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Kidney function depends on the nephron, which comprises a blood filter, a tubule that is subdivided into functionally distinct segments, and a collecting duct. How these regions arise during development is poorly understood. The zebrafish pronephros consists of two linear nephrons that develop from the intermediate mesoderm along the length of the trunk. Here we show that, contrary to current dogma, these nephrons possess multiple proximal and distal tubule domains that resemble the organization of the mammalian nephron. We examined whether pronephric segmentation is mediated by retinoic acid (RA) and the caudal (cdx) transcription factors, which are known regulators of segmental identity during development. Inhibition of RA signaling resulted in a loss of the proximal segments and an expansion of the distal segments, while exogenous RA treatment induced proximal segment fates at the expense of distal fates. Loss of cdx function caused abrogation of distal segments, a posterior shift in the position of the pronephros, and alterations in the expression boundaries of raldh2 and cyp26a1, which encode enzymes that synthesize and degrade RA, respectively. These results suggest that the cdx genes act to localize the activity of RA along the axis, thereby determining where the pronephros forms. Consistent with this, the pronephric-positioning defect and the loss of distal tubule fate were rescued in embryos doubly-deficient for cdx and RA. These findings reveal a novel link between the RA and cdx pathways and provide a model for how pronephric nephrons are segmented and positioned along the embryonic axis. The kidney eliminates metabolic waste in the body using highly specialized structures called nephrons. Individual nephrons are composed of a blood filter (renal corpuscle), a tubule that recovers or secretes solutes, and a collecting duct [1]. The renal corpuscle contains epithelial cells called podocytes that form the slit-diaphragm filtration barrier and allow collection of substances from the blood [2]. In a number of vertebrate species, including some mammals, the renal corpuscle is connected to the tubule by a short stretch of ciliated epithelium called the neck segment that guides filtrate entry into the tubule [3–5]. The mammalian nephron tubule is subdivided into a series of proximal and distal segments connected to a collecting duct [1,6]. The polarized epithelial cells in the tubule segments have a unique ultrastructure and express a select cohort of solute transporters [1]. Thus, each segment is functionally distinct and performs the transport of particular solutes that are required for proper renal function. In higher vertebrates, three kidneys of increasing complexity arise sequentially from the intermediate mesoderm (IM): the pronephros, the mesonephros, and the metanephros [7]. The pronephros and mesonephros degenerate in succession, with the metanephros serving as the adult kidney. Lower vertebrates, such as fish and amphibians, develop a pronephros during embryonic stages, and then form a mesonephros that will be used throughout their adult life [8–10]. Each of these kidneys contains the nephron as its basic functional unit [8]. To date, much of our knowledge of kidney development has come from gene-targeting studies in the mouse [7,11,12]. These experiments have identified a number of genes that play essential roles in the early stages of metanephros development, but there is a limited understanding of the molecular pathways governing the later stages of kidney ontogeny, when individual nephrons form and become segmented [7]. The zebrafish is an ideal genetic and developmental model system for dissecting the molecular mechanisms of nephron formation because of the anatomical simplicity of the pronephros, which contains two nephrons as opposed to the thousands of nephrons in a mammalian metanephros [9]. During zebrafish development, bilateral stripes of IM lying on either side of the trunk undergo a mesenchymal-to-epithelial transition to form the pair of pronephric nephrons. The anteriormost renal progenitors differentiate into podocytes, which migrate medially and fuse at the midline to form a single renal corpuscle. The nephrons also fuse posteriorly at the cloaca to form a shared exitway. From a functional standpoint, these pronephric nephrons have been thought to consist of three parts: (1) the blood-filtering renal corpuscle, (2) a very short tubule region that transports solutes, and (3) long pronephric ducts that convey the resulting waste to the cloaca [9]. Contrary to this model, recent studies have suggested that the ‘duct' region possesses regional segmentation, based on the restricted expression boundaries of solute transporter orthologues known to be expressed in the tubule segments of metanephric nephrons. For example, a rostral stretch of the pronephric duct expresses the endocytic receptor megalin (lrp2) [13] and the sodium bicarbonate transporter NBC1 (slc4a4) [14], which are expressed in the proximal tubule in mammals. These reports raise the possibility that portions of the pronephros considered to be duct might in fact be tubule, thus suggesting that the organization of the zebrafish pronephros is more complex than previously appreciated. However, a complete model of the molecular anatomy of the zebrafish pronephros and whether there is a functional correlation to the segments of the mammalian nephron remain unclear. Furthermore, the pathway (s) directing segmentation of the pronephros along the embryonic axis are unknown. Numerous factors are known to control segmental patterning along the anterior-posterior (A-P) axis during vertebrate development and thus provide candidate pathways that might act to establish pronephros segmentation. Retinoic acid (RA) signaling is vital for directing the A-P regionalization of tissues deriving from all three germ layers, such as the hindbrain, paraxial mesoderm, and gut [15–19]. Control of RA production via retinaldehyde dehydrogenase (RALDH) synthesizing enzymes [20] and the degradation of RA via the CYP26 catabolizing enzymes establishes both the location and timing of RA signaling [21,22]. In addition to RA, the caudal (cdx) transcription factors (Cdx1, Cdx2, and Cdx4 in mammals and cdx1a and cdx4 in zebrafish) are responsible for determining vertebral identity and directing posterior body formation [23–31]. cdx genes are known to act as master regulators of the homeobox (hox) transcription factors [25], and in turn, overlapping domains of hox gene expression along the A-P axis are thought to confer segmental identities [32]. In mice, loss of Cdx function causes posterior shifts in Hox gene expression that are associated with abnormal vertebral patterning, and posterior truncations due to defects in the extension of the embryo axis [23,25–27,33]. Similarly, studies in zebrafish have shown that the loss of cdx4 function or deficiency of both cdx1a and cdx4 causes shifts in hox gene expression domains, a shortened body axis, and altered patterning of the blood, vascular, and neural tissues [24,28–31]. These lines of evidence indicate that the cdx genes play essential roles in controlling cell fates along the embryonic axis; however, the molecular mechanisms underlying these effects have not been elucidated [34]. In this study, we undertook a functional genomics approach to identify new markers of the zebrafish pronephros. From this analysis, we found that the pronephros is composed of at least eight regions, including two proximal and two distal tubule segments. We explored how segmental identity is controlled during nephrogenesis by testing the roles of RA signaling and the cdx genes. We found that RA is required to induce proximal segment fates and prevent the expansion of distal segment fates, whereas the cdx genes are necessary for positioning the pronephros along the embryonic axis. Embryos deficient in cdx1a and cdx4 displayed a posterior shift in the location of the pronephros and formed proximal but not distal nephron segments. The cdx genes were found to control the expression boundaries of raldh2 (aldh1a2) and cyp26a1, suggesting a model in which the cdx pathway influences where the pronephros forms along the body axis by localizing the source of RA, while subsequent RA signaling acts to direct the segmentation of the pronephros. To gain insight into the molecular mechanisms that control vertebrate renal development, we undertook a functional genomics approach to identify genes expressed in the kidney. We mined two gene collections, one comprising developmentally expressed genes from embryonic zebrafish cDNA libraries [35] and another compiled from an adult zebrafish kidney library [36]. Gene expression patterns were analyzed by whole-mount in situ hybridization using wild-type zebrafish embryos between the 5 somite stage and 144 hours post fertilization (hpf). We identified a number of genes, including 15 solute transporters, that were expressed within specific subregions of the pronephros. In total, eight distinct regions could be visualized, with some genes expressed in more than one region (Figure 1A). Representative examples of region-specific genes include wt1b, slc20a1a, trpm7, slc12a1, stc1, slc12a3, and gata3, as compared to expression of cdh17, which is found in all tubule and duct progenitors [37] (Figure 1A). We investigated where the mouse or human orthologues of some of these genes are expressed in the mammalian metanephric kidney, and found that many corresponded to segment-specific domains within the nephron. For example, Slc9a3 is expressed in podocytes, the proximal convoluted segment (PCT) and proximal straight segment (PST) (Figure 1B) [38]. Slc20a1 is expressed throughout the entire nephron epithelium, although stronger expression was observed in proximal tubule segments (Figure 1B). Transcripts for Slc13a3 are found in the PST [39], while Slc12a1 is restricted to the thick ascending limb (TAL) and macula densa (MD) (Figure 1B) [40,41]. Slc12a3 is expressed in the distal convoluted tubule (DCT) (Figure 1B) [41]. Lastly, GATA-3 expression specifically marks the collecting ducts (CD) (Figure 1B) [42,43]. Based on this cross-species gene expression comparison, the following identities were assigned to the zebrafish pronephros segments we observed (going from proximal to distal): podocytes (pod), neck (N), PCT, PST, distal early (DE), corpuscle of Stannius (CS), distal late (DL), and the pronephric duct (PD) (Figure 1A and 1E). Our division of PCT and PST within the tubule is based on the observation that the slc20a1a-expressing PCT cells undergo morphogenesis from a linear tube into a coiled structure by 5 days post-fertilization (dpf), while the trpm7- and slc13a1-expressing PST segment maintains a linear structure (Figure 1C). Expression of trpm7 and slc13a1 is discontinuous within the PST, an observation that has been shown recently to reflect the presence of two cell types in this region: transporting epithelia and multiciliated cells [44,45]. The renal corpuscle connects to the PCT via a short segment of cells that express the transcription factor rfx2, and fails to express almost all of our PCT solute transporters (Figure 1D). As rfx2 marks ciliated cells and rfx genes are essential regulators of ciliogenesis [46,47], we hypothesize that this region corresponds to the ciliated neck segment found in other fish species as well as mammals [3–5]. However, a more detailed analysis is needed to confirm this hypothesis. In addition to the neck segment, rfx2 expression was also detected in presumptive ciliated cells along the length of the PST and DE segments, as described previously [44,45]. For the distal tubule, we adopted the DE/DL nomenclature used in Xenopus [48], although the zebrafish DE appears analogous to the TAL segment in mammals and the DL appears analogous to the mammalian DCT segment according to our gene expression comparison. We included the CS as a discrete segment, as it initially arises from the tubular progenitors within the pronephros, but by 48 hpf, it is located just dorsal to the DE/DL boundary [49,50] (unpublished data). The DL segment expresses slc12a3 and connects to the cloaca via a short segment that expresses gata3 and likely represents the PD. Our data are consistent with the notion that the zebrafish pronephric kidney resembles a ‘stretched-out”' mammalian nephron, and suggests that rather than being composed of mostly nephric duct (as currently believed), it is made up of extensive proximal and distal tubule epithelium (Figure 1E). Between 24 and 48 hpf (the start of blood filtration), the pronephros undergoes significant morphogenesis, including the midline migration of podocytes and the growth/extension of the tubules [9]. In order to better quantitate these morphological changes, as well as to precisely define the anatomical boundaries of each segment, we mapped the expression domains of segment-specific markers relative to the somites by performing double whole-mount in situ hybridization with myosin heavy chain (mhc) at 24 and 48 hpf (Figures 2 and S1–S4). At 24 hpf, podocyte and neck progenitors are arranged in a slight curve at the level of somite 3–4 with the anterior boundary of the PCT level with somite 5 (Figures 2 and S1). By 48 hpf, the podocyte progenitors have fused at the midline (level with somite 3) with the presumptive neck region forming a lateral extension that connects with the PCT also situated at the level of somite 3 (Figures 2 and S3). During this time, the length of the PCT, PST, and DE segments increased, possibly due to cell division within each segment (Figure 2 and S1–S4). This growth may provide the driving force that is responsible for the shift in the anterior boundary of the PCT from somite 5 to somite 3 between 24 and 48 hpf, and for the coiling morphogenesis of the PCT observed between 72 and 144 hpf (Figure 1C). However, the DL segment did not increase in length between 24 and 48 hpf, indicating that there is not a uniform expansion in all segments during development. During juvenile development (2–3 wk post-fertilization) the DL segment is proportionately larger than the other segments, suggesting that its expansion predominates at later stages of development (unpublished data). Interestingly, at 24 hpf, we observed an overlap of the DL and PD expression domains at the level of somite 17 (Figure 2 and Figure S2). This overlap may indicate the presence of an additional segment (such as a discrete CNT equivalent), though to date we have not discovered any genes expressed solely in this domain. In addition to mapping the morphological changes that occur between 24 and 48 hpf, we also noted segment specific changes in gene expression patterns during this time. For example, transcripts for the solute transporters slc13a1 (inorganic sulphate transporter), slc13a3 (sodium-dicarboxylate carrier), and slc22a6 (organic anion transporter) were all absent from the PST segment at 24 hpf but were found expressed at 48 hpf (Figures 2 and S3; and unpublished data). Similarly, transcripts for trpm7 (divalent cation-selective ion channel) and slc41a1 (Mg 2+ transporter) were not found in cells of the CS at 24 hpf but could be detected at 48 hpf (Figure 2 and unpublished data). The up-regulation of these genes likely reflects the maturation/differentiation of the segment epithelia before the onset of blood filtration, which begins around 40 hpf [51,52]. However, the glomerulus does not fully mature until 4 dpf, based on the size exclusion of different-sized fluorescent dextrans [53]. Further profiling of segment expression patterns at later stages is needed to investigate whether the maturation of the transporting epithelial is also an ongoing process. We next sought to characterize the developmental pathways that establish the segmentation pattern of the pronephros. Retinoic acid (RA) is essential for the development of numerous tissues during embryogenesis [25]. In vertebrates, a gradient of RA in the upper trunk is responsible for directing the A-P patterning of the hindbrain into segmental compartments [15,54], and gain or loss of RA also affects vertebral identity [25]. Interestingly, RA has been reported to regulate pronephros formation in Xenopus [55]. To explore whether RA was needed for pronephros segmentation, we injected wild-type zebrafish embryos with a morpholino to retinaldehyde dehydrogenase 2 (raldh2), which encodes an enzyme required to produce RA [20]. We then examined raldh2 morphants at 48 hpf with our panel of segment-specific markers, in combination with mhc expression to map segment length and location relative to the somites. Embryos deficient in raldh2 had fewer podocytes, as evidenced by punctate staining of the podocyte markers wt1b, wt1a, and mafb (Figure 3A and unpublished data). Both proximal tubule segments were slightly shortened, based on the reduced expression domains of slc20a1a (PCT) and trpm7 (PST) (Figure 3). Conversely, the distal tubule was expanded in length, with the clck transporter (marking both the DE and DL) expressed in a greater proportion of the cdh17-positive pronephric tubule (note that the overall length of raldh2 morphants is reduced compared to wild-type) (Figure 3). Each segment within the distal tubule was moderately expanded, with a lengthened DE shown by expanded slc12a1 expression, enlarged clusters of stc1-expressing cells that comprise the CS, and lengthened DL shown by slc12a3 expression (Figure 3). The expression domain of gata3 (PD) was also expanded (Figure 3). In contrast, the cloaca marker aqp3 was unchanged (Figure 3A). These data suggest that RA signaling is necessary for podocyte formation and/or survival, as well as for establishing the normal pattern of nephron segmentation. Multiple enzymes are capable of synthesizing RA and a recent analysis of the neckless (nls) (now called aldh1a2) mutant, which is defective in raldh2, demonstrated that there are additional sources of raldh-like enzyme activity in the zebrafish embryo [56–58]. Based on this, we hypothesized that the nephron phenotype in raldh2 morphants represented the effect of reducing RA production, rather than a complete inhibition. To more fully block RA signaling, we utilized a competitive, reversible inhibitor of raldh enzymes, 4-diethylaminobenzaldehyde (DEAB) [59], which has been used to effectively prevent de novo RA synthesis in zebrafish embryos [58,60]. Wild-type embryos were treated with DEAB starting at 60% epiboly (early gastrula) until the 15 somite stage, and nephron segmentation was assayed at 48 hpf by double whole-mount in situ hybridization using mhc expression to mark the somites. Expression of the podocyte markers wt1b, wt1a, and mafb was absent in DEAB-treated embryos, suggesting that podocyte development was completely blocked (Figure 3 and unpublished data). Expression of the PCT and PST markers slc20a1a and trpm7 was also absent, suggesting that these segments had failed to be specified (Figure 3). In contrast, clck expression (marking the DE and DL segments) was dramatically expanded, such that it was present throughout the entire tubule territory (Figure 3). These findings suggest that the pronephric tubule adopts a distal tubule identity when RA synthesis is inhibited. Within this ‘distal-only' pronephros, the DE marker slc12a1 was expressed from the anterior limit of the tubule to almost the middle of its length, the DL marker slc12a3 was expressed from the middle of the tubule to near its posterior limit, and the PD marker gata3 was expanded by an additional four somite lengths (Figure 3). A marked expansion of stc1-expressing cells was also detected, with multiple clusters of cells arranged in bilateral stripes, as opposed to the small groups of stc1-expressing cells seen in wild-type embryos (Figure 3). Expression of aqp3 was not altered, suggesting that progenitors of the cloaca are unaffected by the inhibition of RA production over this developmental interval (Figure 3). It is not known if the observed DEAB phenotype represents a full loss of RA signaling, as trace amounts of maternal RA have been detected in the yolk [61] and as zygotic raldh2 transcripts are expressed prior to 60% epiboly [56,57]. Nevertheless, our results demonstrate that RA, produced by raldh2 and possibly one or more unknown raldh enzymes, plays an essential role in the formation of proximal nephron fates (podocytes, PCT, PST) and in suppressing the expansion of distal fates (DE, CS, DL, PD). To determine more precisely when RA signaling was needed for the development of proximal segment fates, we performed a DEAB timecourse experiment (Figure 3). Blocking RA signaling from 90% epiboly (late gastrula) to the 5 somite stage caused a milder phenotype than the longer 60%- 15-somite exposure that was characterized by a loss of podocytes, reduced lengths of the PCT and PST segments, and small increases in the lengths of the distal segments (Figure 3). A slightly longer DEAB treatment, from 90% epiboly to the 10 somite stage, expanded distal segments further than the raldh2 morphants or DEAB 90% epiboly-5 somite treated embryos (Figure 3). In addition, examination of the slc20a1a and trpm7 expression patterns showed that the PST segment was ablated, while the PCT was only slightly shortened (Figure 3). Loss of PST identity was also confirmed by the absence of slc13a1 and slc22a6 transcripts, which are additional PST markers (Figure 2 and unpublished data). These findings suggest that, at least for this DEAB time window, the expansion of distal fates occurs at the expense of the PST segment. Finally, we tested whether DEAB treatment during somitogenesis would affect pronephros segmentation. DEAB treatment from 5–15 somites or 10–15 somites had no effect on the segmentation pattern of the pronephros (unpublished data), thus suggesting that an inhibition of RA signaling must be initiated prior to the 5 somite stage in order to affect nephron patterning. Taken together, our DEAB timecourse data indicate that RA signaling is required to induce proximal nephron fates and to prevent an expansion of distal fates, thereby establishing the normal pronephric segmentation pattern. Interestingly, our data suggest that different segments have different temporal requirements for RA signaling. RA is essential between 90% epiboly and the 5 somite stage to induce podocytes, between 90% epiboly and 10 somites to induce PST formation, and between 60% epiboly and 15 somites to form the PCT. The PCT segment is the most refractory to RA inhibition and is only lost following the longest DEAB treatment window (60% epiboly to 15 somites). Given these findings, we tested whether increasing the concentration of RA by exogenous treatment would promote proximal nephron fates at the expense of distal fates. We exposed wild-type zebrafish embryos to RA during similar developmental intervals used for our DEAB experiments, and then assayed segment marker expression at 24 hpf by double in situ hybridization with mhc to mark the somites. Wild-type embryos treated with 1 × 10−7 M RA between 90% epiboly and 5 somites developed a normal number of podocytes, as evidenced by wt1b expression, but displayed expanded proximal tubule domains, shown by expression of slc9a3 (marking both the PCT and PST), slc20a1a (PCT), and trpm7 (PST) (Figure 4). Conversely, the clck-expressing distal tubule domain was reduced, due to reductions in the length of the DL (marked by slc12a3) and PD (marked by gata3) (Figure 4). However, the DE segment (marked by slc12a1) was unaffected (Figure 4). The position of the stc1-expressing CS segment cells was shifted posteriorly, and the level of expression was slightly reduced (Figure 4). A longer treatment window, from 60% epiboly–15 somites resulted in a more severe ‘proximalized' phenotype with a longer expanse of proximal tubule and a greater reduction in each distal tubule domain (Figure 4). In these embryos, the position of the stc1-expressing CS population was shifted even more posteriorly, and was located at the distal edge of the yolk sac extension (Figure 4A). We next treated wild-type embryos with higher dose of RA (1 × 10−6 M) over this same 60% epiboly–15 somites time window, as well as two shorter time periods: 60% epiboly–5 somites, and 90% epiboly–5 somites. Embryos treated for any of these time windows displayed a completely ‘proximalized' phenotype with the tubule domain being comprised entirely of proximal segment identities (Figure 4 and unpublished data). In these embryos, the proximal marker slc9a3 was expressed throughout the cdh17-expressing tubule population (Figure 4 and unpublished data). Within this ‘proximal-only' pronephros, the PCT marker slc20a1a was expressed from the anterior limit of the tubule to somite 13, and the PST marker trpm7 was expressed from somite 14 to the posterior limit of the tubule, where the trpm7-expressing tubules fused at the prospective site of the cloaca (Figure 4 and unpublished data). Expression of all distal segment markers was absent, suggesting that the DE, CS, DL, and PD had failed to be specified (Figure 4 and unpublished data). These results show that exogenous RA treatment from gastrulation stages until the 5 somite stage is sufficient to ‘proximalize' the pronephros, suggesting that this time period is the critical window when RA signaling is required for proximo-distal patterning of pronephric progenitors. To further explore the notion that RA alters the patterning of renal progenitors prior to the 5 somite stage, we examined the expression patterns of the Notch ligands deltaC (dlc) and jagged2a (jag2a), and the renal transcription factors wt1a, pax2a, pax8, and evi1, as these genes are detected in the IM during early somitogenesis and have been implicated in early nephron patterning [44,62–64]. Wild-type embryos were treated with DMSO, DEAB, or 1 × 10−6 M RA from 60% epiboly until the 6 somite stage, and then IM gene expression was assayed by whole-mount double in situ hybridization together with myoD to mark the somites. Transcripts for pax2a and pax8, which label all pronephric progenitors, were found throughout the IM domain in a similar pattern in wild-type, DEAB-treated, and RA-treated embryos (Figure S5). Consistent with our previous results, wt1a expression was absent in DEAB-treated embryos, and wt1a was strongly up-regulated in RA-treated embryos (Figure S5). The expression domains of deltaC and jag2a, normally restricted to a proximal region of the IM adjacent to somites 2–5, were absent in DEAB-treated embryos, and expanded posteriorly in RA-treated embryos (Figure S5). Expression of evi1, found in the distal portion of the IM starting around somite 6, was shifted anteriorly to somite 3 following DEAB treatment and reduced to the most posterior group of IM cells following RA treatment (Figure S5). These findings demonstrate that changes in RA dosage during gastrulation to early somitogenesis are associated with gene expression changes in IM progenitors, far in advance of their mesenchymal-to-epithelial transition that creates the pronephric tubules. In addition to RA, multiple tissues along the embryonic axis are segmentally patterned by the cdx genes [25,34]. Loss of cdx gene function leads to an expansion of anterior trunk fates and axial elongation defects that result in a loss/truncation of the posterior trunk and tail [23–30,34]. In zebrafish, cdx4–/– mutant embryos display expanded wt1a expression at the 15-somite stage, suggesting that the podocyte lineage might be expanded and thus implicating cdx genes in pronephros patterning [24]. We therefore examined the formation of the pronephros in cdx4–/– and cdx1a/4-deficient (herein referred to as cdx-deficient) to assess development of the renal corpuscle, tubule, and pronephric duct. In addition, we used differential interference contrast (DIC) optics to visualize the somite boundaries and determine the size and position of each segment relative to the somites. It is important to note that the size of posterior somites in cdx4–/– embryos, and both the size and number of the somites in cdx-deficient embryos, is greatly reduced toward the posterior, due to the axial elongation defect [24,29–30]. A similar defect has also been observed in mouse Cdx mutants [34]. At 24 hpf, wild-type embryos expressed wt1a in presumptive podocytes (located adjacent to somite 3, marked by an arrow in Figure 5A) as well as a population of presumptive mesenchymal cells located next to somites 1–3 in a broad lateral domain (indicated by a bracket and ‘M' in Figure 5A). cdx4–/– embryos had expanded wt1a expression at 24 hpf that ranged from a position anterior to somite 1 to approximately somite 7, and cdx-deficient embryos showed an even more dramatic posterior expansion that reached somite 12 (Figure 5A). As the expansion of wt1a in cdx mutant embryos could indicate increased numbers of podocytes and/or the mesenchymal population, we examined wt1b expression, which specifically marks podocytes [65]. Equivalent numbers of wt1b-expressing podocytes were formed in cdx4–/– mutant and wild-type embryos, but podocytes in cdx4–/– embryos were located more posteriorly, at the level of somite 5 (Figure 5A). cdx-deficient embryos developed a normal number of wt1b-expressing podocytes, though they were arranged in a pair of somewhat irregular linear groupings (rather than forming bilateral spherical clusters), and they were also located more posteriorly, adjacent to somite 7 (Figure 5A). We conclude from these observations that the loss of cdx4 and cdx1a/4 function progressively expands wt1a expression without increasing the number of podocytes and leads to podocyte formation at more posterior locations along the embryonic axis. To assess tubule formation in cdx mutants, we examined the expression of cdh17 at 24 hpf. While cdx4–/– embryos formed complete tubules that fused at the cloaca, cdx-deficient embryos displayed cdh17 expression that was reduced and discontinuous, with the tubules failing to fuse (Figure 5A). Consistent with the posterior shifts in podocyte position, the A-P position of the tubule, marked by cdh17 transcripts, was shifted caudally in both cdx4–/– and cdx-deficient embryos. In wild-type embryos, the tubule spans the length of somites 4–18, but was located from somites 6–20 in cdx4–/– embryos, and from somites 8–13 in cdx-deficient embryos (Figure 5A). We next characterized the tubule segmentation pattern in cdx mutant embryos by examining the expression patterns of slc20a1a (PCT), trpm7 (PST), slc12a1 (DE), stc1 (CS), slc12a3 (DL), gata3 (PD), and aqp3 (cloaca). cdx4–/– embryos showed a slight expansion of the PCT, which spanned an additional two somites compared to wild-types (Figure 5A and unpublished data). The PST, DE, CS, and PD were all shorter than normal in cdx4–/– embryos, with the PD displaying the most severe reduction in length (Figure 5A and unpublished data). Transcripts for aqp3 were not detected in cdx4–/– embryos, suggesting a defect in cloaca development (Figure 5A). These results indicate that loss of cdx4 leads to a slight expansion in PCT fate with corresponding reductions in more distal fates. In contrast to cdx4–/– embryos, cdx-deficient embryos showed discontinuous slc20a1a expression and failed to express trpm7, slc12a1, stc1, slc12a3, gata3, or aqp3 (Figure 5A). These results suggest that the tubule territory in cdx-deficient embryos acquires a PCT identity, while the remaining nephron segments fail to develop. These defects were not the result of delayed development, as at later developmental stages, cdx-deficient embryos continued to possess tubules that only expressed PCT-markers, and the tubules never fused caudally (unpublished data). In addition, expression analysis of the podocyte marker mafB in cdx-deficient embryos at 48 hpf revealed that podocytes fail to fuse into a single renal corpuscle. Instead, the podocytes formed bilateral corpuscles that were dilated compared to wild-type and cdx4–/– embryos, presumably due to fluid accumulation (Figure 5B). Consistent with this, cdx-deficient embryos had developed glomerular cysts by 72 hpf, as well as severe pericardial edema, indicative of renal failure (Figure 5C). In contrast, cdx4–/– embryos never exhibited glomerular cyst formation or edema, suggesting that although various segment lengths were shortened, these embryos were able to maintain adequate kidney function (Figure 5C). In summary, our expression analyses show that cdx deficiency causes a posterior shift in the location of the pronephros along the embryonic axis (Figure 5D). While the podocytes and PCT populations formed relatively normally, the PST and distal tubule segments were reduced or absent in cdx4–/– and cdx-deficient embryos, respectively (Figure 5D). Thus the cdx genes, acting either directly or indirectly, are required for the formation of the distal nephron segments and establishing the normal segmentation pattern of the pronephros. Given the largely opposite effects of cdx-deficiency and loss of RA signaling on nephron patterning, as well as the recent report that cdx genes control how the hindbrain responds to RA during its patterning [30,31], we wondered if an interplay between these pathways was operative during pronephros segmentation. The location and level of RA within tissues is dependent on the expression of raldh-synthesizing enzymes and cyp26-degrading enzymes [15,54]. We therefore investigated the expression of these genes in cdx-deficient embryos during early somitogenesis, as our previous experiments suggested that the IM is being influenced by RA signaling at this time. At the 5 somite stage, expression of raldh2 in the paraxial mesoderm was expanded posteriorly in cdx4–/– embryos compared to wild-types (Figure 6A). An even greater posterior expansion was seen in cdx-deficient embryos, with raldh2 transcripts being detected throughout the entire unsegmented paraxial mesoderm and tailbud region (Figure 6A). Expression of the RA-catabolizing enzyme cyp26a1 in the upper trunk region was also expanded posteriorly in cdx4–/– embryos at the 5 somite stage, and more extensively expanded in cdx-deficient embryos (Figure 6A). To visualize how the combined changes in raldh2 and cyp26a1 expression altered the source of RA along the trunk, we generated digital overlays of these expression patterns. This analysis suggested that the anterior boundary of RA production (i. e. , the junction of the cyp26a1 and raldh2 expression domains) was located more posteriorly in cdx4–/– mutants compared to wild-types, and that this posterior shift was more pronounced in cdx-deficient embryos (arrows in Figure 6A). To better quantitate these posterior shifts, we examined expression of raldh2 and cyp26a1 at the 10 somite stage when the somites could be visualized by staining for myoD transcripts. We found that the raldh2 expression boundary in cdx4–/– and cdx-deficient embryos was shifted posteriorly by 1 and 2 somites, respectively, compared to wild-type embryos (Figure 6B). The cyp26a1 domain in wild-type embryos occupied the region of somites 2–3, just rostral and slightly overlapping with raldh2, as shown by double in situ hybridization (Figure 6B). In cdx4–/– embryos, cyp26a1 transcripts were detected in the region of somites 3–5, whereas in cdx-deficient embryos they extended from somites 3–7 (Figure 6B). These analyses reveal a striking correlation between the presumptive source of RA at the 10 somite stage and the axial position of the pronephros at 24 hpf in each genotype (i. e. , the source of RA and the position of the pronephros both start at somites 3,5, and 7 in wild-type, cdx4–/–, and cdx-deficient embryos, respectively). The combination of these data, together with the results from our DEAB experiments, suggest a model in which the cdx genes act upstream of raldh2 and cyp26a1 to localize the source of RA along the A-P axis, and that RA, in turn, acts on the IM to induce the proximal segments and prevent an expansion of the distal segment fates. If our model is correct, then inhibiting RA synthesis in cdx-deficient embryos should rescue pronephric positioning and the formation of the distal tubule segments. To test this, we treated cdx4–/– and cdx-deficient embryos with DEAB from 90% epiboly to the 5-somite stage and examined pronephros segmentation. In support of our model, we found that cdx4–/– and cdx-deficient embryos exhibited a one-somite anterior shift in the position of the pronephros, as shown by expression of cdh17 (Figure 7). In addition, the development of podocytes was abrogated, and the length of the PCT was reduced in DEAB-treated cdx4–/– and cdx-deficient embryos (Figure 7), consistent with our findings in wild-type embryos (Figure 3). We observed that the DE segment was increased in DEAB-treated cdx4–/– embryos, as shown by an expansion of the slc12a1 expression domain (Figure 7). DEAB treatment also increased the number of CS cells in cdx4–/– mutants, shown by the expression of stc1, and increased the DL segment length, evidenced by expansion of the slc12a3, romk2, and clck expression domains (Figure 7 and unpublished data). In cdx-deficient embryos treated with DEAB, formation of the DE, CS, and DL segments was rescued, shown by expression of slc12a1, stc1, and slc12a3, respectively (Figure 7). A similar rescue of the expression of the DE and DL markers romk2 and clck was also observed (unpublished data). These findings demonstrate that cdx gene function is not necessary to specify distal segment identity directly, but instead suggests that the abrogation of distal segment formation in cdx-deficient mutants is related to the level of RA that the renal progenitors are exposed to. Taken together with the above results, these finding provide good evidence that the pronephric positioning defect and failure to form the distal tubule identities in cdx-deficient embryos is caused by mis-localization of the RA source along the A-P axis. Retinoid signaling plays essential roles in the A-P patterning of a number of diverse tissues in the embryo. During early development, a source of RA in the upper trunk (cervical) region is produced by the action of the RA synthetic enzyme, Raldh2, which is expressed in the anterior paraxial mesoderm [54]. The coordinate expression of RA-catabolizing Cyp26 enzymes in surrounding tissues creates a so-called ‘sink' for this RA source [54]. Collectively, these enzymes are thought to create a gradient of RA activity that diffuses into surrounding tissues [34,54]. The functions of this RA source have been extensively studied in the developing hindbrain, where the effects of graded RA signaling are thought to create nested expression domains of RA-responsive genes that drive A-P segmentation of the hindbrain into a series of rhombomeres [15,54]. In addition to regionalizing the overlying neurectoderm, RA produced in the upper trunk paraxial mesoderm has been implicated in the regionalization of the underlying endoderm. Studies in zebrafish have shown that RA acts directly on the endoderm to specify hepatopancreatic progenitors that give rise to the liver and pancreas [17,18]. RA also influences mesodermal cell fate decisions during zebrafish development, including the formation of the pectoral fin field—which arises from the lateral plate mesoderm adjacent to the upper trunk somites [56,57,66]—and the heart [67]. In the latter case, inhibition of RA synthesis leads to an expansion of precardiac mesoderm, resulting in an excessive number of myocardial progenitors [67]. These findings indicate that, in addition to acting as an inducer of cell fates such as in the hindbrain and endoderm, RA also plays an important role in restricting certain cell fates. Our study now adds the IM as another mesodermal derivative that is patterned by RA. Our results show that RA production is essential during gastrulation and early somitogenesis for the induction of proximal nephron fates as well as to restrict the expansion of distal nephron fates. Over this period of development, RA is produced by the anterior paraxial mesoderm (PM). The IM, which gives rise to the pronephros, is located lateral to the PM (Figure 8). Given the role of RA as a diffusible morphogen in other tissues, we hypothesize that RA diffuses from the PM and establishes a gradient along the IM, with high levels of RA inducing proximal fates and low RA levels being permissive for distal fates (Figure 8). Our time-course experiments with DEAB support this view, with the most severe reduction in proximal fates (and concomitant expansion in distal segments) corresponding to the longest treatment window. However, further work is needed to determine the nature of the RA gradient, as well as how dynamic fluctuations in retinoid availability [54] affect the dose and length of time that the renal progenitors are exposed to RA. A gradient-free model has recently been proposed for RA-dependent hindbrain patterning, based on the finding that sequential expression domains of the cyp26a1, cyp26b1, and cyp26c1 genes are essential for rhombomere boundary establishment [68,69]. It is unclear if a similar mechanism might operate during pronephros segmentation, as cyp26b1 and cyp26c1 do not show a nested pattern of expression in the IM. Overall, the effects of RA on the patterning of the IM can be regarded as ‘anteriorizing.' Our finding that exogenous RA treatment induces proximal tubule fates to form throughout the pronephros supports this conclusion. Classically, RA is known as a ‘posteriorizing' factor due to its effects on the central nervous system, where an inhibition of RA signaling causes an expansion of anterior neural fates in the hindbrain [15,54]. Thus we conclude that RA can actually have both anteriorizing and posteriorizing activities, depending on the tissue in question. A unified way to characterize these effects would be to consider the upper trunk RA source as an organizing center, akin to the dorsal organizer in the gastrula, that locally patterns cell types in all three germ layers. Previous studies have implicated RA as a regulator of renal development. Animal cap experiments in Xenopus showed that RA, together with Activin, is sufficient to induce the formation of pronephric tubules [70]. A more recent study in Xenopus reported that overexpressing various RA antagonists results in a complete loss of the pronephros (glomus, tubules, and duct) [55]. This phenotype is more severe than what we observed and may reflect differences in the efficacy of DEAB to completely block RA production compared with other RA antagonists. The pronephric tubules in Xenopus are segmented into proximal and distal segments [48], similar to the zebrafish pronephros, however a role for RA in Xenopus nephron segmentation has not been reported. In mammals, it has long been known that vitamin A deficiency causes severe renal malformations [71]. Targeted mutagenesis of the RAR genes in mouse, followed by elegant rescue experiments, established an important role for RA as a dose-dependent inducer of the GDNF receptor Ret [72,73]. GDNF is an essential regulator of ureteric bud branching morphogenesis, and loss of GDNF signaling results most frequently in renal agenesis [74]. Because ureteric bud branching is an essential prerequisite for nephrogenesis, RA serves a key role in stimulating nephron formation. At present it is not known whether RA is also involved in the proximodistal patterning of metanephric nephrons. Interestingly, Raldh2 transcripts are found in podocyte progenitors, whereas Cyp26a1 is expressed by the tubule anlagen during metanephros development, suggestive of a role for RA in mammalian nephron patterning [75]. Transplantation studies in frogs suggest that RA may act directly on pronephric precursors [55]. However, the downstream targets of RA in the IM are not known. In the hindbrain, the presumptive RA gradient is thought to regulate rhombomere segmentation by activating the expression of the anterior, 3' Hox genes, i. e. , those comprising the 1st through 5th paralog groups [15,54,76]. In zebrafish, transcripts for hoxb1a, hoxb1b, and hoxb5a are found in proximal portions of the IM, thus making them potential candidates for mediating the effects of RA during pronephros segmentation [34]. Future studies using single, double, and triple morpholino injections can test the importance of these hox genes for renal development. The effects of RA on pronephric segmentation may also be coordinated by the action of non-Hox pathways. Our results suggest the intriguing possibility that RA signaling targets may include renal transcription factors as well as members of the Notch signaling pathway. The gene evi1 encodes a zinc-finger transcription factor that has been implicated in patterning distal regions of the pronephros in Xenopus, and overexpression of evi1 was found to inhibit proximal segment formation [63]. These data are consistent with our results showing that expression of evi1 in the IM was expanded following DEAB treatment, and reduced following exposure to exogenous RA. Another renal transcription factor candidate is the odd-skipped related transcription factor 1 (osr1) encoding a zinc-finger repressor. Recent studies in Xenopus and zebrafish have shown that osr1 is expressed in the ventral mesoderm during gastrulation and later in an anterior domain of the IM [77]. Morpholino-mediated knock-down of osr1 leads to defects in the formation of podocytes and proximal tubule progenitors [77], consistent with Osr1 participating in a common pathway with RA. The Notch pathway may also interact with RA during nephron segmentation. Conditional knockout of Notch2 in the mouse metanephros results in a loss of podocytes and proximal tubule fates, whereas distal markers are relatively unaffected [78]. In zebrafish, Notch signaling has been shown to regulate the differentiation of multiciliated cells and principle cells in the pronephric tubules [44,45]. However, a role for Notch signaling in the formation of proximal nephron fates is also suggested by the expression pattern of the Notch ligands deltaC, jag1b, and jag2a, which are restricted to proximal portions of the intermediate mesoderm [62,64]. Simultaneous knockdown of jag1b/2a results in an abnormally small renal corpuscle and dysmorphic proximal tubules, consistent with a conserved role for Notch signaling in proximal nephron development [79]. Our finding that DEAB treatment abrogates deltaC and jag2a expression in the proximal IM, while exogenous RA expands their expression, supports a role for RA acting upstream of the Notch pathway. Our study provides evidence that cdx genes control the expression domains of raldh2 and cyp26a1 along the embryonic axis. The boundaries of both raldh2 and cyp26a1 are progressively shifted toward the posterior in cdx4 and cdx1a/4-deficient embryos, suggesting that the upper trunk source of RA is posteriorly shifted. We hypothesize that this posterior shift in RA production results in a posterior shift in the position of the pronephros (Figure 8). We propose that this effect, combined with the axial elongation defects, leads to reduced or absent distal segment fates. The ability to rescue distal segments by treating cdx mutants with a pulse of DEAB is consistent with this model, and also demonstrates that cdx function is not requisite for the induction of distal fates from the intermediate mesoderm. Thus additional, as yet unidentified pathways, are responsible for directing distal fates. While our data supports the notion that Cdx factors exert their effects by the regulation of RA signaling, it does not rule out the possibility that Cdx factors may also function to repress proximal fates independent of RA signaling. Given the mounting evidence that the upper trunk RA source is an important organizing center, we would predict that both the patterning and positioning of numerous organs would be affected in cdx mutants. Consistent with this, defects in several mesodermal fates that arise in the anterior trunk region have been observed in cdx-deficient embryos. Vascular precursors are progressively expanded when cdx activity is abrogated, and blood precursors are both reduced and shifted posteriorly in cdx mutants [24,29]. In addition to mesodermal defects, cdx mutants also display patterning defects in the neurectoderm that gives rise to the anterior spinal cord [30,31]. We hypothesize that many, if not all, of these defects in cdx mutants are caused by the abnormal localization of RA along the A-P axis. The loss of cdx gene function in both zebrafish and murine models has been shown to cause global shifts in hox gene expression in the mesoderm and neurectoderm [24,25,29–30,34]. Given the rostral shifts and expansions of both raldh2 and cyp26a1 expression observed in cdx4 and cdx1a/4-deficient embryos, Hox transcription factors are attractive molecules for regulating raldh2 and cyp26a1 expression. Defects in blood formation in cdx4-null zebrafish can be rescued by the overexpression of several hox genes [24], and the overexpression of hoxa9a also results in a partial rescue of the axis elongation defect in cdx4–/– embryos [29]. Future studies are needed to examine whether hox gene overexpression (s) can rescue pronephros positioning and formation of distal segments in cdx mutant embryos. In conclusion, our studies have revealed an important link between the cdx genes and localization of RA, and provide evidence that RA signaling is a central determinant of pronephros A-P segmentation. Our results establish the zebrafish embryo as a simplified model of vertebrate nephron segmentation that will further our understanding of mammalian nephron segmentation, and provide insights into the causes of kidney birth defects and renal disease in humans. Zebrafish were maintained and staged as described [80,81]. Tübingen strain wild-type embryos were used for all experiments. DEAB and all-trans retinoic acid (Sigma-Aldrich) were dissolved in 100% dimethyl sulfoxide (DMSO) to make a 1 M stock and aliquots were stored at −80°C. For DEAB and RA treatments: embryos were incubated in 1. 6 × 10−5 M DEAB/DMSO in E3 embryo media, 1 × 10−6 M or 1 × 10−7 M RA/DMSO in E3 embryo media, or 1. 6 × 10−5 M DMSO (control) in E3 in the dark over particular developmental intervals, then washed five times with E3 and then fixed at 24 or 48 hpf. These experimental treatments were fully penetrant and produced consistent results at the doses and treatment windows that were examined. raldh2 morpholino (CAACTTCACTGGAGGTCATCGCGTC) was injected into 1-cell wild-type embryos. Incrosses of kggtv205 heterozygous adults (maintained on the Tübingen strain) were used to obtain cdx4–/– embryos and were injected at the 1-cell stage with cdx1a morpholino (CAGCAGATAGCTCACGGACATTTTC) as described [29] to obtain cdx-deficient embryos. Both raldh2 and cdx1a morpholinos produced fully penetrant effects. Embryos were raised to appropriate stages and fixed in 4% paraformaldehyde (PFA) /1×PBST for gene expression analysis. For all reported gene expressions, at least 20 embryos were examined. Whole-mount in situ hybridization of zebrafish embryos was performed as previously described [24]. The expression patterns of cdh17, clck, cyp26a1, evi1, gata3, mhc, myoD, nbc1, pax2a, pdzk1, raldh2, ret1, sall1, sglt1, slc4a2, slc20a1a, wt1a, and wt1b were previously reported [14,24,29,37,55,56,61,82–86]. For antisense probe production, we used the following IMAGE clone template plasmids, restriction enzymes for DNA linearization, and RNA enzymes: mafb: 7995399, pExpress-1, EcoR1, T7; rfx2, pBK-CMV, template was PCR amplified using primers GTGAATTGTAATACGACTCACTATAGGG and TTAACCCTCACTAAAGGGAACAAA, T7; slc9a3: 6996791, pExpress-1, EcoRI, T7; slc26a2: 4760214, pBK-CMV, EcoRI, T7; slc13a1: 6793065, EcoRV, t7; slc13a3: 4744276, pCMV-sport6. 1ccdb, EcoRI, T7; slc22a6: 4744276, pBK-CMV, SalI, T7; slc12a1: pBK-CMV, EcoRI, T7; slc12a3: 7037010, pExpress-1, EcoRI, T7; stc1 was amplified from 24 hpf embryo cDNA using primers ATGCTCCTGAAAAGCGGATTT and TTAAGGACTTCCCACGATGGA and cloned into pGemTEasy, NcoI, Sp6. Gene-specific primers spanning 700–1,000 bp of the coding sequence were used to amplify DNA fragments from E15. 5/P0 kidney cDNA pools, and the PCR products of the right size were cloned into the pCRII-Topo vector (primer sequences available upon request). DNA templates for riboprobe production were generated by PCR with T7 and Sp6 Ready Made primers (Integrated DNA Technologies) from PCRII-TOPO clones or T7 and T3 Ready Made primers from Bmap library clones. Digoxigenin-labeled anti-sense riboprobes were synthesized from the PCR product and purified with Micro Bio-spin columns P-30 Tris RNase-free (Bio-Rad). Probes were diluted with prehybridization buffer (50% formamide, 5×SSC, pH4. 5,50 μg/ml yeast tRNA, 1% SDS, 50 μg/ml heparin) to 10 μg/ml and stored at −80 °C. Neonatal kidneys were dissected free of surrounding tissues except the ureter and fixed with 4% PFA at 4 °C for 24 h. After PBS washes, they were incubated with 30% sucrose at 4 °C overnight. Kidneys were swirled in five dishes of OCT to remove sucrose and mounted in OCT in a dry ice/ethanol bath. The OCT blocks were stored at −80 °C. Sections were cut at 20 μm and air dried. Sections were post-fixed with 4% PFA for 10 min, treated with 10 μg/ml proteinase K for 10 min and post-fixed for 5 min. Slides were acetylated (1. 33% Triethanolamine, 0. 065% HCl, 0. 375% acetic anhydride) for 10 min and dehydrated with 70% ethanol and 95% ethanol for 5 min each. Slides were air dried then incubated with 500 ng/ml digoxigenin-labeled riboprobes at 68 °C overnight. Hybridized sections were washed with 50% formamide, 1×SSC, pH4. 5 for 30 min at 65 °C, treated with 2 μg/ml RNase for 15 min at 37 °C, and washed with 2×SSC, pH4. 5 for 30 min, and twice with 0. 2×SSC, pH4. 5 for 30 min at 65 °C. Slides were washed three times at room temperature with 1×MBST (0. 1 M maleic acid, 0. 15 M NaCl, 0. 1% Tween-20, pH7. 5) for 5 min each, and incubated with blocking solution (2% Boehringer Mannheim (BM) blocking reagent) in 1×MBST, 20% heat-inactivated sheep serum) for 1 h. After incubation with anti-digoxigenin antibody-AP (Roche, 1: 4000) at 4 °C overnight, sections were washed with 1×MBST at room temperature, 5 min for three times, then with NTMT (0. 1 M NaCl, 0. 1 M Tris-HCl, pH9. 5,50 mM Mg2Cl, 0. 1% Tween-20,2 mM Levimasole) for 10 min, and developed with BM purple (Roche). Color reactions were stopped with fixatives (4% PFA, 0. 2% glutaraldehyde) and sections mounted with glycergel mounting media (DAKO). Images were captured with a Nikon DXM1200 digital camera attached to a Leitz DMRB microscope.

In the kidney, structures known as nephrons are responsible for collecting metabolic waste. Nephrons are composed of a blood filter (glomerulus) followed by a series of specialized tubule regions, or segments, which recover solutes such as salts, and finally terminate with a collecting duct. The genetic mechanisms that establish nephron segmentation in mammals have been a challenge to study because of the kidney' s complex organogenesis. The zebrafish embryonic kidney (pronephros) contains two nephrons, previously thought to consist of a glomerulus, short tubule, and long stretch of duct. In this study, we have redefined the anatomy of the zebrafish pronephros and shown that the duct is actually subdivided into distinct tubule segments that are analogous to the proximal and distal segments found in mammalian nephrons. Next, we used the zebrafish pronephros to investigate how nephron segmentation occurs. We found that retinoic acid (RA) induces proximal pronephros segments and represses distal segment fates. Further, we found that the caudal (cdx) transcription factors direct the anteroposterior location of pronephric progenitors by regulating the site of RA production. Taken together, these results reveal that a cdx-RA pathway plays a key role in both establishing where the pronephros forms along the embryonic axis as well as its segmentation pattern.

Abstract Introduction Results Discussion Materials and Methods

developmental biology danio (zebrafish) vertebrates teleost fishes nephrology

2007

The cdx Genes and Retinoic Acid Control the Positioning and Segmentation of the Zebrafish Pronephros

White-nose syndrome is one of the most lethal wildlife diseases, killing over 5 million North American bats since it was first reported in 2006. The causal agent of the disease is a psychrophilic filamentous fungus, Pseudogymnoascus destructans. The fungus is widely distributed in North America and Europe and has recently been found in some parts of Asia, but interestingly, no mass mortality is observed in European or Asian bats. Here we report a novel double-stranded RNA virus found in North American isolates of the fungus and show that the virus can be used as a tool to study the epidemiology of White-nose syndrome. The virus, termed Pseudogymnoascus destructans partitivirus-pa, contains 2 genomic segments, dsRNA 1 and dsRNA 2 of 1. 76 kbp and 1. 59 kbp respectively, each possessing a single open reading frame, and forms isometric particles approximately 30 nm in diameter, characteristic of the genus Gammapartitivirus in the family Partitiviridae. Phylogenetic analysis revealed that the virus is closely related to Penicillium stoloniferum virus S. We were able to cure P. destructans of the virus by treating fungal cultures with polyethylene glycol. Examination of 62 isolates of P. destructans including 35 from United States, 10 from Canada and 17 from Europe showed virus infection only in North American isolates of the fungus. Bayesian phylogenetic analysis using nucleotide sequences of the viral coat protein geographically clustered North American isolates indicating fungal spread followed by local adaptation of P. destructans in different regions of the United States and Canada. This is the first demonstration that a mycovirus potentially can be used to study fungal disease epidemiology. Pseudogymnoascus destructans (Pd; previously named Geomyces destructans) is an emerging fungal pathogen responsible for a fatal disease, white-nose syndrome (WNS) in hibernating bats in North America [1–3]. Experts estimate over 5 millions bats died from WNS in North America since the disease was first noted in New York in 2006 [4–6]. Currently WNS has spread to at least 29 states in the United States (plus three additional states where Pd presence has been confirmed, but not WNS) and five provinces in Canada [4]. The fungus is widely distributed in Europe [6,7] and recently has been reported from the northeast of China and Siberia [8,9], but no mass mortality has been reported in European or Asian bats [6,8]. Pd’s lethal effect on North American bats coupled with its clonal genotype in North American isolates [10,11], its single mating type [12] and the absence of close relatives [13] led many researchers to hypothesize a recent introduction to North America [1,6, 14,15]. Pd is confirmed in seven North American [1,4] and 13 European species of bats [4,9]. The natural history of the genus Pseudogymnoascus and its allies indicate that they are commonly isolated from soils in colder regions of the world [16]. Unlike Pd many of its close relatives are cellulolytic saprobes and non-pathogenic [16,17]. Mycoviruses associated with fungi have drawn interest because of their potential roles in fungal biology and pathogenicity [18]. Mycoviruses are very frequent in fungi and generally maintain a persistent lifestyle [19]. Horizontal transmission is very rare, and is likely restricted to closely related strains, although phylogenetic studies indicate transmission among species has occurred [20]. Transmission has only been documented in a few cases outside the laboratory [21]. Most mycoviruses are cryptic with no known biological effects on their fungal hosts, although there is a lack of research in this area. However, there are significant examples where mycoviruses play important roles in fungal biology and ecology [22]. Here we used mycoviruses of Pd as a tool to study the epidemiology of WNS. We investigated mycoviruses in Pd and show that population variation of a Pd-mycovirus can be useful in tracing the spread of WNS. We examined 62 isolates of Pd from North American and European bats for mycoviruses (Table 1). The isolates were cultured from four North American and one European species of bats and were collected from 2008 to 2015. North American isolates included 14 from Pennsylvania, seven from New York, six from West Virginia, three from North Carolina, three from Vermont, one from Ohio, one from Indiana and 10 from New Brunswick, Canada. We screened 16 isolates of Pd from the Czech Republic and one isolate from Slovakia in Europe. Double-stranded RNA (dsRNA) extracted from all North American isolates showed two bands—a larger band close to 1. 8 kb (RNA 1) and a smaller band close to 1. 6 kb (RNA 2) in electrophoretic analysis (Fig 1A). None of the European isolates contained these dsRNAs, although two, CCF-4127 and CCF-4128, had dsRNAs profiles different from that of the North American isolates (Fig 1B). We found no dsRNAs of viral origin in five isolates of Geomyces sp. from Antarctic soil or in six isolates of Pseudogymnoascus sp. from cave soils in Pennsylvania (S1 Table). The dsRNA enrichment method is based on the premise that uninfected plants or fungi normally do not contain detectable amounts of high molecular weight dsRNA, and, when present, dsRNA is an indicator of a viral genome [23]. Sanger sequencing of cDNA clones from RNAs 1 and 2 of the North American isolates of Pd obtained from random primed RT-PCR provided nearly complete genomic sequences; ends were determined by 5' - primer ligated RNA ligase mediated-rapid amplification of cDNA ends (RLM-RACE) [24] providing consensus genomic sequences for RNAs 1 and 2 of 1761 bp and 1590 bp. Northern-blots using cDNA clones from RNA 1 or RNA 2 as probes confirmed the identity of the dsRNA bands (Fig 1C). We named this new virus Pseudogymnoascus destructans partitivirus-pa (PdPV-pa; the pa indicates the sequenced isolate is from Pennsylvania). A BLASTx search of GenBank showed closest similarity of RNA 1 of PdPV-pa with RNA 1 of Penicillium stoloniferum virus S (PsV-S), with 76% amino acid (aa) identity. Similarly, RNA 2 of Pd showed closest similarity with the RNA 2 of PsV-S with 67% aa identity. PsV-S is the type species of the genus Gammapartitivirus in the family Partitiviridae [25]. Sequence analysis of RNA 1 of PdPV-pa predicted a single open reading frame (ORF) of 540 aa (60 kDa) that codes for a putative RNA-dependent RNA polymerase (RdRp) (Fig 2A). RNA 2 also contained a single ORF of 470 aa (52 kDa) that codes for a putative coat protein (CP) (Fig 2B). Amino acid level sequence identity of PdPV-pa RdRp and CP with the approved members of genus Gammapartitivirus in the family Partitiviridae ranges from 58% - 76% and 36% - 67% respectively, which are within the species demarcation criteria (RdRp ≤ 90%; CP ≤ 80%) of the genus [42]. Further, the 5' termini of PdPV-pa RNAs 1 and 2 coding strand share a conserved CGCAAAA… sequence, where G is followed by A, U, or C but not G in the next 5 to 6 nucleotide positions, characteristic of the genus Gammmapartitivirus [25] (Fig 2C). Similarly, the 3' terminal 50 nucleotides of RNAs 1 and 2 were adenosine (A) rich in the range (7–24 nt) typical of members of the Gammapartitivirus genus [25] (Fig 2D). PdPV-pa particles were purified from mycelia of Pd and negative-stain transmission electron microscopy showed isometric particles of approximately 30 nm diameter, characteristic of members of the Partitiviridae (Fig 3A). PdPV-pa dsRNAs were also extracted from the purified virus particles to reconfirm their presence as genomic RNAs (Fig 3B). Bayesian trees constructed using aa sequences from the RdRp and CP of PdPV-pa clustered PdPV-pa with other members of genus Gammapartitivirus in the Partitiviridae family (Fig 4A & 4B). In both RdRp and CP trees, PdPV-pa appeared as a sister branch to PsV-S with strong posterior probability support of 92% and 100% respectively suggesting PdPV-pa is evolutionary close to PsV-S. The genome structure of PdPV-pa, conserved features in its RNAs explained above, its particle morphology, its RdRp and CP amino acid sequence identity within species demarcation criteria, and phylogenetic analyses all confirmed that PdPV-pa is a novel partitivirus belonging to genus Gammapartitivirus in the family Partitiviridae. We attempted several methods including single spore isolation, hyphal tip culture, protoplast culture, heat therapy and nutritional and chemical stress that involved application of the antiviral drugs cycloheximide or ribavirin, to cure Pd of the PdPV-pa infection. However, only partial success was achieved with high concentrations of cycloheximide (25 μg/ml) and ribavirin (300 μM) treatments after three passages. PdPV-pa remained suppressed in the fungus treated with cycloheximide or ribavirin when grown in media with the drug but once the fungus was transferred to drug-free media the virus reappeared. Finally, our attempt to cure the fungus using polyethylene glycol (PEG) -induced matric potential in minimal nutrition media made PdPV-pa undetectable. PdPV-pa infection in Pd was checked under matric potential gradients starting from -2MPa, -3MPa to -4MPa. We did not observe visible germination of Pd conidia or mycelia growth at -5MPa and -6MPa. PdPV-pa was undetected in PEG treated Pd isolates when evaluated by dsRNA extraction and RT-PCR with RdRp specific primers for PdPV-pa (Fig 5A & 5B). The detection limit of PdPV-pa in Pd was determined to be approximately 380 copies per cell (S1 Appendix). We enriched the viral dsRNA from total nucleic acid extracted from a defined number of Pd conidia followed by measurement of dsRNA concentration, and serial dilutions to determine the end-point of detection. Pd isolates where PdPV-pa was undetected after PEG treatment lost the characteristic gray pigmentation of wild type Pd and appeared white (Fig 6A). The virus-free isolate also produced significantly less conidia in comparison to wild type isolate (Fig 6B). Although PEG treatments were successful in obtaining a PdPV-pa free isolate of Pd, PdPV-pa tolerance to many other stresses mentioned above indicate that PdPV-pa is tightly associated with the Pd isolates from North America. Genetic variability of the RdRp and CP regions was analyzed in 45 North American isolates of PdPV-pa by amplification using specific primers followed by sequence analysis (Fig 7A & 7B). Using a 930 bp region of RdRp amplicons after trimming and alignment, we found the average percentage identity ranged from 99. 7 to 99. 9 among the 45 isolates. The high level of conservation in the RdRp is also reflected by a total of only 15 segregating sites, including seven singletons among the isolates examined. For the CP, nucleotide variability was higher: in a 1088 bp of amplicon of the CP, the average percent identity ranged from 96. 8 to 98. 4 and included 127 segregating sites out of which 69 were singletons. The Bayesian tree based on the RdRp nucleotide sequences of 45 North American isolates of PdPV-pa produced a largely unresolved tree with no clusters with significant support. However, the Bayesian tree constructed from the nucleotide sequences of the CP clustered the 45 PdPV-pa isolates into two major clades based on their geographical distribution (Fig 8). One clade was comprised of Canadian isolates; the other clade included isolates from the USA, although the posterior probability of this separation was lower than for other branching in the tree. The USA clade further included well supported clusters of isolates from New York, Pennsylvania, West Virginia, North Carolina, Vermont, Indiana and Ohio. Indiana and Ohio had one isolate each and separated as sister branches. The separate topologies of USA and Canadian clusters indicate independent diversification of Pd isolates subsequent to movement to particular regions. Within each major clade there were examples of sub-branching topologies representing isolates based on their local distribution although the pattern was not consistent throughout. The phylogeny of the PdPV-pa isolates showed no structure based on the taxonomy of the bats indicating that Pd is a generalist pathogen that is transmitted readily across bat species. In this study, we isolated and characterized a novel virus, PdPV-pa, from the pathogenic filamentous fungus causing WNS in North American bats. Based on the nucleotide sequence, sequence properties at the 5' and 3' termini, genome organization, morphology of the virus particle and phylogenetic analysis, PdPV-pa was confirmed as a new member of the genus Gammapartitivirus, family Partitiviridae. PdPV-pa shows closest similarity with PsV-S within Gammapartitivirus. The branch supports of over 90% in posterior probability in the RdRp and 100% in the CP Bayesian trees separating PdPV-pa from PsV-S (Fig 4A & 4B) and Gammapartitivirus species delimitation criteria (≤ 90% aa-sequence identity in RdRp and/or ≤ 80% aa-sequence identity in CP [26]) confirmed PdPV' s taxonomic placement into a distinct species [25]. The occurrence of PdPV-pa infection in Pd isolates from diverse geographical locations and time suggests PdPV-pa is widely spread in North America. We could not rule out the possibility of PdPV-pa incidence in Europe considering the sample size of 17 isolates that we examined in this study. Previously, Warneke et al. [14] reported a Pd isolate from Germany (MmyotGER2) showing similar mortality effects to North American isolates when inoculated onto North American little brown bat (M. lucifugus) under experimental conditions. Unfortunately, we were not able to obtain the German isolate to evaluate the presence of PdPV-pa. However the close association of PdPV-pa in a diverse subset of the North American population of Pd sampled (35 isolates from 7 states) may provide some indications of the roles of PdPV-pa in WNS. Many mycoviruses have been reported to elicit phenotypic changes, including both hypovirulence and hypervirulence in their fungal hosts [18]. For example, the presence of Helminthosporium victoriae 145S virus (chrysovirus) in the plant pathogenic fungus, Helminthosporium victoria increased virulence in oat plants. The viral dsRNAs up-regulated Hv-p68, an alcohol oxidase/RNA-binding protein in the fungus that is likely responsible for the disease development [27]. Similarly, a high level of virulence was reported in the presence of a six kbp mycoviral dsRNA in Nectria radicicola, the causal fungus of ginger root rot [28]. The opportunistic fungal pathogen, Aspergillus fumigatus causing lung disease in immunocompromised humans and animals also exhibited hypervirulence in the presence of the uncharacterized A78 mycovirus [29]. We have not explored the roles of PdPV-pa in WNS in the present study, but some indirect evidence, including the difficulties in curing the fungus of PdPV-pa, the stability of the virus after numerous generations of laboratory cultures, the changes in pigmentation and the significantly reduced production of conidia in the virus-free isolate indicate close biological relationships between the fungus and the virus; hence future investigation on potential biological effects of PdPV-pa will be important. In our attempts to cure PdPV-pa, PEG-induced stress on the matric potential was found effective. PEG being non-toxic and metabolically inert to fungi is an ideal compound to manipulate matric-induced water stress in media [30]. Matric potential influences water availability of substrates through capillary actions and particle adsorptive forces [31]. Raudabaugh & Miller [32] showed that Pd is sensitive to matric induced water stress beyond -5MPa, which is consistent with our results. In addition to the Pd growth response, normal growth at lower matric stress and significant growth inhibition as negative values of matric potential increases are characteristic of most soil fungi [32,33]. It is possible that Pd may have originated as a soil fungus and the adaptive pressure due to competition expanded its niche. The capacity of a human pathogenic fungus, Cryptococccus neoformans, to infect several animals including cats, dogs, dolphins, sheep and many birds was explained based on the environmental selective pressures imposed on it while surviving in its primary niche: soil [34]. The recent findings that Pd is capable of surviving on various substrates like harvestmen, fungus gnats, moss, and cave soils in addition to bat skin [32,35,36], support this argument. Whether or not Pd susceptibility to matric stress is related to its origin, the inhibitory effect of the matric stress on both Pd and PdPV-pa confirms parallel biological response of both the virus and the fungus. The genetic variation in the RdRp (<1%) and the CP (2–3%) of North American populations of PdPV-pa seems low, but in fact is quite high for partitiviruses. In studies with plant partitiviruses we find less than 1% divergence after extended periods of evolution (MR, personal observation). This higher level of variation implies a recent introduction of PdPV-pa. According to our results, only one species of this virus appears to occur in the North American isolates of Pd. The phylogenetic analysis based on a Bayesian algorithm of CP nucleotide sequences showed geographical clustering of 45 North American isolates into two main clades: USA and Canada. This indicates the diversification of PdPV-pa isolates is the outcome of geographical separation followed by sequence variation. No bat host specialization was observed. This finding is consistence with the clonal populations of Pd reported previously [10,11] with only one mating type [12] despite its infection in several species of bats in North America. The phylogenetic signatures of PdPV-pa isolates relating to geography provide valuable insights on the spread of WNS. The phylogeny supports two major clusters and many sub-clusters corresponding to US States of PdPV-pa isolation, suggesting connections among North American isolates, which is valuable in tracing WNS. Additionally, clustering of Pd isolates based on location was observed in several occasions within the USA clades followed by divergence, most likely for local adaptation. This analysis can be successfully expanded incorporating CP sequences of PdPV-pa from wider geographical locations to study the spread of WNS. Pseudogymnoascus destructans (Pd) was isolated from diseased bat wing tissue, live bat wing punches (2-5mm diameter) or wing swabs, cultured on 0. 5X (7. 5 g/L) Sabouraud dextrose agar (SDA) plates with 20 μg/ml of ampicillin, streptomycin and tetracycline at 10° C for 3 weeks in the dark. Identification of Pd was confirmed based on the species morphological characters i. e. , the presence of curved conidia [1] and DNA sequences from conserved regions: internal transcribed spacer1 (ITS1), elongation factor 1α (EF-1α) and glyceraldehyde 3-phosphate dehydrogenase (gdp) genes. The pure cultures of Pd were obtained either by single spore isolation or hyphal tip cultures. For single spore cultures, actively growing Pd plates (100 mm X 15 mm) of over three weeks old were flooded with 2 ml of sterile water and gently swirled to release the spores (conidia). The spore suspension was vortexed for one minute to avoid clumping of spores. The spore suspension was then picked using an inoculating loop and spread over water agar plate (19 g/L). About 1 ml of sterile water was added in the process to help to spread the spores uniformly. The plate was viewed under a dissecting microscope and concentration of the spore suspension was adjusted so that each plate had 20–30 spores. The plate was then cultured at 7°-10°C in the dark and checked for germination every alternate day. Once the spores germinated, an agar plug was cut containing hyphae from the single germinating spore without damaging growing hyphae and then plated on a regular SDA plate to culture. For hyphal tip culture, we used the protocols described by Kanematsu et. al. [37] with some modification. We plated spore suspension on regular SDA plates as described above but when spores geminated and mycelia mats were formed they were gently overlaid with sterile Whatman cellulose filter paper soaked in SDB. The plates were then cultured for an additional two weeks until the fungal hyphae penetrated the filter paper and started growing on the upper surface. At that point the filter paper was removed and its upper surface was scraped gently and hyphal segments were suspended in sterile water. The method produced hyphal segments ranging from 4–8 cells in length that were appropriate for the hyphal tip culture. The hyphal segment suspension was then plated on SDA plates adjusting the concentration so that each plate had uniform distribution of 20–30 hyphal segments. Finally agar plugs grown from individual hyphal segments were cultured in separate plates to obtain a pure culture. The fungal isolates were stored in SDA plates for short-term storage at 4°C and at -80°C in the form of mycelia in 50% glycerol for long-term storage. All Pd isolates from Pennsylvania, one from Vermont and one from Indiana used in this study were isolated and cultured in our laboratory. The substrates (bat wings, wing punches, swabs) for these cultures were obtained from routine surveys of the Pennsylvania Game Commission (http: //www. pgc. pa. gov/Wildlife/Wildlife-RelatedDiseases/WhiteNoseSyndrome). The isolates from New York, West Virginia, North Carolina, Ohio, the remaining two isolates from Vermont and all European isolates were obtained as sub-cultures from the Center for Forest Mycology Research, United States Forest Service, Madison, WI (http: //www. fpl. fs. fed. us/research/centers/mycology/culture-collection. shtml). The Canadian isolates were obtained as sub-cultures from New Brunswick Museum collections, New Brunswick, Canada (http: //www. nbm-mnb. ca). In addition, we obtained five isolates of Geomyces sp. collected from Antarctic soil from Dr. Robert A. Blanchette’s collection at the University of Minnesota and we used six isolates of Pseudogymnoascus sp. from cave soil in Pennsylvania for this study. We extracted dsRNAs from lyophilized mycelia of Pd with a minor modification in the protocol described by Márquez et. al. [38], specifically Pd was cultured using mycelial plugs or spores in 150 ml of 0. 5X Sabouraud dextrose broth (SDB) supplemented with 20 μg/ml of ampicillin, streptomycin and tetracycline in a shaker at 10°C under dark conditions for three weeks prior to lyophilization. In addition to binding to CF11 cellulose (Whatman) in the presence of ethanol, the chemical nature of the dsRNA was confirmed by its resistance to DNase and RNase with NaCl concentration > 0. 3M. Approximately 2 μg of dsRNA were mixed with 2 μM of random primer-dN10 with a linker sequence (5' CCTTCGGATCCTCCN103' ), 0. 5 mM of Tris-EDTA (pH 8. 0) and nuclease-free water to a final volume of 12 μl, and boiled for 2 min. The mixture was incubated on ice, and 8 μl of Reverse Transcriptase (RT) mix (SuperScriptTM III RT 200U, 5X First-Strand buffer 4 μl, 0. 1M DTT 1 μl and dNTP 0. 5 mM as recommended by the manufacturer) were added and incubation continued at 50°C for 1. 5 hours. The newly synthesized cDNA mixture was then incubated with 10 μg of boiled RNase A (Sigma) for 15 min. at room temperature and cleaned with E. Z. N. A Cycle Pure Kit (Omega Bio-tech) according to the manufactures instruction. About 0. 5 μg of cleaned cDNA was used as a template for a 25 μl polymerase chain reaction (PCR) with Taq DNA Polymerase (ThermoFisher Scientific), buffers, dNTPs supplied with 1μM concentration of the primer (5' CCTTCGGATCCTCC 3' ). The PCR was completed in a Idaho Technologies Rapid Cycler with a slope setting of 5, using the following cycles: 1 cycle of 94°C, 1 min. ; 25 cycles of 94°C, 0 sec. , 45°C, 0 sec. , and 72°C, 15 sec. ; 1cycle of 72°C, 5 min. ; 1 cycle of 37°C, 5 min. The PCR product was cleaned and cloned into the pGEM-T Easy Vector System (Promega) according to the manufacturers instructions. Sequence analysis of the cDNA plasmid clones were done by the Genomic Core Facility of Pennsylvania State University, University Park, PA. The sequences obtained were trimmed for plasmid and primer sequences and assembled using de novo assembly in Geneious version 8. 0. 2 [39]. All cloning and sequence analysis was based on the dsRNA from the LB-01 isolate cultured from a little brown bat from Pennsylvania. RNA ligase mediated-rapid amplification of cDNA ends (RLM-RACE) was performed to determine the terminal sequences of the PdPV-pa dsRNA segments. A 5' -phosphorylated oligodeoxynucletide (5' -PO4-GGAGGATCCGAATTCAGG 3' ) was ligated to the dsRNA termini as an adaptor before synthesizing cDNAs using a complementary primer (5' CCTGAATTCGGATCCTCC3' ) in combination with the internal primers designed for PdPV-pa RNA1 and RNA2 (RNA 1: 5' TTCAAGTTCGCCCTGTACC3' F, 5' TGAGCGAATGGAAGGTTG3' R; RNA 2: 5' CGCGTAATCATGACGACC3' F, 5' CCGAGGAGCACACACTATC3' R) in RLM-RACE. Ligation reactions were done in 50% PEG with 2 U of T4 RNA ligase 2 (New England BioLabs) mixed with approximately 2 μg of dsRNA along with the primers mentioned above and buffer supplied according to the manufacturers instructions, and incubated at 4°C overnight. RT-PCR of the primer-ligated dsRNA was performed exactly like described in the cDNA synthesis above except the enzyme used was Avian Myeloblastosis Virus (AMV) RT (New England BioLabs). The amplicons were cloned followed by sequence determination using Sanger sequencing. The complete nucleotide sequences of PdPV-pa RNA 1 and PdPV-pa RNA 2 have been deposited in GenBank with accession numbers KY20754 and KY207544, respectively. Consensus sequences for PdPV-pa RNA 1 and RNA 2 were analyzed for the open reading frames (ORFs) using ORF finding operation in Geneious version 8. 0. 2. A sequence similarity search was conducted with BLASTn and BLASTx available online from the National Center for Biotechnology Information (NCBI). Northern blotting was performed using non-radioactive isotopes probes, digoxigenin (DIG) -11-dUTP-labeled DNA fragments according to the manufacturers instructions (Roche Diagnostics). Representative clones of PdPV-pa RNA 1 and RNA 2 in the range of 500–700 bp were selected and the labeling was done in a PCR with DIG-11-dUTP and dNTPs mix (DIG-11-dUTP: dTTP = 1: 3; with equimolar amount of dATP, dCTP and dGTP), Taq DNA Polymerase (ThermoFisher Scientific), specific primers and buffer in Idaho Technologies Rapid Cycler as described above. About 2 μg of PdPV-pa dsRNA was electrophoresed in 1. 2% agarose gels and subsequently denatured by saturating with freshly prepared 50mM NaOH for 30 min followed by neutralization in 50mM sodium borate for 5 min. The cycle was repeated three times before dsRNA was transferred to a nylon membrane (Hybond N+ Amersham) by capillary action overnight. The membranes were UV-cross-linked in a Stratalinker at 200 J. Hybridization and washings were carried out as described by Li et al. [40] except we performed prehybridization and hybridization at 52°C instead of 42°C. The blots were incubated in antibody solution, anti-DIG-AP Conjugate (Roche) and CDP-STAR (Roche) for chemiluminescence detection. Virus particles were purified following methods described by Sanderlin and Ghabrial [41] with some modifications. Eight g of lyophilized mycelia of Pd isolate BB-06 was ground to powder in the presence of liquid nitrogen. The homogenates were mixed with extraction buffer (0. 1 M sodium phosphate. pH 7. 6 containing 0. 5% (v/v) thioglycolic acid) and mixed with chloroform followed by low speed centrifugation at 7000 rpm for 15 min at 4°C. The virus containing supernatant was then subjected to two cycles of differential centrifugations (low speed at 7000 rpm for 15 min and ultracentrifuge at 35,000 for 1. 5 hours). During the ultracentrifuge cycle, the virus containing supernatant was underlaid with a 10% sucrose cushion. The final pellets were suspended in 1 ml of 0. 03 M sodium phosphate buffer pH 7. 6. The virus preparation was examined under JEOL 1400 transmission electron microscope after negatively staining with uranyl formate in the Microscopy and Imaging Facility at Penn State College of Medicine, Hersey, PA. For the heat stress, actively growing Pd plates in three replicates were exposed to room temperature (22–23°C), 37°C and 42°C for 2,6, 12 and 24 hours before culturing the mycelia plugs from each treatments in liquid medium (SDB) under normal laboratory culture conditions for Pd described above. During the treatments, Pd plates in three replicates were also grown under normal culture condition as controls. The fungal mycelia were then harvested after three weeks to extract dsRNAs. However, only samples treated at room temperature and 37°C for 2 hours grew. Single spore isolation and hyphal tip cultures were done as described under the section, fungal isolation and culture. The protoplast isolation from Pd was performed on mycelia (~ 1. 7 g) harvested from SDB culture after two weeks at 10°C in a shaker (200 rpm) in the dark. The fungal mycelia were collected by centrifugation at 90 × g for 5 min followed by washing with KCl buffer (0. 6 M, pH 5. 8) as an osmotic stabilizing agent. The mycelia was treated with lysing enzyme mixture (Lysing enzyme from Trichoderma harzianum 20 mg/ml and driselase 20 mg/ml from Sigma) prepared in KCl buffer and incubated at 10°C at 70 rpm in the dark. Protoplast production was checked every half an hour until 35–40 protoplasts were observed under a 40X field with 10 μl of the mixture. The mixture was then passed through double-layered miracloth (VWR) soaked in STC buffer (1. 2 M Sorbitol; 10 mM Tris-HCl, pH 7. 5; 20 mM CaCl2) to filter out the cell debris. The filtrate was centrifuged at 90 × g for 5 min to collect the protoplasts which were resuspended in regeneration media (0. 5% yeast extract, 2% glucose, 0. 6 M Sorbitol and 25 mM CaCl2) followed by incubation at 10°C at 70 rpm in the dark. Once the protoplasts recovered completely with cell wall growth, they were transferred to agar supplemented regeneration media (0. 5% yeast extract, 2% glucose, 20% sucrose and 1% agar) and the concentration adjusted so that each plate had 25–30 uniformly distributed cells. The plates were then incubated under normal culture condition for Pd until hyphae developed uniformly around each protoplast without touching each other. Individual colonies were then picked and cultured in SDA. We also treated Pd with the antiviral drugs cycloheximide and ribavirin at different concentrations in SDA media. Cycloheximide was used at 2 μg/ml, 5 μg/ml, 10 μg/ml, 15 μg/ml and 25 μg/ml concentrations. Ribavirin treatment was at 80 μM, 100 μM, 150 μM, 200 μM and 300 μM concentrations. Three passages with both cycloheximide and ribavirin were also performed with higher concentrations. For PEG induced matric stress on water availability we used PEG 8000 (Fisher BioRegents) in a modified Spezieller Nährstoffarmer liquid media (SN: 0. 02 g sucrose, 0. 02 g glucose, 0. 08 g KNo3,0. 08 g KH2Po4,0. 04 g MgSo4. 7H2O and 0. 04 g NaCl/L) to make media with water potential gradients of -1 MPa, -2 MPa, -3MPa, -4 MPa, -5 MPa and -6 MPa. The amount of PEG 8000 in gram/gram of water was calculated based on Michel [42] equation: Ψ (water potential) = 1. 29 [PEG]2T – 140[PEG]2–4 [PEG] and the value was adjusted to the Pd culture temperature of 10°C. An agar plug containing actively growing Pd was placed in 50 ml autoclaved modified SN liquid media with a targeted amount of PEG 8000 (-1 MPa: ~ 0. 075 PEG g/g of water, -2 MPa: ~ 0. 11 PEG g/g of water, -3 MPa: ~ 0. 14 PEG g/g of water, -4 MPa: ~ 0. 16 PEG g/g of water, -5 MPa: ~ 0. 19 PEG g/g of water and -6 MPa: ~ 0. 21 PEG g/g of water) and grown as described above. After three weeks, pieces of newly growing mycelia of Pd were transferred to normal SBD routinely used to culture Pd and the fungus was harvested after a normal culture period. The fungi from different treatments were examined for PdPV-pa both by dsRNAs gel electrophoresis and RT-PCR with PdPV-pa specific primers. In all methods Pd isolate LB-01 was used. Genetic variation in North American PdPV-pa isolates were determined by sequence analysis of RdRp and CP segments amplified in RT-PCR using specific primers. The primer pairs specific to RdRp (5' ATGGAAGTATCTCCTTTCG3' F, 5' GTATAGAAGATTGAGTGCC3' R) and CP (5' ACTCTGTGTTAACGGAGG3' F, 5' CTGTAGTTGACACCTGTACC3' R) were designed from the consensus sequences of RNA 1 and RNA 2 assembled from LB-01 isolate cloned sequences. PCR products using RdRp and CP specific primers from 45 North American PdPV isolates were sequenced and aligned with MUSCLE default settings in the program Geneious 8. 0 [39]. The RdRp sequences have been deposited in GenBank under accession numbers KY207498 to KY207552 and the CP sequences have been deposited in GenBank under accession numbers KY207453 to KY207497. The alignment was visually corrected as necessary before recording segregating and singleton sites. The average percentage identity for each sequence was calculated by taking the average from a pairwise percentage identity matrix generated from the sequence alignment. Phylogenetic analysis was performed using MrBayes [43] implemented via a plug-in in Geneious. The amino acid sequences were used in studying the evolutionary relationships of PdPV-pa within the genus Gammapartitivirus. The tree was constructed using amino acid sequence (RdRp and CP) of 10 approved species of Gammapartitivirus available in the GenBank. The sequences of Pepper cryptic virus 1, type member of genus Deltapartitivirus, which is the closest group to Gammapartitivirus in Partitiviridae family was used as outgroup. We used nucleotide sequences (CP) to study phylogenetic relationships of PdPV-pa in North American population. The nucleotide sequence of PsV-S CP was used as outgroup in the analysis. In Bayesian trees construction using amino acid sequence of the RdRp and CP ORFs, Jukes-Cantor substitution model was applied and for nucleotide sequences of CP General time-reversible (GTR) model with gamma rate variation was used based on the best model tested out of 28 models.

Many species of bats in North America have been severely impacted by a fungal disease, white-nose syndrome, that has killed over 5 million bats since it was first identified in 2006. The fungus is believed to have been introduced into a cave in New York where bats hibernate, and has now spread to 29 states and 4 Canadian provinces. The fungus is nearly identical from all sites where it has been isolated; however, we discovered that the fungus harbors a virus, and the virus varies enough to be able to use it to understand how the fungus has been spreading. This study used samples from infected bats throughout Pennsylvania and New York, and New Brunswick, Canada, as well a few isolates from other northeastern states. The evolution of the virus recapitulates the spread of the virus across these geographical areas, and should be useful for studying the further spread of the fungus.

Abstract Introduction Results Discussion Materials and Methods

sequencing techniques fungal spores vertebrates animals mammals fungi phylogenetic analysis rna isolation molecular biology techniques rna sequencing fungal reproduction research and analysis methods sequence analysis mycology rna sequence analysis bioinformatics molecular biology molecular biology assays and analysis techniques biomolecular isolation dna sequence analysis database and informatics methods bats biology and life sciences amniotes organisms

2016

Using a Novel Partitivirus in Pseudogymnoascus destructans to Understand the Epidemiology of White-Nose Syndrome

Sustainable dengue intervention requires the participation of communities. Therefore, understanding the health beliefs, knowledge and perceptions of dengue among the local people can help to design locally appropriate strategies for effective interventions. A combination of qualitative semi-structured in-depth interviews (SDIs) and quantitative household questionnaire surveys (HHSs) was used to investigate the beliefs, knowledge and perceptions of dengue among the Shan people in Eastern Shan Special Region IV (ESSR4), Myanmar. The SDI was administered to 18 key informants, and the HHS was administered to 259 respondents. Only 14. 7% (95% CI: 10. 6–19. 6%) of the HHS respondents could confirm that mosquitoes transmit dengue; 14. 3% (95% CI: 10. 3–19. 1%) knew that piebald or Aedes mosquitoes transmit dengue; and 24. 3% (95% CI: 19. 2–30. 0%) believed that dengue-transmitting mosquitoes mainly lived in small ponds. Merely ten (0. 4%) of the 259 respondents of the HHS thought that dengue-transmitting mosquitoes bite in the day time. The people in the villages where there were outbreaks of dengue had more knowledge about dengue. This study demonstrates that the health beliefs of the Shan people were closely associated with their lifestyles, social and natural environments. To stay healthy, the Shan people clean their houses and surroundings regularly. However, their knowledge about dengue was not adequate for effective dengue control because it was mostly learned from previous dengue experiences and in a context that lacks systematic health education. Thus, in this setting, with a weak public health structure, more international support should be provided to promote the knowledge of the Shan people about dengue and to increase their sensitive awareness to dengue, which might be beneficial for social mobilization and community participation during future dengue prevention. Dengue fever (DF) is a mosquito-borne disease caused by the dengue virus. It has become a substantially increasing threat to public health and represents a challenge to health services and a burden to economies. It was recently estimated that there are 390 million dengue infections every year (95% confidence interval 284–528 million), of which 96 million (67–136 million) infections manifest clinically [1,2]. The World Health Organization (WHO) estimated that 3. 9 billion people in 128 countries are at risk for infection with dengue viruses [3] and that approximately 75% of the global population who are at risk for contracting dengue is distributed across the Asia-Pacific region [4]. However, dengue is still one of the most neglected tropical diseases, which are caused and maintained by the social and environmental determinants of health [5]. The social determinants of infectious disease control are difficult to address [6]. For that reason, the Special Programme for Tropical Diseases Research and Training from the World Health Organization has called for more research on the interplay between the demographic, social, and environmental factors in infectious disease occurrence [7]. In recent years, outbreaks of DF have occurred along the China-Myanmar-Laos border, and most of these outbreaks affected Dai or Shan ethnic communities [8,9]. Dengue virus is primarily transmitted by Aedes mosquitoes, particularly Aedes aegypti. Water containers and discarded tires are the main productive habitats for Aedes [10]. The population dynamics of Aedes aegypti are influenced by human behavior and by the weather [11]. Outbreaks of DF may also be attributed to the social and environmental characteristics of the residents. Human behavior change communication (BCC) is one of the currently adopted strategies to reduce the Aedes population and dengue virus transmission [4,11–13]. Understanding people’s health beliefs, knowledge and perception about dengue may help to generate better strategies for dengue control [14,15]. Data about people’s health beliefs, knowledge and perceptions about dengue are still lacking at the China-Myanmar-Laos border. This kind of data may help to explore locally adaptive strategies and solutions for the next steps in dengue prevention and control. This study investigated the health beliefs, knowledge and perception of the Shan people about dengue in Eastern Shan Special Region IV (ESSR4), Myanmar. The purpose of using both qualitative and quantitative methods is to triangulate the study outcomes, to try to gain in-depth understanding and to compare any potential differences between the general community and the key informants who are supposed to have more chances to receive health information. Here, we report our findings and provide a discussion on their implications. This investigation was a cross-sectional study that used mixed methods, including a quantitative HHS and qualitative SDI. Based on the number of reported dengue fever cases in 2017, three types of villages (high, low and no dengue incidence) were chosen as sample sites in Mongla Township in ESSR4, Myanmar. The SDI was administered to six key informants in each type of sampled village, so a total of 18 SDIs were conducted. The targeted sample size of the HHS, i. e. , 250 household heads for the questionnaire survey, was determined based on a 95% confidence interval of the standard value normal distribution, a 5% precision and an estimated 20% of adult people who know that mosquitoes transmit dengue virus [16]. Generally, people’s health beliefs are supposed to affect people’s knowledge, perception and preventive behaviors regarding diseases. In this paper, health beliefs are the general perceptions of elements related to health and diseases, not just special beliefs about dengue, i. e. , people’s perception of the effect of religious, socioeconomic and natural elements on their health and their perception of disease causes. More specifically, in this article, the knowledge and perception were specifically about dengue fever, including their knowledge of the clinical symptoms, vectors, dengue virus transmission, preventive methods and their perceived risk of contracting dengue fever. First, two of the main researchers discussed the study themes, including common diseases and health problems in the community, causes of poor health, knowledge of dengue, perceived risks of dengue and preventive methods, and then drafted the guidelines for the qualitative SDI. Second, the guidelines were pretested through discussions with village leaders and health workers and were then revised and finalized according to the results of the pretests. Third, based on the qualitative SDI guidelines, a questionnaire was developed, pretested by interviewing five heads of households, and then revised and finalized. The questionnaire consisted of 55 questions that covered demographics, education, family economics, beliefs on health and disease causes, the knowledge of dengue (including symptoms, transmission, vectors and prevention), perceived risk and attitudes. ESSR4 of Myanmar adjoins the Xishuangbanna Prefecture of China [17] and has three administrative areas, i. e. , Mongla Township, Nanban and Selei County. The total population of ESSR4 is approximately 110,000, and the majority of residents are Shan people (known as Dai in China, Thai in Thailand and Lao in Lao PDR), which is one of the main ethnicities in the Greater Mekong Subregion (GMS). The hot climate and adequate precipitation in ESSR4 provide a suitable environment for the growth and reproduction of mosquitoes, thereby increasing dengue transmission. Outbreaks of DF have recently occurred in Mongla Township each year [8]. The hospital of ESSR4 is the sole health facility that can perform the laboratory-based diagnosis and treatment of DF, and this hospital reported a total of 114 dengue fever cases in 2017. To compare the responses of communities with different DF experiences and to explore the potential reasons behind these responses, this study was deliberately designed to be carried out in three types of villages (V1 = no DF patients, V2 = low DF incidence, and V3 = high DF incidence). After discussion with the hospital of ESSR4, Mangjingpa and Wangnali (V1), Wangmaidao (V2) and Wangdong (V3) were selected for the study, as a common decision between us and the hospital. In 2017, V1 had 867 people and no reported DF cases; V2 had 389 people and nine reported DF cases; and V3 had 487 people and 36 reported DF cases. The qualitative data were collected by interviewing 18 key informants, with six from each type of village (i. e. , V1, V2 and V3). Participants included one health worker, three village leaders and two villager representatives who were chosen by the other villagers. The interview was conducted by following the SDI guidelines. One researcher interviewed the key informants in the Shan language, and another took notes in Chinese. The contents that were discussed included local health problems, villagers’ beliefs, including religion, the causes of diseases, local dengue situations, the name for DF in the Shan ethnic language and its meaning, the linkage between mosquitoes and dengue, the time that mosquitoes bite humans, the breeding sites of dengue-transmitting mosquito larvae and people’s perceptions of the prevention of DF [18,19]. The unit of sampling of this study was a household, which was defined as all those eating from the same cooking pot. The family wealth index (FWI) was determined by the household’s physical assets, such as housing, walls, roofs, and bicycles, and was classified into five groups (Table 1). Households were selected by a simple computer randomization in V1, V2 and V3. The 50% ±10% of the targeted sample size of the HHS (250 household heads) was decided for villages with reported dengue cases and without, respectively. Chinese is the official language in the ESSR4, so the questionnaire was written in Chinese. Researchers first visited each selected household and introduced the purpose of this study, topics and related questions that would be asked. The questionnaire was subsequently administered to each household head only after oral informed consent was obtained. Investigators who understood both the Shan language and Chinese from the Hospital of ESSR4 asked every question in the Shan language and then filled out the questionnaire in Chinese [20,21]. Records of the qualitative SDI were coded according to the contents of the questions and were then entered into cells in Microsoft Office Excel 2007. The same content records were combined by code sequencing. Two researchers independently analyzed the records of every content to generate themes. Finally, the two researchers discussed and compared their findings to finalize the findings. Quantitative data were entered in Excel 2007 and were analyzed in Epi Info 7. 2. The percentage and its 95% confidence interval (CI) were calculated for each health belief, piece of knowledge and perception. A chi-squared test was used to compare the percentage of each health belief, piece of knowledge and perception across the three village categories (V1, V2 and V3) [20,21]. The study was approved by the Bureau of Health of ESSR4, Myanmar. Ethical approval for this study was also granted by the Ethics Committees of the Yunnan Institute of Parasitic Diseases in China. Verbal consent was approved by the Ethics Committee as an acceptable form, as the study was interview-based and did not include any human specimens. All studied participants were adults (over 18-years-old). According to the World Medical Association Declaration of Helsinki, the purpose and procedures of the study were explained and disclosed to the participants before obtaining their consent. Participation was entirely voluntary, and participants could pass on a question, take a break or withdraw their consent from the study without providing any explanation at any time. Their consent was assumed if they did not refuse to answer questions. The SDI was administered to 18 key informants, including nine males and nine females, ranging from 32- to 54-years-old. The questionnaire was administered to 259 household heads, and all completed questionnaires were considered valid. Among the 259 households, 211 (81. 5%, 95% CI: 76. 2–86%) were less poor (FWI 4–5) (Table 1). The respondents were made up of 149 females and 110 males. The mean age of the respondents was 42. 3-years-old (median: 49. 0, range: 18–99). A total of 225 (86. 9%, 95% CI: 82. 1–90. 7%) respondents said that they had not received any formal health education. Most of the participants and their family members were Buddhists. Only two of the HHS respondents were Papists, and the other three were not religious. All 18 key informants were Buddhist and agreed that, “One good turn deserves another. Evil will be recompensed with evil. The Buddha will bless and protect good people. If anyone does bad things, he or she would be punished due to their evil behaviors”. Half of 18 key informants thought that the natural environment in which they were living affected their health. In V1, three of the key informants agreed that, “In hotter weather, more people get ill”. In V2, two key informants agreed that, “Extremely hot or cold weather can make people ill”. In V3, four of the key informants agreed that, “When there are more trees, there are more mosquitoes and therefore more diseases”. Nine of 18 key informants believed that a certain link existed between household economic income and diseases. One of the key informants explained, “In poorer economic conditions, it is easier to get diseases because the poor may more easily suffer from malnutrition and be less healthy”. Fourteen of the key informants thought that clean living and working environments benefited their health. In V1, the key informants agreed that, “Poor hygiene is one of the disease causes”. In V2, the key informants agreed that, “Dirty water leads to diseases”. In V3, the key informants agreed that, “When hygiene is poor, it is easier to contract diseases” (Table 2). The results of the HHS showed that 79. 9% (95% CI: 74. 5–84. 6%) of the respondents believed that Buddha would bless good people; 54. 1% (95% CI: 48. 2–60. 6%) thought that anyone who did evil would be punished; 47. 5% (95% CI: 41. 3–53. 8%) mentioned that poverty was one of the causes of disease; 62. 9% (95% CI: 56. 7–68. 8%) believed that all natural factors, including climate, weather, water and forests, influenced human health; 87. 0% (95% CI: 92. 2–90. 9%) believed that cleanliness and sound hygiene benefited human health; and 21. 7% (95% CI: 16. 7–27. 2%) thought that unpolluted water was healthier than polluted water (Table 3). DF is named “Paya Yong” in the Shan ethnic language, in which “Paya” represents illness and “Yong” represents mosquitoes. Shan people have connected DF to mosquitoes from the literal meaning of this ethnic language phrase. However, most participants did not know the clinical symptoms of DF. Six of 18 key informants regarded fever as one of the symptoms of DF, and one of the key informants also mentioned rash, pantalgia and joint pain (Table 2). The results of the HHS showed that there was a higher proportion of respondents who did not know the DF symptoms or who did not respond to the question in V1 than in V2 and V3 (P = 0. 0453); only 17. 8% (95% CI: 13. 3–23. 0%) of the respondents mentioned fever; 14. 3% (95% CI: 10. 3–19. 1%) mentioned headache; 4. 2% (95% CI: 2. 1–7. 5%) mentioned orbital pain; 9. 7% (95% CI: 6. 3–13. 9%) mentioned pantalgia; and 3. 5% (95% CI: 1. 6–6. 5%) mentioned rash (Table 3). Seven people perceived DF as a serious or deadly disease among the 18 key informants. In V2 and V3, all 12 key informants thought that local people could easily become infected with DF; however, in V1, only one out of the six key informants thought that local people could easily become infected with DF (Table 2). The HHS results revealed that 68. 7% (95% CI: 62. 7–74. 3%) of the respondents either did not know about the risk or did not respond to the question regarding risk; only 27. 8% (95% CI: 22. 4–33. 7%) of the respondents thought of DF as a serious disease; and 24. 7% (95% CI 19. 6–30. 4%) of the respondents considered DF to be deadly (Table 3). Both the quantitative and qualitative data showed similar results. Ten of the 18 key informants regarded DF as a contagious disease, but they thought that DF could be transmitted from person to person directly by breathing, speaking and physical contact, etc. (Table 2). Furthermore, the HHS results revealed that 52. 9% (95% CI: 46. 6–59. 1%) of the HHS respondents did not answer the transmission question; only 25. 0% (95% CI: 19. 4–31%) the respondents thought that DF was transmittable; 20. 0% (95% CI: 14. 0–24. 9%) thought that DF could be transmitted from person to person directly; and only 14. 7% (95% CI: 10. 6–19. 6%) of the HHS respondents confirmed that DF was transmitted by mosquitoes. Some respondents thought that bacteria, viruses, animals and improper or dirty food as one of the causes of DF (Table 3). Six of the 18 key informants knew that dengue-transmitting mosquitoes were piebald or Aedes; they also knew that dengue-transmitting mosquitoes could bite during all 24 hours of the day. Only one key informant asserted that dengue-transmitting mosquitoes bit in the daytime, compared with four informants who thought that dengue-transmitting mosquitoes only bit at night. Thirteen key informants confirmed that dengue-transmitting larvae live in water; an additional eight of the 13 key informants confirmed that containers or small-scale pond water is the principal breeding sites for the larvae. The other five key informants did not know the breeding habitats of the mosquito larvae or they did not respond to the question (Table 2). The HHS results indicated that 79. 2% (95% CI: 73. 7–83. 0%) of the respondents were unable to answer the vector question; only 14. 3% (95% CI: 10. 3–19. 1%) of the respondents knew that piebald or Aedes were dengue-transmitting mosquitoes, and there were more HHS respondents who knew about dengue-transmitting mosquitoes in V2 and V3 than in V1 (P = 0. 0293). Only 21 out of the 259 respondents knew that dengue-transmitting mosquitoes bit people during the day and night, and ten further confirmed that biting could occur during the daytime. More respondents knew that mosquitoes bite in the daytime in V2 and V3 than in V1 (P = 0. 0467). A total of 33. 2% (95% CI: 27. 5–39. 3%) of the HHS respondents thought that all types of water sites were the habitats of dengue-transmitting mosquitoes, and only 24. 3% (95% CI: 19. 2–30. 0%) confirmed that the dengue-transmitting mosquito larvae were only living in containers or in the water of small ponds. More of the HHS respondents in V1 than in V2 and V3 (P = 0. 0018) could not answer this question or did not respond to the habitat question (Table 3). All participants showed a keen interest in this topic and thus actively discussed this issue with the investigators. Thirteen key informants knew that clearing mosquito breeding sites helps to prevent dengue transmission. Ten key informants said that good hygiene was beneficial for their health and expressed their willingness to participate in the cleaning of the community environment (Table 2). In the HHS, only 20 participants did not answer the questions regarding the control of mosquito larvae, and only five had no response to the questions. The HHS results indicated that 43. 6% (95% CI: 37. 5–49. 9%) of the respondents knew that maintaining good hygiene was important for eliminating the habitats of the dengue-transmitting mosquito larvae; 67. 2% (95% CI: 61. 1–72. 9%) knew that turning containers upside down was important for eliminating the habitats of the dengue-transmitting mosquito larvae; and 20. 5% (95% CI 15. 7–25. 9%) knew that draining small-scale pond waters was important for eliminating the habitats of the dengue-transmitting mosquito larvae. More participants in V2 and V3 than in V1 understood that good hygiene helps to control DF. Regarding adult mosquito control, 20. 1% (95% CI: 15. 4–25. 5) of the HHS respondents knew about the use of door and window screens; 79. 9% (95% CI: 74. 5–84. 6) knew about mosquito coils; 27. 8% (22. 4–33. 7%) knew about spraying or fogging with insecticides; and 69. 9% (63. 9–75. 4%) knew about the use of bed nets. More HHS respondents in V2 and V3 than in V1 thought that using window and door screens (P = 0. 02) and spraying with insecticides is helpful (P = 0. 5891). However, more respondents in V1 than in V2 and V3 thought that using mosquito coils (P = 0. 03) and bed nets (P = 0. 1235) helped with dengue prevention. Additionally, more participants in V1 than in V2 and V3 (Table 3) expressed their willingness to eliminate bamboo and tree stump holes (P = 0. 3302), to clean up dumps and to turn containers upside down (P<0. 0001) (Table 3). The dengue burden is growing and has become large enough currently. More than half of the global population lives in territories that are at risk of becoming infected with dengue [2,4]. However, ability to contain epidemics of the dengue virus is still limited. In addition to supportive treatments, effective antiviral therapies continue to be lacking. The only licensed dengue vaccine is only partially protective [22]. Thus, intensified vector control could still be the most important strategy for DF prevention for a long time to come. Adopting protective behaviors is a multifactorial process that depends on both sociocultural and cognitive factors [23]. The Shan people have beliefs that have originated from their primary social living and culture. They believe that health is associated with all natural and social environmental factors, and that regularly cleaning their houses and surroundings to maintain sound hygiene would benefit human health. The name for DF in the Shan ethnic language (Paya Yong) connects dengue with mosquitoes, however, their knowledge and awareness of DF remain at low level. These low levels might be attributed to the fact that the local Shan people are unable to effectively benefit from the services that are provided by both the national health and education systems. Only nine of the participating household heads had completed more than six years of formal education. Thus, the participant’s low levels of knowledge and perceptions about DF might be explained by the lack of health services that they benefitted from and the inadequate formal education that they received. Meanwhile, few actions involving information, education and communication on dengue have been taken, despite the fact that the burden of DF is increasing [8], shortly after the malaria burden was effectively reduced [24]. In V2 and V3, there was a higher response rate and level of knowledge than in V1 about DF symptoms, transmission, vectors, time of day when mosquitoes bite and the breeding sites of dengue-transmitting mosquitoes. This difference might be attributed to the fact that the V2 and V3 communities have suffered from DF and thus have more experience and knowledge regarding dengue. Over half of the participants could not answer questions about the causes of DF. Only a quarter of the participants understood that dengue is transmittable, but most of the participants thought that dengue could be directly transmitted from person to person by breathing, speaking, physical contact, etc. (Tables 2 and 3). What is worse, almost all key informants did not have enough knowledge to communicate with their fellow villagers to prevent future outbreaks. Most of the participants were unsure if they were at risk of being infected by dengue. Both the qualitative and quantitative data show that the participants had difficulty with answering the questions on the risks and seriousness of DF. Only 15. 8% (95% CI: 11. 6–20. 9%) of the participants perceived the risk of becoming infected. Furthermore, less than one-third of the participants (72/259) considered DF to be a serious disease and lethal. People in V2 and V3 were more susceptible to DF than those in V1. This indicates that their perceived risk may have correlated with their previous experience of DF outbreaks too. This Inappropriate preparedness due to the shortage of necessary resources might be one of the reasons for the outbreaks of DF in ESSR4, Myanmar. More of the participants in V1 than in V2 and V3 showed a willingness to eliminate bamboo and tree stump holes, to clean up dumps and to turn containers upside down. Based on some of the SDI data, local governments have frequently required villagers to clean their houses, public spaces in the community and the surrounding environment during DF outbreaks. In response to DF outbreaks, three ten-person teams in V3 would alternate to conduct spraying and fogging with insecticide once a week. During our communications, we felt that people in V2 and V3 became bored while hearing about these requirements. One villager said, “The campaign just stopped a few days ago”. This ennui might lower their willingness to perform environmental management and may become a new challenge for the sustainability of intensive vector control. Because of the malaria control program, the residents’ knowledge of adult mosquito control was improved to a certain level across ESSR4 [24]. In the HHS, 79. 9% (95% CI: 74. 5–84. 6%) and 69. 9% (95% CI: 63. 9–75. 4%) of the participants mentioned that they had used coils and bed nets, respectively. More of the participants in V1 than in V2 and V3 knew how to use mosquito coils and bed nets. However, only a few people knew that dengue-transmitting mosquitoes bite during the day and that small-scale pond water is the main breeding site of dengue-transmitting mosquito larvae. Most participants knew to protect themselves from the bites of adult mosquitoes, but they did not know that eliminating the habitats of dengue-transmitting mosquito larva is beneficial for the control of dengue vectors (Tables 2 and 3). Combining effective interventions against multiple arboviral diseases has been suggested by some scholars as one of the most cost-effective and sustainable strategies for the reduction of vector-borne diseases [25,26]. However, another study that conducted a meta-analysis documented that indoor residual spraying (IRS) did not significantly impact DF infection risk [27]. Both this literature and our study results illustrate that knowledge regarding the control of adult mosquitoes is not sufficient for controlling the dengue vector. Unavoidably, this study was constrained by two obvious limitations. First, one of the objectives of the qualitative SDI was to triangulate the study outcomes of the quantitative methods. Much more attention was paid to this objective in the qualitative SDI, and not enough nuanced data were collected. This limited our ability to delineate the outcome differences between the qualitative SDI and the quantitative HHS despite the qualitative and quantitative outcomes also showed that the key informants had better knowledge than overall community people. Further qualitative study should been needed. Second, the response rates of the HHS participants to certain questions were not high enough, and thereby might have caused information bias. However, all investigators felt that the Shan People were very friendly, and we very much enjoyed collaborating with them during the field research. This means that the Shan people may have been unlikely to reject answering questions that they could answer. When the individuals had no response to a specific question it was mainly because they did not know the answer. To add to this disclosure, we combined the answers of “I don’t know” and no response into a single category during analysis. In conclusion, the Shan people believe that health is associated with all natural and social environmental factors, and that regularly cleaning their houses and surroundings to maintain sound hygiene would benefit human health. However, their knowledge and aware sensitivity of DF remains at low level, and most of their knowledge and awareness was learned from previous experiences in controlling malaria and dengue. Thus, in this setting with a weak public health structure, more international support should be provided to promote the knowledge of the Shan people about dengue and to increase their aware sensitivity to dengue. With proper guidance, social mobilization and community participation might help increase the perception of DF and the involvement of the Shan people for dengue control.

The burden of dengue has been increasing over the last five decades, and dengue fever (DF) has become one of the most rapidly spreading mosquito-borne diseases. DF has become another disease that threatens public health after malaria has become successfully controlled along the China-Myanmar border. However, it is currently not easy to contain epidemics of the dengue virus. As part of an integrated vector management approach, a community-based method is effective in the prevention of DF by tailoring the approach in a local context. Consequently, mixed methods comprising qualitative semi-structured in-depth interviews (SDIs) and quantitative household questionnaire surveys (HHSs) were used to study the health beliefs, knowledge and perceptions about dengue among the Shan people in Eastern Shan Special Region IV, Myanmar. This study found that the Shan people believed that their health was closely associated with their lifestyle and the social and physical environment in which they lived. Their beliefs originated from their primary social activities and cultural heritage. Most of their knowledge about DF was learned from previous outbreaks and interventions for the disease. The Shan people had a relatively higher level of knowledge about adult mosquito control, which they learned from previous malaria control programs, but they lacked knowledge on DF symptoms, transmission, vectors and Aedes larval breeding sites. Their knowledge about the methods of adult mosquito control could not effectively control DF. More sound health education is urgently needed to increase the local people’s knowledge of dengue and to rouse community awareness and participation in cleaning vector breeding sites. In the context of a lack of the necessary technical and financial resources, these interventions might rely more on international aid and help from neighboring countries, such as China.

Abstract Introduction Methods Results Discussion

invertebrates medicine and health sciences behavioral and social aspects of health tropical diseases social sciences ponds neuroscience animals health care developmental biology cognitive psychology bodies of water neglected tropical diseases insect vectors language public and occupational health infectious diseases dengue fever marine and aquatic sciences life cycles hygiene disease vectors insects arthropoda socioeconomic aspects of health mosquitoes psychology eukaryota earth sciences biology and life sciences species interactions viral diseases cognitive science larvae organisms

2019

The Shan people’s health beliefs, knowledge and perceptions of dengue in Eastern Shan Special Region IV, Myanmar

Despite the central role of estrogen exposure in breast and endometrial cancer development and numerous studies of genes in the estrogen metabolic pathway, polymorphisms within the pathway have not been consistently associated with these cancers. We posit that this is due to the complexity of multiple weak genetic effects within the metabolic pathway that can only be effectively detected through multi-variant analysis. We conducted a comprehensive association analysis of the estrogen metabolic pathway by interrogating 239 tagSNPs within 35 genes of the pathway in three tumor samples. The discovery sample consisted of 1,596 breast cancer cases, 719 endometrial cancer cases, and 1,730 controls from Sweden; and the validation sample included 2,245 breast cancer cases and 1,287 controls from Finland. We performed admixture maximum likelihood (AML) –based global tests to evaluate the cumulative effect from multiple SNPs within the whole metabolic pathway and three sub-pathways for androgen synthesis, androgen-to-estrogen conversion, and estrogen removal. In the discovery sample, although no single polymorphism was significant after correction for multiple testing, the pathway-based AML global test suggested association with both breast (pglobal = 0. 034) and endometrial (pglobal = 0. 052) cancers. Further testing revealed the association to be focused on polymorphisms within the androgen-to-estrogen conversion sub-pathway, for both breast (pglobal = 0. 008) and endometrial cancer (pglobal = 0. 014). The sub-pathway association was validated in the Finnish sample of breast cancer (pglobal = 0. 015). Further tumor subtype analysis demonstrated that the association of the androgen-to-estrogen conversion sub-pathway was confined to postmenopausal women with sporadic estrogen receptor positive tumors (pglobal = 0. 0003). Gene-based AML analysis suggested CYP19A1 and UGT2B4 to be the major players within the sub-pathway. Our study indicates that the composite genetic determinants related to the androgen–estrogen conversion are important for the induction of two hormone-associated cancers, particularly for the hormone-driven breast tumour subtypes. Estrogen exposure is critical for the development of both breast and endometrial cancers and represents the most well-established risk factors for both diseases. Estrogen is a metabolic product whose circulating level is determined by de novo synthesis, conversion from other steroid hormones, and mechanisms of estrogen elimination. These metabolic processes are regulated by a network of enzymes encoded by different genes, suggesting that genetic variation within these metabolic genes may impact on breast and endometrial cancer risk. Genetic variation within the estrogen metabolic pathway has been intensively investigated, mostly by analyzing single variant effects in a limited number of candidate genes, SNPs and study subjects. The inadequacies of study design and analytical methodology have caused these studies to be underpowered for detecting moderate genetic effects which, not surprisingly, has led to inconsistent results [1]–[8]. We surmised that strategies for assessing the synergistic effect of multiple genetic variants within the estrogen metabolic pathway may provide a more realistic determination of genetic effect than a single gene, single SNP approach. Herein, we present a comprehensive analysis of genetic variation in the estrogen metabolism pathway and its association with breast and endometrial cancer risk using a pathway-based approach. We performed single SNP association analysis in 1596 breast cancer cases, 719 endometrial cancer cases and 1730 population controls from Sweden. Of the 239 tagSNPs analyzed, 17 SNPs (7. 1%) had p-values less than 0. 05 for breast cancer, and 18 SNPs (7. 5%) had p-values less than 0. 05 for endometrial cancer (Table S4 and Table S5). For breast cancer, the smallest p-value was 0. 00034 at rs7167936 within CYP19A1, and for endometrial cancer, the smallest p-value was 0. 00017 at rs12595627 in CYP19A1. The single-SNP associations were all moderate. Only rs12595627 (for endometrial cancer) survived the conservative Bonferroni correction for multiple testing at α = 0. 05. Overall, however, the single-SNP p values appeared to deviate from their null distribution of no association (formally tested below). The single-SNP associations were suggestive, but instead of any single variant having a strong effect, there appeared to be multiple weak associations within the metabolic pathway. To evaluate the cumulative effect from multiple variants we employed the AML method [9] that assesses the experiment-wide significance of association by analyzing multiple SNPs through a single global test. The whole metabolic pathway can be sub-divided into three a priori defined sub-pathways, each performing specific metabolic function (Figure 1). Sub-pathway 1 is involved in the synthesis of androgen, sub-pathway 2 is involved in the conversion of androgens to estrogens, and sub-pathway 3 is responsible for removing estrogens. To investigate whether there is multi-SNP association for the whole pathway and whether any of the three sub-pathways is particularly important in influencing disease risk, we performed the progressive pathway-based global test on the whole metabolic pathway as well as the three sub-pathways using the AML method. The global test yielded marginally significant association for the whole metabolic pathway in both breast (pglobal = 0. 034) and endometrial (pglobal = 0. 052) cancers (Table 1). Dividing the metabolic pathway into three functional sub-pathways for the global test revealed strong association between the androgen-to-estrogen conversion sub-pathway and both breast (pglobal = 0. 008) and endometrial (pglobal = 0. 014) cancer (Table 1). The association evidence survived correction for performing 4 pathway-based tests in each cancer (pglobal corrected = 0. 032 for breast and 0. 056 for endometrial). In contrast, the other two sub-pathways showed no association with either form of cancer. For approximately half of the Swedish subjects in the breast cancer study (797 cases and 764 controls) we have genome wide association study (GWAS) data available. We used this to assess the possible influence of population stratification on our results. For the GWAS dataset, the genomic inflation factor, λgc, was1. 015. Assuming an equal level of population stratification (in terms of the fixation index FST) in the current study and the GWAS sub-study, we estimated the genomic inflation factor, λgc, to be 1. 030 in the current study, using the relationship between FST, sample size and λgc described in [10]. Using the λgc value of 1. 030 for genomic control-based correction of population stratification, the corrected global AML p-values for breast cancer are 0. 052 for the entire pathway and 0. 011 for the androgen-estrogen conversion sub-pathway, leaving our results largely unchanged. Even if λgc was as large as 1. 05 in the current study, the global test p-value for the androgen-estrogen conversion sub-pathway would still be as low as 0. 014. To further ensure that the observed associations could not be due to the employment of 319 paraffin-embedded tissue samples in the analysis, we re-ran analyses excluding 319 paraffin-embedded tissue samples, and (at the same time) excluding 33 SNPs with call rates of less than 95%. Results were very similar. For example, for breast cancer, p-values were 0. 028 and 0. 009 for the entire pathway and for the androgen-estrogen conversion sub-pathway, respectively. To validate the association in the androgen-to-estrogen conversion sub-pathway, we genotyped the 120 SNPs of this sub-pathway in an additional 2245 breast cancer cases and 1287 controls from Finland and performed the same AML analysis by using the 118 successfully genotyped SNPs. The validation analysis in the Finnish sample revealed similar evidence of association between the androgen-to-estrogen conversion sub-pathway and breast cancer (pglobal = 0. 015) (Table 1). The non-centrality parameter from the AML analysis of the androgen-to-estrogen conversion sub-pathway, which represents the size of the common effect of the associated SNPs, was estimated as 2. 90 for the Swedish sample and 2. 94 for the Finnish sample. The similar values indicate a consistent size of the genetic effect in the two samples. A joint analysis of the Swedish and Finnish samples further yielded a global p-value of 0. 001 (Table 1). The SNPs with the lowest p-values in the Finnish sample are listed in Table S6. Hormone-related risk factors may play a differential role in breast cancer subtypes. In particular, estrogens appear to drive the development of ER positive tumors. This prompted us to investigate the association in the androgen-to-estrogen conversion sub-pathway in hormone-related breast tumor subtypes. As surrogate markers for hormone driven tumour subtypes we constructed variables as combinations of menopausal status, family history and estrogen receptor (ER) status and divided all the patients into subgroups. We then compared subgroups of patients, defined on values of these variables, with controls, to evaluate the role of the androgen-to-estrogen conversion sub-pathway in different patient subgroups. First, we compared patient subgroups against all the controls in the combined Swedish and Finnish samples. The subgroup results showed that in the combined samples, significant association was observed in postmenopausal patients (pglobal = 0. 009 and 0. 018 respectively), postmenopausal patients without family history (pglobal = 0. 001 and 0. 04 respectively), and postmenopausal patients with estrogen receptor positive (ER+) tumors (pglobal = 0. 0006 and 0. 05 respectively) (Table 2). No significant association was observed in either premenopausal patients or postmenopausal patients with family history or estrogen receptor negative (ER−) tumors. Then, to rule out the possibility that the above subgroup results were caused by the mismatch between the patient subgroups and the controls in terms of the variables which defined patient subgroups, we performed the second subgroup analysis where the controls were also divided into subgroups according to family history and menopausal status (Table 3). The second subgroup analysis was only performed in the Swedish sample, because the Finnish controls lack information on family history and menopausal status. This yielded similar evidence for the association of the sub-pathway with the hormone-driven subtypes of breast cancer as in Table 2. We further investigated the impact of reproductive risk factors on the genetic association of the androgen-to-estrogen conversion sub-pathway with breast cancer. Because the risk factor information is not available for the Finnish controls, the analysis of the reproductive risk factors was performed in the Swedish samples where information on such factors is available. We performed the AML analysis of the androgen-to-estrogen conversion sub-pathway with adjustment for the reproductive risk factors (parity, age at the first birth, age at menarche and age of menopause) and HRT use. We investigated this primarily to assess whether any of the reproductive risk factors could be in the causal pathway. Since p-values remained almost unchanged in all analyses (Table 4), it appears that none of the reproductive risk factors are likely to be in the causal pathway. Attempting to refine the association within the androgen-to-estrogen conversion sub-pathway, we performed a gene-based AML analysis in the combined Swedish/Finnish breast cancer sample and the Swedish endometrial cancer sample. Among the 15 genes tested (Table 5), strong association was observed for CYP19A1 with both breast (pglobal = 0. 003) and endometrial (pglobal = 0. 006) cancer and UGT2B4 (pglobal = 0. 002) with breast cancer only. The associations in breast cancer survived correction for multiple testing of 15 genes (pglobal corrected = 0. 045 for CYP19A1 and 0. 03 for UGT2B4). We also observed suggestive association for UGT2B11 in breast and endometrial cancer as well as for HSD11B1, SULT2A1 and SULT2B1 in breast cancer. Consistent with the pathway-based associations, the gene-based associations are generally more significant in sporadic postmenopausal patient samples than in the whole breast cancer sample (except SULT2B1). Furthermore, the importance of CYP19A1 and UGT2B4 in breast cancer risk is supported by the fact that excluding either gene from the global test of the sub-pathway reduced the global significance of association for the sub-pathway, from 0. 0015 to 0. 011 for CYP19A1, and to 0. 010 for UGT2B4. However, the fact that the association for the sub-pathway remained significant, after excluding either gene, suggests that, although CYP19A1 and UGT2B4 are the major players, genetic variation within other genes also contributes to the association within the sub-pathway. Our pathway-based multi-SNP association analysis revealed a significant association between genetic variants in the androgen-to-estrogen conversion sub-pathway and the risk of two hormone dependent cancers. The association was particularly strong for ER+, sporadic breast cancer. Single SNP analysis did not reveal a similar association. We used the AML-based multi-SNP analysis, which has been shown to be more powerful than single SNP tests to yield significant and consistent association, when genetic risk is carried by multiple risk alleles each with moderate effect [11]. Pathway-based approaches are just beginning to be applied in association analysis [12]. Recently, an association study of 9 candidate gene groups (involving 120 candidate genes) was performed in breast cancer by using the AML approach, and interestingly, only the group of 8 genes involved in the steroid hormone signalling were significantly associated [13]. Our study has moved one step further and highlights the fact that the power of the pathway-based association analysis can be increased when analysis is guided by well-defined biological information. We believe that pathway approaches have potential to move genome-wide association studies beyond their initial success of identifying some ‘low-hanging fruits’ to revealing many weak genetic risk alleles that have been missed by single SNP analysis. Unless one enzyme is the rate limiting step for the entire metabolic pathway, it is not likely that small functional perturbations of individual variants would have a major impact on the overall effect of the metabolic pathway. To test the hypothesis that several genetic variants, each conferring weak to moderate effects, contribute to genetic risk, we adopted a systematic pathway-based approach for association analysis by testing the joint effect of multiple genetic variants in a progressive fashion from the whole metabolic pathway to biochemical sub-pathways and further down to individual genes. Such a progressive approach allows us to not only establish consistent association in three cancer samples from two different populations but also to refine the association of the androgen-to-estrogen conversion component of the metabolic pathway. Our study may therefore have advanced our understanding of the role of estrongen metabolism in breast and endometrial cancers by 1) accounting for the ambiguity surrounding the genetic association results and 2) indicating the androgen-to-estrogen conversion to be the important component of the metabolic pathway in modulating the risk and therefore to be a worthy focus for future studies. After menopause, ovarian estrogen production dramatically declines and conversion of adrenal androgens to estrogens in peripheral tissues becomes the major source of circulating estrogens. The final step of this conversion is catalyzed by aromatase, encoded by CYP19A1 [11]. Thus, there is biological plausibility in the association between CYP19A1 polymorphisms and postmenopausal breast cancer. Moreover, pharmacological inhibition of aromatase prevents recurrences in postmenopausal women with estrogen-receptor-positive breast cancer and new contralateral primaries [14], which has challenged the previous routine of a 5-year course of tamoxifen alone [15]. Our study has advanced our understanding of CYP19A1 by suggesting that the modulation of aromatase activity by either germ-line variation or pharmacological agents can influence the development of ER+ tumour in postmenopausal women. Furthermore, the convergence of genetic and pharmacological effects of CYP19A1 also raises therapeutic possibilities. For example, other genes implicated by our genetic study, such as UGT2B4, might also be pharmacological targets for treating breast cancer. Hormone exposure is a common risk factor for breast and endometrial cancer. Our employment of the three samples of two different hormone-related cancers from two different populations allowed us to apply a very stringent criterion for declaring an association. Furthermore, results of our breast cancer patient subgroup analysis indicate that the genetic determinants within the androgen-to-estrogen conversion sub-pathway may play a more prominent role in postmenopausal women with sporadic ER+ tumors, further suggesting that the modulation of hormone exposure by genetic variation may have a differential impact on breast tumor subtypes. Endogenous sex hormone level appears to be associated with breast cancer risk in postmenopausal women [16], and particularly with the risk of ER+/PR+ breast tumors [17]. The effect of hormone-related factors on breast cancer risk apparently differs by ER status [18] and menopause status [19], [20]. It could also differ by the status of family history of the disease, as suggested by a recent study showing that most cases of hereditary breast cancer are probably not related to cumulative hormone exposure [21]. Our findings may have therefore advanced the development of a general model for breast cancer risk: hormonal factors, both genetic and reproductive, can play a key role in the genesis of post-menopausal and “sporadic” breast cancer, whereas genes involved in DNA repair, checkpoints, and genetic stability (such as BRCA1, BRCA2, p53, ATM, CHK2) appear to be more involved in predominantly breast cancers associated with family history of disease. It is worth noting that the contribution of genetic polymorphisms to risk is a function of both their prevalence and penetrance and thus the relative importance of individual SNPs may vary from population to population. More studies in different populations are needed to fully understand the role of the androgen-to-estrogen conversion sub-pathway in breast cancer. We also want to highlight that our results are of genetic association in nature, and further studies are needed to confirm the findings and to identify functional variants causally linked to cancer risk. Swedish subjects were from a population-based case control study of breast and endometrial cancer as described [22], [23]. Briefly, the study included all incident primary invasive breast and endometrial cancers among Swedish-born postmenopausal women between 50 and 74 years of age at diagnosis, diagnosed with breast cancer between October 1993 and March 1995 and endometrial cancer between January 1994 and December 1995. All cases were identified through six regional cancer registries in Sweden, and all controls were randomly selected from the Swedish Registry of Total Population and frequency matched to the expected age distribution of the cases. Finnish breast cancer cases consist of two series of unselected breast cancer patients and additional familial cases ascertained at the Helsinki University Central Hospital. The first series of 884 patients was collected in 1997–1998 and 2000 and covers 79% of all consecutive, newly diagnosed cases during the collection periods [24], [25]. The second series, containing 986 consecutive newly diagnosed patients, was collected in 2001–2004 and covers 87% of all such patients treated at the hospital during the collection period [26]. An additional 538 familial breast cancer cases were collected at the same hospital as described [27]–[30]. 1287 anonymous, healthy female population controls were collected from the same geographical regions in Southern Finland as the cases and have been used in several studies previously [31]–[33]. Risk factor information and tumour characteristics were available for all the Swedish samples and the Finnish cases, but were missing for the Finnish controls. The Finnish samples (mean age = 56 for the cases and 41 for the controls) were younger than the Swedish samples (mean age = 63 for both the cases and controls). All the risk factor and tumour characteristics information of the subjects are summarized in Table S1 and Table S2. Written informed consent was obtained from all participating subjects, and the study was approved by the Institutional Review Boards in Sweden, Finland and at the National University of Singapore. DNA was extracted from 4 ml of whole blood using the QIAamp DNA Blood Maxi Kit (Qiagen) and non-malignant cells in paraffin-embedded tissue using a standard phenol/chloroform/isoamyl alcohol protocol [34]. We selected 35 genes involved in estradiol or estrone metabolism and expressed in the breast (based on published literatures). We selected 1007 single nucleotide polymorphisms (SNPs) in these genes and their 30kb flanking sequences from the dbSNP (build 124) and Celera databases, aiming for a marker density of at least one SNP per 5kb (Table S3). These SNPs were genotyped in 92 Swedish control samples to assess linkage disequilibrium pattern and coverage. Haplotypes were reconstructed using the PLEM algorithm [35] implemented in the tagSNPs program [36]. A subset of SNPs, tagSNPs, were selected based on the R2 coefficient, which quantifies how well the tagSNP haplotypes predict the genotype or haplotypes an individual carries. We chose tagSNPs so that common SNP genotypes and haplotypes (frequency ≥0. 03) were predicted with R2≥0. 8 [37]. To evaluate our tagSNPs' performance in capturing unobserved SNPs within the genes, we performed a SNP-dropping analysis [38], [39]. In brief, each of the genotyped SNPs was dropped in turn and tagSNPs were selected from the remaining SNPs so that their haplotypes predicted the remaining SNPs with an R2 value of 0. 85. We then estimated how well the tagSNP haplotypes of the remaining SNPs predicted the dropped SNP, an evaluation that can provide an unbiased and accurate estimate of tagSNP performance [38], [39]. Overall, we selected and genotyped 302 tagSNPs from the 35 genes in all the Swedish cases and controls. Genotyping was performed using the Sequenom system (San Diego, California). All genotyping results were generated with positive and negative controls and checked by laboratory staff unaware of case-control status. Of the 302 tagSNPs, 42 SNPs failed in the development stage of Sequenom genotyping assays. SNPs with a call rate <85% (8 SNPs), minor allele frequency <1% (9 SNPs) or out of Hardy-Weinberg Equilibrium (p<0. 05/252,4 SNPs) were excluded from further analysis. Overall, 239 tagSNPs from the 35 genes were successfully genotyped (Table S3). The genotype concordance was >99%, suggesting high genotyping accuracy. The Cochran-Armitage trend test was performed for each of the 239 SNPs. One approach for assessing the departure of the distribution of the (Cochran-Armitage) test statistics from the (global) null distribution (no SNPs associated) has been described by Tyrer et. al. [9]. The approach is based upon fitting a mixture model to the distribution of the test statistics, with two components, one representing SNPs which are independent of the case-control status, the other representing SNPs associated with case-control status. The Cochran-Armitage test statistics for the associated SNPs are assumed to all have the same (chi-squared) non-centrality parameter value. The distributed software for the “admixture maximum likelihood” (AML) test of Tyrer et. al. [9] calculates empirical p-values based on a “pseudo-likelihood ratio” test, comparing the ratio of values of the optimized likelihoods under the null and alternative hypotheses for the observed data, with the corresponding values obtained from a large number of data sets with case-control status permuted randomly. It also provides an estimate of the non-centrality parameter which is a measure of the common effect size of the associated SNPs within the pathway. We performed the AML-based global test of association for the full metabolic pathway as well as for 3 sub-pathways (see results section). In addition, we performed gene-specific analyses, using the AML-based global test on SNPs within genes, within the androgen-estrogen conversion sub-pathway. We also carried out AML tests adjusted for a non-genetic risk factor using software provided by the authors of Tyrer et al. [9].

Estrogen exposure is the most important risk factor for breast and endometrial cancers. Genetic variation of the genes involved in estrogen metabolism has, however, not been consistently associated with these two cancers. We posited that the genetic risk associated with the estrogen metabolic genes is likely to be carried by multiple variants and is therefore most effectively detected by multi-variant analysis. We carried out a comprehensive association analysis of the estrogen metabolic pathway by interrogating SNPs within 35 genes of the pathway in three tumor samples from Sweden and Finland. Through pathway-based multi-variant association analysis, we showed that the genetic variation within the estrogen metabolic pathway is associated with risk for breast and endometrial cancers and that the genetic variation within the genes involved in androgen-to-estrogen conversion is particularly important for the development of ER–positive and sporadic breast tumors in postmenopausal women. Our study has demonstrated that the influence of genetic variation on hormone exposure has an impact on breast cancer development, especially on the development of hormone-driven breast tumor subtypes. Our study has also highlighted that future genetic studies of the estrogen metabolic genes should focus on the androgen-to-estrogen conversion process.

Abstract Introduction Results Discussion Materials and Methods

oncology/breast cancer oncology/gynecological cancers genetics and genomics/genetics of disease genetics and genomics/cancer genetics genetics and genomics/population genetics

2010

Multi-Variant Pathway Association Analysis Reveals the Importance of Genetic Determinants of Estrogen Metabolism in Breast and Endometrial Cancer Susceptibility

Macrophage migration inhibitory factor (MIF) has emerged as a pivotal mediator of innate immunity and has been shown to be an important effector molecule in severe sepsis. Melioidosis, caused by Burkholderia pseudomallei, is an important cause of community-acquired sepsis in Southeast-Asia. We aimed to characterize the expression and function of MIF in melioidosis. MIF expression was determined in leukocytes and plasma from 34 melioidosis patients and 32 controls, and in mice infected with B. pseudomallei. MIF function was investigated in experimental murine melioidosis using anti-MIF antibodies and recombinant MIF. Patients demonstrated markedly increased MIF mRNA leukocyte and MIF plasma concentrations. Elevated MIF concentrations were associated with mortality. Mice inoculated intranasally with B. pseudomallei displayed a robust increase in pulmonary and systemic MIF expression. Anti-MIF treated mice showed lower bacterial loads in their lungs upon infection with a low inoculum. Conversely, mice treated with recombinant MIF displayed a modestly impaired clearance of B. pseudomallei. MIF exerted no direct effects on bacterial outgrowth or phagocytosis of B. pseudomallei. MIF concentrations are markedly elevated during clinical melioidosis and correlate with patients' outcomes. In experimental melioidosis MIF impaired antibacterial defense. Macrophage migration inhibitory factor (MIF) was one of the first cytokines to be discovered almost half a century ago [1]–[4]. Since then MIF has emerged as a pivotal mediator of innate immunity in various inflammatory diseases such as rheumatoid arthritis and atherosclerosis [5], [6] and is considered to be an integral component of the host antimicrobial alarm system [4], [7]. MIF, a classical proinflammatory cytokine, is constitutively expressed by many tissues with environmental contact such as the lung and the gastrointestinal tract, and by numerous cell types, among others T- and B-lymphocytes, monocytes and macrophages [4]. MIF-deficient macrophages are hyporesponsive to lipopolysaccharide (LPS) due to a down-regulation of Toll-like receptor (TLR) -4 [8], [9]. In line, MIF knockout mice were resistant to LPS induced toxic shock [8]–[10]. Recently it was shown that blood concentrations of MIF are elevated in patients with sepsis and able to predict early mortality [11]–[14]. Similarly, MIF is increased in patients with meningococcal disease and highest in the presence of shock [15]. Excitingly, treatment with anti-MIF antibodies protected mice from lethal peritonitis induced by Escherichia coli or cecal ligation and puncture (CLP) [16]. Furthermore, ISO-1 and OXIM-11, new small molecule inhibitors of MIF, offered significant protection to mice from CLP-induced sepsis [17], [18]. These data identified MIF as a potential mediator of lethality following abdominal sepsis. In Southeast-Asia and Northern-Australia the gram-negative bacillus Burkholderia pseudomallei is an important cause of community-acquired sepsis [19], [20]. More than half of these cases of melioidosis, as this severe infection is named, habitually presents with pneumonia, frequently associated with bacterial dissemination to distant sites [19]–[21]. In the present study we aimed to characterize the expression and function of MIF in melioidosis. For this we analysed MIF expression patterns in patients with melioidosis and in a mouse model of B. pseudomallei infection. MIF function was investigated in experimental murine melioidosis using anti-MIF antibodies and recombinant MIF. The patient study was approved by both the Ministry of Public Health, Royal Government of Thailand and the Oxford Tropical Research Ethics Committee, University of Oxford, England. We obtained written informed consent from all subjects before the study. The Animal Care and Use of Committee of the University of Amsterdam approved all murine experiments. We included 34 individuals with sepsis caused by B. pseudomallei and 32 healthy controls in this study. Individuals were recruited prospectively at Sapprasithiprasong Hospital, Ubon Ratchathani, Thailand in 2004. Sepsis due to melioidosis was defined as culture positivity for B. pseudomallei from any clinical sample plus a systemic inflammatory response syndrome (SIRS) [22]. Study design and subjects have been described in detail [23]. Human MIF was measured by ELISA, as described elsewhere [24]. In addition, MIF mRNA levels were measured as follows. Heparin blood samples were drawn from an antecubital vein and immediately put on ice. Leukocytes were isolated using erylysis buffer, dissolved in Trizol and stored at –80°C. Thereafter, RNA was isolated and analyzed by multiplex ligation-dependent probe amplification (MLPA) as described [25], [26] (MRC-Holland, Amsterdam, the Netherlands). Levels of mRNA were expressed as a normalized ratio of the peak area divided by the peak area of the β2 microglobulin (B2M) gene [25]. Male C57BL/6 mice (age 8–10 weeks) were purchased from Harlan Sprague Dawley Inc. (Horst, The Netherlands). Age-matched animals were used in each experiment. For the inoculum, B. pseudomallei strain 1026b, kindly provided by Dr. Don Woods [27], [28], was used and prepared as described [23], [29]–[31]. Pneumonia was induced by intranasal inoculation of a 50 µl (5×101,2. 5×102 or 7. 5×102 colony forming units (CFU) /50 µl) bacterial suspension. 48 hours after infection, mice were anesthetized and sacrificed by bleeding from the vena cava inferior [23], [30], [32]. CFUs were determined from serial dilutions of organ homogenates as described [23], [29]–[31]. In some experiments mice were injected intraperitoneally with 2 mg of anti-MIF or non-immune IgG 2 hours before bacterial inoculation or with 50 µg recombinant mouse MIF or control buffer at the onset of infection as described previously [16], [33], [34]. Rabbit polyclonal anti-MIF and recombinant MIF were generated as described [16], [34]. The ELISA for mMIF developed according to the 4-span approach was used as described in detail [35]. Tumor necrosis factor (TNF) -α, interferon (IFN) -γ, interleukin (IL) -6, IL-10 and IL-12p70 were determined using a cytometric bead array (CBA) multiplex assay in accordance with the manufacturer' s instructions (BD Biosciences, San Jose, CA). Four-µm thick lung tissue sections were sampled 48 hours after infection and mounted on aminopropylmethoxysilane-coated glass slides, deparaffinized in xylol, taken through to absolute alcohol and blocked for endogenous peroxidase with 0. 1% hydrogen peroxide in methanol. They were boiled in 10 mM citrate buffer in a microwave oven and rinsed in Tris-buffered saline (TBS). To reduce non-specific binding, sections were incubated in normal goat serum (Pel-Freez Biologicals, Rogers, AK) 1∶30 in TBS. After 40-minutes incubation with polyclonal rabbit anti-MIF purified IgG diluted 1∶200 in TBS containing 2% bovine serum albumin (final immunoglobulin concentration: 25 mg/l), the sections were incubated with biotinylated goat anti-rabbit IgG (Vector, Burlingame, CA) diluted 1∶400 and then with ABC-peroxidase complex solution (Vector). Peroxidase activity was revealed with 5-5′-diaminobenzidine as chromogen and the sections were counterstained in Meyer' s acid-free hematoxylin. As a negative control, the primary antibody was replaced by pre-immune rabbit purified IgG. Furthermore, to score inflammation, lung and livers from infected mice were harvested 48 hours after infection, fixed in 10%-formalin and embedded in paraffin. Four µm sections were stained with hematoxylin and eosin and analyzed by a pathologist blinded for groups exactly as described previously [23]. B. pseudomallei strain 1026b was used and prepared as described above. In short, B. pseudomallei at concentrations from 3×103–3×106 CFU/ml was grown in the presence of recombinant MIF (dose range from 5 to 50 µg/ml) diluted in LB-growth medium. Phagocytosis was evaluated as described [36], [37]. Heat-killed B. pseudomallei was labeled with carboxyfluorescein-diacetate-succinimidyl-ester (CFSE dye, Invitrogen, Breda, The Netherlands). Peritoneal macrophages (derived from 5 different mice per group) were incubated with CFSE-labeled B. pseudomallei (2. 5×107 CFU/ml) for 0,60 and 120 minutes. Phagocytosis was stopped by placing cells on ice; thereafter cells were washed in PBS and suspended in Quenching solution (Orpegen, Heidelberg, Germany). To determine the neutrophil phagocytosis capacity, 50 µl of whole blood was incubated with bacteria after which cells were suspended in Quenching solution, incubated in FACS lysis/fix solution (BecktonDickinson) and neutrophils were labeled using anti-Gr-1-PE (Pharmingen). Phagocytosis was determined using FACS. Values are expressed as means ± standard error of the mean (SEM). Differences between groups were analyzed by Mann-Whitney U test or Kruskal-Wallis analysis with Dunn' s posthoc test where appropriate. For survival analysis, Kaplan-Meier analysis followed by log rank test was performed. These analyses were performed using GraphPad Prism version 4. 00, GraphPad Software (San Diego, CA). Values of P<0. 05 were considered statistically significant. To obtain an insight into MIF expression during melioidosis, we first measured MIF in plasma from 34 patients with culture proven B. pseudomallei infection and in plasma from 32 local healthy controls. The mortality rate in this cohort of patients was 44%. MIF was markedly elevated in melioidosis patients with mean plasma concentrations that were approximately 2-fold higher than in those of healthy subjects (Figure 1A, P<0. 01). Plasma concentrations of MIF were associated with an adverse outcome: on admission patients who went on to had higher MIF concentrations than those who survived (Figure 1B, P<0. 01). In line, MIF mRNA levels were significantly higher in peripheral blood leukocytes from patients than in leukocytes from healthy controls (Figure 1C, P<0. 001). Since the majority of severe melioidosis cases presents with pneumonia with bacterial dissemination to distant body sites [19]–[21] and considering the fact that it is not feasible to study MIF expression at tissue level in patients with melioidosis, we used a well-established murine model of pneumonia-derived melioidosis in which mice are intranasally infected with B. pseudomallei [23], [29], [31]. In agreement with the data obtained in patients with melioidosis, infected mice showed an abundant upregulation of MIF expression, both in the pulmonary and systemic compartment (Figure 2, both P<0. 01). Immunohistochemical staining of lung tissue was performed to further identify the distribution of MIF expression during melioidosis. Positive immunostaining for MIF was observed in untreated control animals in alveolar macrophages and within the bronchial epithelium (Figure 3A). Granulocytes did not stain positive for MIF. After infection with B. pseudomallei there was a marked increase in immunostaining of the epithelial submucosa, bronchial epithelial cells and inflammatory cells, most notably of alveolar macrophages (Figure 3B). To obtain a first insight into the function of MIF during experimental melioidosis, we treated mice infected with 2. 5×102 CFU B. pseudomallei mice with 50 µg recombinant MIF using a dose similar to that used previously in an experimental septic shock model [16], [33]. Treatment of mice with recombinant MIF at the time of infection resulted in increased MIF concentrations in lung homogenates 48 hours later (from 33±1. 3 to 729±56. 6 ng/ml; P<0. 001). Mice were sacrificed 48 hours after inoculation to determine bacterial loads in lungs (the primary site of the infection), liver and blood (to evaluate to which extent the infection disseminated to distant body sites) (Figure 4). Relative to infected but non-treated controls, mice treated with recombinant MIF displayed almost 10-fold higher bacterial loads in the liver (Figure 4, P<0. 01). In addition, a clear trend was seen towards higher bacterial loads in the pulmonary and systemic compartments of recombinant MIF treated mice, although the differences with control mice did not reach statistical significance (Figure 4). Having found that administration of supra physiological doses of MIF results in a partially impaired bacterial clearance during experimental melioidosis, we next hypothesized that treatment with anti-MIF antibodies would result in decreased bacterial outgrowth and performed the reverse experiment by examining the effect of anti-MIF treatment. Therefore, before inoculating mice with B. pseudomallei, we injected mice with anti-MIF antibodies using a dosing schedule previously found be protective in a mouse model of E. coli or CLP-induced peritonitis [16], [33]. To evaluate whether anti-MIF treatment interferes with bacterial clearance, we first determined bacterial loads 48 hours after infection with an inoculum of 2. 5×102 CFU B. pseudomallei (Figure 5). At this dose no significant differences in bacterial outgrowth in either lungs, liver or blood were observed. To determine whether the effect of anti-MIF therapy is dependent on the size of the infectious dose, we next infected mice with a higher (5×102 CFU B. pseudomallei) and lower (5×101 CFU B. pseudomallei) inoculum (Figure 6). At the highest dose no effect of anti-MIF treatment was seen on the bacterial outgrowth in the lungs, liver or blood (Figure 6B). However, at the lowest inoculum mice treated with anti-MIF had almost 10-fold less B. pseudomallei CFU in their lungs compared to control mice (Figure 6A, P<0. 05). With this low inoculum, none of the mice showed positive Burkholderia cultures in liver or blood, suggesting that anti-MIF treatment inhibits the growth of B. pseudomallei in the lungs after infection with a relatively low bacterial dose. Lastly, we performed a survival experiment in which mice were injected intraperitoneally with 2 mg of anti-MIF or non-immune control IgG 2 hours before intranasal inoculation with B. pseudomallei. In accordance with the modest protective effect of anti-MIF treatment on bacterial outgrowth a limited survival advantage was seen in the anti-MIF treated group (Figure 7). Since cytokines are important regulators of the inflammatory response to acute lower respiratory tract infection [38] and given the observation that protective anti-MIF treatment reduced TNFα concentrations in mouse model of sepsis induced by E. coli or CLP [16], [33], we measured the concentrations of TNFα, IL-6, IL-10, IL-12 and IFNγ in lung homogenates and plasma obtained 48 after infection with 2. 5×102 CFU B. pseudomallei (Table 1). Anti-MIF treatment did not influence pulmonary cytokine concentrations in our model of experimental melioidosis (Table 1). Previously it was shown that plasma TNFα concentrations induced by LPS were lower in MIF-deficient mice compared to wild-type mice [10], [33]. However also in plasma no differences in TNFα or IL-6, IL-10, IL-12 and IFNγ concentrations were seen between anti-MIF treated and control mice after inoculation with B. pseudomallei (data not shown). In addition treatment with 50 µg recombinant MIF did not influence cytokine concentrations in either the pulmonary (Table 1) or systemic compartment (data not shown). Considering that MIF is regarded as an important proinflammatory mediator, we determined whether modulation of MIF concentrations could have an effect on organ inflammation during experimental melioidosis. Therefore, we performed histopathological analyses of lung and liver tissues in control mice and mice treated with anti-MIF or recombinant MIF and infected with B. pseudomallei. Although all mice showed evidence of inflammation as characterized by diffuse infiltrates, interstitial inflammation and bronchitis there were no differences in total organ histopathological scores between groups (data not shown). Having found that anti-MIF treated mice showed lower bacterial loads in their lungs upon infection with a low inoculum while mice treated with recombinant MIF displayed a modestly impaired clearance of B. pseudomallei, we next wished to determine whether MIF has a direct effect on bacterial outgrowth and/or phagocytosis. Therefore, B. pseudomallei (at concentrations from 3×103–3×106 CFU/ml) was grown in the presence of recombinant MIF diluted in the growth medium (dose range from 5 to 50 µg/ml). However, no effects of recombinant MIF on bacterial outgrowth could be observed at any time point (up to 2 hours; data not shown). Lastly, we studied whether MIF contributes to phagocytosis of B. pseudomallei. However no effects of anti-MIF treatment on phagocytosis of B. pseudomallei by peritoneal macrophages or whole blood neutrophils could be observed (data not shown). In the present study we aimed to characterize the expression and role for MIF in melioidosis, linking observational studies in patients with culture-proven disease with functional studies in mice in which we modulated the concentrations of MIF during experimentally induced melioidosis. Our study shows that patients with severe melioidosis have strongly increased MIF plasma and MIF mRNA leukocyte levels. High plasma MIF concentrations were associated with mortality. Similarly, mice intranasally inoculated with B. pseudomallei displayed a strong increase in pulmonary and systemic MIF expression. The functional role of MIF in our model of experimental melioidosis however was modest given the fact that modulation of MIF levels only moderately influenced the innate immune response towards B. pseudomallei. Anti-MIF treatment resulted in a modest survival benefit. Anti-MIF treatment only decreased bacterial outgrowth when mice were inoculated with a low dose of B. pseudomallei whereas - conversely - mice treated with recombinant MIF displayed a modestly impaired clearance of B. pseudomallei. These data are the first to report on the expression and function of MIF during melioidosis. MIF expression is increased in a wide variety of infectious diseases, ranging from viral infections, such as Dengue, HIV and West Nile virus infection [39]–[41], malaria [42], tuberculosis [43] and various forms of sepsis [11], [12], [15]. Our study further extends these findings by demonstrating increased plasma and blood leukocyte mRNA levels of MIF in patients with severe melioidosis. Importantly, we demonstrated a strong association between elevated MIF levels and increased mortality. This is in line with a recent study among pediatric and adult patients with severe sepsis or septic shock caused predominantly by Neisseria meningitides and other gram-negative bacteria in which elevated MIF levels were shown to be predictive of early mortality [12]. MIF, however, is not always upregulated after acute infection or inflammation. For instance, in children with acute malaria circulating MIF levels were significantly lower compared with healthy, malaria-exposed children [44]. Furthermore, MIF release could not be detected in a human endotoxemia model and is not produced by whole blood cells incubated with LPS [15]. Also in HIV seropositive patients low serum MIF levels were associated with a high 1-month mortality [41]. This further highlights the potential diverse roles MIF can play in the host response against various invading pathogens. Melioidosis, which is the most common form of community-acquired sepsis in Northern-Australia and Eastern-Thailand, is associated with a mortality of up to 50% in endemic areas [19], [20]. Severe pneumonia with bacterial dissemination to distant body sites is a common presentation of melioidosis [20], [21]. Sepsis caused by B. pseudomallei is characterized by a markedly proinflammatory cytokine profile; in the current cohort of patients we have demonstrated increased plasma concentrations of IL-6, IL-8 and IL-18 when compared to controls [31], [45]. In addition, high throughput mRNA profiling in these patients suffering from severe melioidosis furthermore demonstrated increased transcription of a whole array of proinflammatory genes in whole blood leukocytes [26]. In light of the proinflammatory properties attributed to MIF in sepsis, we studied the expression and function of MIF in a well-established mouse model of melioidosis [23], [29], [31]. In line with our patient data and in line with various other murine models of sepsis induced by LPS, E. coli or CLP [4], [16], [46], we observed a strong upregulation of MIF expression in both the lungs and blood of mice inoculated with B. pseudomallei. However, MIF seems to play a less important role in the innate immune response in melioidosis, which is in contrast with previous studies pointing towards a central role of MIF in other forms of infection. With regard to bacterial infection, the role of MIF has been first studied in abdominal sepsis caused by either intraperitoneal injection of E. coli or CLP [9], [16]. In these models, anti-MIF from the same source and administered in the exact same dose protected mice from mortality, reduced TNFα concentrations and diminished bacterial growth. Very recently it was shown that polymorphisms associated with higher MIF expression may have a beneficial effect in community-acquired pneumonia [47]. In addition, modulation of MIF may have therapeutic advantages in treating acute lung injury in patients with acute pancreatitis complicated by bacterial infection [48]. The fact that anti-MIF only has a minor impact on the immune response to B. pseudomallei could be related to differences in the primary site of infection and/or differences in the pathogens involved [9], [16]. In this respect it is worthwhile noting that MIF regulates innate immune responses in gram-negative infections through modulation of Toll-like receptor 4 [8]. We obtained recent evidence - counter intuitively for a gram-negative infection - that TLR2 impacts on the immune response of the intact host in vivo, whereas TLR4 does not contribute to protective immunity in melioidosis [23]. As such, the minor role of TLR4 in the innate immune response towards B. pseudomallei could be an explanation for our present findings revealing an equally limited role for MIF in melioidosis. Interestingly, during murine Listeria monocytogenes infection, the elimination of bacteria from the spleen and liver was not affected by anti-MIF antibody although this treatment was able to rescue mice from lethal infection [49]. In reverse experiments we found that treatment of B. pseudomallei infected mice with recombinant MIF caused impairment of the bacterial clearance capability. Earlier studies showed that recombinant MIF increased mortality during E. coli sepsis when co-injected with bacteria in mice [16], [33], [50]. In these investigations the effect of recombinant MIF on bacterial loads was not reported. These findings imply that increased concentrations of MIF can be harmful in the acute host response against invading bacteria. In this respect it is of interest that during the immune suppressed state which occurs in the late phase of the septic response and which is characterized by a reduced capacity of immune cells to produce proinflammatory cytokines such as TNFα, it was shown that treatment with recombinant MIF could protect animals from bacterial superinfection in a mouse model of CLP-induced peritonitis [51]. This further highlights the potential diverse nature of MIF function during the course of sepsis. Our study has several limitations. Our observations were done in patients with sepsis caused by B. pseudomallei and caution is required when extending these findings to less severe or chronic melioidosis, since we focused on the early acute phase of melioidosis. Furthermore, although our in vivo model of melioidosis has been important in elucidating the role of other inflammatory mediators in melioidosis [23], [29], [31], data obtained from a mouse model by definition should be extrapolated to patients with melioidosis with great caution. In addition, it would be of interest to confirm our results in MIF knockout mice, although we consider it less likely that the use of these mice will yield strongly different data in light of the modest differences observed in the different treatment groups. Lastly, obtaining new biological insights from studies using antibodies and recombinant proteins of interest remains a challenge, limited by the notion of considerable cooperation between inflammatory factors involved and extensive redundancy in the host response against invading pathogens [52]. In conclusion, MIF concentrations are markedly increased during melioidosis, and elevated levels correlate with mortality. Although mice with experimentally induced melioidosis showed strongly upregulated expression of MIF in lungs and blood, inhibition of MIF with a specific antibody only modestly influenced the host response. Similarly, administration of recombinant MIF did not strongly impact on the immune response to B. pseudomallei infection. These data argue against an important role for MIF in the pathogenesis of melioidosis.

Melioidosis is a severe tropical infection caused by the bacterium Burkholderia pseudomallei. B. pseudomallei is the major cause of community-acquired septicemia in northeast Thailand with a mortality rate in severe cases of around 40% Little is known, however, about the mechanisms of the host defense to B. pseudomallei infection. Macrophage migration inhibitory factor (MIF) is a proinflammatory cytokine that has emerged as an important mediator of the host defense in severe bacterial infections. In this article, we studied the expression and function of MIF both in patients with melioidosis and in mice during experimental melioidosis. We found that MIF concentrations were elevated in patients with melioidosis. Furthermore, high MIF concentrations are associated with poor outcome in patients with melioidosis. Also, in mice with experimentally induced melioidosis, we observed an upregulation of MIF concentrations. Furthermore, mice with melioidosis that were treated with a MIF blocking treatment showed lower bacterial counts in their lungs during infection. In conclusion, MIF seems to impair host defense mechanisms during melioidosis.

Abstract Introduction Methods Results Discussion

immunology/cellular microbiology and pathogenesis respiratory medicine/respiratory infections infectious diseases/neglected tropical diseases microbiology/innate immunity pathology/histopathology immunology/innate immunity critical care and emergency medicine/sepsis and multiple organ failure pathology/immunology immunology/immunity to infections

2010

Expression and Function of Macrophage Migration Inhibitory Factor (MIF) in Melioidosis

At present, screening of the population at risk for gambiense human African trypanosomiasis (HAT) is based on detection of antibodies against native variant surface glycoproteins (VSGs) of Trypanosoma brucei (T. b.) gambiense. Drawbacks of these native VSGs include culture of infective T. b. gambiense trypanosomes in laboratory rodents, necessary for production, and the exposure of non-specific epitopes that may cause cross-reactions. We therefore aimed at identifying peptides that mimic epitopes, hence called “mimotopes, ” specific to T. b. gambiense VSGs and that may replace the native proteins in antibody detection tests. A Ph. D. -12 peptide phage display library was screened with polyclonal antibodies from patient sera, previously affinity purified on VSG LiTat 1. 3 or LiTat 1. 5. The peptide sequences were derived from the DNA sequence of the selected phages and synthesised as biotinylated peptides. Respectively, eighteen and twenty different mimotopes were identified for VSG LiTat 1. 3 and LiTat 1. 5, of which six and five were retained for assessment of their diagnostic performance. Based on alignment of the peptide sequences on the original protein sequence of VSG LiTat 1. 3 and 1. 5, three additional peptides were synthesised. We evaluated the diagnostic performance of the synthetic peptides in indirect ELISA with 102 sera from HAT patients and 102 endemic negative controls. All mimotopes had areas under the curve (AUCs) of ≥0. 85, indicating their diagnostic potential. One peptide corresponding to the VSG LiTat 1. 3 protein sequence also had an AUC of ≥0. 85, while the peptide based on the sequence of VSG LiTat 1. 5 had an AUC of only 0. 79. We delivered the proof of principle that mimotopes for T. b. gambiense VSGs, with diagnostic potential, can be selected by phage display using polyclonal human antibodies. The chronic form of sleeping sickness or human African trypanosomiasis (HAT) in West and Central Africa is caused by the protozoan parasite Trypanosoma brucei (T. b.) gambiense while T. b. rhodesiense causes a more fulminant, acute form in East and Southern Africa. Both subspecies of T. brucei are cyclically transmitted by tsetse flies of the genus Glossina and mainly affect poor, rural populations. The true burden of this disease is unknown as many cases remain undiagnosed or unreported [1], [2]. Since untreated HAT is almost always fatal and no inexpensive, safe and easily administered drugs are available, accurate case detection is crucial. Parasite detection is laborious and insensitive, and remains therefore limited to disease suspects. In the absence of reliable clinical symptoms or antigen detection tests, HAT suspects are identified through screening of the population at risk for presence of trypanosome specific antibodies. The commonly used antibody detection tests, card agglutination test for trypanosomiasis (CATT) [3], LATEX/T. b. gambiense and ELISA/T. b. gambiense [4], [5] detect antibodies against the highly immunogenic variant surface glycoproteins (VSGs) of T. b. gambiense. Even though the genome of T. brucei contains >1000 VSG genes, only one variable antigen type (VAT) is expressed at a time. Stochastic switching of VSG allows the trypanosome to evade the specific antibody responses that were raised against earlier VATs [6]–[10]. Some VATs, such as LiTat 1. 3 and 1. 5, are recognised by almost all gambiense HAT patients and therefore called predominant. The dense VSG monolayer on the living trypanosome shields all non-specific epitopes. The hypervariable N-terminal VSG domain (300–400 residues) is exposed to the immune system and comprises the VAT-specific epitopes, while the relatively conserved C-terminal domain (40–80 residues) is hidden by the intact VSG coat [6], [9], [11], [12]. Disadvantages of the present antibody detection tests include the occurrence of non-specific reactions. This might be explained by exposure of non-HAT-specific epitopes that are normally shielded on the living trypanosome [12], [13]. In addition, diagnostic test production actually requires culture of infective T. b. gambiense in large numbers of laboratory rodents and poses an important risk of infection to the manufacturing staff [14]. These drawbacks can be circumvented through the use of synthetic peptides that mimic HAT-specific VSG epitopes (mimotopes) and can be produced in a standardised way [15]. One way to identify such mimotopes is by peptide phage display. This technique is based on DNA recombination resulting in foreign peptides with random sequences that are displayed fused to the pIII surface protein of the M13 phage. After an in vitro selection process based on binding affinity and several rounds of enrichment (panning), the encoded peptide insert sequence of the selected phage is deduced from the phage DNA. We previously reported successful identification of mimotopes for VSG LiTat 1. 3 and LiTat 1. 5 by performing phage display with three monoclonal antibodies [16]. However, by the use of only three monoclonal antibodies, representing only a fraction of the VSG-specific antibody response, some mimotopes with diagnostic potential might have been missed. Additionally, the mouse and human immune system may recognise different B cell epitopes. The use of polyclonal human antibodies might therefore increase chances of selecting diagnostic mimotopes [17]. Polyclonal antibodies from human sera have been previously used for selection of mimotopes with diagnostic potential for e. g. hepatitis C [15], typhoid fever [18] and Epstein Barr virus [17]. Some mimotopes have been patented for incorporation in commercially available tests, e. g. for neurocysticercosis [19]. In this manuscript we describe the identification of mimotopes for VSG LiTat 1. 3 and LiTat 1. 5 through phage display, using sera from HAT patients and endemic negative persons. Sera from HAT patients and endemic controls were collected within different diagnostic studies [5], [20]. All individuals gave their written informed consent before providing blood. Permission for these studies was obtained from the national ethical committee of the Democratic Republic of the Congo (DR Congo) and from the Institute of Tropical Medicine Antwerp (ITMA) ethical committee, reference number 03 07 1 413 and 04 44 1 472. Forty additional endemic negative control specimens were obtained from the archived specimen bank of the Parasite Diagnostics Unit at ITMA. All specimens were anonymised. Variant surface glycoproteins were purified from cloned populations of T. b. gambiense Variable Antigen Type (VAT) LiTat 1. 3 and 1. 5 [4]. VSG LiTat 1. 3 or LiTat 1. 5 were coated onto magnetic particles (MP, Estapor, 10% suspension, 1. 04 µm, 9 µeq/g COOH). A volume of 250 µL of MP suspension was washed twice with 1 mL of buffer A (10 mmol/L NaH2PO4, pH 6. 0). The MP were activated with 2. 5 ml of buffer A containing 25 mg of 1-ethyl-3- (3-dimethylaminopropyl) carbodiimide (Pierce) and 15 mg of N-hydroxysuccinimide (Sigma). The MP were rotated for 15 minutes at room temperature (rT) and washed with 1 mL of buffer B (2 mmol/L HCl) where after 350 µg of VSG (LiTat 1. 3 or LiTat 1. 5) in 1. 5 mL of buffer C (20 mmol/L NaH2PO4/Na2HPO4, pH 7. 5) was added to the pellet of MP. After rotating for 2 h at rT, the MP were washed three times with buffer C and resuspended to a final concentration of 8% in buffer C containing 100 mmol/L glycin, 1% bovine serum albumin (BSA) and 0. 1% NaN3. Successful coating of the MP was evaluated by agglutination with a HAT positive serum diluted 1/4 in phosphate buffered saline (PBS, 0. 01 mol/L phosphate, 0. 14 mol/L NaCl, pH 7. 4) containing 0. 02% w/v NaN3. Antibodies specific to VSG LiTat 1. 3 or LiTat 1. 5 were purified from nine HAT positive sera originating from the DR Congo [20]. One mL of LiTat 1. 3 or LiTat 1. 5 coated MP was rotated for 2 h at rT with 125 µL of HAT positive serum. After five washes with 800 µL of PBS, the specific antibodies were eluted from the MP by adding 700 µL of 0. 2 mol/L glycine/HCl (pH 2. 2) followed by magnetic separation after five minutes. The eluates, corresponding to the affinity purified antibody fractions, were neutralised with 100 µL of 1 mol/L Tris/HCl pH 9. 1. Indirect ELISA was used to screen the affinity purified antibody fractions and all human serum samples on reactivity with VSG LiTat 1. 3 and LiTat 1. 5. ELISA plates (Nunc MaxiSorp™) were coated overnight (ON) at 4°C with 100 µL/well of 2 µg/mL of each VSG separately in phosphate buffer (PB, 0. 01 mol/L phosphate, pH 6. 5) or with 1. 7 1011 particle/mL of wild type phage (WTP) in PBS. One plate was left empty as antigen negative control (Ag0). The plates were tapped dry, saturated with 350 µL/well of PBS-Blotto (0. 01 mol/L phosphate, 0. 2 mol/L NaCl, 1% w/v skimmed milk powder, 0. 05% w/v NaN3) during 1 h at rT and washed three times with 0. 05% v/v Tween-20 in PBS (PBST) (ELx50, Bio-Tek ELISA washer). The purified antibody fractions were diluted 1/25 and human serum samples 1/150 in PBS-Blotto. One hundred µL/well of each dilution was applied in duplicate and incubated for 1 h at rT. After three washes with PBST 100 µL/well of horse radish peroxidase (PO) -conjugated goat anti-human IgG (H+L) (Jackson), 1/40000 diluted in PBST, was added. An hour and five washes later, wells were incubated for 1 h at rT with 100 µL/well of 2. 2′-azino-bis- (3-ethylbenzthiazoline-6-sulfonic acid) (ABTS) chromogen/substrate solution (50 mg tablet/100 mL of ABTS buffer, Roche). The plate was shaken for ten seconds and the optical density (OD) was read at 414 nm (Labsystems Multiskan RC 351). The measured OD was corrected (ODc) with the corresponding OD in the Ag0 wells. Three LiTat 1. 3 positive pools, each consisting of three different VSG LiTat 1. 3-specific antibody fractions, three LiTat 1. 5 positive pools, each consisting of three different VSG LiTat 1. 5-specific antibody fractions and one negative pool of four endemic negative sera were prepared. For each pool the antibodies were coated onto anti-human IgG (H+L) functionalised magnetic particles (MP) (1% w/v, 1. 05 µm, Estapor/Merck) according to the guidelines of the manufacturer. The panning was performed with the Ph. D. -12 (12-mer) phage display library (New England Biolabs, NEB) [21] through two rounds consisting of 1) a positive selection with anti-VSG (LiTat 1. 3 or 1. 5, respectively) antibodies coated on MP, 2) a negative selection with endemic negative serum antibodies coated on MP and 3) phage amplification [22]. Each positive selection was followed by phage titration and sandwich ELISA. After these two rounds a third positive selection was performed. Positive selection was performed as previously described [16]. Bound phages were eluted for ten minutes with 600 µL of 0. 2 mol/L glycine-HCl containing 1 mg/mL BSA (pH 2. 2) and neutralised with 90 µL of Tris-HCl (1 mol/L, pH 9. 1). Six hundred µL of the elution from the positive selection was rotated ON at 4°C with 1 mg of MP coated with endemic negative serum antibodies, in a total volume of 1 mL of PBSG. The unbound phages in 900 µL of the supernatant of the negative selection were amplified, in a culture of Escherichia (E.) coli (strain ER2738, NEB) at early log (0. 01–0. 05 A600), and purified with PEG-NaCl as previously described [16], [21]. Phages from the first, second and third positive selection were diluted in PBS 101 to 104,102 to 105,104 to 107, respectively. Ten µL of these dilutions were incubated for five minutes with 200 µL of an E. coli culture in mid-log (0. 4–0. 5 A600). The mixture was then pipetted into 4 mL of Top-Agar (50°C) and poured onto agar plates containing 1 mL/L IPTG/X-gal (1. 25 g isopropyl β-D-thiogalactoside, 1 g 5-bromo-4-chloro-3-indolyl-β-D-galactoside, 25 mL dimethylformamide); ninety-four blue clones were picked and each clone was inoculated in 200 µL of lysogeny broth (LB) in a sterile culture plate (BD Falcon™ Clear 96-well Microtest™ Plate) [21]. This plate was shaken overnight at 30°C, and then the bacteria were pelleted by 5 min centrifugation at 1312 g. The supernatant was tested in a sandwich ELISA. ELISA plates were coated ON at 4°C with 100 µL/well of VSG LiTat 1. 3- or LiTat 1. 5-specific positive antibody pools (5 µg/mL in PBS) or a 1/10000 dilution in PBS of the negative serum pool. The ELISA was performed as previously described [16]. Briefly, the wells were incubated for 1 h at rT with 100 µL of phage dilution in PBS-Blotto (1/3 for culture plate supernatant or 1/20 for PEG-NaCl purified phage). PO-anti-M13 pVIII mAb (GE Healthcare), diluted 1/2000 in PBST was added to the wells for 1 h at rT. The wells were then incubated for 1 h at rT with ABTS and read at 414 nm. Phage clones were withheld after the first two positive selections if 1) the OD with the corresponding positive pool (ODpos) >average ODpos+2*standard deviation (sdpos) and 2) the OD with the negative pool (ODneg) <average ODneg. After the third positive selection, phages were sequenced if 1) ODpos>average ODpos+1* sdpos with at least one of the positive pools, 2) ODpos with the 3rd positive pool >0. 150 or 0. 200 for phages selected for VSG LiTat 1. 3 or LiTat 1. 5 respectively and 3) ODneg<average ODneg. Withheld phage clones were sequenced and tested in a similar sandwich ELISA with as capture antibody the nine individual affinity purified antibody fractions, diluted 1/70 in PBS. Purification of phage DNA was performed according to the NEB manual [21]. Sequence determination was performed as described before [16]. The obtained sequence chromatograms were read with Chromas 2. 33 (Technelysium Pty Ltd). Sequence alignment was performed manually and with RELIC software [23]. A protein data base (pdb) model of the N-terminal domain of VSG LiTat 1. 5, was created using SWISS-MODEL [24], [25]. Modelling was based on the known structure of VSG MITat 1. 2 (pdb 1vsgA), previously derived by X-ray crystallography [26]. For VSG LiTat 1. 3 however the server could not find a template with sufficient sequence homology, hence the pdb was created by Thomas Juetteman from the PyMol helpdesk (PyMOL Molecular Graphics System, Schrödinger, LLC). In order to identify possible conformational epitopes, the 3D-Epitope-Explorer (3DEX) [27] was used to find structural homology between the mimotope sequences and the respective VSG protein sequence. Molecular graphics images were produced using the UCSF Chimera package from the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco (supported by NIH P41 RR001081) (http: //www. cgl. ucsf. edu/chimera). The peptides were synthesised at >85% purity (Peptide 2. 0, Chantilly, VA, U. S.). The GGGS-spacer, separating the library insert and the pIII phage protein, was added to the C-terminus of the peptides that were selected by phage display [16], [21]. All peptides were C-terminally elongated with an additional lysine-biotin and amidated (-CONH2), mimicking the uncharged peptide bond in a protein. All synthetic peptides were reconstituted in sterile deionised H2O to a concentration of 2 mg/mL. First, the reactivity of all biotinylated synthetic peptides was evaluated with the nine sera used for affinity antibody purification and nine endemic negative controls. Second, the diagnostic performance of the synthetic peptides was evaluated with human serum samples that were previously screened (indirect ELISA on VSG, serum dilution 1/100) on reactivity with VSG LiTat 1. 3 and 1. 5. All 102 serum samples from gambiense HAT patients originated from DR Congo [20]. Of the 102 endemic gambiense HAT negative serum samples, 71 originated from the DR Congo and 31 from Benin. The indirect ELISA on biotinylated peptides was performed as the indirect ELISA on VSG but 150 µL/well was applied in all but the saturation and washing steps. ELISA plates were coated with 10 µg/mL streptavidin (NEB) in carbonate buffer (0. 1 mol/L, pH 9. 2) or with 2 µg/mL VSG LiTat 1. 3 and LiTat 1. 5 in PB, or wells were left empty (Ag0). After saturation with PBS-Blotto, the peptides were added at a concentration of 2 µg/mL in PBS to the wells containing streptavidin. The peptide-free wells received only PBS. To the VSG-containing and Ag0 wells PBS-5% w/v sucrose was added. After incubation of 1 h at rT the plates were tapped dry, sealed and frozen at −80°C. The serum samples were centrifuged for 5 min at 15700 g and diluted 1/100 in PBS-Blotto. After thawing of the plates and three washes with PBST, the serum dilutions were applied in duplicate. After one hour, we added PO-conjugated goat anti-human IgG (H+L), 1/40000 diluted in PBST. ABTS was used as chromogen/substrate solution and the OD was read as described above. The measured OD was corrected by subtracting the corresponding OD in the peptide-free or Ag0 wells and the average of the duplicate corrected ODs was taken (ODc). The accuracy of the synthetic peptides to detect VSG-specific antibodies for diagnosis of sleeping sickness was assessed by the area under the receiver operator characteristics (ROC) curve (AUC) [28]. Confidence intervals were calculated according to DeLong [29]. For the whole range of cut-offs the Youden index was determined (Youden index = sensitivity+specificity−1) [30] and the cut-off with maximal Youden index was retained. In indirect ELISA, all affinity purified antibody fractions reacted specifically with their corresponding VSG and not with WTP. The antibody fractions that were purified with VSG LiTat 1. 3, had an average ODc of 0. 533±0. 319 with VSG LiTat 1. 3, and average ODcs of only 0. 032±0. 032 with VSG LiTat 1. 5 and −0. 008±0. 015 with WTP. The antibody fractions purified with VSG LiTat 1. 5 had an average ODc of 1. 406±0. 487 with VSG LiTat 1. 5, and average ODcs of only 0. 037±0. 064 with VSG LiTat 1. 3 and −0. 017±0. 018 with WTP. The negative serum samples did not react with VSG LiTat 1. 3 (ODc 0. 034±0. 078), nor with VSG LiTat 1. 5 (ODc 0. 028±0. 069), nor with WTP (ODc 0. 013±0. 029). During the selection process, none of 94 phage clones of the first positive selection, eight of 188 phage clones of the second positive selection and 11 of 188 phage clones of the third positive selection reacted in the sandwich ELISA and were sequenced, resulting in 18 sequences (table 1). The alignment results of VSG LiTat 1. 3 [GenBank AJ304413] and the eighteen peptide sequences displayed by the phage clones are presented in figure 1. All peptides could be aligned within amino acid stretch (AA) 72 to 116 of the N-terminal domain of VSG LiTat 1. 3 (alignment 1). The common motive (F/W) ExDxK (A/V/L) x (A/V/L) was repeated twice in this VSG AA stretch. Therefore twelve sequences could be aligned twice within this region. The peptide displayed by phage 3-3-F6, ETDNMKPLHLRQ, could even be aligned three times within this region of VSG LiTat 1. 3, having ETD, DNxKP and ExD identical within amino acids 78 to 80,87 to 91 and 102 to 104 of the protein sequence. The peptide sequence displayed by phage 3-2-D10 had the highest identity within AA 72 to 116 of the VSG LiTat 1. 3 sequence (7/16 AA, 44%). The reverse sequence of the peptides displayed by phage clones 3-2-C5 and 3-3-E3 showed respectively 31 and 13% identity within AA 180 to 196 (alignment 2). Within the C-terminal domain (alignment 3), the peptide expressed by phage clone 3-2-C5 and 3-3-E3 had 25% identity (4/16 AA) within AA 404 to 443 of VSG LiTat 1. 3. The peptide expressed by phage clones 3-2-G10,3-2-G5 and 3-2-B12 were respectively 31%, 19% and 19% identical within AA 404 to 443 of VSG LiTat 1. 3. All selected phage clones were tested in a sandwich ELISA with the individual purified antibody fractions. The peptides displayed by the seven phage clones with the highest average ODc were withheld for synthesis as biotinylated peptides (table 1). The peptide displayed by phage clone 3-2-B12 was not withheld, since it was a homologue of 3-2-G10 and 3-2-G5 but had a lower average ODc. Based on the alignment results, AA stretch 78 to 110 and AA stretch 424 to 439 of the protein sequence of VSG LiTat 1. 3 were also synthesised as biotinylated peptides (respectively peptide 1. 3/78-110 and peptide 1. 3/424-439). The reactivity of all nine biotinylated synthetic peptides was evaluated in indirect ELISA with the nine HAT positive sera used for affinity antibody purification, and with nine endemic negative controls. Peptide 1. 3/78-110 was the best performing peptide with ODc 1. 469. Peptide 1. 3/424-439 gave a lower average ODc (0. 246) than peptides 3-2-G10 and 3-2-G5 (0. 564 and 0. 920), sharing the same common motive, and was not withheld for further testing. Peptide 3-2-E2, a homologue of peptide 3-2-D10, also gave a lower average ODc (0. 541 versus 0. 763) and was also not withheld for testing on diagnostic performance. During the selection process, one of 94 phage clones of the first positive selection, two of 94 phage clones of the second positive selection and 20 of 188 phage clones of the third positive selection reacted in the sandwich ELISA and were sequenced, resulting in 20 sequences (table 2). The alignment results of VSG LiTat 1. 5 [GenBank HQ662603] and the 20 peptide sequences displayed by the phage clones are presented in figure 2. The peptide expressed by phage clone 5-1-F9 (19% identity) could be aligned within AA 33 to 47 (alignment 1). Within the N-terminal domain, 18 phage peptides could be aligned with minimum 6% identity within AA 81 to 119 (alignment 2), by analogy with alignment 1 for VSG LiTat 1. 3. The peptides expressed by clones 5-3-C7,5-3-A8 and 5-3-B5 respectively had 0,0 and 6% identity within this AA stretch, but shared the common motive (W/F) Y with the peptide expressed by phage 5-3-B8 (19% identity). The peptide of phage clone 5-3-G6 had only 1/16 AA (6%) identity with VSG LiTat 1. 5 but two more AA were homologous within this region. The reverse sequences of phage clone peptides 5-3-F7 (13% identity), 5-1-F9 (19% identity) and 5-3-D5 (13% identity) could also be aligned within this VSG LiTat 1. 5 region. The peptide expressed by clone 5-3-B9 had the highest identity within the AA 81 to 119 stretch (5/16 AA, 31% identity, if a gap of 1 AA was allowed). Peptides expressed by phage clones 5-3-C1,5-3-A4 and 5-3-A6, with common motive “KLANP”, could also be aligned between AA 145 to 166 of the VSG LiTat 1. 5 protein sequence (alignment 3) with respectively 25,13 and 13% identity. Within the VSG LiTat 1. 5 AA stretch 245 to 281, the peptide expressed by phage clone 5-3-A6, showed 19% identity and the reverse peptide sequence of phages 5-3-B9,5-3-A3 and 5-3-A4 showed respectively 19,19 and 13% identity (alignment 4). Within the boundary with the C-terminal domain of VSG LiTat 1. 5, showed the peptides expressed by phage clones 5-3-A8,5-3-C1 and 5-3-B6 respectively 19,31 and 31% identity within AA stretch 341 to 368, if a gap of three AA was allowed for peptide 5-3-C1 (alignment 5). Within the C-terminal domain of VSG LiTat 1. 5, phage clone peptide 5-3-B9 and 5-3-A4 had respectively 31 and 13% identity between AA 468 to 489 (alignment 6). All selected phage clones were tested in a sandwich ELISA with the individual purified antibody fractions (table 2). The peptides displayed by the seven phage clones with the highest average ODc, were chosen for synthesis as biotinylated peptides, except for the peptide displayed by phage clone 5-3-A6, which was similar to 5-3-C1 but had a lower average ODc. Based on the alignment results and by analogy with VSG LiTat 1. 3, the AA stretch 81 to 109 of the protein sequence of VSG LiTat 1. 5 was synthesised as biotinylated peptide (peptide 1. 5/81-109). The reactivity of all eight biotinylated synthetic peptides was evaluated in indirect ELISA with the nine sera used for affinity antibody purification and with nine endemic negative controls. Peptide 5-3-A4 and 5-3-G6 had the lowest average ODcs (0. 145 and 0. 109) and shared a common motive with peptide 5-3-C1 with a higher average ODc (0. 289) and were therefore not withheld for testing on diagnostic performance. Peptide 1. 5/81-109 had an ODc of 0. 382 and was withheld. The accuracy of the biotinylated peptides to detect VSG-specific antibodies was assessed with sera from 102 gambiense HAT patients and 102 endemic negative controls (table 3). Among the mimotopes for VSG LiTat 1. 3, the highest AUC was obtained with peptide 3-2-G5 (0. 93) and peptide 3-2-G10 (0. 95). Sensitivities and specificities at the cut-off with the highest Youden index were respectively 0. 85 and 0. 94 for peptide 3-2-G5, and 0. 90 and 0. 93 for peptide 3-2-G10. Of the mimotopes for VSG LiTat 1. 5 the highest AUC was obtained with peptide 5-1-F9 (0. 95) and 5-2-D3 (0. 94) with respective sensitivities and specificities of 0. 94 and 0. 95 for peptide 5-1-F9 and 0. 92 and 0. 89 for peptide 5-2-D3. With peptide 1. 3/78-110, an AUC of 0. 95 was observed, with a sensitivity of 0. 96 and a specificity of 0. 85. With peptide 1. 5/81-109, an AUC of 0. 79, a sensitivity of 0. 81 and a specificity of 0. 75 were obtained. VSG LiTat 1. 3 and 1. 5 obtained an area under the curve of respectively 1. 000 and 0. 997. The sensitivity and specificity were both 1. 000 at cut-off 1. 318 for VSG LiTat 1. 3 and 1. 000 and 0. 990 at cut-off 1. 182 for VSG LiTat 1. 5. By using 3DEX software and setting the number of hits at a minimum of 5 AA, none of the VSG LiTat 1. 3 mimotopes with AUC>0. 90 could be mapped as a conformational epitope on the protein model of the VSG LiTat 1. 3. In contrast, among the VSG LiTat 1. 5 mimotopes with AUC>0. 90, peptide 5-2-D3 could be mapped with 8/12 AA (E 168|N 164|D 152|G 153|T 150|K 146|L 144|A 141) on the three-dimensional VSG LiTat 1. 5 protein model (figure 3). In this manuscript we describe how mimotopes and regions that take part in epitope formation for VSGs LiTat 1. 3 and LiTat 1. 5 of T. b. gambiense were identified by screening of a Ph. D. -12 phage display library with polyclonal antibodies that were purified from sera of sleeping sickness patients. As sera from sleeping sickness patients contain an important fraction of trypanosome unrelated antibodies [31], the risk of selecting mimotopes that are unrelated to sleeping sickness by using human sera for the screening was considerable. We identified a linear region between amino acid 72 and 114 of the protein sequence of both VSG LiTat 1. 3 and LiTat 1. 5 wherein most of the peptide sequences could be aligned with the VSG protein sequence. This region is localised in the hypervariable N-terminal domain of the VSG and was for both VSGs synthesised as a linear biotinylated peptide and tested in indirect ELISA with a panel of 102 HAT positive and 102 endemic negative sera. Peptide 1. 3/78-110, corresponding to AA stretch 78 to 110 of VSG LiTat 1. 3, had an AUC of 0. 95, indicating diagnostic potential. The epitope of VSG LiTat 1. 3, recognised by the human serum antibodies used for screening of the peptide library, therefore seems to be linear and located within AA stretch 78 to 110. The peptide sequences that were selected for VSG LiTat 1. 3 had in average 3/16 amino acids in common within AA 72 to 114 with a maximum of 7/16 (44%) identical amino acids. Interestingly, a common motive of the peptide sequences was repeated twice within AA 72 to 114 of VSG LiTat 1. 3: (F/W) ExDxK (A/L/V) x (A/L/V), from AA 77 to 85 and 101 to 109. The two mimotopes of VSG LiTat 1. 3 with the highest AUC, peptide 3-2-G5 and 3-2-G10, seemed to share a common epitope (correlation coefficient of ODcs with human sera in ELISA 0. 71, data not shown), their motive WxxDxK reoccurred twice within AA 72 to 114. Their motive, (I/V/A) (T/S) DSK, could also be aligned within the C-terminal domain (AA 424 to 439). This AA stretch was synthesised as a biotinylated peptide as well, but had a low average ODc upon a first screening with nine HAT sera and was discarded for further evaluation of diagnostic performance. We think it unlikely that peptide 3-2-G5 and 3-2-G10 are mimotopes for a linear epitope localised in the C-terminal domain of VSG LiTat 1. 3. Additionally, epitopes localised in the relatively conserved C-terminal domain are more likely to react with non-VSG-specific antibodies. As for VSG LiTat 1. 3, a repetitive motive was present within AA 81 to 114 of VSG LiTat 1. 5: (Y/F/W) (x or xx) (A/L/I/V) A (A/I/L) (D or K) (A/L) xxxxE, from AA 83 to 94 and AA 99 to 111. The peptide sequences selected for VSG LiTat 1. 5 had in average 2/16 AA in common within AA 82 to 114 of the protein sequence, with a maximum of 5/16 (31%) identical AA. Contrary to the results for peptide 1. 3/78-110, peptide 1. 5/81-109, corresponding to AA stretch 81 to 109 of VSG LiTat 1. 5, had an AUC of only 0. 79, while the AUC of all of the individual peptides aligned in this region was >0. 85. Motive AYSxxxIKL of peptide 5-3-C1 (AUC 0. 87), corresponded to LYSxxxAKL (AA 99 to 106) of the VSG LiTat 1. 5 protein sequence. Peptide 5-3-C1 seems therefore to mimic an epitope that is, albeit partly, localised in this region. The similar peptide 5-2-D3, with motive F (x) xxxxKL, performed better in ELISA (AUC 0. 94). It is possible that peptide 5-2-D3 and 5-3-C1 are mimotopes for a discontinuous epitope as they share the common motive (A/I/L) (Y/F) xxxxxKLANPG with four other peptides, while ANPG was not found in the VSG protein sequence. We therefore suspect the epitope of VSG LiTat 1. 5, recognised by the human serum antibodies used for screening, to be discontinuous and to be at least partly localised within this region. This might explain the weaker performance of the linear peptide 1. 5/81-109 compared to the mimotope peptides. This finding was supported by the results of the 3DEX analysis of the mimotopes that had an AUC>90, locating peptide 5-2-D3 with 8/12 AA on the three-dimensional VSG LiTat 1. 5 protein model (E 168|N 164|D 152|G 153|T 150|K 146|L 144|A 141). In a previous study [16] we were able to identify mimotope peptides for the native trypanosomal variant surface glycoproteins by screening of peptide phage display libraries with monoclonal antibodies. Through phage display with polyclonal human antibodies we now identified different mimotopes and regions taking part in epitope formation. Because the three monoclonal antibodies used in the first study represent only a fraction of the VSG-specific antibody response, some mimotopes with diagnostic potential might have been missed. Additionally, the mouse and human immune system may recognise different B cell epitopes. Other factors may have contributed to finding different motives using the two approaches. As a result of a short infection period of two weeks, the mouse monoclonals do not recognise all VSG-epitopes. They were selected for strict VAT-specificity with purified VSGs and identified mimotopes, not necessarily dominant, that were located near the surface of the VSG N-terminal domain. The polyclonal human antibodies result from a long infection and recognise also less exposed VSG-epitopes. It may be that by affinity purification on purified VSG an antibody fraction that recognises non-surface epitopes was mainly retained, as the mimotopes of VSG LiTat 1. 3 seem to be located in this region. Another explanation may lie in the presence of self-reactive VSG-specific antibodies in sera from uninfected individuals, as has been demonstrated by Müller et al. [32]. Thus the negative selection with human antibodies from control sera may have eliminated the phages expressing the mimotopes for the VSG-specific epitopes also recognised by the mAbs. Both panning strategies thus seem complementary, in contrast to what has been described by Tang et al. [18], who selected a greater number of different 12-mer sequences with polyclonal serum for Salmonella enterica, but some of the common motives were also selected by panning of a monoclonal antibody. In the manuscript of Casey et al. [17] the mimotopes for Epstein-Barr (EBV) virus, selected with polyclonal EBV immune rabbit and patient sera were also not recognised by the monoclonal antibodies used for mimotope selection in a previous study. Diagnostic evaluation of individual mimotopes and combinations [patent application GB1202460. 0] indicates that screening of peptide phage display libraries with patient' s antibodies resulted in a more efficient selection of diagnostic peptides than with monoclonal antibodies. We therefore prefer screening with patient' s antibodies. As an alternative approach to phage display linear epitopes may be replaced by synthetic peptides identified by scanning of overlapping peptides spanning the native protein sequence [33]. Furthermore, there are alternative in vitro methods for phage display such as yeast cell or bacterial display or, non-cellular, ribosome or mRNA display [34]. Our study has nevertheless some limitations. First, considering the broad antibody spectrum in HAT sera due to polyclonal B cell activation [35], we opted to use antibody fractions that were affinity purified for VSG LiTat 1. 3 and 1. 5. Thus, mimotopes for other predominant VSGs or other trypanosome antigens with diagnostic potential have not been selected. An alternative approach to identify additional diagnostic mimotopes may consist of screening peptide phage libraries with patient antibodies against other candidate diagnostic proteins [36]. Examples are the T. b. gambiense-specific glycoprotein TgsGP [37], the T. b. rhodesiense-specific serum resistance associated (SRA) protein [38] and Trypanozoon-specific trypanosome antigens such as invariant surface glycoprotein (ISG) 65 and ISG 75 [39], microtubule associated repetitive protein 1 (MARP1) and GM6 [40]. Some of them have already been tested for their diagnostic potential in the form of recombinant fusion proteins expressed in E. coli but none are yet used in diagnostic tests for HAT. Second, even with affinity purified antibodies there is a risk that non-specific mimotopes are selected with antibodies against VSG epitopes that are normally hidden in the intact VSG coat. Usually, most of the phage particles display a consensus binding sequence after two or three rounds of enrichment [21]. We therefore performed three rounds of positive selection and two selections with negative sera. Remarkably, the mimotopes with the highest AUC for VSG LiTat 1. 3 and 1. 5 were selected after only two or even one round of panning. Third, no affinity measurements e. g. via surface plasmon resonance, have been performed. Considering the polyclonal character of antibodies in patients' sera and the inherent differences in antibody response between individual patients, we opted to assess only the diagnostic potential of the selected peptides by means of ELISA. Before the native T. b. gambiense VSGs LiTat 1. 3 and LiTat 1. 5 in the currently existing diagnostic formats can be replaced by synthetic peptides, further improvements should be considered. It is possible to define critical residues, essential for binding with the antibody, by e. g. alanine scanning mutagenesis [41]. Thus the epitope of the human serum antibodies might be recreated as has recently been done for a linear epitope on the VP1 protein of foot-and mouth disease virus [42]. Other, non-essential, parts of the peptides can then be eliminated in order to increase specificity. Phage clones that express peptides with a higher binding affinity might be selected by increasing the number of selection rounds and/or the stringency of the washing steps. In conclusion, with this study we demonstrate that mimotopes of T. b. gambiense VSG LiTat 1. 3 and 1. 5 can be selected from a phage display library and that these mimotopes and corresponding amino acid stretches within the VSGs have diagnostic potential.

Control of the chronic form of sleeping sickness or gambiense human African trypanosomiasis (HAT) consists of accurate diagnosis followed by treatment. We aim to replace the native variant surface glycoprotein (VSG) parasite antigens that are presently used in most antibody detection tests with peptides that can be synthesised in vitro. Antibodies recognising VSG were purified from HAT patient sera and were used to select phage-expressed peptides that mimic VSG epitopes from a Ph. D. -12 phage display library. The diagnostic potential of the corresponding synthetic peptides was demonstrated in indirect ELISA with sera from HAT patients and endemic negative controls. We proved that diagnostic mimotopes for T. b. gambiense VSGs can be selected by phage display technology, using polyclonal human antibodies.

Abstract Introduction Materials and Methods Results Discussion

medicine infectious diseases test evaluation diagnostic medicine african trypanosomiasis neglected tropical diseases parasitic diseases

2012

Identification of Mimotopes with Diagnostic Potential for Trypanosoma brucei gambiense Variant Surface Glycoproteins Using Human Antibody Fractions

Accurate forecasts of influenza incidence can be used to inform medical and public health decision-making and response efforts. However, forecasting systems are uncommon in most countries, with a few notable exceptions. Here we use publicly available data from the World Health Organization to generate retrospective forecasts of influenza peak timing and peak intensity for 64 countries, including 18 tropical and subtropical countries. We find that accurate and well-calibrated forecasts can be generated for countries in temperate regions, with peak timing and intensity accuracy exceeding 50% at four and two weeks prior to the predicted epidemic peak, respectively. Forecasts are significantly less accurate in the tropics and subtropics for both peak timing and intensity. This work indicates that, in temperate regions around the world, forecasts can be generated with sufficient lead time to prepare for upcoming outbreak peak incidence. Forecasting is an important tool in a number of fields, including weather and climate [1–3], agriculture [4,5], air quality [6,7], and consumer activity [8–10]. When operationalized for use in real time, predictions from probabilistic forecasts can be used in decision-making to inform, for example, emergency food aid allocation [4] or profit maximization [8]. Recently, forecasting systems have also been developed for a range of infectious diseases of high public health concern, including influenza [11–20], norovirus [21], dengue [22–25], Ebola [26–29], and, most recently, Zika [30,31]. The ability to generate accurate, real-time forecasts of infectious disease activity has important implications for public health. Currently, response to infectious disease outbreaks is primarily reactive: medical and public health professionals attempt to deal with unexpected spikes of disease incidence as they occur. By providing information on when an outbreak is expected to peak and how many cases are expected at that peak, forecasts have the potential to create a paradigm shift in infectious disease control and public health decision-making. For example, hospitals expecting a patient surge might ensure that adequate resources are available, avoiding bed and staff shortages. Seasonal influenza produces annual wintertime outbreaks in temperate regions, as well as sporadic outbreaks throughout the year in the tropics and subtropics [32,33]. The World Health Organization (WHO) estimates that influenza causes about 300,000–650,000 deaths and 3–5 million cases of severe illness each year [34]. To date, forecasts of influenza activity in the United States have been generated and operationalized [11,15]. However, while influenza forecasts have been generated for countries outside the US [13,14,17,18,35,36], these efforts are less numerous, and many countries have been ignored entirely. The tropics and subtropics are particularly neglected, with forecasts attempted for only Hong Kong [18] and Singapore [17]. This is true despite evidence suggesting that influenza burden in the tropics is similar to that in temperate regions [33]. The WHO collects influenza data year-round from several member states around the world. To our knowledge, no influenza forecasts have yet been generated using these data. Given differences in data collection procedures by country, and the importance of high data quality for generating accurate forecasts, whether these data can be used to generate accurate forecasts remains an open question. Here, we explore the following research questions: 1) Can the WHO data be used to generate accurate and well-calibrated retrospective forecasts at the country level? ; 2) Does forecast accuracy significantly differ between temperate and tropical regions? ; and 3) What factors are associated with substantial changes in forecast accuracy within both temperate and tropical regions? Based on past work, we expect that forecasting will be feasible in all regions, but that forecast accuracy will be substantially higher in temperate regions. Influenza syndromic and virologic data were obtained from WHO’s FluID [37] and FluNet [38] web-tools, respectively. Briefly, these systems contain aggregated influenza data from WHO member states, which are either submitted by member states directly or downloaded by the WHO from existing regional databases. Good quality (see S1 Text) syndromic and virologic data were available for at least one season from 64 countries, primarily in Europe and North America (see Figs 1 and S1). Countries were classified as temperate or tropical based on both their latitude and whether they demonstrated seasonal or sporadic influenza dynamics (see S1 Text). Overall, eighteen countries were classified as tropical, and three (Australia, New Zealand, and Chile) were located in the southern temperate region. FluID data include diagnostic counts of influenza-like illness (ILI), acute respiratory infection (ARI), severe acute respiratory infection (SARI), and pneumonia, with different countries preferentially reporting different data types (see S1 Text for additional information). Because these data contain no information on laboratory testing, counts include both patients infected with influenza and patients infected by other pathogens that lead to similar signs and symptoms. To adjust for this lack of specificity, we use FluNet data, which includes the total number of tests performed for influenza and the number positive for influenza. Specifically, we multiply the syndromic case counts from the FluID tool by the proportion of tests positive for influenza in that same country during a given week. This calculation eliminates out-of-season syndromic cases that are unlikely to be due to influenza. Further, as the model used in this study (described below) simulates the transmission of a single pathogen, the removal of incidence due to non-influenza illness increases agreement between model input (data) and output. We refer to the resulting measures as ILI+, ARI+, SARI+, or pneumonia+, or, more broadly, syndromic+. For this study, we focused specifically on seasonal influenza outbreaks, and excluded the 2009 pandemic from the main analysis. While pandemic outbreaks often produce a strong incidence signal that is forecastable [17], they typically appear out-of-season in temperate regions. Seasonal influenza outbreaks, on the other hand, occur with enough frequency that, even in the tropics, where outbreak timing is less regular, future epidemics are almost certain to occur within the year. To maintain a consistent forecasting approach, we therefore focus on seasonal influenza. We present results from forecasting the 2009 pandemic alone, as well as associated methods, in S1 Text and S19 Fig. In addition, individual seasons were removed from the final dataset if: a) five or more consecutive weeks of data were missing near the outbreak peak (n = 2); b) the season consisted of fewer than 5 non-NA and non-zero data points (n = 2); c) the total attack rate of the season was less than 5% that of the largest outbreak (in other words, if case counts were unrealistically low; n = 5); d) data collection began or ended at the outbreak peak (n = 4); or e) no consecutive weeks of data were available (in other words, data were only available every other week; n = 1). We also removed data from 2010–11 in Mexico due to the continued disruption of typical seasonal patterns by the 2009 pandemic. Individual data points were removed if they occurred outside of the influenza season (as defined below under “Delineation of Influenza Seasons”) and were greater than 50% of the maximum value for the country over all seasons (n = 1). In total, 15 individual seasons were removed from the dataset, and 64 countries remained. In temperate regions, data were available for between one and seven seasons for each country for a total of 289 seasons. A complete list of countries and seasons used for forecasting can be found in S1 and S2 Tables, and the cleaned influenza data are available as S1 Dataset. Note that, for the seasonal forecasts, we began fitting tropical data at week 40 of 2010. Data on absolute humidity were obtained from NASA’s Global Land Data Assimilation System (GLDAS), which uses both observed data and modeling techniques to produce high-resolution surface meteorological data [39]. Data were available every three hours at a spatial resolution of 1°x1° for the years 1989–2008. Data from each grid cell were aggregated to the daily level, and anomalous records were identified by visual inspection and removed. Then, climatologies for each grid cell were generated by averaging daily specific humidity on each of 365 days across twelve to twenty years, depending on the amount of anomalous data removed. Finally, climatologies were aggregated to the country level by averaging the climatologies for all grid cells lying within a country, weighted by the proportion of the grid cell situated within the country in question. A more detailed description of how the humidity data were processed can be found in the S1 Text, and the processed data are available as S2 Dataset. The influenza season in temperate regions of the northern hemisphere is modeled as beginning in week 40 and ending in week 20 of the following year [40]. We shift these values by one half-year for temperate regions of the southern hemisphere; thus, the influenza season begins in week 14 and continues until week 46. For tropical regions, where consistent seasonality in influenza infection patterns is not observed, the above methods are not sufficient. Individual outbreaks are instead identified using methodology previously described in [18]. Briefly, outbreak onsets are defined as the first of three consecutive weeks where ILI+ rates exceeded the 33rd percentile of non-zero ILI+ values across all available data for a country. The end of an outbreak is defined as the first of two consecutive weeks below this threshold. To ensure that sporadic spikes in influenza are not counted, we remove any outbreaks where ILI+ counts never exceeded three times its respective onset threshold value. Country-level retrospective forecasts are developed using a model-data assimilation system consisting of: (1) influenza observations, as described above, (2) a model of influenza transmission, and (3) a filter to assimilate observations and optimize model simulation and ensemble forecast. The final two components are described here. These components differed slightly for temperate and tropical regions, and are therefore described separately. As described above, model output represents true influenza incidence per 100,000 population. Our data, on the other hand, are obtained by multiplying nonspecific syndromic data by influenza positivity rates among those who actively seek medical care. Furthermore, the majority of countries included in the WHO data provide no information on the total number of patients seen or the size of the catchment areas from which data were obtained. Thus, our data represent counts, not rates. In order to properly use the EAKF as described above, we must therefore first scale the data such that they are compatible with the model-simulated state space. In effect, the scaling factors map the observed syndromic+ data to the model state space. Scaled data, thus, represent the estimated number of syndromic+ cases per 100,000 population, and can be used for data assimilation. Model output—the simulations and forecasts—can then be scaled back to their original units (e. g. ARI+) for use by individual country public health departments. Our previous work has shown that SIRS simulations perform optimally when 15–50% of a model population of 100,000 is infected over the course of a modeled epidemic. Therefore, scaling values, γ, for each country were determined by first calculating the range of scaling values yielding a total attack rate between 15% and 50% for each season, i, ([γ15, i, γ 50, i]), then choosing a single country-specific scaling value based on the following rule: γ={if∃γ∈ℝ: γ15, i<γ<γ50, i∀i: maxi=1n (γ15, i) else: mini=1n (γ50, i) } (4) Although forecasts in the tropics were run continuously rather than by season, scaling factors for tropical countries were determined similarly using influenza outbreaks as identified under “Delineation of Influenza Seasons” above. Scaling values were allowed to vary by country, but not by season: that is, for each country, a single scaling value was chosen and used in retrospective forecasts of all available seasons. As scaling factors are controlling for differences in rates of seeking medical attention, size of the catchment area from which influenza data are collected, and overall population size by country, they vary substantially, from 0. 004 in Mexico to 374 in Peru. Forecasts were evaluated based on their ability to accurately predict outbreak peak timing (the week with the highest number of influenza cases), peak intensity (the number of influenza cases at the peak), and onset timing (the first of three consecutive weeks with influenza activity over some baseline value). Onset baseline values were chosen as 500 simulated cases for temperate countries, and 300 cases for tropical countries (see S1 Text). A forecast was considered accurate for peak timing and onset timing if the predicted value was within one week of the observed, and for peak intensity if the predicted influenza case count was within 25% of the observed. These thresholds, particularly the 1 week cutoff for peak timing accuracy, have been routinely used both in our past work [13,14,19,43,45,48,49] and in evaluating forecasts submitted to the Centers for Disease Control and Prevention’s (CDC) Predict the Influenza Season Challenge [15], allowing for comparison between the results of this work and past work. If the mode predicted onset timing is NA (no outbreak), predicted peak timing, peak intensity, and onset timing were set to NA, and the forecast was removed from consideration. Forecast accuracy was compared for temperate vs. tropical regions, as well as within temperate regions by hemisphere, region, data type, season, and scaling, and within the tropics by region, data type, and scaling. Because, in real time, the actual time to peak is unknown, we evaluated forecast accuracy by predicted lead time (i. e. the difference between the week at which a forecast is initiated and predicted peak timing). For most analyses, forecast accuracy was assessed at predicted lead weeks -6 to 4 (i. e. six weeks before the predicted peak through four weeks after). Comparisons were made for each individual variable using generalized estimating equations (see S1 Text for more details). To assess whether the effects of the explanatory factors change over time, GEE models were also run restricting the data to either before or after the predicted peak. Seasons with no identified onset (in other words, where no outbreak occurred) were removed before analyzing forecast accuracy. Additionally, because individual outbreaks within tropical countries are identified during the forecasting process, and therefore were not checked for quality previously, outbreaks where a) five or more consecutive weeks of data were missing; or b) data collection for an outbreak began at the outbreak peak were removed from tropical countries’ results before GEEs were run. To assess the impact of including humidity forcing in the temperate models, we generated an additional set of forecasts for the temperate regions, this time without including humidity forcing in the model structure (see S1 Text). This resulted in two distinct forecasts for each country, season, start week, and run: one incorporating humidity data and one not. In order to fully take advantage of this paired design, forecast accuracy was compared by observed lead week using the exact binomial test. Because individual comparisons were made for each lead week, we applied a Bonferroni correction and considered differences to be statistically significant when p-values were less than 0. 0045 (p = 0. 05 / 11). Unlike in previous analyses, rather than removing forecasts predicting no onset, we considered these forecasts to be “inaccurate. ” This was done to avoid ignoring pairs of forecasts where one failed to recognize an oncoming outbreak but the other accurately predicted peak timing or intensity. Sensitivity analyses were performed to test how forecast accuracy changes as a function of EAKF observational error variance, onset baseline value, scaling, and accuracy metric. Findings from these sensitivity analyses broadly agree with the results presented here (results in S1 Text). Retrospective forecasts were performed using syndromic+ data from 64 countries, of which 18 were classified as tropical. In the temperate regions, data were available for between 2 and 7 seasons, with each country contributing an average of 6 seasons of data (data in S2 Table). In the tropics, data were available for between 29 and 345 weeks (mean = 166 weeks; median = 140 weeks). In the northern temperate region, onset timing occurred between weeks 45 and 64, and peak timing occurred between weeks 48 and 67. In the southern temperate region, these values were weeks 23 and 33 for onset timing and 29 and 38 for peak timing. Overall, we found that accurate forecasts of both peak timing and peak intensity for influenza outbreaks are possible using publicly available WHO data. In temperate regions, we were able to develop country-level, retrospective forecasts that exceeded 50% accuracy for peak timing (i. e. , 50% of forecasts predicted peak timing within one week of the observed value) up to four weeks before the predicted peak, and for peak intensity (within 25% of the observed value) two weeks before the predicted peak. Forecasts exceeded 75% accuracy for peak timing one week before the predicted peak, and for peak intensity at the predicted peak week (Fig 2A). Forecast accuracy was lower in the tropics, never exceeding 50% for either peak timing or peak intensity (Fig 2B). As expected [11,12,14,17,18], forecast accuracy varied as a function of lead time, with forecasts near and after the forecasted peak typically performing better than forecasts generated several weeks before the peak. Similar patterns were seen by observed lead time, although tropical forecast accuracy was much higher after the observed peak, exceeding 70% (results in S3 Fig). Broadly, these results remained consistent after altering the cutoff point at which forecasts were considered accurate (S11 Fig), and when correlation coefficients and symmetric mean absolute percentage error (sMAPE) over the entire forecast period were assessed (S12 Fig), although forecast accuracy assessed using sMAPE was comparable between temperate and tropical regions. For both temperate and tropical regions, forecasts of outbreak onset timing showed high accuracy post-onset, but forecasts were rarely generated in advance of the predicted onset week (Table 1). Specifically, no temperate forecasts predicted that onset would occur with more than a one week advanced lead, and very few forecasts in the tropics accurately predicted onset with more than a one-week lead. In temperate regions, onset timing accuracy (onset predicted within one week of the observed value) quickly increased and remained above 95% as soon as the predicted onset was in the past. In the tropics, accuracy reached almost 50% at the predicted onset, and remained around 65–70% for all later lead weeks. For the tropics only, we also evaluated how often forecasts correctly recognized an existing or upcoming outbreak, without mistakenly predicting outbreaks during periods in which no outbreaks occurred. Specifically, we calculated sensitivity, specificity, positive predictive value, and negative predictive value. We found that both sensitivity (98. 56%) and the negative predictive value (98. 10%) were high, but that specificity (56. 22%) and the positive predictive value (63. 12%) were much lower. Thus, while forecasts are unlikely to predict dormancy before or during an outbreak, forecasts suggesting a current or upcoming outbreak were inaccurate more often than accurate. It is important to consider not only how accurate forecasts are, but also forecast uncertainty. This is especially true in the case of real-time forecasting: different medical and public health responses might be affected given forecast of an 80% chance of a particular outcome rather than a 20% chance. Because each forecast is based on 300 individual ensemble members, we could assess forecast certainty through the spread of the ensemble variance, where narrower ensemble spread ideally indicated greater certainty. Fig 3A and 3B show average peak timing and intensity forecast accuracy, respectively, for temperate regions plotted against ensemble variance (separated into 10 quantiles). For peak timing, we generally saw a slight decrease in forecast accuracy as ensemble variance increases at all predicted lead weeks, indicating that we can infer expected forecast accuracy from ensemble spread. For peak intensity, this pattern only held prior to the predicted peak. Corresponding plots for the tropics are shown in Fig 3C and 3D. For peak timing, no clear relationship existed between ensemble variance and forecast accuracy, indicating that no information about expected forecast accuracy can be inferred from ensemble spread. For forecasts of peak intensity, on the other hand, increases in ensemble variance corresponded to substantial decreases in forecast accuracy. We also explored how often the observed peak timing and intensity fall within certain prediction intervals of ensemble spread prior to the predicted peak (Fig 4). In a well-calibrated forecast, we expect that the observed intensity will fall within the nth% prediction interval n% of the time. Overall, forecasts appeared to be well calibrated for both peak timing and intensity in temperate regions at all lead times, although prediction intervals tended to be too wide for peak timing, especially several weeks before the peak. In the tropics, peak intensity forecasts appeared well calibrated, while peak timing forecasts rarely included the observed peak timing. Further exploration of forecast calibration can be found in S10 Fig. We have shown that, in temperate regions, accurate and well-calibrated retrospective forecasts of seasonal influenza activity are feasible. Work is currently being conducted to determine whether real-time forecasts are similarly feasible, and future work will incorporate travel between countries with the goal of improving forecast accuracy, particularly onset timing accuracy. Although this work is at an early stage, we note the importance of eventually incorporating forecasts into medical and public health decision-making. Accurate real-time probabilistic forecasts have the potential to inform decisions such as antiviral stockpiling by governments or staff and bed management by hospitals, preventing morbidity and mortality. Therefore, it is critical that these forecasts not be produced solely as an academic exercise.

Influenza is responsible for an estimated 3–5 million cases and 300–650,000 deaths each year worldwide. If produced early enough, accurate forecasts of influenza activity could guide public health practitioners and medical professionals in preparing for an outbreak, reducing the subsequent morbidity and mortality. For example, hospitals could use these forecasts to determine how many beds will be needed when an outbreak is most intense. Despite this potential impact, influenza forecasts are primarily generated for the United States, with forecasts for other countries being comparatively rare. Here, we use publically available influenza data to forecast influenza activity in 64 countries. We find that accurate forecasts can be produced several weeks before the outbreak’s peak in temperate countries, where influenza outbreaks occur regularly during the winter. Forecast accuracy is lower in the tropics and subtropics, where outbreaks occur more sporadically. Overall, our results suggest that forecasts have potential as an important public health tool in many countries, not only in the US.

Abstract Introduction Materials and methods Results Discussion

medicine and health sciences statistics influenza geographical regions atmospheric science temperate regions simulation and modeling seasons mathematics forecasting regional geography humidity research and analysis methods public and occupational health infectious diseases geography mathematical and statistical techniques tropical regions meteorology earth sciences viral diseases physical sciences statistical methods

2019

Development and validation of influenza forecasting for 64 temperate and tropical countries

The cell wall of Gram-positive bacteria is a complex network of surface proteins, capsular polysaccharides and wall teichoic acids (WTA) covalently linked to Peptidoglycan (PG). The absence of WTA has been associated with a reduced pathogenicity of Staphylococcus aureus (S. aureus). Here, we assessed whether this was due to increased detection of PG, an important target of innate immune receptors. Antibiotic-mediated or genetic inhibition of WTA production in S. aureus led to increased binding of the non-lytic PG Recognition Protein-SA (PGRP-SA), and this was associated with a reduction in host susceptibility to infection. Moreover, PGRP-SD, another innate sensor required to control wild type S. aureus infection, became redundant. Our data imply that by using WTA to limit access of innate immune receptors to PG, under-detected bacteria are able to establish an infection and ultimately overwhelm the host. We propose that different PGRPs work in concert to counter this strategy. The complex cell surface of bacteria has been directly or indirectly associated with different strategies that bacterial pathogens use to interact with the host. These include acquisition of specific adhesion factors, formation of biofilms, adaptation to an intracellular environment, production of a protective capsular polysaccharide or evasion of innate immune defences (e. g. lysozyme) [1]. The host counters these strategies by targeting conserved molecules (pathogen associated molecular patterns or PAMPs), unique in bacteria, that are either present at the bacterial surface or are released by bacteria as they attempt to establish infection. Bacterial PAMPs include Peptidoglycan (PG), a heterogeneous polymer of glycan chains cross-linked by short peptides of variable length and amino acid composition [2]. Although PG recognition is essential to trigger an inflammatory response, this macromolecule may not be easily accessible for recognition at the surface of bacteria. In Gram-positive bacteria, PG is buried within a complex cell surface consisting of different molecules [3]–[5]. Such molecules include surface proteins, covalently linked or tightly associated with PG, capsular polysaccharides, usually required for the ability of different bacteria to cause disease [6] and wall teichoic acids (WTA), phosphate-rich glycopolymers involved in the resistance of bacteria to environmental stress and regulation of bacterial division [7]. It is not clear therefore, how the host would be able to sense bacterial PG buried within such complex structures. One hypothesis is that the innate immune system recognises soluble PG fragments that are released from the bacterial cell surface through the activity of enzymes produced by bacteria (such as autolysins) or by the host (such as lysozyme) [2], [8]. However, certain bacteria have the ability to modify their PG, turning it more resistant to the action of such enzymes [9], thus preventing the release of small soluble fragments capable of triggering an innate immune response in the host. This may be the case for Listeria monocytogenes that has the ability to de-N-acetylate its PG allowing them to survive the action of lysozyme and evade the host innate immune system [10]. Another hypothesis is that the components of the host innate immune system are able to bind directly to PG present within the bacterial cell surface. As discussed earlier, PG is decorated with a variety of large molecules that may sterically block access of host receptors to the underlying PG. In Gram-positive bacteria, cell wall glycopolymers, including WTA may play this role [1]. The role of WTA protecting the PG from recognition would have important implications regarding the onset of infection by major human pathogens such as Staphylococcus aureus (S. aureus) [1]. Recently, it has been shown that different components, present at the cell wall of S. aureus bacteria, may determine the survival of infected Drosophila. Specifically, S. aureus strains impaired in the expression of enzymes involved with the metabolism of cell wall components were unable to kill flies [11]. Moreover, it has been proposed that D-alanylation of the WTA produced by S. aureus may inhibit the recognition of PG by host receptors. This inhibitory effect was observed in vitro not only when WTA was covalently attached to polymeric PG but, surprisingly, also when WTA was covalently attached to monomeric PG [12]. The fruit fly Drosophila melanogaster recognises Gram-positive bacteria by either direct binding to PG or its smallest components [13]. Based on in vitro data [14] and infection studies of mutants [14], [15], the current working hypothesis is that a flexible system of pattern recognition receptors (PRRs) can be deployed by the host immune system to detect Lysine-type PG from different Gram-positive bacterial pathogens. Two Peptidoglycan Recognition Proteins (PGRPs), namely PGRP-SA and PGRP-SD are major components of this system [15], [16]. Depending on the bacterium, each, or both of these PGRPs – along with Gram-Negative Binding Protein1 (GNBP1) [17] – interacts with PG and activate a downstream proteolytic cascade, which culminates in Toll receptor signalling. The signal reaches the cytoplasmic NF-κB/I-κB complex via a receptor/adaptor complex comprising dMyD88, Tube and the IRAK homologue Pelle. At that point the I-κB homologue Cactus is phosphorylated and targeted for degradation while the NF-κB homologue Dif is free to enter the nucleus of host cells and regulate target genes [18]. Prominent among these genes, is a group of potent antimicrobial peptides (AMPs), which are synthesised by the fat body and secreted into the haemolymph. An AMP frequently used as a read-out for the Toll pathway is Drosomycin (Drs). AMPs and local melanization, along with the phagocytic activity of haemocytes constitute respectively the humoral and cellular arm of the fruit fly response to infection [18]. Here, we report for the first time that Drosophila PGRP-SA, a non-lytic PGRP was able to bind intact live bacteria in vivo. Access to PG was limited by the presence of WTA: binding of PGRP-SA to various live Gram-positive bacteria was minimal, but binding to purified PG, stripped of covalent modifications (including WTA) was far greater. Through inhibiting WTA synthesis, either by the addition of an antibiotic or genetically, we were able to potentiate detection of these bacteria by PGRP-SA. For S. aureus, this correlated with a reduced ability of the bacteria to proliferate within the host, and a reduced susceptibility of the host to infection in a PGRP-SA/GNBP1 dependent manner. We also observed that PGRP-SD, essential for sensing wild type S. aureus, became redundant as WTA levels were reduced. Overall, our results suggest that WTA may be part of a general mechanism used by Gram-positive bacteria, which limits the access of innate receptors to PG, thereby enabling bacteria to evade detection and establish infection. To address the question of whether Gram-positive bacteria counter host recognition by limiting access of innate sensors to PG, we constructed a fluorescent derivative of the fruit fly Lys-type PG receptor, PGRP-SA (mCherry-PGRP-SA). This construct and an untagged version (rPGRP-SA) were expressed in Escherichia coli and the resulting proteins were purified. As shown in the supplementary material (Figure S1A), injection of mCherry-PGRP-SA, or rPGRP-SA, into PGRP-SA deficient flies restored Drs-GFP production induced by infection with Micrococcus luteus (M. luteus). Endogenous Drs expression was also restored as confirmed by qPCR (Figure S1B). These observations were consistent with our previous results when using a recombinant PGRP-SA expressed in the lepidopteran cell line Sf9 [19]. Taken together, these results showed that the fluorescently tagged PGRP-SA and the untagged versions are functional and capable of restoring an innate immune response in PGRP-SA deficient flies. Initially, we used rPGRP-SA and mCherry-PGRP-SA in co-precipitation experiments in order to study binding to PG from different Gram-positive bacteria. Both bound with similar affinity to PG purified from M. luteus, Enterococcus faecalis (E. faecalis), and S. aureus (data not shown and Figure 1A, respectively). For details of PG composition of these bacteria see Figure S2. Importantly, this indicated that the mCherry-tag appeared not to interfere with PGRP-SA binding, and thus, demonstrated that both proteins were able to bind Lys-type PG of different composition. We therefore assessed in vitro, the binding of mCherry-PGRP-SA to the surface of live bacteria harvested during exponential growth phase. Notably, the binding of the recombinant protein to live bacteria exhibited a range of different affinities in contrast to their respective purified PG. Binding to live E. faecalis and S. aureus was significantly reduced, when compared to binding to M. luteus (Figure 1B). However, the binding levels of PGRP-SA to the purified PG from these bacteria were similar (Figure 1A). We also noticed that while mCherry-PGRP-SA was capable of binding the entire surface of M. luteus cells, it bound at specific sites at the surface of S. aureus cells, similar to what has been described recently for mammalian bactericidal PGRPs [20]. These results suggested that although the three types of bacterial PG were similarly recognized by PGRP-SA, the presence of other components found at the surface of live bacteria might have prevented PGRP-SA from finding its PG ligand. The cell surface of a Gram-positive bacterium is a complex structure consisting of a thick layer of PG, surface proteins and glycopolymers such as capsular polysaccharides and WTA. As previous studies had shown that certain PG-binding proteins, such as bacterial autolysins, have a higher affinity for the surface of bacterial strains lacking WTA [21]–[23], it was decided to investigate whether presence of WTA could be preventing PGRP-SA from binding to the surface of live bacteria. Further support for the choice of WTA came from the fact that different Gram-positive bacteria can produce WTA with a variable composition [24]–[26]. M. luteus, for which mCherry-PGRP-SA displayed the highest affinity, does not produce WTA [24], [27], (Figure S2C). To test whether WTA mediated the differential binding of PGRP-SA, we cultured bacteria in the presence of tunicamycin, thereby inhibiting their ability to synthesize WTA. At lower, sub-inhibitory concentrations as those used in this study, tunicamycin specifically inhibits TagO [28]: a glycosyltransferase that specifically localizes to the division septum of S. aureus [29] and is required for the initial step of WTA biosynthesis, namely, the transfer of GlcNAc to the C55-P lipid anchor bactoprenol. We observed higher levels of mCherry-PGRP-SA binding to the newly synthesized cell material, located at the division septum, when Gram-positive bacteria cells were treated with tunicamycin (Figure 2A). S. aureus and S. saprophyticus exhibited a similar increase in binding, 63× and 84× respectively, whilst E. faecalis binding increased 8×. It should be noted that the effect of tunicamycin in these bacteria was not the same. While addition of the antibiotic resulted in binding of mCherry-PGRP-SA to the entire cell surface of S. aureus, binding was observed predominantly at the division septum in S. saprophyticus and exclusively at this region in E. faecalis. We attribute these differences to how and where the new cell wall synthesis occurs in these bacteria. Nevertheless, the results described above suggested that WTA in different bacteria might protect PG from exposure to host receptors. To confirm that WTA were indeed required to reduce access of PGRP-SA at the cell surface, we quantified the binding of mCherry-PGRP-SA to S. aureus mutants that produced varying amounts of WTA due to mutations in the tagO gene [29]. We chose S. aureus because it is a major human pathogen with a well-characterised WTA synthetic pathway [30], [31]. A complete absence of WTA, which occurs when tagO is entirely deleted (RNΔtagO pMAD), or when two highly conserved residues have been mutated (RNΔtagO ptagOD87A/D88A), resulted in equivalently enhanced levels of mCherry-PGRP-SA binding, when compared to the wild type strain (∼2×103 and ∼3. 3×103-fold respectively, Figure 2B). To verify that the observed result was indeed due to the loss of WTA, we expressed wild type tagO in the RNΔtagO background (RNΔtagO ptagO): this rescued the loss of WTA (WTA levels restored to 90% of wild type levels) [29], and reduced mCherry-PGRP-SA binding to levels close to those observed for the wild type strain (Figure 2B). A tagO mutant that could only support production of a reduced amount of WTA (RNΔtagO ptagOG152A; 24% levels of WTA compared to wild type) exhibited an intermediate level of mCherry-PGRP-SA binding relative to all strains (6×102-fold increase relative to the wild type strain, Figure 2B). Overall, our data indicated that WTA found in the cell wall of different live Gram-positive bacteria restricted PGRP-SA from binding their PG, and in S. aureus this occurs in a dose dependent manner. We next wanted to examine whether increased PGRP-SA binding – due to a lack of WTA – affected the ability of bacteria to survive in an in vivo system. We chose D. melanogaster because it is a well-established model for dissecting pattern recognition in innate immunity [18]. We know for example that in vitro, three PRRs – PGRP-SD/PGRP-SA/GNBP1 – form a ternary complex for binding to the PG of S. aureus [14]. As a first approach wild type and mutant S. aureus strains were injected into wild type flies and also into flies defective for PGRP-SD or PGRP-SA. We then determined the number of CFUs 6 and 17 hours post-infection; the latter time point being when the first flies succumb to infection (Figure 3 and S5). All flies were inoculated with low and statistically identical numbers of bacteria (∼102 CFUs per fly; Figure 3, Time 0). Our rationale was to induce infections that were comparable and that could evolve over time. For example, flies generally succumb to bacterial infection when their numbers increase beyond 106 CFUs per fly [18], [32], and therefore, high initial loads (e. g. 104–105 CFUs per fly) may overwhelm the host and consequently may not be informative regarding the course of an infection. We observed that wild type S. aureus (NCTC8325-4) CFUs increased in all fly backgrounds over the period of infection to numbers that were statistically separable, with PGRP-SA deficient flies carrying the heaviest load (Figure 3). In contrast, the numbers of the S. aureus mutant, which lacked WTA (NCTCΔtagO) [29], did not significantly increase in the wild type or PGRP-SD mutant background. However, the number of NCTCΔtagO bacteria in the PGRP-SA mutant was significantly higher at both the 6 and 17 hours time points (Figure 3). Two-way ANOVA revealed a significant interaction between the bacteria and fly strains, which was due to the large increase of NCTCΔtagO bacteria in the PGRP-SA mutant. Together, these data indicated that WTA were fundamental for S. aureus to counter recognition by PGRP-SA, and consequently, the bacteria were able to increase their number during the initial course of infection. We have previously observed that PG produced by NCTCΔtagO bacteria has reduced levels of cross-linking relative to the wild type strain [29]. To evaluate whether this contributed to the inability of NCTCΔtagO bacteria to increase their number in wild type or PGRP-SD mutant flies, we assessed mCherry-PGRP-SA binding to NCTCΔpbpD and determined CFUs at 6 and 17 hours. NCTCΔpbpD is a derivative of NCTC8325-4 in which pbpD (the gene encoding to penicillin binding protein 4, PBP4) has been deleted. Deletion of pbpD results in a strain that produces PG with a similar level of cross-linking to that found in NCTCΔtagO [29], but which still produces WTA. The inability of NCTCΔpbpD and NCTCΔtagO to produce a highly crosslinked PG did not interfere with bacteria growth in culture, as its duplication time at 30°C was very similar to the parental NCTC8325-4 strain (Figure S3B). In both experiments, NCTCΔpbpD behaved as the wild type bacteria. Firstly, binding mcherry-PGRP-SA similarly (Figure S3C) and secondly, for each fly background attaining numbers that were statistically inseparable from those for NCTC8325-4 (Figure 3, Time +17 hours). To assess whether the developing trend in bacterial numbers at 17 hours post-infection resolved into differences in how flies survive, we monitored the number of flies alive at 24 hour intervals over 3 days. In addition, we infected GNBP1 mutant flies, because GNBP1 has been postulated to work as part of a complex with PGRP-SA [14], [17]. Survival curves for a particular fly background when infected with either NCTC8325-4 or NCTCΔpbpD were statistically inseparable, except for those obtained for the wild type background, where flies succumbed more to NCTCΔpbpD (Figure 4; 62% and 38% survival at 72 hours post-infection, respectively). Nearly all PGRP-SA and GNBP1 mutant flies had died by 24 hours, whereas ∼40% of PGRP-SD mutant flies survived beyond this time point, succumbing to infection around 48 hours (∼5% of flies surviving). In contrast, ∼95% of wild type and PGRP-SD mutant flies survived the NCTCΔtagO infection up to 72 hours (furthermore, taking CFUs at this time-point revealed that NCTCΔtagO had been eliminated from these flies, 0 CFUs per fly). The majority of PGPR-SA and GNBP1 flies had succumbed to infection by 48 hours (3% of flies surviving). A similar trend in survival outcome was observed with NCTC8325-4 after treatment with tunicamycin (Figure 4). These data confirmed that WTA were indeed required to counter host immunity, because without them, infection could be controlled in a PGRP-SA/GNBP1 dependent manner. Differences in CFUs were apparent 6 hours post-infection suggesting that recognition and reduction of propagation or killing of bacteria, occurs rapidly following infection. Interestingly, these results also showed that a requirement for PGRP-SD was bypassed when WTA are removed and PGRP-SA has far greater access to PG. To further demonstrate the necessity for WTA to protect PG from host recognition, we monitored survival of flies infected with the aforementioned TagO point mutations (Figure 2B and Figure 5). In these experiments, we wanted to rule out unknown causes that may occur due to the absence of the TagO protein per se, and also, lessen adverse effects that may occur due to a complete lack of WTA. The survival trend for flies infected with RNΔtagO pMAD, that lacks tagO and carries an empty pMAD plasmid vector (vector control), was similar to that for NCTCΔtagO: the PGRP-SA mutant succumbed rapidly, whereas the PGRP-SD mutant and wild type flies generally survived, their curves being statistically inseparable (Figure 5). The injection of the complemented strain (RNΔtagO ptagO) resulted in survival outcomes that were characteristic of NCTC8325-4, with PGRP-SD mutant and wild type flies succumbing to the infection, with their curves being statistically separated (Figure 5). Notably, wild type and PGRP-SD mutant flies infected with RNΔtagO ptagOG152A (which produces ∼24% WTA relative to RNΔtagO ptagO but produces similar levels of the TagO protein) [29] survived to intermediary levels (Figure 5). Overall, survival of wild type flies decreased as WTA levels increased (with a concomitant decrease in PGRP-SA binding, Figure 2B), and likewise for the PGRP-SD mutant; with the difference between wild type and PGRP-SD mutant survival successively increasing. In contrast, survival of PGRP-SA mutant flies was independent of WTA levels, with flies succumbing strongly for all infections in a statistically inseparable manner (Figure 5). These data confirmed that it was indeed in vivo protection of PG by WTA against the consequences of PGRP-SA binding, and furthermore, suggested that a requirement for PGRP-SD gradually became redundant as WTA levels decreased. It has been reported previously that D-alanylation of WTA is also required for the pathogenicity of S. aureus [11]; D-alanylation is a process that incorporates D-alanine residues into the glycerol-/ribitol-phosphate backbone of WTA, thereby reducing the negative charge of the polymer [33]. We examined therefore, whether a S. aureus mutant that lacks the D-alanylation pathway (RNΔdltABCD) bound mCherry-PGRP-SA equivalently to RNΔtagO. Binding of, mCherry-PGRP-SA to RNΔdltABCD was similar to the binding to the wild type bacteria (Figure S3). This prompted us to assess how RNΔdltABCD affected survival of the wild type, PGRP-SD and PGRP-SA mutant flies. In contrast to RNΔtagO, PGRP-SA mutant flies did not succumb strongly to RNΔdltABCD infection, with 83% surviving at 72 hours post-infection (Figure S4); furthermore, survival was statistically inseparable for the different fly backgrounds (Figure S4). These data demonstrated that D-alanylation is not necessary for WTA to limit the access of PGRP-SA, that neither PGRP-SD nor PGRP-SA were required to control the RNΔdltABCD infection and consequently, the reduced killing effect of RNΔdltABCD had nothing to do with recognition. The results shown here indicate that in respect to Gram-positive bacteria, where the cell wall is not concealed by outer membrane (e. g. staphylococci), pathogen recognition, via recognition of PG, is tightly linked to host survival. Our studies bring forward the notion that one of the strategies used by pathogens to reduce recognition is to restrict accessibility to inflammatory non-self components of the cell wall. Specifically, the results here show that presence of WTA in a range of Gram-positive bacteria impaired PGRP-SA binding. The use of tunicamycin to abolish WTA synthesis dramatically improved receptor recognition of bacteria as well as host survival of flies infected antibiotic treated S. aureus. Genetically deleting a major component of the WTA synthesis (TagO) in S. aureus also increased PGRP-SA binding leading to increased host survival. It should also be noted that, rPGRP-SA was capable of binding in vitro significantly better to WTA-free PG than to WTA-linked PG that were purified from wild type S. aureus bacteria, treated with trypsin to remove any attached surface proteins and adjusted to the same concentration of PG (Figure S2). This observation confirmed the results obtained with live bacteria and allowed us to eliminate the notion that deletion of tagO gene may influence the amount of protein present at the cell surface and that this change in protein levels was influencing the binding of PGRP-SA. Effectively during the course of this work we have removed WTA from PG by treatment with antibiotic, by deletion of the tagO gene and finally we have chemically removed them from PG. In all the cases binding of PGRP-SA to PG has increased. S. aureus produces WTA composed of about 40 ribitol phosphate-repeating units modified with N-acetylglucosamine (GlcNAc) and D-alanine [7]. The latter modification is mediated by the D-Alanine ligase DltA and partially neutralizes the negative charge of the cell surface thus reducing attraction of cationic AMPs [33]. ΔdltA mutants are more susceptible to killing by cationic AMPs and neutrophils in vitro and have markedly reduced virulence in several animal infection models including Drosophila [11], [34]. In one of these studies [11], Tabuchi and colleagues showed that S. aureus producing WTA without D-alanylation were impaired in their ability to kill Drosophila. Surprisingly, the ΔdltA mutant was more impaired in the ability to kill flies than an independently generated tagO mutant [11]; the latter according to the authors had the same pathogenicity as wild type S. aureus [11], contrary to our findings. There is a crucial point to be made in reference to this however, which is at the heart of our experimental design and gives physiological relevance to our results. We propose that WTA are important to reduce S. aureus recognition by the host and thus help the pathogen increase its numbers inside the fly. The host uses PGRP-SA to control bacterial numbers and the more PGRP-SA binds to the cell wall (see Figure 2B) the more the bacterial load is controlled (as seen by comparing CFUs between wild type NCTC8325-4 S. aureus and NCTCΔtagO in Figure 3A). In PGRP-SA mutants the control mechanism is absent and NCTCΔtagO was able to proliferate and kill the host (Figure 3B). We were able to observe this because we started from a low bacterial load (102 cells/initial infection/fly) and followed the progress of pathogen load inside the host. Tabuchi et al. injected 104–106 cells per fly for all bacterial strains used [11]. In our hands this concentration overwhelmed the host from the beginning and it is not surprising that these authors were unable to resolve statistical differences in host survival. In order to rule out possible pleiotropic effects produced by the inactivation of the tagO due to the insertion of non-replicative plasmids or reversion of the mutation by elimination of the plasmid from the chromosome, we have specifically deleted the tagO gene in a manner that left no resistance marker in the bacterial chromosome and thus minimized possible alterations on the transcription of neighbouring genes. Finally, in order to increase the confidence of our results, we have complemented the tagO null mutant with plasmids that allowed the expression of a partially active (TagOG152A), TagO protein and have statistically analyzed the estimated host survival probability curves obtained. Finally we should emphasize that deletion of the tagO gene in NCTC8325-4 strain (an agr positive strain) and in RN4220 (an agr negative strains) resulted in similar outcomes (Figure S3) - a reduced pathogenicity in the Drosophila infection model and the production of a bacterial cell surface that was better recognized by mCherry-PGRP-SA. In parallel experiments we have also generated a ΔdltA deletion mutant (this study) as well as a deletion of the ΔdltA operon (ΔdltABCD) [29] and found that both were indeed less pathogenic than wild type S. aureus (Figure S4), similar to what was previously reported [11]. However, this reduced pathogenicity was also observed in PGRP-SA and PGRP-SD single mutant flies (in contrast to ΔtagO). This indicated that the non-pathogenicity of ΔdltA was not linked to recognition by PGRP-SA or PGRP-SD. We propose that increased “visibility” of PG to PGRP-SA when WTA were removed, dramatically improved survival of the host. However, alternative interpretations of our results may exist. In the following section we will attempt to challenge and rule them out: Our results underline an important aspect of pathogen recognition by the host, which remains relatively unexplored. Namely, how does the host recognition machinery respond to changes in the surface of bacteria? Here we manipulated the amount of WTA on the cell surface of S. aureus. Previously, two host PGRPs, PGRP-SA and PGRP-SD were found to be involved in recognition of wild type S. aureus [14], [15]. We found here that when WTA were genetically removed, the requirement for PGRP-SD was abolished. Flies deficient for PGRP-SD had estimated survival probabilities comparable to wild type flies following infection by S. aureus ΔtagO or ΔtagOptagOD87/D88A. When a small amount of WTA was left on the surface through the residual activity of the S. aureus ΔtagOptagOG152A then PGRP-SD mutants were less able to survive infection. However this sensitivity was not as pronounced as when infected with S. aureus ΔtagOptagO, the strain with reconstituted wild type levels of WTA. Previous studies have established that PGRP-SD does not bind Gram-positive Lys-type PG [14], [39]. However, in its presence, PGRP-SA was able to bind substantially better to cell wall from S. aureus and S. saprophyticus [14]. Our results, combined with the latter observation, support a role for PGRP-SD in neutralizing the effect of WTA obstructing access to PG. The alternate hypothesis that PGRP-SD may directly recognize WTA, and is therefore not necessary when flies are infected with bacteria that lack teichoic acids, is also a possibility. The role of teichoic acids in concealing PG at the surface of Gram-positive bacteria may be also effective in preventing recognition by innate immune sensors of other organisms. It is now established that insect PGRPs have mammalian homologues and mice and humans express four genes encoding members of this family [35]. Our results correlate with data, which attributed a significantly reduced virulence of tagO mutants in cotton rat nasal colonisation model [40] as well as a mouse endophthalmitis model [41] and suggest a mechanism for how this may happen: absence of teichoic acids may render PG at the bacteria surface more exposed to the host immune system. Isogenic wild type flies (Bloomington #25174) were used as the wild type control. For the survival and bacterial Colony Forming Unit (CFU) experiments, and DD1 flies for assaying Drs levels visually or via qPCR; the latter carries a Drs-GFP and a Diptericin-lacz reporter [42]. The PGRP-SA and PGRP-SD mutant backgrounds are, respectively: flies with the semmelweis mutation in PGRP-SA [16] and a 1499 bp deletion in PGRP-SD (PGRP-SDΔ3) [15]. The spzrm7 [43] and spz1 [44] Toll pathway mutant backgrounds, and the Dif1-key1 [45] Toll-IMD pathways double mutant background, were used to assess survival of flies deficient for AMPs. All fly stocks were reared at 25°C. Bacterial strains are listed below. S. aureus strains were grown in tryptic soy broth medium (TSB; Difco) supplemented with antibiotic (erythromycin 10 µg/ml; Sigma-Aldrich) when required. E. faecalis was grown in brain heart infusion medium (BHI; Fluka). M. luteus was grown in Luria-Bertani medium (LB; Difco). Bacteria were plated from -80°C stocks every 7 days Growth of all bacteria cultures were done at 30°C as S. aureus mutants impaired in the synthesis of teichoic acids are thermosensitive [46]. NCTC8325-4 (S. aureus reference strain from R. Novick); NCTCΔtagO (NCTC8325-4 tagO null mutant [29]); RN4220 (Restriction deficient derivative of S. aureus NCTC8325-4 that can be electroporated); RNΔtagO (RN4220 tagO null mutant [29]); RNΔtagOpMAD (RNΔtagO transformed with pMAD [29]– shuttle vector with a thermosensitive origin of replication for Gram-positive bacteria); RNΔtagO ptagO (RnΔtagO transformed with ptagO [29]); RNΔtagO ptagOD87A/D88A (RNΔtagO transformed ptagOD87A/D88A [29]); RNΔtagO ptagOG152A; RNΔtagO transformed with ptagOG152A, [29]); RNΔdltABCD (RN4220 dltABCD null mutant [29]); RNΔdltABCD (RN4220 dltABCD null mutant [29]); RNΔdltA (RN4220 dltA null mutant, this study). M. luteus strain: DMS20030 [47]; E. faecalis strain: JH2-2 [48]; B. subtilis strain MB24 [49]. To delete the dltA gene from the chromosome of S. aureus RN4220 we started by amplifying two 0. 55 Kb DNA fragments from the genome of S. aureus NCTC 8325-4 strain, corresponding to the upstream (primers 5′-AGATCTgaatgtatatatttgcgctgatg-3′ and 5′-gtaaaatcaccatatggaatcatattaagtctccctcattagaactc-3′) and downstream (primers 5′- gagttctaatgagggagacttaatatgattccatatggtgattttac-3′ and 5′-GAATTCcgaaacgtttgtaacgatcg-3′) regions of the dltA gene. The two fragments were joined by overlap PCR using primers P33 and P36 and the resulting PCR product was digested with BglII and EcoRI and cloned into the pMAD vector, producing the plasmid pΔdltA. This plasmid was sequenced and electroporated into S. aureus RN4220 strain. Insertion and excision of pΔdltA into the chromosome of RN4220 was performed as previously described [29] with the exception of the incubation temperature after excision of the plasmid, which was 30°C (instead of 43°C) due to the thermosensitive nature of the cells lacking D-alanylation. Deletion of dltA was confirmed by PCR, and the resulting strain was named RNΔdltA. Overnight 10 ml cultures of bacteria were washed and resuspended in an equal volume of sterile phosphate buffered saline (PBS), and further diluted 1/1000. Healthy looking adult flies from uncrowded bottles, 2–4 days old, were injected in the thorax with 32 nl of a bacterial cell suspension or PBS using a nanoinjector (Nanoject II, Drummond Scientific). For determination of CFUs, injected flies (6 females) were crushed immediately in media appropriate for the bacteria injected and the homogenates were diluted and plated on tryptic soy agar-media (TSA). The plates were incubated at 30°C for 20–30 hours and the colony forming units (CFUs) per fly were measured by counting the number of colonies on each plate, the CFUs per fly were used to adjust the initial dose of bacteria injected to approximately 100 CFUs per fly. For the time course (0,6, 17 hours) determination of CFUs, each value represents an arithmetic average derived from three biological repeat experiments (n = 3). Flies for survival and PGRP-SA mutant rescue assays were inoculated concurrently with those for determining CFUs, with ten or fifteen flies of each sex injected per bacteria-fly strain combination (or PBS-fly strain); each combination being repeated independently three times (n = 3). Following injection, flies were transferred to 30°C and survival assessed every 24 hours over a period of 3 days. Since the trends in survival were the same (i. e. survival curves were positioned similarly relative to one another) for each independent biological repeat, the data for each bacteria-fly strain combination was added (n = 60 or n = 90) and estimates of survival curves constructed. Flies injected with PBS were mostly unaffected for all fly backgrounds. A truncated version of PGRP-SA (in which the N-terminal sorting sequence was replaced with a T7 tag, and a poly-histidine tag was added to the C-terminus) was expressed in E. coli and purified using cobalt affinity resin (Talon; BD Biosciences) under denaturing conditions. A mCherry tagged derivative, mCherry-PGRP-SA was produced using the same procedure. Proteins were stored in 20 mM Tris-HCl pH 8. 0 and 150 mM NaCl. Functionality assays of the rPGRP-SA and mCherry-PGRP-SA proteins were performed as previously described [14]. Drs-GFP expression was monitored after 24 hours of the M. luteus infection through the production of fluorescent signal produced by the infected flies; and by qPCR using as template RNA extracted from 6 infected female flies, similar to what was previously described [50]. Peptidoglycan was prepared from exponentially growing cultures of S. aureus, B. subtilis, M. luteus, and E. faecalis as previously described [13]. 50 µg of recombinant PGRP-SA was incubated with 0. 2 mg of peptidoglycan and 17 µg of BSA (New England Biolabs) in 20 mM Tris-HCL pH 8. 0 and 300 mM NaCl in a final volume of 300 µl. Incubation was at 25°C with agitation for 30 minutes. Peptidoglycan and co-precipitated proteins were harvested by centrifugation, washed twice with 20 mM Tris-HCl pH 8. 0,300 mM NaCl and then resuspended in 1× SDS loading buffer, boiled for 5 minutes and run on 12% SDS PAGE mini gels. An aliquot of the supernatant, representing unbound protein, was also run. Gels were stained with Coommasie stain, destained and imaged using an ImageScanner (Amersham Biosciences/GE Healthcare). Quantifications of bands performed using ImageJ software [51]; each value represents an arithmetic average derived from three biological repeat experiments (n = 3). Bacteria were grown to mid-exponential phase. Washed cell cultures in PBS (500 µl) were incubated with 50 µl of mCherry-PGRP-SA (2 mg/ml in 150 mM NaCl, 20 mM Tris pH 8. 0) for 5 minutes on ice. The cells were washed twice with PBS and harvested at 4°C (3000 rpm, 10 minutes). Finally the bacteria were resuspended in 20 µl PBS. A drop of this culture was placed on a PBS, 1% agarose slide and visualised. Images were obtained using a Zeiss Axio ObserverZ1 microscope equipped with a Photometrics CoolSNAP HQ2 camera (Roper Scientific using Metamorph software, Meta Imaging series 7. 5) and analyzed using ImageJ software. WTA were extracted by alkaline hydrolysis from overnight cultures were analyzed by native polyacrylamide gel electrophoresis and visualized by combined alcian blue silver staining, as previously described [52]. ImageJ software [51] was used to quantify the percentage of WTA produced by each strain as previously described [29]. The signal intensity of each lane was quantified and normalized against the corresponding value for the wild type (considered as 100%). Tunicamycin minimum inhibitory concentration (MIC) assays were performed as previously described [28]. Overnight cultures of bacteria were grown in antibiotic free medium or in the presence of a subinhibitory concentration of tunicamycin (0. 8 ug/ml for E. faecalis – 17× less than the MIC - and 0. 4 ug/ml for S. aureus and S. saprophyticus – 32× less than MIC), that doesn' t interfere with the bacterial growth rate. For mCherry-PGRP-SA binding assays, overnight cultures were diluted 1∶100 into fresh medium, with or without tunicamycin at the appropriate concentration, and were grown until mid-exponential phase. For survival experiments, we used S. aureus overnight culture grown with tunicamycin as above described. As nonparametric tests lack statistical power with small samples, when required, data sets with three biological repeats (n = 3) were transformed to give a normal distribution (Lilliefors test, P>0. 05) and then checked for equal variance (Levene' s test, P>0. 05); subsequently, data was analysed using parametric tests. Data for the PGRP-SA-peptidoglycan co-precipitation assay was normal with equal variance, thus not transformed; One-way ANOVA was applied to the data. For the mCherry-PGRP-SA binding to bacteria assays data (n = 50) was non-normal but with equal variance, therefore nonparametric Kruskal-Wallis test followed by Dunn' s multiple comparison was applied. The complete CFU data set exhibited neither normality nor equal variance, and attempts to rectify this by transforming the data failed. Therefore, the data was separated into 6 groups, which were independently transformed via a Box-Cox transformation (Box-Cox returns a λ number, where a transformed data-point = data-pointλ – 1/λ) to give a normal distribution with equal variance, and statistical analysis performed as described. Firstly, for each bacterial strain (groups 1–3, graphical representations not shown), Repeated Measures Two-way ANOVA was used to look for differences over time and between the fly backgrounds. However, due to interactions between these two factors, Repeated Measures One-way ANOVA with 95% Tukey' s HSD Intervals was used to look for differences over time for each particular bacteria strain and fly background combination (i. e. 9 separate tests, data for each was normally distributed with equal variance). Secondly, at each time point (groups 4–6, Figure 3), Two-Way ANOVA was used to look for differences between the bacterial strains and between the fly backgrounds; where there was an interaction between these two factors, One-way ANOVA with 95% Tukey' s HSD Intervals was used to look for differences between the fly backgrounds for a particular bacterial strain. Estimated survival curves were constructed from the raw data sets and the Log-rank (Mantel-Cox) test used to determine statistical significance between the curves. For clarity in display, 95% confidence intervals have been omitted from the graphs. All data was plotted and analyzed using GraphPad Prism 5 (GraphPad Software, Inc.) or MATLAB R2009a.

Gram-positive bacteria such as the opportunistic pathogen Staphylococcus aureus have their cell wall exposed to the environment found within a host. Following an infection these bacteria need to find ways to evade or reduce recognition by the host in order to survive and potentially proliferate. The cell wall of Gram-positive bacterium consists of an intricate network of glycan chains cross-linked by short peptides called peptidoglycan (PG; a major target for host recognition in a variety of animals) covalently linked to surface proteins and glycopolymers including Wall Teichoic Acids (WTA). It has been proposed that lack of WTA reduce the pathogenicity of S. aureus. We asked whether this was due to better recognition of PG. We found that both bacterial recognition and survival of fruit flies (our model host) infected with bacteria lacking WTA was markedly increased compared to those infected with wild type S. aureus. This result was quantifiable: a reduction in the amount of WTA resulted in greater binding by host receptors and a higher host survival. We propose that the presence of WTA limit access to PG and therefore reduce the recognition ability of the host. Bacteria are thus able to increase in numbers and eventually overwhelm the host.

Abstract Introduction Results Discussion Materials and Methods

medicine infectious diseases model organisms immunology biology microbiology molecular cell biology

2011

Wall Teichoic Acids of Staphylococcus aureus Limit Recognition by the Drosophila Peptidoglycan Recognition Protein-SA to Promote Pathogenicity

In convergent-extension (CE), a planar-polarized epithelial tissue elongates (extends) in-plane in one direction while shortening (converging) in the perpendicular in-plane direction, with the cells both elongating and intercalating along the converging axis. CE occurs during the development of most multicellular organisms. Current CE models assume cell or tissue asymmetry, but neglect the preferential filopodial activity along the convergent axis observed in many tissues. We propose a cell-based CE model based on asymmetric filopodial tension forces between cells and investigate how cell-level filopodial interactions drive tissue-level CE. The final tissue geometry depends on the balance between external rounding forces and cell-intercalation traction. Filopodial-tension CE is robust to relatively high levels of planar cell polarity misalignment and to the presence of non-active cells. Addition of a simple mechanical feedback between cells fully rescues and even improves CE of tissues with high levels of polarity misalignments. Our model extends easily to three dimensions, with either one converging and two extending axes, or two converging and one extending axes, producing distinct tissue morphologies, as observed in vivo. Embryonic development requires numerous changes in tissue morphology. Convergent-extension (CE) is a basic tissue shape change [1–9], during which cells in an epithelial sheet rearrange to narrow (converge) the tissue along one planar axis while lengthening (extending) it along the perpendicular planar axis (Fig 1). Although CE has been observed in the development of many organisms [1–8], the specific cellular mechanisms that drive such movements are still subject of investigation [10]. Both asymmetric external forces on a tissue (passive CE) and asymmetric forces generated by the cells within a tissue (active CE) can lead to CE (Fig 1) [10]. Hypothesized mechanisms for CE include anisotropic cell edge/actin contraction [11,12], anisotropic cell adhesion and elongation [13,14], cell shape extension/retraction [11,15], combinations of a constraining boundary with undirected cell elongation [16] or with directed leading edge protrusion [17], and increased cell adhesion within tissue segments [18] (see Supplemental Material for a more detailed discussion of previous models). Existing models of CE, however, neglect the experimentally observed prevalence of filopodial extension parallel to the direction of tissue convergence [3,9, 19–24], which could produce anisotropic traction forces between cells or between cells and the extracellular matrix [25–28]. The observed asymmetry of filopodial protrusion led us to propose a filopodial-tension mechanism for CE based on anisotropic filopodial pulling forces between cells. We explicitly model the number of cell-cell connections, their range, angular distribution, strength, and frequency of formation and breakage. We define an appropriate set of metrics to quantify both the effects of model parameters and planar-polarization defects (such as misalignments and the passive cells) on the dynamics of tissue-level CE. Since our filopodial-tension model extends naturally to three dimensional tissues, we discuss the two types of 3D CE and their corresponding tissue morphologies. Experiments show that long filopodia continuously form and retract during CE in epithelial sheets and that these filopodia preferentially form in-plane along angles near the axis of tissue contraction. Each model cells therefore extends and retracts filopodia (which we represent using the model concept of a link) distributed within a range of angles around the directions perpendicular to the cell’s planar-polarity axis. To simulate the observed binding of filopodial tips to other cells and the roughly length-independent pulling forces which retracting filopodia generate, in our model, an extending link binds to the cell it contacts, then generates a constant (length independent) tension force between the cells it connects [20,25,29]. We then test whether this tension force is sufficient to explain observed local cell intercalation and global tissue CE. In the filopodial-tension model (Fig 2, S1 Movie) cells form and eliminate links representing filopodia with a defined set of neighboring cells (terms in boldface identify model objects). Each cell carries a polarization vector (perpendicular to its planar-polarity axis) (Fig 2, red arrow) that defines its preferred direction of filopodial protrusion (Fig 2, blue horizontal line). We simplify the model by having the links connect the centers-of-mass of cells rather than connecting the actin cortex of one cell to the actin cortex of the contacted cell, as do real filopodia. Because filopodia typically form in a pair of growth cones roughly along the convergence axis and with a typical maximal length, we allow a cell to form links within a range of angles ±ϑmax around this axis on either side of the cell with a maximum length of approximately rmax. Specifically, a cell can form a link only with those cells whose centers-of-mass lie within a distance rmax from its center of mass and within an angle ±ϑmax of its polarization axis (Fig 2, blue horizontal line). A cell can have at most nmax links to other cells at any time (including links formed and received) and only one link is allowed between any pair of cells. The actual number of links a cell forms may be less than nmax. Each link between a pair of cells exerts a tension force of magnitude λforce along the line connecting the cells’ centers-of-mass. To model the finite lifetimes of filopodia, we define a relaxation time, tinterval, after which we remove the links of all cells and create new ones. In a simulation in which the links form and then persist indefinitely, the cells only move a few microns (lattice sites) from their original locations and the tissue does not converge or extend. We implement the filopodial tension model using the Cellular Potts model (CPM, also known as the Glazier-Graner Hogeweg model, GGH), where each cell is represented as a collection of lattice sites with the same cell index. An effective-energy cost function, H, specifies the cell’s properties (see supplemental material). The tension force along a link between a pair of cells is independent of its length and acts along the vector between their centers-of-mass. In the GGH/CPM formalism, the tension has the form: H=H0+∑σ, σ′λforce (σ, σ′) lσ, σ′ (1) where the sum is over all pairs of linked cells, λforce is the strength of the pulling force between cells σ and σ’, lσ, σ’ is the current distance between the cells, and the term H0 aggregates all the other GGH/CPM cost function terms. The GGH/CPM simulations evolve stochastically from random lattice-site updates subjected to the effective-energy cost function, H. The time unit is the Monte Carlo Step (MCS), defined as the rate of lattice-site updates (see supplemental material for more details on the GGH/CPM formalism). The filopodial-tension model has five intensive parameters (λforce, tinterval, rmax, nmax, ϑmax) and one extensive parameter (N, the number of cells), making a complete sensitivity analysis computationally costly. We therefore fixed all parameters to reference values that are within the ranges observed in vivo and produced biological plausible convergent-extension (Table 1), then studied the effects of varying each intensive parameter one-at-a-time. The biological parameters proposed by the model can be directly measured experimentally, but since the concept of a filopodial-based CE is new and applies more readily to CE of deep tissues, which are not as easily visualized as epithelial sheets, appropriate experimentally-derived values are harder to find. The most studied cases are chicken limb-bud mesenchymal intercalation [30] (tinterval = 2. 2 hours; rmax = 3 cell diameters; nmax = 11; ϑmax = 45°), Xenopus gastrulation and notochord formation [31–33] (tinterval = 2. 0–2. 7 min; rmax = 1. 5 cell diameters; nmax = 8–9; ϑmax = 60°), and Xenopus Keller explants [23,34] (tinterval = 0. 5–1. 0 hour; rmax = 1. 5 cell diameters; nmax = 8–9; ϑmax = 30°). All simulations start with a mass of identical cells uniformly distributed inside a rough circle. Each cell has the same planar-polarization vector (V). To quantify the degree of tissue deformation we calculate the inverse aspect ratio between the length of the minor (L-) and major (L+) axes of the tissue (Fig 3B, green line). Initially the aspect ratio is close to 1 and decreases in time to a final value κ (Fig 3B, dashed red line) that depends on the filopodial tension parameters (λforce, tinterval, rmax, nmax, ϑmax), the number of cells in the tissue (N) and the surface tension of the tissue γ (defined below). The final inverse aspect ratio quantifies the maximum elongation of the tissue, but does not convey how fast the tissue elongates. To quantify the elongation rate, we define the elongation time (τ) the time an initially isotropic tissue takes for its major axis (L+) to double the length of its minor axis (L-), which is equivalent to the time when the inverse aspect ratio (L-/L+) first decreases to 0. 5 (Fig 3B, dashed blue lines). We consider CE to fail if L-/L+ never reaches 0. 5. Since both the filopodial-tension model and the GGH/CPM are stochastic, we average the value of the elongation time (τ) over 10 simulation replicas. Because the tissue inverse elongation ratio converges to the same value independent of the simulation seed or initial conditions (S1 Fig), unless specified otherwise, we calculate the final inverse aspect ratio κ for a single simulation replica, with the standard deviation indicating the fluctuations in κ around its final value for that replica. Successful CE depends on the ability of intercalating cells to generate forces stronger than the internal and external forces that oppose tissue deformation. Here, the opposing forces come from the superficial tension (γ) between the cells and the external medium, defined as [35]: γ=Jc, M−Jc, c2, (2) where Jc, c is the contact energy between cells and Jc, M is the contact energy between cells and medium (see supplemental material). When the filopodial tension is weak compared to the surface tension (λforce < 2γ), cells do not intercalate and CE fails. For larger filopodial tensions, the elongation time (τ) decreases as a power of λforce (τ ∝ λforce-1. 25±0. 03) (Fig 4A, red line). The final inverse aspect ratio (κ) decreases monotonically with increasing λforce (Fig 4B). Increasing γ shifts the κ vs. λforce curve to the right and decreasing γ shifts the κ vs. λforce curve to the left (Fig 4B, inset). Normalizing the filopodial tension by the surface tension (λforce/γ) collapses the κ vs. λforce curves (Fig 4B), showing the linear relationship between λforce and γ. The surface tension (γ), however, has little effect on the elongation time (τ), which depends on λforce, but is relatively insensitive to surface tension (Fig 4A). The κ vs. λforce/γ curve is sigmoidal on a log-log scale (Fig 4B), because the shape of the tissue changes little for weak filopodial tensions and because the total number of cells limits κ for strong filopodial tensions (see Fig S3A). At the inflection point of κ vs. λforce/γ, the tensions of the links (λforce) balances the external surface-tension forces that oppose tissue elongation (λforce/γ ~ 6). Near this inflection point κ varies as an approximate power law of λforce (κ ∝ λforce-1. 51±0. 08). Next we studied how the remaining filopodial tension parameters affect CE, specifically, the mean lifetime of the filopodia, modeled as the time interval between link formation and breakage (tinterval); the maximum length of the filopodia, modeled as the maximum distance of interaction between the cells’ centers-of-mass (rmax); the maximum number of filopodial interactions per cell (nmax); and the maximum angle between the filopodial direction and the cells’ convergence axis (ϑmax). Fig 5A shows that, for the reference parameter values (Table 1) the lifetime of filopodia, tinterval, has no effect on τ or κ for tinterval ≲ 200 MCS. For the reference parameter values, 200 MCS corresponds to the typical time the cells require to rearrange their positions in response to a given set of neighbors interactions. Increasing filopodial lifetimes above 200 MCS slows cell intercalation (increasing the elongation time) and increases the tissue’s final inverse aspect ratio (corresponding to less deformation). The maximum range (rmax) of filopodia interaction has different effects on the final inverse aspect ratio (κ) and elongation time (τ). For rmax < 2 cell diameters, κ decreases as a power law in rmax (κ ∝ rmax-3. 5±0. 2), then saturates for rmax ≥ 2 cell diameters, while the elongation time (τ) decreases monotonically with increasing rmax (Fig 5B). The same effect is seen with respect to the maximum number of links (nmax): κ decreases as a power law in nmax for nmax < 4 (κ ∝ nmax-1. 5±0. 03) and saturates for nmax > 4 (this saturation makes sense since the cell typically has 4 neighbors within the range of its filopodia for rmax = 2 and ϑmax = 45°) while τ decreases monotonically with increasing nmax (Fig 5C). Thus rmax and nmax have affect the rate of cell intercalation more than the final inverse aspect ratio, while the tissue’s surface tension affects only the final inverse aspect ratio and not the rate of cell intercalation (Fig 4). Both κ and τ are concave with respect to the maximum angle of filopodial protrusion (ϑmax), since for small ϑmax the number of cell center-of-mass within the cones defined by ϑmax and rmax is very small, while for ϑmax = 90° the forces on the cell are symmetric since it extends filopodia uniformly in all directions. In both limits CE fails (Fig 5D). Since the net intercalation force is the difference between the tension forces parallel and perpendicular to the convergence axis (roughly ∫0θmax (cos (θ) -sin (θ) ) dθ), we might expect the force to be greatest (and thus κ and τ to be smallest) when ϑmax = 45° and for their values to increase symmetrically away from ϑmax = 45°. The curves, however, have different minima and are not symmetric: the smallest final inverse aspect ratio (κ) is around ϑmax = 40° (Fig 5D, red dots) and the smallest elongation time (τ) is around ϑmax = 30° (Fig 5D, blue squares). This asymmetry is caused by the limited number of neighbors with which a cell can form a link. Both the maximum number of links per cell (nmax) and the number of cells within the link interaction range (rmax) can limit the actual number of links a cell forms. If the maximum number of links per cell is lower than the number of cell neighbors within a cone of range rmax (e. g. nmax = 3) and angle ϑ < ϑmax, increasing ϑmax leads to more links with cells at larger ϑ and thus reduces the net tension force applied along the direction of the convergence axis. In effect, large ϑmax causes the cell to waste its limited number of filopodia. For large nmax, links form to all cells within the cone of range rmax and small θ regardless of the value of ϑmax. Thus, for large nmax (e. g. nmax = 7), the κ and τ curves are roughly symmetrical around their minima at ϑmax ~ 45° (blue and red lines in Fig 5D). The filopodial tension model assumes that cells can extend filopodia, contact and pull other cells that lie within a given distance, even if they do not touch each other before filopodial extension. An example would be the formation of adhesion junctions between cells which coupled to a contractile stress fiber in both cells. To model these cases, we defined a contact-mediated cell tension model, which is identical to the filopodial tension model except that the maximum link length rmax in the filopodial tension model is replaced with the condition that cells must be in touch before pulling on each other (Fig 6A). The qualitative results for the contact-mediated cell tension model do not differ much from the filopodial tension model. The κ x λforce curve is sigmoidal on a log scale, τ decreases with a power law (κ ∝ λforce -1. 18±0. 06) and CE fails for λforce < 20 (Fig 6B). The dependence of κ on the number of filopodial interactions (nmax) is still a power law (κ ∝ nmax-1. 5±0. 03) and saturates when nmax = 4. The elongation time (τ), however, does not keep decreasing as it does for the filopodial tension model, but also saturates around nmax = 4 links (Fig 6C), as few cells have more than 4 neighbors with centers near the convergence plane. The (κ, τ) x ϑmax curves have minima at ϑmax = 40° and ϑmax = 35°, respectively, but are less skewed than in the filopodial tension model (compare Figs 6D and 5D). CE fails for ϑmax < 10° and ϑmax > 70°. Convergent-extension requires cells to have consistent planar polarity throughout an extensive region of tissue. This correlated orientation might result from a long-range bias from a morphogen gradient, cellular or intercellular differences in protein expression [36], or from a boundary-relay mechanism [37,38]. In our previous simulations we assumed that all cells had perfectly aligned polarization vectors (Fig 2, red arrows), i. e. , they all pointed in the same direction with the same magnitude, and they maintained their internal orientation throughout the simulation. To study the effect of polarization misalignment on CE we added a zero-mean Gaussian distributed displacement angle to the cells’ polarization vectors and varied the standard deviation of the distribution (σ) while keeping the mean direction (here, the vertical axis) constant. Since the final elongation ratio is sensitive to the distribution of polarization vectors, the values of κ were averaged over 5 simulations. The filopodial tension model tolerates small polarization misalignments, with a tissue with a displacement angle of σ = 10° reaching the same final inverse aspect ratio as in the perfectly aligned case with little decrease in elongation rate (an 11% increase in τ). The tissue remained aligned with the mean direction of cell polarization (the vertical axis) for small misalignments (σ < 40°, Fig 7B), but bent at around σ = 50° (Fig 7C). For polarization misalignments with σ > 60°, CE fails and the tissue breaks its symmetry, acquiring more complex shapes such as the caltrop (see Fig 7D). Both metrics are exponential functions of the variance σ2 (Fig 7A). So far we have assumed that the polarization vector of the cells remains constant throughout the entire process. In reality, however, cells are constantly communicating with their neighbors either through signaling or through mechanical interactions. During CE cells establish and maintain their polarity through the planar cell polarity (PCP) pathway, however there is growing evidence that mechanical feedback may also play a role in the maintenance of global tissue polarity during development [39–42]. The presence of a mechanical feedback mechanism may rescue CE in tissues with high polarization alignment defects. In order to investigate this we developed a simple model of mechanical feedback and applied it to the misalignment polarization cases that have been described in the last section (Fig 7). The feedback model assumes that the pulling forces on a cell due to filopodial interactions affect its polarization vector. We implement a simple phenomenological version of such an interaction by calculating the line of tension from the sum of all filopodial interactions of the cell with its neighbors (Fig 8A). From this line of tension we extract an orthogonal vector T which is averaged with the previous cell polarization vector V in the following way: Vt+Δt=Vt* (1−w) +Tt*w, (3) where Vt+Δt is the polarization vector of a cell at time t+Δt, Vt* is the normalized polarization of the same cell at time t, Tt* is the normalized tension vector of the cell at time t, and w is a feedback weighting factor ranging from 0 (no feedback) to 1 (no memory) (Fig 8A). This iterative processes repeated at discrete time intervals set equal to filopodia lifetime (Δt = tinterval). For simplicity, we do not distinguish between the pulling forces generated by the cell from the pulling forces that their neighbors exert on it. The normalized tension vector of a cell is calculated from the vector that maximizes the sum of all projections of the normalized lines of force from all the cells’ neighbors: Σi cos (∅i− ∅T) = 0, (4) where the sum is over all the cell’s neighbors that pull on it, ϕi is the angle of the line of force between the cell and the pulling neighbor i, and ϕT is the angle that defines the tension vector T* = (cos ϕT, sin ϕT). For tissues with a high starting level of polarization misalignment (σ ≥ 40°), addition of this mechanical feedback mechanism usually leads to a lower final elongation ratio κ, as long as the feedback factor w is below 0. 1. For these cases, tissue elongation times (τ) decrease with higher feedback levels (Fig 8C, green and red lines), while k usually decreases with lower feedback levels. For tissues with a low starting level of polarization misalignment (σ ≤ 20°), addition of this mechanism leads to lower final elongation ratios only for small levels of feedback (w ≤ 0. 001) (Fig 8B, blue and black lines), while the time of tissue elongation (τ) remains relatively unchanged with respect to the case with no feedback (Fig 8C, blue and black lines). In all simulations where the addition of mechanical feedback rescues CE, the cells established a global polarization axis emergently. We chose to implement the feedback update iteratively, which leads to fast destabilization of the tissue for high levels of feedback, as is expected for a case with no memory. Although a continuous model would be relatively more robust, we expect the same destabilization effect when the weighting factor w approaches 1. Next we varied the number of intercalating cells in the tissue to check if there is a minimum number of active cells needed to drive CE and how this change tissue dynamics. We defined two types of cells without filopodia: passive cells, which lack filopodia but can be pulled by the filopodia of other cells; and non-responsive, or refractory cells, which cannot be pulled by the filopodia of other cells. The former would correspond to cells whose surface adhesion molecules were compatible with those of the cells extending filopodia and the latter to cells with incompatible adhesion molecules. The parameters for cells which produced filopodia were the same as in Table 1. Since the final elongation ratio is sensitive to the distribution of active/non-active cells, the values of κ were averaged over 5 simulations. For tissues with a mixture of active and passive cells, both κ and τ decrease monotonically with the percentage of active cells in the tissue (Fig 9, red dots). However, even a fraction of active cells can drive CE. For 40% or more active cells (S2 Movie), the tissue deforms almost as much as a tissue composed entirely of active cells (Fig 9B, red dots), though the elongation time increases with the percentage of passive cells up to twice that for a tissue of all active cells (Fig 9A, red dots). For higher fractions of passive cells the final inverse aspect ratio increases significantly with the fraction of passive cells (Fig 9B). E. g. , for 90% passive and 10% active cells (Fig 9C and S3 Movie), the tissue’s final inverse aspect ratio never drops below 0. 3 (Fig 9B) and the elongation time τ is more than ten times that for a tissue of all active cells (Fig 9A). In all simulations, the active cells migrate towards the midline of the elongating tissue, leaving the passive cells at the lateral margins (Fig 9D). The presence of relatively high CE despite the presence of only 10% of active cells can be explained by the relative tissue area covered by the filopodia. Every active cell can pull on neighbors that lie up to a distance rmax from its center of mass and within an angle ϑmax on each side of its convergence plane (see Fig 2), thus covering an area of 2ϑmaxrmax2. For the reference parameters (ϑmax = π/2 and rmax = 2 cd, see Table 1) and a population of 10% of active cells (N/10, where N is the number of cells in the tissue) this amounts to ~1. 2N (cd) 2, which more than covers the whole area of the tissue (N (cd) 2). Refractory cells have a stronger effect on CE than passive cells. CE fails when the percentage of refractory cells is above 60% (Fig 9B, blue squares), while with passive cells it only fails for percentages higher than 90% (Fig 9B, red squares). For higher fractions of active cells, the two populations sort out, with the active cells extending normally and the refractory cells displaced to both sides of the elongating tissue (Fig 9F). Surface tension between the cells and the surrounding medium causes the refractory cells to form droplet-like clusters which bend the extending active-cell tissue into a wavy bar (Fig 9F and S4 Movie). The 2D filopodial tension model is a reasonable description of cells within epithelial sheets, where cell movement is confined to a plane. However, in many situations cell intercalation occurs in 3D. That is the case in radial intercalation during epiboly of the developing Xenopus Laevis embryo, where cells in a multilayered epithelium intercalate and converge perpendicular to the plane of the sheet [43]. The filopodial tension model can be easily extended to three dimensions, but due to the extra degree of freedom, it breaks in two versions, depending on which axis is rotated: In equatorial or extensional intercalation, obtained by rotating the 2D model around the polarization vector (the red arrow in Fig 2), the cells pull on all neighbors that lie in a convergence plane (Fig 10A). At the tissue level, equatorial intercalation results in the convergence of the tissue along the two directions perpendicular to the polarization vector and its extension along the polarization vector (Fig 10A’ and 10A”). In bipolar or convergent intercalation, obtained by rotating the 2D model around the convergence line (the blue line in Fig 2), the cells pull on all neighbors that lie along a convergence axis (Fig 10B). At the tissue level, the bipolar intercalation results in the convergence of the tissue along the axis of convergence and its expansion in the other two directions (Fig 10B’ and 10B”). Beginning with a spherical tissue with all the cells polarized in the same vertical direction, the 3D equatorial model produces a tissue resembling a prolate spheroid (cigar shaped, Fig 10A”, S5 Movie), while the bipolar model produces a tissue resembling an oblate spheroid (lentil shaped, Fig 10B”). The bipolar model has more biological correspondence than the equatorial model: cells with unipolar or bipolar protrusive activity are much more common during development than cells with equatorial protrusive activity, and the resulting tissue shape from the 3D bipolar model corresponds to the thinning and expansion associated with radial intercalation. For both versions of the 3D model, the dependence of the parameters κ and τ with λforce, rmax, nmax and tinterval are qualitatively the same as in the 2D model. The results only differ qualitatively with respect to ϑmax. For the same values of rmax and nmax, the 3D convergence model is slightly less skewed than the 2D version, with the best value for κ around ϑmax = 45° and the best value for τ around ϑmax = 35° (Fig 11B). The 3D extension model, however, presents a more drastic change in the (κ and τ) vs. ϑmax curve when compared to the 2D. While the 3D convergence model was slightly more symmetrical around ϑmax = 90°, the 3D extension model is very skewed towards small angles, with the best values for κ around ϑmax = 30° and the best value for τ around ϑmax = 15° (Fig 11A). In the extensional model CE fails for ϑmax < 3° and ϑmax > 60°, while in the bipolar model CE fails for ϑmax < 10° and ϑmax > 75°. The reasons for the asymmetry is that the final shape of the tissue in the 3D extension model—a two cell diameter tube orthogonal to the convergence plane—is more sensitive to perturbations than the lentil shape tissue obtained by the 3D bipolar model. Two cells pulling each other along a convergence axis leads them to be aligned in a plane perpendicular to the direction of the pulling. This plane fully coincides with the 3D bipolar model extension plane (Fig 10B”), but only partially with the extension plane of the 3D extension model (Fig 10A”). In the simulations results shown in Fig 11A, the value of ϑmax = 30° represents the optimal maximum angle value where the pulling forces in the 3D extension model are still able to align the tissue without destabilizing it. Here we developed a new 2D model of for active cell intercalation. The model drastically differs from previous existing models by its explicit use of pulling forces between cells rather than anisotropy on adhesion energies or surface tensions on the cell surface. A recent experimental work by Pfister et al. [28] supports our force-driven model. The use of forces makes the model easily adaptable to three dimensions, but the extra degree of freedom gives two ways in which this can be achieved: either by rotating the model around the polarization vector or around the convergence line (see Fig 10A and 10B). The model was implemented in CompuCell3D using the Cellular Potts formalism. The core of the model, however, is independent of the mathematical formalism and can be easily implemented on other types of agent-based formalisms such as cell-center or vertex models, as long as they provide a volume exclusion mechanism. Although some quantitative results might differ, we expect the same qualitative results. The advantages of implementing and simulating our model using the Cellular Potts formalism include the ease to manipulate and study the effects of the surface tension on the tissue dynamics (Fig 4) and the addition of the model as an integrated part of the CompuCell3D simulation package [44] that allows for it to be immediately reusable by others. Model validation can be done at two different levels quantitative and semi-quantitative: when both microscopic and macroscopic measurements are available for a specific tissue, using model parameters that agree with those measured for the cells in the tissue should result in tissue-level model output (here, the rate of convergence and final aspect ratio) agreeing quantitatively with that of the experimental tissue. This agreement should persist under different experimental conditions. This form of validation shows that the hypothesized mechanisms included in the model are sufficient to reproduce the experiment quantitatively. Note that a model can only show the sufficiency of modeled mechanisms, not their necessity, since a different set of mechanistic hypotheses might yield the same results. In our case, since we are not modelling a specific tissue, model validation can only be semi-quantitative. We show that changes in the properties of the modeled cells (such as average number of filopodia, length and angular distributions) and tissue properties (such ratio of active and non-active cells) predict relative changes in the rate of CE and final tissue aspect ratios in real tissues that agree with those in experiments when the corresponding parameter and conditions are similarly modified. In this case, agreement demonstrates the plausibility of the hypothesized mechanisms, but detailed quantitative validation requires additional experimental measurements. We hope that our semi-quantitative analysis of the filopodial tension model of CE will inspire the additional experimental measurements that a more detailed quantitative validation requires. Our model predicts that external forces, such as surface tension and pressure, can only affect the final degree of tissue elongation (κ) (Fig 4), whereas the internal parameters that regulate cell-intercalation can affect both tissue dynamics (as measured by τ) and the final tissue shape (Figs 4 and 5). This can be easily tested experimentally by either changing the properties of the external environment (the surrounding cells/matrix) of the intercalating tissue or culturing it ex vivo. Of the five cell intercalation parameters, the time interval between link formation/breakage (tinterval) had negligible effects as long as it is below the typical time that the cells take to rearrange positions and/or shapes in response to a given set of external forces. This might be different if a refractory time interval between pulls is added to the model. We expect that in the presence of such refractory time, an increased frequency in link formation/breakage would slow down the speed of intercalation and reduce the final elongation ratio. The model also predicts that the maximum range of cell interaction (rmax) and the maximum number of links per cell (nmax) had no effect on the final tissue elongation after rmax = 2 and nmax = 3, but the time of elongation kept decreasing for higher values of rmax and nmax (Fig 5B and 5C). It was not possible to increase those parameters indefinitely to check if the speed of intercalation would also saturate because the simulated cells start to fragment past rmax ≥ 6 or nmax ≥ 7. The current implementation of the model, however, does not allow for more than one active link between cell pairs, which would likely decrease elongation time. All cell intercalation models assume some type of increased cell activity along the convergence axis, which is often translated, as is the case here, into the assumption that the cells are bipolar (one exception being our 3D equatorial/extensional filopodial tension model). This however is not necessarily true and we expect the model to also work in cases where the simulated cells are either monopolar in opposite directions of the same convergence axis or randomly alternate being monopolar in each direction of the convergence axis. Our model also suggests that CE can be successfully achieved even in the presence of relatively high degrees of polarization defects. We predict that tissues containing polarization misalignments of up to ±10° will be practically indistinguishable to the optimally aligned case. Even severe misalignments (about ±50°) would still lead to some CE, although to a much lesser degree of final elongation ratio and with longer tissue elongation times (Fig 8). Experimental disruptions of the PCP pathway that alter the global alignment of cells in a dose-dependent manner would provide a way to test some of these predictions. Addition of a simple mechanical feedback mechanism by which the cells readjust their polarization in response to the pulling forces from the neighbors does not have major effects on the speed of tissue elongation (Fig 8C), but can fully rescue and even improve on the final elongation ratio of tissues with low or even severe polarization misalignments (Fig 8B). We choose to implement a minimal phenomenological feedback mechanism to explore the general response and self-organization of the tissue, but the formulation of the model allows the replacement of this generic mechanism with more detailed feedback models that reflect a specific tissue. In cases where some cells fail to polarize, the severity of the effects on CE will depend on the type of interaction between the polarized (intercalating) cells and the unpolarized (non-intercalating cells). If the polarized (or active) cells can still pull on the unpolarized cells, then CE still happens even in a situation where the vast majority of cells (95%) are not active (Fig 9), although at a great reduction in both speed and final elongation ratio. If, on the other hand, the unpolarized cells are non-responsive and cannot be pulled, then the reductions in speed and final elongation ratio are much more sensitive to the presence of unpolarized cells (Fig 9) and CE completely fails when the population of active cells falls below 25% (Fig 9C). Another prediction of the model is the separation between the intercalating cells and the non-responsive/refractory cells (Fig 9D). Such defects could be induced experimentally by randomly distributed knock-out of intercalating cells, e. g. using electroporation of tissues with a dominant negative or RNAi, and would provide a way to further test model predictions. Finally, the model reduces to the more common implementations when the maximum range of interaction is replaced by the common contact area condition. In this case, instead of contracting (or increasing the tension of) the cell’s surfaces that are aligned with the polarization vector or the global direction of convergence, we pull the neighbors that are not aligned with it (Fig 6D). In both cases active CE is achieved by the same principle, promoted cell-cell activity along one axis and inhibition along the other.

The development of an embryo from a fertilized egg to an adult organism requires not only cell proliferation and differentiation, but also numerous types of tissue restructuring. The development of a relatively round initial embryo into one elongated along its rostral-caudal axis involves coordinated tissue elongation and cell reorganization in one or more groups of cells or tissues. Counterintuitively, in many organisms, cells in elongating tissues elongate and increase their protrusive activity in the direction perpendicular to the axis of elongation (convergent extension). Experimental and theoretical studies have not determined how this cell-level oriented protrusive activity leads to observed tissue-level changes in morphology. We propose a filopodial-tension model that shows how tension from oriented cell protrusions leads to observed patterns of tissue CE.

Abstract Introduction Methods Results Discussion

cell physiology classical mechanics fluid mechanics condensed matter physics anisotropy surface tension geometry aspect ratio simulation and modeling cell polarity developmental biology mathematics materials science damage mechanics embryos morphogenesis research and analysis methods embryology deformation continuum mechanics physics cell biology planar cell polarity biology and life sciences physical sciences material properties

2016

Filopodial-Tension Model of Convergent-Extension of Tissues

Recent outbreaks of locally transmitted dengue and Zika viruses in Florida have placed more emphasis on integrated vector management plans for Aedes aegypti (L.) and Aedes albopictus Skuse. Adulticiding, primarily with pyrethroids, is often employed for the immediate control of potentially arbovirus-infected mosquitoes during outbreak situations. While pyrethroid resistance is common in Ae. aegypti worldwide and testing is recommended by CDC and WHO, resistance to this class of products has not been widely examined or quantified in Florida. To address this information gap, we performed the first study to quantify both pyrethroid resistance and genetic markers of pyrethroid resistance in Ae. aegypti and Ae. albopictus strains in Florida. Using direct topical application to measure intrinsic toxicity, we examined 21 Ae. aegypti strains from 9 counties and found permethrin resistance (resistance ratio (RR) = 6-61-fold) in all strains when compared to the susceptible ORL1952 control strain. Permethrin resistance in five strains of Ae. albopictus was very low (RR<1. 6) even when collected from the same containers producing resistant Ae. aegypti. Characterization of two sodium channel kdr alleles associated with pyrethroid-resistance showed widespread distribution in 62 strains of Ae. aegypti. The 1534 phenylalanine to cysteine (F1534C) single nucleotide polymorphism SNP was fixed or nearly fixed in all strains regardless of RR. We observed much more variation in the 1016 valine to isoleucine (V1016I) allele and observed that an increasing frequency of the homozygous V1016I allele correlates strongly with increased RR (Pearson corr = 0. 905). In agreement with previous studies, we observed a very low frequency of three kdr genotypes, IIFF, VIFF, and IIFC. In this study, we provide a statewide examination of pyrethroid resistance, and demonstrate that permethrin resistance and the genetic markers for resistance are widely present in FL Ae. aegypti. Resistance testing should be included in an effective management program. Local vector control programs play a part in public health. In many countries, these programs serve as the primary defense against the spread of several mosquito-borne diseases. Effective Integrated Vector Management (IVM) programs rely on surveillance information coupled with multiple vector control strategies such as chemical adulticiding to reduce vector populations and arbovirus transmission. Limited recent transmission of locally acquired dengue and Zika viruses in the southeastern continental US, primarily Florida, has brought renewed attention to the importance of IVM programs where the potential vectors, Aedes aegypti (L.) and Ae. albopictus Skuse, have long been present. However, for IVM programs in Florida to effectively control Aedes vectors and to reduce dengue and Zika virus transmission during an outbreak, it is essential to know which adulticide products are effective against local Ae. aegypti and Ae. albopictus strains. Organophosphates and pyrethroids are the only two classes of insecticides available to public health agencies for control of adult mosquitoes in the US. Compared to organophosphate insecticides, pyrethroids have higher public acceptance, rapid knockdown, relatively low costs, and are generally the product class of choice when adulticiding is required [1]. Unfortunately, years of pyrethroid insecticide use and previous DDT usage has increased the frequency of genetic resistance and enhanced enzymatic detoxification in insects like mosquitoes. Genetic target site changes of the sodium channel, known as knockdown resistance (kdr) mutations, are a relatively common insect response to selective pressure by pyrethroids [2,3]. Although a variety of other single nucleotide polymorphisms (SNPs) have been noted in Ae. aegypti, the primary two SNPs assessed to determine pyrethroid resistance are at codons 1016 and 1534 (positions according to standard M. domestica notation) [4,5]. One allele of the 1016 mutation is geographically distinct to the Western hemisphere and results in the replacement of the normal valine with an isoleucine (V1016I), while the 1534 mutation results in a phenylalanine to cysteine change (F1534C). There is currently debate about the individual toxicological effect of these two mutations, but they are consistently present in resistant strains [4]. Heterozygous and homozygous combinations of these two SNPs could result in nine possible genotypes, but strong linkage between the SNPs has been noted with only six of the genotypes observed in a study of Mexican Ae. aegypti [6]. More recent work indicates that the kdr genotype in Ae. aegypti may be more complicated than the combination of 1016I and 1534C SNPs. An additional SNP at position 410 has been identified and this valine to leucine (V410L) change, in addition to the other two SNPs, may be the strongly resistant phenotype [7]. The noted strong linkage between 410L and 1016I indicates that assessment of 1016 may still be adequate to assess the strongly resistant phenotype [7]. As expected based on the dilocus genotype distribution, several of the trilocus genotypes were also not frequently present in the wild [8]. Pyrethroid resistance and the distribution of kdr alleles have been well documented in Ae. aegypti strains from Central America, South America and the Caribbean [9–13]. Recent testing as part of the Zika emergency response in Puerto Rico has shown that isolated, early reports of resistance were indicative of widespread resistance on the island [13–16]. Studies have also shown the pyrethroid resistance is not specific to a particular pyrethroid but is generally class-wide to type I, type II, and non-ester pyrethroids [13,17,18]. Kdr alleles and pyrethroid resistance are widely distributed in Mexico, including Nuevo Laredo which lies just south of the US-Mexico border [11]. In contrast, little has been published about pyrethroid resistance in Aedes strains within the continental US. In the Garcia et al. [11] study mentioned above, the authors did not find kdr alleles in a Houston, TX collection from 1999. Recent reviews of the Aedes resistance literature listed no reports of resistance in continental US Ae. aegypti [4,19], but Cornel et al. [20] recently demonstrated toxicological resistance and sodium channel mutations in invasive populations of Ae. aegypti in California. Two recent studies using CDC bottle bioassays do indicate resistance in strains from the southern US, but these studies do not provide any quantification of the strength of the resistance nor did they examine the presence of kdr alleles [21,22]. In contrast, Ae. albopictus does not appear to frequently develop kdr resistance, and most US strains tested thus far have only shown very minimal pyrethroid resistance [4,23,24,25]. Resistance surveillance is recommended by the CDC and statewide initiatives to map pyrethroid resistance have begun in Florida and California [22,26]. Resistance information is critically important for operational decision making as part of an effective IVM program. In this study, a collaborative group of government, academic, industry and vector control district stakeholders collected Ae. aegypti and Ae. albopictus adults, eggs or larvae from more than 200 locations throughout Florida to assess the extent and intensity of pyrethroid resistance. The goal of this study was to improve vector control operations by producing a resistance map and apply this information to make more effective control decisions. We determined permethrin resistance ratios (RRs) relative to the susceptible ORL1952 strain for 21 wild type strains of Ae. aegypti and 5 strains of Ae. albopictus using direct topical application, the WHO gold standard assay for determination of intrinsic toxicity [27]. While the CDC bottle bioassay is a more commonly used assay, it is a threshold assay and does not quantitate resistance levels which was one of our primary goals. Nearly 5,000 Ae. aegypti from numerous locations were genotyped by allele-specific PCR to assess the frequency of V1016I and F1534C alleles and rapidly visualize the pattern of resistance throughout the state of Florida. The toxicological profiles and rearing procedures for the susceptible (ORL1952) and resistant (Puerto Rico–BEIResources, NR-48830) strains of Ae. aegypti used in this study have been described previously [17]. Strain information, including specific location information, collectors, and dates of collection are noted in Table 1. Field strains for toxicology testing were often collected as mixed (Ae. albopictus and Ae. aegypti) eggs laid on seed germination paper (Anchor Paper, St. Paul, MN) placed in a variety of oviposition containers including black plastic stadium cups, plastic cemetery vases, and glass containers. Field collected eggs were hatched by soaking papers for 48–72 hours at 27 oC in rearing trays of untreated well water or deionized water (diH2O). Papers were carefully removed, and larvae were reared through adult emergence with a reduced feeding regimen compared to the standard rearing protocol for the ORL1952 susceptible strain [28] due to sensitivity to overfeeding. Strains from Monroe, Seminole, Orange, Hernando and Sarasota counties were collected as larvae from a variety of manmade and natural containers (tires, plant pots, bottles, buckets, etc.), rinsed with diH2O and then placed into the standard larval rearing procedure described above. Pupae were collected from rearing trays and placed in emergence chambers or 12” x 12” screen cages (BioQuip Models 1425 & 1450B, Rancho Dominquez, CA). After emergence, wildtype mosquitoes were briefly chilled to 4 oC and sorted to species. Strains for toxicology testing were produced from locations that had more than 30 wildtype founders. If multiple nearby locations were combined to produce a strain, the GPS location in Table 1 represents the GPS centroid of the sites that contributed the mosquitoes. Individual oviposition cup locations and life stages tested are provided in S1 File. Eggs were produced using standard rearing methods [17]. Colony strains were provided 2–10 bloodmeals on weekly intervals to collect F1 eggs. Warmed bovine blood was provided to produce 2,000 or more eggs per strain. If feeding was poor, warmed blood was spiked with 1 mM ATP as a phagostimulant. Bovine blood for mosquito feeding was purchased by the USDA under contract from a local, licensed abattoir. Blood was collected during normal operations of the abattoir from the waste stream after animal slaughter. Under CFR9, Parts 1–3, tissues, including blood, collected from dead livestock intended as food are exempt from IACUC regulation. Aedes aegypti strains from St. Augustine, Clearwater, and Vero Beach, FL were provided as F1 eggs produced at Anastasia Mosquito Control District, Pinellas County Mosquito Control, and the Florida Medical Entomology Laboratory, respectively. To produce mosquitoes for bioassay testing, eggs from the F1 or F2 generation of field collected strains, the lab susceptible strains (ORL1952) and the pyrethroid-resistant strain were hatched and reared as described above in covered trays at a density of approximately 1,000 mosquito larvae/tray. Adult mosquitoes emerged into 12” x 12” screen cages and provided with cotton saturated with 10% sucrose in diH2O. Females used for bioassays were 3–7 days post-emergence. The adult topical bioassay has been described previously in detail [28,29]. For these studies, the permethrin stock solution in DMSO (Product #N-12848-250MG, Chemservice, Westchester, PA) and all dilutions in acetone were prepared gravimetrically. An initial 10-fold dilution series was prepared over a range of relevant concentrations [17]. Sub-dilutions were prepared from the 10-fold dilution series as necessary to determine the critical region of the dose curve. Three assays (n = 3) were performed for all strains with listed LD50s unless limited numbers of F1 test mosquitoes allowed only two replicates. Average mass/female was calculated for each strain by weighing a cohort of 50–100 females before each replicate. LD50s, standard errors, and goodness of fit were determined from dose-mortality curves using SigmaPlot v13 (Systat software Inc. , San Jose, CA) with data fit to a four-parameter logistic model. To provide a comparative metric between strains that may have different body sizes, doses applied were divided by the average mass of the mosquitoes before curve fitting. This results in an LD50 of ng of active ingredient per mg of mosquito. Resistance ratios for strains were calculated using the LD50 of the field strain divided by the LD50 of the susceptible ORL1952 strain included in the same assay. In this study, we use the WHO scale to define the levels of resistance [30]. When RR is less than 5, the field population is considered susceptible. When the RR is between 5 and10, the mosquitoes are considered to have moderate resistance. A RR greater than 10 indicates that mosquitoes are highly resistant [31]. Genotypes for individual mosquitoes or eggs were determined using melt curve analysis with previously described allele-specific primers for the 1016 and 1534 SNPs [32,33]. Preliminary assays were conducted using eggs and adults from the same generation to verify that the resulting frequencies were similar (within 10%) between life stages rather than showing agenotype bias due to varied hatch rates or differing larval mortality (S2 File). Assays were performed in 96 well plates on a StepOnePlus (Applied Biosystems) or QuantStudio5 (Applied Biosystems, Thermo Fisher Scientific, Waltham, MA) using SYBR green chemistry. Plates were loaded with 8 μl of PCR master mix containing SYBR Select Master Mix (Applied Biosystems, Thermo Fisher Scientific, Waltham, MA), nuclease free water (NFW), and three primers (Table 2). Due to differences in efficiency from the added GC tails, initial primer titering was necessary to ensure that the melt curves of homozygous controls would be accurately called by the analysis software. Individual adult females were homogenized for 60 seconds at max speed in 100 μl of NFW on a bead beater (BioSpec, Bartlesville, OK). Individual eggs, randomly sampled from all available egg papers of the same generation, were similarly treated but homogenized in 50 μl of NFW. Immediately after homogenization, samples were centrifuged at 10,000 relative centrifugal force (rcf) for 60 seconds to pellet solids. Two microliters of each supernatant were added to 8 μl of PCR mix for each primer set and then subjected to standard cycling conditions (3 min @ 95 °C; 40 cycles @ 95 °C for 3 sec, 60 °C for 15 sec). Melt curve analysis followed cycling with acquisition of fluorescence data every 0. 3 °C as the temperature was ramped from 60 °C to 95 °C. Characteristic melting temperature (Tm) peaks in the derivative fluorescence data indicate the presence of specific alleles [32,33]. For the 1016 mutation, a codon for the susceptible valine has a Tm peak at 86±0. 3 °C while the isoleucine codon has a Tm of 77. 3±0. 3 °C. The 1534 phenylalanine has a Tm of 79. 8±0. 3 °C while the mutant cysteine produces a peak at 84. 7±0. 3 °C. Homozygotes for either allele produce one peak while heterozygotes produce peaks at both Tms. All assay plates included two susceptible ORL1952 strain and two resistant PR strain samples as negative and positive controls, respectively. Most plates also contained artificial heterozygotes created by including ORL1952 and PR homogenates in the same sample. Well positions of individual mosquitoes were maintained in both plates (one plate for each locus) to allow genotyping of an individual for both SNPs. Frequencies for each of the nine genotypes (VVFF, VVFC, VVCC, VIFF, VIFC, VICC, IIFF, IIFC, IICC) were calculated by dividing the specific genotype by the total number tested from each area. Base maps were created using ArcGIS software by Esri. ArcGIS® is the intellectual property of ESRI and is used herein under license. Copyright © ESRI. GIS data sources were ESRI and Tele Atlas. All rights reserved. (For more information about Esri® software, please visit www. esri. com). Permission to publish this content was verified from the ESRI Redistribution Rights Matrix at https: //www. esri. com/~/media/Files/Pdfs/legal/pdfs/redist_rights_106. pdf? la=en Maps were exported to GIMP 2. 8 and additional layers with the kdr or resistance ratio data was added. Pie chart representations of kdr allele frequencies were created with Microsoft Excel and exported to layers added to the basemaps. All Florida strains of Ae. aegypti were resistant to permethrin when compared to the ORL1952 strain, which has been in a continuous laboratory colony for nearly seventy years (Fig 1 and Table 3). The field strains showed varied levels of resistance, from 6-fold to 61-fold compared to the ORL1952 strain. The two least resistant Ae. aegypti strains were collected from flowerpots on Big Coppitt Key (Monroe County) and from oviposition cups in Cortez (Manatee County) at 6. 0 and 6. 8-fold, respectively. Ae. aegypti mosquitoes collected from a tire facility in Orlando (Orange County) were about 17. 2-fold more resistant than Ae. aegypti collected from the same area generations earlier and used to produce the ORL1952 strain. While most of the FL strains were 15 to 35-fold resistant compared to the lab strains, several strains with higher RR were identified. The strain from New Port Richey in Pasco County had the highest RR at 61. 3-fold, which is like the RR of the Puerto Rico-resistant reference strain [17]. Strains from Miami Beach and Fort Myers had RRs above 50. The variability we observed in RR throughout the state was also seen at finer resolution within several, but not all, counties. In Miami-Dade County, the Miami Beach and East Wynwood strains were relatively resistant (57-fold and 33-fold), while nearby locations like South Wynwood and central Little River were much less resistant (Fig 1). Manatee County also had a range of RRs. The Anna Maria Island strain, collected from a densely populated barrier island, had a higher RR than nearby strains from Palmetto or Cortez (Fig 1 and Table 3). We also observed this same trend in Lee County but did not observe universal variation in RR. Aedes albopictus competes with Ae. aegypti for oviposition sites, and in several locations Ae. albopictus eggs were collected in conjunction with Ae. aegypti eggs. Ae. albopictus strains were also subjected to topical application along with the Ae. aegypti from the same locations (Fig 1, in blue). In the Ae. albopictus strains we tested, only slight resistance to permethrin (<2-fold) was observed when compared to the ORL1952 strain. In Miami-Dade County, we observed large differences in RR between Ae. albopictus and Ae. aegypti even when collected from the same sites in areas such as south Wynwood and central Little River. St. Johns (St. Augustine), Orange (Orlando), and Lee (Alva) counties also had resistant Ae. aegypti and low RR Ae. albopictus. Examination of kdr alleles in Ae. aegypti strains from 62 locations showed a range of genotypes (Figs 2 and 3). We observed that most strains were fixed or nearly fixed for the F1534C SNP. For many strains, more than 95% of the tested mosquitoes were homozygous for the 1534C (1534CC) and the remainder of the strain was made up of a few 1534 heterozygotes (1534FC). Only strains from Big Coppitt Key (13% FC), Cortez in Manatee County (38% FC), Central Wynwood (22% FC) and 2 strains from Little River (20% and 14% FC) had more than 10% of mosquitoes that were heterozygous at position 1534. Most strains had no mosquitoes without at least one copy of the 1534C allele but we did find two strains from central Wynwood (8% FF) and Cortez (11% FF) that still had appreciable numbers of susceptible alleles. There was much more variation throughout the state at position 1016. Strains from Big Coppitt Key, Longboat Key, Orlando and Clearwater had the lowest percentages of the homozygous 1016II at 11. 0,14. 1,14. 9, and 17. 8%, respectively. We did observe strains with high levels of 1016II. Six of eight strains examined from Pasco County had 1016II frequencies above 75%, including two strains from New Port Richey at 100% 1016II. Due to the 2016 Zika outbreak and resulting massive public health response, we heavily sampled strains from southeast Florida for allele frequencies. Two strains from Broward and several from Miami-Dade County had 1016II frequencies above 75% including a Miami Beach strain at 91% 1016II (Fig 3). Select strains from Lee and Collier counties in southwest Florida were also above 75% 1016II (Fig 2). As with RR, in several counties we observed large differences in allele frequency from one part of the county to another. In Lee County (Fig 2), an inland strain from Alva had a relatively low level 1016II frequency (20%) while four strains from near the Gulf Coast all had 1016II frequencies of greater than 55%. This variation was also observed at much closer scale between strains in neighboring cities. The Manatee County Anna Maria Island strain had a much higher 1016II allele frequency than nearby strains from Palmetto or Longboat Key (Fig 2). While overall IICC frequencies were high in Pasco County, we did find variation at the neighborhood level in south Pasco County, where a strain with 75% 1016II was found just blocks from a strain with 30% 1016II (Fig 2). In Miami-Dade, we were able to test four strains from Wynwood and three from Little River collected during the 2016 Zika response (Fig 3). Again, we observed variations in the levels of 1016II within each neighborhood. North Wynwood was slightly more than 25% IICC while south Wynwood was nearly 50%. In Little River, the four strains ranged from 20 to 60%. Just across the water from Wynwood, the Miami Beach strain was greater than 90% IICC. We regularly observed only six genotypes in the field (Table 4). Only a combined seven mosquitoes of the 4,810 analyzed in this study had genotypes of IIFF, IIFC, or VIFF, which would require an allele coding for a resistant isoleucine at 1016 (1016I) and a susceptible phenylalanine at 1534 (1534F) to be contributed from at least one parent. We did commonly observe the reverse where homozygous or heterozygous susceptible 1016V alleles paired with the homozygous resistant 1534C allele (VVCC or VICC). Plotting the combined dataset for strains with both kdr genotype frequencies and resistance ratios from topical application of permethrin showed a strong correlation between increasing RR and increasing 1016II frequency (Fig 4, Pearson correlation coefficient = 0. 905, P<0. 00001, n = 20). Strains from Miami Beach, Fort Myers, and New Port Richey, with high frequencies of 1016II, which, based on the rarity of IIFF and IIFC mosquitoes implies the genotype IICC, were the strains with the highest resistance ratios. In contrast, the strain from Big Coppitt Key had the lowest 1016II frequency and the lowest RR even though the 1534CC frequency was relatively high. Recent local transmission of Zika virus during 2016 and small outbreaks of dengue virus in 2010 and 2011 demonstrate that an effective IVM plan that attacks multiple life stages to reduce mosquito numbers is a necessity. However, when disease transmission is active, chemical adulticiding can be the only means to immediately reduce the population of potentially infected mosquitoes. While there is some debate as to overall efficacy of adulticiding against Ae. aegypti and Ae. albopictus, there is little question that it is a necessary part of the response that allows other slower methods of control like larviciding and source reduction to gain a foothold. Pyrethroids are the major class of chemical adulticides that Florida mosquito control programs use operationally. Thus, the efficacy of pyrethroids on Aedes vectors is a critically important part of a control program. Direct topical application of permethrin clearly demonstrated that resistance was widespread in Ae. aegypti strains throughout Florida. Resistance ratios ranged from about 6-fold in strains from Manatee County and Big Coppitt Key to approximately 60-fold in Miami Beach, Cape Coral, and New Port Richey. Genotyping thousands of individuals indicated that common kdr alleles were also widely distributed in the state. While these findings are noteworthy as they represent the first published report of widespread pyrethroid resistance and kdr alleles in the southeast US, this result is not surprising and could be predicted based on the results from other locations including Puerto Rico, Mexico and, more recently, in invasive CA Ae. aegypti strains [11,13,20]. The dataset described in this study does reveal wide variations in both RR and kdr alleles within small geographic areas. Examining Miami-Dade County, the strain collected from Miami Beach was highly resistant and had high levels of kdr. However, strains from inland Wynwood and Little River were much less resistant than the Miami Beach strain. Even strains from the opposite ends of neighborhoods differed. We saw this disparate pattern in south Miami-Dade, Manatee, and Lee counties. In Pasco County, we saw very different genotypes in strains geographically close to one another, separated only by a major highway. This wide variation in resistance alleles has been observed in Mexico and has been proposed to predominate over natural gene flow [34]. Considering this variability, along with the relatively short flight ranges and limited immigration in Ae. aegypti [35], the very different allele frequencies we observed in geographically close strains would support performing testing in numerous areas of a control district to get an accurate resistance picture. In contrast to the resistance in the Ae. aegypti strains, we observed very little permethrin resistance in Ae. albopictus statewide. This was true whether Ae. aegypti were present or absent from the same collections. Waits et al. [24] showed very low levels of resistance in St. Johns County strains collected from areas without Ae. aegypti. Miami-Dade, Orange, and Lee counties all had the same pattern of resistant Ae. aegypti and much less resistant Ae. albopictus. It has been proposed that the development of pyrethroid resistance is much more difficult in Ae. albopictus due to sequence differences in the voltage-gated sodium channel (NaV) that make kdr much less likely, although recent reports indicate it may be possible [36,37]. Our laboratory efforts to induce permethrin resistance in wildtype Florida Ae. albopictus by gentle pressuring have failed. At this time, pyrethroid resistance in Florida Ae. albopictus does not appear to be an issue that could lead to adulticide failure. An important observation made due to this work is the correlation between increasing RR and the frequency of the IICC genotype in Ae. aegypti, which has been anecdotally observed in other studies using the CDC bottle bioassay [6,11,13,38]. The linkage between kdr mutations and a strong correlation with resistance ratios has also been observed in other dipterans [39,40]. While more research must be done to validate the correlation, this dataset adds another 20 strains with both resistance and kdr data that support using kdr genotype as a surrogate to estimate pyrethroid resistance levels in mosquitoes [9,41]. The use of allele frequencies has several potential benefits. Genotype data are relatively easy to collect, the results are produced in hours, and dead mosquitoes collected during standard surveillance activities can be used to provide information on resistance. With limited budgets and personnel at the operational vector control district level, predictive estimation of resistance levels could produce useful operational data from activities already being done without requiring additional efforts to collect or produce mosquitoes for bioassay testing. Nearly a decade after Donnelly et al. [41] asked whether kdr genotypes are predictive in mosquitoes there is, to our knowledge, no published study that shows a pyrethroid-resistant strain of Ae. aegypti without also showing the presence of kdr alleles. We suggest that the major benefits to be gained from use of allele frequencies as an estimator of resistance would be improvements in coverage area (this study, 62 strains with allele frequencies vs. 26 by direct topical application) and better operational decision-making as vector control programs could access more timely information on area specific resistance levels. These challenges of getting wide coverage and providing this information on an operationally useful timeline have been present in recent response efforts to Zika virus. The efforts of CDC and vector control units in Puerto Rico and the efforts of the authors and others in Florida to develop wide-area pyrethroid resistance maps by relying strictly on bioassay data show that it is currently a slow, labor intensive process. In Florida at least, the epidemic had passed by the time more than a few strains had been tested by bioassay. Until there is reliable, published evidence to argue against the use of kdr frequencies as a predictor, it is at present the only rapid way to assess strains across a large area. This study shows that permethrin resistance is widely present in variable intensity in Ae. aegypti throughout Florida. This variability points to the need to include resistance testing as part of an IVM plan as well as examine resistance in more than one location. But how do we use this resistance information to improve vector control? Clearly, the strain of Ae. aegypti in Miami Beach is very different from nearby Wynwood and would likely call for different treatment strategies. A treatment with permethrin would likely have much less effect in Miami Beach in comparison to other less resistant locations. Our study also points to the value of a collaborative approach from motivated stakeholders to develop resistance information. The state of Florida began regular conferences to bring together vector control districts, researchers, and public health resources months before the first locally transmitted case of Zika was reported in 2016. Like the CDC response in Puerto Rico, development of resistance information was an early and ongoing part of this process. The dataset in this study represents the result of thousands of hours of effort from vector control districts, vector control contractors, the state of Florida, and the federal government to produce operationally useful resistance information to protect public health and improve the efficacy of control operations. However, even with these efforts, this study is very limited in scope. We examined only permethrin resistance and although the literature shows the patterns we observed would likely be applicable to other pyrethroids [13,17], work to define statewide patterns of resistance to synergized products or organophosphates still needs to be addressed.

Aedes aegypti (Yellow-fever mosquito) and Aedes albopictus (Asian Tiger mosquito) can vector a variety of arboviruses that cause diseases and are thus a public health concern. Pyrethroid insecticide resistance is common in Ae. aegypti in many locations worldwide and can adversely affect vector control operations. However, the resistance status of these vectors in Florida is largely unreported and recent local transmission of dengue and Zika viruses has made this information critical for effective control operations. In this study, we showed that permethrin resistance and two common SNPs of the voltage gated sodium channel (V1016I and F1534C) previously associated with pyrethroid resistance were widely present in Florida Ae. aegypti strains. We also observed a strong correlation between the dilocus knock down response (kdr) genotype and resistance ratio (RR) as determined by topical application, which suggests, as have others, that kdr frequency may be a useful indicator of resistance in Aedes aegypti.

Abstract Introduction Methods Results Discussion

united states invertebrates dengue virus medicine and health sciences pathology and laboratory medicine pathogens variant genotypes geographical locations microbiology animals genetic mapping north america viruses rna viruses infectious disease control insect vectors florida public and occupational health infectious diseases aedes aegypti medical microbiology microbial pathogens disease vectors insects arthropoda people and places mosquitoes eukaryota flaviviruses heredity viral pathogens genetics biology and life sciences species interactions organisms zika virus

2018

Quantification of permethrin resistance and kdr alleles in Florida strains of Aedes aegypti (L.) and Aedes albopictus (Skuse)

During the Ebola virus disease (EVD) epidemic in Liberia, contact tracing was implemented to rapidly detect new cases and prevent further transmission. We describe the scope and characteristics of contact tracing in Liberia and assess its performance during the 2014–2015 EVD epidemic. We performed a retrospective descriptive analysis of data collection forms for contact tracing conducted in six counties during June 2014–July 2015. EVD case counts from situation reports in the same counties were used to assess contact tracing coverage and sensitivity. Contacts who presented with symptoms and/or died, and monitoring was stopped, were classified as “potential cases”. Positive predictive value (PPV) was defined as the proportion of traced contacts who were identified as potential cases. Bivariate and multivariate logistic regression models were used to identify characteristics among potential cases. We analyzed 25,830 contact tracing records for contacts who had monitoring initiated or were last exposed between June 4,2014 and July 13,2015. Contact tracing was initiated for 26. 7% of total EVD cases and detected 3. 6% of all new cases during this period. Eighty-eight percent of contacts completed monitoring, and 334 contacts were identified as potential cases (PPV = 1. 4%). Potential cases were more likely to be detected early in the outbreak; hail from rural areas; report multiple exposures and symptoms; have household contact or direct bodily or fluid contact; and report nausea, fever, or weakness compared to contacts who completed monitoring. Contact tracing was a critical intervention in Liberia and represented one of the largest contact tracing efforts during an epidemic in history. While there were notable improvements in implementation over time, these data suggest there were limitations to its performance—particularly in urban districts and during peak transmission. Recommendations for improving performance include integrated surveillance, decentralized management of multidisciplinary teams, comprehensive protocols, and community-led strategies. In March 2014, Liberia detected its first cases of Ebola virus disease (EVD) in Lofa, a northern county bordering Guinea and Sierra Leone [1]. The Liberian Ministry of Health (MOH) (formerly Ministry of Health and Social Welfare) established a national task force and initiated control efforts, including contact tracing [1,2]. As the epidemic grew, the task force developed into an Incident Management System, which oversaw contact tracing in all 15 counties with support from international partners including World Health Organization (WHO), U. S. Centers for Disease Control and Prevention (CDC), and Action Contre la Faim [3,4, 5]. Continuous, widespread transmission continued until February 2015 [6], and 42 days after the last confirmed case had two negative samples in March 2015 [7], Liberia was declared free of Ebola on May 9,2015—marking an end to the epidemic [8]. Contact tracing is comprised of three main steps: identifying, listing, and monitoring persons who have been exposed to infected individuals, with the goal of rapidly diagnosing and treating new cases and preventing further spread of infection. This approach has been used to control transmission of infectious diseases including smallpox, tuberculosis, HIV, and syphilis [9,10,11,12]. Although contact tracing has been used in prior outbreaks of hemorrhagic fever, these outbreaks were small in scale [13,14]. Contact tracing is most efficient for diseases with low incidence, limited transmissibility [15,16], tight networks, and an incubation period long enough to allow intervention. Conversely, the effectiveness and optimal levels of investment for contact tracing, particularly for emerging diseases and for acute epidemics, are subjects of ongoing research and debate [15,16,17,18,19]. The 2014 EVD epidemic was the largest EVD outbreak in history and the first known EVD outbreak in West Africa [20]. The scale of the control efforts including contact tracing was unprecedented, particularly within both rural and crowded urban settings, which burdened existing surveillance capabilities and required immense commitment and cooperation on the part of government and the affected communities themselves. Furthermore, the strategies and implementation of contact tracing in Liberia evolved—from establishing operations to scaling them up—in order to respond to the changing phases of the epidemic. These aspects warrant the need to further examine contact tracing within this unique context. Here, we describe the scope and characteristics of contact tracing in Liberia and explore its performance during the 2014–2015 EVD epidemic in order to inform future contact tracing strategies in large-scale epidemics. We performed a retrospective descriptive analysis of data collection forms for contact tracing that was conducted for the EVD epidemic in six of Liberia’s 15 counties during June 2014–July 2015. The six counties consisted of both rural and urban areas and represented 72% of the population of Liberia [21]. Three of the counties (Lofa, Bong, and Nimba) are at the border with Cote d’Ivoire, Guinea, or Sierra Leone, while the other three (Montserrado, Margibi, and Sinoe) extend from central areas to the coast. Additionally, both formal and informal sources of information regarding contact tracing organizational structures and implementation within these counties were reviewed to help provide context for the data analysis. A contact was defined as a person who had direct or indirect exposure to any confirmed, probable, or suspect EVD case, or bodily fluids of a case, within the past 21 days [22,23]. This definition also included any persons who had been discharged from an Ebola Treatment Unit (ETU) as not a case, due to their potential exposure to the virus while in the ETU. National contact tracing guidelines and forms, which were initially adapted from existing WHO and CDC materials and finalized during the waning days of the epidemic, were used as the foundation for implementing the three steps of contact tracing: contact identification, listing, and monitoring. Once a case was detected, contact identification and listing were conducted by interviewing the case and/or family members to gather an initial list of potentially exposed persons. In most instances, this process was conducted by case investigation teams, which were distinct from contact tracing teams, and any of the following six types of exposure were added: (1) sleeping or eating in the same household; (2) direct physical contact with the body; (3) touching bodily fluids; (4) manipulating clothes or other objects; (5) through breastfeeding; and (6) attending a case’s funeral. Contact tracers, chosen from within the community, located the listed contacts and identified any additional contacts missed in the initial investigation. Contact tracers transferred the information collected by case investigation teams to paper forms, including the contact’s name, county, district, town, and exposure (s). The name, age, location, and unique case identifier of the case for which contacts were listed, i. e. the “source case”, were also recorded. During contact monitoring, contact tracers were expected to visit contacts twice daily (morning and afternoon) for 21 days post-exposure in order to identify and record whether the contact had EVD symptoms. This was determined initially through self-reports and physical observation, and eventually temperature readings were added for more objective monitoring. Contacts were monitored for nine symptoms: joint pain, fever (>38° Celsius), weakness, nausea, diarrhea, headache, throat pain, red eyes, and mucosal bleeding. Following the outbreak, paper contact tracing forms were requested from all County Health Teams. Forms were received from six counties and the data were entered into a Microsoft Access database. Data were analyzed using Microsoft Access and Epi Info. Each form was considered a unique contact record and unit of analysis, though it was possible for individual contacts to be monitored more than once if re-exposed. Source cases were identified using unique case identifiers, name, age, county, and district. To assess the coverage of contact tracing, or the percentage of cases for which contacts were monitored, we calculated the ratio of source cases in the database to the total number of suspected, probable, and confirmed EVD cases in MOH situation reports for the same counties using the closest approximate dates [24,25,26,27,28]. The mean number of contacts per source case was presented as contact-to-case ratios. We analyzed records by county and district. Urban or rural classifications were assigned based on districts; districts that hold the county headquarters or that have settlements with a population of 5,000 or more persons were classified as urban [21]. Two districts, one each in Nimba and Montserrado counties, were divided into urban and rural sub-districts. Each of the six exposure categories and nine symptoms were analyzed, and medians and interquartile ranges (IQR) were calculated. We divided the timeframe into four phases based on the observed epidemic trends of cases within Liberia [5], per epidemiologic week (EW): “Phase 1”: the initial increase of cases, from June to mid-August 2014 (EW 22–33); “Phase 2”: the peak, from mid-August to mid-November 2014 (EW 34–46); “Phase 3”: a decline in the epidemic, from mid-November 2014 through February 2015 (EW 47–9); and “Phase 4”: sporadic clusters, from March through July 2015 (EW 10–31). The first date of contact monitoring or last date of exposure was used to categorize records by phase. Medians and IQRs for timeliness, determined by the difference between the last date of exposure and first date of follow-up, were calculated and stratified by urban-rural and phases. Each record was assigned one of seven outcomes of monitoring, either designated on the form or imputed using supplemental information: (1) “completed” the monitoring period of 21 days post-exposure; (2) “dropped” if the source was determined to be not a case; (3) “lost to follow-up” if the contact could not be located after three consecutive days; (4) “potential cases” if the contact presented with symptoms and/or died and monitoring was stopped; (5) “restarted” if monitoring was reinitiated due to a new exposure; (6) “transferred” if the contact moved to another jurisdiction; or (7) “unknown” for all remaining contacts with no outcome information. Contacts who presented with symptoms could be referred for medical evaluation without meeting EVD case definitions; hence, we use the terminology “potential cases”. We calculated the positive predictive value (PPV) defined as the proportion of traced contacts—excluding those with dropped and unknown outcomes—who were potential cases. Sensitivity was defined as the ratio of potential cases identified during monitoring to the number of new cases in situation reports in the same counties [24,25,26,27,28]. This analysis assumes all potential cases were infected with EVD, and that all source cases and potential cases in the database were included in the total counts from situation reports. Therefore, to the extent that these assumptions are overstated, the calculations serve as upper limit estimates. PPVs were stratified by urban-rural and epidemic phases, whereas sensitivity and coverage were stratified by phases. We used odds ratios and 95% confidence intervals to examine exposure types, symptom types, phases, and urban-rural amongst potential cases compared with contacts who completed monitoring; for ordinal variables, the lowest category was used as a reference group. Chi-square tests with p-values <0. 05 were statistically significant. Two multivariate logistic regression models were used: (1) urban-rural, phase, and exposure type covariates, limited to records with ≥1 exposures, and (2) urban-rural, phase, and symptom type covariates, limited to records with ≥1 symptoms. Only statistically significant variables in bivariate analysis were included in the models. Nonparametric tests were used for continuous variables. This assessment is included under Johns Hopkins School of Public Health Institutional Review Board no. 6296 with DHP as principal investigator. A letter of agreement was signed with the Liberia MOH concerning the publication of contact tracing analyses. This assessment used retrospective data collected for public health surveillance purposes so informed consent was deemed unnecessary according to the U. S. Common Rule. We followed the Declaration of Helsinki, aiming to provide assurance that the rights, integrity, and confidentiality of participants were protected. We analyzed 25,830 records for contacts who had monitoring initiated or were last exposed between June 4,2014 and July 13,2015 in the six counties. Of these, 25,651 contacts were listed for 2,465 source cases; an additional 179 contacts had no source case provided. The overall contact-to-case ratio was 10: 1 (median = 7, range 1–424). The contact-to-case ratio increased with each subsequent phase and was higher in urban than rural districts. There were 9,241 EVD cases in situation reports in the six counties. The upper limit estimate of coverage, or the maximum percentage of cases for which contacts were monitored, was 26. 7%, and was lowest during Phase 1. (Table 1) In the six counties providing data, 89. 0% of the records were identified in Montserrado County, 8. 6% in Margibi, 1. 6% in Bong, 0. 4% in Lofa, 0. 4% in Sinoe, and 0. 1% in Nimba (Fig 1) (Table 2). Records pertained to 22 of Liberia’s 136 districts (Table 2); data from the remaining districts was unavailable due to no contact tracing records or no reported EVD cases. In total, 21,500 (83. 2%) contacts were in seven urban districts/sub-districts, mainly in the Monrovia capital district in Montserrado, while 4,327 (16. 8%) contacts were in 17 rural districts/sub-districts. Potential cases were less likely to be from urban districts (Table 3). Temporal trends for contact tracing aligned with disease transmission trends (Fig 2). For 25,690 records grouped by phase, 61. 7% were monitored during Phase 2 and 32. 9% during Phase 3. Only contacts in Montserrado were monitored during Phase 4. No contacts were monitored during May–June 2015, corresponding to the Ebola-free period. Based on 25,300 records, contact tracing was timelier in rural districts; overall, the median difference was 1 day (IQR 0–4) (Table 1). Of 25,830 total contacts, 17,876 (69. 2%) contacts reported 34,284 exposure types, and 7,954 (30. 8%) had zero exposures recorded. Among the 17,876 contacts reporting any exposure, direct physical contact with the body was the most common (73. 1%), while funeral attendance (2. 1%) and breastfeeding (0. 2%) were the least common (Table 3). Two or more exposure types were reported in 54. 9% of 17,876 contacts; the median was 2 (IQR 1–3). Multivariate analysis showed the odds of sleeping or eating in the same household, direct physical contact, or touching bodily fluids were higher amongst potential cases than contacts completing monitoring (Table 3). Of 25,569 contacts with an assigned outcome, 22,680 (87. 8%) completed monitoring, 1,768 (6. 8%) were dropped, 637 (2. 5%) restarted, 334 (1. 3%) were potential cases, 136 (0. 5%) were lost to follow-up, and 14 (0. 1%) were transferred. Most contacts completed monitoring during each phase except during Phase 4, when 53. 6% of contacts were dropped (Fig 2). More contacts restarted during phases 2 and 3 than other phases. Potential cases were less likely to be monitored during phases 2 or 3 compared to Phase 1 (Table 3). Twenty-two contacts were not located prior to monitoring. Of 46 recorded contact deaths, 56. 5% were in urban districts and 33 occurred during monitoring (15 after taken to an ETU). The PPV was 1. 4% overall, and was higher in rural (3. 0%) than urban (1. 1%) districts and highest during Phase 1 (4. 7%), after which it decreased for subsequent phases. The sensitivity of monitoring, or the maximum proportion of new cases detected, was 3. 6%, and was highest during phases 1 and 2. (Table 1) Table 4 shows the distribution of reported symptoms. Overall, 326 contacts reported 1,299 symptom types and 3,732 symptom-days; the median symptom types per contact was 4 (IQR 2–5). Contacts of all outcomes reported symptoms except transferred contacts; 218 (66. 9%) of 326 contacts reporting symptoms were potential cases, 92 (28. 2%) completed monitoring, 6 (1. 8%) restarted, 6 (1. 8%) were unknown, 3 (0. 9%) were dropped, and 1 (0. 3%) was lost to follow-up. In multivariate analysis, potential cases were more likely to report fever, nausea, or weakness compared with contacts who completed monitoring. During 2014–2015, more than 25,000 persons in six of Liberia’s 15 counties were identified, listed, and monitored for EVD, representing one of the largest contact tracing efforts during an epidemic in history. Nationwide, these efforts were even more substantial and required the dedication of responders, including the Government of Liberia, counties, and contact tracing teams. As a result, 334 contacts were identified as potential cases with the intention of providing earlier treatment and preventing hundreds of new infections. Relative to the scale of these efforts, however, these data suggest there were limitations to the performance of contact tracing within Liberia. Overall, there was a small proportion of monitored contacts that were identified as potential cases, and more than 97% of reported EVD cases from the six counties were not detected through contact monitoring. This is greater than expected, especially compared to other examples in West Africa where approximately 69% to 78% of cases were not being traced prior to case identification [29,30]. This measure is dependent upon the level of contact tracing coverage, and based on our database, though admittedly not comprehensive, coverage only accounted for a maximum of one-quarter of all EVD cases reported for these six counties. While this ratio is aligned with similar findings in two Guinea prefectures (32% and 39%) and in Sierra Leone (19%) [29,30], it is possible that contact tracing was not initiated for up to three-quarters of the remaining EVD cases in Liberia, potentially due to a combination of factors discussed below. Potential cases were more likely to be identified in rural districts and early in the epidemic, despite intensified efforts as the epidemic progressed. Possible explanations for why contact tracing was less effective in urban areas could include the following: higher population density and complex social networks making it more difficult to identify all contacts; less cooperation within urban settings; higher burden and strained resources; or a combination of these factors. These results support the concept that contact tracing is most successful when transmission is low, and models have shown that expanding implementation of contact tracing yields diminishing reductions in disease prevalence [15,16]. Therefore, it is critical to conduct contact tracing rigorously and comprehensively as soon as an outbreak is identified, and to achieve higher sensitivity and coverage during this phase. There were, however, notable improvements in implementation over time; specifically, greater coverage, fewer contacts lost to follow-up, and higher contact-to-case ratios. During Phase 4, Liberia was able to focus more resources on eliminating the last transmission chains, including expanding the inclusion criteria to ensure no new cases went undetected [6]. This would have resulted in a larger contact-to-case ratio during Phase 4 compared to all other phases. The dynamics of contact tracing are complex, and its success is related to characteristics of the disease and etiologic agent, resources, and socio-political factors that influence its acceptability and implementation. Additionally, the approaches to contact tracing may differ depending on whether there is a vaccine or therapy available. Given that contact tracing remains one of the critical public health tools during outbreaks involving person-person transmission, optimizing its performance is paramount. While not exhaustive, we focus on four key challenges that may have limited the performance of contact tracing for EVD within Liberia, and propose recommendations for future efforts. First, an integrated surveillance and data management system was lacking and had to be established for reporting between the national laboratory, healthcare facilities and ETUs, and contact tracing and case investigation field teams [5]. Consequently, contact tracing was less functional at the beginning of the epidemic when it could have been most effective in slowing the epidemic. Initially, an insufficiently integrated system resulted in missed source cases and contacts, and led to delays in monitoring; this is reflected in that 25% of contacts with available information started monitoring four days after their last exposure. Additionally, contacts were listed and needlessly traced because of delays in receiving negative laboratory results, thereby lowering the PPV. Although mobile applications had the potential to improve reporting and data management, these were not piloted until after the peak of the outbreak. In contrast, contact tracing in urban Nigeria successfully and rapidly contained EVD transmission, largely thanks to robust surveillance systems and leveraging mobile applications for real-time monitoring [31,32]. Strengthening integrated surveillance and electronic data systems, and the early adoption of mobile technology, could help improve timely reporting for listing and monitoring contacts. Secondly, the organizational structure for contact tracing likely led to inefficiencies in its implementation and management, particularly in urban districts. For instance, case investigation teams, who conducted contact listing, were often distinct from contact tracing teams who conducted contact monitoring. In some rural areas, teams responded in tandem thereby reducing gaps, yet this was more difficult in dense urban areas such as in Montserrado County. Additionally, the county level coordinated all aspects of the response—not just contact tracing. In January 2015, Montserrado created decentralized sub-county sectors to oversee and synchronize all operations—a change previously recognized as a critical step for halting transmission [6]. Particularly in urban areas and in the absence of a robust surveillance system, using a decentralized management approach and multidisciplinary teams may improve contact tracing performance. Thirdly, there were challenges with adapting and implementing contact tracing protocols, which had to be used by novice teams during the epidemic. For instance, the number of contacts per source case ranged widely in our analysis, from 1 to 424, and nearly one-third of contacts had no exposure documented, indicating that some contacts may not have met the inclusion criteria, thereby straining resources. Also, written guidance for identifying potential cases during monitoring did not specify how contact tracers should determine when to refer a contact for medical evaluation [23]. Eighteen contacts, who presumably would have shown symptoms prior to death, died during monitoring without being referred for medical evaluation. Among contacts who reported symptoms, including multiple symptoms and symptom-days, 33. 1% continued under monitoring without being referred for further evaluation, indicating that triggers for identifying potential cases was subjective. During future outbreaks, clear and comprehensive protocols need to be initiated early in the epidemic and reinforced throughout implementation. Furthermore, if resources are limited, inclusion criteria could prioritize contacts with multiple exposures, and/or those with household contact or direct contact with the body or bodily fluids. Triggers for identifying potential cases could include contacts reporting multiple symptoms types, fever, nausea, and weakness. Finally, community perceptions, stigma, and mistrust reportedly led to challenges in obtaining complete and reliable information, to delays or an inability to trace contacts due to evasion, and even to violence [5,33]. Underreporting of symptoms due to fear or due to fever-reducing drugs may explain why relatively few symptoms were captured in our database. Also, contacts were instructed to self-isolate within their home, which disrupted normal routines and the ability to maintain jobs; without adequate support from the community or organizations, contacts are less likely to cooperate. These aspects stress the importance of community cooperation, trust, and engagement. Overall, less than 1% of contacts were lost to follow-up, and this improved during each phase along with more contacts listed per source case, suggesting that this cooperation probably improved as the outbreak progressed. For future outbreaks, community-led strategies for contact tracing should be an early priority to foster cooperation, trust, and ownership of the control efforts. This analysis represented both urban and rural settings, and Montserrado specifically, where the response was most intense. However, we were unable to collect forms from all 15 counties nor all forms from the six inclusive counties; for example, no forms were available for the EVD cluster that occurred in Margibi in July 2015. Despite commendable efforts, counties reported that paper forms were lost or destroyed due to perceived contamination risks. Using paper forms also led to variability in data quality, including illegible writing, misspellings, inversed source case and contact information, and difficulty in interpreting marks for visits and the presence of symptoms. Falsifying information on forms was a concern [33], such as documenting visits when the contact had not been seen, and this was an issue early in the epidemic. These factors, combined with the lack of information to ascertain the final status of EVD infection amongst source cases and potential cases, constrained our analysis. Likewise, we could not conclude whether symptoms reported amongst potential cases evidenced EVD infection. Our data primarily represented contact monitoring, as we did not have a comprehensive contact listing. Finally, EVD case counts from situation reports were unavailable to stratify coverage and sensitivity by urban-rural districts and/or phase, and these aggregated totals could not be linked to our individual-level database. Our findings suggest that despite the unprecedented scale of contact tracing for EVD in Liberia, there were limitations in its ability to detect new cases, especially in urban areas and during the peak case load. Since contact tracing remains a critical intervention for controlling outbreaks, we suggest rigorous implementation early in the outbreak and focusing on four key areas to optimize its performance within similar contexts: (1) strengthening integrated surveillance and electronic data systems, (2) decentralizing management of multidisciplinary teams for improved coordination and oversight, (3) instituting and reinforcing clear and comprehensive protocols, and (4) adapting community-led strategies to foster cooperation, trust, and ownership.

Contact tracing is comprised of three main steps: identifying, listing, and monitoring persons who have been exposed to infected individuals, with the goal of rapidly diagnosing and treating new cases and preventing further spread of infection. This approach has been used to control transmission of infectious diseases including smallpox, tuberculosis, HIV, and syphilis, and while contact tracing has been used in prior outbreaks of hemorrhagic fever, these outbreaks were small in scale. During the 2014–2015 Ebola virus disease (EVD) epidemic in Liberia, contact tracing was implemented in all 15 counties on a scale that was unprecedented, particularly within both rural and crowded urban settings. This work provides insight into the magnitude that which contact tracing was implemented, its characteristics, as well as an assessment on its performance. Given that contract tracing is a critical tool for controlling disease spread, these findings aid in informing future planning and decision making for its implementation.

Abstract Introduction Methods Results Discussion

medicine and health sciences body fluids pathology and laboratory medicine infectious disease epidemiology geographical locations signs and symptoms infectious disease control africa infectious diseases geography epidemiology people and places nausea diagnostic medicine anatomy urban areas fevers physiology earth sciences liberia geographic areas biology and life sciences

2018

Contact tracing performance during the Ebola epidemic in Liberia, 2014-2015

Culex species are widespread across Cameroon and responsible for high burden of nuisance in most urban settings. However, despite their high nuisance, they remain less studied compared to anophelines. The present study aimed to assess Culex species distribution, susceptibility to insecticide, bionomics and role in Lymphatic Filariasis (LF) transmission in the city of Yaoundé. Mosquito collections were conducted from March to December 2017 using Centre for Disease Control light traps (CDC-LT), human landing catches (HLC) and larval collections. Mosquitoes were identified using morphological identification keys. Mosquitoes from the Culex pipiens complex were further identified using Polymerase Chain Reaction (PCR) to assess the presence of sibling species. Bioassays were conducted with 2–5 day-old unfed females to assess mosquito susceptibility to DDT, permethrin, deltamethrin and bendiocarb following WHO guidelines. Dead, control and surviving mosquitoes from bioassays were screened by PCR to detect the presence of knockdown resistance (kdr) alleles. Pools of mosquitoes were examined by PCR to detect the presence of Wuchereria bancrofti. A total of 197,956 mosquitoes belonging to thirteen species were collected. The density of mosquito collected varied according to the collection methods, districts and seasons. Culex quinquefasciatus emerged as the most abundant and the only species of the Culex pipiens complex in Yaoundé. Culex species were found breeding in different types of breeding sites including polluted and unpolluted sites. All Culex species including Cx antennatus, Cx duttoni, Cx perfuscus and Cx tigripes were found to be highly resistant to permethrin, deltamethrin and DDT. Culex quinquefasciatus was also found to be resistant to bendiocarb. A high frequency of the West Africa kdr allele was recorded in resistant Cx. quinquefasciatus. Out of the 247 pooled samples of 25 Culex spp. examined for the presence of Wuchereria bancrofti, none was found infected. The study confirms the high adaptation of Culex species particularly Culex quinquefasciatus to the urban environment and no implication of this species in the transmission of LF in Yaoundé Cameroon. Culex species predominance in urban settings highlight potential transmission risk of West Nile and rift valley fever in Yaoundé. Study site- The study was conducted in Yaoundé (03°52’N; 11°31’E), the capital city of Cameroon from March to December 2017. The city has a population estimated at 2. 8 million inhabitants. Yaoundé belongs to Guinean subequatorial climate type, characterized by four distinct seasons: the short rainy season (Mars-June), the short dry season (June-July), the long rainy season (August-November) and the long dry season (November-February). The city receives annually over 1600 mm of rainfall and the annual average temperature is 24°C. Yaoundé is located about 750 m above sea level and surrounded by many hills. Although occurring at very low endemicity, human infection by Wuchereria bancrofti was estimated at 2. 3% during surveys conducted between 2009–2010 in Yaoundé and it surroundings [19]. The study was conducted under the ethical clearance N° 2016/11/832/CE/CNERSH/SP delivered by the Cameroon National Ethics Committee for Research on Human Health (CNERSH) Ref N°D30-172/L/MINSANTE/SG/DROS/TMC of 4 April 2017. For human landing catches all adult men who took part in the collection signed a written informed consent form before being enrolled in the study as recommended by the validated protocol and were given free malaria prophylaxis. Mosquito’s collection and breeding sites characterization- Adult and immature stages of Culicine mosquitoes were sampled in 32 districts of Yaoundé. Culicine collections were undertaken in the context of a big survey intended to assess mosquito distribution and malaria transmission pattern in the city of Yaoundé before a larval control trial and will allow in the future additional analysis with more data. Adult mosquitoes were collected using CDC light traps (CDC-LTs) and Human Landing Catches (HLCs) from 7pm to 6am. All potential larval breeding sites were inspected and positive sites (with at least one Culicine larvae or pupae) recorded. Three dips were undertaken for small breeding sites of less than 1 m2; and 5 to 10 dips were undertaken in breeding sites of more than 1m2. The average larval density (N) was estimated. Once collected larvae were classified according to their stages: early instars larvae (L1&L2) and late instars (L3&L4 and pupa). Other parameters measured included the type of breeding sites sampled (stagnant water pools, gutters, well, tyre print, footprint, pit latrine….), depth, the status organically polluted or not, the distance to the nearest house, the presence/absence of predators, the proportion of water surface covered by vegetation or algae. Larvae collected were kept in plastic containers and brought to the insectary for rearing. After emergence, adult mosquitoes were identified to species level under a binocular magnifying glass using morphological identification keys [31–33]. For mosquitoes collected using either CDC-LTs or HLC, a subsample of 50 culicine specimens per district was randomly selected for identification during each collection month. All mosquitoes collected were stored at -20°C for further molecular analyses. Susceptibility tests to insecticides-Bioassays were performed with 2–5 days old females emerging from larval collection. Mosquitoes were tested against permethrin 0. 75%, DDT 4%, bendiocarb 1% and deltamethrin 0. 05% following WHO guidelines [34]. For each test, batches of 25 mosquitoes per tube were exposed to impregnated papers for 1 hour. The number of mosquitoes knocked down by the insecticide was recorded every 10 minutes during exposure. After exposure, mosquitoes were fed with a 10% glucose solution and the number of dead mosquitoes was recorded 24 hours post-exposure. Mosquitoes used as controls were exposed to untreated papers. The mortality rates were corrected using the Abbot formula [35] whenever the mortality rate of the controls was between 5 and 20%. Susceptibility and resistance levels were assessed according to WHO criteria [34]. At the end of the assay, mosquitoes were classified into three different groups: 98%-100% mortality indicates susceptibility, 80%-97% mortality suggests possible resistance that needs to be confirmed, <80% mortality suggests resistance. The study objective was to assess culicine species distribution, bionomic and potential role in W. bancrofti transmission in the city of Yaoundé. High Culicine species diversity was recorded with up to 13 species collected. Culex species were the most prevalent and this was consistent with previous studies conducted in Cameroon and across Africa indicating the high adaptation capacity of species of this genus particularly Cx. quinquefasciatus to the urban environment [27,41–44]. The diversity of culicine species recorded could result from the presence of different landscapes across the city of Yaoundé made up of an alternation of highland and marshland covered with vegetation and exploited for agriculture, lakes invaded by vegetation, and rural environment. It is still unknown whether there is an intense competition between culicine species sharing similar habitats. Species such as Cx. tigripes larvae are known to be predators for early instars of different species. Culex quinquefasciatus emerged after molecular analysis, as the sole member of the Cx. pipiens complex in Yaoundé; its presence was consistent with the known distribution of members of the complex [37]. Species diversity and abundance were all found to vary according to collection methods and seasons. High species diversity was recorded using CDC-LT compared to HLC or larval collection and reflects the high efficiency of CDC-LT method for collecting culicines. The use of CDC-LT has now become common for sampling mosquito populations across the world and has been shown to be particularly effective for sampling Culex mosquitoes [27,45]. This tool was rather found to underestimate anophelines densities [27,45,46]. Both HLC and CDC-LT techniques were used because there was so far no available data on the efficiency of CDC-LT for collecting Culex species from Yaoundé. Seasonal variations in species composition was detected for mosquitoes collected from breeding habitats, however, no similar trend was detected for mosquitoes collected using CDC-LT or HLC. This likely suggest different breeding habitats preference for culicine species at different periods of the year or the influence of physico-chemical parameters [47,48] or xenobiotics selection [49] on Culex species distribution. Cx. quinquefasciatus larvae were found to be highly prevalent in polluted sites. It is likely that females of Culex species are more attracted by oviposition cues released by the microbial fauna in this type of habitats. In addition, these habitats are rich in nutrients and could thus reduce competition for resources between species. This could also be because mosquitoes in polluted sites are also frequently exposed to intensive selective pressure induced by pollutants and xenobiotics [27,50–52], different strategies were reported to promote Culex species adaptation to different ecological constraints. This include the development of resistance or detoxification mechanisms to a large set of insecticides and xenobiotics [53–55], the capacity for eggs to resist desiccation [56] and development of cuticle resistance in larvae [3,57,58]. Several Culex species including Cx. quinquefasciatus, Cx. antennatus, Cx. duttoni were found to display resistance to DDT, permethrin and deltamethrin. This is the first time that insecticide resistance in different Culex species is documented in Cameroon. The level of pyrethroid resistance was similar to data recorded for An. gambiae populations in the city of Yaoundé [59,60]. In addition to the fact that Culex species are known to breed in polluted environment and could thus be affected by xenobiotics selection, the high level of resistance recorded could also result from increased use of LLINs for malaria vector control and pesticides use in agriculture in the city of Yaoundé [27,61]. Our study also suggested the presence of kdr allele in Cx. quinquefasciatus populations. It is likely that resistance in Culex species is sustained by both kdr mutations and other mechanisms such as the metabolic detoxification machinery [62]. The present study also permitted to evaluate the role of Culex species in LF transmission after mass drug administration (MDA) scale up in Cameroon. Culex quinquefasciatus is the predominant vector of LF in both urban and rural settings in East Africa [3,23] but less so in Central and West Africa. However, with potential gene flow and changing climate, one cannot rule out that Cx. quinquefasciatius in Central Africa such as in Cameroon may also emerge as LF vector. Furthermore, because of the rapid expansion and predominance of this species in Cameroon cities, it’s potential implication in LF transmission in Yaoundé was examined. Analysis conducted with pool samples of Culex mosquitoes recorded no infection. In Cameroon LF is considered to be endemic with prevalence rates varying from 1 to 8% [19,63]. It is likely that the prevalence of parasite may have decreased over years due to the implementation of mass drug administration of ivermectin and abendazole to the population since 2009 [19]. So far, five to six rounds of MDA have been successfully conducted in endemic settings across the country and interruptions of LF transmission have been documented in some parts of the country [64]. The fact that only Culex species were screened during this study could have limited the capacity of detecting any ongoing transmission since mosquito species such as An. gambiae and An. funestus are also good vectors of LF [3,23]. Another important dimension which could explain the absence of W. bancrofti infection in Culex is that the area may have not been endemic for W. bancrofti before the introduction of MDA. Recent studies conducted in Cameroon and DRC suggested that the perceived endemicity of LF established by ICT test in the central African region could result from the presence of Loa filariasis which cross react to the ICT tests which was used to detect W. bancrofti in Central Africa, leading to false positivity [64–66]. During the last decade, several arboviral diseases such as chikungunya, dengue, yellow fever, West Nile, Sindbis, Tahyna, O’nyong-nyong and spondweni virus have been reported in circulation in human adults in both urban and rural settings [67–70]. With the rapid distribution of Culex species in the urban environment, the potential role that these species could play in spreading of these arboviral diseases deserves further consideration. The present study confirms high abundance of Cx. quinquefasciatus in the city of Yaoundé and high insecticide resistance in most Culex species populations. The study also suggests no transmission of W. bancrofti by Culex species in Yaoundé. In Cameroon, apart from malaria vectors, surveillance activities are not regularly conducted on other vectors of diseases because of lack of funding or technical capacities for these activities. In this context, combining surveillance activities of malaria vectors with other culicine species and strengthening capacities of medical entomologists on taxonomy, sampling, processing and calculation of key entomological indicators for endemic vector borne diseases could be cost effective and will enable better understanding of the distribution and epidemiology of various diseases. This could lead to the establishment of sustainable surveillance systems.

Culex species are highly prevalent in both urban and rural settings in Cameroon and are responsible for high nuisance and transmission of pathogens such as Wuchereria bancrofti and arbovirus. Despite the important epidemiological role, that Culex could play, they are still less studied. The current study was conducted to assess Culex species distribution, susceptibility to insecticide and role in W. bancrofti transmission in the city of Yaoundé. Mosquito collection was conducted using three collection methods human landing catches, CDC light traps and larval collection. Once collected, mosquitoes were identified using morphological identification keys and PCR diagnostic tools. They were later processed to determine their infection status. Bioassays with Culex females of 2 to 5 days old were conducted to determine their susceptibility level to different insecticide families. Culex quinquefasciatus emerged as the most abundant species. Up to 13 different culicine species were recorded. Culex species were recorded to be highly resistant to DDT, permethrin and deltamethrin. A high frequency of the West Africa kdr allele was recorded. No mosquito was detected to be infected by LF. The study confirms the need for further xenomonitoring activities in order to control the risk of outbreaks due to Culex mosquitoes in the city of Yaoundé.

Abstract Introduction

death rates invertebrates medicine and health sciences geographical locations animals developmental biology molecular biology techniques population biology insect vectors africa research and analysis methods infectious diseases cameroon agrochemicals wuchereria bancrofti artificial gene amplification and extension wuchereria culex quinquefasciatus life cycles molecular biology disease vectors insects agriculture arthropoda people and places insecticides population metrics mosquitoes eukaryota polymerase chain reaction nematoda biology and life sciences species interactions larvae organisms

2019

Culex species diversity, susceptibility to insecticides and role as potential vector of Lymphatic filariasis in the city of Yaoundé, Cameroon

A constellation of metabolic disorders, including obesity, dysregulated lipids, and elevations in blood glucose levels, has been associated with cardiovascular disease and diabetes. Analysis of data from recently published genome-wide association studies (GWAS) demonstrated that reduced-function polymorphisms in the organic cation transporter, OCT1 (SLC22A1), are significantly associated with higher total cholesterol, low-density lipoprotein (LDL) cholesterol, and triglyceride (TG) levels and an increased risk for type 2 diabetes mellitus, yet the mechanism linking OCT1 to these metabolic traits remains puzzling. Here, we show that OCT1, widely characterized as a drug transporter, plays a key role in modulating hepatic glucose and lipid metabolism, potentially by mediating thiamine (vitamin B1) uptake and hence its levels in the liver. Deletion of Oct1 in mice resulted in reduced activity of thiamine-dependent enzymes, including pyruvate dehydrogenase (PDH), which disrupted the hepatic glucose–fatty acid cycle and shifted the source of energy production from glucose to fatty acids, leading to a reduction in glucose utilization, increased gluconeogenesis, and altered lipid metabolism. In turn, these effects resulted in increased total body adiposity and systemic levels of glucose and lipids. Importantly, wild-type mice on thiamine deficient diets (TDs) exhibited impaired glucose metabolism that phenocopied Oct1 deficient mice. Collectively, our study reveals a critical role of hepatic thiamine deficiency through OCT1 deficiency in promoting the metabolic inflexibility that leads to the pathogenesis of cardiometabolic disease. Hepatic energy metabolism is a major determinant of systemic glucose and lipid levels as well as total body adiposity, which in turn are key risk factors for cardiovascular and metabolic diseases [1,2]. Genome-wide association studies (GWAS) have provided a wealth of information on the genes and pathways involved in hepatic energy metabolism, including apolipoprotein E (APOE), proprotein convertase subtilisin/kexin type 9 (PCSK9), and low-density lipoprotein receptor (LDLR) [3–5]. In follow-up studies in cells and in preclinical animal models, most of these genes have been linked mechanistically to lipid metabolism [6]. In contrast, the mechanisms responsible for the genome-wide–level significant association of SLC22A1 (encoding the organic cation transporter, OCT1) with total and low-density lipoprotein (LDL) cholesterol [3] remains unexplored. In humans, the OCT1 gene is highly polymorphic. A number of reduced-function variants with high prevalence in European populations have been characterized [7–9]. In particular, 40% of Caucasians carry one and 9% carry two reduced-function OCT1 variants [7,8]. OCT1, which is highly expressed in the liver, has been widely characterized as a drug uptake transporter. Reduced-function polymorphisms of OCT1 have been associated with changes in the pharmacokinetics and pharmacodynamics of several drugs, including the opiate receptor agonist, morphine, and the anti-diabetic drug, metformin [10–12]. Recently, GWAS and fine mapping analysis showed that OCT1 functional variants are associated with acylcarnitine levels through efflux mechanism [13]. Previously, through metabolomic studies in Oct1-/- mice and in cells overexpressing human OCT1, our laboratory identified thiamine, vitamin B1, as a major endogenous substrate for OCT1, and Oct1 knockout mice were shown to exhibit hepatic thiamine deficiency [14]. Although systemic thiamine deficiency is well known to cause nerve damage and lead to beriberi and Wernicke-Korsakoff syndrome [15,16], the pathophysiologic effects of thiamine deficiency in the liver are not understood. Thiamine pyrophosphate (TPP), the active metabolite of thiamine, is an essential cofactor for several metabolic enzymes, including pyruvate dehydrogenase (PDH), α-ketoglutarate dehydrogenase (α-KGDH), and transketolase (TK), which have fundamental roles in regulating cellular energy metabolism [15]. In particular, in 1963 Randle proposed that PDH acts as a key metabolic switch in the glucose–fatty acid cycle, which underlies the metabolic disturbance of diabetes. Under the theory of substrate competition between glucose and fatty acids, an increase in fatty acid oxidation and a reduction in glycolytic flux result in a critical imbalance in energy metabolism in tissues. As noted by Randle, regulation of PDH activity greatly influences selection of fuel source [17,18]. Failure to flexibly adjust the choice of fuel (e. g. , fatty acids or glucose) for metabolic energy production has recently been proposed to underlie metabolic inflexibility and lead to the pathogenesis associated with metabolic disorders [19]. Metabolic inflexibility and indeed metabolic syndrome have been linked to an excess of macronutrients (e. g. , carbohydrates or fat); however, the role of micronutrients such as thiamine in metabolic syndrome has been largely ignored. Although many reports have identified a high prevalence of thiamine deficiency in patients with diabetes or obesity [20–23] and a beneficial effect of thiamine supplementation in these patient populations [24–26], the molecular mechanisms contributing to thiamine-associated metabolic disturbance are unknown. Here, we hypothesize that reduced OCT1 function or reduced dietary thiamine intake leading to decreases in hepatic thiamine levels modulates the activity of multiple enzymes and the levels of key metabolites involved in glucose and lipid metabolism. These effects result in dyslipidemias, increases in circulating glucose levels, and peripheral adiposity. Through extensive experiments in Oct1-/- mice, our data show that Oct1 deficiency results in substantial changes in hepatic energy metabolism, i. e. , reduction in glucose utilization, increased gluconeogenesis, and alterations in lipid metabolism. Similarly, feeding wild-type mice a thiamine deficient diet (TD) results in comparable effects on hepatic energy metabolism. Taken together, our studies suggest that hepatic thiamine deficiency, through deletion of Oct1 in mice, results in the development of metabolic inflexibility. Our studies provide a mechanistic explanation for the striking metabolic findings in large-scale human genetic studies, demonstrating that common OCT1 reduced-function polymorphisms are associated with dyslipidemias, obesity, and increased risk for type 2 diabetes. The GWAS Catalog, database of Genotypes and Phenotypes (dbGAP) Association Results Browser, and Genome-Wide Repository of Associations Between SNPs and Phenotypes (GRASP) identified two major phenotypes (total cholesterol and LDL cholesterol levels) that were associated with genetic variants in SLC22A1 (OCT1) (Fig 1 and S1A and S1B Fig). In particular, rs1564348 and rs11753995 were associated with LDL cholesterol (p = 2. 8 × 10−21) and total cholesterol (p = 1. 8 × 10−23), respectively (Fig 1 and Table 1). Using HaploReg v4. 1 to obtain linkage disequilibrium information from 1000 Genomes Project, we noted that these two SNPs are in linkage with the OCT1 with methinone420 deletion (420Del), a common genetic variant in OCT1 that shows reduced uptake and altered kinetics of its substrates. Thus, the results suggest that reduced OCT1 function is significantly associated with higher total cholesterol and higher LDL levels. The GRASP database identified other phenotypes with significant, but weaker, p-values, relevant to glucose traits and coronary artery disease. Recent results from the UK Biobank cohort (http: //geneatlas. roslin. ed. ac. uk/), available in the Gene ATLAS database and from the Global Lipids Genetic Consortium, are also included in Table 1. As shown, the reduced-function OCT1 nonsynonymous variants, OCT1-R61C, OCT1-G401S, OCT1-420Del, and OCT1-G465R, were significantly associated with high total cholesterol, LDL cholesterol, and/or TG levels in at least one study (Table 1). In addition, two of the missense OCT1 variants, OCT1-P341L and OCT1-V408M, which are associated with lower SLC22A1 expression levels in several tissues [13,27,28], were also associated with higher cholesterol levels in at least one study. The OCT1 nonsynonymous variants in Table 1, except OCT1-P341L, are not in linkage disequilibrium (r2 < 0. 1) with SNPs in lipoprotein (a) (LPA) and lipoprotein (a) like 2 (LPAL2) genes (a known locus for plasma lipoprotein levels) [29–31] (S1C Fig), indicating that OCT1 constitutes an independent locus for association with plasma lipids, which was also recently shown in other studies [32,33]. Notably, the effect size of the OCT1 variants for associations with lipids traits are small; thus, larger sample sizes are needed for genome-wide level significance (p < 5 × 10−8) (Table 1). In the Type 2 Diabetes Knowledge Portal, weaker but significant associations (p < 0. 05) between OCT1 reduced-function variants and higher 2-hour glucose levels, higher fasting insulin levels, increased risk for type 2 diabetes, increased risk for coronary artery disease, and higher BMI were cataloged (Table 1). We performed burden test analysis using the data available in the portal. Interestingly, in the analysis, in which we included possibly or probably deleterious missense or protein truncating variants of OCT1, we observed strong associations of the reduced-function OCT1 variants with increased body weight (p = 0. 0002–0. 0005, beta = 0. 23–0. 3). When we performed a similar burden test analysis with type 2 diabetes, the significance was weaker and the results were only significant when we included only protein truncating variants of OCT1 (p = 0. 015, odds ratio = 2. 10). Consistent with our previous studies, deletion of Oct1 protected the mice from hepatic steatosis [14] (Fig 2A, S2A Fig). In this study, we observed that glycogen content was 3. 3-fold greater in livers from Oct1-/- mice compared to livers from Oct1+/+ mice after an overnight fast (Fig 2A and S2B Fig). Consistent with these results, hepatic glucose levels were 5. 9-fold higher (p = 0. 0006) in Oct1-/- mice compared to Oct1+/+ mice (S2C Fig). Significantly greater body weights were observed for Oct1-/- mice compared to their wild-type counterparts, starting at the age of 6 weeks (Fig 2B and S2D Fig). Body composition also differed, with dual-energy X-ray absorptiometry (DEXA) scans showing a higher percent of body fat in Oct1-/- compared to Oct1+/+ mice (p = 0. 001) (Fig 2C). Consistent with the greater proportion of body fat, Oct1-/- mice had greater epididymal fat pad weights and reduced liver weight compared to Oct1+/+ mice (p < 0. 0001) (Fig 2D and S2E Fig). To further assess the potential mechanism leading to increased weight gain in Oct1-/- mice, we analyzed energy expenditure, food intake, and activity by the comprehensive laboratory animal monitoring system (CLAMS). Before placing the mice into the CLAMS, the body composition of all mice was measured by EchoMRI. As shown in Fig 2E, Oct1-/- mice had greater fat and lower lean mass in comparison to Oct1+/+ mice (p < 0. 0001). When normalized to total body weight, Oct1-/- mice had significantly lower respiratory oxygen (O2) consumption and energy expenditure (Fig 2F and 2G), indicating lower metabolic rates of Oct1-/- mice in comparison to Oct1+/+ mice. These data are consistent with the lower lean mass of the Oct1-/- mice compared to Oct1+/+ mice, because lean mass contributes more to energy expenditure than more inert tissue, such as adipose tissue [39,40]. In fact, no differences in respiratory O2 consumption or energy expenditure normalized to lean mass were observed between Oct1+/+ and Oct1-/- mice (S2F and S2G Fig). Thus, the differences in metabolic rate between Oct1+/+ and Oct1-/- mice appear to be due to significant differences in body composition. Additionally, our Oct1-/- mice had no difference in activity but had slightly lower food intake and respiratory exchange ratio (RER) during the dark cycle compared to Oct1+/+ mice (S2H–S2J Fig). There were no deleterious effects of Oct1 deficiency on hepatic function and, in fact, some of the liver function tests improved in the Oct1 knockout mice in comparison to wild-type mice (S2K Fig). There were no major differences in the expression levels of thiamine transporters (Slc19a2 and Slc19a3) in the liver. In contrast, levels of organic cation transporter, Oct2, which also transports thiamine, were increased, albeit the expression levels of Oct2 in the liver were extremely low relative to Oct1 and Slc19a2 (S2L Fig). Collectively, our data suggest that Oct1 deletion had a significant effect on hepatic and peripheral energy homeostasis. We hypothesized that the systemic plasma levels of thiamine are higher in Oct1-/- mice as a result of reduced hepatic extraction of dietary thiamine (Fig 3A). As expected, Oct1-/- mice had significantly higher plasma levels of thiamine (Fig 3B and S3A Fig) compared to Oct1+/+ mice on thiamine-controlled and thiamine-enriched diets. In addition, Oct1 deletion preserved plasma thiamine levels in mice on TDs (Fig 3B). Thiamine deficiency is associated with life-threatening diseases, such as beriberi and Wernicke-Korsakoff syndrome [15,41]. We hypothesized that preserved circulating thiamine levels would delay the development of severe thiamine deficiency syndromes and increase the rate of survival when mice were challenged with a TD. As shown in Fig 3C, there was a significant improvement in the overall survival of Oct1-/- mice (p = 0. 012, Gehan-Breslow-Wilcoxon test; p = 0. 018, log-rank test) compared to Oct1+/+ mice. Modulation of Oct1 expression levels provides a means of studying the effect of hepatic thiamine levels per se as opposed to systemic thiamine levels or thiamine levels in other tissues. Manipulation of dietary thiamine may have additional effects, for example, in the central nervous system. Notably, Liu and colleagues determined that reduced levels of thiamine in the systemic circulation in mice resulted in neurological effects in the hypothalamus, with anorexia and resultant reduction in peripheral adiposity [42]. In human populations, the OCT1 gene is highly polymorphic [7–9,43]. Many loss-of-function polymorphisms of OCT1 have been characterized and found to affect hepatic uptake of drugs, leading to altered treatment response [43]. Here, in the uptake studies, cells expressing human OCT1 genetic variants (420Del or 420Del+G465R) had significantly reduced uptake of thiamine compared to the reference allele (Fig 3D), although they have comparable levels of OCT1 transcript (S3C Fig). In kinetic studies performed at 4 minutes, the maximum velocity (Vmax) of thiamine in cells expressing human OCT1 with methinone420 deletion (hOCT1-420Del) was 70% lower than in cells expressing the human OCT1 reference (hOCT1-Ref) (1. 80 ± 0. 09 nmol/mg protein/minute versus 5. 36 ± 0. 30 nmol/mg protein/minute) (Fig 3D). In contrast to humans, who express OCT1 primarily in the liver, mice express Oct1 in both the liver and the kidney; therefore, deletion of Oct1 in the kidney could potentially affect systemic levels of thiamine in mice. To address this limitation of the Oct1 knockout mice as a model for humans, we used hydrodynamic tail vein injection of mouse Oct1 short hairpin RNA (shRNA) lentiviral particle (or empty vector shRNA lentiviral particle as control) to specifically knock down Oct1 in the liver in both Oct1+/+ and Oct1-/- mice. Following a single intraperitoneal injection of 2 mg/kg thiamine (with 4% 3H-thiamine), we observed that the area under the plasma concentration-time curve (AUC) of thiamine was significantly greater in wild-type mice treated with Oct1 shRNA lentiviral particles compared to wild-type mice treated with vector control shRNA lentiviral particles (Fig 3E). Although not significant, similar trends were observed in the maximum concentration (Cmax) values (S3D Fig). Notably, the Oct1 shRNA did not affect Oct1 expression levels in the kidney (S3D Fig). Compared to wild-type mice with Oct1 shRNA lentiviral particle knockdown, higher systemic levels of thiamine were observed in Oct1-/- mice (Fig 3E), potentially reflecting an incomplete Oct1 knockdown (50% liver Oct1 expression reduction, S3D Fig) or an additive effect of renal Oct1 deletion in Oct1-/- mice. The data provide strong evidence that reduction of OCT1 expression in the liver alone can result in increased systemic thiamine exposure. Although the liver plays a role in pre-systemic thiamine metabolism, it should be noted that thiamine is metabolized in most tissues in the body; therefore, other tissues, such as the intestine, may contribute to pre-systemic metabolism of the vitamin. Collectively, alterations in OCT1 function through genetic polymorphisms affect thiamine uptake and disposition. Our previous studies indicated that Oct1 deletion resulted in reduced hepatic thiamine levels and levels of TPP [14], the cofactor of PDH. It is shown that reduced TPP levels directly affect the activity of PDH [44,45]. As PDH plays a key role in energy metabolism linking glycolysis to the tricarboxylic acid (TCA) cycle and fatty acid metabolism [17], we hypothesized that the activity of hepatic PDH was impaired in Oct1-/- mice. Because phosphorylation of PDH results in inactive forms of the enzyme [46], we measured levels of phosphorylated PDH (at two phosphorylation sites, Ser232 and Ser300) and mRNA levels of pyruvate dehydrogenase kinase 4 (PDK4). Both phosphorylated PDHs and PDK4 transcripts were significantly higher in livers from Oct1-/- mice (Fig 4A and S4A Fig). In addition, in Oct1-/- mice, glycogen synthase (GS), and glucose transporter 2 (Glut2) were present at significantly higher levels (Fig 4A). Although glycogen phosphorylase (PYGL), which plays a key role in breakdown of hepatic glycogen, was also expressed at higher levels, the ratio of GS to PYGL was significantly higher in livers from Oct1-/- mice (Fig 4A and S4B and S4C Fig). These data suggest that Oct1-/- mice had higher rates of glycogen synthesis, which could explain the higher hepatic glycogen content in Oct1-/- mice. Our data suggested that livers from Oct1-/- mice would have less activity of PDH, which in turn would result in a lower rate of conversion of pyruvate to acetyl-CoA entering the TCA cycle [47,48] and thus an overall reduction in oxidative phosphorylation of glucose. We hypothesized that the reduction of oxidative phosphorylation of glucose would increase the accumulation of the intermediates of gluconeogenic substrates. These intermediates would lead to increased gluconeogenesis as glycolysis and gluconeogenesis are reciprocally regulated and highly depend on the availability of gluconeogenic substrates [1,49]. The levels of glucose-6-phosphate (G6P), a strong allosteric activator of GS [50], were 2. 3-fold (p < 0. 0001) higher in the livers of Oct1-/- mice (Fig 4B). In addition, the ratio of phosphorylated GS to total GS was significantly lower in Oct1-/- mice (Fig 4C), consistent with a higher activity of GS in Oct1-/- mice. To further investigate the role of OCT1 in hepatic glucose metabolism, we performed three standard tests related to glucose homeostasis [51]. In the glucose tolerance test (GTT), the blood glucose rose following oral glucose dosing and fell back to normal in both Oct1+/+ and Oct1-/- mice (S4D Fig), although the Oct1-/- mice had higher blood glucose levels at baseline. After adjusting for baseline, there was a trend toward higher blood glucose levels and an overall greater glucose AUC after a bolus dose of glucose in Oct1-/- mice (Fig 4D). The GTT indicated that both Oct1+/+ and Oct1-/- mice could produce insulin in response to rising glucose. In contrast, pyruvate tolerance tests (PTTs) were different between Oct1+/+ and Oct1-/- mice (Fig 4E and S4E Fig). In particular, blood glucose was significantly higher at each time point after pyruvate injection in Oct1-/- mice, which suggested that Oct1-/- mice had higher rates of hepatic gluconeogenesis. In the insulin tolerance test (ITT), there was a trend toward higher blood glucose levels after insulin injection in the Oct1-/- mice and an overall greater glucose AUC (Fig 4F). Blood glucose levels are maintained by glucose uptake mainly in peripheral tissues and glucose output primarily from the liver [52]. Data from the PTT suggested that the knockout mice had significantly higher hepatic gluconeogenesis, which may have contributed to the higher glucose exposure in Oct1-/- mice following the ITT. To understand the role of thiamine in regulating glucose metabolism, age-matched mice were placed on dietary chow containing three different doses of added thiamine, following the experimental design shown in Fig 5A. Wild-type mice fed a TD for 10 days had higher levels of hepatic glycogen, hepatic glucose, and plasma glucose compared to mice fed control diets (Fig 5B–5D). In contrast, varying thiamine content in the diet resulted in no significant differences in hepatic glycogen, hepatic glucose, or plasma glucose levels among Oct1-/- mice (Fig 5B–5D). Furthermore, wild-type mice fed TDs had similar levels of hepatic glycogen, hepatic glucose, and plasma glucose as Oct1-/- mice irrespective of the thiamine content in their diets, consistent with the idea that Oct1 deficiency mimics thiamine deficiency in wild-type mice. Levels of G6P, an activator of GS, were significantly higher in livers from wild-type mice fed a TD diet and were comparable to liver levels of G6P in Oct1-/- mice irrespective of thiamine content in the diet (Fig 5E). As shown by western blotting (Fig 5F), livers from Oct1-/- mice in the control thiamine diet group and from both Oct1+/+ mice and Oct1-/- mice in the TD group had higher GS and Glut2 protein levels compared to Oct1+/+ mice in the thiamine control group. Taken together, our data suggest that thiamine deficiency impairs glucose metabolism in wild-type mice and that Oct1 deficiency phenocopies thiamine deficiency in wild-type mice. Oct1-/- mice exhibited increased adiposity (Fig 2C and 2D), and examination of fat cells through staining revealed significantly larger adipose cells in the epididymal fat pad (epididymal white adipose tissue [eWAT], p = 0. 004) and a trend toward larger adipose cells in retroperitoneal adipose tissue (rpWAT) from Oct1-/- mice (Fig 6A). To probe the mechanism of increasing adiposity and adipose cell size in the Oct1-/- mice, we measured the mRNA expression levels of genes related to adipose metabolism. Fat gain may be due to imbalances between rates of TG synthesis and lipolysis. The mRNA expression of patatin-like phospholipase domain-containing protein 2 (Pnpla2) and lipase, hormone sensitive (Lipe) involved in adipose lipolysis was reduced in adipose tissue from Oct1-/- mice compared to adipose tissue from Oct1+/+ mice (Fig 6B). In contrast, levels of genes involved in TG synthesis were similar between the two strains of mice (S5A Fig). Pnpla2 (coding for adipose triglyceride lipase [ATGL]), Lipe (coding for hormone sensitive lipase [HSL]), and Mgll (coding for monoglyceride lipase [MGLL]) are responsible for three major steps in mobilizing fat through hydrolysis of TGs to release free fatty acids from the adipocytes [53]. Lower expression levels of these genes are consistent with lower rates of lipolysis in adipose tissue from Oct1-/- mice. Insulin has antilipolytic effects in adipose tissue, regulating ATGL expression and promoting lipid synthesis, and chronic insulin treatment results in increased adipose mass [54,55]. Corresponding to the higher levels of glucose (Fig 6C), we observed higher circulating levels of insulin in the Oct1-/- mice (Fig 6D and S5B Fig), which suppressed lipolysis. Furthermore, fasting free fatty acid levels were lower in the plasma of Oct1-/- mice (Fig 6E), which may reflect the lower rates of lipolysis in adipose tissue [56]. Data in Oct1 knockout mice were corroborated by data from inbred strains of mice. In particular, Oct1 mRNA levels in the liver inversely associated with percent fat growth and fat mass among various strains of mice (S1 Table). In addition, down-regulation of mitochondrial uncoupling protein 2 (Ucp2) was observed in brown adipose in Oct1-/- mice (S5G Fig), which may associate with the reduced energy expenditure. Examination of total cholesterol, HDL cholesterol, LDL cholesterol, and TG in plasma samples revealed significant differences in the two strains of mice. Notably, Oct1-/- mice had higher plasma levels of total cholesterol and LDL cholesterol compared to Oct1+/+ mice, without significant differences in TG and HDL cholesterol (Fig 6F). The increase in LDL was due primarily to smaller LDL particles (Fig 6G). We observed no differences in the transcript levels of lipoprotein lipase (Lpl) and Ldlr in livers from Oct1+/+ mice and Oct1-/- mice. However, livers from Oct1-/- mice had higher transcript levels of 3-hydroxy-3-methylglutaryl-CoA reductase (Hmgcr), and Acyl-CoA: cholesterol acyltranferase 2 (Acat2) (S5E Fig). Consistent with lower activity of PDH, pyruvate levels were significantly higher in the livers from Oct1-/- mice, as less pyruvate was converted to acetyl-CoA. Interestingly, contrary to our expectation, Oct1-/- mice had higher levels of acetyl-CoA in their livers (Fig 6H and 6I). The higher accumulated acetyl-CoA may have resulted from higher fatty acid β-oxidation in Oct1-/- mice [17,48]. Our data suggest that up-regulation of enzymes involved in cholesterol synthesis and higher levels of the substrate precursor, acetyl-CoA, in the liver of Oct1-/- mice result in alterations in hepatic cholesterol metabolism, leading to increased production of LDL particles. Furthermore, lower thiamine levels were correlated with higher levels of cholesterol in plasma and liver in male mice from various inbred strains of mice (S2 Table and S6 Fig). Through extensive characterization of Oct1 knockout mice, our data provide compelling evidence that Oct1 deficiency leads to a constellation of diverse effects on energy metabolism that are consistent with GWAS demonstrating strong associations between OCT1 polymorphisms and a variety of metabolic traits in humans (Fig 1 and Table 1). Our data support the notion that hepatic thiamine deficiency is the underlying mechanism for the phenotypes associated with reduced OCT1 function. Five major effects of OCT1 deficiency emerge from the current study: (1) a shift in the pathway of energy production from glucose to fatty acid oxidation due to lower activity of key thiamine-dependent enzymes in the liver; (2) increased gluconeogenesis and hepatic glucose output, with associated increases in liver glycogen and glucose levels; (3) increased peripheral adiposity stemming from alterations in energy metabolism; (4) changes in hepatic cholesterol homeostasis and plasma lipids that may contribute to cardiovascular disease risk; and (5) beneficial effects on life-threatening thiamine deficiency syndromes. As the major energy-generating organ, the liver has high metabolic flexibility in selecting different substrates to use in energy production in response to various metabolic conditions. The glucose–fatty acid cycle, first proposed by Randle in 1963, plays a key role in regulating metabolic fuel selection, and impairment in metabolic flexibility contributes to insulin resistance and metabolic syndrome [17,18,47,57,58]. Many studies have shown that there is a failure to shift from fatty acid to glucose oxidation during the transition from fasting to feeding in individuals with obesity or diabetes [48,57]. We observed lower hepatic steatosis (Fig 2A), largely due to increases in fatty acid oxidation in the liver [1,59,60]. Importantly, the observation that the Oct1-/- mice had significantly lower RERs during the dark cycle than their wild-type counterparts suggests that overall, Oct1-/- mice have a greater reliance on energy production from fatty acids over glucose during feeding than wild-type mice [61,62] (S2J Fig). PDH is the key enzyme switch for the glucose–fatty acid cycle [17,63]. Thus, alterations in its activity by reduced levels of the cofactor TPP in Oct1-/- mice (Fig 4A) disrupt the hepatic glucose–fatty acid cycle, resulting in an impairment of hepatic energy homeostasis. In livers from Oct1 deficient mice, β-oxidation of fatty acids becomes a major source of energy production, leading to impaired homeostasis in both lipid and carbohydrate metabolism. In both the current study and our previous study [14], we observed increased levels of phosphorylated 5′ adenosine monophosphate-activated protein kinase (AMPK) and its downstream target, acetyl-CoA carboxylase (ACC), in livers from Oct1-/- mice compared to livers from wild-type mice, indicative of a lower hepatic energy status in the knockout mice. As a result, fatty acid β-oxidation was stimulated in the livers from Oct1-/- mice. However, as was evident by lower hepatic ATP content, the increased rates of fatty acid oxidation were not sufficient to compensate for normal rates of ATP production. Reduced glucose oxidation as well as a reduction in flux through the TCA cycle due to reduced activity of α-KGDH, another TPP-associated enzyme, may have contributed to the lower ATP production. Consistent with a lower flux through the TCA cycle as well as increases in β-oxidation of fatty acids, we observed higher levels of acetyl-CoA in livers from Oct1-/- mice. Studies have shown that acetyl-CoA allosterically inhibits PDH, which results in further inhibition of glucose utilization [48,58,63]. Thus, in the livers from Oct1-/- mice, this loop continued to stimulate fatty acid β-oxidation, which further suppressed hepatic glucose utilization, shifting the major energy source from glucose to fatty acids. Increases in hepatic glycogen and glucose content in the Oct1-/- mice (Fig 2A, Fig 5B and 5C) were likely due to changes in intermediary metabolites resulting directly from alterations in the activity of the TPP-dependent enzyme, PDH (Fig 4A). Reduced PDH activity resulted in higher levels of pyruvate in the liver of Oct1-/- mice (Fig 6H), which is consistent with results from previous studies [63,64]. Higher pyruvate levels can drive hepatic gluconeogenesis, resulting in increased hepatic glucose production and associated increases in hepatic glucose and glycogen levels [1,52]. Whereas our data suggested that the glycogen accumulation in the livers of Oct1-/- mice resulted from changes in key intermediate metabolites that are involved in hepatic energy metabolism, other regulatory paths such as hormonal, transcriptional, and neural regulation need to be further studied. Oct1-/- mice exhibited increased adiposity (Fig 2), which was more likely due to downstream effects of reduced transporter expression in the liver rather than in extra-hepatic tissues. Multiple factors contributed to the increased adiposity, such as hyperinsulinemia, hyperglycemia, increased hepatic glycogen, and reduced energy expenditure. Consistent with the increased adiposity observed in the Oct1-/- mice, hepatic expression levels of Oct1 are inversely correlated with fat growth and fat mass in inbred strains of mice (S1 Table). In addition, hyperinsulinemia in Oct1 knockout mice may further result in increasing storage of TGs and suppression of lipolysis in peripheral adipose tissue, which reduced flux of fatty acids to the liver. Chronic insulin treatment has been shown to result in increased adipose mass due to suppression of lipolysis and increased lipid storage [54,55], and in the current study, a high correlation between plasma insulin levels and fat mass in both wild-type and Oct1-/- mice (S5D Fig) was observed. Furthermore, high insulin levels have been associated with low expression levels of the lipolytic enzyme Pnpla2 in adipose tissue [55], consistent with results in the Oct1-/- mice (Fig 6B), which is in agreement with the increased adiposity in these mice. In addition, high hepatic glycogen levels may have contributed to the increased adiposity. In particular, hepatic glycogen levels regulate the activation of the liver–brain–adipose axis [65]. Glycogen shortage during fasting triggers liver–brain–adipose neurocircuitry that results in stimulation of fat utilization. In contrast, in mice with elevated liver glycogen resulting from overexpression of GS or knockdown of PYGL, the liver–brain–adipose axis action is turned off, which preserves fat mass [65]. The greater stores of glycogen in the livers of Oct1-/- mice may have shut off the liver–brain–adipose axis, contributing to the increased peripheral adiposity in the mice. Overall, our data in Oct1-/- mice suggest that OCT1 plays a key role in regulation of peripheral metabolism, likely because of its effects on circulating glucose and insulin as well as increased stores of hepatic glycogen triggering a feedback loop mechanism between the liver, the brain, and the adipose tissue. Parallels between phenotypes observed in GWAS in humans and those in the Oct1-/- mice were striking. High plasma LDL, total cholesterol, and TG levels were observed in individuals with reduced-function polymorphisms of OCT1 (R61C, F160L, G401S, V408M, 420del, and G465R) (Table 1) as well as in the Oct1-/- mice (Fig 6F and 6G). In particular, the Oct1-/- mice, and humans with reduced-function polymorphisms of OCT1 (S3 Table) [37], have increased levels of small dense LDL particles (Fig 6G), which in humans are predictive of increased risk of cardiovascular disease [66] and are a characteristic feature of the dyslipidemia associated with excess adiposity [67] and insulin resistance [68]. We speculate that the increased LDL levels in Oct1-/- mice may result from a relative deficiency of hepatic thiamine. Specifically, livers from rats with thiamine deficiency have been shown to have lower TG but higher cholesterol content [69], consistent with our data in inbred strains of mice, in which inverse correlations were observed between plasma thiamine and cholesterol levels in both plasma and liver (S2 Table and S6 Fig). Importantly, our in vivo studies in mice showed that reduction of liver Slc22a1 expression levels resulted in higher systemic thiamine levels (Fig 3B and 3E). Furthermore, individual OCT1 polymorphisms were nominally associated with systemic plasma levels of thiamine in humans (S4 Table) [70] as well as when combining six of the OCT1 nonsynonymous variants that were genotyped in the cohort by Rhee and colleagues (see S5 Table) [71]. Published studies in animals and humans suggest that thiamine supplementation may improve blood lipid profiles [24,25]. Higher levels of the precursor for cholesterol synthesis, acetyl-CoA, as well as higher expression levels of enzymes involved in cholesterol synthesis [59,72] (S5E Fig), potentially leading to greater rates of hepatic cholesterol production, could have contributed to the higher cholesterol and LDL levels in plasma. Although we were unable to detect differences in total hepatic cholesterol content between Oct1+/+ mice and Oct1-/- mice (S5F Fig) corresponding to the observed differences in plasma cholesterol levels between the mouse strains, many factors that can modulate hepatic cholesterol content [73,74], including perhaps increased export, need further investigation. Further studies are warranted to investigate the mechanisms underlying the effects of reduced OCT1 function as well as thiamine bioavailability on cholesterol and lipoprotein metabolism. In addition to the metabolic changes that were observed in Oct1-/- mice, the knockout mice were found to survive substantially longer on TDs. This may have been due to the higher systemic levels of thiamine (Fig 3B), which would spare essential organs such as the brain and heart from thiamine deficiency, as well as to the increased adiposity in the knockout mice (Fig 2C), which could protect the mice from the starvation that ensues from thiamine deficiency [42,75,76]. Nevertheless, the results have implications for human ancestors who harbored reduced-function genetic polymorphisms of OCT1. Because of differences in the tissue distribution of OCT1 between humans and mice, our study has limitations in directly extrapolating all the results obtained in mice to humans. In particular, because Oct1 is also abundantly expressed in the kidney of mice, OCT1-mediated renal secretion of thiamine is another important determinant of systemic thiamine levels in mice. In contrast, in humans, OCT1 plays a role in modulating thiamine disposition largely in the liver and not in the kidney. Deletion of Oct1, particularly in the kidney of the knockout mice, therefore, may have modulated systemic thiamine levels and, thus, survival during TDs as well as other phenotypes observed in the current study. Today, thiamine deficiency is associated with aging, diabetes, alcoholism, and poor nutritional status [23,77–79]. In the setting of thiamine deficiency, OCT1 reduced-function polymorphisms today would have mixed effects. On the one hand, individuals who harbored reduced-function variants would have higher systemic levels of thiamine, which may protect essential organs from thiamine depletion. On the other hand, the individuals would have low hepatic thiamine levels, which may predispose them to the deleterious effects of dysregulated plasma lipids and to obesity and diabetes (Table 1). In fact, lower thiamine levels have been reported in individuals with diabetes [23,78] and, as noted, some studies have shown that high-dose thiamine supplementation has beneficial effects on diabetes [25,80,81]. Overall, the current study shows that OCT1 deficiency triggers a constellation of effects on hepatic and overall energy homeostasis (Scheme in Fig 6J). That is, reduced OCT1-mediated thiamine uptake in the liver leads to reduced levels of TPP and a decreased activity of key TPP-dependent enzymes, notably PDH and α-KGDH. As a result, there is a shift from glucose to fatty acid oxidation, which leads to imbalances in key metabolic intermediates, notably, elevated levels of pyruvate, G6P, and acetyl-CoA. Because of these imbalances, metabolic flux pathways are altered, leading to increased gluconeogenesis and glycogen synthesis in the liver. In addition, the increased acetyl-CoA levels along with elevated expression levels of key enzymes involved in cholesterol synthesis likely contribute to increases in plasma levels of total and LDL cholesterol observed in mice with Oct1 deficiency and in humans with reduced-function genetic polymorphisms of OCT1. Although many of the details of the mechanisms have still to be worked out, our study provides critical insights into the role of thiamine in the liver in maintaining metabolic balance among energy metabolism pathways. Finally, our studies provide mechanistic insights into findings from GWAS implicating reduced-function variants in the SLC22A1 locus as risk factors for lipid disorders and diabetes. Animal experiments were approved by the Institutional Animal Care and Use Committee (IACUC) of University of California, San Francisco (AN119364), in accordance with the requirements of the National Research Council Guide for the Care and Use of Laboratory Animals and the Public Health Service Policy on the Humane Care and Use of Laboratory Animals. Humane end points were determined by body condition score of 2 or less or 15% body weight loss. Animals were euthanized once the humane end points were reached during the treatment in accordance with IACUC approved protocol. To limit pain and stress, mice were anesthetized deeply by isoflurane vaporizer and intraperitoneal injection of ketamine/medetomidine cocktail (75/1 mg/kg) prior to the physical cervical dislocation of euthanasia. Various publicly available databases were used to determine whether there are significant genetic associations of SLC22A1 reduced-function variants with human diseases and traits. The following databases were used: GWAS Catalog, dbGAP Association Results Browser, GRASP: Genome-Wide Repository of Associations Between SNPs [82], GIANT Consortium, Type 2 Diabetes Knowledge Portal, and Phenotypes and Genome Wide Associations Studies for Lipid Genetics. The first three databases, GWAS Catalog, dbGAP Association Results Browser, and GRASP, provide an easy to use interface to allow first-step information gathering of the types of human diseases and traits that have been reported in all published GWAS. Based on the results from the three databases, other specific databases relevant to the findings were then used. This includes searching for specific databases that have the GWAS summary statistics (beta coefficient and p-values). These are GIANT Consortium for body weight, Type 2 Diabetes Knowledge Portal for glucose and insulin traits, and all GWAS for lipid traits. These databases allow investigators to download the association studies for obtaining the p-values and the beta coefficients for the associations. In this study, we focused our search on nonsynonymous variants of OCT1 (SLC22A1) with minor allele frequencies ≥1% in populations with European ancestries (1000 Genome Project): R61C (rs12208357), F160L (rs683369), P341L (rs2282143), G401S (rs34130495), M408V (rs628031), 420Del (rs202220802) (rs662138 and rs1564348, which are in linkage disequilibrium to 420Del with r2 ≥ 0. 77, D′ > 0. 95), and G465R (rs34059508). All experiments on mice were approved by the IACUC of UCSF. Oct1-/- mice were generated as previously described [83] and backcrossed more than 10 generations to FVB/N background. Mice were housed in a pathogen-free facility with a 12-hour light and 12-hour dark cycle and given free access to food and water. Five- or six-week-old experimental mice were fed with thiamine control diet Cat# TD. 09549 (thiamine 5 mg/kg) containing 17. 5% protein, 65. 8% carbohydrate, and 5. 0% fat by weight (Envigo, Madison, WI). Other thiamine diets contained the same composition as the thiamine control diet but differed in thiamine levels (TD Cat# TD. 81029,0 mg/kg; adjusted thiamine diet with different thiamine doses added, Cat# TD. 120472,25 mg/kg; Cat# TD. 140164,50 mg/kg). The periods of dietary treatments and time for mouse being humanely killed are indicated in the Results section and figure legends. The animal studies were conducted in male mice; however, overall body weight and liver weight were assessed in female mice. Mice treated with TD developed thiamine deficiency syndromes, resulting in reduction of food intake and body weight loss. During the treatment period, mice were closely monitored and weighed daily. Animals were euthanized once the humane end points (body condition score of 2 or less or 15% body weight loss) were reached during the treatment. To limit pain and stress, mice were anesthetized deeply by isoflurane vaporizer and intraperitoneal injection of ketamine/medetomidine cocktail (75/1 mg/kg) prior to the physical cervical dislocation of euthanasia. Hydrodynamic tail vein injection procedure was conducted as described previously [84], with minor modifications. Briefly, the body weights of the mice were used to calculate the total volume (mL) required for injection based on the formula: body weight (g) * (mL/10g) + 0. 1 mL (dead volume). The injection solution included 5*10^7 TU virus/mouse and saline to just the final volume. Instead of anesthetizing the mice, we used TransIT-QR kit MIR5210 (Mirus Bio LLC, US) and followed the online protocol (https: //www. mirusbio. com/delivery/tailvein/). Dosing of 3H-thiamine was performed 48 hours after hydrodynamic tail vein injection. Mouse Slc22A1 shRNA lentiviral particle (TRCN0000070156) and nonmammalian shRNA control pLKO. 1 (SHC002V) were purchased from Sigma. Viruses were verified in HEK-293 cells. Briefly, pcDNA5 containing mouse Slc22a1 was cotransfected with the Slc22a1 knockdown vector or pLKO. 1 control virus. mRNA was isolated after 48 hours transduction, and mRNA expression of Oct1 was measured. Before and during dietary treatment, body composition was determined by either quantitative magnetic resonance on the EchoMRI-3in1 body composition analyzer (EchoMRI, Houston, TX) or by DEXA. For DEXA, live animals were anesthetized with isoflurane and scanned on the Lunar PIXImus densitometer (Lunar PIXImus Corporation Headquarters, Madison, WI). After 8 weeks diet treatment, mice were placed in single housing cages for 3 days before initiating the CLAMS (Columbus Instruments, Columbus, OH) experiments. CLAMS was used to monitor food and water intake, oxygen consumption (VO2) and carbon dioxide production (VCO2), and locomotor activities for a period of 96 hours. All these experiments were performed in the Diabetes and Endocrinology Research Center Mouse Metabolism Core at UCSF. Blood glucose levels from mice were measured using the FreeStyle Freedom Lite blood glucose meter (Abbott Laboratories, Chicago, IL) in samples obtained by the tail milking method. For oral glucose tolerance tests (OGTTs), mice were fasted for 5 hours and dosed with glucose 2 g/kg (Sigma-Aldrich, St. Louis, MO) by oral gavage. For ITTs, mice were fasted for 5 hours and dosed with 0. 75 U/kg humulin R insulin 100 U/ml (Henry Schein Animal Health, Dublin, OH) by intraperitoneal injection. Blood was sampled at 0,15,30,60,90, and 120 minutes. For PTTs, mice were fasted overnight for 16 hours and dosed with pyruvate 2 g/kg (Sigma-Aldrich, St. Louis, MO) by intraperitoneal injection. Blood was sampled at 0,15,30,60,90,120,150, and 180 minutes. For adipose tissues and liver glycogen staining, mice were fasted for 16 hours and perfused with 20 mL 4% paraformaldehyde (PFA) in PBS. Epididymal fat pad, rpWATs, and liver were incubated in 4% PFA for 48 hours at 4°C and transferred to 70% ethanol. For Oil Red-O (ORO) staining in liver, mice were fasted for 16 hours and perfused with 10 mL PBS. Livers were fixed via sucrose infiltration steps prior to freezing. After incubating in 30% sucrose in PBS at 4°C for 24 hours, tissues were frozen in OCT molds. Fixed or frozen tissues were transferred to the histology and light microscopy core at Gladstone Institutes for staining, imaging, and analysis. For the cell-size analysis, hematoxylin and eosin-stained paraffin-embedded sections (https: //labs. gladstone. org/histology/pages/section-staining-haematoxylin-and-eosin-staining) of mouse adipose tissues were imaged using a Nikon Eclipse E600 upright microscope equipped with a Retiga camera (QImaging, Vancouver, BC, Canada) and a Plan Fluor 20×/0. 3NA objective. For each sample, four independent fields were imaged for analysis and adipocyte size was determined using ImageJ (v. 2. 0. 0-rc-3) software (US National Institutes of Health) and the Tissue Cell Geometry macro (http: //adm. irbbarcelona. org/image-j-fiji). For quantifying the lipid droplets, ORO stained frozen sections of mouse liver were imaged as above. For each sample, four independent fields were imaged for analysis. RGB images were then color thresholded to ORO, and the total area of ORO-positive pixels was summed for each image using the Analyze Particles function. For quantifying the glycogen levels in liver sections, Periodic-Acid Schiff’s stained mouse liver was imaged as above. For each sample, four independent fields were imaged and the mean intensity and integrated density values were averaged for each image using the Analyze Particles function. Mice were humanely killed and blood was collected via posterior vena cava to BD microtainer tubes with dipotassium EDTA (365974) or heparin (365985). Plasma was sent to the Clinical Laboratory of San Francisco General Hospital for measurement of total, LDL, and HDL cholesterol and TGs and liver panel. Plasma was send to Children' s Hospital Oakland Research Institute for the measurement of lipoprotein particles size, as described previously [85]. Glucose (GAGO20), glycogen (MAK016), free fatty acid (MAK044), acetyl-Coenzyme A (MAK039), and G6P (MAK014) quantification kits were purchase from Sigma-Aldrich (St. Louis, MO). Pyruvate (ab65342), cholesterol (ab102515), and TG (ab65336) quantification kits were purchase from Abcam (Cambridge, MA). Plasma insulin was measure by ELISA (EMINS) from Thermo Fisher Scientific (Waltham, MA). Plasma was sent to Molecular MS Diagnostics, Inc. (Warwick, RI), for thiamine quantification, as previously described [14]. Tissues were homogenized in CelLytic MT lysis buffer (Sigma-Aldrich, St. Louis, MO) with cOmplete ULTRA protease inhibitor tablet and PhosSTOP phosphatase inhibitor tablet freshly added (Roche). After normalization, equal protein amounts from each sample were loaded in to 4%–20% criterion Tris-HCl gel (Bio-Rad, Hercules, CA) and run at 110 V. Protein was transferred to EMD milllipore immobilon PVDF membranes at 100 V for 1 hour at 4°C using the criterion blotter. GS (15B1) (#3886; 1: 1,000 dilution), phospho-glycogen synthase (Ser641) (#3891,1: 1,000 dilution), phospho-acetyl-CoA carboxylase (Ser79) (#3661; 1: 2,000 dilution), AMPKα (23A3) (#2603,1: 1,000 dilution), and phospho-AMPKα (Thr172) (#2535; 1: 1,500 dilution) were purchased from Cell Signaling Technology (Danvers, MA). Anti-PDH-E1α (pSer232) (#AP1063; 1: 2,000 dilution) and Anti-PDH-E1α (pSer300) (#AP1064; 1: 2,000 dilution) were purchased from EMD Millipore (Billerica, MA). PDH-E1α (D-6) (#sc-377092; 1: 200 dilution) and β-actin (C4) (#sc-47778; 1: 4,000 dilution) were purchased from Santa Cruz Biotechnology, Inc. (Dallas, TX). Anti-PYGL (#ab198268; 1: 1,000 dilution) was purchased from Abcam (Cambridge, MA). Anti-Glut2 (#600-401-GN3; 1: 1,000 dilution) was purchased from Rockland (Limerick, PA). Primary antibodies were incubated overnight at 4°C. Secondary antibodies, goat anti-rabbit IgG-HRP (#sc-2030; 1: 5,000; Santa Cruz Biotechnology, Inc.), and anti-mouse IgG-HRP (#7076; 1: 5,000; Cell Signaling Technology) were incubated for 2 hours at room temperature. Either Amersham ECL western blotting detection reagents (RPN2106) or ECL Prime western blotting detection reagents (RPN2232) were used for detection. Membranes were imaged by ProteinSimple western blot imaging system (San Jose, CA). For the quantification of western blot bands, the ImageJ (US National Institutes of Health) method was used. Total RNA from mouse tissues or cell lines was isolated using RNeasy Mini kit (Qiagen, Valencia, CA). Total RNA (2 μg) from each sample was reverse transcribed into cDNA using SuperScript VILO cDNA Synthesis kit (Life Technologies, CA). Quantitative real-time PCR was carried out in 384-well reaction plates using 2X Taqman Fast Universal Master Mix (Applied Biosystems, Foster City, CA), 20X Taqman specific gene expression probes, and 10 ng of the cDNA template. The reactions were carried out on an Applied Biosystems 7900HT Fast Real-Time PCR System (Applied Biosystems, Foster City, CA). The relative expression level of each mRNA transcript was calculated by the comparative method (ΔΔCt method), normalized to the housekeeping gene, β-actin. The stably overexpressing pcDNA5 empty vector, mouse OCT1, human OCT1-reference, OCT1-420 del, and OCT1-420 del+G465R cell lines were maintained in Dulbecco’s Modified Eagle Medium (DMEM H-21) supplemented with hygromycin B (100 ug/mL) (Thermo Fisher Scientific, Waltham, MA), penicillin (100 U/mL), streptomycin (100 mg/mL), and 10% fetal bovine serum. Cell culture supplies were purchased from the Cell Culture Facility (UCSF, CA). Cells were cultured on poly-D-lysine coated 96-well plates for 24 hours to reach 95% confluence. Before the uptake experiments, the culture medium was removed and the cells were incubated in Hank’s balanced salt solution (HBSS) (Life Technology, CA) for 15 minutes at 37°C. Radiolabeled thiamine [3H (G) ] hydrochloride (20 Ci/mmol) was purchased from American Radiolabeled Chemicals Incorporation (St. Louis, MO). Thiamine hydrochloride was purchased from Sigma-Aldrich (St. Louis, MO). Chemicals and radiolabeled compounds were diluted in the HBSS for uptake experiments. The details for drug concentrations and uptake time are described in the Results section and figure legends. The uptake was performed at 37°C, and then the cells were washed three times with ice-cold HBSS. After that, the cells were lysed with buffer containing 0. 1 N NaOH and 0. 1% SDS, and the radioactivity in the lysate was determined by liquid scintillation counting. For the transporter study, the Km and Vmax were calculated by fitting the data to a Michaelis-Menten equation using GraphPad Prism software 6. 0 (La Jolla, CA). All mice were randomly assigned to the control or each treatment group. No statistical method was used to predetermine sample size, and sample size was determined on the basis of previous experiments. Numbers of mice for each experiment are indicated in figure legends. Mice that were dead or sick before the end of experiments were excluded from the final analysis. Investigators were not blinded during experiments. Data were expressed as mean ± SEM. Appropriate statistical analyses were applied, as specified in the figure legends. Data were analyzed using GraphPad Prism software 6. 0 (La Jolla, CA). Differences were considered statistically significant at p < 0. 05; *p < 0. 05, **p < 0. 01, and ***p < 0. 001.

The liver is the major organ for glucose and lipid metabolism; impairment in liver energy metabolism is often found in metabolic disorders. Traditionally, excesses in macronutrients (fat and glucose) are linked to the development of metabolic disorders. Our study provides evidence that imbalances in a micronutrient, vitamin B1 (thiamine), can serve as an etiological cause of lipid and glucose disorders and implicates the organic cation transporter, OCT1, in these disorders. OCT1 is a key determinant of thiamine levels in the liver. In humans, reduced-function polymorphisms of OCT1 significantly associate with high LDL cholesterol levels. Using Oct1 knockout mice, we show that reduced OCT1-mediated thiamine uptake in the liver leads to reduced levels of TPP—the active metabolite of thiamine—and decreased activity of key TPP-dependent enzymes. As a result, a shift from glucose to fatty acid oxidation occurs, leading to imbalances in key metabolic intermediates, alterations in metabolic flux pathways, and disruptions of various metabolic regulatory mechanisms. The extensive characterization of Oct1 knockout mice provides evidence for the molecular mechanisms responsible for various metabolic traits and indicates an important role for imbalances in micronutrients in cardiometabolic disorders.

Abstract Introduction Results Discussion Materials and methods

b vitamins carbohydrate metabolism genome-wide association studies medicine and health sciences body fluids chemical compounds carbohydrates glucose metabolism organic compounds glucose glycobiology genome analysis lipids genomics chemistry vitamins blood plasma cholesterol biochemistry blood organic chemistry anatomy glycogens physiology monosaccharides genetics biology and life sciences physical sciences computational biology fatty acids metabolism thiamine human genetics

2018

Organic cation transporter 1 (OCT1) modulates multiple cardiometabolic traits through effects on hepatic thiamine content

The peptide repertoire that is presented by the set of HLA class I molecules of an individual is formed by the different players of the antigen processing pathway and the stringent binding environment of the HLA class I molecules. Peptide elution studies have shown that only a subset of the human proteome is sampled by the antigen processing machinery and represented on the cell surface. In our study, we quantified the role of each factor relevant in shaping the HLA class I peptide repertoire by combining peptide elution data, in silico predictions of antigen processing and presentation, and data on gene expression and protein abundance. Our results indicate that gene expression level, protein abundance, and rate of potential binding peptides per protein have a clear impact on sampling probability. Furthermore, once a protein is available for the antigen processing machinery in sufficient amounts, C-terminal processing efficiency and binding affinity to the HLA class I molecule determine the identity of the presented peptides. Having studied the impact of each of these factors separately, we subsequently combined all factors in a logistic regression model in order to quantify their relative impact. This model demonstrated the superiority of protein abundance over gene expression level in predicting sampling probability. Being able to discriminate between sampled and non-sampled proteins to a significant degree, our approach can potentially be used to predict the sampling probability of self proteins and of pathogen-derived proteins, which is of importance for the identification of autoimmune antigens and vaccination targets. Major histocompatibility complex (MHC) class I molecules play a crucial role in the adaptive immune response of higher vertebrates. These molecules, in humans referred to as human leukocyte antigen (HLA) class I molecules, bind peptides derived from endogenous proteins of host or, in the case of infected cells, of pathogen origin and present them to circulating CD8+ T lymphocytes and natural killer (NK) cells. The presentation of self peptides by an individual' s HLA class I molecules has an impact on positive and negative selection of CD8+ T lymphocytes in the thymus [1], [2], maintenance of naive T cells in the periphery [3], [4], and inhibition of NK cells through recognition of self peptides in the context of HLA class I molecules by killer cell immunoglobulin-like receptors (KIR) [5]. Generally, HLA class I ligands are derived from intracellular proteins, which are degraded by the proteasome into peptide fragments. These peptides are then translocated by the transporter associated with antigen processing (TAP) into the lumen of the endoplasmic reticulum (ER), where they may be loaded onto an HLA molecule if the peptide sequence fits the HLA molecule' s binding preference. The C-terminus of an HLA ligand is assumed to be mainly determined by the proteasome (even though recently a carboxypeptidase has been found to contribute to C-terminal editing [6]), whereas the N-terminus may be trimmed by cytosolic and endoplasmic aminopeptidases after proteasomal cleavage [7]. Finally, the HLA-peptide complexes are transported to the cell surface for presentation to CD8+ T cells and NK cells. Several studies analyzed peptide data sets obtained by peptide elution from specific cell lines and peptide sequencing by mass spectrometry to characterize the HLA peptide repertoire [8], [9], [10], [11]. Most of these studies focused on characterizing the function and subcellular localization of source proteins and suggested that HLA class I presented peptides are sampled from functionally and compartmentally diverse proteins, with a functional bias towards RNA-binding proteins [8]. In human cells, a weak correlation has been found between the abundance of HLA class I ligands presented and the corresponding mRNA levels [10], [11], whereas peptides eluted from murine thymocytes were preferentially derived from highly abundant mRNAs [12]. Here, we take a different angle to the question of what fraction of the human proteome is represented on the cell surface. We studied two large HLA ligand data sets obtained by peptide elution [13], [14] with the aim to quantify the role of several factors shaping the peptide repertoire of HLA class I molecules. We show that the gene expression level, protein abundance, rate of potential binding peptides in a protein and the processing quality of these peptides all contribute to which proteins are sampled and which peptides are chosen to be presented on the cell surface. Having studied the impact of each of these factors separately, we subsequently combined all factors in a logistic regression model in order to quantify their relative impact. This model can potentially be used to predict the sampling probability of self proteins and of pathogen-derived proteins. We studied two different peptide elution data sets. One large set, which we will call the Johnson data, comprises 4717 human peptides and 105 vaccinia peptides eluted from vaccinia infected cells [13]. A second set of eluted peptides, the Ben Dror data, comprises 569 human peptides eluted from soluble HLA-B*27: 05 (see Materials and Methods for details) [14]. By mapping each eluted peptide to the human proteome, we were able to uniquely identify the source protein for 90% (4243 of 4717) of the Johnson data and 81. 9% (466 of 569) of the Ben Dror data. Peptides that mapped to several human proteins (about 9%) or for which no source protein could be identified (only 0. 4–1. 1%) were excluded from further analysis. The cell line used for the generation of the Johnson data was homozygous for HLA-A*02: 01, B*15: 01 and C*03. In order to assess which of these three HLA molecules each of the reported peptides was eluted from, we employed NetMHC 3. 2 [15], [16], a tool for HLA-peptide binding prediction. This tool is applicable for peptides of 8 to 13 amino acids in length [15]. Of all eluted peptides of appropriate length and that could be mapped uniquely to a human source protein (4113 of 4243 peptides), we were able to assign 86. 4% (3552 of 4113) to either being eluted from A*02: 01 or B*15: 01. The remaining 561 peptides could potentially have been eluted from C*03. Binding predictions, however, suggested C*03 binding only for a minor fraction of these (28% for all peptide lengths and 37% for 9mers), and therefore we decided to exclude these peptides from further analysis. Surprisingly, we identified twice as many potential B*15: 01 binders as A*02: 01 binders, originating from a larger number of source proteins (Fig. 1). This observation is in agreement with the estimation given by the original study [13], in which the assignment to the restricting HLA molecule was solely determined based on the C-terminal residues of the eluted peptides. Likewise, among the vaccinia-derived peptides, a larger number of peptides (1. 5-fold) were eluted from B*15: 01 than from A*02: 01, mapping to a larger number of vaccinia proteins, even though in this case the difference was less pronounced (Fig. 1). Of the 569 eluted peptides in the Ben Dror data, 466 peptides (81. 9%) mapped uniquely to 396 human source proteins. Among these peptides, 420 (90. 1%) were predicted to bind to HLA-B*27: 05, the soluble HLA molecule expressed by the cells studied. For all three HLA alleles studied, the majority of sampled proteins were represented by only a single peptide: 86. 9% (457 of 526) of the proteins sampled by A*02: 01,76. 1% (981 of 1289) of the ones sampled by B*15: 01, and 86. 4% (342 of 396) of the proteins sampled by B*27: 05 gave rise to only one eluted peptide. In total, the two elution data sets had 160 source proteins in common. GO-term enrichment analysis (see Materials and Methods) revealed that for this set of proteins biological processes relating to the cell cycle and its regulation as well as nucleic acid metabolic processes were overrepresented. The observation that a protein is represented on the cell surface by one or more peptides allows the assumption that the protein must have been available in sufficient amounts or must have been present at an accessible subcellular location to be available for the antigen processing machinery. What factors then determine which of the potential HLA binders of a given protein will be found on the cell surface? In order to characterize the obtained peptide set, we employed prediction methods for HLA binding and antigen processing (see Materials and Methods). For the identified source proteins, we predicted all potential (9mer) binders to HLA-A*02: 01, B*15: 01, and B*27: 05 and compared the predicted binding affinity of the eluted peptides (which form a subset of all potential binders) with the predicted binders from the same source protein that were not found in the elution. To ensure an unbiased comparison, the set of eluted peptides was limited to 9mers that were predicted to bind to the respective HLA molecule. We found that eluted peptides bind their HLA molecule with a significantly higher (predicted) binding affinity than other potential binders (Fig. 2A). However, not all predicted high-affinity binders were found in the elution. In order to investigate whether this observation may be due to inefficient processing of these peptides, we predicted the probability of C-terminal processing (using NetChop [17]) for all eluted peptides and all predicted binders that were not found in the elution data set. Based on these predictions, the set of eluted peptides is indeed more likely to arise from antigen processing as compared to the set of predicted binders (Fig. 2B). Possibly due to co-evolution between HLA class I molecules and the immunoproteasome [18], predicted binding affinity and C-terminal processing probability show a weak (but significant) correlation (Kendall' s tau = −0. 065, p-value<0. 0001). Therefore, we investigated the effect of processing without the influence of HLA binding by comparing the eluted peptide set to an affinity-matched subset of predicted binders to ensure that eluted and predicted peptides show the same distribution of binding affinities (Fig. S1A). Also for this subset of predicted binders we observed a significantly lower C-terminal processing probability (Fig. S1B). NetChop was trained on the C-termini of known HLA ligands and therefore predicts the combined effect of proteasome and TAP. Investigating the impact of these two processes separately (by employing prediction methods that are part of mhc-pathway [19], [20]) suggested that the C-termini of eluted peptides are more likely to be produced by the immunoproteasome and that these peptides are more efficiently transported by TAP (Fig. S2). For non-self peptides, we observed the same characteristics. Eluted peptides that originated from vaccinia proteins showed a significantly higher binding affinity to the respective HLA allotype than other potential binders derived from the same set of proteins (Fig. S3A). A difference in predicted C-terminal processing probability between eluted and other peptides was, however, only found for A*02: 01-binding vaccinia peptides (Fig. S3B). Interestingly, we did not observe a difference in predicted HLA binding affinity between the eluted peptides that originate from human proteins and vaccinia proteins (results not shown). This is in line with an earlier study, which showed that the HLA alleles analyzed here do not show a preference for presentation of non-self over self peptides, while others, foremost HLA-A alleles, do [21], [22]. After having investigated what factors determine which peptides of a given protein are chosen to be presented, we turned to investigate which features of a protein impact protein sampling itself. In other words, why are some proteins sampled while others are not? Previous studies have shown that proteins giving rise to HLA ligands are foremost intracellular, distributed over various intracellular compartments with a slight bias towards the cytosol [8], [23]. Predicting the subcellular localization of each of the sampled proteins in the two data sets of our study, we found similar results: Overall, the distribution of cellular compartments for the sets of source proteins significantly differed from the distribution for the complete human proteome (Fig. S4), and specifically, extracellular proteins were significantly underrepresented in both elution data sets (p<1e-09, Chi-squared test), while proteins resident in the cytosol were overrepresented (p<2e-05). In addition, we tested several protein characteristics for their ability to discriminate source proteins from proteins that were not sampled by the antigen processing pathway. For all three HLA allotypes studied, source proteins are longer, more abundant, and the corresponding genes are more highly expressed (Fig. 3). These factors, however, are not independent. As expected, gene expression level and protein abundance are moderately correlated (Spearman' s rho = 0. 3, p-value<2e-16). Additionally, we noticed that protein length and abundance are inversely correlated to each other, with shorter proteins being more abundant (rho = −0. 41, p-value<2e-16). Since we found that sampled proteins were longer but at the same time more abundant, correcting for protein length (by choosing a random subset of non-sampled proteins with the same length distribution as the set of sampled proteins) enhanced the difference in protein abundance even (Fig. S5). In addition, proteins that were sampled in both elution studies (n = 160) were found to be more abundant than source proteins that emerged only in one of the data sets (median abundance = 17. 54 ppm (parts per million, see Materials and Methods) compared to 3. 15 ppm, p = 1e-10). Moreover, we found a significantly higher rate of predicted binders (in the following referred to as the “predicted hit rate”) in sampled proteins, most pronounced for A*02: 01-specific source proteins (median hit rate = 0. 3 for sampled proteins vs. 0. 025 for non-sampled proteins, p = 7e-14). Interestingly, within the same cell line, proteins that were sampled only by B*15: 01 show a significantly lower predicted hit rate for A*02: 01 than proteins that have been sampled by A*02: 01 (median hit rate = 0. 030 vs. 0. 026, p = 7e-13), further emphasizing that the relative number of potential binding peptides does have an influence on sampling probability. We do not have abundance data for vaccinia proteins available, but Assarsson et al. [24] measured vaccinia gene expression at several time points after infection. For each time point, we found a positive correlation between the gene expression level and the sampling frequency of the proteins when comparing single-sampled and multiple-sampled source proteins with those that did not give rise to eluted peptides (Fig. S6). Additionally, as for human proteins, sampled vaccinia proteins are longer than the remaining vaccinia proteins (p<0. 01, data not shown). Overall, all tested factors - protein length, gene expression level, protein abundance, and predicted hit rate - show differences between the set of sampled proteins and the proteins that were not sampled. In order to quantify the contribution of each factor and to determine which combination of factors best describes the data, we performed a multiple logistic regression analysis. Starting from a maximal model including all factors as explanatory variables, we obtained a minimal model by iterative exclusion of non-significant factors. Before running the regression, we first randomly picked a subset of non-sampled proteins to form a “negative” set of equal size as the positive set of sampled proteins. This balanced set of negative and positive data points was then used to perform a logistic regression and performance analysis (see Materials and Methods), which was repeated 100 times with different random negative subsets. The performance was measured as the Spearman correlation coefficient between the known sampling status (i. e. , a binary value) and the predicted sampling probability. For all three HLA allotypes, a regression model combining protein abundance, protein length and predicted hit rate showed the best performance (the best examples are given in Fig. 4A–C). Since we found that eluted peptides are more efficiently processed than other HLA-binding peptides, we tested whether we could improve the model by filtering the set of predicted binders for processing efficiency. For all three HLA allotypes, this filtering step improved the prediction performance only to a minor extent (results not shown). As gene expression and protein abundance are moderately correlated, we tested which of these two factors would carry more information for predicting protein sampling. We found that protein abundance clearly outcompetes gene expression (Fig. 5). Among the three HLA allotypes, the prediction performance of the B*27: 05 model was best (Fig. 4C), with an average AUC (area under the receiver operating characteristic curve [25]) value of 0. 74 compared to 0. 70 for A*02: 01 and 0. 68 for B*15: 01 (Fig. 4D). Overall, the resulting logistic regression models were able to discriminate between sampled and non-sampled proteins to a significant degree (Fig. 4A–C). Only a small fraction of the human proteome is sampled by the class I antigen processing pathway and presented on HLA class I molecules. Previously it was suggested that the cellular localization of a protein and its function play a role in this sampling process [8], [23]. Here we show that other protein characteristics like protein length, abundance, and rate of predicted binders also largely influence the sampling probability of a protein and thereby shape the peptide repertoire of an HLA class I molecule. We analyzed two large peptide elution datasets; one derived from a vaccinia virus infected cell line, and one obtained from cells transfected with a gene encoding a soluble HLA class I molecule. Identification of the source protein of each peptide showed that, in spite of the huge difference in proteome size between human and vaccinia virus, a similar fraction of either proteome (10–12% of all proteins) was sampled by the antigen processing pathway. We characterized the set of eluted peptides in terms of antigen processing and presentation and observed a significantly higher binding affinity to the respective HLA molecule and more efficient processing for the eluted peptides than for other potential binders derived from the same set of source proteins. The predicted median affinity of eluted peptides was 14 nM IC50 for A*02: 01,65 nM IC50 for B*15: 01, and 107 nM IC50 for B*27: 05. These values are much lower than the 500 nM, which are often used as a threshold to discriminate HLA-binding peptides from non-binders [26]. This observation could reflect that high-affinity binders are preferentially loaded onto the HLA molecule among others with the help of the ER resident chaperone tapasin [27], [28], or that they have a longer “life span” on the cell surface because they form a more stable complex with the HLA molecule, which increases their chance of being eluted (even though this is rather related to the off-rate of a peptide than to its affinity). Especially for the elution studies involving soluble HLA molecules, it is not surprising to identify foremost high-affinity binders after the long affinity purification process [14]. A higher binding affinity of eluted peptides has also been found for mouse MHC class I molecules [12]. If high-affinity peptides are able to outcompete lower affinity-peptides in binding to the HLA, this may result in a higher copy number of these peptides on the cell surface which in turn increases their chance to be detected by mass spectrometry. The nature of the data sets we analyzed does not allow us to study this because we do not have abundance data on the peptides. Instead we merely know whether a peptide was present in the eluate or not. To our surprise, most proteins were represented by only a single peptide in the elution data sets we studied. This is in line with the observation by Hickman et al. [8] who found only 9 of 189 source proteins (4. 8%) to be represented by more than one peptide. For the prediction of C-terminal processing, we employed a method that has been trained on the C-termini of known HLA ligands [17]. Initially intended as a predictor of proteasomal cleavage, the method automatically accounts for the contribution of other potential peptidases that are able to further process the carboxy terminus of proteasome products, as for example the carboxypeptidase ACE [6]. It does, however, not account for the activity of aminopeptidases in the cytosol and ER, which may further trim the amino termini of peptides. There is some evidence for the existence of N-terminal processing motives, which differ in specificity between cytosol and ER [29], [30]. However, the lack of appropriate prediction methods prevented us from assessing the effect of N-terminal trimming of peptides in our analysis. For the data sets we studied, we observed that (i) source proteins are longer and more abundant than non-source proteins, (ii) the corresponding genes show higher expression levels, and (iii) source proteins show a higher rate of predicted binders than proteins that were not sampled. We combined these factors in a logistic regression model and conclude that prediction of protein sampling probability is possible to some degree. The best model made use of protein length, abundance, and predicted hit rate to predict the sampling probability of a protein. Fortier et al. [12] observed that MHC-presented peptides are preferentially derived from highly abundant mRNAs. Our analysis confirmed the impact of gene expression reported earlier by Fortier et al. , but in addition, our results suggest that protein abundance carries more information for the prediction of protein sampling than transcript levels do. It has been argued that antigen processing should be correlated with protein turnover rather than cellular abundance of proteins [10]. In addition, a recent study suggested that the pioneer round of mRNA translation, which serves as a “proof-reading” step during mRNA maturation, might be a major source of HLA ligands [31]. We believe that the model presented in this paper will improve considerably when more data is available describing the specificity and kinetics of peptide generation via these processes. Finally, another source of antigenic peptides are so-called defective ribosomal products (DRiPs), which are truncated and/or misfolded polypeptides that are directly targeted to proteasomal degradation [32], [33]. The DRiP hypothesis suggests that the set of MHC-presented peptides reflects recent protein synthesis rather than the protein content of the cell, which should manifest itself in our analysis as a higher correlation with gene expression level than with protein abundance. Even though this is not what we see, the fact that HLA ligands are preferentially derived from long proteins is in accordance with the DRiP hypothesis, because the chance of incorporating errors and of misfolding increases with protein length. A limitation of our analysis is the presumably high noise in the protein abundance and gene expression data. The abundance data was derived through meta-analysis from a multitude of different tissue types, even though there is considerable variation of protein abundance between cells and tissues. However, as Weiss et al. [34] report, the abundance data set consists mainly of house-keeping genes whose tissue-to-tissue expression variability is limited. Ideally, the analysis presented here should be repeated on a data set where mRNA levels, protein abundance, and HLA peptide presentation are measured simultaneously for a single cell type or tissue sample to minimize noise. All the more striking it is, however, that we see a clear signal for both gene expression and protein abundance in their impact on protein sampling in spite of the noise introduced by averaging over cell types. In conclusion, the results presented in this paper demonstrate that protein characteristics such as gene expression level, protein abundance, and the rate of HLA ligands determine which protein will be sampled for antigen presentation. Moreover, our results suggest that sampling prediction may be extended to the proteomes of pathogens, allowing us to identify promising targets for vaccination studies. Johnson et al. [13] performed peptide elution and mass-spectrometry analysis of vaccinia virus infected Epstein-Barr virus-transformed B-cells, homozygous for HLA-A*02: 01, B*15: 01, and C*03 (for details see [13]). With a false positive rate (FPR) of 5%, they identified 4717 unique human-derived peptides and 119 vaccinia derived peptides. Ben Dror et al. [14] eluted peptides from cultured cartilage cells and HeLa cells transfected with a soluble form of HLA-B*27: 05. Based on several criteria to assess the confidence in identified peptides, they categorized eluted peptides into three subsets: certain (569 peptides), probable (582 peptides), and possible (116 peptides). As the certain peptide set corresponds to a FPR of 4. 7%, we limited our analysis to this data set. Of note, in the original publication, peptides were selected as correct only if they contained the amino acids arginine or glutamine at their second position [14], which according to the authors (personal communication) was necessary in order to filter out peptides that were eluted from other HLA allotypes expressed by the cell line (which may become soluble due to cellular stress). We obtained the human proteome from Ensembl Genomes (ftp. ensembl. org/pub/, release 56) and used this collection of proteins to identify the source protein for each peptide in our elution data sets. Source protein identification required identical mapping of a peptide to the source protein sequence. Peptides that could not be uniquely mapped to one single protein were omitted from further analysis. In the case of several splice variants of the same protein (i. e. , the peptide matched to several protein sequences which all originate from the same gene), the longest splice variant was chosen for sequence analysis. Likewise, the longest splice variant was chosen for the set of non-sampled proteins. We were able to map 105 of the vaccinia peptides to the Vaccinia Western Reserve proteome (GenBank identifier: AY243312). We had abundance data available for 12,021 human proteins [34]. The abundance is expressed in parts per million (ppm), relative to the molecule counts of all other proteins in the proteome. The measured abundance for different proteins spans several orders of magnitude. The protein abundance data covers 1986 (78. 4%) of the 2533 Johnson source proteins and 340 (85. 8%) of the 396 Ben Dror source proteins. We used gene expression data from Juncker et al. [23], who provide the median of normalized mRNA levels of haematopoietic tissues originally obtained from the GNF gene expression database [35]. Expression levels of vaccinia virus genes were obtained from Assarsson et al. [24]. Throughout the study we used the method NetMHC 3. 2 [15], [16] to predict peptide-binding to the HLA molecules A*02: 01, B*15: 01, and B*27: 05. Binding predictions for C*03 were done using NetMHCpan 2. 4 [36]. We define predicted binders as peptides that have a predicted binding affinity of <500 nM IC50 for a particular HLA molecule. For the Ben Dror data, where all identified peptides were eluted from a known HLA molecule (namely, HLA-B*27: 05), this proved to be a suitable threshold, predicting 89. 6% (510 of 569) of the eluted peptides as binders. The NetChop version Cterm-3. 0 [17], [37] was used for the prediction of C-terminal processing. Furthermore, we used weight matrices provided by the mhc-pathway package [19], [20] for the prediction of cleavage probability by the immunoproteasome and for TAP transport efficiency. We employed WoLF PSORT [38] to predict subcellular localization of proteins and confirmed our results by GO-term enrichment analysis using the Cytoscape [39] plug-in Bingo [40]. The predicted hit rate for a given protein is defined as the ratio of the number of predicted binders for a particular HLA allotype to the total number of unique 9mer peptides in this protein. Multiple occurrences of the same peptide within one protein were counted as a single occurrence, because they would also not be detected as separate peptides in the elution analysis. The hit rate is calculated per HLA allele and hit rates may differ between alleles, because we use a fixed affinity threshold of 500 nM IC50 to define binders (instead of assigning a fixed fraction of peptides, e. g. top 1%, as binders). For this reason we did not directly compare hit rates between alleles, but instead performed separate analyses per HLA allele. Two-sided Mann-Whitney tests, correlation tests, Chi-squared tests, and logistic regression analysis were performed using R (http: //www. R-project. org). We used a generalized linear model with a binomial response distribution and a logit function for data transformation to model the impact of various factors on sampling probability. All figures were produced using R.

HLA class I molecules are expressed on the cell surface of almost all cells of the human body in complex with short fragments (peptides) of cytosolic proteins, thereby providing a snapshot of the intracellular state of a cell to circulating CD8+ T cells. Several processes are involved in shaping the peptide ligand repertoire of an HLA class I molecule, which generally represents only a small fraction of the proteins available in the cytosol. In our work we addressed protein sampling by HLA class I molecules to answer two questions: 1) Which proteins are sampled by the antigen processing pathway and why, and 2) which peptides of a given protein are picked to represent the source protein on the cell surface? To this end we quantified the contribution of each process involved in peptide processing and presentation individually and combined them into a logistic regression model. This simple model enabled us to predict the sampling probability of self proteins and may aid in the identification of autoimmune antigens.

Abstract Introduction Results Discussion Materials and Methods

sequence analysis major histocompatibility complex immunology biology computational biology

2012

Proteome Sampling by the HLA Class I Antigen Processing Pathway

A statistical thermodynamics approach is proposed to determine structurally and functionally important residues in native proteins that are involved in energy exchange with a ligand and other residues along an interaction pathway. The structure-function relationships, ligand binding and allosteric activities of ten structures of HLA Class I proteins of the immune system are studied by the Gaussian Network Model. Five of these models are associated with inflammatory rheumatic disease and the remaining five are properly functioning. In the Gaussian Network Model, the protein structures are modeled as an elastic network where the inter-residue interactions are harmonic. Important residues and the interaction pathways in the proteins are identified by focusing on the largest eigenvalue of the residue interaction matrix. Predicted important residues match those known from previous experimental and clinical work. Graph perturbation is used to determine the response of the important residues along the interaction pathway. Differences in response patterns of the two sets of proteins are identified and their relations to disease are discussed. Transfer of information between two points in a protein is a fundamental problem relating to function [1], [2]. Fluctuations of residues in the native protein are the essential determinants of information transfer. The three dimensional native conformation or the topology of a protein determines the fluctuations of its residues. Relationships between topology and fluctuations offer important clues for the function of the protein. Structure-function relations can conveniently be understood by treating the protein as a graph of interacting residues [1]. Significant progress has been made in this direction over the past decade. Residue fluctuations, correlations, locations of conserved and important residues, stability of the native state, information transfer, energy fluctuations, and recently the protein-protein and protein-ligand binding have been studied by recourse to the graph-like state of the native topology [3]–[14]. The residue interaction graph contains important information in this respect that allows the determination of important interactions in a protein. The criticality of important interactions in a complex system is often the determining factor of stability of graphs [1], [15]. The lack of rearrangements in over determinate and highly constrained graphs result in decreased stability and robustness [15]. Our work here is centered on the determination of the subset of important interactions in proteins and the relationships between this set and function. We apply our analysis to a set of ten HLA class I proteins, HLA-B27, which are relevant examples of the relationship between critical interactions, robustness, and function. The specific purpose of the present paper is to present a statistical thermodynamics model that gives a consistent explanation of structure-fluctuation-function relations in terms of the graph-like features of native proteins. We use the widely adopted Gaussian Network Model (GNM) based on a harmonic potential of residue-residue interactions, and propose a model for determining structurally and functionally important residues in relation to ligand-protein interactions as well as the path that the protein uses in transferring information form one point to the other. Our treatment is essentially an extension of the three recent papers [9]–[11] which we briefly summarize in the method section below in order to reduce cross-referencing. In the cited papers we showed, using statistical thermodynamics arguments, that the mode corresponding to the largest eigenvalue of the connectivity graph obtained from the contact map indicates the structurally and functionally important residues and that these residues are the ones for which energy and residue fluctuations are strongly correlated. We show that a few residues belong to the set of energetically active residues that are at the surface of the protein and are most efficient in energy exchange with the surroundings. We call these the ‘energy gates’. We also show that the residues that connect any such two surface residues along an interaction path are the ‘hub residues’ over which information is transmitted. From statistical mechanical arguments, a surface residue that is efficient in energy exchange with the surroundings is expected to be active in binding of a ligand, as the ligand-binding problem is an energy exchange problem. We also show that changes in the binding/interaction capacity of an energy gate or a hub residue changes the binding/interaction capacity of the other energy gate or hub residues. This has significant consequences relating to allostery and cooperative binding. The harmonic approximation that we adopt here is a coarse graining approach. However, many of the features obtained by this coarse graining are also indicated by more accurate treatments of protein behavior [16]–[19]. The GNM approach allows for a faster and easier visualization of structure-function relations. We study the structure-function, ligand binding and allosteric activities of ten models of HLA-B27 Class I proteins of the immune system. Five of these models, which belong to the HLA-B*2705 allele of the HLA-B27 protein, are known to be strongly associated with a tendency to develop a chronic inflammatory rheumatic disease, known as ankylosing spondylitis, by causing yet unknown functional abnormalities. The remaining five are chosen from the HLA-B*2709 allele of the same protein. These are the corresponding properly functioning ones with almost no susceptibility for ankylosing spondylitis [20]–[31]. Each pair of the protein structures, one from the HLA-B*2705 and the other from the HLA-B*2709 allele, contains the same peptide in their antigen binding groove to present to immune cells, and therefore serves as an excellent benchmark to test the predictions of the GNM. The only difference between the B*2705 and B*2709 alleles is that residue 116 in the former is always an ASP, whereas it is HIS in the latter. This single residue difference between the two alleles causes structural differences in the two types, and therefore in their contact maps. We show that these differences in the contact map of the two types lead to significant and consistent changes in the fluctuation profile, making the members of the HLA-B*2705 allele respond too strongly to perturbation. Based on these changes, we propose a mechanism that is responsible in the functional differences of the two types. The system consists of the protein and its environment. The latter may contain ligands that are capable of binding to the protein. The protein and the environment form a closed system with fixed energy and amount of molecules. The protein exchanges energy with the environment. Since the total energy of the protein and the surroundings is constant, we have (1) (2) where, Uprot and Usurr are the energies of the protein and the surroundings, respectively. In the statistical thermodynamics treatment of proteins that we propose here, the thermodynamic variables for the protein are S = Entropy, U = energy, V = Volume of protein, R = Position of the residues. In the remainder of the paper, the thermodynamic variables are used for the protein only, without the subscript prot. The thermodynamic variables are averages. The instantaneous values of the energy, volume and residue positions are shown by, , , respectively. The fluctuations, , , result from the deviations of the instantaneous extensive variables from their thermodynamic averages. In the GNM model, the emphasis has been on the fluctuations, visualized as resulting from coupled harmonic motions of the residues from their mean positions [4]. The present treatment is based on the extension of the mechanistic description of the GNM to include the role of energy fluctuations, , as well. As in previous treatments, we adopt a coarse-grained model and represent each residue in terms of its alpha carbon. Thus, for a protein of n residues, is defined as (3) Here, represents the Cartesian coordinates of the ith residue alpha carbon. and are similarly defined. The probability of the instantaneous values, , , and, of the energy, volume and residue positions, is determined from the interrelation of the thermodynamic functions given in the Text S1. Therein, this probability function is used to derive the correlations between the fluctuations of residue positions and energy, as well as the cross correlations between the fluctuations of the energy and residue positions. The statistical thermodynamics interpretation of the GNM was given in full detail by Yogurtcu et al. , [11], which was successfully applied to the prediction of binding sites in receptor-ligand complexes [10], of specific sites for binding [9]. In the present paper, we use the statistical thermodynamics approach to predict the important residues along an interaction pathway. The starting point of the model is the equation relating fluctuations to thermodynamic averages. The derivation of this equation is given in the Text S1. We reproduce the resulting expression here to reduce cross-referencing (4) Here, represents the position vector of the alpha carbon of the ith residue and the superscript T indicates the transpose. k is the Boltzmann constant, T is the temperature. is the force on the jth alpha carbon. The subscripts of the parenthesis of the right hand side indicate that the temperature, pressure and the force on each residue except the ith is kept constant. Angular brackets indicate an average over all possible values of the argument. The right hand side is a thermodynamic quantity that expresses the change in the position of residues by the application of a force. The left hand side, on the other hand denotes an average of fluctuations. Thus, this equation relates fluctuations to average quantities. If fluctuations are associated with function, as is done in several previous studies [3], [6], [32], [33]. The right hand side of Eq. 4 requires the knowledge of a force-displacement relation. The simplest of such relations is that for the linear spring (5) where, Γ is the spring constant matrix. Multiplying both sides of Eq. 5 with the inverse of Γ and performing the differentiation shown in Eq. 4 leads to (6) In the GNM model, the Γ matrix is obtained by inserting a constant to the ij' th position if residues i and j are in contact and zero otherwise. Two residues are assumed to be in contact if they are separated by less than 7 Å. This value of the cutoff is approximately equal to the radius of the first coordination shell for residues in a protein. Each diagonal element of Γ is the negative sum of its row. In this way the protein is visualized as a graph, and the off-diagonal terms of Γ is the connectivity graph of the protein. Although the ij' th elements of the Γ matrix is set to zero when residues i and j are separated by more than 7Å, the ij' th element of the inverse matrix Γ−1 is not zero. This implies correlations between residues that are not in contact. The correlation between the fluctuations of the ith and jth residues is determined by the full graph structure, since the ij' th element of the inverse of Γ contains contributions from all nodes of the graph, i. e. , from all other residues, and not just from those in the close neighborhood of the ith and jth residues. As will be discussed in detail below, the essential features of the correlations may be understood largely by considering the largest eigenvalue of Γ. Energy fluctuations of the protein are assumed to result from fluctuations of inter-residue interactions. The correlation of the energy fluctuations of the protein with fluctuations of residue positions is derived as (See Text S1) (7) The left hand side gives the correlations of energy fluctuations with the fluctuations of residues. The right hand side consists of correlations among residue fluctuations only. Writing the difference (8) and using the right hand side of Eq. 7 with the appropriate choice of the indices, we can write (9) For the case of harmonic fluctuations, i. e. , GNM, this relation is derived in the Text S1. If is assumed to represent the mean-square fluctuation in the ‘spring length’ connecting residues i and j, the left hand side of Eq. 4 becomes proportional to the fraction of the incoming energy from the surroundings absorbed by the spring. The right hand side of Eq. 9 is positive. The terms in the angular brackets on the left hand side may be positive or negative depending on the sign of ΔU. But, for the average to be positive, there must be a constraint on the elements of the left hand side: Positive fluctuations of the energy, which indicates energy transfer into the protein from its surroundings, should couple to large values of and negative fluctuations should couple to small values. Stated in another way, energy that is absorbed from the surroundings are stored in pairwise interactions between residues i and j according to Eq. 8. In the application of the model to the HLA proteins, we define the variable as the sum of the ith row of the correlation matrix (10) A finite value of indicates that residue i belongs to the subset of energetically active residues that are either energy gates or lie along an interaction pathway. It also is a measure of the energy absorbed from the surroundings as may be seen from the second equality in Eq 10. According to graph theory, important features of the graph, such as graph perturbation that relates to allostery for example, may be obtained by considering the largest eigenvalue and eigenvector of the graph [34]. The choice of the largest eigenvalue mode is specifically relevant, because (i) it corresponds to localized effects where only a few residues are excited [3] and (ii) the largest eigenvalue is the most sensitive to perturbation of the graph [34]. In the case that the residues identified by the highest mode are adjacent in space, then they interact and form a path that is active in long distance communication. Our calculations for a large number of ligand-protein systems show that the largest eigenvalue and the corresponding mode of the matrix is in general sufficient to point to the known functionally relevant residues. Within the present approximation, we adopt the maximum eigenvalue interpretation. A residue at the surface with a large value of is an ‘energy gate’ through which the protein executes its energy interactions with the surroundings. If the residue with high is not at the surface but inside the protein, then it is a ‘hub residue’ that has important function along the interaction pathway connecting to an energy gate. Although there is no proof, hubs are generally located between two energy gates in allosteric processes [35], [36]. Examples shown below are in support of this statement. For a residue i at the surface, is a measure of whether residue i will interact with the ligand. For the hub residues, is a measure of the importance of that residue within the network of information exchange. The quantity introduced above indicates the extent of correlation of the given residue i with the rest of the protein. Any change in the connectivity state of residue i will affect the behavior of the rest of the protein through the subset of energetically active residues. One way to apply this change would be to bind a ligand to i, and to the residues within the cutoff distance of i. This corresponds to perturbing the entries in the ith row and column of the Γ matrix. The relation of this to allosteric manipulation is obvious. In this section we discuss the changes in the interaction energies of residues j when the parameters of residue i are modified. Binding to a point i on the protein may increase or decrease the residue interaction energy of other points. The calculations and the analysis are carried out with respect to the heavy chain A, taking the structure from the complex structure of chains A, B (beta-2-microglobulin) and C (peptide bound on the antigen binding groove of chain A) in the alleles HLA-B*2705 and HLA-B*2709 alleles. Ten HLA-B27 protein structures are analyzed here. Five of the structures belong to the HLA-B*2705 allele and the remaining five belong to the HLA-B*2709 allele, where the residue 116 is ASP in the former and HIS in the latter. Each pair has the same peptide sequence bound. The PDB codes of the proteins and their alleles are presented in the first and third columns of Table 1. In Figure 1A, the ribbon diagram of the heavy chain of 1OF2. PDB of the HLA-B*2709 allele is shown. The nine residue peptide is shown in indigo. It sits in the groove between the two helices shown in red. In the same figure, we show the positions of the energetically active and functionally important residues 6,7, 27,101 and 164 for 1OF2 in yellow, suggested by the GNM calculations. Figure 1B is an enlarged version of Figure 1A where each residue is shown with a different color and the rest of the protein is not shown. Figure 1B clearly shows the important residues that form an interaction pathway, with GLU161 at one end and TYR27 on the other end of this path. The prediction of these residues, and the detailed discussion of their role in the functioning and malfunctioning of the HLA-B*2709 and HLA-B*2705 will be given below. In all of the calculations presented in this paper, we concentrate only on the largest eigenvalue which suffices for presenting a proof-of-principle discussion. A more detailed discussion may need to involve eigenvalues other than the largest, which might be plausible for additional functionally and structurally residues. We use Eq. 7 (see Methods) to obtain the energetically active residues using the high frequency mode. In Figure 2, we show the plots of the two proteins 1OF2 and 1OGT, where the subscript i indicates the residue index. The plots are obtained as follows: First, the Γ matrix is constructed with a cutoff distance of 7 Å and the correlations are calculated using Eq. 3. The components of the correlation matrix corresponding to the largest eigenvalues of the Γ matrix are determined by reconstructing the correlation matrix keeping the largest eigenvalue only, and the columns of the resulting matrix are added according to Eq. 10 in order to obtain the values presented in the figures. Figure 2 displays that residue CYS101plays the most significant role in the interactions of the protein and has the strongest correlations with other residues such as ARG6, TYR27, LEU160 and CYS164 in both 1OF2 and 1OGT. This is also observed in the other HLA-B*2709 and HLA-B*2709 alleles. The residues with high values that are observed in the ten proteins are shown in Table 1. The other feature observed that is in common for all of the ten proteins is that the value for the 101st residue is always larger for the HLA-B*2709 alleles than for HLA-B*2705. Since these residues are calculated from the largest eigenvalue of the Γ matrix, we call them the important residues and show with the examples below that these play role in the stability and function of the protein. From the definition given by Eq. 10, is the sum of the distance fluctuations of the intermolecular bonds which the ith residue makes with others. Equation 4 shows that reflects the energetic interactions of residue i with other residues. In this sense, CYS101 acts as the central hub, which controls the system. There are two different types of terms on the right hand side of Eq. 8, the self terms, and the cross term. In order for to be large, both and should be large, and should be negative, i. e. , residues i and j should make anti-correlated motions. Only in this case can be large and energy can be transferred from one to the other via the spring that connects them. Figure 2 shows that there are essentially four groups of residues that are of significance: (i) residue 6, (ii) residue 27, (iii) residues 101–116, and (iv) residues 160–164. In the remaining sections, we elaborate on the characteristic features of these four groups of residues that are also observed in the other HLA-B*2709 and HLA-B*2709 alleles (Table 1). In this section, we study the differences of the response of the residues for a perturbation along the interaction pathway between the two families. These differences arise from the presence of the negatively charged ASP116 in HLA-B*2705 and positively charged HIS116 in HLA-B*2709 that induce energetic changes along the interaction pathway, resulting in functional differences. The differences will be outlined in the following sections. In this section, we present the results of our calculations based on Eq. 10. In the interest of observing the response of a protein to an external stimulus, we induce changes in the interaction strength of each important residue and observe the response of the remaining residues. This is done by increasing the interacting strength of contacts of the ith residue by 1%. This amounts to multiplying the off diagonal elements of the ith row and column of the Γ matrix by 1. 01, and recording the difference in the values of obtained after and before this perturbation for each residue j. In this notation Δ is the change in, and the subscript i indicates that the perturbation is applied on the ith residue. A perturbation of 1% was chosen to ensure that the system was in the linear response region. Trial choice of values above 10% resulted in nonlinear response. In the linear response regime, changing the perturbation from 1% to 2%, for example, doubled the output. Figures 3A–F, given for the case of 1OF2-1OGT, show that the B*2705' s respond to perturbations strongly compared to B*2709' s. The other B*2705' s and B*2709' s, also show the same difference. This difference has its roots in the differences of residue-residue interaction energies. The residue ASP116 in B*2705' s results in strong interactions with its surrounding residues, making the protein respond strongly to perturbations. An examination of Figure 3 shows that positive perturbation of ARG6 induces a decrease in the response of CYS101 which is stronger for all of the B*2705 alleles than for B*2709. It is worth noting that the residue that is directly involved in the binding of the ligand is TYR7. However, its perturbation does not result in any noticeable perturbation in the rest of the protein, suggesting that it does not directly lie on the interaction pathway. However, perturbation of its neighbors MET5 and ARG6 induces strong changes in the behavior of the protein. This is because in the native structure, the environment of TYR7 is less compact than that of ARG6. In this respect, ARG6 plays a special role in the pathway we identified. For example, mutating ARG6 into ALA6 in 1OGT (see the following section describing energy calculations) caused four times more energy increase then mutating TYR7 into ALA7. Based on this evidence we hypothesize that the direct interaction of the ligand with TYR7 induces a perturbation of ARG6 which affects the protein structure significantly. In order to see the differences in response of the two alleles, we subtracted the values of B*2709 from those of B*2705 for ARG6 for each allele pair, and presented the results in Figure 4. The figures show that although ARG6 is perturbed positively by the same amount for both alleles, i. e. , the related elements of the Γ matrix are perturbed by 1% in both cases, the response of B*2705' s is stronger at ARG6 and the CYS101 response to this perturbation is always negative, and stronger again in all B*2705' s. Energy calculations show that the interactions of ARG6 with its environment is 9 kcal/mole stronger in B*2705 than in B*2709, which means that ARG6 is more rigidly embedded in its surroundings in B*2705. This is the result of the differences of residue 116 that affect the two alleles in different ways and the effects are seen on residues ARG6 and CYS101. The only difference between the sequence of B*2705' s and B*2709' s is in the residue 116. This residue is located at the bottom of the B-pocket where the peptide binds. This single mutation is thought to cause differences in the stiffness of the structure around 116 that result in the differences observed and reported above for the two alleles [21]. In order to understand the energetic differences of the two alleles, we calculated the interaction energy of residue 116 with its surroundings in the two alleles. The knowledge of the interaction energies of specific residues in the system may be helpful for understanding and comparing the behavior of the two alleles under study. Here, we present approximate calculations of interaction energies obtained by static minimization of the energies, briefly described in Text S1. The energy minimization calculations are only for comparison of the B*1705 and B*2709, where we either compare two different systems, or compare two different situations on the same system. Thus, the relative values rather than the absolute values of the energies reported here are of interest here to have an estimate on the differences between the B*1705 and B*2709. In our calculations, we first minimized the energy of the system. To calculate the interaction of a given residue with the rest of the protein at its minimum energy conformation, the residue is chosen in the matrix of the remaining residues that are kept in their native states. The interaction energy of the chosen residue is then minimized around the given conformation. In this calculation, only the residue of interest is left flexible and the conformations of the remaining protein residues are kept fixed at their native values. As the energies are sensitively dependent on the value of the dielectric constant chosen, different values of the dielectric constant are used to see the effect on the calculated energies. In the absence of explicit water, the value range of 1–4 is usually considered in the calculations of biological systems. We minimized the energy of 1OGT. PDB and 1OF2. PDB and calculated the energy of residue 116 in each structure as described in the preceding paragraph. We then removed the rest of the protein and minimized the energy of the isolated residue 116. The energy calculated in this way is the intra-residue energy for 116 and contains bond, bond angle, electrostatic, hydrogen bonded and nonbonded energies of the atoms that all belong to 116 only. The difference between the energy in the presence and absence of the surroundings gives an idea on how strongly the residue interacts with its neighbors in the protein. For the dielectric constant equal to unity, the energy of residue 116 is 156 kcal/mol lower in 1OGT (B*2705) because of the charge differences of the two residues, where HIS is positively charged with a pK of 6. 5 while ASP is negatively charged with a pK of 3. 1, which is the ‘random coil’ or ‘model compound’ small peptide pKa value. In 1OGT, the carbonyl group of the negatively charged ASP116 is within 2. 9 Å of the positively charged amino end of LYS70, and within 5. 5 Å of the positively charged HIS114, whereas in 1OF2 (B*2709), the positively charged HIS116 is 6 Å to the nearest negatively charged ASP122. In 1OGT, the two residues of the peptide binding site, ARG6 and ASN97 have lower energies in 1OGT compared to 1OF2. This means, these two residues are embedded strongly in their environments in 1OGT, and perturbing their states results in strong responses in the protein. The unrealistically high energy values reported here are upper bounds that are obtained by taking the dielectric constant as unity. We calculated the binding energies by varying the dielectric constant over a wide range. The results are presented in Figure 5. The difference between the two proteins vanishes when the dielectric constant is around 20. The realistic values of ε used for biological systems vary in the range 1–4. Even with a value of ε = 4, the energy difference is as high as 30 kcal/mole. In Table 2, we present the differences in these energies for 1OGT (B*2705) and 1OF2 (B*2709) for residue 116 and for a few other residues with the values calculated for ε = 1. These values are the upper bounds. We see that GLU163 in 1OGT is bound to its neighborhood less strongly than the one in 1OF2 by an energy difference of 61. 0 kcal/mole. This difference comes from the presence of the negatively charged LYS3 of the peptide in close vicinity of GLU163 in 1OF2. In the energy minimized structure, the oxygen of the carbonyl group of GLU163 is 2. 3 Å from the hydrogen of the amino group of LYS3 of the peptide, whereas this distance is 5. 6 Å in 1OGT. This interaction indicates the specificity of binding of the peptide to 1OF2, which is lacking in 1OGT. In Table 3, differences in the interaction energy of the peptides of the two alleles and the energy of the residue GLU163 are presented. The approximations involved in these calculations are explained in the Text S1. In the second column, we compare the binding energy of the full peptide to the proteins. The values are the differences of the binding energies to alleles B*2705 and B*2709. Among the different residues, GLU163 exhibits a peculiar difference in that its energy is much lower in B*2709' s. For this reason we calculated the difference in the energy of GLU163 for the two alleles and presented the results in the third column of Table 3. Although the overall binding energy of the peptide to B*2705 is more favorable in all cases, the energy of GLU163 is more favorably in B*2709. It is worth noting that the computations by using the largest eigenvalue approach indicated LEU160 and CYS164 as the important residues and GLU163 does not appear as an important residue. A similar trend is observed for several other systems not reported here, where the maximum eigenvalue approach points to a close neighbor of an important residue as in the present study. The difference arises mostly from the presence of electrostatic interactions in the neighborhood of the important residue. Predictions by the maximum eigenvalue method do not directly consider the electrostatic interactions. However, their presence affects the topology that is reflected in the maximum eigenvalue method. In the energy calculations described above, in order to see why ARG6 appeared in the interaction path, we mutated ARG6 into ALA6 in 1OGT, which resulted in fourfold increase in the energy of the system (See Text S1). Same calculations are performed by mutating TYR7 into ALA7. These values, though approximate and relative, it consistently points to some important features of the system. Nevertheless, it is worth stating here that the appropriate and rigorous computational practice in computational biology is to perform an extensive molecular dynamics simulation of the protein and the ligand in aqueous medium and extract the required energies as thermodynamic averages, which may include the evaluation of the free energy as well. Our present energy minimization approach is only for exploratory purposes. We identified the interaction paths of the B*2705 and B*2709 alleles. This path contains the residues TYR27 at one end and CYS164 at the other. Along the path lies CYS101 as the most interactive residue, which we termed as the hub residue. The important residues along this path are shown in Figure 6 for 1OF2, of the B*2709 allele. Differences on this path for the B*2705 alleles are summarized in Figures 7A–D for 1OGT. The roles of the residues shown in Figures 6 and 7 relating to the structural and functional features of this path are discussed in this section. (1) Substitution of ASP116 for HIS116 in the B*2705 allele results in stronger bonds both between the peptide and the protein and between the residues of the protein in the neighborhood of 116. In Figure 6 for B*2709, HIS116 exhibits no interactions with other residues along the path. For B*2705, on the other hand, ASP116 shown for 1OGT in Figure 7A makes three hydrogen bonds with ARG5 of the peptide and one hydrogen bond with ASN97. In other members of B*2705 and B*2709, interactions other than the ones shown in Figures 6 and 7 are also present. In molecular dynamics simulations by Starikov et al. [21], for example, a salt bridge between LEU9 of the peptide and ASP116 in B*2705 was observed to limit the relative motions of these two residues. This causes changes along the interaction path and makes the system B*2705 becomes more fragile against nontrivial rearrangements, in parallel with recent findings on graphs [1]. Apart from differences in the important residues on the pathway predicted by various works the major common finding relates to the effects of replacing HIS116 in B*2709 with ASP116 on B*2705. (2) Our calculations show that ARG6 exhibits strong response to external perturbation in B*2705' s compared to those in B*2709. This implies that ARG6 is more rigidly embedded in its surroundings in B*2705. In Figure 6, ARG6 of 1OF2 is observed to make a single hydrogen bond directly with TYR99. In B*2705, however, ARG6 makes more bonds to its neighbors. In Figure 7B for 1OGT, for example, ARG6 and its neighbor MET6 are hydrogen bonded to CYS101, which in turn is covalently bonded to CYS164. Similarly, in Figure 7C, ARG6 and its neighbor TYR7 are hydrogen bonded to TYR27. In Figure 7D, ARG6 is observed to be an element of a cycle that is a loop of hydrogen bonded elements. This loop contains the residues in clockwise order: ARG6, MET5, CYS101, CYS164, GLU163, bridged by ARG1, ARG2, LYS3 of the peptide, followed by TYR99, TYR 7, terminating with ARG6. All of the residues along this loop are identified by the GNM and the maximum eigenvalue method. Perturbation of B*2705 and B*2709 at ARG6 results in a strong change in the correlations of CYS101 with its neighbors. The response of CYS101 is stronger in B*2705. Comparative energy calculations reported above, show that the interactions of ARG6 with its environment is stronger in B*2705 than in B*2709. (3) Although ASN97 is not identified as a path member by the maximum eigenvalue method, this residue is shown in Figure 6 to make a hydrogen bond with TYR99. Although the latter does not show up as a path member, it makes a short loop of hydrogen bonding with ASN97 which is expected to reinforce the binding pocket. In Figure 7A, ASN97 is observed to make a hydrogen bond with HIS9 and with ARG5 of the peptide, thereby accentuating the tight binding of the peptide in the B*2705 allele. The pocket region that contains HIS9 is referred to as the B-pocket. According to comparative energy calculations reported above, ASN97 shows energetic differences for the two alleles, being more strongly bound to its environment in B*2705 than in B*2709. (4) There is a major difference between the binding modes of the peptides to B*2705 and B*2709. In B*2709, ARG1 and ARG2 of the peptide bind strongly to the B pocket and the rest of the peptide remains relatively flexible contributing to the entropic advantage. The residues of the peptide in the C terminal are subject to nonpolar interactions. These interactions allow for only a few residue types, thus restricting the number of different peptides to only a few. B*2705 on the other hand, is capable of forming bonds with a multitude of residues because of the presence of ASP116. Hence, several different peptides may bind to B*2705. Therefore binding is not specific to a few peptides. Furthermore, the stronger bonding in the B*2705' s presented in Table 3 results in an enhanced entropy penalty. More specifically, for the B*2709 allele shown in Figure 6, ARG1 of the peptide is hydrogen bonded to GLU163, and ARG2 of the peptide is hydrogen bonded to GLU45 and GLU63. For the B*2705 allele, more extensive hydrogen bonding is observed between the peptide and the protein: In Figure 7A, ARG5 of the peptide binds to ASN97 and ASP116; in Figure 7B, ARG1 of the peptide binds to GLU163; in Figure 7C, ARG2 of the peptide binds to THR24; in Figure 7D, ARG2 of the peptide is bonded to TYR7 and LYS3 of the peptide is bonded to TYR99. Peptide flexibility is observed only for the HLA-B*2709 [37]. This suggests an entropic control of peptide recognition. The constraints on the strongly bound peptides in B*2705 constitutes an entropy disadvantage, or an entropy penalty. This hypothesis is supported by thermodynamic data [37]. Figures 7A–D show that the peptide is capable of forming several hydrogen bonds with various residues of the protein. Among these, the interaction with ASP116 and ARG5 of the strong peptide binding capability of the B*2705 binding groove that we observed raises the possibility that B*2705 allele may be capable of binding various different peptides. On the contrary, B*2709 exhibits a limited peptide binding capacity. There is indeed significant amount of experimental work aimed at understanding the differences in binding capacities of the two alleles. B*2709 shows a high specific preference for ligands with nonpolar C-terminal residues. The reason for this is the lack of ASP116. B*2705 accepts other residues at this position [38]–[39]. This is one reason of the ligand specificity of the B*2709' s. (5) In Figure 6, GLU163 is hydrogen bonded to ARG1 of the peptide. Its neighbor CYS164, is also shown in Figure 6. The corresponding conformation for the HLA-B*2705 allele is presented in Figure 7B. In both cases, the crystal structures show that CYS164 is covalently bonded to CYS101. Thus, in both alleles, the gate residue GLU163 can transfer the effects of the peptide to the rest of the protein through the bridge over the CYS164-CYS101 pair. We found that CYS101 is the residue that is strongly correlated with several other residues of the protein. In this sense, we call it the hub residue that controls the function of the protein. The present analysis shows that perturbation at the peptide binding site affects the behavior of CYS101. As shown in Figure 4, this response is stronger in B*2705 when compared with B*2709. A decrease of correlations of CYS101 is expected to result in an important change in the behavior of the protein. Warburton et al. mutated the residue CYS101 in another HLA Class I protein by replacing the CYS with SER, denoted by C101S mutation [40]. Due to the loss of the disulfide bond between CYS101 and CYS164 located between the alpha-helix and beta-sheet portions of the alpha2 domain of the A protein (heavy chain), the proteins lost stability and function. (6) The stronger response of CYS101 to perturbations is a consequence of the strong inter-residue interactions around the binding region. The presence of ASP116 in B*2705 leads to strong inter-residue interaction. On the contrary, the presence of HIS116 in B*2709 makes the protein more flexible due to weaker interactions in the F pocket [21]. This socket is the region that contains the residues 114 and 116 and accommodates the carboxy terminus of the bound peptide. (7) In Figure 6, the CO group of ASN97 is seen to make a hydrogen bond with the backbone NH of TYR99. The importance of this hydrogen bond for the stability of the protein has been shown by Blanco-Gelaz et al. [22] In that work, ASN97 was mutated to ASP97 which prevented the protein from gaining a stable conformation. (8) As a general rule, if a residue is strongly coupled to its environment, then it leads to stronger response when its environment is perturbed, for example, when the residue is replaced with another amino acid of different size. It is therefore expected that when a protein exhibits strong inter-residue interaction energies at a given site, then it is less stable against external perturbations. This parallels the reasoning behind the stability of graphs [1], [8], [41]. (9) In Figure 7, we see that TYR27 makes a hydrogen bond with TYR63 of the beta-2-microglobulin. This is true for both the B*2705 and B*2709 alleles. Interaction of the heavy chain with the light chain is known to be a necessary determinant of stability, and any change in this interaction may be one reason for the misfolded or unfolded protein response [42]. However, although TYR27 appears as a significant residue on the interaction pathway, perturbation of the structure presented in Figures 4 and 5 does not induce a strong response in TYR27. The contribution of TYR27 to the unfolded protein response may not therefore be significant. However, although misfolding is associated with the activity of the peptide, the possible role of the B pocket of the heavy chain in unfolding has not been discarded [43]. The B pocket contains ARG6, TYR7, HIS9, THR24, GLU45, GLU63, and TYR99. The hub residue CYS101 makes two hydrogen bonds, one with MET5 and the other with ARG6, and ARG6 in turn makes two hydrogen bonds with TYR27. There are two different lines of thought or hypothesis from the patogenetic perspective in the association of ankylosing spondylitis disease with HLA-B27 alleles [44]–[50]: Our model shows a difference between the structural stability between B*2705 and B*2709 but also corroborates both hypothesis together by suggesting a role of binding peptides on the stability of structure. Due to strong interactions between the peptide and B*2705, specifically the presence of ARG at positions 2 and 5 in the ligand, this allele of HLA-B27 protein can bind a multitude of different peptides. Strong binding of these peptides influences the stability of the protein through interactions extending from residues ARG6 and TYR7 all the way to CYS101. The interaction between certain peptides and B*2705 heavy chain may result in the enhanced folding problems and an inflammatory reaction due to unfolded protein response. B*2709 on the other hand is highly selective for the peptides, having only a binding residue in the F-pocket, the B-pocket being rather floppy, leading to stable binding if the peptide is extremely suitable for this purpose. This selectivity may be an advantage for B*2709 allele by avoiding the binding of certain peptides which may increase the likelihood of structural instability.

We propose a statistical thermodynamics model for determining structurally and functionally important residues in ligand-protein interactions. Our method identifies the path that the protein uses in transferring information from one point to the other. We show that a few energetically active residues are most efficient in energy exchange with the surroundings acting as ‘energy gates’. The remaining important residues that we identify are situated along the interaction path. These are the hub residues. Strong correlations exist between energy gates and hub residues along the interaction path, thus relating to allostery and cooperative binding. We studied the structure-function, ligand binding and allosteric activities of ten models of HLA Class I proteins of the immune system. Five of these models belong to the HLA-B*2705 allele and are strongly associated with a chronic inflammatory rheumatic disease. The remaining five from the HLA-B*2709 allele of the same protein are the corresponding properly functioning ones. We show that differences in the contact maps of the two types lead to significant and consistent changes in the fluctuation profile, making the HLA-B*2705 alleles respond too strongly to perturbation.

Abstract Introduction Methods Results Discussion

biophysics biophysics/theory and simulation biophysics/biomacromolecule-ligand interactions

2010

Predicting Important Residues and Interaction Pathways in Proteins Using Gaussian Network Model: Binding and Stability of HLA Proteins

The scaling relationship between the size of an appendage or organ and that of the body as a whole is tightly regulated during animal development. If a structure grows at a different rate than the rest of the body, this process is termed allometric growth. The zebrafish another longfin (alf) mutant shows allometric growth resulting in proportionally enlarged fins and barbels. We took advantage of this mutant to study the regulation of size in vertebrates. Here, we show that alf mutants carry gain-of-function mutations in kcnk5b, a gene encoding a two-pore domain potassium (K+) channel. Electrophysiological analysis in Xenopus oocytes reveals that these mutations cause an increase in K+ conductance of the channel and lead to hyperpolarization of the cell. Further, somatic transgenesis experiments indicate that kcnk5b acts locally within the mesenchyme of fins and barbels to specify appendage size. Finally, we show that the channel requires the ability to conduct K+ ions to increase the size of these structures. Our results provide evidence for a role of bioelectric signaling through K+ channels in the regulation of allometric scaling and coordination of growth in the zebrafish. Organ growth is a complex process that requires attaining not only a certain shape but also an appropriate size. The maintenance of proper proportions between organs is tightly regulated [1]. The growth of a structure at a different rate with respect to the rest of the body results in changes in proportions during development. Such allometric growth accounts for the morphological differences between juvenile and adult stages in numerous organisms. This process also contributes to changes in shape and morphology during evolution [2], [3]. Growth is regulated by both organ-intrinsic signals as well as growth factors and hormones that originate outside the target organ. Their relative contribution can vary depending on the species or even between different structures within the same organism [4], [5]. Analysis of chimeras, obtained from transplantation experiments during embryonic stages, has shown that in many cases the final size of an organ is independent of extrinsic factors, such as nutrients or hormones, suggesting that determination of size and shape are organ-autonomous properties [6]. For instance, reciprocal xenografts of limb buds between salamander species of different sizes lead to limbs that attain the final size of the donor species [7]. Further, grafting experiments in avian models have shown that the mesenchyme harbors the instructive information that specifies the final size and shape of structures such as the limb and the beak [8]–[11]. The final size of an organ or appendage results from a combination of cell number and cell size. Perturbation of the Hippo pathway causes massive proliferation of Drosophila tissues and tumorigenesis in mouse [12], while hyperactivation of the TOR pathway stimulates cell growth and can trigger entry into the cell cycle [13]. Locally acting molecules such as insulin-like growth factors (IGFs) and fibroblast growth factors (FGFs) are essential regulators of growth [6]. Yet, how these components are integrated to establish proper patterning and size during development as well as during regeneration is still unclear. Two-pore domain potassium (K2P) channels are a family of potassium (K+) channels that play an important role in determining membrane potential and cell excitability [14]. These leak K+ channels conduct instantaneous currents that are independent of voltage and show open rectification, i. e. they mediate primarily outward currents under physiological conditions. K2P channel function is modulated by neurotransmitters and pharmacological compounds as well as physiological parameters such as temperature, oxygen, osmolarity and pH [15]. Due to their ability to respond to multiple biological stimuli and their wide expression across tissues, they are thought to control many physiological processes besides determining the membrane potential. Although these ion channels have not been implicated in organ size control so far, evidence has been accumulating that endogenous bioelectrical signals orchestrate patterning and growth [16]. Endogenous electrical currents are associated with limb development and regeneration in vertebrates [17], [18] and changes in voltage accompany cessation of regenerative growth in earthworms [19]. In Xenopus laevis, a species with limited regenerative capacity, artificial induction of currents can enhance the regeneration process [20], [21], while chemical, pharmacological or molecular inhibition of ionic currents can abrogate regeneration in this species [22]–[24]. Fins are structures that show an enormous diversity in shape and size in different fish species. They also possess a remarkable regenerative capacity [25]; they can easily be manipulated and unlike internal organs, fins do not have obvious limitations on growth. The skeleton of zebrafish fins consists of a proximal endochondral and a distal dermal skeletal component. The latter is formed by segmented, concave fin rays, the lepidotrichia, which originate from mesenchymal condensations [26]. Fins grow through sequential addition of lepidotrichial segments at their distal tip via migration of mesenchymal cells along the actinotrichia, clusters of collagenous fibers that emerge from the tip of each lepidotrichium [27], [28]. Segment length slightly decreases along the proximo-distal axis [26], but does not change once joints are formed and segment boundaries are established [29]. In zebrafish numerous fin mutants have been isolated over the years [30]–[33]. Most of these mutants have reduced fins [34]. For example, impairment of the ectodysplasin signaling causes loss of fin rays in finless and nackt mutants [35], while in short fin (sof) mutants defects in connexin 43 (cx43) lead to decreased fin size with shorter segments [36]. A few mutants exhibit increased allometric growth of the fin. Among these, longfin (lof) and rapunzel (rpz) mutants have an increased number of ray segments [32], [37], whereas another longfin (alf) mutants tend to have elongated segments [36]. So far, the genetic lesion has only been identified for rpz, which is mutated in a novel teleost-specific gene with unknown function [38]. Here, we report that the allometric fin overgrowth displayed by alf mutants is due to the altered function of Kcnk5b, a K2P channel. Our analysis indicates that mutant Kcnk5b acts locally within the mesenchyme of fins and barbels to increase appendage size. Furthermore, we demonstrate that K+ conductance is required to cause allometric growth during development. Genetic experiments suggest that kcnk5b may act independently of, or in parallel to, cx43. Taken together our results provide in vivo evidence for a role of K+ channels in the determination of appendage size and proportion in the zebrafish. another longfin (alfdty86d) was identified in a large-scale mutagenesis screen as a mutant with elongated fins and irregular segmentation of the fin rays [30], [34]. In a subsequent mutagenesis screen we isolated a second mutation (alfd30mh) showing an identical phenotype and mapping to the same chromosomal region as the original alf allele (see below). Besides the longer fins, alf mutants show overgrowth of the barbels, (Figure 1A, arrows). Homozygous mutants have a stronger phenotype (Figure S1) and their fins tend to be particularly susceptible to breakage leading to accretion of bone around the lesions. Overgrown fins and barbels in alf mutants retain their general organization; however, the fins have an altered segmentation pattern, as joint formation is variable in the mutants. On average, the length of lepidotrichial segments is increased [36] (Figure 1B and 1C); however, structures appearing as very short segments are occasionally observed (arrows in Figure 1B). In contrast to other fin overgrowth mutants such as lof or rpz [32], [37], the number of segments is not increased in alf mutants (Figure 1C). Analysis of the caudal fins during development showed that the increase in size seen in the mutants is due to an increased growth rate (Figure 1D). Wild type (wt) fins exhibit only a slight increase in relative growth during development (k = 1. 29) as growth is essentially isometric [32]. alf heterozygotes showed positive allometric growth during development of the fin with an allometric coefficient k near 2 (Figure 1D). Histological analysis of fins from heterozygous fish does not reveal appreciable differences in the size of scleroblasts and epidermal cells over those seen in wild type sections (Figure 2A). However, increased staining of the proliferating cell nuclear antigen (PCNA) during fin regeneration suggests that proliferation is increased in the mutants (Figure 2B). In sof mutants defects in cx43 are known to cause a reduction in both fin size and segment length [36]. We therefore tested whether the alf overgrowth phenotype requires the function of cx43. Crosses between alfdt30mh and a dominant sof allele, sofdj7e2, showed no epistatic interaction between the two genes (Figure 1E), suggesting that the two mutations most likely affect independent processes that both contribute to the determination of final appendage size during fin development. We mapped the alf mutations to overlapping regions on chromosome 20 (Figure 3A). We further refined alfdty86d to a genomic interval of 125 kb coding for 4 genes (bpnt1, ylpm1, kcnk5b, and syt14). In both alf alleles, distinct missense mutations (W169L and F241Y) were identified in the coding sequence for kcnk5b (Figure 3B). This gene encodes a K2P channel. The affected residues are highly conserved in kcnk5b homologs of other vertebrate species (Figure 3C). Thus, the alf phenotype is due to allelic mutations in kcnk5b. To assess the nature of these alleles we generated a phenotypic revertant (j131x8) of the dominant alfdty86d mutant (Figure 3D). PCR analysis of genomic DNA showed the presence of a 384 bp deletion leading to a frameshift and a premature termination codon. The resulting protein is predicted to lack 3 of the 4 transmembrane (TM) domains (Figure 3E). This suggests that the revertant is a null mutation for kcnk5b. Homozygotes harboring the deletion are viable and fertile; thus, kcnk5b is not essential for zebrafish development. As kcnk5b has a close paralog in zebrafish, kcnk5a (Figure 4A), which is expressed in similar tissues (Figure 4B), the lack of a loss-of-function phenotype in normal development may be due to functional redundancy between the paralogs. Together, these data endorse our finding that kcnk5b is the gene responsible for the alf overgrowth phenotype and demonstrate that these mutations are due to a gain of function rather than haploinsufficiency. We used the known structure of human KCNK4 (K2p4. 1) [39] as a template for modeling Kcnk5b and assessing the mutations. These models revealed that the affected amino acids are positioned in two distinct TM domains towards the cytoplasmic side of the protein (Figure 5A). To assess how the identified amino acid substitutions might affect Kcnk5b function, the channel properties were tested in a two-electrode voltage clamp experiment in Xenopus oocytes. This technique permits measurement of currents across the cell membrane when the membrane potential is clamped to a given value. Oocytes injected with kcnk5b (wt) cRNA react steadily to a change in voltage and do not exhibit a delay in current flow, as is expected for a K2P channel. A similar situation is also seen with kcnk5b (W169L) or kcnk5b (F241Y) cRNAs. However, oocytes injected with either of both mutant cRNAs show an almost two-fold increase in K+ conductance over that of oocytes injected with wild type cRNA (Figure 5B). The current-voltage relationship of the wild type channel shows the typical outward rectification of a K2P channel, i. e. current flows preferentially out of the cell, from the side of high K+ concentration to the side of low K+ concentration [40]. In contrast, the increase in K+ currents in the Kcnk5b mutant variants is accompanied by reduced outward rectification (Figure 5C) suggesting that the change in K+ conductance results from altered biophysical features of Kcnk5b rather than a simple increase in the number of channels at the plasma membrane. K2P channels are often referred to as leak channels since they account for the constant leaking current that sets the resting membrane potential observed in neurons. They are known to control both cell excitability and membrane potential [41], and the human homolog of kcnk5b, KCNK5 (TASK2), was shown to contribute significantly to the stabilization of the membrane potential in articular chondrocytes [42]. Therefore, we hypothesized that zebrafish Kcnk5b might also play a role in setting the membrane potential. Indeed, the membrane potential values of oocytes injected with wild type and mutant kcnk5b cRNAs are correlated with the amplitude of the ion current measured at a constant voltage of 50 mV (Figure 5D): the higher the conductance for K+ measured at 50 mV, the more negative the membrane potential of the oocyte. Consistently, the mutant channels lead to stronger hyperpolarization causing a shift in the membrane potential towards −90 to −100 mV, the equilibrium potential for K+ in Xenopus oocytes. To show where kcnk5b is expressed we performed in situ hybridization experiments on adult fins, however no specific signal above background was observed, indicating that expression levels might be below detection with this technique. Nevertheless, RT-PCR analysis showed that kcnk5b is expressed in fins of adult fish (Figure 4B). To assess whether kcnk5b acts locally within fins and barbels to control growth, we transplanted kcnk5bdt30mh/+ mutant cells into wild type hosts (Figure S2A). Local overgrowth of these structures was detected in 29 out of 120 chimeras raised to adulthood (Figure S2B–D), whereas global overgrowth of all fins and barbels was never observed. This suggests that the mutations act locally within the appendages to increase their size. We further attempted to induce the mutant phenotype by local overexpression of the channel within fins and barbels of wild type fish. Whereas the electrophysiological analysis indicated that the dominant kcnk5b mutations lead to an increase of channel conductance, the current of K+ ions through the plasma membrane depends not only on individual channel conductance, but also on the number of channels present in the membrane. Therefore, we argued that increasing the number of channels should also promote fin overgrowth. We generated a construct in which either kcnk5b (wt) or kcnk5b (W169L) expression is driven by the elongation factor 1 alpha (ef1a) promoter from Xenopus laevis; this promoter was recently shown to be active in all major fin tissues [43]. To mark the cells that express the transgene, DsRed expression was driven under a second ef1a promoter positioned in cis within the same plasmid (Figure 6A). This plasmid was injected into wild type one-cell stage zebrafish zygotes along with Tol2 transposase mRNA as described before [43]. Injected fish were raised to adulthood, screened for DsRed positive cells in the fins and the effects on growth were recorded. No overgrowth was observed in fish injected with a control plasmid expressing only DsRed under the ef1a promoter (0/240), despite the presence of DsRed-positive cells in various tissues within the fin (Figure S3). In about 40% of the fish injected with plasmids encoding wild type or mutant kcnk5b and showing DsRed positive cells in the fins we found a local overgrowth phenotype (Figure 6B and H). Analysis showed a strong correlation of overgrowth with DsRed positive mesenchymal tissue (89. 2%, N = 37, Figure 6C, D and I), whereas DsRed positive cells in other tissues were not associated with increases in size. The fin ray segments were enlarged in the overgrown fins similar to alf mutants (Figure 6D). The marked fibroblast-like cells typically occupied the intra-ray space and were excluded from the arteries (Figure 6E). These vessel-surrounding clones extended along the actinotrichia to the distal ends of the overgrown fins (Figure 6F). In the case of barbel overgrowth, DsRed positive cells were found in the mesenchymal tissue surrounding the central rod (Figure 6G), an acellular, non-cartilaginous, non-mineralized structure that supports this organ [44]. In a few cases no DsRed fluorescence signal could be detected within or next to overgrown fin tissue (kcnk5b (W169L), 2/26; kcnk5b (wt), 2/11), probably due to variegation of promoter activity [43]. In conclusion, these findings indicate that kcnk5b overexpression within fibroblasts of the mesenchyme is sufficient to induce fin outgrowth. To test whether kcnk5b-induced overgrowth requires conductance of K+ ions by the channel, we generated an overexpression construct encoding a non-conductive version by mutating the GFG motif of the selectivity filter to AAA, kcnk5b (GFGAAA). This modification has previously been shown to block ion conductance in K+ channels [45]. Electrophysiological measurements in Xenopus oocytes showed that this channel is unable to conduct K+ (Figure 6J). The plasmid was injected into wild type embryos along with Tol2 transposase mRNA and injected fish were reared to adulthood and assessed for overgrowth. No overgrowth was detected in these fish (Figure 6H), although fins containing DsRed positive tissue (n = 32), including fibroblasts (Figure 6J, inset), were found. These data indicate that the increase in conductance of the Kcnk5b channel is essential for the coordinated overgrowth of the fins and barbels in the mutants. The size of an organ depends on cell size and cell number. The mammalian homolog of kcnk5b has been implicated in both, regulation of cell volume [50], [51] and cell proliferation [52], [53]. In alf mutants we could detect an increase in cell proliferation but not in cell size (Figure 2). Importantly, the mutant phenotype does not arise simply by dysregulation of cell proliferation, which would cause tumorous overgrowth; rather the overgrown structures in the mutants preserve tissue organization and patterning. It is unclear how K+ channels regulate proliferation. Studies have proposed that this might occur through regulation of the membrane potential [54]. In apparent contrast to some studies [55]–[58] but in agreement with others [59], [60], we found that hyperpolarization caused by mutations in a K+ channel can lead to tissue overgrowth. Although we observed a hyperpolarizing effect of the alf mutation in Xenopus oocytes, we cannot exclude that this, in turn, triggers a depolarization, either at cellular level or in the surrounding tissues during development of the fin. In fact, experiments employing depolarization-sensitive dyes, suggest that this might indeed be the case (Figure S4). The importance of hyperpolarization during growth is supported by regeneration studies in Xenopus [22], [24]. Regenerating tadpole tails are initially depolarized, but, unlike tails in the refractory state, subsequently undergo hyperpolarization. Notably, impairing hyperpolarization through inhibition of V-ATPase activity leads to a reduction of cell proliferation and failure to regenerate [22]. Transient hyperpolarization of the cell might lead to a cytosolic increase of the second messenger Ca2+, activate integrin-dependent or PTEN phosphatase-dependent cascades, or favor the uptake of mitogens such as serotonin through voltage-dependent transporters [61]. Recent reports suggest that in some cases K+ channels can induce cell proliferation independently of their effect on membrane potential [62], [63]. We show that, in the case of Kcnk5b, conductance is essential for the regulation of fin growth. Overexpression of a non-conducting version of the channel does not cause a phenotype, whereas wild type and alf variants induce local overgrowth. Our analysis of transgenic mosaics indicates that cells of the mesenchyme are sufficient to provide cues that alter the size of the fins. This is consistent with results of classic xenograft studies between chicken and quail where cells of the mesenchyme impart donor-specific characteristics to the limbs [8], [64]. During development tetrapod limbs are patterned by signaling interactions between mesenchymal cells and the overlying ectoderm. A prominent signaling center, the apical ectodermal ridge (AER), is active at the distal tip of the limb bud during this process. The AER and the mesenchyme of the progress zone continuously communicate with each other to direct limb outgrowth and development. Similar epithelial-mesenchymal interactions from the apical fin fold are likely to be required for the patterned overgrowth of fins in alf mutants. In support of this mechanism, we consistently find labeled mesenchymal cells in the distal-most regions of overgrown tissue in mosaic animals. AER signaling in amniotes requires connexin-mediated electrical connectivity between cells to coordinate pattern and growth of the vertebrate limb [65]–[67]. An analogous mechanism may be functioning in fish. We show here that altering ionic communication in the developing fin of the zebrafish is sufficient to induce growth. Our analysis of the genetic interactions between alf and sof indicate that Kcnk5b and Cx43 may act in parallel pathways to modulate final fin size. In both mutants segment length and fin size are correlated, however the role of segment patterning in size regulation of the fin is unclear. In contrast to alf [36], [68] (Figure 1) and sof [33], the overgrowth mutants lof and rpz have wild type sized lepidotrichial segments [32]. Moreover, the evx1 mutation, which leads to fins rays devoid of joints, does not affect final fin size in a wild type nor lof background [69]. Several experiments suggest that bioelectrical signaling is a shared common mechanism used across bilaterians to control organ growth and patterning [21], [70], [71] and indicate that ion flow may have an instructive role during development [72], [73], as well as regeneration [18], [74]. Here, we provide genetic evidence showing that changes in K+ channel activity result in allometric scaling of an organ, rather than causing uncontrolled proliferation. We favor a model for size regulation in which modulation of ionic current by K+ channels within the organ shifts positional information, thereby setting a different register of size during development. In fact, there is evidence for a rostro-caudal and medio-lateral gradient of voltage within the developing embryo suggesting that electric fields are a component of the positional information [75], [76]. External electrical currents have been shown to alter positional information in axial regenerates of planaria [77]. However, the underlying mechanism of signaling from electrical fields is largely unknown, and possibly depends on electrical coupling between cells [78]. This hypothesis is supported by studies in pigment pattern formation, where both K+ channels and connexins have been implicated in proper formation of the zebrafish stripes [49], [79]–[81]. Further studies will be needed to uncover the signaling mechanism from K+ channels to regulate size and pattern. However, our work, in concert with that of others, clearly shows that ion flow is not just an epiphenomenal event accompanying growth but one of the major factors specifying pattern and form during development and regeneration. Zebrafish were bred and maintained as previously described [82]. alfdty86d was isolated in the 1996 Tübingen screen [30], [34] as a mutant affecting adult fin formation. The alfdt30mh (pfau) mutant was identified in F1 fish of a standard F3 screen (ZF Models) and isolated based on its fin and barbel phenotype. Fish were anesthetized in tricaine solution for measurements; fin length and standard length was measured using handheld calipers. Fish were imaged under a stereo microscope (Zeiss, SteREO Discovery) and measurements were performed using AxioVision software (Zeiss). p-values from unpaired Student' s t-test were obtained with Microsoft Excel. Fin regenerates were fixed at 4 dpa in 4% PFA overnight and decalcified with 0. 5 M EDTA for 24 h. Sample were embedded in paraffin and sectioned at 5 µm. Immunohistochemistry with anti-PCNA antibody (Sigma) was performed as described [83]. Percentage of PCNA positive nuclei over Hoechst positive nuclei was determined on three to four sections of four independent samples for each genotype. Adult zebrafish organs were dissected on ice and stored in RNALater (Invitrogen) at 4°C. Total RNA was isolated using RNeasy Mini kit (Qiagen). cDNA was synthesized from 200 ng RNA from each sample with SuperScript III and oligo (dT) primers (Invitrogen). PCR analysis was performed using Taq polymerase S (Genaxxon) with intron spanning primers (β-actin forward OSP-31, TGC GGA ATA TCA TCT GCT TG, β-actin reverse OSP-32: AGC ATC ATC TCC AGC GAA TC, kcnk5b forward OSP-390: CAT TCC TCT GTG CCT CAC CT; kcnk5b reverse OSP-324 AGG CCA TCC ACA GAC TCA TC, Tm = 61°C, 30 cycles). Mapping was performed as described [82]. The alfdty86d mutation mapped between z11841 (5 recombinants/96 meioses) and z21067 (2/96) and fine mapped using SNPs. alfdt30mh mapped between z7803 (1/48) and z21067 (1/48). Full length kcnk5b was cloned into pGEM-T Easy from cDNA of fin blastema amplified with LA Taq polymerase (TaKaRa) (forward primer OSP-379: TGG GAG TGT GGA GTG TGT GT, reverse OSP-382: TTT TTG GTC CAG CTT TGG TC, Tm = 60°C, 45 cycles). Sperm from alfdty86d homozygotes was irradiated with X-rays (1125 rads, Faxitron 43855D) and used to fertilize wild type eggs (AB strain). F1 progeny was reared to approximately three weeks of age (9433 fish) and screened for the alf phenotype. 11 fish showed wild type fins. 10 of these survived to adult stages. SSLP analysis revealed that 9 of these were deletions of some or all of the upper arm of chromosome 20. q-RT-PCR of candidate genes in the remaining revertant (j131x8) showed no change in transcript levels for bpnt, ylpm1 and syt14, but little or no transcript for kcnk5b. PCR analysis of genomic DNA showed that this revertant has a 384 bp deletion of the 3′ end of intron 2 and the 5′ end of exon 3. This deletion results in a frameshift and early truncation of the protein. The amino acid sequence of zebrafish Kcnk5b was retrieved from Ensembl (http: //www. ensembl. org) and used to search the PDB database with HHpred (http: //toolkit. tuebingen. mpg. de/) [84]. The first hit in the search (human KCNK4, PDB ID: 3um7 [39] identity 36%, similarity 0. 646; 22nd March 2012) was used to build the 3D model. The model was processed with MacPyMol (http: //pymol. org). kcnk5b was subcloned from pGEM-T Easy to pSGEM expression vector via SacII and SpeI sites. After linearization with NheI, cRNA was synthesized with Ambion mMessage mMachine (Invitrogen) and cleaned up with mRNeasy Mini Kit (Qiagen). X. laevis oocytes were injected as described previously [85] (kcnk5b single alleles: 4 ng wild type or mutant kcnk5b cRNA; co-injections of two kcnk5b alleles: 2 ng cRNA each, for a total of 4 ng per oocyte). Measurements were done from a holding potential of −80 mV with 0. 5 s long pulses from −100 to +60 mV with increments of 20 mV. Recorded currents (n = 5–26) were averaged and normalized to the mean value recorded for oocytes injected with the wild type channel at +60 mV. PCR mutagenesis was performed as described [86] using Pfu polymerase (Fermentas) (OSP-15 CCC TGA CGA CTG TCG CTG CAG CTG ACT ATG TGG CAG GGG C; OSP-16 CCT GCC ACA TAG TCA GCT GCA GCG ACA GTC GTC AGG GTG G, Tm = 70°C, 30 cycles) on pSGEM: kcnk5b (wt). ef1a: DsRed vector. A ef1a: DsRed cassette generated with KOD Hot Start DNA Polymerase (Toyobo) (primers: TAA TTT AAA TAG ATC TTC GAG CAG GGG GAT CAT CTA ATC A; CTA GAT GGC CAG ATC TGC CCG GGA CTT GAT TAG GGT GAT GGT TCA CGT AGT G, Tm = 59°C, 30 cycles) from plasmid Ale237 (kind gift of Alessandro Mongera) was inserted in plasmid 587jk (kind gift of Dr. Jana Krauß) using BglII restriction sites through In-Fusion Advantage (Clontech) cloning according to manufacturer' s protocol. ef1a: DsRed; ef1a: kcnk5b wild type and mutant vectors. The ef1a promoter was amplified from plasmid Ale237 (primers: ATT AAT TCG AGC TCG GTA CCC CTC GAG CAG GGG GAT CAT CT; GAA CAA GCA AGC TGG GTA CCC CGG CCG TCG AGG AAT TCT TTG, Tm = 59°C, 30 cycles) and inserted into the pSGEM vector at the KpnI restriction site using In-Fusion Advantage (Clontech) cloning. The ef1a: kcnk5b cassette was amplified from the resulting plasmid as above (primer: AAA CCT AGG TCG AGC AGG GGG ATC ATC T; AAA CCT AGG ATG ACC ATG ATT ACG CCA AGC TAT), digested with AvrII and inserted into ef1a: DsRed vector using the SpeI restriction site. Plasmids (5–20 ng/µl), Tol2 mRNA (25 ng/µl) and 20% (v/v) phenol red solution (Sigma- Aldrich, P0290-100ML) were injected into the zygote of 1-cell stage embryos under a dissecting microscope (Zeiss, Stemi 2000) using 275 Pa (40 psi) injecting pressure for 100 ms (World Precision Instruments, Pneumatic PicoPump PV820). Adults were analyzed with Zeiss, SteREO Discovery and Zeiss LSM 5 Live. Transplantations were performed as previously described [82]. At mid blastula stage (1000 cell stage), about 20–40 cells were transplanted from the pfaudt30mh/+ donors into the recipient close to the yolk cell and chimeras were raised to adulthood. Fluorescent dye experiments were performed by adapting described protocols [22], [87]. Briefly, wild type and mutant juvenile fish (STL = 16–18 mm) were incubated in fluorescent dye diluted 1∶2000 in fish water (stock solutions: DiSBAC2 (3) (Bis- (1,3-Diethylthiobarbituric Acid) Trimethine Oxonol, Life Technologies): 1 mg/ml in DMSO) for 30 min in the dark, anesthetized with tricaine solution and placed on a custom-made chamber for confocal imaging. The chamber was obtained by removing the bottom of a 55 mm plastic dish and by replacing it through a round cover slip fastened with silicone. Fish were held in place with a tissue soaked in dye and imaged upon excitation at 561 nm. Unstained animals were imaged as a negative control. p-values from unpaired Student' s t-test were obtained with Microsoft Excel.

The proportions of an animal can change during its lifetime. This often occurs through the phenomenon of relative growth, or allometry. In humans, for example, the head grows at a lower rate than the body resulting in a change in proportion between children and adults. The regulation of size and proportion is not well understood. We investigated fin growth in zebrafish as a model to understand this phenomenon. The mutant another longfin develops proportionally larger fins and barbels. Here, we show that another longfin mutants carry a mutation in kcnk5b, a gene coding for a potassium channel. Potassium channels control the electrical potential of cells and are known to regulate processes such as heart beat, neurotransmission and hormone secretion. We demonstrate that increased potassium channel activity can drive changes in growth in the zebrafish. Clonal analysis reveals that the channel acts directly in the fins and barbels to increase their size in a coordinated manner. Our work shows that potassium channels are involved in the determination of appendage size in zebrafish and suggests consistent with recent studies, an instructive role for bioelectrical signaling in development.

Abstract Introduction Results Discussion Materials and Methods

animal models mutagenesis developmental biology zebrafish animal genetics genetic mutation organism development model organisms genetic screens genetics biology anatomy and physiology electrophysiology morphogenesis pattern formation limb development

2014

Bioelectric Signaling Regulates Size in Zebrafish Fins

Chagas disease is a vector-borne disease of major importance in the Americas. Disease prevention is mostly limited to vector control. Integrated interventions targeting ecological, biological and social determinants of vector-borne diseases are increasingly used for improved control. We investigated key factors associated with transient house infestation by T. dimidiata in rural villages in Yucatan, Mexico, using a mixed modeling approach based on initial null-hypothesis testing followed by multimodel inference and averaging on data from 308 houses from three villages. We found that the presence of dogs, chickens and potential refuges, such as rock piles, in the peridomicile as well as the proximity of houses to vegetation at the periphery of the village and to public light sources are major risk factors for infestation. These factors explain most of the intra-village variations in infestation. These results underline a process of infestation distinct from that of domiciliated triatomines and may be used for risk stratification of houses for both vector surveillance and control. Combined integrated vector interventions, informed by an Ecohealth perspective, should aim at targeting several of these factors to effectively reduce infestation and provide sustainable vector control. Chagas disease is a vector-borne disease of major importance in the Americas where it is endemic. It affects an estimated 8–10 million people, and nearly 25 millions are at risk of infection [1]. In terms of disability-adjusted life years (DALYs), and with a burden of 0. 7 million DALYs, it is the fourth most important disease in Latin America, following only hookworm, ascaris and trichuris infections [2]. The disease is caused by the protozoan parasite Trypanosoma cruzi and most transmission occurs by hematophagous triatomine vectors [3]. Disease control and prevention are mostly limited to vector control to reduce triatomine infestation of human dwellings and concomitant transmission of T. cruzi to humans [4]. Indoor residual spraying of pyrethroid insecticides and housing improvement are the main methods of interventions. Inter-governmental vector control initiatives during the 1990s are believed to have eliminated vectorial transmission to humans in several Latin American regions [4]. However, this success is mitigated by difficulties in sustaining vector control activities [5] and the emergence of insecticide resistance [6]. Additionally, some triatomine species are not well domiciliated but transiently invade house to feed on humans, representing an important source of infection. However, traditional control methods are less effective at preventing these triatomines from entering houses [3]. Integrated vector control based “on a rational decision-making process for the optimal use of resources in the management of vector populations” [7], targets ecological, biological and social determinants of vector-borne diseases to achieve improved control. It shares a common Ecohealth perspective, an ecosystem approach to health that promotes use of transdisciplinary participatory research to attain better health outcomes through environmental management [8]. Such multidisciplinary strategies are emerging as more rational, sustainable, and cost-effective than widespread empirical insecticide spraying [7], [8]. However, they require extensive knowledge of the eco-bio-social determinants leading to disease transmission. Most research on Chagas disease vectors has focused on domiciliated Triatoma infestans, the major vector species in South America. Risk factors for house infestation (and colonization) by T. infestans are typically associated with housing structure and quality; houses providing abundant hiding refuges for bug resting and reproduction (cracked adobe walls, dirt floors, thatched roofs, poor hygiene, darkness, etc. .) and easily accessible feeding sources (indoor dogs or chickens, large families) are more likely to be infested [9]–[11]. Similar results have been observed for other domiciliated vector species and populations, including T. dimidiata in Central America [12]–[16]. Accordingly, integrated vector control interventions targeting these risk factors such cement floors or roofs, or wall plastering are being evaluated [17]–[19] as alternatives to conventional insecticide spraying [20], [21]. Less is known about determinants of invasion by non-domiciliated triatomines, which have limited ability to establish domestic colonies, but transiently enter houses for blood feeding on animal and human hosts. In urban areas, infestation by T. pallidipennis or T. dimidiata has been found to be much less dependent on housing characteristics, but is instead associated with the availability of various peridomestic refuges (large peridomestic area, adjacent empty or abandoned lots) and feeding sources such as dogs, squirrels, and opossums [22], [23]. In the Yucatan peninsula, sibling species from the T. dimidiata complex typically infest houses on a seasonal basis during the months of March–July, with very limited ability to colonize houses [24]–[27]. This contrasts with its level of domiciliation in Central America and has been hypothesized to be associated with genetic differences within the T. dimidiata complex [20], [28]. This infestation is responsible for a seroprevalence of T. cruzi infection in humans of about 1–5% in the region [29], [30] and these invasive vectors cannot be fully controlled by conventional insecticide spraying [31], [32]. While we have now an accurate description of the dynamics of house infestation [33], [34], we still have limited understanding of the factors driving this process, limiting the design of vector control interventions. For example, houses located in the periphery of rural villages, in close proximity to the surrounding vegetation, were found twice as likely to be infested compared with houses located closer to village centers [35], suggesting that specific spatial targeting of vector control may be appropriate [36]. Proximity of public street lights was also found to be a significant contributor to infestation [37]. Importantly, housing quality seems irrelevant for this transient infestation [24], but more detailed ecosystemic and social studies are needed to fully identify and understand the interplay of factors contributing to this infestation pattern and to develop integrated vector control interventions. In the present study, we performed a detailed analysis of the eco-bio-social characteristics of rural villages in the Yucatan peninsula, Mexico, to identify the key determinants associated with transient house infestation by T. dimidiata. The study was carried out from July 2010 to July 2011 in the rural villages of Bokoba (21. 01°N, 89. 07°W), Teya (21. 05°N, 89. 07°W) and Sudzal (20. 87°N, 88. 98°W), located about 15–20 km apart in the central part of the Yucatan state, Mexico. The regional climate is warm and humid, with an average annual temperature of 26°C and 1150 mm of rainfall, and the villages are surrounded by a mixture of secondary bush vegetation and agricultural/pasture land. There are a total of 570,702 and 416 houses in Bokoba, Teya and Sudzal, respectively, all of which have been georeferenced previously [35]. The respective populations are of about 2,000 inhabitants in both Bokoba and Teya and 1,600 in Sudzal, with about 40% of the population below 14 years of age. Most of the population (over 90%) is of Mayan descent and culture. Each of these communities has a health center run by the state public health system (Secretaria de Salud de Yucatán), as well as public primary and secondary schools and at least one church. The population is largely Catholic (over 95%). Previous work indicated that there was no significant difference in the overall housing characteristics and living conditions of these villages, all three of them being rather representative of the conditions in rural Yucatan [32]. There has been no systematic vector control program in these villages, but entomologic monitoring by community participation has been performed since 2006 and pilot interventions were implemented in a limited number of houses in 2007 [32]. An extensive survey was developed and validated to identify the key eco-bio-social dimensions of the households. The survey included a total of 127 variables describing in detail housing structure and characteristics (34 variables), peridomicile structure and characteristics (56 variables), socio-demographic characteristics and cultural practices (38 variables) (see Table S1 for the complete list of those variables). Housing structure variables included the number of rooms and construction materials of the different parts of the house (floor, wall, roof). Peridomicile variables included data on the size of the peridomicile, its vegetation, the presence of different structures (storage, corrals, others) and the presence of different species of domestic animals. Socio-demographic characteristics and cultural practices included a detailed description of the composition of the household, its socioeconomic status, and common practices related to the maintenance and care of the house and the peridomicile, including use of insecticides and other potential vector control measures (e. g. storage of grains or construction material, cleaning habits, etc…). Practices related to the care of domestic animals were also investigated. A total of 346 households randomly sampled in the three villages was selected for the survey (representing 20% of the total number of households). We used a random sampling scheme to avoid any bias in selecting specific households and ensure that all types of households would be included in the survey, irrespective of the housing type, structure, position in the village, infestation status. Teams of two trained field workers performed individual visits to each household to apply the survey following obtention of written informed consent. The protocol was approved by both the World Health Organization and the Autonomous University of Yucatan institutional bioethics committees. We were able to obtain data from 308 households, the remaining being abandoned houses, households that declined to participate, or households in which inhabitants were unavailable after three visits. In all three villages, house infestation was monitored from July 2010 to July 2011 by community participation, which we have found highly reliable and more sensitive than timed manual searches for entomologic surveys of houses with low and transient infestation [24], [25]. Infestation was defined as the catch and notification of at least one triatomine (adult or nymph) inside the domicile at any time of the year. Community members were asked to collect any triatomine-like bug observed in their houses, using plastic bags to avoid direct contact with the bugs, and take them to the health center where the bugs were registered together with basic information on the household. We later visited each household that had collected a bug to confirm the coordinates we had previously georeferenced [35]. Regular community meetings were held during the study to promote Chagas disease awareness and ensure community participation. Conventional indices were calculated to describe infestation: infestation index (percent of houses with indoor triatomine presence at any time of the year), colonization index (percent of infested houses with nymphal stages), density index (number of triatomines per infested house) [23], [24]. We used a mixed modeling approach by first performing univariate analyses of the 127 variables describing the eco-bio-social conditions of the 308 houses using logistic regression, to explore the potential association between the transient presence of T. dimidiata at any time of the year (house infestation) and these variables. A few data were missing in the database, corresponding to different houses for different variables, and thus missing data were considered randomly distributed (up to 8% of missing values for one variable retained in the model). We then used a multiple imputation method implemented in the R package ‘amelia II’ to estimate the missing values [38], [39]. Following established guidelines we constructed 5 datasets with imputed data [38], [40]. For each logistic regression, the value of each coefficient was calculated as its mean value across the 5 datasets, and standard errors were calculated taking into account the mean intra-dataset variance and the between-dataset variance [40]. We then selected for further multivariate analysis a sub-set of 29 variables that had P-values<0. 1 from a likelihood ratio test in the univariate analysis. Because of the redundancy and overlap of several of these variables, as assessed by correlation analysis, these were further reduced to a subset of 9 variables selected to incorporate the maximum independent information in our model while keeping covariation among variables at a minimum (see results and Table S2). We evaluated the goodness of fit of the complete model (i. e. the model with all nine variables) based on a generalised coefficient of determination [41], and the potential for over-dispersion in the data [42]. Multivariate analyses were performed using the framework of multimodel inference and selection followed by model averaging [42] to assess the support of each model in terms of Akaike' s Information Criterion (AIC). We also determined the odds ratio associated with each of the 9 variables, with their 95% confidence intervals (95%CI) constructed using multi-model estimations of each coefficient' s variance [42], [43]. All analysis were performed in Matlab (R2012b, The Mathwork). We performed 512 logistic regressions including different combination of zero up to nine of the selected variables and determined the likelihood () for each of the models. The AIC of each model (corrected for small sample size) was calculated as where k is the number of parameters estimated in the model. The final AIC was taken as the mean AIC from the 5 imputed datasets [44]. Models were then ranked from the best supported model (with the minimum value of AIC, AICmin) to the least supported one (maximum value of AIC). Akaike differences were calculated as and models with were considered to have a considerably lower support than the best supported model [43]. The Akaike weight (WAIC) for each model was defined as: and it provides the probability for each model to be the best model. The relative importance of a particular variable was then calculated as the sum of the Akaike weights of all models that contained this particular variable. Finally, to obtain a model including the most complete information and the best predictive ability, we performed model averaging, in which each parameter was weighted by the WAIC of each model and averaged for all 512 models [45]. Thus, for parameter linking infestation with a variable in the logistic regression, the average value over all R models is: A confidence interval was calculated for each parameter assuming a normal distribution with a total variance associated to the parameter. This variance accounted for (1) the uncertainty in the parameter value given a certain model, and for (2) the uncertainty associated with model selection, as follows: We then derived a confidence interval for the odds ratio associated with each parameter by taking the exponential of the lower and upper bounds of the parameter confidence interval. Finally, we evaluated a posteriori interactions among the six most supported variables of the best model, and constructed 15 models including one pairwise interaction. The statistical significance of each interaction was tested using a Student' s t test. For each household, the probability of infestation was calculated based on the model' s averaged parameters to assess the reliability of the model in terms of sensitivity and specificity. We thus calculated the generalized coefficient of determination (following [41]) to assess the fit of the model to observed data. To assess predictions at a coarser level, we also defined 15 groups of 20 houses according to their predicted probability of infestation. Houses from the first group had the 20 lowest predicted probabilities of infestation, and those from the last group had the 20 highest predicted probabilities (due to a sample size of 308, the first and last group actually consisted of 23 and 24 houses). For each group, we calculated the observed infestation probability defined as the proportion of infested houses in the group. The relationship between predicted and observed infestation was evaluated by correlation and regression analysis. The eco-bio-social characteristics of a total of 308 households were obtained from the field survey. A typical household was composed of a family of 4–5 persons (4. 1±0. 1), led by a man in 77% of the cases. Most worked as subsistence farmers (38%), some in construction or manufacture (14%) and only 22% had a regular work contract. Sixty-three percent received social welfare benefits (“Oportunidades” program). Education level reached primary school for most men and women (63%). The houses had been built 20±1 years ago and consisted of 2. 1±0. 1 adjacent rooms. This included 1. 6±0. 1 bedrooms and rooms had an average of 1. 5±0. 1 windows (Fig. 1). Houses were of cement block construction (96%), often fully plastered walls (63%), with cement floors (93%). Similarly, roofs were made of cement/concrete; only 5% were thatched and 8% from tin. Fourteen percent of houses had no sanitation system. Houses were surrounded by a peridomestic area of on average 1300 m2, limited by a fence of piled rocks (84% of houses). Vegetation was scarce close to the house and trees were somewhat denser further from it (>10 m). Many families kept domestic animals all year round (58%), the most frequent being dogs (52%), chickens (49%), cats (34%), and songbirds (14%). Other animals such as rabbits, pigs, sheep, horses and cows were rather rare (<3%). Animals were usually kept close to the house (<10 m, 83% of cases). Songbirds were kept in suspended cages attached to the fronts of houses, chickens and turkeys were sometimes kept in a corrals/coops (22%), while other animals had free range in the peridomicile. Construction materials were sometimes stored in the peridomicile (25%), while corn or other grains (13%) and firewood (10%) were stored inside the house. The peridomicile area was cleaned at least once a week in most cases (66%), usually by men. About 55% of families used domestic insecticide products such as mosquito coils (45%), plug-in repellents (32%) or spray insecticides (55%) on a regular basis, but only a few (5%) resorted to professional insecticide spraying. Thirty-nine percent of households reported having seen triatomines in their house. Triatomine transient domestic infestation was detected in 46 of the 308 households (14. 9%), corresponding to 10/70 houses (14. 3%) in Sudzal, 9/106 houses (8. 5%) in Bokoba, and 27/132 houses (20. 5%) in Teya. The colonization index was of 5/46 (10. 8%) and the density index was of 2. 9 triatomines/house, ranging from 1 to a maximum of 43 triatomines/house. All these data were similar to what has been previously observed in these same villages [37], which are also very representative of other villages from the region [23], [24], [46]. The potential association of house transient infestation with eco-bio-social characteristics was first assessed by logistic regressions and 29/127 variables included in the survey were found correlated with house infestation at a P<0. 1 level (Table S2). Importantly, none of the variables related to the socio-economic status of the household, the education level or general cultural practices such as sleeping or cleaning habits of the house or peridomicile were found to be associated with infestation. The use of various domestic insecticide products or peridomestic pesticides had also no relationship with infestation. Similarly, most variables describing housing and peridomicile structure and organization, including floor, walls or roof type, number of rooms, number of inhabitants, peridomestic vegetation type and density, were not significantly associated with infestation. On the other hand, the 29 variables associated with transient domestic infestation (Table S2) were related to the presence of specific domestic animals such as dogs, chickens and perching birds, housing condition such as wall plastering, and the surroundings of the houses such as the presence of piles of rocks in the backyard/peridomicile, the proximity of a street light and the location of a house in the periphery of the village. We first further reduced the number of variables because of the redundancy and correlation of several of these, so that we could incorporate the maximum independent information in our model and limit covariation among explanatory variables (Table S3). For example, three variables described the presence of dogs: ‘Presence/absence of dogs’, ‘Number of dogs’ and whether dogs were ‘free ranging, enclosed in a corral or tied on a leash, or absent’. We thus eliminated the first variable because of its complete redundancy with the other two and further selected the variable with the lowest P-value in the univariate analysis, which led to retain the variable ‘Number of dogs’ for further modelling. This process resulted in the elimination of 20 variables and only 9 variables were kept for multivariate analysis, with pairwise correlations among them always lower than 0. 25 (Table S3). These variables included the number of dogs, presence of chickens in a corral or free-ranging, proximity to the periphery of the village, practice of removing trash from the peridomicile, presence of rock piles close to the house, presence of songbirds, the storage of firewood inside the house, complete wall plastering and the proximity of a public street light. We then evaluated the complete model (i. e. the model with all 9 variables) and found no obvious over-dispersion in the data (Pearson' s goodness of fit, , , or using the residual deviance:). Furthermore, this model accounted for 29% (R2) of the variation in infestation. Analysis of all 512 models including different combinations of the 9 variables indicated rapidly increasing AIC scores and, but 10 models presented a ΔAIC of less than 4. 5 and were thus considered to receive support from the data (Table 1). These 10 models included the complete model, but the best supported model was comprised of only 8 of the variables and had a coefficient of determination of. Since all 10 models contained at least 6 variables, infestation by triatomines may not be attributed to a single or few factors, but rather seemed to depend on a complex combination of conditions. We then proceeded to model averaging to identify the strongest determinants for house infestation and evaluate the predictive power of our model. The Akaike weight of each variable in the averaged model indicated that five variables could be considered of high importance in defining house infestation with WAIC>0. 9, two additional variables were of secondary importance with 0. 7<WAIC<0. 9, and the remaining had limited contributions (Table 2). As reported before [35], the location of a house at the periphery of a village increased the risk of infestation and had a very high weight. Keeping chickens in a coop or corral was a major determinant of infestation - it resulted in a 2. 4 fold higher risk of infestation by triatomines - while having free-ranging chickens had no effect (Table 2). Cleaning trash from the peridomicile area similarly doubled the risk of infestation, and the presence of more than two dogs also significantly increased this risk. The storage of firewood inside the house was a major protective factor in the averaged model, although the individual effect was not statistically significant. Risk factors of secondary importance consisted of the proximity of a public street light, which had been identified before [37], and the presence of rock piles in the peridomicile. Again, the individual contributions of these factors in terms of odds ratio was not significant. Finally, the complete plastering of walls and the presence of perching birds had very minor weights in the model and may thus be of limited relevance as determinants for infestation. Potential interactions between the six most supported variables were further evaluated a posteriori, but none of them reached statistical significance. We then tested if these determinants of infestation could be used to predict house infestation, and thus be used to target potential vector control interventions. The generalized coefficient of determination of the averaged model was, indicating that about a third of the variance in the observed pattern of infestation at the level of a single house could be predicted by the model. The model allowed the correct identification of 90% of non-infested houses (specificity), while 41% of infested houses could be correctly identified (sensitivity). We also grouped houses according to their predicted probability of infestation to assess the reliability of the model at a somewhat larger scale (groups of 20 houses) by comparing the predictions with the observed infestation index of each group of houses. In this case, the averaged model provided an excellent prediction of infestation probability compared with the observed infestation index (R2 = 0. 85, P<0. 0001, slope = 0. 878) and was thus able to explain most of the variance in the infestation pattern (Fig. 2). Transient house infestation by non-domiciliated triatomine vectors remains a key challenge for the design of sustainable vector control interventions and further reduction of the burden of Chagas disease [47]. This infestation pattern makes conventional insecticide spraying poorly effective, and a better understanding of the determinants of infestation is needed to formulate novel vector control strategies [31], [32], [36], [48]. In fact, risk factors for house infestation by these non-domiciliated triatomines remain poorly understood, limiting the breadth of potential control interventions to be tested. We performed here the first detailed analysis aimed at identifying possible determinants of domestic infestation by T. dimidiata. From the initial univariate screening of 127 eco-bio-social variables describing the rural ecosystem, it was clear that variables associated with infestation by invasive triatomines are distinct from the determinants usually associated with infestation and colonization with domiciliated triatomines. Indeed, socio-economic status or housing quality were clearly not relevant for infestation [23], [24]. Similarly, even indoor use of a variety of domestic insecticide products was found irrelevant to prevent triatomine infestation. On the other hand, the strongest five determinants for infestation that were identified through our model selection and averaging approach included the number of dogs, having chickens in a corral, the practice of cleaning of trash from the peridomicile, and being located in the periphery of the village, which all favoured infestation, while the presence of firewood inside houses appeared protective. To a lesser extent, the proximity of public lights and the presence of rock piles in the peridomiciles were also associated with infestation according to our model. The relationship between the storage of firewood and the cleaning of the peridomicile with house infestation is difficult to interpret. Indeed, the presence of firewood has usually been associated with increased infestation risk [49] due to the passive transport of bugs and the potential refuge it provides. Alternatively, households may use smoke from firewood to repel insects from their house, as reported in the state of Chiapas, Mexico [50]. Peridomicile cleaning would also be expected to limit peridomestic infestation, and as a consequence the dispersal of peridomestic bugs towards houses. Studies on peridomicile management aimed at eliminating peridomestic bug colonies suggest it may indeed contribute to integrated vector control [32], [51]. Alternatively, peridomicile cleaning may reduce the availability of refuges and increase bug dispersal, and as a consequence favour domestic infestation. This potential effect of environmental management has not been considered yet, and may have contributed to the pattern observed in this study. On the other hand, all other determinants for transient infestation that we identified here are consistent with our previous hypothesis that poorly-fed bugs from both the peridomicile and surrounding sylvatic areas, foraging for blood sources, are infesting houses [26], [52]. Thus, houses located in the periphery would be more at risk of infestation as shown before [34], [35], [52]. The presence of dogs and chickens, the most common domestic animals, would be attractive food sources that may be effectively detected by foraging bugs [53]. Further studies of T. cruzi infection in dogs may provide additional information on their role as domestic reservoirs in the villages. Interestingly, only chickens held in a corral or coop seem to contribute to infestation, while free-ranging chickens do not play a role. Thus, a concentrated and captive chicken population may provide a stronger signal to attract bugs and an easier food source. The contribution of dogs and chickens in infestation by T. infestans has been observed previously [11], [54], [55]. Public street lights may then interfere with the dispersal process and attract bugs to nearby houses as suggested before [37] and rock piles may provide additional peridomestic refuges for bugs. The reliability of our model was further assessed by evaluating its ability to predict house transient infestation based on the determinants identified. At the level of an individual house, our averaged model was able to account for about 30% of the presence/absence of infestation by T. dimidiata. However, when houses were grouped according to their predicted infestation, the model then accounted for up to 85% of the variations in the observed infestation index. Unfortunately, the predictive value of models is rarely reported by authors attempting to identify risk factors, even though they often base subsequent vector control interventions on such studies, with little certainty that infestation will actually be affected [9], [12]–[15], [17]–[19]. The general practice is to use odds ratio statistics to identify key risk factors, but the actual capacity of the key factors to predict the level of risk is not assessed. This lack of assessment occurs with risk studies of other vector borne diseases as well, including malaria or dengue [56]–[60]. To our knowledge such evaluation has been attempted only once from a multimodel inference approach similar to the one adopted in this paper. This study identified a limited number of risk factors contributing to the infestation of domiciles or of chicken coops by T. infestans in the Grand Chaco, Argentina [54] and the authors genuinely tested the predictive capacity at the site level of their best statistical model. They reported levels of sensitivity and specificity of 49% and 82% in domicile infestation, and 65% and 71% in chicken coops infestation, which is similar to the sensitivity (41%) and specificity (90%) we reported. In our study, despite the fact that bug dispersal is a complex dynamic process with potentially many interacting factors, which can lead to very variable infestation outcomes, the few variables we identified account for most of the variability in terms of infestation at the level of groups of houses (R2 = 0. 85). These data suggest that we have identified the key determinants of infestation in this model and that additional determinants we may have missed will be minor contributors to infestation. While the model was not tested on additional villages, the representativeness of the studied villages suggests that our findings may be extrapolated to villages with similar characteristics. Additional studies should help explore further generalization of our results. Thus, infestation is actually associated with a rather limited set of risk factors, each having a modest contribution, and their particular combinations and synergism is what seems to be associated with infestation of specific houses. Importantly, our results suggest that targeting a single risk factor may be ineffective for vector control and that combined integrated interventions targeting multiple variables may be required to adequately reduce infestation by T. dimidiata. Nonetheless, the small number of factors to be modified suggests that such integrated control might be feasible. In conclusion, our search for eco-bio-social determinants for house transient infestation by non-domiciliated T. dimidiata clearly identified several factors, including the presence of dogs and chickens in the peridomicile, potential refuges such as rock piles, and the location of the house close to the vegetation at the periphery of the village, and proximity to public street lights. These factors allowed us to explain most of the variation in infestation within villages in rural Yucatan, Mexico. These results may be used for risk stratification within villages for both vector surveillance and control. They also suggest that combined integrated vector interventions, informed by an Ecohealth perspective, should target several of these factors to effectively reduce infestation and provide sustainable vector control.

Chagas disease is a parasitic disease of major importance in the Americas, transmitted by triatomine insects. Integrated control interventions targeting a combination of factors associated with the presence of the insect vectors are increasingly investigated for improved control. Here we identified the factors associated with the seasonal intrusion of triatomine vectors in houses from the Yucatan peninsula, Mexico, by studying the characteristics of 308 houses from 3 villages. The presence of triatomine vectors was associated with the presence of dogs, chickens and potential bug refuges, such as rock piles, and the proximity of houses to vegetation at the periphery of the villages and to public light sources. Thus, factors favoring seasonal intrusion of triatomines appear different from those favoring their domiciliation. Integrated control interventions based on this Ecohealth perspective should focus on several of the factors identified in this study to achieve effective and sustainable vector control.

Abstract Introduction Materials and Methods Results Discussion

2013

Eco-Bio-Social Determinants for House Infestation by Non-domiciliated Triatoma dimidiata in the Yucatan Peninsula, Mexico

GABAergic interneurons (INs) in the dorsal lateral geniculate nucleus (dLGN) shape the information flow from retina to cortex, presumably by controlling the number of visually evoked spikes in geniculate thalamocortical (TC) neurons, and refining their receptive field. The INs exhibit a rich variety of firing patterns: Depolarizing current injections to the soma may induce tonic firing, periodic bursting or an initial burst followed by tonic spiking, sometimes with prominent spike-time adaptation. When released from hyperpolarization, some INs elicit rebound bursts, while others return more passively to the resting potential. A full mechanistic understanding that explains the function of the dLGN on the basis of neuronal morphology, physiology and circuitry is currently lacking. One way to approach such an understanding is by developing a detailed mathematical model of the involved cells and their interactions. Limitations of the previous models for the INs of the dLGN region prevent an accurate representation of the conceptual framework needed to understand the computational properties of this region. We here present a detailed compartmental model of INs using, for the first time, a morphological reconstruction and a set of active dendritic conductances constrained by experimental somatic recordings from INs under several different current-clamp conditions. The model makes a number of experimentally testable predictions about the role of specific mechanisms for the firing properties observed in these neurons. In addition to accounting for the significant features of all experimental traces, it quantitatively reproduces the experimental recordings of the action-potential- firing frequency as a function of injected current. We show how and why relative differences in conductance values, rather than differences in ion channel composition, could account for the distinct differences between the responses observed in two different neurons, suggesting that INs may be individually tuned to optimize network operation under different input conditions. The dorsal lateral geniculate nucleus (dLGN) receives input from retinal ganglion cells and transmits processed information to visual cortex. About 75–80% of the neurons in the dLGN are thalamocortical (TC) neurons, also called relay neurons, as they relay information from the retina to the cortex. Local GABAergic interneurons (INs) constitute the remaining 20–25%, and are responsible for most of the intra-nuclear connections [1]. By providing feed-forward inhibition from retinal ganglion cells to TC neurons, INs control the number of visually evoked spikes in TC neurons, and refine the receptive fields of TC neurons (e. g. [2]–[5]). The INs are also important for synchronizing thalamic oscillations [6]–[7]. Comprehensive mathematical network models will likely be important for a comprehensive understanding of the key functional features of early sensory processing [8]–[11]. Due to the relatively high abundance of experimental data, the early visual system has attracted particular interest from theoretical neurobiologists. Several mechanistic network models aimed at mimicking responses of neurons in primary visual cortex, and neurons in the dLGN that provide the feed-forward input to visual cortex, have been developed [12]–[21]. Such network models require (i) detailed information about network connectivity and (ii) mathematical neuron models that capture the salient physiological properties of the individual neurons types. For dLGN, a host of physiological and anatomical experiments have provided detailed information about the neuronal connectivity, as well as the morphology and physiology of TC neurons and INs (see e. g. reviews in [22]–[24]). Different electrophysiological characteristic of TC neurons have been captured in a series of modeling works [25]–[31], and the accumulated insight has been incorporated in a high-resolution model which comprises a detailed description of the cell morphology, a set of different ion channels and their distribution over the somatodendritic membrane [32]. For the INs, the situation is more problematic: models are few and less detailed [33]–[36]. Until now, network models for the early visual system have either omitted INs entirely, or represented them in a very simplified manner (but see [37]). A satisfactory theoretical understanding of the computational properties of the dLGN circuit, and thus also the input to visual cortex, will likely require network models incorporating more detailed IN models. The development of such models is the topic of the present paper. INs may exhibit a rich variety of firing patterns, including (i) initial “sags” by hyperpolarizing current injections, (ii) rebound bursts when released from hyperpolarization, (iii) tonic firing of action potentials (APs) by depolarizing current injections, (iv) initial bursts by depolarizing current injections, (v) spike-time adaptation during depolarizing current injections, and (vi) periodic bursting during depolarizing current injections [36], [38]–[44]. Most previous models focus on aspects of passive signal propagation in INs [33]–[35]. To our knowledge, the only currently available model that includes a variety of the active mechanisms in INs was primarily developed in order to study the mechanisms behind the rebound bursts [36], whereas other properties such as dendritic conductances and the relationship between somatic current injections and action potentials frequency (hereby referred to as the I/O curve) were not taken into account. The morphology and distribution of dendritic ion channels are crucial for the integration of synaptic input (reviewed in [45]), and can even influence the neuron' s response to somatic current injections [46]–[49]. Several active conductances have been identified in the dendrites of INs [38], [50]–[54]. Dendritic ion channels are likely of particular importance in INs, as their dendrites have not only postsynaptic contacts for excitatory retinal and cortical input, but also presynaptic terminals for inhibitory output to TC dendrites [2]. An understanding of how INs provides feed forward inhibition to TCs thus requires models that incorporate the electrically active processes in the dendritic tree. No previous IN model includes such properties. We here propose a new and detailed multi-compartment model of the IN, which advances previous models in several aspects. Firstly, it includes a detailed 3D reconstruction of IN morphology, and a set of somatic and dendritic active conductances which could reproduce some of the key response patterns of INs (including (i) - (vi) listed above). In this way it lays the foundation for simulating active dendritic signaling on a fine spatiotemporal scale. Secondly, we present two different parameterizations (P1 and P2) of the model, which were constrained by current-clamp data from two example neurons (IN1 and IN2). This approach allowed us to do a comparative study, and relate differences in the parameterizations P1 and P2 to differences in the firing patterns between IN1 and IN2. The two parameterizations contain the same set of ion channels such that differences in the responses of the two neurons could be explained solely in terms of relative differences in the peak values of the conductances. Thereby, we demonstrate that relative differences in conductance values of the included ion channels may account for the substantial variations in firing patterns observed between INs. Finally, we were able to explain the experimentally observed responses in different neurons (IN1 or IN2) under 8 different input conditions, obtaining quantitative agreement with the experimental I/O-curves over the entire input range studied. This is a significant advance compared to previous models of similar cells. Since the overall input to any given cell in a network generally varies with time over a wide dynamic range, our model will likely allow a more accurate representation of the integration and computational properties of these neurons when included in a realistic dLGN network. Preliminary results from this model have previously been presented in abstract form [55]. The model will be publicly available on ModelDB (http: //senselab. med. yale. edu/modeldb). Brain slices containing dLGN were prepared from GAD67-GFP (Δneo) knock-in mice [56] in accordance with the guidelines and approval of the Animal Care Committee in Norway. Mice, 29–33 days old, were deeply anaesthetized with halothane and sacrificed by rapid decapitation. A block of the brain was dissected out and 250–300 µm thick coronal slices were cut in 4°C oxygenated (5% CO2–95% O2) solution containing (mM): 75 glycerol, 87 NaCl, 25 NaHCO3,2. 5 KCl, 0. 5 CaCl2,1. 25 NaH2PO4,7 MgCl2, and 16 D-glucose, and kept submerged in oxygenated (5% CO2–95% O2) artificial cerebrospinal fluid (ACSF) containing (mM): 125 NaCl, 25 NaHCO3,2. 5 KCl, 2 CaCl2,1. 25 NaH2PO4,1 MgCl2 and10 D-glucose at 34°C for at least 30 min before the experiment. During experiments, slices were kept submerged in a small (∼1. 5 ml) chamber and perfused with ACSF at the rate of 5 ml min−1 heated to at 36°C through an inline heater. In some of the experiments, as indicated, 4-Ethylphenylamino-1,2-dimethyl-6-methylaminopyrimidinium chloride (ZD 7288; 20 µM; Tocris Bioscience, Bristol, UK) was included in the perfusion ACSF to block the hyperpolarization-activated cation current Ih. Whole-cell voltage- or current-clamp recordings were made from INs in dLGN. Neurons were visualized using DIC optics and infrared video microscopy. INs were identified by expression of GFP which was specifically expressed in GABAergic neurons under control of the endogenous GAD67 promoter in the GAD67-GFP knock-in mice [56] we used. Recordings were obtained with borosilicate glass electrodes (4–6 MΩ) filled with (mM): 115 potassium gluconate, 20 KCl, 10 HEPES, 2 MgCl2,2 MgATP, 2 Na2ATP, 0. 3 GTP (pH adjusted to 7. 3 with KOH). For morphology reconstruction, biocytin (0. 25%; Sigma-Aldrich, St Louis, USA) was included in the intracellular solution. Current traces were recorded and filtered at 3 kHz with a HEKA EPC 9 amplifier (HEKA Electronik, Lambrecht, Germany), while voltage traces were recorded and filtered at 10 kHz with an Axoclamp 2A amplifier (Molecular Devices, Palo Alto, CA, USA). After recordings, slices were fixed by 0. 1 M phosphate buffer containing 4% paraformaldehyde and kept there for at least 24 h. After biocytin histochemistry with avidin-biotin complex (Vectastain ABC kit, Vector Laboratories, Inc, USA) and diaminobenzidine (DAB; Sigma-Aldrich, St. Louis, USA), interneurons were drawn under a x100 objective using software for neuron reconstruction (Neurolucida, MicroBrightField, Inc. USA). The set of empirical data used for constraining the model is presented in Figure 2. The firing patterns in two different INs (IN1 and IN2) under 8 different experimental conditions are shown in Figure 2A–B. IN1 had a resting potential of -63 mV (Figure 2A1). The onset of a strong hyperpolarizing current injection (-150 pA) made the membrane potential drop rapidly to a hyperpolarized peak value, before it increased to a less hyperpolarized plateau value. This initial sag is a trademark of the Ih current. IN2 had a resting potential of -69 mV (Figure 2A2). The initial sag for hyperpolarizing current injections was less pronounced in IN2 than in IN1. Depolarizing stimuli gave rise to an initial, transient response (characterized by a high AP-firing frequency), followed by regular AP- firing at lower frequency. In IN2 the initial response tended to be distinct and burst-like (see e. g. Figure 2A2,40 pA), whereas IN1 tended to have a more gradual transition between the initial response and the regular AP-firing (see e. g. Figure 2A1,70 pA). We did not quantify these differences, but use the term initial burst for all initial responses that can be distinguished from the later, more regular AP firing. IN1 needed stronger depolarizing input (≥55 pA) to initiate AP-firing than IN2 (≥40 pA). However, IN1 had the steepest I/O-curve (see below), so that both neurons had about the same firing frequencies when stimulated at 70pA. In one experiment, small current injections were used to hold the membrane potential at a depolarized value (-57 mV in IN1 and -58 mV in IN2). In this case, strong hyperpolarizing current pulses (-150 pA) were followed by a rebound burst in IN1 (Figure 2B1), but not in IN2 (Figure 2B2). The Ih -current in rat INs has no calcium dependence [82] and has been measured and thoroughly described [41]. A comparison of the time course of the initial sag for recordings from rat INs [36], [41] with ours from mouse INs, especially IN1 (Figure 2A1), indicated that Ih may differ between the two species. We therefore recorded the Ih response to different hyperpolarizing voltage steps in our mouse INs (Figure 2C), and used these data to estimate the Ih kinetics. We derived Ih -activation curves for mouse INs using the following procedure [41]: With Imax denoting the maximum observed amplitude throughout the trial (e. g. , about -180 pA in the dataset shown in Figure 2C), and Iinf denoting the steady-state value of the response for a given command potential (V), the ratio Iinf/Imax was plotted against V for three data sets (Figure 3A). The data points were then fitted by a Boltzmann curve (Figure 3A; full line), given by: Iinf/Imax = (1+exp ( (V-shift) /stp) ) −1, where shift is the potential at half-inactivation, and stp determines the steepness of the activation curve. Using the inbuilt optimizer fminsearch in Matlab, we obtained the optimal parameters shift = -96 mV and stp = 10 mV. The corresponding values estimated for rat INs were -79 mV and 7. 4 mV [36], [41]. In order to determine the voltage dependence of the time constants for steady-state activation, the current traces (interval from 0–1000 ms after stimulus onset in Figure 2C) were fitted by exponential curves on the form Ih = (1-exp (-t/τh) ) ·Iinf. Only a single exponential term was used, as it yielded a good fit. In this way we estimated the time constant (τh) at each command potential as plotted in Figure 3B. The data points for τh were in turn fitted with a bell shaped curve (as in [41]) of the form: τh (V) = exp[ (V+a1) /a2]/ (1+exp[ (V+a3) /a4]), with V measured in mV and τh in ms. Using fminsearch, we obtained the optimal fit for [a1, a2, a3, a4] = [250,30. 7,78. 8,5. 78]. Note that the maximum value of τh is about 200 ms, while the corresponding value found for rat INs was about 1000 ms [36], [41]. The faster kinetics was in good agreement with the time course of the initial sag (see Figure 4). We sought two parameterizations of the model (P1 & P2), which separately should explain the characteristic features of the respective data sets (IN1 and IN2) under all 8 experimental conditions (Figure 2A–B). As an additional constraint we assumed that the two neurons contained the same types of ion channels and had the same axial resistivity (Ra). This means that we aimed to explain the differences between IN1 and IN2 in terms of differences in three passive parameters (Rm, Epas, Cm), seven parameters representing the density of ion channels (gh, gNa, gKdr, gCaT, gCaL, gAHP, gCAN), and two parameters (ShNa, ShKdr) representing shifts in the activation/inactivation curves of Na and Kdr along the voltage axis. Shifts in the kinetics of the AP-generating currents were allowed to be free parameters as we observed clear differences in spiking threshold between neurons IN1 and IN2. In order to limit the number of free parameters, the voltage and calcium dependence of the remaining ion channels were assumed to be identical in the two neurons. The experimental protocols for the two example neurons (Figure 2A–B) were replicated in the simulations shown in Figure 4, where we show that many significant features of the experimental results are reproduced by the model using the two sets of parameters (P1 and P2, Table 1). P1 had resting potential -63 mV, and P2 had resting potential -69 mV (as in the empirical data sets). Stimulus intensities between -150 pA and 20 pA gave rise to sub-threshold responses in both models. The higher response amplitudes in P2 were due to the significantly higher membrane resistance found for this neuron. Initial sags in P1 and P2 for strong hyperpolarizing current injections (-150 pA) were due to Ih. Simulated action potentials had a width (at half max response) of about 0. 4 ms in P1 as well as in P2 (Figure 5A), which is typical for INs [39], [40], and agreed well with the AP width of IN1. The APs elicited by IN2 were somewhat broader, and had a width of about 0. 6 ms. We did not change the kinetics of Na and KDR channels to account for the variability in AP shapes. The mechanisms behind other characteristics in the response patterns are discussed in the following subsections. As in the data sets (Figure 2A–B), depolarizing current injections to the model (55–70 pA to P1 and 40–70 pA to P2) gave rise to an initial burst (or a few APs with short intra-spike intervals) followed by regular activity with a lower AP firing frequency (Figure 4A). The slope of the I/O curve was mainly regulated by the interplay between calcium entering through CaL channels and a single, calcium activated potassium channel (IAHP). As the high-voltage activated CaL channels open during APs, the intracellular calcium concentration will accumulate during high firing frequencies, so that also IAHP increases with firing frequency. In this way the CaL/IAHP-mechanism flattens the I/O curves, as has been well described in earlier modeling studies (e. g. [83]). Without this regulatory mechanism, the firing frequency (as resulting from the Na and Kdr channels) was generally too high, and the I/O curves were too steep compared to the data in Figure 2A (results not shown). With parameters as in Table 1, the I/O curves of P1 and P2 agreed well with the data sets, being steeper in P1, as the conductance values for both these channels (gCaL and gAHP) were higher in P2 (Figure 5B). In order to investigate the impact of IAHP, we interchanged the conductance values (gAHP) between P1 and P2, leaving all other parameters at their original values. The high gAHP resulted in a lower spike frequency in P1 (Figure 6B1), and in a threefold reduction in the slope of the I/O-curve (from the original ∼0. 8 spikes/pA to ∼0. 25 spikes/pA (results not shown) ). Correspondingly, the low gAHP increased the spiking frequency in P2 (Figure 6B2), and made the I/O curve three times steeper (from the original ∼0. 3 spikes/pA to ∼0. 9 spikes/pA). The initial bursts for depolarizing current injections were well reproduced by the model (Figure 4A), and depended mainly on CaT. The Ih conductance had negligible impact on these initial bursts (results not shown), but the bursts vanished when gCaT was set to zero (Figure 5C), and became stronger when gCaT was increased (Figure 5D). Despite P1 having the highest gCaT, the initial bursts were most pronounced in P2. This may seem counterintuitive, but we found that this is mainly due to differences in resting membrane potential. The lower resting potential in P2 means less inactivation of CaT at rest, and compensates for the lower gCaT. Note that our CaT kinetics (Figure 1D) was shifted +8 mV compared to the empirical data set for rats [44]. Using the CaT activation from that empirical study would thus give even stronger bursts (Figure 2A). Due to the high intra-burst firing frequencies, initial bursts gave rise to strong IAHP activation, resulting in a period of afterhyperpolarization which distinctly separated initial bursts from the regular AP firing. The afterhyperpolarization was most pronounced in P2, as P2 had the higher gAHP (Figure 4A), but became most pronounced in P1 when gAHP was interchanged between the two parameterizations (Figure 6B). Previous experiments have shown that some INs respond to depolarizing input by periodic bursting [42], [52]. Although periodic bursting was not observed in our experiments (IN1 and IN2), we ran test simulations to see if the mechanism that explained the initial bursts and subsequent afterhyperpolarization in IN1 and IN2 also could give rise to periodic bursting. Using the parameter sets P1 and P2 as a starting point, an increase in gCaT (by a factor 2) increased the intensity of the initial burst, but also the overall AP firing frequency, suggesting a nonzero CaT-activity throughout the stimulus period. In P2, the increased gCaT also gave rise to a periodic firing of pairs of APs (Figure 6D2), indicating a periodic interplay between CaT-driven bursts and IAHP-driven afterhyperpolarizations. By also increasing gAHP (by a factor 2), both the afterhyperpolarization (triggering the bursts) and the bursts (triggering the afterhyperpolarization) became more intense, and periodic bursting was obtained in both P1 and P2 (Figure 6E). The model thus predicted that the interplay between CaT and IAHP could explain the periodic bursting observed in some INs [42], [52], and that periodically bursting neurons have high CaT and IAHP conductances relative to neurons that do not show this behavior (e. g. IN1 and IN2). Small current injections (22 pA in P1 and 12 pA in P2) were used to shift the membrane potential from rest to the holding potentials of -57 mV in P1, and -58 mV in P2. When held at these depolarized potentials, strong hyperpolarizing current injections (-150 pA) were followed by a rebound burst in P1, but not in P2 (Figure 4B). In order to elicit bursts, a preceding hyperpolarization of the membrane potential is often required [36], [53]. However, the somatic current injections used in vitro to study this effect do not occur in vivo. For example, the -150 pA current injections used in our experiments gave rise to a membrane potential much more hyperpolarized than the typical GABAergic reversal potential. We therefore simulated a more realistic, synaptic GABAergic input, to investigate whether our model could elicit rebound bursts under more realistic conditions. We found that 50 synapses of moderate strength (maximum conductances of 1 nS), activated with 10 ms intervals over a time period of 300 ms, reproduced the essential findings from the current-clamp experiments. When held at normal resting potentials, no rebound burst was elicited in either of the parameterizations (Figure 7A). However, when held at -57 mV, the series of synaptic activations provoked a rebound burst in P1, but not in P2 (Figure 7B). Rebound bursts in various neurons are normally mediated by CaT and/or Ih channels [39], [64], [84]–[86]. We used the simulation setup with synaptic input to explore the contributions from these two ion channels to the rebound bursts in INs. In the original parameterizations (Table 1), P1 had higher values than P2 for both gCaT and gh, which explains why P1 and not P2 elicited rebound bursts. When gh was interchanged between P1 and P2, leaving all other parameters as in Table 1, the rebound burst in P1 was reduced to include only a single AP, while the (sub-threshold) rebound response in P2 became more pronounced (Figure 7C). Similar results were obtained when gCaT was interchanged between the two parameterizations: The rebound response in P1 fell below the AP firing threshold, while the (sub-threshold) rebound response in P2 became stronger (Figure 7D). Finally, if both parameters (gh and gCaT) were interchanged between P1 and P2, also the rebound responses were entirely interchanged: P1 showed a small sub-threshold rebound response, while P2 elicited a pronounced rebound burst, resembling that originally seen in P1 (Figure 7E). This suggests that CaT and Ih are of comparable importance for rebound bursts in mouse INs. To investigate how sensitive our results are to the IN morphology, we ran test simulations using three additional IN morphologies with (a) a similar, (b) a smaller and (c) a larger total membrane area compared to the original morphology (o). We replaced the original morphology (o) with the new morphologies (a–c), but kept all other model parameters fixed at the values in Table 1. As the ion channel densities (i. e. conductances per µm2) were kept fixed, the new morphologies (a–c) corresponded to INs with (a) a similar, (b) a higher, and (c) lower input resistance compared to the original morphology (o). The AP shape was not strongly affected by the morphology changes, except from small variations in the afterdepolarization (Figure 5A). The I/O curve in cases (o) and (a) nearly coincided (Figure 5B). As expected from the differences in total input resistance, morphology (b) gave rise to a I/O curve that was steeper and shifted in the negative direction along the current axis, whereas morphology (c) gave rise to an I/O curve that was flatter and shifted in the positive direction along the current axis compared to the cases (o) and (a) (Figure 5B). Although occurring at different current input levels, the characteristic responses of P1 and P2 did not change qualitatively with morphology. Depolarizing current injections of sufficient intensity gave rise to initial bursts followed by regular AP firing, and with the initial bursts being most pronounced for the parameterization P2. Furthermore, strong hyperpolarizing current injections (-150 pA in case of morphology (o), (a) or (b) and -200 pA in case of morphology (c) ) were followed by rebound bursts when using parameter set P1 at a holding potential of -57 mV, but not when using the parameter set P2 at a holding potential of -58 mV. The essential firing patterns of P1 and P2 were thus preserved under changes of morphology, and the I/O curve was always steeper for parameterization P1 (Figure 5C). The set of seven ionic conductances that we used to fit the experimental findings (Table 1) is the same as in the previous model by Zhu et al. [36], but with kinetics that were updated to account for recent findings for activation/inactivation kinetics and somatodendritic distributions, and with conductance values constrained by a broader range of I/O data. Although these seven channel types were successful in reproducing the observed spiking patterns, we cannot exclude the presence of additional mechanisms, either overlapping with an included conductance type in terms of their action on the firing properties, or with a minor or negligible effect on the somatic response observed in the current clamp experiments. Several of the included ion channels have well documented roles in INs, including the role of CaT in burst generation [36], [39], [44], the role of Ih in generating initial sags [41] and the role of CaL in increasing the intracellular calcium concentration [51], [53]. ICAN is involved in making the dendrites leakier in connection with cholinergic modulation [52]. Its influence on the somatic response pattern of INs is less clear, although a previous modeling study has suggested that ICAN may be involved in generating plateau potentials and prolong bursts [36]. Due to the high calcium sensitivity of this channel [36], [88], we found that even small depolarizations of the membrane gave rise to a tonically active ICAN. In our model, the main function of ICAN was to reduce the magnitude of the depolarizing current required for the neuron to reach AP firing threshold. In comparison to somatic voltage recordings from rat INs [39], [41], our recordings from mouse INs show more pronounced initial sags for strong hyperpolarizing current injections (see Figure 2A, -150 pA stimuli). This suggests that the kinetics of Ih may differ between INs in rats and mice, as is the case in CA1 pyramidal neurons [89]. This we confirmed by measuring the voltage dependence of Ih in three mouse INs (Figure 3). Simulations with Ih kinetics based on our own measurements not only agreed better with the sag-shape in the mouse INs, but also predicted that the impact of Ih on rebound burst generation was comparable to that of CaT (Figure 6). This differs from the situation in rat INs, where rebound responses are mainly due to CaT, with no measurable contribution from Ih [39]. Conversely, in CA1 pyramidal neurons it has been shown that rebound spiking can be generated by Ih alone [86]. However, a joint involvement of CaT and Ih in burst generation, were found to underlie intrinsic rhythmic bursting in subpopulations of TCs during inattentiveness in guinea pigs [64], [84]. Intrinsic rhythmic bursting was not observed in our neurons. The presence of CaL-conductances in IN dendrites [51], [53], [54] makes it likely that also inhibitory, calcium-dependent mechanisms (such as IAHP) are present. The role of IAHP in INs has not been previously documented. Our model predicted that the interplay between CaL and IAHP conductances was sufficient for explaining the modulation of the I/O curves, although additional mechanisms could be involved. For example, a slowly activating potassium channel (KM) with a high threshold and no inactivation gave similar results to our CaL and IAHP mechanism, but without any calcium dependence (simulations were made using KM kinetics taken from [90], results not shown). However, no clear functional role could be assigned to KM other than that covered by IAHP. We thus did not explore this issue further, since IAHP gave a better agreement than KM with the time course of the intra-spike membrane potential and, especially, with the afterhyperpolarization following the initial bursts in P2. However, the possibility that KM and IAHP have overlapping functions in regulating the spiking frequency cannot be excluded. This could be experimentally tested by blocking the respective channels. We presented two parameterizations of the model (P1 and P2), which reproduced the electrophysiological properties of two different INs (IN1 and IN2). Rebound bursts as those generally observed only in a small subset of INs [39] were elicited by P1 but not P2. On the other hand, P2 elicited more pronounced initial bursts than P1 when exposed to depolarizing stimuli. P2 also had a less steep I/O curve and required weaker depolarization than P1 in order to reach AP-firing threshold. Our simulations showed that differences between P1 and P2 in terms of response properties and preferred input conditions arose from relative differences in specific conductances. Our model thus supports the idea that conductances values (i. e. channel density) in different subgroups of INs may be tuned in such a way as to optimize network operation under different input conditions. Under in vivo conditions, changes in input conditions (e. g. in synaptic input and/or shifts in membrane potential) may be mediated by mGlu5-receptor activation, GABAergic input from the reticular nucleus, or cholinergic modulation [4], [53], [54], [91], [92]. Data on the somatic voltage responses to somatic current injections do not uniquely determine the distribution of passive and active properties in the dendrites (see e. g. [48], [93]). Assumptions on the somato-dendritic distributions of CaT and CaL in our model were therefore based on calcium imaging data [50], [51]. This may be very useful in future studies, as the specific sub-cellular localization of different types of calcium channels may be particularly important for their specific functional role [43]. The assumed distributions of the remaining ion-channels were based on what we judged to be the most relevant literature to date. With the assumption that dendritic Na and Kdr densities were 10% of those in the soma, simulations showed that APs got broader during propagation in the dendrites, whereas the amplitude did not get significantly attenuated (results not shown). This is, at least at a qualitative level, in good agreement with recent experiments using voltage-sensitive dye [70]. We cannot exclude the possibility that important aspects of dendritic signaling are not captured by our model at this stage. For instance, there is an uncertainty regarding the distribution of our dendritic CaT channels. We based our CaT distribution on electrophysiological data [50], which indicated that the CaT density increases with distance to soma, while other, anatomy-based studies, have indicated a uniform distribution of dendritic CaT channels [94]. In test simulations we showed that we could obtain essentially the same results as in Figure 4 and Figure 5 also with a uniform CaT distribution, simply by rescaling the total CaT conductances (results not shown). However, although it may not be crucial for an IN' s response to somatic current injections, the CaT distribution will likely influence aspects of dendritic signaling, such as the probability for dendritic GABA-release. A related source of uncertainty concerns the presence of dendritic A-type potassium channels (KA), which, suggested by early studies, counteracted the CaT-channels in the dendrites of INs, and suppressed bursting [38]. This mechanism has, however, not been observed consistently, and several experiments have reported bursting INs [36], [39], [40], [43]. For thalamic reticular neurons, the ability versus inability to burst was rather explained by a varying density of CaT-channels [95], as also fits well with our findings. KA channels are often most densely present in distal dendrites and might have an influence on backpropagating action potentials [96]–[98]. However, recent experiments on INs found that attenuation of dendritic Ca-signals was relatively small [53]. As the importance of KA is unclear, these channels were not included in our model. Test simulations, using a moderate KA density (channel kinetics from [97]) in the dendrites, did not affect the somatic response to somatic stimuli significantly (results not shown). Such channels could therefore be readily added to the model if future experiments identify a clear functional role of KA in IN dendrites. Parts of the distal dendrites of INs form so called triadic synapses with axons from retinal ganglion cells and dendrites from TC neurons [4], [99]. In these triads, the IN terminals are, at the same time, postsynaptic to retinal input, and presynaptic to TC neurons. The conditions for GABAergic release from IN dendrites are not fully known, but may depend on intracellular calcium levels which are elevated by (backpropagating) sodium spikes as well as signals evoked by local synapses [4], [53], [54], [99]. It is known that cholinergic modulation reduces the membrane resistance of INs, through the M2-receptor mediated activation of Ih, ICAN and a linear, unspecified potassium current [52]. During sleep, when the cholinergic tone tends to be low, interneurons are likely to be electronically compact. INs may then provide long range inhibition, as synaptic input at one location in the dendritic tree then result in GABA release throughout the dendritic and axonal arbors. During awake states, when the cholinergic tone is high, distal dendritic regions may become electronically isolated from each other and from the soma. During these conditions, the triads may function as independent units, being excited by the presynaptic retinal afferents and then directly inhibiting only the postsynaptic dendrites of TC neurons [33], [52], [100], [101]. In our model, somatic action potentials successfully invaded distal dendritic region, and it is thus likely that our parameterizations (P1 and P2), correspond to conditions with a low cholinergic tone. The present model includes dendritic sodium-, potassium-, and calcium channels, and at least some of the mechanisms that are affected by cholinergic modulators. It computes the time course of the intracellular calcium levels in each compartment, and contains essential mechanisms for addressing signaling in IN dendrites on a fine spatiotemporal scale. In a network model including the dLGN circuitry, this will be of paramount importance for simulating the interactions between TCs and INs, as well as inputs to these cells from retina, cortex, thalamic reticular nucleus and possibly modulatory input from the brain stem.

The dorsal lateral geniculate nucleus (dLGN) is a part of the visual thalamus. This region contains two types of neurons: thalamocortical neurons and local interneurons. Thalamocortical neurons receive information from the retina and transmit information to visual cortex. The interneurons regulate the activity of thalamocortical neurons through inhibitory connections. This regulation is not properly understood, but it is believed to promote contrast enhancement and other vital visual functions. A powerful tool for development of a mechanistic understanding of dLGN functions is computer models that include the involved neurons, their interconnections and their interactions. Quite sophisticated models are available for thalamocortical neurons, but previous interneuron models are too simple for adequate mechanistic understanding of the functional properties of interneurons. We here present a detailed compartmental interneuron-model based on experimental data. The typical response patterns vary between different interneurons, but also within a given neuron, depending on the stimulus it receives. The model identifies a set of ionic mechanisms that can explain this diversity of activity patterns. In addition to being a useful building block for future network simulations of the dLGN, the model gives useful insight into the operating principles of dLGN interneurons.

Abstract Introduction Methods Results Discussion

systems biology visual system computer science theoretical biology computational neuroscience single neuron function biology computational biology sensory systems neuroscience genetics and genomics

2011

A Multi-Compartment Model for Interneurons in the Dorsal Lateral Geniculate Nucleus

The microbial communities that inhabit the distal gut of humans and other mammals exhibit large inter-individual variation. While host genetics is a known factor that influences gut microbiota composition, the mechanisms underlying this variation remain largely unknown. Bile acids (BAs) are hormones that are produced by the host and chemically modified by gut bacteria. BAs serve as environmental cues and nutrients to microbes, but they can also have antibacterial effects. We hypothesized that host genetic variation in BA metabolism and homeostasis influence gut microbiota composition. To address this, we used the Diversity Outbred (DO) stock, a population of genetically distinct mice derived from eight founder strains. We characterized the fecal microbiota composition and plasma and cecal BA profiles from 400 DO mice maintained on a high-fat high-sucrose diet for ~22 weeks. Using quantitative trait locus (QTL) analysis, we identified several genomic regions associated with variations in both bacterial and BA profiles. Notably, we found overlapping QTL for Turicibacter sp. and plasma cholic acid, which mapped to a locus containing the gene for the ileal bile acid transporter, Slc10a2. Mediation analysis and subsequent follow-up validation experiments suggest that differences in Slc10a2 gene expression associated with the different strains influences levels of both traits and revealed novel interactions between Turicibacter and BAs. This work illustrates how systems genetics can be utilized to generate testable hypotheses and provide insight into host-microbe interactions. The intestinal microbiota has profound effects on host physiology and health [1–3]. The composition of the gut microbiota is governed by a combination of environmental factors, including diet, drugs, maternal seeding, cohabitation, and host genetics [4–7]. Together, these factors cause substantial inter-individual variation in microbiota composition and modulate disease risk [8,9]. Alterations in the composition of the microbiota are associated with a spectrum of cognitive, inflammatory and metabolic disorders [10–12], and a number of bacterial taxa have been causally linked with modulation of disease [13–15]. A major challenge in the field is deciphering how host genetics and environmental factors interact to shape the composition of the gut microbiota. This knowledge is key for designing strategies aimed at modifying gut microbiota composition to improve health outcomes. Several mouse and human studies have examined the role of host genetics in shaping the composition of the gut microbiota [16]. Mouse studies comparing gut bacterial communities from inbred mouse strains [17,18] and strains harboring mutations in immune-related genes [19–22] support this notion. Additionally, quantitative trait locus (QTL) analyses in mice have identified genetic regions associated with the abundance of several bacterial taxa and community structure [23–26]. Twin studies and genome-wide association studies (GWAS) in humans have identified heritable bacterial taxa and SNPs associated with specific gut microbes. While comparing these studies is often difficult due to differences in environmental variables among populations, some associations are consistently detected among geographically discrete populations, such as the association between Bifidobacterium abundance and the lactase (LCT) gene locus [27–29], indicating the abundance of specific taxa is influenced by host genetic variation. Gut microbes and the host communicate through the production and modification of metabolites, many of which impact host physiology [30–34]. Bile Acids (BAs) are host-derived and microbial-modified metabolites that regulate both the gut microbiome and host metabolism [35–37]. BAs are synthesized in the liver from cholesterol, stored in the gallbladder and are secreted in the proximal small intestine where they facilitate absorption of fat-soluble vitamins and lipids. Once in the intestine, BAs can be metabolized by gut bacteria through different reactions, including deconjugation, dehydroxylation, epimerization, and dehydrogenation, to produce secondary BAs with differential effects on the host [33,35]. In addition to their direct effects on the host, BAs shape the gut microbiota composition through antimicrobial activities [38,39]. The detergent properties of BAs cause plasma membrane damage. The bactericidal activity of a BA molecule corresponds to its hydrophobicity [40]. Additionally, the microbiota modulates primary BA synthesis through regulation of the nuclear factor FXR [41]. Thus, we hypothesized that host genetic variation associated with changes in BA homeostasis mediates alterations in gut microbiota composition. To investigate how genetic variation affects gut microbiota and BA profiles, we used the Diversity Outbred (DO) mouse population, which is a heterogenous population derived from eight founder strains: C57BL6/J (B6), A/J (A/J), 1291/SvImJ (129), NOD/ShiLtJ (NOD), NZO/HiLtJ (NZO), CAST/EiJ (CAST), PWK/PhJ (PWK), and WSB/EiJ (WSB) [42,43]. These eight strains capture a large breadth of the genetic diversity found in inbred mouse strains. Additionally, the founder strains harbor distinct gut microbial communities and exhibit disparate metabolic responses to diet-induced metabolic disease [18,44,45]. The DO population is maintained by an outbreeding strategy aimed at maximizing the heterozygosity of the outbred stock. The genetic diversity and large number of generations of outbreeding make it an ideal resource for high-resolution genetic mapping of microbial and metabolic traits [43]. We characterized the intestinal microbiota composition and plasma and cecal BA profiles in ~400 genetically distinct DO mice fed a high-fat/high-sucrose diet for ~22 weeks and performed quantitative trait loci (QTL) analysis to identify host genetic loci associated with these traits. Specifically, we focused our analysis on potentially pleiotropic loci, which we defined as a single genetic locus that associates with both bacterial and BA traits. Our analysis revealed several instances of bacterial and metabolite traits attributed to the same DO founder haplotypes mapping to the same position of the mouse genome, including a locus associated with plasma BA levels and the disease-modulating organism Akkermansia muciniphila. Additionally, we identified the ileal BA transporter Slc10a2 as a candidate gene that regulates both the abundance of Turicibacter sp. and plasma levels of cholic acid. We investigated the impact of genetic variation on gut microbiota composition and bile acid (BA) profiles using a cohort of ~400 DO mice maintained on a high-fat high-sucrose diet (45% kcal from fat and 34% from sucrose) for ~22 weeks (range 21–25 weeks), starting at weaning. We previously showed that this diet elicits a wide range of metabolic responses in the eight founder strains that are associated with microbiome changes [18,46]. Furthermore, we incorporated in our analyses previously published clinical weight traits collected from the same DO mice [47]. All animals were individually housed throughout the duration of the study to measure food intake and minimize microbial exchange. We performed LC-MS/MS analyses of plasma and cecal contents to assess relative variation in the levels of 27 BAs. Both plasma and cecal bile acids were measured to provide a comprehensive picture of systemic BA homeostasis. There was substantial variation in the plasma and cecal BA profiles across the 384 mice (Fig 1A and 1B; S1 Table). Additionally, we examined gut microbiota composition (n = 399) using 16S rRNA gene amplicon sequencing of DNA extracted from fecal samples collected at the end of the experiment. Within the cohort, there were 907 unique Exact Sequence Variants (ESVs), (100% operational taxonomic units defined with dada2 [48]), which were agglomerated into 151 lower taxonomic rankings (genus, family, order, class, phyla). The microbial traits represented each of the major phyla found in the intestine and the relative abundance of these phyla was highly variable among the DO mice (Fig 1C). For instance, the abundance of taxa classified to the Bacteroidetes phylum ranged from 1. 17–89. 28%. For subsequent analysis, we identified a core measurable microbiota (CMM), which we defined as taxon found in at least 20% of the mice [24]. This was done to remove the effects of excessive variation in the data due to bacterial taxa that were low abundance and/or sparsely distributed. In total, the CMM was comprised of 86 ESVs and 42 agglomerated taxa (S2 Table). The CMM traits represent a small fraction of the total microbes detected, but account for 94. 5% of the rarefied sequence reads, and therefore constitute a significant portion of the identifiable microbiota. Since mice were received in cohorts (i. e. , waves) of 100, we examined whether animals in each wave were more similar to each other than mice in other waves. The fecal microbiota composition significantly clustered by wave (p < 0. 001, PERMANOVA) and sex (p < 0. 001, PERMANOVA) (S1 Fig). PCA analysis of plasma and cecal bile acids showed a significant effect of sex, but not wave, on both plasma (p < 0. 0001, Kruskal Wallis) and cecal BA profiles (p < 0. 05, Kruskal Wallis) (S2 Fig). There is substantial evidence implicating gut microbiota and BAs in metabolic disease development [36,37]. To identify potential relationships among these traits, we performed correlation analysis which yielded many significant associations after FDR correction (FDR < 0. 05) (S3 Table, discussed in S1 Data). To identify associations between regions of the mouse genome and the clinical and molecular traits discussed above, we performed QTL analysis using the R/qtl2 package [49]. We used sex, days on the diet, and experimental wave as covariates. We identified 13 significant QTL (LOD ≥ 7. 66; P ≤ 0. 05) and 50 suggestive QTL (LOD ≥ 6. 80; P ≤ 0. 2) for bacterial [36], bile acid [13], and body weight [1] traits (Fig 2, S4 Table). Of the microbial QTL, we found 23 QTL for 17 distinct bacterial ESVs from the Bacteroidetes and Firmicutes phyla that met the LOD ≥ 6. 80 threshold. ESVs with the strongest QTL (LOD > 8) are classified to the Clostridiales order and map on chr 12 at ~33 Mbp, the Lachnospiraceae family on chr 2 at 164 Mbp, and the S24-7 family on chr 2 at ~115 Mbp. We also identified 12 QTL for microbial taxa collapsed by taxonomic assignment (i. e. , genus to phylum). The genera Lactococcus and Oscillospira were also associated with host genetic variation, which is consistent with previous studies [23,24,50,51]. Similarly, BA QTL mapped to multiple loci spanning the mouse genome and most BA traits mapped to multiple positions. BA synthesis and metabolism are regulated by multiple host signaling pathways: there are >17 known host enzymes involved in the production of BAs [36], transporters, which play a critical role in maintaining the enterohepatic circulation and BA homeostasis, and receptors that respond to BA in a variety of host tissues [52–54]. Therefore, it is not surprising that our results indicate that BA levels are polygenic and shaped by multiple host factors. To identify instances of overlapping QTL, we applied a less stringent threshold of LOD ≥ 6. 1 (P < 0. 5). We observed multiple instances of related BA species associating to the same genetic locus, indicating the presence of pleiotropic loci. Interestingly, several of these loci associate with levels of related BA species in different stages of microbial modification. For example, cecal taurocholic acid (TCA) and plasma CA QTL overlap on chr 7 at 122 Mbp. Likewise, QTL for plasma TDCA and cecal DCA, overlap on chr 12 between ~99–104 Mbp. For the cecal DCA, the WSB founder haplotype was associated with higher levels of this BA, while the NOD founder haplotype was associated with lower levels. The opposite pattern was observed for plasma TDCA, where the NOD and WSB haplotype were associated with higher and lower levels, respectively (S3A and S3B Fig). We also identified overlapping QTLs on chr 11 at ~71 Mbp for cecal levels of the secondary BAs lithocholic acid (LCA) and isolithocholic acid (ILCA), the isomer of LCA produced by bacterial epimerization (S3C Fig). Higher levels of these cecal BAs are associated with the 129 founder haplotype and lower levels are associated with the A/J founder haplotype (S3D and S3E Fig). We identified the positional candidate gene Slc13a5 (S3F Fig), which is a sodium-dependent transporter that mediates cellular uptake of citrate, an important precursor in the biosynthesis of fatty acids and cholesterol [55]. Recent evidence indicates that Slc13a5 influences host metabolism and energy homeostasis [56–58]. Slc13a5 is a transcriptional target of pregnane X receptor (PXR) [59], which also regulates the expression of genes involved in the biosynthesis, transport, and metabolism of BAs [60]. We searched for regions of the chromosome that were associated with both BA and bacterial abundance, as this may provide evidence of interactions between the traits [61]. We identified 17 instances of overlapping microbial and BA QTL on 12 chromosomes (LOD ≥ 6. 1; P ≤ 0. 5). This QTL overlap indicates there might be QTL with pleiotropic effects on BAs and the microbiota, suggest that genetic variation influencing host BA profiles has an effect on compositional features of the gut microbiota, or genetic-driven variation in microbiota composition alters BAs. Examples of notable instances of overlapping bacterial and BA QTL, including Akkermansia muciniphila and Peptostreptococcaceae family are discussed in the Supporting Information (S1 Data). We focused our co-mapping analysis on chr 8 at ~ 5. 5 Mbp, where Turicibacter sp. QTL and plasma cholic acid (CA) QTL overlap (Fig 3A and 3B). These traits were particularly interesting because both have been shown to be influenced by host genetics by previous studies. Turicibacter has been identified as highly heritable in both mouse and human genetic studies [24,27,45,50], and multiple reports have found differences in CA levels as a function of host genotype [18,46]. Furthermore, CA levels are influenced by both host genetics and microbial metabolism since it is synthesized by host liver enzymes from cholesterol and subsequently modified by gut microbes in the intestine. Notably, these co-mapping traits also share the same allele effects pattern, where the A/J and WSB haplotypes have strong positive and negative associations, respectively (Fig 3C and 3D). To assess whether the trait patterns observed in the DO founder strains correspond to the observed allelic effects in the QTL mapping, we performed a separate characterization of the fecal microbiota composition and plasma bile acids in age-matched A/J and WSB animals fed the HF/HS diet. The founder strain allele patterns inferred from the QTL mapping closely resembled the observed levels of Turicibacter sp. (Fig 3E) and plasma CA in the founder strains (Fig 3F), where A/J animals had significantly higher levels of Turicibacter sp. and CA than WSB animals. However, Turicibacter levels in the founder strains do not completely mirror the estimated allele effects. This may be due to other genetic factors that also influence Turicibacter levels, as this taxa may be influenced by multiple host genes and levels of Turicibacter have previously been associated on chr 7 [24], 9 and 11 [50] in mice. Furthermore, Turicibacter and plasma CA were positively correlated in the DO mice (r = 0. 43, p = 3. 53e-10). This finding is consistent with a previous study that found positive correlations between Turicibacter and unconjugated cecal BAs [62]. Taken together, the overlap between the Turicibacter sp. QTL and plasma CA QTL, along with the similar allele effects pattern, which reflect the values observed in the founder strains, provide strong evidence that these traits are related and they are responding to the common genetic driver. We searched in the QTL confidence interval for candidate genes via high-resolution association mapping on chr 8 and identified SNPs associated with both microbial and BA traits. Among these we identified SNPs upstream of the gene Slc10a2, which encodes for the apical sodium-bile transporter (Fig 3G). Slc10a2 is responsible for ~95% of BA reabsorption in the distal ileum and plays a key role in BA homeostasis [63]. In humans, mutations in this gene are responsible for primary BA malabsorption, resulting in interruption of enterohepatic circulation of BAs and decreased plasma cholesterol levels [64]. Likewise, Slc10a2-/- mice have a reduced total BA pool size, increased fecal BA concentrations and reduced total plasma cholesterol in comparison to wild-type mice [63]. Additionally, a comparison between germ-free and conventionally-raised mice found that expression of Slc10a2 is downregulated in presence of the gut microbiota, suggesting microbes may influence the expression of the transporter [41]. Our analysis identified SNPs associated with levels of Turicibacter sp. and plasma CA at the QTL peak (Fig 3G). The SNPs with the strongest associations were attributed to the WSB and A/J haplotypes and fell on intergenic regions near Slc10a2. There is growing evidence that non-coding intergenic SNPs are often located in or closely linked to regulatory regions, suggesting that they may influence host regulatory elements and alter gene expression [65,66]. To assess if candidate gene expression patterns in the DO founders corresponds to the estimated allelic effects in the QTL mapping, we quantified Slc10a2 expression in distal ileum samples from A/J and WSB mice by quantitative reverse transcriptase PCR (qRT-PCR). A/J mice exhibited significantly higher expression of Slc10a2 compared to WSB mice (Fig 3H), which is consistent with estimated allele patterns for the overlapping Turicibacter and plasma CA QTLs on chr 8 (Fig 3A and 3B). Remarkably, several studies have noted concomitant changes in microbiota composition and Slc10a2 mRNA levels [67–69]. We mapped QTL for Turicibacter sp. and for plasma CA levels to a common locus on chr 8 at 5–7 Mbp. Since the LOD profiles and allelic effects are highly similar, the QTL may be due to a single shared locus (pleiotropy) or multiple closely linked loci. We examined this question using a likelihood ratio testing of the null hypothesis of pleiotropy versus the alternative of two independent genetic regulators of these traits [70]. Analysis of 1000 bootstrap samples resulted in a p-value of 0. 531, which is consistent with the presence of a single pleiotropic locus that affects both traits. We next sought to understand the causal relationships between the microbe and the BA. We asked whether the relationship between the microbe and BA was causal, reactive or independent. To establish the directionality of the relationship, we applied mediation analysis where we conditioned one trait on the other [71]. When we conditioned Turicibacter sp. on plasma CA (QTL → BA → Microbe), we observed a LOD drop of 3. 2 (Fig 4A and 4B). Likewise, when we conditioned the plasma cholic acid on the microbe (QTL → Microbe → BA) there was a LOD drop of 3. 32 (Fig 4C and 4D). The partial mediation seen in both models suggests that the relationship between the microbe and the BA could be bidirectional, where they exert an effect on one another. From this analysis, we can hypothesize this relationship can be explained by a pleiotropic model, where a single locus influences a microbial and a BA trait, and the microbial trait is also reactive to changes in the BA trait. It is important to note that statistical inference only partially explains the relationship between the traits and there may be other hidden variables that may further explain the relationship. The complex relationship depicted by the causal inference testing is consistent with the interplay between gut microbes and BAs in the intestine and their known ability to influence the other. Due to the strong correlative relationship between the QTL, we tested whether there was a direct interaction between bile acids and Turicibacter. Turicibacter inhabits the small intestine where BAs are secreted upon consumption of a meal [72,73]. We screened the human isolate Turicibacter sanguinis for deconjugation and transformation activity in vitro by HPLC/MS-MS. We found that T. sanguinis deconjugated ~96–100% of taurocholic acid and glycochenodeoxycholic acid (Fig 5A) within 24 hours. It also transformed ~6 and 8% of CA and CDCA to 7-dHCA and 7-ketolithocholic acid (7-KLCA), respectively (Fig 5B and 5C). Both of these transformations require the action of the bacterial 7α-hydroxysteroid dehydrogenase. Based on these results, we asked if conjugated and unconjugated bile acids differentially modulate T. sanguinis growth. BA concentrations range from ~1–10 mM along the small intestine [74] to ~0. 2–1 mM in the cecum [75]. Therefore, we grew T. sanguinis in the presence of either conjugated or unconjugated bile acids at physiologically relevant concentrations ranging from 0. 1–5 mM. T. sanguinis growth decreased with increasing concentrations of BAs and growth was completely inhibited at 1 mM for unconjugated BAs and 5 mM for conjugated BAs (Fig 5D and 5E). Growth rate was significantly slower in the presence of 1 mM conjugated and 0. 5mM unconjugated bile acids (Fig 5F). These results suggest that levels of BAs may affect abundance of Turicibacter in the gut. To compare T. sanguinis sensitivity to conjugated bile acids relative to other small intestine colonizers, we grew four taxa (Bacteroides thetaiotaomicron, Clostridium asparagiforme, Lactobacillus reuteri and Escherichia coli MS200-1) known to colonize this region of the intestine with or without 1 mM conjugated bile acids. Members of these genera are known to have bile salt hydrolase (BSH) activity to deconjugate bile acids [35]. Unlike T. sanguinis, the addition of high levels of conjugated bile acids had little to no effect on the growth of these four gut microbes (S4 Fig). Consistent with these findings, Turicibacter abundance was negatively correlated with cecal TCA levels in the DO mice (r = -0. 262, p = 0. 0035). Taken together, these data indicate that T. sanguinis is sensitive to higher concentrations of BA compared to other small intestine colonizers. These reciprocal effects between the BA and the bacterium provide biological evidence for the correlative relationship shown by the causal model testing. In summary, using a genetic approach, we identified and provide validation of a relationship between a genetic locus containing the BA transporter Slc10a2, and levels of Turicibacter and plasma cholic acid. Based on our findings, we hypothesize that the identified locus regulates expression of Slc10a2, altering active BA reabsorption in the ileum, leading to increased intestinal BA concentrations and alterations in the intestinal BA environment. Consequently, the resulting environmental change provides an unfavorable habitat for Turicibacter. In turn, lower levels of Turicibacter BA deconjugation activity leads to a decrease in circulating free plasma cholic acid levels. In this study, we performed the first known genetic mapping integration of gut microbiome and BA profiles. Using DO mice, we identified multiple QTL for gut microbes and bile acids spanning the host genome. These included loci that associated with individual microbial and BA traits, as well as loci with potential pleiotropic effects, where a single genetic region influenced both the abundance of a gut microbe and levels of a BA. While several studies suggest that host genetic variation has a minor impact on microbiota composition, there are overlapping findings among different studies in both human and mouse populations that indicate that specific bacterial taxa are influenced by host genetics. Our results in the DO population corroborate several of these key findings (discussed in S1 Data). Turicibacter sp. is among the microbes consistently associated with host genetics. This work plus data from previous reports suggest that alterations in the BA pool driven by Slc10a2 genetic variation and concomitant changes in expression/activity elicit an impact on gut microbiota community structure and influence the ability of Turicibacter to colonize and persist in the intestine. Although this microbe deconjugates primary BAs, we found that it is also sensitive to elevated concentrations of both conjugated and unconjugated BAs. Future experiments are needed to examine how a decrease in Slc10a2 expression changes intestinal BA profiles and the consequences on Turicibacter colonization. Additionally, this work identified multiple host-microbe-metabolite interactions that need to be validated with additional molecular studies. More broadly, our work demonstrates the power of genetics to identify novel interactions between microbial and metabolite traits and provides new testable hypotheses to further dissect factors that shape gut microbiota composition. Animal care and study protocols were approved by the University of Wisconsin-Madison Animal Care and Use Committee (A005821) and were in compliance with all NIH animal welfare guidelines. Animal care and study protocols were approved by the University of Wisconsin-Madison Animal Care and Use Committee. DO mice were obtained from the Jackson Laboratories (Bar Harbor, ME, USA) at ~4 weeks of age and maintained in the Department of Biochemistry vivarium at the University of Wisconsin-Madison. Mice were housed on a 12-hour light: dark cycle under temperature- and humidity-controlled conditions. Five waves of 100 DO mice each from generations, 17,18,19,21, and 23 were obtained at intervals of 3–6 months. Each wave was composed of equal numbers of male and female mice. All mice were fed a high-fat high-sucrose diet (TD. 08811, Envigo Teklad, 44. 6% kcal fat, 34% carbohydrate, and 17. 3% protein) ad libitum upon arrival to the facility. Mice were kept in the same vivarium room and were individually housed to monitor food intake and prevent coprophagy between animals. DO mice were sacrificed at 22–25 weeks of age. The eight DO founder strains (C57BL/6J, A/J, 129S1/SvImJ, NOD/ShiLtJ, NZO/HILtJ, PWK/PhJ, WSB/EiJ and CAST/EiJ) were obtained from the Jackson Laboratories. Mice were bred at the University of Wisconsin-Madison Biochemistry Department. Mice were housed by strain and sex (2–5 mice/cage), with the exception of CAST that required individual housing. Inbred founder mice were housed under the same environmental conditions as the DO animals. Like the DO mice, the eight founder strains were maintained on the HF/HS diet and were sacrificed at 22 weeks of age, except for NZO males that were sacrificed at 14 weeks, due to high mortality attributable to severe disease. For both DO and founder mice, fecal samples for 16S rRNA sequencing were collected immediately before sacrifice after a 4 hour fast. Cecal contents, plasma, and additional tissues were harvested promptly after sacrifice and all samples were immediately flash frozen in liquid nitrogen and stored at -80°C until further processing. DNA was isolated from feces using a bead-beating protocol [18]. Mouse feces (~1 pellet per animal) were re-suspended in a solution containing 500μl of extraction buffer [200mM Tris (pH 8. 0), 200mM NaCl, 20mM EDTA], 210μl of 20% SDS, 500μl phenol: chloroform: isoamyl alcohol (pH 7. 9,25: 24: 1) and 500μl of 0. 1-mm diameter zirconia/silica beads. Cells were mechanically disrupted using a bead beater (BioSpec Products, Barlesville, OK; maximum setting for 3 min at room temperature), followed by extraction with phenol: chloroform: isoamyl alcohol and precipitation with isopropanol. Contaminants were removed using QIAquick 96-well PCR Purification Kit (Qiagen, Germantown, MD, USA). Isolated DNA was eluted in 5 mM Tris/HCL (pH 8. 5) and was stored at -80°C until further use. PCR was performed using universal primers flanking the variable 4 (V4) region of the bacterial 16S rRNA gene [76]. Genomic DNA samples were amplified in duplicate. Each reaction contained 10–30 ng genomic DNA, 10 μM each primer, 12. 5 μl 2x HiFi HotStart ReadyMix (KAPA Biosystems, Wilmington, MA, USA), and water to a final reaction volume of 25 μl. PCR was carried out under the following conditions: initial denaturation for 3 min at 95°C, followed by 25 cycles of denaturation for 30 s at 95°C, annealing for 30 s at 55°C and elongation for 30 s at 72°C, and a final elongation step for 5 min at 72°C. PCR products were purified with the QIAquick 96-well PCR Purification Kit (Qiagen, Germantown, MD, USA) and quantified using Qubit dsDNA HS Assay kit (Invitrogen, Oregon, USA). Samples were equimolar pooled and sequenced by the University of Wisconsin–Madison Biotechnology Center with the MiSeq 2x250 v2 kit (Illumina, San Diego, CA, USA) using custom sequencing primers. Demultiplexed paired end fastq files generated by CASAVA (Illumina) and a mapping file were used as input files. Sequences were processed, quality filtered and analyzed with QIIME2 (version 2018. 4) (https: //qiime2. org), a plugin-based microbiome analysis platform [77]. DADA2 [48] was used to denoise sequencing reads with the q2-dada2 plugin for quality filtering and identification of de novo exact sequence variants (ESVs) (i. e. 100% exact sequence match). This resulted in 20,831,573 total sequences with an average of 52,078 sequences per sample for the DO mice, and 2,128,796 total sequences with an average of 34,335. 4 sequences per sample for the eight DO founder strains. Sequence variants were aligned with mafft [78] with the q2-alignment plugin. The q2-phylogeny plugin was used for phylogenetic reconstruction via FastTree [79]. Taxonomic classification was assigned using classify-sklearn [80] against the Greengenes 13_8 99% reference sequences [81]. Alpha- and beta-diversity (weighted and unweighted UniFrac [82] analyses were performed using q2-diversity plugin at a rarefaction depth of 10000 sequences per sample. For the DO mice, one sample (DO071) was removed from subsequent analysis because it did not reach this sequencing depth. For analysis of the eight DO founder strains, one sample (NOD5) was removed because it did not reach this sequencing depth. Subsequent processing and analysis were performed in R (v. 3. 5. 1), and data generated in QIIME2 was imported into R using Phyloseq [83]. Sequencing data was normalized by cumulative sum scaling (CSS) using MetagenomeSeq [84]. Summaries of the taxonomic distributions were generated by collapsing normalized ESV counts into higher taxonomic levels (genus to phylum) by phylogeny. We defined a core measurable microbiota (CMM) [24] to include only microbial traits present in 20% of individuals in the QTL mapping. In total, 86 ESVs and 42 collapsed microbial taxonomies comprised the CMM. 40 μL of DO plasma collected at sacrifice (30 μL used for founder strains) were aliquoted into a tube with 10 μL SPLASH Lipidomix internal standard mixture (Avanti Polar Lipids, Inc.). Protein was precipitated by addition of 215 μL MeOH. After the mixture was vortexed for 10 s, 750 μL methyl tert-butyl ether (MTBE) were added as extraction solvent and the mixture was vortexed for 10 s and mixed on an orbital shaker for 6 min. Phase separation was induced by adding 187. 5 μL of water followed by 20 s of vortexing. All steps were performed at 4°C on ice. Finally, the mixture was centrifuged for 4 min at 14,000 x g at 4°C and stored at -80°C. For targeted bile acids analysis, samples were thawed on ice. 400 μL of ethanol were added to further precipitate protein, as well as 15 μL of isotope-labeled internal standard mix (12. 5 μM d4-TαMCA, 10 μM d4-CDCA). The samples were vortexed for 20 s and centrifuged for 4 min at 14,000 g at 4°C after which the supernatant (ca. 1000 μL) was taken out and dried down. Dried supernatants were resuspended in 60 μL mobile phase (50%B), vortexed for 20 s, centrifuged for 4 min at 14,000 g and then 50 μL were transferred to vials with glass inserts for MS analysis. 30 ± 7. 5 mg cecal contents along with 10 μL SPLASH Lipidomix internal standard mixture were aliquoted into a tube with a metal bead and 270 μL MeOH were added for protein precipitation. To each tube, 900 μL MTBE and 225 μL of water were added as extraction solvents. All steps were performed at 4°C on ice. The mixture was homogenized by bead beating for 8 min at 25 Hz. Finally, the mixture was centrifuged for 4–8 min at 11,000 x g at 4°C. Subsequent processing for the DO mice and eight DO founder strains differed due to other analyses performed on the samples that are not presented in this paper. For DO samples, 100 μL of the aqueous and 720 μL of organic layer were combined and stored at -80°C. For analysis, these were thawed on ice and 400 μL of ethanol were added to further precipitate protein, as well as 15 μL of isotope-labeled internal standard mix (12. 5 μM d4-TαMCA, 10 μM d4-CDCA). The samples were vortexed for 20 s and centrifuged for 4 min at 14,000 g at 4°C after which the supernatant (ca. 1000 μL) was taken out and dried down. Dried supernatants were resuspended in 100 μL mobile phase (50%B), vortexed for 20 s, centrifuged for 8 min at 14,000 g and then 50 μL were transferred to vials with glass inserts for MS analysis. For the eight DO founder strains, the mixture was dried down including all solid parts and stored dried at -80°C. For targeted bile acid analysis, these dried down samples were then thawed on ice and reconstituted in 270 μL of methanol, 900 μL of MTBE, and 225 μL of water. 400 μL of ethanol were added to further precipitate protein, as well as 15 μL of isotope-labeled internal standard mix (12. 5 μM d4-TαMCA, 10 μM d4-CDCA). The mixture was bead beat for 8 min at 25 Hz and centrifuged at 14,000 g for 8 minutes after which the supernatant (ca. 1500 μL) was taken out and dried down. Dried supernatants were resuspended in 100 μL mobile phase (50%B), vortexed for 20 s, centrifuged for 4 min at 14,000 g and then 90 μL were transferred to vials with glass inserts for MS analysis. LC-MS analysis was performed in randomized order using an Acquity CSH C18 column held at 50°C (100 mm × 2. 1 mm × 1. 7 μm particle size; Waters) connected to an Ultimate 3000 Binary Pump (400 μL/min flow rate; Thermo Scientific). Mobile phase A consisted of 10 mM ammonium acetate containing 1 mL/L ammonium hydroxide. Mobile phase B consisted of MeOH with the same additives [85]. Mobile phase B was initially held at 50% for 1. 5 min and then increased to 70% over 13. 5 min. Mobile phase B was further increased to 99% over 0. 5 min and held for 2. 5 min. The column was re-equilibrated for 5. 5 min before the next injection. Twenty microliters of plasma sample or ten microliters of cecum sample were injected by an Ultimate 3000 autosampler (Thermo Scientific). The LC system was coupled to a TSQ Quantiva Triple Quadrupole mass spectrometer (Thermo Scientific) by a heated ESI source kept at 325°C (Thermo Scientific). The inlet capillary was kept at 350°C, sheath gas was set to 15 units, auxiliary gas to 10 units, and the negative spray voltage was set to 2,500 V. For targeted analysis the MS was operated in negative single reaction monitoring (SRM) mode acquiring scheduled, targeted scans to quantify selected bile acid transitions, with two transitions for each species’ precursor and 3 min retention time windows. Collision energies were optimized for each species and ranging from 20–55 V. Due to insufficient fragmentation for unconjugated bile acids, the precursor was monitored as one transition with a CE of 20 V. MS acquisition parameters were 0. 7 FWHM resolution for Q1 and Q3,1 s cycle time, 1. 5 mTorr CID gas and 3 s Chrom filter. In total, 27 bile acids, including 14 unconjugated, 9 tauro- and 4 glycine-conjugated species, were measured. The resulting bile acid data were processed using Skyline 3. 6. 0. 10493 (University of Washington). For each species, one transition was picked for quantitation, while the other was used for retention time confirmation. Normalization of the quantitative data was performed to the internal standard d4-CDCA as indicated in Eq 1. Genotyping was performed on tail biopsies as previously described [42] using the Mouse Universal Genotyping Array (GigaMUGA; 143,259 markers) [86] at Neogen (Lincoln, NE). Genotypes were converted to founder strain-haplotype reconstructions using a hidden Markov model (HMM) implemented in the R/qtl2 package [49]. We interpolated the GigaMUGA markers onto an evenly spaced grid with 0. 02-cM spacing and added markers to fill in regions with sparse physical representation, which resulted in 69,005 pseudomarkers. We performed QTL mapping using the R package R/qtl2 [49]. QTL mapping was done through a regression of the phenotype on the founder haplotype probabilities estimated with an HMM designed for multi-parental populations. Genome scans were performed for each phenotype with sex, cohort (wave), and days on diet included as additive covariates. Genetic similarity between mice was accounted for using a kinship matrix based on the leave-one-chromosome-out (LOCO) methods [87]. For microbial QTL mapping, normalized gut microbiota abundance data transformed to normal quantiles. For bile acid QTL mapping, normalized plasma and cecal bile acid levels were log2 transformed. The mapping statistic reported is the log10 likelihood ratio (LOD score). The QTL support interval was defined using the 95% Bayesian confidence interval. Significant and suggestive QTL were determined at a genome-wide threshold of P ≤ 0. 05 (LOD ≥ 7. 66) and P ≤ 0. 2 (LOD ≥ 6. 80), respectively. We used a common significance threshold for all phenotypes, by pooling the permutation results for the individual phenotypes. No adjustment was made for the search across multiple phenotypes. To assess whether two co-mapping traits were caused by a pleiotropic locus, we used a likelihood ratio test implemented with the open source R package R/qtl2pleio [70]. Here, we compared the alternative hypothesis of two distinct loci with the null hypothesis of pleiotropy for two traits that map to the same genetic region. Parametric bootstrapping was used to determine statistical significance. Mediation analysis was applied to identify whether a microbe or bile acid were likely to be a causal mediator of the QTL as presented in Li et al. [88]. This analysis was adapted from a general approach previously described to differentiate target from mediator variables [89]. The effect of a mediator on a target was evaluated by performing an allele scan or SNP scan using the target adjusted by mediator. Only individuals with both values for both traits were considered for mediation analysis. Traits with a LOD drop >2 after controlling for the mediator were considered for further causality testing. To statistically assess causality between microbial and bile acid trait sets (causal, reactive, independent, undecided), a causal model selection test [90] was applied using the R packages R/intermediate and R/qtl2. Causal model selection tests were evaluated on both alleles and SNPs in peak region. Total RNA was extracted from flash-frozen distal ileum tissues by TRIzol extraction and further cleaned using the RNeasy Mini Kit (Qiagen, Germantown, MD, USA). DNA was removed by on-column DNase digestion (Qiagen). Purified RNA was quantified using a Nanodrop 2000 spectrophotometer. SuperScript II Reverse Transcriptase with oligo (dT) primer (all from Invitrogen, Carlsbad, CA, USA) was used to synthesize 20 μl cDNA templates from 1 μg purified RNA. cDNA was diluted 2X before use and qRT-PCR reactions were prepared in a 10 μl volume using SsoAdvanced Universal SYBR Green Supermix (Bio-Rad, Hercules, CA, USA) and 400 nM specific primers targeting the gene of interest (SLC10A2-F [5’- TGGGTTTCTTCCTGGCTAGACT-3’]; SLC10A2-R [5’- TGTTCTGCATTCCAGTTTCCAA-3’] [91]). All reactions were performed in triplicate. Reactions were run on a CFX96 Real-Time PCR System (Bio-Rad, Hercules, CA, USA). The 2-ΔΔCt method [92] was used to calculate relative changes in gene expression and all results were normalized to GAPDH. Bacterial strains were obtained from DSMZ and ATCC. All strains were cultured at 37°C under anaerobic conditions using an anaerobic chamber (Coy Laboratory Products) with a gas mix of 5% hydrogen, 20% carbon dioxide and 75% nitrogen. Strains were grown in rich medium (S5 Table) that was filter sterilized and stored in the anaerobic chamber at least 24 hours prior to use. L. reuteri was grown in medium supplemented with 20 mM glucose. For all in vitro assays, cultures used for inoculation were grown overnight at 37°C in 10 mL 14b medium in anaerobic Hungate tubes. Stock solutions of conjugated bile acids (TCA, GCDCA) and unconjugated bile acids (CA, CDCA, DCA) were prepared to a final concentration of 100 mM and used for all in vitro assays. All bile acids used were soluble in methanol. Stock solutions of conjugated and unconjugated bile acids (100 mM) were added to 3 ml 14b medium to obtain a final concentration of 100 μM total bile acid. Tubes were inoculated with a T. sanguinis cultured overnight, then incubated in the anaerobic chamber at 37°C for 48 hours. At the 24- and 48-hour timepoints, 1 mL of each culture was removed and the supernatant was collected after brief centrifugation. Each culture supernant was diluted 10x in initial running solvent (30: 70 MeOH: 10 mM ammonium acetate). Samples were spun at max speed for 3 minutes to remove suspended particles prior to loading on the uHPLC. Samples were analyzed using a uHPLC coupled with a high-resolution mass spectrometer. 10 μL aliquots of diluted supernatant samples were analyzed using a uHPLC-MS/MS system consisting of a Vanquish uHPLC coupled by electrospray ionization (ESI) (negative mode) to a hybrid quadrupole-high-resolution mass spectrometer (Q Exactive Orbitrap; Thermo Scientific). Liquid chromatography separation was achieved on an Acquity UPLC BEH C18 column (2. 1-by 100-mm column, 1. 7-μm particle size) heated to 50°C. Solvent A was 10 mM Ammonium acetate, pH 6; solvent B was 100% methanol. The total run time was 31. 5 minutes with the following gradient: 0 min, 30% B; 0. 5 min, 30% B; 24 min, 100% B; 29 min, 100% B; 29 min, 30% B; 31. 5 min, 30% B. Bile acid peaks were identified using the Metabolomics Analysis and Visualization Engine (MAVEN) [93]. Bacterial growth rate was measured in medium 14b supplemented with either 100 μM, 300 μM, 1 mM bile acids or methanol control. Medium was dispensed inside an anerobic chamber into Hungate tubes. Tubes containing 10 mL of medium were inoculated with 30 μL of an overnight culture and incubated at 37°C for 24 hours. T. sanguinis was grown with shaking to disrupt the formation of flocculent colonies. Growth was monitored as the increase in absorbance at 600 nm in a Spectronic 20D+ spectrophotometer (Thermo Scientific, Waltham, MA, USA). Growth rate was determined as μ = ln (X/Xo) /T, where X is the OD600 value during the linear portion of growth and T is time in hours. Values given are the mean μ values from two independent cultures done in triplicate. All statistical analyses were performed in R (v. 3. 5. 1) [94]. Unless otherwise indicated in the figure legends, differences between groups were evaluated using unpaired two-tailed Welch’s t-test. For multiple comparisons, Krustkal-Wallis test was used if ANOVA conditions were not met, followed by Mann-Whitney/Wilcoxon rank-sum for multiple comparisons and adjusted for multiple testing using the Benjamini-Hochberg FDR procedure. The correlation between the abundance of microbial taxa was performed using Spearman’s correlation in the “Hmisc” (v. 4. 1–1) R package [95]. The p-values were adjusted using the Benjamini and Hochberg method, and correlation coefficients were visualized using the “pheatmap” (v. 1. 0. 10) [96]. Multiple groups were compared by Kruskal-Wallis test and adjusted for multiple testing using the Benjamini-Hochberg FDR procedure. Significance was determined as p-value < 0. 05. To assess magnitude of variability of the CMMs, summary statistics were calculated on each CMM (taxa and ESVs). Non-parametric-based PERMANOVA statistical test [97] with 999 Monte Carlo permutations was used to compare microbiota compositions among groups using the Vegan R package [98].

Inter-individual variation in the composition of the intestinal microbiota can in part be attributed to host genetics. However, the specific genes and genetic variants underlying differences in the microbiota remain largely unknown. To address this, we profiled the fecal microbiota composition of 400 genetically distinct mice, for which genotypic data is available. We identified many loci of the mouse genome associated with changes in abundance of bacterial taxa. One of these loci is also associated with changes in the abundance of plasma bile acids—metabolites generated by the host that influence both microbiota composition and host physiology. Follow up validation experiments provide mechanistic insights linking host genetic differences, with changes in ileum gene expression, bile acid-bacteria interactions and bile acid homeostasis. Together, this work demonstrates how genetic approaches can be used to generate testable hypothesis to yield novel insight into how host genetics shape gut microbiota composition.

Abstract Introduction Results and discussion Materials and methods

medicine and health sciences body fluids gut bacteria microbiome quantitative trait loci population genetics bile microbiology genetic mapping population biology microbial genetics bacteria microbial genomics genetic polymorphism medical microbiology genetic loci haplotypes anatomy heredity physiology genetics biology and life sciences genomics evolutionary biology organisms

2019

Genetic determinants of gut microbiota composition and bile acid profiles in mice

Monocytes and T-cells are critical to the host response to acute bacterial infection but monocytes are primarily viewed as amplifying the inflammatory signal. The mechanisms of cell death regulating T-cell numbers at sites of infection are incompletely characterized. T-cell death in cultures of peripheral blood mononuclear cells (PBMC) showed ‘classic’ features of apoptosis following exposure to pneumococci. Conversely, purified CD3+ T-cells cultured with pneumococci demonstrated necrosis with membrane permeabilization. The death of purified CD3+ T-cells was not inhibited by necrostatin, but required the bacterial toxin pneumolysin. Apoptosis of CD3+ T-cells in PBMC cultures required ‘classical’ CD14+ monocytes, which enhanced T-cell activation. CD3+ T-cell death was enhanced in HIV-seropositive individuals. Monocyte-mediated CD3+ T-cell apoptotic death was Fas-dependent both in vitro and in vivo. In the early stages of the T-cell dependent host response to pneumococci reduced Fas ligand mediated T-cell apoptosis was associated with decreased bacterial clearance in the lung and increased bacteremia. In summary monocytes converted pathogen-associated necrosis into Fas-dependent apoptosis and regulated levels of activated T-cells at sites of acute bacterial infection. These changes were associated with enhanced bacterial clearance in the lung and reduced levels of invasive pneumococcal disease. Innate immunity is critical for the rapid recognition and response to pathogenic micro-organisms [1]. A complex relationship exists between innate immune responses and T-cells. Innate immune responses recruit and activate T-cells at sites of infection but T-cells in turn regulate phagocyte function and can therefore modify inflammatory responses. Monocytes are key effectors of the innate immune response to bacteria and contribute to recruitment of T-cells at sites of infection [2]. In contrast to differentiated macrophages, however, monocytes have not been viewed as having a major role in the downregulation of the inflammatory response [3]. Streptococcus pneumoniae is one of the leading causes of infection-related mortality globally [4]. T-cells are key to host defense against pneumococci, making this a useful model with which to study the regulation of T-cells during bacterial infection [5], [6]. CD4+ T-cells are found at sites of pneumococcal colonization in the upper airway [7] and T-cells migrate to sites of infection in the lung [8]. In murine models CD4+ T-cell Th17 responses facilitate clearance of colonizing bacteria [7], [9] while CD4+ T-cells enhance clearance of bacteria from the lungs [5]. Other studies have emphasised an important role for CD8+ T-cells during pneumococcal pneumonia by demonstrating CD8+ T-cells limit the extent of the inflammatory response [10]. Despite these observations, CD4+ T-cell inhibition may also limit inappropriate degrees of inflammation in some models of pneumococcal infection and improve disease outcome, emphasizing that numbers of T-cell populations must be carefully regulated to ensure effective clearance of bacteria while limiting lung pathology [10], [11]. There is limited information on how T-cell numbers are regulated during the immune response to pneumococci and in particular what role cell death plays in this process. Lymphocyte apoptosis has been observed in peripheral blood of patients with pneumococcal infection [12] and is a well-recognized feature of bacterial sepsis [13]. Nevertheless it remains unclear whether the lymphocyte apoptosis described during pneumococcal infection is part of a non-specific response, associated with microbial products and the altered cytokine responses that are a feature of infection, or whether it might be the result of a more specific host programme regulating the immune response. We therefore examined whether the interaction of T-cells with pneumococci results in cell death and have characterized features of this process. In particular we observed that the pneumococcal protein pneumolysin induces T-cell necrosis but that in the presence of monocytes T-cells undergo Fas-dependent apoptosis. Moreover we have found a requirement for Fas-signaling in the regulation of CD3+ T-cell death, during the early T-cell dependent phases of the host response to pneumococci. We first examined whether peripheral blood mononuclear cells (PBMC) incubated with pneumococci demonstrated features of apoptosis, using a range of morphologic and biochemical assays of apoptosis. Almost all PBMC became Annexin V+ after 24 h of incubation with pneumococci (Figure 1A). PBMC also showed evidence of loss of inner mitochondrial transmembrane potential (Δψm) (Figure 1B) and of increased caspase activation (Figure 1C), cell shrinkage (Figure 1D) and DNA strand breaks (Figure 1E), following exposure to pneumococci. We confirmed cell death of purified lymphocytes 6 h after exposure to bacteria with evidence of both early apoptotic Annexin V+/TO-PRO-3− cells and late apoptotic/necrotic Annexin+/TO-PRO-3+ cells (data not shown), that was directly proportional to the MOI (Figure 1F). We also confirmed accumulation of hypodiploid DNA, a feature of apoptosis [14], in CD3+ T-cells (Figure 1G) and that the CD3+ T-cell death was caspase dependent (Figure 1H). The combination of features confirmed an apoptotic form of cell death. Apoptosis was apparent with an MOI as low as 0. 1 (Figure 1B). Cell death required cell contact between bacteria and PBMC and was not the consequence of utilization of growth factors in the media by bacteria since the insertion of transwells between bacteria and PBMC inhibited cell death at representative early and late time points after bacterial challenge (Figure S1). In PBMC cultures as expected T-cells predominated but cultures also contained 17. 5±1. 7% CD19+ B-cells and 8. 7±1. 1% CD14+ monocytes. CD19+ B-cells showed a trend towards an increased rate of cell death following 16 h pneumococcal challenge (Figure S2A). As we have previously reported during pneumococcal infection [15], CD14+ monocytes were highly susceptible to cell death (Figure S2B). We also confirmed that different subsets of T-cells were highly susceptible with early evidence of cell death (Figure S2C–E). These subsets included CD161+ cells, which are Th17 cells or cells with the potential to differentiate into this subset [16]. We did not find evidence for the selective involvement of any of these T-cell sub-sets as they were equally susceptible to cell death following bacterial challenge. Since monocytes/macrophages can induce apoptosis in T-cells in specific circumstances [17], [18] we addressed whether monocytes influenced CD3+ T-cell death in cultures of PBMC following exposure to pneumococci. The percentage of CD14+ monocytes in PBMC was 11. 8±1. 1%, while the number in the non-adherent fraction of plastic purified PBMC cultures was reduced to 1. 6±0. 4% (Figure S3) and the percentage of CD14+ monocytes in highly pure CD3+ T-cells cultures was negligible at only 0. 1±0. 05%. In these experiments the highly pure CD3+ T-cell cultures contained 95±0. 7% CD3+ T-cells. As shown in Figure 2A, the increase in total Annexin V+ cells, in PBMC cultures following challenge with pneumococci, was similar in magnitude despite varying numbers of monocytes. However, the PBMC cultures demonstrated significantly more Annexin V+/TO-PRO-3− cells and significantly fewer Annexin V+/TO-PRO-3+ cells than the highly purified CD3+ T-cell cultures (Figure 2B–C), while purified CD3+ T-cell cultures showed no increase in Annexin V+/TO-PRO-3− cells above baseline. Moreover few cells in the purified CD3+ T-cell culture showed accumulation of hypodiploid DNA or cytochrome c translocation into the cytoplasm, a specific feature of apoptosis indicating mitochondrial outer membrane permeabilization [19]. In contrast PBMC or plastic purified PBMC had significant numbers of cells with hypodiploid DNA and evidence of cytochrome c translocation (Figure 2D–F). To ensure cytochrome c translocation was measured only in CD3+ T-cells these cells were purified from PBMC cultures exposed to bacteria. We confirmed the lack of apoptotic features, such as hypodiploid DNA accumulation, in the purified CD3+ T-cell culture was not just the result of altered kinetics of cell death since the differences in accumulation of hypodiploid DNA between purified CD3+ T-cell cultures and PBMC were seen at all time points from 6–16 h after pneumococcal challenge, the latest time point being a time at which cell death was extensive in both cultures using less selective assays (Figure S4). The ability of monocytes to induce apoptosis in T-cells was not altered in the presence of neutrophils or apoptotic neutrophils, demonstrating this role of monocytes is not subverted during an acute inflammatory response or during the resolution of this response (Figure S5). To prove that monocytes were responsible for the enhanced levels of apoptotic T-cells we purified both CD3+ T-cells and CD14+ monocytes and confirmed that addition of monocytes to CD3+ T-cells was sufficient to induce accumulation of hypodiploid DNA and caspase 3 activation in CD3+ T-cells (Figure 3A–B). Apoptosis was not apparent in CD3+ T-cells cultured without CD14+ monocytes, even when cultured with high doses of bacteria for prolonged periods. Cultures containing CD14+ monocytes also showed evidence of a further apoptosis marker, loss of full length Bid indicative of Bid activation (Figure 3C–D). Bim expression remained constant in these experiments. In cultures containing only purified CD3+ T-cells there was no evidence of Bid activation. We were able to show that as the percentage of CD14+ monocytes increased the numbers of CD3+ T-cells with hypodiploid DNA increased (Figure 3E). Our isolation method initially only collected ‘classical’ CD14+ monocytes (Figure S6). ‘Non-classical’ CD14loCD16+ monocytes have emerged as important effectors of innate immunity [2]. We therefore modified our isolation protocol to include ‘non-classical’ and also ‘intermediate’ populations [20], as shown in Figure S6. Addition of these sub-populations to the ‘classical’ monocytes did not alter the level of CD3 T-cells with hypodiploid DNA in comparison to co-cultures with only CD14+ monocytes (Figure 3F). In the absence of monocytes CD3+ T-cells underwent a form of necrotic cell death. There was evidence of extensive loss of membrane integrity in the purified CD3+ T-cells and evidence of morphological features of necrosis such as prominent membrane disruption without features of cell shrinkage or of the nuclear condensation observed in the CD3+ T-cells isolated from PBMC (Figure 4A). Purified CD3+ T-cells showed prominent membrane disruption as evidenced by greater levels of Trypan blue staining (Figure 4B). The cell death in purified CD3+ T-cell cultures was not altered by necrostatin (Figure 4C) and death was not converted to apoptosis (Figure 4D) suggesting the death process did not involve necroptosis. Purified CD3+ T-cells did not demonstrate caspase 1 activation suggesting pyroptosis was unlikely to be the mechanism of cell death (Figure S7A). Cell death in PBMC required live bacteria (Figure S7B). The pneumococcal virulence factor pneumolysin induces not only cell lysis with membrane permeabilization but also apoptosis in mammalian cells [21]. Distinct regions of pneumolysin govern cytolytic and non-cytolytic activity such as complement activation. Overall cell death in CD3+ T-cells in PBMC cultures, which we show above is apoptotic, was partially reduced but not restored to baseline by a mutant (Δ6), which produces toxin that lacks cytolytic activity (Figure 4E and Figure S7C). Purified pneumolysin could induce cell death of lymphocytes (Figure 4F) but these cells did not have features of apoptosis such as accumulation of hypodiploid DNA (Figure 4G). These results demonstrate that the purified T-cells die by a primarily necrotic process with prominent membrane permeabilization and implicate pneumolysin in the cell death. We next addressed whether monocytes altered CD3+ T-cell activation in our model, since monocytes can enhance MHC-independent T-cell activation [22] and enhanced activation can alter susceptibility to apoptosis [23]. Monocytes/macrophages can also induce death receptor mediated cell death in activated T-cells [18]. We showed enhanced expression of the CD3+ T-cell activation markers CD69, CD25 and HLA-DR in PBMC cultures exposed to viable or heat-killed pneumococci (Figure 5A–B). CD3+ T-cells from HIV-1 seropositive individuals are known to have enhanced levels of activation and to be particularly susceptible to macrophage-mediated cell death [18]. In keeping with this we documented that cells isolated from HIV-seropositive individuals who were not receiving antiretroviral therapy (since HIV viral load influences activation state and susceptibility to apoptosis [24]), had increased levels of CD3+ T-cell death, as compared to controls, when exposed to pneumococci (Figure 5C). These results suggested T-cell activation influenced levels of cell death and activation was enhanced in PBMC co-cultures containing monocytes. To determine the apoptotic pathway inducing monocyte-dependent CD3+ T-cell apoptosis we next examined the potential role of Fas ligand (FasL). Fas signaling contributes to T-cell receptor mediated activation-induced cell death but has also been implicated in macrophage-induced cell death of activated T-cells [18], [23]. PBMC were challenged with pneumococci and then CD3+ T-cells were purified to prevent other mononuclear cells from confounding the results. This demonstrated increased levels of caspase 8 activation in CD3+ T-cells co-cultured with monocytes (Figure 6A), suggesting death receptor-mediated apoptosis [23]. We next examined involvement of Fas in the death process using an infectious dose at the low end of the range we had shown causes death (see Figure 1B) to limit competing effects of necrosis. A Fas blocking antibody prevented Bid activation and cytochrome c accumulation in the cytosol of CD3+ T-cells present in PBMC (Figure 6B–E). The numbers of CD3+ T-cells with accumulation of hypodiploid DNA was also reduced following treatment with anti-Fas antibody (Figure 6F). In addition, when we supplied a Fas-blocking antibody and measured the level of Annexin V+ cells in CD3+ T-cells isolated from PBMC following pneumococcal infection, there was also a reduction of cell death (Figure 6G). Similar experiments in purified CD3+ T-cells revealed Fas inhibition failed to reduce the levels of Annexin V+ CD3+ T-cells (data not shown). In addition, a granzyme B inhibitor (added since it has been suggested that pneumolysin can substitute for perforin and combine with granzyme B to induce lymphocyte apoptosis [25]) did not alter levels of T-cell apoptosis (Figure S8). TRAIL blocking antibody or PDL-1 blocking antibody also had no effect on levels of T-cell apoptosis (data not shown). These results confirmed that Fas mediated CD3+ T-cell apoptosis in PBMC cultures. To confirm the relevance of these findings in vivo we documented T-cell apoptosis in the spleen, an environment where macrophage-mediated T-cell death would be anticipated to occur in vivo [26], in mice with invasive pneumococcal disease (Figure 7A). Differences in levels of bacteremia can influence levels of T-cell apoptosis [27]. Therefore our investigation of apoptosis in vivo was carried out using a high dose of serotype 4 S. pneumoniae, a virulent strain of bacteria that establishes high levels of bacteremia [28], which we confirmed were similar between FasL deficient gld and control mice (data not shown). Under these conditions, which normalized blood bacterial colony counts, we documented a significant reduction in the levels of hypodiploid DNA in CD3+ T-cells from gld mice, as compared to TRAIL deficient or wild-type mice (Figure 7B). However, T-cells play an important role in the early stages of pneumonia [5], [6]. To test the relevance of FasL to pneumonia outcomes we confirmed that gld mice had increased levels of bacteria in the lung and blood in a model when there were very low levels of recruited neutrophils (Figure 7C–E). In association with this gld mice had increased levels of TNF-α (Figure 7F), though not IL-6 (Figure 7G). We also established that T-cells in this model play an important role in host defense by establishing that control mice treated with cyclosporine, a known T-cell immunosuppressant and cause of T-cell lymphopenia [11], had reduced control over bacterial replication and that this T-cell mediated control was lost in the gld deficient mice (Figure 7H–I). This suggested FasL was critical for T-cell dependent host defense in the early stages of pneumococcal infection when there are still relatively few recruited neutrophils. Monocytes are usually regarded as contributing to the ramp up of inflammatory responses rather than triggering their cessation [2]. We now demonstrate an important role for monocytes in controlling the number of activated T-cells during bacterial infection. Although a variety of T-cell populations contribute to the immune response to bacteria such as pneumococci [5], [7], [10], the regulation of these cell populations and the role of cell death in this process are incompletely defined. We show that monocytes play an important and unrecognized role ensuring Fas-dependent apoptosis to ensure the safe disposal of activated T-cells. In the absence of monocytes pathogen driven T-cell necrosis predominates. Moreover we also demonstrate that in mice T-cell dependent early host responses to pneumococcal infection are attenuated in the absence of FasL, suggesting that monocyte-mediated Fas-dependent apoptosis represents a critical component of the early stages of an effective host response against pneumococci. We were also able to show this response could occur in the presence of viable or apoptotic neutrophils, suggesting it may also be important at later stages of infection. Cholesterol-dependent cytolysins, including listeriolysin, are recognized stimuli for T-cell death [27]. We found the related pneumococcal toxin pneumolysin triggered non-apoptotic death with early membrane permeabilization in purified T-cells. Pneumolysin enhanced monocyte-dependent T-cell death, though in this case via apoptosis without evidence of early membrane disruption, suggesting apoptosis did not require outer cell membrane pore formation. Cytolytic activity of pneumolysin has been traditionally viewed as a virulence determinant but recent evidence suggests that pneumolysin is also an important trigger for effective innate immune responses against the pneumococcus, including those mediated by the nucleotide-binding oligomerization domain-like receptor family pyrin domain containing 3 (NLRP3) inflammasome [29]. In our model of monocyte-mediated apoptosis it appears that the requirement for cytolytic toxin was as a trigger for the host response that induced maximal levels of apoptosis. Mutants that lack cytolytic activity such as ST306 serotype 1 and ST53 serotype 8 pneumococci have emerged as major causes of invasive pneumococcal disease [30]. On the basis of our findings we would predict these strains would induce less T-cell apoptosis and that persistence of activated T-cells could contribute to the invasive potential of these non-cytolytic strains. In contrast, once these strains have invaded the propensity to cause lower rates of apoptosis might explain the lower mortality associated with them since lymphocyte apoptosis may influence outcomes during sepsis [31]. Surprisingly PBMC needed to be in contact with the bacteria for the toxin to mediate its effect. Although the toxin is soluble when released from lysed bacteria it may also remain associated with the bacterial cell wall [32], which might explain this finding. Alternatively cell contact with PBMC might upregulate toxin production or enhance release by increasing bacterial lysis [33]. LPS can stimulate MHC-independent T-cell activation via a monocyte-dependent process involving CD80 [22]. Pneumolysin is also a TLR4 ligand that activates T-cells; therefore a similar mechanism may explain the enhanced T-cell activation we observed in PBMC cultures [5], [21], although the fact that heat killed bacteria also activated T-cells suggests active toxin is not essential. Other microbial factors could also stimulate monocytes to activate CD3+ T-cells. One consequence of increased T-cell activation is to enhance susceptibility to apoptosis after a period of sustained activation and this paradigm is exemplified by the apoptosis of uninfected CD3+ T-cells during HIV infection [34]. In keeping with this PBMC from HIV-seropositive individuals had elevated levels of CD3+ T-cell apoptosis following challenge with pneumococci. In addition to enhancing susceptibility to apoptosis T-cell activation could increase resistance to the competing toxin-related cytolytic cell death through cytokine production. For example, IFN-γ, produced by activated Th1 cells, reduces the susceptibility of epithelial and monocytic cell lines to pneumolysin associated cytolytic death [35]. Consistent with this possibility mice lacking IFNγ are more susceptible to listeriolysin dependent cell death [27]. In the presence of ‘classical’ monocytes we found that CD3+ T-cell death had classic features of apoptosis and this effect was maintained even in the presence of small numbers of monocytes, as evidenced by high levels of CD3+ T-cell apoptosis in plastic purified PBMC. ‘Non-classical’ monocytes played less of a role in this process though the numbers we isolated were relatively small so we cannot exclude a possible role when their numbers expand. The absence of early membrane permeabilization or of inhibition by necrostatin argued against the cell death being a form of programmed necrosis and the absence of cell swelling, early loss of membrane integrity or caspase 1 activation was inconsistent with pyroptosis, which has recently been described in T-cells [36]. In contrast cell death in purified CD3+ T-cells which were not co-cultured with monocytes was characterized by early membrane disruption without significant mitochondrial outer membrane permeabilization, Bid activation or accumulation of hypodiploid DNA, consistent with necrosis. These results define a novel role for monocytes in downregulating the inflammatory response during acute bacterial infection. A key role for monocytes in downregulating the inflammatory response has been previously ignored. Unlike macrophages, monocytes are not thought to clear apoptotic cells by efferocytosis [3]. Nevertheless, monocytes express FasL [37] and evidence that monocytes induce Fas-mediated apoptosis of activated T-cells has been provided during HIV infection [17] and chronic periodontitis [38]. Nevertheless this role of monocytes has not been demonstrated previously during acute bacterial infections, where downregulation of the inflammatory response is a critical component of inflammation resolution. In our model monocytes did not influence overall T-cell viability (levels of death were similar in the presence or absence of monocytes) but their role was to ensure the induction of an injury limiting apoptotic form of cell death. There are a number of potential benefits to the host of monocyte-dependent CD3+ T-cell apoptosis: ensuring the timely removal of activated T-cells downregulates pro-inflammatory cytokine expression by T-cells, induction of apoptosis rather than necrosis limits secondary tissue injury and the removal of apoptotic cells by efferocytosis further detunes pro-inflammatory cytokine expression by macrophages during pneumococcal pneumonia [39]. Consistent with these possibilities we noted that FasL deficient mice had reduced T-cell apoptosis (when we normalized the level of bacteraemia to exclude confounding effects of differences in bacterial number) and that during the early T-cell dependent stages of pneumococcal pneumonia FasL deficiency was associated with impaired bacterial clearance, in a model that had few recruited neutrophils and was therefore unlikely to be confounded by any direct effects of Fas-signalling on neutrophil function [40]. FasL deficiency was associated with upregulation of TNF-α which would be anticipated to in turn upregulate receptors involved in the translocation of bacteria into the blood such as the PAF receptor [41]. Activated T-cells enhance TNF-α production by monocytes, through release of IFN-γ, but also through membrane interactions with monocytes [42], [43]. Induction of T-cell apoptosis not only would be anticipated to reduce these stimuli but would also provide apoptotic cells which when ingested by macrophages would further reduce TNF-α expression [39]. IL-6 production, however, was not reduced even though production is also decreased in the presence of apoptotic cells [39]. The reason for this finding was not apparent but may reflect the pleiotropic role of IL-6 both as an inflammatory cytokine and as a cytokine which can downregulate T-cell responses [44]. It remains possible that its production was sustained since it is produced as part of a counter-regulatory response to the sustained presence of activated T-cells [44], although we were not able to formally test this possibility. Recently it has been shown that selective CD4+ T-cell depletion, non-selective depletion of T-cells or reduction of T-cell activation can improve survival in murine models of invasive pneumococcal disease, suggesting excessive T-cell activation can be harmful and must be tightly controlled [11]. Our observations provide a mechanism for this process. Prior observations of T-cell apoptosis and of increased FasL expression in a small cohort of patients with pneumococcal disease are also consistent with this [12]. We established a hierarchy of cell death programs such that monocytes induced apoptosis as the dominant mechanism of T-cell death during pneumococcal infection. In the absence of monocytes the combination of non-apoptotic cell death and the unopposed effects of pro-inflammatory microbial factors, such as pneumolysin, would be predicted to result in increased tissue injury and bacterial invasion [21]. In the context of pneumolysin-mediated T-cell activation [5], and in light of the potential for pneumococci to induce a predominantly cytolytic cell death in purified CD3+ T-cells, our observation that monocytes regulate T-cell death, ensuring Fas-mediated apoptosis predominates, is significant. These findings emphasise the important and unrecognised role of monocytes in downregulating the inflammatory response to bacterial infection by regulating numbers of activated T-cells during the immune response to infection. Human PBMCs were isolated from whole blood donated by healthy volunteers as previously described with written informed consent as approved by the South Sheffield Regional Ethics Committee of Royal Hallamshire Hospital (Sheffield, United Kingdom). All animal experiments were performed in accordance with the UK Animals (Scientific procedures) Act, authorized under a UK Home Office License, and approved by the animal project review committee of the University of Sheffield. Experiments were performed with serotype 2 S. pneumoniae, strain D39 (NCTC7466) or its Δ6 mutant unless otherwise stated. In murine experiments serotype 1 (SSISP strain; Statens Seruminstitut) or serotype 4 S. pneumoniae (TIGR4 strain) were utilized. To test the contribution of the pneumococcal endonuclease EndA [45] in increasing the extent of cleavage of hypodiploid DNA we screened a panel of serotype 1 strains for EndA production using the method of Beiter et al [45] and used strain NCTC7465 that is EndA negative and INV104B that expresses EndA. All bacterial stocks were prepared as described previously [46]. In certain experiments bacteria were heat-killed by boiling for 10 min prior to incubation with cells and inhibition of growth was confirmed by documenting no growth following plating on blood agar. The Δ6 mutation refers to deletion of amino acid residues A146 and R147 from pneumolysin, introduced by site-directed mutagenesis [47]. The altered gene replaced the chromosomal gene [48]. The Janus cassette, which encodes markers for kanamycin resistance and streptomycin sensitivity, was linked to 300 bp of upstream and downstream flanking DNA from the pneumolysin gene by splice overlap PCR [48]. This construct was transformed into a streptomycin resistant version of D39. Homologous recombination in the flanking DNA introduced the Janus cassette in place of the pneumolysin gene giving an intermediate form of D39 that was resistant to kanamycin and sensitive to streptomycin. The intermediate form was isolated by growth on plates containing 150 mg/ml kanamycin and transformed with the Δ6 gene. Homologous recombination in the flanking DNA replaced the Janus cassette with the altered form of the pneumolysin gene giving D39 that was resistant to streptomycin and sensitive to kanamycin. Recombinants were selected by growth on plates containing 300 mg/ml streptomycin. The selected strain was checked for production of pneumolysin by western blot and the toxin produced was shown to be non-haemolytic using a standard hemolytic assay of cell extracts as described previously [49], see Figure S7C. Peripheral blood mononuclear cells (PBMC) plated at 1×106 cells/ml were isolated and cultured as previously described [50]. Plastic purified lymphocytes (PBL) were obtained by plating PBMC at 2×106 cells/ml in 25 cm2 flasks (Costar) for 1 h, transferring the non-adherent cells to a new flask for a further 1 h, removing, washing and counting the non-adherent cells and then resuspending at 1×106 cells/ml in a 24-well plate. Purified CD3+ T-cells and monocytes with and without CD16 depletion were isolated from PBMC by magnetic immunoselection using EasySep human T cell enrichment kit, EasySep human monocyte enrichment kit and EasySep human monocyte enrichment kit without CD16 depletion (Stemcell Techonologies). In the absence of CD16 depletion monocyte sub-populations corresponding to ‘non-classical’ and ‘intermediate’ monocytes, as described by Ziegler-Heitbrock and colleagues [20] were identified (Figure S6). Neutrophils were isolated by dextran sedimentation and plasma-Percoll (Sigma) density gradient from the peripheral blood of healthy volunteers and were approximately 98% pure. Neutrophils were made apoptotic as previously described [51]. In brief neutrophils were cultured for 20 h to ensure a population that was approximately 80% apoptotic and <5% necrotic, as defined by trypan blue. Neutrophils immediately after isolation or apoptotic neutrophils were added to PBMC cultures at a ratio of 10 neutrophils per PBMC for the indicated periods. Freshly isolated PBMC, plastic purified lymphocytes and CD3 enriched T-lymphocytes or cultures of purified CD3+ T-lymphocytes and purified monocytes were infected with S. pneumoniae or mock-infected for 4–24 h. In certain experiments transwells with 0. 4 µm pore size (BD Biosciences) were used to separate bacteria from lymphocytes [52]. To inhibit caspase activation 10 µM of N-benzyloxycarbonyl-Val-Ala-Asp (O-methyl) fluoromethyl ketone (Z-VAD-FMK) (R&D Systems Inc.) or DMSO (Sigma) as vehicle control was added for 30 min before infection. ZB4 neutralizing Fas antibody at 1 µg/ml (Enzo Life Sciences), or isotype control, was added to cultures 30 min prior to infection [18]. In additional blocking experiments, cells were pre-treated for 30 min with 2. 5 µg/ml of neutralizing TRAIL antibody 2E5 (Enzo Life Sciences), 10 µg/ml of mouse anti-human CD274 (PD-L1) (MIH1, eBioscience), 30 nM of necrostatin-1 (Sigma) to inhibit necroptosis, or 5 µM of granzyme B inhibitor (Calbiochem). In experiments involving antibodies monocyte Fcγ receptors were pre-blocked with 100 µg of human IgG1 (Sigma). Cytospin preparations were generated from non-adherent PBMC after infection (Cytospin 3; Thermo Shandon) and slides stained with TUNEL (terminal deoxynucleotidyl transferase dUTP nick end labelling) reagents per the manufacturer' s instructions (Oncor), counterstained with 4′6′-diamidino-2-phenylindole (DAPI (Vectashield) ) and mounted with cover slips containing Vectashield. TUNEL and DAPI-positive cells were viewed via fluorescence light microscopy (Leica DMRB 1000). PBMC or CD3 enriched T-cells were challenged with S. pneumoniae (serotype 2, D39) or mock-infected for 6 h then fixed in ice-cold 3% glutaraldehyde/0. 1 M phosphate buffer overnight at 4°C. The cell pellets were processed as previously described [15]. Sections (85 nm) were cut on a Reichert Ultracut E ultramicrotome and stained with 1% toluidine blue in 1% borax. Sections were examined using an FEI Tecnai transmission electron microscope at an accelerating voltage of 80 kV and micrographs were taken using a Gatan digital camera. Flow cytometric measurements were performed using a four-colour FACSCalibur flow cytometer (Becton Dickinson). Forward and side scatter light was used to identify cell populations by size and granularity. Fcγ receptor blockade was with 100 µg/ml human IgG1 (Sigma). Cell surface marker expression was with 1 µg/ml mouse anti-human anti- CD14 (61D3) phycoerythrin (PE), (eBioscience), anti-CD3 (SK7) fluorescein isothiocyanate (FITC), (BD Pharmingen), anti-CD161 (HP-3G10) allophycocyanin (APC), (eBioscience), anti-CD4 (CSK3) peridinin chlorophyll protein (PerCP) and anti-CD19 (HIB19) FITC (BD Pharmingen) with appropriate isotype controls. To determine percentages of monocyte sub-populations after each isolation procedure we used 1 µg/ml mouse anti-human anti-CD14 (TuK4) pacific blue (Invitrogen) and anti-CD16 (B73. 1) PE (eBioscience) or appropriate isotype controls. Activation marker expression was with 1 µg/ml of mouse anti-human anti-CD69 (FN50) APC (eBioscience); anti-human anti-CD25 (M-A251) APC-H7 (eBioscience) and anti-human anti-HLA-DR (L243) PE (BD Pharmingen). In murine experiments T-lymphocytes were identified using rat anti-mouse anti-CD3 (17A2) eFluor 450 (eBioscience). Annexin V-PE and TO-PRO-3 was used to assess cell death. Annexin V+/Topro3− cells were regarded as early apoptotic and Annexin V+/TO-PRO-3+ cells as late apoptotic/necrotic [46]. To measure loss of the inner mitochondrial transmembrane potential (Δψm), the cationic dye JC-1 was used. Loss of Δψm was confirmed by loss of red and gain of green fluorescence. DNA fragmentation was confirmed by the hypodiploid peak assay [14]. Apoptotic cells were visible as a distinct population with low fluorescence compared to healthy cells in growth phase 0/1. Since the events recorded were predominantly on the far left of the hypodiploid peak when depicted on a linear scale, we have presented the histograms on a log scale [53]. We tested the role of the pneumococcal endonuclease EndA in further degrading the DNA of the apoptotic cells [45]. Using pneumococcal strains that are sufficient in functional EndA, or lack significant EndA activity, we confirmed that EndA increased the degree of DNA degradation, pushing events to the left on a log scale and that this appearance could be reproduced by incubating apoptotic cells with 1 µg/ml of DNAse (Promega) (Figure S9). In all flow cytometry experiments 10,000 events were captured and analyzed with FlowJo software version 9. 3. 2 (Tree Star, Inc.). Caspase 3 and 8 activities were measured using the Caspase-Glo 3/7 Assay (Promega, USA) and Caspase-Glo 8 Assay (Promega, USA). After challenge with D39 (16 h, MOI 10), 4×104 PBMC (or in some experiments CD3+ T-cells isolated from the PBMC by magnetic immunoselection as above) were suspended in 50 µl of media and were combined with 50 µl of the Caspase-Glo 3/7 or Caspase-Glo 8 reagent for 1 h at room temperature. Luminescence was measured with a Packard Bioscience Fusion universal microplate analyser (Perkin Elmer, Beaconsfield, UK). Cells were lysed, quantified and separated by SDS-PAGE as described with 15–40 µg protein/lane [49]. Proteins were transferred to Immobilon-P membrane (Millipore), blocked in PBST/5% non-fat dry milk powder and incubated with primary and secondary antibodies. Antibodies used were: mouse anti-human cytochrome c (7H8. 2C12, BD Biosciences), mouse anti-human Cox-4 (Molecular Probes), rabbit anti-human Bid (Cell Signalling), rabbit anti-human Bim (Chemicon Ltd.), rabbit anti-human caspase 1 (Abcam) and actin (A2086, Sigma-Aldrich). Detection was with HRP-conjugated goat anti-rabbit and anti-mouse immunoglobulins (Dako), and enhanced chemiluminescence (Amersham Pharmacia). PBMC were isolated from 8 HIV-seropositive individuals who were not receiving antiretroviral therapy. Individuals had CD4 T-cell counts of 498±49/µl (Mean ± SEM) and HIV viral loads of 42379±15823 as assessed by Roche Cobas Ampliprep/Cobas TaqMan HIV-1 version 2. 0. PBMC were challenged with pneumococci and apoptosis in CD3+ T-cells was measured by estimating hypodiploid DNA accumulation as above. C57BL/6 mice (Harlan, Oxford, U. K.), mice homozygous for the FasLgld mutation (B6Smn. C3-Faslgld/J, gld) [54] and TRAIL deficient mice [55] both on a C57BL/6 background, received 5×105 or 1×107 cfu serotype 1 or 4 S. pneumoniae by intratracheal instillation for 24 h as described [46]. Excised spleens were homogenized and splenocytes separated from tissue using a 100 µm cell strainer (BD biosciences) before staining and analysis by flow cytometry. Bacterial colony counts were estimated in lung and blood as previously described [46]. Bronchoalveolar lavage (BAL) was performed by instilling 5×0. 8 ml of ice-cold heparinized-saline intratracheally (Leo laboratories) and total cell counts estimated by hemocytometer [46]. Cytospins were prepared (Cytospin 3, Thermo Shandon) and the percentage neutrophils in the BAL estimated as described [46]. TNF-α and IL-6 were measured by EIA as described [39]. Results were recorded as mean and standard error of the mean with the number of individual donors cells contributing to each data set shown as the ‘n’ value. Differences between groups of treatments were calculated by ANOVA (Bonferroni' s post-test), with Kruskal-Wallis test with Dunn' s Multiple Comparison Test for non-parametric comparisons or with paired t-test for comparison of means using GraphPad Prism 5 (Graphpad Software, Inc.). Significance was defined as p<0. 05.

T-cells are important contributors to the early host response to pneumonia, but their numbers must be tightly regulated to limit inflammatory lung injury. Cell death regulates T-cell numbers but the mechanism of execution influences the inflammatory cost with apoptosis viewed as predominantly anti-inflammatory and necrosis as pro-inflammatory. We show that monocytes determine the mechanism of T-cell death during acute bacterial infection. Monocytes triggered Fas-dependent T-cell apoptosis but in the absence of monocytes T-cells died by necrosis, which required the pneumococcal virulence factor pneumolysin. We also show that Fas ligand is required to regulate the early T-cell dependent host response to pneumococci during pneumonia. Although monocytes have previously been associated with enhancement of the inflammatory response our results imply that a key role of monocytes is to dampen the inflammatory response through induction of Fas-mediated apoptosis of activated T-cells during S. pneumoniae pneumonia. Our data identifies a critical and unrecognized regulatory role for monocytes during pneumonia.

Abstract Introduction Results Discussion Materials and Methods

medicine cell death infectious diseases molecular cell biology cell biology biology microbiology host-pathogen interaction

2012

Monocytes Regulate the Mechanism of T-cell Death by Inducing Fas-Mediated Apoptosis during Bacterial Infection

Zaire ebolavirus (ZEBOV), a highly pathogenic zoonotic virus, poses serious public health, ecological and potential bioterrorism threats. Currently no specific therapy or vaccine is available. Virus entry is an attractive target for therapeutic intervention. However, current knowledge of the ZEBOV entry mechanism is limited. While it is known that ZEBOV enters cells through endocytosis, which of the cellular endocytic mechanisms used remains unclear. Previous studies have produced differing outcomes, indicating potential involvement of multiple routes but many of these studies were performed using noninfectious surrogate systems such as pseudotyped retroviral particles, which may not accurately recapitulate the entry characteristics of the morphologically distinct wild type virus. Here we used replication-competent infectious ZEBOV as well as morphologically similar virus-like particles in specific infection and entry assays to demonstrate that in HEK293T and Vero cells internalization of ZEBOV is independent of clathrin, caveolae, and dynamin. Instead the uptake mechanism has features of macropinocytosis. The binding of virus to cells appears to directly stimulate fluid phase uptake as well as localized actin polymerization. Inhibition of key regulators of macropinocytosis including Pak1 and CtBP/BARS as well as treatment with the drug EIPA, which affects macropinosome formation, resulted in significant reduction in ZEBOV entry and infection. It is also shown that following internalization, the virus enters the endolysosomal pathway and is trafficked through early and late endosomes, but the exact site of membrane fusion and nucleocapsid penetration in the cytoplasm remains unclear. This study identifies the route for ZEBOV entry and identifies the key cellular factors required for the uptake of this filamentous virus. The findings greatly expand our understanding of the ZEBOV entry mechanism that can be applied to development of new therapeutics as well as provide potential insight into the trafficking and entry mechanism of other filoviruses. Zaire ebolavirus (ZEBOV, Genbank: AF086833), a member of the family Filoviridae, genus Filovirus, causes a highly fatal hemorrhagic fever in humans and non-human primates. Over the past three decades numerous human outbreaks have occurred in Central Africa involving hundreds of cases with fatality rates ranging from 50–89% [1]. In addition, outbreaks of ZEBOV infection have been implicated in deaths of tens of thousands of gorillas, chimpanzees and duikers in Central and Western Africa posing a considerable threat to the wildlife and ecology in those areas [2]. Due to a very high case fatality rate in humans, significant transmissibility of the virus, lack of effective preventive or therapeutic measures against the disease, ZEBOV is considered a serious emerging viral pathogen. Currently no specific therapy or vaccine is approved for human or animal use against this pathogen. As for other members of Filoviridae, ZEBOV is morphologically distinct from other animal viruses. The virions are long and filamentous with an average length of 800–1000 nm and a diameter of about 80 nm but can form a variety of shapes ranging from straight rods to closed circles [3]. Virions are surrounded by a host cell-derived lipid envelope. The envelope contains virally-encoded glycoprotein (GP) spikes composed of homotrimers of two virally-encoded glycoproteins, GP1 and GP2. The approximately 19 kb single-stranded, negative-sense genomic RNA complexed with nucleocapsid, VP35, VP30 and L proteins form the nucleocapsid, while VP40 forms the matrix that underlies the viral membrane [4]. Like all viruses, ZEBOV largely relies on host cell factors and physiological processes for key steps of its replication cycle. Identification of these processes and factors will not only allow a better insight into pathogenic mechanism, but may identify novel targets for future therapeutic development. As the first step of replication, entry into the host cell is an attractive target for therapeutic intervention as infection can be stopped before virus replication disrupts cellular functions. However, the entry mechanism of ZEBOV, and that of other large enveloped viruses is very limited. Many enveloped viruses, including ZEBOV, require endocytosis to infect cells. The internalized virus is transported through successive endocytic vesicles to reach a vesicle/compartment where conditions are conducive (low pH and/or presence of proteolytic enzymes) for the GP to attain a suitable conformation needed for membrane fusion [5], [6], [7]. Upon fusion of viral and endocytic membranes, the capsid moves into the cell cytoplasm to begin genome replication. Several distinct endocytic mechanisms exist in mammalian cells. They are distinguished from each other on a number of criteria including the size and morphology of endocytic vesicles, the type of cargo they carry, the cellular factors involved in their control and their origins and destinations [8]. Different viruses employ different routes of endocytosis, and the route taken by a given virus largely depends on the receptor it interacts with. Clathrin-mediated endocytosis (CME) is the best understood endocytic pathway. A number of viruses including Influenza A (Genbank: M73524), Semliki forest (Genbank: X04129) and vesicular stomatitis viruses (VSV; Genbank: J02428) employ this pathway for entry [9], [10]. The internalization of the virus-receptor cargo occurs in specialized areas of cell membrane called clathrin-coated pits (CCPs). CCPs are formed on the cytoplasmic face of the plasma membrane through sequential assembly of proteins including clathrin that form a cage-like structure lining the cytoplasmic side of the pit. The pit then invaginates and buds from the plasma membrane forming a clathrin-coated vesicle approximately 120 nm in diameter containing the internalized cargo. Subsequently, the vesicle sheds its clathrin coat, a prerequisite for further trafficking and merging with other compartments. A number of accessory, adaptor and signaling molecules participate in this process, and provide a tight regulation of the pathway. Some, such as accessory protein-2 (AP2) and Eps15, are specifically associated with CCPs, while others such as dynamin, which is responsible for vesicle budding from the plasma membrane, are shared with other endocytic pathways [8]. Caveolin-mediated endocytosis (CavME), first observed for the cellular uptake of simian virus 40 (SV40) [11], differs from CME in terms of internalization mechanism and vesicular transport route. Caveolae are flask-shaped invaginations in the plasma membrane that are rich in caveolin protein, and are predominantly associated with cholesterol-rich plasma membrane microdomains termed lipid rafts. Therefore, extraction or perturbation of membrane cholesterol severely impedes entry of viruses that use CavME. Vesicles derived from CavME are indicated by the presence of caveolin and are termed caveosomes. Other cellular factors such as Eps15-related (Eps15R) protein are thought to be specific for CavME, but as with CME, dynamin is still required for severing of caveolae from the plasma membrane. Another distinguishing feature is that caveloae are smaller than CCPs and have an average diameter of approximately 60–80 nm [8]. Recently, the importance of macropinocytosis, as a distinct endocytic uptake mechanism for virus infection, has started to be realized for some viruses [9]. Macropinocytosis is associated with membrane ruffles such as those formed by filopodia and lamellipodia, which are outward extensions of the plasma membrane driven by actin polymerization underneath the membrane surface [12]. When a ruffle folds back upon itself a cavity can be formed. Subsequent fusion of the distal end of the loop with the plasma membrane results in formation of a large vesicle called a macropinosome. These can range in size from 200 to 10,000 nm across and take up cargoes of similar dimensions [9]. Morphological and regulatory characteristics that distinguish macropinocytosis from other endocytic processes have also begun to emerge [8], [9], [13], [14]. Macropinosomes are best characterized for uptake of fluid phase markers such as high molecular weight dextran and horse-radish peroxidase and is sensitive to inhibitors of Na+/H+ exchangers, such as amilorides [14]. As for CavME, they are dependent on cholesterol-rich lipid rafts, but dynamin is not required. Instead, scission of macropinosomes appears to require CtBP/BARS [9], [15], [16]. Other work indicates the involvement of cell signaling factors PI3K, Akt, PKC, PLCγ and PLC-A2 that act to promote membrane ruffling by stimulating actin remodeling through Rac and cdc42 [9]. All endocytic pathways used by viruses serve to deliver virus to vesicles and compartments conducive to virus membrane fusion and release of the core into the cell cytoplasm at a site where replication proceeds optimally. Many endocytic pathways share common features, such as acidification, yet each virus type appears to prefer one trafficking pathway over others and misdirection into alternative pathways can result in inhibition of infection. For most enveloped viruses, the point at which membrane fusion occurs appears to be at the early or late endosome stage. This evidence has been gathered by comparing pH-sensitivity of the GP to known pH of the endosome at different stages of maturation. More recently, the use of dominant negative GTPases, that are involved in endosomal maturation, have been used in determining virus exit points from the endosome [17], [18], [19]. In general, the two methods agree but provide little detail as to whether viruses have additional requirements in terms of site of release other than the early or late endosome. Currently, a detailed understanding of ZEBOV endocytosis and trafficking is lacking. Each of the previous studies on understanding ZEBOV entry pathway have indicated involvement of different pathways, including CME [20], [21], CavME [20], [22] and a Rho GTPase-dependent pathway that may suggest involvement of macropinocytosis [23]. These conflicting findings may be due to the use of surrogate models of ZEBOV such as pseudotyped retroviruses, which are morphologically and biochemically distinct from wild type filamentous ZEBOV and/or reliance on one analytical approach, such as use of pharmacological agents, which are likely to act on more than one cellular target [24]. Here we have used multiple independent approaches employing replication-competent, infectious ZEBOV and/or morphologically comparable virus-like particles (VLPs). We have examined the contribution of each endocytic pathway to ZEBOV entry and infection by quantitative analysis. The work involves measuring the impact of drugs, siRNA and/or expression of well characterized dominant negative (DN) mutants of cell trafficking proteins on virus entry and infection. We also use fluorescently-labeled virus-like particles (VLPs) to follow virus internalization and trafficking through different endocytic compartments. The product of combining all these approaches provides, for the first time, an accurate and detailed description of ZEBOV uptake mechanism. Our data clearly indicate that wild type ZEBOV enters and infects Vero and HEK293T cells independently of clathrin, caveolae and dynamin. Instead, virus entry required the presence of cholesterol in the cell membrane and was inhibited by the amiloride derivative, EIPA. A marked induction in fluid-phase uptake was also observed shortly after virus binding to cells and internalized virus particles showed significant colocalization with high molecular weight dextran. In addition, inhibition of p53-activated kinase (Pak1) or CtBP/BARS resulted in significant reduction in virus entry and infection. Importantly, the virus particles appear to stimulate uptake through this pathway directly by promoting localized actin polymerization and this is consistent with our previous work where the GP triggered the PI3 kinase signaling cascade and Rac1 activity [25]. No evidence for involvement of clathrin- or caveolae-dependent endocytosis was seen. Instead, the primary mechanism of virus uptake appears closely related to macropinocytosis. Subsequent to internalization, the virus utilizes the conventional endolysosomal pathway and is trafficked through early and late endosomes before membrane fusion takes place. This study provides novel information regarding ZEBOV entry, and is likely to be useful in understanding the entry mechanism of other filoviruses. A recent study suggested that Ebola virus uses CME for cellular entry [21], while an earlier study had implicated both CME and CavME [20], [21]. However, these studies utilized either pseudotyped virus, which due to morphological and/or biochemical differences may not accurately depict the ZEBOV entry pathway, and/or relied solely on the use of pharmacological agents, which may alter multiple processes important for membrane trafficking. To more closely examine the role of the each endocytic pathway we used replication-competent infectious virus and morphologically comparable ZEBOV virus-like particles (VLPs) to determine the impact of specific dominant-negative (DN) forms of Eps15 (OMIM: 600051) and caveolin-1 (cav-1, OMIM: 601047) on infection and VLP uptake into cells. Eps15 and caveolin-1 (cav-1) are required for the formation and trafficking of CME and CavME vesicles respectively, and their DN forms inhibit the respective endocytosis with high specificity [26]. HEK293T cells were transfected with plasmid encoding GFP alone or GFP-tagged forms of DN-Eps15 or DN-Cav1, and subsequently infected with ZEBOV. Infected cells were detected by immunofluorescence staining for ZEBOV matrix protein (VP40) protein and the proportion of cells that co-expressed the transfected protein and ZEBOV VP40 (infection marker) was calculated. It was found that the proportion of ZEBOV-infected cells in cultures expressing DN-Eps15-GFP or DN-Cav1-GFP was not significantly different (P>0. 05) to that in cultures expressing GFP alone, indicating that neither DN protein had a significant impact on ZEBOV infection (Fig. 1A). This lack of effect of either DN protein on ZEBOV entry was confirmed using a sensitive contents-mixing entry assay that measures virus-endosomal membrane fusion by monitoring luciferase release from VLPs into the cell cytoplasm [25]. Expression of either DN protein failed to have any significant effect on entry of ZEBO-VLP (P>0. 05; Fig. 1B). As control, VLPs bearing VSV envelope glycoprotein (VSV-VLP) were used. VSV is known to use CME for cellular entry [27]. Entry of VSV-VLP was significantly inhibited only in cells expressing DN-Eps15 (Fig. 1B, P<0. 001). To confirm that DN-Cav1 expression impacted caveolar endocytosis, murine leukemia virus 10A1 (MLV-10A1) infection and cholera toxin B subunit (CTxB, Pubchem: 53787834) uptake were measured as both processes are known to require CavME [28], [29]. 10A1 infection of cells expressing DN-Cav-1 was reduced by half (Fig. 1C) and is consistent with previously reported observations [28]. Uptake of CTxB (Fig. 1D) was more strongly inhibited, as cells expressing DN-Cav1 had little CTxB inside the cytoplasm, indicating that DN-Cav1 was functional, blocking caveolar endocytosis. As a further test, colocalization of internalized GFP-labeled ZEBO-VLPs (gfpZEBO-VLP) with established markers of CME (clathrin light chain A; OMIM: 118960 and transferrin; OMIM: 190000) or CavME (caveolin-1) pathways was examined. Confocal microscopy revealed no significant colocalization of gfpZEBO-VLP with any of the markers used (Fig. 1E). Similar results were obtained when Vero cells were used (not shown). Taken together, the above findings indicated that neither CME nor CavME plays a major role in entry and infection of ZEBOV into HEK293T or Vero cells. Dynamin (OMIM: 602377) is a large GTPase and plays a critical role in numerous endocytic pathways including CME and CavME as well as some of the non-clathrin/non-caveolin-dependent (NC) pathways [8]. Dynamin acts by mediating the release of newly-formed endocytic vesicles from the plasma membrane. To determine ZEBOV dependence on dynamin, the effect of dynasore (Pubchem: 56437635), a potent and specific dynamin inhibitor [30] was tested. A recombinant infectious ZEBOV that encodes GFP (gfpZEBOV) was used. This virus is comparable to wild-type ZEBOV in terms of replication and cytopathic effects (CPE) in cultured cells but has been engineered to express GFP as an infection marker [31]. As control, a recombinant infectious VSV that encoded red fluorescent protein (rfpVSV) was used. Dynasore treatment of Vero cells greatly reduced rfpVSV infection but failed to have any significant effect on infection by gfpZEBOV even at the highest concentration tested (Fig. 2A, B). Similar results were obtained in HEK293T cells (not shown). This result was confirmed using the VLP-based entry assay. Just like the gfpZEBOV, entry of ZEBO-VLP was unaffected at any of the doses used while VSV-VLPs were strongly inhibited by dynasore in a dose-dependent manner (Fig. 2C). To ensure that dynasore inhibited dynamin-mediated endocytosis, its effect on internalization of transferrin (CME marker) or CTxB (CavME marker) was determined. Confocal microscopy revealed that treatment reduced internalization of both markers by >80% and 96% respectively in Vero cells (Fig. 2D). As a further test of the dynamin independence of ZEBOV infection cells were made to express a DN form of dynamin-2 (Dyn2-K44A) and VLP entry assays were performed. As with dynasore, there was a significant drop in the entry of VSV-VLP in cells transfected with Dyn2-K44A (P<0. 05). In contrast, the entry of ZEBO-VLPs actually increased significantly (P<0. 05), suggesting that the suppression of dynamin function may enhance entry of ZEBOV (Fig. 2E). Furthermore, the majority of GFP labeled ZEBO-VLPs (gfpZEBO-VLPs) did not colocalize with endogenous dynamin at any point up to 60 min after cell contact (Fig. 2F). These findings indicated that cell entry of ZEBOV is independent of dynamin and that dynamin activity may actually redirect virus to a non-productive pathway. Many of the cellular endocytic pathways including CavME, macropinocytosis and certain NC pathways occur in cholesterol-rich membrane microdomains such as lipid rafts. An earlier study suggested that lipid rafts may play a role in ZEBOV infection [32]. Consistent with this, we found significant co-localization of gfpZEBO-VLPs with lipid rafts during entry (Fig. 3A). Furthermore, methyl-β cyclodextrin (Pubchem: 3889506) or nystatin (Pubchem: 6433272), which disrupt lipid rafts by extracting or sequestering cholesterol out of the plasma membrane, respectively were able to block gfpZEBOV infection in a dose-dependent manner (Fig. 3B). Similar effects of both drugs were observed when tested using the ZEBO-VLP-based entry assay (not shown). These data indicated that cholesterol-rich lipid raft domains are the likely site of ZEBOV entry. The above findings clearly indicated that ZEBOV uptake occurs through a dynamin-independent, lipid raft-dependent, non-clathrin/non-caveolar endocytic mechanism but remains cholesterol dependent. Macropinocytosis is one pathway that is known to be cholesterol-dependent, but independent of clathrin, caveolin and dynamin and has been shown important for uptake of vaccinia virus into cells as well as bacteria [9]. Also, our previous work indicated involvement of PI3K and Rac1 (a rho family GTPase) in ZEBOV entry and infection [25]. Work by others had also indicated involvement of Rho GTPase in Ebola virus entry [23]. Each of these signaling proteins is also thought to be important for macropinocytosis [13], [33]. To assess the involvement of macropinocytosis, the effect of EIPA (5- (N-ethyl-N-isopropyl amiloride; Pubchem: 1795) on ZEBOV infection was determined. EIPA, an amiloride, is a potent and specific inhibitor of Na+/H+ exchanger activity important for macropinosome formation [34], [35], [36], [37]. Consistent with this activity, EIPA caused a significant reduction (>80%) in the uptake of high molecular weight dextran, a marker of macropinosomes (Fig. 4A and B). When tested in Vero cells, a dose-dependent inhibition of gfpZEBOV infection was observed in the presence of EIPA (Fig. 4C, top panels; Fig. 4D), while infection by VSV was not significantly affected (Fig. 4C, middle panels; Fig. 4D). As used, EIPA had no significant cytotoxic effect as assessed by cell monolayer integrity (Fig. 4C, bottom panels). Counting of infected cells revealed that VSV infection was inhibited by 30% but increasing the dosage of the drug did not further reduce infection, indicating a small portion of VSV infection may occur through an EIPA-sensitive pathway. In contrast, the majority of ZEBOV infection inhibition was dosage dependent, potent and indicative of inhibition of a single uptake pathway (Fig. 4D). Similar results were observed in HEK293T cells (data not shown). To rule out the possibility that EIPA blocked ZEBOV infection at a post-entry step, the VLP entry assay was used. Here, EIPA treatment had no significant effect on entry of VSV-VLP (P<0. 05) while the level of ZEBO-VLP entry was inhibited similarly to that seen for infectious virus (Fig. 4E). The impact of EIPA on virus binding to cells was also tested. Cells were pretreated with EIPA and then incubated with luciferase-containing ZEBO-VLPs for 10 min on ice. Unbound particles were washed away and then the amount of VLP associated with cells was measured by lysis in non-ionic detergent to release virus-encapsulated luciferase. Compared to DMSO-treated (control) cells, no significant difference was observed in luciferase activity in samples that were treated with EIPA, indicating that ZEBO-VLP binding to cells was unaffected (Fig. 4F). Finally, to directly visualize the effect of EIPA on virus uptake, Vero cells treated with DMSO or EIPA were incubated with gfpZEBOV-VLPs. Confocal microscopy revealed that there was a marked drop (3. 5-fold) in gfpZEBO-VLP uptake in cells treated with EIPA as compared to that in DMSO-treated cells (Figs. 4G and H). Vaccinia virus was shown to induce fluid phase uptake and exhibit colocalization with fluid phase markers such as dextran [36]. To see if a similar induction of fluid phase uptake was seen with ZEBOV, dextran and virus were incubated together on cells and examined by confocal microscopy. Starting within 10 min and continuing until at least 60 min post-binding, at any one time, approximately 20% of internalized VLPs overlapped with dextran (Fig. 4I). An additional 20–30% of the remaining VLPs were also found juxtaposed to vesicles containing dextran, indicating a close association with this compartment. Furthermore, while performing these experiments we observed that cells incubated with ZEBO-VLPs appeared to have more dextran containing vesicles than intact cells. Indeed, when studied in detail, a (2–3 fold) increase in the number of dextran-containing vesicles per cell was seen after incubation with ZEBOV as compared to cells incubated with VSV or medium alone (Fig. 4J). A similar outcome was seen for cells incubated with VLPs (not shown). The above data indicated that cellular uptake of ZEBOV primarily occurs through a pathway that has characteristics of macropinocytosis. Another hallmark of macropinocytosis is its dependence on the activity of Pak1 [9]. Therefore, the role of Pak1 in entry of ZEBOV was investigated. First, we measured the effect of siRNA-mediated suppression of endogenous Pak1 and found that the infection of gfpZEBOV was significantly reduced in cells transfected with Pak1 siRNA (Fig. 5A). To confirm, cells transfected with plasmid encoding wild-type Pak1 or DN Pak1 were challenged with gfpZEBOV. The infection of gfpZEBOV was reduced >95% in cells expressing the DN Pak1 protein than in cells expressing the wild-type Pak1 protein (Fig. 5B). A similar effect of DN Pak1 was observed when ZEBO-VLPs were tested in the entry assay (not shown). The protein CtBP/BARS, is also known as important for macropinocytosis [15], [16] and substitutes for dynamin in promoting vesicle scission from the plasma membrane. Again, siRNA were used to suppress expression and the impact on infection measured. Two independent siRNA were able to suppress expression of CtBP/BARS by >70% and 80% respectively (Fig. 5C, left panels). Infection was also reduced by 50% and 90% respectively (Fig 5C, right). This observation indicated that the suppression of CtBP/BARS expression must cross a threshold before becoming limiting to ZEBOV infection but plays an important role. Both sets of data support roles for Pak1 and CtBP/BARS in ZEBOV infection. Macropinocytosis is heavily actin-dependent. Actin is required for the formation of plasma membrane ruffles in macropinosome formation, as well as trafficking of macropinosomes into the cell [38]. Ligands that utilize macropinocytosis often promote changes in the cell actin dynamics by regulating various cellular proteins involved in controlling F-actin assembly and disassembly. Arp2 protein is an integral component of a multi-protein complex that serves as a nucleation site for de novo actin assembly. We observed a significant increase in the size of Arp2-containing complexes shortly after ZEBOV binding to cells (Fig. 5D, E). Analysis of the data indicated a 2-fold increase in the number of large (>0. 25 µm2) Arp2-containing complexes (Fig. 5D). A similar outcome was seen with cells incubated with VLPs (not shown) and a significant proportion of VLPs were associated with Arp2 complexes (Fig. 5F). Further support for a role of actin in ZEBOV entry came from the observation that gfpZEBO-VLPs were associated with F-actin foci within the interior of the cell but this was not seen for VSV-VLPs (Fig. 5G). Similarly, the gfpZEBO-VLPs were also seen associated with vasodilator-stimulated phosphoprotein (VASP), an actin-associated protein that promotes actin nucleation (Fig. 5H). In each of these cases, VLPs and staining for each marker often did not completely overlap. Instead VLP and actin or VASP often were closely juxtaposed and is consistent with nucleation occurring around vesicles containing the VLP. These observations suggested that the virus actively promotes actin assembly and associates with actin-based structures to facilitate its uptake and/or trafficking. The above findings indicated that ZEBOV is primarily internalized by a macropinocytosis-like pathway in Vero and HEK293 cells. However, the subsequent trafficking route to the site of penetration into the cytoplasm remained unknown. We found that fluorescently-labeled ZEBOV particles significantly co-localize with early endosomal antigen-1 (EEA1; OMIM: 605070) shortly after incubation with cells (Fig. 6A). At any time up to 60 min after the start of incubation, more than 30% of VLPs were associated with this marker (Fig. 6B). This confirmed a role for endocytic uptake into cells and suggested that following internalization, ZEBOV is delivered to an EEA1-positive compartment, likely sorting endosomes. Typically, the cargo from EEA1-positive compartments is delivered to early endosomes followed by trafficking to late endosomes. These vesicles are characterized by the presence of Rab5 (OMIM: 179512) and Rab7 (OMIM: 602298) GTPases on the cytoplasmic face of the vesicle, respectively, which play a key role in regulating their trafficking. Consistent with a role for early and late endosomes and in contrast to the lack of effect of DN Eps15 and Cav-1 expression, GFP-tagged DN Rab5 or DN-Rab7 resulted in significant reduction (P<0. 001 for each) in infection by gfpZEBOV (Fig. 6C). To determine if the effect was due inhibition of virus entry, VLP entry assays were performed. As compared to the negative control (GFP alone), wild-type Rab5 had no significant effect on entry of either ZEBO-VLP or VSV-VLPs, while there was >50% reduction in entry of both ZEBO-VLPs in cells transfected with either DN-Rab5 or DN-Rab7. The level of entry inhibition seen for ZEBO-VLP was similar to that of VSV-VLPs (Fig. 6C) and indicated that like VSV, ZEBOV is taken up by Rab5-dependent early and Rab7-dependent late endosomes. However, this does not mean that both virus types are present in the same vesicle population but that similar trafficking proteins are required at this stage of endocytosis. Currently, it is unknown whether ZEBOV envelope fusion occurs in late endosomes or further trafficking to a different compartment is needed. Endocytosis offers an efficient way for viruses to cross the significant physical barrier imposed by the plasma membrane and to traverse the underlying cortical matrix. Viruses have also evolved to target distinct endocytic pathways that are capable of delivering the capsid into the cell cytoplasm at sites suitable to initiate replication and to avoid destructive compartments like the lysosome. Understanding the pathway of virus entry and deciphering the mechanism regulating it is important for understanding viral pathogenesis as virus entry into host cell is the first critical step in pathogenesis of infection. While there is ample evidence that ZEBOV enters cells through endocytosis in a pH-dependent manner [6], [7], [20], the specific endocytic and trafficking pathways have not been clearly defined. Previous studies to elucidate the ZEBOV entry pathway have produced conflicting findings. Most of these studies relied on the use of retrovirus-based pseudotypes in which the Ebola virus GP is coated onto the surface of a retrovirus capsid containing a recombinant genome. The use of this system overcomes the need for high bio-containment but suffers from not having native virus morphology, GP density, and other biochemical characteristics. One early study on ZEBOV uptake using pseudotyped virus indicated caveolae as important [22] but later work indicated that caveolin activity was not required [39]. In contrast, a recent study concluded that clathrin-mediated endocytosis was the major entry pathway for ZEBOV [21]. While multiple approaches were used, including dominant-negative mutant expression and siRNA to specifically disrupt clathrin-mediated endocytosis, the key data was obtained using lentivirus-based retroviral pseudotypes. In comparison, previous work using wild type virus [20] implicated both clathrin and caveolar endocytosis in entry of ZEBOV. However, only pharmacological inhibitors were used in this study and drug specificity was not examined, making interpretation difficult. Indeed, the only evidence of clathrin involvement in infection was provided using chlorpromazine. Chlorpromazine is a useful drug and there is ample evidence indicating that it disrupts clathrin-coated pits, but it has recently been demonstrated to also interfere with biogenesis of large intracellular vesicles such as phagosomes and macropinosomes [24]. Here, by combining distinct and independent approaches we have performed a detailed analysis of each major endocytic pathway and have obtained, a clear and accurate picture of how ZEBOV enters the cell and identified important cellular proteins that are required. Careful assessment of specificity and functionality of each pathway was performed and correlated to infection and virus uptake. Replication-competent infectious ZEBOV, as well as ZEBO-VLPs (which are morphologically similar to infectious ZEBOV and contain the native matrix protein in addition to GP) were used to study the virus entry mechanism. Drugs were used to inhibit pathways but issues of specificity and pleiotropy were assessed by testing the function of each pathway after treatment. This was done by using independent markers such as transferrin, CTxB and high molecular weight dextran for CME, CavME and macropinocytic uptake respectively. We also assessed the association of fluorescent VLPs with each marker as well as markers of each endocytic compartment being examined. Furthermore, highly specific dominant-negative mutants and/or siRNAs were also used to corroborate the data obtained by pharmacological inhibitors. Importantly, throughout this work a sensitive contents-mixing virus entry assay was used in discriminating against blocks in virus entry versus blocks in downstream steps in the infection cycle. This is particularly important to do when using drugs that often affect multiple cellular functions. It is noteworthy that in each case, virus infection with wild type or the GFP-expressing ZEBOV correlated exactly with the outcomes of the VLP-based assays. This approach gives a highly detailed view of the mechanism of ZEBOV uptake into cells. Unlike previous studies [20], [21], [22], we found no evidence for the involvement of either CME or CavME in ZEBOV entry and infection. However, there was strong association of fluorescently-labeled ZEBO-VLPs with lipid rafts, and a marked reduction of ZEBOV infection by MBCD or nystatin, as reported previously [32]. This signified that cholesterol-rich lipid raft domains are required for productive entry of the virus. However, cholesterol-rich membrane microdomains play important roles in many forms of endocytosis including caveolae-dependent, non-clathrin/non-caveolar pathways, and macropinocytosis [38], [40]. Our previous work indicated that entry of ZEBOV was dependent on signaling through PI3K and Rac1 [25], which are important regulators of macropinocytosis [38]. Work by others also showed that Rho GTPases play a role in ZEBOV uptake [23]. Each of these cellular signaling proteins are known to be important in macropinocytosis. Macropinocytosis is also distinguished from the other pathways principally by criteria that include actin-dependent structural changes in the plasma membrane, regulation by PI3K, PKC, Rho family GTPases [9], [13], [33], Na+/H+ exchangers, Pak1, actin, actin regulatory factors, involvement of CtBP/BARS [14], [38] as well as ligand-induced upregulation of fluid phase uptake and colocalization of the internalized ligand with fluid phase markers [14], [36], [37]. In our examination of ZEBOV entry mechanism, we found that EIPA, a potent and specific inhibitor of the Na+/H+ exchanger [34], [35], [36], [37] blocked ZEBOV infection and entry. Furthermore, ZEBOV caused significant induction of dextran uptake (a fluid phase marker) and the internalized virus particles colocalized with dextran. Pak1 regulates macropinocytosis by promoting actin remodeling and macropinosome closure through phosphorylation of proteins LIMK and CtBP/BARS, respectively [9], [16]. We found that suppression of both Pak1 and CtBP/BARS activity by siRNA or expression of a DN form of Pak1 reduced virus entry and infection. Actin plays a central role in formation and trafficking of macropinosomes. Actin remodeling is a key event during macropinocytosis and is often triggered by stimuli that promote macropinocytosis. Arp2, among other actin regulatory proteins, has been implicated in macropinocytosis [9]. Arp2 also plays an important role in actin remodeling. It is an integral component of a large multi-protein complex that forms in response to stimuli that trigger actin assembly, and serves as a nucleation site for assembly of actin monomers to form F-actin [41]. We observed a significant increase in the size of the Arp2-containing complexes shortly after ZEBOV binding to cells, indicating stimulation of actin nucleation by the virus. The increase in Arp2 nucleation paralleled an increase in large dextran containing vesicles inside cells corresponding to macropinosomes. This activity appears to be associated with the ZEBOV glycoprotein as VLPs were also capable of inducing a similar increase in Arp2 nucleation and dextran uptake (data not shown). Additionally, we found marked association of fluorescently-labeled ZEBO-VLPs with F-actin foci, as well as with the Arp2-containing complexes and actin-regulatory protein, VASP, that resides in membrane ruffles and promotes actin foci formation. Together, these data provide evidence for a role of actin in ZEBOV entry and suggest that the virus can actively promote localized actin remodeling to facilitate its uptake through macropinocytosis or a similar mechanism. Despite using multiple approaches, we found no evidence for a role of dynamin in ZEBOV entry. Dynamin is a large GTPase that is involved in scission of newly-formed endocytic vesicles at the plasma membrane [42], [43]. Dynamin-independent entry of ZEBOV further ruled out roles for clathrin or caveolae-mediated pathways as both require dynamin activity [38], [44], [45]. In contrast, the majority of studies suggest that macropinocytosis is independent of dynamin [9]. Recently a novel mechanism has been described for scission of shigatoxin-containing vesicles in which Arp2-dependent actin-triggered membrane reorganization directly leads to vesicle severance [46]. As indicated above, we observed a marked increase in the size of Arp2 complexes shortly after incubation with ZEBOV and a significant association of ZEBO-VLPs with these and F-actin foci but it is unclear if this resulted in membrane scission. In addition, several reports have indicated that C-terminal binding protein (CtBP/BARS), originally identified as a nuclear transcription factor, likely replaces dynamin in scission of nascent macropinosome from the plasma membrane [15], [16]. As discussed above, suppression of CtBP/BARS by siRNA reduced infection and is consistent with the requirement for macropinocytosis in ZEBOV infection. Interestingly, ZEBOV VP40-based VLPs bearing VSV envelope glycoprotein were found to enter cells through clathrin-mediated endocytosis, as has been reported for the wild-type virus [27]. This suggested that the choice of the internalization pathway is primarily determined by envelope glycoprotein specificity. This is in contrast to a study in influenza virus, where profound differences were seen in the entry characteristics of early passage filamentous virus compared to the laboratory grown spherical isolates that tend to use clathrin-mediated endocytosis [47]. These data indicated a more pronounced role of virion morphology on the choice of endocytic pathway. The apparent reason for this discrepancy is not clear but may relate to the differences in biological characteristics of the viruses and/or cell types used in the two studies. Overall, our data provide strong evidence that in HEK293T and Vero cells infection by ZEBOV occurs by a process that is closely related to macropinocytosis. We cannot say that entry occurs exclusively by this pathway, but that its disruption blocks the majority of infection and particle uptake. Our work also indicates that clathrin and/or caveolar endocytosis play at most, only a minor role in infection by wild type virus. A few other viruses as well as bacteria, require macropinocytosis to establish infection [14]. Each uses different mechanisms to induce macropinocytosis. Vaccinia virus has been shown to trigger macropinocytosis by mimicking apoptotic bodies [36]. In contrast, Coxsackie virus and adenovirus activate macropinocytosis by binding to the cell surface proteins occludin and integrin αV, respectively [48], [49]. The mechanism by which ZEBOV triggers macropinocytosis is currently unknown but likely involves GP interaction with cell receptors. Axl (a receptor tyrosine kinase) and integrin βI have been suggested to act as virus receptors [50], [51]. Although, the role of Axl or integrin βI has not been studied in the context of macropinocytosis, there is evidence that several other receptor tyrosine kinases and integrins can trigger macropinocytosis [52], [53], [54]. Therefore, it will be important to analyze the role of Axl and/or integrin βI in this context. After formation, macropinosomes traffic further into the cytoplasm and may acquire new markers and/or undergo heterotypic fusion with other vesicles of the classical endolysosomal pathway thereby successively transferring the cargo to more acidic compartments such as early and late endosomes [38], [55]. Consistent with this, we found that ZEBO-VLPs co-localized with EEA1-positive vesicles soon after binding [25]. Interestingly, the timing of colocalization of VLPs with EEA1 positive vesicles coincided with their appearance in dextran-containing macropinosomes (within 10 min after binding). Possible explanations may be that the macropinosomes acquire EEA1 shortly after formation or that they undergo prompt fusion with EEA1 positive vesicles. Our data also provided evidence that ZEBOV infection and entry was dependent on Rab5 and Rab7 function, indicating the involvement of early as well as late endosomes in ZEBOV uptake and infection. While a role of early endosomes in Ebola virus entry has not been previously reported, our finding that ZEBOV is trafficked to late endosomes is consistent with prior studies that showed inhibition of Ebola pseudovirion infection by dominant-negative Rab7 [56] and proteolytic processing of Ebola GP1 by late endosome-resident cathepsins [6], [7]. However, it is important to note that many distinct endocytic vesicles associate with Rab5 and Rab7 during maturation but differ by the ligands they carry [57]. This explains why transferrin, a marker of CME, was never seen associated with ZEBOV containing vesicles, even though both require Rab5 and Rab7 for endocytosis. The intracellular trafficking of the macropinosome is not well understood and existing data provide evidence both for and against the involvement of classical endolysosomal pathway [58]. However, little mechanistic information is available with respect to virus entry by macropinocytosis. Prior to our work only one study analyzed trafficking in any detail, using vaccinia virus and found that virus particles did not colocalize with markers of classical endolysosomal pathway [59]. This difference is likely due to the fact that ZEBOV requires transport to an acidic compartment for membrane fusion while vaccinia virus, which is a pH-independent virus, may undergo nucleocapsid release prior to fusion of macropinosomes with more acidic compartments of the endolysosomal pathway. Our findings now add novel and valuable information regarding macropinosome trafficking mechanism in general and in the context of virus entry. In conclusion, the evidence presented here demonstrates that ZEBOV utilizes a macropinocytosis-like pathway as the primary means of entry into HEK293T and Vero cells. Once taken up by endocytosis, virus trafficking occurs through early and then late endosomes; however, the exact site where envelope fusion and nucleocapsid release occur is unknown. We do not know if ZEBOV and other filoviruses follow the same pathway into other cell types, like macrophages, that are thought to be a primary target for infection. However, most cell types are capable of macropinocytosis and it is likely that the same or a similar pathway will be used. These findings are important as they not only provided a detailed understanding of ZEBOV entry mechanism, but also identified novel cellular factors that may provide new potential targets for therapies against this virus. It will be important to determine if other filoviruses share the same pathway. If so, it may be possible to develop broad-spectrum therapies that temporarily block this pathway in cells. Human Embryonic Kidney HEK293T and Vero cells were maintained in Dulbecco' s modified Eagle' s (DMEM) medium supplemented with 10% fetal bovine serum (Gemini Bioproducts, GA), 1% non-essential amino acids (Sigma, MO) and 1% penicillin-streptomycin solution (Sigma, MO). All pharmacological inhibitors were purchased from Calbiochem (San Diego, CA) or Sigma (St. Louis, MO). Stock solutions were prepared either in water, DMSO, or methanol, as per manufacturer' s recommendation, and stored at −80°C in small aliquots. Alexafluor-labeled reagents including cholera toxin B subunit, transferrin, dextran (10,000 MW) and secondary antibodies were from Invitrogen (Eugene, OR). Specific antibodies against clathrin light chain, caveolin, dynamin, cholera toxin B, Arp2, CtBP/BARS, phospho-VASP and Pak1 were purchased from Santa Cruz Biotechnology, Inc. (Santa Cruz, CA) or Cell Signaling Technology (Beverly, MA). siRNA were from Qiagen (Valencia, CA) and transfections were performed using Dharmafect transfection reagent 1 according to the manufacturer' s (Dharmacon, Lafayette, CO) instructions. 4–6 pmol of siRNA were used per transfection of cells in 0. 1 ml of medium per well of a 96-well plate. Assays were performed 48 h after transfection. All plasmids were prepared using Qiagen kits or by CsCl gradient centrifugation following standard procedures. The plasmid encoding VSV-G envelope glycoprotein (pLP-VSVG) was purchased from Invitrogen. Construction of the plasmid encoding Nef-luciferase fusion protein (pCDNA3-nef-luc) has been described previously [60]. Plasmids encoding ZEBOV matrix protein (VP40), ZEBOV envelope glycoproteins were kindly provided by Christopher Basler (Mount Sinai School of Medicine), Paul Bates (University of Pennsylvania), and Luis Mayorga (Universidad Nacional de Cuyo, Argentina) respectively. Plasmids expressing dominant-negative Eps15, caveolin-1, and dynamin-2 K44A have been described previously [19]. The Pak1 expression plasmids were obtained from Addgene (Cambridge, MA). ZEBOV-VLPs were produced by co-transfecting HEK293T cells with plasmids encoding ZEBOV matrix (VP40) protein, ZEBOV envelope glycoproteins, and Nef-luciferase fusion protein using the calcium phosphate method. For VSV-VLP, plasmid encoding ZEBOV glycoproteins was replaced with one encoding VSV-G. Cell culture supernatant was collected 48 h after transfection and cell debris was cleared by centrifugation (1,200 rpm for 10 min at 4°C). Subsequently, VLPs were purified by centrifugation (25,000 rpm in SW28 rotor for 3. 5 h at 4°C) through a 20% (w/v) sucrose cushion in PBS. The VLP pellet was resuspended in 0. 01 volume of DMEM, aliquoted and stored at 4°C. Assays were performed within 2–3 days after purification of VLPs. HEK293T cells were used for contents mixing assays to measure nucleocapsid release into the cell cytoplasm. The cells were removed from plates by trypsin treatment, pelleted by centrifugation and then resuspended in fresh medium. Cells (106 per assay point) were mixed with nef-luciferase containing VLPs in a volume of 0. 2 ml and incubated at 37°C on a rotating platform for 3 h. Subsequently, the cells were washed 2–3 times with DMEM to remove the unbound VLPs and the final cell pellet was resuspended in 0. 1 ml of luciferase assay buffer lacking detergent (Promega, WI). Luciferase activity was then measured using a Turner Design TD 20/20 luminometer and expressed as counts/sec. To study drug activity on virus entry, cells were pre-treated with drug for 1 h, followed by incubation with VLPs in the continued presence of the drug. Virus entry was then measured as described above. For measuring the effect of ectopic gene expression, cells were transfected with the control plasmid or one encoding the protein of interest. Cells were then used for entry assays 36 h after transfection. Typical transfection efficiency was 50–70%. Wild type ZEBOV (Mayinga strain) was provided by Michael Holbrook (UTMB, TX) and the recombinant virus encoding GFP (gfpZEBOV) was from Heinz Feldman (NIH, Rocky Mountain Laboratory, MT). The virus was cultivated on Vero-E6 cells by infection at an MOI of approximately 0. 1. All infected cells expressed GFP approximately 24 h post-infection. Culture supernatants were collected after 7 d and clarified by centrifugation at 2000 x g for 15 min. Virus titer was determined by serial dilution on Vero-E6 cells. Cells were incubated with virus for 1 h and then overlaid with 0. 8% tragacanth gum in culture medium. 10 d post-infection cells were fixed with formalin, and stained with crystal violet 10 d post-infection for plaque counting. All experiments with ZEBOV were performed under biosafety level 4 conditions in the Robert E. Shope BSL-4 Laboratory at UTMB. Cells were pre-treated with inhibitors for 1 h and then incubated with gfpZEBOV at 37°C for 2 h (except in the case of MBCD and nystatin, where cells were washed to remove the inhibitors prior to incubation with the virus). Subsequently, the unbound virus particles were removed by washing with PBS, and cells incubated in fresh growth medium. Twenty-four h later, cells were washed and fixed with 10% formalin for 48 h. Images were taken by epifluorescence microscopy and infected foci counted. Counting was performed using the Cell Profiler software package [61]. The processing pipeline used by the software is available upon request. HEK293T or Vero-E6 cells were cultivated overnight on chambered coverglass slides (Nunc, Rochester, NY) at a density of 50%. The following day, cells were incubated with GFP-tagged ZEBO-VLPs. Cells were then washed three times in DMEM and fixed in 4% fresh paraformaldehyde in PBS. After one wash in PBS residual paraformaldehyde was neutralized by addition of 0. 1 M glycine buffer, pH 7. 4 and cells were permeabilized using 0. 1% Triton X-100 for 1 min at room temperature. For immunofluorescence, cells were incubated with the appropriate primary antibody, typically diluted 1∶200 in PBS. After washing in PBS, the cells were then incubated with the indicated secondary Alexafluor conjugated secondary antibody. Cells were imaged using a Nikon TE Eclipse inverted microscope with a 100× oil immersion lens or a Zeiss LSM 510 confocal microscope in the UTMB optical imaging core. Cells were transfected with plasmids encoding GFP-tagged forms of the protein of interest. For work with Pak1, myc-tagged or GST-tagged expression constructs were also used. After 24 h, the cells were challenged with wild type ZEBOV. After an additional 48 h, the cells were fixed in formalin. Cells were then stained for ZEBOV VP40 using a rabbit polyclonal antiserum (Ricardo Carrion, Southwest Foundation for Biomedical Research, San Antonio, TX) followed by an Alexa633 secondary antibody. For Pak1 work, cells were also stained for myc-tag or GST-tag using the corresponding primary and a fluorescently labeled secondary antibody. Cell nuclei were also stained using DAPI (Invitrogen). Images were taken using an epifluorescence microscope and the intensity of GFP fluorescence and VP40 staining was evaluated on a cell per cell basis using the Cell profiler software package [61]. For this, cells were first identified by DAPI staining of the cell nuclei. Then cytoplasmic fluorescence intensity for GFP and VP40 staining was determined. The algorithm pipeline used for this part of the analysis is available from R. Davey upon request. The output data, which gives intensities on a scale of 0 to 1, was converted to a scale of 0–1024 using Excel. This data file was then converted to a text file and processed using A2FCS software, which is part of the MFI/FCS Verification Suite (Purdue University) and is available at http: //www. cyto. purdue. edu/flowcyt/software/Catalog. htm. This conversion makes the data accessible to conventional FACS analysis software. The data were then analyzed using FlowJo v7. 5 (http: //www. flowjo. com). Gates were set to exclude cells that were not infected and not expressing the tagged protein, as determined in control experiments. These were done for normal cells infected by virus but not stained, cells not infected by virus but stained with VP40 specific antibody and cells expressing the tagged protein and not infected (stained with antibody against the tagged protein when used). To quantitate the infection dependency of ZEBOV on expression of each construct, the proportion of cells that were expressing each tagged protein construct and infected by ZEBOV was calculated as a fraction of the total cell population. While not used here, this analytical approach can be extended further by setting gates for low, moderate and high levels of ectopic gene expression and then correlating the outcome on infection. To measure the effect of DN-Cav1 on 10A1 MLV infection, HEK293 cells were transfected with plasmid encoding GFP or GFP tagged DN-Cav1 protein. Thirty six hours after transfection cells were infected with 10A1 MLV pseudotype encoding truncated CD4 receptor (Miltenyi, Germany) as a marker for infection. 36 h after infection the cells were stained for CD4 expression with PE-labeled mouse anti-human CD4 antibody (BD Pharmingen Cat#555347). After 1 h the cells were washed in PBS and fixed in 4% paraformaldehyde. Cells were stained with DAPI to identify cell nuclei and were imaged by a Nikon TE eclipse microscope with an automated motorized stage. To analyze the effect of DN-Cav1 on infection, images were analyzed using Cell Profiler software (Broad Institute, Cambridge, MA) to detect total cells, cells expressing the expression construct and those infected by detection of CD4. Analysis was then performed as above.

Filoviruses, including Zaire ebolavirus (ZEBOV), are among the most pathogenic viruses known. Our understanding of how these viruses enter into host cells is very limited. A deeper understanding of this process would enable the design of better targeted antiviral therapies. This study defines in detail, key steps of ZEBOV cellular uptake and trafficking into cells using wild type virus as well as the host factors that are responsible for permitting virus entry into cells. Our data indicated that the primary mechanism of ZEBOV uptake is a macropinocytosis-like process that delivers the virus to early endosomes and subsequently to late endosomes. These findings aid in our understanding of how filoviruses infect cells and suggest that disruption of macropinocytosis may be useful in treatment of infection.

Abstract Introduction Results Discussion Materials and Methods

virology/emerging viral diseases cell biology/membranes and sorting virology microbiology microbiology/cellular microbiology and pathogenesis virology/host invasion and cell entry

2010

Cellular Entry of Ebola Virus Involves Uptake by a Macropinocytosis-Like Mechanism and Subsequent Trafficking through Early and Late Endosomes

GcvB is an archetypal multi-target small RNA regulator of genes involved in amino acid uptake or metabolism in enteric bacteria. Included in the GcvB regulon is the yifK locus, encoding a conserved putative amino acid transporter. GcvB inhibits yifK mRNA translation by pairing with a sequence immediately upstream from the Shine-Dalgarno motif. Surprisingly, we found that some target sequence mutations that disrupt pairing, and thus were expected to relieve repression, actually lower yifK expression and cause it not to respond to GcvB variants carrying the corresponding compensatory changes. Work prompted by these observations revealed that the GcvB target sequence in yifK mRNA includes elements that stimulate translation initiation. Replacing each base of an ACA trinucleotide near the center of the target sequence, by any other base, caused yifK expression to decrease. Effects were additive, with some triple replacements causing up to a 90% reduction. The enhancer activity did not require the ACA motif to be strictly positioned relative to the Shine-Dalgarno sequence, nor did it depend on a particular spacing between the latter and the initiating AUG. The dppA mRNA, another GcvB target, contains four ACA motifs at the target site. Quite strikingly, replacement of all four ACAs by random trinucleotide sequences yielded variants showing over 100-fold reduction in expression, virtually inactivating the gene. Altogether, these data identify the ACA motif as a translation-enhancing module and show that GcvB' s ability to antagonize the enhancer function in target mRNAs is quintessential to the regulatory effectiveness of this sRNA. A relevant chapter in the expanding field of RNA-mediated gene regulation is devoted to the activities of multi-target trans-encoded small RNAs in bacteria. Acting in concert with chaperon protein Hfq, these RNA regulators function by base-pairing with short, often imperfectly complementary sequences in the 5′ untranslated regions (UTR) of target messenger RNAs. They can affect translation and turnover of several mRNAs simultaneously thus reprogramming gene expression of whole gene families in a coordinate manner in response to environmental cues (reviewed in [1]–[3]). Archetypal examples of this class of regulators are the RyhB small RNA (sRNA) which represses expression of mRNA for dispensable iron-sequestering proteins when iron is limiting [4]–[8]; RybB, which downregulates several outer membrane protein mRNAs under envelope stress conditions [9]–[13], Spot 42, which amplifies the regulatory range of catabolite repression by targeting several mRNAs involved in sugar uptake and consumption [14] and GcvB, which downregulates dozens of different mRNAs involved in amino acid uptake or metabolism in E. coli and Salmonella [15]–[18]. GcvB, a 200 nucleotide-long sRNA, was identified serendipitously during a study of gcvA, the gene for the main transcriptional regulator of the glycine cleavage operon gcvTHP [18]. The latter encodes the enzymes of the glycine cleavage system, the pathway generating one-carbon units from the oxidative cleavage of glycine [19]. The gcvB gene is located immediately adjacent to gcvA in the opposite orientation with its promoter partially overlapping the gcvA promoter. In the presence of excess glycine, the GcvA protein activates transcription of the gcvTHP operon as well as of gcvB [18]. Initial characterization of GcvB showed this sRNA to downregulate the synthesis of DppA and OppA proteins, main components of dipeptide- and oligopeptide-transport systems, respectively [16], [18]. Since then, the number of genes found to be regulated by GcvB has increased exponentially. A recent transcriptomic study in Salmonella enterica set this number to more than 40, making the GcvB regulon the largest of its kind [17]. The vast majority of these loci are linked directly or indirectly to amino acid metabolism and are negatively controlled by GcvB. Typically, regulation is exerted during exponential growth in nutrient rich environments and possibly aimed at coordinating the expression of interconnected metabolic pathways [16], [17]; however, its precise role remains incompletely understood. GcvB uses a specific sequence region to pair with most, although not all [20] of its mRNA targets. This pairing domain – named the R1 region [16] – is characterized by its high degree of sequence conservation, the lack of secondary structure and a typical GU-rich sequence bias. Hence, most sequences targeted by GcvB include CA-rich repeats. They are typically found inside, or immediately adjacent to, the ribosome binding sites (RBS) of target mRNAs. In one of these targets - the gltI mRNA for a glutamate-aspartate transport protein – the CA-rich element is located 45 nucleotides (nt) upstream from the translation initiation codon. Removal of this sequence (as part of a 27 nt deletion), besides causing the loss GcvB regulation, affected gltI translation, suggesting that the CA-rich element acts as a translational enhancer. Consistent with this interpretation, crafting the 27 nt segment at the corresponding position of an unrelated mRNA conferred simultaneously GcvB control and increased translational efficiency [16]. Some years ago, our laboratory performed a lac-based genetic screen aimed at identifying genes controlled by trans-encoded small RNAs in Salmonella. A random library of lacZ fusions to chromosomal genes was generated using a phage Mu-derived transposon (MudK) and screened for isolates whose LacZ levels changed (either increased or decreased) upon inactivating Hfq [21]. Among the candidates that were found, two independent isolates upregulated in the hfq mutant background, carried the lacZ insert translationally fused to the yifK gene [21]. Presumptive identification of this gene as an amino acid transporter suggested that yifK might be a GcvB target. We thus proceeded to test this hypothesis and characterize yifK regulation. While this work was underway, Sharma and coworkers identified yifK mRNA as a member of the gcvB regulon by microarray analysis; however, these authors could not confirm direct regulation by GcvB due to low reporter fluorescence of the yifK-gfp fusion used in the study [17]. Since this study also identified global regulator Lrp as a GcvB target [17], the possibility remained that the GcvB effects on yifK expression might be indirect. Here we present in vivo and in vitro evidence that GcvB downregulates yifK directly by pairing with a sequence immediately preceding the Shine-Dalgarno (SD) motif in yifK mRNA. A surprising observation in the course of this study was that some target sequence mutations that disrupted pairing did not cause yifK expression to increase – as expected for the relief of GcvB repression – but had the opposite effect. The drop in expression was not suppressed by deleting gcvB nor was it accentuated in a GcvB mutant carrying the appropriate compensatory changes. Closer analysis revealed that the GcvB target sequence includes elements that stimulate yifK mRNA translation. In the absence of such elements, the role of GcvB pairing in regulation becomes marginal. Our original screen for Hfq-regulated genes yielded two isolates carrying the MudK (lac) transposon in the yifK gene; one predicted to produce a LacZ protein fusion to the 48th amino acid (aa) of the 461 aa YifK (yifK87: : MudK); the other with LacZ inserted after the 95th aa of YifK (yifK88: : MudK) [21]. Preliminary tests showed both fusions to be regulated in a closely similar manner; however, yifK87: : MudK produced significantly higher ß-galactosidase activity and was chosen for the present study. A survey of protein sequence databases showed YifK to be a highly conserved protein with the characteristic signature of amino acid transporters. The known role of GcvB in the regulation of some members of this family made this small RNA the likeliest candidate to control yifK expression. This was confirmed by deleting the gcvB gene and testing the effects of the deletion on the expression of the yifK87: : MudK fusion (hereafter referred to as yifK-lacZY). As shown in Figure 1, the gcvB deletion causes a nearly 5-fold increase of ß-galactosidase activity in exponentially growing cells, while effects decline in stationary phase. Somewhat surprisingly, LacZ levels in the gcvB-deleted strain are not as high as the levels measured in a strain deleted for hfq (Figure 1). This might reflect the existence of one or more additional sRNA (s) participating in yifK repression. Alternatively, Hfq could repress yifK directly [22]. The data in Figure 1 show that loss of Hfq is epistatic to the gcvB deletion. Primer extension experiments mapped the 5′ end of yifK mRNA to 64 nucleotides upstream from the initiating AUG (Figure 2). This 5′ untranslated region (UTR) includes a 14-nt stretch complementary to the 3′ half of GcvB' s R1 region. As an initial step to characterize GcvB involvement in yifK regulation, we tested whether point mutations in the gcvB gene or in the promoter-proximal portion of yifK relieved GcvB-mediated repression. For this, DNA fragments spanning either of these two regions were randomly mutagenized by the polymerase chain reaction (PCR) under error-prone conditions and introduced into the chromosome of a strain harboring the yifK-lacZY reporter fusion via lambda red recombination. Most of the isolates originating from the gcvB mutagenesis carried changes in the gcvB promoter or in the promoter of the adjacent gcvA gene (Figure S1). Thus, these mutations appeared to lower the levels rather than the activity of GcvB and were not further considered. Mutagenesis of yifK promoter-proximal segment yielded three mutants with elevated yifK-lacZY expression. One isolate carried a C: G to A: T change 33 base-pairs upstream from the 5′ end of yifK mRNA. The position and the nature of the change (producing a -35 promoter consensus match, TTGACA, Figure 2A), strongly suggest that the mutation increases the strength of the yifK promoter. The mutation leads to a sharp rise in the intensity of the primer extension product (lane “-33A” in Figure 2B) and a more than 10-fold increase in ß-galactosidase activity (data not shown). These findings confirmed that the 5′ end identified by primer extension corresponds to yifK transcription initiation site. The remaining two mutations affect residues within the 5′ UTR (Figure 2A). One allele, resulting in a U to C change at position +21, falls within a AU-rich segment (AUAACAAUAA) that might constitute a site for Hfq binding [3], [23]. Consistent with this interpretation, the mutation has no effect in Δhfq background (Figure 2C). Finally the third allele (G to A at +27) affects the CG-rich stem of a presumptive secondary structure immediately adjacent to the AU-rich segment. The change causes a generalized increase of yifK-lacZY expression by an unidentified mechanism. Northern blot analysis was used to assess the effects of GcvB regulation on yifK mRNA levels. This study critically benefited from the availability of the -33 promoter mutant (see above), yifK mRNA being otherwise undetectable when expressed from the wild-type promoter (data not shown). The analysis identified two yifK mRNA species, a 1. 4 kilobase (Kb) mRNA covering just the yifK coding portion and a longer, 2. 0 Kb RNA extending into the adjacent argX-hisR-leuT-proM tRNA operon. As shown in Figure 3A, both RNAs accumulate upon RNase E inactivation, whereas only the shorter species accumulates in cells lacking GcvB or Hfq. This suggested that derepression of yifK translation in ΔgcvB or Δhfq cells protects the 1. 4 Kb RNA against RNase E cleavage. To confirm this interpretation, the analysis was repeated with strains that, besides the promoter “up” mutation, carried a mutation in the Shine-Dalgarno sequence (G to Cat position +59; described in more detail below). As shown in Figure 3C, the SD mutation causes the intensity of 1. 4 Kb band to sharply decrease in the ΔgcvB or Δhfq strains but not in the rne ts mutant, consistent with the idea that reduced translation renders yifK mRNA susceptible to RNAse E degradation. Absence of any obvious transcription termination signals in the intercistronic region between yifK and the tRNA operon suggests that the 1. 4 Kb RNA originates from processing of the longer form. Likely, under normal conditions (i. e. , wt yifK promoter) yifK transcription contributes only to a small fraction of the four tRNAs, as most the tRNA operon transcription results from a strong promoter located immediately upstream from the argX gene [24]. The approximately 500 nt RNA accumulates in the RNase E mutant (Figure 3B). Previous work in E. coli, showed that this tRNA precursor is processed by the concerted actions of RNase E and RNase P in a pathway that, intriguingly, also sees the participation of Hfq [25]. Early on in this study, it became apparent that yifK expression was exquisitely sensitive to the growth medium and virtually silenced in minimal medium. As a result, a strain with the yifK-lacZY fusion is phenotypically Lac− when plated in minimal medium. We exploited this phenotype to positively select for spontaneous Lac+ mutants. The selection yielded two classes of mutations, one genetically linked to the yifK-lacZY locus, the second mapping elsewhere. All of the linked mutants that were analyzed were found to harbor the -33 C: G to A: T promoter change obtained previously (see above). The unlinked mutations mapped in a chromosomal interval encompassing the gene for leucine response regulator, Lrp. Prompted by this observation, we introduced an lrp insertion mutation into the yifK-lacZY-containing strain. The resulting strain acquired a Lac+ phenotype (Figure 4A), indicating that yifK silencing in minimal medium results from Lrp repression. Addition of leucine efficiently relieves repression (Figure 4). The data in Figure 4 also show that GcvB does not contribute to yifK repression to any significant extent in minimal medium. This is not surprising as GcvB is transcribed at very low level under these conditions [16] and inactivating Lrp does not reverse this pattern (Figure S2). The data in Figure S2 differ from those of Modi et al [26] who reported an approximate 30-fold increase in GcvB levels in an lrp deletion mutant of in E. coli. This discrepancy might reflect differences in the organisms used or in media composition. The above approach yielded no mutations affecting the presumptive pairing sequences of GcvB or yifK. Reasoning that single base changes might not disrupt regulation enough to be revealed by the MacConkey plate screen, we resorted to introducing multiple changes by site-directed mutagenesis. An initial test involved changing a UGUG quadruplet in the GcvB segment thought to pair with yifK mRNA. The alteration caused expression of the yifK-lacZY fusion to increase approximately threefold, thus corroborating the postulated role of this sequence in yifK repression. Unexpectedly, however, when the ACAC sequence at the corresponding position in yifK mRNA was changed, yifK-lacZ expression did not increase but actually declined (data not shown). Trying to clarify this observation, portions of the region of interest were mutagenized separately. As shown in Figure 5A, converting the AAA sequence in the middle of the target sequence to UGU, or making the opposite change (UGU to AAA) in GcvB, similarly relieves yifK-lacZY repression. Repression is restored upon combining the compensatory alleles. Thus, this portion of the target sequence behaves as expected, and the behavior of the compensatory mutant strongly suggests that GcvB represses yifK through a base-pair interaction. In vitro toeprint experiments, showing that GcvB inhibits the binding of ribosomal 30S subunit to yifK translation initiation site, specifically and in a dose-dependent manner (Figure 6), provided independent support to this conclusion. Again, however, changing the CA doublet on the 3′ side of the AAA sequence to UC produced an unusual pattern: like in the quadruplet mutant above, yifK-lacZY expression decreased rather than increase, becoming insensitive to a GcvB variant carrying the compensatory change (Figure 5B). To verify that the compensatory change did not hamper GcvB' s function in an unpredictable way, we took advantage of the fact that the replaced nucleotides do not participate in the pairing with dppA [16] and tested the mutant' s ability to repress a dppA-lacZ translational fusion. This analysis showed both GcvB variants to be as efficient as wild-type in repressing dppA, indicating that both alleles remain fully functional (Figure S3). Besides being insensitive to the compensatory GcvB allele, the yifK CA49,50 to UC49,50 mutant fails to respond to gcvB or hfq deletions (Figure 7A). We interpreted these findings to suggest that the CA to UC conversion lowers translation efficiency and under such conditions, GcvB action is no longer rate-limiting for yifK expression. The effects of the mutation on yifK translation were examined in vitro using a reconstituted system. Results in Figure 7B showed an epitope-tagged Cat protein to accumulate at significantly greater levels when made from a gene fusion to the wt yifK 5′ UTR than from an equivalent construct carrying the CA to UC change. These data confirmed that the CA49,50 doublet stimulates translation and suggested that GcvB effectiveness in regulation reflects the targeting of an activating element. To characterize the enhancer element, an 8-nt segment preceding the SD sequence was modified by systematically changing individual residues to each of the three alternative bases. Effects on expression of the yifK-lacZ fusion were measured in a strain deleted for the gcvB gene. As shown in Figure 8, any change in the ACA sequence at positions +48 to +50 lowers yifK-lacZY expression. Variations range between 23% and 62%, with G residues exerting the most adverse effects at any position. Significantly, the effects appear to be additive since a separate experiment in which all three bases in the ACA sequence were randomized yielded alleles undergoing as much as 92% reduction in yifK-lacZY expression (bottom portion of Figure 8). Alteration of a second ACA motif between +53 and +55 produced a somewhat different pattern. Changes in the central C were either neutral or stimulatory; in contrast, having a C at the first position was highly deleterious resulting in nearly 95% reduction of ß-galactosidase activity (Figure 8). Altogether, these data suggested that ACA motifs enhance the efficiency of yifK translation. Conservation of these motifs in distant members of the Enterobacteriaceae family (Figure S4) is consistent with their functional importance. A peculiarity of the yifK translation signal is the unusually short distance (four nucleotides) between the most conserved base of the SD motif [27] and the initiating AUG. We thus envisaged that the role of the enhancer could be to somehow compensate for such suboptimal arrangement. To test this possibility, we generated a 7-nt tandem direct duplication of the SD region and then inactivated either copy of the SD by changing the GAGGA motif to GACGA (Figure 9). Thus, the resulting constructs have the functional SD sequence positioned either 4 or 11 nt from the AUG. As shown in Figure 9, these two variants (n. 4 and n. 5) express ß-galactosidase levels that are similar to each other and to the strain in which both SD are functional (n. 1). However, when the upstream ACA motif is replaced by GGG, yifK-lacZ expression drops sharply in both constructs (compare n. 4 ton. 6, and n. 5 ton. 7). Similar effects were observed in a separate construct where the segment between SD sequence and the initiating AUG was replaced by the sequence found at the corresponding position in the chiP gene [28] where the spacing (9-nt) is optimal (n. 8 and n. 9). While construct n. 8 is tightly repressed by GcvB, its variant lacking the upstream ACA (n. 9) shows a weak response to this sRNA (Figure S5). In conclusion, these results indicate that the enhancer activity does not require strict positioning of ACA relative to the initiation site, nor it depends on the spacing between the SD and the initiation codon. Loss of the enhancer function causes yifK expression to be less sensitive to repression by GcvB. To assess the generality of the ACA effects, we turned to the dppA gene, a major GcvB target [16], [18]. The target sequence of GcvB in dppA mRNA includes four ACA motifs clustered within a 15 nt segment near the SD sequence (Figure 10). As an initial test, this 15-nt sequence was deleted in a strain carrying a dppA-lacZY translational fusion. The resulting mutant showed 96% lower of ß-galactosidase activity than the parental strain (data not shown). In the next experiment, we randomly mutagenized all four ACA repeats (in the same lacZ fusion background) and screened the mutants on MacConkey lactose indicator plates. Out of 43 mutants analyzed, 18 formed white colonies and had ß-galactosidase activities ranging between 0. 5 and 5% of the wild-type levels. Four representative isolates from this group are shown in Figure 10. They are essentially Lac− mutants. 4 of the initial 43 mutants formed red colonies and expressed significant levels of ß-galactosidase (three shown in Figure 10). Interestingly, in two of these strains, the mutagenic process regenerated an ACA sequence. The remaining 21 isolates had an intermediate phenotype (pink colonies) and were not analyzed. Examination of the mutant sequences by the Mfold algorithm [29] showed a complete lack of correlation between presence/absence of secondary structures (or free energy values) and lacZ expression. Hence the most likely conclusion from this analysis is that the variations in lacZ expression levels are solely dictated by primary sequence determinants. Although the possibility that decreased expression in some of the mutants could be due to reduced mRNA stability, independent of ribosome binding, cannot be formally ruled out, it seems most likely that the observed differences reflect variations in translation initiation rates. Thus, on one hand, the data in Figure 10 further corroborate the positive role of the ACA motif in translation initiation; on the other hand, they reiterate the notion that an SD motif and a properly spaced AUG are not sufficient to promote initiation if placed in unfavorable sequence contexts [30]. In the present work, we have characterized the regulation of Salmonella' s yifK locus encoding a putative amino acid transporter highly conserved in Enterobacteriaceae. Our analysis showed yifK to be negatively controlled at the transcriptional level by the leucine response regulator Lrp, and at the post-transcriptional level by GcvB sRNA. These findings place yifK at the intersection of two global regulatory networks devoted to amino acid management [17], [31]. The relative impacts of two systems on yifK expression vary as a function of growth conditions, with the Lrp control predominating in leucine-deprived poor media and the GcvB control operating when amino acids are plentiful, possibly in excess. The sole condition where yifK appears to escape negative control is leucine-supplemented minimal medium, where Lrp repression is relieved. This response closely parallels that of the oligopeptide permease operon, oppABCDF [31] whose transcript is also a target of GcvB repression [16], [18]. Likely, the overlap of Lrp and GcvB networks reflects the link between amino acid metabolism and one-carbon units production; however, the precise physiological role and the implications of the above responses remain incompletely understood. Genetic analysis of GcvB: yifK mRNA interactions revealed that the GcvB target sequence in yifK mRNA contains an enhancer element. Intriguingly, mutations that disrupt the enhancer - and lower yifK expression as a result - render yifK expression totally insensitive to GcvB repression. This suggests that the effectiveness of GcvB regulation is dependent on the enhancer function and that when this component is removed, GcvB-mediated repression no longer constitutes a rate-limiting step in yifK expression. Sharma and coworkers (2007) previously showed that GcvB' s target sequence in the gltI gene of Salmonella acts as transferable translation enhancer (see Introduction). Unlike in our study, the effects of GcvB as a translational repressor were much greater than the effects of removing the enhancer, leading the authors to conclude that GcvB did not simply block the enhancer effect [16]. It seems possible that the plasmid-borne nature of the gcvB gene in the study by Sharma and coworkers made the GcvB repression tighter than when the sRNA is expressed from the chromosome. Alternatively, the contribution of the enhancer to gltI expression might be less important than in yifK expression. The gltI enhancer, located 45 nt upstream from the initiation codon, was characterized as part of a 27 nt segment and not analyzed in any further detail [16]. Here we found that nucleotide replacement in either of two ACA triplets within GcvB target site in yifK can result in more than 90% reduction in yifK expression. Although our data do not allow defining the contours of the enhancer element, they unequivocally identify the ACA motif as a determinant of its activity. We also found that the enhancer activity is maintained following a 7 nt shift in the position of the initiation site, suggesting the absence of strict spatial requirements for the functioning of the element. This is consistent with the data from the gltI system and with a report showing CA repeats to stimulate translation even when placed downstream from the start codon [32]. Translation initiation efficiencies have been known to vary greatly as a function of the sequence context of the initiation region [30], [33]. Computational analysis of sequences surrounding translation initiation sites of E. coli genes showed that the spacing between the SD and the initiation codon affects SD sequence conservation and its pattern. This study did not reveal significant biases outside these main elements [27]; however, conserved patterns occurring at variable positions might have been difficult to identify by the statistical analysis. Indeed, separates lines of evidence point to the role of the ACA motif in translation initiation. The motif is found in other translation enhancer sequences [34], [35] and, as an ACAA repeat, was shown to promote translation initiation in the absence of a SD sequence [36]. ACA is also found in the loops of pseudoknots formed by RNA ligands to ribosomal protein S1, obtained through Systematic Evolution of Ligands by Exponential Enrichment (SELEX) [37] and is part of the SELEX-determined consensus sequence for binding of protein CsrA, a translational regulator [38]. Finally, ACA is the recognition sequence of the MazF endonuclease that inactivates E. coli mRNAs by preferentially cleaving near the translation initiation codon [39]. The lack of position requirements for the functioning of the enhancer suggests that its role is to provide an anchor point for the 30 S ribosomal subunit so as to facilitate subsequent recognition of the SD sequence. Some of the evidence reviewed above tentatively identifies protein S1 as the possible candidate for the interaction. In vitro S1-binding studies with some of the mutants constructed in the course of this work should allow testing of this idea. Combined with the mutational analysis of other GcvB-regulated mRNAs, this approach might provide further insight into how the ACA motif participates in the translation initiation step. Strains used in this study were derivatives of Salmonella enterica serovar Typhimurium strain LT2 [40]. Strain SV4280 was a gift of J. Casadesús. Except for the latter strain and for strain MA7224, all other strains were derived from MA3409, an LT2 derivative cured for the Gifsy-1 prophage [41]. The genotypes of the relevant strains used are listed in Table S1. Bacteria were cultured at 37°C in liquid media or in media solidified by the addition of 1. 5% Difco agar. LB broth [42] was used as complex medium. Carbon-free medium (NCE) [43], supplemented with 0. 2% glycerol or 0. 2% lactose was used as minimal medium. Antibiotics (Sigma-Aldrich) were included at the following final concentrations: chloramphenicol, 10 µg ml−1; kanamycin monosulphate, 50 µg ml−1; sodium ampicillin 100 µg ml−1; spectinomycin dihydrochloride, 80 µg ml−1; tetracycline hydrochloride, 25 µg ml−1. MacConkey agar plates containing 1% lactose [44] were used to monitor lacZ expression in bacterial colonies. Liquid cultures were grown in New Brunswick gyratory shakers and growth was monitored by measuring the optical density at 600 nm with a Shimazu UV-mini 1240 spectrophotometer. T4 polynucleotide kinase and Taq DNA polymerase were from New England Biolabs, Pfu-Turbo DNA polymerase was from Stratagene, T4 DNA ligase was from New England Biolabs. DNA oligonucleotides were custom synthesized by Sigma Aldrich or Eurofins MWG/Operon. The complete list of DNA oligonucleotides used in this study is shown in Table S2. DNA sequencing was performed by GATC biotech. Acrylamide-bisacrylamide and other electrophoresis reagents were from BioRad. Agarose was from Invitrogen. Hybond-N+ membranes and hybridization buffer used for Northern blot analysis were from GE Healthcare and from Applied Biosystems-Ambion, respectively. The rNTPs were from Promega and the 32P-NTPs were from PerkinElmer or Hartmann Analytic. 32P-labeled nucleic acids were detected by phosphorimaging using ImageQuant software. Generalized transduction was performed using the high-frequency transducing mutant of phage P22, HT 105/1 int-201 [45] as described [46]. Chromosomal engineering (recombineering) was carried out by the λ red recombination method [47]–[49] implemented as in [47]. Donor DNA fragments were generated by PCR using plasmid DNA or chromosomal DNA or DNA oligonucleotides as templates. Amplified fragments were electroporated into appropriate strains harboring the conditionally replicating plasmid pKD46, which carries the λ red operon under the control of the PBAD promoter [47]. Bacteria carrying pKD46 were grown at 30°C in the presence of ampicillin and exposed to arabinose (10 mM) for 3 hours prior to preparation of electrocompetent cells. Electroporation was carried out using a Bio-Rad MicroPulser under the conditions specified by the manufacturer. Recombinant colonies were selected on LB plates containing the appropriate antibiotic. Constructs were verified by PCR and DNA sequence analysis (performed by GATC company). PCR amplification of DNA fragments under error-prone conditions was carried out as previously described [50]. Scarless modification of chromosomal DNA sequences at the single base-pair level was achieved with a two-step recombineering procedure as previously described [51]. Briefly, this involved: 1) inserting a tetAR module (produced by PCR) at the chromosomal site to be modified and: 2) replacing the tetAR module by a DNA fragment carrying the desired changed through positive selection tetracycline-sensitive recombinants [52]. Typically, the DNA fragment in the second step was also obtained by PCR using oligonucleotides with complementary sequences at their 3′ ends priming DNA synthesis on each other (“reciprocal priming”). In site-directed mutagenesis experiments, one of the two primers contained the desired nucleotide changes or randomized sequence stretches. All constructs were verified by DNA sequencing. Table S3 shows the list of alleles made by standard or scarless recombineering. RNA was prepared by the acid-hot-phenol method from exponentially growing cells (OD600 of 0. 35) as previously described [50]. Reverse transcriptase reactions (enzyme Superscript II from Invitrogen) were carried out using 5 µg of bulk RNA and 32P-labeled primer ppF49. The same DNA primer was used for the sequencing reactions. Reactions were performed with the fmol DNA Cycle Sequencing System from Promega, according to the manufacturer' s protocol. Reaction products were fractionated on a 10% polyacrylamide-8 M urea gel. For Northern blot analysis, RNA was fractionated on a 1% agarose-formaldehyde gel, blotted onto a nylon membrane, and hybridized to the appropriate 32P-labeled DNA oligonucleotide probes. In vitro coupled transcription/translation was performed using New England Biolabs' PURExpress In vitro Protein Synthesis kit (NEB #E6800) according to the manufacturer instructions. Genes to be analyzed were cloned under T7 promoter control in the DFRH plasmid provided with the kit. The hybrid genes carried yifK wt or mutant 5′ UTR sequences fused to the cat-3×FLAG coding sequence (chloramphenicol acetyl transferase in-frame fusion to the 3×FLAG epitope). Final volume of the transcription/translation reaction was 25 µl in all cases. In addition to kit solutions A and B, reaction mix contained, 10 U of RNase inhibitor SUPERase (Ambion) and template plasmid DNA added to either 0. 5 or 5 pM final concentration. Incubation times at 37°C varied from 15 to 90 min. Reactions were stopped by addition of equal volume of 2× Laemmli buffer and immediate freezing. Aliquots were loaded on 12. 5% Acrylamide gels and Western analysis performed as previously described [53]. Toeprinting reactions were carried out as described by Darfeuille et al [54] with minor modifications. RNA fragments spanning positions +1 to +135 of yifK mRNA were synthesized in vitro from T7 DNA templates generated by PCR amplification of chromosomal DNA (from strains MA8020 or MA11793) with primers ppI22 and ppI23. 2 pmol of RNA were annealed with 5′end-labeled primer ppI23 (1 pmol) in 10 mM Tris-acetate [pH 7. 6], 0. 1 M potassium acetate, and 1 mM DTT for 1 min at 90°C and chilled in ice for 5 min. Then, all dNTPs (final concentration 1 mM), Mg Acetate (10 mM final) were added; this was followed by preincubation with 2 pmol of 30S ribosomal subunit (a gift of Dominique Fourmy and Satoko Yoshizawa) at 37°C for 5 min. In experiments involving GcvB, 5,1 or 0. 5 pmol of sRNA were added prior to both, addition of the 30S ribosomal subunit and the preincubation step. After the 5-min period, 2 pmol of tRNAfMet were added and preincubation at 37°C continued for 15 additional min. Finally, Reverse Transcriptase (Superscript II, Invitrogen, 200U) was added and samples incubated for 15 min at 37°C. Following phenol chloroform extraction and ethanol precipitation, resuspended samples were loaded onto a 10% polyacrylamide-8 M urea gel along with the sequencing reaction samples generated with the same primer. β-galactosidase activity was assayed in toluene-permeabilized cells as described in [55] and is expressed in Miller units throughout this work. Typically, measurements were performed on duplicate or triplicate cultures grown in late exponential phase (OD600≈0. 7). All experiments included parental or reference strains as normalization controls. Standard deviations were generally less than 5% of the mean.

The majority of small RNA (sRNA) regulators in bacteria act by inhibiting translation initiation in target messenger RNAs. The study of this regulatory mechanism not only allows a better understanding of sRNA function but it can also provide new insight into aspects of the translation initiation process that remain incompletely characterized. This was the case in the work described here. Analyzing the mechanism by which GcvB, a multi-target sRNA, downregulates a putative amino acid transporter in Salmonella, we discovered that the sequence base-pairing with GcvB in the target mRNA functions as a translational enhancer. Replacing an ACA motif near the center of the sequence with unrelated trinucleotide sequences leads to a decrease in translational initiation efficiency that can be as severe as more than 90%. Interestingly, some of these replacements concomitantly render the mRNA insensitive to GcvB variants carrying the appropriate compensatory changes, suggesting that targeting the enhancer element is paramount for GcvB regulatory effectiveness. Overall the data presented in the paper unveil the role of the ACA motif in the translation initiation process and lay the grounds for further analysis of the mechanism involved.

Abstract Introduction Results Discussion Materials and Methods

2014

Translation Enhancing ACA Motifs and Their Silencing by a Bacterial Small Regulatory RNA

Sjögren’s syndrome (SS) is a common, autoimmune exocrinopathy distinguished by keratoconjunctivitis sicca and xerostomia. Patients frequently develop serious complications including lymphoma, pulmonary dysfunction, neuropathy, vasculitis, and debilitating fatigue. Dysregulation of type I interferon (IFN) pathway is a prominent feature of SS and is correlated with increased autoantibody titers and disease severity. To identify genetic determinants of IFN pathway dysregulation in SS, we performed cis-expression quantitative trait locus (eQTL) analyses focusing on differentially expressed type I IFN-inducible transcripts identified through a transcriptome profiling study. Multiple cis-eQTLs were associated with transcript levels of 2' -5' -oligoadenylate synthetase 1 (OAS1) peaking at rs10774671 (PeQTL = 6. 05 × 10−14). Association of rs10774671 with SS susceptibility was identified and confirmed through meta-analysis of two independent cohorts (Pmeta = 2. 59 × 10−9; odds ratio = 0. 75; 95% confidence interval = 0. 66–0. 86). The risk allele of rs10774671 shifts splicing of OAS1 from production of the p46 isoform to multiple alternative transcripts, including p42, p48, and p44. We found that the isoforms were differentially expressed within each genotype in controls and patients with and without autoantibodies. Furthermore, our results showed that the three alternatively spliced isoforms lacked translational response to type I IFN stimulation. The p48 and p44 isoforms also had impaired protein expression governed by the 3' end of the transcripts. The SS risk allele of rs10774671 has been shown by others to be associated with reduced OAS1 enzymatic activity and ability to clear viral infections, as well as reduced responsiveness to IFN treatment. Our results establish OAS1 as a risk locus for SS and support a potential role for defective viral clearance due to altered IFN response as a genetic pathophysiological basis of this complex autoimmune disease. Sjögren’s syndrome (SS) is a common systemic autoimmune disease with a prevalence rate (~0. 7% of European Americans) second only to rheumatoid arthritis (RA) [1]. SS is distinguished by immune cell infiltration, functional destruction, and irreversible dysfunction of exocrine glands, most notably salivary and lacrimal glands [2]. Secondary manifestations of exocrine gland dysfunction may include severe dental decay and corneal scarring. Approximately one-third of patients experience extra-glandular manifestations of disease, such as debilitating fatigue, a 16-fold increased risk of developing lymphoma, neuropathies, Raynaud’s phenomenon, arthralgia, and dermatologic symptoms [2–6]. Both glandular dysfunction and extra-glandular manifestations are associated with autoantibodies, a hallmark of autoimmunity [7–9]. Approximately 70% and 40% of SS patients exhibit autoantibodies targeting ribonucleoproteins, Ro/SSA (Ro52 and Ro60) and La/SSB, respectively [10]. These autoantibodies have the capacity to bind necrotic and apoptotic material, thus creating RNA-immune complexes that can activate cells of the immune system and aggravate autoinflammation [11]. Such RNA-containing immune complexes are taken up by the Fc gamma receptor IIa on plasmacytoid dendritic cells (pDCs) [12], which activates intracellular Toll-like receptors 7 and 9 and stimulates type I interferon (IFN) responsive loci [13]. The etiology of SS is still largely unknown, though it involves a complex interplay between both genetic and environmental factors [14–16]. Viral infections, such as Epstein-Barr virus (EBV) and cytomegalovirus [17–19], may initiate prolonged inflammation in glandular lesions and formation of germinal center-like structures commonly linked to autoantibody production in SS [14,19]. Autoantibodies can be detected up to 18–20 years prior to diagnosis in 81% of SS patients [20]. Indeed, cross-reactivity between antibodies against EBV and the Ro60 antigen has previously been reported [21], and possible subclinical reactivation of the virus has been associated with active joint involvement in SS [22]. Recently, the virus-like genomic repeat element L1 was identified as an endogenous trigger of the IFN pathway, and its expression correlates with type I IFN expression and L1 DNA demethylation [23]. Type I IFNs are key antiviral immune mediators of innate immune responses in infected cells, while at the same time enhancing antigen presentation and inducing production of pro-inflammatory cytokines and chemokines [24], thus initiating adaptive immunity [25]. Overexpression of type I IFN-inducible genes, known as “the IFN signature”, is a common feature of many autoimmune diseases [26,27], including RA patients with poor clinical outcome [28–30] and systemic lupus erythematosus [31,32], where the predominant IFN producing cells, pDCs, are reduced in number in the blood but are abundant in skin and lymph nodes [33]. In SS, the IFN signature is observed in both peripheral blood and salivary glands [12,34–37], and associates with systemic manifestations, greater disease severity, and autoantibody titers [8,9]. It has been proposed that viral infections contribute to perpetual activation of type I IFN signaling and the resulting dysregulation of innate immunity, ultimately resulting in activation of the adaptive immune response and autoantibody production in SS and other autoimmune diseases [34,38]. Genome-wide association [GWA] studies in autoimmune diseases have identified multiple genetic risk variants involved in type I IFN signaling pathways [39–41], including associations of IRF5 and STAT4 with SS susceptibility [15,42,43]. Suggestive associations of FCGR2A, PRDM1 (PR domain containing 1, regulated by IRF5) [44], and IRF8 with SS have also been reported [15], along with two genes within the NF-κB pathway (TNIP1 and TNFAIP3), which regulates early phase type I IFN production during viral infection [45]. Despite the evidence indicating an important role of the type I IFN pathway in SS, no direct functional mechanisms for SS-associated variants contributing to the substantial upregulation of IFN signature transcripts have been described. The vast majority of disease associated single-nucleotide polymorphisms (SNPs) identified in GWA studies are non-coding [46] and are not likely to impact protein function directly, thus requiring a combination of genetic studies and gene expression analyses to point towards mechanisms that link genetics with functional effects [47]. Specifically, the extensive linkage disequilibrium (LD) observed between associated polymorphisms renders it hard to identify causal variant (s) of disease. Systemic evaluation of genome-wide functional elements by the Encyclopedia of DNA Elements (ENCODE) project reveals that 80% of the human genome has at least one biochemical function, and many of the genetic variants are within cis- or trans- regulatory sites that impact gene expression [48]. Furthermore, genome-wide cis-expression quantitative trait locus (eQTL) mapping studies in different tissues have identified more than 3,000 genes associated with nearby genetic variants [49,50]. Through combining GWA and gene expression data from SS patients, we sought to identify and characterize SS-associated variants that influence the expression of genes within the IFN signature by utilizing a genomic convergence approach (Fig 1). Through cis-eQTL analyses we identified an association of a SNP rs10774671, located within the OAS1 gene locus, with SS. Functional studies were performed to assess biological consequence of the OAS1 variants. To select candidate genes in the IFN signature, we first evaluated dysregulated transcripts in SS through a microarray-based gene expression profiling study. Whole blood transcriptome profiles from 115 anti-Ro/SSA positive SS cases and 56 healthy controls of European ancestry were compared, as the IFN signature is enriched in SS patients seropositive for anti-Ro/SSA [34]. After quality control (QC) and normalization, 13,893 probes (in 10,966 genes) remained, for which Welch’s t-tests, false discovery rate (FDR) -adjusted P values (q values), and fold changes (FC; the difference of the mean between log2-transformed values from cases and controls) were calculated (see Methods for details). Differentially expressed genes were selected by q < 0. 05 and FC > 2 or < -2. We found 73 differentially expressed genes in our dataset, among which 57 genes are regulated by type I IFNs (S1 Table). The majority of dysregulated genes (66 out of 73) were overexpressed in SS patients and formed the IFN signature in cases after unsupervised hierarchical clustering (Fig 2A). Of note, the IFN signature was observed in most but not all anti-Ro/SSA positive SS cases, in accordance with our previous work [34], and the intensity of this feature was heterogeneous among patients (Fig 2B). These results suggest that the expression of IFN signature genes might be influenced by genetic variants, which could be identified through cis-eQTL analysis. We hypothesized that variants near the differentially expressed IFN signature genes may potentially influence disease susceptibility through cis-regulatory mechanisms. We would, however, expect to identify many cis-eQTLs in these regions regardless of whether they associate with disease susceptibility or not. Therefore, instead of performing cis-eQTL analyses for all of the 73 dysregulated IFN signature genes, we first sought to identify variants that showed a disease association of Passoc<0. 05 for subsequent evaluation of their role in altering gene expression. Genetic associations with SS susceptibility for 2,163 SNPs in regions of the 73 differentially expressed genes were tested using a combined dataset (Dataset 1; Table 1) from genome-wide genotyping arrays consisting of 765 SS cases and 3,825 population controls of European ancestry. We identified suggestive associations (Passoc<1×10−4; this threshold was determined by Bonferroni correction for independent variants with r2<0. 2) of genetic variants within the OAS1 region (top association at rs10774671, Passoc = 8. 47×10−5), which is regulated by type I IFNs (S1 Table). Furthermore, we identified suggestive associations in ARGN (S1 Table) and observed nominal associations (1×10−4≤Passoc<0. 05) with SS susceptibility in 42 additional regions (S1 Table). To determine whether these disease-associated genetic variants (Passoc<0. 05) were related to the altered expression levels of their nearby differentially expressed IFN signature genes, we performed cis- and trans-eQTL analyses for all SNPs with Passoc<0. 05 (173 SNPs in 44 regions; S1 Table) using a linear model by integrating the transcriptome and genotype datasets in 178 European individuals (108 anti-Ro/SSA positive SS cases, 55 anti-Ro/SSA negative SS cases, and 15 healthy controls). Variants within and near OAS1 showed significant association with OAS1 transcript expression (Fig 3A; S1 Table). In particular, three microarray probes targeting OAS1 passed QC and were evaluated for cis-eQTLs (Fig 3B). The OAS1 transcript levels measured by all of the three probes were found to be associated with nearby genetic variants (Fig 3C). The most significant cis-eQTL for all the three probes targeting OAS1 was rs10774671 (PeQTL-Probe1 = 5. 14×10−4, PeQTL-Probe2 = 2. 86×10−6, and PeQTL-Probe3 = 6. 05×10−14; Fig 3C). No eQTL was detected in any other differentially expressed genes (S1 Table). We also determined that none of these eQTL variants were associated with the two nearby genes, OAS2 and OAS3. Additionally, no significant trans-eQTL was detected for OAS1. Therefore, we identified a variant associated with both SS susceptibility and gene expression in the IFN signature gene OAS1. To fine map this disease-associated region, imputation was then performed for the SS-associated OAS1 region to increase the informativeness of the genetic association and eQTL analyses results. After imputation, the most significant association with SS in the OAS1 region was at rs4767023 (Passoc = 3. 82×10−5; r2 = 0. 98 with the most significant genotyped SNP rs10774671; Fig 4A), whereas the top eQTL remains at rs10774671 (Fig 3A). All the variants with Passoc<1×10−4 in the OAS1 region were strongly correlated to each other (r2>0. 9; Fig 4B) and could explain the association of the whole region through conditional analyses (Fig 4C and 4D). The top SS-associated variants and cis-eQTLs in the OAS1 region, including rs10774671, were in strong LD (r2>0. 9; Fig 4B), thus challenging the selection of potentially functional variant (s) based on results from the association analyses. However, the top eQTL variant, rs10774671, is an A/G substitution within the consensus sequence of a splice acceptor site at the junction of the 5th intron and the 6th exon of OAS1 (Fig 3B), and is known to alter normal splicing and induce isoform switching of OAS1 [51]. In addition, all other SS-associated variants (Passoc<1×10−4) in the OAS1 locus were either intronic or outside of coding regions, lacking functional genomic elements mapped to the SNP as determined by the ENCODE project [48,52]. Also, we performed a co-localization analysis using eCAVIAR [53] to identify the potential causal variant in the OAS1 region. We estimated colocalization posterior probability (CLPP) scores for all the tested 453 variants, and rs10774671 has the highest CLPP score among all the variants (S2 Table). Therefore, we prioritized this SS-associated cis-eQTL variant, rs10774671, for further replication and functional studies. We replicated the genetic association of rs10774671 with SS susceptibility in an independent dataset (Dataset 2; Table 1) consisting of 514 European SS cases and 3,466 European population controls (genotyped using TaqMan assays, Prep = 5. 16×10−6; odds ratio = 0. 71; 95% confidence interval = 0. 63–0. 83). Meta-analysis was performed to combine the results between the initial genetic association study (Dataset 1) and the replication cohorts (Dataset 2) and established the association of rs10774671 with SS risk (Pmeta = 2. 59×10−9; odds ratio = 0. 75; 95% confidence interval = 0. 66–0. 86; risk allele [the A allele] frequency: case = 0. 70, control = 0. 64; with no heterogeneity between the two datasets as determined by I2 = 0). We also performed a stratified analysis and a permutation analysis using merged samples from Dataset 1 and Dataset 2 to determine whether the observed genetic association was restricted to anti-Ro/SSA positive or negative patients. We did not find any evidence to support the genetic effect to be specific to any sub-group of the patients (S1 Fig). In summary, we identified a potential causal variant, rs10774671, that was associated with SS susceptilibty, likely through its impact on the expression of a key IFN signature gene, OAS1. Following establishment of the association between rs10774671 and SS susceptibility, we further determined the influence of different genotypes on the alternative splicing of OAS1. Four isoforms of OAS1 are annotated in the NCBI Reference Sequence (RefSeq; http: //www. ncbi. nlm. nih. gov/refseq) database, of which we analyzed p46, p42, and p48, and p44, an un-annotated isoform previously reported in RNA-sequencing (RNA-seq) studies [54–56] (Fig 3B). The difference between these isoforms is confined to their 3' end where rs10774671 influences alternative splicing, yielding amino acid sequences of different lengths and composition. In the microarray experiments, one probe targeting OAS1 specifically recognized the 3' end of the p42 isoform (Fig 3B). The risk allele A of rs10774671 was correlated with higher expression levels of p42 (Fig 3C, right panel). However, we were not able to determine the influence of rs10774671 on the expression of other isoforms due to lack of isoform-specific probes on the microarray. In order to determine the influence of rs10774671 on the expression of each alternatively spliced isoform of OAS1 and compare OAS1 isoform composition, we performed RNA-seq on whole blood from 57 SS cases and 27 healthy controls. After QC, the reads were aligned to the human genome using TopHat [57] without gene annotation to facilitate the detection of potentially novel isoforms of OAS1. The transcript level of each isoform was compared across samples with different genotypes of rs10774671 based on the measurement of fragments per kilobase of transcript per million mapped reads (FPKM) using Cufflinks [58]. Consistent with our microarray results, the SS risk allele A of rs10774671 was correlated with higher expression levels of p42 (P = 1. 30×10−7; Fig 5A). Increased production of other alternatively spliced isoforms of OAS1, including p48 and p44, was also observed in subjects with the SS risk genotypes (GA and AA) of rs10774671 (Fig 5B and 5C). In contrast, transcript levels of the p46 isoform, was decreased in samples with the A allele (P = 3. 48×10−10; Fig 5D), consistent with previous reports that interruption of the splicing consensus sequence inhibit formation of the p46 isoform [56]. These results were further confirmed by quantitative real-time PCR using primer sets targeting the specific OAS1 isoforms (S2 Fig; S3 Table). Therefore, we found that the SS-associated variant rs10774671 is a functional variant that influences alternative splicing of OAS1. Since OAS1 is part of the IFN signature and its expression levels are correlated with the autoantibody status, we also performed a stratified eQTL analysis to investigate whether the eQTL effects are specific to any sub-group of the SS patients based on their anti-Ro/SSA positivity. We stratified the SS case samples into anti-Ro/SSA positive patients (n = 27) and anti-Ro/SSA negative patients (n = 30), and performed eQTL analyses on each of the OAS1 isoforms using linear regression while adjusting for sex. Despite reduced statistical power, we identified significant eQTL results for the p46, p42, and p48 isoforms in both subsets of samples. By using the Z-test as described in S1 Fig, we did not find any significant difference of the eQTL effects between the two sub-groups (S3 Fig). Comparing the total OAS1 transcript level from the microarray study within each genotype revealed significantly higher gene expression in SS patients as compared to control in the GA group (Fig 6A). There is a trend towards higher total OAS1 transcripts in the AA and GG groups of SS patients as well, though it is not statistically significant. Interestingly, the highest transcript levels are seen in the anti-Ro/SSA positive cases (Fig 6B), significantly higher than both anti-Ro/SSA negative cases and healthy controls. The same results were observed in the RNA-seq data (S4 Fig), indicating that the total OAS1 transcript levels regardless of isoform are also influenced by disease status or the presence of autoantibodies. To further dissect the functional mechanism of rs10774671 in predisposing disease risk, we utilized Western blots to evaluate the difference in protein levels of the normally spliced isoform p46 (formed by the protective allele G of rs10774671) and the alternatively spliced isoforms of OAS1 in EBV-immortalized B cells from SS patients. Consistent with the RNA-seq results, the protein expression of p46 was substantially lower in subjects carrying the A allele of rs10774671, whereas p42 was the dominant isoform in the GA and AA subjects without stimulation (Fig 7A). Interestingly, both protein and mRNA levels of the p46 isoform were upregulated after stimulation by type I IFN in the GG and GA subjects (P = 3×10−4 and P = 2×10−3, respectively; Fig 7A and 7B). However, protein expression of the p42 isoform remained unchanged upon IFN stimulation (Fig 7A), even though its transcript level significantly increased after stimulation (Fig 7B). The protein expressions of p48 and p44 were low in all of the samples, and not responsive to type I IFN stimulation (Fig 7A). We then cloned and transfected each isoform into human embryonic kidney (HEK) cell line 293T cells and observed similar protein expression results as in EBV cells: lower protein levels for p48 and p44 compared to p46, even though their transcript levels were equivalent (Fig 7C). These results suggest that the alternative isoforms of OAS1 that are associated with the disease risk variant of rs10774671 fail to generate proteins after transcription. The catalytic OAS1 domain is located at the N terminus, though the isoforms differ in their C terminus. It has been suggested that these differences affect affinity of OAS1 protein for different viruses [59]. However, our data suggested that the alternatively spliced 3' -terminus influenced the lack of post-transcriptional expression of the p48 and p44 isoforms. To test this hypothesis, we generated several truncated forms of p48 and p44 at the 3' -terminus and transfected them into HEK 293T cells. The truncation of both p48 and p44 transcripts at the 3' -end resulted in restoration of protein expression (Fig 7D). Our results demonstrated that the alternatively spliced 3' -end between 1,047 and 1,155 bp of the p48 isoform and the 3' -end between 1,083 and 1,137 bp of the p44 isoform were responsible for the impaired protein expression (Fig 7D). In addition, we recombined the green fluorescent protein (GFP) transcript with the 3' -end from different OAS1 isoforms and expressed them in HEK 293T cells. The alternatively spliced 3' -terminus of p48 and p44 resulted in reduced expression of GFP when linked to the 3' end of GFP transcript (Fig 7E; S5 Fig). These results further confirmed the impact of the alternatively spliced 3' -end of OAS1 on protein expression. Determining mechanisms for how the 3' -terminus from the alternatively spliced OAS1 isoforms influences protein expression and type I IFN responsiveness needs further study. Overexpression of genes in the IFN pathway is a distinctive feature of multiple autoimmune diseases, though no evident mechanism has thus far been revealed. We identified and established rs10774671 as a risk locus for SS. The A allele of rs10774671 is correlated with reduced OAS1 enzymatic activity in human peripheral blood mononuclear cells [51], and is associated with increased susceptibility to West Nile virus [60] and chronic hepatitis C virus infections [61]. OAS1 is a member of the 2' -5' -oligoadenylate synthetase family, which is upregulated by type I IFNs during innate immune responses to viral infection and activates latent RNase L, leading to viral RNA degradation and clearance [62,63]. The SS risk allele A of rs10774671 causes alternative transcript splicing and consequently less functional isoforms are activated by type I IFNs. Failure to clear virus might lead to subclinical, chronic infection that drives the sustained overexpression of IFN, but viral proteins may also indirectly cause IFN production through adaptive immune responses. For example, antibodies generated towards EBV nuclear antigen-1 cross-react with Ro/SSA [21], and anti-Ro/SSA antibodies may in turn form immune complexes that stimulate type I IFNs [64]. As viruses evidently play a role in SS pathophysiology [18], genetic variants affecting the antiviral properties of OAS1 might be a contributing factor. A recent study showed that while the presence of antibodies to hepatitis D virus was equal in SS patients and otherwise healthy controls, the virus itself was present in significantly more patients [65], indeed suggesting that viral clearance is restrained. Epitope spreading [66,67], antibody cross-reaction [21], or molecular mimicry [68,69] are likely consequences of subclinical, chronic or recurrent infection. The basal activity of OAS1, which varies greatly among individuals, is thought to be under strong genetic control [51]. The enzyme activity in the GG genotype with predominantly the p46 isoform is higher than GA (intermediate) and AA (low) [51]. OAS1 isoforms p42 and p46 have been detected at the protein level in human cells, whereas the p44a/p44b, p48, and p52 isoforms have been detected at mRNA levels [54,70–72]. In addition to the RNase L activation properties, the tetramer forming p48 isozyme also exhibits proapoptotic activity [73], a property partly accredited to IFN-γ [74], and is shown to interact with Bcl-2 [75]. Bcl-2 is an anti-apoptotic protein negatively regulated by Ro52 [76], and in salivary gland epithelial cells Bcl-2 is essential in regulation of IFN-γ induced apoptosis [77]. It has been postulated that p46 is a more efficient synthetase than p48, explaining the increased basal activity of p46 [51]. Although there is no evidence showing that the differences in the C-terminus alter protein function [78], our truncation experiments indicated that the alternatively spliced C-terminus governs the post-transcriptional protein expression. Interestingly, we found lower protein expressions of p44 and p48 after type I IFN stimulation despite equivalent transcript levels compared to p46. This indicates that p44 and p48 production, which is governed by the A allele, is less responsive to IFN stimulation as compared to p46. Lack of response to IFNs has also been seen in multiple sclerosis (MS), in which patients carrying the homozygous rs10774671 GG genotype, a protective genotype in MS associated with less active disease, were more responsive to IFN-β treatment than AA and AG patients, as measured by time to first relapse [79]. We searched the Genotype-Tissue Expression (GTEx) database and confirmed the association between rs10774671 and OAS1 expression in whole blood [80]. We also searched eQTL for all our top variants in the 43 SS-associated genes (besides OAS1) as well as any variants in LD with those top variants (r2>0. 8). Out of the 614 variants we checked, two variants were also eQTLs for their corresponding genes: the top SS-associated variant in ANKRD22 (rs1147601, Passoc = 2. 38×10−3, PeQTL-GTEx = 6. 4×10−6) and the top SS-associated variant in EPSTI1 (rs7323736, Passoc = 1. 79×10−2, PeQTL-GTEx = 2. 6×10−6). However, both of these variants were only nominally associated with SS susceptibility and did not pass our suggestive significance threshold for disease association (Passoc<1×10−4). Nevertheless, these variants and genes could be plausible targets for future replication studies to assess their disease associations. The rs10774671 A/G variant is a common splice site variation, and there is a skewed distribution of genotypes in autoimmune diseases like type I diabetes (T1D) [81] and MS [79] despite ambiguous genetic association with disease: the alternative allele A renders risk to SS and MS, whereas the reference allele G increases susceptibility to T1D. We hypothesized that these opposite risk effects may be due to different functional isoform usages in different disease-relevant tissues. Through searching the GTEx database for rs10774671 eQTLs, we found rs10774671 is a significant eQTL of OAS1 in 5 tissues (S6A Fig). Interestingly, the eQTL effect in the Esophagus—Mucosa tissue is in the opposite direction compared to other tissues. In whole blood, the p46 isoform is predominant, thus the A allele caused reduced expression of OAS1 as a whole (S6B Fig); however, in Esophagus Mucosa, the isoform is p42 (S6C Fig) and results in an opposite effect of rs10774671 on the total OAS1 expression. We propose that the ambiguous genetic effects of rs10774671 on different diseases might be due to different functional isoforms in disease-relevant tissues (not necessarily Esophagus—Mucosa). While reduced expression of the functional isoform p46 in whole blood increases risk of SS and MS, it protects individuals from T1D. The downstream differences of various isoforms in protein levels, isoform expression, responsiveness to IFN, and basal activity between genotypes flag OAS1 as a highly relevant protein in autoimmune diseases, despite no direct effect on IFN expression. OAS1 is one of several genes relevant in overall IFN response found to be disease associated in SS. Others include IL-12A [15], which can induce both type I and type II IFNs [82]; STAT4 [15], which, although not explicitly overexpressed in the IFN signature, plays an important role in the cross-talk between type I and type II IFNs [83–85]; and IRF5 [15], a transcription factor in the IFN pathway [86]. The rs10774671 is a known cis-eQTL and splicing QTL, observed in whole blood [87] as in our study, in lymphoblastoid cells [88], and in monocytes, both naïve CD14 and in cells stimulated with LPS and IFN-γ [89]; but no trans-eQTLs are known. It is possible that other variant (s) in high LD with rs10774671 could contribute additional functional impact (s), such as the rs11352835 in exon 7 seen in MS [59]. Genomic editing approaches that introduce single point mutations or deletions in the OAS1 region will further advance the dissection of the causal SS-associated variant in this haplotype. Animal models that express the risk isoforms due to the risk allele can also be used to observe whether they spontaneously develop SS-like symptoms, and whether the chances for developing such symptoms increase after exposure to viral infections. Our study also highlights the importance of utilizing genomic convergence to identify and prioritize susceptibility genes for human complex disease. The complex mechanisms underlying the IFN signature in SS cannot be explained as a single eQTL driven overexpression. However, we have in this study established OAS1 as a risk locus with functional consequences affecting isoform composition, and that may play a fundamental role in dysregulation of both viral clearance and apoptosis. All patients in this study fulfilled the 2002 American-European Consensus Group (AECG) criteria for primary SS [7]. Seropositivity of anti-Ro/SSA autoantibodies was determined by the antibody index ≥1 using the Bio-Plex assay (Bio-Rad) following the manufacturer’s protocol. The present study was approved by the Oklahoma Medical Research Foundation Institutional Review Board (IRB#1—Biomedical), operation under Federalwide Assurance (FWA) # 00001389 and IRB # 00000114 under IORG 0000079 approved by the Office for Human Research protection (OHRP), Department of Health and Human Services (DHHS). The OMRF IRB is in compliance with local regulations and the regulations of the United States Food and Drug Administration as described in 21 CFR Parts 50,56 and 11, the International Conference on Harmonization (ICH) E6, and the United States Department of Health and Human Services at 45 CFR 46. The current study was approved under IRB#07–12 and all patients provided written informed consent. Quantitative levels of the differentially expressed transcripts from the microarray analysis were used as phenotypic traits in 178 European subjects described above. Variants showing nominal association with SS (Passoc<0. 05) in the genetic association analysis were selected to test for cis-eQTLs, defined by variant-transcript pairs within 50kb of the target genes or trans-eQTLs for variants at least 1Mb away. Association of genotype with transcript expression was evaluated using both linear regression (adjusted for gender and disease status) and analysis of variance (ANOVA) in Matrix-eQTL [107]. FDR-adjusted P values were calculated to determine the significance of the eQTL. The results of the cis-eQTL analyses were plotted in Prism 6. We also used a tool, PEER, based on a Bayesian framework to adjust for unknown non-genetic factors in gene expression [108]. We transformed our expression values using all the genes that passed QC by running PEER for 15 factors. We then used the PEER residuals from the 44 SS-associated (Passoc<0. 05) IFN signature genes as quantitative traits to determine eQTL while adjusting for other known potentially confounding factors: sex, disease status, anti-Ro/SSA status, and age. In addition to additive genetic models, we also performed eQTL analyses using linear regression by three other models: recessive (recode genotype from 0,1, 2 [where 2 equals to AA] to 0,0, 1), dominant (0,1, 1), and overdominant (0,1, 0). We used the coefficient of determination (R2) to evaluate the goodness-of-fit in each of these models. As shown in S4 Table, both the p42 and p48 isoforms fit the additive model best (highest R2), whereas the recessive model outperformed in the p46 isoform regression (R2rec = 0. 57 vs. R2add = 0. 54). However, the difference of the R2 between the dominant and additive models in the p46 eQTL analysis is subtle. Also, the outperformance of the recessive model cannot be confirmed by qPCR (where the additive model has the highest R2). Therefore, we only reported the additive results in the main text. However, the alternative genetic models for the eQTL effect observed in different isoforms may reflect distinct disease mechanisms rendered by these isoforms, and thus detailed contribution of different isoforms on disease susceptibility warrant further functional study. The co-localization analysis between genetic association and cis-eQTL results in the OAS1 region was performed using eCAVIAR [53]. We used the z-scores (calculated by β/standard error) and the LD matrix (calculated using PLINK—r) from both the genetic association and cis-eQTL results as input and assumed one causal variant to obtain colocalization posterior probability (CLPP) scores for all the tested 453 variants in the OAS1 region. Peripheral blood mRNA transcripts from 27 anti-Ro/SSA positive SS cases, 33 anti-Ro/SSA negative SS cases, and 30 healthy controls were isolated and measured as described above. RNA-seq was performed using the Illumina HiSeq 2000 employing standard procedures. Multiplexing of 6 samples per lane was utilized. Post sequence data were processed with Illumina Pipeline software v. 1. 7. Quality of raw sequence data was assessed using FASTQC. We assessed the quality of each sample using AQM [92] as described above. A total of 6 samples were removed from analysis due to significantly different expression patterns revealed by PC analysis. Raw FASTQ files were aligned to the human reference genome (hg19) using TopHat [57] that aligns the reads across splicing junctions independent of gene annotations, which benefits de novo detection of alternative splicing events. The total gene transcript level was determined by normalized read counts (raw read counts divided by estimated size factor) in DESeq [109]. To determine alternative splicing events, the reference-independent construction of the transcripts was performed using Cufflinks [58] to identify transcripts >1% of the most abundant isoform in each sample. We only kept the transcripts that were detected in more than 10% of the samples for further analysis. The previously annotated isoforms (p46, p42 and p48) and an un-annotated isoform identified across multiple samples (p44) were used as reference to reconstruct the isoforms of OAS1. The novel identified isoforms of OAS1 were also checked manually in the Integrative Genomics Viewer (IGV) [110] to confirm the transcripts and cross-exon reads. The FPKM values calculated by Cufflinks were used to determine the expression levels of each isoform of OAS1. Total RNA was extracted using TRIzol reagents (Life Technologies) from EBV-immortalized B cells pre-selected for the presence of target OAS1 isoforms based on the RNA-seq results from whole blood. Following DNase treatment (Life Technologies) and cDNA synthesis (iScript kit from Bio-Rad), full-length and truncated OAS1 transcripts were amplified from cDNA using primer sets specific for the different OAS1 isoforms and truncated forms (S3 Table). Each OAS1 isoform transcript was individually cloned into pcDNA3. 1 (Invitrogen) with an Xpress epitope tag at the 5' -terminus to facilitate the detection of transfected protein using Western-blot with anti-Xpress antibody. The plasmid was transfected into the HEK 293T cells using FuGENE transfection reagents (Promega) following manufacturer’s protocols. The protein expression of OAS1 isoforms was evaluated in EBV-immortalised B cells from SS patients, four independent samples from each genotype group GG, GA and AA, treated or not treated with type I interferon (universal type I IFN, 1500 U/mL, for 24 hours). The cells were lysed in RIPA buffer and cell lysate protein concentration determined using the Qubit Protein Assay kit (Thermo Fisher Scientific). A total of 30 μg protein from each cell extract was separated on a 10% Bis-Tris gel (10% Criterion™ XT Bis-Tris Gel, BioRad, Cat #: 3450112) following the manufacturer’s instructions, the gels cut according to the weight of the OAS1 protein, and simultaneously transferred to a single PVDF membrane, thus ensuring the comparability of Western blot bands from all gels. The OAS1 isoforms were visualized using an anti-OAS1 antibody targeting the shared epitope (Rabbit polyclonal anti-human OAS1, Abcam, Cat #: ab86343) and ECL Prime Western Blotting Detection Reagents (Amersham, Cat #: RPN2232).

Sjögren’s syndrome (SS) is a common autoimmune condition where immune cells infiltrate moisture-producing glands, leading to dryness typically in the eyes and mouth. SS patients also manifest debilitating fatigue as well as other diseases in liver, lung, kidney, and skin. The cause of this complex disease is still not fully understood; however, an environmental trigger, such as viral infections, in individuals with genetic risk factor (s) is thought to contribute to the development of SS. Type 1 interferons (IFNs) are one of the first defenders after viral infection and induce the expression of various virus-responding genes. Perpetual elevation of type 1 IFN signaling has been observed in SS patients. Here, we first replicated previously identified RNA transcripts contributing to the abnormal type 1 IFN signaling in SS patients. We then identified a disease-associated genetic variant in an IFN-inducible gene, OAS1. This variant governs splicing of OAS1, altering the transcript into multiple isoforms that lack protein expression and responsiveness to IFNs. The results of this study may provide insight into the genetic basis of SS, as well as other autoimmune disease with similar dysregulation in the type 1 IFN system.

Abstract Introduction Results Discussion Materials and methods

medicine and health sciences immune physiology body fluids immunology autoantibodies alternative splicing protein expression bioassays and physiological analysis molecular biology techniques antibodies research and analysis methods immune system proteins proteins gene expression molecular biology molecular biology assays and analysis techniques microarrays biochemistry gene expression and vector techniques rna rna processing anatomy nucleic acids blood physiology interferons genetics biology and life sciences genetics of disease

2017

Identification of a Sjögren's syndrome susceptibility locus at OAS1 that influences isoform switching, protein expression, and responsiveness to type I interferons

Enteroviruses, members of the family of picornaviruses, are the most common viral infectious agents in humans causing a broad spectrum of diseases ranging from mild respiratory illnesses to life-threatening infections. To efficiently replicate within the host cell, enteroviruses hijack several host factors, such as ACBD3. ACBD3 facilitates replication of various enterovirus species, however, structural determinants of ACBD3 recruitment to the viral replication sites are poorly understood. Here, we present a structural characterization of the interaction between ACBD3 and the non-structural 3A proteins of four representative enteroviruses (poliovirus, enterovirus A71, enterovirus D68, and rhinovirus B14). In addition, we describe the details of the 3A-3A interaction causing the assembly of the ACBD3-3A heterotetramers and the interaction between the ACBD3-3A complex and the lipid bilayer. Using structure-guided identification of the point mutations disrupting these interactions, we demonstrate their roles in the intracellular localization of these proteins, recruitment of downstream effectors of ACBD3, and facilitation of enterovirus replication. These structures uncovered a striking convergence in the mechanisms of how enteroviruses and kobuviruses, members of a distinct group of picornaviruses that also rely on ACBD3, recruit ACBD3 and its downstream effectors to the sites of viral replication. Enteroviruses are small RNA viruses that belong to the Enterovirus genus of the Picornaviridae family. They are non-enveloped positive-sense single-stranded RNA viruses with icosahedral capsids, currently consisting of 15 species. Seven enterovirus species (Enterovirus A-D and Rhinovirus A-C) contain human pathogens, such as polioviruses, numbered enteroviruses, echoviruses, coxsackieviruses, and rhinoviruses. They cause a variety of diseases ranging from common cold to acute hemorrhagic conjunctivitis, meningitis, myocarditis, encephalitis, or poliomyelitis [1]. The genome of the enteroviruses encodes the capsid proteins and seven non-structural proteins (named 2A-2C and 3A-3D). The latter carry out many essential processes including genome replication, polyprotein processing, host membrane reorganization, and manipulation of intracellular trafficking. To facilitate these functions, several host factors are recruited to the sites of enterovirus replication through direct or indirect interactions with viral proteins. For instance, the enterovirus non-structural 3A proteins directly bind to the Golgi-specific brefeldin A-resistant guanine nucleotide exchange factor-1 (GBF1) [2] and acyl-CoA-binding domain-containing protein-3 (ACBD3, also known as GCP60) [3]. ACBD3 is a Golgi resident protein involved in the maintenance of the Golgi structure [4] and regulation of intracellular trafficking between the endoplasmic reticulum and the Golgi [5]. ACBD3 is a multidomain protein composed of several domains connected by flexible linkers. Its central glutamine rich domain (Q domain) interacts with the lipid kinase phosphatidylinositol 4-kinase beta (PI4KB) and with the Rab GTPase-activating proteins TBC1D22A and TBC1D22B [6]. The interaction of ACBD3 and PI4KB causes membrane recruitment of PI4KB and enhances its enzymatic activity [7]. The C-terminal Golgi-dynamics domain (GOLD) of ACBD3 has been reported to interact with the Golgi integral protein giantin/golgin B1, which results in the Golgi localization of ACBD3 [5]. However, in enterovirus-infected cells, the ACBD3 GOLD domain interacts preferentially with viral non-structural 3A proteins, which causes re-localization of ACBD3 to the sites of virus replication [8]. The role of ACBD3 in enterovirus replication is not yet fully understood. It has been proposed that recruitment of ACBD3 to the sites of viral replication can lead to the indirect recruitment of its interactors and downstream effectors such as PI4KB, a well-known host factor essential for generation of PI4P-enriched membranes suitable for enterovirus replication [9,10]. The 3A-ACBD3-PI4KB route represents one of the major described mechanisms of PI4KB recruitment to the sites of enterovirus replication [3,11], although some other mechanisms employing the viral proteins 2BC [12] or 3CD [13] might be involved as well. Moreover, the formation of the 3A-ACBD3-PI4KB complex represents the major described mechanism of PI4KB recruitment to the replication sites of kobuviruses, members of a distinct group of picornaviruses [3,14–16]. Previously, it has been suggested that PI4P directly recruits the viral RNA-dependent RNA polymerase (3Dpol) [9]. Further studies, however, revealed that the affinity of PI4P to 3Dpol is too weak to attract 3Dpol to target membranes by itself, suggesting that other factors may be involved [17]. Notably, PI4P gradients between various membranes can be used for the transport of other cellular lipids against their concentration gradient [18,19]. The PI4P/cholesterol exchange machinery was implicated in replication of several enteroviruses [12,20], suggesting that PI4P can be used by the viral machinery as a mediator to prepare membranes with a specific lipid composition suitable for viral replication. ACBD3 is an important host factor of various enterovirus species [21], however, the structural determinants of its recruitment to the viral replication sites are poorly understood. To date, the structural information about any picornavirus 3A proteins is limited to a solution NMR structure of the uncomplexed poliovirus 3A protein [22] (pdb code 1NG7) and our previously published crystal structure of the aichivirus 3A protein in complex with the ACBD3 GOLD domain [23] (pdb code 5LZ3). Unfortunately, the latter cannot be used for homology modeling of the enterovirus 3A proteins, given the unrelated primary sequences of the enterovirus and kobuvirus 3A proteins, which indicates distinct mechanisms of hijacking ACBD3 by these two groups of viral pathogens. In this study, we present a structural, biochemical, and biological characterization of the complexes composed of human ACBD3 and the 3A proteins of four representative enteroviruses. The crystal structures revealed the details of the ACBD3-3A interaction, the 3A-3A interaction causing the assembly of the ACBD3-3A heterotetramers, the interaction between the ACBD3-3A complex and the lipid bilayer, and the roles of these interactions in facilitation of enterovirus replication. The comparison of the structures of the ACBD3: enterovirus 3A complexes and the previously known structures of the ACBD3: kobuvirus 3A complexes [23] uncovered a striking convergence in the mechanisms of how the two distinct groups of picornaviruses recruit ACBD3 and its downstream effectors to the sites of virus replication. For the structural characterization of the enterovirus 3A proteins in complex with the host ACBD3 GOLD domain, we selected 3A proteins of six human-infecting enteroviruses each representing different species as follows: enterovirus A71 (EVA71), coxsackievirus B3 (CVB3), poliovirus 1 (PV1), enterovirus D68 (EVD68), rhinovirus A2 (RVA2), and rhinovirus B14 (RVB14) (Fig 1a). The recombinant cytoplasmic domains of all the 3A proteins were poorly soluble and tended to aggregate and precipitate at the required concentrations. Therefore, we used 3A proteins N-terminally fused to a GB1 solubility tag. For the crystallographic analysis of the complexes composed of the ACBD3 GOLD domain and the viral 3A proteins (hereafter referred to as GOLD: 3A complexes), the GB1-fused cytoplasmic domains of the 3A proteins were directly co-expressed with the ACBD3 GOLD domain in bacteria. The GOLD: 3A complexes were then purified, and the GB1 tag was cleaved off. The GOLD: 3A complexes exhibited better protein solubility than the uncomplexed 3A proteins, sufficient for the subsequent crystallographic analysis. Of the six GOLD: enterovirus 3A complexes, only GOLD: 3A/EVD68 and GOLD: 3A/RVB14 formed crystals that diffracted to a resolution suitable for subsequent structure determination (i. e. 2. 3 Å and 2. 9 Å, respectively). Both structures were solved by molecular replacement using a previously published structure of the unliganded ACBD3 GOLD domain (accession number 5LZ1 [23]) as a search model (Fig 1b, Table 1). To improve the crystallization properties of the other four GOLD: 3A complexes, we used two different strategies. The first strategy was based on mutagenesis of selected surface-exposed hydrophobic residues of the 3A proteins to improve the solubility of the respective GOLD: 3A complexes and their capability to be crystallized at higher protein concentrations. This approach led to a successful crystallization of the GOLD: 3A/PV1 complex with an L24A point mutation within the PV1 3A protein. Its structure was then solved at a resolution of 2. 8 Å (Fig 1b, Table 1). The second strategy took advantage of the fact that in all three solved GOLD: 3A structures the C terminus of the ACBD3 GOLD domain was located in the vicinity of the N terminus of the ordered part of the 3A protein. This allowed us to design GOLD-3A fusion proteins with the last residue of ACBD3 (R528ACBD3) fused through a short peptide linker (GSGSG) to the first predicted ordered residues of the respective 3A proteins (e. g. K153A/EVA71). This approach led to a successful crystallization of the GOLD-3A/EVA71 fusion protein and its structure solution at a resolution of 2. 8 Å (Fig 1b, Table 1). The GOLD: 3A/CVB3 and GOLD: 3A/RVA2 complexes, however, failed to form diffracting crystals even after extensive optimization using both the mutagenesis and fusion-protein strategies. The overall structures of all solved GOLD: 3A complexes are highly similar to each other. This suggests that neither the L24A mutation in the GOLD: 3A/PV1 complex nor the fusion-protein strategy used for the GOLD: 3A/EVA71 complex affected the overall fold of the complexes (Fig 1b). No electron density was observed for the N termini of the 3A proteins (approximately the first 15 residues) and we, therefore, assume that this region is intrinsically disordered. This part of the 3A proteins has been previously reported to be involved in the interaction with another host factor GBF1 [2] or it is largely absent (e. g. RVA2) (Fig 1a). In order to determine the strength of the interaction between the ACBD3 GOLD domain and multiple enterovirus 3A proteins in vitro, we used microscale thermophoresis (Fig 1c). The dissociation constants of the GOLD: enterovirus 3A complexes ranged approximately from 1 μM (EVD68 and RVB14) to 15 μM (EVA71). In summary, our experiments confirmed that the enterovirus 3A proteins interact with the host ACBD3 protein through the GOLD domain of ACBD3 and the cytoplasmic domains of the 3A proteins. These proteins interact directly with dissociation constants within the low micromolar range. Using several approaches, four GOLD: enterovirus 3A complexes were crystallized and their structures were solved. Taken together, these structures document a conserved mechanism how diverse enterovirus species recruit the host ACBD3 protein. We performed an analysis of the GOLD: 3A interface to identify amino acid residues important for the ACBD3: 3A interaction, co-localization, stimulation of PI4KB recruitment, and facilitation of virus replication in human cells. For this analysis, we chose the GOLD: EVD68 3A complex because we resolved its structure at the highest resolution. Given the high similarity of the various GOLD: enterovirus 3A structures, we assume that the conclusions drawn from the ACBD3: EVD68 3A complex can be applied to the other ACBD3: enterovirus 3A complexes as well. In the GOLD: EVD68 3A crystal structure, we could trace the polypeptide chain of the 3A protein from T163A to I583A. It contains four secondary elements: two alpha helices P193A-V293A (α13A, Fig 2a) and Q323A-K413A (α23A, Fig 2b), and two beta strands I443A-I463A (β13A, Fig 2c) and V533A-I583A (β23A, Fig 2d). All these segments contribute to the GOLD: 3A interaction mediated through multiple hydrophobic interactions and hydrogen bonds (Fig 2a–2d). The helices α13A and α23A bind to a mild cavity of the GOLD domain that is formed by four antiparallel beta strands of ACBD3. The strand β13A interacts with the strand K518ACBD3-R528ACBD3 of the ACBD3 GOLD domain, while the strand β23A binds to the strand V402ACBD3-P408ACBD3, both in the antiparallel orientation. The conformation of all these secondary elements is highly conserved among various GOLD: enterovirus 3A complexes. The lowest homology of the tertiary structures of these complexes within short linkers between the β13A and β23A strands of the 3A proteins corresponds to the lowest homology of the primary sequences of these proteins within this region (Fig 1a and 1b). Calculations [24] of the changes of the interaction energies of various to-alanine mutants of these complexes based on their crystal structures uncovered that multiple amino acid residues of both 3A proteins and ACBD3 are involved in the interaction (S1 Fig). To evaluate the relative importance of various segments of the 3A protein on the complex formation, we designed the following EVD68 3A mutants: NLD (N23A/L26A/D30A), QRD (Q32A/R35A/D36A), IVH (I44A/V45A/H47A), and LVK (L52A/V54A/K56A) (Fig 3a; S1 Fig, panel a). For all the mutants, the ACBD3: 3A interaction was significantly attenuated both in the mammalian-two-hybrid assay (Fig 3b) and in the co-immunoprecipitation assay (Fig 3c), confirming that all four segments of the 3A protein are important for the ACBD3: 3A interaction. Nevertheless, some residual affinity of the 3A mutants to ACBD3 was still observed. All the 3A mutants co-localized with endogenous ACBD3 in the Golgi as did the wild-type 3A protein. The lipid kinase PI4KB, however, was recruited to the Golgi significantly more effectively in the cells expressing wild-type 3A compared to the cells expressing the 3A mutants (Fig 3d and 3e). Under physiological conditions, PI4KB cycles between the cytoplasm and Golgi, where it is recruited by a direct interaction with ACBD3 [7]. In enterovirus-infected cells, the viral 3A protein has been proposed to promote the ACBD3: PI4KB interaction [11]. Thus, considering that no direct interaction between the enterovirus 3A proteins and PI4KB has ever been observed, our data indicate that the stimulation of the ACBD3: PI4KB interaction by the 3A protein and the subsequent increase of the PI4KB recruitment to target membranes in infected cells depends on the ACBD3: 3A interaction. The Golgi-localized PI4P lipid was redistributed in the 3A-expressing cells possibly due to the Golgi disintegration caused by 3A overexpression, nevertheless, no significant change in the PI4P levels was observed in the wild-type 3A-expressing cells compared to the mock-transfected or mutant 3A-expressing cells (S2 Fig). Thus, a cooperation with some other viral proteins can be required to increase the PI4KB activity during viral infection. To analyze the impact of these 3A mutations on enterovirus replication, we established a reporter subgenomic replicon assay for EVD68. To determine the background reporter expression directly from the transfected RNA, we used a viral polymerase-lacking mutant (Δ3Dpol). Unexpectedly, no significant replication of the wild-type replicon RNA compared to the Δ3Dpol mutant was observed in HeLa cells. However, screening of several human cell lines uncovered the U-87 MG glioblastoma cells and HaCaT keratinocytes in which the wild-type replicon RNA significantly replicated. For all analyzed mutants, the viral RNA replication was attenuated in both cell lines (Fig 3f; S3 Fig). We observed no replication of the NLD, QRD, and IVH mutants, and a significantly reduced replication of the LVK mutant. Notably, this mutant was the weakest ACBD3 interactor in both co-immunoprecipitation and mammalian-two-hybrid assays, indicating additional unknown important effects distinct from the strength of the ACBD3-3A interaction affecting virus replication. We tested whether this mutant gained resistance to the PI4KB inhibition, nevertheless, we found that this mutant was still sensitive to a highly specific PI4KB inhibitor (compound 10 in Mejdrova et al. [10]) (Fig 3g). To address the effect of mutagenesis of selected residues within ACBD3, we designed the following ACBD3 mutants: WR (W375A/R377A), VTVRV (V403A/T404A/V405A/R406A/V407A), SYLF (S414A/Y415A/L416A/F417A), and RVYYT (R523A/V524A/Y525A/Y526A/T527A) (Fig 4a; S1 Fig, panels b-c). In the mammalian-two-hybrid assay (Fig 4b) and in the co-immunoprecipitation assay (Fig 4c), all these ACBD3 mutants displayed a significantly reduced ability to interact with the 3A protein. A weak yet significant effect was observed for the SYLF and RVYYT mutants, while a strong effect resulting in no detectable interaction in both assays was achieved for the WR and VTVRV mutants. Proper intracellular localization of these ACBD3 mutants was verified by their ectopic expression in ACBD3 knock-out cells derived from HeLa cells by CRISPR/Cas9 technology [21]. All these ACBD3 mutants co-localized with giantin, an integral Golgi protein, which has been proposed to directly recruit ACBD3 to the Golgi [5] (Fig 4d). Finally, we tested the ability of these ACBD3 mutants to rescue enterovirus replication in ACBD3 knock-out cells. The ACBD3 F258A/Q259A mutant, which does not interact with the lipid kinase PI4KB and cannot rescue virus replication [21], was used as a control. The ACBD3 WR and VTVRV mutants failed to rescue virus replication as expected. However, virus replication was still sufficiently restored by the other tested ACBD3 mutants SYLF and RVYYT (Fig 4e). These data document that the remaining affinity of these ACBD3 mutants to the viral 3A protein is still sufficient to fully facilitate enterovirus replication. Surprisingly, the SYLF mutant supports viral replication significantly better than wild-type ACBD3. It is possible that this mutation affects some other ACBD3 properties, such as its ability to interact with some other (known or unknown) proteins involved in enterovirus replication, nevertheless, the exact mechanism of the enhanced enterovirus replication in the ACBD3 SYLF mutant-expressing cells remains unclear. Compared to the ACBD3 WR mutant, the single mutants W375A and R377A still could rescue virus replication (S4 Fig, panels a-b), indicating that both mutations at the ACBD3: 3A interface are required to sufficiently disrupt the ACBD3: 3A interaction to affect virus replication. Several other tested ACBD3 mutants (such as V403A/V405A/ V407A, Y415A/F417A, and R523A/Y525A/Y526A) displayed a reduced affinity to the enterovirus 3A protein and still were able to restore enterovirus replication (S4 Fig, panels c-d). Alternatively, virus replication can be inhibited by single mutations interfering with a proper intracellular localization of ACBD3 (through ACBD3 misfolding and/or loss of the interaction with giantin) as documented by the E419A mutant (S4 Fig, panels e-f). In conclusion, our data document that the ACBD3: 3A interaction is essential for enterovirus replication. The viral replication, however, can be facilitated by weakly interacting ACBD3 mutants, provided that they are correctly folded and localized in the Golgi in non-infected cells. The enterovirus 3A proteins have been proposed to form homodimers [22,25]. Analysis of the crystal structures of the GOLD: 3A complexes revealed that the 3A proteins formed either one of the crystal-packing contacts (as in the case of EVD68 and PV1) or contacts with the second 3A molecule when two GOLD: 3A complexes per asymmetric unit were present (as in the case of EVA71 and RVB14). This putative dimerization interface is formed by the two central alpha helices of the 3A proteins, which are bent 180° to form a helical hairpin (Fig 5a). These helices are amphipathic with several hydrophobic residues oriented towards the hydrophobic residues of the other 3A monomer. Surprisingly, the C termini of the 3A proteins, which in the cellular environment are anchored to the membranes, are located on the opposite sides of the GOLD: 3A heterotetramers. Therefore, we were interested whether the plasticity and flexibility of the 3A dimerization interface together with the plasticity of the lipid bilayer allows to form the GOLD: 3A heterotetramers at the intracellular membranes. To identify amino acid residues of the 3A proteins involved in the dimerization of the GOLD: 3A complexes, we calculated [24] the changes of the dimerization energies of various to-alanine mutants of these complexes based on the crystal structures (S5 Fig, panel a). The dimerization interface of the GOLD: 3A/EVD68 complex consists of the hydrophobic core formed by the residues L25, V29, V34, and Y37, and an additional intermolecular salt bridge between the residues D24 and K41 (Fig 5a). To analyze the dimerization of the GOLD: 3A complexes in more detail, we generated a mutant EVD68 3A protein (hereafter referred to as LVVY mutant) with the following four mutations at the putative dimerization interface: L25A, V29A, V34A, and Y37A. As expected, retention volumes of the recombinant wild-type 3A and its LVVY mutant in size exclusion chromatography were significantly shifted corresponding to the dimeric and monomeric state of the wild-type 3A and its LVVY mutant, respectively (Fig 5b). At the request of a reviewer of our manuscript, we analyzed the dimerization of the L25V, V29Y, L25V/V34L, and V29Y/Y37V mutants (S6 Fig). Both L25V and V29Y mutations attenuated the 3A dimerization. The dimerization of the L25V mutant was restored by the V34L mutation, likely due to the compensation of weakening the L25-L25 interaction by strengthening the V34-V34 and V34-V29 interactions. On the other hand, the potential" rescue" Y37V mutation had a negative impact on the 3A dimerization, likely due to the attenuation of the Y37-L25 interaction and a loss of the hydrogen bond between Y37 and D24 (S6 Fig). Next, we investigated the stoichiometry of the GOLD: EVD68 3A complexes. To ensure that the 3A protein is fully complexed with the ACBD3 GOLD domain and to avoid the formation of partial complexes with 1: 2 stoichiometry, we designed a GOLD-EVD68 3A fusion protein using a similar approach as for the GOLD-EVA71 3A fusion protein used for the crystallographic analysis as described earlier. Taking advantage of the vicinity of the C terminus of the ACBD3 GOLD domain and the N terminus of the ordered part of the EVD68 3A protein, we connected the last residue of ACBD3 (R528ACBD3) through a short peptide linker (GSGSG) to the first ordered residue of the EVD68 3A protein (T163A/EVD68) (S5 Fig, panel b). Both GOLD-3A wild-type and LVVY mutant fusion proteins formed crystals, which diffracted to a resolution suitable for further structure determination (S5 Fig, panel c). The crystal structures of the GOLD: 3A complex consisting of two individual proteins, the GOLD-3A fusion protein, and its LVVY mutant were almost identical with low root-median-square deviations (S5 Fig, panel b), confirming that neither the fusion-protein approach nor the LVVY mutation affected the correct folding of these proteins. Three lines of evidence document the dimeric state of the wild-type GOLD-3A fusion protein and the monomeric state of its LVVY mutant in vitro. First, the retention volumes of these proteins in size exclusion chromatography are significantly shifted (Fig 5c). Secondly, the small-angle X-ray scattering (SAXS) profiles of these proteins correspond to the calculated scattering curves of a dimer of the wild-type GOLD-3A fusion protein (Fig 5d; S7 Fig, panel a) and of a monomer of its LVVY mutant (Fig 5e). Thirdly, crystal contacts corresponding to the 3A dimerization interface are not preserved in the crystal structure of the GOLD-3A LVVY mutant (S7 Fig, panels b-c), indicating that this mutant cannot dimerize through this interface even at very high protein concentrations (of approximately 20 mM) present within the protein crystal. Next, we investigated the stoichiometry of the GOLD: 3A complexes in cells. For this purpose, we ectopically co-expressed either wild-type GOLD-3A fusion protein or its LVVY mutant N-terminally fused to mAmetrine and mPlum fluorescent proteins in HeLa cells and determined the Förster resonance energy transfer (FRET) efficiency by flow cytometry. Owing to the crowding effect, the energy transfer was observed in the case of both proteins. Nevertheless, we observed a significant difference in FRET efficiency between the wild-type GOLD-3A fusion protein and its LVVY mutant (Fig 5f and 5g). These results confirm that the GOLD: 3A complexes are flexible enough to allow the formation of the heterotetramers consisting of two molecules of the viral 3A protein and two molecules of host ACBD3 even in cells at the respective intracellular membranes (Fig 5h). Finally, we analyzed the impact of the LVVY mutation on enterovirus replication. Using a reporter subgenomic replicon assay for EVD68 established earlier, we found replication of this mutant significantly attenuated in both U-87 MG and HaCaT cells (Fig 5i; S3 Fig). These findings document that the intact dimerization interface of the viral 3A proteins is required for enterovirus replication. In a previous study [23], we identified a novel ACBD3 membrane binding site (MBS) consisting of the residues R399, L514, W515, and R516. The hydrophobic residues L514 and W515 can be inserted directly into the lipid bilayer, while the positively charged residues R399 and R516 can interact with the negatively charged phospholipid head groups (Fig 6a). A vicinity of ACBD3 MBS and the expected position of the transmembrane domain of the enterovirus 3A protein within the ACBD3: 3A complexes suggests that ACBD3 MBS may be involved in the stabilization of the ACBD3: 3A complexes at the membrane as well. To experimentally evaluate this hypothesis, we designed the following ACBD3 mutants with several point mutations within MBS: LWR514AAA and, to increase repulsion between ACBD3 MBS and the lipid bilayer, LWR514DDD. Then, we ectopically expressed wild type ACBD3 or its MBS mutants N-terminally fused to EGFP in HeLa ACBD3 knock-out cells. We found that wild-type ACBD3 co-localized with the Golgi marker giantin, while both ACBD3 MBS mutants LWR514AAA and LWR514DDD were mostly released to the cytoplasm, although minor yet significant fractions of their pools were still preserved at the Golgi (Fig 6b). Remarkably, when the ACBD3 MBS mutants were co-expressed with the enterovirus 3A protein, they were completely (LWR514AAA mutant) or partially (LWR514DDD mutant) re-localized back to the Golgi (Fig 6c and 6d). Thus, an intact MBS is required for ACBD3 recruitment to the Golgi by the action of giantin or other cellular factors, however, it is dispensable for ACBD3 stabilization at target membranes through its interaction with enterovirus 3A proteins. Finally, we tested the capacity of wild-type ACBD3 and its MBS mutants to rescue virus replication in ACBD3 knock-out cells. Both ACBD3 wild type and the LWR514AAA mutant, but not the LWR514DDD and FQ258AA (used as a control [21]) mutants, effectively restored virus replication (Fig 6e). Thus, it seems that not ACBD3 MBS itself but rather the orientation of the ACBD3: 3A complex with respect to the membrane plays a role in facilitation of enterovirus replication. To-alanine mutations of ACBD3 MBS still allow the ACBD3: 3A complex at the membrane to adopt a conformation suitable for viral replication. On the contrary, to-aspartate mutations of ACBD3 MBS, which repel the negatively charged phospholipids of the lipid bilayer, result in an orientation of the ACBD3: 3A complex with respect to the membrane that does not support enterovirus replication. In summary, ACBD3 MBS is not required for ACBD3 recruitment to target membranes by the action of the enterovirus 3A proteins, however, the proper conformation of the ACBD3: 3A complexes at the membrane mediated by ACBD3 MBS is essential for enterovirus replication. Considering the commonness of enterovirus-mediated infections within human population, it is surprising that no antiviral therapy for enteroviruses has been approved yet. Targeting essential host factors instead of rapidly mutating viral enzymes represents a promising strategy. Several host factors essential for enterovirus replication are recruited to the sites of viral replication by a direct protein-protein interaction between the host factor and a viral protein. A detailed knowledge of the structures of such complexes can open up prospects for a structure-guided development of small chemical compounds targeting these interactions, yielding a novel class of antivirals to combat infections caused by these pathogens. In this study, we present a series of crystal structures of complexes composed of the non-structural 3A proteins of four enterovirus species and the 3A-binding GOLD domain of the host factor ACBD3. Previously, the genetic inhibition of ACBD3 mediated by siRNA has yielded conflicting results on the importance of ACBD3 for virus replication [26,27]. This conflict in the literature has been recently addressed using CRISPR/Cas9-generated ACBD3 knock-out cells, in which enterovirus replication was severely impeded [8,21]. This confirmed that ACBD3 is, indeed, an essential host factor for enterovirus replication. However, it seems that a very low concentration of ACBD3 within the cells is still fully capable of facilitating enterovirus replication. This hypothesis is in agreement with our observations that enterovirus replication in ACBD3 knock-out cells can be restored by several ACBD3 mutants with a very low affinity to the viral 3A proteins even at the detection limit of conventional methods assessing the protein-protein interactions, such as protein co-immunoprecipitation. Among picornaviruses, the interaction between the viral 3A protein and host ACBD3 is not unique for enteroviruses. ACBD3 has been proposed to interact also with the 3A proteins of kobuvirus (e. g. aichivirus), hepatovirus, salivirus (klassevirus), and parechovirus, but not with those of cardiovirus (e. g. Saffold virus) or aphthovirus (foot-and-mouth disease virus, FMDV) [6]. To our best knowledge, all picornaviruses sensitive to PI4KB specific inhibitors (such as enteroviruses and kobuviruses) are able to hijack ACBD3, arguing for ACBD3 as a main mediator of PI4KB recruitment by these viruses. Notably, hepatovirus replicates independently of PI4KB [28], indicating either functionally irrelevant interaction with ACBD3 or another, PI4KB-independent, role of ACBD3 in hepatovirus replication. Picornaviruses that cannot hijack ACBD3 through their 3A proteins are either PI4P-independent (such as FMDV [29]) or their replication depends on another PI4P-producing lipid kinase PI4KA (e. g. cardioviruses [30]). During the past decades, multiple enterovirus mutants resistant to the inhibitors of PI4KB and the oxysterol binding protein (OSBP), which acts downstream of PI4KB, were isolated and characterized. Most of the resistance-conferring mutations were localized to the 3A-encoding regions of these viruses, e. g. PV1 N45Y, R54W, N57D, A70T, and A71S, CVB3 V45A, I54F, and H57Y, and RVB14 E30D/V/Q, I42V, and M54I (S8 Fig) [31–34]. Although many of the PI4KB/OSBP-inhibition resistance-conferring mutations are localized within the ACBD3-interacting regions of the 3A proteins, they seem unlikely to act through modulation of the ACBD3-3A interaction. On the other hand, it is possible that the mutations clustered within the β2 strands of the 3A proteins, such as CVB3 H57Y or PV1 R54W, can act through modulation of the interaction of the ACBD3-3A complex (or the uncomplexed 3A protein) with the membrane. The loss of the positive charge of the mutated residues can possibly compensate for the loss of the negative charge of the PI4P head groups upon PI4KB inhibition. Nevertheless, at least the mechanism of action of the mutations located distally with respect to the membrane, mostly clustered within the β1 strands of the 3A proteins, remains unclear. Apart from the crystal structures of the enterovirus 3A: GOLD complexes, to date only the structures of the kobuvirus 3A: GOLD complexes are known [23]. The enterovirus (e. g. poliovirus) and kobuvirus (e. g. aichivirus) 3A proteins share a common overall architecture, i. e. a similar size of approximately 10 kDa, large N-terminal soluble cytoplasmic domains followed by hydrophobic membrane-anchoring regions and small C-terminal domains (Fig 7a). Despite this common architecture, primary and predicted secondary structures of the enterovirus and kobuvirus 3A proteins are unrelated and cannot be aligned. Furthermore, positions of the ACBD3 binding regions of the enterovirus and kobuvirus 3A proteins are distinct. The ACBD3 binding region of the enterovirus 3A proteins forms the C-terminal segments of the cytoplasmic domain (and is preceded by the N-terminal GBF1 binding region), while the ACBD3 binding region of the kobuvirus 3A proteins is located at the N terminus (and a GBF1 binding region is completely missing). Superposition of the crystal structures of the enterovirus and kobuvirus 3A: GOLD complexes reveals that the enterovirus and kobuvirus 3A proteins bind to the same regions of the ACBD3 GOLD domain, nevertheless, the polypeptide chains of the enterovirus and kobuvirus 3A proteins have opposite polarities (Fig 7b and 7c). For instance, the poliovirus 3A strand β23A/PV1 binds in the antiparallel orientation to the strand V402ACBD3-P408ACBD3 of ACBD3, while the aichivirus 3A strand β13A/AiV1 binds at the same position to the same strand of ACBD3, but in the parallel orientation (Fig 7b and 7c). The reverse orientation of the enterovirus 3A proteins compared to the kobuvirus 3A proteins may be caused by the specific need of the enterovirus 3A proteins to bind GBF1. A notable difference between the enterovirus and kobuvirus 3A proteins is represented in the way they are anchored to the membrane. In addition to the C-terminal hydrophobic membrane binding regions, kobuvirus 3A proteins are membrane-anchored by the myristoylated N-terminal glycines, which is very unusual among picornaviruses [3]. Among enteroviruses, the N-terminal myristoylation is not present. To gain more insight into the membrane binding mode of the enterovirus 3A: GOLD protein complex, we performed all-atom molecular dynamics simulation of this complex at the membrane and compared it with our previously published [23] simulation of the kobuvirus 3A: GOLD protein complex at the surface of the lipid bilayer (Fig 7d). These simulations uncovered a similar conformation of the ACBD3 GOLD domain recruited to the membrane by the poliovirus or aichivirus 3A protein, including the insertion of the ACBD3 membrane binding site residues into the lipid bilayer. The position of the N-terminal myristoylation of the aichivirus 3A protein functionally substitutes the position of the C-terminal transmembrane domain of the poliovirus 3A protein, whereas the C-terminal transmembrane domain of the aichivirus 3A protein has no equivalent in the case of the poliovirus 3A protein. In summary, our findings reveal structural details of how two groups of viral pathogens, enteroviruses and kobuviruses, developed a similar mechanism of hijacking the same host factor (ACBD3) and its downstream effectors (such as PI4KB). These viruses use their 3A proteins with a common architecture yet totally unrelated primary sequences to bind to the same regions of the host ACBD3 protein in the opposite orientations, representing a striking case of convergence in picornavirus evolution. Our results are in agreement with a pioneering work by Greninger and colleagues [6,35], which has forecast such convergent evolutionary strategies of kobuviruses and enteroviruses based on extensive mutagenesis of the ACBD3-3A interface. There are still several other picornavirus genera proposed to recruit ACBD3 through the 3A: GOLD interaction (such as salivirus, hepatovirus, or parechovirus [6]) whose 3A: GOLD complexes remain structurally unexplored. Structural details of their ACBD3 recruitment uncovering whether they utilize the mechanism described for enteroviruses, kobuviruses, or another mechanism distinct from those two, remain to be further elucidated. For expression in E. coli, full-length human ACBD3 and various enterovirus 3A proteins and their deletion mutants were cloned into pRSFD vector (Novagen) with an N-terminal 6xHis tag followed by a GB1 solubility tag and a TEV protease cleavage site using PCR and restriction cloning. For bacterial expression of the EGFP-fusion proteins, the EGFP encoding sequence was inserted between the TEV cleavage site and the target gene encoding regions. For expression of the EGFP-fusion proteins in human cells, target genes encoding regions were recloned into pEGFP-C1 vector (Clontech) with an N-terminal EGFP tag. For expression of the GST-, mAmetrine-, and mPlum-fusion proteins in human cells, the EGFP encoding region was replaced by GST or corresponding fluorescent protein encoding sequence by PCR and restriction cloning. The pRib-EVD68/mCherry plasmid for viral subgenomic replicon assays was generated by subcloning of the EVD68 cDNA of a prototypical Fermon strain under T7 promoter and replacing the capsid proteins-encoding region with the mCherry fluorescent protein-encoding gene by Gibson assembly. Mutations were generated using the Q5 Site-Directed Mutagenesis Kit (New England BioLabs). All DNA constructs were verified by sequencing. The pEGFP-ACBD3 and pBIND-ACBD3 plasmids were kindly provided by Carolyn Machamer and Jun Sasaki, respectively. The mAmetrine-C1 and mPlum-C1 plasmids were gifts from Robert Campbell and Michael Davidson (Addgene plasmids #54660 [36] and #54839 [37]). The pEGFP-GalT plasmid was a gift from Jennifer Lippincott-Schwartz (Addgene plasmid #11929 [38]). All recombinant proteins used in this study were bacterially expressed as fusion proteins with an N-terminal 6xhistidine (His6) tag followed by a GB1 solubility tag and a TEV protease cleavage site. For the crystallographic analysis of the GOLD: 3A complexes, the N-terminally His6-GB1-TEV site-fused cytoplasmic domains of the 3A proteins were directly co-expressed with the untagged ACBD3 GOLD domain. The proteins were expressed in E. coli BL21 DE3 NiCo cells (New England Biolabs) using the autoinduction ZY medium. Bacterial cells were harvested and lysed in the lysis buffer (50 mM Tris pH 8,300 mM NaCl, 3 mM β-mercaptoethanol, 30 mM imidazole, 10% glycerol), the lysate was incubated with the HisPur Ni-NTA Superflow agarose (Thermo Fisher Scientific), and the bound proteins were extensively washed with the wash buffer (50 mM Tris pH 8,300 mM NaCl, 1 mM β-mercaptoethanol, 20 mM imidazole). The protein was eluted with the elution buffer (50 mM Tris pH 8,200 mM NaCl, 3 mM β-mercaptoethanol, 300 mM imidazole). For the biochemical analysis of the 3A proteins by microscale thermophoresis or SAXS, the N-terminal His6-GB1 tags were preserved uncleaved to increase protein solubility and to avoid aggregation at required concentrations. For the crystallographic analysis of the GOLD: 3A complexes and for the biochemical analysis of the GOLD-3A fusion proteins by SAXS, the N-terminal His6-GB1 tags were removed with home-made TEV protease. Next, the proteins were purified using the size exclusion chromatography at HiLoad 16/60 Superdex 75 or Superdex 200 prep grade columns (GE Healthcare) in the storage buffer (10 mM Tris pH 8,200 mM NaCl, 3 mM β-mercaptoethanol). In addition, the GOLD: 3A complexes used for the crystallographic analysis were further purified by reverse immobilized metal affinity chromatography using the HisTrap HP column (GE Healthcare), while the EGFP-fused ACBD3 GOLD domain used for microscale thermophoresis was further purified using the ion exchange chromatography at a MonoQ 10/100 GL column (GE Healthcare) and then dialyzed back into the storage buffer. The molecular weight and purity of all proteins was verified by SDS-PAGE (S9 Fig) and Matrix-Assisted Laser Desorption/Ionisation (MALDI). Purified proteins were concentrated to 1–10 mg/ml, aliquoted, flash frozen in the liquid nitrogen, and stored at -80 °C until needed. Crystals grew at 291 K in sitting drops by the vapor diffusion method. They were cryoprotected, flash frozen in liquid nitrogen, and analyzed. Measurements were carried out at the MX14. 1 beamline of the synchrotron BESSY II at Helmholtz-Zentrum Berlin [39]. The crystallographic datasets were collected from single frozen crystals. Data were integrated and scaled using XDS [40] and XDSAPP [41]. Structures were solved by molecular replacement using the uncomplexed ACBD3 GOLD domain structure (pdb code 5LZ1) as a search model. The initial models were obtained with Phaser [42] from the Phenix package [43]. The models were further improved using automatic model building with Buccaneer [44] from the CCP4 suite [45], automatic model refinement with Phenix. refine [46] from the Phenix package [43], and manual model building with Coot [47]. Statistics for data collection and processing, structure solution and refinement are summarized in Table 1. Structural figures were generated with PyMol [48]. The atomic coordinates and structural factors were deposited in the Protein Data Bank (www. pdb. org). MST measurements were carried out using the Monolith NT. 115 instrument (NanoTemper Technologies) according to the manufacturer' s instructions. The Monolith NT. 115 standard treated capillaries were loaded with a mixture of a recombinant EGFP-fused protein at a constant concentration of 150 nM in the MST buffer (30 mM Tris pH 7. 4,150 mM NaCl, 3 mM β-mercaptoethanol) and its binding partner in the appropriate series of concentrations. The thermophoretic motion of the fluorescent protein and its temperature-dependent changes of fluorescence were analyzed with the Monolith NT Analysis Software. Human cervical-carcinoma cells HeLa (American Type Culture Collection / ATCC), embryonic kidney cells HEK293T (ATCC), and keratinocytes HaCaT (AddexBio) were maintained in Dulbecco' s modified Eagle' s medium (Sigma) supplemented with 10% fetal calf serum (Gibco). Human glioblastoma cells U-87 MG (ATCC) were maintained in Minimum Essential Medium Eagle (Sigma) supplemented with 10% fetal calf serum (Gibco), GlutaMAX Supplement (Thermo Fisher Scientific), and non-essential amino acids (Biowest). HeLa cells were transfected using Lipofectamine2000 reagent (Thermo Fisher Scientific) or X-tremeGENE HP DNA Transfection reagent (Sigma/Roche) according to manufacturer' s instructions. Transfections of HEK293T cells were performed using polyethylenimine (Sigma) or Fugene6 (Promega). HEK293T cells were transfected with the appropriate mutants of the EGFP-fused EVD68 3A protein and GST-fused ACBD3. The next day, cells were harvested, washed twice with phosphate-buffered saline (PBS) and lysed in the ice-cold lysis buffer (20 mM Tris pH 7. 4,100 mM NaCl, 50 mM NaF, 10 mM EDTA, 10% glycerol, 1% NP-40), supplemented with protease inhibitors (Complete protease inhibitor cocktail, Sigma/Roche). After solubilizing for 15 min on ice, the lysate was pre-cleared by centrifugation at 16,000g for 15 min. The resulting supernatant was incubated with sepharose beads coupled either to GFP nanobody (GFP-Trap, ChromoTek) or glutathione (Protino Glutathione Agarose, Macherey-Nagel) for 1h at 4 °C. After three washes with 10 volumes of the lysis buffer, the bound proteins were directly eluted with the Laemmli sample buffer, subjected to SDS-PAGE, and analyzed by immunoblotting. The whole cell lysates and eluted proteins were stained with mouse monoclonal antibodies to ACBD3 (Santa Cruz Biotechnology, sc-101277) and GFP (Santa Cruz Biotechnology, sc-9996). The images were acquired using the LI-COR Odyssey Infrared Imaging System. HeLa cells were co-transfected with plasmids encoding target proteins fused to the FRET pair of fluorescent proteins mAmetrine (" donor" ) and mPlum (" acceptor" ). The next day, cells were harvested, washed twice with PBS, and analyzed by flow cytometry using BD LSR Fortessa (BD Biosciences). The donor and acceptor fluorescence as well as the energy transfer was determined using the optical configurations as follows: mAmetrine—405 nm laser—525/50 nm bandpass filter; mPlum—561 nm laser—670/30 nm bandpass filter; FRET—405 nm laser—655/8 nm bandpass filter. Acquired data were analyzed with the FlowJo software. The acquired fluorescence intensities were compensated and the same gate corresponding to the live transfected cells with the approximately 1: 1 donor: acceptor ratio was applied to all samples. The acquired events were binned on the basis of the acceptor fluorescence intensity, and the average FRET fluorescence intensities of each bin were plotted against the respective acceptor fluorescence intensity. HEK293T cells grown in 96-well plates were co-transfected with 50 ng of each pACT, pBIND, and pG5Luc plasmids using Fugene6 (Promega). At 24 hours post transfection, the cells were lysed, and both firefly and Renilla luciferase activities were measured using the Dual-Luciferase assay kit (Promega) and Centro LB 960 luminometer (Berthold Technologies) according to the manufacturer' s instructions. The firefly luciferase activity was normalized to the Renilla luciferase activity (used as an internal control of the transfection efficiency) and then to the activity determined in cells co-expressing wild-type ACBD3 and 3A (which was set to 100%). HeLa cells grown on coverslips in 24-well plates were transfected with 400 ng of the plasmid DNA using Lipofectamine2000 (Thermo Fisher Scientific). At 16 hours post transfection, the cells were fixed with 4% paraformaldehyde for 15 min at room temperature, permeabilized with 0. 1% Triton X-100 in PBS for 5 min, and immunostained with the appropriate primary and secondary antibodies diluted in 2% normal goat serum in PBS. Sources of the antibodies were as follows: anti-ACBD3 (Sigma, WH0064746M1), anti-PI4KB (Merck, 06–578), anti-GM130 (BD Biosciences, 610822), anti-giantin (Enzo Life Science, ALX-804-600-C100), anti-myc (Thermo Fisher Scientific, PA1-981), anti-PI4P (Echelon, Z-P004), and goat-anti-mouse and goat-anti-rabbit secondary antibodies conjugated to AlexaFluor 488,596, or 647 (Molecular Probes). Nuclei were stained with DAPI. Coverslips were mounted with FluorSave (Calbiochem), and confocal imaging was performed with a Leica SpeII confocal microscope. The pRib-EVD68/mCherry wild-type and mutant plasmids were linearized by cleavage with SalI-HF (Thermo Fisher Scientific) and purified using the mini spin columns (Epoch Life Science). Viral subgenomic replicon RNA was generated with TranscriptAid T7 High Yield Transcription Kit (Thermo Fisher Scientific) and purified using the RNeasy mini spin columns (Qiagen). For replicon assays, U-87 MG or HaCaT cells grown in 12-well plates were transfected with T7-amplified RNA using the TransIT mRNA transfection kit (Mirus Bio). At 12 hours post transfection, the reporter mCherry fluorescence was determined by flow cytometry using BD LSR Fortessa (BD Biosciences) and the following optical configuration: 561 nm laser, 670/30 nm bandpass filter. Acquired data were analyzed with the FlowJo software. The level of RNA replication was expressed as a transfection efficiency-normalized percentage of cells with the mCherry signal above the threshold determined using the viral polymerase-lacking mutant Δ3Dpol. To test the effect of PI4KB inhibition on virus replication, a PI4KB-specific inhibitor (compound 10 from [10] kindly provided by Radim Nencka) was added to the medium at a final concentration of 1 μM 30 min prior transfection of the viral subgenomic replicon RNA. Wild-type or ACBD3 knock-out HeLa cells grown in 96-well plates were transfected with plasmids encoding wild-type or mutant ACBD3 or another Golgi-resident protein GalT as a control. At 24 hours post transfection, the cells were infected with the Renilla luciferase-expressing CVB3 virus (RLucCVB3) [49]. At 8 hours post infection, the intracellular Renilla luciferase activity was determined using the Renilla luciferase assay system (Promega) and a Centro LB 960 luminometer (Berthold Technologies). The Renilla luciferase activity was normalized to the activity determined in wild-type ACBD3-expressing cells (which was set to 100%). Proteins were dialyzed against the SAXS buffer (30 mM Tris pH 7. 4,150 mM NaCl, 1 mM TCEP) and concentrated as follows: c1 = 1. 03 mg/ml, c2 = 1. 4 mg/ml and c3 = 1. 54 mg/ml for the wild-type GOLD-3A fusion protein, and c1 = 0. 96 mg/ml, c2 = 2. 36 mg/ml and c3 = 3. 26 mg/ml for the GOLD-3A LVVY mutant. The SAXS data were collected using the beamlines BioSAXS Beamline BM29 (ESRF, Grenoble) and EMBL SAXS beamline P12 (Petra III DESY, Hamburg) that are both equipped with the 2M Pilatus detector (Dectris). The three datasets overlay after rescaling, indicating no protein aggregation in the samples. To structurally interpret the SAXS data, we incorporated the missing loop (D437-K473) into the structures of the wild-type GOLD-3A dimer and GOLD-3A LVVY mutant monomer. Next, we performed the coarse-grained molecular simulations [50] in which only the conformations of the D437-K473 loop were sampled while the crystallized portion of the protein was kept rigid, yielding 10,000 structural models of both proteins. For all structural models, we computed the SAXS intensity profiles using the previously developed algorithms [51], compared them individually to the experimental SAXS data, and selected the models best fitting the SAXS data collected on the samples with the highest protein concentrations. The best models fit the SAXS data with χ = 1. 5 for the wild-type GOLD-3A dimer and χ = 1. 4 for the GOLD-3A LVVY mutant monomer. The intrinsically disordered region of the ACBD3 GOLD domain, which was missing in the crystal structure (D437-K473), was modeled as described previously [23]. The C-terminal segment of the poliovirus 3A protein (N57-Q87) was modeled as a loop (N57-M60) followed by a transmembrane alpha helix (T61-F83) and a short C-terminal tail (A84-Q87). This segment was positioned in a planar segment of a lipid bilayer using the PPM server [52]. MD simulations of the ACBD3 GOLD domain in complex with the poliovirus 3A protein in the environment of a lipid bilayer were performed following the procedures recently used to study the ACBD3 GOLD domain in complex with the aichivirus 3A protein [23]. The initial system for MD simulations was prepared using VMD version 1. 9. 2 [53]. Namely, a POPC bilayer segment with the lateral dimensions of 10 nm by 10 nm was formed using the Membrane Plugin version 1. 1 in VMD. The GOLD: 3A complex was placed on top of the resulting lipid patch. The lipids overlapping with the transmembrane alpha helix of the 3A protein were removed. The system was solvated using the Solvate Plugin version 1. 5 in VMD. Sodium and chloride ions were added to neutralize the simulated system and to reach a physiological concentration of 150 mM. The MD simulations were performed using the NAMD package [54] version 2. 9. The CHARMM22 force field [55,56] with the CMAP correction [57] and the TIP3P water model were used. The simulations were carried out in the NPT ensemble. Temperature was kept at 298K through a Langevin thermostat with a damping coefficient of 1/ps. Pressure was maintained at 1 atm using the Langevin piston Nose-Hoover method with a damping timescale of 50 fs and an oscillation timescale of 100 fs. Short-range non-bonded interactions were cut off smoothly between 1 and 1. 2 nm. Long-range electrostatic interactions were computed using the particle-mash Ewald method with a grid spacing of 0. 1 nm. Simulations were performed with an integration time step of 2 fs. After initial energy minimization with a conjugate gradient method, a 10 ns simulation was performed with constraints on the protein backbone atoms in order to equilibrate the lipids, ions and water molecules. Namely, a harmonic potential with the spring constant of 5 kcal/ (mol Å2) was applied to all backbone atoms of the GOLD: 3A complex. After the equilibration, the system was simulated with no constrains for 200 ns. The resulting MD trajectory was visualized and analyzed using VMD. To localize the PI4KB/OSBP-inhibition resistance-conferring mutations within the structure of the GOLD: CVB3 3A complex, a homology model of this complex was generated by the I-TASSER server [58] using the crystal structure of the GOLD: EVD68 3A complex as a template. In the graphs, data are presented as mean values ± standard errors of the means (SEMs) based on three independent experiments, unless stated otherwise. For statistical analyses, two-tailed two-sample Student' s t-tests were used. P-values below 0. 05 were considered significant. The crystal structures of the ACBD3 GOLD domain in complex with the 3A proteins from EVD68, RVB14, and PV1 (L24A mutant), and the ACBD3 GOLD domain fused to the 3A proteins from EVA71, EVD68 (wild type), and EVD68 (LVVY mutant) from this publication have been submitted to the Protein Data Bank (www. pdb. org) and assigned the identifiers 6HLN, 6HLT, 6HLV, 6HLW, 6HM8, and 6HMV, respectively.

Enteroviruses are the most common viruses infecting humans. They cause a broad spectrum of diseases ranging from common cold to life-threatening diseases, such as poliomyelitis. To date, no effective antiviral therapy for enteroviruses has been approved yet. To ensure efficient replication, enteroviruses hijack several host factors, recruit them to the sites of virus replication, and use their physiological functions for their own purposes. Here, we characterize the complexes composed of the host protein ACBD3 and the ACBD3-binding viral proteins (called 3A) of four representative enteroviruses. Our study reveals the atomic details of these complexes and identifies the amino acid residues important for the interaction. We found out that the 3A proteins of enteroviruses bind to the same regions of ACBD3 as the 3A proteins of kobuviruses, a distinct group of viruses that also rely on ACBD3, but are oriented in the opposite directions. This observation reveals a striking case of convergent evolutionary pathways that have evolved to allow enteroviruses and kobuviruses (which are two distinct groups of the Picornaviridae family) to recruit a common host target, ACBD3, and its downstream effectors to the sites of viral replication.

Abstract Introduction Results Discussion Materials and methods

medicine and health sciences crystal structure protein interactions pathology and laboratory medicine pathogens condensed matter physics microbiology viruses rna viruses crystallography physical chemistry chemical properties infectious diseases solid state physics dimerization proteins medical microbiology enterovirus infection microbial pathogens chemistry viral replication physics enteroviruses biochemistry virology poliovirus viral pathogens protein domains biology and life sciences physical sciences viral diseases organisms

2019

Convergent evolution in the mechanisms of ACBD3 recruitment to picornavirus replication sites

Coarse-grained (CG) simulations have become an essential tool to study a large variety of biomolecular processes, exploring temporal and spatial scales inaccessible to traditional models of atomistic resolution. One of the major simplifications of CG models is the representation of the solvent, which is either implicit or modeled explicitly as a van der Waals particle. The effect of polarization, and thus a proper screening of interactions depending on the local environment, is absent. Given the important role of water as a ubiquitous solvent in biological systems, its treatment is crucial to the properties derived from simulation studies. Here, we parameterize a polarizable coarse-grained water model to be used in combination with the CG MARTINI force field. Using a three-bead model to represent four water molecules, we show that the orientational polarizability of real water can be effectively accounted for. This has the consequence that the dielectric screening of bulk water is reproduced. At the same time, we parameterized our new water model such that bulk water density and oil/water partitioning data remain at the same level of accuracy as for the standard MARTINI force field. We apply the new model to two cases for which current CG force fields are inadequate. First, we address the transport of ions across a lipid membrane. The computed potential of mean force shows that the ions now naturally feel the change in dielectric medium when moving from the high dielectric aqueous phase toward the low dielectric membrane interior. In the second application we consider the electroporation process of both an oil slab and a lipid bilayer. The electrostatic field drives the formation of water filled pores in both cases, following a similar mechanism as seen with atomistically detailed models. Since the first introduction of physics-based coarse-grained (CG) models in computational biology [1], CG models have become increasingly popular in the simulation of complex biological systems [2]. They significantly reduce the computational complexity in comparison to all-atom (AA) models and allow sampling over much longer time scales and of larger system sizes. One of the most widely applied CG models is the MARTINI force field [3]. The MARTINI model was initially developed for lipid systems [4] and has recently been extended for proteins [5] and carbohydrates [6]. In general a four-to-one mapping is used in MARTINI, which means that on average four atoms and associated hydrogens are represented by one CG bead. The CG particles interact with the other CG particles in the system by means of Lennard-Jones (LJ) interactions; in addition charged groups (e. g. ions, lipid head groups, charged amino acid side chains) interact via a Coulombic energy function. Water is treated explicitly, at the same level of coarse-graining as all other molecules implying that four water molecules are combined into a single coarse-grained bead. MARTINI water beads, just as many other CG water models, do not bear charges and, consequently, are blind to electrostatic fields and polarization effects. To compensate for the neglect of explicit polarization, screening of electrostatic interactions is done implicitly, assuming a uniform relative dielectric constant. While this is a reasonable approximation for bulk water, problems arise at the interfaces between water and other phases and in the vicinity of charged particles. Because of the implicit screening, the interaction strength of polar substances is underestimated in non-polarizable solvents. Correct modeling of the partitioning of polar and charged compounds into a low dielectric medium, e. g. a lipid bilayer, has proven a big challenge for CG models in general [7]. Applications involving the formation of polar/charged complexes in a non-polar environment are especially prone to be affected. A potential solution is to make the interaction potentials dependent on the local environment (see e. g. [8]), especially useful in solvent free approaches. With explicit solvent particles present, more flexibility is achieved with a polarizable water model. Attempts to include the effect of polarization in simplified water models date already back to the early days of biomolecular modeling. Notably the development of the soft sphere dipole model is worth mentioning [9], [10]. In this model, water molecules are represented by point dipoles that can reorient in response to the electrostatic field of an embedded (macro) molecule. Recently, induced dipoles were also added to a CG solvent model, and made compatible with a CG protein force field [11]. The polarizability challenge also stands in all-atom (AA) force fields at a more fine-grained level. The AA force fields lack electronic polarizability, which has proven to be a significant drawback in simulations of ions and highly polarizable systems [12], [13]. Several approaches to develop polarizable AA force fields, such as the inducible point dipole model [14], the model with Drude oscillators [15], [16], the fluctuating charge model [17] and the multipole expansion model [18] exist. The general idea of all these methods is to introduce a fluctuating dipole to each polarizable particle, which responds to the local electric field in the vicinity of this particle. In this work, we introduce orientational polarizability to the water beads of the MARTINI force field using an approach similar to that of the Drude oscillator [15], [16]. The resulting polarizable CG water model, in combination with the MARTINI force field, allows modeling the interaction of water with charged particles in a more realistic way. In the parameterization of the polarizable water model the following three criteria were used: i) The dielectric constant of bulk polarizable water should be sufficiently close to the value in real water; ii) The particle density of the polarizable water should be close to the particle density of the water in standard MARTINI; iii) The reproduction of partitioning free energies between water and organic solvents for a large variety of small compounds, one of the corner stones of the MARTINI model, should remain unaffected. The rest of this paper is organized as follows. In the next section, we first describe the details of the model, and the way we set out to parameterize it. This is followed by the Results section in which we explore the parameter space and arrive at the optimal parameter set, based on reproduction of the density and dielectric constant of bulk water, and the water/oil partitioning behavior of the MARTINI building blocks. We then test a number of properties of the new model, including the dynamical behavior of bulk water, the surface tension of the water/vapor interface, and structural properties of ionic solutions and of a lipid bilayer. We also look at the effect of long-range electrostatic interactions. Finally, two applications are shown which would not have been feasible with the standard MARTINI model, nor with most other CG models. The applications are a realistic description of the free energy of ion transport across a lipid bilayer, and the electroporation process of both an octane slab and a lipid bilayer. A discussion section about the limitations and prospects of the model concludes this paper. The polarizable CG water consists of three particles instead of one in the standard MARTINI force field (Fig. 1). The central particle W is neutral and interacts with other particles in the system by means of the Lennard-Jones interactions, just like the standard water particle. The additional particles WP and WM are bound to the central particle and carry a positive and negative charge of +q and −q, respectively. They interact with other particles via a Coulomb function only, and lack any LJ interactions. The bonds W-WP and W-WM are constrained to a distance l. The interactions between WP and WM particles inside the same CG water bead are excluded, thus these particles are “transparent” toward each other. As a result the charged particles can rotate around the W particle. The dipole momentum of the water bead depends on the position of the charged particles and can vary from zero (charged particles coincide) to 2lq (charged particles are at the maximal distance). A harmonic angle potential with equilibrium angle θ and force constant Kθ is furthermore added to control the rotation of WP and WM particles and thus to adjust the distribution of the dipole momentum. The average dipole momentum of the water bead will depend on the charge distribution and is expected to be on average zero in an apolar environment, such as the interior of the lipid bilayer. In contrast, some non-zero average dipole will be observed in bulk water or in some other polar environment. The masses of the charged particles as well as of the central particle are set to 24 amu, totaling 72 amu (the mass of four real water molecules). There are five adjustable parameters in the polarizable water particle: The charge q, the distance l, the angle parameters θ and Kθ, and the atom type of the central particle W. The accessible range of the dipole momentum of the water bead is determined by both l and q; to restrict our parameter space we used q as the only adjustable parameter and fixed l at the value 0. 14 nm. This distance is small enough to prevent the overlap of the charged particles of adjacent water beads (which could result in very large forces) and large enough to represent the instantaneous dipole of a cluster of four water molecules. Similarly, only Kθ was varied. The equilibrium angle was fixed at θ = 0 to ensure that the water bead in an apolar solvent has a vanishing dipole moment (recalling that one CG water bead effectively represents a cluster of four real water molecules). It is clear that the polarizable water beads attract each other stronger than the standard CG water beads because of additional electrostatic interactions between their charged particles WP and WM. This additional attraction should be counter-balanced by a reduced LJ self-interaction of W particles. Thus we tested less attractive interaction levels II, III and IV (the standard MARTINI water has the atom type P4, which has a self-interaction strength level I. Note that, for each of these levels, the LJ parameter σLJ = 0. 47 nm. The LJ well depth εLJ = 5. 0,4. 5,4. 0,3. 5 kJ mol−1 for levels I–IV, respectively). Concerning the LJ interactions between the W particles and other particles in the MARTINI force field, our expectation was that these could stay unaffected, i. e. correspond to those for a P4 particle (for the full interaction matrix, see [3]). However, as we will show below, the cross-interaction strength has to be reduced slightly in order to reproduce the correct partitioning behavior. Since the polarization of water is treated explicitly in our polarizable model, the global dielectric constant εr = 15 used in the standard MARTINI should be adjusted accordingly. This value of εr compromises between large ε in water and small ε in the hydrophobic regions like the core of the lipid membrane. In the polarizable model, the global dielectric constant is reduced to εr = 2. 5 to ensure a realistic dielectric behavior in the hydrophobic regions. Other force field parameters are the same as in standard MARTINI [3]. All simulations were performed with the GROMACS suite of programs, versions 3. 3. 1 [19], 4. 0. 2 and 4. 0. 5 [20]. Standard simulation parameters associated with the MARTINI force field [3] were used unless stated otherwise. A time step of 20 fs was used in all simulations. We have repeated some of the simulations using 10 fs and 30 fs time steps; the results were virtually identical to the ones reported below. Temperature and pressure were kept constant by using weak coupling schemes [21], with time constants of τT = 0. 3 ps and τp = 3. 0 ps, respectively. The distance l between the central W particle and the charged WP/WM particles was constrained using the LINCS algorithm [22]. Visualization of the results was done with VMD [23]. Error estimates were obtained using a block-averaging procedure [24]. Details of the system composition and set-up are given alongside the presentation of the results. Times are reported as actual simulation time, except when explicitly stated as effective time in order to compare the kinetics to either all-atom simulations or experiment. The effective time accounts for the speed-up in coarse-grained dynamics (see [4]) and equals four times the actual simulation time. The parameter files are available in Dataset S1. They can also be downloaded from http: //cgmartini. nl, together with some example applications. The neglect of orientational polarizability in many water models associated with CG lipid force fields [54], [55], [56], [57], [58] is arguably one of the crudest approximations made. Water in those force fields is represented by spherically symmetric interaction sites either based on analytic potentials or effective potentials derived from atomistic simulations. None of these water models include electrostatic interactions, implying they are non-polarizable. The MARTINI model suffers from the same approximation. The inability to form a transmembrane water pore upon dragging a lipid across the membrane [3], or upon binding of antimicrobial peptides [41], [59], [60] are examples pointing at the shortcoming of the standard MARTINI water model. To improve the behavior of the water model, inclusion of electrostatic interactions is needed; to account for the orientational polarizability, the minimum requirement is a point dipole, as in the models of e. g. Warshel and coworkers [9], [10], [13] and Orsi et al [61]. Our new water model is a three-bead model, consisting of a central particle with two charges-on-a-spring embedded, and was chosen as it combines simplicity with versatility. It is similar to the classical Drude model used in polarizable all-atom (AA) force fields to mimic electronic polarization [15], [16]. In contrast to the AA case, were the charged particles are massless and their position is solved in an expensive, self-consistent way, in our CG model the particles carry mass and follow the normal equation of motions. The model has only few adjustable parameters, yet enough of them to reproduce the dielectric properties of bulk water on the one hand and keeping at par with the standard MARTINI philosophy on the other. Despite the limited amount of free parameters in the model, a full exploration of parameter space is practically impossible; guided partly by intuition and partly through extensive testing we eventually settled on a combination of parameters which, overall, perform very well. Compared to the standard MARTINI water model, the polarizable model has improved properties, not only with respect to its dielectric behavior, but also for instance in the somewhat reduced freezing point. It can not be excluded that other combinations of parameters might perform even better, and we anticipate that further optimization of the model will take place in the future alongside with extending the range of applications of the model. The main reason for having included polarizability into the model is the expectation that processes involving interactions between charged and polar groups in a low-dielectric medium are more realistically described. As an example we presented two applications for which standard CG models, including MARTINI, are less well suited, namely the translocation of ions across a lipid membrane and the electroporation of an octane slab and a lipid bilayer. Both processes involve the movement of charges from a high dielectric environment (water) to a low dielectric medium (membrane interior). A realistic description of such processes requires a model capable of performing local electrostatic screening. The two applications presented show that, despite being coarse-grained, our polarizable water model can do this at a level comparable to that of atomistic simulations. This opens the way to explore a number of important (bio) physical processes using the MARTINI model, including membrane poration by antimicrobial and cell penetrating peptides, DNA transfection, salt-induced membrane fusion, functioning of the voltage gated membrane channels, electroporation, and electrokinetic phenomena in general. Finally, it is important to point out a few limitations of the polarizable water model: First, it is slightly more expensive from a computational point of view (for a pure water system the simulations are slowed down by a factor of approximately three). Second, the current parameterization of the model is not as thoroughly tested yet in comparison to the standard MARTINI model. For example lipid phase behavior, or the effect on proteins and peptides is largely unexplored. Third, despite an overall improved performance, some properties are still not at par with experimental measurements or data from atomistic simulations. These include the air/water surface tension, which is significantly too low, and also the sign of the membrane dipole potential which is opposite to that observed with more detailed force fields. Further improvement could be obtained by changing the analytical form of the non-bonded potential (i. e. moving away from the LJ 12-6 form), and by adding polarizability to other beads in the force field. The latter idea may also lead to a more realistic description of the protein backbone, allowing secondary structure formation to be described with MARTINI, an option we are currently exploring. We finally note that the polarizable MARTINI water model is not meant to replace the standard MARTINI water model, but should be viewed as an alternative with improved properties in some, but similar behavior at reduced efficiency in other applications.

Many biomolecular processes involve charged species moving between regions of high polarity, such as the water phase, and regions of lower polarity, such as the lipid membrane. Due to the change in electrostatic screening between these two environments, the strength of the interactions between the moving charge and the surrounding molecules also changes. This has important consequences for the way biological activity is controlled. To help understand the forces driving the movement of biomolecules, we developed a computational model which is capable of describing these processes at near-atomic detail. To do so efficiently, we use a coarse-grained description of the molecules, in which some of the atomistic detail is averaged out. To capture the inhomogeneous nature of the dielectric response, we re-introduce some detail in the water model; the new model effectively mimics the orientational polarizability of real water molecules, and screens electrostatic interactions realistically. This enables the study of a number of important biological processes that were hitherto considered challenging for coarse-grained models, such as the permeation of ions across a lipid membrane and the rupture of membranes due to an electrostatic field, at relatively low computational cost.

Abstract Introduction Methods Discussion

biophysics/theory and simulation biochemistry/theory and simulation

2010

Polarizable Water Model for the Coarse-Grained MARTINI Force Field

Schistosomiasis japonica is a serious debilitating and sometimes fatal disease. Accurate diagnostic tests play a key role in patient management and control of the disease. However, currently available diagnostic methods are not ideal, and the detection of the parasite DNA in blood samples has turned out to be one of the most promising tools for the diagnosis of schistosomiasis. In our previous investigations, a 230-bp sequence from the highly repetitive retrotransposon SjR2 was identified and it showed high sensitivity and specificity for detecting Schistosoma japonicum DNA in the sera of rabbit model and patients. Recently, 29 retrotransposons were found in S. japonicum genome by our group. The present study highlighted the key factors for selecting a new perspective sensitive target DNA sequence for the diagnosis of schistosomiasis, which can serve as example for other parasitic pathogens. In this study, we demonstrated that the key factors based on the bioinformatic analysis for selecting target sequence are the higher genome proportion, repetitive complete copies and partial copies, and active ESTs than the others in the chromosome genome. New primers based on 25 novel retrotransposons and SjR2 were designed and their sensitivity and specificity for detecting S. japonicum DNA were compared. The results showed that a new 303-bp sequence from non-long terminal repeat (LTR) retrotransposon (SjCHGCS19) had high sensitivity and specificity. The 303-bp target sequence was amplified from the sera of rabbit model at 3 d post-infection by nested-PCR and it became negative at 17 weeks post-treatment. Furthermore, the percentage sensitivity of the nested-PCR was 97. 67% in 43 serum samples of S. japonicum-infected patients. Our findings highlighted the key factors based on the bioinformatic analysis for selecting target sequence from S. japonicum genome, which provide basis for establishing powerful molecular diagnostic techniques that can be used for monitoring early infection and therapy efficacy to support schistosomiasis control programs. Schistosomiasis is caused by Schistosoma haematobium, S. mansoni, S. japonicum, and less frequently, S. mekongi and S. intercalatum. It occurs in the tropics and subtropics and it is among the most important parasitic diseases worldwide, with a significant socio-economic impact [1]. The disease affects people living in 74 endemic countries, with approximately 120 million individuals being symptomatically infected and 20 million being severely affected [2], [3]. Moreover, schistosomiasis represents an increasing problem in non-endemic areas, due to the growing number of immigrants and tourists [4], [5]. As schistosomiasis control programs are chiefly based on treatment of infected populations, adequate case-finding is important for the effective consecution of the control programs. Herein, diagnosis plays a crucial role in the monitoring of early infection and therapy efficacy. However, the currently available diagnostic assays are not ideal, since the examination of eggs in stools, such as Kato-Katz assay, and detection of circulating antigens lack sensitivity due to low disease prevalence, post-treatment situations where chemotherapeutic agents mask the presence of existing disease and methodologically, antibody detection lacks specificity [6]. In addition, current ELISA methods cannot be used to evaluate the efficacy of chemical treatment as IgG antibody levels remained elevated despite the fact that the disease was cured, indicating that there may be false positive results [7]. In the last several years, several research groups have developed specific and sensitive PCR-based methods for detecting S. japonicum, S. mansoni and S. haematobium DNA from humans and the intermediate hosts [7]–[15]. These PCR assays have proven useful alternatives for the accurate diagnosis of human schistosomiasis. Numerous factors influence the sensitivity and specificity of PCR assays for the diagnosis of schistosomiasis, in particular, the target sequences selected for PCR amplification. Several research groups have developed PCR methods using various target sequences and these PCR assays showed different sensitivities for detection of Schistosoma genomic DNA. An 121-bp rDNA sequence was the major target sequence for detecting S. mansoni [8], [9], [11], [13]. Mitochondrial NADH I gene (nad1) has been used as genetic marker for detection of S. mansoni and S. japonicum DNA [15]–[17]. SjR2, a new RTE-like, non-long terminal repeat retrotransposon from S. japonicum, is firstly described by Laha et al. [18], and its 230-bp sequence was first used as target sequence in PCR and LAMP assays for detecting S. japonicum DNA [7], [12]. Recently, 29 retrotransposons were identified in the genome sequence of S. japonicum [19], including known Gulliver, SjR1, SjR2 and Sj-pido elements as well as 25 novel elements. In the present study, primers targeting these 25 novel retrotransposons were designed and used in PCR assays for detecting S. japonicum DNA, and their sensitivities and specificities were compared. This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The protocol was approved by the Committ-ee on the Ethics of Animal Experiments of the Soochow University (Permit Number: 2007–13). All surgeries were performed under sodium pentobarbital anesthesia, and all efforts were made to mini-mize suffering of animals. Serum samples of healthy individuals were obtained from Suzhou, Jiangsu Province, China. The protocol was approved by the Scientific Committee of the Soochow University. Serum samples of patients were obtained from the endemic area of Hunan Province, China. Written informed consent was obtained from all donors. Ethical clearance for the project was obtained from the Scientific Committee at the Hunan Institute of Parasitic Diseases, which is responsible for schistosomiasis control within the Hunan Province where targeted villages are located. Schistosome-infected snails were obtained from Jiangsu Institute of Parasitic Diseases, China. The institute provided live Oncomelania hupensis snails exposed to the Chinese strains of S. japonicum. All living snails were putting into a tray which was filled with 4/5 volume of water and exposed to a light source to induce shedding of live S. japonicum cercariae. Clonochis sinensis adult worms were provided by Prof. Kuiyang Zheng, Xuzhou Medical College, China, Trichinella spiralis adult worms were provided by Prof. Zhongquan Wang, Medical College of Zhengzhou University, China, and S. mansoni adult worms were obtained from Dr. Donato Cioli, Institute of Cell Biology, Monterotondo, Italy. Six female New Zealand rabbits, weighing 1. 8–2. 2 kg, were randomly divided into three groups of two rabbits each. Blood collected before infection from all rabbits was served as negative control. Group I and Group II were percutaneously infected with 200 mixed sexual cercariae of S. japonicum (light infection), and Group III was percutaneously infected with 500 mixed sexual cercariae of S. japonicum (medium infection). On the seventh week, the eggs were detected in the feces. The EPG was 96 (Group I), 92 (Group II) and 397 (Group III), respectively. In Group I, between 1 and 7 weeks post-infection, blood were collected weekly. On the seventh week, the rabbits were anaesthetized with sodium pentobarbital and the adult worms and liver samples were collected. Between 1 and 24 weeks post-infection, blood was collected from rabbits in Group II. Serum of each rabbit was separated by centrifugation (1500 g for 15 min) and kept at −80°C until use. The rabbits infected with S. japonicum in Group III were treated with two doses of 150 mg/kg praziquantel on the seventh and eighth week post-infection. In this group, blood was collected weekly between 1 and 30 weeks post-infection (23 weeks post-treatment) and all of the rabbits were sacrificed on the last week, and no adult worms were found in portal system. Serum of each rabbits was separated by centrifugation (1500 g for 15 min) and kept at −80°C until use. Forty-three S. japonicum-positive serum samples of patients with positive Kato-Katz stool examination results were obtained from the endemic areas in Hunan Province, China, and the egg counts of these patients ranged from 8 to 1160 eggs per gram (EPG). 51 serum samples from healthy individuals were collected from healthy donors at the Center of Health Examination, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, China, and were used as negative control to evaluate the specificity of the PCR assay. DNA from all of the collected samples was extracted using the method described by Steiner et al [20]. as modified by Xia et al. [7]. Briefly, 0. 2 g liver homogenate samples was mixed with 200 µl distilled water, grinded and dissolved in 200 µl Triton-X-100 (10 mMol/L Tris-HCL; 0. 45% Triton-X-100; 0. 45% NP-40; 300 µg/mL protease K, pH 7. 4) and were incubated at 60°C for 2 h with vigorous agitation for 10 min on a shaker, and then centrifuged at 12,000 g for 10 min. 300 µl supernatant of the liver homogenate was collected in a separate tube and incubated at 100°C for 10 min, centrifuged at 12,000 g for 10 min and the supernatant was collected. DNA from adult worms was also extracted using the method modified by Xia et al [7]. Five adult worms of each S. japonicum, S. mansoni, C. sinensis and T. spiralis were homogenized in 300 µl physiological saline and then digested with equal volume of extraction buffer containing 3 mg/ml proteinase K, 0. 1 mol/L Tris-HCl, pH 8. 5,0. 05 mol/L EDTA, and 1% SDS. Then the mixture was incubated at 60°C for 1 h. 200 µl serum of infected rabbits or humans were diluted in 400 µl serum extraction buffer containing 150 mol/L NaCl, 10 mol/L EDTA, 10 mol/L Tris-HCl, pH 7. 6,2% SDS, 5 µg/ml salmon sperm DNA, 4 µg 25 mg/ml proteinase K, and were incubated at 37°C overnight. All of the extraction mixtures (liver homogenate, adult samples and serum) were extracted twice with phenol, chloroform, isoamyl alcohol (25∶24∶1), once with chloroform, isoamyl alcohol (24∶1), and then precipitated with dehydrated alcohol and 3 M sodium acetate. The supernatant was discarded and the pellet was washed twice with 1 mL of 75% ethanol, finally the pellet was left to dry at 37°C for 30 min and then re-suspended with 100 µl of TE (10 mmol/L Tris-HCl, 1 mmol/L EDTA, pH 8. 0) for DNA of liver homogenates and adult worms, and 20 µl of TE (10 mM Tris-HCl, 1 mM EDTA, pH 8. 0) for DNA of serum samples, respectively. The DNA extracted from adult worms, liver and serum were used as the template. Primers were designed targeting the 25 novel retrotransposons repeat DNA sequences of S. japonicum (Table S1). PCR reaction (25 µl) contained 2. 5 µl of buffer, 1. 5 µl of 25 mmol/L MgCl2,2 µl of 2. 5 mmol/L dNTP, 0. 5 µl of each 20 pmol/L primer, 0. 4 µl of 5 U/L Taq polymerase (Takara) and 4 µl of template. The conditions for PCR were as follows (with the exception of SjCHGCS4 and SjCHGCS6 using 2-step PCR: 94°C for 4 min, followed by 30 cycles of 94°C for 30 s, 68°C for 60 s, and a final extension of 72°C for 7 min): 94°C for 3 min, followed by 35 cycles of 94°C for 60 s, Tm/ (list in Table S1) for 60 s, 72°C for 60 s and a final extension of 72°C for 7 min. The PCR was performed using GeneAmp PCR System (Eppendorf, Hamburg, Germany). Finally, a 5 µl aliquot of the PCR product was run on a 2. 0% agarose gel along with DNA ladder marker (Takara, Dalian) in TBE buffer containing 0. 5 µg/ml ethidium bromide, and the bands were visualized under UV light on a transilluminator. The nested-PCR reaction system was similar to the normal PCR as described above. The first-round amplification was carried out in the same manner except that the degenerate temperature was 55°C. Primers were designed on the basis of the SjCHGCS19 retrotransposons repreat DNA sequence of S. japonicum, the primers employed were P3 (5′-CCAAATCGCAACACTACGC-3′ (forward) and P4 (5′-ATCGGATTCTCCTTGTTCAT-3′) (reverse). DNA samples extracted from serum of rabbits and humans were used as the template. The expected length of the amplification product was 607 bp. Second-round amplification (nested-PCR) was carried out in the same manner as the first-round except that the DNA sample was a 1∶10000 dilution of the first-round PCR product and the degenerate temperature was 65°C. The sequences of these primers are listed in Table S1. The expected length of the amplification product was 303 bp. The first-round PCR product targeting SjCHGCS19 was cloned into plasmid by means of a pMD20-T II cloning reagent Kit (Tiangen, Beijing, China). Plasmid purification was done with a TIAN pure Mini Plasmid Kit (Tiangen, Beijing, China). Plasmids were quantified by spectrophotometry. Sequencing of the cloned amplification product confirmed that it was identical to part of the S. japonicum retrotransposon SjCHGCS19. The standard plasmid was tested in 10-dilution series by nested-PCR. Primers were designed from the 25 novel retrotransposons of S. japonicum and a series of diluted genomic DNA of S. japonicum adults were used as the template. The target fragments were amplified by nested-PCR assay and the minimum amounts detectable were different among 25 new retrotransposons and SjR2. In addition to the 230-bp fragment from SjR2, a new 303-bp fragment from non-LTR retrotransposon (SjCHGCS19) displayed high sensitivity and specificity. Bioinformatic analysis showed that both the SjCHGCS19 (303-bp sequence) and SjR2 (230-bp sequence) have higher genome proportions (4. 09% and 4. 43%), higher repetitive complete copies (793 and 400) and partial copies (17,373 and 23,755), and higher EST numbers (39 and 79) than the other retrotranposons in the genome of S. japonicum (Table 1). Furthermore, to determine the limit of the 303-bp DNA fragment for detecting S. japonicum DNA, 10-fold serial dilutions of the standard plasmid clones of SjCHGCS19 and SjR2 were amplified by nested-PCR assay. The minimum detectable amount of standard plasmid from SjCHGCS19 was only 2. 02 copies, whereas the 230-bp fragment from SjR2 was 10. 2 copies. In addition, the 303-bp DNA fragment was amplified by nested-PCR assay from male and female adults of S. japonicum, from liver homogenate and sera of infected rabbits (Figure 1). Interestingly, the target DNA was amplifiable from both S. japonicum and S. mansoni, but no cross-reaction was detected with DNA samples representing C. sinensis and T. spirals (Figure 2). To clarify the potential diagnostic value of the 303-bp DNA fragment for early infection and chemotherapy evaluation in the rabbit model, a series of rabbit sera in Group II (light infection) and Group III (praziquantel treatment) were examined by nested-PCR assay. As shown in Figure 3, the 303-bp DNA fragment was amplified at 3 d sera post-infection in Group II and could be amplified until 24 weeks post-infection by nested-PCR assay. The 303-bp DNA fragment was amplifiable between 1 and 24 weeks post-infection and it became negative from 25 weeks post-infection (17 weeks after praziquantel treatment) in Group III (Figure 4). To illuminate the clinical utility of the 303-bp DNA from SjCHGCS19 as the target sequence for the diagnosis of human schistomomiasis, 43 human serum samples from patients with S. japonicum infection confirmed by stool examination and 51 serum samples from normal healthy individuals were examined. Of the 43 patient serum samples, 42 (97. 67%) were positive by the nested-PCR assay. Of the 51 serum samples from healthy individuals, 49 (96. 07%) were detected as negative by the nested-PCR assay. the percentages of sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for nested-PCR assay were 97. 67%, 96. 07%, 97. 67% and 96. 07%%, respectively. For patients with heavy infection with S. japonicum (EPGs≥400) and medium infection (100≤EPG<400), the percentage of sensitivity was 100%. For patients with light infection (EPG<100), the percentage of sensitivity was 96. 87% (Table 2, Figure S1). Because schistosomiasis control programs are chiefly based on treatment of infected populations, adequate case-finding is important for the effective consecution of the control programs. Herein, accurate diagnosis plays a crucial role in the monitoring of early infection and therapy evaluation. Nucleic acid-based diagnosis has been used in the clinical testing of a wide variety of pathogenic infections, such as human immunodeficiency virus [21], Mycobacterium tuberculosis [22], Plasmodium falciparum [23], Trypanosoma cruzi [24] and Leishmania braziliensis [25]. In parasitic diseases such as schistosomiasis, it was possible to detect cell-free parasite DNA circulating in plasma, and this could be used to diagnose schistosomiasis. Importantly, nucleic acid-based diagnostic methods have the same diagnostic value as parasitological diagnostic methods. Recently, several PCR-based methods for detecting Schistosoma DNA from various samples have been developed [7]–[15]. Numerous factors may influence the sensitivity and specificity of PCR assays for the diagnosis of schistosomiasis, in particular the target sequences selected. A 121-bp tandem repeat rDNA sequence was the major target sequence for detecting S. mansoni DNA in mouse serum samples and in human plasma by PCR [11], [13]. Although a real-time PCR assay using nad1 as target sequence for detecting S. japonicum DNA has proven highly sensitive, even for samples containing less than 10 EPG, it was negative for examining serum and urine samples from the infected pigs [14]. While previous efforts of PCR diagnosis of S. mansoni and S. japonicum infections have been relied on mitochondrial or rDNA sequences, we have focused on the sequence of the highly repetitive retrotransposon SjR2 in S. japonicum. In our previous studies, the 230-bp sequence from the highly repetitive retrotransposon SjR2 of S. japonicum was used as target sequence and it showed high sensitivity and specificity in detecting S. japonicum DNA, and the 230-bp fragment was amplified with DNA equivalent of 1. 1 egg from feces [7]. In particular, the 230-bp sequence was able to be amplified from the sera of the rabbit model at 1 week post-infection, which is one week earlier than that of the 121-bp sequence in the mouse-S. mansoni model [7], [11], [12]. SjR2 was 3. 9 kb in length and was constituted of a single open reading frame encoding a polyprotein with apurinic/apyrimidinic endonuclease and reverse transcriptase domains [18]. Phylogenetic analyses based on conserved domains of reverse transcriptase or endonuclease revealed that SjR2 belonged to the RTE clade of non-long terminal repeat retrotransposons and SR2 elements are members of a non-LTR retrotransposons typified by the RTE-1 non-LTR retrotransposon of Caenorhabditis elegans[26]. According to the phylogenetic tree, both SjCHGCS19 and SjR2 belonged to RTE clade of non-LTR retrontransposon (Figure S2). SjCHGCS19 showed the closest relationship with Perere-3 of S. mansoni, while SjR2 located in the same branch with SR2. At amino acids level, SjCHGCS19 showed 74% identity with Perere-3, while only 30% identity with SR2 and SjR2. Between SR2 and SjR2, the polyprotein showed 55% identity. Furthermore, hybridization analyses indicated that 10,000 copies of SjR2 were dispersed throughout the S. japonicum genome, accounting for up to 14% of the nuclear genome [18]. In our previous study, 29 retrotransposons were identified, including the known Gulliver, SjR1, SjR2 and Sj-pido elements as well as 25 novel elements, together constituting 19. 8% of the genome. Of the 25 novel retrotransposons, 18 were LTR forms, four were non-LTR forms and three were Penelope-like elements—enigmatic retroelements that retain introns. Each type of retrotransposons was represented by 1 to 793 intact copies or hundreds to thousands of partial copies [19]. The non-LTR retrotransposons such as SjR1, SjR2, Sj-pido, SjCHGCS19, SjCHGCS20, SjCHGCS21 and SjCHGCS22 have significantly higher copy numbers, constituting 12. 6% of the genome. We then wanted to determine whether there are any associations between the numbers of copies of target sequences and the sensitivity of the PCR detection. In the present study, primers were designed to amplify the 25 novel S. japonicum retrotransposons. The target fragments were amplified by nested-PCR assay for comparing sensitivity and specificity of detecting S. japonicum DNA of the target sequences. The results showed that a new 303-bp fragment from the highly repetitive retrotransposon, SjCHGCS19, had high sensitivity for detecting S. japonicum DNA in addition to the 230-bp fragment from SjR2. Importantly, bioinformatic analysis of 26 S. japonicum retrotransposons showed that both the SjCHGCS19 (303-bp sequence) and SjR2 (230-bp sequence) have higher genome proportions, repetitive complete copies and partial copies, and active ESTs than the others in the chromosome genome (Table 1). The minimum amount of the standard plasmid detectable using nested-PCR assay was 2. 02 copies per reaction using the 303-bp target sequence from SjCHGCS19, whereas 10. 2 copies per reaction of S. japonicum DNA was detected targeting the 230-bp sequence from SjR2 by the nested-PCR assay. This indicated that the 303-bp fragment from SjCHGCS19, as target sequence for detecting S. japonicum DNA, was more sensitive than the 230-bp fragment from SjR2 previously identified. Additionally, as shown in Figure 1, we evaluated the specificity of the 303-bp target sequence. The expected product was amplified by the nested-PCR assay from S. japonicum male and female adults, from liver homogenate and from sera of infected rabbits. We did not find any false positive bands in non-infected rabbit sera. Interestingly, the target DNA was amplified from both S. japonicum and S. mansoni, but no cross-reaction was detected in DNA samples representing C. sinensis and T. spirals (Figure 2). Furthermore, in the present study, the 303-bp sequence was amplified in rabbit sera at 3 d day post-infection, which is 4 d earlier than that of the 230-bp sequence in the rabbit-S. japonicum model by nested-PCR (Figure 3). The results showed that higher sensitivity is achieved using the 303-bp sequence for the detection of S. japonicum DNA in serum samples. In addition to the obvious advantage of an early diagnosis, our findings showed that the 303-bp target sequence might be valuable for the evaluation of chemotherapy efficacy, and it became negative at 25th week post-infection (17 weeks after praziquantel treatment) by nested-PCR, and was 7 weeks longer than detection by using the 230-bp sequence from SjR2, indicating a higher sensitivity of the 303-bp sequence than that of the 230-bp sequence (Figure 4). The effectiveness of the 303-bp target sequence was validated by examining serum samples from patients infected with S. japonicum (Figure S1). The findings showed that the sensitivity was 97. 67%, and the specificity was 96. 07% (Table 2). In particular, the PPV was 100% in patients with heavy infection (EPGs≧400) and medium infection (100≦EPG<400), indicating its high sensitivity. For patients with light infection (EPG<100), the percentage of sensitivity was 96. 87%. Only 1 serum sample was detected as false negative, and this sample was from a patient with particularly low infection (EPG = 56). This could be due to little S. japonicum DNA in human blood circulation and/or DNA loss during template extraction procedures. There appeared to be a correlation between PCR results and the EPGs of the patients (Table 2). However, in this study, the examined number of patient serum samples was small. It is imperative to carry out further investigation using a large number of patient serum samples. Since the prevalence of human infection with S. japonicum has been decreasing year by year [27], the rapid and reliable diagnosis of Schistosoma infection is central to the control as well as to the environmental monitoring and disease surveillance, especially for evaluation of treatment efficacy. DNA amplification assays provide alternative approaches for sensitive and specific diagnosis of Schistosoma infection, provided that reliable genetic markers are employed in the tests. Our results demonstrated that this new 303-bp sequence from non-LTR retrotransposon (SjCHGCS19) had high sensitivity and specificity for the detection of S. japonicum DNA, which may provide the new target sequence useful for the early diagnosis and for the evaluation of chemotherapy efficacy of schistosomiasis. More importantly, although many factors may affect the sensitivity and specificity in the target sequence selection, such as the length of target sequence, the specificity of the conserved sequence, the present study highlighted the key factors based on bioinformatic analysis for selecting a new perspective sensitive target sequence from genome sequences, which provides new insights into selecting suitable target sequence which may play a key role for the sensitive and specific detection of Schistosoma DNA. These findings would provide basis for establishing powerful molecular diagnostic techniques that can be used in clinical settings and as laboratory tools for surveillance and for environmental monitoring to support schistosomiasis control programs.

Schistosomiasis remains a serious parasitic disease worldwide. Adequate case-finding is important and crucial for the effective control programs as schistosomiasis control programs are chiefly based on treatment of infected populations. However, the currently available diagnostic assays are not ideal, and DNA detection can provide useful alternative approaches for the sensitive and specific diagnosis of schistosomiasis, provided that reliable genetic markers are employed in the tests. However, different target sequences used for DNA detection of samples showed different sensitivity. In previous studies, we identified a 230-bp sequence of high sensitivity and specificity from the retrotransposon SjR2 of Schistosoma japonicum. Here, new primers based on 25 novel retrotransposons were designed and their sensitivities and specificities for detecting S. japonicum DNA were compared. Of these, a new 303-bp DNA sequence from the non-LTR retrotransposon (SjCHGCS19) had high sensitivity and specificity in detecting S. japonicum DNA. More importantly, we also found that both the SjCHGCS19 (303-bp sequence) and SjR2 (230-bp sequence) have high repetitive copies, higher genome proportions and active ESTs in the genome of S. japonicum. Our findings provide new insights into selecting suitable target sequences which may play a key role for the sensitive and specific detection of S. japonicum DNA.

Abstract Introduction Materials and Methods Results Discussion

medicine biochemistry infectious diseases nucleic acids neglected tropical diseases dna biology

2012

Sensitive and Specific Target Sequences Selected from Retrotransposons of Schistosoma japonicum for the Diagnosis of Schistosomiasis

The C. elegans germline is pluripotent and mitotic, similar to self-renewing mammalian tissues. Apoptosis is triggered as part of the normal oogenesis program, and is increased in response to various stresses. Here, we examined the effect of endoplasmic reticulum (ER) stress on apoptosis in the C. elegans germline. We demonstrate that pharmacological or genetic induction of ER stress enhances germline apoptosis. This process is mediated by the ER stress response sensor IRE-1, but is independent of its canonical downstream target XBP-1. We further demonstrate that ire-1-dependent apoptosis in the germline requires both CEP-1/p53 and the same canonical apoptotic genes as DNA damage-induced germline apoptosis. Strikingly, we find that activation of ire-1, specifically in the ASI neurons, but not in germ cells, is sufficient to induce apoptosis in the germline. This implies that ER stress related germline apoptosis can be determined at the organism level, and is a result of active IRE-1 signaling in neurons. Altogether, our findings uncover ire-1 as a novel cell non-autonomous regulator of germ cell apoptosis, linking ER homeostasis in sensory neurons and germ cell fate. Apoptosis, also known as programed cell death (PCD), is a highly conserved fundamental cellular process that provides a self-elimination mechanism for the removal of unwanted cells. PCD is critical for organ development, tissue remodeling, cellular homeostasis and elimination of abnormal and damaged cells [1], [2]. The apoptotic machinery that actually executes cell death is intrinsic to all cells and can be activated in response to extracellular or intracellular cues. These are thought to be mediated by cell death receptors or by cytotoxic stress respectively [3]. In C. elegans, 131 somatic cells invariably undergo apoptosis during hermaphrodite development [4], [5]. In contrast, in the adult C. elegans, only germ cells undergo apoptotic cell death. These cell deaths can be either physiological or stress-induced [6], [7]. So far, stress-induced germ cell apoptosis has been associated with DNA damage, pathogens, oxidative stress, osmotic stress, heat shock and starvation [7]–[9]. These apoptotic events are restricted to germ cells at the pachytene stage which are located in the loop region of the gonad [6], where oogenesis transition normally occurs [10]. The physiological germ cell apoptosis pathway acts during oogenesis and is thought to act either as a part of a quality control process, preferentially removing unfit germ cells from the gonad, or as a resource re-allocation factor important for maintaining oocyte quality or simply as a gonad homeostatic pathway removing excess germ cells [6], [11], [12]. Both somatic and germ cell apoptosis rely on the highly conserved core apoptotic machinery comprised of the Caspase-3 homolog ced-3, the Apaf-1 homolog ced-4 and the anti-apoptotic Bcl-2 homolog ced-9 [6], [13]–[16]. All germ cell apoptosis, physiological and stress-induced, relies on the core apoptotic machinery [7]–[9]. However, different upstream genes activate the core apoptotic machinery in the germline in response to different stresses. For example, DNA damage-induced germ cell apoptosis involves the proteins EGL-1, CED-13, and the DNA damage response protein p53 homolog CEP-1 [9], [17]–[19]. In contrast, oxidative, osmotic, heat shock and starvation stresses induce germ cell apoptosis through a CEP-1 and EGL-1 independent pathway and rely on the MEK-1 and SEK-1 MAPKs instead [7]. The endoplasmic reticulum (ER) fulfills many essential cellular functions, including a role in the secretory pathway, in lipid metabolism and in calcium sequestration. Accordingly, ER homeostasis is essential for proper cellular function [20]. A specialized, conserved cellular stress response, called the unfolded protein response (UPR), is in charge of detecting ER stress and adjusting the capacity of the ER to restore ER homeostasis. In C. elegans, as in humans, three proteins located at the ER membrane sense ER stress and activate the UPR: the ribonuclease inositol-requiring protein-1 (IRE-1), the PERK kinase homolog PEK-1 and the activating transcription factor-6 (ATF-6) [21]. Of the three, IRE-1 is the major and most highly conserved ER stress sensor. In response to ER stress, IRE-1 activates the ER stress-related transcription factor XBP-1, which induces the transcription of genes that help restore ER homeostasis [22]–[24]. Accordingly, ire-1 and xbp-1 deficiencies perturb ER homeostasis [25], [26]. Although IRE-1 typically protects cells, upon excessive and prolonged ER stress, IRE-1 can also trigger cell death, usually in the form of apoptosis [27], [28]. For example, IRE-1 can lead to activation of the cell death machinery via JNK and caspase activation [29], [30] or by mediating decay of critical ER-localized mRNAs through the RIDD pathway, tipping the balance in favor of apoptosis [31]. These functions of IRE-1 are independent of XBP-1 [29]–[33]. Highly proliferating cells with a high protein and lipid biosynthetic load are thought to rely on ER function to a greater extent than other cells. This together with the general sensitivity of the germline to cellular stresses prompted us to investigate the effects of ER stress on germ cell fate. Strikingly, we discovered that ER stress does not simply kill the germ cells by not meeting their biosynthetic demands. Instead, we found that ER stress initiates a signaling cascade in neurons that regulates germ cell survival non-autonomously. Thus, our findings reveal that germ cell sensitivity to ER stress conditions can be regulated at an organismal level and can be uncoupled from germ cell stress. To investigate whether ER stress induces apoptosis in the C. elegans germline, we first assessed the number of apoptotic corpses in the gonads of animals treated with tunicamycin, a chemical ER stress inducer which blocks N-linked glycosylation. Apoptotic corpses in the gonad were identified by staining with the vital dye SYTO12 and by their discrete cellularization within the germline syncytium. We found that tunicamycin treatment increased the number of apoptotic germ cells present in wild-type gonads by approximately 3 fold compared to control DMSO treatment from day-1 to day-3 of adulthood (P<0. 001, Figure 1A–B). If indeed the increased number of germline corpses in tunicamycin-treated animals is a consequence of ER stress, then additional manipulations that disrupt ER homeostasis should also increase germ cell apoptosis. tfg-1 encodes a protein that directly interacts with SEC-16 to control COPII subunit accumulation at ER exit sites and is required for the vesicular export of cargo from the ER [34]. We hypothesized that ER homeostasis would be disrupted in tfg-1-deficient animals. To examine the effect of tfg-1 deficiency on ER homeostasis, we assessed the effect of tfg-1 RNAi treatment on the levels of the ER stress response reporter Phsp-4: : gfp [22]. tfg-1 RNAi efficacy was confirmed by the reduction in the animals' body size compared to control RNAi treated animals [35]. We found that treatment with tfg-1 RNAi specifically activated the ER stress response, as it increased the level of the ER stress response reporter without increasing the expression of oxidative stress response, heat shock response or mitochondrial stress response reporters (Figure S1). In terms of germ cell apoptosis, we observed that tfg-1 RNAi consistently increased the number of apoptotic germ cells in the gonad by approximately 4 fold from day-1 to day-3 of adulthood compared to wild-type animals (P<0. 001, Figure 2A, B). A similar 4 fold increase in germ cell apoptosis was observed by scoring germ cell engulfment by neighboring cells that expressed GFP-labeled CED-1, a transmembrane receptor that mediates cell corpse engulfment in C. elegans [36] (Figure 2C). tfg-1 RNAi treatment did not increase the number of SYTO12-labeled cells in the gonads of apoptosis-defective ced-3 (n1286) mutants, confirming that the dye specifically labels apoptotic cells (Figure S2). Together, these results indicate that conditions that disrupt ER homeostasis, including tunicamycin treatment or blocking secretory traffic from the ER, increase apoptosis frequency in the gonad compared to non-stressed animals. We next asked which apoptotic machinery is implicated in ER stress-induced germ cell apoptosis. To this end, we examined mutants deficient in core-apoptotic genes as well as mutants deficient in genes specifically implicated in germ cell apoptosis. This array of apoptosis-related mutants was treated with control or tfg-1 RNAi, and germ cell apoptosis was scored by SYTO12 labeling. As expected, we found that the core apoptosis machinery genes ced-3 and ced-4 [6], [13], [15], [16] were required for germ cell apoptosis in response to ER stress (Figure 3A). Importantly, the cep-1, egl-1 and ced-13 genes, previously implicated in DNA damage-induced apoptosis [9], [17]–[19], were also found to be completely essential for germ cell apoptosis in response to tfg-1 RNAi treatment (Figure 3A). Accordingly, the levels of a CEP-1: : GFP translational fusion transgene driven by the cep-1 promoter were increased within the germ cells of tfg-1 RNAi-treated animals (P<0. 001, Figure 3B). In contrast, pmk-1 and sek-1, previously implicated in oxidative stress-induced and pathogen-induced germ cell apoptosis respectively [7], [8], were dispensable for germ cell apoptosis in response to tfg-1 RNAi treatment (Figure 3C). Thus, the genetic analysis clearly implicated the apoptotic machinery that mediates DNA damage-induced germ cell apoptosis in ER stress-induced germ cell apoptosis as well. The strong changes in CEP-1 levels observed in tfg-1 RNAi-treated animals suggest that ER stress controls CEP-1 activation within the germ cells. One possible explanation for the involvement of genes implicated in DNA damage-induced germ cell apoptosis is that ER stress indirectly damages DNA, which in turn leads to CEP-1 activation and DNA damage-induced germ cell apoptosis. However, whereas a previous study implicated the intestinal kri-1 gene in non-autonomous regulation of ionizing-radiation induced germ cell apoptosis [37], we found that tfg-1 RNAi treatment efficiently induced germ cell apoptosis in kri-1-deficient animals (Figure 3C). This genetic uncoupling between the requirements for ionizing-radiation induced germ cell apoptosis and ER stress-induced germ cell apoptosis suggest that ER stress does not simply induce DNA damage which in turn leads to germ cell apoptosis. To further substantiate this conclusion, we examined directly whether the DNA damage response is activated in the germ cells of ER stressed- animals treated with tfg-1 RNAi. To this end, we followed the nuclear aggregation of HUS-1: : GFP, which encodes a DNA damage checkpoint protein that relocalizes to distinct nuclear foci upon induction of DNA damage [38]. Whereas nuclear HUS-1: : GFP aggregates were clearly observed in the germ cells of DNA-damaged rad-51 RNAi treated animals (P<0. 001 compared to control RNAi), HUS-1: : GFP aggregates were not detected in tfg-1 RNAi treated animals (P = 0. 78 compared to control RNAi, Figure 3D). Thus, ER stress activates CEP-1/p53 to induce germ cell apoptosis without generating DNA damage. We next asked whether any of the canonical ER stress sensing genes was implicated in ER stress-induced germ cell apoptosis. To this end, we examined mutants deficient in the ER stress-response sensor genes that comprise the UPR: ire-1, pek-1 or atf-6. This array of mutants was treated with DMSO or with tunicamycin and germ cell apoptosis was scored by SYTO12 labeling. We found that similarly to its effect in wild-type animals, tunicamycin treatment increased the number of germline corpses in atf-6 and pek-1-deficient animals by approximately 3 fold from day-1 to day-3 of adulthood (P<0. 001, Figure 1A, C). In contrast, ER stress induced by tunicamycin treatment failed to increase germ cell apoptosis in ire-1 mutants (Figure 1A, B). Therefore, the insensitivity of the germline to tunicamycin is unique to ire-1-deficient animals and not seen in animals deficient in other UPR sensors. ire-1 mutants are abnormal in terms of their gonad anatomy and their reproductive capacity: ire-1 mutants have approximately 2 fold less progeny and 2 fold less mitotic germ cells within their proliferative zones compared to ire-1 (+) wild-type animals (P<0. 001, Figures S3A, D). Thus, we wondered whether these abnormalities affected the ability of their germ cells to undergo apoptosis in general or whether they were specifically defective in their ability to undergo apoptosis in response to ER stress. First we assessed germline apoptosis in ire-1 (−) mutants under normal growth conditions, from day-0 (L4) to day-3 of adulthood. At all timepoints, we detected approximately half the amount of germline corpses in ire-1 (−) gonads compared to ire-1 (+) wild-type gonads, as assessed by SYTO12 labeling and by the CED-1: : GFP engulfment marker (Figure 2A–C). The low levels of germ cell apoptosis persisted in ire-1 mutants treated with vps-18 RNAi (Figure 2D), a treatment that impairs germ cell corpse clearance [39]. However, normalization of the number of apoptotic corpses to the number of mitotic germ cells resulted in comparable levels of germ cell corpses in ire-1 mutants and in non-stressed wild-type animals (P = 0. 12, Figure S3C). This indicates that in spite of their reproductive abnormalities, physiological germ cell apoptosis in ire-1 (−) and ire-1 (+) animals is comparable. Next, we assessed stress-induced germline apoptosis in ire-1 mutants. We found that although manipulations that disrupt ER homeostasis fail to increase germ cell apoptosis in ire-1 mutants (Figure 1B, 1D–E), DNA damage and oxidative stress conditions did increase germ cell apoptosis in ire-1 mutants (P<0. 001 compared to non-stressed ire-1 mutants, Figure 1D–E). This resulted in a similar level of germline apoptosis as in stressed wild-type animals upon normalization of the number of apoptotic corpses to the number of mitotic germ cells (P = 0. 44 for DNA damage and P = 0. 42 for oxidative stress, Figure 1D–E). Thus, in spite of the reproductive abnormalities of ire-1 mutants, the germ cells of these mutants undergo stress-induced apoptosis similarly to wild-type animals, however not in response to ER stress. The inability of ire-1 mutants to increase germline apoptosis specifically in response to perturbations in ER homeostasis suggests that IRE-1 may be a critical mediator of ER stress-induced germ cell apoptosis. The most established mode of action of IRE-1 under ER stress conditions is via the activation of the UPR-related transcription factor XBP-1 [22]–[24]. Therefore, if IRE-1 enabled ER stress-induced germ cell apoptosis via its downstream target xbp-1, then the number of germline corpses detected in xbp-1 (−) mutants should remain low under ER stress conditions, similarly to ire-1 (−) mutants. In order to test this, we first examined germ cell apoptosis in xbp-1 (tm2457) null mutants. Surprisingly, in contrast to ire-1 (−) mutants, we consistently detected increased germ cell apoptosis in xbp-1 (−) gonads compared to wild-type gonads under normal growth conditions. A 2. 5 fold increase in the number of germline corpses in xbp-1 (−) mutants was detected by SYTO12 labeling of gonads from day-1 to day-3 of adulthood compared to wild-type animals (P<0. 001, Figure 2A–B). A similar observation was apparent by using the CED-1: : GFP engulfment marker (Figure 2C). The 2. 5 fold increase in the number of germ cell corpses was still apparent in engulfment defective vps-18 RNAi-treated animals (Figure 2D) and upon normalization to the number of mitotic germ cells located in the proliferative zone (Figure S3C). Thus, in contrast to ire-1 mutants and wild-type animals, xbp-1 mutants exhibit a high basal level of germ cell apoptosis. We hypothesized that the increase in germline apoptosis in xbp-1 mutants may be due to perturbed ER homeostasis in these animals [25], [26], If so, then it should be mediated via ire-1, similarly to other ER stress conditions that induce germ cell apoptosis. Accordingly, we found that in an ire-1 (−) background, the xbp-1 mutation did not increase germ cell apoptosis. This observation was consistent along different time points spanning from day-0 (L4) to day-3 of adulthood (Figure 2A–B). This also persisted upon normalization to the number of mitotic germ cells located in the proliferative zone (Figure S3C). Interestingly, the amount of mitotic germ cells of xbp-1; ire-1 double mutants was similar to that of xbp-1 single mutants (P = 0. 072, Figure S3A), whose germ cells were responsive to ER stress-induced apoptosis (Figure S3B–C). This indicates that the reduced amount of mitotic cells in the gonad of ire-1 mutants can be uncoupled from the inability of their germ cells to undergo ER stress-associated apoptosis. The finding that xbp-1 deficiency per se promotes ire-1-dependent ER stress-induced germ cell apoptosis suggests that xbp-1 is dispensable for increasing germ cell apoptosis in response to ER stress. Consistent with this, we found that tunicamycin treatment further increased germ cell apoptosis in xbp-1 mutants (P<0. 001, Figure 1A–B). Altogether, these results lend further support to the notion that ire-1 is a critical signaling molecule in mediating ER stress-induced germline apoptosis, whereas its' downstream canonical target xbp-1 is not. Furthermore, since ER function is compromised both in ire-1 and in xbp-1 deficient mutants [25], [26], the differential ability to induce germ cell apoptosis in these mutants suggests that germ cell apoptosis may be the result of active IRE-1 signaling, rather than simply a consequence of ER dysfunction. In mammalian cells, activation of IRE1 can cell-autonomously activate JNK via the adaptor protein TRAF. Consequently, IRE1-mediated activation of JNK initiates proapoptotic signaling, independently of XBP1 [29]. Thus, we examined whether the C. elegans homologs of TRAF and JNK proteins were required for ire-1/ER stress-induced apoptosis in C. elegans, which is also independent of xbp-1. To this end, trf-1 mutants or mutants deficient in all three C. elegans JNK homologs were treated with control or tfg-1 RNAi. We found that tfg-1 RNAi increased germ cell apoptosis independently of the trf-1 and the JNK-like genes (Figure 3E). Thus, since ER stress can effectively induce germ cell apoptosis in the absence of xbp-1, trf-1 and JNK homologs, the signaling mediated by IRE-1, in this case, must be executed by an alternative xbp-1-independent output of IRE-1. Next, we examined whether ER stress triggers programmed cell death autonomously within the germ cells, or non-autonomously from the soma. To test this, we used tfg-1 RNAi to induce ER stress specifically in the germline or in the soma. To induce ER stress primarily in the germ cells, mutants in the rrf-1 gene, encoding an RNA-directed RNA polymerase (RdRP) homolog required for most somatic RNAi but not for germline RNAi [40], were treated with tfg-1 RNAi. No increase in the amount of germline corpses was observed as a result of tfg-1 RNAi treatment in rrf-1 mutants (P = 0. 19, Figure 4A). To induce ER stress specifically in the soma, mutants in the ppw-1 gene, which is required for efficient RNAi in the germline [41], were treated with tfg-1 RNAi. This resulted in a 4. 5 fold increase in the amount of apoptotic corpses in the gonads (P<0. 001, Figure 4A). Thus, ER stress in the soma, rather than in the germ cells, is sufficient for the induction of germ cell apoptosis. Does germ cell apoptosis occur upon disruption of ER homeostasis in the entire soma or does it occur in response to ER stress in a particular part of the soma? To answer this, ER stress was induced locally in specific somatic tissues. This was achieved by treating animals expressing functional RNAi machinery only in specific tissues with tfg-1 RNAi and assessing germ cell apoptosis in these animals. We found that tfg-1 RNAi treatment did not increase germ cell apoptosis in animals which respond to RNAi only in the intestine, in the muscle, in the hypodermis, in the uterine or in the distal tip cells (P>0. 1 in each one of these strains, Figure 4B). In contrast, tfg-1 RNAi treatment increased germ cell apoptosis by approximately 7 fold in animals which respond to RNAi specifically in the neurons (P<0. 001, Figure 4A). Next, we examined whether ER stress-induced germline apoptosis is under pan-neuronal control or under the control of specific neurons. To this end, we introduced ER stress-inducing tfg-1 RNAi into animals expressing functional RNAi machinery specifically in the cholinergic, glutamatergic, GABAergic, dopaminergic or in a subset of sensory neurons. Importantly, we found that tfg-1 RNAi treatment increased germ cell apoptosis only in animals whose sensory neurons responded to RNAi (Figure 4C). Among the sensory neurons whose exposure to ER stress increased germline apoptosis were the ASI neurons, which have been previously implicated in the regulation of germ cell proliferation and maturation [42]. Hence, we examined whether ER stress in the ASI sensory neurons alone is sufficient for the induction of germ cell apoptosis in the gonad. To this end, we first assessed germline apoptosis in daf-28 (sa191) mutants, which produce a toxic insulin peptide that activates the UPR specifically in the ASI neurons [43]. We found that germ cell apoptosis in the gonads of daf-28 (sa191) mutants was increased by approximately 4 fold compared to wild-type animals (P<0. 001, Figure 4D). Importantly, germ cell apoptosis was not increased in a daf-28 (tm2308) null strain, which is deficient in daf-28 and does not produce the toxic insulin peptide which induces ER stress (P = 0. 15, Figure 4D). tfg-1 RNAi treatment of the two daf-28 mutant strains increased the number of germline corpses in daf-28 (tm2308) null strain (P<0. 001), but did not further increase germline apoptosis in the daf-28 (sa191) strain (P = 0. 09, Figure 4D). tfg-1 RNAi treatment did not alter ASI overall morphology as assessed by the expression pattern of a GFP reporter driven by an ASI-specific promoter (Figure S4A). Together, these findings suggest that expression of the toxic form of DAF-28 and tfg-1-deficiency increase germ cell apoptosis by similar means; most likely by causing ER stress and activating the UPR in the ASI neurons. We have demonstrated that in the absence of the ER stress sensor ire-1, ER stress does not increase germline apoptosis. We further demonstrated that ER stress in the ASI sensory neurons is sufficient to induce germ cell apoptosis. Thus, we next examined whether it is also sufficient to express ire-1 in the soma, and specifically in the ASI neurons, to restore germ cell apoptosis in response to ER stress. To this end, we restored ire-1 expression in the entire soma, pan-neuronally or specifically in the ASI/ASJ neurons of ire-1 (−) mutants. This was achieved using multi-copy ire-1 transgenes under ire-1, rgef-1 and daf-28 promoters respectively. Since the expression of multi-copy transgenes is normally suppressed in germ cells [44], and due to the specificity of their promoters, these transgenes restore ire-1 expression within different parts of the soma but not in the germline. We found that expression of each of these ire-1 transgenes completely restored the increase in germline apoptosis in response to treatment with tfg-1 RNAi (P<0. 001, compare white and black bars within each strain in Figure 5A). Similarly, we restored ire-1 expression in muscle cells and in the PVD and OLL neurons using multi-copy ire-1 transgenes under myo-3 and ser-2 promoters respectively. No increase in germline apoptosis in response to tfg-1 RNAi treatment was apparent in these two transgenic lines compared to control RNAi treatment (P>0. 1, Figure 5A). The fact that not all ire-1 transgenes induced apoptosis supports the notion that ire-1-induced germline apoptosis is not the result of leaky expression of the transgenes in other tissues. Altogether, this implies that not all tissues and not all neurons are involved in the regulation of this process. Next, we asked whether increasing IRE-1 levels to a greater extent may be sufficient for inducing germline apoptosis even in the absence of ER stress. To this end, we overexpressed ire-1 transgenes in various tissues or cells of ire-1 (+) wild-type animals. This was achieved by using multi-copy ire-1 transgenes under ire-1, rgef-1, daf-28 and daf-7 promoters. This is consistent with the interpretation that some activation of IRE-1 is achieved merely by its over-expression, as has been previously observed in yeast and in mammalian cells [45], [46]. We found that this artificial activation of IRE-1 in the soma, pan-neuronally or specifically in the ASI/ASJ neurons of ire-1 (+) animals was sufficient to induce high levels of germ cell apoptosis (P<0. 001, compare white bars of transgenic animals to that of wild-type animals in Figure 5B). No increase in germ-cell apoptosis was observed upon overexpression of an ire-1 transgene in muscle cells or in the AIY neurons in ire-1 (+) animals (P>0. 1, Figure 5B). These findings support the claim that the rescuing activity of the ire-1 transgenes stems from their expression in specific neurons. tfg-1 RNAi treatment of ire-1 (+) animals over-expressing the ire-1 transgenes in the soma, in the neurons and specifically in the ASI/ASJ neurons did not further increase germline apoptosis (P>0. 5, compare white and black bars within the strains, Figure 5B). This suggests that IRE-1 overexpression and tfg-1-deficiency increase germ cell apoptosis by similar means, i. e. by activating IRE-1. Taken together, our data demonstrate that activation of ire-1 specifically in the ASI neurons, either by ER stress in the ASI neurons or by IRE-1 overexpression, can non-autonomously regulate germ cell apoptosis. Furthermore, since over-expression of transgenic IRE-1 is sufficient for its artificial activation in a manner that is independent of ER stress, this further suggests that active IRE-1 signaling in the ASI neurons per se, rather than neuronal ER stress or ER dysfunction, is the cause of germ cell apoptosis. Understanding the molecular events that regulate the life-death decision of cells is of fundamental importance in cell biology research, cell development, cancer biology and disease biology [47]. In this study, we gained new and fascinating insights into the complex coupling between ER stress in the nerve system and germ cell apoptosis. We report for the first time that germ cells undergo apoptosis in response to ER stress. We find that activation of the ER stress response gene ire-1 is required and sufficient to induce germ cell apoptosis in response to several ER stress-inducing conditions. Strikingly, we find that germ cell fate is regulated non-autonomously by ER stress and/or through IRE-1 activation specifically in the ASI neurons. This implies that ER homeostasis and UPR signaling in the germ cells themselves is not a factor in determining their fate, ruling out the possibility that these apoptotic events are part of a quality control process that removes “stress-damaged” germ cells from the gonad [6], [11], [12]. Furthermore, this assigns a central neuroendocrine role for the ASI neuron pair in coupling between stress sensing and the onset of germ cell apoptosis. This is in addition to other central physiological processes in C. elegans, such as dauer formation [48], [49] and longevity [50], [51], that are also controlled by the sensory ASI neuron pair. Interestingly, another pair of sensory neurons, the ASJ neurons, has been previously implicated in the protection of germ cells from apoptosis under hypoxic conditions [52]. Thus, depending on the stress condition, different neurons can shift germ cell fate from survival to death or vice versa. How might IRE-1 activation in the ASI neurons dictate germ cell survival or death? One possibility is that defects associated with ire-1 deficiency and/or ire-1 activation indirectly abrogate the communication between the neurons and the gonad. However, several lines of evidence undermine this hypothesis: (1) We find that ER stress-induced germ cell apoptosis proceeds normally in animals with a severely defective nervous system (Figure S4B, C). This implies that germline apoptosis does not result from a generic neuronal defect. (2) ire-1 deficiency is associated with germline abnormalities which include a significant reduction in the number of mitotic germ cells and in reduced progeny number. However, these gonad-related defects do not confer generic resistance to stress-induced apoptosis as the germ cells of ire-1 mutants do undergo apoptosis in response to a variety of stresses. Furthermore, a mutation in xbp-1, which improved the reproductive abnormalities of ire-1 mutants, did not restore responsiveness to ER stress induced germ cell apoptosis in ire-1; xbp-1 double mutants, thus uncoupling the two. (3) Whereas the comparison of germ cell apoptosis in ire-1 and wild-type animals may be confined by the basal discrepancy of their reproductive systems, this concern does not exist in the analysis of ire-1 overexpressing strains, whose gonad appears to be normal (P>0. 1 for Pire-1: : ire-1 and Pdaf-7: : ire-1 compared to wild-type animals Figure S3A, D). Similarly, this concern does not exist in the intra-strain comparisons of germline apoptosis within the ire-1 (−) strain under control and stress conditions. If ire-1 misregulation in the ASI neurons does not indirectly abrogate the communication between the neurons and the gonad, how might it dictate germ cell survival or death? IRE-1 is a dual-activity enzyme, bearing both kinase and endoribonuclease activities and a propensity to self-aggregate at the ER membrane in response to ER stress. The most characterized mode of action of IRE-1 is the activation of its downstream transcription factor XBP-1 [53]. Significantly less characterized are XBP-1 independent targets of IRE-1, that include activation of the cell death machinery via JNK/TRAF signaling and degradation of ER-localized mRNAs that encode secreted and membrane proteins in a process called RIDD [29], [30], [32], [54]–[58]. Since we find that ER stress can effectively induce germ cell apoptosis in the absence of xbp-1, trf-1 and JNK homologs, the signaling mediated by IRE-1 in this case may be executed by the RIDD pathway or via a novel, yet undescribed, xbp-1-independent output of IRE-1. We propose that activation of IRE-1 in the neurons (either as a result of ER stress or merely by its over-expression) actively regulates the production of a germ cell regulatory signal. In principle this may be a germ cell proapoptotic signal produced by the neurons upon IRE-1 activation. Alternatively, this may be a germ cell anti-apoptotic signal that is down-regulated by IRE-1 upon its activation. This ASI-regulated signal, whose identity and nature remain to be elucidated, propagates in the animal and affects the gonad where it acts upstream to the p53 homolog cep-1, activating the same apoptotic machinery in the germ cells as the DNA damage response, without inducing DNA damage in the germ cells (Figure 6). This indicates the existence of a new pathway that can activate CEP-1 independently of DNA damage upon activation of neuronal IRE-1. Interestingly, in adult animals, exposure to ER stress or activation of IRE-1 in the soma induce apoptosis only in germ cells, as we did not detect any apoptotic corpses outside of the gonad of these animals. This is in contrast to the developing embryo, where exposure to ER stress can induce apoptosis in the soma [35], [59]. We propose that as the organism completes development, its ability to respond or execute programmed cell death upon exposure to ER stress is maintained in mitotic germ cells while being selectively abrogated in the post-mitotic soma, as has been demonstrated for their ability to execute apoptosis in response to DNA-damage [60]. This resistance of the soma is important in terms of survival of the animal as it prevents cell death of somatic tissues that lack stem cell pools and regenerative capacity, while allowing cell death of immortal germline cells at times of stress. What could be the advantage in diluting the germ cell pool when neurons “feel” ER-stressed (i. e. when IRE-1 is activated naturally by ER stress or artificially by overexpression)? Recent studies demonstrate a tight inverse correlation between germ-cell proliferation and the maintenance of somatic proteostasis and longevity [61]–[65]. This inverse correlation is thought to be due to a limitation of resources shared by the germline and the soma and due to altered metabolic and cellular repair mechanisms in the soma that are enabled upon germ cell loss. Previous studies implicated the nervous system in systemic and hierarchical control of cellular stress responses elsewhere in the soma to maintain organismal homeostasis [66]–[72]. Our data further imply that neurons also have the ability to communicate with the germ cells to promote their death in response to stress in the ER. This, in turn, may orchestrate a proteostasis switch in the soma at the expense of a replenishable germ cell pool in times of stress. This adds a new layer of complexity to our understanding of how protein homeostasis is regulated and coordinated across tissues in multicellular organisms. For single time-point experiments, the number of apoptotic germ cells was scored in day-2 animals stained with SYTO12 (Molecular Probes) as previously described [6]. For time course experiments, the number of SYTO12-labeled apoptotic corpses per gonad arm was scored in animals from day-0 (L4) to day-3 of adulthood. Where indicated, the average number of apoptotic corpses was normalized to the number of mitotic germ cells within the proliferative zone of the gonads, determined by section analysis of DAPI-stained gonads. Day-1 adult animals were placed in 200 µl of M9 (control) or 10 mM paraquat (oxidative stress) for 1. 5 h at 20°C. After the incubation period, 1 ml of M9 was added to dilute the paraquat. Animals were then transferred to eppendorfs with SYTO12 staining for 4. 5 hrs. Animals were allowed to recover on plates for 40 min. Finally, the animals were mounted and observed under the microscope to determine cell corpse numbers. Gonads of day-1 adults were dissected, fixed, and stained with DAPI as previously described [10]. Bacteria expressing dsRNA were cultured overnight in LB containing tetracycline and ampicillin. Bacteria were seeded on NGM plates containing IPTG and carbenicillin. RNAi clone identity was verified by sequencing. Eggs were placed on plates and synchronized from day-0 (L4). The efficacy of the tfg-1 RNAi was confirmed by the animals' reduced body size [35]. Animals were anaesthetized on 2% agarose pads containing 2 mM levamisol. Images were taken with a CCD digital camera using a Nikon 90i fluorescence microscope. For each trial, exposure time was calibrated to minimize the number of saturated pixels and was kept constant through the experiment. The NIS element software was used to quantify mean fluorescence intensity as measured by intensity of each pixel in the selected area. Error bars represent the standard error of the mean (SEM) of at least 3 independent experiments. P values were calculated using the unpaired Student' s t test. The following lines were used in this study: N2, CF2012: pek-1 (ok275) X, CF2988: atf-6 (ok551) X, CF2473: ire-1 (ok799) II, CF3208: xbp-1 (tm2457) III, SHK62: ire-1 (ok799) II; xbp-1 (tm2457) III, MD701: Plim-7 ced-1: : gfp V, xbp-1 (tm2482) III, CF2185: ced-3 (n1289) IV, MT2547: ced-4 (n1162) III, TJ1: cep-1 (gk138) I, MT8735: egl-1 (n1084n3082) V, FX536: ced-13 (tm536) X, WS1433: hus-1 (op241) I; unc-119 (ed3) III; opIs34, CF2052: kri-1 (ok1251) I, KU25: pmk-1 (km25) IV, AU1: sek-1 (ag1) X, CF3030: kgb-1 (um3) kgb-2 (gk361) jnk-1 (gk7) IV, NS2937: trf-1 (nr2014) III, CF2260: zcIs4[Phsp-4: : gfp] V, CL2166: Pgst-4: : gfp (dvls19) V, CF1553: muIs84 [ (pAD76) Psod-3: : gfp+rol-6], SJ4100: Phsp-6: : gfp (zcIs13) V, CL2070: Phsp-16. 2: : gfp (dvls70) V, SHK57: xbp-1 (tm2457) III; ced-3 (n1286) IV, SHK189: zcIs4[Phsp-4: : gfp] V; Pire-1: : ire-1, NL2098: rrf-1 (pk1417) I, NL2550: ppw-1 (pk2505) I, SHK185: ire-1 (ok799) II; Prgef-1: : ire-1, SHK4: Pire-1: : ire-1, SHK182: Prgef-1: : ire-1, BB22 rde-4 (ne299) III; adr-2 (gv42) III, TG12: cep-1 (lg12501) I; unc-119 (ed4) III; gtIs1 [CEP-1: : GFP+unc-119 (+) ], SHK8: Pmyo-3: : ire-1, SHK14: Pmyo-3: : ire-1; ire-1 (ok799) II, Pser-2: : ire-1; ire-1 (ok799) II, SHK15: Pdaf-28: : ire-1; ire-1 (ok799) II, SHK6: Pdaf-28: : ire-1, SHK237: Pttx-3: : ire-1; Pdaf-7: : gfp, SHK234: Pdaf-7: : ire-1; Pdaf-7: : gfp, daf-28 (tm2308) V, CF2638: daf-28 (sa191) V, VB1605: svls69[Pdaf-28: : daf-28: : gfp], SHK11: ire-1 (ok799) II; svls69[Pdaf-28: : daf-28: : gfp], SHK27: ire-1 (ok799) II; Pire-1: : ire-1; svls69[Pdaf-28: : daf-28: : gfp], SHK60: unc-13 (e51) I, SK7: unc-64 (e246) III; unc-31 (e928) IV and MT6308: eat-4 (ky5) III. The following strains were used for tissue-specific RNAi experiments: TU3401: sid-1 (pk3321) V; Punc-119: : sid-1 (neuron only RNAi), VP303: rde-1 (ne213) V; Pnhx-2: : rde-1 (intestine only RNAi), WM118: rde-1 (ne300) V; Pmyo-3: : rde-1 (muscle only RNAi), NR222: rde-1 (ne219) V; Plin-26: : rde-1 (hypodermis only RNAi), NK640: rrf-3 (pk1426) II; rde-1 (ne219) V; Pfos-1A: : rde-1 (uterine only RNAi), JK4143: rde-1 (ne219) V; Plag-2: : rde-1: : gfp (distal tip cell only RNAi). The following strains were used for neuron-specific RNAi treatments as previously described [73]: XE1581: wpSi10 II [unc-17p: : rde-1: : SL2: : sid-1+Cbr-unc-119 (+) ]; eri-1 (mg366) IV; rde-1 (ne219) V; lin-15B (n744) X - Cholinergic neuron-specific RNAi strain. XE1375: wpIs36 I [unc-47p: : mCherry]; wpSi1 II [unc-47p: : rde-1: : SL2: : sid-1+Cbr-unc-119 (+) ]; eri-1 (mg366) IV; rde-1 (ne219) V; lin-15B (n744) X - GABAergic neuron-specific RNAi strain. XE1582: wpSi11 II [eat-4p: : rde-1: : SL2: : sid-1+Cbr-unc-119 (+) ] II. ; eri-1 (mg366) IV; rde-1 (ne219) V; lin-15B (n744) X - Glutamatergic neuron-specific RNAi strain. XE1474: wpSi6 II [dat-1p: : rde-1: : SL2: : sid-1+Cbr-unc-119 (+) ] II; eri-1 (mg366) IV; rde-1 (ne219) V; lin-15B (n744) X - Dopaminergic neuron-specific RNAi strain, SHK231: sid-1 (pk3321) V; Pche-12: : sid-1 (+); rol-6 (su1006). Prgef-1: : ire-1 - ire-1 cDNA was cloned under the 3. 5 kb rgef-1 (F25B3. 3) promoter and injected at 5 ng/µl with Pmyo-3: : mCherry at 50 ng/µl. Pire-1: : ire-1 - ire-1 cDNA was cloned under the 4. 5 kb ire-1 (C41C4. 4) promoter in the L3691 vector and injected at 25 ng/µl with rol-6 at 100 ng/µl. Pttx-3: : ire-1 - was created by cloning the ire-1 cDNA into a Pttx-3 vector [74] using KpnI/SphI. Pdaf-7: : gfp and Pdaf-7: : ire-1 - daf-7 promoter fragment [49] was cloned into pPD95. 75 (gift from A. Fire, Carnegie Institute) using SphI/XbaI to create daf-7p: : gfp transcriptional fusion. The gfp fragment was then replaced by ire-1 cDNA using XmaI/AflII to make daf-7p: : ire-1. daf-7p: : ire-1 or ttx-3: : ire-1 were injected at 10 ng/µl with daf-7p: : gfp and pRF4 (rol-6) at 20 ng/µl each. ser-2prom-3: : ire-1 - was created by cloning ire-1 cDNA under ser-2prom-3 fragment [75] using XmaI/AflII. ser-2prom-3: : ire-1 was injected at 10 ng/µl with ttx-3: : mCherry at 40 ng/µl.

Cells in the C. elegans germline undergo programmed cell death as part of the normal developmental program and in response to various stresses. Here, we discovered that more germ cells undergo programmed cell death under stress conditions associated with the accumulation of misfolded proteins in the endoplasmic reticulum, a cellular organelle responsible for protein folding and trafficking. Surprisingly, we found that germ cell death is a consequence of stress in neurons rather than in the germ cells themselves. This implies that germ cell death under ER stress conditions is regulated at the organismal level and implicates signaling between tissues.

Abstract Introduction Results Discussion Materials and Methods

model organisms cell biology genetics biology and life sciences organisms research and analysis methods

2014

It's All in Your Mind: Determining Germ Cell Fate by Neuronal IRE-1 in C. elegans

Subcutaneous immunization delivers antigen (Ag) to local Ag-presenting cells that subsequently migrate into draining lymph nodes (LNs). There, they initiate the activation and expansion of lymphocytes specific for their cognate Ag. In mammals, the structural environment of secondary lymphoid tissues (SLTs) is considered essential for the initiation of adaptive immunity. Nevertheless, cold-blooded vertebrates can initiate potent systemic immune responses even though they lack conventional SLTs. The emergence of lymph nodes provided mammals with drastically improved affinity maturation of B cells. Here, we combine the use of different strains of alymphoplastic mice and T cell migration mutants with an experimental paradigm in which the site of Ag delivery is distant from the site of priming and inflammation. We demonstrate that in mammals, SLTs serve primarily B cell priming and affinity maturation, whereas the induction of T cell-driven immune responses can occur outside of SLTs. We found that mice lacking conventional SLTs generate productive systemic CD4- as well as CD8-mediated responses, even under conditions in which draining LNs are considered compulsory for the initiation of adaptive immunity. We describe an alternative pathway for the induction of cell-mediated immunity (CMI), in which Ag-presenting cells sample Ag and migrate into the liver where they induce neo-lymphoid aggregates. These structures are insufficient to support antibody affinity maturation and class switching, but provide a novel surrogate environment for the initiation of CMI. Secondary lymphoid tissues (SLTs) are highly organized structures with defined compartments consisting of B and T cell areas. These distinct locations support the rapid circulation and concentration of Ag and the interaction of Ag-presenting cells (APCs) with lymphocytes. Prevailing dogma dictates that only if competent APCs transport Ag into SLTs, an adaptive immune response is initiated; otherwise, the Ag is ignored by the immune system [1]. For the initiation of humoral antibody (Ab) -mediated immunity in mammals, the formation of B cell follicles and germinal centers (GCs) appears to be a prerequisite. The dynamic nature of such GCs, including the interaction of follicular dendritic cells (FDCs) with B cells and Ag, was recently elegantly demonstrated by others [2]. However, in contrast to the B cell-dominated cortex, T cell areas, where T cells encounter mature APCs and their cognate Ag, are structurally ill defined. Whereas intravital confocal microscopy has provided compelling evidence for the capacity of SLTs to host T cell priming [3], definitive data supporting their absolute requirement for the initiation of T cell-mediated immunity (CMI) do not exist. In addition, cold-blooded vertebrates lacking conventional SLTs generate potent immune responses upon immunization. However, in the mammalian system, the apparent immunodeficiency of mice that lack SLTs strongly supports the notion that the initiation of effective immune responses requires the dedicated structures provided by SLTs [4]–[8]. Alymphoplasia (aly/aly) mice are characterized by a complete lack of lymph nodes (LNs) and Peyer' s patches, and structural alterations of the spleen and thymus due to a point mutation in the NFκB-inducing kinase (NIK) [9]. NIK is vital for the initiation of the noncanonical NFκB cascade, which appears to play a discrete role, for instance, in the function of CD40 and lymphotoxin-β receptor (LTβR) signaling in some cell types [10]–[12]. Aly/aly mice display impaired Ab responses and loss of CMI, demonstrated by their inability to reject allogeneic grafts or tumors [4], [13], [14]. The developmental deficits in aly/aly mutants are readily explained by the requirement of NIK in LTβR signaling. LTβR is vital for the development of SLTs, and LTβR−/− mice display similar developmental defects as do aly/aly mice or NIK−/− mice [12], [15]. In this study, we describe that the immunodeficiency of aly/aly mice is not due to the absence of SLTs, but due to the impact of the underlying genetic defect on cellular immunity. Using different strains of alymphoplastic mice and T cell migration mutants in an experimental paradigm in which the site of Ag-delivery is distant from the site of priming and again distant from the site of inflammation, we can detect both TH cell-driven autoimmune disease as well as systemic CTL-mediated antitumor immunity initiated through classical subcutaneous (s. c.) immunization/vaccination independent of SLTs. APCs present at the site of immunization migrate to and select the liver as a natural extra-lymphoid tissue for the initiation of CMI, which we propose to be an evolutionary hard-wired pathway already found in cold-blooded vertebrates. This alternative pathway, undescribed to this day, can potently drive CMI but fails to elicit B cell immunity, indicating that the immunization-induced T cell accumulation within conventional lymphoid organs mainly serves humoral immunity but that CMI can be initiated elsewhere. We first sought to determine whether LNs are an absolute requirement for the induction of a complex TH cell-driven autoimmune response initiated by the s. c. delivery of auto-Ag. Experimental autoimmune encephalomyelitis (EAE) is a B cell-independent, TH cell-mediated demyelinating autoimmune disease of the central nervous system (CNS) and serves as the animal model for multiple sclerosis (MS). The conversion of TH cells from the naive to effector state is vitally dependent on the structures provided by LNs [5], [6]. Cervical LNs are widely held to constitute the predominant intrinsic priming site for encephalitogenic T cells, based on the observation that these LNs support the expansion of PLP-TcR transgenic (Tg) T cells [16], [17]. However, draining inguinal LNs drive the polyclonal, endogenous T cell population after s. c. immunization with encephalitogenic peptides. To assess the role of SLTs in the transition of TH cells from a naive to effector state (T cell priming), we induced EAE in aly/aly or aly/+ mice by s. c. immunization with myelin oligodendrocyte glycoprotein peptide and complete Freud adjuvant (MOG35–55/CFA). Figure 1A shows that aly/aly mice are completely resistant to EAE compared to aly/+ control mice (the latter developing normal SLTs as NIK is haplosufficient). To verify the notion that pathogenic T cells cannot be raised in aly/aly mice, they were immunized s. c. with MOG35–55. Eleven days postimmunization (dpi), splenocytes were harvested, and MOG35–55-reactive cells were expanded in vitro and subsequently transferred into aly/aly as well as aly/+ recipients. Figure 1B shows that only cells derived from aly/+ donors were able to induce disease regardless of whether the recipients had SLTs (aly/+) or not (aly/aly). In contrast, MOG35–55-reactive T cells derived from aly/aly donors were not pathogenic and did not mediate CNS inflammation. To assess the capacity of LN-less mice to initiate T cell expansion in response to s. c. -delivered Ag, CFSE-labeled TcR Tg T cells (2D2) specific for the encephalitogenic MOG35–55 peptide [18] were adoptively transferred into either aly/aly or aly/+ mice prior to immunization with their cognate Ag. After 4 d, splenocytes were analyzed for T cell expansion by flow cytometry (Figure 1C). Ag-specific T cell proliferation can be observed in aly/aly mice; however, they display slightly delayed kinetics in comparison to aly/+ mice. Similar results were obtained with Ovalbumin (OVA) TcR Tg T cells (OTII) transferred into aly/aly and aly/+ mice (unpublished data), indicating that T cell expansion can be initiated independent of SLTs, whereas efficient effector function is dependent on the microenvironment provided by SLTs. The fact that aly/aly mice do not develop T cell-driven autoimmune disease could be explained by their inability to prime self-reactive T cells (a) due to the lack of dedicated draining LNs [5], [6], or (b) due to a direct impact of the NIK mutation on immune cells [19], [20]. In order to define whether their EAE resistance is due to the lack of LNs or an intrinsic defect of aly/aly mice to prime T cells, we generated a series of bone marrow (BM) -chimeric mice. To restrict the NIK mutation to the hematopoietic system, lethally irradiated aly/+ mice were injected with BM cells from aly/aly donor mice (aly/aly→aly/+). Conversely, to conserve the developmental structural defects, without the NIK lesion of the hematopoietic compartment, aly/aly mice were reconstituted with BM cells of normal aly/+ donors (aly/+→aly/aly). As previously reported, spontaneous development of lymphoid tissues in aly/aly recipients upon reconstitution was expectedly not detected [21]. Surprisingly, we discovered that aly/+→aly/aly BM-chimeras were fully susceptible to EAE after s. c. immunization with MOG35–55 (Figure 2A), clearly demonstrating that s. c. immunization can mount a productive T cell-driven autoimmune response even in the absence of draining LNs. Using the reciprocal approach, by generating aly/+→aly/+ (WT-NIK immune system and normal SLTs) as well as aly/aly→aly/+ BM-chimeras (NIK-deficient immune system and normal SLTs), we found that the NIK mutation lead to EAE resistance, even when the lymphoreticular compartment is unperturbed (Figure 2B). This finding clearly demonstrates that the reported immunodeficiency of aly/aly mice can largely be explained by the requirement of NIK for the initiation of immunity rather than the lack of LNs. In support of this, we found that unmanipulated LTβR−/− mice, which also lack all LNs but have normal NIK function, are also fully susceptible to EAE (Figure 2C). The formation of IFNγ and IL-17–secreting autoreactive T cells has been demonstrated to be a prerequisite for the development of autoimmunity [22]. In aly/aly→aly/+ mice we observed a substantial reduction in IL-17– and IFNγ-producing cells compared to the control mice aly/+→aly/+ (Figure 2D), indicating that the resistance to EAE in the absence of NIK could be related to the function of NIK in T cell polarization. The mechanistic underpinnings of this phenomenon are currently being investigated, but it is clear that the loss of NIK signaling impairs the capacity of aly/aly mice to generate pathogenic TH cells regardless of their structural defects. Given the dogma that in mammals, CMI initiated by s. c. or intramuscular Ag-delivery requires the presence of SLTs, it is feasible that the remaining SLT (i. e. , the spleen) in aly/+→aly/aly BM-chimeras compensates for the absence of LNs. In order to test this notion, we splenectomized aly/+→aly/aly BM-chimeras (aly/+→aly/alyspl) 14 d prior to the induction of EAE. Upon immunization, aly/+→aly/alyspl mice developed EAE with the same disease severity as control mice (Table 1). We noted a slight delay in disease onset when all SLTs are absent, while histopathological analysis of diseased mice revealed no difference between aly/+→aly/+ and aly/+→aly/alyspl mice (Figure S1). In contrast to T cell activation, we found that B cell activation requires the structural environment provided by SLTs. To investigate the impact of immunization on T versus B cell responses, we used Keyhole limpet hemocyanin (KLH) as a model of foreign Ag to elicit delayed-type hypersensitivity (DTH) responses. Aly/aly as well as aly/+ mice were immunized with KLH, and 11 dpi, they were challenged by intradermal injection with KLH into the ear. As illustrated in Figure 2E, both groups were able to mount a solid DTH reaction measured by ear swelling, which was only marginally lower in aly/aly than in aly/+ mice. However, in contrast to ear swelling, which is indicative of CMI, aly/aly mice did not mount Abs against KLH when compared to aly/+ mice, demonstrating that the development of a humoral immune response is ablated in the absence of lymphoreticular structures (Figure 2F). We could reproduce functional DTH responses using other Ags including OVA and MOG35–55 (unpublished data). Similarly, in our EAE paradigm using BM-chimeras, whereas control mice (aly/+→aly/+ and aly/+→aly/+spl) elicit high Ab titers, anti-MOG Abs are virtually absent in mice without LNs (either aly/+→aly/aly or aly/+→aly/alyspl) (Figure 3A). Analysis of isotype subtypes revealed that in splenectomized alymphoplastic mice, elevated anti-MOG IgM could be detected, which has previously been reported [7], [23], [24], whereas class switching to IgG could not be observed (Figure 3B). Taken together, and in agreement with the notion that SLTs are vital for B cell activation, highly organized SLTs are obligatory for the generation of high-affinity Igs and class switching, whereas potent cellular immunity can be induced successfully upon s. c. immunization even in the absence of SLTs. Since the loss of SLTs in aly BM-chimeric mice does not hinder the development of T cell immunity, we wanted to determine at which alternative site T cell priming could take place and to which organ the Ag travels from the site of immunization (s. c.). Therefore, aly BM-chimeras were injected s. c. with yellow green (YG) carboxylate microspheres emulsified in CFA. At 7 dpi, various organs were isolated and analyzed for the presence of fluorescent cells by flow cytometry. Figure 4A shows that in control mice (aly/+→aly/+), fluorescently labeled APCs were exclusively detected in LNs upon s. c. immunization. It was previously shown that the BM has the capacity to drive an enriched population of high-affinity TcR Tg T cells in response to blood-borne Ag [25]. As expected, upon intravenous (i. v.) delivery of Ag, the vast majority of it accumulates in the spleen, BM, and liver, regardless of the presence of SLTs (Figure S2). However, after (s. c.) immunization of aly/+→aly/alyspl BM-chimeras lacking SLTs, APCs carrying fluorescent microspheres migrate primarily to the liver and not the other organs analyzed (thymus, CNS, and gut; unpublished data) (Figure 4A). Only a small amount of Ag reaches the liver when draining SLTs are present. Next, we wanted to determine the means of the Ag transport from the s. c. reservoir to the liver. To determine whether the Ag diffuses to the liver or is actively transported by APCs, aly/+→aly/+ and aly/+→aly/alyspl chimeric mice were separated into two groups. One received YG microspheres/CFA in the left flank and polychromatic red (PR) microspheres/CFA in the right flank. The other group received a mixture of YG- and PR-coupled beads in both flanks (see scheme in Figure 4B). After 7 d, mice were sacrificed, perfused, and a single-cell suspension of livers, LNs, and spleens was generated for cytofluorometric analysis. We found that the mixture of PR/YG-coupled beads generated a large proportion of dual-labeled CD11b as well as CD11c-positive APCs. Conversely, the injection of either PR- or YG-coupled microspheres into each flank revealed merely single-labeled APCs in the liver. The presence of single-labeled cells within the liver strongly suggests that the Ag is delivered to the liver by the migration of APCs initially present at the site of immunization. Passive diffusion of the Ag from the site of immunization via the bloodstream to the liver cannot be fully excluded, but is evidently not the dominant means of Ag delivery. In addition, only a negligible amount of Ag reaches the liver when dedicated SLTs are present (Figure 4). We could also confirm these findings by using soluble FITC painted on shaved flanks (without the adjuvant CFA). Twenty-four hours after FITC skin painting, we found FITC+ APCs primarily in the liver, again supporting the notion that the liver can serve as an alternative Ag-presenting site when draining LNs are not available (Figure 4C). In order to determine whether lymphoid-like structures can be found in the liver, we analyzed the livers of BM-chimeric mice immunized s. c. with MOG35–55/CFA by histology (7 dpi). Livers of aly/+→aly/alyspl BM-chimeras showed massive infiltration of leukocytes in comparison to aly/+→aly/+ control mice (Figure 5). Histological analysis displays dendritic cells (DCs) in close proximity to T cells in the infiltrated periportal areas of the liver, indicative of T cell priming by Ag-laden APCs (Figure 5B). In spite of the stroma' s inability to respond to LTα/β, detailed histological analysis revealed the presence of VCAM and ICAM in the infiltrates as well as B cells (Figure S3) and even the presence of CXCL13 transcripts indicative of aggregates ability to recruit B cells (unpublished data). However, no evidence for GC formation could be obtained (Figure S3). We also transferred TcR Tg T cells from Luciferase-2D2 (Luc-2D2) mice into recipient BM-chimeras and observed the accumulation of Ag-responsive T cells in the liver 2 dpi with MOG35–55/CFA by bioluminescence imaging (Figure 6A). Figure 6B shows that the number of DCs (CD11c+) and adoptively transferred 2D2 T cells (CD4+/Vβ11+) is drastically increased in the liver in mice lacking SLTs. In order to demonstrate that the observed lymphocyte accumulations in the liver can support cell expansion, we injected naive (CD62L+) CD4+ T cells derived from 2D2 Tg mice into aly BM-chimeras and subsequently immunized them with MOG35–55/CFA. At 5 dpi, livers were analyzed for Ag-specific CD4+ T cell proliferation. Even in normal mice, we find a large number of expanded T cells within the liver (Figure 6C), but one could argue that they have immigrated from their initial priming site, the draining LN. However, in the absence of SLTs, the livers of aly/+→aly/alyspl BM-chimeric mice are sufficient to propagate Ag-driven T cell expansion and accumulation. In order to confirm that Ag-specific T cell proliferation occurs in situ in the liver, we administered BrdU intraperitoneally (i. p.) into aly BM-chimeras 7 dpi with MOG35–55/CFA. Thirty minutes after BrdU injections, the mice were sacrificed, and livers were analyzed for proliferating (BrdU+) CD4+ T cells by flow cytometry. Figure 6D and 6E reveal the presence of BrdU+ cells in the livers of both aly/+→aly/+ and aly/+→aly/alyspl BM-chimeras. The number of BrdU+ T cells in the liver is increased in aly/+→aly/alyspl BM-chimeras compared to the controls. The fact that we found such a rapid (30 min) emergence of proliferating T cells even in normal mice in which SLTs are present, indicates that some degree of liver-initiated CMI occurs simultaneously to the priming within draining LNs. In contrast to our findings, which show that mice lacking SLTs do not generate high-affinity Ab-responses, intranasal influenza infection of splenectomized LTα−/− mice reconstituted with wild-type (wt) stem cells, for instance, can initiate the formation of extra-lymphoid follicles within the lung, which support some degree of B cell maturation and Ab secretion [24], [26]. One possible explanation for these contrasting observations regarding Ab production is that in our case, stroma cells such as FDCs cannot signal through LTβR due to the mutation within NIK and that this could be the reason for our inability to observe GC formation and Ab secretion, whereas Moyron-Quiroz et al. [24], [26] used mice in which the stroma compartment can be engaged by LTα/β. To definitively address whether the stroma' s inability to signal through NIK is the reason for the weak B cell response, we obtained LN-deficient LTα−/− mice and reconstituted their hematapoietic system with wt stem cells. The resulting chimeras were splenectomized and lacked all peripheral SLTs (analogous to the aly/+→aly/alyspl). Yet in contrast to aly/+→aly/alyspl, wt→LTα−/−spl chimeras have normal stromal cell function, and FDCs are capable of responding to LTα/β. These mice were immunized s. c. , and the formation of B cell maturation and Ab production was analyzed. Figure 7A demonstrates that these wt→LTα−/−spl chimeras behave exactly like aly/+→aly/alyspl in regards to their inability to generate high Ab titers and to class switch. In a comparative fashion, we analyzed the histological parameters of wt→wt, aly/+→aly/alyspl, and wt→LTα−/−spl chimeras (Figure 7B). Although only alymphoplastic mutants revealed the presence of lymphoid aggregates surrounding periportal areas of the liver, neither FDCs nor PNA-positive clusters could be found, again supporting the notion that the surrogate structures in the liver support T cell function but fail to initiate the formation of GCs needed for Ab-affinity cell maturation and class switching. Lastly, the large number of Ki67+ cells within the liver aggregates again support our conclusion, that active proliferation within the liver can be induced by s. c. immunization (Figure 7B). Although we have demonstrated the development of TH cell-driven autoimmune disease in mice lacking SLTs, we wanted to elucidate whether these mice are also capable of inducing successful CTL immunity. We used the B16. F10 murine melanoma model, which represents a lethal and poorly immunogenic cancer. Irradiated GM-CSF expressing B16. F10 cells are used as s. c. vaccine to initiate potent CD8+-antitumor immunity against live parental B16. F10 tumor cells [27]. We injected irradiated B16. F10-GM-CSF cells s. c. into one flank of aly/+→aly/+ and aly/+→aly/alyspl chimeric mice. At 12 dpi, mice were challenged with parental B16. F10 cells injected into the opposite flank. Figure 8A shows that aly/+→aly/alyspl chimeric mice can elicit potent antitumor CTL responses revealed by the inhibition of tumor growth. Next, we transferred CFSE-labeled MHC class I-restricted OVA-TcR Tg OTI T cells into aly/+→aly/alyspl and aly/+→aly/+ BM-chimeric mice and subsequently injected irradiated B16. F10 cells expressing OVA. At 12 dpi, livers and, in control animals, also spleen and LNs were analyzed by FACS for Ag-specific CD8+ T cell expansion. As demonstrated in Figure 8B, proliferation of CD8+ OTI cells was detected in the liver of mice lacking SLTs. Hence, even under conditions in which the draining LNs are considered a compulsory site hosting the encounter of captured Ag and infiltrating CD8+ T cells, we can detect potent T cell responses, which originate in the liver when SLTs are absent. We next wanted to address the relevance of the liver to serve as an alternative priming site in a setting where LNs are present but T cell migration into LNs is defective. To this end, we analyzed plt/plt (paucity of LN T cells) mice, which display undisturbed B cell zones but severely abrogated T cell zones due to the loss of CCL19 and CCL21, which results in the inhibition of both naive T cell and DC homing into SLTs [28]. We found that plt/plt mice also developed delayed but fulminant EAE after s. c. immunization with MOG35–55/CFA (Figure 8C). Examination of liver sections of immunized plt/plt mice again revealed lymphocyte aggregates consisting mainly of CD4+ T cells and DCs within the liver (Figure 8D). S. c. immunization instigates a situation in which draining LNs are widely held to be absolutely obligatory for the initiation of adaptive immunity. In the absence of such draining LNs, we found however, that APCs take up the Ag at the site of immunization and subsequently select the liver as an extra-lymphoid environment for the initiation of CMI. These findings are consistent with the propensity of alymphoplastic mice (NIK−/−, LTα−/−, and LTβR−/−) to develop abnormal lymphocytic infiltrates primarily in the liver [15], [29]. The lymphocyte accumulation seen in the liver of naive alymphoplastic mice does not coincide with any overt tissue damage, nor do they develop any secondary sign of hepatic injury (M. Heikenwaelder, Zurich, Switzerland, personal correspondence). Such surrogate structures are evidently not as sophisticated as true SLTs and fail to support B cell priming, but are clearly sufficient to support CMI. Such neo-lymphoid structures in the liver are not restricted to alymphoplastic mouse strains, but can be reproduced in mice in which T cells do not migrate into the LNs (plt/plt). The fact that we observe the rapid emergence of immunization-induced T cell expansion in the liver of normal mice supports the notion that the adult liver provides an efficient niche for the initiation of CMI. Moyron-Quiroz et al. [26] elegantly demonstrated that the lymphoid tissue in the lung (BALT) is sufficient to generate immunity against an infectious agent attacking the lung. In their experimental paradigm, peripheral SLTs are not compulsory for the initiation of protective immunity, and they could even observe some degree of B cell maturation. In our report, however, after s. c. immunization, the local APCs must sample the Ag and then actively migrate to and select the liver as a site for T cell priming, which then is even capable of driving autoimmune responses within the CNS. In our experimental paradigm, the site of Ag deposition, priming, and inflammation are distinct. The liver is thus not like the BALT or the NALT, a site where local immune responses can be initiated, but represents a niche for systemic T cell priming under conditions in which the draining LNs are widely held to be absolutely compulsory. The fact that Ag-laden APCs migrate from the site of immunization to the periportal areas in the liver could be explained by the presence of chemoattractive factors in the liver aggregates observed in SLT mutants. Alternatively, the extensive lymphatic network of the liver makes it an ideal niche for the accumulation of leukocytes as a reservoir when regular SLTs are inaccessible. Although the induction of CMI is not a function traditionally attributed to the adult liver, the fetal liver is a primary lymphoid organ hosting early hematopoiesis. Our findings suggest that the liver has the potential to “remember” its lymphoid function. The phenomenon, that, for instance, food allergies can be transferred by the transplantation of livers from an allergic donor to a previously nonallergic recipient [30], can be explained by our findings. Such transplant-acquired food allergy has only been described for the liver and not for other transplanted organs of the same donor [30]. It has been hypothesized that this occurrence is due to donor-derived allergen-specific lymphocytes residing in the liver. In support of this, Klein and Crispe [31] reported recently that after liver transplantation in a mouse in which Ag presentation was restricted to resident cells of the liver grafts, efficient CD8+ T cell priming can be induced locally in the transplanted liver. The situation also is reminiscent of the effect of immunizations on some cold-blooded vertebrates that are much more primitive than mammals in their SLT organization (i. e. , lacking GCs and showing only minimal affinity maturation). Frog tadpoles (Alytes obstetricans) immunized with rabbit serum in CFA developed a large accumulation of lymphocytes in the liver visible 2–3 wk after injection (L. Dupasquier, Basel, Switzerland, personal correspondence). Interestingly, during evolution, the emergence of RAG was permissive for the development of adaptive immunity in jawed fish [32]. RAG mediates somatic recombination and is required for the formation of both B and T cell receptors, which appear to have emerged simultaneously during evolution. However, whereas the adaptive immune system is well developed in the oldest jawed vertebrates (cartilaginous fish, e. g. , sharks), potent affinity maturation, Ig-class switching, and GC formation are lacking. Class switching only appeared at the time of the divergence of amphibians [33]. The fact that CMI evolved earlier than modern humoral immune responses corroborates our discovery that T cells can function outside of dedicated lymphoreticular structures. In summary, we demonstrate that the structural requirements for the initiation of B and T cell responses differ significantly. We found that B cells are dependent on the topography of dedicated lymphoid tissues, whereas CD4+ as well as CD8+ T lymphocytes retain the capacity to recognize Ag in a structure-independent fashion. This finding has obvious implications for our understanding of adaptive immunity and vaccination. As for the development of autoimmune diseases, our findings show that self-reactive T cells may not need to be primed in tissue-draining LNs, but could occur at the inflammatory site or even in organs distant to the target tissue. C57BL/6 mice were purchased from Janvier Laboratories. Alymphoplasia (aly/aly) mice were obtained from Clea Laboratories and bred in-house under specific pathogen-free (SPF) conditions. Heterozygous aly (aly/+) mice were used as controls for homozygous aly mice (aly/aly); 2D2 (MOG-TCR Tg) mice were provided by V. Kuchroo (Harvard Medical School, Boston, Massachusetts); LTβR−/− and LTα−/− mice were provided by A. Aguzzi and M. Heikenwalder (University Hospital Zurich, Zurich, Switzerland); and OTII and OTI mice were purchased from Jackson Laboratories. Luciferase (pbActin-Luciferase) Tg mice were obtained from C. Contag (UCSF) and crossed to the 2D2 mice (Luc-2D2). Plt/plt mice were obtained from B. Ludewig (Kantonsspital St. Gallen, Switzerland). All mice were bred in-house under SPF conditions. BM-chimeras were generated as described previously [34]. Mice were splenectomized as described previously [35]. Animal experiments were approved by the Swiss veterinary Office (68/2003,70/2003,10/2006, and 13/2006). MOG35–55 peptide (MEVGWYRSPFSRVVHLYRNGK) was obtained from GenScript. EAE was induced as described previously [34] with the modification that BM-chimeras were generally not boosted with pertussis toxin. For adoptive transfer, MOG-reactive lymphocytes were generated as described [34]. Each time point shown is the average disease score of each group±the standard error of the mean (SEM). Mice were euthanized with CO2, and various organs were removed to isolate leukocytes: For isolating lung cells, lungs were incubated with DNase (0. 5 mg) /Liberase (1 mg/ml) (Roche) for 30 min at 37°C. Spleen, LNs, thymus, and lung were homogenized, and BM cells were isolated by flushing the bones with PBS. Cells were strained through a 100-µm nylon filter (Fisher) and washed. Erythrocytes of whole blood, BM, and spleen were lysed. For isolating hepatic nonparenchymal cells, the liver was incubated with DNase/Liberase for 30 min at 37°C, homogenized, and then centrifuged at room temperature (RT) for 2 min at 50g. The supernatant was then centrifuged at 1,500 rpm for 10 min, and the pellet was resuspended in 30% Percoll (Pharmacia) and centrifuged at 12,000 rpm for 30 min at 4°C. The interphase cells were collected and washed. For isolating intestinal lymphocytes, intestines were opened longitudinally, washed, and then cut into small pieces. Tissues were then incubated with DNase/Liberase and leukocytes were isolated using a percoll gradient as described above. Isolation of CNS lymphocytes has been described previously [35]. Mice were injected i. v. with 20×106 CFSE (carbofluorescein diacetate succinimidyl ester) -labeled (Invitrogen/Molecular Probes) (10 µM) splenocytes obtained from either 2D2, OT-II, or OT-I TcR Tg mice or with 8×106 CFSE-labeled naive CD4+ 2D2 Tg T cells (isolated with CD4+CD62L+ isolation kit from Miltenyi). Mice were subsequently immunized s. c. with 200 µg of MOG35–55. /CFA (Adjuvant complete H37 Ra. . ; DIFCO) (for 2D2), OVA323–339/CFA (for OT-II), or with a 1∶1 mix of irradiated 2×106 B16. F10-GM-CSF/B16. F10-OVA cells (for OT-I). At 4 or 5 dpi (12 dpi for OT-I), mice were sacrificed, and spleen, LNs (if present), and livers were analyzed by fluorescence-activated cell sorting (FACS) for the proliferation of CD4+ T cells using the clonotypic TcR and CFSE fluorescence (2D2: TCR Vα3. 2 Ab; OT-II and OT1: Vα2 Ab). Tissues were freshly snap-frozen in liquid nitrogen. To determine infiltration of inflammatory cells, tissue sections were stained with hematoxylin and eosin (H&E) or with the following mouse-specific Abs as previously described [34]: anti-CD11c (Jackson ImmunoResearch Labs), anti-CD11b (BMA Biomedicals), anti-CD3, anti-CD4, anti-CD19, anti-FDC M1, and anti-Thy1. 1 (BD-Pharmingen), anti-ICAM, anti-VCAM, and anti-CD8 (Serotec). GC cells were stained with peanut agglutinin (PNA; Vector Laboratories). For FACS analysis, the following Abs were used: anti-CD11c, anti-CD4, anti-CD8, anti-CD11b, anti-Vα3. 2, anti-Vα3, and anti-Thy1. 1 (BD-Pharmingen). The cells were analyzed using a FACS-Canto (BD) with Cell-Diva software. Postacquisition analysis was performed using FLOWJO software. To trace the distribution of Ag after immunization, mice were injected s. c. with 200 µl of yellow-green (YG) or polychromatic red (PR) 1. 0-µm microspheres (Polysciences) emulsified in CFA. At 7 dpi, mice were euthanized with CO2, and organs were removed to isolate lymphocytes as described above. Single-cell suspensions were analyzed by FACS for the presence of fluorescein isothiocyanate (FITC+) or PE+ cells. For FITC skin painting, mice were painted on the shaved flanks with 100 µl of 5 mg/ml FITC (Molecular Probes) dissolved in 1∶1 acetone: dibutylphtalate. On day 1, mice were euthanized with CO2, and organs removed and analyzed by FACS as described above. Mice were immunized s. c. with 100 µg/flank of KLH (Sigma) emulsified in CFA. At 11 dpi, mice were challenged by injecting 10 µg/10 µl KLH, PBS into the dorsal surface of the ear. DTH responses were determined by measuring the ear thickness using a caliper micrometer (Mitutoyo) 24 h after challenge, and Δ ear swelling was established by the increase in ear thickness over baseline (prechallenge ear thickness). Plates were coated with 10 µg of rMOG1–121 in 0. 1 M NaHCO3 (pH 9. 6) at 4°C overnight or KLH (Sigma), and blocked with 1% (w/v) bovine serum albumin (BSA). Diluted sera were incubated for 2 h at RT. After washing, peroxidase-conjugated antibodies to mouse immunoglobulins, IgG, IgA, and IgM (Sigma) were added (1∶1,000 diluted) and incubated for 1 h at RT. Plates were washed, and chromogen (Biosource) was added. Absorbance was measured on a microplate reader (450 nm) (Bio-Rad). A total of 2×105 cells were plated in medium containing 10% FCS and 50 µg/ml of MOG35–55 in 96-well plates (Millipore) coated with the capture Ab against either IFNγ or IL-17A [36]. Elispots were revealed as described previously [36] and subsequently analyzed on an Elispot reader (CTL immunospot). To visualize Luc-2D2 cells, mice were injected i. p. with 3 mg of luciferin (Xenogen) prior to bioluminescence imaging using an IVIS100 imaging station (Xenogen). The luminescent image was overlaid on the photographic image. Mice were immunized s. c. with MOG35–55/CFA. At 7 dpi, BrdU (BD Pharmingen) (2. 5 mg) was injected i. p. 30 min before the mice were sacrificed and analyzed for proliferating (BrdU+) CD4+ T cells by flow cytometry with anti-BrdU Ab (eBioscience). Mice were s. c. vaccinated into one flank with irradiated (6,000 rads) 1×106 B16. F10-GM-CSF cells. At day 12 after vaccination, mice were injected with live 2×105 B16. F10-Luc cells into the opposite flank. Each time point shown is the average tumor size of each group±SEM, measured using a caliper.

Lymph nodes (LNs) are believed to be the most important tissues initiating immune responses by facilitating the activation of T and B lymphocytes. Mice lacking such LNs (called alymphoplastic) are severely immune compromised and resistant to immunizations. We discovered that the immune-deficiency of such alymphoplastic mice is actually not caused by the loss of LNs, but rather by the underlying genetic lesion. Surprisingly, mice lacking all lymph nodes can still mount potent T cell-mediated immune responses. We also discovered that T and B cells have completely different structural requirements for their activation/maturation. Whereas B cells rely on LNs to become efficient antibody-producing cells, T cells can be activated successfully outside of such dedicated tissues. So—in the absence of LNs—antigens delivered by immunization are actively transported into the liver where cellular immunity is initiated. The mammalian fetal liver is responsible for the early formation of blood and immune cells, and we propose that the adult liver can still provide a niche for T cell–antigen encounters. During evolution, T and B cells emerged simultaneously, allowing cold-blooded vertebrates (which lack LNs) to launch adaptive immune responses. The development of LNs in mammals coincided with a drastic improvement in antibody affinity maturation, whereas T cells remain LN-independent to this day.

Abstract Introduction Results Discussion Materials and Methods

immunology/immunomodulation immunology/immune response immunology/innate immunity surgery/transplantation immunology evolutionary biology neuroscience/neurobiology of disease and regeneration immunology/autoimmunity immunology/leukocyte activation

2009

Neo-Lymphoid Aggregates in the Adult Liver Can Initiate Potent Cell-Mediated Immunity

Oncolytic viruses replicate selectively in tumor cells and can serve as targeted treatment agents. While promising results have been observed in clinical trials, consistent success of therapy remains elusive. The dynamics of virus spread through tumor cell populations has been studied both experimentally and computationally. However, a basic understanding of the principles underlying virus spread in spatially structured target cell populations has yet to be obtained. This paper studies such dynamics, using a newly constructed recombinant adenovirus type-5 (Ad5) that expresses enhanced jellyfish green fluorescent protein (EGFP), AdEGFPuci, and grows on human 293 embryonic kidney epithelial cells, allowing us to track cell numbers and spatial patterns over time. The cells are arranged in a two-dimensional setting and allow virus spread to occur only to target cells within the local neighborhood. Despite the simplicity of the setup, complex dynamics are observed. Experiments gave rise to three spatial patterns that we call “hollow ring structure”, “filled ring structure”, and “disperse pattern”. An agent-based, stochastic computational model is used to simulate and interpret the experiments. The model can reproduce the experimentally observed patterns, and identifies key parameters that determine which pattern of virus growth arises. The model is further used to study the long-term outcome of the dynamics for the different growth patterns, and to investigate conditions under which the virus population eliminates the target cells. We find that both the filled ring structure and disperse pattern of initial expansion are indicative of treatment failure, where target cells persist in the long run. The hollow ring structure is associated with either target cell extinction or low-level persistence, both of which can be viewed as treatment success. Interestingly, it is found that equilibrium properties of ordinary differential equations describing the dynamics in local neighborhoods in the agent-based model can predict the outcome of the spatial virus-cell dynamics, which has important practical implications. This analysis provides a first step towards understanding spatial oncolytic virus dynamics, upon which more detailed investigations and further complexity can be built. Oncolytic viruses replicate selectively in tumor cells and have been explored as a targeted treatment approach against cancers [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15]. In principle an oncolytic virus will spread though the tumor cell population and lyse the infected cells, leading to eradication or control of the tumor. Because of the selectivity of such viruses for cancer cells rather than normal human cells, side effects also should be less pronounced than those associated with traditional treatments, such as chemotherapy or ionizing radiation. Oncolytic virus therapy has been explored in the context of several different virus species. While some non-human viruses display natural selectivity for cancer cells in humans [16], modern approaches use genetically engineered viruses to achieve tumor selectivity. The first engineered virus generated in the 1990s was a herpes simplex virus-1 [17]. Engineered adenoviruses have been of major interest in recent clinical trials, especially in the context of head and neck cancer [15]. Indeed the adenovirus H101 (Shangahi Sunway Biotech, Shanghai, China) was approved in China for the treatment of head and neck cancer in combination with chemotherapy [18]. A variety of other virus types has also been explored [19]. However despite initial promising results and observations in the laboratory and clinic, oncolytic viruses have so far failed to demonstrate sustained and reliable treatment success [15]. Besides experimental research, mathematical and computational modeling has increasingly become a tool to study the dynamics of oncolytic viruses. Mathematical models can help us understand the emerging properties of cancer-virus interactions, to interpret experimental results, and to design new experiments. The first mathematical models of oncolytic virus therapy considered ordinary differential equations that described the basic interactions between a replicating virus and a growing population of tumor cells, and also immune responses [20], [21]. Further work extended this type of approach in a number of ways, describing different scenarios and applying models to specific virus-tumor systems [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35]. One of the assumptions that is implicit in such modeling approaches is that cells and viruses mix perfectly with each other (mass action). While this might hold true in the context of some in vitro experiments, and while this might be a reasonable approximation of the dynamics occurring in some non-solid tumors, the majority of tumors have intricate spatial structures where cells and viruses do not mix well, but where interactions are limited to local neighborhoods. Hence, to gain a better understanding about the dynamics of oncolytic viruses, spatially explicit models are required. While some spatial modeling studies have been performed and have given rise to interesting results [30], [36], [37], [38], they commonly include, in addition to basic spatial dynamics, one or more additional assumptions that introduce further complexity. We still do not, however, have a good understanding of the basic principles that govern spatially restricted virus spread through a population of target cells, what outcomes can be expected, and what determines those outcomes. Obtaining such basic knowledge is a necessary foundation for building predictive models of virus therapy. This knowledge can be used as a basis for examining the effects of further biological complexities on the outcome of virus therapy, such as immune responses, tumor-microenvironment interactions, cellular heterogeneity, cell-cell interactions, among others. Therefore, the aim of this paper is to study the basic dynamics of virus spread through a spatially arranged population of growing cells in a simple setting. To achieve this, we have constructed an in vitro experimental system in which a fluorescent labeled virus spreads through a target cell population in a two-dimensional geometry, such that an infected source cell can transfer the virus only to target cells in the direct neighborhood. Besides quantifying the number of infected cells, this also allows us to track the emerging spatial patterns over time. We found three distinct patterns of virus spread and determined the frequency of their occurrence. An agent-based model was used to simulate these experiments and to interpret the data. The model can qualitatively reproduce the experimental observations and suggests key parameters that determine the different growth patterns. Using this model, we explore the implications of the observed growth patterns for the long-term outcome of the dynamics, and obtain insights about the conditions required for the virus to drive the target cell population extinct in this setting. This is a first step towards understanding the basic principles of virus spread and the correlates of successful virotherapy in spatially structured cell populations, and provides a basis for more detailed explorations and for the incorporation of other complexities that are relevant for virus-tumor dynamics in vivo. In order to examine spatial virus spread in a relatively simple setting, we constructed a recombinant adenovirus type-5 (Ad5) that expresses enhanced jellyfish green fluorescent protein (EGFP), AdEGFPuci, and grows on human 293 embryonic kidney epithelial (293) cells [39]. The experiment was set up such that cells are arranged in a two-dimensional layer, and virus spread is most likely to occur to neighboring cells. An agar overlay prevents long-range spread of the virus away from infected cells in the culture medium. This set-up allows us to not only quantify the number of infected cells over time, but also the spatial patterns of infected cells that are formed as the virus population expands. In addition, we used fluorescent markers to visualize the spatial distribution of all cells (infected and uninfected) by generating HEK293-H2BmCherry cells, that stably express the core nuclear histone protein H2B fused to mCherry (a highly photostable, monomeric red fluorescent protein (RFP) ) [40]. Thus, using HEK293-H2BmCherry cells allows us to visualize all the cell nuclei (i. e. , intact cells) in any particular culture. The culture was infected at a very low multiplicity of infection (MOI), such that any area of infection resulted from a single “founder” infected cell. Each culture contained several such founder cells that were sufficiently separated from each other, allowing us to track multiple growth foci across the dish. Details of the experimental procedures are given in the Methods section. The earliest stages of virus growth starting from a single founder infected cell were characterized in detail in a separate study [39]. This gave rise to the interesting observation that while virus extinction was a likely event as long as the number of infected cells in a given area was less than three, spreading virus growth was always observed once the number of infected cells reached three or higher. In the current study, we followed the growth of such spreading infections and characterized the consequent growth patterns. We observe three basic patterns of virus spread, which interestingly occur under identical experimental conditions and even within the same culture. They are shown in Figure 1A and described as follows. (i) In the first pattern, the virus infection spreads rapidly outwards as a ring, leaving no cells behind in the core of the ring (Figure 1A, pattern (i) ). This classic plaque pattern is observed in virus growth experiments. We call this the “hollow ring” structure. In the second and third patters there is viral spread, but it is limited. (ii) In the second case, a “disperse” growth pattern is observed, where the virus population expands as a mixed cluster of infected and uninfected cells (Figure 1A, pattern (ii) ). Finally, the virus population expands as a thinner ring, but in contrast to the first case, uninfected cells are left behind in the core of the ring (Figure 1A, pattern (iii) ). We call this the “filled ring” structure. A limited growth pattern is magnified in Figure 1B, in which uninfected cells are visible within the center of the virus infected population. In the top right panel of Figure 1B, an AdEGFPuci infected (fluorescent) cell is indicated (arrow, inf.), whereas an uninfected cell in the center of the spreading infection does not fluoresce green (arrow, un inf.). The same cells are indicated in the middle right panel of Figure 1B, showing red fluorescence. In the bottom left panel of Figure 1B, images of the top and middle panels are merged; infected cell (arrow, inf.) fluoresces yellow, while the uninfected cell, (arrow, un inf.) remains red. As mentioned the area over which the infection spread remained limited in patterns (ii) and (iii) and persisted throughout the infection (through 19 dpi). In contrast, in pattern (i) the ring of infected cells continued to spread outward as long as there was space; cell clearing in the center of the plaque was apparent at 13 dpi, as shown in Figure 1A. Similar patterns of spreading infection were also seen in Ad293 cells, a HEK293 cell derivative optimized for adenovirus plaque assays. Overall, among 436 scored growth foci, the hollow ring structure was found in 45%, and the limited patterns in 55% of cases. In the following sections, we simulate these experiments with a computational model. We examine conditions for the formation of different patterns and examine implications of the different growth patterns for the ability of the virus to eradicate the target cell population. Our in silico studies of the interactions between oncolytic viruses and cancerous cells rely on the agent-based modeling technique, where each cell is represented as an “agent” occupying a certain position on a grid, and interacting with other cells according to some (probabilistic) rules. Our modeling approach is spatial, that is, it takes into account the spatial distribution of the uninfected and infected cells The model, based on previous work [41], describes target cell-virus dynamics on a two dimensional grid that contains N×N spots. Each spot is either occupied by a cell (infected or uninfected), or it is empty. We model the development of the populations in discrete time. Given the state of the system at time t, a set of rules is applied to each spot, and this gives rise to the state of the system at time t+1. At each time step, the grid is randomly sampled N2 times. If the chosen spot is occupied by an uninfected cell, it can die with a probability D, leaving the spot empty. Alternatively, the cell can reproduce with a probability R, and a destination spot is randomly chosen for the offspring from the set of eight nearest neighboring spots. If the destination spot is empty, the offspring is placed there, otherwise, no reproduction occurs. If the chosen spot contains an infected cell, it can die with a probability A, or attempt to transmit the virus with a probability B. A destination spot is chosen randomly from the eight nearest neighbors, and infection only proceeds when a susceptible cell is present. Infected cells do not reproduce since adenoviruses lock the cell in the S-phase for replication, thus preventing further divisions [34]. The particular formulation of the infection process used in this paper corresponds to virus transmission that is not coupled with cell death, but simulations indicate that transmission coupled to cell death does not qualitatively alter the results reported here. For reference, parameters and their meaning are summarized in Table 1. In this section, we explore the initial virus growth patterns. As starting conditions we assume that the grid is filled with uninfected cells and that a relatively small square of infected cells (30×30 spots) is placed in the middle of the grid (which overall contains 300×300 spots). The emerging growth pattern depends on parameters that influence the rate of virus spread, in particular the probability for an infected cell to die, A, and the probability for an infected cell to transmit the virus, B. The patterns that we observe are presented in Figure 2. In Figures 2a and b, the infected cell population expands as a ring or wave that leaves no cell behind in its core. The two pictures differ in the death probability of infected cells. In Figure 2a, the probability for infected cells to die is relatively low such that during the time frame of the simulation a hollow ring has not yet formed and the infected cell population expands as a relatively solid mass. In Figure 2b, the death probability of infected cells is higher such that during the time frame of the simulation a hollow ring has formed. In Text S1 we derive an approximate growth law for these scenarios. The total number of cells is proportional to, such that for short time-scales (or smaller death rates) the growth is quadratic in time, and for longer times scales (or larger death rates) it is linear in time. This is exactly what is observed. Figure 2a, characterized by smaller values of A, shows a growth law of the infected cell population that is close to quadratic. In Figure 2b, where the death rate is larger, the infected cell population grows linearly once the hollow ring is present. Note that these two scenarios are identical in principle because in Figure 2a, the formation of the hollow ring requires more time (and a larger grid). The higher the death rate of infected cells, the faster the ring is formed, and the faster the growth law changes from square to linear. Lowering the rate of virus spread (decreasing the value of B and increasing the value of A) gives rise to patterns of a different nature. In Figure 2c, uninfected cells are left behind in the core of the expanding ring. When they grow and become infected by virus, a coupled expanding ring of uninfected and infected cells forms. This can occur repeatedly, giving rise to concentric rings. The persistence of cells in the core of the ring is probabilistic in nature, and that is reflected in the growth laws that are observed in multiple runs of the simulation. In cases where uninfected cells are not left behind inside the ring, the infected cell population grows linearly. When concentric rings do occur, the growth becomes quadratic. Finally, no expanding ring structure is formed in Figure 2d because the viral spread kinetics are even slower. Instead, the area of virus growth is characterized by a mix of infected and uninfected cells that expands over time. In this case, quadratic growth of infected cells is observed (see Text S1). Note that if the viral spread kinetics are in the lower end of this spectrum, it is possible to observe a variation of this pattern, shown in the inset of Figure 2d: While the spreading infection leaves uninfected cells behind, the viral spread kinetics are too low to maintain significant numbers of infected cells throughout this area. Most of the infected cells will be at the outer edge of the infection due to a higher density of target cells. In this case, a relatively thin, ring-like structure can be formed, with a large area of uninfected cells remaining in its core. This pattern, however, is temporary. With time, one of two scenarios can be observed. A mixed pattern can be generated, characterized by a large number of uninfected cells and a low number of infected cells, because the virus eventually spreads to the remaining susceptible cells. Alternatively, there is a chance that the virus population goes extinct due to the slow rate of spread. Long term outcomes are discussed further below. Figures 2a and b qualitatively correspond to experimental pattern (i), the hollow ring structure. Figure 2d corresponds to experimental pattern (ii), the disperse growth structure. The pattern shown in the inset of Figure 2d likely corresponds to experimental pattern (iii), where a limited ring with mainly uninfected cells in its core temporarily forms before either developing into a mixed pattern or resulting in virus extinction. According to this interpretation, the experimentally observed patterns (ii) and (iii) are variations of the same theme. The concentric rings observed in the model simulations are not found in the experimental data. This is not surprising because they only occur in relatively narrow parameter regions in the model. In order to go beyond the qualitative comparison of model and data, we fit the model to two sets of experimental data, one showing an expanding hollow ring, and the other the disperse growth pattern. A least squares algorithm (see Text S1) was used to fit the number of infected cells over time, and a relatively good fit was obtained for both cases (Figure 3). The types of spatial patterns that emerged matched the observed ones qualitatively (Figures 4 and 5). While this procedure found best fitting parameter values, their biological meaning remains questionable, since different parameter combinations can give rise to similarly good fits. A more solid validation would require an independent estimation of parameter values, and a subsequent generation of the predicted growth patterns. Due to the complexity of the experimental observations, this is not currently possible and is discussed in detail below. The fitting procedure does, however, indicate that the model is at least consistent with experimental data. Here, we explore the long term dynamics, investigating how the above described patterns play out and correlate with the overall outcome if both the uninfected and infected cell population can expand in space. We seek to define conditions under which the virus can eliminate the target cell population in this system. All simulations are started with a small number of infected cells placed in a compact vicinity into a larger space filled with uninfected cells, which is in turn embedded into an even larger “empty” space (for the exact initial conditions for particular cases, see appropriate figure legends). In contrast to the simulations reported above, here we go beyond the initial virus growth stage, and focus on time-scales where the population of target cells experiences significant changes (grows in size in the absence of infection). The outcomes of this system include extinction of the target cells and thus the virus; extinction of the virus and persistence of the target cells; coexistence of virus and target cells. The dependency of these outcomes on the parameters is shown in Figure 6, which is the result of at least 104 instances of the simulation, where the log10 of all the parameters was varied between −4 and 4. Figures 7 and 8 show corresponding spatial and temporal patterns. We examine the outcomes first in a relatively small 30×30 grid, and subsequently in a larger, 300×300 grid. We used a newly designed virus, AdEGFPuci, replicating on 293 embrionic kidney epithelial cells in order to study the spatial dynamics of virus growth in a two-dimensional setting in which a source cell transmits the virus to target cells in the immediate vicinity. We found that when the virus is placed into the midst of a target cell population, the initial growth pattern can be divided into two categories. Either the infected cells expand as a wave and eventually form a hollow ring, or the hollow ring is not formed and we observe cells remaining in the core of the ring which can lead to a disperse growth pattern. An agent-based model was used to qualitatively simulate these experiments. The model produced the same types of patterns found in the experiments and model fitting to the experimental data has shown that our description is consistent with observation. The data, together with the model, gave rise to a number of insights. When a ring is formed, the initial virus growth is quadratic and then becomes linear. In the absence of a ring, the entire virus growth is quadratic. These growth patterns are obviously related to and have implications for the dynamics of viral plaque formation, which have been previously investigated mathematically from other angles [53], [54], [55]. Initial virus growth patterns are correlated with the long-term outcomes of the dynamics. If a disperse growth structure is formed, persistence of the target cells at high levels is the only outcome, corresponding to definite treatment failure. On the other hand, the long-term outcome of the dynamics when a ring structure is formed includes extinction or low-level persistence of the target cells. In the best case scenario (which corresponds to parameter region A in our notations), the wave of infected cells catches up with the spreading target cells and drives the cell population extinct, which is the most desirable treatment outcome. Low-level persistence occurs when the virus wave fails to overtake and eliminate the target cell wave, resulting in a non-equilibrium persistence situation that requires larger grids in the model. These outcomes are likely to be relevant to tumors since the number of cells in a tumor is significantly larger than the number of cells present in any of our simulations. Cell persistence in this case, however, occurs at relatively low levels and this can be thought of as controlled persistence. Since a tumor is less likely to cause morbidity or mortality in such a state, this outcome can be considered as partially successful treatment. An important theoretical finding is that the outcome of the spatial system (extinction versus persistence) can be predicted well from the local dynamics, characterized by the interactions among neighboring cells. In our model, this is given by the neighborhood of 3×3 spots. This result suggests that ordinary differential equations, which describe mass action dynamics, can be a valid approach to study the correlates of successful virus therapy even for tumors that exhibit spatial structure. The results of such approaches have to be correctly interpreted to include the notion of local neighborhoods. If interpreted correctly, insights gained from such previous modeling studies are applicable to the treatment of spatial tumors. In addition, this result tells us that simple in vitro experiments (where viruses and cells mix well) can be used to compare the basic replication and spread efficiencies of different viruses, and that this directly correlates with their ability to fight a spatially structured tumor. Of course, the correlates of successful treatment derived from such simple modeling studies are qualitative in nature. That is, it is for example possible to predict the effect of increasing a certain parameter, such as the death rate of infected cells, on the outcome of treatment. However, predicting whether or not virus treatment will result in the eradication/control of tumor cells is a very difficult problem, and so far no modeling approach exists that is suited to perform this task. Not only do we lack sufficient biological information, the same biological processes can be described mathematically in different ways, adding uncertainty to these models. As a next step it will be important to examine whether the global spatial dynamics can still be predicted from local mass-action dynamics in models when further complexity is introduced, including three-dimensional spatial structures, immune responses, physical barriers to spread, or cell populations with differential susceptibility to infection. If our result holds, then relatively simple ordinary differential equation models can be used to guide the exploration of other, more complex modeling approaches, as well as the design of experiments aimed at evaluating candidate viruses. To put this work in the context of the existing literature, we note that experimental research has made much progress in the construction of viruses, in elucidating the molecular biology of virus replication in tumor cells, and in investigating clinical trials and correlates of success for several different types of candidate viruses and different tumors. Yet, the complex and multi-factorial interactions between viruses, their target cells, and other relevant components make it difficult to predict the outcomes of such dynamics, an area where mathematical tools can be of great help to complement experimental analysis, interpret data, and make experimentally testable predictions. In the recent years, there have been several mathematical/computational studies which examined spatially explicit models in more or less complex settings, taking into account an array of relevant factors that might affect the in vivo virus spread through a tumor [30], [34]–[36]. Here, we took a step back and examined the basic spatial dynamics between a virus and its susceptible tumor cells, ignoring more complex factors such as the immune system, tumor vascularization, or physical barriers to virus spread within tumors. In principle, the simpler setting allows for more solid experimental testing and validation of models. As it turns out, the dynamics in such a simplified setting already exhibit very complex behavior, and it is imperative to gain a thorough understanding of such a basic system before venturing on towards more comprehensive scenarios. This paper provides a first step to such an understanding. Due to the complexity of the experimentally observed dynamics, further questions remain that are subject to ongoing investigation. The most puzzling observation was that identical experimental conditions, using the same virus-target cell system, gave rise to different patterns of virus growth. This indicates the existence of so far unidentified factors that influence virus spread in this in vitro system. It is possible that initial events, stochastic in nature, might determine the remaining fate of the virus population. One hypothesis is that infection of cells triggers the production of anti-viral factors by the infected cell, which could induce an anti-viral state in neighboring cells. An example of such a factor could be interferons. Indeed adenovirus infection has been reported to induce interferon beta production [56], and 293 cells respond to interferon beta by activation of interferon-responsive genes (System Biosciences. Interferon Response Detection Kit – User Manual. Mountain View, CA). Cells in the anti-viral state can have both a reduced susceptibility to infection and/or an increased death rate through the induction of apoptosis if they do become infected [57]. In such a setting, it is possible that a race occurs between the spread of the virus and the anti-viral factors to neighboring cells. The population that initially gains the upper hand in this race could determine the emerging pattern of virus growth. The hollow ring structure could be formed if the virus out-runs the anti-viral factors, and the disperse growth pattern could be observed if the virus population fails to do so. While parameter estimates from the model fit to the data remain inconclusive, the fit accounted for the disperse pattern through an increased death rate of infected cells, which might suggest that apoptosis significantly contributes to the anti-viral state. This concept could also explain the observation that the area of infection remains limited when the disperse growth pattern is found in the experiments. In our model, which does not take into account such anti-viral factors, the infection area continues to grow, albeit slowly. Interferons have been shown to influence the development of plaques before in the context of herpes simplex virus [58], and the dynamics have been examined with a similar modeling approach [59]. In this study, however, different dynamics and outcomes were observed. The finding that different patterns can form in identical experimental conditions also blocks our ability to turn our model into a truly predictive one. This would require parameters to be measured independently in the experimental system, and to demonstrate that these parameters yield a satisfactory fit of the model to the data. However, the dynamics are most likely characterized by different parameter combinations in the context of the different observed patterns. Before we fully understand the reasons for the various patterns, parameterization of the model remains an impossible task. Nevertheless, the model analysis presented here does highlight the parameters that determine the different outcomes, which guides our search for the responsible mechanisms. Identifying these mechanisms still requires extensive work which goes beyond the scope of the current paper. We conclude that the dynamics of virus spread in the simplest spatial setting can be very complicated. We interpreted these dynamics with a computational model, and shed light onto the meaning of the different spatial patterns observed. We further found theoretically that mass-action dynamics in local areas can be indicative of the outcome of virus spread in a spatially structured cell population. This suggests that previous insights, gained from the analysis of ordinary differential equations, remain relevant for the spread of viruses in a spatial setting. Both an agent-based model and a metapopulation model were used to examine the spatial dynamics of virus spread through a population of growing target cells. Analytical and computational methods are described in detail in the Text S1.

Traditional chemotherapy of cancers is characterized by strong side effects, while showing a low success rate in the long term control of tumors. Besides small molecule inhibitors, which have shown great promise, oncolytic viruses present an emerging specific treatment approach. They are engineered viruses that spread from tumor cell to tumor cell, killing them in the process. Non-tumor cells are generally not infected. While clinical trials have given rise to promising results, reliable success remains elusive. Besides experiments, computational approaches provide a valuable tool to better understand the dynamics of virus spread through a growing population or tumor cells. Combining in vitro experimental approaches with computational models, we study the principles of virus spread through a spatially structured population of cells, which is of fundamental importance to understanding virus treatment of solid tumors. We describe different growth patterns that can occur, interpret them, and explore how they relate to the ability of the virus to induce tumor regression. We further define how these spatial dynamics relate to settings where cells and viruses mix more readily, such as in many cell culture experiments that are used to evaluate candidate viruses.

Abstract Introduction Results Discussion Methods

theoretical biology biology

2012

Complex Spatial Dynamics of Oncolytic Viruses In Vitro: Mathematical and Experimental Approaches

A powerful way of gaining insight into biological systems is by creating a nonlinear differential equation model, which usually contains many unknown parameters. Such a model is called structurally identifiable if it is possible to determine the values of its parameters from measurements of the model outputs. Structural identifiability is a prerequisite for parameter estimation, and should be assessed before exploiting a model. However, this analysis is seldom performed due to the high computational cost involved in the necessary symbolic calculations, which quickly becomes prohibitive as the problem size increases. In this paper we show how to analyse the structural identifiability of a very general class of nonlinear models by extending methods originally developed for studying observability. We present results about models whose identifiability had not been previously determined, report unidentifiabilities that had not been found before, and show how to modify those unidentifiable models to make them identifiable. This method helps prevent problems caused by lack of identifiability analysis, which can compromise the success of tasks such as experiment design, parameter estimation, and model-based optimization. The procedure is called STRIKE-GOLDD (STRuctural Identifiability taKen as Extended-Generalized Observability with Lie Derivatives and Decomposition), and it is implemented in a MATLAB toolbox which is available as open source software. The broad applicability of this approach facilitates the analysis of the increasingly complex models used in systems biology and other areas. Mathematical modelling has become a fundamental tool in present day biology [1], and system identification is one of the key tasks of this process [2]. Building a dynamic model usually requires establishing the values of some unknown parameters, which raises the issue of parameter identifiability [3]. A model is structurally identifiable if it is possible to determine the values of its parameters from observations of its outputs and knowledge of its dynamic equations [4]. While the related concept of practical identifiability refers to quantifying the uncertainty in parameter values when estimated from noisy measurements, structural identifiability does not take into account limitations caused by the quality or availability of experimental data. It is, however, a necessary (a priori) condition for practical identifiability, which, in turn, is a prerequisite for model calibration, also known as parameter estimation [5]. Any identification efforts aimed at estimating unidentifiable parameters will fail, leading to wrong estimates, waste of resources, and possibly misleading model predictions [6]. Furthermore, if structural unidentifiability is mistaken for practical unidentifiability, it may lead to trying to overcome it by investing additional efforts in designing and performing new experiments [7], which will nevertheless be sterile. Hence it is essential to assess the structural identifiability of any unknown parameters in a model before attempting to calibrate it. As stressed in the conclusions of a recent parameter estimation challenge [8], “modelers must avoid creating structurally unidentifiable parameters that can never be estimated”. However, in real applications structural identifiability is seldom checked before performing parameter estimation [9]. This is at least partly due to the computational complexity of the problem: structural identifiability methods generally require symbolic manipulations, which can quickly give rise to long expressions as the system size increases [10]. This is a major challenge in systems biology, as the models constructed are increasingly complex, large [11], and more difficult to identify [8]. However, the development of structural identifiability tools has been lagging behind, and, despite the wide variety of methods developed for this task (some of which have publicly available implementations [12–16]), the analysis of some models remains elusive. For example, although recent improvements in efficiency [16,17] have enabled the analysis of increasingly large rational models (those that can be expressed as fractions of polynomial functions), non-rational systems such as those including trigonometric expressions or Hill-type kinetics (which are common in mechanical and biochemical models, respectively) can currently be analysed only for small sizes. While in certain cases non-rational models can be rewritten in rational form, by introducing additional variables and equations, it is not always possible or convenient to do so. Furthermore, the results obtained for the rational counterpart are not necessarily valid for the original non-rational model in the case of unidentifiability [18]. Recent studies [9,10,19–21] show that, in general, the choice of a structural identifiability method involves trade-offs between generality of the application, computational cost, and level of detail of the results. In conclusion, there is currently a lack of structural identifiability methods of the sufficient generality and robustness to be applied to nonlinear models of general form and realistic size [21,22]. To address this issue, we propose a methodology applicable in principle to any analytic system and geared towards computational efficiency. This method approaches local structural identifiability as a generalized version of observability, a classic concept in systems and control theory [23]. A system is observable if it is possible to determine its internal state from output measurements in finite time. If the model parameters are considered as state variables with zero dynamics, structural identifiability analysis can be recast as a generalization of observability analysis [17,24,25]. In this way it is possible to assess the structural identifiability of nonlinear systems using results from differential geometry [21]. Essentially, identifiability is determined by calculating the rank of a generalized observability-identifiability matrix, which is constructed using Lie derivatives. When this rank test classifies a model as unidentifiable, the procedure determines the subset of identifiable parameters. In some cases it is also possible to find identifiable combinations of the remaining parameters. This approach is directly applicable to many models of small and medium size; larger systems can be analysed using additional features of the method. One of them is decomposition into more tractable submodels, which is performed with a combinatorial optimization metaheuristic as in [26]. Another possibility is to build identifiability matrices with a reduced number of Lie derivatives. In some cases these additional procedures allow to determine the identifiability of every parameter in the model (complete case analysis); when such result cannot be achieved, at least partial results—i. e. identifiability of a subset of parameters—can be obtained. We illustrate the applicability of this method to systems biology models of different types, including genetic, signalling, metabolic, and pharmacokinetic networks. Some of them are non-rational systems exhibiting Hill kinetics, that is, with expressions containing terms of the form k1xn/ (k2 + xn), such as the Goodwin model of transcriptional repression [27], the mitogen-activated protein kinase (MAPK) signalling cascade [28], and the genetic network that controls the circadian clock in Arabidopsis thaliana [29]. Other models analysed here include drug uptake into hepatocytes [19], NF-κB [30] and JAK/STAT [31] signalling pathways, and the central carbon metabolism of Chinese hamster ovary cells [32]. These case studies include models whose identifiability had not been previously determined, and for some of them we found unidentifiabilities that had not been reported before. In those cases, we obtained identifiable reparameterizations by removing redundant parameters and fixing the values of other parameters a priori. In this work we consider identifiability as an augmented observability property. We begin the description of the approach by defining observability and showing how it can be assessed. A system is (locally) observable at a state x0 if there exists a neighbourhood N of x0 such that every other state x1 ∈ N is distinguishable from x0. Two states x0 ≠ x1 are said to be distinguishable when there exists some input u (t) such that y (t, x0, u (t) ) ≠ y (t, x1, u (t) ), where y (t, xi, u (t) ) denotes the output function of the system for the input u (t) and initial state xi (i = 0,1). The concept of observability was initially formulated by Kalman for linear systems [34], and then extended to the nonlinear case by Hermann and Krener [23]. For a nonlinear system given by Eq (1) it is possible to obtain information about the states x from its outputs y by calculating the derivatives y˙, y¨, …. These differentiations are performed by taking Lie derivatives of the output function g. The Lie derivative of g with respect to f is: L f g (x) = ∂ g (x) ∂ x f (x, u) (3) For a system with n states and m outputs, ∂ ∂ x g (x) is an m × n matrix, and L f g (x) = ∂ g (x) ∂ x f (x, u) is an m × 1 column vector. The ith order Lie derivatives are recursively defined as follows: L f 2 g (x) = ∂ L f g (x) ∂ x f (x, u) ⋯ L f i g (x) = ∂ L f i - 1 g (x) ∂ x f (x, u) (4) Stacking n sub-matrices, we obtain the nonlinear observability matrix: O (x) = ∂ ∂ x g (x) ∂ ∂ x (L f g (x) ) ∂ ∂ x (L f 2 g (x) ) ⋮ ∂ ∂ x (L f n - 1 g (x) ) (5) We can now formulate the Observability Rank Condition (ORC) as follows: if the system given by Eq (1) satisfies rank (O (x 0) ) = n, where O is defined by Eq (5), then it is (locally) observable around x0 [35]. The rank condition provides a result about local observability of any possible state x0. That is, if the matrix is full rank then for every state x0 there exists a neighbourhood N (x0) in which x0 can be distinguished from any other state x*. In other words, every state can be distinguished from its neighbours, but not necessarily from other distant states. In contrast, global observability is a property that must hold for every possible N (x0). The difference is clearly shown with the following example [23]: x ˙ = u, y 1 = cos (x), y 2 = sin (x) (6) While this system satisfies the observability rank condition and is therefore locally observable, it is not globally observable because it is impossible to distinguish between x0 and xk = x0 + 2kπ, for any integer k. We remark that the observability rank condition does not require the assumption of constant inputs u; analytic differentiable input functions can be used [36,37]. As noted in [38], this entails that u can be treated symbolically in rank calculations. While identifiability problems can be addressed by a number of techniques not explicitly related to nonlinear observability, it is possible to consider the parameters p as additional states with trivial dynamics p ˙ = 0 and, in this way, the identifiability problem can be recast in the framework of observability [17,21,24]. Thus, by augmenting the state variable vector so as to include model parameters, x ˜ = [ x, p ], we obtain a generalized observability-identifiability matrix, O I (x ˜): O I (x ˜) = ∂ ∂ x ˜ g (x ˜) ∂ ∂ x ˜ (L f g (x ˜) ) ∂ ∂ x ˜ (L f 2 g (x ˜) ) ⋮ ∂ ∂ x ˜ (L f n + q - 1 g (x ˜) ) (7) With this formulation we can define a generalized Observability-Identifiability Condition (OIC) as follows: if the system given by Eq (1) satisfies rank (O I (x ˜ 0) ) = n + q, it is (locally) observable and identifiable in a neighbourhood N (x ˜ 0) of x ˜ 0. Since we have recast the analysis of structural identifiability as a particular case of observability, the same remark that was made in the preceding subsection about the difference between local and global properties applies here. It has been noted [39] that in certain cases a system may become unreachable for specific values of the initial conditions, leading to the impossibility of determining the values of parameters classified as identifiable by structural identifiability methods. This situation can be detected if rank (O I (x ˜ 0) ) is calculated using a vector of specific initial conditions instead of a generic symbolic vector. Finally, we note that the idea of treating parameters and state variables similarly is also adopted by estimation methods such as extended Kalman filtering [40]. However, the context is different, since the goal of such techniques is to determine the value of states and parameters from data, while structural identifiability analysis aims at establishing whether such estimation is theoretically possible. In practice, checking the aforementioned Observability-Identifiability Condition (OIC) is often computationally inefficient (or even infeasible) because building O I and calculating its rank is a highly demanding, memory-consuming task. Fortunately, sometimes this cost can be decreased by building a smaller matrix. Let us first note that each of the n + q sub-matrices vertically stacked in the generalized observability-identifiability matrix of Eq (7) has dimension m × (n + q), and the full matrix O I has dimensions (m ⋅ (n + q) ) × (n + q). Therefore it may not be necessary to calculate the n + q − 1 Lie derivatives in order to test whether O I is full rank, since full rank may be achieved with a lower number of derivatives. The minimum number of Lie derivatives for which the matrix may be full rank is n d = n + q m - 1 (8) that is, the smallest integer not less than (n + q) /m − 1, where n, q, and m are the numbers of states, parameters, and outputs, respectively. The maximum number of Lie derivatives is also known a priori: derivatives of order higher than n + q − 1 cannot increase the matrix rank [38]. Having lower and upper bounds for the necessary Lie derivatives is an advantage of this methodology compared to, e. g. , power series approaches, for which the maximum number of derivatives is in principle infinite [10]. Our method builds O I recursively. Once nd is reached, addition of a new Lie derivative is followed by calculation of the rank. This process is repeated until the maximum number n + q − 1 is reached, or until adding a new Lie derivative does not increase the matrix rank; in both cases no further derivatives are necessary [38]. At that point, if O I is full rank the corresponding model is observable and identifiable, as seen in the previous subsection. If O I is not full rank, the algorithm proceeds to find identifiable parameters, as explained in the following subsection. Further improvements in the computational burden can be obtained by calculating the rank numerically instead of symbolically. A way in which this can be performed is by replacing the symbolic variables in the O I with prime numbers to minimize the risk of accidental cancellations, which would reduce the rank. If O I is not full rank, the Observability-Identifiability Condition (OIC) does not inform us about which parameters are identifiable and which are not. This can be achieved by realizing that each column of O I corresponds to a parameter-to-output relation (or state-to-output): ∂ ∂ x 1 g (x ˜) ∂ ∂ x 2 g (x ˜) ⋯ ∂ ∂ p q g (x ˜) ∂ ∂ x 1 (L f g (x ˜) ) ∂ ∂ x 2 (L f g (x ˜) ) ⋯ ∂ ∂ p q (L f g (x ˜) ) ⋮ ⋮ ⋮ ⋮ ∂ ∂ x 1 (L f n + q - 1 g (x ˜) ) ∂ ∂ x 2 (L f n + q - 1 g (x ˜) ) ⋯ ∂ ∂ p q (L f n + q - 1 g (x ˜) ) Therefore, if deleting the ith column of the generalized observability-identifiability matrix does not change its rank, then the corresponding ith state (parameter) is non-observable (unidentifiable). This fact can be exploited to determine which of the parameters in an unidentifiable model are identifiable and which are not, using a sequential procedure: after the matrix rank has been calculated and the model has been found to be unidentifiable, each of the columns in O I corresponding to a particular parameter is removed one by one and the rank is recalculated. In this way the identifiability of each of the parameters is evaluated. The procedure outlined in the preceding subsections classifies the model parameters as either identifiable of unidentifiable. A question that naturally follows is: are there combinations of the unidentifiable parameters which are themselves identifiable? If the answer is affirmative, the model can be reparameterized and converted to a structurally identifiable model. However, this is a difficult problem, which few methods can address, and only for models of moderate size. An example is COMBOS [41,42], which is based on differential algebra. Here we suggest an approach based on ideas presented in [43,44] and on the method for finding symmetries proposed by [38]; related work has been recently presented in [45]. The procedure is as follows: if O I is rank-deficient, remove the columns corresponding to identifiable parameters and obtain a reduced submatrix, O U [38]. Then, obtain a basis for the kernel (null space) of this matrix, N (O U) (step 2 in [44]). Its coefficients define one or several partial differential equations whose solution (s) are the identifiable combinations (step 3 in [44]). This procedure is illustrated in the Methods section with the JAK/STAT signalling pathway, for which an identifiable combination of two parameters is found. While this example shows the potential of this procedure, it must be acknowledged that the computational complexity of calculating the kernel of O U limits its applicability to models with a moderate number of unidentifiable parameters. The methodology described in the previous subsections can be used to analyse the identifiability of whole models and, if the model is unidentifiable, of its parameters individually. However, since it relies heavily on symbolic operations, it may be computationally infeasible for large or complex models. It should be noted that the main limiting operations are: The minimum number of derivatives necessary for building O I (x ˜) is given by nd as defined in Eq (8). The limit of what is computationally possible is difficult to quantify a priori, since it depends on the model equations and the machine used in the calculations. As a rule of thumb, analyses involving nd ≥ 10 are infeasible except for very small models. As model size or complexity increases, this upper bound decreases; some examples will be shown in the Results section. One solution is to decompose those models into smaller submodels whose analysis is possible computationally. Thus, we seek to decompose a model M into submodels {M1, M2, …} which require few Lie derivatives for their analysis, that is, they have a small nd. Each submodel Msub includes a subset of the model states, xsub. Its outputs, ysub, are the outputs of M which are functions of at least one state included in xsub. The submodel parameters and inputs are those appearing in the equations of xsub and ysub. There may be states that appear in the equations of xsub or ysub but are not part of xsub; they are considered as additional unknown parameters of Msub. The submodels can be found by optimization as follows. For each submodel Mi we select a subset of the states in M by performing a combinatorial optimization where we minimize nd: min s n d (s) (9) where s = {s1, s2, …, sn} is a binary vector of size n, whose entries sj ∈ {0,1} denote inclusion (sj = 1) or exclusion (sj = 0) of the corresponding state. The combinatorial optimization is performed with the Variable Neighbourhood Search metaheuristic [46]. We carry out n optimizations (one per state); in the jth optimization we force sj = 1, so that each state appears in at least one solution. This, in turn, guarantees that all the parameters will eventually be evaluated. A penalty term is included in the objective function to penalize solutions that have more states than a chosen maximum. Apart from this optimization-based decomposition, it may sometimes be useful to specify a particular submodel in order to explore the identifiability of a specific part of the model. Let us clarify how we can conclude identifiability of a parameter from analysis of a submodel. As an example, consider M to be the model of Arabidopsis thaliana described in the Results section; its equations are given in the Supplementary Information (S1 Text). Let us consider a submodel Msub consisting of two states, xsub = {x1, x7}. The equations of Msub are those that correspond to the states {x1, x7}, that is: { x˙1=n1x6ag1a+x6a−m1x1k1+x1+q1x7u (t), x˙7=p3−m7x7k7+x7− (p3+q2x7) u (t), x1 (0) =0, x7 (0) =0 (10) The outputs of Msub are those outputs of M which are functions of at least one of the states in Msub (in this example, y1 = x1). The parameters and inputs of Msub are those present in Eq (10): respectively, {n1, g1, a, m1, k1, q1, p3, m7, k7, q2} and u. Additionally, we must also include as parameters the states that do not belong to xsub but appear in Eq (10) or in ysub (in this case, x6). Thus in this example the submodel parameters would be {n1, g1, a, m1, k1, q1, p3, m7, k7, q2, x6}. By including states such as x6 as parameters we are considering them as unknown and constant. In contrast, if they were included as inputs to the submodel, we would be implicitly assuming that they provide sufficient excitation for identification purposes. Thus, including them as parameters is a conservative assumption in terms of identifiability. Therefore, if a parameter is classified as identifiable in a submodel under these conditions, it will also be identifiable when considering the whole model. When the nd of the full model is so high that it is not feasible to build O I, one solution is to decompose the model into smaller submodels as described in the previous subsections. Another possibility is to build O I with i < nd derivatives. In this case we know that full rank cannot be achieved, so even if the model is identifiable we will not be able to determine it in this way. However, it may be possible to determine identifiability of at least some of the parameters. Note that this procedure can be helpful exactly in the same circumstances as decomposition. In some cases one approach will be more appropriate than the other, but both can be used to determine the identifiability of different parameters, and may therefore be complementary. Fig 1 shows a diagram of the methodology presented so far. The next sections discuss the types of analyses that can be performed with this methodology and show how to refine the solutions iteratively in order to obtain more complete diagnoses. By assessing identifiability as explained in sections “Assessing the OIC efficiently” and “Determining identifiability of individual parameters” we are performing a “Complete Case Analysis” (CCA): every parameter in the model is either classified as identifiable or as unidentifiable. However, it may not always possible to carry out the aforementioned procedure due to computational limitations, as explained in sections “Decomposing large models into submodels to facilitate their analysis” and “Building O I with less than nd Lie derivatives”, which presented two different alternatives. In certain cases these alternatives can yield incomplete results, that is, they may fail to determine the (un) identifiability of some parameters. For example, this may happen in the following scenarios: The two cases mentioned above will be called “Partial Analyses for Identifiability” (PAI): some parameters are conclusively classified as identifiable, but nothing can be said about the rest. It is also possible to perform similar analyses to guarantee unidentifiability of some parameters, leading to what we will call “Partial Analyses for Unidentifiability” (PAU). In such tests, some parameters are classified as unidentifiable while the analysis of the rest is not conclusive. This can happen in at least two situations: The different types of analyses that can be performed are summarized in Table 1. As shown in the preceding subsection, for some complex problems a complete analysis—that is, classifying all the parameters as identifiable or unidentifiable—may not be possible, at least in a first approach, due to computational limitations. In such cases, one can try to obtain more complete diagnoses by running the procedure iteratively. At each time, the computational cost can be reduced by removing from the augmented state vector, x ˜ = [ x, p ], those parameters that were already found to be identifiable in previous steps. This operation, which leads to a smaller O I matrix, does not alter the result of the identifiability test, because the resulting O I is identical to the one obtained with the original vector x ˜ = [ x, p ] after removing the columns corresponding to identifiable parameters—which results in a decreased rank. Note that this equivalence is made possible by the fact that p ˙ = 0, so the procedure cannot be applied to the model states, since x ˙ ≠ 0. In summary, if a model M is too large to be analysed as a whole—i. e. to directly calculate the rank of its identifiability matrix and perform a complete case analysis (CCA) —identifiability analysis can be approached as follows: The present method has been implemented as a MATLAB toolbox named STRIKE-GOLDD (STRuctural Identifiability taKen as Extended-Generalized Observability using Lie Derivatives and Decomposition). It is an open source tool licensed under the GNU General Public License version 3 (GPLv3). It is freely available from https: //sites. google. com/site/strikegolddtoolbox/ and as supplementary information accompanying this article (S1 File). It requires a MATLAB installation with the Symbolic toolbox. Additionally, to use optimization-based decomposition it is necessary to install the MEIGO toolbox [48]. The usage of the STRIKE-GOLDD software is discussed in detail in the manual (S2 Text); in the following lines we provide a brief description of the key options. The toolbox allows limiting the number of Lie derivatives that are calculated when building O I (x ˜). This is useful to prevent the algorithm from getting stuck in excessively lengthy calculations. To adapt this limit to the computer where the algorithm is running, it is specified as a machine-dependent criterion: the user can set a limit on the time invested in calculating these derivatives by entering it in opts. maxLietime (that is, the algorithm will not calculate the ith + 1 derivative if the time spent in obtaining the ith one was ti > opts. maxLietime). Furthermore, the user can choose what to do if this time limit is reached without O I (x ˜) being full rank: by setting opts. decomp = 0, STRIKE-GOLDD will perform a partial analysis of the whole model with the current O I (x ˜); if opts. decomp = 1, it will decompose the model. It is also possible to enforce the use of decomposition from the start, i. e. without checking whether the time limit is reached, with the option opts. forcedecomp. The submodels can be found by optimization or specified by the user; this choice is made by opts. decomp_user. Another option, opts. numeric, allows deciding whether to calculate rank (O I (x ˜) ) numerically or symbolically. The symbolic calculation is always exact. It is possible to perform a numerical calculation by replacing the symbolic variables with prime numbers. This usually reduces the computational cost, although in some cases it might lead to accidental cancellations that decrease the rank artificially. However, the risk of obtaining a spurious result is low, and it can be minimized by running the procedure several times, since the substitutions are random. In all of the case studies analyzed in the Results section we found agreement between numeric and symbolic rank calculations. Finally, it is possible to assess identifiability for specific values of the system’s initial conditions. As mentioned in subsection “Structural identifiability as augmented observability: the OIC”, this can be useful in order to detect situations in which loss of reachability from particular initial conditions leads to loss of identifiability. Such pathological cases are not detected if rank (O I (x ˜ 0) ) is calculated using a generic symbolic vector of initial conditions. However, they can be tested by setting the option opts. knowninitc = 1 and entering the corresponding vector of initial conditions in the script that creates the model. In [19], Grandjean and coworkers proposed 18 alternative pharmacokinetic nonlinear compartmental models of the uptake process of Pitavastatin (a drug used to treat hypercholesterolaemia) into hepatocytes. They applied five different methods to analyse their structural identifiability: similarity transformation, differential algebra, Taylor series, and two approaches based on a non-differential input/output observable normal form and an algebraic input/output relationship approach. With these techniques they established the identifiability of most of the models. However, for several model formulations none of the methods was able to produce results. This was the case for two candidate models (with or without pseudo-state assumption) that accounted for drug metabolising within the cell. A diagram of these Pitavastatin uptake models is shown in panel A of Fig 2. The upper part of the panel shows the system’s functional diagram. The lower part shows a graph drawn following the same convention as in [47], in which a directed arrow from A to B indicates that B appears in the dynamic equation of A. This graphical approach was originally proposed to study observability, and hence in [47] only the states were shown in the graphs. Since here we use it for identifiability purposes, we extend it to include both states and parameters (see figure caption for more details). The method presented here determines that both Pitavastatin uptake models (with and without pseudo steady state assumption) are structurally identifiable. The classical model of oscillations in enzyme kinetics proposed by Goodwin in 1965 [27] and shown in panel B of Fig 2 is still the subject of analyses [28]. It was selected by [10] to compare the performance of several structural identifiability methods, considering two different scenarios or variations of the model: when the three states are measured, or when only one of them—the enzyme concentration, x1—can be measured. The latter situation is more realistic, but its analysis is particularly challenging, and none of the methods tested by [10] managed to reach a conclusion due to computational complexity. According to Eq (8), the minimum number of Lie derivatives for which the identifiability matrix may be full rank is nd = 10 for this model. While the subsequent rank calculation is very demanding, the computational cost is substantially reduced by building O I (x ˜) with only 9 Lie derivatives. In this way the method classifies four parameters as identifiable: b, σ, β, δ. Then, removing these parameters from the model as explained in “Iterative refinement of the diagnosis” enables the analysis of the remaining parameters (a, A, α, γ), which are found to be unidentifiable. Thus this model is unidentifiable. It can be made identifiable by considering two parameters as known, one from each of the pairs {A, a} and {α, γ}. For example, if we fix the values of {A, α}, the remaining six unknown parameters in the model are identifiable. An alternative solution is to measure more states, if it is experimentally possible. In this case, if all three states are outputs, the model is structurally identifiable. Measuring only two of the three states, however, increases the number of identifiable parameters but does not render the model fully identifiable. The subsets of unidentifiable parameters for y = {x1, x2}, y = {x1, x3}, and y = {x2, x3} are, respectively, {a, A, γ}, {α, γ} and {a, α}. This model was presented in [28] as an example of a system exhibiting both oscillation and bistability. It is a three-layer signalling cascade with positive and negative feedback loops and Hill nonlinearities, shown in panel C of Fig 2. It has three states, which are the phosphorylated forms (x1, x2, x3), and 14 parameters (k1, k2, k3, k4, k5, k6, s1t, s2t, s3t, K1, K2, n1, n2, α). This system requires that all its three states are measured in order to be identifiable. However, if just one of the states is left unmeasured, some parameters become unidentifiable: if x1 is not measured, k3 and s1t are unidentifiable; if x2 is not measured, k5 and s2t are unidentifiable; and if x3 is not measured, K1, K2, and s3t are unidentifiable. This model was presented by [30] and was used by both [10] and [16] as a benchmark for structural identifiability methods. In the formulation of [10], only 13 parameters are considered unknown. In that case, all of them are identifiable. The general case, in which all 29 parameters are in principle unknown, is more challenging. For this case STRIKE-GOLDD classifies 5 parameters as unidentifiable: c1c, c2c, c3c, c4, and k2, and the remaining as identifiable. Part of this diagnosis can be confirmed by inspection of the connection diagram in the right side of Fig 3, which shows that c1c, c2c, and c3c only appear in the equation of state x15. Since x15 is in turn “disconnected” from the rest of the model (i. e. it does not appear in the equation of any other state), and it is not measured, there is clearly no way of determining its value. Hence x15 is unobservable, and the three parameters associated with it are unidentifiable. In contrast, the unidentifiability of c4 and k2 is by no means apparent from the figure. However, it can be determined with the methodology that they are not only unidentifiable, but related: fixing any of the two renders the other one identifiable. In summary, this 29-parameter model can be converted into a structurally identifiable 25-parameter model by fixing the values of four parameters: c1c, c2c, c3c, and either c4 or k2. This model of the IL13-Induced JAK/STAT signalling pathway was presented in [31] and later used in [20] to benchmark three identifiability analysis methods. The network interaction diagrams are shown in panel A of Fig 4. The results of our method coincide with those reported in [20], that is, five of the 23 parameters are unidentifiable, pu = [θ11, θ15, θ17, θ21, θ22]. Following the procedure outlined in the Methods section, it is possible to find an identifiable combination of unidentifiable parameters. To do this we remove the columns corresponding to identifiable parameters and obtain a reduced submatrix, O U. Calculation of a basis of the kernel of O U yields the following vector: v = [ 0,0, - θ 17 θ 22,0, 1 ] (11) which in turn leads to the following PDE: - θ 17 θ 22 · ∂ Φ ∂ θ 17 + ∂ Φ ∂ θ 22 = 0 ⇒ Φ = θ 17 · θ 22 (12) Thus, Φ = θ17 ⋅ θ22 is an identifiable parameter combination. The methodology does not report any combination involving θ11, θ15, θ21. If, additionally, we fix the value of θ11 a priori, we obtain a structurally identifiable model with 21 unknown parameters. The genetic subnetwork that controls the circadian clock in the plant A. thaliana was modelled in [29]; its diagram is shown in Fig 4. This model uses both Michaelis-Menten and Hill kinetics. Two Hill coefficients of transcription (a, b) were considered as known parameters in the original publication [29]. Although it was argued in [29] that there is evidence that b = 2, coefficient a was fixed to a = 1 without experimental evidence. In [10] it was reported that (for the case of a = 1) no global structural identifiability method was capable of successfully analysing the model; at most, identifiability of five parameters was established. While the choice of a = 1 makes the system rational and reduces the problem dimension, here we consider the more general case in which a is an unknown parameter. According to Eq (8), the minimum number of Lie derivatives for which O I (x ˜) may be full rank is very high for this model (nd = 16). This is the same situation as with the previously analysed Goodwin model, that is, the computational cost of the construction and subsequent rank calculation of O I (x ˜) with nd derivatives is too high. Furthermore, we found that the approach adopted for the Goodwin model—building the matrix with less than nd derivatives—was not successful in the case of this example, at least when performed with few derivatives. Hence we turned to the alternative procedure, i. e. decomposing the model using optimization. In this way, identifiability of ten parameters was established: a, k1, k4, m1, m4, n1, n2, q2, r2, and r4. Removing these parameters from the model decreases the number of required derivatives nd to 12, which is still very high; however, building O I (x ˜) with 9 derivatives reports identifiability of an additional parameter, r1. By performing partial analyses for unidentifiability (PAUs) we confirmed that the model is indeed unidentifiable. This can be remedied in several ways. A possible solution is to measure more states, if it is experimentally feasible. In the model it has been assumed that only mRNA concentrations are measured (i. e. states x1 and x4); however, if protein concentrations (i. e. the remaining states) are also measured, then all the parameters become structurally identifiable. Alternatively, if we assume that only the original outputs can be measured, it is possible to obtain an identifiable reformulation of the model by fixing some parameters. For example, choosing fixed values for the five degradation constants that were not found to be identifiable (k2, k3, k5, k6, k7) yields a structurally identifiable model with 23 parameters. This large-scale model was taken from the BioPreDyn-bench collection [32], where it was included as benchmark B4. It models a batch fermentation process for protein production using Chinese Hamster Ovary cells. Its diagrams are shown in Fig 5. It contains 34 states (which are metabolites present in three compartments: fermenter, cytosol, and mitochondria), of which 13 are measured outputs. Its 32 reactions include protein product formation, the Embden-Meyerhof-Parnas pathway (EMP), the TCA cycle, a reduced amino acid metabolism, lactate production, and the electron transport chain. The reactions are modelled using lin-log kinetics [49], resulting in non-rational equations with 117 unknown parameters. While it was noted in [32] that the parameter estimation results suggested practical identifiability issues, possible deficiencies in structural identifiability were not mentioned. Given the size of this model, its analysis is very challenging. Using decomposition it is possible to classify most of the parameters in the model as identifiable. However, we also found that at least four parameters are structurally unidentifiable: they are the parameters numbered 47,48,55, and 57, which correspond to the following kinetic constants (elasticities): {e54, − e55, − e62, e64}. After inspecting the model, we realised that it is possible to rewrite its dynamic equations in such a way that these parameters appear as (e54 + e55) and (e62 + e64); clearly, the individual parameters appearing in these sums are not identifiable. Thus, we replaced these four parameters with two new ones, en1 = e54 + e55 and en2 = e62 + e64. In this way we obtained a new model with 115 parameters, and confirmed that the newly introduced ones are structurally identifiable. Overall, we determined the identifiability of 97 parameters. While we did not manage to assess the identifiability of the remaining 18, we did find that fixing six of them (e. g. {p28, p72, p77, p101, p105, p115}) results in a structurally identifiable model. This action is slightly conservative, since those parameters can in principle be s. l. i. However, since the model has practical identifiability deficiencies [32] (as is typical of models of this type and size [49]), and given that it would be necessary to perform many Lie derivatives to relate these parameters to the model outputs, it is likely that in practice their values will be difficult to estimate. Therefore, fixing a subset of them appears as a reasonable solution. In summary, we found that: (i) this model is structurally unidentifiable, (ii) there exist two identifiable combinations of parameters, which convert 4 unidentifiable parameters into 2 identifiable ones, (iii) of the remaining 113 parameters, at least 95 are identifiable, and (iv) fixing the values of 6 parameters ensures that the remaining 12 (and the model as a whole) are identifiable. We have presented a methodology for analysing the structural identifiability of dynamic models described by a system of ordinary differential equations. It builds on concepts and techniques originally presented in the context of nonlinear observability analysis. More specifically, it adopts a differential geometry approach, which is based on building an augmented observability matrix—with the parameters considered as additional state variables—and calculating its rank. This formulation, as opposed to other approaches based on differential algebra, allows handling any analytic models, without requiring them to be in rational or polynomial form. If a model is structurally unidentifiable the method determines the identifiability of each parameter individually, by recalculating the matrix rank after removing the corresponding column. Realising that the structural identifiability analysis of nonlinear dynamic models is a challenging task, and that this difficulty increases rapidly with the problem size, our method is geared towards computational efficiency. To this end it includes several algorithmic developments to facilitate the analysis of models of larger size. One is the possibility of decomposing the model into smaller submodels, which can be found by optimization or specified by the user. Another is the calculation of the matrix rank with a reduced number of Lie derivatives. These alternatives lead in some cases to partial analyses, whose result is only conclusive if a parameter is classified as identifiable, but not as unidentifiable (or vice versa, depending on the type of analysis). In these situations the method also allows for an iterative refinement of the diagnosis: by removing parameters already classified as identifiable, the problem size is reduced and more complete analyses are made possible. To facilitate the application of this methodology, we have provided it as a free MATLAB (The MathWorks, Natick, MA) toolbox called STRIKE-GOLDD (STRuctural Identifiability taKen as Extended-Generalized Observability with Lie Derivatives and Decomposition), available under the GNU General Public License from https: //sites. google. com/site/strikegolddtoolbox/. We expect that STRIKE-GOLDD will contribute to fill the gap between the complexity of current systems biology models and their usability, which can be compromised unless structural identifiability is assessed. We have validated the methodology using a set of nonlinear systems biology models whose size and/or complexity make them challenging case studies. They range from a classic model of enzymatic oscillations with 8 parameters proposed by Goodwin in 1965 [27] to a metabolic model of more than 100 parameters published in 2015 [32]. Interestingly, we found structural identifiability issues even in models of relatively small size, such as the aforementioned Goodwin model. Indeed, the results show that identifiability issues are likely to appear in over-parameterized models (with many parameters per state), specially if only few of their states are available for measurement (in order words, if there are few outputs). A large parameter-to-output ratio also implies that the structural identifiability of the model will be difficult to analyse, because it will be necessary to perform many Lie derivative calculations in order to build the augmented observability matrix, thus incurring a high computational cost. Could this common cause mean that the difficulty in analysing a model is a hint of lack of identifiability? We ask this question because we know that, on the other hand, it is possible to analyse models with many parameters as long as sufficient measurements are available. Among the models analysed here, the JAK/STAT pathway had already been studied [20], and for that case our method confirmed previously reported results. In other cases we established the identifiability of systems that had not been analysed before, such as the mixed feedback MAPK pathway [28] or the model of Pitavastatin hepatic uptake (which had been reported to resist analysis when attempted with other methods, although it was suspected that it was identifiable [19]). Perhaps more interestingly, we also found some unidentifiabilities that had not been previously reported. An example is the Goodwin oscillator [27], for which it was established that half of its parameters are structurally unidentifiable. Despite the relatively small size of this model (3 states and 8 parameters), the fact that it is not a rational system, combined with the high parameter-to-output ratio (given that only one of its states is measured) make it a very challenging problem. Similar issues were found in the NF-κB signalling pathway [30] and in the genetic subnetwork of the circadian clock in Arabidopsis thaliana [29]. In these cases it can be noted that the ratio of unidentifiable parameters is larger in models with a lower ratio of measured outputs. Finally, we also detected unidentifiabilities in a recently presented large-scale dynamic model of metabolism of Chinese Hamster Ovary cells [32] with 117 parameters. We have also shown how to turn unidentifiable models into identifiable ones. With the procedure described in this paper it is sometimes possible to combine several unidentifiable parameters into a single identifiable combination. More often the solution is to reparameterize the model by considering some of the unidentifiable parameters as known constants, fixing them to values that appear reasonable according to available knowledge. In this way the remaining unknown parameters are rendered identifiable. Finally, a model can also be made identifiable by increasing the number of its outputs, if it is experimentally possible to measure more of its states.

Advances in computing power have facilitated the development of increasingly larger dynamic models of biological processes, which usually have many unknown parameters. Often times, such models contain parameters that are structurally unidentifiable, i. e. , they cannot be uniquely determined from experiments. Any parameter estimation algorithm will fail when trying to estimate unidentifiable parameters, leading to waste of resources and possibly wrong model predictions. Hence, it is essential to assess structural identifiability before exploiting a model. However, performing such analysis can be hard, especially as models become increasingly complicated. To address this challenge, we developed a methodology for structural identifiability analysis that aims at generality—it can handle any analytic model written as a set of ordinary differential equations—and computational efficiency—it includes features that facilitate the analysis of large systems. We provide an implementation of the methodology as a MATLAB toolbox called STRIKE-GOLDD. We illustrate its applicability to systems biology models of genetic, signalling, metabolic, and pharmacokinetic networks, showing which of them are unidentifiable and how they can be made identifiable.

Abstract Introduction Methods Results Discussion

medicine and health sciences biological cultures brassica optimization model organisms systems science mathematics pharmacology plants drug metabolism mapk signaling cascades research and analysis methods arabidopsis thaliana computer and information sciences nonlinear systems cho cells cell lines pharmacokinetics systems biology signal transduction plant and algal models cell biology biology and life sciences physical sciences cell signaling organisms signaling cascades

2016

Structural Identifiability of Dynamic Systems Biology Models

Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-order disease traits. Therefore, identifying the molecular phenotypes that vary in response to changes in DNA and that also associate with changes in disease traits has the potential to provide the functional information required to not only identify and validate the susceptibility genes that are directly affected by changes in DNA, but also to understand the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. Toward that end, we profiled more than 39,000 transcripts and we genotyped 782,476 unique single nucleotide polymorphisms (SNPs) in more than 400 human liver samples to characterize the genetic architecture of gene expression in the human liver, a metabolically active tissue that is important in a number of common human diseases, including obesity, diabetes, and atherosclerosis. This genome-wide association study of gene expression resulted in the detection of more than 6,000 associations between SNP genotypes and liver gene expression traits, where many of the corresponding genes identified have already been implicated in a number of human diseases. The utility of these data for elucidating the causes of common human diseases is demonstrated by integrating them with genotypic and expression data from other human and mouse populations. This provides much-needed functional support for the candidate susceptibility genes being identified at a growing number of genetic loci that have been identified as key drivers of disease from genome-wide association studies of disease. By using an integrative genomics approach, we highlight how the gene RPS26 and not ERBB3 is supported by our data as the most likely susceptibility gene for a novel type 1 diabetes locus recently identified in a large-scale, genome-wide association study. We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease and plasma low-density lipoprotein cholesterol levels in the process. Recent large-scale, genome-wide association studies have now delivered a number of novel findings across a diversity of diseases, including age-related macular degeneration [1–3], heart disease [4,5], host control of HIV-1 [6], type I and II diabetes [7,8], and obesity [9]. However, despite this astonishing rate of success, the major challenge still remains to not only confirm that the genes implicated in these studies are truly the genes conferring protection from or risk of disease, but to elucidate the functional roles that these implicated genes play with respect to disease. Most of the genetic association studies reporting novel, highly replicated associations to disease traits do not provide experimental data supporting the putative functional roles a given candidate susceptibility gene may play in disease onset or progression. Even in cases where susceptibility genes are well studied, with well known functions, nailing down how these genes confer disease susceptibility can linger for years, or even decades, as has been the case for genes like ApoE, an Alzheimer disease susceptibility gene identified more than 15 years ago [10]. Complex networks of molecular phenotypes—gene expression (mRNA, ncRNA, miRNA, and so on), protein expression, protein state, and metabolite levels—respond more proximally to DNA variations that lead to variations in disease-associated traits. These intermediate phenotypes respond to variations in DNA that in turn can induce changes in disease associated traits. Because a majority of single nucleotide polymorphisms (SNPs) detected as associated with disease traits from the recent wave of genome-wide association studies (GWASs) do not appear to affect protein sequence, it is likely that these SNPs either regulate gene activity at the transcript level directly or link to other DNA variations involved in this type of regulatory role. Therefore, to uncover the genetic determinants affecting expression in a metabolically active tissue that is relevant to the study of obesity, diabetes, atherosclerosis, and other common human diseases, we profiled 427 human liver samples on a comprehensive gene expression microarray targeting more than 39,000 transcripts, and we genotyped DNA from each of these samples at 782,476 unique SNPs. The relatively large sample size of this study and the large number of SNPs genotyped provided the means to assess the relationship between genetic variants and gene expression with more statistical power than many previous studies allowed [11–13]. A comprehensive analysis of the liver gene expression traits revealed that thousands of these traits are under the control of well-defined genetic loci, with many of the genes having already been implicated in a number of human diseases. Here we demonstrate directly how integrating genotypic and expression data in mouse and human can provide much-needed functional support for candidate susceptibility genes identified in a growing number of genetic loci that have been identified as key drivers of disease from GWASs. Specifically, we highlight how the gene RPS26 and not ERBB3 is most strongly supported by our data as a susceptibility gene for a novel type 1 diabetes (T1D) locus that was recently identified in a large-scale GWAS [14] and subsequently extensively replicated in a number of cohorts [15]. We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease [16] and plasma low-density lipoprotein (LDL) -cholesterol levels [17,18]. To identify expression quantitative trait loci (eQTL) that have putative cis and trans [19] regulatory effects on the liver gene expression traits, we tested all expression traits for association with each of the SNPs in the analysis SNP set typed in the HLC. The strongest putative cis eQTL for a given expression trait was defined as the SNP most strongly associated with the expression trait over all of the SNPs typed within 1 megabase (Mb) of the transcription start or stop of the corresponding structural gene. The association p-values were adjusted to control for testing of multiple SNPs and expression traits using two different methods: (1) a highly conservative Bonferroni correction method to constrain the study-wise significance level, and (2) an empirical false discovery rate (FDR) method that constrains the overall rate of false positive events. For cis eQTL, we only test for associations to SNPs that are within 1 Mb of the annotated start or stop site of the corresponding structural gene. To achieve a study-wise significance level of 0. 05, the Bonferroni adjusted p-value threshold was computed as, where Ni denotes the number of SNPs tested for trait i, over all 39,280 expression traits tested. At this threshold, 1,350 expression traits corresponding to 1,273 genes were identified. The Bonferroni adjustment method can be conservative when there is dependence among the expression traits and among the SNP genotypes. Given that strong correlation structures exist among expression traits and among SNP genotypes in a givenlinkage disequilibrium (LD) block, the Bonferroni adjustment may be overly conservative. Therefore, we used an empirical FDR method based on permutations that accounts for the correlation structures among the expression traits and among the SNP genotypes. We constrained the empirically determined FDR to be less than 10% (see Methods). At this level, we identified 3,210 expression traits corresponding to 3,043 genes that were significantly associated with at least one SNP near the corresponding gene region (referred to here as a putative cis eQTL). The full list of association results are provided in Table S2. The magnitude of the effects ranged from SNPs that explained roughly 2% of the in vivo expression variation (p ∼ 0. 003) to those that explained roughly 90% of the expression variation (p < 10−65). Several recent studies have been published that examine the extent of genetic control in blood [20–22], brain [23], and adipose [21] gene expression via genetic association testing. In one of these studies [21], we performed the study on human blood and adipose tissues profiled on the same expression platform as the HLC, providing a straightforward way to compare the extent of cis eQTL overlap between blood, adipose, and liver tissues. In our characterization of blood and adipose tissue eQTL, there were 2,573 and 2,789 expression traits, respectively, represented on the microarray used to profile the HLC samples and that gave rise to cis eQTL. Of these, 752 blood and 881 adipose cis eQTL overlapped the set of 3,210 cis eQTL detected in the HLC. Therefore, in both adipose and blood, roughly 30% of the cis eQTL detected in these tissues were also detected in the HLC, confirming that there is significant, common genetic control between tissues. However, these overlaps also highlight that a majority of cis eQTL detected in one tissue may be specific to that tissue, potentially reflecting the genetic control of tissue-specific biological functions. The significance of the trans eQTL in the HLC were also assessed by the Bonferroni method and by constraining the empirically determined FDR to be less than 10%. In the case of trans eQTL, all 782,476 SNPs were tested for association to each of the 39,280 expression traits. Therefore, the Bonferroni adjusted p-value threshold was computed as 0. 05/ (782,476 × 39,280) = 1. 6 × 10−12. At this threshold, 242 expression traits corresponding to 236 genes were significantly associated with a SNP in trans (referred to here as a trans eQTL). On the other hand, by constraining the FDR to be less than 10%, 491 expression traits corresponding to 474 genes were identified as significantly associated with a SNP in trans. For the FDR-computed cis and trans eQTL signatures, the trans eQTL signature was only 15% the size of the cis eQTL signature, consistent with findings in other human genetics of gene expression studies [12,13]. The smaller trans eQTL signature likely reflects a lack of power to detect the small-to-moderate eQTL effects, given the sample size of this study in the context of testing 782,476 SNPs and profiling 39,280 expression traits. Other studies have noted strong heritability estimates for a majority of the expression traits that, when taken together with the small number of associations detected, suggests that expression in general is a complex trait under the control of many loci [21]. With the more stringent threshold required to constrain the FDR in searching for trans eQTL, the magnitude of the trans eSNP effects (mean R2 = 0. 19) was larger than the cis eSNP effects (mean R2 = 0. 14). In this study we used both the Affymetrix and Illumina genotyping platforms, providing for increased power to detect cis and trans eQTL in the HLC compared to the detections achieved using the Affymetrix and Illumina sets independently [49]. Conditional on the sample size and FDR, the Illumina SNP set provided for roughly 15% more eQTLs than the Affymetrix SNP set, corresponding to a 15% increase in the relative power. This increase in power is primarily due to the higher genetic coverage of the Illumina SNP set compared to the Affymetrix SNP set. Further, given the ∼40,000 expression traits profiled in the HLC, we were able to estimate the genetic coverage of the Illumina and Affymetrix SNP sets on a cohort that is independent of the HapMap CEU (Utah residents with ancestry from Northern and Western Europe) subjects. Interestingly, we observed significantly lower genetic coverage (78%) than previously reported (90%) (electronic database: http: //www. cidr. jhmi. edu/download/HumanHap650Y_info. pdf). Finally, in comparing whether more samples or more SNPs enhanced power most dramatically, we found that a modest increase in sample size (19%) had a more profound impact on the power to detect gene expression associations (a 21% increase in this case) than increasing the genetic coverage. These power and genetic coverage issues are fully detailed in a separate report [49]. The cis and trans eQTLs identified from the first pass analysis provide a significantly reduced set of SNPs on which to focus (∼3,700 versus 782,476). The set of SNPs associated with expression (eSNPs) can be considered a functionally validated set, given that the SNPs in this set have been found to associate with biologically relevant control of gene expression. In fact, many of the gene expression traits associated with eSNPs correspond to genes that have previously been associated with many different human diseases (Table S3). For example, BRCA1, a well-known susceptibility gene for breast cancer, and CFH, a susceptibility gene for age-related macular degeneration identified in one of the first published GWASs, are each strongly associated with an eSNP (p = 9. 73 × 10−17 for BRCA1 and p = 6. 94 × 10−22 for CFH) that falls within 1Mb of the corresponding structural gene (Table S3). Genes associated with drug response are also represented in this set. For example, VKORC1, a gene recently associated with warfarin dosing [24], has liver gene expression values that are significantly associated with an eSNP near the 3′ end of the gene (p = 1. 66 × 10−23). To characterize further the effect that this set of eSNPs has on the liver transcriptional network, we re-analyzed the association results by restricting attention to this panel of SNPs. We again constrained the FDR to be less than 10% with respect to the eSNP set and identified an additional 3,053 expression traits, corresponding to 2,838 genes that were significantly associated with at least one of the eSNPs (Table S2). We assessed the significance of this new set of expression traits by randomly sampling five sets of SNPs from the full set of SNPs typed in the HLC, such that the size and minor allele frequency distribution matched that of the eSNP set. For each of the randomly selected SNP sets, we analyzed the associations between all expression traits and SNPs in this set. The maximum number of associations detected in any of the five sets at a 10% FDR was only 20, and the mean detection rate over all sets was 12. This demonstrates well the biological utility of the eSNP set, given that this set is significantly enriched for SNPs that associate with expression traits, beyond the initial set of expression traits that defined the eSNP set, compared to comparable sets of randomly selected SNPs. A number of eQTL hot spots emerged as well in this full set of expression traits, where a given locus was identified as a hot spot if greater than 20 expression traits linked to a single eSNP at the locus. The significance of these hot spots was assessed by permuting the genotypes and examining the distribution of associations in the permuted sets. In each permutation set, we identified the maximum number of traits associated with a single marker over all markers. The mean of the maximum counts over 10 permutation sets was only 12, compared to a maximum of 283 in the observed data (Table S2). Identifying candidate susceptibility genes in regions associated with disease using the proximity of the candidate genes to SNPs in that region may be misleading a lot of the time. For example, from Table S2, for the 3,210 expression traits giving rise to cis eQTLs, only 627 of the corresponding cis eSNPs are located within the corresponding gene region, whereas 1,282 are located downstream of the 3′ untranslated region (UTR) and 1,301 are located upstream of the 5′ UTR. Further, of the cis eSNPs located up- and downstream of the corresponding genes, 490 and 526, respectively, are >100 kb away. That is, greater than 30% of all cis eSNPs fall greater than 100 kb away from the transcription start and stop sites of the corresponding gene. Therefore, at least for expression traits, the nearest SNP rule for inferring genes given an association finding would result in an unacceptably high miss-call rate. Genes with expression values that are strongly associated with variations in DNA provide a different path to elucidate the gene or genes and their respective functions underlying genetic loci associated with disease in a more objective fashion. Previous studies on the genetics of gene expression in humans have focused primarily on lymphoblastoid cell lines or other blood-derived samples [13,14,17]. We have provided a large-scale assessment of the genetics of gene expression in human liver, a metabolically active tissue that is critical to a number of core biological processes and that plays a role in a number of common human diseases. After profiling 427 human liver samples on a comprehensive gene expression microarray and genotyping the DNA from these samples at greater than one million SNPs, we identified a significant genetic signature underlying the expression of more than 6,000 genes, with many of these genes already implicated as causal for a number of different diseases, including heart disease, breast cancer, inflammatory bowel disease, age-related macular degeneration, schizophrenia, and Alzheimer disease. This set of data highlights the utility of monitoring molecular phenotypes that underlie the higher order clinical states of a system. Whereas the eQTL data in the human liver cohort is valuable in its own right, when integrated with other GWAS data and with genetics of gene expression and clinical data in segregating mouse populations, there is the potential to directly identify experimentally supported candidate susceptibility genes for disease. We demonstrated directly how genetics of gene expression data can complement multiple GWAS datasets by highlighting SORT1 and CELSR2 as candidate susceptibility genes for CAD and LDL cholesterol levels at a recently identified locus associated with CAD [16]. In this instance, the association to LDL cholesterol levels is novel and based on publicly available GWAS data and a mouse cross designed specifically to study lipid and other metabolic syndrome traits. In addition to the CAD locus, we highlighted RPS26 as a candidate susceptibility gene for T1D from a novel, highly replicated T1D locus on Chromosome 12q13, which was identified in a separate GWAS [15]. Not only was the expression of this gene in the HLC strongly associated with the T1D SNP at this locus, but it was observed to operate in a part of the molecular network that is significantly enriched for genes associated with T1D (like HLA-DRB1), whereas the gene inferred as the most likely susceptibility gene at that locus (ERBB3) [15] was not supported by any of our experimental data. Recent studies have demonstrated that ribosomal proteins may be involved in auto-immune diseases like systemic lupus erythematosus [36]. In addition, recent work has demonstrated a connection between endoplasmic reticulum (ER) stress in the cytoplasm and diabetes, where protein unfolding in response to ER stress is hypothesized to disrupt processes associated with diabetes [37]. Given RPS26' s protein translation role as part of the ribosomal complex on the ER, its association to T1D is particularly intriguing. The unfolded protein response has also been linked to inflammation and oxidative stress [38], hence the putative connection between RPS26 and an auto-immune disease like T1D is worthy of further consideration. Cells with high secretory capacity like pancreatic beta cells are also more likely to be susceptible to ER stress, making the link between RPS26 and T1D even more plausible. In fact, previous work has indicated higher ER stress levels in T1D patients [39]. It is important to note that a lack of association between expression traits in the HLC and disease-associated SNPs is not a valid filter for excluding a gene as a candidate disease susceptibility gene, given that variation in a gene leading to disease may affect protein function and not expression, or it may affect expression in a different tissue or under different environmental conditions. However, the approach of analyzing the genetics of gene expression in human populations does provide a more objective view into the functioning of genes in a given disease-associated region. This view has the potential to lead to higher confidence candidates in the absence of direct functional support for any one gene, which is typically the case in GWASs where the SNPs identified have no known functional role. Given the potential that genetics of gene expression studies have to affect our understanding of common human diseases, generating even larger-scale molecular profiling datasets in segregating populations may provide a path to more rapidly elucidating not only the genetic basis of disease, but the impact the genetic basis of disease has on molecular networks that in turn induce variations in disease associated traits. The HLC was assembled from a total of 780 liver samples (1–2 g) that were acquired from Caucasian individuals from three independent liver collections at tissue resource centers at Vanderbilt University, the University of Pittsburgh, and Merck Research Laboratories (Table S1). The Vanderbilt samples (n = 504) included both postmortem tissue and surgical resections from organ donors and were obtained from the Nashville Regional Organ Procurement Agency (Nashville, Tennessee), the National Disease Research Interchange (Philadelphia, Pennsylvania), and the Cooperative Human Tissue Network (University of Pennsylvania, Ohio State University, and University of Alabama at Birmingham). The Pittsburgh samples were normal postmortem human liver and were obtained through the Liver Tissue Procurement and Distribution System (Dr. Stephen Strom, University of Pittsburgh, Pittsburgh, Pennsylvania). The University of Pittsburgh samples (n = 211) were all postmortem, as were the Merck samples (n = 65), which collected by the Drug Metabolism Department and reported previously [40]. All samples were stored frozen at −80 °C from collection until processing for RNA and DNA; some samples had been stored for over a decade before being processed for this study. Demographic data varied across centers for these samples and were missing in many cases. In cases where age, sex, or ethnicity data were not available in the patient records, we imputed it from the gene expression and/or genotype data (see below). Of the 780 samples collected, high-quality DNA was isolated on 548 samples, and 517 of these were successfully genotyped on the Affymetrix genotyping platform (see Methods below). Of the 517 successfully genotyped samples, high-quality RNA was isolated and successfully profiled on 427 samples. This set of 427 genotyped and expression profiled samples comprised the HLC. Table S1 gives a summary of the demographics and other annotations on the 427 individuals that were successfully genotyped and expression profiled. All counts and descriptive statistics include the imputed data. All samples and patient data were handled in accordance with the policies and procedures of the participating organizations. C57BL/6J (B6) mice were intercrossed with C3H/HeJ (C3H) mice to generate 321 F2 progeny (161 females, 160 males) for the BXH wild type (BXH/wt). C57BL/6J (B6) mice were intercrossed with Castaneus (CAST) mice to generate 442 F2 progeny (276 females, 166 males) for the BXC cross. All mice were maintained on a 12 h light–12 h dark cycle and fed ad libitum. BXH mice were fed Purina Chow (Ralston-Purina) containing 4% fat until 8 wk of age. From that time until the mice were killed at 20 wk, mice were fed a western diet (Teklad 88137, Harlan Teklad) containing 42% fat and 0. 15% cholesterol. BXC mice were fed Purina Chow until 10 wk of age, and then fed western diet (Teklad 88137, Harlan Teklad) for the subsequent 8 wk. Mice were fasted overnight before they were killed. Their livers were collected, flash frozen in liquid nitrogen, and stored in −80 °C prior to RNA isolation. The BXH cross on an ApoE null background (BXH/apoE) was previously described [41]. Briefly, C57BL/6J ApoE null (B6. ApoE–/–) were purchased from Jackson Laboratory. C3H/HeJ ApoE null (C3H. Apo E–/–) were generated by backcrossing B6. ApoE–/– to C3H for ten generations. F1 mice were generated from reciprocal intercrossing between B6. ApoE–/– and C3H. ApoE–/–, and F2 mice were subsequently bred by intercrossing F1 mice. A total of 334 (169 female, 165 male) were bred, and all were fed Purina Chow containing 4% fat until 8 wk of age, and then transferred to western diet containing 42% fat and 0. 15% cholesterol for 16 wk. Mice were killed at 24 wk, and liver, white adipose tissue, and whole brains were immediately collected and flash-frozen in liquid nitrogen. All procedures of housing and treatment of animals were performed in accordance with Institutional Animal Care and Use Committee regulations. Array design and preparation of labeled cDNA and hybridizations to microarrays for the human liver cohort. RNA preparation and array hybridizations were performed at Rosetta Inpharmatics. The custom ink-jet microarrays used in this study were manufactured by Agilent Technologies and consisted of 4,720 control probes and 39,280 noncontrol oligonucleotides extracted from mouse Unigene clusters and combined with RefSeq sequences and RIKEN full-length cDNA clones (Table S4). Liver samples extracted from the 427 Caucasian individuals were homogenized, and total RNA extracted using TRIzol reagent (Invitrogen) according to manufacturer' s protocol. Three micrograms of total RNA was reverse transcribed and labeled with either Cy3 or Cy5 fluorochrome. Purified Cy3 or Cy5 complementary RNA was hybridized to at least two single microarrays with fluor reversal for 24 h in a hybridization chamber, washed, and scanned using a laser confocal scanner. Arrays were quantified on the basis of spot intensity relative to background, adjusted for experimental variation between arrays using average intensity over multiple channels, and fitted to an error model to determine significance (type I error), as previously described [42]. Gene expression is reported as the mean-log ratio relative to the pool derived from 192 liver samples selected for sex balance from the Vanderbilt and Pittsburgh samples, because the RNA from the Merck samples had been amplified at an earlier date. The error model used to assess whether a given gene is significantly differentially expressed in a single sample relative to a pool composed of a randomly selected subset of samples has been extensively described and tested in a number of publications [42–44]. The age, sex, race, center, alcohol use, drug use, and steatosis variables presented in Table S1 were tested for association to the gene expression traits. Only age, sex, race, and center were significantly associated with the expression traits beyond what would be expected by chance. As a result, all gene expression traits were adjusted for these covariates. The lack of association between the expression traits and alcohol use, drug use, and steatosis was somewhat surprising, but may be due to the sparseness of these data, resulting in a lack of power to detect significant associations. Array design and preparation of labeled cDNA and hybridizations to microarrays for the mouse liver and adipose tissue samples. RNA preparation and array hybridizations were again performed at Rosetta Inpharmatics. The custom ink-jet microarrays used in the BXH/wt, BXH/apoE, and BXC crosses were manufactured by Agilent Technologies. The array used for the BXH/apoE and BXH/wt samples consisted of 2,186 control probes and 23,574 noncontrol oligonucleotides extracted from mouse Unigene clusters and combined with RefSeq sequences and RIKEN full-length cDNA clones (Table S5). The array used for the BXC cross consisted of 39,280 noncontrol oligonuceotides again extracted from the mouse Unigene clusters and combined with RefSeq sequences and RIKEN full-length cDNA clones (Table S6). Mouse adipose and liver tissues from all of the crosses were homogenized, and total RNA extracted using Trizol reagent (Invitrogen) according to manufacturer' s protocol. Three micrograms of total RNA was reverse transcribed and labeled with either Cy3 or Cy5 fluorochrome. Labeled complementary RNA (cRNA) from each F2 animal was hybridized against a cross-specific pool of labeled cRNAs constructed from equal aliquots of RNA from 150 F2 animals and parental mouse strains for each of the three tissues for each cross. The hybridizations for the BXH/apoE cross were performed in fluor reversal for 24 h in a hybridization chamber, washed, and scanned using a confocal laser scanner. The hybridizations for the BXH/wt and BXC crosses were performed to single arrays (individuals F2 samples labeled with Cy5 and reference pools labeled with Cy3 fluorochromes) for 24 h in a hybridization chamber, washed, and again scanned using a confocal laser scanner. Arrays were quantified on the basis of spot intensity relative to background, adjusted for experimental variation between arrays using average intensity over multiple channels, and fitted to a previously described error model to determine significance (type I error) [42]. Gene expression measures are reported as the ratio of the mean log10 intensity (mlratio). DNA isolation. DNA isolation was performed at Rosetta Inpharmatics. DNeasy tissue kits from QIAGEN were used to carry out all DNA extractions. For each liver sample, 20–30 mg of liver was placed in a 1. 5-ml microcentrifuge tube along with 80 μl buffer ATL and 20 μl proteinase K. The contents of each tube were then mixed thoroughly by vortexing, followed by incubation at 55 °C until the tissue was completely lysed. Transcriptionally active tissues such as liver and kidney contain high levels of RNA, which will co-purify with genomic DNA. Because RNA-free genomic DNA was required for processing, 4 μl RNase A (100 mg/ml) was added and mixed by vortexing, followed by incubation for 2 min at room temperature before continuing. Samples were then vortexed and 200 μl buffer AL was added to the sample and mixed thoroughly. After 10 min incubation at 70 °C, 200 μl ethanol (96%–100%) was then added and mixed again. The mixture was placed into the DNeasy Mini column and centrifuged at 6,000g (8,000 rpm) for 1 min. The DNeasy Mini spin column was then placed in a new 2-ml collection tube, and 500 μl buffer AW1 was added, followed by placement in a centrifuge for 1 min at 6,000g (8,000 rpm). The DNeasy Mini spin column was then placed in a new 2-ml collection tube again, and 500 μl buffer AW2 was added and centrifuged for 3 min at 20,000g (14,000 rpm) to dry the DNeasy membrane. Then the DNeasy Mini spin column was placed in a clean 1. 5-ml or 2-ml microcentrifuge tube and 200 μl buffer AE was pipetted directly onto the DNeasy membrane. This was incubated at room temperature for 1 min and then centrifuged for 1 min at 6,000g (8,000 rpm) to elute. Two 200-μl elutions were performed followed by ethanol/sodium acetate precipitation and resuspension of the resultant pellet with TE buffer. Genotyping data from the Affymetrix 500K panel. SNP genotyping was performed with the commercial release of the Affymetrix 500K genotyping array. The genotyping was carried out at the Perlegen genotyping facility in Mountain View, California. Genotyping was attempted on 548 samples. 18 samples were unable to be genotyped because of poor DNA quality, and an additional 13 samples were removed after genotyping because their overall call rate did not exceed the 90% cutoff we required. We then applied SNP-wise quality checks on the 517 samples that were successfully genotyped. The Affymetrix 500K array consisted of 500,568 SNPs in total, 429,545 SNPs provided quality data from the genotyping assay, and we rejected those SNPs with a call rate < 75%, resulting in a final panel of 393,494 SNPs. We further filtered out SNPs with minor allele frequencies < 4% (81,646 SNPs) or SNPs that deviated from Hardy-Weinberg equilibrium (p < 10−4; 1,104 SNPs). The resulting set of 310,744 SNPs were used to carry out tests for association to the liver gene expression traits in the HLC. Genotyping data from the lllumina 650Y panel. SNP genotyping was performed on the same set of samples that were genotyped on the Affymetrix 500K panel using the Sentrix humanHap650Y genotyping beadchip from Illumina. The genotyping was carried out at the Illumina genotyping facility in La Jolla, California. This chip consists of 655,352 tag SNP markers derived from the International HapMap Project (http: //www. hapmap. org) on a single BeadChip, with ∼100,000 Yoruba-specific tag SNPs to provide more comprehensive coverage in African and African-American populations. Genotyping was attempted on 517 samples. A total of 497 samples were genotyped successfully, and 654,069 SNP assays genotyped successfully. The same genotype quality control measures applied to the Affymetrix 500K dataset were applied to Illumina HumanHap 650Y dataset to determine the analysis set. The sample set for analysis (n = 397) was restricted to those identified or imputed as Caucasian. Of the 397 samples we attempted to genotype, 13 failed the Illumina genotyping assay (overall call rate < 75%), resulting in a set of 384 genotyped samples carried forward for the expression analysis. In total, 652,648 SNPs were called, with only two SNPs rejected because the call rate was <75%. We then sequentially removed 94,915 SNPs with MAF <4% and 491 SNPs that deviated from the Hardy-Weinberg equilibrium (p < 10−4). The resulting set of 557,240 SNPs was used to carry out tests for association to the liver gene expression traits in the HLC. A total of 85,508 SNPs were represented in both the Illumina and Affymetrix SNP sets. Therefore, there were 782,476 unique SNPs successfully genotyped in the HLC such that the call rate was greater than 75%, the MAF >4%, and there was not significant deviation from Hardy-Weinberg equilibrium at the 0. 0001 significance level. The sample set for analysis was restricted to the 427 HLC samples that had both genotype and gene expression data available, passed the criteria outlined above and those that were identified as Caucasian, or imputed to be Caucasian when data was missing (see below). Sex confirmation. Sex identifiers were available for most of the liver samples obtained from the three study centers. We independently confirmed the sex of each individual providing a liver sample by two methods. First, we looked for expression of Y-specific genes in the liver gene expression based on three probes representing three distinct transcripts. Second, we scored heterozygosity of X-chromosome markers. We excluded any individual for which there was a discrepancy in any of the three measures of sex in order to ensure a coherent data set for analysis and that we had excluded as many potential cases of annotation or sample-handling errors as possible. For samples where sex was not noted in the records, we imputed the sex call if both the genotype and gene-expression data were concordant. Ethnicity. Ethnicities were confirmed or imputed using STRUCTURE [45]. A panel of 106 autosomal markers was randomly selected from around the genome to be unlinked and ancestry informative. Markers were selected from the HapMap data [46] that were present on the Affy 500K panel such that the minor allele frequency was >0. 05 and the absolute allele frequency difference in the Caucasians and African Americans ∼0. 5, with average minor allele frequency 0. 5 (standard deviation = 12). Several K were tested (K = 1–6) with burn-in 100,000 and 100,000 reps of MCMC before any information was collected. In all cases, the greatest support was for K = 2. Admixture was detected for some individuals in some runs and some individuals were reclassified. For those unknown and reclassified, population reassignment was made if the probability of group membership was >0. 9 for that individual. This resulted in 469 individuals assigned to the Caucasian group, 28 individuals assigned to the African descent or African American group, and 18 individuals assigned as “unknown”. The data set for further analysis was restricted to Caucasian samples. Age. Ages were imputed using the Elastic Net method [47]. This method performs model selection and parameter estimation in a manner that is a combination of ridge-regression and the lasso. The prediction method is also explained in [47]. For computational reasons, λ was set to zero, in which case the Elastic Net method reduces to the lasso method. For most applications, experience demonstrates that the optimal value for λ is zero or quite near zero. Ages were imputed using separate models for each data source, due to evidence of a source effect, and each sex separately. In cases where the sex was missing or the reported sex was different from the sex implied by the expression data, the sex implied by the expression data was used. This was done so that in the case the annotation data and expression data were mismatched, the imputed age would correspond to the data used to predict it. The 5,000 genes with the highest correlation to age were used as potential regressors. Cross-validation was used to select the number of steps in the model selection procedure. The number of predictors in the model was between 67 and 76 for the four different models. The percentage of variation explained in the training set is quite high (97%–99%) for three of the models. For the fourth, the model for Vanderbilt females, the percentage of variation explained was slightly lower, 0. 92. This is a vast improvement over more naïve imputation methods that are used when adjusting for covariates with missing data, where mean values of the nonmissing data are used to fill in the missing values. Very few of the predictors we constructed were common between the different models. Given the number of predictors with high correlation to age, this is not surprising. Nonetheless, within a given data source (i. e. , Pittsburgh or Vanderbilt samples), the male model is a reasonable predictor for the ages of the females and vice-versa. This same trend did not hold for predicting the ages of same-sex individuals across data sources. Expression trait processing. Expression traits were adjusted for age, sex, and medical center. Residuals were computed using rlm function from R statistical package (M-estimation with Tukey' s bisquare weights). In examining the distributions of the mean log ratio measures for each expression trait in the HLC set, we noted a high rate of outliers. As a result, we used robust residuals and nonparametric tests to carry out the association analyses in the HLC. For each expression trait, residual values deviating from the median by more than three robust standard deviations were filtered out as outliers. Genome-wide eQTL association analysis. The Kruskal-Wallis test was used to determine association between adjusted expression traits and genotypes. We chose this nonparametric method because of its robust nature to underlying genetic model and trait distribution. p-Values were computed using nag_mann_whitney (for loci with two observed genotypes) and nag_kruskal_wallis_test (for loci with three observed genotypes) routines from NAG C library (http: //www. nag. co. uk). We used FDR for multiple-test correction. FDR was estimated as the ratio of the average number of eQTLs found in datasets with randomized sample labels to the number of eQTLs identified in the original data set. Since the number of tests was large (∼1,010), we found the empirical null distribution was very stable and three permutation runs were sufficient for convergence to estimate FDR. FDR computation was performed separately for cis (<1 Mb probe to SNP distance) and trans associations resulting in nominal p-value cutoffs of 5. 0 × 10−5 and 1. 0 × 10−8 for cis and trans eQTLs, respectively. Targeted set association analysis. The 3,346 SNPs identified in the first round of analysis as associating with expression traits in cis at an FDR < 0. 1 were picked for a second round of analysis. To assess the significance of the resulting set of expression traits detected as associated with this set of SNPs, sets of randomly selected SNPs of size 3,346 with MAF distributions identical to the original set were generated. All sets of SNPS were then analyzed using the same method described above for genome-wide associations. Identifying differentially expressed genes. To assess whether a gene in a given sample was differentially expressed, we used a previously described and validated error model for testing whether the mean log ratio of the intensity measures between the experiment and reference channels was significantly different from zero [42,43,48]. Based on this error model we obtained p-values for each of the individual gene expression measures in each sample as previously described [33]. We then computed the standard deviation of –log10 of the p-value for each gene expression measure over all samples profiled for a given tissue, and then rank ordered all of the genes profiled in each tissue based on this standard deviation value (rank ordered in descending order). Genes that fall at the top of this rank ordered list can be considered as the most differentially expressed or variable genes in the study. We have previously shown that this type of ordering approach well captures the most active genes in a set of samples [33]. For demonstrating the number of genome-wide significant eQTLs and eSNPs as a function of differential gene expression, we binned the expression traits into quartiles (Q1-Q4) based on the rank-ordered gene list, with each bin containing 10,025 genes and the bins increasing in significance with respect to differential expression, from Q1 to Q4. Visualization of networks. Networks were visualized using the Target Gene Information (TGI) Network Analysis and Visualization (NAV) desktop application developed at Rosetta Inpharmatics. This tool enables rapid, real-time, graphical analysis of pathway network models built from a comprehensive and fully integrated set of public and proprietary interaction databases available through a back-end central database, described in detail in a separate report. Additionally, the TGI NAV tool supports experimentally generated systems biology data such as the statistical associations and causal relationships described here. TGI NAV enables integration and visualization of orthogonal data sets using network models as a framework and facilitates dissection of networks into smaller, functionally significant subnetworks amenable to biological interpretation. To construct the local networks for H2-Eb1, Erbb3, and Rps26, the whole-gene probabilistic causal networks were loaded into the database and the TGI NAV tool was used to extract all edges from this network involving the central gene of interest. In the case of the Erbb3 network, the local network was expanded by extracting all additional edges involving any genes directly connected to Erbb3. Note that while the underlying networks describe causal relationships between transcripts, TGI NAV was used to translate this network into the space of genes using an integrated mapping database that clusters transcripts into gene models utilizing their genomic coordinates. As a result, multiple causal relationships between gene pairs can be observed in cases where multiple transcripts for a single gene were profiled. Visualization properties of nodes (e. g. , color) are specified in TGI NAV either for individual nodes, or in a data-driven manner by associating attributes, such as KEGG pathway membership, with groups of nodes and mapping visualization properties to these attributes. All microarray data associated with the HLC have been deposited into the Gene Expression Ominbus database under accession number GSE9588.

Genome-wide association studies seek to identify regions of the genome in which changes in DNA in a given population are correlated with disease, drug response, or other phenotypes of interest. However, changes in DNA that associate with traits like common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in the higher-order disease traits. Therefore, identifying molecular phenotypes that vary in response to changes in DNA that also associate with changes in disease traits can provide the functional information necessary to not only identify and validate the susceptibility genes directly affected by changes in DNA, but to understand as well the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. To enable this type of approach we profiled the expression levels of 39,280 transcripts and genotyped 782,476 SNPs in 427 human liver samples, identifying thousands of DNA variants that strongly associated with liver gene expression. These relationships were then leveraged by integrating them with genotypic and expression data from other human and mouse populations, leading to the direct identification of candidate susceptibility genes corresponding to genetic loci identified as key drivers of disease. Our analysis is able to provide much needed functional support for these candidate susceptibility genes.

Abstract Introduction Results Discussion Materials and Methods Supporting Information

diabetes and endocrinology computational biology genetics and genomics cardiovascular disorders

2008

Mapping the Genetic Architecture of Gene Expression in Human Liver

To form a percept of the environment, the brain needs to solve the binding problem—inferring whether signals come from a common cause and are integrated or come from independent causes and are segregated. Behaviourally, humans solve this problem near-optimally as predicted by Bayesian causal inference; but the neural mechanisms remain unclear. Combining Bayesian modelling, electroencephalography (EEG), and multivariate decoding in an audiovisual spatial localisation task, we show that the brain accomplishes Bayesian causal inference by dynamically encoding multiple spatial estimates. Initially, auditory and visual signal locations are estimated independently; next, an estimate is formed that combines information from vision and audition. Yet, it is only from 200 ms onwards that the brain integrates audiovisual signals weighted by their bottom-up sensory reliabilities and top-down task relevance into spatial priority maps that guide behavioural responses. As predicted by Bayesian causal inference, these spatial priority maps take into account the brain’s uncertainty about the world’s causal structure and flexibly arbitrate between sensory integration and segregation. The dynamic evolution of perceptual estimates thus reflects the hierarchical nature of Bayesian causal inference, a statistical computation, which is crucial for effective interactions with the environment. In our natural environment, our senses are exposed to a barrage of sensory signals: the sight of a rapidly approaching truck, its looming motor noise, the smell of traffic fumes. How the brain effortlessly merges these signals into a seamless percept of the environment remains unclear. The brain faces two fundamental computational challenges: First, we need to solve the ‘binding’ or ‘causal inference’ problem—deciding whether signals come from a common cause and thus should be integrated or instead be treated independently [1,2]. Second, when there is a common cause, the brain should integrate signals taking into account their uncertainties [3,4]. Hierarchical Bayesian causal inference provides a rational strategy to arbitrate between sensory integration and segregation in perception [2]. Bayesian causal inference explicitly models the potential causal structures that could have generated the sensory signals—i. e. , whether signals come from common or independent sources. In line with Helmholtz’s notion of ‘unconscious inference’, the brain is then thought to invert this generative model during perception [5]. In case of a common signal source, signals are integrated weighted in proportion to their relative sensory reliabilities (i. e. , forced fusion [3,4, 6–10]). In case of independent sources, they are processed independently (i. e. , full segregation [11,12]). Iin a particular instance, the brain does not know the world’s causal structure that gave rise to the sensory signals. To account for this causal uncertainty, a final estimate (e. g. , object’s location) is obtained by averaging the estimates under the two causal structures (i. e. , common versus independent source models) weighted by each causal structure’s posterior probability—a strategy referred to as model averaging (for other decisional strategies, see [13]). A large body of psychophysics research has demonstrated that human observers combine sensory signals near-optimally as predicted by Bayesian causal inference [2,13–16]. Most prominently, when locating events in the environment, observers gracefully transition between sensory integration and segregation as a function of audiovisual spatial disparity [12]. For small spatial disparities, they integrate signals weighted by their reliabilities, leading to cross-modal spatial biases [17]; for larger spatial disparities, audiovisual interactions are attenuated. A recent functional MRI (fMRI) study showed how Bayesian causal inference is accomplished within the cortical hierarchy [14,16]: While early auditory and visual areas represented the signals on the basis that they were generated by independent sources (i. e. , full segregation), the posterior parietal cortex integrated sensory signals into one unified percept (i. e. , forced fusion). Only at the top of the cortical hierarchy, in anterior parietal cortex, the uncertainty about the world’s causal structure was taken into account and signals were integrated into a spatial estimate consistent with Bayesian causal inference. The organisation of Bayesian causal inference across the cortical hierarchy raises the critical question of how these neural computations unfold dynamically over time within a trial. How does the brain merge spatial information that is initially coded in different reference frames and representational formats? Whereas the brain is likely to recurrently update all spatial estimates by passing messages forwards and backwards across the cortical hierarchy [18–20], the unisensory estimates may to some extent precede the computation of the Bayesian causal inference estimate. To characterise the neural dynamics of Bayesian causal inference, we presented human observers with auditory, visual, and audiovisual signals that varied in their spatial disparity in an auditory and visual spatial localisation task while recording their neural activity with electroencephalography (EEG). First, we employed cross-sensory decoding and temporal generalisation matrices [21] of the unisensory auditory and visual signal trials to characterise the emergence and the temporal stability of spatial representations across the senses. Second, combining psychophysics, EEG, and Bayesian modelling, we temporally resolved the evolution of unisensory segregation, forced fusion, and Bayesian causal inference in multisensory perception. Combining psychophysics, multivariate EEG pattern decoding, and computational modelling, we next investigated the computational principles and neural dynamics underlying audiovisual integration of spatial representations using a general linear model (GLM) -based wAV and a Bayesian modelling analysis. As shown in Fig 3, both analyses were applied to the spatial estimates that were either reported by participants (i. e. , behaviour, Fig 3B left) or decoded from EEG activity patterns independently for each poststimulus time point (i. e. , neural, Fig 3B right, for further details, see the Methods section and the Fig 3 legend). The GLM-based wAV analysis quantifies the influence of the true auditory and true visual location on (1) the reported or (2) EEG decoded auditory and visual spatial estimates in terms of an audiovisual weight index wAV. The Bayesian modelling analysis formally assessed the extent to which (2) the full-segregation model (s) (Fig 3C, encircled in light blue, red or green), (2) the forced-fusion model (Fig 3C, yellow), and (3) the Bayesian causal inference model (i. e. , using model averaging as decision function, encircled in dark blue; see supporting material S1 Table for other decision functions) can account for the spatial estimates reported by observers (i. e. , behaviour) or decoded from EEG activity pattern (i. e. , neural). Integrating information from vision and audition into a coherent representation of the space around us is critical for effective interactions with the environment. This EEG study temporally resolved the neural dynamics that enable the brain to flexibly integrate auditory and visual signals into spatial representations in line with the predictions of Bayesian causal inference. Auditory and visual senses code spatial location in different reference frames and representational formats [26]. Vision provides spatial information in eye-centred and audition in head-centred reference frames [27,28]. Furthermore, spatial location is directly coded in the retinotopic organisation in primary visual cortex [29], whereas spatial location in audition is computed from sound latency and amplitude differences between the ears, starting in the brainstem [27]. In auditory cortices of primates, spatial location is thought to be represented by neuronal populations with broad tuning functions [30,31]. In order to merge spatial information from vision and audition, the brain thus needs to establish coordinate mappings and/or transform spatial information into partially shared ‘hybrid’ reference frames, as previously suggested by neurophysiological recordings in nonhuman primates [30,32]. In the first step, we therefore investigated the neural dynamics of spatial representations encoded in EEG activity patterns separately for unisensory auditory and visual signals using the method of temporal generalisation matrices [21]. In vision, spatial location was encoded initially at 60 ms in transient neural activity associated with the early P1 and N1 components and then turned into temporally more stable representations from 200 ms and particularly from 350 ms (Fig 2, upper right quadrant, S2 Fig). In audition, spatial location was encoded by relatively stable EEG activity from 95 ms and particularly from 250 ms, which is associated with the auditory long latency P2 component [22–24] (S3 Fig). Activity patterns encoding spatial location generalised not only across time but also across sensory modalities between 160 and 360 ms. As indicated in Fig 2, SVR models trained on visual-evoked responses generalised to auditory-evoked responses and vice versa (upper left and lower right quadrant, significant cross-sensory generalisation encircled by thick grey line). These results suggest that unisensory auditory and visual spatial locations are initially represented by transient and modality-specific activity patterns. Later, at about 200 ms, they are transformed into temporally more stable representations that may rely on neural sources in frontoparietal cortices that are at least to some extent shared between auditory and visual modalities [22,33,34]. Next, we asked when and how the human brain combines spatial information from vision and audition into a coherent representation of space. The brain should integrate sensory signals only when they come from a common event but should segregate signals from independent events [1,2, 12]. To investigate how the brain arbitrates between sensory integration and segregation, we presented observers with synchronous audiovisual signals that varied in their spatial disparity across trials. On each trial, observers reported either the auditory or the visual location. Our results show that a concurrent yet spatially disparate visual signal biased observers’ perceived sound location towards the visual location—a phenomenon coined spatial ventriloquist illusion [17,35]. Consistent with reliability-weighted integration, this audiovisual spatial bias was significantly stronger when the visual signal was more reliable (Fig 1C left, grey solid versus dashed lines). Furthermore, observers reported different locations for auditory and visual signals, and this difference was even greater for large- relative to small-spatial-disparity trials. This significant interaction between spatial disparity and task relevance indicates that human observers arbitrate between sensory integration and segregation depending on the probabilities of different causal structures of the world that can be inferred from audiovisual spatial disparity. Using EEG, we then investigated how the brain forms neural spatial representations dynamically post stimulus. Our analysis of the neural audiovisual weight index wAV shows that the spatial estimates decoded from EEG activity patterns are initially dominated by visual inputs (i. e. , wAV close to 90°). This visual dominance is most likely explained by the retinotopic representation of visual space that facilitates EEG decoding of space leading to visual predominance (for further discussion, see the Methods section). From about 65 ms onwards, visual reliability significantly influenced wAV (Fig 4A): as expected, the location of the visual signal exerted a stronger influence on the spatial estimate decoded from EEG activity patterns when the visual signal was more reliable than unreliable. By contrast, the signal’s task relevance influenced the audiovisual weight index only later, from about 190 ms (Fig 4B). Thus, visual reliability as a bottom-up stimulus-bound factor impacted the sensory weighting in audiovisual integration prior to top-down effects of task relevance. We observed a significant interaction between task relevance and spatial disparity as the characteristic profile for Bayesian causal inference from about 310 ms: the difference in wAV between auditory and visual report was significantly greater for large- than for small-disparity trials (Fig 4D, Table 2). Thus, spatial disparity determined the influence of task-irrelevant signals on the spatial representations encoded in EEG activity from about 310 ms onwards. A task-irrelevant signal influenced the spatial representations mainly when auditory and visual signals were close in space and hence likely to come from a common event, but it had minimal influence when they were far apart in space. Collectively, our statistical analysis of the audiovisual weight index revealed a sequential emergence of visual dominance, reliability weighting (from about 100 ms), effects of task relevance (from about 200 ms), and finally the interaction between task relevance and spatial disparity (from about 310 ms, Fig 4A–4D). This multistage process was also mirrored in the time course of exceedance probabilities furnished by our formal Bayesian model comparison: The unisensory visual segregation (SegV) model was the winning model for the first 100 ms, thereby modelling the early visual dominance. The audiovisual forced-fusion model embodying reliability-weighted integration dominated the time interval of 100–250 ms. Finally, the Bayesian causal inference model that enables the arbitration between sensory integration and segregation depending on spatial disparity outperformed all other models from 350 ms onwards. Hence, both our Bayesian modelling analysis and our wAV analysis showed that the hierarchical structure of Bayesian causal inference is reflected in the neural dynamics of spatial representations decoded from EEG. The Bayesian causal inference model also outperformed the audiovisual full-segregation (SegV, A) model that enables the representation of the location of the task-relevant stimulus unaffected by the location of the task-irrelevant stimulus. Instead, our Bayesian modelling analysis confirmed that from 350 ms onwards, the brain integrates audiovisual signals weighted by their bottom-up reliability and top-down task relevance into spatial priority maps [36,37] that take into account the probabilities of the different causal structures consistent with Bayesian causal inference. The spatial priority maps were behaviourally relevant for guiding spatial orienting and actions, as indicated by the correlation between the neural and behavioural audiovisual weight indices, which progressively increased from 100 ms and culminated at about 300–400 ms. Two recent studies have also demonstrated such a temporal evolution of Bayesian causal inference in an audiovisual temporal numerosity judgement task [38] and an audiovisual rate categorisation task [39]. The timing and the parietal-dominant topographies of the AV potentials (see S2 and S3 Figs) that form the basis for our spatial decoding (and hence for wAV and Bayesian modelling analyses) closely match the P3b component (i. e. , a subcomponent of the classical P300). Although it is thought that the P3b relies on neural generators located mainly in parietal cortices [40,41], its specific functional role remains controversial [42]. Given its sensitivity to stimulus probability [43–45] and discriminability [46] as well as task context [42,47,48], it was proposed to reflect neural processes involved in transforming sensory evidence into decisions and actions [49]. Most recent research has suggested that the P3b may sustain processes of evidence accumulation [50] that are influenced by observers’ prior [51], incoming evidence (i. e. , likelihood [52]), and observers’ belief updating [53]. Likewise, our supplementary time-frequency analyses revealed that alpha/beta power, which has previously been associated with the generation of the P3b component [54], depended on bottom-up visual reliability between 200 and 400 ms and top-down task relevance between 350 and 550 ms post stimulus (see S5 Fig, S2 Table and S1 Text), thereby mimicking the temporal evolution of bottom-up and top-down influences observed in our main wAV and Bayesian modelling analysis. Yet, our main analysis took a different approach. Rather than focusing on the effects of visual reliability, task relevance/attention, and spatial disparity directly on event-related potentials (ERPs) or time-frequency power, the wAV analysis investigated how these manipulations affect the spatial representations encoded in EEG activity patterns, and the Bayesian modelling analysis accommodated those effects directly in the computations of Bayesian causal inference. Along similar lines, two recent fMRI studies characterised the computations involved in integrating audiovisual spatial inputs across the cortical hierarchy [14,16]: whereas low level auditory and visual areas predominantly encoded the unisensory auditory or visual locations (i. e. , full-segregation model) [55–64], higher-order visual areas and posterior parietal cortices combined audiovisual signals weighted by their sensory reliabilities (i. e. , forced-fusion model) [65–68]. Only at the top of the hierarchy, in anterior parietal cortices, did the brain integrate sensory signals consistent with Bayesian causal inference. Thus, the temporal evolution of Bayesian causal inference observed in our current EEG study mirrored its organisation across the cortical hierarchy observed in fMRI. Fusing the results from EEG and fMRI studies (see caveats in the Methods section) thus suggests that Bayesian causal inference in multisensory perception relies on dynamic encoding of multiple spatial estimates across the cortical hierarchy. During early processing, multisensory perception is dominated by full-segregation models associated with activity in low-level sensory areas. Later audiovisual interactions that are governed by forced-fusion principles rely on posterior parietal areas. Finally, Bayesian causal inference estimates are formed in anterior parietal areas. Yet, although our results suggest that full segregation, forced fusion, and Bayesian causal inference dominate EEG activity patterns at different latencies, they do not imply a strictly feed-forward architecture. Instead, we propose that the brain concurrently accumulates evidence about the different spatial estimates and the underlying causal structure (i. e. , common versus independent sources) most likely via multiple feedback loops across the cortical hierarchy [18,19]. Only after 350 ms is a final perceptual estimate formed in anterior parietal cortices that takes into account the uncertainty about the world’s causal structure and combines audiovisual signals into spatial priority maps as predicted by Bayesian causal inference. Sixteen right-handed participants participated in the experiment; three of those participants did not complete the entire experiment: two participants were excluded based on eye tracking results from the first day (the inclusion criterion was less than 10% of trials rejected because of eye blinks or saccades; see the Eye movement recording and analysis section for details), and one participant withdrew from the experiment. The remaining 13 participants (7 females, mean age = 22. 1 years; SD = 3. 0) completed the 3-day experiment and are thus included in the analysis. All participants had no history of neurological or psychiatric illnesses, had normal or corrected-to-normal vision, and had normal hearing. All participants gave informed written consent to participate in the experiment. The study was approved by the research ethics committee of the University of Birmingham (approval number: ERN_11_0470AP4) and was conducted in accordance with the principles outlined in the Declaration of Helsinki. The visual (‘V’) stimulus was a cloud of 20 white dots (diameter = 0. 43° visual angle, stimulus duration: 50 ms) sampled from a bivariate Gaussian distribution with vertical standard deviation of 2° and horizontal standard deviation of 2° or 12° visual angle presented on a dark grey background (67% contrast). Participants were told that the 20 dots were generated by one underlying source in the centre of the cloud. The visual cloud of dots was presented at one of four possible locations along the azimuth (i. e. , −10°, −3. 3°, 3. 3°, or 10°). The auditory (‘A’) stimulus was a 50-ms-long burst of white noise with a 5-ms on/off ramp. Each auditory stimulus was delivered at a 75-dB sound pressure level through one of four pairs of two vertically aligned loudspeakers placed above and below the monitor at four positions along the azimuth (i. e. , −10°, −3. 3°, 3. 3°, or 10°). The volumes of the 2 × 4 speakers were carefully calibrated across and within each pair to ensure that participants perceived the sounds as emanating from the horizontal midline of the monitor. In a spatial ventriloquist paradigm, participants were presented with synchronous, spatially congruent or disparate visual and auditory signals (Fig 1A and 1B). On each trial, visual and auditory locations were independently sampled from four possible locations along the azimuth (i. e. , −10°, −3. 3°, 3. 3°, or 10°), leading to four levels of spatial disparity (i. e. , 0°, 6. 6°, 13. 3°, or 20°; i. e. , as indicated by the greyscale in Fig 1A). In addition, we manipulated the reliability of the visual signal by setting the horizontal standard deviation of the Gaussian cloud to a 2° (high reliability) or 14° (low reliability) visual angle. In an intersensory selective-attention paradigm, participants reported either their auditory or visual perceived signal location and ignored signals in the other modality. For the visual modality, they were asked to determine the location of the centre of the visual cloud of dots. Hence, the 4 × 4 × 2 × 2 factorial design manipulated (1) the location of the visual stimulus (−10°, −3. 3°, 3. 3°, 10°; i. e. , the mean of the Gaussian), (2) the location of the auditory stimulus (−10°, −3. 3°, 3. 3°, 10°), (3) the reliability of the visual signal (2°, 14°; SD of the Gaussian), and (4) task relevance (auditory-/visual-selective report), resulting in 64 conditions (Fig 1A). To characterise the computational principles of multisensory integration, we reorganised these conditions into a 2 (visual reliability: high versus low) × 2 (task relevance: auditory versus visual report) × 2 (spatial disparity: ≤6. 6° versus >6. 6°) factorial design for the statistical analysis of the behavioural and EEG data. In addition, we included 4 (locations: −10°, −3. 3°, 3. 3°, or 10°) × 2 (visual reliability: high, low) unisensory visual conditions and 4 (locations: −10°, −3. 3°, 3. 3°, or 10°) unisensory auditory conditions. We did not manipulate auditory reliability, because the reliability of auditory spatial information is anyhow limited. Furthermore, the manipulation of visual reliability is sufficient to determine reliability-weighted integration as a computational principle and arbitrate between the different multisensory integration models (see Bayesian modelling analysis section). On each trial, synchronous audiovisual, unisensory visual, or unisensory auditory signals were presented for 50 ms, followed by a response cue 1,000 ms after stimulus onset (Fig 1B). The response was cued by a central pure tone (1,000 Hz) and a blue colour change of the fixation cross presented in synchrony for 100 ms. Participants were instructed to withhold their response and avoid blinking until the presentation of the cue. They fixated on a central cross throughout the entire experiment. The next stimulus was presented after a variable response interval of 2. 6–3. 1 s. Stimuli and conditions were presented in a pseudo-randomised fashion. The stimulus type (bisensory versus unisensory) and task relevance (auditory versus visual) was held constant within a run of 128 trials. This yielded four run types: audiovisual with auditory report, audiovisual with visual report, auditory with auditory report, and visual with visual report. The task relevance of the sensory modality in a given run was displayed to the participant at the beginning of the run. Furthermore, across runs we counterbalanced the response hand (i. e. , left versus right hand) to partly dissociate spatial processing from motor responses. The order of the runs was counterbalanced across participants. All conditions within a run were presented an equal number of times. Each participant completed 60 runs, leading to 7,680 trials in total (3,840 auditory and 3,840 visual localisation tasks—i. e. , 96 trials for each of the 76 conditions were included in total; apart from the four unisensory auditory conditions that included 192 trials). The runs were performed across 3 days with 20 runs per day. Each day was started with a brief practice run. Stimuli were presented using Psychtoolbox version 3. 0. 11 [69] (http: //psychtoolbox. org/) under MATLAB R2014a (MathWorks) on a desktop PC running Windows 7. Visual stimuli were presented via a gamma-corrected 30” LCD monitor with a resolution of 2,560 × 1,600 pixels at a frame rate of 60 Hz. Auditory stimuli were presented at a sampling rate of 44. 1 kHz via eight external speakers (Multimedia) and an ASUS Xonar DSX sound card. Exact audiovisual onset timing was confirmed by recording visual and auditory signals concurrently with a photodiode and a microphone. Participants rested their head on a chin rest at a distance of 475 mm from the monitor and at a height that matched participants’ ears to the horizontal midline of the monitor. Participants responded by pressing one of four response buttons on a USB keypad with their index, middle, ring, and little finger, respectively. To address potential concerns that results were confounded by eye movements, we recorded participants’ eye movements. Eye recordings were calibrated in the recommended field of view (32° horizontally and 24° vertically) for the EyeLink 1000 Plus system with the desktop mount at a sampling rate of 2,000 Hz. Eye position data were on-line parsed into events (saccade, fixation, eye blink) using the EyeLink 1000 Plus software. The ‘cognitive configuration’ was used for saccade detection (velocity threshold = 30°/sec, acceleration threshold = 8,000°/sec2, motion threshold = 0. 15°) with an additional criterion of radial amplitude larger than 1°. Individual trials were rejected if saccades or eye blinks were detected from −100 to 700 ms post stimulus. Participants’ stimulus localisation accuracy was assessed as the Pearson correlation between their location responses and the true signal source location separately for unisensory auditory, visual high reliability, and visual low reliability conditions. To confirm whether localisation accuracy in vision exceeded performance in audition in both visual reliabilities, we performed Monte-Carlo permutation tests. Specifically, we entered the subject-specific Fisher z-transformed Pearson correlation differences between vision and audition (i. e. , visual–auditory) separately for the two visual reliability levels into a Monte-Carlo permutation test at the group level based on the one-sample t statistic with 5,000 permutations [70]. Continuous EEG signals were recorded from 64 channels using Ag/AgCl active electrodes arranged in a 10–20 layout (ActiCap, Brain Products GmbH, Gilching, Germany) at a sampling rate of 1,000 Hz, referenced at FCz. Channel impedances were kept below 10 kΩ. Preprocessing was performed with the FieldTrip toolbox [71] (http: //www. fieldtriptoolbox. org/). For the decoding analysis, raw data were high-pass filtered at 0. 1 Hz, re-referenced to average reference, and low-pass filtered at 120 Hz. Trials were extracted with a 100-ms prestimulus and 700-ms poststimulus period and baseline corrected by subtracting the average value of the interval between −100 and 0 ms from the time course. Trials were then temporally smoothed with a 20-ms moving window and downsampled to 200 Hz (note that a 20-ms moving average is comparable to a finite impulse response [FIR] filter with a cutoff frequency of 50 Hz). Trials containing artefacts were rejected based on visual inspection. Furthermore, trials were rejected if (1) they included eye blinks, (2) they included saccades, (3) the distance between eye fixation and the central fixation cross exceeded 2°, (4) participants responded prior to the response cue, or (5) there was no response. For ERPs (S2 and S3 Figs), the preprocessing was identical to the decoding analysis, except that a 45-Hz low-pass filter was applied without additional temporal smoothing with a temporal moving window. Grand average ERPs were computed by averaging all trials for each condition first within each participant and then across participants. For the multivariate pattern analyses, we computed ERPs by averaging over sets of eight randomly assigned individual trials from the same condition. To characterise the temporal dynamics of the spatial representations, we trained linear SVR models (LIBSVM [72], https: //www. csie. ntu. edu. tw/~cjlin/libsvm/) to learn the mapping from ERP activity patterns of the (1) unisensory auditory (for auditory decoding), (2) unisensory visual (for visual decoding), or (3) audiovisual congruent conditions (for audiovisual decoding) to external spatial locations separately for each time point (every 5 ms) over the course of the trial (S2, S3 and S4 Figs). All SVR models were trained and evaluated in a 12-fold-stratified cross-validation (12 ERPs/fold) procedure with default hyperparameters (C = 1, ε = 0. 001). The specific training and generalisation procedures were adjusted to the scientific questions (see the Shared and distinct neural representations of space across vision and audition section and the GLM analysis of audiovisual weight index wAV section for details). Combining psychophysics, computational modelling, and EEG, we addressed two questions: First, focusing selectively on the unisensory auditory and unisensory visual conditions, we investigated when spatial representations are formed that generalise across auditory and visual modalities. Second, focusing on the audiovisual conditions, we investigated when and how human observers integrate audiovisual signals into spatial representations that take into account the observer’s uncertainty about the world’s causal structure consistent with Bayesian causal inference. In the following sections, we will describe the analysis approaches to address these two questions in turn. First, we investigated how the brain forms spatial representations in either audition or vision using the so-called temporal generalisation method [21]. Here, the SVR model is trained at time point t to learn the mapping from, e. g. , unisensory visual (or auditory) ERP pattern to external stimulus location. This learnt mapping is then used to predict spatial locations from unisensory visual (or auditory) ERP activity patterns across all other time points. Training and generalisation were applied separately to unisensory auditory and visual ERPs. To match the number of trials for auditory and visual conditions, we applied this analysis to the visual ERPs pooled over the two levels of visual reliability. The decoding accuracy as quantified by the Pearson correlation coefficient between the true and decoded stimulus locations is entered into a training time × generalisation time matrix. The generalisation ability across time illustrates the similarity of EEG activity patterns relevant for encoding features (i. e. , here: spatial location) and has been proposed to assess the stability of neural representations [21]. In other words, if stimulus location is encoded in EEG activity patterns that are stable (or shared) across time, then an SVR model trained at time point t will be able to correctly decode stimulus location from EEG activity patterns at other time points. By contrast, if stimulus location is represented by transient or distinct EEG activity patterns across time, then an SVR model trained at time point t will not be able to decode stimulus location from EEG activity patterns at other time points. Hence, entering Pearson correlation coefficients as a measure for decoding accuracy for all combinations of training and test time into a temporal generalisation matrix has been argued to provide insights into the stability of neural representations whereby the spread of significant decoding accuracy to off-diagonal elements of the matrix indicates temporal generalisability or stability [21]. Second, to examine whether and when neural representations are formed that are shared across vision and audition, we generalised to ERP activity patterns across time not only from the same sensory modality but also from the other sensory modality (i. e. , from vision to audition and vice versa). This cross-sensory generalisation reveals neural representations that are shared across sensory modalities. To assess whether decoding accuracies were better than chance, we entered the subject-specific matrices of the Fisher z-transformed Pearson correlation coefficients into a between-subjects Monte-Carlo permutation test using the one-sample t statistic with 5,000 permutations ([70], as implemented in the FieldTrip toolbox). To correct for multiple comparisons within the two-dimensional (i. e. , time × time) data, cluster-level inference was used based on the maximum of the summed t values within each cluster (‘maxsum’) with a cluster-defining threshold of p < 0. 05, and a two-tailed p-value was computed. To characterise how human observers integrate auditory and visual signals into spatial representations at the behavioural and neural levels, we developed a GLM-based analysis of an audiovisual weight index wAV and a Bayesian modelling analysis that we applied to both (1) the reported auditory and visual spatial estimates (i. e. , participants’ behavioural localisation responses) and (2) the neural spatial estimates decoded from EEG activity pattern evoked by audiovisual stimuli (see Fig 3 and [14,16]).

The ability to tell whether various sensory signals come from the same or different sources is essential for forming a coherent percept of the environment. For example, when crossing a busy road at dusk, seeing and hearing an approaching car helps us estimate its location better, but only if its visual image is associated—correctly—with its sound and not with the sound of a different car far away. This is the so-called binding problem, and numerous studies have demonstrated that humans solve this near-optimally as predicted by Bayesian causal inference; however, the underlying neural mechanisms remain unclear. We combined Bayesian modelling, electroencephalography (EEG), and multivariate decoding in an audiovisual spatial localisation task to show that the brain dynamically encodes multiple spatial estimates while accomplishing Bayesian causal inference. First, auditory and visual signal locations are estimated independently; next, information from vision and audition is combined. Finally, from 200 ms onwards, the brain weights audiovisual signals by their sensory reliabilities and task relevance to guide behavioural responses as predicted by Bayesian causal inference.

Abstract Introduction Results Discussion Methods

acoustics medicine and health sciences engineering and technology audio signal processing visual signals signal processing brain electrophysiology social sciences electrophysiology neuroscience clinical medicine brain mapping bioassays and physiological analysis vision event-related potentials electroencephalography neuroimaging research and analysis methods imaging techniques clinical neurophysiology behavior electrophysiological techniques research assessment physics psychology research validity physiology biology and life sciences sensory perception physical sciences neurophysiology acoustic signals

2019

To integrate or not to integrate: Temporal dynamics of hierarchical Bayesian causal inference

Although the major structural transitions in molecular motors are often argued to couple to the binding of Adenosine triphosphate (ATP), the recovery stroke in the conventional myosin has been shown to be dependent on the hydrolysis of ATP. To obtain a clearer mechanistic picture for such “mechanochemical coupling” in myosin, equilibrium active-site simulations with explicit solvent have been carried out to probe the behavior of the motor domain as functions of the nucleotide chemical state and conformation of the converter/relay helix. In conjunction with previous studies of ATP hydrolysis with different active-site conformations and normal mode analysis of structural flexibility, the results help establish an energetics-based framework for understanding the mechanochemical coupling. It is proposed that the activation of hydrolysis does not require the rotation of the lever arm per se, but the two processes are tightly coordinated because both strongly couple to the open/close transition of the active site. The underlying picture involves shifts in the dominant population of different structural motifs as a consequence of changes elsewhere in the motor domain. The contribution of this work and the accompanying paper [36] is to propose the actual mechanism behind these “population shifts” and residues that play important roles in the process. It is suggested that structural flexibilities at both the small and large scales inherent to the motor domain make it possible to implement tight couplings between different structural motifs while maintaining small free-energy drops for processes that occur in the detached states, which is likely a feature shared among many molecular motors. The significantly different flexibility of the active site in different X-ray structures with variable level arm orientations supports the notation that external force sensed by the lever arm may transmit into the active site and influence the chemical steps (nucleotide hydrolysis and/or binding). Coupling between chemical events and conformational transitions is crucial for the function of many biomolecules [1]. Notable examples include molecular motors [2–4], signaling proteins [5], and membrane transporters [6], in which large-scale conformational transitions are often coupled to the binding and hydrolysis of Adenosine triphospate (ATP) or phosphorylation. Despite many previous efforts from both experimental and theoretical studies [2–4,7, 8], the complex nature of the phenomena still makes it an outstanding challenge to establish a concrete understanding of “mechanochemical coupling” in those “molecular machines” with structural and energetic details. One particularly interesting question in this context is whether the ATP hydrolysis reaction itself is capable of driving any major structural transitions. Although the answer appears to be positive according to most biochemistry textbooks [1], the popular modern view in the biophysical community [2,9, 10] is that the large-scale structural transitions implicated in most “molecular machines” are directly coupled to the binding of ATP, and the hydrolysis of ATP mainly functions as a switch to allow the system to move forward into the next functional cycle. Indeed, considering the highly charged nature of ATP (or ATP·Mg2+), it is physically reasonable to expect the binding process to involve substantial local structural adjustments that can then propagate into larger structural transitions. The hydrolysis of ATP, by contrast, involves only subtle structural changes and therefore may not be particularly capable of driving major structural transitions. Along this line, conventional myosin (which will be referred to as myosin-II or myosin in the subsequent discussions) is interesting to study because it is a prototypical molecular motor [11] that utilizes ATP; more importantly, there is solid experimental evidence [12] that supports the explicit role of ATP hydrolysis in driving the key structural transition (see below). The fundamental functional cycle of myosin-II has been well-established via kinetic studies and is summarized in Figure 1 [13–17]. The ATP hydrolysis step implicates the detached kinetic states, for which relevant high-resolution X-ray structures are available for the truncated motor domain. As shown in Figure 2A, two representative X-ray structures were solved with ATP (PDB code 1FMW [18]) and ADP·VO4− (PDB code 1VOM [19]) as the ligands, respectively, and they were postulated to reflect the conformations before and after (or during) ATP hydrolysis, correspondingly. Fitted into the kinetic scheme of the functional cycle (Figure 1), they are referred to as the “post-rigor” and “pre-powerstroke” states, respectively. The most visible structural change occurs in the C-terminal converter region, which undergoes a major rotation between the two structures and is believed to propagate into the lever-arm rotation; with the two structures best-fitted based on the first 650 residues, the backbone atom root mean square deviation (RMSD) in the converter is as large as 18. 7 Å while it is only 2. 4 Å for the rest. In addition to this striking rearrangement, there are also more subtle structural changes in the nucleotide binding site (Figure 2B) and the “relay helix” (Figure 2C) that connects the binding site and converter. There has been a large body of experimental [12,17–28] and computational [29–32] studies aimed at understanding the mechanism of the “recovery stroke” between the post-rigor and pre-powerstroke states and its connection to the hydrolysis of ATP. The FRET study of Shih et al. [12] has firmly illustrated that the rotation of the converter, which is the major part of the recovery stroke, is dependent on and tightly coupled to the hydrolysis of ATP. This is consistent with the functional cycle of myosin (Figure 1): (i) the binding of ATP is used to detach the motor domain from the actin and therefore the recovery stroke needs an alternative driving force; (ii) without a tight coupling between the hydrolysis and recovery stroke, the motor may rebind to the actin with a lever-arm configuration that is incapable of performing the powerstroke, which leads to futile ATP hydrolysis. An interesting point further arises when one considers the energetics of the functional cycle (see Discussion). Since ATP hydrolysis occurs in the detached kinetic states, which are not capable of exerting force on actin and performing work, the reaction is expected to have a small free-energy drop [33], which is consistent with experimentally measured equilibrium constants [14,28]. Naively, the nearly thermoneutral nature of the hydrolysis reaction seems inconsistent with its observed role [12] in driving the major structural transitions in the recovery stroke. Therefore, a question of critical functional importance is: what are the properties of the myosin motor domain that ensure a tight coupling between ATP hydrolysis and the converter rotation, despite not only the long distance separating the nucleotide binding site and the converter but also the nearly thermoneutral nature of the hydrolysis reaction? To answer this question, it is important to address not only the structural features of the active site but also the energetics of various processes involved in the recovery stroke. In this work, we meet this challenge with a combination of equilibrium simulations of the myosin motor domain. Specifically, potential of mean force (PMF) simulations [34,35] are used to quantitatively characterize the open/closed transition of the nucleotide binding site and analyze how this transition is coupled, thermodynamically, to the structure of the relay helix and orientation of the converter domain. Equilibrium simulations, using both molecular mechanics (MM) and combined quantumn mechanics/molecular mechanics (QM/MM), are also carried out for different nucleotide states (ATP versus ADP·Pi) to probe the effect of ATP hydrolysis on the active-site behavior, which aims to explore how ATP hydrolysis might induce the subsequent rebinding of the motor domain to actin (Figure 1). In the accompanying article [36], several alternative computational approaches, which include targeted molecular dynamics (MD) [37,38], normal mode [39,40] hinge analysis, and statistical coupling analysis [41,42] are used to explore transition pathways for the recovery stroke and to identify residues important to the coupling between distant functional sites. Compared with previous simulation studies on the mechanochemical coupling in myosin [29–32,43] and other molecular motors [8,44–46], the present set of studies represents the first serious attempt to examine energetic properties concerning the conformational transitions in these complex systems and a systematic analysis of residues critical to mechanochemical coupling. With all the results taken together, a clearer mechanistic picture emerges for the coupling between ATP hydrolysis and various essential structural motifs, which includes some features that might be shared by different classes of motors. Although the converter domain and the active site were seen to adopt different conformations in the two X-ray structures (1FMW and 1VOM) [18,19], to what degree these distant regions are coupled thermodynamically is not well-established; whether the nucleotide chemical state (e. g. , ATP versus ADP·Pi) contributes directly to the coupling is also not clear. These issues are probed with PMF calculations for the open/closed transition in the active site with different X-ray structures of the motor domain, which mainly differ in the lever-arm orientations (down versus up) and relay helix conformations. The PMFs for the open/closed transition of the active site have been calculated in both one dimension (ΔRMSD) and two dimensions (RMSDFMW, RMSDVOM); see Methods. The general trend is very similar; thus, we will only refer to the 1-D PMFs in the discussion, although both sets are shown in Figure 3 for comparison. With the lever arm down (1FMW: ATP), the PMF has the double-well feature, with the open configuration slightly preferred (∼−0. 6 kcal/mol) and a low barrier of ∼5 kcal/mol (Figure 3A and 3B). Experimentally it was found that Switch II closure is a rapid (∼1000 s−1) event with an equilibrium constant close to 1 (ΔG0 ∼ 0 kcal/mol) [14,28]. The drastic difference between the measured open/closed rate (∼1000 s−1) and the calculated barrier (∼5 kcal/mol, which corresponds to a rate of 1. 4 × 109 s−1 using a pre-factor of kBT/h in a transition state rate expression, ignoring the effect of damping) might be due to the approximate nature of the reaction coordinate used in the PMF calculations; an alternative explanation is that the reporting Trp501 fluorescence might be more directly related to the position of the lever arm rather than the open/closed of the Switch II motif [48] (also see [36]). Nevertheless, the trend that the open and closed configurations of the active site have similar energetics in post-rigor is found in both experiments [27] and the present PMF calculations. When the lever arm is up (1VOM: ATP, 1VOM: ADP·Pi), the free-energy well corresponding to the open active site (ΔRMSD ∼ −1. 5 Å) disappears and the closed state becomes the much preferred one (Figure 3C–3F). The PMF profile is qualitatively the same with ATP and ADP·Pi bound, although the open configuration (ΔRMSD ∼ −1. 5 Å) appears to be even higher in free energy in the latter case; this is in qualitative agreement with the expectation based on QM/MM studies of ATP hydrolysis in different active-site conformations that ADP·Pi favors the closed configuration compared with ATP [29,49]. Due to the high energy cost, the open configuration of the active site is essentially inaccessible when the lever arm is up, regardless of the nucleotide chemical state. In other words, the lever-arm orientation is clearly tightly coupled to the active-site conformation, presumably through residues in the relay helix (see below). It is worth noting that the free-energy minimum corresponding to the closed active site has an RMSD ∼ 1. 5 Å (Figure 3G and 3H) relative to the 1VOM X-ray structure in all three sets of simulations shown in Figure 3, which suggests that the ΔRMSD reaction coordinate offers an unbiased description for the open/closed transition in different X-ray structures. The effectiveness of the simulation protocol is also demonstrated by the water structure in the active site. As emphasized in previous simulations and experimental studies [22,29], there are two water molecules in the closed active site and they form stable hydrogen bonds with a number of residues and the γ-phosphate of ATP; this hydrogen-bonding network, which does not exist in the open configuration of the active site, helps to orient the lytic water for a favorable nucleophilic attack. Encouragingly, the correct hydrogen-bonding network is formed spontaneously in 1FMW: ATP simulations when the active site is closed with umbrella sampling simulations based on ΔRMSD (Figure 3G). Although the active site and the converter are separated by more than 40 Å, the different active-site flexibilities reflected by the open/closed PMFs indicate that variation in the lever-arm orientation produces changes that propagate to the active site, which in turn affects the relative stability of the open and closed configurations of the active site. To identify residues that make major contributions to the open/closed PMF and their dependence on the lever-arm orientation, free-energy maps for local torsions of active-site residues are obtained using Equations 5 and 6. The (χ2, χ3) conformational free-energy profile for Arg238 and (ϕ, ψ) conformational free-energy profiles for residues Asp454, Ile455, Ser456, Gly457, Phe458, and Glu459 are shown in Figure 4. As references, the dihedral angles describing the conformation of those residues in the crystal structures are summarized in Table 2. The main chain conformational spaces accessed in the PMF simulations are within allowed regions in the Ramachandran plot [50,51], which reassures us that the sampled conformations do not involve unphysical ones. The most striking difference between the 1FMW: ATP and 1VOM: ATP simulations is the Ramachandran plot for the side-chain conformation of Arg238, which involves flipping around χ3 during the open/closed transition. When the lever arm is down, the side chain can adopt two different conformations about (−80°, 90°) (A) and (−50°, −60°) (B). Region A is preferred over region B with a free-energy difference of ∼3 kcal/mol. When the lever arm is up (1VOM), by contrast, region A is no longer accessible. All other residues within Switch II involve a population shift between different conformations, most notably Gly457 and Glu459. This is consistent with the proposal that Gly457 is involved in a rotation to allow the formation of the Arg238-Glu459 salt bridge and to make an amide hydrogen bond to the γ-P of ATP during the Switch II closure [15]; a mutation of Gly457 to even an Ala affects the flexibility in this region and therefore reduces the stabilization of the γ-P, which in turn causes the loss of the basal ATPase activity [52]. Although only Switch I/II residues are subject to bias in the umbrella sampling simulations, substantial changes in other regions of the motor domain are observed. As shown in Figure 5, opening the active site in the 1VOM: ATP simulations induces significant changes in the actin-binding interface (regions C, D), the loop between Switch II, and the N-terminal of the relay helix (region B), as well as the C-terminal of the second central ß-sheet (region A); the displacements in those regions are substantially larger than their equilibrium fluctuations (unpublished data). As illustrated by the Ramachandran (ϕ, ψ) conformational free-energy profiles (Figure 6) for the residues between Switch II and the N-terminal of relay helix (region B as defined in Figure 5), the accessible conformational space is more restricted in the lever-up (1VOM: ATP) simulations compared with the lever-down case (1FMW: ATP), which partly explains why the closed state is preferentially stabilized when the lever arm is up. For example, the (ϕ, ψ) of Ile460 in the X-ray structures of 1FMW and 1VOM are (−130°, 85°) and (−133°, 127°), respectively. With the lever arm down, Ile460 can have a widely accessible conformation space surrounding its conformations in both X-ray structures; with the lever arm up, Ile460 preferentially adopts its conformation in the closed state. To further explore how ATP hydrolysis initiates structural changes that ultimately induce the rebinding of the motor domain to actin, substantially longer equilibrium simulations (up to 12 ns) of the closed active site are performed for the ATP and ADP·Pi chemical states with both MM and QM/MM potentials. The results of MM and QM/MM simulations are largely consistent; thus, only MM values are discussed below, although both sets of results are shown in Figure 7 for comparison. We focus on the interaction between Mg2+-nucleotide and the Switch I region because it has been speculated that changes in Switch I are coupled to transitions in actin-binding motifs [17]. In the ATP state (Figure 7A), the two distances between Mg2+ and the nucleotide oxygen atoms are significantly shorter than those between Mg2+ and Thr186/Ser237. The average distances for O1ß-Mg2+ and O1γ-Mg2+ are 1. 83 ± 0. 04 Å and 1. 78 ± 0. 03 Å, respectively; the corresponding values for OSer237-Mg2+ and OThr186-Mg2+ are 2. 14 ± 0. 11 Å and 2. 09 ± 0. 08 Å, respectively. This trend is expected considering that the oxygen atoms in the nucleotide are more negatively charged (∼−0. 85e) than those in polar amino acid side chains (∼−0. 66e) [53]. In the ADP·Pi simulations (Figure 7B), the most striking change is observed for the OSer237-Mg2+ distance, for which the average is 2. 48 Å, 0. 34 Å longer than that in the ATP simulation. The fluctuation in the distance also increases more than twice that in the ATP state, to 0. 25 Å. The largest distance sampled in ∼12 ns reaches almost 4 Å, as compared with 2. 75 Å in the ATP simulations. In the QM/MM simulations (Figure 7C and 7D), the Mg2+-O distances are consistently shorter, but the trend of a weaker interaction between Mg2+ and Ser237 in the ADP·Pi simulation is also evident. Therefore, both MM and QM/MM simulations show that following ATP hydrolysis the interaction between the Mg2+-nucleotide and Switch I becomes substantially weakened; the fact that MM simulations produce similar results as QM/MM simulations suggests that the cause is largely electrostatic in nature and due to the difference in the charge distribution in the nucleotide before and after hydrolysis. A careful examination of the structures in the simulations suggests that the increase in the distance has contributions from both the structural relaxation of Mg2+-nucleotide following hydrolysis and the slight displacement of the Ser237 side chain (Figure 7E); the average backbone configuration of the Switch I motif, however, is not perturbed significantly during the nanosecond scale simulations (unpublished data). Nevertheless, the weakened interaction between Mg2+-nucleotide and Ser237, as reflected by the larger fluctuations of Ser237 (Figure 7F), is expected to make structural transition of Switch I more facile. Analysis of sidechain internal fluctuations also indicates that Ser237 is the only residue that is affected significantly by hydrolysis (see Protocol S1). Before discussing the results from the current work in detail, it is useful to recapitulate the fundamental questions regarding mechanochemical coupling in myosin, and other molecular motors, that ultimately drive our study. As pointed out elegantly by Hill and others in their pioneering analysis of coupled biological processes [33,54,55], biological systems need to meet specific thermodynamic and kinetic constraints to acquire the corresponding characteristics necessitated by their biological functions. Specifically for myosin, the constraints based on functional considerations are [55]: (i) processes that occur in the detached states have small free-energy drops; (ii) in particular, the ATP hydrolysis reaction itself is close to being thermoneutral; (iii) the ATP hydrolysis step is tightly coupled to the reorientation of the lever arm. The first two constraints arise because for a motor to achieve a high thermodynamic efficiency, only the steps capable of performing useful work should be associated with large free-energy drops, since no work can be done in the detached states (no force can be exerted on the actin), and, considering that the sum of free-energy changes through a functional cycle is strictly a constant (the hydrolysis free energy of ATP in solution), the detached states should have small free-energy drops. In principle, large free-energy increases and drops associated with the detached states may cancel out to still leave a large free-energy drop for the powerstroke, but the large free-energy increases for certain step (s) may compromise the speed of the motor; in other words, meeting the first two constraints is likely correlated with a fast motor without any significant kinetic bottleneck. In this context, we emphasize that one has to distinguish the free-energy drop for ATP hydrolysis in the motor domain, which is small in magnitude (as shown experimentally for not only myosin [14] but also a number of other motors such as F1-ATPase [56]), from that for the entire functional cycle; the latter is strictly equivalent to the hydrolysis free energy of ATP in solution and forms the ultimate thermodynamic driving force for the vectorial motion of the motor. The third constraint minimizes the amount of futile ATP hydrolysis, because otherwise a loose coupling may lead to rebinding of the motor to actin with a lever-arm orientation incapable of powerstroke. Although these “idealized” constraints are derived by considering a motor with the optimal efficiency, and most biological motors do not need to have perfect thermodynamic efficiency for their biological functions [7], they provide an interesting set of guidelines to investigate the properties of molecular motors in the context of mechanochemical coupling. Indeed, it is of interest to investigate to what degree these constraints are satisfied in realistic systems and, if so, how they are implemented in structural and energetic terms [49,57]. For constraint I, although our study here explicitly deals with only a few steps in the functional cycle, a number of relevant observations can be made. First, normal mode analyses of multiple conformational states clearly demonstrate that the structural transitions are highly correlated with the low-frequency modes and, therefore, intrinsic structural flexibility of the motor domain [36,43]. Although no explicit free-energy cost can be inferred from such normal mode analyses, the significant degree of observed correlation strongly suggests that conformational transition among those states is associated with a modest free-energy change. A series of hydrophobic and polar interactions is observed in both minimum energy path [30] and biased MD simulations [36] to facilitate the large structural rearrangements in the relay helix and converter domain and ensures the corresponding low energy cost. Second, for the open/close of the active site, which has been shown to activate ATP hydrolysis [22,29,49], both current PMF calculations and recent fluorescence experiments [27] suggest that the transition is nearly thermoneutral with the lever arm down (the expected orientation following detachment from actin). The PMF calculations are worthwhile because the interpretation of the fluorescence is not straightforward and the signal due to Trp501 might be better correlated with the lever-arm position rather than the active-site configuration per se [48]. Third, for processes other than the powerstroke, although not explicitly studied here, it is natural to envision how they can be nearly thermoneutral. The detachment of the motor domain from actin is compensated for by the binding of ATP; the rebinding of the motor domain to actin following ATP hydrolysis is known to be only a weak association; the weak → strong actin-binding transition is compensated for with dissociation of hydrolysis product (s) and may partially couple to the powerstroke. For constraint II, which is consistent with the measured equilibrium constant [14,27], both our [29] and others' [31] QM/MM simulations suggest that the small free-energy drop is likely controlled by well-orchestrated electrostatic environment of the active site. Both favorable and unfavorable contributions from residues/water in the closed active site have been identified, which together result in a small hydrolysis-free energy. Importantly, as argued below, the small free-energy drop for the hydrolysis step itself does not compromise the coupling between ATP hydrolysis and lever-arm orientation as far as both couple tightly to the open/close transition of the active site. As the major focus of this work, understanding how constraint III is implemented is fascinating because of its allosteric nature: ATP hydrolysis in the active site is strongly coupled to the rotation of the converter domain (thus, lever arm) at 40 Å away. It is physically reasonable to argue that the two processes are coupled indirectly by both being directly coupled to variations in the active-site configuration. Indeed, both structural studies [19,22] and QM/MM simulations [29] support the idea that ATP hydrolysis is only possible with the closed active site and is prohibited in the open active site. In addition, PMF calculations (Figure 3) show explicitly that the active site open/close transition is sensitive to the conformation of the lever arm and relay helix. In the post-rigor state (lever arm down, relay helix straight), the open/close transition is a rapid and thermoneutral process, while with the pre-powerstroke state (lever arm up, relay helix kinked) the active site is strongly biased (by more than 15 kcal/mol) toward the closed configuration. Taken together, at the thermodynamic level, the simulation results show explicitly how ATP hydrolysis is coupled to the lever-arm rotation through a series of population shifts in local conformations (Figure 8). Starting with the post-rigor conformation, the active site (more precisely, Switch II) rapidly explores the open and closed configurations; with the closed configuration, ATP hydrolysis is a relatively rapid and reversible process. Based on the thermodynamic linkage between the hydrolysis and active-site closure (see the O-C-DP·Pi-TP plane in Figure 8), the dominant population of the active site is shifted toward the closed configuration once ATP is hydrolyzed (with ADP·Pi bound). In turn, based on the thermodynamic linkage between the active-site open/close transition and the converter rotation (see the D-U-O-C plane in Figure 8, assuming that the converter and relay helix are tightly coupled), closure of the active site drives the dominant population of the lever arm from the post-rigor to the pre-powerstroke orientation. Although this description is purely thermodynamic in nature and does not specify the sequence of events (see discussions in the accompanying paper [36]), the clear energetic relations make it possible to refine models concerning the functional cycle, which is difficult to do with a structure-only analysis [30]. For example, most kinetic schemes in the literature assume that activating the hydrolysis depends on (at least partial) rotation of the lever arm [12], because it is difficult in experiments to simultaneously monitor the active-site motion and lever-arm rotation due to their significantly different length scales [26,27,48]. Our computational analyses taken together, however, suggest that the activation of ATP hydrolysis does not require the rotation of the lever arm per se, because closure of the active site, which directly regulates ATP hydrolysis, is a facile process even with the lever arm in the post-rigor orientation. This is consistent with the phenotype of many “uncoupling mutants” [23,24], in which the ATPase activity is similar to the wild-type but motility (which presumably is correlated with lever-arm rotation) is significantly compromised or abolished. As mentioned in the Introduction, the tight coupling between ATP hydrolysis and converter rotation is even more striking considering the near-thermoneutral nature of processes in the detached states. With the energetics-based analysis, it is worth noting that a nearly thermoneutral ATP hydrolysis (in the motor) does not imply a weak coupling to lever-arm orientation. Provided that the lever arm is strongly correlated to the active-site open/close transition through the relay helix, the only requirement for ATP hydrolysis (for the sake of avoiding futile hydrolysis) is that it is tightly coupled to the same active-site transition; the magnitude of this coupling depends only on the relative hydrolysis free energy in the open and closed active-site configurations. As a near-thermoneutral reaction in the closed state but unfavorable reaction in the open state, the ATP hydrolysis is strongly coupled to the open/close transition and, simultaneously, satisfies the thermodynamic constraint II imposed on the motor. Another point clearly underlined by the energetics-based analysis is that the coupling between ATP hydrolysis and the lever-arm rotation relies on the intrinsic flexibility of the motor domain at both the small (active-site) and large (converter-rotation) scales [29]. In this sense, the current description is reminiscent of the “rectified Brownian diffusion” framework [58] proposed for a number of motors including myosin. The current analysis, however, is built on an atomistic description of the relevant processes with concrete structural and energetic features; therefore, we are able to provide mechanistic details regarding how “Brownian motions” of the motor domain are “rectified” by the hydrolysis of ATP, or, in the language of physical chemistry, how the dominant population of different structural motifs is shifted by events that occur elsewhere in the motor domain. Moreover, we note that although many previous analyses of “molecular machines” based on normal modes [29,44,59–63] emphasized the role of intrinsic structural flexibility at the domain scale, the current study highlights the comparable significance of local flexibility (e. g. , active site) and, more importantly, how these flexibilities at different scales are modulated by chemistry. Conversely, the significantly different flexibility of the active site, as captured by the PMF calculations, in different X-ray structures of the motor domain, supports the notion that external force sensed by the lever arm may transmit into the active site and perturb processes such as ATP hydrolysis or product release, which ultimately leads to the change in the motor speed [64]. In addition to the coupling between hydrolysis and the converter, an issue of major importance concerns the coupling between the active-site activities and the actin interface. In the Lymn−Taylor scheme [13–15], hydrolysis of ATP leads to the reattachment of the motor domain to actin, although the corresponding mechanism is not well-understood, due largely to the lack of a high-resolution structure for the myosin−actin complex. The current set of equilibrium simulations of the active site, although limited by the nanosecond time scale and the boundary condition, have provided useful hints. First, significant distortion of the actin-binding interface and the central ß sheet has been observed to be induced by the opening of the active site in the umbrella sampling simulations (Figure 5), strongly suggesting the coupling between the open/close of the active site and motion in the actin-binding interface. This hypothesis is consistent with the structural analysis of Geeves and Holmes [17], who proposed that during the powerstroke the opening of Switch I induces the twisting of the central ß sheet as well as the closure of the actin-binding cleft. In addition, an interesting observation from simulations with different nucleotide chemical states found that the interaction between the Mg2+ and Switch I (in particular, Ser 237) is substantially reduced once ATP is hydrolyzed; this is observed with both MM and QM/MM potential functions, which suggests that the cause is largely electrostatic and structural in nature rather than due to any subtle quantum mechanical effects. This supports the hypothesis of Reubold et al. [65] and Coureux et al. [66], who analyzed the X-ray structures of nucleotide-free myosin II and myosin V and proposed that hydrolysis of ATP leads to the destabilization of the closed configuration of Switch I. This might prepare for the opening of the Switch I loop, which has been postulated to occur upon binding of actin and is necessary for the release of the hydrolysis products [17]. We note that as this work was in progress, a similar observation was reported in a QM/MM minimization study of myosin [31] and an MD simulation using MM potential [32]. Our unique contribution here is the confirmation of this important effect with both MM and QM/MM potential functions with extensive sampling. An interesting observation in [32] is that Asn475 breaks its hydrogen bond with Switch II upon ATP hydrolysis. This, however, was not observed in either the MM or QM/MM simulations here; to what extent this is due to the spherical boundary condition of these simulations is being investigated (also see Protocol S1). Interestingly, we note that Koppole et al. [32] used a mixture of the force-field parameters in their model, which involves polar-hydrogen parameters for nonaromatic residues and all-atom parameters for the aromatic residues. It is not clear what justifies such mixing of force-field parameters and what is the impact of this on the simulation result. Vectorial biological systems such as molecular motors and pumps are fascinating to study [57] because their unique functional characteristics may impose specific thermodynamic and kinetic constraints [33,54,55] that distinguish them from regular enzymes. With remarkable progress in structural and dynamical characterizations of complex biomolecules in both experimental and theoretical arenas, the challenge is to establish to what degree these “idealized” constraints are satisfied and to reveal the physical and chemical principles behind their implementation and regulation in structural and energetic terms. Specifically for myosin II, a characteristic molecular motor, we argue that the key constraints the motor domain is likely to satisfy include small free-energy drops for all steps in the detached kinetic states, and that events at the active site are tightly coupled to distant structural motifs, which are consistent with a broad range of experimental observations. Molecular simulations are then carried out to reveal how these constraints, or “mechanochemical coupling, ” are reflected in the structural, energetic, and dynamical features of the motor domain. Specifically in this work, equilibrium active-site simulations with explicit solvent molecules have been carried out to probe the behavior of the motor domain as functions of the nucleotide chemical state and conformation of the converter/relay helix. In conjunction with our previous QM/MM studies of ATP hydrolysis in different active-site conformations [29] and normal mode analysis of the motor domain [43], the results here help establish an energetics-based framework for understanding the mechanochemical coupling, in which the active-site closure tightly regulates both the hydrolysis activity and orientation of the converter (thus, lever arm). The flexibilities at both the small (active-site open/close) and large (converter-rotation) scales inherent to the motor domain make it possible to implement these couplings while maintaining small free-energy changes for all key changes occurring in the detached states; in this sense, the current framework is reminiscent of the “rectified Brownian motion” model of molecule motors [58]. The contribution of this work and the accompanying paper [36] is to propose the actual mechanism for “rectifying” these “Brownian motions” and residues that play important roles in the process. In addition, the significant coupling between the converter orientation/relay helix and the active-site configuration as identified by the PMF calculations provides a concrete energetics basis for how active-site activities can be affected by the external force sensed by the lever arm. It is anticipated that many motors, not only those in the myosin family, share many similar features in their mechanochemical coupling cycles. In the near future, the results reported here together with additional simulations of the lever-arm transition will facilitate the development of an effective phenomenological model [67,68] that can simulate the entire functional cycle of myosin (s), which ultimately provide the missing link between microscopic structural and energetic details with the behavior of motors at the macroscopic length and time scales. Before such a model can be developed, however, key questions regarding myosin–actin interactions need to be answered [17,69], which requires contributions from both experimental and theoretical efforts. To estimate the PMF for the active-site open/close transition, umbrella sampling calculations with a stochastic boundary condition and explicit solvent molecules are carried out using the CHARMM program (c32a2 version) [70]. Three systems are simulated: post-rigor with ATP bound (denoted as 1FMW: ATP), pre-powerstroke with ATP bound (denoted as 1VOM: ATP), and pre-powerstroke with ADP and Pi bound (denoted as 1VOM: ADP·Pi). As starting coordinates, the X-ray structures solved by Rayment and co-workers for the Dictyostelium discoideum myosin II motor domain are used; the lever arm is down and the active site is open in 1FMW [18] (resolution 2. 15 Å), while the lever arm is up and the active site is closed in 1VOM [19] (resolution 1. 90 Å). The missing residues (1,203–208,500–508,622–626 in 1FMW, and 1,205–208,711,716–719,724–730 in 1VOM) are modeled by using the program ModLoop [71,72]. The protein atoms and the Mg2+ ion in the active site are described with the all-atom CHARMM force field for proteins [53], and the water molecules are described with the TIP3P model [73]. The hydrogen atoms are added with HBUILD [74]. In all cases the system is partitioned into a 32-Å inner region centered at the geometric center of ATP, P-loop, Switch I, and Switch II (about 12,800 atoms), with the remaining portion of the system in the outer region. Newtonian equations-of-motion are solved for the MD region (within 28 Å), and Langevin equations-of-motion are solved for the buffer region (28–32 Å) with a temperature bath of 300 K [75]. All the atoms in the inner region are subject to a weak GEO type of restraining potential to keep them inside the inner sphere with the MMFP module of CHARMM; the effect of the restraint on most inner-region atoms is negligible. All protein atoms in the buffer region are harmonically restrained with force constants determined directly from the B-factors in the PDB file [75]. Langevin atoms are updated heuristically during the simulation to consistently treat protein groups and water molecules that may switch regions during the simulation. All bonds involving hydrogen are constrained using the SHAKE algorithm [76], with a relative geometric tolerance of 10−10, and the time step is set to 1 fs. Nonbonded interactions within the inner sphere are treated with an extended electrostatics model, in which groups beyond 12 Å interact as multipoles [77]. The entire system is heated gradually to 300 K and equilibrated for 160 ps prior to the production simulations. To account for the electrostatics between the inner and outer region atoms and the effect of solvation, the generalized solvent boundary potential approach developed by Roux and co-workers is used [78,79]. In the generalized solvent boundary potential, the contribution from distant protein charges (screened by the bulk solvent) in the out region is represented in terms of the corresponding electrostatic potential in the inner region, where qα is the partial charge on atom α. The dielectric effect on the interactions among inner-region atoms is represented through a reaction field term where Qm/n is the generalized multipole moment and Mmn is the element of the reaction field matrix, M. The static field due to the outer region is evaluated with the linear Poisson–Boltzmann equation using a focusing scheme that place a 70-Å cube of fine grid (0. 4 Å) into a larger 120-Å cube of coarse grid (1. 2 Å). The reaction field matrix, M, is evaluated using 400 spherical harmonics. In the Poisson–Boltzmann calculations, the protein dielectric constant ɛp = 1, the water dielectric constant ɛw = 80, and 0. 0 M salt concentration is used. The optimized radii of Roux and Nina [80,81], based on experimental solvation energies of small molecules as well as the calculated interaction energy with explicit waters, are adopted to define the solvent–solute dielectric boundary. In the umbrella sampling simulations, intermediate structures along the transition pathway between the open and closed active site are generated by using a 1-D RMSD restraint on all atoms in the active site (Switch I: 233–238 and Switch II: 454–459). Two harmonic restraining potentials are applied to rmsd (Xt − Xopen) and rmsd (Xt − Xclosed), respectively, with the condition rmsd (Xt − Xopen) + rmsd (Xt − Xclosed) = 2. 4 Å; Xt is the instantaneous structure, and Xopen and Xclosed are the two reference configurations of the active site based on the 1FMW and 1VOM X-ray structures. The intermediates are generated at 0. 1-Å RMSD intervals, which produced 25 intermediate structures spanning the range of ∼2. 4 Å RMSD separating the two reference structures. These intermediate structures are then used as the starting points for umbrella sampling simulations [34,35,82] with the restraining potential wj on ΔRMSD as a reaction coordinate [83,84] where ΔDmin specifies the position of the minimum for the harmonic umbrella potential in a specific window. The ΔRMSD is the difference between the all-atom RMSD values for the Switch I and Switch II regions of the instantaneous structure (Xt) from the two reference structures (Xopen and Xclosed), The force constant KRMSD is gradually reduced from 500 kcal/ (mol·Å2) to 25 kcal/ (mol·Å2) in the 50-ps equilibration simulation for each window and kept fixed for another 100 ps of production simulation. Additional windows are added or additional simulations are performed for some windows by inspecting the 2-D distribution of RMSD, ρbias (rmsd (Xt − Xopen), rmsd (Xt − Xclosed) ). The Weighted Histogram Analysis Method (WHAM) [82,85,86] is used to obtain the PMF along the 1-D reaction coordinate (ΔRMSD) from the instantaneous values of ΔRMSD saved every step during the MD simulations. The convergence of the PMF profiles is checked by calculations using substantially longer trajectories, up to 450 ps per window and in total about 11 ns (see Protocol S1). The MD simulations with the umbrella potential applied along ΔRMSD as the reaction coordinate contain information about many individual degrees of freedom that contribute to the overall transition between the open and closed states. It is possible to estimate the free-energy profile along other additional degrees of freedom using trajectories from these umbrella sampling MD simulations [83,84]. For example, one can obtain the PMF surface as a function of the 2-D reaction coordinates defined by (rmsd (Xt − Xopen) and (Xt − Xclosed). The procedure to obtain such PMF begins by considering the biased probability distribution ρbias (ξ1, ξ2, ξ3) where ξ1 = rmsd (Xt − Xopen), ξ2 = rmsd (Xt − Xclosed), and ξ3 = ΔRMSD. The unbiased probability distribution ρ (ξ1, ξ2, ξ3) can be obtained using the standard Weighted Histogram Analysis Method approach (by setting the biased potential in ξ1, ξ2, direction to zero). The probability ρ (ξ1, ξ2) can then be obtained by integrating ρ (ξ1, ξ2, ξ3) along ξ3: and the corresponding PMF surface can be then obtained from In a similar way, the PMF along the local (ϕ, ψ) space for some key residues are calculated from the trajectories saved every 100 steps during the MD simulations. To probe the effect of ATP hydrolysis on the active-site structure, especially the Switch I region that has been proposed to communicate with the distant actin-binding interface, we carry out both MM and QM/MM [87,88] MD simulations for the pre-powerstroke state (1VOM [19]) with different nucleotides (ATP or ADP·Pi). QM/MM simulations are also carried out to investigate if the qualitative behavior is sensitive to the treatment of charge transfer and polarization associated with the nucleotide. The simulation setup is essentially the same as that in the PMF simulations described above, except that a smaller inner region of 20-Å radius is used to allow much longer simulations of the active site (∼12 ns for MM and ∼4 ns for QM/MM simulations). In the QM/MM simulations, 48 atoms are described quantum mechanically, including tri-phosphate and part of the ribose group of ATP, the lytic water (or the corresponding atoms in ADP·Pi), the Mg2+ ion and all its ligands (the side chains of Thr186, Ser237, and two water molecules), as well as the side chain of the conserved Ser236. These atoms are described at the SCC-DFTB level [89,90] with the recent parameterization for phosphate systems (Yang et al. , unpublished data). Four link atoms are introduced to saturate the valence of the QM atoms at the QM/MM interface [88], which interact with all MM atoms electrostatically except with the link host (Cα atoms in Thr186, Ser236, Ser237, and C5′ in the ribose); no van der Waals interactions are considered for the link atoms. This link atom treatment has been shown to work well when the nearby MM charges are small [91–93], which is the case here. The generalized solvent boundary potential approach is used for treating electrostatics in both MM [78,79] and QM/MM [94] simulations.

The hydrolysis of Adenosine triphosphate (ATP) provides the energy for most life processes, including the motility of molecular motors. How the chemical energy of hydrolysis is converted into mechanical work in these fascinating “nanomachines” is a central question that has only been answered in an outline form for almost all molecular motors. The fundamental challenge is that the working cycle of molecular motors involves processes of different physicochemical natures and scales, including ATP chemistry and protein structural transitions of diverse magnitudes (from a few Angstroms to a few nanometers), which makes mechanistic analysis using experiments alone difficult. Combined with previous computational studies from this lab, molecular dynamics simulations help identify energetic and structural properties of myosin, a prototypical molecular motor, that are essential to its energy conversion function. In addition to the role of flexibilities at the domain scale, which has been emphasized in previous studies of similar systems, the current results highlight the comparable significance of local flexibilities and how these flexibilities at different scales are modulated by ATP chemistry.

Abstract Introduction Results Discussion Materials and Methods

in vitro none molecular biology computational biology

2007

Mechanochemical Coupling in the Myosin Motor Domain. I. Insights from Equilibrium Active-Site Simulations

Intracellular acting protein exotoxins produced by bacteria and plants are important molecular determinants that drive numerous human diseases. A subset of these toxins, the cytolethal distending toxins (CDTs), are encoded by several Gram-negative pathogens and have been proposed to enhance virulence by allowing evasion of the immune system. CDTs are trafficked in a retrograde manner from the cell surface through the Golgi apparatus and into the endoplasmic reticulum (ER) before ultimately reaching the host cell nucleus. However, the mechanism by which CDTs exit the ER is not known. Here we show that three central components of the host ER associated degradation (ERAD) machinery, Derlin-2 (Derl2), the E3 ubiquitin-protein ligase Hrd1, and the AAA ATPase p97, are required for intoxication by some CDTs. Complementation of Derl2-deficient cells with Derl2: Derl1 chimeras identified two previously uncharacterized functional domains in Derl2, the N-terminal 88 amino acids and the second ER-luminal loop, as required for intoxication by the CDT encoded by Haemophilus ducreyi (Hd-CDT). In contrast, two motifs required for Derlin-dependent retrotranslocation of ERAD substrates, a conserved WR motif and an SHP box that mediates interaction with the AAA ATPase p97, were found to be dispensable for Hd-CDT intoxication. Interestingly, this previously undescribed mechanism is shared with the plant toxin ricin. These data reveal a requirement for multiple components of the ERAD pathway for CDT intoxication and provide insight into a Derl2-dependent pathway exploited by retrograde trafficking toxins. Cytolethal distending toxins (CDTs) are produced by a variety of Gram-negative pathogens including the oral pathogen Aggregatibacter actinomycetemcomitans, the sexually transmitted pathogen Haemophilus ducreyi, and the gastrointestinal pathogens, Escherichia coli and Campylobacter jejuni. These toxins belong to a larger, emerging group of intracellular-acting “cyclomodulins” whose expression is associated with increased persistence, invasiveness and severity of disease [1]–[7]. Rather than inducing overt cytotoxicity and tissue damage, cyclomodulins drive more subtle alterations in the host through changes in cell cycle progression. CDTs cause DNA damage in susceptible host cells, resulting in the induction of DNA repair signaling mechanisms including phosphorylation of the histone H2AX, cell cycle arrest at the G2/M interface and disruption of cytokinesis [8]. Inhibiting the cell cycle interferes with many functions of rapidly dividing eukaryotic cells, including lymphocytes and epithelial cells, which play a role in immunity and provide a physical barrier to microbial pathogens [5], [9], [10]. In cultured cells, the DNA damage response ultimately leads to apoptotic cell death, while in vivo, persistent DNA damage may give rise to infection-associated oncogenesis [11]. Although the cellular response to CDTs is well characterized [8], [12], the mechanism by which CDTs bind to host cells and ultimately gain access to their nuclear target is less clear. CDTs generally function as complexes of three protein subunits, encoded by three contiguous genes (cdtA, cdtB, cdtC) in a single operon [13]. Consistent with the AB model of intracellular acting toxins [14], CdtB functions as the enzymatic A-subunit and possesses DNase I-like activity responsible for inducing DNA damage within the nuclei of intoxicated cells [15], [16]. CdtA and CdtC are thought to function together as the cell-binding B-moiety of AB toxins to deliver CdtB into cells [17]–[20]. To exert their cyclomodulatory effects, CDTs must be taken up from the cell surface and transported intracellularly in a manner that ultimately results in localization to the nucleus. Recent data suggest that the endosomal trafficking pathways utilized by CDTs from unrelated pathogens are different, but that all CDTs are trafficked in a retrograde manner through the Golgi apparatus and into the ER [21], [22]. CDTs and other retrograde trafficking toxins lack the ability to translocate themselves across the ER membrane and must therefore rely on host cellular processes to access their intracellular targets. Toxins such as cholera toxin, Shiga toxin, and ricin use a host-encoded protein quality control process known as ERAD [23]–[31]. ERAD is a normal physiological process by which misfolded proteins in the ER lumen and membrane are translocated to the cytoplasm for degradation by the proteasome. The core machinery driving ERAD in mammalian cells consists of the Hrd1/Sel1L ubiquitin ligase complex, the Derlin family of proteins and may also involve Sec61 [32]. Translocation of misfolded proteins across the ER membrane is energetically unfavorable and is facilitated by the AAA-ATPase p97 [33]–[35]. While toxins use various components of the ERAD pathway to exit the ER lumen, they avoid proteasomal degradation, thereby hijacking the host quality control mechanism to gain access to the cytosol. In contrast to other retrograde trafficking toxins, several reports have suggested that ERAD does not play a role in the translocation of CDT across the ER membrane. Mutant cell lines deficient in the retrotranslocation of several retrograde trafficking toxins, such as cholera toxin, Pseudomonas aeruginosa exotoxin A, E. coli heat labile-toxin IIb, plasmid encoded toxin, and ricin were sensitive to CDT [22], [36]. Overexpression of Derlin-GFP fusions, which can act as dominant negative proteins to inhibit ERAD, did not block CDT intoxication [22]. Thermal stability of CdtB suggested that this catalytic subunit does not unfold prior to translocation and thus may not be an ERAD substrate [37]. Finally, CdtB was not found in the cytoplasm of intoxicated cells prior to nuclear localization, but rather was localized with ER membrane projections into the nucleus (i. e. nucleoplasmic reticulum), leading to the model that CDTs translocate directly from the ER lumen into the nucleoplasm [37]. Contrary to these data, others have described requirements for nuclear localization signals within the CdtB subunits, implicating a requirement for retrotranslocation to the cytosol prior to trafficking to the nucleus [38]–[40]. Identifying host factors required for translocation of CDT across the ER membrane would provide insight into mechanism of toxin entry; however, these data have been elusive [22], [41], [42]. Here we describe the results of two genetic screens aimed at identifying host genes required for intoxication by CDT from four human pathogens. These results implicate key components of the ERAD pathway in retrotranslocation of CDT and thereby provide insight into the mechanism by which host cells are intoxicated by this family of bacterial toxins. In order to identify genes that confer sensitivity to CDT, we performed two separate forward somatic cell genetic screens. First, we utilized the frameshift mutagen ICR-191 to induce mutations in ten separate pools of CHO-pgs A745 cells (A745). Each pool of 1×106 cells was selected with 20 nM A. actinomycetemcomitans CDT (Aa-CDT), a toxin concentration high enough to cause death in parental cells. Five of the ten pools yielded Aa-CDT resistant clones; the most resistant clone isolated (CHO-CDTRA2) was resistant to the highest dose of Aa-CDT tested (Fig. 1a). Interestingly, CHO-CDTRA2 cells were also resistant to the highest dose of H. ducreyi CDT (Hd-CDT) tested (Fig. 1b) and more modestly resistant to CDTs from E. coli (Ec-CDT; Fig. 1c) and C. jejuni (Cj-CDT; Fig. 1d). To identify the gene responsible for CDT resistance in CHO-CDTRA2 cells, we utilized a high throughput cDNA expression-based complementation approach. A custom cDNA library consisting of approximately 3. 7×103 arrayed clones was prepared from the mammalian gene collection [43]. Plasmid DNA was isolated from the library, normalized for concentration, plated individually into 384-well plates and reverse transfected into CHO-CDTRA2 cells. After 72 hours, the transfected cells were intoxicated with 20 nM Aa-CDT and immunostained using fluorescent anti-pH2AX antibodies to identify activation of CDT-mediated DNA damage response. Cells were stained with Hoechst 33342 to enumerate nuclei, imaged by automated fluorescence microscopy and scored using automated image analysis software. We identified Mus musculus Derlin-2 (Genbank ID: BC005682), a gene involved in the ERAD pathway, as able to complement the sensitivity of CHO-CDTRA2 cells to Aa-CDT. CHO-CDTRA2 cells were transduced with a retroviral vector encoding Derl2 to verify this finding and test whether Derl2 was able to complement resistance to the remaining three CDTs. CHO-CDTRA2 cells expressing Derl2 regained sensitivity to all four CDTs tested to near parental levels (Fig. 1a–d). In a parallel effort to identify genes required for CDT intoxication, a retroviral mutagenesis approach was employed [44]. Approximately 1×107 A745 cells expressing the tetracycline repressor protein fused to the Krüppel associated box from human Kox1 (A745TKR) were transduced with murine leukemia virus (MLV) encoding the tetracycline repressor element at a multiplicity of infection of 0. 1 and selected with 5 nM Hd-CDT, a toxin concentration high enough to cause death in parental cells. Two independent pools produced Hd-CDT-resistant clones. Subsequent characterization of one clone from each pool, CHO-CDTRC1 and CHO-CDTRF1, revealed that they were resistant to cell killing by the highest concentrations of the four CDTs tested (Fig. 2a–2d) as well as cell cycle arrest induced by lower CDT concentrations (Fig. S1). The site of mutational proviral integration was determined using a combination of sequence capture, inverse PCR and sequencing [44]. Proviral integration sites in the mutants were distinct; the mutagenic integration in CHO-CDTRC1 cells occurred between the first and second Derl2 exons and occurred in the opposite orientation in CHO-CDTRF1 cells between the fourth and fifth Derl2 exons (Fig. 2e). Overexpression of Derl2 in these mutants complemented sensitivity to all CDTs tested (Fig. 2a–2d, S2). In contrast, overexpression of the functionally related Derl1, which shares 51% homology and 35% amino acid identity with Derl2, failed to complement sensitivity to Hd-CDT in CHO-CDTRC1 cells (Fig. 2f). Both CHO-CDTRC1 and CHO-CDTRF1 mutant cells displayed decreased Derl2 expression by immunoprecipitation followed by western blot (Fig. 2g). Targeted deletion of Derl2 was performed in HeLa cells using the Cas9 clustered regularly interspaced short palindromic repeats (CRISPR) system [45]. HeLa cells lacking Derl2 were resistant to Hd-CDT (Fig. 2h, 2i). Additionally, siRNA mediated knockdown of Derl2 in HeLa cells rendered them resistant to Hd-CDT (data not shown). Although the demonstration of a direct physical interaction between Derl2 and CDT would support the hypothesis that Derl2 is part of a retrotranslocation apparatus, attempts to co-immunoprecipitate CDT with Derl2 were unsuccessful, likely due to very small quantities of CDT reaching the ER during intoxication. Although Derlins have been most intensely studied as important factors in the translocation of ERAD substrates, these proteins have also been implicated in the trafficking of the plant toxin ricin from endosomes to the Golgi apparatus [46]. To identify which step of the CDT retrograde trafficking pathway was blocked in Derl2-deficient cells, the intracellular trafficking of Hd-CDT in parental A745TKR and mutant CHO-CDTRC1 and CHO-CDTRF1 cells was assessed by immunofluorescence microscopy as a function of time. After 10 minutes of intoxication, Hd-CdtB was clearly internalized into all the cell types tested (Fig. 2j–2l, S3). However, after 60 minutes, significantly more CdtB had localized to the nucleus of the parental A745TKR cells than in the Derl2-deficient CHO-CDTRC1 and CHO-CDTRF1 cells. In the CHO-CDTRC1 and CHO-CDTRF1 cells, Hd-CdtB was clearly localized to the ER, even after 60 minutes, but nearly absent within the ER of the parental A745TKR cells. Together, these data support a model that Derl2 is required for retrograde translocation of Hd-CdtB from the ER lumen. Derl2 is part of the Hrd1-containing “retrotranslocon”, a protein complex that mediates retrotranslocation of ERAD substrates [47]. Indeed, Hrd1 was co-immunoprecipitated with Derl2 from wildtype but not Derl2-deficient cells (Fig. 3a). Similarly, Derl2 could be co-immunoprecipitated from wildtype cells, but not from cells in which Hrd1 was targeted by CRISPR (Fig. 3b–3c). Intoxication of Hrd1-deficient cells revealed that this gene, like Derl2, is required for cell killing by multiple CDTs (Fig. 3d–3g, S4). Interestingly, cells lacking Hrd1 displayed full sensitivity to intoxication by Cj-CDT (Fig. 3g). Similar to Derl2 deficient cells, deletion of Hrd1 resulted in retention of Hd-CDT in the ER 240 minutes post-intoxication (Fig. 3h–3j). These data suggest that the Derl2 and Hrd1-containing retrotranslocon is required for intoxication by multiple CDTs, implicating a role for the ERAD pathway in cellular entry for a subset of this family of toxins. Derlins have been implicated in retrotranslocation of misfolded proteins out of the ER [35], [48]. In order to evaluate whether Derl2 might function by a similar mechanism to retrotranslocate CDTs, we investigated the importance of several Derlin functional motifs required for the retrotranslocation of previously characterized ERAD substrates. A carboxyl terminal SHP box (FxGxGQRn, where n is a non-polar residue) was recently demonstrated to be required for the interaction of Derlins with the AAA ATPase p97 [49], which provides energy to extract ERAD substrates from the lumen into the cytosol [33], [34], [50]. To assess the importance of p97-Derl2 interactions for the escape of CdtB from the cytosol, we tested whether Derl2 with a deletion of the C-terminus (Derl2ΔC) that removes the SHP box could complement Derl2 deficiency in CHO-CDTRC1 cells. Additionally, we tested a dominant negative form of Derl2 with a C-terminal GFP tag (Derl2-GFP) [22], [48]. Similar to what had been shown previously, Derl2ΔC was unable to bind p97 (Fig. 4a) [47], [49]. Further, Derl2-GFP was also unable to bind p97 (Fig. 4a). Surprisingly, intoxication studies revealed that despite failing to interact with p97, Derl2-GFP did not act as a dominant negative inhibitor, and that both Derl2-GFP and Derl2ΔC complemented sensitivity to Hd-CDT (Fig. 4b–d). These results suggest that Hd-CDT has evolved to use a Derl2-dependent retrotranslocation pathway that is independent of interaction between Derl2 and p97. Although the interaction between Derl2 and p97 is not required for Hd-CDT retrotranslocation, this does not preclude a requirement for p97 in intoxication. To investigate this, dominant negative (R586A) and control (R700A) versions of p97 were overexpressed in 293 cells. Activity of the dominant negative p97 was confirmed by an increase in fluorescence signal from the ERAD substrate TCRαGFP [51] (Fig. 4e). Expression of dominant negative p97 caused a reduction in cell cycle arrest in G2 mediated by Hd-CDT, compared to control p97 (Fig. 4e). Consistent with a role for p97 in egress of CdtB from the ER lumen, expression of the dominant negative p97 resulted in retention of Hd-CDT in the ER after 240 minutes of intoxication (Fig. 4g–4i). We next evaluated the importance of a second functional domain required for Derl2-mediated retrotranslocation of ERAD substrates. Derlins were recently classified as members of the rhomboid protease family of proteins, although they lack key residues required for proteolytic activity [49]. Rhomboid proteases are unique in that they contain an aqueous membrane-embedded cavity that allows for hydrolytic catalysis within the lipid bilayer [52]. Similar to other rhomboid proteases, Derl2 contains a “WR motif” (Q/ExWRxxS/T) in the sequence between the first and second transmembrane domains and a GxxxG motif in the sixth transmembrane domain. The WR motif protrudes laterally into the bilayer and plays a role in rearrangement of the local lipid environment [52], [53] while GxxxG motifs enable intra- and inter-molecular dimerization of transmembrane domains [52], [53]. Mutation of either of these domains in Derl1 renders it unable to retrotranslocate a constitutively misfolded protein to the cytosol for proteosomal degradation [49]. To test for a role for these motifs in CDT egress from the ER, Derl2 variants with single point mutations in the residues that comprise the WR and GxxxG motifs were expressed in Derl2 deficient CHO-CDTRC1 cells. Expression of Derl2 variants Q53A, W55A and T59A complemented the resistance to Hd-CDT in CHO-CDTRC1 cells to the same levels as that of wildtype Derl2 (Fig. 4f). One point mutant in the WR domain (R56A) and mutants in either residue of the GxxxG domain (G175V, G179V) failed to complement CHO-CDTRC1 cells; however, these mutants were poorly expressed as determined by immunoprecipitation and western blot, and therefore no conclusion can be made regarding a role for these residues (data not shown). These data suggest that although the WR motif is required for retrotranslocation of misfolded proteins by Derl1 [49], it is not required for retrotranslocation of Hd-CDT. In order to provide insight into the mechanism by which Derl2 supports intoxication, we set out to identify Derl2 domains that are required for intoxication by Hd-CDT. Taking advantage of the knowledge that Derl1 is sufficiently divergent from Derl2 such that it cannot complement Derl2 deficiency (Fig. 2f), we constructed chimeric proteins comprised of fusions between homologous segments of Derl1 and Derl2 to map Derl2 segments that support intoxication by Hd-CDT. Replacing the C-terminal cytoplasmic tail of Derl2 with that from Derl1 (Derl21–187: Derl1189–251) gave a chimera that retained function and complemented sensitivity to Hd-CDT in CHO-CDTRC1 cells, consistent with a dispensable role for this domain (Fig. 5a). Likewise, CHO-CDTRC1 cells expressing a fusion protein in which the third ER luminal loop of Derl2 was replaced with that from Derl1 (Derl21–112: Derl1114–121: Derl1120–239) were sensitive to Hd-CDT, indicating that this domain is not required for intoxication (Fig. 5b). In contrast, two distinct domains were identified in Derl2 that were each independently required for intoxication by Hd-CDT. Three fusion proteins comprised of Derl1 from the N-terminus through the second, fourth and fifth transmembrane domains respectively fused to the remaining portions of Derl2 (Derl11–88: Derl288–239; Derl11–138: Derl2138–239; Derl11–162: Derl2162–239) were unable to complement sensitivity to Hd-CDT in CHO-CDTRC1 cells, implicating a Derl2-specific sequence within the first 88 N-terminal residues as required for CDT intoxication (Fig. 5c). Second, a fusion protein consisting of Derl2 with the second ER luminal loop of Derl1 (Derl21–161: Derl1163–171: Derl2171–239) was unable to complement sensitivity, demonstrating that one or more of the six amino acids in the second luminal loop unique to Derl2 were also required for intoxication by Hd-CDT (Fig. 5b). We attempted to express several other Derl1: Derl2 chimeric proteins; however, these were expressed at levels lower than their wildtype counterparts and therefore these results were deemed inconclusive (data not shown). Taken together, these data identify two distinct domains of Derl2 required for Hd-CDT intoxication. Similar to CDT, several other protein toxins such as ricin, Shiga toxin and cholera toxin rely on retrograde trafficking from the cell surface through the ER in order to gain access to the cytoplasm [25], [54]. Recently, RNAi-mediated repression of members of the Derlin family was shown to cause a slight resistance to ricin [26], [46] that was attributed to reduced trafficking from endosomes to the Golgi apparatus [46]. Similarly, the Derl2 deficient mutant cell line CHO-CDTRC1 displayed four-fold resistance to ricin, which was complemented by transduction with Derl2 (Fig. 6a). CRISPR mediated deletion of Hrd1 in 293 cells caused resistance to ricin, albeit to a lesser degree than resistance to Hd-CDT (Fig. 6b, 2h). This low-level resistance to ricin suggests that Derl2 and Hrd1 contribute to, but are not absolute requirements for ricin intoxication. In contrast, a high level of resistance to multiple CDTs resulted from Derl2 or Hrd1 deficiency (Fig. 2). Interestingly, the novel Derl2 SHP box- and WR motif-independence characterized for CDT was shared with ricin. Derl2ΔC and Derl2 WR mutants were able to restore sensitivity to Derl2 deficient CHO-CDTRC1 cells (Fig. 6c, 6d), suggesting that Derl2 may have multiple functions that are independent of the conserved WR motif and SHP box-mediated interactions with p97. In order to gain access to their intracellular targets, retrograde trafficking toxins such as CDT bind the plasma membrane, are endocytosed and then trafficked though endosomes, the Golgi apparatus and ultimately the ER. At this point they must cross the formidable barrier posed by the host cellular membrane. The current model is that retrograde trafficking toxins commandeer the host ERAD pathway to cross the ER membrane, thereby gaining access to the cytosol. Various components of the ERAD machinery have been identified for cytoplasmic delivery of ricin, Shiga, and cholera toxins as well as for Pseudomonas aeruginosa exotoxin A [23]–[29]. These ERAD components include members of the HRD ubiquitin ligase complex, Hrd1 and Sel1L [27], [28], Derlins 1–3 [26], [30], [31], ER proteins involved in substrate recognition and unfolding of ERAD substrates [23]–[25], and the Sec61 translocon [26], [29]. Interestingly, different toxins appear to require distinct ERAD components, suggesting that multiple pathways exist by which toxins are translocated out of the ER lumen [26]. In contrast to these toxins, the pathway (s) by which CDTs exit the ER and ultimately gain access to the host nucleus was previously unknown. An ERAD-independent pathway was suggested based on failure of Derl1-GFP and Derl2-GFP fusion proteins to block intoxication by Hd-CDT, as well as susceptibility of mutant cells to CDT that were resistant to multiple other retrograde trafficking toxins [22], [36]. Here we provide evidence that three core components of the ERAD machinery, Derl2, Hrd1 and p97, are in fact required for intoxication by multiple CDTs and that abrogation of these key members of the ERAD pathway leads to Hd-CDT accumulation in the ER, consistent with a role in retrotranslocation. The inability of Derl1 to complement Derl2 deficiency further enabled identification of novel domains within Derl2 required for intoxication by CDT. Derl2 is a six-pass transmembrane protein with three predicted loops in the ER lumen [49]. Replacing the third luminal loop from Derl2 with Derl1 sequences supported intoxication, indicating that this loop is not required, though we cannot exclude a more minor role. However, replacing the second luminal loop, which consists of just eight amino acids, two of which are conserved with Derl1, resulted in loss of function. This finding supports a key role for specific amino acids within this small domain in sensitivity to Hd-CDT. The first luminal loop may also be important, though chimeras consisting of this loop from Derl1 swapped with Derl2 and vice versa were not expressed and thus this could not be tested directly. However, replacing the first 88 N-terminal residues, inclusive of the first two transmembrane domains and the first luminal loop, with those from Derl1 did express well but failed to support Hd-CDT intoxication. This N-terminal region also contains the WR motif conserved among rhomboid proteases and required in Derl1 for retrotranslocation of misfolded proteins. However, the WR motif is conserved between Derl1 and Derl2 and point mutations within this WR motif in Derl2 still supported intoxication. These findings suggest that another functional domain exists within this region that is required for intoxication by Hd-CDT. Further studies are needed to determine whether additional requirements for intoxication map to the first luminal loop, the two transmembrane domains, or perhaps the N-terminal tail that extends into the cytosol. In addition to identifying Derl2, Hrd1, and p97 as host factors usurped by CDTs to exit the ER, the studies presented here provide insight into the mechanism by which Derlin-GFP fusions act as dominant negative proteins. These constructs have been used to study the role of derlin family members in retrograde translocation of misfolded proteins, cytomegalovirus mediated degradation of class I MHC, infection by murine polyomavirus, and intoxication by ricin and cholera toxin [25], [30], [48], [49], [55]; however, the mechanism by which these constructs inhibit ERAD function was unknown. Interestingly, overexpression of Derl1-GFP or Derl2-GFP was previously shown to have no effect on the intoxication of HeLa cells by ricin or Hd-CDT, leading the authors to conclude that derlins are not required for these toxins [22], [25]. Similarly, we found that overexpression of Derl2-GFP (Fig. 4b) or Derl1-GFP (not shown) had no effect on CDT intoxication of parental A745TKR cells. Rather, overexpression of Derl2-GFP actually complemented sensitivity to Hd-CDT in Derl2-deficient CHO-CDTRC1 cells. Expression of Derl2ΔC complemented resistance to both ricin and CDT. The data presented here suggest that Derlin-GFP constructs act in a dominant negative manner by blocking interactions mediated by the C-terminus such as SHP box-mediated interactions with p97, and therefore may only exert dominant negative effects on ERAD and trafficking processes that require these interactions. Interestingly, although the interaction of p97 with Derl2 is not required for CDT interaction, p97 activity is indeed required for intoxication as expression of dominant negative p97 causes reduced sensitivity to Hd-CDT. p97 may supply energy for the retrotranslocation process that is common to both misfolded proteins and CDT through interactions with other proteins such as Hrd1 [50], or may be required for other entry or trafficking steps [56]. Determining the precise roles for this multifunctional protein requires more detailed studies and it remains possible that p97 contributes to more than one step in the intoxication pathway. Previous somatic cell genetic screens identified twelve host genes required for intoxication by CDTs and ricin, but failed to identify Derl2, Hrd1 or p97 [41], [42]. The reason for this difference is unclear, though any single genetic model system is unlikely to provide a complete picture of such a complex biological process. Indeed, the host genes identified thus far only begin to explain the host processes required for cellular binding and entry by CDTs [21], [41], [42]. Only ten of the fifteen host factors identified thus far are required for intoxication by more than one CDT and of these, only two, sphingomyelin synthase 1 (SGSM1) [42] and Derl2 (Fig. 1,2) have been shown to be required for all four CDTs tested here. These results suggest that various members of the CDT family have evolved distinct strategies to gain access to the host nucleus [21], [57]. Cj-CDT is the most evolutionarily divergent CDT studied here and displays unique requirements for host factors compared with Ec-, Aa-, and Hd-CDTs [42], [57]. Consistent with these prior findings, Cj-CDT had the least dependence on Derl2 and no requirement for Hrd1 (Fig. 2d). Future studies will likely identify many more host requirements for this family of toxins and provide further insight into their cellular entry pathways. Comparison of multiple members of the CDT family will elucidate a core set of host factors required for entry of all CDTs, but will also provide insight into unique solutions evolved by distinct CDTs to gain access to the host nucleus. Chinese hamster ovary cells (CHO) and derivatives were maintained in F-12 media (Gibco) supplemented with 10% fetal bovine serum (Sigma Aldrich), 100 U/mL penicillin, 100 µg/mL streptomycin, 5 mM L-glutamine (Invitrogen) and 1 µg/mL doxycycline (Sigma Aldrich). HeLa and 293 cells (American Type Culture Collection) were maintained in Dulbecco' s Modified Eagle Medium (DMEM; Cellgro) containing 25 mM HEPES, 4. 5 g/L sodium pyruvate, 4. 5 g/L glucose, 10% fetal bovine serum (Sigma Aldrich), 100 U/mL penicillin, 100 µg/mL streptomycin, and 5 mM L-glutamine (Invitrogen). In some cases, 293 culture medium was supplemented with 1% non-essential amino acids (Gibco). All cells were cultured at 37°C in a humid atmosphere containing 5% CO2. To isolate chemically mutagenized CDT-resistant clones, ten pools of CHO-pgs A745 cells (A745, provided by Jeff Esko, UCSD) were treated with ICR191 (Sigma Aldrich) at a concentration high enough to kill 90% of the cells [58]. The resulting cells were counted, seeded at 1×106 cells per 10 cm plate and selected with 20 nM Aa-CDT. Resulting resistant cells were subjected to limiting dilutions to obtain single cell clones, expanded and reselected with Aa-CDT. Selection of retrovirally mutagenized CDT-resistant clones was performed similar to a previously reported protocol [44]. Briefly, an Hd-CDT-sensitive clonal A745 cell line expressing tetR-KRAB (A745TKR) was established. Ten pools of 1×106 A745TKR parental cells were mutagenized by transduction with murine leukemia virus encoding the transcription response element TetO7 in the long terminal repeat (pCMMP. GFP-NEO-TRE) at a multiplicity of infection of 0. 1. These pools were transcriptionally repressed at proviral integration sites for 96 hours in the absence of doxycycline then selected with 5 nM Hd-CDT for 24 hours. After selection, two of the ten pools yielded colonies; these colonies were picked, expanded and reselected with Hd-CDT. None of the CDT-resistant clones displayed doxycycline dependant sensitivity to CDT, so they were further maintained in the presence of doxycycline. Mammalian cells were trypsinized, counted and seeded at approximately 1×103 cells per well in 384-well plates. The following day, medium was removed and toxin containing medium was added for 48 hours, followed by addition of ATPlite 1-step reagent (Perkin Elmer). Recombinant CDTs were cloned, expressed, and purified as described previously [57] and ricin was purchased commercially (List Biological Laboratories). Each biological replicate intoxication was performed in triplicate. Analysis of intoxication was performed either by quantitation of pH2AX immunofluorescence (as described previously [57]) or by using ATPlite reagent (Perkin Elmer) according to manufacturer recommendations. Intoxication data obtained by ATPlite reagent was normalized by dividing the luminescence relative light unit (RLU) signal of each replicate by the average of the unintoxicated control cells. All intoxication results presented are representative of at least three biological replicates. In order to identify the location of the provirus in the CDT-resistant clones, genomic DNA was purified from each clone according to manufacturer recommendations (Qiagen), followed by digestion of 2 µg of genomic DNA with BamHI restriction enzyme (New England Biolabs). Digested genomic DNA was purified by column chromatography (Qiagen) and resuspended in 100 mM Tris-HCl, 150 mM NaCl, 50 mM EDTA, pH 7. 5, containing 10 pmol biotinylated oligonucleotide complimentary to the 3′ pCMMP long terminal repeat (Sigma Aldrich; [Biotin]GTACCCGTGTTCTCAATAAACCCTC). The samples were heated to 95°C for 5 minutes then plunged on ice, followed by end over end rotation at 55°C for 14 hours. Streptavidin coated magnetic beads were washed three times with 10 mM Tris-HCl, 2 M NaCl, 1 mM EDTA, pH 7. 5 and added to the samples. Samples were vortexed for 0. 5 hours at room temperature then the beads were immobilized on a magnet and supernatant removed, followed by three washes with 5 mM Tris-HCl, 1 M NaCl, 0. 5 mM EDTA, pH 7. 5 and resuspension in 100 µL water. The tubes were heated to 95°C in the presence of the magnet and the supernatant was removed and self-circularized with T4 DNA ligase according to manufacturer recommendations (Fermentas). PCR was performed using the following primers (GAGGGTTTATTGAGAACACGGGTAC and GTGATTGACTACCCGTCAGCGGGGTC) followed by nested PCR with the following primers (CGAGACCACGAGTCGGATGCAACTGC and GTTCCTTGGGAGGGTCTCCTCTG). Amplicons were run in a 1% agarose gel, bands were cut out, column purified (Qiagen) and sequenced (Genewiz). In order to confirm that the MLV proviral integration occurred at the Derl2 locus, PCR amplification was performed on the genomic DNA from the retrovirally induced CDT resistant clones and the parental A745TKR cells. The primers used for amplification annealed to the fifth exon in the Derl2 open reading frame (CCATGAGCACCCAGGGCAGG) and either forward proviral elements (TGATCGCGCTTCTCGTTGGG) or reverse proviral elements (AGCGCATCGCCTTCTATCGC). Murine Derl1 and Derl2 cDNA were subcloned by PCR amplifying using the following primers (restriction sites and kozak consensus sequences shown underlined and capitalized, respectively): Derl1 forward aaaagatctTCCACCATGtcggacatcggggactggttcagg; Derl1 reverse aaactcgagctggtctccaagtcggaagc; Derl2 forward aaaagatctTCCACCATGgcgtaccagagcctccggctgg; Derl2 reverse aaactcgagcccaccaaggcgctggccctcacc. The amplicons and the empty retroviral vector pMSCVpuro (Clontech) were digested with BglII and XhoI (New England Biolabs), gel purified (Qiagen) and ligated with T4 DNA ligase (Fermentas). The Gibson assembly reaction was utilized to construct the chimeric Derl1: Derl2 and Derl2: Derl1 [59]. Briefly, primers (Table S1) were designed to span the ends of the segments to be cloned by using the NEBuilder (TM) tool (New England Biolabs). PCR amplification and gel purification were performed to isolate segments to be cloned. Segments were assembled and cloned into pMSCVhygro (Clontech) by using Gibson assembly mastermix according to manufacturer' s protocol (New England Biolabs). In order to generate retroviral vectors, plasmid DNA was purified and transfected into human 293 cells along with MLV gag/pol and vesicular stomatitis virus G-spike protein expression plasmids, as previously described [44]. 48 and 72 hours later, resulting retroviral particles were harvested, filter sterilized and used to transduce target cells in the presence of 8 µg/mL polybrene (Sigma Aldrich). Approximately 1×107 cells were lysed in 1% digitonin, 25 mM Tris-HCl, 150 mM NaCl, 5 mM EDTA, 1 U/mL DNAse (Promega), and protease inhibitors (Roche), pH 7. 0. The lysates were centrifuged at 14,000× G and supernatants were mixed with either 1 µg/mL rabbit α-Derl2 antibody or 5 µg/mL mouse anti-Hrd1/SYVN1 monoclonal antibody (Sigma Aldrich) and incubated overnight at 4°C with agitation. Protein-A sepharose beads (Santa Cruz Biotechnology) were washed, blocked with 5% bovine serum albumin (EMD Millipore) and incubated with the lysates for 1 hour at room temperature with agitation. Following incubation, the beads were washed three times, mixed with SDS reducing buffer and subjected to SDS-PAGE followed by transfer to PVDF membranes. Membranes were probed with either rabbit anti-Derl2 antibody (Sigma Aldrich) or rabbit anti-Hrd1 polyclonal antibody (Novus Biologicals) at a 1∶2000 dilution followed by HRP conjugated α-rabbit antibody (Invitrogen) to allow detection. To test interactions between Derl2 and p97,293 cells were seeded at 1×106 per 10 cm plate and allowed to adhere overnight. The following day, cells were transfected with 10 µg of plasmid DNA by calcium phosphate method. Seventy-two hours post-transfection, the cells were lysed in 1% digitonin lysis buffer (as described above). S-protein agarose beads were blocked in 5% bovine serum albumin for 1 hour and incubated with the lysates overnight at 4°C. The beads were washed with 0. 1% digitonin, 25 mM Tris-HCl, 150 mM NaCl, 5 mM EDTA, pH 7. 0 and protease inhibitors and then mixed with 1X SDS reducing buffer. Samples were subjected to SDS-PAGE, transferred to PVDF membranes then probed with rabbit anti-S-tag antibody (Cell Signal Technologies) and mouse anti-p97 antibody (Santa Cruz Biotechnology). One hundred thousand Hela or 293 cells were transfected with 1 µg Cas9 expression plasmid (AddGene) [45] and 1 µg DNA derived from RT-PCR amplification of gRNA (Integrated DNA Technologies; Derl2 target sequence: AAGAAGTTCATGCGGACAT; Hrd1 target sequence: TGATGGGCAAGGTGTTCTT) using lipofectamine 2000 (Invitrogen) according to the manufacturer' s protocol in a 12-well plate. Twenty four hours following transfection, cell culture medium was aspirated and replaced with complete DMEM containing 300 µg/mL of G418 to select for cells successfully transfected with the human codon optimized pcDNA3. 3 TOPO vector carrying the Cas9 gene sequence and neomycin resistance cassette. After 72–96 hours under G418 selection the remaining viable cells were expanded to 10 cm tissue culture plates in complete DMEM without G418 and allowed to reach ∼80% confluence, after which toxin resistant cells were selected by intoxication with 5 nM Hd-CDT holotoxin. Cells surviving Hd-CDT intoxication were further expanded and the loss of either Derl2 or Hrd1 was confirmed by IP-western blot. 8 well-chambered slides (Nunc) were seeded with cells and allowed to adhere overnight. The following day, they were chilled on ice for 30 minutes then incubated on ice with 100–200 nM Hd-CDT for 30 minutes. The monolayers were washed with ice-cold PBS pH 7. 4 (Lonza), and then incubated at 37°C with complete medium. After 60 minutes at 37°C, the cells were washed with ice-cold PBS pH 7. 4, and fixed with ice-cold 2% formaldehyde (Sigma). After fixing for 30 minutes at room temperature, the cells were permeabilized by incubating in PBS 7. 4 containing 0. 1% Triton X-100 for 15 min, and blocked with 3% BSA (Sigma) for 30 minutes. To probe for Hd-CdtB, cells were incubated with rabbit polyclonal anti-Hd-CdtB antibodies (generated by The Immunological Resource Center, University of Illinois, Urbana, IL) at 4°C overnight, followed by incubation with goat anti-rabbit antibody labeled with either Alexa Fluor 488 or Alexa Fluor 568 (Invitrogen) at room temperature for 2 hours. Where indicated, the ER is labeled with either Alexa Fluor 594 conjugated Concanavalin A (Invitrogen) or mouse monoclonal anti-calreticulin antibody (Abcam) at 4°C overnight, followed by incubation with goat anti-mouse Alexa Fluor 647-labeled antibody (Invitrogen). Where indicated, nuclear counterstaining was performed by either incubating with DAPI for 30 minutes at room temperature or transfecting with 1 µg of plasmid encoding Histone-GFP (pH2B-GFP; Addgene, Cambridge, MA). The slides were mounted with ProLong Gold antifade reagent (Invitrogen) and images were collected using DIC/fluorescence microscopy and deconvoluted by using SoftWoRX constrained iterative deconvolution tool (ratio mode), and analyzed using Imaris 5. 7 (Bitplane AG). For each cell, images were collected from an average of 30 z-planes, each at a thickness of 0. 2 µm. Nuclear localization analysis was conducted by using the DeltaVision SoftWoRx 3. 5. 1 software suite. For nuclear localization, the percentage of Hd-CdtB localization into nucleus in parental and Derl2 deficient cells were calculated from approximately 30 cells from each group over at least two independent experiments. To test the colocalization of Hd-CdtB with the endoplasmic reticulum, results were expressed as the localization index, which was derived from calculating the Pearson' s coefficient of correlation values, which represent the colocalization of Hd-CdtB and the ER in each z plane of the cell. In these studies, a localization index value of 1. 0 indicates 100% localization of Hd-CdtB to the ER, whereas a localization index of 0. 0 indicates the absence of Hd-CdtB localization to the ER. The localization index was calculated from the analysis of a total of 30 images collected over at least two independent experiments. One hundred thousand 293 cells expressing T-cell receptor alpha fused to green fluorescent protein were seeded the day prior to transfection with 1 µg of plasmid encoding either dominant negative p97 (R586A) or control p97 (R700A) co-expressed with CD4 as a surface marker of positive expression (plasmids generously provided by Ron Kopito, Stanford University). Seventy-two hours after transfection, the cells were intoxicated with a concentration of Hd-CDT sufficient to cause cell cycle arrest in 48 hours. Intoxicated cells were rinsed with PBS, detached from the wells with PBS+1 mM EDTA, rinsed with PBS again and incubated with phycoerythrin conjugated rabbit anti-CD4 antibody (Invitrogen) in PBS+3% bovine serum albumin on ice for 30 minutes. Following staining, the cells were washed with PBS, fixed with 1% formaldehyde, washed with PBS again and stained with Hoechst 33342 for 10 minutes. Cells were then washed with PBS, resuspended in PBS and analyzed for phycoerythrin, Hoechst and GFP fluorescence by flow cytometry (LSR II; Becton Dickinson). Cell cycle analysis was performed on CD4 expressing cells. The half maximal lethal dose (LD50) of ricin intoxication was calculated by log transforming ricin concentrations and calculating sigmoidal variable slope dose response curves using the least squares (ordinary) fitting method. Paired t-tests were performed on average LD50 values calculated from three independent experiments performed in triplicate to determine two tailed p-values. Data analysis was performed using Prism version 5. 0d (GraphPad software).

Cytolethal distending toxins (CDTs) are produced by several bacterial pathogens and increase the ability of these bacteria to cause disease. After being taken up by host cells, CDTs are trafficked to the endoplasmic reticulum (ER) where they must translocate across the ER membrane to gain access to their intracellular target; however, this translocation process is poorly understood for CDTs. Here we provide evidence that CDTs require components of the ER-associated degradation (ERAD) pathway, a normal cellular process utilized to translocate terminally misfolded ER lumenal and membrane proteins across the ER membrane for degradation in the cytosol. Deletion of a key member of this pathway, Derl2, makes cells resistant to multiple CDTs. Interestingly, two domains within Derl2 which are required for ERAD of misfolded proteins are dispensable for intoxication by CDT. Further, we report two previously uncharacterized domains within Derl2 that are each required for intoxication. Consistent with a role of Derl2, abrogation of two other members of the ERAD pathway, Hrd1 and p97, results in retention of CDT in the ER and resistance to intoxication. Taken together, these data provide novel insight into how CDTs exit the ER and therefore gain access to their cellular targets.

Abstract Introduction Results Discussion Materials and Methods

bacteriology gram negative bacteria medicine and health sciences pathology and laboratory medicine host-pathogen interactions medical microbiology microbial pathogens biology and life sciences microbiology pathogenesis bacterial pathogens

2014

Cytolethal Distending Toxins Require Components of the ER-Associated Degradation Pathway for Host Cell Entry

In contrast to large GWA studies based on thousands of individuals and large meta-analyses combining GWAS results, we analyzed a small case/control sample for uric acid nephrolithiasis. Our cohort of closely related individuals is derived from a small, genetically isolated village in Sardinia, with well-characterized genealogical data linking the extant population up to the 16th century. It is expected that the number of risk alleles involved in complex disorders is smaller in isolated founder populations than in more diverse populations, and the power to detect association with complex traits may be increased when related, homogeneous affected individuals are selected, as they are more likely to be enriched with and share specific risk variants than are unrelated, affected individuals from the general population. When related individuals are included in an association study, correlations among relatives must be accurately taken into account to ensure validity of the results. A recently proposed association method uses an empirical genotypic covariance matrix estimated from genome-screen data to allow for additional population structure and cryptic relatedness that may not be captured by the genealogical data. We apply the method to our data, and we also investigate the properties of the method, as well as other association methods, in our highly inbred population, as previous applications were to outbred samples. The more promising regions identified in our initial study in the genetic isolate were then further investigated in an independent sample collected from the Italian population. Among the loci that showed association in this study, we observed evidence of a possible involvement of the region encompassing the gene LRRC16A, already associated to serum uric acid levels in a large meta-analysis of 14 GWAS, suggesting that this locus might lead a pathway for uric acid metabolism that may be involved in gout as well as in nephrolithiasis. Nephrolithiasis is a multifactorial disorder of complex etiology characterized by the presence of stones in the urinary tract. Kidney stones are a common disorder, with a prevalence of urinary calculi between 4% and 10% in the adult population, with an increasing incidence in Western societies [1]. For instance, in the US the prevalence has risen from 3. 2% to 5. 2% in just over two decades from the mid-1970s to the mid-1990s [2]. Wide geographical variations exist in renal stone incidence and composition, and specific geographic belts have been identified [3], where increased incidence has been attributed to genetic and environmental factors, such as hot climate (fluid loss) and sun exposure that increases the rate of vitamin D. Kidney stones are composed of inorganic and organic crystals amalgamated with proteins. Crystallisation and subsequent lithogenesis can happen with many solutes in the urine. Calcareous stones are still by far the most common type of nephrolithiasis, accounting for more than 80% of stones [4]. Uric acid nephrolithiasis (UAN) represent about 5–10% of the remaining stones, trailed by cystine, struvite, and ammonium acid urate stones. Genetic contribution to renal stones formation has been extensively recognized, and a number of studies have established a link between several genes and predominantly oxalate kidney stones, including vitamin-D receptor gene (VDR) and calcitonin receptor (CTR) gene, heparan sulfate (HSPG2) gene, and fibronectin gene (FN1) [5], [6]. There are a number of factors that can contribute to the formation of renal stones, including diet and obesity, specific drugs, other diseases, climate changes, metabolic disorders, and genetic predisposition [7], [8]. The complexity of this disease has led researchers to consider nephrolithiasis as one feature of a broader systemic disease, rather than a disease specific to a single organic system. This is especially interesting in relation to gout and metabolic syndrome, which are both systemic disorders in close relation with nephrolithiasis [9], [10]. UAN primarily results from low urinary pH, which increases the concentration of the insoluble undissociated uric acid, causing formation of both uric acid and mixed uric acid/calcium oxalate stones. A persistently low urinary pH (<5. 5, the pKa for uric acid is 5. 35) is a distinctive feature of idiopathic UAN previously named gouty diathesis [11]. In this study we focused on a Sardinian isolated population, the village of Talana, located in a mountain area of the island. The Talana population has been extensively studied, and has been characterized by a limited number of original founders, a long-term, slow population growth rate and isolation [12], [13]. Studying founder, isolated populations like the Talana, allows to reduce genetic complexity underlying disease etiology and to increase environmental homogeneity, as inhabitants share a common and uniform lifestyle. In the extant population of Talana the frequency of nephrolithiasis is approximately 20%, with a strong prevalence of UAN stones (half of all renal stone formers). In our previous study, we performed a genome-wide linkage search in 14 closely-related affected individuals using 382 microsatellites. Suggestive regions were investigated in 37 individuals who were more distantly-related affecteds [14], allowing us to fine-map a susceptibility locus on the chromosomal region 10q21–q22, and to identify a possible candidate gene [15]. The advent of high-throughput technologies for single nucleotide polymorphisms (SNPs) genotyping has allowed for a rapid and an economical way to do GWA analysis, and it might now be possible to achieve adequate power for identifying risk variants associated to complex diseases such as nephrolithiasis. In this new study we perform a GWA scan in a larger sample of well characterized cases and controls from Talana, utilizing a highly-dense SNPs map. Association analysis of our cohort of cases and controls, all related through multiple lines of descent and belonging to a single, large, and well-characterized genealogy, is particularly challenging, due to the complex relatedness in the sample. A number of methods have been proposed in the recent years for case-control association testing in samples that include related individuals from a single population provided that the pedigrees are completely specified [16]–[19]. It is well known that in association studies, spurious association can arise if ancestry differences among the cases and controls are not properly accounted for. An improved association method, named ROADTRIPS, for samples with related individuals and population structure, has recently been implemented in a software program [20]. ROADTRIPS uses an empirical genotypic covariance matrix calculated from genome-screen data to allow for population structure and cryptic relatedness in a sample that may not be captured by the available genealogical information. This method is appropriate for sampled individuals (both cases and controls) from a founder population, who are related through multiple lines of descent, with pedigrees only partially specified. In simulation studies with related individuals from outbred populations and population structure, including admixture, ROADTRIPS has been demonstrated to provide a substantial improvement over a number of existing methods in terms of power and type 1 error. Furthermore, in a recent review investigating the current progresses on methods that correct for stratification while accounting for additional complexities, ROADTRIPS has been shown to have appropriate characteristics [21]. We applied ROADTRIPS to a sample of related cases, affected by UAN, and a sample of unaffected controls selected from the same isolated population, all related through a complex genealogy. We also investigated the properties of ROADTRIPS, as well as other association methods, in our highly inbred population. To our knowledge this is the first application of this recent method to a case/control sample of closely related individuals from a founder population with extended genealogical data. We then followed up on the more promising regions and the top associated SNPs identified in our initial sample from the genetic isolate and performed an association analysis in an independent sample collected from the Italian population (including a general Sardinian sub-sample). The study subjects were 861 individuals from Talana, linked through a multi-generation 4446-member pedigree, with a mean (median) kinship coefficient of 0. 0201 (0. 0115) (SD = 0. 0231). During physical examination of each individual, a blood sample was collected for DNA extraction, and different phenotypic traits, and pathologies, were recorded. For this study, we collected information on age at diagnosis, medications, hospital admissions, and family history. Individuals with a history of urinary tract infection or with any secondary condition that might predispose to kidney stones (e. g. , inflammatory bowel disease or gout) were not included. The diagnostic procedures have been carried out essentially as described elsewhere [14]. In brief, all subjects affected by renal stones and their family members underwent renal ultrasound examination to confirm reported disease occurrence and to identify asymptomatic cases. Clinical renal ultrasonography is used to image calculi, such as UAS, that are non-opaque on X-rays [22]. From an initial set of 173 renal stone formers, we selected 80 severe cases that showed uric acid as the principal component. Disease severity was established on the basis of the presence of stones during ultrasonography and past history of kidney stones, with more than one episode of abdominal colic. Subjects with mild to moderate disease symptoms (e. g. , having only a single episode or spots but no episodes) were not classified as affected in the present study. We identified 94 control subjects, who were examined by ultrasonography to exclude individuals with asymptomatic disease. The mean age at observation of unaffected controls was sufficiently high (∼55 years) to have given an elevated probability of developing stones. All subjects gave written informed consent, and all samples were taken in accordance with the Helsinki declaration. Genotyping for the initial GWA study was carried out using the Affymetrix 500K chips using standard protocols, and the 50K chips with SNPs distributed around known genes. SNPs genotyping was performed on the Affymetrix Gene-Chip platform. We used the GeneChip Human Mapping to genotype the 500K Array Set that comprises two arrays (the Nsp and Sty arrays) capable of genotyping ∼262,000 and ∼238,000 SNPs, respectively. We followed the recommended protocol described in the Affymetrix manual. In total, 861 individuals were genotyped for the 500K set and 528 individuals for the 50K set. Details on QC analysis in Talana are provided in the Text S1. Briefly, we first checked for gender mismatches to make sure that individuals in our database align with individual DNA samples in the genetic data, dropping problematic samples. Individuals with a missing rate >90% were removed, and SNPs showing a missing rate >95% and a MAF <0. 05 were dropped in both the 50K and 500K sample sets. HWE was tested and two different thresholds (due to the different number of SNPs) were used to exclude SNPs that showed extreme deviation from HWE (threshold of p<1E-6 for the 500K, and of p<1E-4 for the 50K). Furthermore, we estimated the proportion of IBD sharing derived from the genome between each pair of genotyped individuals and compared it with the proportion expected based on the genealogical information. Relatedness between examinees was estimated from an LD-pruned dataset of SNPs derived from the whole genome data using PLINK [23]. From this analysis we identified and excluded individuals that showed recurrent inconsistencies between the two IBD sharing proportions. For the statistical analysis of our sample of related cases and controls derived from a single large genealogy, we used the recently proposed method implemented in the ROADTRIPS software [20]. This program allows for single SNP (currently just for autosomes) case-control association testing in samples from isolated founder populations with partially or completely unknown genealogies. A significant improvement over the previously proposed tests for founder populations, implemented in the CC-QLS and the MQLS software packages [16], [18], is that ROADTRIPS uses an empirical covariance matrix, denoted by Ψ, calculated from genome-wide SNP data to correct for unknown population and pedigree structure, while maintaining high power by taking advantage of known pedigree information when it is available. The structure matrix estimated from genome-wide data is used in the variance calculation to account for structure that may not be captured by the kinship coefficient matrix, denoted by Φ, derived from the known genealogy. Additional advantages of this approach are that it allows for two different types of controls, unaffected controls and controls of unknown phenotype (e. g. , general population controls), to be included in the same analysis, and it can incorporate phenotype information on relatives with missing genotype data at the SNP being tested. We now give a brief overview of the different test statistics used in the analysis. The ROADTRIPS extension of the statistics implemented in the MQLS software, namely, MQLS, WQLS, and the corrected Pearson' s χ2 test, are RM, RW, and Rχ, respectively. The MQLS, WQLS, and the corrected Pearson' s χ2 tests were developed for related samples from a single population with known pedigrees, and ROADTRIPS extends these statistics to allow for population structure and pedigrees that are partially or completely unknown. For two-allele disease models, optimal properties of the MQLS test (and the RM test when the individuals are from a single population) is that it is most powerful in a general class of linear statistics for general two-allele disease models in outbreds and for additive disease models in inbreds, as effect size tends to 0. The MQLS and RM tests improve power by taking advantage of the enrichment of predisposing alleles in affected individuals with affected relatives. The WQLS (and the RW test when the individuals are from a single population) is optimal when the true genetic trait model is a rare, fully penetrant dominant allele. The corrected Pearson' s χ2 test and Rχ are extensions of the Pearson' s χ2 test for independence of trait value and marker genotype. The Rχ statistic has a correction factor that is similar to the correction factor used in genomic control [24]. When the aforementioned test statistics have been applied to various association studies in the context of complex trait mapping, where the traits of interest are influenced by numerous genes as well as environmental factors, the tests have given complimentary as well different results, with the MQLS (and RM) test often having slightly higher power to detect association than the corrected Pearson' s χ2 test (and Rχ) and with WQLS (and RW) having the lowest power [16], [18], [20]. A summary of the characteristics of the statistics that were used is shown in Table 1. The p-values for the test statistics in the ROADTRIPS software are based on a χ2 asymptotic null distribution with 1 degree of freedom. To assess whether or not the p-value is “exact”, the ROADTRIPS software uses a similar criterion to what is commonly used for Pearson' s χ2 test for independence between trait and marker genotype, where the expected counts in each cell for a 2×2 table should be at least 5 in order for the χ2 distribution assumption to hold. The asymptotic null distribution assumption will hold for SNPs with rare alleles provided that there are enough minor allele counts observed for the SNPs in the sample. The ROADTRIPS software provides a warning message “The p-value might not be exact because of the small number of type 1 alleles in …” referring to cases, controls, or both, when the asymptotic null distribution assumption for the statistics may not be satisfied, which can occur for SNPs with low minor allele counts. The Rχ test is calculated using naïve allele frequency estimates, i. e. , allele frequency estimates based on giving equal weights to the sample individuals, while both the RM, and RW tests use BLUE estimates [25]. The latter allele frequency estimator is the best linear unbiased estimator and is calculated conditioned on the genealogy of the sample individuals. The BLUE takes into account relatedness in the sample and the estimator allows for inbreeding and for sample individuals to be related through multiple lines of descent. We collected an independent sample from the Italian general population, and in particular 69 cases from the Department of Nephrology and Dialysis of Bergamo, and 98 controls deriving from randomly ascertained blood donors in the same area. We also collected 56 affected individuals, and 59 controls from randomly ascertained blood donors in Sardinia. The Sardinian affected individuals were collected from the Clinics of Urology of Cagliari and Lanusei. All cases were selected to have pure uric acid stones or uric acid as the principal component. In total we analyzed 282 individuals (125 cases and 157 controls) in the replication study, but we considered the two population samples (continental Italy and Sardinia) as two different clusters, in order to exclude potential bias in the analysis derived from the geographical origin of the samples. We genotyped 96 SNPs in the independent replication sample as well as in the 73 cases and 93 controls from Talana analyzed in the initial study. A total of 28 SNPs were selected either from the top results in the initial study (10 SNPs), or in the candidate regions on chromosomes 2,6 and 10, based on a Rχ p-value <0. 05 and RM p-value<0. 01 (18 SNPs). For 11 out of 28 of these SNPs, only 48 cases and 67 controls were genotyped in the initial set (i. e. these SNPs belonged to the 50K set). We also genotyped 4 cSNPs (missense) in the candidate genes SLC17A1, ADAMTS14, and UNC5B, selected from Hapmap with a MAF in CEU >0. 01. The remaining SNPs (64) were selected using Tagger [26] to cover the candidate regions on chromosomes 2,6 and 10. We selected tSNPs with the criteria of “pick only the N best tags”, where N was based on the specific size and recombination pattern of each region. We used the “pairwise tagging only” mode, providing the Illumina design score for preferential picking of the tSNPs, to capture only SNPs with MAF>0. 05. The tagged regions and the resulting coverage based on r2 are shown in Table S1. The initial set of cases and controls from Talana was also genotyped for the SNPs typed in the replication cohort. SNPs were typed by using the VeraCode GoldenGate Genotyping Assay from Illumina according to the manufacturer' s protocol (Illumina, San Diego, CA). Briefly, the technology is based on allele-specific primer extension. Genomic DNA (250 ng) was activated chemically with biotin and then hybridized to a pool of locus-specific oligos (OPA, OligoPool All; Illumina). After removal of nonspecific unbound oligos, a PCR reaction was performed by using fluorescent-labeled primers (Cy3 and Cy5). PCR products were cleaned and denatured, and single-stranded fluorescent-labeled DNAs were hybridized to VeraCode beads, which were scanned on a BeadXpress reader by using Illumina VeraScan V1. 1 software. Raw data, consisting of intensities of fluorescence, were then imported into the analysis software GenomeStudio and the automatic allele calling was done using GeneCall threshold of 0. 25. The final SNP call rate (the number of SNP successfully genotyped for each sample) was >0. 97. Standard QC was performed and only 1 SNP was excluded due to extreme deviation from HWE, where this SNP had only the two homozygous genotypes. For the replication set, the sample of unrelated cases and controls was analyzed with PLINK using standard methods, based on allele frequencies differences. The Cochran-Mantel-Haenszel (CMH) tests for stratified tables, which allow for tests of association conditional on cluster of samples was used for merged sets (we clustered individuals based on the geographic origins, namely continental Italy and Sardinia). The Breslow-Day (BD) test was computed to test the homogeneity of odds ratios within clusters. We also performed a global association test by including to the replication set a Talana sub-sample that consisted of distantly related cases/controls, as an additional cluster using the CMH and BD tests. The Talana sub-sample was extracted from the whole sample of cases and controls using a pairwise sampling approach [27] basing on a kinship<0. 125 between each pair (resulting in 41 cases and 38 controls). The 73 cases and 93 controls from Talana, used in the initial study, were all typed for the 96 SNPs and analyzed with ROADTRIPS. For 11 SNPs identified in the initial study (28 SNPs), only 48 cases and 67 controls were genotyped in the initial set (i. e. these SNPs belonged to the 50K set), and the remaining 66 tSNPs were not typed in the initial GWAS. It should also be noted that for these SNPs we could not use the option in ROADTRIPS to include all unknown population controls in the analysis as was done in the initial GWAS, since only cases and unaffected controls were typed for these SNPs, while the remaining 668 sample individuals from Talana were not. This smaller sample size can lead to a reduction in power for the replication analysis, since samples sizes strongly influence the power of the test. For SNPs that have a low minor allele count in either the cases or the controls (unaffected and unknown phenotype) such that the asymptotic χ2 null distribution assumption with 1 degree of freedom for the statistics may not be valid, ROADTRIPS provides a warning message. In our GWAS we observed 22,502 warning messages for RM (∼7% of the tests), and 26,772 for Rχ (∼8% of the tests). We investigated the occurrences of warning messages for the RM and Rχ statistics in relation to MAF. Since in our GWAS we used all available information, thus also including in the analysis 668 unknown population controls, the RM statistics did not show any warning message referring to controls, but only to cases. In Figure S1 we show box-plots of MAF (naïve estimates) for the RM and Rχ statistics tagged by a warning message for each specific group (for RM in controls only; for Rχ in cases, controls, or both), stratified by the 500K and 50K sets as a different number of individuals were genotyped in the two sets (829 and 514 subjects, respectively). Figure S2 shows the Q-Q plots for the RM and Rχ statistics stratified by the presence of a warning message in the ROADTRIPS output. This figure illustrates that the asymptotic null distribution assumption does not hold for SNPs with low minor allele frequency and counts in our sample (due to the small sample size used in this analysis), particularly for the RM statistic. We also investigated whether the lower minor allele count SNPs are contributing to the excess of smaller p-values of the RM and Rχ statistics than what is expected under the null. From the figure it is evident that for RM these SNPs do not contribute to any inflation of the type 1 error, as none of the SNPs with warning messages for the RM test are anywhere near the significance level threshold, and for the Rχ test the vast majority of the SNPs with warning messages are also not close to being significant. Q-Q plots for the different statistics (RM, Rχ, RW, MQLS, corrected χ2 and WQLS) obtained from the GWAs are shown in Figure 1, where we stratified by naïve allele frequencies of the SNPs in the whole sample. From Figure 1, it is clear that for the SNPs with lower minor allele frequency, the type 1 error distribution is in general inflated for all statistics based on the BLUE estimation. In particular, the RM test may be quite sensitive to allele frequencies, and therefore hard to calibrate. Figure 1 also illustrates that for SNPs with a minor allele frequency of at least 0. 1 the asymptotic null distribution assumption for the RM test appears to be adequate for this sample, and the test may actually be conservative for this particular sample in the right tail of the distribution based on the −log (p-values), which may result in a slight loss of power for the RM test for the analysis of this sample. It is evident that ROADTRIPS provides a significant improvement of the RM test over MQLS test in this dataset, in terms of type 1 error, since there appears to be cryptic relatedness in this study that is not being accounted for in the MQLS statistic, and for which inflated p-values are observed over the whole genome (independently from MAF). In contrast, not much difference is observed between Rχ (which corrects for both population and pedigree structure using genome-wide data) and the corrected χ2 implemented in MQLS (which corrects for relatedness using the genealogical data) in our data. Finally, we were interested in gaining a better understanding for why some of the SNPs give large p-value differences for the RM and Rχ statistics in our data. We investigated SNPs with discordant RM and Rχ results for the analyses that included the unknown population controls. Specifically we investigated SNPs for which the RM test gives a p-value <1E-4 and the Rχ p-value is not close to significance level (>0. 05), and vice versa. In the Text S2 we present a formal investigation of the different behavior of the test statistics for the specific SNPs. The large difference that is observed for RM and Rχ for these SNPs is due to the small number of founders and the high degree of relatedness among the sample individuals. Even though there are 842 individuals in the sample, when comparing the allele frequency variance of the BLUE for this sample to the number of independent (i. e. , unrelated non-inbred) individuals that would give the same variance, we estimate the number of independent alleles in the sample [28] to be equivalent to having approximately 61 founders in the sample, i. e. , 61 independent individuals (60. 52 to be more precise). This estimate is based on the kinship and inbreeding coefficients for the 842 individuals that were calculated from the available genealogical data. The number of independent alleles in this sample may actually be less than our estimate since there is evidence of cryptic relatedness in this sample, as we previously mentioned. Based on the more stable characteristics of the Rχ statistics that we observed in our sample over the entire range of minor allele counts (low to high), we focused on results obtained with the Rχ statistics (p-value<1E-4), but we also required validation of the SNPs by the RM statistic with a p-value <1E-2. We therefore included in our follow up analysis potentially interesting SNPs with small p-values that did not necessarily reach the conservative Bonferroni genome-wide significance threshold. The final SNPs dataset used in this study consisted of a 334,674 SNPs in the merged set (500K and 50K) on the autosomes. The final sample set, that passed the QC, consisted of 73 affected and 92 controls. The characteristics of the case/control sample used in this analysis are summarized in Table 2. The 73 affected subjects are all related through a large pedigree of 1666 individuals. In total 80 cases and 94 controls were analyzed with ROADTRIPS, which allows additional phenotyped relatives that are not genotyped (namely 7 cases and 2 controls) to contribute to the analysis. Results from the GWAS for Rχ, are shown in Figure 2, where consistent results obtained with the RM statistics are also highlighted (RM p-value<1E-2). In Table 3 we summarize the top results obtained for SNPs that have a Rχ p-value<1E-4 and RM p-value<1E-2. On chromosome 2p, different SNPs in LD which each other showed Rχ below the 1E-4 threshold. These SNPs and the top SNP (rs11125301) are located in introns of the NRXN1 gene. Two other SNPs at different locations on 2q, rs1864466 and rs2359681, were identified with both Rχ and RM. The former, rs1864466, located in the 3′ of the ALS2CR8 gene, and the latter, rs2359681, is located in an intron of DYTN, and it is in LD with other SNPs located in the nearby ADAM23 gene, for which suggestive association is observed with the Rχ test (p-value = 0. 00018, Figure 3 and Table S2). On 6p three SNPs were associated with a Rχ p-value<1E-4, but only one, rs10946741, had also a RM p-value <1E-2 (p-value = 0. 00026). Other SNPs in the region and in LD with rs10946741 showed marginal association. The highest association is found in a region near the 5′ of the LRRC16A gene, found to be associated in a large meta-analysis with serum uric acid levels [29], although different SNPs located in introns of LRRC16A or in introns in flanking genes (SLC17A4 and SLC17A1) showed association with either the Rχ or the RM statistics (Figure 3 and Table S3). Evidence of association through the RM test was observed at different SNPs located in introns of LRRC16A, and at SLC17A4, where a nonsense SNP provided a p-value = 0. 00354. Strong LD is observed at SNPs in the SLC17A2 gene, but no evidence of association is observed with any of the test statistics. On chromosome 8, the Rχ test resulted in the most significant p-value, 8. 95E-08 over the genome (genome-wide significant after Bonferroni correction, p-value corrected = 0. 03), at rs12707927. The closest gene to rs12707927, PVT1, lies 90kb upstream, and neighbouring SNPs located within the gene were only showing marginal significance. Also, this SNP was tagged by a warning message that the minor allele count was small, and as a result the p-value, which is calculated based on a null distribution assumption of a χ2 with 1 degree of freedom, may not be exact. Indeed the allele frequency estimated with the BLUE was 0. 082 in cases and 0. 038 in controls for the A allele (allele frequency is 0. 062 in Hapmap-CEU). Note that this SNP was not removed in the QC stage because the estimated naïve allele frequency was 0. 054, and therefore slightly above the set MAF threshold of 5%. In a region on 10q, three SNPs in strong LD showed association (rs12784847, rs3740434, and rs11591930) in all statistics, with a lowest Rχ p-value of 0. 00003 at rs11591930 (Figure 3 and Table S4). One of this SNP, rs12784847, is located in an intron of the ADAMTS14 gene. Further, different SNPs in LD with rs11591930, and located either in introns or in the 5′UTR of the gene showed nominal association (Rχ p-value<0. 05), and one SNP (rs10999500) was a synonymous coding variant of the gene. Further, rs11591930 is tagging additional SNPs located in introns of the LRRC20 gene which showed marginal association (best Rχ p-value = 0. 00469, and RM p-value = 0. 00552), and in introns of the UNC5B gene (best Rχ p-value = 0. 00116, and RM p-value = 0. 00034). Finally, a SNP located on chromosome 22, rs12167903, was a missense variant of the CCDC157 gene. ROADTRIPS gave the warning that Rχ p-value might not be correct because of the low MAF (which is 0. 024 in the whole sample estimated by BLUE). This variant is indeed rare in the literature (2%), and resulted in a BLUE estimated frequency in our cases of 0. 125 (SD = 0. 060). Other regions identified in the initial GWAS did not contain genes with a direct role in stones formation or were regions devoid of known genes. These regions were not examined further adding tSNPs in the replication set, but only the top SNPs obtained in the initial GWAS were typed in the independent sample. The results of the analysis carried out for the 96 SNPs in the continental Italian, Sardinian, and merged sets (for a total of 282 individuals, 125 cases and 157 controls) are shown in Table 4. When considering the Sardinian and the continental Italian sample separately and merged together, using the CMH tests allowing for two strata, we observed association to UAN in the chromosome 2 and 6 regions. In the replication set on 2q33. 3 no significant association was observed when considering the merged sample, but nominal significance was obtained at three different SNPs in the continental Italian sample. The SNPs are located in the introns of ADAM23, with the highest evidence at rs11891267 (p-value = 0. 02732). The same SNPs were also found to be associated in the Talana sample, but the allele frequencies were oppositely distributed in cases and controls. In the whole Talana sample (Table S5) two SNPs intronic to ADAM23 (rs1025077 and rs3755224, Rχ p-value = 0. 01884, and Rχ p-value = 0. 00069, respectively), and a SNP in the intron region of DYTN (rs2163033, Rχ p-value = 0. 00818) were additionally found to be associated to UAN. In the region encompassing the LRRC16 gene on 6p22. 2–p21. 3, different SNPs showed marginal significance in the upstream region of the gene with peak evidence at rs12665174 with a CMH p-value of 0. 00146. One SNP (rs2149228) located in the intronic region of the gene also showed nominal significance in the merged replication set. All the SNPs but rs10946741 (identified in the initial study and not found significant in the replication set, i. e. CMH p-value of 0. 09925) had the same allele more frequent in cases compared to controls, both within each strata (Sardinia and continental Italy samples) and in the Talana cohort. Therefore, when considering the distantly related cases and controls from Talana as an additional strata in a merged dataset, evidence for association in the region increased for all SNPs with the highest evidence at rs12665174 (CMH p-value = 0. 00085). When analyzing the continental Italy and Sardinian samples separately, significant evidence for association was observed only in the Sardinian sample, with the highest evidence at rs12665174 (p-value = 0. 00502). When looking at the results obtained in the chromosome 6 region with ROADTRIPS in the whole Talana sample (Table S5), two additional SNPs showed nominal significance in the LRRC16 region (rs9461102, located upstream of LRRC16, and rs880226, located in an intron of the gene), and one additional SNP showed a Rχ p-value of 0. 015627 at SLC17A1. None of these SNPs showed evidence for association in the replication set. No other SNP identified in the initial study showed nominal significance in the merged replication sample, but two SNPs showed evidence for association in specific sub-samples: rs11125301 located in an intron of NRXN1 on chromosome 2 was associated in the Italian sample only, and with a different allele more frequent in cases compared to Talana; and rs12707927 on chromosome 8, that provided the highest evidence in the initial study, and for which a different allele was associated only at a nominal level in the Sardinian replication set (p-value = 0. 02260), but with allele frequencies in cases and controls in the opposite direction compared to Talana. For this SNP, in the analysis with ROADTRIPS without considering the unknown population controls, the RM and the Rχ tests were both tagged with a warning message due to the low MAF of the SNP, and were less significant than in the initial scan RM p-value = 0. 00020 and Rχ p-value = 0. 00079. On chromosome 10 only one SNP (rs826460) showed marginal evidence for Rχ in the flanking 5UTR region of ADAMTS14 in the whole Talana sample with ROADTRIPS, whereas none of the replication sets, nor the merged sample showed any evidence in this region. Finally, in the initial scan, we identified a missense variant of the CCDC157 gene, located on chromosome 22, whose frequency estimated with BLUE was increased in affected cases (12. 5%), compared to either unaffected controls (5. 0%) or population controls (2. 2%). The population control frequency is comparable to the 2% frequency reported in the literature. This variant is too rare to be identified in the relatively small replication set, and we did observed any significant results, nor in the merged Italian samples, not considering the two distinct geographical origins. In the present case-control GWAS including 73 stones formers and 92 controls, all related to each other, and deriving from an isolated Sardinian village, we identified different SNPs that showed suggestive associations with UAN. We applied a recently proposed method [20], ROADTRIPS, that allows for the analysis of the complex type of data we have, and we showed the improvement of the method in this founder population over previously proposed methods implemented in the MQLS software [18]. Indeed, providing the pair-wise kinship for all pairs of cases and controls was not sufficient to control for spurious association in our dataset using the MQLS test, as additional structure was still present. The statistics implemented in the MQLS software do not use an empirical structure matrix, and, in the presence of additional cryptic relatedness or unknown population structure, we observed inflated type 1 error. The remaining population structure was accounted for in the RM test implemented in the ROADTRIPS software by using an empirical covariance matrix calculated using genome-wide data, while also incorporating known genealogical information about the cases and controls into the analysis. In contrast, both χ2 corrected statistics (either corrected on pedigree or on genome data) showed similar results, indicating that with a sufficiently well-characterized genealogy data, the corrected χ2 test as implemented in the MQLS software shows less inflation of type 1 errors over the genome. A deviation from the χ2 null distribution was observed throughout the genome for both RM and MQLS for SNPs with rarer alleles (MAF<0. 1), which is an artifact of the small number of samples in our study. Furthermore, we observed that for a number of SNPs, the difference between RM and Rχ was largely being driven by the complex pedigree structure in the sample and the small number of founders (see Text S2). For samples like the Talana sample (as well as samples from founder populations like the Hutterites) with only a small number of founders, it actually is not clear at this time if a reasonable assessment of p-values can be obtained in the extreme tail of the χ2 distribution (e. g. , genome-wide significance p-values <1E-8), and this is future research to be conducted. A small sample is expected and unavoidable when focusing on small, isolated villages like Talana with only 1,200 inhabitants. Nevertheless, we were able to identify suggestive candidate genes for UAN in the initial GWAS, and to validate some of them in an independent Italian sample of well characterize cases and controls. Based on the associated SNPs in the initial scan, and their tagged SNPs (basing on LD pattern in Talana), we identified candidate genes on 2q33. 3,6p22. 2–p21. 3 and 10q22. 1, that were particularly interesting for UAN due to their physiological function. These regions were also investigated in the independent samples by typing additional tSNPs. Since the geographical origin of the replication samples were either continental Italy or Sardinia, we considered these two distinct groups in the statistical analysis using the CMH test and tested the homogeneity of odds ratio by the BD test. The 6p22. 2 region contains the LRRC16A, SLC17A1, SLC17A4 genes, and was identified in the initial scan with a significance at LRRC16A of Rχ p-value = 0. 00863, and RM p-value = 0. 00306, at SLC17A1 of Rχ p-value = 0. 01048, and at SLC17A4 for RM p-value = 0. 00354 at a nonsense SNP (rs2328894). Interesting, Kolz and colleagues [29], in a meta-analysis of 14 GWAs including a total of 28,141 participants, identified the same genes (except SLC17A4) significantly associated with serum UA levels. Therefore, peak SNPs in the region (Table S3) and additional 16 tSNPs for LRRC16A and 6 tSNPs for SLC17A4-SLC17A1 were typed in the replication set. Interestingly, different SNPs showed significant association in the upstream and intronic regions of the LRRC16 gene in the merged replication sample, with the highest evidence at rs12665174 (CMH p-value = 0. 00146). Most of the associated SNPs in the region showed the same allele more frequent in cases compared to controls, both within each strata (Sardinia and Italy samples) and in the Talana cohort. Therefore, when considering the distantly related cases and controls from Talana as an additional strata in a merged sample, evidence for association in the region increased with the highest evidence of CMH p-value = 0. 00085 at rs12665174. When analyzing the Italian and Sardinian samples separately, significant evidence for association was observed only in the Sardinian sample, with the highest evidence at rs12665174 (p-value = 0. 00502). When looking at the results obtained in the chromosome 6 region with ROADTRIPS in the whole Talana sample (Table S5), 2 additional SNPs showed nominal significance in the LRRC16 region (rs9461102, located in the upstream region of LRRC16, and rs880226, located in an intron of the gene), and one additional SNP showed a Rχ p-value of 0. 01563 at rs1165208, located in the intronic region of SLC17A1 (Table S5). None of these SNPs showed evidence for association in the replication set, and no association was observed in the replication set in the SLC17A4-SLC17A1 region. The LRRC16A gene is, for the larger part, located in an LD block encompassing also SCGN. In this study the coverage obtained by adding tSNPs in this region was only 51% of the total variation with an r2 of at least 0. 8, therefore further studies are needed to validate the involvement of these genes to UAN. On 2q33. 3, different SNPs showed association in the initial scan, with a peak at rs2359681 identified with both Rχ and RM (p-value = 0. 00008 and p-value = 0. 00254, respectively). The SNP is located in an intron of DYTN, and it is in LD with other associated SNPs located in the nearby ADAM23 gene, for which suggestive association is observed (Figure 3 and Table S2). In the replication set no significant association was observed when considering the Italian and Sardinia samples together, but nominal significance was obtained at different SNPs located in the introns of ADAM23, with the highest evidence at rs11891267 (p-value of 0. 02732) in the Italian sample alone. The allele frequencies were oppositely distributed in cases and controls compared to Talana, suggesting that putative causal variant/s at the gene implicated in UAN etiology are in LD with different alleles at the SNPs examined. In the whole Talana sample (Table S5) two tSNPs intronic to ADAM23 (rs1025077 and rs3755224, Rχ p-value = 0. 01884, and p-value = 0. 00069, respectively) and a SNP in the intron region of DYTN (rs2163033, Rχ p-value = 0. 00818) were additionally found to be associated in the replication study. The other interesting region identified in the initial study and investigated further in the replication set was on 10q22. 1, with the highest association evidence observed in the initial study at the ADAMTS14 gene. (Rχ p-value = 0. 00003; RM p-value = 0. 00022). Two other genes were found to be associated in the region on 10q, namely LRRC20 and UNC5B (most significant RM p-value = 0. 00552, and RM p-value = 0. 00034, respectively). Interestingly, the SNPs identified on chromosome 10 are located within the critical region identified through linkage analysis in Talana [14]. In the previous study we performed a genome-wide linkage search in 14 closely-related affected individuals using 382 microsatellites, and followed up suggestive regions on 37 individuals more distantly-related affecteds. The original linkage region spanned approximately 9 Mb, with the second highest peak at D10S537 (position ∼72,065 kb), located in the upstream region of ADAMTS14. In the replication set we did not observe any signal of association in either the ADAMTS14 region or in the UNC5B region, although by typing additional tSNPs. In the Talana sample a SNP located in the upstream region of ADAMTS14 showed marginal evidence of association (Rχ p-value = 0. 04647 at rs826460, Table S5), but the SNPs that showed association in the original scan when also including the unknown phenotype controls in the analysis, were not found to be significantly associated in the replication study. Further analyses are needed to evaluate the role of this region in UAN etiology. Among the remaining top SNPs identified in the initial GWAS only one showed marginal evidence for association in a specific sub-sample: rs11125301 located in an intron of NRXN1 on chromosome 2 was only found to be associated in the Italian sample and with a different allele that is more frequent in cases compared to the Talana sample. In conclusion, we obtained evidence for association to UAN for some interesting genes in this study, whereas further investigation is needed to validate the involvement of other genes/regions identified in the initial GWAS. In particular, LRRC16A, already associated to serum UA levels from previous studies, encodes for CARMIL protein, an inhibitor of actin capping protein (CP) and has profound effects on cell behavior. Removal of CP may be a means to harness actin polymerization for processes such as cell movement and endocytosis and plays important roles in intracellular transport (the movement of vesicles and organelles). It is interesting that this protein showed the highest expression in kidney and other epithelial tissues [30]. The mechanism by which variants at this gene regulate UA remains unclear. We can envisage that this gene may be involved with the kidney, for example, in podocytes that are glomerular cells with an actin-based contractile apparatus and they are insulin sensitive [31]. The insulin response of the podocytes occurs via the facilitative glucose transporters GLUT1 and GLUT4, and this process is dependent on the filamentous actin cytoskeleton [32]. Insulin responsiveness in this key structural component of the glomerular filtration barrier may have a central role in the establishment of states of insulin resistance. Different studies have emphasized the increasing importance of insulin resistance in the pathogenesis of UA stones and insulin resistance is strongly correlated with low urine pH [33]. Numerous epidemiologic studies have shown a significant association between nephrolithiasis, obesity, glucose intolerance, type 2 diabetes mellitus, hypertension and chronic kidney disease [10], [34]–[37]. There are likely still many unrecognized renal manifestations of the metabolic syndrome. UAN, secondary to low urine pH, might only be the tip of the iceberg. Nevertheless, UA stone formers may have yet undisclosed mechanisms leading to unduly low urinary pH that are not entirely accounted for by insulin resistance [33]. Similarly, we can envisage that the F-actin reorganization is important also in tubular cells of kidney for proteins sorting directly involved in metabolism of UA. For the different endophenotypes we examined (Table S6), we observed normal serum parameters and not significant differences between cases and controls (after correcting for age and sex). Indeed in Talana we observed a general low urinary pH (Figure S3), significantly lower than the distribution in the general population (95%CI = [5. 4; 5. 7]), that could explain the high proportion of UAN cases among renal stones formers. The ADAM23 gene, for which nominal significance was observed in the Italian replication sample, encodes a member of the ADAM (a disintegrin and metalloprotease domain) family. ADAMs are membrane-anchored cell surface proteins with putative roles in cell–cell and/or cell–matrix interactions and in protease activities [38]. Members of this family have a unique structural organization including metalloprotease, desintegrin, cystein-rich, epidermal growth factor-like, transmembrane and cytoplasmatic domains [38]. The available data indicate that three of the ADAM family members are expressed at high levels in normal brain (ADAMs 11,22, and 23) while other members are either expressed in the testis or are ubiquitous. More recently Ru et al. [39] detected the ADAM23 protein in Human urine samples. ADAM23 exhibits the typical structure of the ADAM family members; however, the metalloproteinase domain is inactive, suggesting that it is exclusively involved in cell adhesion. The disintegrin and cysteine-rich domain of ADAMs have been shown to interact with cell adhesion molecules including the receptors of the extracellular matrix, integrins [40], as well as proteoglycans (e. g. syndecans) [41]. It is interesting that the proteoglycans (GAGs) are inhibitors of crystallization and appear to be involved in kidney stone formation. In a previous study we showed that the lower excretion of GAGs in stone formers could impair their inhibitory activity on UA stone formation, and, as a consequence, it may represent a risk factor for this form of urolithiasis [42]. Furthermore, a proteoglycan like Syndecan-4 was up-regulated in proliferative renal disease and mice deficient in syndecan-4 were more susceptible to κ-carrageenan induced renal damage indicating that syndecan-4 plays an important role in renal diseases [43]. Finally, Hwang et al. [44] reported a strong association with ADAM23 for urinary albumin excretion, that is a marker of kidney function. Due to the small sample of affected subjects used in the initial scan, statistical power was consequently relatively low in this study, and indeed the significance of the evidence for association with the identified SNPs is lower than genome-wide significance considering Bonferroni correction. On the other hand, we have the advantage of using a homogenous cohort of individuals, sharing a very similar life style and dietary habits, and with an increased genetic homogeneity, as a consequence of a strong founder effect and of genetic drift deriving from isolation that endured for centuries. A consequence of association studies in founder populations can be lower statistical power due to having small sample sizes. A compelling advantage, however, for such samples is increased homogeneity in terms of both environmental and genetic factors involved in disease etiology, which can ultimately improve the power to detect association. Although our sample was relatively small, we were nevertheless able to identify different candidate genes with a potential role in UAN, and to provide evidence for association in an independent sample for the gene LRRC16A on 6p, already found to be associated to serum UA levels in a large meta-analysis of 14 GWAS and possibly for ADAM23 on 2q. To our knowledge this GWAS is the first one carried out for UAN. It is also the first application of ROADTRIPS to a founder population. The original application of ROADTRIPS [20] was to both simulated and real data in samples from outbred populations. The sample sizes of the cases and/or the controls in the previous applications used to evaluate the method were also more than five times the sample we analyzed in this study. We were able to evaluate the performance of the method using real data from a small sample in a genetic isolate, which likely has different properties and complexities than the data sets previously used to evaluate the type 1 error and power of ROADTRIPS.

There are a number of factors that contribute to renal stone formation, including diet and obesity, specific drugs, other diseases, climate changes, metabolic disorders, and genetic predisposition. In this article, we focus on identifying genomic regions that may be involved with nephrolithiasis associated with a uric acid component. We analyze data from a genetic isolate in Sardinia to take advantage of the potential improvement in power to detect association with complex traits when related, homogeneous affected individuals are selected. To take into account the correlations among our related sample of cases and controls, we applied a recently proposed method that corrects for both known and unknown population and pedigree structure using genome-wide data. In simulation studies for outbred populations with related individuals and population structure, the method has been demonstrated to provide a substantial improvement over a number of existing methods in terms of power and type 1 error. We investigate the properties of this new method, as well as other association methods, in our inbred sample. To our knowledge, this is the first application of this recently proposed method to a founder population. This study is also the first genome-wide association study carried out for uric acid nephrolithiasis.

Abstract Introduction Methods Results Discussion

genetics and genomics/genetics of disease genetics and genomics/complex traits genetics and genomics genetics and genomics/population genetics

2011

Application of a New Method for GWAS in a Related Case/Control Sample with Known Pedigree Structure: Identification of New Loci for Nephrolithiasis

The Gran Chaco ecoregion, a hotspot for Chagas and other neglected tropical diseases, is home to >20 indigenous peoples. Our objective was to identify the main ecological and sociodemographic determinants of house infestation and abundance of Triatoma infestans in traditional Qom populations including a Creole minority in Pampa del Indio, northeastern Argentina. A cross-sectional survey determined house infestation by timed-manual searches with a dislodging aerosol in 386 inhabited houses and administered questionnaires on selected variables before full-coverage insecticide spraying and annual vector surveillance. We fitted generalized linear models to two global models of domestic infestation and bug abundance, and estimated coefficients via multimodel inference with model averaging. Most Qom households were larger and lived in small-sized, recently-built, precarious houses with fewer peridomestic structures, and fewer livestock and poultry than Creoles’. Qom households had lower educational level and unexpectedly high residential mobility. House infestation (31. 9%) was much lower than expected from lack of recent insecticide spraying campaigns and was spatially aggregated. Nearly half of the infested houses examined had infected vectors. Qom households had higher prevalence of domestic infestation (29. 2%) than Creoles’ (10. 0%), although there is large uncertainty around the adjusted OR. Factors with high relative importance for domestic infestation and/or bug abundance were refuge availability, distance to the nearest infested house, domestic insecticide use, indoor presence of poultry, residential overcrowding, and household educational level. Our study highlights the importance of sociodemographic determinants of domestic infestation such as overcrowding, education and proximity to the nearest infested house, and corroborates the role of refuge availability, domestic use of insecticides and household size. These factors may be used for designing improved interventions for sustainable disease control and risk stratification. Housing instability, household mobility and migration patterns are key to understanding the process of house (re) infestation in the Gran Chaco. The strong association between neglected tropical diseases (NTDs), poverty and particular combinations of ecological, social, political and economic determinants explains the occurrence of global hotspots of NTDs [1]. One of such hotspots occurs in the Gran Chaco ecoregion in South America, where the prevalence rates of geohelminthic infections and Chagas disease are very high [1]. Chagas disease, caused by Trypanosoma cruzi, is considered the main regional vector-borne disease in terms of disease burden and affects 8–10 million people in Latin America [2]. Triatoma infestans, the main vector in the Southern Cone countries and southern Peru, has been the target of an insecticide-based regional elimination program that interrupted the transmission of human T. cruzi infection by T. infestans in various countries [2–4]. Progress in the Gran Chaco lagged behind and vector-mediated transmission of T. cruzi still occurs albeit at lower incidence levels than 20 years ago [5–8]. The Gran Chaco is home to more than 20 ethnic groups [9]. Indigenous populations usually are among the most marginalized groups, with more precarious health and living conditions than other peoples [10–12]. Indigenous communities of the Gran Chaco showed high seroprevalence of human T. cruzi infection [13–18]. One of the most numerous ethnic groups in this region is the Qom (Toba) people [19]. Qom households were exposed to a greater risk of T. cruzi infection than Creole households in a well-defined rural section of Pampa del Indio (Argentine Chaco) mainly inhabited by Creoles (denominated Area I), but there were large heterogeneities between and within ethnic groups [20,21]. Further studies on the ecological, biological and social (eco-bio-social) determinants of vector-borne diseases are needed [22], more so in the case of vulnerable indigenous populations affected by Chagas disease and other NTDs. The main identified determinants of house infestation with the major domestic vectors of T. cruzi (T. infestans, Rhodnius prolixus, Panstrongylus megistus, and Triatoma dimidiata) include housing construction characteristics that create refuges for the bugs to hide in (e. g. , cracks in walls, thatched roofs, precarious peridomestic structures); the presence and number of human and domestic animal hosts (dogs, chickens) that serve as bloodmeal sources, and little or no domestic application of insecticides by house residents [20,23–32]. These factors are the expression of various underlying processes that ultimately create conditions that facilitate house infestation and T. cruzi transmission [33]. A full understanding of complex systems [34] involving infectious diseases requires more integrative approaches such as the ecosystem approach to human health (ecohealth) [35], which gives proper attention to eco-bio-social factors and their eventual interactions. However, very few studies have explicitly addressed these factors simultaneously in relation to Chagas disease [31,36,37]. This limited knowledge curtails our ability to design and implement innovative vector and disease control strategies adapted to resource-constrained settings. The current study therefore addressed traditional ecological determinants and selected sociodemographic factors related to poverty and ethnicity. As part of a longitudinal study on the eco-epidemiology and control of Chagas disease in northeastern Argentina, we expanded the scope and geographic scale of our previous studies [20,21,37] conducted in Area I of Pampa del Indio to focus on Qom communities living in ancestral territories which also included a Creole minority (denominated Area III). The living conditions of Qom households most likely differed substantially from those of Creoles, and their association with house infestation has not been investigated at a sufficiently large spatial scale. The objective of the current study was to identify the main ecological and sociodemographic determinants of domestic infestation and abundance of T. infestans (two surrogate indices for transmission risk) in Area III, where Qom communities predominated, using generalized linear models in a multimodel inference frame with model averaging. In addition to the above-mentioned factors known to be closely associated with house infestation in multiple settings, we examined the effects of distance to the nearest infested house, residential overcrowding, household education level, wealth indicators, and preventive practices. The first two factors were predicted to exert positive effects on domestic infestation and bug abundance whereas the remaining factors were expected to exert negative effects. We also re-examined whether ethnic background modified both response variables when other relevant risk factors were accounted for. Our study highlights the relevance of various ecological and sociodemographic factors whose effects have not been investigated simultaneously, and provides guidance on improved control interventions specifically adapted to the Gran Chaco. Field work was conducted in a rural section (95 km2) of Pampa del Indio municipality (25° 55’S 56° 58’W), Chaco province, Argentina (Fig. 1). The municipality was inhabited by approximately 22,000 people by late 2013, and 45% of residents belonged to the Qom ethnic group according to local municipal authorities. Official decennial census records in 2001 and 2010 indicated that the population of Pampa del Indio municipality increased remarkably from 11,558 to about 18,000 people, respectively (annual population growth rate, 4. 9%). The climate, landscape and demographic features of a contiguous section of the municipality inhabited mainly by Creole households were described elsewhere [20,21]. The last insecticide spraying campaign conducted in the municipality occurred in 1997–1998 according to the Chagas disease control program from Chaco province. Selection of the study area took into account the lack of recent history of community-wide insecticide spraying; preliminary evidence of house infestation ranging from 30 to 40%; the predominance of indigenous households; and the presence of at least 350 adjacent households in order to achieve a sufficiently large study base for statistical inference. A household is defined as all the people who occupy a housing unit including the related and nonrelated family members [38]. A house compound was defined as the set of domicile (i. e. , an independent structure used as human sleeping quarters, S1 Fig.), patio and other structures included within the peridomestic area (kitchens, storerooms, latrines, corrals, chicken coops and chicken nests (“nidero”), ovens, trees where chickens roosted, others) as illustrated elsewhere [20]. House compounds sometimes had more than one domicile used as sleeping quarters by related family (S1 Fig.). Before initiating field operations local key actors were interviewed to gather background data that may allow a better assessment of the preintervention situation; discuss the initial and long-term goals of the research program (see below); and assist the interpretation of the study outcomes. Local key actors included the mayor, health and education authorities and other personnel, rural health-care workers and school teachers, representatives of third-sector organizations, and community leaders. The stated long-term goals of the research program were to interrupt the human transmission of T. cruzi through intensified vector control, human diagnosis and treatment, and to promote long-term sustainability of disease control efforts through local empowerment. A cross-sectional survey aimed at enumerating all house compounds in the area and assessing house infestation was conducted in October 14–31,2008. The study area included seven villages with 407 inhabited houses, 19 abandoned dwellings and 17 public buildings (4 primary schools, 1 health-care post, 6 churches and 6 community centers) (Fig. 1). One member of the research team explained to each householder the aims of the survey and requested permission to access their premises and identify the house with a numbered aluminum plate. The location of each house was georeferenced with a GPS receiver (Garmin Legend). Householders were asked for the presence of triatomines within their premises after showing them dry specimens of T. infestans, Triatoma sordida and other Reduviidae to prevent confusion with other insects; from these reports we derived the index “householders’ notification of house infestation”. All households were provided with a labeled self-sealing plastic bag to contain any triatomine they sighted, and instructed on how to manipulate the bugs safely. This additional source of bugs was denominated “householders’ bug collections”. Householders’ bug collections were only considered if the date and collection site were reported to us. The study protocol was approved by the Dr. Carlos A. Barclay Independent Ethical Committee for Clinical Research, Buenos Aires, Argentina. A total of 386 inhabited houses (94. 9%) were included in the current study of triatomine infestation; 21 houses closed during the survey were not searched for bugs. In all of the 386 study houses the following methods were performed to assess bug infestation: i) inspection by timed-manual searches; ii) collection of bugs that were spotted during insecticide spraying operations; and iii) promotion of householders’ collection of any triatomine they sighted (as explained above). Multiple methods were used as a cross-check of the outcome of timed-manual searches. All domestic and peridomestic sites of the study houses were searched for triatomine bugs (timed-manual collections) by four teams including one supervisor and two or three skilled bug collectors who used 0. 2% tetramethrin (Espacial, Argentina) to dislodge the insects as described [20]. Each domicile and peridomestic site was searched by one person for 15 min. Immediately after the vector survey, vector control personnel sprayed every house with suspension concentrate deltamethrin (K-Othrin, Bayer) or beta-cypermethrin (Sipertrin, Chemotecnica) using standard doses (25 and 50 mg/m2, respectively) and routine procedures [39]. Bugs sighted during insecticide spraying operations were also collected. The collected bugs were stored in plastic bags labeled with the house number and specific bug collection site and were transported to the field laboratory where they were identified taxonomically and counted according to species, stage or sex. Two to six weeks after bug collection, feces of all the third-instar nymphs and older stages that were alive were microscopically examined for infection with T. cruzi at 400× as described [21]. The bugs examined for infection were collected from 72. 8% of the infested houses. This survey was conducted in parallel to the vector survey in October 2008. An adult household member fluent in Spanish was asked for information on the following items: full name of householder (i. e. , head of household) [40]; the number of resident people by age class (0–5,6–14, and 15 or more years of age); the number of domestic animals of each type (dogs, cats, chickens, other poultry, goats, pigs, cows, and equines) and their resting places; use of domestic insecticides (type, frequency, purpose); and date of the last insecticide spraying of house premises conducted by vector control personnel or the local hospital or any other third party using manual compression sprayers. Because the study area encompassed traditional Qom communities, assignment of a household to ethnic group was based on whether they spoke Qom language (Qomlaqtaq); participated in traditional Qom organizations; and took into account their physical features. When in doubt, assignments to ethnic group were subsequently checked with local Qom health-care personnel and were corroborated in all cases. Households with a mixed ethnic background were considered to be Qom because they resided in ancestral indigenous territories and fulfilled the above mentioned attributes. A sketch map of the spatial location of all structures in each house compound was performed, and each structure was given a unique code according to its use. We recorded the building materials used in roofs and walls, presence of wall plaster, condition of wall surface, and plaster material. The availability of refuges for bugs was determined visually by a skilled member of the research team and scored in one of five levels ranging from absence to very abundant refuges [20]; only the three top categories were recorded in domiciles. As our knowledge of the study area increased during the vector surveillance phase, additional sociodemographic variables potentially associated with house infestation were taken into consideration and recorded mostly in November 2012: educational level attained by each household member (number of schooling years completed); land ownership (no ownership; individual: the householder owned the land they inhabited; familial: a relative owned the land; communal: the community owned the land which therefore could not be sold); agricultural activities (present and past); monthly public welfare support; household electricity and time since first connection; age of house (years since construction); size of each domicile’s area; source of drinking water; presence of window screens (wire mesh); use of bed nets; and participation in a local social organization. The data collected in 2012 were back-corrected to extant conditions in 2008 (e. g. , access to electricity, age of house, agricultural activities). Overall changes in several respects (e. g. , drinking water source, domicile’s area, participation in social organizations) were negligible during the four-year period. For some of the back-corrected variables it was possible to assess the validity of the reports. The comparison of domestic area and age of house recorded both in 2009 and 2012 showed minor differences. Land tenure, access to electricity and householders’ reports of time since last insecticide spraying were checked with other local sources of information and whether they were spatially clustered. Comparison between the list of houses sprayed with insecticides in 2006 (identified by the name of the head of household) and the date of the last insecticide spraying each individual household reported to us in 2008 showed either large or perfect agreement in two communities (75% and 100%) and a very low degree of agreement in another community (8%). The recorded data were used to compute household-level surrogate indices for wealth, educational level and overcrowding. The goat-equivalent index represents a small stock unit to quantify the total number of livestock (cows, pigs, goats) and poultry owned by the household in terms of goat biomass. To calculate this index the average weight of each type of animal was considered (cow, 453 kg; pig, 159 kg; goat, 49 kg; chicken, 2. 5 kg) [41]. The household educational level was defined as the mean number of schooling years attained by household members aged 15 years old or more [42]. The overcrowding index was defined as the number of human occupants per sleeping quarter; the presence of 3 or more occupants per room is taken as critical overcrowding [43]. Housing quality (a three-level categorical variable) was represented by the combination of mud walls (versus brick-cement walls) and tarred-cardboard sheets on the roof (versus corrugated metal-sheets); no house had brick-cement walls and tarred-cardboard sheets. As part of annual vector surveillance after community-wide insecticide spraying in October 2008, all extant households in the study area were re-surveyed in August 2009, April 2010 and November 2012, whereas a sample of 86 houses was re-surveyed in December 2011. For each house we recorded its current and previous existence; fate (destruction, movement and construction); destination of moving households and underlying reasons (whenever possible), among other variables. The sociodemographic information was collected at every new house as in the baseline survey. House infestation data only included inhabited houses because no local public building or abandoned house was found to be infested. Similarly, latrines and trees used by chickens were not infested by T. infestans and therefore were not included in the number of peridomestic sites per household. The prevalence of house (or site-specific) infestation by T. infestans was calculated based on the finding of at least one live bug (except eggs) by any of the bug collection methods used (i. e. , timed-manual searches, during insecticide spraying operations, and householders’ bug collections) relative to the total number of houses (or sites for each ecotope) inspected. The abundance of triatomine bugs was calculated as the number of live bugs collected per 15 min-person among houses positive by timed-manual searches. If a house compound had more than one domicile, the average domestic bug abundance was calculated as the total number of live triatomines collected per 15 min-person across domiciles divided by the number of domiciles inspected. A matrix of distances to the nearest infested house was calculated using qGIS [44]. Agresti–Coull binomial 95% confidence intervals (95% CIs) were used for infestation prevalence [45]. Householders’ notification of the domestic presence of T. infestans and timed searches of domestic infestation were compared using the kappa index implemented in Stata 12 [46]. Kappa index values greater than 0. 6 may be considered substantial to perfect agreement and values less than 0. 4 represent a poor agreement beyond chance. Risk factor analyses of the presence and relative abundance of T. infestans were restricted to human sleeping quarters because peridomestic infestations were relatively few. Owing to the occurrence of house compounds with more than one domicile (including related family) and that several variables were measured at the household level, in these cases data for all domiciles were pooled to obtain a single observation per compound. Availability of refuges for bugs and age of house were averaged over domiciles within a house compound, and the total domestic area was the sum of each domicile’s area. The number of domestic hosts (dogs or cats and poultry, mostly chickens) used in the analyses (not in the census) only included animals reported to rest or nest inside domiciles. Bivariate logistic and negative binomial regressions on each explanatory variable were performed with domestic infestation and bug abundance as response variables, respectively. Relative bug abundance (RA), labeled in Stata output as' incidence-rate ratios’, and their CIs were calculated from the estimated coefficients (b) of the negative binomial regression as eb. The association between selected explanatory variables and both response variables were tested through multiple logistic and negative binomial regressions, respectively. The global models included 10–12 explanatory variables with complete data selected a priori based on background evidence (e. g. , [20,25,30]) and additional hypothesis on the predicted effects of selected sociodemographic determinants as mentioned above. Some variables measured in 2012 (i. e. , age of house, electricity, time since last insecticide spraying) had a large number of missing data and therefore were not included in these analyses. We also compared the fit of the negative binomial models for bug abundance with those returned by mixture and two-part models for zero-inflated distributions, and found strong evidence of the superiority of the negative binomial regression model (S2 Text). Two global models were analyzed. The first model included 10 explanatory variables (from 386 households) which described building characteristics (housing quality, refuge availability), domestic host availability (number of persons, number of dogs or cats and presence of poultry indoors), socioeconomic features (ethnicity, goat-equivalent index), household preventive practices (reported insecticide use), peridomestic infestation by T. infestans, and distance to the nearest infested house. The second model included 12 variables (i. e. , the 10 variables mentioned before, residential overcrowding and household educational level) recorded at 274 households. Some continuous variables were rescaled in order to give more meaning to the unit of increment of risk estimates: distance to the nearest infested house (one unit every 50 m), household educational level (every 6 years) and the goat-equivalent index (every 10 goats). For comparative purposes we also analyzed the second data set after removing overcrowding and educational level data. On a post hoc basis we investigated the effects of the interactions between ethnicity and every other factor in the global models on both response variables, which proved not to be significant. These terms were added one by one to each global model and tested separately to avoid convergence problems. Potential multicollinearity among explanatory variables was evaluated through the variance inflation factor (VIF) and condition numbers as implemented in Stata 12. The condition numbers were less than 10 and VIF < 2 for all explanatory variables, indicating that the significant correlation found between some pairs of variables (ethnic group with housing quality, refuge availability, goat-equivalents, which had correlation coefficients ranging from 0. 35 to 0. 4) would not cause serious multicollinearity. We used an information theoretic approach and Akaike’s information criterion (AIC) to identify the best-fitting models describing variations in domestic infestation and abundance of T. infestans, given the data collected. Multimodel inference was especially conceived to account for model selection uncertainty; it allows a quantitative ranking of the models and identification of the set of models having best support given the data [47,48]. Because the ratio between the number of parameters and the number of observations (i. e. , houses) was less than 40, we used the AIC corrected for small sample size (AICc). Akaike differences (ΔAICc) were calculated for each model as ΔAICc = AICc—AICmin; models with ΔAICc > 2 were considered to have less support than the best model (AICmin), given the data and models analyzed. Several models had substantial support; therefore, we performed multimodel inference through model averaging. The Akaike weight (wi) of a model represents the support or probability of being the “best model”. The relative importance (RI) of each variable is defined as the sum of Akaike weights in each model in which the variable is present; RI takes values from 0 to 1. The overall fit of the logistic models was assessed by the Hosmer-Lemeshow test using the model-averaged coefficients and pooling the data in 10 equal-sized groups. Odds Ratios (ORs) and their 95% confidence intervals were calculated from model-averaged coefficients. Unconditional standard errors were calculated according to equation 4 in [49] with the default option (revised. var = TRUE). The area under the receiver operating curve (ROC) was also calculated; a value of 1 indicates a perfect fit. Sensitivity and specificity were assessed using the observed infestation prevalence of each data set as the cutoff values. The analyses and calculations were performed in R (version 2. 15. 1) [50]. Package MuMIn (version 1. 9. 5) was used for multimodel averaging; ResourceSelection (0. 2–2) for performing the Hosmer-Lemeshow test; and ROCR (version 1. 0–5) for calculating sensitivity, specificity and the area under the ROC curve. The spatial distribution of domestic infestation was assessed through global and local point pattern analyses (PPA) [51]. The former estimates the spatial aggregation of the outcome event across the entire study area whereas the latter detects the location of aggregated events. The spatial distribution of houses was examined to determine whether the potential aggregation of house infestation was influenced by a non-random dispersion of house locations. The global spatial analysis of domestic infestation was performed in Programita using the weighted K-function [52] and random labeling as the null hypothesis (i. e. , to assess the spatial distribution of infested houses given the fixed spatial distribution of all houses). The maximum distance considered was 2,000 m (i. e. , one-third of the smallest dimension of the area) [51], and the cell size was 40 m. A total of 999 Monte Carlo simulations was performed and the 95% confidence envelope was calculated with the 25th upper and lower simulations. Local spatial aggregation of infestation was tested through the Getis statistic (G*) [53] implemented in PPA [54]. This analysis distinguishes between positive and negative aggregation of events (i. e. , infested houses); parameter settings were the same as for the global analysis. The house-to-house census enumerated a total of 2,389 inhabitants in 386 inhabited houses as of October 2008, and 2,356 persons in 445 inhabited households in November 2012. The population included 18. 0% up to five years of age; 27. 7% between 6 and 14 years; and 54. 3% with 15 or more years of age at baseline, and displayed a nearly indistinguishable age distribution in 2012. The mean age was 20. 3 yr whereas the country-wide average was 34. 4 yr. The number of men per 100 women was 109. 2 whereas the average for Argentina was 95. 8 as of 2010 [55]. A summary of the housing and sociodemographic characteristics of the study population by ethnic group is shown in Table 1. The detailed frequency distribution of study variables appears in Tables 2,3 and S1. Qom households comprised 89. 6% of the inhabited houses. Unlike Creoles, most Qom households lived in houses with mud walls and a tarred-cardboard roof, in small-sized (< 30m2), recently-built domiciles with high refuge availability, < 2 peridomestic structures, and with little access to electricity (Table 1). Qom households were larger, more often experienced critical overcrowding (S1 Text), and had lower household educational level than Creoles. The average goat-equivalent index of Creoles (median, 68. 9; first-third quartiles, 5. 7–168. 6) was 69 times larger than that of Qom households (1. 0,0. 1–7. 7). Most Creole households applied insecticides in domestic premises (90. 0%) and had window screens (59. 5%), unlike Qom households (Table 1, S1 Text). Creole households applied high-concentration pyrethroid or carbamate insecticides (27. 5%) much more frequently than Qom households (5. 8%). Householders’ reports indicated that 68. 0% of houses had never been sprayed with insecticides by vector control personnel whereas 21. 5% had been sprayed two years before (Table 3). Local health personnel reportedly sprayed with insecticides 36 and 49 houses, mainly from Cuarta Legua villages in 2000 and 2006, respectively. A key feature of the study population was the very frequent mobility of households to a new house (i. e. , housing instability). Of all the inhabited houses enumerated in 2008,20. 2% (78) were demolished or abandoned by 2012, whereas 142 new houses were built over the four-year period, of which only 52 (36. 7%) were present in 2012. Most of the new houses (90. 1%) and the demolished or abandoned ones (98. 7%) belonged to Qom households. Among the latter, movers were relatively disadvantaged compared to nonmovers. On average, movers had a lower goat-equivalent index (median, 0. 5 versus 1. 9); smaller domiciles (24 versus 36 m2); more recently-built houses (69. 2% versus 45. 6%); smaller household size (5. 2 versus 6. 8); and fewer peridomestic sites (1. 3 versus 2. 1). Triatoma infestans was found by timed-manual searches in 108 (28. 0%) of the 386 inhabited houses and in 6. 9% of the 1,744 sites inspected. The median relative abundance was 3 bugs (first-third quartiles, 1–11) per unit of catch effort. Fifth-instar nymphs (24%), males (20%) and females (16%) were the stages most frequently captured. When the finding of bugs by any collection method was considered, the prevalence of house infestation slightly rose to 31. 9% (123 of 386), and was 27. 2% (105 of 386) in domestic sites and 7. 8% (30 of 386) in peridomiciles. A total of 2,362 T. infestans was caught. Triatoma sordida was found in 4. 5% (17 of 386) of houses exclusively in peridomiciles. The contribution of each collection method to detection of domestic infestation is shown in S3 Table. Although the majority of domestic infestations was detected by timed-manual searches, bugs collected by householders (almost exclusively in domestic areas) and during insecticide spraying operations contributed to additional detection of 19 infested houses that timed searches had missed. Detection of domestic infestations by timed-manual searches and householders’ notifications were in poor agreement (kappa index = 0. 3). The ecotopes most frequently infested at site level (as determined by any bug collection method) were domiciles (23. 1%), storerooms (14. 0%), kitchens (6. 3%), chicken nests (6. 0%), and chicken coops (4. 6%) (Fig. 2). The median abundance of T. infestans per unit of catch effort was higher in kitchens, storerooms and chicken nests, but did not differ significantly among ecotopes by negative binomial regression (P > 0. 1 in all cases). The overall prevalence of bug infection with T. cruzi was 25. 0% among 719 live bugs examined, and ranged from 23. 9% (150/628) in domiciles to 33. 0% (30/91) in peridomiciles. Infected bugs were collected in domiciles of 45. 2% (28/62) of the houses with bugs examined for infection, and in peridomiciles of 44. 4% (8/18) of them. Although house infestation occurred across the study area, some communities showed larger domestic infestation than others (range, 12. 2–50. 8%) (Fig. 3, S1 Table). Domestic infestation was significantly aggregated at a global scale at distances ranging from 600 to 2,000 m (S2 Fig.); this means that infested houses were clustered, and on average, for every infested house there was a higher probability of finding another infested house within 600–2,000 m than expected by chance. Local spatial analyses of bug abundance identified clusters of houses located within 40–600 m in some communities (Cuarta Legua, Pampa Chica). The apparent cold spot at the NE angle (Pampa Grande village) was not statistically significant (P > 0. 05). Wall and roof materials were significantly associated with domestic infestation (Table 2). The prevalence of domestic infestation increased significantly with increasing refuge availability levels and numbers of human residents, and declined steadily with increasing age of house and domestic area. Infested domiciles had a significantly smaller area (37. 8 ± 27. 2 m2, n = 84) than non-infested ones (50. 6 ± 39. 9 m2, n = 200; Mann-Whitney test, P < 0. 001). Domestic infestation was higher in houses with at least one infested peridomestic site and fewer peridomestic structures (S1 Table). Domestic bug abundance only was significantly associated with refuge availability, domestic area and the number of dogs. Qom households had a nearly threefold domestic infestation (29. 2%) than Creoles’ (10. 0%), whereas domestic bug abundance was similar between ethnic groups (Table 3). Domestic infestation increased steadily with increasing residential overcrowding from 0% up to 42. 9%, and decreased with increasing household educational level from 30. 1% to 0%. Households reporting insecticide use had a significantly lower infestation (21. 2%) than those that did not (32. 7%). Bug abundance was also significantly associated with residential overcrowding, household educational level, the goat-equivalent index, number of peridomestic sites, land ownership, and access to electricity (Table 3, S1 Table). The signs of the individual effects were the same as for domestic infestation. Households with no window screens had increased domestic infestation and bug abundance, whereas the use of bed nets was inversely associated (S1 Table). No significant association was found between domestic infestation and time since last insecticide spraying (Table 3). Using the first global model including 386 houses, we identified 11 and 6 models with considerable support (ΔAICc < 2) for domestic infestation and bug abundance, respectively. Refuge availability (RI = 1. 00), distance to the nearest infested house (RI = 1. 00–0. 83) and insecticide use (RI = 0. 75–0. 69) were the most important factors (Table 4). Refuge availability exhibited a strong positive association whereas insecticide use and distance to the nearest infested house had a negative one. The presence of poultry indoors (RI = 0. 75) was only moderately and directly associated with domestic bug abundance. The number of people (RI = 0. 73) had moderate importance and marginally positive effects on infestation only, with CIs including the null value. Other factors presented lower RI for both response variables. Ethnicity showed low RI and widely variable CIs. The average logistic model for infestation (Hosmer-Lemeshow test, χ2 = 8. 03; d. f. = 8; P = 0. 43) and the area under the ROC curve (0. 73) indicated a good fit. The model had moderate specificity (0. 62) and sensitivity (0. 71). For the second global model including 274 houses, refuge availability (RI = 1. 00), overcrowding (RI > 0. 98) and distance to the nearest infested house (RI = 0. 78–0. 88) were the most important factors and showed strong to moderate effects, whereas household educational level had moderate importance (RI = 0. 74–0. 68) and rather small negative effects on domestic infestation only (Table 4). The average logistic model for infestation (Hosmer-Lemeshow test χ2 = 3. 66; d. f. = 8; P = 0. 89) and the area under the ROC curve (0. 79) indicated a good fit. This model showed higher specificity (0. 71) and similar sensitivity (0. 72). Removing overcrowding and household educational level data yielded results that were qualitatively similar to those in the first global model (not shown). Additional analyses including only Qom households identified the same set of factors with high and moderate RI in both global models (not shown). Our study documented threats of active vector-borne transmission of T. cruzi in approximately 27% of the households (as determined by the occurrence of domestic infestations), and identified manageable variables that may be targeted for improved interventions and risk stratification. Improving housing quality and living conditions is urgently needed and largely exceeds Chagas disease vector control because housing improvements will impact positively on family health. Reducing the presence of chickens in human sleeping quarters [20,21,25,26,64,67] and applying insecticides in more effective ways when required may contribute to improved vector control. Although these factors are frequently construed as environmental or ecological, the types of housing, land ownership, habits of raising livestock or poultry, frequency of insecticide use and type of preventive practices have historical, sociodemographic, cultural and political roots. The household mobility patterns recorded have serious implications for vector and disease control. In the preintervention context of an infested area under marginal vector control (as in 2008), the mobility of Qom households implied the potential carriage of bugs in their belongings to the new houses, while leaving bugs behind in the rubble of knocked down walls. The recently-built houses represented new habitat patches susceptible to bug invasion and colonization, and therefore decreased the fraction of all houses effectively protected by the long-lasting residual effects of recent insecticide sprays. On the flip side, the processes of house destruction and reconstruction are expected to cause major negative impacts on the local abundance of bugs by increasing bug mortality and dispersal. The mobility of some indigenous populations may pose special challenges to traditional housing improvement programs relying on stable settlement and secure land tenure. More knowledge of the drivers of household mobility, migration and the desired types of housing of Qom and other indigenous peoples which had a nomadic or seminomadic tradition are needed. The design of Chagas disease prevention programs and other health interventions directed to indigenous populations should address their specific needs and beliefs [80,81]. Improving housing quality in isolation, while traditional agricultural activities continue in decline and other sources of local employment are rare, may not stop the rural-to-peri-urban exodus across ethnic groups. The links between household educational levels and domestic infestation require more elaboration and specific research on the mechanisms involved. This area offers new opportunities for innovative interventions through health education and promotion workshops [82] that include, but are not restricted to, community-based vector and disease surveillance, control and treatment. Better access to formal education may also contribute directly and indirectly to primary and secondary disease prevention (e. g. , by increasing awareness of treatment opportunities). The large fraction of Qom and Creole households who managed to keep their premises free from triatomine bugs using the scarce means available to them holds promise for further improvements with a modest investment of resources. Households performing good practices of vector control may contribute as agents of change to further reduce infestation and transmission risks in community-based control programs. The strong heterogeneities in the distribution of ecological and sociodemographic factors associated with house infestation may be used for risk stratification and targeted interventions. Large households residing in small-sized, precarious houses, with few or no livestock or poultry and lower educational levels, appear to be especially vulnerable for Chagas and other infectious diseases. These households and the affected communities may benefit from targeted disease prevention activities channeled through a more vigorous, adequately staffed, primary healthcare system deployed in the affected rural areas.

Indigenous peoples are especially affected by Chagas and other neglected tropical diseases. One of the most numerous ethnic groups in the Gran Chaco region of South America is the Qom (Toba) people. The living conditions of Qom households most likely differ substantially from those of Creoles, and their association with house infestation by triatomine bugs has not been investigated. This is important because the major vector species have adapted to thrive in human sleeping quarters, and in addition to housing construction patterns, other ecological and sociodemographic factors may affect house infestation. We found that Qom households had much higher domestic infestation than Creole ones, in conjunction with more precarious housing, fewer poultry and livestock. The unexpectedly high local residential mobility of Qom households combined with the large fraction of recently-built houses (derived from a rapidly increasing population with a very young age structure during recent decades) may explain why domestic infestation was much lower than expected from the lack of recent insecticide spraying campaigns. Domestic infestation and bug abundance increased with overcrowding and refuge availability, and decreased with household education levels and insecticide use. These results are useful for designing improved interventions and household risk stratification.

Abstract Introduction Materials and Methods Results Discussion

2015

Ecological and Sociodemographic Determinants of House Infestation by Triatoma infestans in Indigenous Communities of the Argentine Chaco

Widespread but particularly incident in the tropics, leptospirosis is transmitted to humans directly or indirectly by virtually any Mammal species. However, rodents are recognized as the most important reservoir. In endemic regions, seasonal outbreaks are observed during hot rainy periods. In such regions, hot spots can be evidenced, where leptospirosis is “hyper-endemic”, its incidence reaching 500 annual cases per 100,000. A better knowledge of how rodent populations and their Leptospira prevalence respond to seasonal and meteorological fluctuations might help implement relevant control measures. In two tribes in New Caledonia with hyper-endemic leptospirosis, rodent abundance and Leptospira prevalence was studied twice a year, in hot and cool seasons for two consecutive years. Highly contrasted meteorological situations, particularly rainfall intensities, were noted between the two hot seasons studied. Our results show that during a hot and rainy period, both the rodent populations and their Leptospira carriage were higher. This pattern was more salient in commensal rodents than in the sylvatic rats. The dynamics of rodents and their Leptospira carriage changed during the survey, probably under the influence of meteorology. Rodents were both more numerous and more frequently carrying (therefore disseminating) leptospires during a hot rainy period, also corresponding to a flooding period with higher risks of human exposure to waters and watered soils. The outbreaks of leptospirosis in hyper-endemic areas could arise from meteorological conditions leading to both an increased risk of exposure of humans and an increased volume of the rodent reservoir. Rodent control measures would therefore be most effective during cool and dry seasons, when rodent populations and leptospirosis incidence are low. Leptospirosis is an endemic bacterial disease in many tropical and sub-tropical areas. Various Leptospira strains, maintained in different animal species, are excreted in the urine of asymptomatic chronically infected individuals [1]–[3]. Humans get infected when abraded skin or mucous membranes come into contact with contaminated kidneys, urine or urine-contaminated environments [2]. The detailed epidemiology of leptospirosis, both a zoonosis and an environmental disease, both an occupational and a recreational disease, is highly complex. Though veterinary leptospirosis is often studied, little is usually known on how wild or feral Mammals contribute to leptospirosis dynamics [4]. Virtually any Mammal species can act as a reservoir of a co-adapted Leptospira strain [1], but among animal reservoirs, rodents are recognized as the most significant Mammal species maintaining and disseminating leptospires worldwide [2], [3], [5]. The Norway (or brown) rat Rattus norvegicus is notably known to be a reservoir of Leptospira interrogans of the serogroup Icterohaemorrhagiae, whereas the domestic mouse (Mus musculus) is a reservoir for Leptospira borgpetersenii of the serogroup Ballum [2], [3]. In New Caledonia, Mammal biodiversity is low: no native terrestrial Mammal is known, except 9 bat species (both micro- and megabats) [6]. However, four rodent species are known to be present, all resulting from importation by the early human settlements: three rat species (Rattus exulans, Rattus rattus, and R. norvegicus) and the domestic mouse (M. musculus) [7]. Leptospirosis in farm animals has been well studied (see [4] for review) but feral Mammals have not been investigated. Even though several strains and serovars are involved in human cases [8]–[10], Icterohaemorrhagiae is the most frequent serogroup, pointing to the importance of rodents as a major reservoir. In most tropical regions where leptospirosis is known to be endemic, a seasonality is observed, with highest incidence during hot rainy periods, particularly after tropical storms and floods [11], [12] or during the monsoons [13]. During seasonal or post-cyclone outbreaks, there are particular areas of New Caledonia where leptospirosis incidence can reach 500 annual cases per 100,000 habitants [14]. Patchy distributions of leptospirosis have been described in New Caledonia [14], [15] but also in Brazil [16]–[18]. How human exposure to environmental contamination, reservoir abundance (especially rodents) and their Leptospira prevalence each contribute to such outbreaks and “leptospirosis hot spots” remains unknown. The aim of our study was to determine rodent abundance and the dynamics of Leptospira prevalence in their populations, in a site previously determined as a leptospirosis hot spot. Based on previous studies [9], [19], the municipality of Bourail (see Figure 1) was known as a place of high incidence of leptospirosis. Using the diagnostic data of the early 2008 outbreak [14], we more precisely described the probable contamination area of these cases. Demographic data obtained from the Institut de la Statistique et des Études Économiques (http: //www. isee. nc/index. html) were also used to evaluate the incidence of leptospirosis in each district. This incidence was then plotted on a map using PopGis version 1. 0 (Secretariat of the Pacific Community). This allowed identifying three hot spots within the municipality of Bourail (Figure 1). The districts of Pötê and Buiru were chosen for our survey, based on their limited surface area and the good acceptance of our project by custom chiefdom and populations. These two study sites correspond to two Melanesian tribes where many outdoor activities are part of the everyday life, including fishing and bathing in freshwater streams, agriculture, maintenance of backyard pig pens, hunting (deers and wild hogs). Most of the households have one or more dogs that freely stray from the houses. Most of the inhabitants go bare foot and know the presence of rodents in and around their houses. After adequate contacts with the customs authorities, the study period was determined as a two-year period, one survey being conducted for each hot (January–April) and each cool (July–October) seasons. Relative rodent abundance was evaluated both in the hot season (March) and during the cooler months (September-October). The sampling strategy was aimed at evaluating both the rodent abundance and the prevalence of Leptospira in the kidneys of the different rodent species. The method used was based on the standard index trapping technique developed in New Zealand for the study of rodents [20]: Hundred snap traps were placed by pairs (one rat- and one mouse-sized lethal snap trap) every 25 meters along a transect line close to the households within the study site. In Pötê, two transects were used with 50 traps each: one along the stream close to the households, the other one starting close to a farmer' s house and extending in a cattle pasture along the same stream (Figure 2). In Buiru, the transect also extended along the streams close to the household, thus dividing at a stream confluence. Trap stations were set for 3 consecutive nights and baited with cheese and peanut butter. The processing of the trapped rats used the methods of Cunningham & Moors [20] and included the identification of species based on measurements for head-body length (HBL) and tail length (TL), assignment to a developmental stage (either adult or juvenile), and sex determination. Any animal that had been damaged by predators was identified to the species level, sex and age class was determined only if possible. Traps were checked and re-baited daily, captures and whether baits were taken or the trap sprung was also recorded. Trapping success was corrected for sprung and bait-taken traps by subtracting half a trap night for each such occurrence as described [21]. This allowed calculating an index of abundance, expressed as a number of captured animals per 100 corrected trap-nights. Meteorological data were kindly provided by Météo-France (http: //www. meteo. nc/) and corresponded to the closest automated meteorological station (Bourail). Rain (monthly accumulated rainfall) and temperature (monthly average of daily minima and monthly average of daily maxima) were plotted for the two years of the study (2009 and 2010). Rodents were killed when caught by the snap traps. Each individual rodent was aseptically dissected and one kidney was immediately put in 95% alcohol for postponed DNA extraction. During the first survey (March 2008), one small piece of kidney was also aseptically transferred into a EMJH culture tube supplemented with 300 µg. ml−1 5-Fluoro-Uracile as an inhibitor of contaminant bacteria [22] for Leptospira isolation. A few rodents captured had been attacked by cats or birds at the time of collection; some could therefore not be completely identified or dissected. These were considered for abundance calculations but not studied for Leptospira prevalence. Back at the lab (at the end of the 3-day sampling period), EMJH culture tubes were incubated aerobically for 14 weeks at 30°C with weekly dark field microscopic observation. Positive cultures were immediately subcultured in fresh EMJH and then frozen at −80°C with 10% glycerol. A small piece of each individual kidney (ca. 10 mg) was aseptically dissected, rehydrated in 3 successive ultrapure sterile water baths for 6–12 hours each. It was then grinded in 50 µL sterile Phosphate Buffer Saline, pH 7. 4 (Sigma) and 180 µL ATL Buffer (QIAamp DNA mini kit, QIAGEN) using a sterile single-use Piston Pellet (Kimble Chase). DNA was then extracted using QIAamp DNA mini kit (QIAGEN) following the manufacturer' s instructions for tissue. The proteinase K digestion step was set for 4 hours. Additional proteinase K (20 µL) was added to samples incompletely digested at this time and incubation prolonged until complete tissue lysis (up to 8 hours). One millilitre of each Leptospira culture was centrifuged and extracted using the same QIAamp DNA mini kit (QIAGEN) using the manufacturer' s instructions for cultured cells. The elution volume was 100 µL for either rodent kidney or Leptospira isolate. Leptospira carriage in the rodent kidneys was assessed using two previously described diagnostic real time PCR assays, both using SYBR Green I technology, namely the detection and amplification of lfb1 [23] or lipL32 [24]. To check the absence of PCR inhibitors that would lead to false negative results, all kidney DNA extracts were also amplified with a “universal” 18S rDNA PCR, using primers previously described [25] and SYBR Green I technology. All oligonucleotide sequences are shown in Table S1. The lfb1 PCR products amplified from positive kidneys were purified using the MinElute PCR Purification Kit (Qiagen) and directly sequenced as previously described [10]. For Leptospira isolates, a MLST scheme [26] was used as described before [10]. Alignments and phylogenies were then obtained using previously described techniques [10]. All sampling points were referenced using a handheld GPS device (Garmin). Data were transferred to MapInfo version 7. 0 (Pitney Bowes Software Inc.) on a 1/10,000 map, kindly provided by the Direction des Infrastructures, de la Topographie et des Transports Terrestres – Gouvernement de la Nouvelle-Calédonie. All captured animals were similarly plotted on the map. Qualitative variables were expressed as percentages or proportions. Groups were compared using Fisher' s exact test. A test with a p value lower than 0. 05 was considered statistically significant. Statistical analyses were performed by using Stata 11. 0 (StataCorp LP, College Station, TX, USA). Because rodents are introduced invasive Mammals in New Caledonia, they are legally classified as “dangerous detrimental species” and no particular authorization is required for their capture and study [27]. Protocols for animal experiments were prepared and conducted according to the guidelines of the Animal Care and Use Committees of the Institut Pasteur. The protocol was approved before the start of the experiments by a scientific committee and an animal care committee of the Institute Pasteur in New Caledonia. To obtain the permission of conducting surveys in tribal lands, custom chiefs were met and the project of the research explained during a public information meeting on leptospirosis. A custom council (directed by the tribe and senior council chiefs) gave the necessary agreement for working on their land. All field studies were conducted with the help of a salaried tribe guide. Fieldwork surveys were carried out in highly contrasted meteorological conditions with major differences in monthly accumulated rainfalls and temperatures (Figure 3): 2009 had a very rainy hot season, while 2010 had a relatively dry hot season. When compared to historical ten-year average rainfall and temperature data, the hot season in 2009 had a large rainfall excess, and stream overflows and floods actually occurred, whereas the 2010 hot season was both cooler and much dryer than a ten-year average. Although the cool season was warmer in 2010 than in 2009, both cool seasons had quite similar patterns, especially regarding (near normal) rainfall intensities. All four rodent species known to be present in New Caledonia, namely the three rat species (R. rattus the black rat, R. norvegicus the brown or Norway rat and R. exulans the Polynesian rat) and the domestic mouse (M. musculus) were actually captured during our study. A total of 239 rodents were captured, out of which 231 could be identified (species) and 210 could be sampled for Leptospira carriage. Similarly due to predation of the captures, the age class could only be ascertained for 213 individuals. The black rat R. rattus was the most frequently captured species, accounting for 60. 6% of the captures (140 rats), whereas mice M. musculus accounted for 25. 5% (59 mice), Norway rats R. norvegicus for 9. 1% (21 individuals), the rarest species being Polynesian rats R. exulans (4. 8%, 11 rats). The greatest number of captures was achieved during the hot season 2009, with 113 rodents (47. 3%). During the 2009 cool season, 2010 hot and cool seasons, the numbers of rodents captured were 28,56 and 42 respectively. A greater number of rodents were systematically captured in Buiru (a total of 142 captures, 59. 4%) than in Pötê (a total of 97 captures, 40. 6%). The corresponding values in terms of abundance, an index used to compare rodent densities between different places and seasons and expressed as a number of captured rodents per 100 corrected trap × nights, are shown in Figure 4. A significantly higher proportion of juveniles was found in all rat species in hot seasons (63. 6%) when compared to cool seasons (18. 9%) (p<0. 001). Contrastingly, in mice, a lower proportion of juveniles was captured in hot seasons (14. 3%) compared to cool seasons (46. 7%) (p = 0. 027). From the universal 18S amplification, only one kidney DNA extract demonstrated PCR inhibition. This rodent was therefore considered for abundance calculations but excluded from prevalence studies. The detailed results are shown in Figure 5 and Table S2. In total, 56 rodents out of 210 (26. 7%) were found as carrying Leptospira in their kidneys. This prevalence however considerably varied according to species, age class and seasons. The Leptospira prevalence was significantly higher in mice (24/51 = 47. 1%) and Norway rats (7/19 = 36. 8%) than in black rats (23/129 = 17. 8%) or Polynesian rats (2/11 = 18. 2%). Considering all species together, adults were more frequently carrying Leptospira (40/119 = 33. 6%) than juveniles (15/88 = 17%) (p = 0. 01), though this difference was not significant in every individual species. As an example, the prevalence was 21. 2% in adult black rats, whereas it was 14. 3% in juveniles (p = 0. 36). Similarly, it was 75% (6 out of 8) in adult Norway rats and only 10% (1/10) in juveniles (p = 0. 01). The culture technique, used only during the first survey (March 2008) allowed the collection of 8 Leptospira isolates, from 6 mice (6 isolates), one black rat and one Norway rat. Because of its low yield and frequent failure in Leptospira positive kidneys, it was not used in further surveys. The difference in Leptospira prevalence between the different surveys (see Figure 5) was not significant. However, trends could be evidenced in all rodent species, regardless of species or age class. The prevalence was higher in hot seasons (30. 1%) than in cool seasons (19. 4%) this difference however is not significant (p = 0. 13). This difference was highest between the hot and rainy season in 2009 (33. 7%) than all other seasons grouped together (21. 2%) (p = 0. 59) and was significant when considering only adult rodents (20/43 = 46. 5% vs. 20/76 = 26. 3% p = 0. 0284). Similarly, the Leptospira prevalence was significantly higher during the hot and rainy season in 2009 (31/92 = 33. 7%) than during the cool season in 2010 (5/40 = 12. 5%) (p = 0. 011). The 8 isolates obtained from mice (6), one Norway rat (1) and one black rat (1) were typed using a MLST scheme described previously [10], [26]. The results pointed to L. borgpetersenii putatively belonging to the serogroup Ballum for mice isolates and L. interrogans putatively belonging to the serogroup Icterohaemorrhagiae for the isolates from both rat species. In the 2 Polynesian and 7 Norway rats, the lfb1 PCR product sequence presumptively identified L. interrogans belonging to the Icterohaemorrhagiae serogroup as the infecting Leptospira [10]. Identical lfb1 sequences pointing to the serogroup Icterohaemorrhagiae were obtained from 20 out of 26 (76. 9%) black rats, the remaining 6 (23. 1%) were infected with a L. borgpetersenii with an lfb1 sequence presumptively pointing to the serogroup Ballum. From 24 Leptospira-infected mice, 22 were also infected with a L. borgpetersenii presumptively belonging to the serogroup Ballum, one with a L. interrogans presumptively identified as serogroup Icterohaemorrhagiae, whereas the last one was infected with an unidentified Leptospira sp. Actually, its kidney DNA extract gave a positive PCR amplification using the lipL32 PCR [24] but failed to be amplified using the other diagnostic PCR targets tested, namely lfb1 [23], secY [28], even if using degenerated primers (see Table S1) or different primers targeting a larger product [29]. Similarly, the TaqMan-based lipL32 assay [30] gave negative results using this DNA extract. The lipL32 PCR product was purified and sequenced, yielding a 352 bp sequence (Accession Number JN092329) that did not match any known Leptospira strain when compared with sequences available in GenBank using the Blast algorithm. Attempts to specifically amplify the 16S rRNA gene using Leptospira specific primers [31] for species identification were conducted on a gradient thermocycler but despite many attempts only allowed the sequencing of 207 bp of this gene. This 16S rDNA sequence (Accession Number JN092330) demonstrated a highest identity of 98% with L. kirschneri. The phylogenetic position of this uncultured Leptospira as deduced from this short 16S ribosomal sequence is shown in Figure S1. We were able to capture all four rodent species (the black rat R. rattus, the Norway rat R. norvegicus, the Polynesian rat R. exulans and the mouse M. musculus) present in New Caledonia. The Norway rat (R. norvegicus) was very rarely captured in Pötê while this species was captured in all four seasons in Buiru. Whatever the site, the black rat (R. rattus) was the species most frequently captured and accounted for about 60% of captures. This coexistence of the black rat with Polynesian rats (R. exulans) and domestic mice (M. musculus) is consistent with previous studies realized in other locations in New Caledonia in uninhabited sclerophylls or rainforests. The sympatric behaviour of these rodent species in New Caledonia is regarded as a peculiarity. The four introduced rodents are usually not been found to coexist in the same habitat notably in New Zealand except possibly on the Chatham Islands [32]. In New Caledonia however, like in Hawaii [33], we found the four species to be sympatric. Our surveys allowed sampling rodents during highly contrasted seasons: the hot season 2009 was especially wet and warm, contrasting with the hot season 2010 which was much drier. The cool seasons 2009 and 2010 were quite similar, except that succeeding to either a wet (2009) or a dry (2010) hot season. Significant differences were noted in rodent abundance, highest abundances being observed during the hot and rainy period in 2009. A marked seasonality of rodent dynamics is well-known and has notably been considered as a major concern when considering rodents as reservoirs of infectious diseases [34], [35] and was modelled for an African rodent in the context of leptospirosis [36]. It is also recognized that seasonal factors must be considered when rodent control programs are to be implemented [37], [38]. The overall prevalence of Leptospira spp. in our rodent sample was 26. 7%, a finding in accordance with former studies [39], [40]. No correlation was shown between sex and prevalence but age had a major impact on prevalence, adult animals being much more frequently infected (33. 6%) than juveniles (17%) (p = 0. 01), as already described in other locations [41]–[43]. Mice were more frequently infected than rats (p<0. 001), no difference being evidenced between the three rat species (p = 0. 16). Interestingly, when considering the ecological habits of the different rodent species, mice and Norway rats that are considered as commensal species (living closer to humans) have a higher prevalence (44. 3%) than the more sylvatic black and Polynesian rats (17. 9%) (p<0. 001). Higher Leptospira prevalence in mice and Norway rats compared to black rats was frequently observed in some locations [44], [45], though contrasting results were reported in others [46]. As expected and already largely recognized, mice appeared to maintain L. borgpetersenii strains, the DNA sequences pointing to Ballum as the putative serogroup, Norway rats maintaining L. interrogans presumptively identified as belonging to the Icterohaemorrhagiae serogroup, both being evidenced in black rats in which Ballum appeared to be less frequent (23%). The simultaneous carriage of these two leptospires in a single (and probably panmictic) black rat population was also already described, e. g. in New Zealand [43] or Argentina [42]. Oppositely, no L. borgpetersenii carriage was detected in Norway rats, as was sometimes observed in Hawaii [41] or New Zealand [43]. Only two Polynesian rats were found as carrying leptospires, both from the species L. interrogans and presumptively identified as belonging to the serogroup Icterohaemorrhagiae, while leptospires from the serogroup Ballum found in other R. exulans populations [41] were not evidenced in our captures. Unexpectedly, an unknown leptospire was also detected using various PCR techniques. This leptospire was found in the kidney of a domestic mouse. Its sequences clearly point to a species belonging to the pathogenic cluster of Leptospira spp. (see Figure S1) but its exact species identification cannot be ascertained. Interestingly, this strain could not be detected using the lfb1 PCR [23] routinely used for diagnosis in New Caledonia, nor using the TaqMan-based lipL32 technique [30] or the secY technique [28], all supposed to detect all pathogenic Leptospira spp. This surprising finding not only highlights the rich biodiversity of the Leptospira phylum but also questions about the existence of other pathogenic Leptospira species in New Caledonia that would be undetected using several of the PCR techniques described and currently used for diagnosis. Water and rodents are known to play pivotal roles in the epidemiology of leptospirosis. Taken together, Icterohaemorrhagiae and Ballum serogroups have been responsible for more than 75% of human leptospirosis cases in New Caledonia [10], again highlighting the major contribution of rodents to human leptospirosis. The increased incidence of human leptospirosis in hot rainy seasons observed in New Caledonia [9], [14] and elsewhere [3] could result from the combined effects of an increased exposure of humans to mud and surface waters and of an increased Leptospira contamination of these environments. This latter would also result from both a higher survival probability of leptospires in wet environments during hot rainy periods and higher leptospire abundance due to increased seeding by reservoir populations. We actually evidence a higher rodent abundance and an increased Leptospira prevalence in rodent populations during one hot period with heavy rainfall. The results of our study are therefore in agreement with this global scheme, notably suggested as a factor contributing to a leptospirosis epidemics in Guadeloupe, West Indies [47] and with the rural model proposed by Holt and colleagues [36]. Additionally, our study in an area of leptospirosis hyper-endemicity highlights a higher Leptospira prevalence in mice and Norway rats, both rodent species which ecology and behavior bring in closer contact to humans compared to the more sylvatic black and Polynesian rats. Taken together, our data strongly suggest that all parameters studied might contribute to the occurrence of human leptospirosis epidemics during hot periods with heavy rainfalls: increased rodent populations with higher Leptospira carriage, leading to an increased contamination of an environment more favorable to leptospire survival. Our data, though in complete agreement with prior knowledge on rodent dynamics elsewhere, only rely on two consecutive years and even more significantly only one season with heavy rain. Because interactions between climate variables, reservoir hosts and the pathogen are especially complex, additional surveys are needed to ascertain the influence of climate on rodents and their Leptospira carriage dynamics in the context of New Caledonia. With regard to rodent control measures, our results are also in agreement with previous knowledge and model predictions [36]–[38]. Should the impact of climate and meteorological variability be confirmed, the best rodent management strategy to minimize leptospirosis burden in New Caledonia would probably be the use of rodenticides before the start of a hot rainy period, a situation similar to rodent control for agricultural crops [38], therefore at times of low rodent density and low leptospirosis incidence, also corresponding to periods of low political and public awareness. Nevertheless, because economical modeling tends to demonstrate a similar cost-benefit effect of rodent control measures compared to post-exposure treatments [48], a better control of rodent populations should be increasingly considered as a possible approach for leptospirosis management in endemic areas. Similarly to a study in Guadeloupe [47], the climatic conditions leading to leptospirosis epidemics in New Caledonia are under strong influence of the El Niño Southern Oscillation [9], [49]. The major advances in the modeling and prediction of this climatic phenomenon probably provides opportunities for predicting leptospirosis epidemics in some regions (e. g. [50]), in turn permitting to implement leptospirosis prevention measures (like river dredging, drainage or rodent control actions) in areas of high leptospirosis endemicity, in a timely manner.

In this study, we surveyed rodents and their Leptospira carriage in an area where human leptospirosis is hyper-endemic. We evidenced the well-known associations between specific rodent species and particular leptospires in both mice and rats. Overall, the observed Leptospira prevalence was in the range 18–47% depending on species, similar to other descriptions. However, significant variations were observed both in the abundance of rodents and their Leptospira carriage, one hot period with heavy rain being associated with both a highest abundance and an increased prevalence. Similar meteorological conditions could lead to increased leptospires dispersal by the rodent reservoir and increased exposure of humans to risk situations (e. g. flood, mud). Because rodent control measures were demonstrated elsewhere to be cost-effective if correctly planned and implemented, this contribution to a better knowledge of rodent and leptospires dynamics provides useful information and may in turn allow to develop relevant rodent control actions aimed at reducing the burden of human leptospirosis.

Abstract Introduction Methods Results Discussion

medicine public health and epidemiology microbiology bacterial diseases emerging infectious diseases infectious disease control applied microbiology infectious diseases disease ecology environmental epidemiology epidemiology biology public health leptospirosis community ecology ecological risk ecology

2011

Rodent Abundance Dynamics and Leptospirosis Carriage in an Area of Hyper-Endemicity in New Caledonia

Genetic interactions, a phenomenon whereby combinations of mutations lead to unexpected effects, reflect how cellular processes are wired and play an important role in complex genetic diseases. Understanding the molecular basis of genetic interactions is crucial for deciphering pathway organization as well as understanding the relationship between genetic variation and disease. Several hypothetical molecular mechanisms have been linked to different genetic interaction types. However, differences in genetic interaction patterns and their underlying mechanisms have not yet been compared systematically between different functional gene classes. Here, differences in the occurrence and types of genetic interactions are compared for two classes, gene-specific transcription factors (GSTFs) and signaling genes (kinases and phosphatases). Genome-wide gene expression data for 63 single and double deletion mutants in baker’s yeast reveals that the two most common genetic interaction patterns are buffering and inversion. Buffering is typically associated with redundancy and is well understood. In inversion, genes show opposite behavior in the double mutant compared to the corresponding single mutants. The underlying mechanism is poorly understood. Although both classes show buffering and inversion patterns, the prevalence of inversion is much stronger in GSTFs. To decipher potential mechanisms, a Petri Net modeling approach was employed, where genes are represented as nodes and relationships between genes as edges. This allowed over 9 million possible three and four node models to be exhaustively enumerated. The models show that a quantitative difference in interaction strength is a strict requirement for obtaining inversion. In addition, this difference is frequently accompanied with a second gene that shows buffering. Taken together, these results provide a mechanistic explanation for inversion. Furthermore, the ability of transcription factors to differentially regulate expression of their targets provides a likely explanation why inversion is more prevalent for GSTFs compared to kinases and phosphatases. Understanding the relationship between genotype and phenotype of an organism is a major challenge [1,2]. One of the difficulties for unravelling genotype-phenotype relationship has been genetic interactions, when combinations of mutations lead to phenotypic effects that are unexpected based on the phenotypes of the individual mutations [3–5]. Large-scale analyses of single and double deletion mutants have revealed that genetic interactions are pervasive in many model organisms [6–11]. Recently, efforts have been initiated to investigate genetic interactions in human cell lines too, using large-scale RNA interference and Crispr-Cas9 knock downs [12–15]. Our understanding of the molecular mechanisms that underlie genetic interactions lags behind our ability to detect genetic interactions. Understanding the molecular basis of genetic interactions and their interplay with cellular processes is important for unraveling how different processes are connected [16–18], to what degree genetic interactions shape pathway architecture [6], as well as for understanding the role genetic interactions play in human disease [5,19]. One of the phenotypes that is frequently used to investigate genetic interactions is cell growth [6,20–28]. Based on this phenotype, genetic interactions can be broadly subdivided in two types, negative genetic interactions where the double mutant is growing slower than expected given the growth rate of the single deletion mutants, and positive genetic interactions where the double mutant is growing faster than expected [3]. Negative genetic interactions have frequently been associated with a redundancy relationship between two functionally related genes [29]. The redundancy mechanisms by which two genes can compensate for each other’s loss has been linked with close paralog genes or redundant pathways [30,31]. Positive genetic interactions have been associated with genes participating in the same protein complex or pathway [32]. There are however many exceptions to these rules and it also has become clear that there are many other hypothetical mechanisms underlying these genetic interactions that require further investigation [3,18]. Another phenotype that has been much less frequently used for investigating genetic interactions is gene expression [16,17,33–36]. Expression-based genetic interaction profiling provides detailed information at the molecular level which is beneficial for unraveling mechanisms of genetic interactions [16,17,33–36]. Unlike growth-based profiling, which gives a subdivision into either positive or negative interactions, expression-based genetic interaction profiling provides further subdivision into more specific genetic interaction patterns. These patterns have recently been systematically classified and include buffering, quantitative buffering, suppression, quantitative suppression, masking and inversion [17]. A more detailed sub classification that includes information on expression of downstream genes, can also contribute to understanding the underlying mechanisms by which two genes interact [16,17,37]. To provide mechanistic insights into biological networks, Boolean modeling has been used successfully [38,39]. It has also been applied to unravel regulatory networks underlying genetic interaction patterns between kinases and phosphatases [16]. Due to their intrinsically simple nature, such Boolean network models allow exhaustive enumeration of network topologies. The outcomes of these models can then be easily compared to the patterns observed in experimental data. Boolean operators however, are limited to on and off values and cannot easily accommodate quantitative measurements, which limits the types of genetic interaction patterns that can be investigated using this approach. Unravelling the regulatory network underlying genetic interaction patterns would benefit from modeling approaches that allow some degree of quantitativeness to be introduced while still being computationally feasible to exhaustively explore all potential models. In this way, Petri nets may be considered an extension of Boolean modeling that provides more flexibility, without the need to incorporate detailed prior quantitative knowledge [40–44]. Petri nets are able to capture both qualitative and quantitative traits and have successfully been applied to investigate genetic interactions before [45,46]. Petri net modeling would therefore allow investigation of all possible genetic interaction patterns in an exhaustive and semi-quantitative manner. It is evident that genetic interactions are widespread in Saccharomyces cerevisiae [6] as well as other organisms [7,8]. Nevertheless, extensive characterization of the molecular mechanisms underlying genetic interactions, as well as a comparison of the molecular mechanisms underlying genetic interactions between different functional classes have, as yet, not been performed. Here, based on two existing data sets and corresponding functional classes, gene specific transcription factors (GSTFs) and signaling related genes (kinases and phosphatases) have been compared with regard to negative genetic interaction patterns and the possible underlying molecular mechanisms. This revealed that the two most common genetic interaction patterns are buffering and inversion. The prevalence of inversion however, is much stronger in GSTFs. Inversion, whereby genes show opposite behavior in the double mutant compared to the corresponding single mutants, as well as the underlying mechanism of inversion, are poorly understood. Exhaustive enumeration of network topologies using Petri net modeling reveals that the minimum requirement for observing inversion is having a quantitative difference in interaction strength (edge weight) from the two upstream transcription factors to a shared downstream gene. In addition, this quantitative edge difference is frequently accompanied by an intermediate node, that displays a buffering pattern. The proposed model provides a mechanistic explanation for inversion, thereby further aiding a better understanding of genetic interactions. GSTFs, more so than kinases/phosphatases, can modulate or fine-tune the activation levels of their target genes, which suggests quantitative differences in regulating downstream target genes are important for the functioning of GSTFs. This is consistent with the fact that inversion occurs more often between GSTFs than between signaling genes, as well as our observation that quantitative edge differences are required for inversion to occur and provides a likely explanation why inversion is more prevalent for transcription factors. To investigate potential differences in mechanisms of genetic interactions between groups of genes with a different function, data from two previously published datasets using the same technical setup and platform were combined [16,17]. Both datasets include DNA microarray gene expression measurements as a result of deleting genes in the yeast Saccharomyces cerevisiae and have been subjected to rigorous quality control and statistical analyses [47]. The first dataset consists of genome-wide gene expression measurements of 154 single and double gene-specific transcription factor (GSTF) deletion mutants [17]. The second dataset contains genome-wide gene expression measurements of 54 single and double kinase/phosphatase (K/P) deletion mutants [16]. These studies applied different criteria to select for interacting pairs. Whereas the GSTF dataset includes both positive and negative genetic interactions, the kinase/phosphatase dataset was restricted to negative genetic interactions only. To avoid potential systematic biases, the selection criteria of the kinase/phosphatase dataset [16] were adopted and applied to both datasets. To summarize, selection was based on pairs having a significant growth-based negative genetic interaction score (adjusted p-value < 0. 05, Methods) to include redundancy relationships that influence fitness. In addition, for a given double mutant, at least one of the corresponding single mutants has an expression profile similar to wildtype (WT) to ensure that genetic interactions such as redundancy are considered. An expression profile is considered similar to wildtype if no more than eight genes are changing significantly (adjusted p-value < 0. 05, fold-change > 1. 7). These selection criteria yield a uniform dataset consisting of 11 GSTF double mutants and 15 kinase/phosphatase double mutants as well as their respective single mutants (63 single and double mutants in total; S1 Table). Genetic interactions can be investigated in different ways. Here, both growth as well as genome-wide gene expression is used to compare genetic interactions between GSTFs and kinases/phosphatases, as described before [17]. To summarize, a growth-based genetic interaction score εgrowth, XY between two genes X and Y is obtained by comparing the observed fitness for double mutant WxΔyΔ to the fitness that is expected based on both single mutants WxΔ · WyΔ (εgrowth, XY = WxΔyΔ - WxΔ · WyΔ) [48]. A gene expression-based genetic interaction score between two genes X and Y is calculated in two consecutive steps [17]. First, the effect of a genetic interaction between two genes X and Y on any downstream gene i is calculated as the deviation between the expression change observed in the double mutant Mi, xΔyΔ and the expected expression change based on the corresponding single mutants Mi, xΔ + Mi, yΔ (εtxpn_i, XY = |Mi, xΔyΔ – (Mi, xΔ + Mi, yΔ) |). The overall genetic interaction score between gene X and Y is then obtained by counting the total number of genes for which εtxpn_i, XY is greater than 1. 5 [17]. Gene expression changes from single and double mutants were subsequently grouped into the six genetic interaction patterns, buffering, suppression, quantitative buffering, quantitative suppression, masking and inversion, as previously described (Fig 1A) [17]. Gene expression profiles of GSTFs and kinases/phosphatases do not show any obvious differences in the number of genes significantly changing (30 vs. 27 on average), show similar gene expression ranges (Fig 1B and 1C) and correlations between pairs involving either kinases/phosphatases or GSTFs are highly similar (S1 Fig). When investigating the genetic interaction profiles of GSTFs (Fig 1B) as well as kinases/phosphatases (Fig 1C), it is clear that buffering is prevalent in many of the larger genetic interaction profiles, but the degree of buffering differs for the smaller genetic interaction profiles. Hierarchical clustering was applied to group pairs with similar genetic interaction patterns (S2 Fig), thereby disregarding the identity of individual downstream genes. From this clustering, it is clear that there is no distinct separation between pairs consisting of GSTFs and kinases/phosphatases. Instead, most pairs are characterized by large buffering effects, grouped together in a single large cluster (S2A Fig, red branch labeled as 1). This is not surprising, since all pairs are selected for having a significant growth-based negative genetic interaction score. This in turn is based on double mutants growing slower than expected based on the single mutants. Slow growing strains are known to display a common gene expression signature [49,50]. This slow growth gene expression signature is caused by a change in the distribution of cells over different cell cycle phases [51]. To facilitate investigating mechanisms of genetic interactions, such effects are better disregarded. Removing the slow growth gene expression signature is therefore expected to improve identification of direct effects and thereby aid systematic unravelling of the underlying mechanisms. As described before [51], the dataset was transformed by removing the slow growth signature (Methods). Removing the slow growth signature and thereby reducing indirect effects should also improve the identification of direct downstream target genes of the GSTFs included in the dataset. Four GSTF double deletion mutants have binding data available [52]. Investigating the enrichment before and after data transformation for direct downstream targets of Hac1/Rpn4 (S3A Fig), Met31/Met32 (S3B Fig), Gat1/Gln3 (S3C Fig) and Cbf1/Hac1 (S3D Fig) show a clear improvement in enrichment after data transformation for three out of four GSTF pairs as also shown before for individual GSTFs [51]. These results confirm that removing the slow growth signature improves the identification of direct effects and is therefore probably more suited when investigating mechanisms of genetic interactions. Hierarchical clustering of the slow growth corrected genetic interaction profiles was then applied to unravel potential differences in observed genetic interactions patterns between GSTFs and K/P (Fig 2A–2C). Three striking differences emerge when comparing this clustering with the clustering of the original, untransformed data (S2 Fig). First, pairs are grouped into distinct clusters, whereas previously, most were grouped into a single large cluster. Second, a cluster of predominantly kinase/phosphatase pairs emerges (Fig 2A, green branch, labeled as 1). These contain mixtures of different genetic interaction patterns, corresponding to ‘mixed epistasis’ [16]. Third, a smaller cluster dominated by buffering appears (Fig 2A, red branch, labeled as 2). This cluster also has strong growth-based negative genetic interaction scores (Fig 2C), which are known to be associated with redundancy. The ‘buffering’ cluster, with its strong growth-based negative interactions, mostly consists of pairs with a high sequence identity (average 43. 7%) compared to the others (average 21%). These include Nhp6a-Nhp6b, Met31-Met32, Ecm22-Upc2 and Ark1-Prk1, for all of which redundancy relationships have been described previously [53–56]. The high sequence identity here indicates a homology-based redundancy, in which both genes can perform the same function [30,31,57,58]. The only exception here, is the kinase/phosphatase pair Elm1-Mih1. This pair may be explained through pathway-based redundancy where two parallel pathways can compensate for each other’s function [59]. Elm1 is a serine/threonine kinase, and Mih1 a tyrosine phosphatase, which are both involved in cell cycle control (S4 Fig, left panel) [60,61]. Mih1 directly regulates the cyclin-dependent kinase Cdc28, a master regulator of the G2/M transition [61]. Elm1, on the other hand, indirectly regulates Cdc28 activity by promoting Swe1 degradation through the recruitment of Hsl1 [62,63]. The timing of entry into mitosis is controlled by balancing the opposing activities of Swe1 and Mih1 on Cdc28, and both Swe1 and Mih1 are key in the checkpoint mediated G2 arrest [64,65]. Deletion of Elm1 does not result in many gene expression changes (Fig 1C) which can be explained through compensatory activity of Mih1 (S4 Fig, middle panel). Downregulation of Mih1 activity has also been suggested before as an effective mechanism to counter stabilization of Swe1, as neither stabilization of Swe1 or elimination of Mih1 in itself is sufficient to promote G2 delay, but simultaneous stabilization of Swe1 and elimination of Mih1 does cause G2 arrest [63]. Simultaneous deletion of Elm1 and Mih1 leads to higher levels of inactive Cdc28 causing a G2 delay and stress (S4 Fig, right panel) [63]. All pairs within this cluster can therefore be associated with a redundancy mechanism. Taken together, these results suggest that the clustering of the slow growth corrected genetic interaction profiles is able to discern potential differences in mechanisms. Even though most pairs in the four clusters (Fig 2A) show negative genetic interactions (Fig 2C), different mechanisms are likely underlying each individual cluster. Within the slow growth corrected genetic interaction profiles another interesting cluster stands out: the orange branch where five out of six pairs involve GSTFs which predominantly show the inversion pattern (Fig 2A, branch 3). This suggests that inversion may be strongly associated with a particular group of GSTFs, whereas this does not seem to be the case for kinases and phosphatases. The overall percentage of genes showing inversion is already much higher for GSTFs (28. 6%) than for kinases/phosphatases (18. 7%) (S2 Table). When investigating the GSTF pairs within the cluster, it is clear that these display an even higher percentage of inversion compared to kinases and phosphatases (Fig 2D; adjusted p-value = 0. 00026) as well as compared to other GSTF pairs (Fig 2D; adjusted p-value = 0. 0043). In order to determine whether inversion was specific to the set of GSTFs analyzed here, or part of a more general phenomenon common to GSTFs, we included both positive and negative genetic interactions between GSTF pairs, expanding the number of GSTF pairs to 44. Clustering of all 44 GSTF pairs (S5 Fig) also shows that a large fraction of the GSTF pairs contain many genes showing inversion, with most of the inversion dominated GSTF pairs still clustering together (S5 Fig, indicated with an asterisk). Note though, that because the 44 GSTF pairs include both positive and negative genetic interactions, the results are not directly comparable to the kinase/phosphatase pairs as these only include negative genetic interactions. Taken together, this indicates that not only is inversion more frequently associated with GSTFs compared to kinases and phosphatases, but one particular subset of GSTFs is also predominantly defined by inversion. Unlike buffering, where redundancy is a likely mechanistic explanation, the underlying mechanism of inversion is still unknown [17]. The GSTF pairs within the inversion dominated cluster also do not share a common biological process, function, pathway or protein domain other than general transcription related processes and functions. To investigate potential mechanisms of inversion, an exhaustive exploration was initiated. Previously, Boolean modeling has been applied to exhaustively explore all mechanisms underlying two genetic interaction patterns for the Fus3-Kss1 kinase phosphatase pair [16]. However, to explore all potential mechanisms underlying inversion, a Boolean approach may not suffice as more subtle, quantitative effects, may be needed to obtain inversion. At the same time, any modeling approach must remain computationally feasible. For this purpose, a modeling approach based on Petri nets was devised to exhaustively evaluate all possible three and four node models but taking into account the possibility of quantitatively different effects (Fig 3, Methods). Interactions between nodes (edges) can be activating (positive) or inhibiting (negative). In order to incorporate quantitative differences, both strong and weak edges were used (Methods). Counting all possible combinations of different edges results in 152,587,890,625 possible edge weight matrices. To reduce the number of models, three conditions were imposed, as used previously [16]. In short, nodes contain no self-edges, the number of incoming edges on any node is limited to two and the model includes at least two edges from one of the regulators (R1, R2) to the downstream genes (G1, G2). Applying these requirements and filtering for mirror edge weight matrices results in 2,323,936 matrices. By including AND/OR logics the final number of models to be evaluated was 9,172,034 (Methods). Petri net simulations were then run and genetic interaction patterns determined for G1 and G2, analogous to what was done for the original data (Methods) (Fig 1A). Depending on the topology, Petri net models can be stochastic, in other words, they do not show the same behavior when simulated multiple times and therefore result in unstable models. Only 2. 3% of the models were found to be unstable, i. e. showed inconsistent genetic interaction patterns for G1 and G2 across five simulation runs. Thus, stochasticity hardly influences the observation of genetic interaction patterns in our simulations (Fig 3). Nevertheless, unstable models were excluded from further analysis. In total, 168,987 models (1. 8%) show inversion in either G1, G2, or both downstream nodes. To investigate which potential regulatory patterns underlie the 168,987 models showing inversion, low complexity models with few edges were analyzed first. Two interesting observations can be made. First, although there are many high complexity models involving four nodes and many edges (up to eight), three nodes and three edges are sufficient to explain inversion (Fig 4A). Second, only two three-node models exist that show inversion (Fig 4A). These two models only differ in the strength of the inhibiting edge from R1 to R2. Both models involve inhibition of R2 through R1 and weak activation of G1 by R1 in combination with a strong activation of G1 by R2, i. e. a quantitative edge difference between the incoming edges of G1. Deletion of R1 in these two models results in activation of R2, and therefore upregulation of G1 due to a strong activating edge. Deletion of R2 however, will not result in any changes compared to WT as it is normally inhibited by R1. Deletion of both R1 and R2 will lead to downregulation of G1 as the weak activating edge from R1 to G1 is lost. Taken together, the analysis of the low complexity models indicates that a quantitative difference in interaction strength is required to explain inversion. To investigate whether this requirement also holds for higher complexity models, all models containing two to eight edges were further analyzed. Inversion models were grouped by the number of edges (complexity) and then analyzed for their relative frequency of having a quantitative edge difference (Fig 4B, top left panel, note that the number of possible models grows exponentially with the number of edges). Almost all of these models show a quantitative edge difference, with only a very small fraction (1. 3% overall) of models not having a quantitative edge difference. To exclude these results being based on a particular choice of edge weights (1 for weak and 5 for strong, or ‘1/5’ for short), we repeated the simulations with strong interactions represented by an edge weight of 9 (named ‘1/9’). Of the 168,987 models that show inversion in the ‘1/5’ simulations, 144 299 (85. 4%) also show inversion in the ‘1/9’ simulations. Moreover, both of the three edge models (Fig 4A) also show inversion in the ‘1/9’ simulations. Finally, also in the 144,299 ‘1/9’ inversion models, only 1,696 (1. 18%) have no quantitative edge difference. Except for masking, the other genetic interaction patterns show different behavior, indicating that the relative ratio of quantitative versus non-quantitative edges is not an inherent network property. Based on both the low complexity models as well as the high complexity models showing inversion, it is evident that a quantitative difference in interaction strength of two genes or pathways acting on a downstream gene is required to explain inversion. With the exception of the two models discussed above, all other inversion models consist of four nodes with two regulator nodes and two downstream effector nodes. To better understand the interplay between all four nodes, besides the node displaying inversion (G1), the second downstream gene (G2) was also analyzed for the occurrence of different genetic interaction patterns (Fig 5A). Most G2 nodes tend to have no genetic interaction pattern (27%). The most common genetic interaction patterns are buffering (23%) and quantitative buffering (18%). These both are very alike in their genetic interaction pattern (Fig 1A) and only show slight differences in their quantitative behavior. They may therefore be considered as part of the same superclass of “buffering”. As can be expected, the buffering node is frequently positioned upstream of the inversion node (Fig 5B). The combination of inversion and buffering is also significantly overrepresented within inversion models when compared to all models (Table 1, p < 0. 005). Taken together this shows that a quantitative difference in interaction strength of two genes or pathways acting on a downstream gene is frequently accompanied by an intermediate gene or pathway that displays buffering. One gene pair within the inversion dominated GSTF cluster (Fig 2A, branch 3; Fig 6A) that largely consists of inversion is Gat1-Gln3. By combining the three node model derived from the Petri Net modelling (Fig 4A, left panel) with existing literature, a potential mechanistic explanation for the interaction between this pair can be obtained (Fig 6B). Both Gln3 and Gat1 are activators involved in regulating nitrogen catabolite repression (NCR) sensitive genes [66–68]. When cells are grown under nitrogen rich conditions, as was done here, Gat1 is repressed by Dal80 [67]. Dal80 in turn can be activated by Gln3 [67,69], which provides a plausible mechanism for the predicted inhibition edge between Gln3 and Gat1 (Fig 6B). The degree to which Gln3 and Gat1 influence downstream genes has also been reported to differentiate between individual genes [70], which is fully consistent with the quantitative edge difference as predicted in the model (Fig 6B). The set of inversion related genes (Fig 6A, gene set 1) is enriched for nuclear encoded mitochondrial respiratory genes compared to non-inversion related genes (Fig 6A, denoted with a dot, adjusted p-value 3. 2x10-17). Previously, NCR has been linked with mitochondrial-to-nuclear signaling through the retrograde signaling pathway [71,72], although an alternative mitochondrial-to-nuclear signaling pathway, such as the intergenomic signaling pathway, may instead be involved [73]. Taken together, this suggests that Gat1 and Gln3 might differentially influence mitochondrial-to-nuclear signaling, although additional experiments would be needed to confirm this initial hypothesis. Another interesting pair of genes within the GSTF cluster dominated by the inversion pattern (Fig 2A, branch 3) is Hac1-Rpn4. This pair displays a substantial amount of both inversion as well as buffering (Fig 7A) and lends itself well for testing some of the model predictions. Hac1 and Rpn4 are both involved in the processing of inappropriately folding proteins, either by activating genes of the unfolded protein response [74] (UPR, Hac1) or via the endoplasmic reticulum-associated degradation [75] (ERAD, Rpn4). Two genes that display inversion, Pdr5 and Pdr15, show stronger expression changes compared to the other genes in the same gene set (Fig 7A, gene set 1). Both Pdr5 and Pdr15 are multidrug transporters involved in the pleiotropic drug response [76]. Expression of these two genes is tightly regulated by Pdr1 and Pdr3 [77,78]. Pdr5 is also positively regulated by expression of Yap1, a basic leucine zipper transcription factor that is required for oxidative stress tolerance [79]. Of the three transcription factors Pdr1, Pdr3 and Yap1, only PDR3 shows a clear upregulation in the hac1Δ rpn4Δ double mutant and hardly any change in the respective single mutants (Fig 7B). This is consistent with the role of the intermediate buffering gene as derived from our Petri net modeling results. If Pdr3 acts as the intermediate buffering gene mediating the quantitative effect as predicted based on our model, it is also expected that deletion of PDR3 leads to a more severe downregulation of PDR5 and PDR15 expression levels when compared to expression levels of PDR5 and PDR15 in the rpn4Δ mutant. To test this prediction, mRNA expression changes of PDR5 and PDR15 where investigated in the pdr3Δ and rpn4Δ mutants. As expected, deletion of PDR3 results in a much stronger downregulation of PDR5 (adjusted p-value = 7. 26x10-4) and PDR15 (adjusted p-value = 5. 95x10-5) compared to deletion of RPN4 (Fig 7C), thereby confirming the model prediction. Taken together, these results provide a likely mechanistic explanation where Pdr3 acts as the intermediate buffering gene in regulating Pdr5 and Pdr15 (Fig 7D). To investigate genetic interactions in a high-throughput manner, growth-based assays have frequently been deployed, resulting in the identification of an overwhelming number of both negative and positive genetic interactions [6,20–28]. Based on these surveys, several theoretical mechanisms have been proposed to explain genetic interactions [3,18,80,81]. More efforts, also using different types of assays, are however still needed to systematically and thoroughly investigate the underlying mechanisms. Alongside growth-based genetic interactions, genome-wide gene expression measurements have been applied to elucidate potential molecular mechanisms underlying genetic interactions [16,17,33–36]. Although more laborious, expression-based genetic interactions potentially allow for more in-depth characterization of the genetic interaction landscape. Here, we show that buffering is the most frequently occurring pattern underlying most negative genetic interactions. These are however to a large degree related to slow growing strains, hindering the investigation of the underlying mechanisms. By applying a slow growth transformation that removes a cell cycle associated gene expression signature, many such effects can be filtered out [51]. The transformation results in distinct clusters that can be more easily aligned with potential underlying mechanisms. Recent advances using Crispr-Cas9 single and double knock-down screens, followed by single cell RNA sequencing have also shown that results are greatly influenced by the cell-cycle phase in which different cells are found [35,82]. It is therefore essential for future studies on genetic interactions to incorporate methods that decompose such large confounding effects, as they greatly influence the ability to deduce mechanism. To infer underlying mechanisms from the genetic interaction landscape as obtained from genome-wide gene expression measurements, systematic modeling approaches are warranted [3,18]. Various modeling techniques have been instrumental in understanding various aspects of experimental data (reviewed in [83]). Different modeling methods have different applications, depending on the question asked and available data types. To understand the underlying mechanisms for many genetic interactions, an approach is needed that is able to exhaustively explore the complete genetic interaction landscape while at the same time incorporating (semi-) quantitative values. Thus, the simulated gene expression levels are coarse-grained semi-quantitative representations of the actual expression levels and cannot be linearly translated to experimental output. Therefore, we here used Petri net models to exhaustively explore more than nine million models. Inversion, a pattern strongly associated with a group of GSTF pairs was investigated in more detail, resulting in the striking conclusion that a quantitative difference in interaction strength is needed to explain inversion, independent of the particular value of the edge strength parameter chosen in the model. The approach taken here, by combining slow growth corrected genome-wide gene expression measurements with the exhaustive semi-quantitative Petri-net modeling thus highlights the benefits of using such an approach to understand mechanisms of genetic interactions. Applying this approach to other types of genetic interactions or across many more genetic interaction pairs can help us in further characterizing mechanisms of genetic interactions and relating these to pathway organization and cellular states. Previously, a mechanism termed “buffering by induced dependency” was proposed to explain parts of the genetic interaction patterns observed between Rpn4 and Hac1 (Fig 8, dotted inset) [17]. This mechanism links the endoplasmic reticulum-associated degradation (ERAD) by the proteasome (Rpn4) with the unfolded protein response (UPR, Hac1), two distinct processes dealing with misfolded and unfolded proteins. By combining the “buffering by induced dependency” mechanism with the model proposed for inversion here, most genetic interaction patterns observed for Rpn4 and Hac1 can be explained (Figs 7A and 8). The combined model introduces a third, compensatory process, the pleiotropic drug response (PDR; Fig 8, bottom light gray inset). Even though the exact relationship between ERAD, UPR and pleiotropic drug response is unclear, the interplay between UPR and drug export has been shown in mammalian cells [84]. In yeast, Pdr5 and Pdr15 have been implicated in cellular detoxification [78,85] and may also be required for cellular detoxification under normal growth conditions [85]. Both Pdr5 and Pdr15 have been reported to be regulated through Pdr1 and Yap1 [79,86], as well as through Rpn4 [87,88]. This is also confirmed here by downregulation of both Pdr1 and Yap1 as well as downregulation of their target genes Pdr5 and Pdr15 in rpn4Δ (Fig 7B and 7C). It is therefore likely that in the wildtype situation when Rpn4 is active, both ERAD and the PDR are functioning (Fig 8). Deletion of RPN4 leads to deactivation of the ERAD and PDR pathways and activation of the UPR through Hac1 (Fig 8, rpn4Δ dotted red line). Deletion of both RPN4 and HAC1 results in a major growth defect and accumulation of misfolded and unfolded proteins, most likely leading to a stronger activation of the PDR through Pdr3 compared to the wildtype situation (Fig 7B and 7C; Fig 8, hac1Δ rpn4Δ dotted red line) [77,78]. Taken together, this model thus provides a potential regulatory mechanism in which two redundant processes, each with slightly different efficacies, can be differentially regulated, or fine-tuned, through a third, compensatory process. The requirement to fine-tune slightly different efficacies of different cellular processes then also provides a potential explanation why inversion is observed more frequently for gene-specific transcription factors since these allow for more fine-grained control than protein kinases and phosphatases. In conclusion, we have shown how exhaustive exploration of regulatory networks can be used to generate plausible hypothetical regulatory mechanisms underlying inversion. Almost all models showing inversion contain a quantitative difference in edge strengths, which suggests quantitative differences in regulating downstream target genes are important for the functioning of GSTFs. These hypothetical mechanisms have subsequently been tested against known and new experimental data. For GSTFs we show a validated example of Hac1-Rpn4 where differential regulation of gene expression is key to understanding the genetic interaction pattern inversion. Two selection criteria were applied to select genetically interacting GSTF and kinase/phosphatase pairs. First, one of the mutants of each individual pair should show genome-wide gene expression measurements similar to wildtype (WT). DNA microarray data from Kemmeren et al [47] was used to determine whether a single deletion mutant is similar to WT. A deletion mutant is considered similar to WT when fewer than eight genes are changing significantly (adjusted p-value < 0. 05, FC > 1. 7) in the deletion mutant gene expression profile, as previously described [16]. Second, selected pairs should show a significant growth-based negative genetic interaction score. Growth-based genetic interaction scores for GSTF [28] and kinase/phosphate [26] pairs were converted to Z-scores. A negative Z-score significance of p < 0. 05 after multiple testing correction was used as the significance threshold. Applying these selection criteria resulted in 11 GSTF pairs and 15 kinase/phosphatase pairs (S1 Table). Genome-wide gene expression measurements of single and double mutant GSTF pairs were obtained from Sameith et al [17]. Genome-wide gene expression measurements of single and double mutant kinase/ phosphatase pairs were obtained from van Wageningen et al [16]. Genome-wide gene expression measurements of pdr3Δ and rpn4Δ were obtained from Kemmeren et al [47]. Statistical analysis of these gene expression profiles was performed as previously described [47]. In summary, mutants were grown in Synthetic Complete (SC) medium with 2% glucose and harvested during exponential growth. WT cultures were grown alongside mutants in parallel to monitor for day to day effects. For each mutant statistical analysis using limma was performed versus a collection of WTs [16,47]. Reported FC for each transcript is the average of four replicate expression profiles over a WT pools consisting of 200 WT strains. Growth measurements for single and double mutant GSTF and kinase/phosphatase pairs were obtained from Sameith et al [17] and van Wageningen et al [16] respectively. Growth-based genetic interaction scores were calculated for both GSTF and kinase/phosphatase pairs as performed before [17]. In summary, the fitness W of single and double mutants was determined as the ratio between the WT growth rate and the mutant growth rate. The growth-based genetic interaction score ɛgrowth, XY was calculated as the deviation of the observed fitness in a double mutant from the expected fitness based on the respective single mutants (ɛgrowth, XY = WxΔyΔ - WxΔ. WyΔ). P-values were assigned to genetic interaction scores based on the mean and standard deviation of a generated background distribution [17]. P-values were corrected for multiple testing using Benjamini-Hochberg. Adjusted p-values lower than 0. 05 were considered significant. Fitness values of all single and double mutants, as well as calculated genetic interaction scores can be found in S1 Table. Expression-based genetic interaction scores were calculated for both GSTF and kinase/phosphatase pairs as described before [17]. In summary, the effect of a genetic interaction between two genes X and Y on gene i is calculated as the deviation between the observed expression change in the double mutant and the expected expression change based on the corresponding single mutants (εtxpn_i, XY = |Mi, xΔyΔ − (Mi, xΔ + Mi, yΔ) |). The overall genetic interaction score between X and Y is calculated as the sum all genes i for which εtxpn_i, XY > log2 (1. 5). All genetic interaction scores consisting of at least 10 genes were kept for further downstream analyses. Genes with similar gene expression changes were divided into the 6 different patterns (buffering, quantitative buffering, suppression, quantitative suppression, masking, inversion), as previously described [17] (Fig 1A). Genetic interaction profiles for both classes of proteins were grouped together based on the number of occurrences of the six different patterns using hierarchical clustering. Average linkage was applied for the clustering. Identity of genes in each genetic interaction profile was disregarded. Slow growth signature transformation of the gene expression profiles was performed as previously described [51]. In short, for each mutant, the correlation of its expression profile with the first principal component of 1,484 deletion strains [47] was removed, thus minimizing correlation with the relative growth rate. The transformation reduces correlation with the relative growth rate from 0. 29 to 0. 10 on average [51]. Exhaustive modeling of possible network topologies underlying the genetic interaction patterns was carried out by creating Petri net models consisting of four nodes, representing two regulator genes (R1 and R2) and two downstream genes (G1 and G2). With four nodes and directed edges, there are 42 = 16 possible edges, and 216 = 65536 possible edge weight matrices, which is a tractable number. However, each interaction can in addition be positive or negative, and weak or strong (and absent), leading to 516 = 1. 5⋅1011 possible interaction graphs (edge weight matrices), which becomes intractable. Many of these models, however, will be irrelevant for the understanding the biological behavior of genetic interaction patterns of two genes. To exclude these types of models, the following conditions were applied: 1) No self-edges are allowed. 2) The number of incoming edges on any node must be limited to two. 3) At least two incoming edges from at least one of the regulators (upstream nodes) to the genes (downstream nodes). Applying these conditions reduces the number of relevant edge weight matrices to 9,287,616. Furthermore, most generated matrices have mirror counterparts, therefore only one of the matrices was included in downstream analyses. Applying this filtering step results in 2,323,936 matrices. Fig 3 gives an overview of the various filtering steps, and shows which representation of the models was relevant in different stages of the filtering. Edge weight matrices were generated in R, version 3. 2. 2 (the function expand. grid was used to generate all combinations of edges per row in a given matrix). Regulatory effects of two potentially interacting genes (R1 and R2) on two downstream genes (G1 and G2) were simulated using a Petri net approach [42,89–91] to recapitulate genetic interaction patterns observed in the gene expression data. In the Petri net notation, nodes in a given model are represented by places (denoted as circles). Tokens are denoted in the places and indicate the availability of the resource represented by the place. Interactions between nodes always go via a transition (denoted as squares), connected via directed arcs (drawn as arrows). An incoming arc to a transition can be either activating or inhibiting. The weight on arcs going to a transition is always fixed to 1. The weight on arcs going from a transition to a place depends on the edge weight between two nodes, 1 for weak and 5 for strong (Fig 3). To establish sensitivity of our results with respect to the particular edge weights chosen, we also performed simulations with an edge weight of 9 for the strong interaction. For nodes with two incoming edges, one has to decide how these two inputs should be combined: does the transition require both inputs to be activated (AND logic), or can one or the other activate it (OR logic). To incorporate this, for each pair of incoming edges with the same weight, two Petri net models were generated: one using the AND logic, and one using the OR logic (Fig 3, bottom right panel). For two incoming edges with different weights only the Petri net model using the OR logic was generated. For cases with two incoming edges to a node with two different directions, activation and inhibition, inhibition dominates. To simulate the regulatory effects of two upstream genes (R1 and R2), 200 tokens were provided to represent the mRNA resources for each regulator, except when one of the regulators has an incoming edge from the other regulator as shown in (S6A Fig). Each step in the simulation process comprises of firing all enabled transitions (maximal parallel execution) [92,93]. A transition is enabled to fire when resources (tokens) in the input place (s) match or exceed the weight (s) on the respective incoming arc (s) to the transition (S6B Fig). In total 50 consecutive transition firing steps were performed. To incorporate deletion mutants in the simulation process, tokens were removed from corresponding regulators. To prevent accumulation of tokens in deleted regulators, each outgoing arc from a transition to the corresponding deleted places were also removed in simulated deletion strains. The number of tokens in G1 and G2 after 50 steps of firing transitions represent their expression levels. The final token levels are coarse-grained semi-quantitative representations of expression levels. Since they cannot be linearly translated to the experimental output, we compare the relative difference between single and double mutants and the WT situation where both R1 and R2 are active. To avoid division by zero one token was added to the total number of tokens in G1 and G1. These fold changes were then log2 transformed (M values). Simulation-based genetic interaction scores for G1 and G2 were calculated based on the deviation between observed M values in the double mutant and the expected M value based on the single mutants, as follows: εsim, R1R2i = |MR1ΔR2Δi − (MR1Δi + MR2Δi) |, where i can be either G1 or G2. Each node with εsim, R1R2i > log2 (1. 7) was further divided into genetic interaction patterns, as defined before based on gene expression data [17]. Simulated expression levels for single and double mutants are considered to be increased relative to WT when M > log2 (1. 7) and decreased when M < -log2 (1. 7). Functional enrichment analyses were performed using a hypergeometric testing procedure on Gene Ontology (GO) biological process (BP) annotations [67] obtained from the Saccharomyces Cerevisiae Database [68]. The background population of genes was set to 6,359 and p values were corrected for multiple testing using Bonferroni. Models were visualized in R, version 3. 2. 2, using diagram package (version 1. 6. 3). Weak and strong activation/inhibition edges are represented as thin and thick lines, respectively.

The relationship between genotype and phenotype is one of the major challenges in biology. While many previous studies have identified genes involved in complex genetic diseases, there is still a gap between genotype and phenotype. One of the difficulties in filling this gap has been attributed to genetic interactions. Large-scale studies have revealed that genetic interactions are widespread in model organisms such as baker’s yeast. Several molecular mechanisms have been proposed for different genetic interaction types. However, differences in occurrence and underlying molecular mechanism of genetic interactions have not yet been compared between gene classes of different function. Here, we compared genetic interaction patterns identified using gene expression profiling for two classes of genes: gene specific transcription factors and signaling related genes. We modelled all possible molecular networks to unravel putative molecular differences underlying different genetic interaction patterns. Our study proposes a new mechanistic explanation for a certain genetic interaction pattern that is more strongly associated with transcription factors compared to signaling related genes. Overall, our findings and the computational methodologies implemented here can be valuable for understanding the molecular mechanisms underlying genetic interactions.

Abstract Introduction Results Discussion Materials and methods

gene regulation enzymes regulatory proteins dna-binding proteins enzymology phosphatases dna transcription regulator genes transcription factors gene types molecular biology techniques mutagenesis and gene deletion techniques genetic interactions research and analysis methods proteins gene expression molecular biology biochemistry gene identification and analysis genetics biology and life sciences deletion mutagenesis

2019

The ability of transcription factors to differentially regulate gene expression is a crucial component of the mechanism underlying inversion, a frequently observed genetic interaction pattern

Although transposable elements (TEs) are known to be potent sources of mutation, their contribution to the generation of recent adaptive changes has never been systematically assessed. In this work, we conduct a genome-wide screen for adaptive TE insertions in Drosophila melanogaster that have taken place during or after the spread of this species out of Africa. We determine population frequencies of 902 of the 1,572 TEs in Release 3 of the D. melanogaster genome and identify a set of 13 putatively adaptive TEs. These 13 TEs increased in population frequency sharply after the spread out of Africa. We argue that many of these TEs are in fact adaptive by demonstrating that the regions flanking five of these TEs display signatures of partial selective sweeps. Furthermore, we show that eight out of the 13 putatively adaptive elements show population frequency heterogeneity consistent with these elements playing a role in adaptation to temperate climates. We conclude that TEs have contributed considerably to recent adaptive evolution (one TE-induced adaptation every 200–1,250 y). The majority of these adaptive insertions are likely to be involved in regulatory changes. Our results also suggest that TE-induced adaptations arise more often from standing variants than from new mutations. Such a high rate of TE-induced adaptation is inconsistent with the number of fixed TEs in the D. melanogaster genome, and we discuss possible explanations for this discrepancy. The recent years have seen a burst in studies searching for signatures of genetic adaptation in a variety of organisms, including natural populations and domesticated plants and animals [1–12]. These studies suggest that adaptation is a pervasive force in evolution. However, many fundamental questions, for instance, the relative contribution of coding versus regulatory changes, point mutations versus structural changes, or different functional genic classes to adaptation, remain largely unanswered. Despite its central significance for all of biology, the genetics of adaptation remains very poorly understood. One question that is still unanswered is the role that transposable elements (TEs) play in adaptation. One might expect that TEs participate in adaptation since TEs are potent sources of mutation and are known to contribute to the function and evolution of genes and genomes in a variety of ways [13–15]. TEs (1) play an important role in the structural evolution of genomes through the generation of various types of rearrangements [14,16], (2) donate regulatory sequences that control the expression of nearby genes [17–20], (3) become incorporated into coding sequences at the transcript level [21–24], and (4) have their genes recruited by the host genomes for key functions [25]. A common genomic effect of TEs is the induction of mutations. For instance, in Drosophila melanogaster, TEs are responsible for approximately 80% of the visible spontaneous mutations [26–28]. Most of the TE insertions are found at low frequencies, suggesting that the majority of the mutations they generate are deleterious [29,30]. TEs may be deleterious because they disrupt genes, because the translation of TE-encoded proteins may be costly, and also because they may mediate deleterious chromosomal rearrangements [31]. Only a few examples of TEs found at high population frequencies have been reported [32–39]. In two cases, there is good evidence that these high-frequency TEs have been adaptive in the recent evolution of D. melanogaster [33,38]. However, a systematic search for adaptive TEs in the D. melanogaster genome has never been carried out. D. melanogaster is a particularly good model to analyze the contribution of TEs to adaptive evolution since it has one of the highest-quality genome sequences and annotations of TEs in eukaryotes [40,41]. D. melanogaster is also a particularly good model to study specifically recent TE-induced adaptation, since this species, originally from sub-Saharan Africa, expanded its population size worldwide very recently [42,43]. It appears that the expansion out of Africa into Europe took place approximately 10,000–16,000 y ago or equivalently 0. 1 to 0. 3 Ne generations ago [44,45]. As a result, we might expect that adaptations to the out-of-Africa environments that D. melanogaster is likely to have experienced [1,4, 46] might still be detectable as partial or complete selective sweeps [47]. In addition, it should be easier to carry out genetic, phenotypic, and functional analyses of recent TE-induced adaptations given that such TEs would still be segregating in the D. melanogaster population, allowing for straightforward genetic manipulations. Note that the inference of partial or complete selective sweeps is complicated by the bottleneck that D. melanogaster appears to have experienced during the spread out of Africa [44,45]. It has been shown that bottlenecks alone can produce patterns of nucleotide variability that mimic those expected under selection [4,46,48–50]. Demography must therefore be taken into account before making any inferences of selective sweeps due to putatively adaptive TEs. We used the annotated TEs in Release 3 of the D. melanogaster genome [51] as the starting point for our search for TEs that contributed to recent adaptation outside of Africa. We provide evidence for a high rate of TE-induced recent adaptive changes. The analysis of the set of adaptive TE insertions allows us (1) to estimate the minimum contribution of TEs to adaptive evolution, (2) to gain insight into the type of genes that have been targets of positive selection, (3) to assess the relative contributions of adaptive evolution in coding versus regulatory regions, and (4) to estimate the relative importance of new mutations versus standing variation. The estimated rate of adaptive transposition is unexpectedly high and inconsistent with the relatively small number of fixed TEs in the D. melanogaster genome. We discuss the implications of these results for the understanding of adaptation in D. melanogaster. The third release of the D. melanogaster genome sequence identified 1,572 TEs belonging to 96 distinct families scattered across the euchromatic portion of the genome [51]. These TEs were identified by using BLAST to compare a reference dataset of canonical TE sequences against the genomic sequence. Only the euchromatic TE sequences displaying over 90% identity over more than 50 base pairs of sequence with the canonical TEs have been included in this set [51]. We used these 1,572 TEs as a starting point to determine population frequencies of the majority of euchromatic TEs. To obtain these frequencies, we employed a pooled-PCR strategy. PCRs were run with six DNA pools containing DNA from five different North American (NA) populations and one pool containing DNA from one sub-Saharan (Malawi) African (AF) population. Each DNA pool contained DNA from eight to 12 individual, isofemale, or highly inbred strains (see Materials and Methods). Not all the TEs in the Release 3 have been assayed—for 415 of them, specific primers could not be designed because the regions flanking the insertion were repetitive (see Materials and Methods). Release 4 of the D. melanogaster genome [41] corrected the annotation of approximately half of the elements in Release 3. The reannotation revealed that the primers had not been designed correctly for 225 TEs—we discarded the results for such TEs. As a result, we have information for 932 out of 1,572 TEs. For 695 of these TEs, we have information from all six NA pools, and for an additional 207 TEs, we have information from at least four NA pools. These 902 TEs form the starting point for our search for recent adaptive TE insertions (Figure 1; Table S1). Our goal is to identify TEs that may have contributed to adaptation after the expansion of the D. melanogaster population out of Africa. Therefore, we focused on identifying TEs that are rare or absent in Africa and are frequent or fixed in North America. We start by identifying TEs present in all of the NA pools and not fixed in the AF pool. Specifically, we searched for insertions that (1) were clearly present in at least four NA pools, (2) were not clearly absent in any of the NA pools, and (3) were not fixed in the AF pool. Most of the 902 TEs are present at low population frequencies: 347 are only present in the sequenced strain, and another 341 TEs are either present at low frequencies in the five analyzed NA populations or gave ambiguous PCR results for more than two pools. A total of 214 TEs fulfill the first two criteria and therefore are more likely to be present at intermediate frequencies in the NA populations (Figure 1; Table S1); 113 of these 214 TEs appear fixed in all of the pools, including the AF pool. An additional 27 are polymorphic in NA pools but are fixed in the AF pool. Some of these TEs may have contributed to adaptation but are less likely to be recent and to have specifically contributed to adaptation associated with the out-of-Africa expansion. We eliminated these TEs from further consideration, leaving us with the set of 74 TEs. Some of these remaining 74 TEs are present in the regions of low recombination (Table S1). TEs in the low-recombination areas are likely to be subject to weaker purifying selection due to a lower rate of ectopic recombination [35,52–54] and higher population stochasticity due to stronger background selection and stronger effect of linked positive selection [55–58]. The high-frequency TEs found in low-recombination areas are more likely to be neutral, and therefore to represent false positives, than those found in high-recombination areas. We eliminated the TEs present in low-recombination regions of the genome (<1. 4 cM/Mb) from further analysis. At the end, based on pool frequency data, we identified 38 TEs that are located in regions of high recombination, are not fixed in sub-Saharan Africa, and which might be frequent in NA (Figure 1; Table S1). We focus on these 38 TEs for the remainder of this paper. We assessed the age of the 38 TEs by comparing their sequences to the consensus sequences of their families. We considered a TE insertion to be old when its divergence from the consensus sequence was higher than 1%. Using this criterion, we identified elements FBti0019418 and FBti0019634 from the 1360 family, FBti0019372 and FBti0020119 from the S-element family, and FBti0020114 and FBti0019081 from the transib2 family as potentially old (Table 1). However, the possibility remains that these insertions are recent insertions of TEs whose sequence differ from that of the consensus. For the six putatively old insertions, we compared the sequence to other annotated TEs in the same family and also performed BLAST queries against the whole genome to search for closely related, but not annotated, copies (see Materials and Methods). For FBti0019372, FBti0020119, FBti0020114, and FBti0019081, we found other elements in their respective families that showed less than 1% divergence, indicating that they are likely to be recent insertions (Table 1). FBti0019418 showed more than 1% divergence when compared to all the other identified copies belonging to the 1360 family. However, we discovered a new 1360 TE copy that is nested inside an element annotated as a Cr1a TE (FBti0059655) and is very similar to FBti0019418 (0. 18% divergence). Only one 1360 copy (FBti0019634) is more than 1% divergent both from the 1360 consensus sequence and from any other 1360 copy in the genome. Consistently with its age estimate, FBti0019634 appears fixed in the AF pool (Table 1). The presence of a TE in all six NA pools suggests, but does not guarantee, that it is present at high frequency in the NA population. Indeed, a TE present at a 10% frequency in the population has an approximately 13% chance of being present in all six pools containing 12 strains each. To filter out the TEs present at a low frequency in the NA population and to verify the pooled-PCR results, we carried out PCRs with individual strains for all 38 putatively frequent TEs. Results are shown in Table 1. Overall, we confirmed the results obtained with the pooled-PCR strategy. For two elements, FBti0020042 and FBti0020056, we could not detect the presence of the TE in any of the tested strains within a pool. In both cases, we can explain this by the inability to test every strain from the original pools because some strains were no longer available. For most of the PCRs, we obtained a single band of the expected size, indicating that the primers were specifically amplifying the region of the genome where the TE was identified. We only found three exceptions. For a pogo element, FBti0019627, we obtained several bands besides the expected band for the presence of the element in all the strains assayed. We cloned and sequenced these amplification products and identified the band that contained this particular TE. We considered FBti0019627 to be present only when the PCR amplification products contained this specific band (Table 1). For a 297 element, FBti0018868, and for a roo element, FBti0019985, the results obtained with the primers designed to check for the presence of the TE were not consistent with the results obtained with the primers designed to check for its absence. FBti0019985 also showed variability in the amplicon length. The specific reasons for these results are being currently investigated. These two elements were not considered further in this analysis (Table 1). Of the remaining TEs, a number are rare in North America and/or frequent in Africa. We used an ad hoc cutoff of 30% to define frequent TEs. Using this cutoff, we eliminated eight TEs present at 30% or lower frequency in the NA strains and seven TEs present at 30% or higher frequency in the AF strains (Table 1). At the end, we have 21 TEs for which we have unambiguous evidence that they are frequent in North America and rare in Africa. Some TEs belong to families in which the majority of copies are present at high frequency in the NA population and thus are unlikely to be adaptive. Instead, it is more plausible that such TE families are subject to relaxed purifying selection as a whole [35]. Using a maximum likelihood approach (see Materials and Methods), we estimated the selection coefficient for the 11 families represented in our list of 21 putatively adaptive TEs based on the NA pooled PCR data (Table 1). Three of the families, BS, X-element, and hopper, show selection coefficients that are not significantly different from zero, indicating that these families are likely to be under relaxed purifying selection (Table S2). For one of these families, the BS family, we have additional sequencing data that show that a number of the TEs in this family appear to have increased in frequency neutrally [50]. Eight elements in our list belong to one of these three families (Table 1). We considered these eight TEs to be putatively neutral and the remaining 13 TEs to be putatively adaptive (Figure 1). Only one of the 13 putatively adaptive insertions is present in the analyzed AF population. However, we sampled only 11 Malawi strains. In addition, there might be substantial structure in the D. melanogaster population in sub-Saharan Africa [59] that might be further exacerbated by natural selection acting on the putatively functional TE insertions studied here. Moreover, we already know that one TE in our set, FBti0019430, is absent in the analyzed Malawi population but is present in 17% of the strains from a population collected in Kenya [38]. For the 11 TEs that were not present in the Malawi population, we extended the analysis to three other sub-Saharan populations: two from Zimbabwe and one from Kenya (Table 2). Seven out of 11 insertions were present in at least one of the pools assayed. Only two of them, FBti0018880 and FBti0019372, are absent in all three additional pools of AF strains. No results were obtained for the remaining two TEs (Table 2). We conclude that most putatively adaptive TEs are present in sub-Saharan Africa. We investigated whether the identified 13 TEs are truly adaptive by searching for signatures of a partial selective sweep in the regions flanking the TEs. We sequenced the flanking regions in four out of the 13 insertions. Two of these four TEs, a Bari1 element (FBti0018880) and a pogo element (FBti0019627) (Table 1), are present in 93% of the assayed NA strains. We also sequenced two TEs present at lower frequencies: another pogo element (FBti0019065) and an F element (FBti0019170). These insertions were found in 42% and 35% of the assayed NA strains, respectively (Table 1). First, we performed individual strain PCRs for the three additional AF populations to estimate the frequency of these four TEs in the AF populations. FBti0018880 is absent in all the tested strains, and the three other insertions, FBti0019065, FBti0019170, and FBti0019627, are present at low frequencies (3% to 10%) in the tested AF populations (Table 2). Therefore, we are confident that they have increased in frequency either during or after the expansion out of Africa. Figures 2 through 5 show the sequencing data for the flanking regions around these four TEs. The sequences from the strains with and without the TEs are separated by a black line, and the filled-in box indicates the position where the TE is inserted. A summary of the sequencing data is given in Table 3. FBti0019627 was present in all but three of the assayed NA strains (Table 1). We sequenced the three strains that did not contain the element: Wi98, We4, and We47. In two cases, We4 and We47, we found evidence for an independent excision event of the TE. Both strains contain the two-nucleotide target site duplication (TA) and two nucleotides that belong to the TE. We only consider a strain not to have the insertion if it does not show any evidence of excision. Therefore, we excluded these two sequences from the subsequent analysis. As can be seen in Figures 2 through 5, similar polymorphism patterns are found around all four analyzed TEs. The strains with the TE show a reduced amount of polymorphism and fewer haplotypes compared to the strains without the insertion. These observations are consistent with the expectations of a selective sweep. However, the recent bottleneck likely experienced by the NA strains [42–45] can produce patterns on DNA sequence variation that mimic signatures of positive selection in a population of constant size [8,48–50,60,61]. Specifically, a search for the D. melanogaster TEs (or any polymorphisms) that are rare in the ancestral AF population and are common in the derived NA populations is expected to bias the results toward finding patterns resembling those of partial selective sweeps [50]. Macpherson et al. [50] employed coalescent simulations to explore how ascertainment biases, demography, purifying selection against the TE, and suppression of recombination caused by the TE affect the interpretation of polymorphism data. They analyzed the flanking sequences of five TEs. One TE belongs to our set of 13 putatively adaptive TEs—it is a Doc element (FBti0019430) that is quite likely to be adaptive as it disrupts a conserved gene and is linked to resistance to organophosphate and carbamate pesticides [38] (Y. T. Aminetzach, T. Karasov, and D. A. Petrov, unpublished data). The other four are BS elements: FBti0018879, FBti0019133, FBti0019410, and FBti0019604. These four BS elements belong to our set of eight putatively neutral TEs (Table 1). Macpherson et al [50] showed that the null model of neutrality and constant population size was strongly rejected for all five datasets. However, when the null models included the demographic scenarios specified in Thornton and Andolfatto [45] or Li and Stephan [44], only the presumably adaptive TE insertion (FBti0019430 [38]) showed signatures of positive selection. Incorporating purifying selection and recombination suppression to the null model strengthens this result although it did not change the conclusions qualitatively [50]. In view of these results, we decided to explore whether the haplotype configuration of the four insertions sequenced in this work depart from neutrality by considering a null model that incorporates the bottleneck scenario specified in Thornton and Andolfatto [45] and ascertainment of a derived polymorphism at a prespecified frequency matching that found in the data (see Materials and Methods). We estimated several statistical measures of polymorphism and compared them with the distributions obtained by simulation under this null model (Table 4 and Table S3). The integrated haplotype score (iHS) statistic is expected to be the most powerful indicator of a partial selective sweep [8]. We also estimated the proportion of nucleotide diversity within the haplotypes linked to the TE relative to the total nucleotide diversity in the sample, fTE = πTE/ (πTE + πnon−TE). Table 4 shows iHS and fTE statistics both for the four elements sequenced in this work and for the five other elements studied previously [50]. In all five putatively adaptive cases, we found significant departures from neutrality. The fTE values observed for the elements FBti0019065 and FBti0019170 are seven and four standard deviations away from the expected values, respectively. The iHS statistic for these two TEs was not significant potentially because only NA strains were sequenced [62]. For the other two TEs, FBti0018880 and FBti0019627, the iHS statistic showed significant deviations in the direction expected under a partial selective sweep. The observed values were five and four deviations away from the expectation, respectively. These results demonstrate that all five investigated putatively adaptive TEs show stronger signatures of positive selection than four investigated putatively neutral TEs. This suggests that the rest of the 13 insertions might be highly enriched for adaptive TEs as well. The sequencing allowed us to determine whether the four newly sequenced TEs were the causative agents of the sweeps rather than being passively linked to such causative mutations. In all four cases, the TE was located in the center of the apparent sweep, with the haplotype structure decaying on both sides. It is theoretically possible that the TE is in perfect linkage with a causative polymorphism located in the immediate vicinity of each TE. However, we did not find any such polymorphism in any of the four datasets. The age of a partial selective sweep can be estimated by measuring the extent to which linkage disequilibrium decays at a known distance from the presumed focal site of adaptation [63]. For all four studied TEs, we sequenced 500-bp regions at approximately 10 kb away from each TE in several strains with and without the insertion (see Materials and Methods). We used the method of Slatkin and Rannala [63] to estimate that the partial selective sweeps associated with the elements FBti0019065 and FBti0019627 are approximately 0 to 500 y old, and those associated with the elements FBti0018880 and FBti0019170 are approximately 0 to 800 y old. Although being rough estimates of the age of the alleles, they agree with the scenario in which these partial selective sweeps have taken place after the out-of-Africa expansion of D. melanogaster. Testing for the presence or absence of these TEs in the M strains can also yield insight about the time of the spread of the TEs in the NA population. M strains are old laboratory stocks that were established before the 1940s and can be molecularly defined by the absence of the P elements in their genome [64]. Therefore, TEs found at a high frequency in the modern D. melanogaster populations, but which are absent or rare in the M strains, most likely have increased in frequency in the last 70 y. We checked the frequency of the 13 putatively adaptive TEs in ten M strains originally sampled from around the world (see Materials and Methods). All 13 TEs are present in the M strains at frequencies comparable to those found in the recently sampled NA strains (Table 1). Thus, there is no evidence of very recent expansions of these TEs. We have also investigated the M strain frequency for all 38 TEs present in the initial list of putatively adaptive TEs (Table 1). Only two TEs, FBti0020042 and FBti0019418, are absent in all of the M strains assayed (Table 1). However, these two TEs are present at low frequencies in the modern NA strains, suggesting that all the TEs in the list of possibly adaptive TEs have reached their current frequencies prior to the 1940′s. The results of the haplotype tests described above are suggestive of positive selection. However, they should not be taken as conclusive evidence for selection since the true demographic model for D. melanogaster is unknown. Moreover, the frequency of the TE in the ancestral population and the extent of recombination suppression in heterozygotes due to the presence of the TE are also unknown. We know that the variation of these parameters might affect the distribution of tested statistics under neutrality and thus affect our inference of positive selection [50]. Consequently, we decided to perform an additional, independent test of the adaptive role of these elements—whether the frequencies of these TEs are higher in more temperate compared to more tropical out-of-Africa populations of D. melanogaster. Such a pattern would be expected if these TEs provide adaptive benefits in the temperate, but not in the tropical, habitats. We analyzed the frequency of the 21 TEs, including the 13 putatively adaptive and the eight putatively neutral TEs, in 44 strains from two Eastern Australian populations. These two populations are located close to the ends of a latitudinal cline along the Eastern coast of Australia [65]. The Northern population is located near Innisfail, Queensland, where the climate is similar to that of the likely ancestral, sub-Saharan African habitat. The Southern population is located at the Yering Station, Victoria, and has a much colder, less tropical climate characteristic of the more temperate out-of-Africa–derived habitats. Note that D. melanogaster likely colonized Australia only 100 y ago, most likely through a single northern invasion [65], and that the Australian population had not been used by us for the identification of the 13 putatively adaptive TEs. Thus, the differentiation of the TE frequencies across these two populations would serve as an independent test of adaptation both in the historical and the experimental sense. We used a maximum likelihood procedure to estimate the frequencies of the TEs in the two Australian populations (see Materials and Methods). The set of 13 adaptive TEs shows significant heterogeneity of frequencies between these two populations (p < 0. 0001), whereas the set of eight putatively neutral TEs does not show such heterogeneity (p = 0. 19). Moreover, only one of the eight putatively neutral elements showed heterogeneity in its population frequency (p = 0. 009), whereas eight out of the 13 elements in the putatively adaptive set showed such heterogeneity (p < 0. 05) (Figure 6; Table S4). We tested whether there is significantly more differentiation for the putatively adaptive TEs compared to the putatively neutral TEs, and we found that indeed this is the case (p = 0. 023, G-test with Yates correction for continuity). In all nine instances of significant heterogeneity, the TE frequency was higher in the temperate Southern population compared to the tropical Northern population, consistent with our expectations. Three of the eight putatively adaptive TEs and the one putatively neutral TE that showed population differentiation are located inside the cosmopolitan chromosomal inversion In (3L) Payne or In (3R) Payne (Table S4). Both inversions show latitudinal patterns in Australian populations (see Hoffmann and Weeks for a review [65]). Only one of these two inversions, In (3L) Payne, has been characterized at the molecular level and therefore can be scored by PCR (see Materials and Methods). We checked for the presence of this inversion in the 44 strains analyzed in this work and found that it is only present in one strain. It so happens that the two putatively adaptive TEs that are located inside this inversion (FBti0020091 and FBti0020119) failed to be amplified in this particular strain. Therefore, we can conclude that the presence of In (3L) Payne is not affecting our results. For the other two TEs that showed population differentiation and are included in In (3R) Payne, we cannot discard the potential confounding effects of In (3R) Payne on their population frequency. The exclusion of these two TEs does not affect the significance of the comparison of the putatively adaptive and putatively neutral TEs, however. There is still significantly more differentiation for the putatively adaptive TEs (p = 0. 014, G-test with Yates correction for continuity). Note also that the TEs showing heterogeneity are distributed across all three major chromosomes and are unlinked with each other, suggesting that these patterns are independent cases of adaptive differentiation between these two populations (Table S4). The 13 TEs included in our list of putatively adaptive insertions belong to eight different families from all three major classes of TEs: long terminal repeat (LTR) retrotransposable elements (one family), long interspersed nucleotide element (LINE) -like retroposons (four families), and DNA transposons with terminal inverted repeats (TIR) (three families) (Table 1). Some numerous families such as roo or jockey are not represented, whereas other families like pogo, Doc, or S-element contribute more than one element [51] (Table 1). LTR elements, the most abundant class of elements in the genome [51], are significantly underrepresented in our set (p = 0. 006). The 13 putatively adaptive TEs are evenly distributed among the chromosomal arms (p = 0. 57). We wanted to test whether the 13 putatively adaptive TEs (the “adaptive” set) are peculiar in any way compared to the putatively nonadaptive TEs (the “nonadaptive” set) within the same families that are also found in regions of high recombination (95 TEs total). Specifically, we focused on three properties: size, distance to the closest flanking gene, and functional properties of the flanking genes. We compared the size of the TEs in the adaptive and nonadaptive sets to the canonical size of their families [51]. We classified the elements into three categories: near full length (>90% of the canonical length), medium length (20%–90% of canonical length), and small (<20% of the canonical length) (Table S1). TEs in the adaptive set are not significantly different in size from the TEs in the nonadaptive set (χ2 = 0. 5; p = 0. 778). Most of the known cis-regulatory sequences in Drosophila are located within 1 kb of the transcriptional start site [66]. Taking this into account, we classified the TEs into three categories in relation to their distance to the nearest gene: inside genes, located less than 1 kb from a gene, and located more than 1 kb from a gene (Table S1). Again, we failed to detect any differences in the distribution of these distances for the TEs in the adaptive and nonadaptive sets (χ2 = 0. 75; p = 0. 687). Finally, we analyzed the functional association of the genes found next to the TEs in our two sets using the Gene Ontology (GO) database. Some of the genes next to the adaptive TEs are associated with more than one GO term, a representative list of which is given in Table 2. Ten of the 13 genes have a GO term for the biological process and/or the molecular function categories. For example, three of them are associated with genes involved in response to stimulus: FBti0018880, FBti0019386, and FBti0020119 (Table 2). We used FatiGO+ [67] to search for terms that are significantly over- or underrepresented in the adaptive set compare to the nonadaptive set. The biological process term “response to stimulus” appears overrepresented in the set of genes associated with the putatively adaptive TEs (p = 0. 003). However, the false discovery rate–adjusted p-value is above 0. 05. In conclusion, none of the terms in the molecular function or cellular component categories is over- or underrepresented in the adaptive set. It is possible that the failure of FatiGO+ to find significant differences between the two sets of genes is at least partly due to the small size of the adaptive TE set further compounded by the sparse functional and molecular annotations of the neighboring genes in both sets. Finally, for the 13 putatively adaptive insertions, we searched for expressed sequence tags (ESTs) containing both the TE and the gene they are associated with in the Ensembl database [68]. Only insertions FBti0019430 and FBti0019627 form chimeric transcripts with genes CHKov1 and Kmn1, respectively, supported by EST evidence. For the rest of the TEs, we did detect ESTs that contain part of or the whole TE sequence, but none of these ESTs also contain genic sequence. We found no ESTs for FBti0018880. Twelve out of 13 adaptive TEs are located outside of coding regions, in many cases in close proximity to genes or inside introns. This suggests that the adaptive effects of these TEs are likely due to their effects on gene regulation. To confirm this inference, we analyzed the expression of the 12 genes located closest to the putatively adaptive TEs (Table 2). The 13th TE (FBti0019430) is inserted into the exon of CHKov1 and had been previously shown to truncate the original CHKov1 protein and to generate a new functional protein [38]. For each tested gene, we searched for differences in expression between the allele carrying the TE and the allele lacking the TE in the F1 heterozygous adults. Differential expression of the two alleles in the same cellular environment of the F1 individual is indicative of functional cis-regulatory differences [69]. For each TE, we identified two highly inbred NA strains that both differ by the presence/absence of this TE and by the presence/absence of a diagnostic SNP in the coding region of the adjacent gene. The exact procedure is described in Materials and Methods. Pyrosequencing was then used to measure the relative abundance of the two alleles [69]. This technique has been demonstrated to be a sensitive tool to quantify allele-specific expression, enabling discrimination of subtle differences in transcript abundance [70]. We obtained data for five genes (Figure 7; Table S5). Four of them showed differences in expression between the two alleles: in three cases, the allele of the nearby gene that carries the TE in cis is down-regulated, and in one case, it is up-regulated. As can be seen in Figure 7, for some genes, the results depend on the direction of the cross, suggesting that there could be a parental effect on the regulation of the genes close to the TE. These results further suggest that the majority of the recently adaptive TEs in D. melanogaster have an effect on the expression of the adjacent genes. The spread of D. melanogaster out of sub-Saharan Africa within the last 10 to 20 thousand years ago exposed D. melanogaster to new ecological and physiological challenges. These new challenges likely led to adaptive genetic changes in the non-African populations of D. melanogaster. In this study, we set out to search for such recent adaptations driven specifically by insertions of TEs. We started our search from a set of 1,572 individual insertions annotated in Release 3 of the D. melanogaster genome [51]. Starting from a set of TEs found in the sequenced genome biases our ascertainment toward preferentially finding TEs present at higher population frequencies. All things being equal, we are bound to find every fixed TE; on average, 50% of TEs present at 50% frequency in the population; 10% of TEs at 10% frequency; and so on. However, in addition, the choice of the sequenced strain introduces its own bias. The sequenced strain of D. melanogaster (y1; cn1 bw1 sp1) is an old laboratory strain likely to have been isolated from the wild at the beginning of the 20th century in the United States [71]. Therefore, our ascertainment is biased toward finding TEs that were frequent in NA populations at the beginning of the century. These biases are in many ways helpful for the search of the TEs that contributed to the out-of-Africa adaptation of D. melanogaster. Indeed, such TEs should be frequent in the NA populations and would be discovered at a reasonable chance using our procedure. We would miss all of the very recent adaptations, however. We obtained population frequency data for 902 TEs both in NA and AF populations and found that most of these 902 TEs are present at low population frequencies. These results confirm previous findings based on the analysis of individual families suggesting that in Drosophila, TEs are under purifying selection [29,30]. Based exclusively on their population frequencies, we identified 13 TEs in the highly recombining regions of the D. melanogaster genome that could plausibly play a role in the out-of-Africa adaptation (Table 2). Note that we define high-recombination areas as those where recombination rate is greater than 1. 4 cM/Mbp. However, our results are not very sensitive to the exact value of the cutoff. Varying the cutoff between 1 and 2 cM/Mbp changes the number of putatively adaptive TEs from 13 to 11. These 13 TEs are segregating at high frequencies (>30%) in North America and at low frequencies (<30%) in Malawi. They also belong to the TE families that appear to be evolving under purifying selection in general, making it less likely that these 13 TEs rose to high frequencies by genetic drift alone. We also identified eight TEs that are also frequent in North America and rare in Africa but which belong to TE families that are under reduced purifying selection. This makes these eight TEs more likely to be neutral. We use them as a control set of putatively neutral TEs in our population genetic analyses. To start testing whether at least some of these 13 TEs are in fact adaptive, we sequenced the flanking regions of four of them (Figures 2–5; Table 2). We used coalescent simulations to test whether the nucleotide polymorphism pattern surrounding these four TE insertions depart significantly from a null model that incorporates the demographic scenario specified in Thornton and Andolfatto [45] and ascertainment of a derived polymorphism at a prespecified frequency matching that found in the data [50]. Another putatively adaptive TE (FBti0019430) had been sequenced previously along with four out of the eight neutral TEs we have identified here [50]. We did find significant departures from neutrality in the direction expected under a partial selective sweep for all five tested putatively adaptive TEs, but not for four putatively neutral TEs (Table 4). This provides highly suggestive evidence for the role of positive selection in the increase of frequency of the majority of the putatively adaptive TEs. Different statistics were significant for the different TEs: fTE was significant for FBti0019065 and FBti0019170, and iHS was significant for FBti0018880 and FBti0019627. Both fTE and iHS are significant for FBti0019430. The observation that not all of the tests are significant for each TE is not entirely unexpected given that these TEs were already present in Africa prior to the putative partial sweeps. Simulations have previously demonstrated that positive selection from standing variation may not leave as strong a signature in the patterns of linked polymorphisms as positive selection acting on de novo mutations [62,72,73]. We used the sequencing data to also assess whether these TEs are likely to be causative adaptive mutations or whether they just happen to hitchhike to high frequencies with linked adaptive mutations. In all five studied cases, (1) the TE appears to be completely linked to the partial sweep, (2) the partial sweep decays on both sides of the TE, and (3) there are no polymorphism other than the TE itself that are in perfect linkage disequilibrium with the partial sweep. These results suggest strongly that the TE is likely to be the causative mutation of the partial sweep rather than to be merely linked to such a causative mutation. The signatures of selective sweeps in the regions flanking the putatively adaptive TEs provide some evidence for their adaptive increase in frequency but should be treated with caution. The uncertainty about the starting frequency of the TE in the ancestral population and about the appropriate demographic model makes it difficult, if not impossible, to come with very robust neutral expectations about the distributions of tested statistics [50]. We therefore performed an additional independent test of the adaptive role of these elements. These 13 TEs are expected to be adaptive in the environments characteristic of the out-of-Africa expansion, but not adaptive in Africa. We thus expect these TEs to be less frequent in the out-of-Africa populations located in the more tropical regions compared to the populations located in the more temperate regions. To test this prediction, we analyzed the frequencies of the 13 putatively adaptive and eight putatively neutral TEs in two populations located close to the ends of a latitudinal cline in the Eastern coast of Australia. The Northern populations experience tropical climates, whereas the Southern ones experience more temperate ones. Consistent with our predictions, eight of 13 putatively adaptive TEs are significantly more frequent in the Southern population, whereas only one of eight neutral ones shows such differentiation (Figure 6; Table S4). We also ensured that these patterns are not due to the linkage of these TEs to inversions that show clinal patterns of variation along the Eastern coast of Australia [65] or to each other. Note that because D. melanogaster colonized Australia less than 100 years ago and because we did not use the Australian population data in defining the set of adaptive and neutral TEs, these results provide a powerful independent test of adaptive significance of the 13 identified TEs. The evidence of the partial selective sweeps due to all five putatively adaptive TEs tested combined with the population heterogeneity between tropical and temperate habitats for eight out of 13 putatively adaptive TEs indicate very strongly that most, if not all, of the identified 13 TEs play adaptive roles in the out-of-Africa D. melanogaster population. The analysis of the location of the 13 recent adaptive insertions identified in this work gives insight into the relative contribution of protein-coding versus regulatory changes in adaptation [74,75]. Most of the TEs in our set are located in introns or intergenic regions (eight and three TEs, respectively), whereas only two are located in the mature transcripts: one within an exon (FBti0019430) and one in a 3′ UTR (FBti0019627; Table 2). This distribution suggests that recent adaptive insertions are mostly involved in regulatory changes. To further explore this possibility, we analyzed the expression of the genes located next to the adaptive insertions. Changes in gene expression can arise from cis-regulatory changes that affect transcription and/or transcript stability in an allele-specific manner, or from trans-regulatory changes that influence expression of both alleles [69,76]. We searched for cis-regulatory differences by comparing the relative abundance of transcripts in F1 hybrids in which one allele contains the TE in cis and the other one does not. In four out of the five genes for which we obtained results, we showed that the expression of the allele carrying the TE in cis is significantly different from the expression of the allele lacking the TE (Figure 7). In three cases, the expression was down-regulated, and in one case, it was up-regulated. These results support the role of the adaptive TEs in the regulation of the adjacent genes and agree with the analysis of chimeric gene–TE proteins in the human genome, suggesting that the role of young TEs is probably most often limited to regulatory functions [23]. The analysis of the types of genes associated with the 13 adaptive insertions might provide an insight into the type of biological processes that have been targets of selection in the expansion of D. melanogaster out of Africa (Table 2). Three of the genes associated with our set of adaptive insertions are involved in processes grouped under the GO category “response to stimulus”: Ago2, sra, and Jheh3 (Table 2). This category has been previously associated with genes under positive selection in Drosophila [33,38,77,78]. Another three genes, kuz, rdx, and Jon65Aiv, are associated with protein metabolism. An overrepresentation of genes associated with protein metabolism has been found in the analysis of genes likely to be under positive selection after the expansion of D. simulans out of Africa [79]. However, there is no overlap between the two gene datasets, suggesting that the same type of biological processes, but not exactly the same genes, have been the target of selection in the expansion of both D. melanogaster and D. simulans out of Africa. For some of the genes that do not have a GO annotation, there is additional information that suggests the biological processes in which they might be involved. For example, CG34353 has been described as an immunoglobulin-like (Ig-like) gene [80]. Many of the proteins in the Ig-like family are cell surface or secreted proteins that have important roles during development. Such genes have been previously shown to exhibit signatures of positive selection [81,82]. Some of the adaptive TEs are located close to or inside genes belonging to highly conserved pathways. Such insertions are likely to be involved in the fine-tuning of these processes. For example, FBti0018880 is inserted in the 0. 7-kb intergenic region between Jheh2 and Jheh3 genes and down-regulates at least one of them (Jheh3, Figure 7). Both of these genes are involved in Juvenile Hormone (JH) metabolism [83]. This hormone has major effects on various aspects of development and life history, not only in Drosophila, but also in other insects [84]. FBti0019170 is inserted in the intron of kuz, a gene in the Notch (N) signaling pathway, and up-regulates it (Figure 7). N is a transmembrane receptor that mediates local cell–cell communication and coordinates a signaling cascade present in all animal species studied to date [85]. Finally, FBti0019372 is inserted in the first intron of rdx, a gene involved in the Hedgehog (Hh) signal transduction pathway [86] and down-regulates it. Hh plays essential roles in a multitude of developmental processes via a complex signaling cascade conserved from insects to mammals [87]. Overall, there is no clear overriding pattern in the types of genes that are located near the adaptive TEs. It is possible that the number of adaptive TEs is too small or our understanding of the functional role of many genes is too limited to see this pattern. Future investigation of the functional effects of the adaptive TEs will be required to understand the phenotypic and ecological nature of adaptation due to these TEs. Adaptive mutations can arise in two different ways. On the one hand, adaptation can start out as a new mutation that is favored as soon as it arises. Most of the searches for recent adaptations are guided by this model of positive selection [88–90]. However, this assumption may not be realistic, especially if adaptation takes place in response to range expansions. Environmental changes associated with range expansions can lead to previously neutral or slightly deleterious alleles that were segregating in the ancestral population to become beneficial [48,72,73]. This seems to be the scenario for the majority of the 13 adaptive TEs described here. We found that the majority of them were already present in the ancestral AF populations (Table 2). Only two out of 13 putatively adaptive TEs were absent from all four sub-Saharan AF populations, suggesting that the majority of recent TE-induced adaptations in D. melanogaster came from standing variation. Furthermore, all 13 adaptive TEs are very similar in their sequence from the other TEs in their families (divergence less than 1%), suggesting that these 13 TEs inserted into the genome very recently (Table 1) and therefore are unlikely to have been subject to long-term balancing selection. Hence, it appears that these TEs were either neutral or slightly deleterious in the ancestral African population and became adaptive upon the expansion of D. melanogaster into temperate habitats. The goal of this study was to identify recent TE insertions highly likely to be adaptive in the recent evolutionary past of D. melanogaster. We followed a conservative approach that undoubtedly led us to miss some adaptive insertions. For example, since we focused on recent insertions likely to have contributed to adaptation during or after the expansion of D. melanogaster out of Africa, we ignored all TEs present at high frequencies in the Malawi population. These insertions are less likely to have increased in frequency specifically in the out-of-Africa populations. However, some of these TEs may still have contributed to adaptation in the out-of-Africa populations. For example, all parallel TE-induced adaptations in the African and out-of-Africa populations will be missed by this approach. We will also miss all of the TEs that contributed to adaptation prior to the expansion of D. melanogaster out of Africa. There are 114 insertions that appear fixed in all of the analyzed populations; 25 of them are located in regions of high recombination and therefore are more likely to be enriched for adaptive insertions (Table S1). We also did not consider insertions present at high frequencies in genomic regions characterized by low recombination rates since they are more likely than those found in high-recombination areas to be neutral [55–58,91]. However, some of these insertions, particularly the ones that are present at high frequencies in the NA populations and absent in the AF populations, are also likely to be adaptive. There are 15 such insertions; nine of them are most probably not adaptive since they belong to families under relaxed purifying selection (D. A. Petrov, J. González, M. Lipatov, A. S. Fiston-Lavier, and K. Lenkov, unpublished data), but the other six TEs might be adaptive (Table S1). All of these TEs deserve further study. As stated before, the starting point of our search for adaptive TEs were the insertions described in one D. melanogaster strain that was probably collected at the beginning of the last century in North America. This ascertainment bias implies that we are undercounting some, especially less frequent TEs. Given the frequency distribution of the 13 putatively adaptive TEs and the PCR failure rate, we can estimate that the NA populations at the beginning of the century had approximately 25 adaptive TEs in the high-recombination regions of the genome. If we suppose that the rate of adaptation is the same in low- and high-recombination regions, then as many as 50 TE insertions anywhere in euchromatin have been adaptive since the out-of-Africa migration of D. melanogaster. Note also that we are missing all TEs that may have contributed to adaptation in other out-of-Africa populations but were rare in NA at the beginning of the century. For instance, the TEs that increased in frequency after the collection of the sequenced strain are bound to be missed by this study. As a case in point, we did not sample the insertion of an Accord TE previously found to confer resistance to insecticides because it is not present in the sequenced genome [33] and is rare in the M strains in general (Y. T. Aminetzach, T. Karasov, and D. A. Petrov, unpublished data). Thus, the list of 13 putatively adaptive TEs is likely an underestimate of all adaptive TEs. We can consider that at least 13, and more likely 25–50, adaptive TEs have increased in frequency in the NA populations since the expansion of Drosophila out of Africa approximately 10,000 to 16,000 y ago [44,45] and before the collection of the sequenced strain (∼70 y ago). This corresponds to one adaptive TE increasing to intermediate frequencies in the D. melanogaster euchromatin every 200 to 1,250 y. If all of the identified TEs are destined to reach fixation and the rate of adaptation was similarly high prior to the expansion of D. melanogaster out of Africa, then this rate appears incompatible with the number of fixed TEs in the D. melanogaster genome. Indeed, even if we conservatively estimate that we should only be able to detect TEs fixed within the past approximately 1 million years (Myr) (corresponding to the expected neutral divergence of ∼3%), we should see 800 to 2,500 fixed TEs in euchromatic regions of high recombination and up to 5,000 TEs in euchromatin in general. This assumption is conservative, given that all TEs less than 10% divergent from its consensus sequence are expected to be found and the average time to loss of 50% of the DNA in Drosophila is substantially greater than 1 Myr [92]. In contrast to this large expected number of fixed TEs, only 25 fixed insertions in high-recombination regions of the genome, and 114 in total, have been detected. There are at least three distinct, but not mutually exclusive, scenarios that would explain why we see so few fixed TEs in the D. melanogaster genome. First, it is possible that the rate of adaptation is not constant. The rate that we estimated could be reflecting a burst in adaptations that took place during the expansion of D. melanogaster out of Africa. A higher rate of adaptive evolution in the derived populations compared to the African populations could be expected and in fact has been suggested by previous studies [44]. Second, it is likely that these TEs are adaptive in some, but not other, environments. Supporting this, we found that eight of them appear to be adaptive to temperate climates (Figure 6). Moreover, we did not find any TE fixed in the NA populations of D. melanogaster and polymorphic or absent in AF. Our estimates of the frequencies of these 13 TEs in the M strains also show that the current frequencies have been stable for the last 70 y (∼700 generations) (Table 1). If this is the explanation for the observed low number of fixed TEs, then our results suggest that the majority of local adaptations are destined to be lost. Such local adaptations might be common for other, non–TE-derived recent adaptations and, similar to the TE-derived adaptations, they might be ephemeral. Finally, we might be underestimating the number of fixed insertions in the genome if the adaptive TEs undergo faster sequence divergence compared to the neutral TEs. This is not entirely far-fetched as newly adaptive TEs might undergo a bout of fast sequence changes driven by positive selection. If many of these adaptive substitutions are indels, then the sequence of the TEs might quickly become obscured. A more sensitive search for degenerate TE sequences in the D. melanogaster genome might be productive in this case. Our estimate of the rate of TE-induced adaptations, one every 200–1,250 y or one every 2,000–25,000 generations, suggests that, at least since the expansion of D. melanogaster out of Africa, TEs have contributed considerably to adaptive evolution. Several recent studies based on the analysis of both coding [2,3, 9] and noncoding regions [5] suggest that the genomic rate of adaptive evolution is high. For example, Smith and Eyre-Walker [2] estimated that approximately 45% of amino acid substitutions in Drosophila were driven by positive selection, which translates into one adaptive substitution every 450–900 generations. This rate is even higher, approximately one adaptation every 70–520 generations when only the noncoding regions of the genome are considered [5]. The above estimates focus on adaptations that fix in the genome. Using a different approach, based on the spatial correspondence between neutral polymorphism and nonsynonymous divergence, Macpherson et al [12] also argued for a high rate of adaptive substitution. These authors estimated that approximately one adaptation every 3,000 generations is taking place in the Drosophila species. The rate of TE-induced adaptation is of the same order of magnitude and thus might be a significant source of adaptive mutations in Drosophila. The high rate of adaptation estimated in these various studies is surprising. In order to increase our confidence in these estimates and to understand the nature of adaptation, it is clearly important to connect putatively adaptive mutations to their phenotypic effects. The adaptive TE insertions that we identified in this study represent a promising set for such functional analyses. Again, most of the adaptive insertions identified in this paper are closely linked to genes of at least partly known functional roles. For example, insertion FBti0018880 is likely to affect the expression of genes involved in the degradation of JH. JH affects a significant number of processes and traits in Drosophila development and life history, including metamorphosis, behavior, reproduction, diapause, stress resistance, and aging [84]. Any of these processes could have been affected by the insertion of this TE in this particular region of the genome. They are therefore likely candidates to be analyzed in order to assess the functional consequences of the insertion. For the insertions closely located to genes with no functional information, components of fitness such as male and female fertility, survival rates through development or temperature and desiccation resistance can be studied. A systematic identification of adaptive insertions described in this work allows us to infer that TEs are a considerable source of recently adaptive mutations in the Drosophila genome. Most of the adaptive TEs are located close, but not inside, protein coding regions of genes and appear to affect the expression of these genes. Functionally diverse genes located next to the putatively adaptive TEs provide a rich collection for a follow-up investigation of adaptive processes in D. melanogaster. The adaptive TE insertions appear to have been present in Africa as neutral or deleterious polymorphisms prior to the expansion of D. melanogaster out of Africa and are only adaptive in some, specifically temperate environments. The high rate of recent adaptive changes due to TEs appears to be incompatible with a low number of fixed TEs in the D. melanogaster euchromatin. This most likely indicates that most locally adaptive TEs are destined to be lost over long periods of time. It is tempting to speculate that such local adaptations (1) are common for other types of mutations as well and (2) tend to be ephemeral and lost fairly quickly in general. This would imply that genetic variation within species might often be due to different mutations than that between species. Thus, the extent to which functional genetic variation within species is ephemeral rather than “a phase in molecular evolution” [93] remains to be determined. DNA from five different NA populations (8–12 strains per population; 64 strains in total) and one AF population collected in Malawi (11 strains) were combined into seven different pools. Six pools contained DNA from the NA populations, and one pool contained DNA from the AF population. The composition and the geographical origin of each pool is given in Table S6. Strains from the Wi pool were subject to over 30 generations of brother–sister matings. Strains from the We1 and We2 pools were subject to 10–15 generations of brother–sister matings. Strains from NA, NB, and CSW pools are isofemale strains. The final concentration of DNA in each pool was 2. 5 ng of each individual strain per PCR reaction. Genomic DNA from all these strains was extracted using DNeasy Tissue kit (Qiagen). The absent/polymorphic/fixed status of each TE in all the pools was determined using the polymerase chain reaction (PCR). All PCR primers were designed using Primer 3 [94] and were checked with Virtual PCR [95]. One set of primers was intended to assay for the presence of the TE insertion and consists of a “Left” (L) primer which lay within the TE sequence and a “Right” (R) primer that lay in the flanking region to the right of the TE insertion. We expect this PCR to give a band only when the element is present. The other set of primers was intended to assay for the absence of the TE insertion and consisted of a “Flank” (FL) primer which lay in the left flanking region of the TE sequence and the R primer mentioned above. In this case, the absence of a TE in the pool should give a shorter, “absence” band, and the presence of a TE should give a longer, “presence” band. We assumed that the presence band is unlikely to be amplified if the TE sequence is longer than 800 bp. For the insertions that overlap with another TE, specific R or FL primers could often not be designed, and therefore, the frequency of such TEs was not assayed. PCR reaction mix was made using Redtaq Readymix from Sigma Aldrich and primers at a final concentration of 1 μM/μl. The PCR conditions were: 94 °C for 5 s, 27 cycles of 94 °C for 30 s, 62 °C for 30 s, and 72 °C for 1 min. We classified an element as absent when the L-R primer pair did not yield a band, and the FL-R primer pair yielded an absence band only. We classified an element as polymorphic if the combined L-R and FL-R primer pairs produced both a presence and an absence band. Finally, we classified an element as fixed if the L-R primers yielded a presence band and the FL-R primers yielded either a presence band or no band at all (if the element is longer than 800 bp, the FL-R primers were not expected to amplify the presence band). For the TEs shorter than 800 bp, the failure of FL-R primer was interpreted as PCR failure and the PCR results as ambiguous. Here, we only analyzed in detail those TEs for which both primer pairs gave a mutually consistent result. The same two sets of primers described above were used to detect both the presence and the absence of a subset of the TEs in each individual strain present in the different pools (Table S6). Besides the above strains, for some of the insertions, three additional AF pools were assayed. Two of the pools contained strains collected in Zimbabwe, and the other one contained strains collected in Kenya. The composition of these three pools is also given in Table S6. In addition, we used ten M strains from Bloomington Drosophila Stock Center at Indiana University that were collected worldwide: Canton-S, Oregon-R-C, Oregon-R-S, Amherst 3, Lausanne-S, Samarkand, Swedish-C, ORiso-2, CSiso-2, and Berlin-K. First, we confirmed that these were truly M strains by checking for the presence/ absence of P elements. As a positive control, we used a classic P strain (Harwich stock, also from Bloomington Drosophila Stock Center at Indiana University). An inverted repeat–specific primer of the D. melanogaster canonical P element was used [96]. Amplification consisted of a first step of 7 min at 94 °C and then 30 cycles of 45 s at 94 °C, 45 s at 57 °C, and 1. 5 min at 72 °C. A final extension step at 72 °C for 10 min was carried out. Finally, we also checked the frequency of a subset of the TEs in two Australian populations collected in 2007 close to the ends of a latitudinal cline: Innisfail in far North Queensland, and Yering Station in South Victoria. For each population, a total of 22 stocks were analyzed (Table S6). For these 44 stocks, we also checked for the presence of inversion In (3L) Payne. Primers were designed in the region spanning the distal breakpoint of this inversion [97]. Primer pair 5′-CCGGATGGACCACATAGAAC-3′ and 5′-CATTCTGGGCCTTATCATCT-3′ amplify the standard, but not the inverted, chromosome. Primer pair 5′-CCGCAAACGAACACTTA-3′ and 5′-GATTATGGACCTAATGAAAGC-3′ amplify the inverted, but not the standard, chromosome. For all the individual strain PCRs, the following conditions were used: 94 °C for 2 min, 13 cycles of 94 °C for 30 s, 63 °C for 30 s (−0. 5 °C per cycle), 72 °C for 1 min, and then 20 cycles of 94 °C for 30 s, 56 °C for 45 s, 72 °C for 1 min, and one last extension step of 10 min at 72 °C. For each of the elements for which we obtained individual strain frequency data, levels of divergence from their consensus sequence (available at http: //flybase. bio. indiana. edu) were estimated. Sequences were aligned using Sequencher software (v. 4. 7; Gene Codes Corporation). The minimum size of the aligned regions was 180 bp. We considered a TE to be old if its divergence from the consensus sequence is greater than 1%. However, it could also be that these apparently old insertions are recent insertions generated by active TEs whose sequence differs from the consensus sequence of the family. To test for this possibility, we aligned the insertions showing greater than 1% divergence from the consensus sequence to the rest of sequences that belong to the same families. To detect the existence of copies closely related to our insertions that have not been previously identified, we performed BLAST queries against the whole D. melanogaster genome with the sequence of these apparently old insertions. We calculated pairwise distance using Mega 3. 1 [98] and identified the sequence most closely related to our insertion. We then estimated the percentage of divergence between those two sequences. For each group of TEs, we performed nested likelihood analysis, following the work of Petrov et al. [35]. Assuming that all TEs within a family are subject to uniform selection with a selection coefficient s along with a heterozygous effect h = 1/2, and given the size of the D. melanogaster population N, we can calculate the sojourn time of each new insertion at any given frequency x—i. e. , the time this insertion is expected to spend in a short interval between x and x + Δx. To do so, we made use of a diffusion approximation and the resulting sojourn time density function (equations 4. 22 and 4. 23 in Ewens [99]) [35]: The probability that a randomly chosen TE insertion will be found at frequency x is proportional to the above sojourn time τ (x). However, the insertions we studied were all originally found in the sequenced strain. Thus, Pr[an insertion we detect is at frequency x] = Pr[we detect the insertion | the insertion is at frequency x] × Pr[a random insertion is at frequency x] ∝ x × τ (x | s, N) ≡ α (x | s, N). In the above, we defined α (x | s, N), a function that is proportional to the probability that any given insertion is at frequency x. The probability itself, then, is this function normalized by its integral over all possible values of x: Here, we integrated α (x | s, N) between 1/ (2N) and 1 − 1/ (2N), because in reality, a polymorphic element insertion cannot be present at frequencies that are outside this range. The incomplete gamma function that appears in the denominator is given by Γ (a, x) ≡ e−tdt. For each TE, our data come in the form m ≡ {m1, m2, m3, m4, m5, m6}, where m1 is the number of NA strain pools in which the element is absent, m2 is the number of pools in which it is polymorphic, and m3, the number of pools in which it is fixed. m4 and m5 give the numbers of pools with partial information—those where the element is either absent or polymorphic, and those where the element is either polymorphic or fixed. Finally, m6 is the number of pools about which we have no reliable information. The sum of m1 through m6 is always equal to 6, since that is the number of NA strain pools. Note that the numbers of strains vary between eight and 12 for different pools. However, the estimates of selection coefficients and population frequencies consistently vary by a factor of less than 1. 5 as we switch between these two pool sizes. Consequently, in the following treatment, we adopted an intermediate value of 11 strains per pool, close to the pool average. In the following, we also need to consider the fact that some of the pools in which the element is polymorphic, may appear to have the element either as absent or fixed. We estimate that the rates of both types of errors are approximately equal to 0. 046 (unpublished data). Conditional on x, the element' s frequency in the population, for the pools in which we can distinguish perfectly between the three classifications (absent, polymorphic, and fixed), the probability of m1 pools classified as absent, m2 as polymorphic, and m3 as fixed is The probability of finding that the element is absent or polymorphic (as opposed to fixed) in m4 pools is Finally, the probability of finding that an element is polymorphic or fixed (as opposed to absent) in m5 pools is Combining Equations 2 through 5, and integrating over the entire range of possible element frequencies in the population, we get the total probability of obtaining the data (m1 through m5), given a selection coefficient s and population size N. Since we assumed that the same selection coefficient and population size apply to every element in the group, the combined probability of obtaining a particular set of data for all the group' s elements is where M denotes the combined set of data for the n elements in a group, and {m1, m2, m3, m4, m5}j is the data for a given element j. We noted that this is also the likelihood of the population genetic parameters s and N given the data, i. e. , L[s, N | M]. Previous studies showed that the size of the NA D. melanogaster population is likely to be between 105 and 106 [100,101]. Furthermore, we have shown that the qualitative conclusions of our analysis do not change as we switch between these two values [35]. Accordingly, we proceeded by fixing N at 105 in the probability distributions and likelihood functions below. For each of the family/recombination rate groups, we found the value of s that gives us the maximum likelihood of the group' s combined data (Equation [7]). We then constructed a likelihood model in which each element within a group may come from one of two subgroups with different selection coefficients s1 and s2. The probability that each element comes from a subgroup with a selection coefficient s1 is p, and that it comes from the other subgroup is 1 − p. In order to get the probability of element' s frequency x under the new model with three parameters, instead of one, we extended Equation 2 as follows: Using Equation 8, we constructed the new likelihood function analogous to the one described by Equations 6 and 7: and For each group of TEs, we found parameters s1, s2, and p that maximize this new likelihood function, and wanted to test whether the improvement in likelihood is significant from the value we obtained for a one-parameter model. To do this, we used the likelihood ratio test, which involves calculating and comparing −2*ln (Λ) to the χ2 distribution with the number of degrees of freedom equal to the difference in the numbers of parameters between the two models. In practical terms, since that difference is 2, and the 95th percentile of the corresponding distribution is equal to 5. 991, we need ln (Λ) to increase additively by at least 5. 991/2 = 2. 996 as we increase the number of parameters in our model. Whenever we saw such an improvement, we interpreted it as evidence of heterogeneity of selection coefficients within a group of TEs. We went on to construct a likelihood model with five parameters: s1, s2, s3, p1, and p2. Here, s1, s2, and s3 are the selection coefficients of possible element subgroups, and p1 and p2 are the proportions of elements that come from the first two subgroups. However, the maximum likelihood value under this model never showed improvement above the threshold value of 2. 996 (see above) for any of the TE groups we considered. Finally, we estimated the confidence intervals on the selection coefficients. In case of either of the two likelihood functions above (either with one or with two selection coefficients), we calculated the confidence intervals on each selection coefficient si by holding all parameters except si constant, and noting the values of si where the function drops under two units below its maximum value. This procedure is based on a likelihood ratio test, where the test statistic is the likelihood ratio of the zero-parameter (or two-parameter) model with si fixed at the value of its maximum likelihood estimate to the two-parameter (or three-parameter) model with si that is allowed to vary. This statistic is distributed as a χ2 distribution with one degree of freedom. When the difference in log-likelihoods increases above two, the likelihood ratio increases above e2 = 7. 39, where e is the base of the natural logarithm. This value is the 99. 3% quantile of the χ2 distribution (corresponding to p = 0. 007,1 d. f.). Based on the sequenced genome of D. melanogaster, we designed primers in an overlapping fashion to amplify the 5′ and 3′ flanking regions of four of the insertions: FBti0018880, FBti0019065, FBti0019170, and FBti0019627 (Table S7). For a particular insertion, the same set of primers were used to sequence both strains with and without the element except for the primer pairs amplifying the regions immediately 5′ and 3′ to the insertion: primer pairs FL and FL_R, and L and R were used only in the strains with the insertion, given that primers FL_R and L are designed inside the element. On the other hand, the primer pair FL and R was used to sequence the strains without the element. Only populations from Davis, CA, and Raleigh, NC, appeared truly isogenic based on previous sequencing data [38]. For the rest of the strains, DNA was amplified using a proofreading DNA polymerase (Platinum Pfx; Invitrogen) and cloned into Zero Blunt TOPO PCR cloning kit (Invitrogen) before sequencing. The number of strains sequenced for each element varies between 20 and 33. For two of the elements, FBti0019065 and FBti0019170, only NA strains were sequenced, and for the other two elements, FBti0018880 and FBti0019627, both NA and AF strains were sequenced. To assess the statistical significance of the polymorphism patterns in the TE datasets, we compared several summary statistics calculated over the datasets to distributions of these statistics obtained by neutral coalescent simulations. The statistics we computed included π [102], iHS [8], and fTE. We evaluated π over various subsets of the sequences: NA sequences, AF sequences, TE-bearing sequences, and non–TE-bearing sequences. iHS was calculated only with respect to the presence of the TE and without respect to whether a TE-bearing strain was NA or AF. The coalescent simulations attempt to account for both the demographic history of D. melanogaster and the sample configuration, as follows. The simulations assume a demographic model derived from Thornton and Andolfatto [45]. In this model, the modern NA population derives from a founder population of African origin. We assume this emigration event to have occurred 0. 022 Ne generations before the present, where Ne is the effective population size of the modern AF population. From 0. 022 Ne generations until 0. 0042 Ne generations before the present, the NA population is assumed to have size 0. 03 Ne. From 0. 0042 Ne generations ago until the present, the NA population is assumed to have effective population size Ne. The transitions in NA population size at 0. 022 Ne and 0. 0042 Ne occur instantaneously. No migration occurs between the NA and AF populations from 0. 022 Ne generations until the present. The African population size Ne is assumed to remain constant throughout. These estimates correspond to the high-recombination scenario (ρ = 10) considered in Thornton and Andolfatto [45]. We accounted for the sample configuration using a simple acceptance–rejection algorithm. We set the numbers of contemporary AF and NA strains to those obtained in TE-typing assays (Table 1). Many more strains were typed in these assays than were sequenced, which implies that the typing assays provide better estimates of the true TE frequency in each subpopulation, and this is why the typing assay values were used. Only simulations in which a derived-state allele could be found segregating in the exact numbers the TE was found to be segregating in the NA and AF population were accepted. Following this, the simulated sample was randomly pruned to match the sample configuration of the respective flanking-sequence dataset, such that the segregating site that matched the TE segregation pattern in the TE-typing assay now matched the TE segregation pattern in the respective flanking sequences. Finally, the sample was only accepted if it had the same number of segregating sites to the left and to the right, respectively, of the TE locus as in the actual sample. To improve the acceptance rate, we typically simulated conditional on some small multiple of the observed number of segregating sites, multiplying the length in base pairs of the sequence by the same value, and then truncating to the required number of segregating sites. Coalescent simulations were conducted using the program ms [103]. In these simulations, Ne was set to 106, and the respective estimates of local recombination rate reported in Table 1 were used. One thousand replicates were obtained for each locus. We estimated the age of the TEs, i. e. , the time elapsed since the TE inserted, based on the decay of linkage disequilibrium between the TE locus and “distal flanking sequences” of approximately 500 bp at loci roughly 10 kb away from the TE, using the method of Slatkin and Rannala [63]. Because this method requires that each distal sequence be classified as one of at most two alleles, we employed the resampling method of Tang et al. [104] to partition the sequences into two allelic groups. For both distal flanking sequence datasets, we performed for each of the four TE datasets, 1,000 replicates of the partition method, yielding a distribution of allele ages that accounts for the uncertainty in the partitioning. In the distal flanking alignments, any site having a gap was ignored. Primers used to amplify and sequence these regions are given in Table S7. We estimated the frequency of each TE in the Australian populations and evaluated the heterogeneity of the frequencies between the Northern and the Southern populations using a maximum likelihood procedure. The Australian strains are not fully isogenized, as evidenced by the heterozygosity of many TEs for presence and absence in many strains (Table S4). We assumed that each tested strain effectively contains two different haploid genomes and that different strains within a tested set come from a panmictic population. The data for each TE in each population come in the form {m1, m2, m3}, where m1 is the number of strains homozygous for the presence of the TE, m2 is the number of strains heterozygous for the presence of the TE, and m3 is the number of strains that are homozygous for the absence of the TE. The log-likelihood of observing such data conditional on the frequency p is: The L (m1, m2, m3 | p is maximized at the value p̂: To determine whether the frequencies in the two tested populations are different from each other, we compare the log-likelihoods of two models. Under H1, we assumed that the frequencies in the two populations are different and estimated them using Equation 13 with the data that come from each population separately. We also calculated the two corresponding maximum log-likelihoods. Under H2, we assumed that the frequency of the TE is the same in both populations and estimate this frequency using Equation 13 with the combined data from the two populations. We also estimate the maximum log-likelihood under H2. The heterogeneity is detected when the difference between the sums of the two maximum log-likelihood values under H1 and the maximum log-likelihood value under H2 (denoted by ΔL) is greater than 3. 84, corresponding to the 5% critical value of the χ2 test with one degree of freedom. The total heterogeneity across a group of TEs is evaluated by comparing the sum of the ΔL values for each element with the critical values of a one-tailed χ2 distribution with the number of degrees of freedom equal to the number of TEs in the group. We analyzed the expression of the genes close to 12 of the 13 putatively adaptive TEs. FBti0019430 is inserted in an exon of gene CHKov1 and has been shown previously to truncate this gene and generate a new functional protein [38]. Consequently, it was not included in this analysis. First, we sequenced fragments of the coding regions of the genes next to the TEs, and we searched for SNPs in linkage disequilibrium with the TE. Primers used to amplify and sequence these genes are given in Table S8. For TEs FBti0018880 and FBti0019627, we used the SNPs previously discovered in this work (Figures 2 and 3). Sequencing these SNPs allowed us to distinguish between the mRNA originating from the chromosome carrying the insertion and the mRNA originating from the chromosome lacking the insertion. Then, we established crosses between a strain homozygous for the presence and a strain homozygous for the absence of each adaptive insertion that also differ by the presence/absence of the diagnostic SNP. For each insertion, we established two different crosses: in one cross, the mother is homozygous for the presence of the element, and in the other cross, the father is homozygous for the presence of the element. The specific stocks used for each insertion are given in Table S9. We used the F1 progeny of these crosses to check for differences in expression between the allele carrying the insertion and the allele lacking the insertion. For each cross, we collected 3–5-day-old males and females and analyzed them separately. Flies were snap frozen in liquid nitrogen and stored at −80 °C until use. Total RNA was extracted using TRIzol reagent (Invitrogen). RNA was treated with DNase to remove any contaminating DNA and purified using RNeasy mini kit (Qiagen). The concentration of purified total RNA was determined spectophotometrically at 260 nm. First-strand cDNA synthesis was performed with SuperScriptIII First-Strand synthesis system for reverse transcriptase PCR (RT-PCR) (Invitrogen). To check for genomic contamination, RT-PCR controls without retrotranscriptase were performed. Specific primers to amplify and sequence each SNP were designed (Table S10). A universal sequence was appended to one primer of each set. PCR was done in the presence of 2. 5 μM tailed primer, 10 μM nontailed primer, and 10 μM universal biotin labeled primer. Each sample was analyzed in triplicate. Pyrosequencing of the PCR products was performed by EpigenDx. The use of this sequencing technique for gene expression analysis at the allele level has been shown to enable discrimination of subtle differences in transcript abundance [70]. The allelic ratio in the cDNA was normalized to remove systematic artifacts caused by unequal amplification or biases in peak heights due to inequality of light emission from incorporation of different nucleotides [105]. To do that, we used the same primers to amplify genomic DNA of the F1 adults. The allelic ratio in the cDNA was then normalized, taking into account the allelic ratio obtained for the genomic DNA. Significance was tested by an unpaired t-test since genomic DNA and cDNA come from different individuals.

Transposable elements (TEs) are present in virtually all species and often contribute a substantial fraction of the genome size. Understanding the functional roles, evolution, and population dynamics of TEs is essential to understanding genome evolution and function. Much of our knowledge about TE population dynamics and evolution comes from the studies of TEs in Drosophila. However, the adaptive importance of TEs in the Drosophila genome has never been assessed. In this work, we describe the first comprehensive genome-wide screen for recent adaptive TE insertions in D. melanogaster. Using several independent criteria, we identified a set of 13 adaptive TEs and estimate that 25–50 TEs have played adaptive roles since the migration of D. melanogaster out of Africa. We show that most of these adaptive TEs are likely to be involved in regulatory changes and appear to be involved in adaptation to the temperate climate. We argue that most identified adaptive TEs are destined to be lost from the D. melanogaster population but that they do contribute significantly to local adaptation in this species.

Abstract Introduction Results Discussion Materials and Methods

evolutionary biology molecular biology genetics and genomics

2008

High Rate of Recent Transposable Element–Induced Adaptation in Drosophila melanogaster

Buruli ulcer (BU) is a neglected tropical skin disease caused by Mycobacterium ulcerans with more than two thirds of the global cases reported in West Africa. A nationwide active BU case search conducted in 1999 identified two health districts along the Offin River as two of the three most endemic districts in Ghana. Based on recent anecdotal accounts that transmission is unstable along the Offin River, we conducted from March to June 2013 an exhaustive household survey and active case search in 13 selected communities within a five-kilometer radius along the Offin River. The overall prevalence of BU was 2. 3% among the surveyed population of 20,390 inhabitants and 477 of the total 480 cases detected (99. 4%) were historical (healed) cases. By estimating the year of occurrence for each case per community and taking into account available passive surveillance records of health facilities and the District Health Directorate, we observed a general trend of continuous emergence of cases in communities located midstream the Offin River whereas downstream communities showed more sporadic patterns. We monitored the incidence of cases after the survey and recorded a cumulative incidence rate of 0. 04% for the 13 communities over a 17-month active surveillance period from August 2013 to December 2014. Our data reveal an overall decline in BU incidence along the Offin River similar to the general decline in BU incidence in recent years reported by the World Health Organization for West Africa. Buruli ulcer (BU) is a necrotizing skin disease caused by Mycobacterium ulcerans [1]. The disease has been reported in over 30 countries worldwide, mainly in the tropics, but the brunt of it seems to be mainly experienced in West Africa with Côte d’Ivoire, Ghana, Benin and Cameroon reporting more than 80% of the global number of cases [2]. Within the endemic countries, BU occurs in foci typically affecting inhabitants of impoverished and rural settings where access to medical care is a big challenge [3]. Control of BU is based mainly on early case detection and adequate antibiotic treatment and wound management. The current treatment regimen recommended by the World Health Organization (WHO) includes daily administration of streptomycin and rifampicin for eight weeks [4]. Advanced lesions may require debridement and/or skin grafting as an adjunct to improve healing and to prevent or correct deformities [5–7]. Nevertheless BU treatment is often associated with long hospital stays and represents a major socio-economic burden in the affected communities [8]. In Ghana, nearly 1,200 BU cases were reported between 1993 and 1998 by the first passive surveillance system established in the country and between 2004 and 2014 more than 9,000 cases have been reported. A nation-wide active case search conducted in 1999 resulted in an overall crude prevalence rate (clinically diagnosed active lesions) of 20. 7 per 100,000 inhabitants [9]. The most endemic district identified within this study was the Amansie West District, which is drained by the Offin river. The river takes its source from the Mampong scarp in the Ashanti region, flows from Boanim community through Bipoa community and eventually joins the Pra river in the Central Region (Fig 1). With farming and alluvial gold mining being the characteristic human activities in the river basin, spatio-epidemiological studies have consistently associated these activities with BU [10,11]. From June to July 2012, we paid reconnaissance visits to all 11 Health Districts drained by the Offin River. Anecdotal accounts gathered from interactions with local health staff and leaders of some selected communities indicated the absence of recent (in the past 3 to 5 years) cases in some communities historically known to be BU endemic and the emergence of new cases in some non-endemic communities. Since BU case data at the local and district health facilities were scanty, we set up this study with the following specific objectives: i) to determine the prevalence of BU in 13 selected communities in the Offin river basin, ii) to characterize the retrospective occurrence of BU cases for each community based on active surveillance data and available case data at the district and local health centers. We also report on active surveillance activities conducted to prospectively monitor the emergence of new BU cases in the selected communities. Ethical clearance for this study was obtained from the institutional review board of the Noguchi Memorial Institute for Medical Research, NMIMR, with a federal wide assurance number FWA00001824 and the ethics review committee of the Ghana Health Service (ethical approval ID number GHS-ERC: 06/07/13). Participation in all aspects of the study was voluntary and all confirmed cases—independent of their participation- were treated according to the treatment guidelines established by the National Buruli Ulcer Control Program (NBUCP). Written informed consent was obtained from all patients before their lesions were sampled for laboratory diagnosis. Parents or guardians provided written consent on behalf of all child participants. This study was conducted in the Offin river valley of Ghana (Fig 1). The Akans, who form the largest ethnic group in Ghana, are the main inhabitants of five out of the ten administrative regions (Ashanti, Central, Western, Eastern and Brong–Ahafo). The Offin river drains two of these regions namely the Ashanti and Central Regions. Due to the intense gold mining activities carried out in the river basin, anecdotal accounts suggest that this preponderance breaks down at the community level due to the influx of migrants from other parts of Ghana and neighboring West African countries. Our study area presents major landscape differences between communities located upstream the river on one hand, and those located mid and downstream on the other hand. As illustrated in Fig 2, land cover of upstream communities Bedomase (A) and Krakrom (B) was mainly farmland. In contrast, the peripheries of the mid and downstream communities were generally characterized by heavy mining activities as exemplified by Ntobroso (C) and the downstream community Pokukrom (D). In addition, our assessment of the elevation patterns with respect to the Offin river course revealed that the highlands of Mampong in the Ashanti region from which the river takes its source were at least 401m (1,315 ft) high (Fig 3C). Thus communities A to C located upstream the river were situated in areas with altitudes of at least 256m. Conversely, the midstream to downstream region of the Offin river recorded relatively lower elevation values ranging from 173m to 98m (569–320 ft). Following a nation-wide active BU case search [9], the NBUCP maintained a database of geographic co-ordinates of communities visited. We obtained from the NBUCP geographic co-ordinates of all communities in the 11 health districts of the Offin river valley. Using the ArcGIS 10. 0 mapping software, we created a buffer of five kilometers along the river. This seeded a total of 199 communities from which we selected 10 by simple randomization using a randomization tool embedded in the software (Fig 3A). The selected communities were Bedomase (A) and Krakrom (B) of the Sekyere south district, Kapro (C) of the Atwima Nwabiagya district, Ntobroso (E) of the Atwima-Mponua district, Keniago (G) and Tontonkrom (H) of the Amansie West district, Dominase (I) and Nkotumso (J) of the Upper Denkyira West district, Wromanso (K) of the Amansie Central district and Pokukrom (M) of the Upper Denkyira East district (Fig 3A). Based on recommendations of local health staff, three additional communities known to be BU endemic were included to bring the total number to 13. These communities were; Akomfore (D) and Achiase (F) of the Atwima Mponua district, and Mfantseman (L) of the Upper Denkyira East district. Other selected communities known to be endemic based on available passive surveillance records were Ntobroso (E), Keniago (G), Tontonkrom (H), Dominase (I), Wromanso (K), Pokukrom (M) and Nkotumso (J). Passively, no BU case has ever been reported for Bedomase (A) and Krakrom (B). We were unable to substantiate the endemicity of Kapro (C) prior to the survey. Entry into each community was carried out by a team consisting of a research assistant from the NMIMR, a field officer from NBUCP, a local disease control officer (DCO) and a community based surveillance volunteer (CBSV). We met chiefs, traditional and opinion leaders of each community to whom we explained the structure, aims and benefits of the study. Once we received their approval, information on the study was relayed to the community members through the CBSV and community information delivery systems. Between February and May 2013, we conducted an exhaustive household survey and active case search for BU. We formed teams each consisting of one research assistant from NMIMR, one local health staff routinely involved in the management of BU cases and one community volunteer. To aid in the description of the disease to participants being interviewed, each team was equipped with posters and picture charts illustrating the clinical forms of BU. Members of the teams received training on the clinical signs of BU, how to fill the survey forms and also how to take GPS co-ordinates. The teams went out to each inhabitable structure, numbered them serially and interviewed all inhabitants who were present. Households of inhabitants who were absent were noted and in two follow-up sessions on study participants (July and December 2013), we interviewed those absentees who were now available. Information on minors was provided by their parents or guardians. In addition to demographic data, clinical data were also collected for detected cases and GPS co-ordinates for every household. An inhabitant was classified as a part-time resident of a community if in the past year prior to the survey the person; a) travelled and stayed out of the community for more than 3 months and b) had his livelihood (such as occupation and education) in another community such that he spent more than 6 hours of his day-time there. A full-time resident on the other hand a) never travelled out of the community in the past year or b) travelled but stayed no longer than 3 months and c) had his livelihood in the community being surveyed and thus spent more than 6 hours of his day time there. Each time we completed surveying a community, we extracted information on inhabitants with suspected lesions and revisited them together with a clinician of extensive experience in diagnosing BU. The clinician then examined the lesions and diagnosed them as either active or inactive (healed) BU or as BU-unrelated lesions. Generally, depressed stellate scars were anecdotally considered as healed BU lesions whereas healed lesions with cleared skin areas were considered as BU-unrelated. Thus, cases with both active and healed lesions were included in this study. Clinically diagnosed active lesions were microbiologically confirmed by the detection of M. ulcerans insertion sequence (IS) 2404 by diagnostic PCR. In addition, BU lesions were clinically classified according to the WHO guidelines. Category I lesion was defined as a single lesion of size less than or equal to five centimeters at the widest diameter, category II as a single lesion between five and 15 cm and category III as either a single lesion greater than 15 cm or multiple lesions or a lesion at critical areas of the body. Using posters and charts the clinical features of BU was described to the participants after which, we asked participants if they have ever had an episode of BU at any point in their lives. This was done to determine the overall crude life time prevalence of BU in the population surveyed. However, in order to assess the historical emergence of cases for each community, we restricted our analysis of the healed cases to a 24 year prevalence period (1990 to 2013) and compared our data with passive surveillance records obtained from 2000 to 2013. Admittedly, such retrospective analyses are prone to recall bias particularly for BU where the appearance of symptoms does not reflect the moment of contracting the disease due to a relatively unknown incubation period. For BU cases with healed lesions, we estimated the year of developing the symptoms of the disease by i) examining health records and BU 01 forms (if available) and ii) verbally interviewing the cases and confirming from at least two other household inhabitants or nearby neighbors who were present and witnessed the case having the disease. To facilitate recall in healed cases of more than one year history, estimation was done by reference to any other household member or close neighbor who was born around the period the case developed symptoms of the disease. Once this estimation was confirmed by two other inhabitants present at the time of developing the clinical symptoms of BU, the period was estimated and noted using the year of birth of the inhabitant referenced. Subsequent to the exhaustive household survey, we continued to monitor the emergence of BU cases using two approaches: i) community outreach program and ii) monthly household visits by community volunteers. We conducted the community outreach once every three months in all 13 selected communities. Specifically in the months of July 2013, September 2013, December 2013 to January 2014, March 2014, July to August 2014 and November 2014. This program involved one evening of educating the community members on transmission, early case detection and treatment by showing BU documentaries and interacting with them through questions and answers. The following morning, the inhabitants were screened and those with clinically suspected BU lesions were sampled for laboratory confirmation. For seven of the 13 communities (Achiase (F), Ntobroso (E), Akomfore (D), Keniago (G), Pokukrom (M), Wromanso (K) and Mfansteman (L) ), we employed a monthly household-visit based surveillance as an additional tool to the surveillance by the community outreach program. All seven communities were selected based on the willingness of the CBSV to voluntarily carry out the exercise. We trained and equipped one CBSV from each of the afore-mentioned communities with android phones (HTC wildfire S) pre-loaded with a BU surveillance questionnaire. The questionnaire was designed using the Epicollectplus mobile application (http: //plus. epicollect. net/). From August 2013 to December 2014, the CBSVs were mandated to visit all households in a month and record any suspected case using the mobile application and a notebook. Suspected cases were then sampled by a local health staff and the samples were sent to the NMIMR for laboratory confirmation. We then assessed the feasibility and efficiency of this approach by evaluating the number of cases detected, the severity of lesions sampled and the community coverage (the proportion of households inventoried during the exhaustive survey that could be visited in a month by the volunteer). To confirm suspected active BU lesions, two swab specimens were collected from the undermined edges of ulcerative lesions. For pre-ulcerative lesions, one fine needle aspirate (FNA) was transferred into 500 μl phosphate buffered saline (PBS) as previously described [12] and transported to NMIMR at 4°C. Laboratory confirmation was conducted as previously described. Briefly, suspensions from pooled swabs of the same lesion containing 2 ml of PBS were concentrated by centrifuging at 3,000 x g for 15 minutes. The supernatant was then decanted and the sediment mixed with PBS to make up a final volume of 500 μl suspension from which 100 μl was used to prepare slides for Ziehl-Neelsen (ZN) microscopy. From the residual 400 μl suspension, we extracted M. ulcerans DNA for IS2404 PCR. Summary data from household surveys were mapped and analyzed using the ArcMap (Economic and Social Research Institute, version 10. 0). The elevation values (presented in feet) were derived from 15 meter resolution Advanced Spaceborne Thermal Emission and Reflection (ASTER) Satellite digital elevation model (DEM) data, obtained at a sun angle of 59. 6 degrees. All statistical analyses were carried out using GraphPad Prism version 6. 0 (GraphPad Software, San Diego California USA) and Stata 12 (Statacorp 2011 statistical software Release 12. College Station, TX: StataCorp LP). A total of 2,822 households of three hamlets, seven villages and three peri-urban settings along the Offin river were visited to survey the population for BU. In all, we surveyed a total population of 20,390 inhabitants, 50. 08% (n = 10,211) females and 49. 92% (n = 10,179) males (Table 1). The majority (90. 0%) of these inhabitants were full time residents. Characteristic of a youthful population, the mean age recorded for the survey was 23. 6 (S. D +/- 18. 8) years and 39. 7% of the surveyed population below the age of 15 years (Fig 4A). As expected, in all of the visited communities, the Akans represented the major ethnic group (46. 9% to 96. 5%). 82. 8% of the population between the ages of 4 and 18 years were students. We observed that miners formed less than 1% of the population we surveyed upstream the river (0. 4% from Bedomase and none from Krakrom and Kapro). On the other hand, miners were well represented in mid and downstream communities like Nkotumso and Tontonkrom where they formed the highest and second highest occupational group, respectively. Based on clinical signs and symptoms, we identified seven suspected active BU cases of which three were confirmed; one by both ZN and PCR and two by PCR only. All three confirmed cases (one male and two females) had ulcerative lesions. The male patient who was 6 years old presented with a category I ulcer on his thorax. One 35 year old female presented with category I on the upper left limb and the other 22 year old female had a category II ulcer on her lower left limb. Based on the clinical assessment of healed lesions by an experienced clinician, we could detect a total of 477 cases with healed BU lesions from the 13 communities. Of these 477 healed cases, 138 (28. 9%) had been previously diagnosed (clinical and/or laboratory) as active BU and noted in existing passive surveillance records. We estimated 0. 01% and 2. 34% prevalence of active and healed BU cases respectively in the surveyed population. We observed no significant difference (P = 0. 12) between the proportion of historical cases in females (2. 6%, 266/10209) when compared to the proportion recorded for males (2. 0%, 211/10178). The age of the 477 healed cases identified during the survey at the estimated onset of the disease ranged from 0. 75 to 87 years with a mean age of 31 years (SD = 18). Children under 15 years accounted for 48. 1% of the total number of cases. When we computed the age adjusted prevalence based on the age distribution of the population surveyed, we observed the highest peak for children between 10 and 12 years (Fig 4B). The BU prevalence for all surveyed communities is listed in Table 1. The disease burden was not uniform along the river. By grouping all 13 study communities according to their location along the Offin river, we observed that BU cases detected formed 0. 1% (2 out of 2,491) of the total population surveyed upstream the river (communities A to C). This was significantly smaller (P<0. 001) than the proportion recorded for communities D to H located mid-stream (2. 4%, 274/11,160) and I to M located down-stream the river (3. 0%, 204/6,739). As illustrated in Fig 3B, upstream, we detected no case in Bedomase (A) but recorded a prevalence of 0. 90% and 0. 14% in Krakrom (B) and Kapro (C), respectively. Prevalence was higher midstream (between 1. 22% and 3. 89%). The highest overall prevalence (8. 9%) was recorded downstream the river in the Mfantseman (L) community. In the other downstream communities we recorded a prevalence of 2. 46%, 3. 61%, 2. 31% and 1. 33% for Dominase (I), Nkotumso (J), Wromanso (K) and Pokukrom (M), respectively. Based on the BU history data compiled for all 480 cases detected during the active case survey (ACS), we estimated the time span during which cases emerged for each community included in the survey. In a second step, we compared our results with the annual passive case surveillance (PCS) data available at the local and district health centers. We estimated a population growth rate of 3. 8% per annum for communities A to H of the Ashanti region and 3. 2% per annum for communities I to M of the Central region using data from the regional population census conducted in 2000 and 2010. The annual BU case estimates for both ACS and PCS were expressed per 1,000 inhabitants to facilitate the comparison of trends between communities (Figs 5 and 6). While PCS data prior to the year 2000 was not available for any of the 13 communities surveyed, our annual ACS trend corroborated reasonably well with PCS data available for the years later than 2000. Altogether, we observed slightly differing trends in BU case emergence for upstream and downstream communities. The most upstream-localized community Bedomase (A) was non-endemic, as no case has ever been detected by PCS or ACS. In upstream communities Krakrom (B) and Kapro (C), we identified only one index case each corresponding to nine cases per 1,000 inhabitants and one case per 1,000 inhabitants respectively. While no PCS data was available for both cases detected, the cases were estimated to have emerged in 2009 and 2005 for Krakrom (B) and Kapro (C) respectively. Among all the mid-stream communities (D-H), we observed a continuous emergence of BU cases with at least one case per 1,000 inhabitants being recorded each year by either ACS or PCS or both from 2000 to 2014 (Fig 5). The downstream communities (I to M) which incidentally represent communities located within the Central region were generally characterized by a less continuous trend (Fig 6). For the historically endemic community Nkotumso (J), we observed an absence of cases from 2010 to 2014 after a long period of rather consistent emergence of cases. The further downstream communities Wromanso (K) and Mfansteman (L) were characterized by sporadic emergence of cases. Additionally, the highest number of cases per 1,000 inhabitants for a single year (25 cases in 2011) was observed in Wromanso (K). In all, 95. 8% (460/480) of the total BU cases detected have resided in their respective communities for more than three months. Additionally, when we stratified the population surveyed by their length of stay in the communities, we observed that BU cases made up 3. 2% (460/14,332) of the group with more than three months of residence. This was significantly higher (P<0. 001) when compared to the proportion recorded for those who have resided for three months or less (0. 6%, 20/3613). By employing both community outreach and household-visit surveillance, we continued to monitor the emergence of BU cases in all 13 selected communities over the 17-month period from August 2013 to December 2014. In all, we detected 29 clinically suspected BU cases during this active surveillance period. Five of these cases were detected through the community outreach program (four from Ntobroso and one from Dominase) and 24 were detected through the monthly household visits. Eight of them (27. 6%) were laboratory confirmed by IS2404 PCR. As expected for an active surveillance activity, all eight cases (five males and three females aged between 3 and 35 years, with a mean age of 18 (SD = 9) ) ) had lesions in their early stages; five were detected with pre-ulcerative lesions (four nodules and one plaque) and three presented with Category II ulcers. The 21 non-confirmed suspected BU cases were referred to the district hospital for further assessment and alternative diagnosis. The monthly household visits resulted in the detection of seven out of the eight laboratory confirmed cases. All seven cases were detected in three communities: Achiase (four cases), Ntobroso (two cases) and Akomfore (one case). Laboratory case confirmation rate for Achiase (4 out of 6 (66. 7%) ) was significantly higher (P = 0. 03) than the combined rate recorded for all other communities (4 out of 23 (17. 4%) ). As shown in Fig 7, all four cases from Achiase were detected in months when the volunteer had covered more than 50% of the households. Limited access of the population to health facilities and the reluctance of BU patients to seek medical care have made house-to-house surveys an attractive tool in studies on the disease epidemiology [13–15]. Here, we investigated recent anecdotal accounts of unstable M. ulcerans transmission along the Offin river. We observed during our survey, scanty passive surveillance data which made our annual BU case trend analysis for each community a challenge. BU patients’ records preserved over time could also serve as reliable source of information for retrospective analyses which could add to the existing clinical knowledge of the disease. The importance of maintaining good patient records was demonstrated in a recent analysis of 1,227 BU case data collected from 2005 to 2011 in Benin. The study revealed a higher risk of developing osteomyelitis among male patients than female patients and a significant association between clinical presentation and development of permanent functional sequelae [16]. Inhabitants having both healed and active lesions were recorded in the study in order to account for historical trends as well as to get an overview of the current BU situation for each community. While only laboratory confirmed BU patients with active lesions were included, case definition for patients with healed lesions was based on clinical judgment. In view of the lack of passive surveillance data on BU for the study communities, findings of this study may thus serve as a reference for future longitudinal follow-up of the community. In line with the decline in BU incidence in West Africa, Ghana recorded in 2014 nearly 50% less cases than in 2009 [2]. Along these lines, one key observation of this study is the overall decline in the prevalence of BU along the Offin river, even in known historically endemic communities. The high focal BU incidence recorded for Wromanso (K) in 2011 was perhaps due to an increase in community sensitization and awareness campaigns about BU conducted within the year. Consequently, this may have led to an increase in cases reporting to the health facilities or detection of more cases by the health staff through the awareness programs. However, some studies have reported similar upsurges in BU incidence in association with climatic and environmental changes [17–19]. In endemic communities of Africa, children have been reported as forming the majority of BU cases [20–25]. While children below 15 years of age formed nearly half of all cases detected in our study, we observed an underrepresentation of children below 5 years among cases, consistent with the findings of a recent study conducted in Cameroon [14]. Moreover, sero-epidemiological studies in Ghana and Cameroon demonstrated that children of this age group were less exposed to M. ulcerans [26]. Consistent with findings of a previous study conducted along the Densu river in Ghana [13], we observed a very low prevalence of BU upstream the river whereas mid and downstream communities recorded high prevalence. Similar to the repeated association of BU with man-made modification of aquatic environments [11,17,18,23,27–29], the intense gold mining activities observed in our study area were also localized mid and downstream the river. Recently, a detailed study on land cover and its association with BU in the downstream region of the Offin demonstrated a significant association between mining and the occurrence of the disease [10]. In the same study, the distribution of alluvial gold mining patches in areas of BU foci was made evident using high resolution satellite images. The low-lying feature of the mid and downstream regions of the river also supports previously reported association of BU with landscapes of low elevation [30,31,28]. The monthly household-based surveillance conducted by the community volunteers in the course of this study resulted in the detection of seven of the eight laboratory confirmed active BU cases, all of whom had early stage lesions. This underscores the important role of community volunteers in early case detection and uncomplicated treatment of BU [32–34]. However, ideally, only category I lesions should have been detected considering that active surveillance strategies were employed to monitor the emergence of new cases after the exhaustive case search. This could be explained that some early lesions were missed since the volunteers couldn’t consistently achieve 100% household coverage. Alternatively some patients may have ignored the very early clinical BU signs and may have only presented themselves to the volunteers on such visits when lesions have progressed to category II. As reviewed [35] and previously reported [36] patients with large active lesions may play a role in transmission of M. ulcerans by disseminating the bacteria from their active lesions into the environment which may then serve as a source of infection to others. This implies that intense continuous early case detection and timely antibiotic treatment in an endemic area may result in the interruption of this cycle leading to the gradual decline of cases, as observed for example in Ghana and Benin. The current active surveillance approach employed by the BU control programs is mainly community based. One cost effective way to sustain the monthly household surveillance will be to integrate the BU surveillance with other prevalent skin diseases like yaws, cutaneous leishmaniasis and leprosy [37]. As a tool, the mobile phone data collection can serve as a back-up to address the gaps in data collected at the health facility or district levels.

Buruli ulcer (BU) is a tropical skin disease caused by Mycobacterium ulcerans and more than two thirds of the global cases reported in West Africa. The Offin has been considered the most endemic river valley in Ghana following a nationwide active case search conducted in 1999. Here, we present findings of an exhaustive household survey and case search of 13 selected communities along the Offin river aimed at addressing recent anecdotal accounts of unstable transmission of M. ulcerans within the river basin. We observed among the surveyed population of 20,390 inhabitants, an overall 2. 3% prevalence of BU with 99. 4% of the total cases detected being historical cases. We also observed a general trend of continuous and sporadic emergence of cases in mid and downstream communities, respectively. Subsequently, we detected a total of eight cases (0. 04% cumulative incidence rate) in a prospective 17-month active surveillance of all 13 communities. These data confirm the recent decline in BU incidence in historically endemic communities along the Offin river basin, analogous to the observation made in recent years by the World Health Organization for West Africa.

Abstract Introduction Materials and Methods Results Discussion

dermatology ecology and environmental sciences medicine and health sciences rivers pathology and laboratory medicine tropical diseases geographical locations bacterial diseases signs and symptoms aquatic environments bodies of water neglected tropical diseases bacteria africa skin diseases infectious diseases buruli ulcer epidemiology marine and aquatic sciences lesions actinobacteria people and places mycobacterium ulcerans ghana freshwater environments diagnostic medicine earth sciences disease surveillance biology and life sciences organisms

2016

Burden and Historical Trend of Buruli Ulcer Prevalence in Selected Communities along the Offin River of Ghana

Chikungunya virus (CHIKV) has reemerged as a life threatening pathogen and caused large epidemics in several countries. So far, no licensed vaccine or effective antivirals are available and the treatment remains symptomatic. In this context, development of effective and safe prophylactics and therapeutics assumes priority. We evaluated the efficacy of the siRNAs against ns1 and E2 genes of CHIKV both in vitro and in vivo. Four siRNAs each, targeting the E2 (Chik-1 to Chik-4) and ns1 (Chik-5 to Chik-8) genes were designed and evaluated for efficiency in inhibiting CHIKV growth in vitro and in vivo. Chik-1 and Chik-5 siRNAs were effective in controlling CHIKV replication in vitro as assessed by real time PCR, IFA and plaque assay. CHIKV replication was completely inhibited in the virus-infected mice when administered 72 hours post infection. The combination of Chik-1 and Chik-5 siRNAs exhibited additive effect leading to early and complete inhibition of virus replication. These findings suggest that RNAi capable of inhibiting CHIKV growth might constitute a new therapeutic strategy for controlling CHIKV infection and transmission. Chikungunya virus (CHIKV) is a mosquito-transmitted alphavirus belonging to the family Togaviridae. CHIKV is responsible for an acute infection, characterized by high fever, arthralgia, myalgia, headache, and rash [1], [2], [3]. Although having immense medical importance, effective vaccine or specific therapy is not commercially available. Currently, strict attention is given to good infection control practices that emphasizes mosquito control program. RNA interference (RNAi) is the process of sequence-specific, post-transcriptional gene silencing (PTGS) in animals and plants, which is induced by 21- to 23-nucleotide (nt) small interfering RNA (siRNA) that is homologous in sequence to the silenced gene [4], [5], [6]. RNAi not only regulates gene expression in the mammalian cells but also acts as a cellular defense mechanism against the invaders, including the viruses. In recent years, inhibition of specific genes by siRNAs has proven to be a potential therapeutic strategy against viral infection. For instance, inhibition of virus replication and gene expression by directly introducing siRNAs into the cells have been reported for several RNA viruses, including several important human pathogens, such as poliovirus, HIV, Hepatitis, Chandipura and influenza virus [7]–[24]. It has been also shown that alphaviruses such as Semliki Forest virus [7], Venezuelan equine encephalitis [20], O' nyong-nyong virus [14] are susceptible to small interfering RNA action. Recently Dash et al. , [22] have demonstrated that introduction of exogenous siRNAs can inhibit replication of CHIKV in vitro. The success of this study is limited as siRNAs used against ns3 and E1 genes of CHIKV were shown to reduce replication by 65% by 48 h p. i. and not evaluated in-vivo [22]. Cell clones expressing shRNAs against CHIKV E1 and nsP1 genes showed significant inhibition of CHIKV replication as compared to the scrambled shRNA cell clones and non-transfected cell controls [25]. Alphaviruses contain a linear, positive sense, single stranded RNA genome of approximately 11. 8 kb. RNA genome consists of a capped 5′ non-coding region (NCR) and 3′ polyadenylated NCR. The non-structural proteins, nsP1, nsP2, nsP3 and nsP4 are required for the virus replication; the structural proteins, which consist of capsid and envelope proteins (E1, E2, E3 and 6K), are synthesized as polyproteins and are cleaved by capsid autoproteinase and signalases [26]. Given the similarity of the CHIKV genomic structure to those of other alphaviruses, CHIKV is expected to encode spikes on the virion surface that is each formed by three E1–E2 heterodimers where the E1 glycoproteins mediate the fusion and the E2 glycoproteins interact with the host receptor [26], [27], [28]. Nsp1 protein is involved in the RNA synthesis and capping. E2 and ns1 genes are highly conserved in CHIKV strains and are important in the entry and the multiplication in the host cell and, therefore, represent the rational targets for antiviral therapy. In the current study, based on consensus sequence of CHIKV strains, the siRNA were designed to target the conserved regions in the E2 and ns1 genes of CHIKV. The efficacy of siRNAs targeted against E2 and ns1 genes individually or in combination in inhibiting the replication of CHIKV were evaluated in vitro (Vero cells) and in vivo (mice). All animals were handled in strict accordance with good animal practice as defined by Institutional Animal Ethics Committee (IAEC). The experiments were done in a biosafety level-2 animal facility at the National Institute of Virology. All animal work was approved by the IAEC. Animal experiments were carried out in strict compliance with Committee for the Purpose of Control and Supervision of Experiment on Animals (CPCSEA) guidelines, India. Swiss albino and C57BL/6 mice (3–4 wks old; 20–25 grams) were maintained in the BSL-2 facility with controlled temperature (22°C), humidity, and a 12 h light/dark cycle. Mice received the CHIKV via one of three delivery methods: 1) Intra nasal (i. n.) 100 µl, 2) standard intra venous tail vein injection (i. v.) 200 µl, 3) Intra muscular injection (i. m.) 200 µl. siRNA (∼20–25 µg/mouse) mixed with Hiperfect transfection reagent (Qiagen, Germany) and PBS (final volume 200 µl) via i. v. delivery method. African Green monkey kidney (Vero-E6) cells were maintained in minimum essential medium with 10% fetal bovine serum, 100 U/mL penicillin, 100 µg/mL streptomycin and Neomycine 50 µg/mL. Vero-E6 cells grown under similar conditions were used for the propagation of CHIKV (African genotype, Strain No. 061573; Andhra Pradesh 2006; Accession Number EF027134), Dengue-2 (DENV-2) (Trinidad; TR1751) virus and Chandipura virus (CHPV) (Strain No. 034627; Andhra Pradesh; 2003) stock. CHIKV, DENV-2 and CHPV strains were obtained from virus repository of National Institute of Virology, Pune, India. Virus strains were passaged twice in Vero-E06 cells. Cell supernatants were harvested when 75% of the cells showed cytopathic effect, aliquoted, and stored at −80°C and used throughout the study. The virus stock titers were determined using real time PCR (8. 26×108 CHIKV RNA copies/ml) and standard plaque assay (7×107 plaque-forming units/mL). CHIKV whole genome sequences were retrieved from GenBank NCBI database (http: //www. ncbi. nlm. nih. gov) and consensus sequence was used to design the siRNA. All siRNAs were designed using HP OnGuard siRNA design (Table 1 and Fig. 1). siRNAs were then checked for the homology to all other sequences of the genome using non-redundant sequence database and the homology analysis tool. Four siRNAs each, targeting E2 and ns1 genes were designed and synthesized (Qiagen, Germany) (Table 1, Fig. 1). Negative control siRNA [ncsiRNA; siRNA against Chandipura virus (24) with no significant homology to any known mammalian gene was used as a non-silencing control in all RNAi experiments and were purchased from Qiagen, Germany. Fluorescent labeling of siRNA was performed using the Cy3 Silencer labeling kit (Ambion, USA) and modified as described in the manufacturer' s protocol. Vero E6 cells were infected with CHIKV (Multiplicity of infection MOI 5. Two h post infection (p. i.), cells were transfected with E2 (Chik-1, Chik-2, Chik-3, Chik-4), ns1 (Chik-5, Chik-6, Chik-7, Chik-8) siRNA and control using the Amaxa Nucleofector device II (Amaxa biosystems). After electroporation, Vero-E6 cells were incubated at 37°C until analyzed for inhibition of CHIKV replication. Cells were harvested at 24,36 and 48 h p. i. and inhibition of CHIKV replication was determined by quantitative RT-PCR (qRT-PCR), plaque assay and ImmunoFluorescnce Assay (IFA). At two hours p. i. with CHIKV, Vero E6 cells were transfected with Chik-1, Chik-5 and combination of both siRNAs at different concentration (10,50,100,150 and 200 pmol). After 24 h post transfection, total RNA was isolated from the tissue culture supernatant and cells. One step qRT-PCR was carried out to evaluate the inhibitory effect of siRNA. Swiss albino and C57BL/6 mice (3–4 weeks) were infected with approximately 1×106 pfu of CHIKV (100 µl of 107 pfu/ml; ∼4. 5×108 RNA copies/ml) by three routes viz. ; i. v. , i. n. and i. m. and RNA copies were checked in muscles on 2nd, 4th, 7th and 14th day p. i. . C57BL/6 mice (4–6 weeks) were infected by CHIKV (100 µl of 107 pfu/ml; ∼4. 5×108 RNA copies/ml) and CHIKV RNA copies were measured daily in the blood and muscles by one step qRT-PCR for fourteen days. siRNAs were complexed with HiPerfect™ (QIAGEN, Valencia CA) according to the manufacturer' s instructions and ∼25 µg/mouse (1 mg/Kg body wt) was administered i. v. once after 48 or 72 h p. i. . Chik-1 siRNA, Chik-5 siRNA and combination of Chik-1 and Chik-5 siRNAs (Comb-siRNA) were used in different mice groups. Blood (∼200 µl) was collected from siRNA, ncsiRNA, or saline injected mice groups at 0,1, 2,3 and 4 day post treatment. CHIKV E3 RNA was quantitated from the sera using qRT-PCR. In C57BL/6 mice, the 72 h time point was chosen for siRNA treatment. Blood and hind limb muscle tissues were harvested from C57BL/6 mice at 0,1, 2,3 and 4 day post siRNA injection. The tissues were dissected, weighed, crushed and macerated in liquid nitrogen using mortar pestle, and used for the RNA isolation. RNA from the Vero-E6 cells, serum and mice tissues was extracted using QIAmp viral RNA minikit (QIAGEN, Valencia, CA) and trizol (Invitrogen USA) method respectively following the manufacturer' s instructions. Viral load in serum and/or tissue samples were determined by absolute quantification using the standard curve method. One step RT-PCR was performed in 25 µl reaction mixture containing 5 µl RNA, 12. 5 µl TaqMan One-Step RT-PCR 2× Master Mix, 1 µl 40× (RT+RNAasin) (Applied Biosystems) each 1 µl sense (µM), 1 µl anti-sense (µM) primer and 1 µl TaqMan probe. Primers were selected from the E3 structural protein region. Real-time one step RT-PCR was performed in a 96-well format using 7300 real time PCR system and SDS software V 1. 0. 2 (Applied Biosystems). The amplification program included: reverse transcription at 48°C for 30 min, initial denaturation at 95°C for 10 min, and 50 cycles of denaturation (95°C for 15 sec) and annealing and extension (60°C for 1 min). After the amplification, a melting curve was acquired to check the specificity of PCR products. A standard curve was generated by the amplification of serial dilutions of in vitro transcribed RNA of CHIKV (108 to 102 serial dilutions). After generation of standard curve, we compared unknowns to the standard curve and extrapolated the value. Viral titers were expressed as RNA copies per ml of serum or per ml of tissue culture suspension or per well or per mg tissue. Detection limit of real time PCR was 10 copies per reaction. Serial dilutions of tissue culture supernatants of infected and siRNA transfected cells were added to a monolayer of Vero E6 cells and the plates were incubated at 37°C for 1 h. After the incubation, the medium was replaced by overlay medium (2× MEM, 2% CMC and 10% FBS (Gibco, USA). The plates were incubated at 37°C for 72 h and the cells were stained with amido black and the plaques were counted. Groups of 3–4 weeks old swiss albino mice were inoculated intra-peritonealy with the CHIKV African strain (1∶1 vol∶vol mixture of CHIKV and Freund' s complete adjuvant) and were maintained under standard laboratory conditions. Two booster doses with CHIKV along with Freund' s incomplete adjuvant (1∶1) were administered at weekly intervals. Blood samples were collected at pre and post immunization (7 days after the last dose). IgG antibodies were then purified using protein A column (Merck Biosciences, India) according to the instructions of the manufacturer. Immunofluorescence assay (IFA) was carried out as described by Sudeep et al. (29). Vero E6 cells were fixed with acetone and blocked with 2% BSA in the phosphate buffered saline (pH 7. 4) for 1 h. The cells were incubated with (1∶100) mouse anti CHIKV antibody followed by incubation with FITC-conjugated rabbit anti-mouse (1∶500) antibodies (Invitrogen, USA). Cells were counter stained with Evan' s blue for one min. The slides were visualized using fluorescence microscope (Nikon eclipse T2000S and Q capture pro 5. 0 software). Negative controls were similarly processed using pre-immune sera. Hind limb tissues, excluding the femur bone, were fixed in 4% formaldehyde and were embedded in the paraffin. Thin section of 8 µm size were prepared. Tissues were stained with haematoxylin and eosin. Histopathological evaluation was performed on the muscle tissues of the hind legs from the control (saline injected, ncsiRNA), CHIKV infected (4,5, 6 and 7 day p. i.), treatment groups (Chik-1 siRNA, Chik-5 siRNA and Comb-siRNA). siRNA treatment was given on third day p. i. and the tissues were harvested at 4,5, 6 and 7 days p. i. and evaluated for necrosis, inflammation, regeneration, mineralization, fibrosis and the edema. Concurrently IFA was carried out to check the presence of CHIKV. Immunofluorescence assay (IFA) was carried out as described by Sudeep et al. [29]. The slides were incubated with (1∶100) mouse anti CHIKV antibody followed by incubation with Alexa flor 546-conjugated rabbit anti-mouse (1∶200) antibodies (Invitrogen USA). Cells were counter stained with DAPI for 10 seconds. The slides were visualized using fluorescence microscope (Nikon eclipse T2000S and Q capture pro 5. 0 software). Negative controls were processed similarly. For real-time reverse transcription RT-PCR analysis, hind limb muscle tissues were crushed in liquid nitrogen. RNA was extracted by using TRIzol reagent (Invitrogen) according to the manufacturer' s instructions. One step RT-PCR was performed using Quantitect SYBR Green RT PCR kit (Qiagen, Germany). Real-time PCR analysis used the following nucleotide primers: 5′-GGCCGAGGACTTTGATTGCACATT-3′ and 5′- AGGATGGCAAGGGACTTCCTGTAA-3′ for actin beta, 5′- AGGAGGAGTTTGATGGCAACCAGT -3′ and 5′- TCCTCATCCCAAGCAGCAGATGAA-3′ for Interferon alpha (INF-α) (NM_010502), 5′- TGTGGCAATTGAATGGGAGGCTTG-3′ and 5′- TCTCATAGATGGTCAATGCGGCGT -3′ for interferon beta (IFN-β), and 5′-AGCGGCTGACTGAACTCAGATTGT-3′ and 5′- ACTGCTTTCTTTCAGGGACAGCCT-3′ for interferon gamma (IFN-γ) (NM_008337). The 25-µl amplification reaction mixture contained 500 ng total RNA, 0. 5 µM each primer pair, 0. 25 of reverse transcriptase enzyme and 12. 5 µl of 2× SYBR green qPCR Supermix (Qiagen, Germany). Cycling conditions were as follows: one cycle of 50°C for 30 min and one cycle of 95°C for 15 min followed by 45 cycles at 94°C for 15 s, 57°C for 30 s, 72°C for 30 s and 68°C for 15 s. The real-time PCR was performed by using a Rotor-Gene 3000 PCR machine. The data were analyzed with Rotor-Gene real-time analysis software. Each sample was analyzed in duplicate and normalized to actin beta mRNA. Expression changes in interferon alpha, beta and gamma genes in CHIKV infected group, CHIKV infected mice treated with Chik-1, Chik-5 and Comb-siRNA group, and the control mice with Chik-1, Chik-5 and Comb-siRNA treatment were investigated using real time PCR analysis. Mice were mock-infected with CHIKV and treated with siRNA on third day p. i. and then gene expression determined at days 4,5, 6, and 7 p. i. . Three mice were used for each treatment and each time point. All data were expressed as mean ± standard deviation. The viral loads were log-transformed for improvement of normality. Statistical significance was determined by Dunnet' s test using ANOVA. A value of p<0. 05 was considered statistically significant. Fold change was compared using one way ANOVA and the groups were also compared by nonparametric Kruskal-Wallis test for confirmation of results. For the initial comparison of antiviral activity of different siRNAs, Vero-E6 cells were infected with CHIKV and transfected with different siRNAs (Chik-1 to Chik-8) 2 h p. i. . Chik-1 and Chik-5 were the most effective siRNAs, suppressing CHIKV copies by 5 log10 (p<0. 001) and ∼2. 5log10 (p<0. 05) RNA copies respectively (Fig. 2). The pool of siRNAs Chik 1–4 (4 log10; p<0. 001) and Chik5–8 (3 log10; p<0. 001) did not increased the CHIKV suppression in Vero E6 cells (Fig. 2). Results obtained with the individual siRNAs and pool of siRNAs indicated that only siRNAs Chik-1 and Chik-5 possessed the antiviral activity against CHIKV. Therefore only Chik-1 and Chik-5 and Comb-siRNA were used for further studies. The reduction in the CHIKV copies by Chik-1 and Chik-5 was initiated at the siRNA concentrations of 50 pmol, and reached a plateau at 100 pmol (Fig. 3 A). Chik-1 and Chik-5 showed sequence dependent inhibition and showed no reduction in the dengue-2 (Fig. 3B) and the Chandipura virus (Fig. 3C) replication in Vero-E6 cells. Effect of Chik-1, Chik-5 and Comb-siRNAs (100 pmol) transfection on survival of Vero-E6 cells was assessed by the MTT assay. At 24 h, transfection of Chik-1 siRNA (95. 1±6. 51), Chik-5 siRNA (89. 46±3. 19), Comb-siRNA (92. 11±7. 11) and Hiperfect reagent (95. 88±11. 47) do not exhibited any significant change in proliferation of Vero E6 cells (Fig. 3D). At 48 h, transfection of Chik-1 siRNA (83. 71±9. 24), Chik-5 siRNA (82. 13±2. 71), Comb-siRNA (86. 1±1. 65) and Hiperfect reagent (88. 02±2. 58) displayed small reduction in viable cell number (Fig. 3D). Transfected Cyanine 3 dye labeled siRNAs showed signal at 4 h and 6 h whereas at 24 h signal was minimal, but still present compared to control treatment (Supplementary Information Fig. S1). Chik-1 and Chik-5 siRNAs were stable till 24 h. Figure 4A depicts the effect of Chik-1, Chik-5 and Comb-siRNAs on the CHIKV replication at different time points. At 24 h p. i. , treatment of Chik-1 and Chik-5 siRNAs resulted in the reduction of 5 log10 and 3 log10 CHIKV RNA copies respectively in cells and the supernatant (Fig. 4A). At 36 h p. i. , 3 log10 (Chik-1), and 2 log10 (Chik-5) reduction in CHIKV RNA copies was observed in tissue culture supernatant whereas 2 log10 reduction was recorded in cells with Comb-siRNAs (Fig. 4A). At 48 h p. i. , no significant reduction in CHIKV RNA copies was noted in the cells and the supernatant. Overall, the siRNAs directed against E2 gene (Chik-1) were more efficient in inhibiting CHIKV replication than the siRNA directed against ns1 region (Chik-5). We further evaluated the additive advantage of treatment with Comb-siRNAs. In supernatant, 5 log10 (p<0. 001), 2. 5 log10 (p<0. 05) and 2. 5 log10 (ANOVA Dunnet' s test p<0. 05) reduction in CHIKV copies was observed at 24,36 and 48 h respectively when compared to virus infected control. In cells, 4. 5 log10 (p<0. 001), 3 log10 (p<0. 05) and 2 log10 (p<0. 05) reduction was obtained at 24,36 and 48 h respectively. Importantly, the Comb-siRNA could prolong the inhibitory effect as compared to individual siRNAs (Fig. 4A). When plaque assay was used as the measure of CHIKV replication, Chik-1 siRNA yielded a reduction of 5 log10 at 48 h p. i. (Table 2). Chik-5 reduced 3 log10 and Comb-siRNA showed reduction of 3 log10 in the virus titer. At 24 and 36 h p. i. , cytopathic effects were not observed in the treated cultures where as commencement of cytopathic effects was observed in the untreated control from 24 h p. i. demonstrating the inhibitory effect of the siRNAs. These results were consistent with our real time RT-PCR results and the plaque assay results, IFA also showed the reduction of viral antigen in Chik-1 and Chik-5 siRNAs treated cells (Fig. 4 B). As human muscle cells are the target of CHIKV infection, we evaluated the i. m. route along with i. v. and i. n. route for CHIKV infection. Infection by all the three route resulted in CHIKV replication in the thigh muscles (Fig. 5A). CHIKV RNA copies were not detected in uninfected mice at 0,2, 7 and 14 days p. i. . Mice inoculated with in i. v. , i. m. and i. n. routes; CHIKV was not detected in the muscle tissues at 0 day p. i. (Fig. 5A). CHIKV appeared in the muscle tissues by 2 days p. i. , persisted till 7 days p. i. and disappeared on 14 days p. i. (Fig. 5A). CHIKV inoculated via i. m. route could be detected in thigh muscle tissues at 2 h p. i. (753±101 CHIKV RNA copies). Therefore i. m. route was not preferred as it was difficult to distinguish newly replicated virus from the virus innoculum. Intra nasally inoculated mice exhibited the lower viral RNA copies in the thigh muscles (Fig. 5A). Since i. m. and i. n. routes were not yielded satisfactory results, therefore we used i. v. route for CHIKV infection. Swiss albino and C57 BL/6 mice were infected by i. v. route and CHIKV RNA copies were measured in serum and muscle tissues from 1 day p. i. to 14 day p. i. . Infection of adult swiss albino and C57BL/6 mice with 1×106 PFU CHKV (100 µl of 107pfu/ml) CHIKV by i. v. route did not cause mortality. Clinical symptoms such as sluggishness and foot swelling were observed. A definite evidence of the replication of the virus was observed in muscle tissues (Fig. 5B). CHIKV RNA copies were detected in mice serum from 1 day p. i. till 9 days p. i. (Fig. 5 C & D). Viremia in i. v inoculated mice reached a peak by 3 days p. i. , with viral loads ranging from 7×105 to 5×107 viral RNA copies/ml (Fig. 5 C & D). To assess whether siRNAs could protect mice from CHIKV infection, groups of CHIKV infected mice (1×106 PFU CHIKV; 100 µl of 107pfu/ml) were administered Chik-1 and Chik-5 siRNAs at 72 h p. i. . Swiss albino mice treated with E2 or ns1 siRNA with 250 µg per kg body weight (∼6 µg/mice) showed ∼3log10 inhibition, 500 µg per kg body weight (∼12 µg/mice) showed 3log10 inhibition of CHIKV whereas at 1 mg per kg body weight (∼25 µg/mice) siRNA led to 7log10 reduction in CHIKV copies (Fig. 6 A & B). Similar results were obtained in C57BL/6 mice (Fig. 6 C & D). We therefore administered 1 mg/kg body weight (∼25 µg/mice) siRNA in subsequent experiments. For all in-vivo experiments, Hiperfect reagent was used for delivery of siRNA. Chik-1, Chik-5 and Comb-siRNA administered at 72 h p. i. provided significant reduction in serum viral load as assessed by real time PCR (Fig. 7). At 48 h post siRNA injection, reduction with Chik-1 and Chik-5 was around 2. 5 log10 (ANOVA Dunnet' s test p<0. 05) as compared to 0 h and ncsiRNA whereas 100% inhibition (7log10) was observed with Comb-siRNA (ANOVA Dunnet' s test p<0. 01). At 72 h p. i. , administration of Chik-1, Chik-5 and Comb-siRNAs showed complete inhibition (7log10, ANOVA Dunnet' s test p<0. 01). Chik-1, Chik-5, Comb-siRNA and ncsiRNA administered 72 h p. i. (1×106 PFU CHKV; 100 µl of 107pfu/ml) provided significant reduction in the serum viral load as assessed daily by real time PCR (Fig. 8). At 24 h and 48 h post siRNA treatment, 2. 5 log10 and 3. 5 log10 (ANOVA Dunnet' s test p<0. 05) reduction was recorded for all siRNAs, when compared to ncsiRNA. At 72 h post treatment, reduction with siRNA Chik-1, and Chik-5 was around 3. 5 log10 (ANOVA Dunnet' s test p<0. 05) while Comb-siRNA showed 100% inhibition (7log10, ANOVA Dunnet' s test p<0. 01). Importantly, Comb-siRNA produced prolonged inhibitory effect when compared to individual siRNAs. In muscle tissues, CHIKV RNA reached peak by third day p. i. , with viral loads ranging from 1×104 to 7×105 viral RNA copies/mg tissue (Fig. 8). At 24 h post-siRNA treatment ∼2. 5 log10 reduction in CHIKV RNA was noted with all the three siRNAs as compared to ncsiRNA control. At 72 h, all the siRNAs produced 4log10 reduction in CHIKV RNA (100% inhibition, ANOVA Dunnet' s test p<0. 01). Similar results were seen when IFA was used to evaluate the effect of siRNA on CHIKV replication in muscle tissues that corroborated with real time PCR-based data (Fig. 9). Having demonstrated that Chik-1 and Chik-5 siRNA treatment significantly reduced the CHIKV titer in serum and muscle tissues, histopathology analysis was performed to determine the inflammation and infiltration in chikungunya infected and siRNAs treated tissues. Histopathological examination of CHIKV infected mice (1×106 PFU CHKV; 100 µl of 107pfu/ml) showed pathological changes such as extensive necrosis, inflammation, pronounced monocyte/macrophage infiltrates and edema (Fig. 10). Such histopathological changes were prevented by systemic treatment either with Chik-1, Chik-5 individually or in Comb-siRNAs. At 3 days p. i. , the muscle tissues chikungunya infected mice showed mild inflammation of lymphocytes and monocytes. At 4 days p. i. , chikungunya infected mice muscle tissues showed moderate inflammation of lymphocytes and monocytes, focal edema and focal necrosis whereas siRNA treated mice muscle tissues showed only mild inflammation. At 7 days p. i. , extensive muscular necrosis with dense inflammation of lymphocytes and monocytes was observed in CHIKV infected and ncsiRNA treatment mice. On other hand, siRNA treatment preserved the morphological integrity of the muscle tissues with regeneration (Fig. 10). The muscle tissues from control mice infected with saline showed no pathological changes such as necrosis, edema, inflammation and infiltration of polymorphs (Fig. 10). We tested if inhibition of CHIKV replication in mice was indeed sequence dependent and not because of non-specific antiviral interferon response. In the absence of CHIKV, Chik-1, Chik-5 and Comb-siRNA treatment did not significantly induced the α, β and γ interferon mRNA expression in hind limb muscle tissues (Table 3; Kruksal Wallis p>0. 05). Similarly, siRNA treatment of CHIKV-infected mice did not show significant elevations in α, β and γ interferon gene expression in the hind limb muscle tissues (Table 3; Kruksal Wallis p>0. 05). These results suggest that siRNA mediated reduction in CHIKV replication is sequence specific without any deleterious effect on the host. Taken together, this first in vivo experiment clearly revealed that siRNA therapy is effective in vivo by reducing the clinical symptoms in the challenge-infected animals and was capable of significantly reducing virus replication in the serum and muscles. This study for the first time clearly shows the efficacy of ns1 and E2 siRNAs in combination, in inhibiting CHIKV replication in mice infected with the CHIKV (1×106 PFU CHIKV; 100 µl of 107pfu/ml, ANOVA Dunnet' s test p<0. 01). Importantly, a single i. v. inoculation of the siRNAs, 72 h after CHIKV infection could completely inhibit viral replication as evidenced by the absence of viral RNA in the muscles and serum. Though attractive, the therapeutic potential of siRNAs in treating viral diseases has been limited primarily because of the failure when evaluated in animal models and the absence of appropriate delivery systems. However, the success of in-vivo use of siRNAs in viral infection is noteworthy [17], [23], [24]. E2 and ns1 genes were chosen as the target genes because of the critical roles in viral replication. E2 and ns1 genes are highly conserved in CHIKV strains. CHIKV is expected to encode spikes on the virion surface, each formed by three E1–E2 heterodimers where the E1 glycoproteins mediate fusion and the E2 glycoproteins interact with the host receptor [26], [27], [28], [30]. Together with nsP4, nsP1 is expected to catalyze the initiation of negative strand RNA synthesis. Nsp1 protein is also involved in methylation and capping of positive RNA [31]. Indeed, CHIKV nsp1, a 535 amino acid long protein contains consensus sequence at the N terminal region which is characteristic of Alphaviruses. Elimination of nsp1 abolishes the ability of CHIKV to replicate. We evaluated four different siRNAs each against ns1 and E2 genes when administered two h p. i. in vitro. Of these, Chik-1 (siRNA for E2 gene) and Chik-5 (siRNA for ns1 gene) were most efficient in inhibiting CHIKV replication as assessed by multiple parameters such as real time PCR, plaque assay and IFA (Fig. 2, Fig. 3, Fig. 4, Table 2). The work described here utilizes targets against viral RNA sequences that are conserved, and invariant between different strains. The target sites of Chik-1 and Chik-5 siRNA are 100% conserved in all sequenced CHIKV genome. As compared to individual Chik-1 and Chik-5, the pool of Chik-1–4 and Chik5–8 siRNAs does not have any additive effect on the CHIKV inhibition. The combination therapy of Chik-1 and Chik-5 siRNAs provided additive effect and was found to be most effective as compared to individual siRNA in inhibiting CHIKV replication even at later time points. Thus, simultaneous silencing of multiple genes of CHIKV appeared to be better strategy for preventing viral replication. Recently Dash et al. , [22] have used siRNA against ns3 and E1 genes of CHIKV and showed reduction in CHIKV replication by 65% by 48 h p. i. and not evaluated in-vivo [22]. In vitro studies employing Chik-1, Chik-5 and Comb-siRNAs showed more than 90% reduction in CHIKV replication with all three formulations, whereas complete inhibition of CHIKV replication was observed in in vivo studies. The observed efficient inhibition of CHIKV might be due to targeting of conserved sites, route of delivery or combination of both. The next step was to evaluate these siRNAs in a suitable animal model. Recently C57BL/6 mice were established as susceptible murine models for CHIKV infection [32], [33]. In the absence of an immunocompetent mouse model replicating clinical symptoms in humans, we used Swiss albino and C57BL/6 adult mice for the evaluation of siRNAs. Though mice strains showed mild clinical symptoms (sluggishness and swelling of foot), replication of CHIKV was evident and the effect of siRNAs on CHIKV replication could be assessed employing several parameters (Fig. 5, Fig. 6, Fig. 7 and Fig. 8). SiRNAs were stable in Vero-E6 cells till 24 h post transfection (Fig. S1). We could not study the stability of siRNAs in mice because of low signal of cyanine 3 dye labeled siRNAs. However, it may be noted that previous studies have demonstrated uptake of siRNAs in the liver, kidney, lung endothelium and jejunum using standard i. v. injection of siRNA [34][35][36]. Despite the weaker siRNA uptake with standard intra venous administration, earlier reports suggest that this technique is still effective and may offer a potential route for systemic therapeutic use. Standard i. v. administration of siRNA efficiently delivered the siRNAs to various organs and resulted in efficient reduction of CHIKV titer. Although chikungunya viremia has not been extensively studied in humans, studies on non human primates and mice suggest viremia of short duration, with viral loads ranging from 1×103 to 1. 2×1010 viral RNA copies/ml [33], [37], [38], [39], [40]. It has been demonstrated that CHIKV can persist for longer time in animal models [37], [40]. The findings of Morrison et al. , [40] and Labadie et al. , [37] indicate that though CHIKV is readily cleared from most tissues after the acute stage of infection, CHIKV RNA persists in joint tissues for at least 3–4 weeks after inoculation. In monkey model a clear relationship has been demonstrated between the inoculation dose and the period and magnitude of the viremia [37]. In current study we used the high dose of CHIKV (1×106 PFU) which might resulted in longer persistence of CHIKV RNA (7–8 days post inoculation). The results obtained in this study are consistent with findings of Morrison et al. , [40] and Labadie et al. , [37]. It has been demonstrated that CHIKV actively suppresses STAT activation by both type I and type II interferon [37], [41]. In current study moderate suppression in Interferon α and Interferon β was observed in CHIKV infected mice. It is possible that CHIKV persistence observed in current study might be the combined effect of high dose of CHIKV, route of inoculation, active evasion of host innate or adaptive immune responses by the CHIKV. It will be interesting to evaluate the mechanisms involved in CHIKV persistence in C57BL/6 mice. Similar to in-vitro studies, Comb-siRNA was more efficient than the individual components in-vivo studies also. Important point is that the siRNA was administered 48 or 72 h p. i. suggesting utility in CHIKV-infected hosts. CHIKV was detected in muscle tissues of infected mice inducing pathological changes such as severe necrosis and dense infiltration of monocytes and lymphocytes (Fig. 9 and 10). On the contrary, ∼1 day after siRNA treatment, mild inflammation and infiltration of monocytes was observed while after ∼4 days, regeneration and intact muscle morphology with no evidence of inflammation was recorded (Fig. 10). These results clearly demonstrate the therapeutic effect of siRNAs, especially Comb-siRNA, in virus-infected mice. Even a single dose administered 3 days p. i. could protect mice suggesting ability of the siRNAs in treating an established virus infection. Under certain circumstances, siRNAs can induce the interferon (IFN) pathway and trigger inflammation [42], [43], [44]. It has been suggested that canonical small interfering RNA (siRNA) duplexes are potent activators of the mammalian innate immune system [42], [43], [44]. Synthetic siRNA in delivery vehicles that facilitate cellular uptake can induce high levels of inflammatory cytokines and interferons after systemic administration in mammals and in primary human blood cell cultures [42], [43]. To differentiate the modes of protection offered by siRNAs, we determined expression levels of interferon α, β and γ interferon genes in the muscle tissues of different mice groups (Table 3). siRNAs alone did not induce significant induction of interferon genes; CHIKV-infected, siRNA treated mice did not show siRNA-induced interferon gene expression when compared to the virus infected mice. These results revealed that the observed inhibition of CHIKV replication was mainly because of characteristic activity of siRNAs. In conclusion, Comb-siRNAs (E2 and ns1 genes) described by us provide an excellent therapeutic agent for chikungunya and should be further assessed in non-human primates. Need for a proper delivery system for use in humans remains an important issue.

Despite having immense medical importance, still vaccine, chemoprophylactic, or effective therapeutic measures are not commercially available for chikungunya. Only strict attention to good infection control practices may prevent CHIKV infection. The pathogenic properties of CHIKV necessitate the development of an efficient antiviral therapies. Four siRNAs each, targeting the E2 and ns1 genes of chikungunya were designed and evaluated for their efficiency in inhibiting CHIKV growth in in vitro and in vivo model systems. Efficiency of these siRNAs in controlling CHIKV replication in vitro and in vivo was assessed by the real time PCR, IFA and plaque assay. Chik-1 and Chik-5 siRNA ids efficiently inhibited CHIKV replication in the virus-infected Vero-E6 cells and mice. CHIKV replication was completely inhibited in the virus-infected mice when administered 72 hours post infection (p. i.). The combination of Chik-1 and Chik-5 siRNAs exhibited additive effect leading to early and potent inhibition of virus replication. Taken together, these findings suggest the promising efficacy of RNAi ids in silencing sequence-specific genes of CHIKV and might constitute a new therapeutic strategy for controlling the CHIKV infection and transmission.

Abstract Introduction Materials and Methods Results Discussion

biology microbiology

2013

Administration of E2 and NS1 siRNAs Inhibit Chikungunya Virus Replication In Vitro and Protects Mice Infected with the Virus

The segmented structure of the influenza virus genome plays a pivotal role in its adaptation to new hosts and the emergence of pandemics. Despite concerns about the pandemic threat posed by highly pathogenic avian influenza H5N1 viruses, little is known about the biological properties of H5N1 viruses that may emerge following reassortment with contemporary human influenza viruses. In this study, we used reverse genetics to generate the 63 possible virus reassortants derived from H5N1 and H3N2 viruses, containing the H5N1 surface protein genes, and analyzed their viability, replication efficiency, and mouse virulence. Specific constellations of avian–human viral genes proved deleterious for viral replication in cell culture, possibly due to disruption of molecular interaction networks. In particular, striking phenotypes were noted with heterologous polymerase subunits, as well as NP and M, or NS. However, nearly one-half of the reassortants replicated with high efficiency in vitro, revealing a high degree of compatibility between avian and human virus genes. Thirteen reassortants displayed virulent phenotypes in mice and may pose the greatest threat for mammalian hosts. Interestingly, one of the most pathogenic reassortants contained avian PB1, resembling the 1957 and 1968 pandemic viruses. Our results reveal the broad spectrum of phenotypes associated with H5N1/H3N2 reassortment and a possible role for the avian PB1 in the emergence of pandemic influenza. These observations have important implications for risk assessment of H5N1 reassortant viruses detected in surveillance programs. The emergence of an influenza virus that will cause a pandemic is inevitable and therefore preparedness is mandatory. The new pandemic influenza virus is likely to carry a hemagglutinin (HA) gene other than the currently circulating H1 and H3 lineages in order to escape immunity in the human population. However, we cannot predict the mechanism by which the pandemic influenza virus will emerge. One possibility is the transfer of an avian influenza virus from birds to humans, made possible by adaptive mutations, as postulated for the 1918 pandemic [1], [2]. Another possible scenario would follow the paradigm of the H2N2 and H3N2 influenza pandemics of 1957 and 1968 in which avian virus genes were incorporated into circulating human influenza viruses by reassortment [3], giving rise to viruses with novel surface antigens; i. e. antigenic shift. The segmented structure of the viral genome facilitates exchange of gene segments between two viruses co-infecting a single host cell. Dual infection with avian and human influenza viruses and subsequent reassortment may occur in hosts that are susceptible to both kinds of viruses and serve as mixing vessels that generate novel reassortants [4], [5]. Wild aquatic birds are the natural reservoirs for influenza A viruses and have been found to harbor each of the 16 known HA subtypes [6]. Highly pathogenic avian influenza (HPAI) H5N1 viruses are now enzootic among wild birds and poultry in three continents (http: //www. who. int). Since 1997, when HPAI H5N1 viruses first emerged in Hong Kong to cause human respiratory illness and death, over 360 laboratory-confirmed human infections have been reported. Most human infections are caused by contact with infected poultry and to date H5N1 viruses have not yet acquired the ability to transmit efficiently among humans. A major obstacle to transmission of the H5N1 virus among humans is thought to be the preferred receptor specificity of the H5 HA towards sialic acid (SA) with α2,3 linkage to galactose (the so-called avian receptor) [7], [8]. A switch of receptor specificity towards α2,6 linked SA (the human receptor) is considered to be a pre-requisite for sustained human to human transmission [9], [10]. However, it is not known whether other genes from H5N1 viruses would confer virulence and transmissibility in humans. It has been shown that a reassortant virus with the HA and NA from an H3N2 human virus and the PB2, PB1, PA, NP, M, and NS (so-called internal genes) of an H5N1 virus did not transmit efficiently in a ferret model [11]. (In this report, the term “internal genes” refers to the gene constellation comprising PB2, PB1, PA, NP, M, and NS, although the M gene encodes for the M2 protein, which is surface-exposed in virions.) The internal genes from this avian H5N1 virus were therefore postulated to lack at least one essential functional attribute to initiate a human pandemic. These critical function (s) might be acquired through a reassortment event between the H5N1 virus with a circulating human H3N2 influenza virus that generates the appropriate gene constellation. In theory, a single reassortment event between two influenza A viruses can yield up to 254 (28 minus two parental viruses) hybrid genotypes. However, the few available reports suggest that the number of natural or experimental reassortants is likely to be smaller [5], [12], [13], [14], [15], [16]. Reliable estimates of the expected frequency of hybrid genotypes resulting from dual infections are not possible in the absence of systematic studies on human-avian influenza reassortment. Comprehensive in vivo co-infection studies and in vitro evaluations of all the reassortant genotypes derived from a human influenza virus and an HPAI virus would help bridge this gap of knowledge. In this report, we analyze the repertoire of reassortants between contemporary avian H5N1 and human H3N2 viruses by evaluating the phenotypes of 63 (26-1) viral reassortants with HA and NA genes from an avian H5N1 virus and the six internal genes from either parental virus, assigned higher priority because only viruses with novel surface antigens may cause a pandemic. We used reverse genetics to derive the reassortant virus panel, and subsequently examined their replication characteristics in cell culture and their virulence in a mammalian system. Our in vitro and in vivo analyses revealed a high frequency of viable reassortants with a wide spectrum of virulence for mice, providing insight into their potential for future emergence in nature. To generate the collection of human-avian reassortant viruses for this study, we first developed plasmid-based reverse genetics (rg) systems for the two parental viruses; A/Wyoming/3/2003 (subtype H3N2) (WY03) and A/Thailand/16/2004 (H5N1) (TH04) [17]. The parental WY03 virus showed α2,6 linked SA receptor specificity [18], replicated to high titers in MDCK cell culture, and was avirulent in mice (data not shown). The TH04 virus showed α2,3 receptor specificity [8], replicated efficiently in MDCK cells and was highly virulent for mice [17]. Virus recovery from plasmid DNA transfections was evaluated by quantitative plaque analysis at 72 hours (h) post-transfection; herein referred to as rescue efficiency. Cell cultures transfected with WY03 and TH04 rg plasmids yielded >107 plaque-forming units (pfu) /ml of progeny virus, termed rgWY03 and rgTH04, which formed 4–5 mm diameter plaques, comparable to those of parental wildtype (wt) viruses (Figure 1). A wide range of virus yields and plaque diameters were obtained for each of the 63 H5N1 human-avian reassortant (rH5N1) plasmid transfections. In order to categorize the in vitro properties of each reassortant, rH5N1 genotypes were segregated into 4 phenotypic groups, according to their rescue efficiencies (Figures 1 and 2): (1) rH5N1 genotypes with wt or near-wt replication efficiency. Twenty-eight rH5N1 viruses (cell culture phenotype group 1) consistently yielded ≥106 pfu/ml in the transfected cell cultures (Figure 1), which represented rescue efficiencies similar to those of rgWY03 and rgTH04. Most of the cell culture group 1 viruses formed ∼2–4 mm plaques in diameter (Figure 1). The efficient in vitro growth phenotypes of nearly one-half of the rH5N1 viruses in the group revealed a high frequency of functional compatibility between avian and human virus genes. (2) rH5N1 genotypes with moderate cell culture replication impairment. Fourteen rH5N1 viruses (22% of the rH5N1 genotypes) had rescue efficiencies between 104 and 106 pfu/ml (cell culture phenotype group 2), and most of these viruses formed 1–3 mm plaques (Figure 2). (3) rH5N1 genotypes with severe cell culture replication impairment. Eight reassortants (13% of the rH5N1 genotypes) yielded ∼102–104 pfu/ml from transfected cell cultures, with plaque size ranging from 0. 5–2 mm (cell culture phenotype group 3 in Figure 2). (4) Non-viable or marginally viable rH5N1 genotypes. Thirteen rH5N1 viruses (∼20% of rH5N1 genotypes) yielded <100 pfu/ml from transfected cell cultures (cell culture phenotype group 4 in Figure 2). Their rescue efficiencies were 5 log10 pfu/ml lower than their rg parent viruses. The severe replication defects of viruses in this group may reflect structural or functional incompatibilities in avian-human viral RNA and/or protein complexes. Collectively, these categories guided our rationale for excluding reassortants with severe replication defects from further in vivo studies. Notably, the severely impaired rH5N1 viruses in group 4 (Figure 2) were all characterized by the association of the nucleoprotein (NP) gene from WY03 virus with matrix (M) and/or nonstructural (NS) genes derived from TH04 virus. For example, the single gene reassortant r5 (group 4), which carried the NP from WY03 and the five other internal genes from TH04 had a rescue efficiency of <102 pfu/ml. However, replacement of the TH04 NS gene with the WY03 NS in the same background increased rescue efficiency to ∼104 pfu/ml (r5/8 virus, group 2, Figure 2), which was significantly higher than r5 (P≤0. 0001). Further introduction of the WY03 M segment into this gene constellation restored the rescue efficiency and plaque size of the reassortant virus (r5/7/8, group 1, Figure 1) to nearly wt level (P≤0. 0001). In contrast, introduction of polymerase complex genes did not improve replication (r5 replication is similar to r1/5, r2/5, r3/5, and r1/2/3/5; P>0. 9) (Figure 2). Conversely, only 6 out of 28 rH5N1 viruses (group 1) that replicated efficiently had NP of human origin, in every case along with human NS (Figure 1). These observations suggest that the NP gene of WY03 origin preferentially interacts with M and NS genes of the same origin for optimal replication. In contrast, the NP gene of the TH04 avian virus appears to be more compatible with the M or NS of heterologous origin (e. g. , r1/2/3/7/8 virus replication was similar to r1/2/3/7 or r1/2/3/8; P = 1. 0). Although not all viruses with human NP and avian M or NS were severely impaired, they generally displayed significantly reduced replication, suggesting that avian M and/or NS may not be incorporated into seasonal human H3N2 viruses in the absence of avian NP. Another remarkable gene incompatibility was noted with the r2/3/5/7/8 virus, bearing TH04 PB2 and the remaining five internal genes from WY03 virus (Figure 2, group 3). This reassortant virus formed tiny plaques (0. 5 mm diameter) and had a very low rescue efficiency. This defect was repaired by providing a PA gene of avian virus origin; i. e. , the rescue efficiency of r2/3/5/7/8 was significantly lower than r2/5/7/8 (Figure 1, group 1) (P<0. 0001), suggesting a functional interaction of TH04 PB2 with the cognate avian PA gene. This finding suggests that reassortment of avian PB2 genes with human viruses may be linked to co-incorporation of the avian PA gene. A set of 38 rH5N1 viruses with cell culture replication efficiencies comparable to those of parental viruses, or with only modest reductions, were chosen for study in a BALB/c mouse model to assess their virulence in a mammalian host. The plasmid-derived rgTH04 virus was highly virulent for mice, as indicated by a very low intranasal 50% mouse infectious dose (MID50 = 101. 5 pfu) and lethal dose (LD50 = 101. 8 pfu) (Figure 3). This virus replicated to high titers (>107 pfu/ml) in lungs by day 4 following an intranasal inoculation of 104 pfu and caused >19% body weight loss. Viral replication was also detected at systemic sites, such as brain and spleen, recapitulating the outcome of infection with the wt TH04 isolate [17]. In contrast, replication of the rgWY03 virus in mice was very inefficient as evidenced by an MID50 of 106 pfu and an LD50 of >106 pfu (determination of MID50 for rgWY/03 required additional doses of 105 and 106 pfu to detect virus in tissues; data not shown). However, the reassortant virus bearing HA and NA from TH04 and the remaining genes from WY03 virus (r1/2/3/5/7/8) replicated efficiently (MID50 of 101. 8 pfu and titer of 107 pfu/ml in the lung), suggesting that the HA and/or NA from WY03 lack appropriate interaction with receptors or other host factors in the mouse respiratory tract [19]. Most importantly, the internal genes from WY03 mediated efficient viral replication of r1/2/3/5/7/8 virus in mice validating the BALB/c mouse as a useful model to evaluate the influence of human/avian internal gene combinations on the virulence phenotypes of rH5N1 viruses. Three rH5N1 viruses were highly virulent for mice, with an LD50<103 pfu (Figure 3, group A1). Each of these rH5N1 had an MID50 of ≤101. 5 pfu, replicated to high titers in the lung (≥106. 5 pfu/ml), and caused >17% weight loss by 6–7 days post-infection (dpi) on average. The virulence of these viruses was comparable to that of wt TH04. In addition, the high frequency of virus detection in the spleen and brain of mice indicated systemic spread of these viruses, resembling infection with wt TH04. The ten viruses in group A2 were moderately virulent, with a mean LD50 value that was significantly different from that of the highly virulent group A1 viruses (P<0. 001). The remaining 25 rH5N1 viruses in groups B1 and B2 exhibited low virulence phenotypes in mice with LD50 values >104 pfu. However, five rH5N1viruses (Figure 3, virulence group B1) caused significant transient weight loss (>16%), clinical signs, such as ruffled haircoat and lethargy, and three viruses (r1/3/7/8, r1/2/3/8, r1/5/7/8) each caused mortality in a single mouse infected at 104 pfu, suggesting potential for increased virulence at higher virus inoculums (data not shown). The other 20 rH5N1 viruses (Figure 3, virulence group B2) caused subclinical infections in mice, with minor weight loss (<15%). These viruses spread to the spleen and /or brain sporadically and their pulmonary replication capacity ranged from substantially efficient to nil. Although many rH5N1 viruses with high rescue efficiencies and large plaque phenotypes also displayed highly virulent phenotypes in mice, several rH5N1 viruses belonging to virulence group B2 (i. e. , r2/7/8 and r1/3/8) had high rescue efficiencies but did not replicate well in mice. This finding highlights the limitations of inferring in vivo virulence properties solely from efficient in vitro replication characteristics. Interestingly, r1/3/5/7/8, one of the most virulent rH5N1 among the 38 reassortants inoculated into mice had a gene constellation resembling that of the pandemic viruses from 1957 and 1968. In 1957, HA, NA and PB1 genes from an avian H2N2 virus were introduced into the circulating human H1N1 virus and caused the so-called “Asian flu” pandemic, whereas the 1968 “Hong Kong” pandemic virus incorporated the HA and PB1 genes from an avian donor [3]. In this study, a virus carrying HA, NA and PB1 of avian origin and the remaining genes from a human virus, namely r1/3/5/7/8, was highly virulent for mice (LD50 = 102. 5 pfu). In contrast, a reassortant virus (r1/2/3/5/7/8) with all the internal genes from WY03 virus, including PB1, caused minimal mortality and had a significantly different LD50 (1. 3 log10 pfu increase; P<0. 001), suggesting that the PB1 of contemporary H5N1 viruses can reassort into circulating H3N2 viruses and increase their virulence for mice. Efficient viral replication at the lower temperature of the upper respiratory tract is thought to be essential for droplet transmission of influenza virus between humans. Avian influenza viruses with a PB2 polymerase bearing glutamic acid at position 627 instead of lysine have decreased replication at 33°C in mammalian cells [20], [21], [22]. Although both WY03 and TH04 viruses have lysine at position 627 in PB2, it is not known whether new avian and human gene constellations would compromise viral replication at lower temperature. To address this question, we determined reassortant viral titers in the nasal turbinates collected at 4 dpi. We found that in general, rH5N1 viruses replicated less efficiently in nasal turbinates than in lungs, as reported previously [21]. Interestingly, some reassortants (i. e. , r2/7, r2/7/8, and r3/7/8) showed extremely low replication in nasal turbinates despite considerable titers in lungs (Figure 3). These reassortants would be expected to lack efficient transmissibility by generation of nasal secretion droplets. Although mice are regarded as a useful mammalian model for studying the replication of HPAI viruses, the species differences between humans and mice mandate studies with models from the target species to complement the data. The epithelial cells of the respiratory tract are the primary targets of influenza infection. Therefore, we used in vitro differentiated HTBE cultures to evaluate the replication potential of the rH5N1 viruses in humans [23]. HTBE cells were infected with rgWY03 and rgTH04 viruses, or each of 38 rH5N1 viruses that were previously analyzed for virulence in mice. We quantified virus progeny released into the apical side of the pseudostratified epithelium because budding of HPAI H5N1 virus in the HTBE model remains polarized (data now shown). As shown in Figure 4A, both rgTH04 and rgWY03 parental viruses replicated efficiently in the HTBE cells. The rapid rise of WY03 virus titers to 108 pfu/ml at 32 h post-infection was consistent with the efficient spread of human viruses in HTBE cells, as described previously [23]. The plateau in WY03 virus production may be caused by virus-induced cell death, first noted at 40 h post-infection. In contrast, HTBE cells infected with rgTH04 virus showed no cytopathology and virus progeny increased steadily throughout the 56 h infection. The majority of rH5N1 viruses produced ≥104 pfu/ml by 24 h post-infection, and the growth kinetics were similar to parental rgTH04 or slightly delayed (e. g. , r1/2/3/5/7/8 in Figure 4B and data not shown for others). In comparison, four rH5N1 viruses, r/3, r2/3, r3/8, r3/7/8, replicated substantially less efficiently in the HTBE cells (Figure 4, C and D). Interestingly, these four viruses also replicated poorly in mice; had MID50 values of ≥4 log10 pfu and caused minimal weight loss (Figure 3). These results supported the virulence data provided by the mouse model. To study the mechanisms underlying the differences in the replication phenotypes of certain rH5N1, we exploited a mini-genome reporter assay which dissects the function of the viral ribonucleoprotein (RNP) complex from the rest of the viral gene products [24], [25]. The 16 possible RNP combinations of PB2, PB1, PA and NP from either the TH04 or WY03 viruses were studied at 33°C or 37°C, to recapitulate the temperatures of the upper and lower respiratory tract, as reported previously [20]. Another panel of RNP combinations with A/Vietnam/1203/2004 (VN04) viral genes replacing TH04 genes was also analyzed in parallel to extend the results for other H5N1 viruses. The human RNP was almost equally active at 33°C and 37°C, whereas the avian RNP activity was substantially reduced at 33°C despite the presence of lysine at position 627 of PB2, in both TH04 and VN04 backgrounds (Figure 5). The RNP constituted by PB1 and PA from WY03 virus and PB2 from TH04 (or VN04) virus resulted in extremely low polymerase activities at 33°C and 37°C (Figure 5A and B, RNP denoted by asterisks). Although the RNP complexes carrying PB2 and PB1 from TH04 and PA from WY03 virus showed partially reduced polymerase activity, a similar combination derived from VN04 and WY03 viruses showed a more pronounced loss of replication activity (Figure 5A and B, RNP denoted by arrows). The reduced polymerase activities of these gene constellations were consistent with the low viral titer from lungs and nasal turbinates of mice infected with reassortant viruses r3/7/8, r3/8, r3 and r2/3 (Figure 3). Interestingly, the polymerase activity of the RNP with PB1 from TH04 and the other proteins from WY03 was comparable to that of the wt WY03 RNP. These findings indicated that the increased mouse virulence attributed to avian PB1 in the WY03 genetic background (r1/3/5/7/8) may not directly result from stimulation of the polymerase activities of the RNP. Alternative hypotheses to reconcile these observations would include an in vivo role for PB1 in RNP function, a possible modulation of host cell function by PB1-F2, or unknown interactions of PB1 with the remaining 4 genes absent in this assay: HA, NA, M and NS. Although H5N2 subtype viruses have been isolated from poultry in North America and Asia for many years [26], it is not clear whether the N2 derived from contemporary human H3N2 virus can support the efficient replication of a reassortant virus bearing the HA from circulating H5N1 virus. Balanced HA and NA activities are critical for efficient influenza virus infection and replication in various hosts. The HA of contemporary H5N1 viruses has retained a strong preference for α2,3 linked SA [7], [8]. In contrast, the NA derived from H3N2 human seasonal isolates has adapted over time to acquire α2,6 SA specificity [27], [28]. To evaluate the H5N2 reassortant, the NA of TH04 was replaced with the N2 from WY03 virus by reverse genetics. This virus, termed r6 (H5N2), was virtually identical to wt TH04 in rescue efficiency and replication in HTBE cells (Figure 1 and Figure 4B). In addition, this reassortant was highly virulent in mice, with an LD50 comparable to the parental TH04 virus (Figure 3). These results suggested that the NA activity from circulating H3N2 viruses can functionally support the activity of the H5 HA and promote H5N2 virus spread in the mammalian host. The influenza pandemics of the past century were caused by viruses carrying at least one internal gene of avian origin and a novel HA subtype that acquired α2,6 SA receptor binding specificity [3]. While many studies have focused on adaptive mutations in the avian HAs required for acquisition of human receptor specificity, little is known about the importance of the avian virus internal genes in pandemic emergence [7], [29], [30]. In this report, we used reverse genetics to systematically study reassortants with each of the 63 possible combinations of internal genes from contemporary avian and human viruses; all with H5N1 surface protein genes. Collectively, these studies revealed that certain genes, such as human NP and avian PB2, often caused severe replicative impairment in cell culture when transferred alone to the heterologous virus background, whereas transfer of other genes, such as PB1, was less detrimental. The incompatibility of the human NP with a full complement of avian influenza genes was noted in early studies with Fowl Plague virus [13]. This is significant because the NP gene of influenza virus plays an important role in host range specificity [5], [31], [32]. In this report, we provide evidence suggesting that reassortants with NP of avian origin in a human virus background can replicate efficiently in mammalian cell cultures. This phenotype does not require the presence of other avian virus internal genes, whereas the NP gene of human origin depends on cognate NS and M genes for expression of the efficient replication phenotype. The best characterized event of the viral infectious cycle involving NP, M, and NS gene products is the nuclear export of viral RNP. In the nucleus, the influenza nuclear export protein (NEP; encoded by the NS gene) interacts with the M1 protein, which binds to the newly assembled viral RNP. NEP also interacts with host protein CRM1, thereby directing the nuclear export of the viral RNP complex [33], [34], [35]. Although a direct interaction between NEP and NP proteins has not been shown, the severely defective growth of reassortants possessing heterologous M and NS relative to NP suggests an unidentified crosstalk between these viral proteins, with the possible involvement of a host protein (s). Striking viral phenotypes were also evident in rH5N1 viruses with heterologous polymerase subunits. The PB1 protein interacts with PA and PB2 forming transcriptionally active heterotrimers [36], [37], [38]. Although a direct interaction between PB2 and PA has never been reported, our genetic analyses pointed towards a specific interdependence between PB2 and cognate PA genes of avian origin, either through direct protein-protein interaction or concerted interaction with other viral or host factor (s). Interestingly, natural avian-mammalian reassortant viruses isolated from humans and swine possess PB2 and PA of the same host origin and sometimes carry a PB1 derived from a virus adapted to a third host species [39], [40]. Thus, linkage between avian PB2 and PA would be expected in the event of reassortment between an H5N1 virus and a seasonal H3N2 virus from humans. The role of the avian PB1 genes in the emergence of reassortant viruses that caused the 1957 and 1968 influenza pandemics has remained enigmatic. This study shows that incorporation of an avian PB1 gene into a background of human virus internal genes significantly increased mouse virulence. We postulate that acquisition of the avian PB1 gene, as was seen in the 1957 and 1968 pandemic influenza strains may be a critical factor in the early stages of a pandemic, allowing the emerging reassortant to overcome competition with seasonal influenza viruses by enhancing its replication or virulence. Our results, therefore, have implications for assessing the potential virulence of novel reassortant viruses possessing human virus internal genes and PB1 from currently circulating H5N1 viruses. Although reassortment between two different viruses could yield 254 possible new genotypes, this study characterized the subset of 63 genotypes with H5N1 surface antigens, of highest public health significance. In addition, these studies show that a reassortant virus with NA from a contemporary human H3N2 virus and the remaining 7 genes from TH04 replicated efficiently and was as lethal as wt H5N1 virus in mice, indicating that the current human N2 is compatible with the receptor binding function of the H5 HA. Although we did not analyze all the 63 additional genotypes carrying H5N2 surface genes, we anticipate that their virulence would be similar to their rH5N1 counterparts. However, these data should be interpreted in a broader context of human and avian influenza virus replication and evolution. The genotype of the rH5N1 that would emerge from natural co-infection is dictated by many factors besides the replication competency of a given reassortant. Dual infection of a single cell with human and avian influenza viruses involves co-replication of two genomes that may complement, interfere, and compete with each other. These events and the subsequent expansion of the reassortan will be further conditioned by the host species and tissue tropisms of the parental viruses and resulting reassortants. Ultimately, while use of reverse genetics technology to generate reassortants provides an experimental platform free of these many variables, natural reassortment between two viral genomes, and the consequences therein, are more complex. In summary, we report a strikingly high level of compatibility between avian and human virus genes. Because few studies have described naturally occurring or experimentally derived avian-human reassortants, our results were surprising in that almost half of the rH5N1 viruses tested showed a high frequency of functional compatibility between avian and human virus genes. In addition, approximately 1 in 5 of all possible H5N1 reassortants was lethal for mice at doses below 104 pfu. The highly virulent reassortant genotypes identified in this study suggest that introduction of certain H5N1 viral segments into circulating human H3N2 viruses may increase their virulence for mice and perhaps other mammalian species. In addition, the moderately virulent reassortant viruses could circulate in a mammalian host, evolve by compensatory and/or adaptive mutations, and become more virulent for humans. The results of this study, therefore, underscore the necessity for enhanced viral surveillance strategies, which monitor reassortment events in nature to reduce the public health threat posed by H5N1 HPAI viruses currently circulating in three continents. A/Thailand/16/2004 (TH04) and A/Vietnam/1203/2004 (VN04) H5N1 viruses and A/Wyoming/3/2003 (WY03) H3N2 virus obtained from the WHO Global Influenza Surveillance Network were provided by Alexander Klimov (CDC, Atlanta, USA). Madin-Darby canine kidney (MDCK) and human lung carcinoma (A549) cells were obtained from the American Type Culture Collection and propagated in Dulbecco' s Modification of Eagle' s Medium with 10% fetal bovine serum. Viral infectivity was determined by plaque assay on MDCK cells as described [41]. Reassortant viruses containing any segment derived from the H5N1 virus were generated in compliance with the Institutional Biosafety Committee and NIH Guidelines for Research Involving Recombinant DNA Molecules. Viruses were handled in biosafety level 3 containment, including enhancements required by the U. S. Department of Agriculture and the Select Agents program http: //www. cdc. gov/od/ohs/biosfty/bmbl5/bmbl5toc. htm. RT-PCR amplicons of the eight viral genes from WY03 and TH04 viruses were cloned into a dual-promoter plasmid for influenza A reverse genetics [42]. Virus rescue was performed by plasmid DNA transfection into co-cultured 293T/MDCK cells [42]. Culture medium from the transfected cells was harvested at 72 h and analyzed by plaque assay on MDCK monolayers. The plaque count and diameter were recorded as a measure of the virus rescue efficiency from plasmid DNA. DNA transfection of each genotype was performed at least three times independently. WY03 and TH04 rg plasmid sets were included as controls during each reassortant rescue to evaluate experimental variation. Viruses with H5 HA were propagated in 10–11 days old embryonated chicken eggs. The H3N2 virus was propagated in MDCK cells in the presence of 1 μg/ml TPCK-treated trypsin. Following propagation, the full genomes of reassortant viruses were sequenced to confirm presence of parental virus sequence. A549 cells cultured in 24-well tissue culture plates were co-transfected with pPol1-NS-Renilla (100 ng) encoding a reporter mini-genome under transcriptional control of the human RNA polymerase I, pSV-Luc (200 ng) encoding firefly luciferase under SV40 virus RNA polymerase II promoter control, and four plasmids expressing viral PB2, PB1, PA, NP (50 ng each) from the strain of interest. Twenty-four hours after the transfection, the cell lysates were harvested and further diluted to perform the dual luciferase assay according to the manufacturer' s protocol (Promega). The influenza polymerase catalytic activity derived from the Renilla luciferase plasmid (pPol1-NS-Renilla) was corrected to account for well-to-well differences in transfection efficiency using the firefly luciferase activity values from pSV-Luc plasmid. Groups of 6-8 week old female BALB/c mice (Jackson Laboratories, Bar Harbor, ME) were placed under light anesthesia and inoculated intranasally with 50 μl of serial 10-fold dilutions of infectious virus in PBS. For reassortant viruses tested, 104 pfu of virus was the highest dose used to infect mice; for WY03 virus, 106 pfu of virus was tested. Three mice from each group were euthanized at 4 days post-infection (dpi) and nasal turbinates, lungs, spleens, and brains were harvested, immediately frozen on dry ice, and stored at −80°C until further processing. Whole tissues were thawed, homogenized in 1 ml of cold PBS, and clarified by centrifugation (2,200×g) at 4°C. Virus titers of homogenates were determined by plaque assay in MDCK cells. Five additional mice in each group were monitored daily for clinical signs for 14 dpi. Mice that lost more than 25% of their body weight were euthanized humanely. The fifty percent mouse infectious dose (MID50) and fifty percent lethal dose (LD50) were calculated and expressed as the pfu value corresponding to 1 MID50 or LD50. Animal studies were conducted per approved Institutional Animal Care and Use Committee protocols. Statistically significant differences of rescue efficiencies of avian-human reassortants in cell culture were determined by F-test adjusted for multiple comparisons. LD50 and MID50 values were calculated using the method of Reed and Muench [43]. Statistically significant differences between LD50 values of viruses in virulence group A1 and A2 were determined by comparing groups A1 and A2 to TH04 WT and group A1 to group A2 using an analysis of variance performed by an F test for multiple comparisons. Growth and differentiation of primary human tracheobronchial epithelial cells were performed as described previously [23], [30], [44]. Briefly, primary cells (passage level 3) were seeded in porous membrane inserts (Corning, 4. 5 μm, 12 mm diameter) at the density of 5×104 cell/cm2. Three days after seeding the cells, the medium from the apical side was removed and the confluent monolayers were cultured at an air-liquid interface. The medium from the basal compartment was replaced daily, and the in vitro differentiation of primary cells was achieved after 4–6 weeks. Differentiated cells with trans-epithelial electrical resistance of ≥300Ω cm2 were used in our study. Kinetic analysis of reassortant virus growth was performed after infection of the monolayer at a multiplicity of infection (moi) of 0. 02 pfu/cell as described [23], [30]; apically released virus was harvested at the appropriate times and analyzed by plaque assay. The GenBank (http: //www. ncbi. nlm. nih. gov/sites/entrez) accession numbers for the genes described in this paper are: EU268216 (A/Thailand/16/2004, PB2 gene), EU268217 (A/Thailand/16/2004, PB1 gene), EU268218 (A/Thailand/16/2004, PA gene), EU268219 (A/Thailand/16/2004, HA gene), EU268220 (A/Thailand/16/2004, NP gene), EU268221 (A/Thailand/16/2004, NA gene), EU268222 (A/Thailand/16/2004, M gene), EU268223 (A/Thailand/16/2004, NS gene), EU268224 (A/Wyoming/03/2003, PB2 gene), EU268225 (A/Wyoming/03/2003, PB1 gene), EU268226 (A/Wyoming/03/2003, PA gene), EU268227 (A/Wyoming/03/2003, HA gene), EU268228 (A/Wyoming/03/2003, NP gene), EU268229 (A/Wyoming/03/2003, NA gene), EU268230 (A/Wyoming/03/2003, M gene), EU268231 (A/Wyoming/03/2003, NS gene).

The influenza pandemics of 1957 and 1968 were caused by hybrid viruses consisting of a mixture of human and avian influenza genes. The introduction of avian genes resulted in a sudden change of the virus surface antigens, allowing its worldwide spread due to lack of immunity in the population. The highly pathogenic avian influenza H5N1 virus has continued its spread in domestic and wild birds in Asia, Europe, and Africa. Although H5N1 infection in humans is rare and person-to-person transmission is very inefficient, the steady accumulation of human cases has raised concern over the possible reassortment between H5N1 and human seasonal influenza resulting in a virus with new surface antigens and pandemic potential. In this study, we used recombinant DNA technology to generate a systematic collection of hybrid viruses (with genes from human and avian viruses) bearing H5N1 surface antigens and analyzed their properties in cell culture and in mice. The H5N1 hybrid viruses revealed a broad range of viability and multiplication capacity in cell cultures. In addition, several H5N1 hybrid viruses were highly virulent in mice. Results from this systematic analysis provide important insight to support risk assessment of reassortant H5N1 avian influenza viruses.

Abstract Introduction Results Discussion Materials and Methods

virology/virus evolution and symbiosis virology/animal models of infection virology/emerging viral diseases

2008

Genetic Compatibility and Virulence of Reassortants Derived from Contemporary Avian H5N1 and Human H3N2 Influenza A Viruses

Leprosy reversal reactions type 1 (T1R) are acute immune episodes that affect a subset of leprosy patients and remain a major cause of nerve damage. Little is known about the relative importance of innate versus environmental factors in the pathogenesis of T1R. In a retrospective design, we evaluated innate differences in response to Mycobacterium leprae between healthy individuals and former leprosy patients affected or free of T1R by analyzing the transcriptome response of whole blood to M. leprae sonicate. Validation of results was conducted in a subsequent prospective study. We observed the differential expression of 581 genes upon exposure of whole blood to M. leprae sonicate in the retrospective study. We defined a 44 T1R gene set signature of differentially regulated genes. The majority of the T1R set genes were represented by three functional groups: i) pro-inflammatory regulators; ii) arachidonic acid metabolism mediators; and iii) regulators of anti-inflammation. The validity of the T1R gene set signature was replicated in the prospective arm of the study. The T1R genetic signature encompasses genes encoding pro- and anti-inflammatory mediators of innate immunity. This suggests an innate defect in the regulation of the inflammatory response to M. leprae antigens. The identified T1R gene set represents a critical first step towards a genetic profile of leprosy patients who are at increased risk of T1R and concomitant nerve damage. Leprosy is a chronic human infectious disease caused by Mycobacterium leprae. If left untreated the disease results in pronounced skin deformities and nerve disabilities due to preferential invasion of macrophages and Schwann cells by M. leprae. Efforts by the World Health Organisation (WHO) to eliminate leprosy resulted in a substantial reduction of global disease prevalence from 5. 35 million in 1985 to 211,903 by 2010. The number of newly registered cases, however, remained at high rates (244,796 in 2009) [1]. Leprosy displays a wide spectrum of clinical manifestations. Tuberculoid (TT) and lepromatous leprosy (LL), characterized by the presence and absence of specific cellular immune responses, respectively, represent the opposite ends of the clinical spectrum [2], [3]. Based on histopathological, immunological, bacteriological, and clinical criteria, Ridley and Jopling classified three additional intermediate, or “borderline, ” types as borderline tuberculoid (BT), mid borderline (BB), and borderline lepromatous (BL) leprosy [4]. Leprosy reactions, acute episodes of dysregulated inflammation, are a major cause of nerve damage in leprosy patients and present as two types [5], [6]. Type-2 reactions remain rather infrequent (<5% of leprosy patients) and occur nearly exclusively in BL and LL patients [7]. In contrast, reversal reactions type-1 (T1R) can occur in any leprosy subtype, although they are most prevalent in the borderline forms (BT-BB-BL) [8]. T1R are characterized by sudden episodes of exacerbated local delayed-type hypersensitivity to M. leprae in skin and/or nerves. Histological assessment of T1R lesions demonstrated an influx of mononuclear cells that lead to skin swelling and neural compression [9], [10]. Immunological analysis of the skin lesions and peripheral blood samples of patients with T1R showed the predominance of CD4+ T cells and Th1-associated cytokines, especially IFN-γ, IL-2, IL-12, and TNF-α [11]–[15]. The clinical care and management of T1R patients is a major challenge of current leprosy control efforts. In Vietnam, prevalence of T1R is 29% among leprosy patients and approximately one third of T1R cases are detected at the time of leprosy diagnosis [8]. However, the prevalence of T1R differs widely among different geographical and epidemiological settings and ranges from 6% to 67% of all patients with leprosy [7], [16]–[19]. It is not known if this large spread in the occurrence of T1R reflects the variable impact of environmental triggers or an innate predisposition of certain leprosy patients towards T1R. In the absence of acute T1R, we compared the transcriptome response to M. leprae sonicate of leprosy patients that developed T1R with those that did not. In the discovery set we employed a retrospective design (patients who present at leprosy diagnosis with T1R) and we validated the results obtained in a prospective study (patients who present with T1R after diagnosis of leprosy). This design allowed us to identify a T1R gene set signature that captures differences in gene expression of whole blood following exposure to M. leprae antigens that are characteristic for persons with an innate predisposition to undergo T1R. Our study followed a two-step design (Figure 1). First, we enrolled a retrospective sample of 12 former leprosy patients of which half had remained T1R-free while the other six had been diagnosed with T1R at the time of leprosy diagnosis. For these patients, irrespective of T1R, the time from clinical cure to participation in the present study was on average nine years [range 5–13 years]. The patients with simultaneous diagnosis of T1R and leprosy can be considered as early-onset T1R and we hypothesized that genetic effects should be most pronounced in such patients. By comparing M. leprae sonicate triggered gene expression in whole blood between the two groups (i. e. T1R-positive and T1R-free), we derived a gene set that was either over- or under-stimulated among the T1R group (Figure 1). Next, we employed the genes that were differentially induced (the so-called T1R-specific gene set) in a prospective design. We enrolled 43 leprosy patients who were T1R-free at the time of leprosy diagnosis and obtained RNA from whole blood assays stimulated with M. leprae sonicate. We then followed these patients for 3 years and recorded episodes of T1R among 11 patients. At that point, we conducted an analysis that validated the T1R gene set in the prospective arm (Figure 1). Since none of the subjects in the prospective phase had developed clinical signs of T1R at the time of the experiment, this validation showed that the T1R-specific set captured an innate characteristic of T1R susceptibility. In the retrospective samples, we conducted a careful transcriptional response profile of whole blood to M. leprae sonicate. Independent of the clinical phenotype, a strong transcriptional response to M. leprae antigens was observed in subjects of both groups. Antigen stimulation altered the expression of 581 genes by at least two-fold in at least one group. Of these, 462 genes showed the same direction and strength of regulation (absolute fold-change equal or above 2) in both groups. Even though not all of the remaining 134 genes reached the two-fold regulation cut-off in each patient group, these genes showed identical direction of M. leprae-triggered expression changes in both groups. The comparison of the baseline (unstimulated) and antigen-stimulated expression levels between T1R-free andT1R-affected patients did not detect significant differences in gene induction or suppression (data not shown). We utilized the DAVID analysis tool to assess the enrichment of particular functional clusters among the 581 regulated genes. Out of 581 genes, 572 were annotated by DAVID for further analysis. The DAVID Functional Annotation Clustering tool assigns each gene to a set of functional groups according to its role in cellular processes [20]–[22]. To avoid listing multiple sources for clustered functional entities we focused on GO terms and KEGG/PANTHER pathways. As significance thresholds we used p-values adjusted by the Benjamini-Hochberg procedure (PBH). For GO terms we selected PBH<10−5 and for pathways PBH<0. 05 with the false-discovery rate fixed at 0. 05 in each instance. The highest levels of significance were observed for GO terms “Defense response” (PBH = 6. 2×10−24), “Inflammatory Response” (PBH = 1. 6×10−21) and “Response to wounding” (PBH = 1. 44×10−20; Table 1). More specific and still highly significant terms highlighted the overabundance of genes involved in innate immunity (e. g. “Vacuole”, “Regulation of immune system process”, “Response to bacterium”, “Cytokine activity”, “Chemotaxis”, “Regulation of cell death”, “Regulation of cell proliferation”; Table 1). Consistent with the observed GO terms, we also detected significant enrichment of three signalling pathways implicated in lysosomal function, cytokine and chemokine signalling cascades (Table 2). Taken together, these data showed a strong innate immune response to M. leprae antigens irrespective of a history of T1R. Although there were no significant T1R-specific differences in induction or suppression of individual transcripts, we were still interested to evaluate the T1R-specific differences in the intensity of response to M. leprae antigen. To quantitate differences in the intensity of the transcriptional response, we defined the delta fold change (ΔFC) value. This value captured differences in the M. leprae antigen triggered FC of gene expression between the groups of T1R-affected and T1R-free patients. We considered a gene to be differentially regulated if its ΔFC was greater than 1. 3 or smaller than 1/1. 3. The justification for this cut-off was derived from the overall distribution of ΔFC values for the probes with a |FC| of ≥2 and captures approximately the 10% of extreme ΔFC values for those probes (Figure 2A). Indeed, ΔFC ≥1. 3 or ≤1/1. 3 probes represent a reasonable selection of probes in terms of differential intensity of triggered probes (Figure 2B). The 50 probes identified represent a total of 44 genes (Table 3). Since all of these genes belonged to the cluster of transcripts that were up-regulated by the M. leprae sonicate stimulation, the identified genes differed in their intensity of transcriptional up-regulation. Hence, reference to over-stimulated or under-stimulated genes in T1R patients is always relative to T1R-free leprosy patients. We classified the 44 differentially regulated genes by their functional roles in immunological processes. More specifically, we assigned 32 genes into 3 distinct groups: i) genes promoting a pro-inflammatory response; ii) genes belonging to the arachidonic acid (AA) metabolic pathway, iii) genes involved in down-regulation of the inflammatory response. The remaining 12 genes were not assigned to any specific functional class, i. e. they were regarded as unclassified genes (Table 3). There were larger proportions of under-stimulated genes in the groups of “negative regulation of inflammation” and “unclassified genes” as compared to the groups “pro-inflammatory regulators” and “arachidonic acid pathway” (Table 3). We defined these 44 genes as T1R gene set signature. Approximately two-thirds of T1R patients develop clinical symptoms of T1R only after diagnosis of leprosy and initiation of treatment. Such patients can be enrolled in a prospective design. We collected blood samples from 43 recently diagnosed borderline leprosy patients with no signs of T1R at the time of leprosy diagnosis. Blood stimulation with M. leprae sonicate at the time of enrolment was performed by the identical procedure used in the retrospective study. All patients were followed for at least 3 years and a total of 11 individuals with T1R episodes were recorded. After 3 years no additional episodes of T1R are expected to occur [8]. Transcriptome analysis of whole blood assays from the prospective samples detected 752 genes regulated by M. leprae sonicate with ≥2-fold change in T1R-affected and/or T1R-free patients. The gene sets and pathways represented by these genes largely overlapped those detected in the retrospective sample (Table S1). As in the retrospective discovery arm, no single gene was significantly differentially regulated between T1R-free and T1R-affected leprosy patients in the prospective arm. Therefore, we focused our approach on the systematic analysis of differentially expressed groups of genes with special focus on the T1R-specific gene set. To test for the significance of differential regulation of groups of genes between T1R-affected and T1R-free patients we performed receiver operator characteristic scoring (ROC) analysis [23], [24]). The ROC algorithm, an equivalent of the Wilcoxon rank sum test, performs ranking of genes based on their scores. We used the absolute log2 transformed ΔFC values as gene scores. Subsequently, ROC clusters genes according to GO terms and user-defined gene sets and tests for the overrepresentation of high scoring genes in each gene-set. As the proportion of top ranking genes in a gene set increases, the set becomes more significant. We performed the initial analysis by comparing T1R patients to T1R-free leprosy patients in the retrospective sample, and validated the results in the prospective sample. Contrary to the approach used for the T1R-specific set in the discovery phase, for the ROC analysis we used all available probes without a specified score cut-off point to avoid bias in the results. We restricted the number of genes representing an individual gene set to be between 5 and 100. As the ΔFC value can have two directions we split the probe sets into “over-regulated” (ΔFC≥1) and “under-regulated” (ΔFC<1) groups of genes. Since the direction of differential stimulation is an important replication criterion, groups of over- or under-stimulated genes from the same gene set were analysed separately. The T1R gene set included 29 genes that were over-regulated and 15 genes that were under-regulated (Table 3). As expected, in the retrospective discovery sample the set of 29 over-regulated genes was very significantly enriched (PBH = 1. 07×10−53). Importantly, the same set of genes was very significantly replicated in the prospective validation sample (PBH = 2. 33×10−9; Table 4). Next, we analysed the 10 GO terms most significantly overrepresented among T1R patients in the retrospective sample for replication in the prospective sample. As no gene score cut-off was used for ROC analysis the GO terms included up to 100 genes. Several of these GO terms concerned immune cells activation. For example, 72 genes of the GO term “Regulation of leukocyte activation”, were significantly more up-regulated in T1R patients in both the discovery (PBH = 2. 33×10−6) and the validation sample (PBH = 4. 91×10−3; Table 4). Likewise, a set of 97 genes of the lymphocyte activation GO term was significantly stronger regulated in T1R patients in both discovery (PBH = 3. 28×10−6) and replication sample (PBH = 3. 91×10−4). In addition, several replicated GO terms related to control of cellular immune response, including “Regulation of cell activation” (Retrospective PBH = 6. 88×10−6 and Prospective PBH = 9. 44×10−3), “Anti-apoptosis” (Retrospec PBH = 1. 00×10−5 and Prospec PBH = 1. 74×10−4) and “Response to virus” (Retrospec PBH = 1. 36×10−5 and Prospec PBH = 5. 74×10−6; Table 4). Of note, among the ten highly significant GO terms with up-regulated genes in retrospective T1R patients, four did not replicate in the prospective sample (Table 4). When we analysed the list of 15 genes under-stimulated in the T1R gene set signature in the retrospective arm, we also observed a very significant differential regulation T1R (PBH = 7. 7×10−31) which was expected since the T1R gene set had been derived from the same data. More importantly, this result was replicated in the prospective sample (PBH = 2. 36×10−3; Table 5). Among the ten most significantly under-stimulated GO terms we observed a preponderance of gene sets related to RNA processing the majority of which was replicated in the prospective sample (Table 5). For example, we detected two large gene sets involved in negative regulation of cellular protein metabolic processes (100 genes) and protein modification processes (82 genes) that were significant under-stimulated in T1R patients in both the retrospective and prospective samples (Retrospec PBH = 5. 98×10−4/Prospec PBH = 3. 97×10−4, and Retrospec PBH = 1. 00×10−3/Prospec PBH = 1. 18×10−4, respectively; Table 5). Overall, from 20 tested gene sets we successfully replicated thirteen with the combined T1R gene set showing the strongest evidence for replication for any gene set. We evaluated a possible impact of gender and age at diagnosis on the significance of the T1R gene set. In the discovery arm of the study we had equal numbers of males (3) and females (3) among the T1R patients. We tested ΔFC ranking for heterogeneity by gender and found significant over-representation of both over- and under-stimulated genes of the T1R gene set among males (PBH = 9. 5×10−20 and PBH = 2. 1×10−10, respectively). In the prospective arm, only males developed T1R and an impact of gender could not be tested. To test for impact of age, we subdivided subjects into 15 years and younger (n = 3) or older than 15 years (n = 3) and found significantly better ranking of the T1R gene set in older patients for over- but not for under-stimulated genes (PBH = 1×10−14 and PBH = 0. 16, respectively). The impact of age on the T1R set was not unexpected since young age (<16 years) is highly protective for T1R in Vietnam [8]. In the prospective sample, only one T1R case was younger than 16 years and no further tests were conducted. While the T1R-gene set ranked significantly in all sub-groups, the data suggested that the T1R gene set might have better characteristics for older male patients. Next, we investigated the impact of time to onset of T1R after leprosy diagnosis on replication of the T1R gene set. First, we compared ΔFC values between four subjects who presented with T1R within one month of leprosy diagnosis and the seven patients that developed T1R more distant to initial diagnosis (2 to 21 months). The ΔFC values for the T1R gene set ranked significantly better among late onset T1R (PBH = 1. 89×10−16 and PBH = 1. 14×10−5 for over-and under-stimulated genes, respectively). These results suggest that rapid onset patients already displayed preclinical forms of T1R and that the onset of T1R immune dysregulation in those patients overwhelmed the predictive signature. Despite the effective treatment of T1R by corticosteroids, neurological impairment persists in about 30% to 50% of cases [25]. Thus, the early identification of patients at risk of T1R, and consequently at risk of neurological injury, is a critical challenge in leprosy care [26]. The identity of the factors that commit certain leprosy patients to T1R while others remain free of such complications is unknown. Episodes of T1R are characterized by a highly dysregulated inflammatory response which makes it difficult to discern specific functional indicators of T1R risk in patients with ongoing reactions. To avoid this problem, we enrolled former T1R patients that had undergone an episode of T1R at least seven years earlier and compared their reaction to M. leprae antigens with the one displayed by former leprosy patients that had remained T1R-free. This sample was too small to assess a possible impact of time since cure on the transcriptional response but the spread of time since cure across all subjects was rather narrow suggesting that this would not bias results. As assay we used transcriptional profiling of whole blood cultures stimulated with high doses of M. leprae sonicate. The gene sets that were preferentially regulated in former T1R patients were then validated in a prospective study where we followed newly diagnosed leprosy patients at risk of T1R for three years. Transcriptome responses to M. leprae antigens were compared between patients who developed T1R and those who did not. Hence, our study was not designed to study pathogenic networks of host response during T1R but to evaluate characteristics of the transcriptomic response of T1R patients to M. leprae antigen prior to the onset of any clinical symptoms of T1R. The important result of our study was that we were able to define a T1R gene set signature based on the magnitude of expression differences in the retrospective sample that we replicated very significantly in an independent prospective sample. This T1R gene set now needs to be investigated for its possible predictive value in follow-up studies. An important aspect of our analysis was the finding that for individual transcripts we failed to detect significant differences in M. leprae antigen triggered gene induction between T1R-affected and T1R-free leprosy patients. The lack of significant differences for individual gene transcripts prompted us to analyse differences of induction in sets of genes involved in the same cellular functions. Gene sets reflect biological pathways or processes and therefore represent a higher level of host responses to stimuli than individual genes. The fact that gene set analyses can identify changes in host responsiveness that cannot be detected through the study of any individual gene is a powerful feature of such analyses. Since our interest was in the difference of gene induction by M. leprae antigen between the two groups of patients, we used the difference in gene induction as a “score” to characterize the transcriptional response of individual genes. These gene scores were then used in the gene set analysis. By considering the scores of all genes on the expression chip we were able to identify gene sets that significantly differed between the two patient groups in a global and unbiased fashion. In addition, the genes that are part of a gene set need to have a strong biological link to the phenotype to which the gene set is assigned. A substantial number of genes in the T1R set signature are strong mediators of a pro-inflammatory response which is driven by monocytes and lymphocytes reflecting the clinical picture of T1R episodes which are characterized by excessive cell-mediated immunity [9], [27]. This is best shown by the large number of chemokine-coding genes that are more strongly upregulated in T1R patients since the encoded molecules mediate the recruitment of monocytes and lymphocytes to the site of inflammation (Table 3). For example, identification of CCL2 as a part of the T1R gene set is not surprising and could be considered as a positive control of the experiment as it is entirely consistent with the reported increase of CCL2 expression in lesions of active T1R patients [28]. The T1R gene set contains a substantial number of genes involved in arachidonic acid metabolism. The release of AA and its derivatives is a crucial step in the regulation of pro- and anti-inflammatory signalling. For example, PTGS2, encoding COX-2, a central gene in the AA pathway, was one of the preferentially upregulated genes in the T1R gene set signature. Clinical detection of increased levels of COX-2 in edemas, vessels and nerves of T1R patients [29] is supported by the drastic upregulation of PTGS2 in T1R patients. In line with this observation, TNFAIP6, which was highly expressed in the early onset T1R samples, encodes TNF-stimulated gene 6 (TSG6), an inducer of COX-2 expression in macrophages [30]. COX-2 oxidizes AA leading to the production of prostaglandins which are powerful mediators of pro- and anti-inflammatory responses [31]. A substantial number of genes in the T1R gene set represent strong mediators of the anti-inflammatory response. For example, alpha-1-acid glycoprotein (AGP), encoded by the ORM1 gene, is a potent inhibitor of neutrophil chemotaxis and superoxide anion generation [32]. The serum level of AGP is used as a biomarker of leprosy type-2 reactions and Crohn' s Disease management [33], [34]. Elafin, coded by PI3, can effectively de-activate neutrophil elastase thus preventing excessive tissue damage [35]. Additionally, by inhibiting NF-κB and AP-1 activity elafin controls the extent of the inflammatory response in tissues [36]. IDO1 and KYNU encode the anti-inflammatory regulators indoleamin-2,3-dioxygenase (IDO) and kynureninase (KYNU), respectively, that are involved in the tryptophan metabolic pathway [37]. The metabolites of the tryptophan breakdown represent potent mediators of the anti-inflammatory response [38]. The oxidative action of IDO on tryptophan is dependent on the presence of the superoxide radical anion (O2−) which is utilized by IDO both as a substrate and a cofactor. In a competitive reaction O2− is inactivated by superoxide dismutase (SOD2). Therefore, the observed relative down-regulation of SOD2 in retrospective T1R patients favours the presence of superoxide radical anions providing both substrate and cofactor for IDO. The coordinated up-regulation of IDO1 and KYNU and down-regulation of SOD2 reveals a strong anti-inflammatory response in the retrospective T1R patients. Finally, PLA2G7 encodes a phospholipase A that inactivates the potent pro-inflammatory mediator platelet-activating factor (PAF) [39]. The usefulness and robustness of the T1R gene set was demonstrated by the validation of the T1R set signature in a prospective study. For this, we conducted whole blood assays for newly diagnosed leprosy patients before the onset of clinical symptoms of T1R. The validity of the T1R gene set signature in this design directly demonstrates that T1R patients have an innate predisposition to mount a strong pro-inflammatory response to M. leprae antigens despite the up-regulation of major anti-inflammatory genes. Hence, the apparent breakdown of communication between pro- and anti-inflammatory responses in T1R patients appears as characteristic of T1R susceptibility. However, it is important to realize that the T1R gene set does not capture all aspects of predisposition to T1R nor does it allow conclusions about the effector mechanism at work during ongoing T1R. Hence, while our data point to a primary role of innate immunity in predisposition it is possible that acquired immunity responses missed by our design make an important contribution to T1R predisposition. Nevertheless, the results of our experiments raise two immediate questions. What are the genetic factors that predispose a person to undergo T1R after exposure to M. leprae antigens and is the observed breakdown in the regulation of pro-inflammatory responses specific to T1R patients? The answers to these questions may impact on a multitude of human inflammatory diseases. The study was conducted according to the principles expressed in the declaration of Helsinki. Informed consent was obtained for all subjects participating in the study. The study was approved by the regulatory authorities and ethics committees in Ho Chi Minh City, Vietnam, and the Research Ethics Board at the Research Institute of the McGill University Health Centre, Montreal, QC, Canada. For the retrospective study, we recruited 12 unrelated Kinh Vietnamese subjects at the Dermato-Venereology (DV) Hospital in Ho Chi Minh City, Vietnam. These individuals had previously been diagnosed with borderline leprosy (BT: n = 3, BB: n = 7, BL: n = 2) and six of them had presented with T1R at the time of their leprosy diagnosis. At the time of recruitment for the present study, all individuals had been cured and had remained asymptomatic for at least five years. Males (n = 5) and females (n = 7) were approximately equal distributed across both groups. Age at the time of leprosy diagnosis ranged from 9 to 28 years, with a median age of 18 years. For the prospective study, we recruited 43 individuals recently diagnosed with borderline leprosy without T1R. Blood samples were collected from patients within less than 3 months of their leprosy diagnosis and before undergoing T1R. Enrolled individuals presented with five borderline leprosy subtypes (BT: n = 10, BT/BB: n = 6, BB: n = 19, BB/BL: n = 2, BL: n = 6). Among the recruited patients 34 were males and 9 were females; the median age was 27 years (range 9 to 41 years). The recruitment of younger individuals for the retrospective study is explained by age being a risk factor for the occurrence of T1R at the time of diagnosis [8]. The male∶female ratio is representative of the patient hospital turn in. All prospective patients were closely followed for 3 years during which 11 patients developed T1R. M. leprae whole cell sonicate was generated with support from the NIH/NIAID Leprosy Contract N01-AI-25469 at Colorado State University. Inactivated (irradiated) armadillo-derived M. leprae whole cells were probe sonicated with a Sanyo sonicator to >95% breakage to produce whole cell sonicate. A total of 20 ml of whole blood was obtained from each subject by venipuncture in EDTA vacutainers. Blood samples were split in two aliquots and each aliquot was mixed with RPMI medium containing L-glutamine (300 mg/L) and HEPES (10 mM) at 1∶2. One aliquot was stimulated with M. leprae sonicate at a concentration of 20 µg/ml, which approximately corresponds to an MOI of 50 M. leprae per white blood cell. The second aliquot was left untreated. Each aliquot, the stimulated one and the control, was divided into four 50 ml polystyrene tubes to facilitate better leukocytes adhesion and aeration of blood. Tubes were incubated for 26–32 hours at 37°C, 5% CO2. Total RNA from blood samples was extracted employing a modified protocol of the LeukoLOCK RNA extraction kit (Ambion, CA, USA). Briefly, blood aliquots were filtered by gravity through LeukoLOCK filters to isolate leukocytes. Collected cells were rinsed to eliminate red blood cells and lysed directly on the LeukoLOCK filters. Extraction of total RNA was performed according to the manufacturer' s instructions. Isolated RNAs were kept under ethanol and ammonium acetate at −80°C. Prior to further experiments, all samples were cleaned with the RNeasy kit (Qiagen, Germany). The quality of 110 RNA samples (stimulated with M. leprae sonicate or not for each of 55 individuals) was assessed by the BioAnalyzer (Agilent). All samples showed RNA Integrity Numbers above 8. 5, indicating a good RNA quality, were reverse transcribed, amplified and labelled for hybridization following standard protocol. The retrospective 24 samples (12 stimulated and 12 non-stimulated) from healthy controls and former leprosy patients were hybridized to Illumina HumanRef_6_v3 BeadChips and screened for expression changes of 48,804 individual probes (representing 37,804 loci). The prospective 86 samples were hybridized to Illumina HumanHT_12_v4 BeadChips and screened for 47,323 probes (34,695 loci). Raw data were collected by BeadStudio v3. 3. 7 (Illumina Inc. , CA). Utilizing FlexArray 1. 6. 1. 1 (http: //genomequebec. mcgill. ca/FlexArray) raw data were subjected to variance-stabilization transformation (VST) and quantile normalization. In the retrospective arm, the regulation of transcription was determined by comparing mean expression values for each probe in stimulated and unstimulated samples of each phenotype group. The expression FC was estimated using the formula: To focus on highly M. leprae-regulated transcripts we selected all genes whose expression levels were increased or decreased by a fold-change of 2 or more. To compare the extent of transcription regulation between T1R-affected and T1R-free leprosy patients we looked at the ratio in post-stimulatory expression changes, ΔFC = FCT1R/FCLep. We selected genes that were differentially regulated between two groups with ΔFC≤1/1. 3 (termed under-regulated) and ΔFC≥1. 30 (termed over-regulated). We employed DAVID version 6. 7 (http: //david. abcc. ncifcrf. gov/; [20], [21]) to estimate the enrichment of Gene Ontology (GO, [22]) terms and metabolic or signalling pathways within the list of genes regulated by M. leprae sonicate. We selected the GO terms of levels 3 to 5 which assign more specific functional annotation to each gene. Due to involvement in multiple processes some genes are assigned to multiple GO terms. We used Benjamini-Hochberg correction for multiple testing (PBH), controlling the false-discovery rate (FDR) at 0. 05. A particular GO term was considered significantly overrepresented in a gene list when its PBH-value was <10−5. ROC was performed using ErmineJ software (http: //www. chibi. ubc. ca/ermineJ/, [23], [24]). ROC is used as the standard method of evaluating genes scores by their ranking. The algorithm is based exclusively on the order of the underlying values (gene scores). The ROC method tests the null hypothesis that genes represented in gene sets are randomly distributed in their ranking. For each gene set the ROC value is calculated, which reflects the area under the curve, ranging from 0. 5 (genes in the set are ranked randomly) to 1. 0 (the gene set includes only the highest-scoring genes). Thus, ROC evaluates the probability of high-scoring genes to belong to a specific gene set while accounting for the number of genes in the set. We used absolute values of log2 transformed ΔFC values for the probes as gene scores in the ROC algorithm. All available probes were used for the analyses. For genes represented by multiple probes the mean score was used. For the analysis we only considered biological processes GO terms containing 5–100 genes. In addition to the T1R gene set, the ten most significant differentially regulated GO terms in the retrospective arm were tested for replication in the prospective arm.

Leprosy type 1 reversal reactions (T1R) are an important cause of nerve damage in leprosy patients and accurate prediction of patients at increased risk of T1R is a major challenge of current leprosy control. The incidence of T1R differs widely from 6% to 67% of leprosy patients in different leprosy endemic settings. Whether or not this reflects the impact of unknown environmental triggers or differences in the genetic background across ethnicities is not known. We performed a comparative transcriptome analysis between leprosy patients affected and free of T1R in response to M. leprae antigens. As the discovery sample we enrolled cured leprosy patients who had been diagnosed with T1R at the time of leprosy diagnosis and leprosy patients who had never undergone T1R (retrospective arm). Whole genome transcriptome analysis after stimulation of blood with M. leprae antigen resulted in the definition of a T1R signature gene set. We validated the T1R gene set in RNA samples obtained from T1R-free patients at the time of leprosy diagnosis and followed for 3 years for development of T1R (prospective arm). These results confirm the role of innate factors in T1R and are a first step towards a predictive genetic T1R signature.

Abstract Introduction Results Discussion Materials and Methods

genetics of disease genome expression analysis medicine genome analysis tools infectious diseases bacterial diseases gene expression gene regulation genetics molecular genetics leprosy biology genomics neglected tropical diseases gene networks transcriptomes

2013

Gene Set Signature of Reversal Reaction Type I in Leprosy Patients

In the prevailing model of HIV-1 trans-infection, dendritic cells (DCs) capture and internalize intact virions and transfer these virions to interacting T cells at the virological synapse. Here, we show that HIV-1 virions transmitted in trans from in vitro derived DCs to T cells principally originate from the surface of DCs. Selective neutralization of surface-bound virions abrogated trans-infection by monocyte-derived DCs and CD34-derived Langerhans cells. Under conditions mimicking antigen recognition by the interacting T cells, most transferred virions still derived from the cell surface, although a few were transferred from an internal compartment. Our findings suggest that attachment inhibitors could neutralize trans-infection of T cells by DCs in vivo. To ensure their survival, microbial pathogens have evolved strategies to subvert the action of cellular components of the host immune system, including dendritic cells (DCs). DCs patrol peripheral mucosal sites, capturing and processing potential pathogens into antigenic peptides for presentation by major histocompatibility complex (MHC) class II to CD4 T cells in lymphoid organs, initiating an immune response (for a review, see [1]). HIV-1 has been proposed to usurp this natural function of DCs to spread efficiently. HIV-1 entering the body via the mucosa and other peripheral sites may be transported by DCs to CD4 T cells deeper in the mucosa or in lymphoid organs [2–4]. HIV-1 that reaches lymphoid organs can also take advantage of the formation of DC–T-cell conjugates to promote its replication and spread [5–7]. DCs can transmit HIV-1 to T cells via two pathways. In the de novo pathway, DCs are actively infected with HIV, leading to the budding and spread of new virions to neighboring CD4 T cells. In the prevailing model of the trans pathway, intact HIV-1 virions are captured by alternative HIV-1 receptors, which bind virions without triggering fusion, and internalized into clustered compartments resembling late endosome/multivesicular bodies (MVBs) [8,9]. After interacting with a CD4 T cell, HIV-1–loaded DCs redistribute the virion-containing vesicles to the virological synapse [8,10,11]; CD4, CXCR4, and CCR5 receptors on T cells are recruited to this region, facilitating trans-infection [10]. How HIV-1 virions survive the uptake pathway designed to capture and cleave pathogens into peptides for antigen presentation remains unknown. HIV-1 could divert the intracellular trafficking of immunological synapse components to avoid degradation and thus survive until later transmitted to T cells. Alternatively, external virions could be transmitted to CD4 T cells since some HIV-1 virions remain deeply tangled in membrane protrusions and microvilli of the plasma membrane [8]. Using functional assays that detect virion fusion and productive infection of CD4 T cells, we investigated whether trans-infection is mediated through internalized or external HIV-1 virions in monocyte-derived DCs (MDDCs) and CD34-derived Langerhans cells (LCs). The potential effects of the state of DC maturation and coreceptor utilization by HIV virions in the trans and the de novo pathways in HIV-1 transmission from DCs to T cells were evaluated. These studies were performed with immature MDDCs or MDDCs matured with tumor necrosis factor α and poly (I: C) [12] and with two laboratory-adapted viral strains, CXCR4-tropic NL4–3 and CCR5-tropic 81A (Figure 1). MDDCs were incubated with virions at 4 °C to promote viral binding and were then either added to autologous activated T cells immediately or incubated for 1 to 5 d at 37 °C before mixing the autologous T cells. The MDDCs were then incubated with T cells for 24 h to allow virion transfer to T cells. After an additional 2 d of incubation in the presence of azidothymidine (AZT), productive infection of T cells was measured by immunostaining with anti-p24Gag (Figure 1A). Transmission of 81A (R5-tropic) virions from immature MDDCs to T cells was biphasic, as reported [11]. The early phase (0 to 1 d) involved the trans pathway; the later phase (1 to 5 d) involved the de novo pathway and was sensitive to the HIV protease inhibitor amprenavir (not shown). During the first day, 25% of R5-tropic 81A virions were transmitted by the trans pathway; 75% were transmitted by the de novo pathway over the ensuing 4 d (Figure 1B). In mature MDDCs, however, approximately 93% of virions were transmitted by the trans pathway during the first day. X4-tropic NL4–3 virions were transmitted by both immature and mature MDDCs principally by the trans pathway. Similar results were obtained when MDDCs were analyzed from nine different normal donors (Figure 1C). In vivo, DCs may not immediately interact with T cells after virion capture. Accordingly, we investigated how a delay in T-cell contact might affect transmission through the trans pathway with a virion-based HIV-1 fusion assay [13,14]. MDDCs loaded with HIV-1 virions containing β-lactamase-Vpr (BlaM-Vpr) were incubated with autologous T cells, and fusion to CD4 T cells was monitored by the changes in fluorescence of CCF2, a BlaM substrate loaded into the cells (Figure 1D). When NL4–3 virions were presented immediately after binding to mature MDDCs, up to 24% of CD4 T cells displayed BlaM activity, indicating virion fusion. Transmission was less efficient when virions were presented by immature MDDCs. Fewer 81A than NL4–3 virions were transmitted, likely because there were fewer CCR5- than CXCR4-expressing cells in resting peripheral blood lymphocytes (PBLs). When virions were presented by MDDCs after incubation at 37 °C for up to 120 min, transmission efficiency decreased sharply (Figure 1E) in both immature and mature MDDCs. This rapid decrease was not due to a relative lack of sensitivity of the fusion assay in the context of trans-infection. As in our previous studies of T-cell infection with free virions, the fusion assay proved to be both sensitive and quantitative over a broad range of viral inputs in these DC–T-cell mixing experiments [13] (Figure S1). Thus, HIV-1 trans-infection from MDDCs to autologous CD4 T cells is efficient only for a limited time after virion capture. Our results show that immature DCs preferentially transmit R5-tropic HIV-1 by the de novo pathway, as described [11,15–17], and X4-tropic HIV-1 by the trans pathway. Mature DCs transmit both R5- and X4-tropic virions mainly by the trans pathway. Since immature DCs are present at mucosal sites of viral entry, while mature DCs reside in lymph nodes and in the gut-associated lymphoid tissue (GALT), HIV-1 may exploit different transmission strategies at different anatomic sites in vivo. In healthy mucosa, immature DCs are likely to transmit R5-tropic HIV-1 principally via the de novo pathway, especially since the efficiency of the trans pathway declines rapidly (Figure 1E and [11,16]); the trans pathway might contribute to the local spread of virus from mucosal immature DCs to macrophages and CD4 T cells. However, in inflamed mucosal epithelium, which contains a greater proportion of mature DCs, HIV-1 transmission might preferentially involve the trans pathway, as in human cervical explants [4]. In lymph nodes and GALT, the proximity of mature DCs to T cells would further favor the trans pathway. Since DC–T-cell conjugates are major sites of HIV-1 production [5–7], trans-infection could be critical in the intense viral replication that characterizes the acute and chronic phases of untreated HIV-1 infection. To identify the cellular compartment from which HIV-1 is transmitted in trans, we selectively neutralized surface-bound HIV-1 virions with truncated recombinant soluble CD4 (sCD4; AIDS Reagent Program [18]), which binds the HIV-1 envelope gp120 protein and prevents engagement of CD4 on T cells. Cells were treated at 4 °C to protect internalized virions from sCD4 exposure. NL4–3 virions containing BlaM-Vpr were bound to MDDCs and allowed to internalize, and cell-surface virions were neutralized with sCD4. In the absence of an internalization step, surface-bound virions on immature and mature MDDCs effectively fused to CD3+CD4+ cells (Figure 2A; bars 1 and 3); this fusion was effectively blocked by sCD4 (bars 2 and 4). However, when HIV-1 virions were internalized at 37 °C for 30 min before treatment (bars 5 to 8), sCD4 still inhibited virion fusion (bars 6 and 8). To confirm that sCD4 neutralized surface-bound virions without impairing subsequent virion transfer, two sets of HIV-1 virions were successively bound to immature MDDCs, but only the first was neutralized with sCD4 (Figure 2B). Similar amounts of HIV-1 fused to T cells regardless of the presence of previously neutralized cell-surface virions on the DCs (bars 3 and 5). Thus, sCD4 does not interfere with virological synapse formation or the transfer of virions bound after sCD4 treatment. To further confirm the absence of virion transfer from internal cellular compartments, we performed sequential loading of HIV-1 expressing green fluorescent protein (GFP) (GFP-HIV) and then HIV-1 expressing cyan fluorescent protein (CFP) (CFP-HIV) [19] (Figure 2C). GFP-HIV was bound to MDDCs and incubated at 37 °C to allow virion internalization. Some residual GFP-HIV virions remained at the surface. Next, CFP-HIV was bound but kept at 4 °C to prevent virion internalization. MDDCs were then incubated with autologous T cells for 48 h. sCD4 treatment after binding and internalization of GFP-HIV but before CFP-HIV binding fully blocked transmission of GFP-HIV (Figure 2C, middle panel), indicating that residual surface-bound GFP-HIV was the source of virus for transmission to T cells (left panel). Under these conditions, CFP-HIV was still transferred to T cells, confirming that sCD4 treatment did not affect subsequent transfer of virions and was not inherently harmful. In the absence of treatment, many double-positive GFP+CFP+ T cells were observed, indicating that more than one virion can be transmitted to each T cell (left panel). Thus, the neutralization studies showed that only surface-bound, but not internalized, HIV virions mediated trans-infection from MDDCs to T cells. sCD4 likely neutralizes HIV-1 virions by competing for viral binding to the CD4 receptor on T cells, as intact HIV-1 virions, including gp120, remained associated with the MDDCs after treatment (unpublished data). Although sCD4 has been reported to allow HIV-1 fusion to cells that do not express CD4 [20], sCD4 did not induce fusion to CD4– cells in our experiments, as demonstrated by the absence of BlaM transfer to CD8 T cells or B cells (not shown). Next, we stripped surface HIV virions from MDDCs by proteolytic digestion (Figure 2D). GFP-HIV was again allowed to bind and internalize. CFP-HIV was restricted to the surface of MDDCs and served as a control for neutralization by the proteolytic enzymes. MDDCs loaded with GFP-HIV and CFP-HIV were then treated with trypsin as described [10,21] or pronase and incubated for 2 d with T cells. Trypsin treatment did not effectively remove externally bound CFP-HIV virions (unpublished data). However, in the presence of increasing amounts of pronase, fewer surface-bound CFP-HIV virions were transmitted to T cells, indicating increasingly effective removal the surface-bound HIV-1 by this protease cocktail. However, pronase had the same effect on the transfer of GFP-HIV, whether it had been internalized or not. Thus, successfully transmitted GFP-HIV virions appear to originate from the cell surface rather than from an internal, pronase-resistant compartment in MDDCs. We considered the possibility that the low levels of transmission in the presence of sCD4 could correspond to transmission events from internalized HIV-1, masked under our experimental conditions. However, changes in viral input, internalization time, and viral strains failed to reveal significant transfer from internal compartments (Figure S2). We also studied a second type of DC, LCs derived from CD34+ cord blood cells (MatTek; http: //www. mattek. com). As observed in MDDCs, the transfer of HIV virions from LCs to allogenic T cells again was mediated by virions bound at the surface of the LCs (Figure S3). Since MDDCs and LCs derived from CD34 progenitors are excellent surrogates of in vivo DCs, we conclude that most virions transmitted in trans in vivo likely originate from the cell surface. These results in MDDCs and LCs sharply contrast with the report that formed the basis for the prevailing model of trans-infection [21]. In that report, surface-bound virions were neutralized by proteolytic digestion with trypsin. Although in our hands trypsin did not digest surface-bound virions as potently as pronase, the discordance of results likely lies in the use of different reporter systems. Kwon et al. [21] used a luciferase reporter that does not allow the distinction between infection of T cells or MDDCs. Since the immature MDDCs used in that study are highly susceptible to fusion with the R5-tropic BaL envelopes [12] and efficiently replicate CCR5-tropic HIV-1 [15,22], the luciferase activity may have derived from infected immature MDDCs, not T cells, in the coculture. Our flow-based assays, which permit a clear distinction between infection of T cells and MDDCs in coculture, reveal that HIV-1 virions transmitted in trans are sensitive to pronase treatment. Our results also differ from three later studies, further supporting the notion that HIV infection of T cells by DCs involves the transfer of internalized virions from DCs to interacting CD4 T cells [9,10,15]. McDonald et al. [10] showed the recruitment of “trypsin-resistant” HIV-1 virions to the immunological synapse; however, functional assays were not performed to confirm that the interacting CD4 T cells were actually infected by the recruited virions. We suspect that, while virions may be transported to the synapse, these virions are not successfully transmitted. Wiley et al. [9] showed the release of infectious virions from HIV-1–loaded MDDCs, even after surface-bound virions were removed with trypsin. However, the efficacy of the trypsin treatment was only controlled in the experiments measuring the release of p24Gag in the supernatant, not in studies measuring the infectivity of these virions. Finally, Ganesh et al. [15] observed the transfer of some virions from MDDCs to T cells in the presence of neutralizing antibodies, a surprising result in light of our findings with sCD4. In their study, the efficacy of the antibody neutralization was measured with free virions but not with MDDCs bearing only surface-bound HIV-1 virions. The amount of antibody required to neutralize free virions might be lower than the amount needed to neutralize surface-bound virions on MDDCs, a possibility that would explain our divergent results. Of note, our findings are supported by a recent study showing that, in Raji cells expressing DC-SIGN (DC-specific ICAM-3 grabbing nonintegrin), surface-bound rather than internalized virions are transmitted in trans to 293T cells expressing CD4 and CCR5 [17]. Several reports have suggested that captured HIV-1 virions are stored in MVBs, raising the possibility that HIV-1 mediates trans-infection of T cells by highjacking a pathway involved in the trafficking of internal vesicles to the immunological synapse. In DCs, the transport of MHC class II from the MVB to the immunological synapse requires a T-cell–mediated signal [23]. Only T cells of the appropriate antigen specificity trigger this transport. Since antigen recognition could mobilize the release of HIV-1 virions from the MVB, we investigated trans-infection in the context of stimulation with a superantigen, staphylococcal enterotoxin B (SEB) (Figure 3). SEB activates T cells by crosslinking the variable region of T-cell receptor β-chain and the MHC class II molecule expressed on the DC surface [24]. NL4–3 virions containing BlaM-Vpr were bound at 4 °C to SEB-pulsed MDDCs. Viral transfer to autologous purified CD4 T cells was measured after allowing virion internalization, or not, by MDDCs. Again, sCD4 completely blocked HIV-1 transmission (Figure 3A). However, since the fusion assay does not require T-cell activation to generate a positive signal, trans-infection could be detected in the absence of engagement of the T-cell receptor and MHC class II. To further ensure that transfer was analyzed only when the MDDCs and T cells were effectively engaged, we measured productive infection of resting T cells by immunostaining for p24Gag (Figure 3B). Only T cells stimulated by SEB-loaded MDDCs are rendered permissive by releasing a postentry restriction block created by APOBEC3G (apolipoprotein B mRNA-editing enzyme, catalytic polypeptide-like 3G) [25]. Again, the vast majority of transmission events were neutralized by sCD4. However, under these experimental conditions, a few virions were transmitted from an sCD4-resistant compartment, as evidenced by the slight increase in transfer when SEB-loaded MDDCs were allowed to internalize HIV-1 virions at 37 °C. These transmission events were not due to new virion production by MDDCs since similar results were observed when AZT was added after 24 h of coincubation of MDDCs and T cells. When SEB-pulsed MDDCs were allowed to internalize HIV-1 virions for a longer period, HIV-1 virion transfer slightly increased peaking at 1 h of internalization (Figure 3C). Subsequently, the efficiency of transfer from internal compartments decreased, likely as a consequence of degradation or inactivation of the internalized virions. In conclusion, HIV-1 virions transmitted in trans from DCs to T cells principally originate from the surface of DCs, except during antigen recognition, when a few internalized virions may also be transmitted to the antigen-specific T cells. Whether these rare events contribute to the preferential infection and elimination of HIV-specific T cells in vivo [26] is not known. Nevertheless, even within this context, the vast majority of transmitted virions are derived from the surface of DCs. Our results do not challenge the prevailing view that HIV-1 virions are internalized in the DCs. Indeed, we detected large amounts of internalized HIV-1 virions by microscopy. However, unless HIV-1–loaded DCs encounter T cells of the appropriate specificity, virion internalization appears to be a dead end for HIV-1 trans-infection. Since the C-type lectin receptors involved in trans-infection are localized in lipid rafts [27], surface-bound HIV-1 likely exploits the clustering of lipid rafts at the immunological synapse to enhance trans-infection of CD4 T cells. Because trans-infection principally involves surface-bound virions, our findings suggest that attachment inhibitors could be used to limit trans-infection of T cells by DCs, in vivo. To study HIV-1 transmission from DCs to T cells, MDDCs (2 × 106) were incubated with 81A or NL4–3 virions (50 μg of p24Gag /ml) for 1 h at 4 °C, washed four times in cold PBS, incubated for 0 to 5 d at 37 °C, diluted 1: 10, and added to autologous phytohemagglutinin-activated PBLs (2 × 106). Cocultures were maintained for 3 d in RPMI with 10% FBS, 20 IU/ml IL-2,25 ng/ml IL-4,50 ng/ml GM-CSF, and penicillin and streptomycin (100 μg/ml each); at 24 h, AZT (10 μM) was added to prevent further infection. Infected T cells were identified by intracellular immunostaining for p24Gag combined with antibodies against CD3, CD4, and CD1a. Productively infected T cells represent the percentage of p24Gag+CD4– cells in the CD3+CD1a– population and correspond to infected CD4 T cells that had effectively downregulated CD4 receptors due to expression of select viral gene products, including nef, vpu, and env [28]. In some experiments, an HIV protease inhibitor, amprenavir (40 nM) (Division of Acquired Immunodeficiency Syndrome, National Institute of Allergy and Infectious Diseases; http: //www. niaid. nih. gov), was added during the binding step and maintained for the rest of the experiment. MDDCs derived from CD14+ monocyte were induced to mature with poly (I: C) and tumor necrosis factor α [12]. The 81A or NL4–3 virions containing BlaM-Vpr (500 ng of p24Gag) [12–14] were incubated with MDDCs (2 × 106) or with CD34-derived LCs for 1 h at 4 °C, washed four times in cold PBS, and incubated at 37 °C for the indicated time to allow virion internalization or kept at 4 °C. Aliquots (2 × 105 cells) were added to autologous resting PBLs (2 × 106), and incubated at 37 °C for 1 h. HIV-1 fusion to CD4+CD3+ cells was measured using the virion-based fusion assay combined with immunostaining with CD1a-APC, CD4-PE Cy7, and CD3-APC Cy7 antibodies [13,14]. Cells were analyzed by flow cytometry (BD LSRII; Becton Dickinson, http: //www. bd. com) and analyzed with FlowJo software (Treestar Software, http: //www. flowjo. com). GFP-HIV virions were bound to MDDCs for 1 h at 4 °C and allowed to internalize at 37 °C for 30 min; CFP-HIV virions were only bound to MDDCs. As indicated, surface virions were neutralized with sCD4 before or after the binding of CFP-HIV or by pronase after the binding of GFP-HIV and CFP-HIV. MDDCs were then incubated with autologous T cells for 48 h. Cells were immunostained with CD1a-APC, CD4-PE Cy7, and CD3-APC Cy7 antibodies and analyzed by flow cytometry. To neutralize surface-bound virions, MDDCs or CD34-derived LCs loaded with HIV-1 virions were incubated for 90 min at 4 °C with 20 μg/ml sCD4 in RPMI and 10% FBS and extensively washed with PBS before MDDCs or CD34-derived LCs were added to T cells. To neutralize virions with pronase, the HIV-1–loaded MDDCs were incubated for 30 min at 4 °C with 50 to 400 μg/ml pronase (Roche, http: //www. roche. com). MDDCs (2 × 106) were pulsed with SEB (0. 5 μg/ml) at 37 °C for 1 h. NL4–3 virions containing BlaM-Vpr (500 ng of p24Gag) were allowed to bind at 4 °C to the MDDCs; as indicated, cells were incubated for 30 min to 4 h at 37 °C to allow internalization. Surface-bound virions were then neutralized or not with sCD4. The HIV-loaded MDDCs were added to autologous purified resting CD4 T cells, and trans-infection was measured with the fusion assay at 2 h or by measuring productive infection after 3 d of coculture. Productively infected T cells were identified by intracellular immunostaining for p24Gag combined with antibodies against CD3, CD4, and CD1a. We then measured the percentage of p24Gag+CD4– cells in the CD3+CD1a– population.

Dendritic cells (DCs) patrol peripheral mucosal sites, capturing and processing potential pathogens into antigenic peptides for presentation to T cells of lymphoid organs, and thereby initiating an immune response. HIV-1 had been proposed to use DCs as “Trojan horses, ” hiding inside the DCs and surviving the degradation pathway to gain access to the lymph nodes and spread to the T cells. Our study challenges this “Trojan horse” model by showing that only HIV-1 virions bound to the surface of DCs, and not internalized virions, are transmitted to T cells. Even when T cells specifically recognized the antigen presented by DCs, the infection of T cells was principally mediated by virions remaining at the surface of the DCs. Interestingly, in this context of antigen-specific recognition, which increases the trafficking toward the immunological synapse of DC internal vesicles, where HIV-1 virions seem to hide, a few internal virions could infect T cells. Our findings suggest that in vivo transmission to T cells of HIV-1 virions captured by DCs should be more sensitive to neutralization than previously expected.

Abstract Introduction Results/Discussion Materials and Methods

viruses homo (human) infectious diseases eukaryotes

2007

In Vitro Derived Dendritic Cells trans-Infect CD4 T Cells Primarily with Surface-Bound HIV-1 Virions

The cellular response to DNA double-strand breaks (DSBs) is initiated by the MRX/MRN complex (Mre11-Rad50-Xrs2 in yeast; Mre11-Rad50-Nbs1 in mammals), which recruits the checkpoint kinase Tel1/ATM to DSBs. In Saccharomyces cerevisiae, the role of Tel1 at DSBs remains enigmatic, as tel1Δ cells do not show obvious hypersensitivity to DSB-inducing agents. By performing a synthetic phenotype screen, we isolated a rad50-V1269M allele that sensitizes tel1Δ cells to genotoxic agents. The MRV1269MX complex associates poorly to DNA ends, and its retention at DSBs is further reduced by the lack of Tel1. As a consequence, tel1Δ rad50-V1269M cells are severely defective both in keeping the DSB ends tethered to each other and in repairing a DSB by either homologous recombination (HR) or nonhomologous end joining (NHEJ). These data indicate that Tel1 promotes MRX retention to DSBs and this function is important to allow proper MRX-DNA binding that is needed for end-tethering and DSB repair. The role of Tel1 in promoting MRX accumulation to DSBs is counteracted by Rif2, which is recruited to DSBs. We also found that Rif2 enhances ATP hydrolysis by MRX and attenuates MRX function in end-tethering, suggesting that Rif2 can regulate MRX activity at DSBs by modulating ATP-dependent conformational changes of Rad50. DNA double-strand breaks (DSBs) are among the most cytotoxic DNA lesions, because failure to repair them can lead to genome instability. DSBs can be repaired by either nonhomologous end joining (NHEJ) or homologous recombination (HR). While NHEJ directly ligates the DNA ends, HR requires the 5′ ends of a DSB to be nucleolytically processed (resected) to generate 3′ single-stranded DNA (ssDNA) tails that initiate HR by invading an undamaged homologous DNA template [1]. Generation of DSBs activates a DNA damage response (DDR), which regulates DSB repair and coordinates it with cell cycle progression [2]. In both yeast and mammals, the DDR is initiated by the MRX/MRN complex (Mre11-Rad50-Xrs2 in yeast; Mre11-Rad50-Nbs1 in mammals), which recognizes unprocessed DSBs and activates the checkpoint kinase Tel1/ATM [3]. MRX/MRN recruits Tel1/ATM to the DSB ends through its interaction with the C-terminal domain of Xrs2/Nbs1 and stimulates Tel1/ATM catalytic activity [4–8]. MRX/MRN also plays critical functions in DSB resection and in maintaining the DSB ends tethered to each other [9]. Several studies have shown that the MRX complex consists of a globular head domain from which the long coiled-coil domain of Rad50 protrudes [10–13]. The coiled-coil apex contains a CXXC amino acid motif that can dimerize via tetrahedral coordination of a zinc ion, thereby forming molecular bridges for keeping the DNA ends tethered to each other [14,15]. Mre11 is active as an exo- and endonuclease in vitro [16–19] and initiates DSB resection [20–24]. The functions of MRX in end-tethering and DSB resection are regulated by Rad50, whose ATP binding and hydrolysis activities result in MRX conformational changes [11,25–28]. Mutants that promote the ATP-bound conformation of Rad50 exhibit a higher level of tethering [29], indicating that end-tethering depends on this MRX conformation. In turn, the ATP-bound conformation sterically blocks the Mre11 nuclease activity [29–32], whereas release from this ATP-bound state that occurs with ATP hydrolysis opens Mre11 nuclease active sites so that they can be engaged in DSB resection [13]. Thus, ATP hydrolysis triggers a switch between a closed state, in which Mre11 nuclease domain is occluded, to an open configuration with exposed Mre11 nuclease sites. In addition to its role in DSB repair, MRX works in the same epistasis group of Tel1 to maintain telomere length [33,34]. Interestingly, the lack of Tel1 in Saccharomyces cerevisiae cells causes telomere shortening and a decrease of MRX binding at DNA ends flanked by telomeric DNA repeats [35,36]. On the other hand, telomere length is negatively regulated by Rif2, which is recruited to telomeric DNA ends by Rap1 [37]. Artificial tethering of Rif2 at DNA ends reduces the amount of telomere-bound Tel1, but not that of MRX [35]. This observation, together with the finding that Rif2 appears to compete with Tel1 for binding to the C-terminus of Xrs2 in vitro [35], suggests that Rif2 interferes with MRX-Tel1 interaction to shelter telomeric ends from Tel1 recognition. Although Tel1 is recruited to DSBs and participates in DSB end resection [4,38], its function in DSB repair remains enigmatic because Tel1-deficient S. cerevisiae cells do not show obvious hypersensitivity to DNA damaging agents and are not defective in checkpoint activation in response to a single DSB [38]. To better understand the function of Tel1 in the cellular response to DSBs, we performed a genetic screen aimed at identifying mutants that require Tel1 to survive to genotoxic treatments. We found that the rad50-V1269M allele makes tel1Δ cells hypersensitive to DNA damaging agents. The MRV1269MX complex associates poorly to a DSB and the lack of Tel1 further reduces its retention at DSB ends. As a consequence, rad50-V1269M tel1Δ cells are severely defective in maintaining the DSB ends tethered to each other. These findings indicate that Tel1 promotes proper MRX association to DNA ends, and this function is required to support the end-tethering activity of MRX. The Tel1 function in promoting MRX retention to DSBs is counteracted by Rif2, which is recruited to DSB ends. Rif2 also enhances MRX ATPase activity and attenuates MRX function in end-tethering, suggesting that it modulates MRX function not only by inhibiting MRX association to DSBs but also by regulating ATP-dependent Rad50 conformational changes. To gain insights into the role of Tel1 at DSBs, we searched for mutations that caused hypersensitivity to DNA damaging agents only in the absence of Tel1. For this purpose, tel1Δ clones were screened for decreased viability in the presence of camptothecin (CPT) and/or phleomycin. Hypersensitive tel1Δ clones that lost the DNA damage hypersensitivity after transformation with a plasmid containing wild-type TEL1 were crossed to a wild-type strain followed by sporulation and tetrad analysis to verify that the DNA damage hypersensitivity was due to the combination of tel1Δ with a mutation in an unknown single gene. This procedure allowed us to identify five single-gene mutations belonging to three distinct allelism groups. Genome sequencing of the clone that showed the most severe synthetic phenotype and subsequent genetic analyses established that the mutation responsible for the DNA damage hypersensitivity of tel1Δ cells was a single nucleotide change in the RAD50 gene, resulting in substitution of valine 1269 with methionine in the C-terminal ATPase domain (Fig 1A). Both rad50-V1269M and tel1Δ single mutant cells were as sensitive as wild type to phleomycin, methyl methanesulfonate (MMS), and low CPT doses, while the sensitivity to the same drugs was greatly increased in tel1Δ rad50-V1269M double mutant cells (Fig 1B), indicating that the Rad50-V1269M variant requires Tel1 to support cell viability in the presence of genotoxic stress. As Tel1 is a protein kinase, we asked whether the rad50-V1269M allele also exacerbated the sensitivity to DNA damaging agents of cells expressing a Tel1 mutant variant (Tel1-kd) carrying G2611D, D2612A, N2616K, and D2631E amino acid substitutions that abolished Tel1 kinase activity in vitro [39]. Telomeres in tel1-kd cells are shorter than in wild-type cells and indistinguishable from those of tel1Δ cells [39], indicating that these mutations abolish Tel1 function at telomeres. Surprisingly, the viability of tel1-kd rad50-V1269M double mutant cells in the presence of DNA damaging agents was similar to wild-type cells (Fig 1C), suggesting that Rad50-V1269M mutant variant requires the presence of Tel1 but not its kinase activity to support cell viability in the presence of genotoxic stress. Rad50 binds DNA and has ATPase activity [27]. These functions reside in the globular domain formed by the N- and C-termini of the protein, which are separated by an antiparallel coiled-coil domain [9]. The V1269M mutation is very closed to the H-loop (Fig 1A), whose histidine residue has been proposed to promote ATP hydrolysis by positioning the first water molecule needed for the reaction and/or by forming a catalytic dyad with the Walker B glutamate [11,40]. Thus, we asked whether and how the rad50-V1269M mutation affects MRX ATPase and/or DNA-binding activities. The Rad50 and the Rad50-V1269M proteins were purified to near homogeneity by following our published procedure (Fig 2A) [18]. Purified Rad50 and Rad50-V1269M were then individually incubated with Mre11 and Xrs2, and the fully assembled complexes were separated from free proteins by gel filtration. Rad50-V1269M could be expressed to the same level as the wild-type protein, behaved well chromatographically, and yielded the same amount of trimeric complex with Mre11 and Xrs2. As shown in Fig 2A, the stoichiometry of the three components in the MRV1269MX mutant complex was very similar to that of the wild-type MRX complex. As we reported previously [41], Rad50 hydrolyzed ATP only within the context of the MRX complex. The MRV1269MX mutant complex exhibited a reduced ATPase activity compared to wild-type MRX (Fig 2B), indicating that the rad50-V1269M mutation affects ATP hydrolysis. Aside from Xrs2 and Mre11, Rad50 also binds DNA [41], and we found that Rad50-V1269M is compromised for DNA binding in vitro (Fig 2C). Subsequently, we examined DNA binding by MRX wild-type and MRV1269MX mutant complexes and noticed insignificant difference between the two complexes (Fig 2D). We note that the DNA binding deficiency of the Rad50-V1269M mutant may be masked by the DNA binding attribute of Mre11 and Xrs2 within the MRX complex [19,41]. We next analyzed DSB association of Rad50-V1269M and MRV1269MX in vivo by chromatin immunoprecipitation (ChIP) followed by quantitative real-time PCR (qPCR). To generate a single DSB at a specific chromosomal locus, we used a strain expressing a galactose-inducible HO endonuclease. In this strain, induction of HO by galactose addition leads to the generation at the MAT locus of a single DSB that cannot be repaired by HR because the strain carries the deletion of the homologous donor loci HMLα and HMRa [42]. Consistent with the finding that the Rad50-V1269M mutant variant is compromised in DNA binding (Fig 2C), the amount of Rad50-V1269M bound at the HO-induced DSB was lower than that of wild-type Rad50 (Fig 3A). Furthermore, although binding to DNA of the MRV1269MX mutant complex was not affected (Fig 2D), the amount of Mre11 associated to the HO-induced DSB was significantly lower in rad50-V1269M than in wild-type cells (Fig 3B). This decreased Rad50-V1269M and MRV1269MX association to the DSB is not due to reduced protein levels or altered MRV1269MX complex formation. In fact, protein extracts from wild-type and rad50-V1269M cells contained very similar amounts of Rad50, Rad50-V1269M, and Mre11 proteins (Fig 3C). Furthermore, equal amount of Rad50-V1269M and Rad50 could be immunoprecipitated with the Mre11 protein (Fig 3D). As MRV1269MX binding to DNA was not significantly affected (Fig 2D), these data indicate that the rad50-V1269M mutation impairs MRX retention to DSBs. As expected from the previous finding that MRX is required to load Tel1 at the DSB ends [4], the amount of Tel1 bound at the HO-induced DSB was lower in rad50-V1269M cells than in wild type (Fig 3E). This attenuated Tel1 association to the DSB is not due to either reduced Tel1 level in rad50-V1269M cells or impaired MRV1269MX-Tel1 interaction. In fact, similar Tel1 amounts could be detected in protein extracts prepared from wild-type and rad50-V1269M cells (Fig 3F). Furthermore, wild-type and mutant MRX complexes could be coimmunoprecipitated equally well with Tel1 (Fig 3G). The MRX complex plays multiple functions in DSB repair: it promotes DSB resection and checkpoint activation [20,21,38] and it keeps the DSB ends tethered to each other [43–46]. The severe DNA damage hypersensitivity of tel1Δ rad50-V1269M cells is not due to defect in DNA damage-induced checkpoint activation, as wild-type and tel1Δ rad50-V1269M cells phosphorylated the checkpoint kinase Rad53 with similar kinetics in response to phleomycin (Fig 4A) or MMS treatment (Fig 4B). Furthermore, tel1Δ rad50-V1269M cells phosphorylated Rad53 with wild-type kinetics in response to an irreparable HO-induced DSB (Fig 4C). Recent data indicate that MRX in the ATP-bound state promotes end-tethering, whereas ATP hydrolysis opens the MRX conformation to promote Mre11 nuclease activity and DSB resection [13,29]. As MRV1269MX exhibits reduced ATPase activity and MRX variants impaired in ATP hydrolysis are endonuclease-defective [27,47], we asked whether rad50-V1269M cells were defective in DSB resection. To monitor directly the generation of ssDNA at the DSB ends, we used strains expressing a galactose-inducible HO endonuclease, which generates at the MAT locus a single irreparable DSB [42]. Resection of the HO-induced DSB renders the DNA sequence flanking the HO break resistant to cleavage by restriction enzymes, resulting in the appearance of resection intermediates that can be detected by Southern blot analysis with a probe that anneals to the 3′ end at one side of the break. Because resection was much slower in cells arrested in G2/M than in replicating cells [48], HO was induced by galactose addition to cell cultures that were arrested in G2 with nocodazole and kept blocked in G2 by nocodazole treatment to detect even subtle differences in resection efficiency. Consistent with MRV1269MX deficiencies in ATP hydrolysis and DNA binding, rad50-V1269M mutant cells showed a slight defect in DSB resection compared to wild-type cells (Fig 4D). Importantly, the lack of TEL1, which caused per se a slight delay in DSB resection (Fig 4D) [38], did not reduce further the resection efficiency of rad50-V1269M cells, as rad50-V1269M and tel1Δ rad50-V1269M cells resected the DSB with similar kinetics (Fig 4D). These findings indicate that the severe DNA damage sensitivity of tel1Δ rad50-V1269M double mutant cells is not due to a resection defect. Further supporting this conclusion, EXO1 overexpression, which suppresses the hypersensitivity to DNA damaging agents and the DSB resection defect of mre11Δ cells [38,49], did not suppress the hypersensitivity to DNA damaging agents of tel1Δ rad50-V1269M double mutant cells (Fig 4E). MRX function in end-tethering is largely dependent on Rad50 coiled-coil domains [12,14]. Nonetheless, structural studies suggest that also DNA binding of the globular domain is important for end-tethering, possibly because it increases intercomplex hook-hook dimer formation by causing the Rad50 coils to become more rigid and parallel to one another [12]. As the MRV1269MX mutant complex was poorly recruited to the DSB (Fig 3A and 3B), we asked whether rad50-V1269M and tel1Δ rad50-V1269M cells were defective in keeping the DSB ends tethered to each other. To detect the association of broken DNA ends, we used a yeast strain where the DNA proximal to an irreparable HO-induced DSB can be visualized by binding of a LacI-GFP fusion protein to multiple repeats of the LacI repressor binding site (LacO) that are integrated on both sides of the HO cleavage site on chromosome VII at a distance of 50 kb [43]. HO was induced by galactose addition to cell cultures that were arrested in G2 with nocodazole and kept blocked in G2 by nocodazole treatment in order to ensure that all cells would arrest in metaphase. The majority of wild-type cells showed a single LacI-GFP focus both before and after HO induction, indicating their ability to hold the broken DNA ends together (Fig 4F). Consistent with previous results [45], tel1Δ cells showed a slight increase of two LacI-GFP spots at 1–3 h after HO induction (Fig 4F). An increase of two LacI-GFP spots compared to the uninduced condition could be detected also in rad50-V1269M cells (Fig 4F). Strikingly, the number of cells showing two LacI-GFP spots after HO induction was greatly increased in tel1Δ rad50-V1269M double mutant cells compared to each single mutant, reaching a percentage similar to that observed in mre11Δ cells (Fig 4F). The MRX complex has been implicated in sister chromatid cohesion [50], prompting us to evaluate whether the increase frequency of two LacI-GFP foci after HO induction in tel1Δ rad50-V1269M cells was due to end-tethering and/or cohesion defects. We therefore induced HO expression in α-factor-arrested cells that were kept arrested in G1 by α-factor in the presence of galactose. About 50% of G1-arrested tel1Δ rad50-V1269M cells showed two LacI-GFP foci 1 h after HO induction similarly to mre11Δ cells (Fig 4G), indicating that the appearance of two LacI-GFP foci in these cells is primarily due to defective end-tethering. We also monitored the ability of tel1Δ, rad50-V1269M, and tel1Δ rad50-V1269M cells to maintain cohesion between sister chromatids by determining formation of LacI-GFP foci in nocodazole-arrested cells in the absence of HO induction. Under these conditions, the amount of tel1Δ and rad50-V1269M cells showing two LacI-GFP foci was similar to that found in wild-type cells (Fig 4H), indicating that cohesion is not affected by either the lack of Tel1 or the presence of Rad50-V129M variant. By contrast, a slight cohesion defect was detectable in nocodazole-arrested tel1Δ rad50-V1269M cells, which showed a ~5% increase of two LacI-GFP foci compared to wild-type cells (Fig 4H). Because the ability of the above strains to held together the DSB ends was determined by using target sequences integrated at a distance of 50 kb from the DSB, we also monitored end-tethering by using a strain expressing LacI-YFP and TetR-RFP fusion proteins, which bind LacO and TetO tandem arrays, respectively [51]. These arrays are integrated at a distance of 7 kb from the DSB that is generated by the endonuclease HO on chromosome III, with each kind of array marking one specific side of the break. The frequency of cells showing separated LacI-YFP and TetR-RFP foci dramatically increased after HO induction in G2-arrested tel1Δ rad50-V1269M cells compared to wild-type cells (Fig 4I), confirming that tel1Δ rad50-V1269M cells are defective in end-tethering. Thus, the absence of Tel1 severely reduces the end-tethering activity of MRV1269MX, indicating a role for Tel1 in supporting this MRX function. The maintenance of the DSB ends tethered to each other is a relevant event in the repair of a DSB by both NHEJ and HR [43,44,46,52]. Thus, we asked whether tel1Δ rad50-V1269M cells were defective in HR and/or NHEJ. Among the HR pathways, single-strand annealing (SSA) is devoted to repair a DSB that is flanked by direct repeats and requires resection of the DSB ends followed by Rad52-dependent annealing of the resulting complementary ssDNA sequences [53]. To investigate possible HR defects, we first monitored the ability of tel1Δ rad50-V1269M cells to repair a DSB by SSA. To this end, we used a strain carrying a galactose-inducible GAL-HO construct, as well as tandem repeats of the LEU2 gene, with a recognition site for the HO endonuclease adjacent to one of the repeats (S1A Fig) [54]. Galactose was added to G2-arrested cells to induce HO production and it was maintained in the medium so that continuously produced HO could re-cleave the HO sites eventually reconstituted by NHEJ. When kinetics of DSB repair was monitored by Southern blot analysis with a LEU2 probe, accumulation of the 8 kb SSA repair product was slightly delayed in both tel1Δ and rad50-V1269M single mutants, whereas it was severely defective in tel1Δ rad50-V1269M double mutant compared to wild-type cells (S1B and S1C Fig). This finding indicates that Tel1 is important to support MRX function in DSB repair by SSA. The observation that tel1Δ, rad50-V1269M and tel1Δ rad50-V1269M cells all delay resection to the same extent (Fig 4D) indicates that the SSA defect of tel1Δ rad50-V1269M cells cannot be explained by a resection defect. We noticed that all the galactose-induced cell cultures exhibited a DNA band that migrated slower than the uncut band and appeared concomitantly with the SSA products (S1C Fig). This band was not detectable in rad51Δ cells, indicating that it was generated by Rad51-mediated recombination events (S1D Fig). Because SSA repair pathway does not involve strand invasion and therefore does not require the recombination protein Rad51 (S1D Fig) [55], we also monitored the HR events that depend on the Rad51-dependent invasion and pairing of broken DNA ends with intact homologous sequences present on a sister chromatid or at an ectopic location in the genome. In the major HR pathway, the 3′-ended ssDNA tail invades an intact duplex homologous, creating a loop structure (D-loop) consisting of a region of heteroduplex DNA and displaced ssDNA. If this ssDNA anneals with the complementary sequence on the other side of the DSB (second end capture), subsequent extension and ligation result in the formation of a double Holliday junction intermediate, whose random cleavage yield an equal number of noncrossover (NCO) and crossover (CO) products [53]. Alternatively, if the newly synthesized strand is displaced, it can anneal with the 3′ ssDNA end at the other end of the DSB. This event leads to the generation of NCO products in a process called synthesis-dependent strand-annealing (SDSA) [56–58]. To monitor CO and NCO formation, we used haploid strains that bear two copies of the MATa sequence. A MATa gene introduced in chromosome V can be cleaved by a galactose-inducible HO endonuclease and repaired by Rad51-dependent HR using a uncleavable MATa donor on chromosome III that contains a single base pair substitution preventing HO cleavage (MATa-inc) (Fig 5A) [59]. This repair event can lead to NCO and CO outcomes, with the proportion of COs being ~5% among the overall repair events [59]. Galactose was added to induce HO production and then it was maintained in the medium to cleave the HO sites that were eventually reconstituted by NHEJ-mediated DSB repair. The 3 kb MATa band resulting from NCO recombination events re-accumulated less efficiently in tel1Δ rad50-V1269M double mutant cells compared to both tel1Δ and rad50-V1269M single mutant cells, which generated NCO products similar to wild-type cells (Fig 5B and 5C). Interestingly, while tel1Δ rad50-V1269M double mutant cells showed decreased amount of NCOs compared to wild-type cells, the percentage of COs in the same cells was similar to that observed in wild-type cells (Fig 5B and 5C). As most of the NCO products are generated by the SDSA mechanism, this finding suggests that tel1Δ rad50-V1269M cells are specifically defective in SDSA. Because SDSA is thought to be the main mechanism responsible for mating type switching [60], we investigated the ability of tel1Δ rad50-V1269M cells to switch the mating type. HO expression was induced for 30 min by galactose addition to MATa cells and was then rapidly shut off by the addition of glucose to allow repair of the HO-induced break by gene conversion. Since there is a strong mating type-dependent preference for the choice of the two silent donor loci HMLα and HMRa [60], the MATa sequence will be replaced preferentially with the HMLα donor sequence to generate the MATα product. Strikingly, tel1Δ rad50-V1269M cells accumulated the MATα repair product less efficiently than rad50-V1269M cells, which generated this product with almost wild-type kinetics (Fig 5D and 5E). These findings indicate that tel1Δ rad50-V1269M double mutant cells are defective in mating type switching, supporting the hypothesis that they are specifically impaired in SDSA-based recombination mechanisms. Next, we investigated whether tel1Δ rad50-V1269M cells were defective in NHEJ. To this purpose, we used the strains previously used to monitor DSB repair by SSA. HO expression was induced for 30 min by galactose addition and was then rapidly shut off by the addition of glucose to allow NHEJ-mediated repair of the DSB. To ensure that repair of the HO-induced DSB occurred mainly by NHEJ, HO was induced in G1-arrested cells that were kept arrested in G1 with α-factor (Fig 6A). In fact, the low Cdk1 activity in G1 cells prevents resection of the HO-induced DSB and therefore its repair by SSA [61,62]. NHEJ-mediated DSB repair was severely affected in tel1Δ rad50-V1269M cells. In fact, the 14. 5 kb uncut band resulting from NHEJ-mediated ligation of the DSB ends failed to re-accumulate in tel1Δ rad50-V1269M cells compared to wild type (Fig 6B and 6C), which also showed the expected decrease of both the 2. 5 and 12 kb HO-cut band signals due to DSB repair by NHEJ (Fig 6B). Some defective re-accumulation of the 14. 5 kb uncut band could be detected also in both rad50-V1269M and tel1Δ single mutant cells, although this defect was much less severe compared to that observed in tel1Δ rad50-V1269M cells (Fig 6B and 6C). To confirm the NHEJ defect, we used the GAL-HO strain, where HO induction by galactose addition generates a DSB at the MATa locus. This strain lacks the homologous donor sequences HMLα and HMRa and therefore can repair the HO-induced DSB only by NHEJ. HO expression was induced for 30 min by galactose addition to G1-arrested cells and then shut off by glucose addition to allow DSB repair by NHEJ (Fig 6D). The MATa sequence resulting from NHEJ repair events re-accumulated in wild-type cells but not in tel1Δ rad50-V1269M double mutant cells (Fig 6E), thus confirming a severe NHEJ defect. Finally, we measured NHEJ efficiency also as the ability of the above cells to re-ligate a plasmid that was linearized before being transformed into the cells [63]. Both rad50-V1269M and tel1Δ mutants showed only a slight reduction in the efficiency of plasmid re-ligation compared to wild-type cells (Fig 6F). By contrast, the re-ligation efficiency in tel1Δ rad50-V1269M cells dramatically decreased to a level similar to that found in dnl4Δ cells that lack the NHEJ enzyme responsible for DSB end ligation (Fig 6F). Thus, DSB repair by NHEJ, which is only slightly affected by the lack of Tel1 or the presence of the Rad50-V1269M mutant variant, is lost in tel1Δ rad50-V1269M double mutant cells, indicating that Tel1 supports MRV1269MX function also in NHEJ. A structural study has proposed that DNA tethering requires proper binding to DNA of the MRX globular domain to induce intercomplex hook-hook dimer formation [12]. The finding that the lack of Tel1 exacerbates the end-tethering defects of rad50-V1269M cells raises the possibility that Tel1 supports MRX function in DNA tethering by promoting proper MRX association to damaged DNA. It has been previously shown that the lack of Tel1 decreases Mre11 binding at DNA ends flanked by telomeric DNA repeats [35,36], whereas only a slight reduction of Mre11 association, if any, can be detected in tel1Δ cells at 1 kb from an HO-induced DSB [35]. However, since only a limited amount of MRX is bound at this distance from the DSB (Fig 3A and 3B) [35], we analyzed Rad50 and Mre11 association at 0. 6 kb from the DSB, where Mre11 is strongly enriched (Fig 3B). The amount of Rad50 (Fig 7A) and Mre11 (Fig 7B) bound at the HO-induced DSB ends was lower in tel1Δ cells than in wild-type cells, indicating that Tel1 promotes MRX association to DSBs. Consistent with the finding that the lack of Tel1 kinase activity did not exacerbate the DNA damage sensitivity of rad50-V1269M cells (Fig 1C), the association of Rad50 and Mre11 to DSBs was not affected in tel1-kd cells (Fig 7A and 7B). The lack of Tel1 also reduced the association to the DSB of Rad50-V1269M (Fig 7C) and Mre11 in rad50-V1269M cells (Fig 7D). The decreased DSB association of Rad50, Rad50-V1269M and Mre11 in tel1Δ compared to wild type was not due to reduced protein levels, as similar amounts of Rad50 and Mre11 could be detected in protein extracts prepared from wild-type, tel1Δ, rad50-V1269M and tel1Δ rad50-V1269M cells (Fig 7E). As MRX is required to load Tel1 on the DSB ends, these findings indicate that Tel1, once loaded onto the DSB by MRX, promotes MRX association/persistence in a feedback loop and this function is necessary to support the end-tethering activity of MRX. Rif2 was shown to counteract MRX association at telomeres by inhibiting the recruitment of Tel1, which in turn promotes MRX accumulation at telomere ends [35]. We asked whether Rif2 can modulate MRX association also at intrachromosomal DSBs by investigating the effect of RIF2 deletion on MRX binding at the HO-induced DSB in tel1Δ, rad50-V1269M, and tel1Δ rad50-V1269M cells. ChIP and qPCR analysis showed that the amount of MRX (Fig 8A) and MRV1269MX (Fig 8B) bound at the HO-induced DSB ends was slightly higher in the absence than in the presence of Rif2, although similar amount of Mre11 could be detected in protein extracts prepared from wild-type, rif2Δ, and rif2Δ rad50-V1269M cells (S2 Fig). This finding indicates that Rif2 counteracts MRX and MRV1269MX association to DSBs. This Rif2 function was completely dependent on Tel1, as the amount of both MRX (Fig 8A) and MRV1269MX (Fig 8B) bound at the DSB decreased to similar levels in both tel1Δ and tel1Δ rif2Δ cells. Consistent with a previous finding that Rif2 competes with Tel1 for the binding to MRX [35], the interaction between Tel1 and MRV1269MX was strongly attenuated by Rif2 (Fig 8C). All together, these data suggest that Rif2 inhibits MRX association at DSBs by counteracting MRX-Tel1 interaction. Consistent with a direct role of Rif2 in DSB metabolism, purified Rif2 turned out to bind dsDNA in vitro (Fig 8D). Furthermore, following HO induction by galactose addition, a fully functional Myc-tagged Rif2 variant was efficiently recruited close to the HO-induced DSB and its binding increased over 3 h, spreading to 2 kb from the HO cleavage site (Fig 8E). As Rif2 physically interacts with MRX [35], we asked whether MRX may contribute to Rif2 binding at the DSB. Indeed, the amount of Rif2 bound at the DSB decreased in both rad50-V1269M and mre11Δ cells (Fig 8E), indicating that Rif2 association to the DSB is partially dependent on MRX. The finding that the lack of Rif2 increases MRX and MRV1269MX association to DSBs prompted us to ask whether RIF2 deletion could restore end-tethering in rad50-V1269M mutant cells. Indeed, RIF2 deletion suppressed the end-tethering defects of G1- and G2-arrested rad50-V1269M cells (Fig 9A and 9B) and this suppression is specific for rad50-V1269M, as rif2Δ did not restore end-tethering in G1- and G2-arrested tel1Δ cells (Fig 9A and 9B). As a consequence, the lack of Rif2 also rescued the NHEJ defects of rad50-V1269M cells, as rif2Δ rad50-V1269M cells re-ligated the BamHI-cut plasmid more efficiently than rad50-V1269M cells (Fig 9C). Interestingly, both rad50-V1269M and tel1Δ single mutant cells did not lose viability in the presence of phleomycin, MMS or low CPT doses (Fig 1B), but they exhibited hypersensitivity to high doses of CPT (Fig 9D and 9E). Consistent with a role of Rif2 in limiting MRX functions, rif2Δ also suppressed the CPT hypersensitivity of rad50-V1269M (Fig 9D), but not that of tel1Δ mutant cells (Fig 9E). The lack of Rif2 increased the association of MRX and MRV1269MX at DSBs in a Tel1-dependent manner (Fig 8A and 8B), indicating that Rif2 counteracts the ability of Tel1 to enhance MRX association to DSB. If the lack of Rif2 suppressed the CPT hypersensitivity and the end-tethering defect of rad50-V1269M cells by increasing the amount of MRV1269MX bound to the DSB, this rif2Δ-mediated suppression should require Tel1 and therefore rif2Δ should not be able to suppress the same defects in tel1Δ rad50-V1269M cells. However, we found that rif2Δ restored both end-tethering (Fig 9A and 9B) and NHEJ (Fig 9C), as well as DNA damage resistance (Fig 9F) also of tel1Δ rad50-V1269M double mutant cells. This finding indicates that the restored DNA damage resistance and end-tethering in tel1Δ rif2Δ rad50-V1269M cells do not simply depend on increased amount of MRX bound at the DSB. The finding that the lack of Rif2 restores DNA damage resistance and end-tethering in rad50-V1269M cells even in the absence of Tel1 suggests that Rif2 has other functions in regulating MRX activity besides limiting MRX recruitment to DNA ends. It has been proposed that ATP hydrolysis induces the change from a closed MRX complex, required for end-tethering, to an open configuration that promotes Mre11 nuclease activity and DSB resection [29]. Thus, we asked whether Rif2 attenuates MRX function in end-tethering by influencing its ATPase activity. The ATPase assay was performed in the presence of 200 nM of 100-bp double-stranded DNA (dsDNA) to fully activate MRX ATPase activity. Since efficiently shifting 10 nM of the same DNA requires at least 300 nM Rif2 (Fig 8D), 2 μM Rif2 was used to investigate the potential effect of Rif2-mediated DNA binding on ATP hydrolysis by MRX in reactions that contained 200 nM DNA. We found that the addition of purified Rif2 increased the ATP hydrolysis activity by both wild-type MRX (Fig 10A) and MRV1269MX complexes (Fig 10B). Since end-tethering requires the MRX ATP-bound state [29], this finding suggests that the lack of Rif2 suppresses the hypersensitivity to DNA damaging agents and the end-tethering defect of rad50-V1269M and rad50-V1269M tel1Δ cells by increasing the time spent by MRX in an ATP-bound closed conformation. As an earlier study has shown that Rif2 binds to the C-terminus of Xrs2 [35], we asked whether this Rif2-mediated regulation of MRX activity requires Rif2-MRX interaction. Interestingly, the lack of Rif2 cannot suppress the hypersensitivity to DNA damaging agents of rad50-V1269M cells carrying the xrs2-11 mutation (Fig 10D), which causes the lack of the Xrs2 C-terminal part and therefore of the MRX-Rif2 interaction. This finding suggests that regulation of MRX function by Rif2 requires its interaction with Xrs2. This requirement can be bypassed in vitro where these proteins are already in close proximity, as Rif2 can also enhance the ATPase activity of the MR complex (Fig 10C). Mutants that promote the ATP-bound closed conformation of Rad50 exhibit a higher degree of tethering activity [29], and Rif2 enhances ATPase activity not only of MRV1269MX, but also of wild-type MRX (Fig 10A and 10B). If this Rif2 function is physiologically relevant also in a wild-type context, then rif2Δ cells should show improved efficiency of DNA tethering. We found that the percentage of rif2Δ cells showing two LacI-GFP spots was reproducibly decreased compared to wild-type cells, indicating that the tethering efficiency is higher in rif2Δ cells than in wild type (Fig 9A and 9B). Furthermore, Rif2 limits DSB repair by NHEJ, as rif2Δ mutant cells re-ligated the BamHI-cut plasmid more efficiently than wild-type cells (Fig 9C). These observations, together with the finding that Rif2 enhances ATP hydrolysis by MRX, suggest that Rif2 regulates MRX function by promoting ATP-driven Rad50 conformational changes. We provide evidence that Tel1, once loaded to a DSB by the MRX complex, promotes/stabilizes MRX association to the DSB in a positive feedback loop. Tel1 exerts this function independenly of its kinase activity, suggesting that it plays a structural role in promoting/stabilizing MRX retention to DSBs. This Tel1-mediated control of MRX association can be important to ensure that MRX binding to DNA is end-specific and becomes crucial for cell viability after genotoxic treatment when MRX accumulation at DSBs is suboptimal, such as in rad50-V1269M mutant cells. The rad50-V1269M mutation impairs MRX association at DSBs and the lack of Tel1 reduces further the amount of MRV1269MX bound at DSBs. As a consequence, tel1Δ rad50-V1269M double mutant cells are much more sensitive to genotoxic agents compared to each single mutant. This DNA damage hypersensitivity is not due to defective DSB resection. Instead, tel1Δ rad50-V1269M cells are severely defective in keeping the DSB ends tethered to each other and in repairing a DSB by HR and NHEJ. Since the mainteinance of the DSB ends in close proximity is a relevant event in the repair of DSBs by both NHEJ and HR [43,44,46,52], the low degree of end-tethering in tel1Δ rad50-V1269M cells can explain the poor ability of the same cells to repair a DSB by both repair mechanisms. During HR, the second DSB end can be captured by the D-loop to form an intermediate with double Holliday junctions, whose random cleavage results in equal number of NCO or CO products. However, if the newly synthesized strand is displaced by the D-loop and anneals to the other DSB end, this results in NCO products by the SDSA mechanism [53]. Consistent with the finding that only some MRX functions are lost in tel1Δ rad50-V1269M cells, COs are generated with wild-type kinetics in tel1Δ rad50-V1269M cells, whereas formation of these repair products are impaired by the lack of any MRX subunit [64]. Interestingly, tel1Δ rad50-V1269M cells are specifically defective in the generation of NCO products, suggesting that these cells can be specifically impaired in SDSA. This observation raises the possibility that the function of MRX in keeping the DSB ends in close proximity can be particularly important to facilitate the annealing of the displaced strand to the other DSB end. By contrast, this function can be escaped when the second DSB end is already captured by the D-loop and the DNA intermediate is stabilized by the formation of a double Holliday junction. MRX association to DNA has been shown to induce parallel orientation of the Rad50 coiled-coils that favours intercomplex association needed for DNA tethering [12]. Our results support a model wherein Tel1, once loaded at DSBs by MRX, exerts positive feedback by promoting an end-specific association of MRX with DNA (Fig 10E). This Tel1-mediated regulation of DNA-MRX retention is important for proper MRX conformation needed for the tethering of broken DNA ends. Previous data have shown that the lack of Sae2 impairs end-tethering and increases MRX association to DSB ends [65,66]. These and our findings suggest that it is not the amount of MRX bound at DNA ends per se that simply dictates the integrity of end-tethering. Instead, a proper MRX-DNA interaction is required to allow the establishment of a productive MRX intercomplex association that is needed to maintain DNA strands in close proximity. The amount of MRX and Tel1 bound at telomeres is lower than that found at DSBs [36]. This difference is due to a Rif2-mediated inhibition of Tel1 accumulation at telomeric ends, which has been proposed to protect telomeric DNA ends from over-elongation and checkpoint activation [35,36,67]. This Rif2 function in modulating MRX activity is not restricted to telomeric DNA ends. In fact, although the amount of Rif2 bound at an HO-induced DSB flanked by telomeric repeats is higher than that found at HO-induced DSB containing no telomeric sequences [35], we show that the lack of Rif2 increases the association of MRX in a Tel1-dependent manner also to intrachromosomal DNA ends. As Rif2 competes in vitro with Tel1 for the binding to MRX, Rif2 can limit MRX association to DSBs by reducing MRX-Tel1 interaction. Consistent with a direct role of Rif2 at DSBs, Rif2 can bind DNA both in vitro and in vivo and its binding at DSBs is partially dependent on MRX. We also found that the lack of Rif2 suppresses the DNA damage sensitivity and the end-tethering defects of tel1Δ rad50-V1269M double mutant cells. As rif2Δ increases MRX association to DSBs only in the presence of Tel1, the finding that Tel1 is not required for rif2Δ-mediated suppression of the rad50-V1269M phenotypes suggests that Rif2 has other functions in regulating MRX activity besides limiting its association to DNA ends. Based on the characterization of Rad50 variants that either promote or destabilize the ATP-bound state, it has been proposed that the ATP-bound conformation of MRX promotes end-tethering, whereas release from this ATP-bound state by ATP hydrolysis is necessary to allow access to DNA of the Mre11 nuclease active site and subsequent DSB resection [29]. Our data show that Rif2 enhances the ATP hydrolysis activity of the MRX complex, suggesting that the lack of Rif2 might restore end-tethering and DNA damage resistance in tel1Δ rad50-V1269M cells by increasing the time spent by MRX in the ATP-bound closed conformation. Consistent with this hypothesis, rif2Δ cells show an increased efficiency of both end-tethering and NHEJ compared to wild-type cells. The finding that the lack of Rif2 suppresses the end-tethering defect and the DNA damage hypersensitivity of tel1Δ rad50-V1269M cells without increasing MRX association at DSBs suggests that the transition between closed and open MRX conformations does not necessarily result in different amounts of MRX bound at DSBs. Thus, we propose that Rif2 has a dual function in regulating MRX functions at DSBs: (i) it counteracts MRX association at DSBs by inhibiting MRX-Tel1 interaction; (ii) it enhances MRX ATPase activity, promoting the transition of the complex from a closed state, required for tethering, to an open state that unmasks Mre11 nuclease active sites and thus is competent for DSB resection (Fig 10E). Interestingly, Rif2 is known to counteract NHEJ at telomeres [68]. Whether this Rif2 function depends on a Rif2-mediated regulation of MRX conformational changes is an interesting question that remains to be addressed. Cancer therapies targeting ATM/Tel1 have been developed to increase the effectiveness of standard genotoxic treatments and/or to set up synthetic lethal approaches in cancers with DNA repair defects [69]. Interestingly, analysis of the mutational landscape in 7,494 sequenced tumors across 28 tumor types revealed that approximately 4% of all human tumors harbor mutations in the MRN/MRX complex [70]. Our finding that MRX dysfunctions can be rendered synthetically lethal with tel1Δ in the presence of genotoxic agents suggests that ATM inhibitors in combination with DNA-damaging chemotherapy could be beneficial in patients whose tumors are defective in MRN function. Strain genotypes are listed in S1 Table. Strains JKM139 and YMV45 were kindly provided by J. Haber (Brandeis University, Waltham, United States). Strains JYK40. 6 and W4441-11C, used to detect end-tethering, were kindly provided by D. P. Toczyski (University of California, San Francisco, US) and M. Lisby (University of Copenhagen, Denmark), respectively. Strain tGI354, used to detect ectopic recombination, was kindly provided by J. Haber. Cells were grown in YEP medium (1% yeast extract, 2% bactopeptone) supplemented with 2% glucose (YEPD), 2% raffinose (YEPR) or 2% raffinose and 3% galactose (YEPRG). Gene disruptions were generated by one-step PCR disruption method. All the synchronization experiments have been performed at 26°C. To search for mutations that sensitize tel1Δ cells to DNA damaging agents, tel1Δ cells were mutagenized with ethyl methanesulfonate and plated on YEPD plates. Approximately 100,000 survival colonies were replica plated on YEPD plates with or without phleomycin or CPT. Phleomycin and/or CPT sensitive clones were recovered and transformed with plasmid containing wild-type TEL1 to identify those that lost DNA damage hypersensitivity. The corresponding original clones were then crossed to wild-type cells to identify by tetrad analysis the clones in which the increased sensitivity to DNA damaging agents was due to the simultaneous presence of tel1Δ and a single-gene mutation. Subsequent genetic analyses of the positive clones allowed grouping them in three allelic classes. In one class, the mutation responsible for the tel1Δ hypersensitivity to CPT and phleomycin was identified by genome sequencing and genetic analyses. To confirm that the rad50-V1269M mutation was responsible for the hypersensitivity to DNA damaging agents of tel1Δ cells, a KANMX gene was integrated downstream of the rad50-V1269M stop codon and the resulting strain was crossed to tel1Δ cells to verify by tetrad dissection that the increased sensitivity to CPT and phleomycin of tel1Δ cells co-segregated with the KANMX allele. DSB end resection at the MAT locus in JKM139 derivative strains was analyzed on alkaline agarose gels, by using a single-stranded probe complementary to the unresected DSB strand, as previously described [71]. This probe was obtained by in vitro transcription using Promega Riboprobe System-T7 and plasmid pML514 as a template. Plasmid pML514 was constructed by inserting in the pGEM7Zf EcoRI site a 900-bp fragment containing part of the MATα locus (coordinates 200870 to 201587 on chromosome III). DSB repair by NHEJ and SSA in YMV45 strains were detected by Southern blot analysis using an Asp718-SalI fragment containing part of the LEU2 gene as a probe, as previously described [71,72]. To determine the efficiency of NHEJ, the intensity of the uncut band at 30 min after HO induction (maximum efficiency of DSB formation), normalized respect to a loading control, was subtracted to the normalized values of the same band at the subsequent time points after glucose addition. The obtained values were divided by the normalized intensity of the uncut band in raffinose (100%). To determine the efficiency of DSB repair by SSA, the normalized intensity of the SSA product band at different time points after HO induction was divided by the normalized intensity of the uncut band at time zero before HO induction (100%). The loading control was obtained by hybridization of the filters with a probe that anneals to the RAD52 gene. DSB repair by NHEJ in JKM139 strains was detected by Southern blot analysis of SspI-digested genomic DNA with a MATa probe (200870–201587 coordinates of chromosome III). Strain W303 was transformed with a plasmid carrying HO under the control of a galactose inducible promoter. Mating type switching was detected by Southern blot analysis using a MATa probe (201082–201588 coordinates of chromosome III). This probe is complementary also to the HMLα locus (13826–13918 coordinates of chromosome III). To determine the efficiency of mating type switching, the intensity of the MATα band at different time points after glucose addition, normalized respect to a loading control, was divided by the normalized intensity of the uncut MATa band in raffinose (100%). DSB repair by ectopic recombination was detected by using the tGI354 strain as described in [72]. To determine the repair efficiency, the intensity of the uncut band at 2 h after HO induction (maximum efficiency of DSB formation), normalized respect to a loading control, was subtracted to the normalized values of NCO and CO bands at the subsequent time points after galactose addition. The obtained values were divided by the normalized intensity of the uncut MATa band at time zero before HO induction (100%). The centromeric pRS316 plasmid was digested with the BamHI restriction enzyme before being transformed into the cells. Parallel transformation with undigested pRS316 DNA was used to determine the transformation efficiency. Efficiency of re-ligation was determined by counting the number of colonies that were able to grow on medium selective for the plasmid marker and was normalized respect to the transformation efficiency for each sample. The re-ligation efficiency in mutant cells was compared to that of wild-type cells that was set up to 100%. ChIP analysis was performed as previously described [73]. Input and immunoprecipitated DNA were purified and analyzed by qPCR using a Biorad MiniOpticon. Data are expressed as fold enrichment at the HO-induced DSB over that at the non-cleaved ARO1 locus, after normalization of each ChIP signals to the corresponding input for each time point. Fold enrichment was then normalized to the efficiency of DSB induction. All the protein purification steps were carried out at 0–4°C. Rad50, Mre11 and Xrs2 were overexpressed in yeast and purified as described previously [18,41]. The Rad50-V1269M mutant was expressed and purified with a similar yield using the procedure devised for the wild-type protein. To assemble the MRX complex [74], Rad50, Mre11, and Xrs2 were incubated together for 5 h on ice. The resulting MRX complex was separated from unassembled proteins in a Sephacryl S-400 gel filtration column. Tel1 was overexpressed in the protease deficient yeast strain BJ5464 (MATa ura3-52 trp1 leu2Δ1 his3Δ200 pep4: : HIS3 prb1Δ1. 6R can1 GAL) using pGAL-FLAG-Tel1 (a kind gift from K. Sugimoto). An overnight yeast culture was diluted 1: 100 into 8 L of omission medium with 2% raffinose. Cells were further cultured at 30°C until the OD660 reached 0. 8, when 2% galactose was added to induce Tel1 expression. Cells were cultured for another 16 h before harvest. The pellet (~40 g), after being agitated with dry ice in a coffee grinder, was resuspended in 40 ml of ice-cold lysis buffer (40 mM KH2PO4, pH 7. 4,20% glycerol, 1 mM EDTA, 0. 1% NP-40,2 mM DTT, 600 mM KCl, and a cocktail of protease inhibitors consisting of aprotinin, chymostatin, leupeptin, and pepstatin A at 5 μg/ml each, and also 1 mM phenyl-methylsulfonyl fluoride). The lysate was clarified by centrifugation (20,000 xg, 30 min) and the supernatant was incubated with 0. 5 ml of anti-FLAG-M2 agarose resin for 2 h. After washing the matrix with 30 ml of K buffer (20 mM KH2PO4, pH 7. 4,10% glycerol, 0. 5 mM EDTA, 0. 01% NP-40,1 mM DTT) with 500 mM KCl, Tel1 was eluted with 1 ml of K buffer with 500 mM KCl and 200 μg/ml FLAG peptide for 1 h. The eluate containing purified Tel1 was concentrated and filter dialyzed against K buffer with 500 mM KCl in an Ultracel-30K micro-concentrator (Amicon). Purified Tel1 was stored at -80°C in small aliquots. To express (His) 6-tagged Rif2, the pET-Rif2 plasmid (a kind gift from K. Sugimoto) was introduced into BL21 Rosetta cells. Early log phase culture was treated with 0. 3 mM IPTG to induce Rif2 expression. After 4 h of incubation at 37°C, cells were harvested and Rif2 was purified using the following procedure. Briefly, the clarified cell lysate from 38 g pellet prepared by sonication in 100 ml T buffer (25 mM Tris-HCl, pH 7. 5,10% glycerol, 0. 5 mM EDTA, 0. 01% Igepal, and 1 mM DTT) containing 300 mM KCl and the protease inhibitor cocktail was mixed gently with 6 ml Ni-NTA resin (Qiagen) to capture the (His) 6-tagged Rif2. After washing extensively with 100 ml T buffer containing 1M KCl and 20 mM imidazole, bound proteins were eluted with 8 ml T buffer containing 150 mM KCl and 200 mM imidazole. The eluate was applied onto a SP Sepharose column (6 ml), which was developed with a 90 ml gradient of 50–350 mM KCl in T buffer. The peak fractions containing (His) 6-Rif2 were pooled and then applied onto a Mono S column (1 ml), which was developed with a 30 ml gradient of 75–450 mM KCl in T buffer. After concentrating the pooled peak fractions to 1. 5 ml in an Ultracel-30K concentrator (Amicon), the preparation (0. 5 ml protein) was further fractionated in a Superdex 200 column (24 ml) in T buffer with 300 mM KCl. The highly purified (His) 6-Rif2 protein (1 mg) was concentrated and stored in small portions at -80°C. The ATPase assay was performed as described previously with the level of ATP hydrolysis being measured by thin layer chromatography and phosphorimaging analysis [27]. Briefly, wild-type MRX or MRV1269MX (100 nM) and Rif2 (2 μM) were used in the presence of 100-bp dsDNA (200 nM). The effect of Rif2 on the ATPase activity of the MR complex (100 nM) was examined as described above. The DNA binding assay for Rad50 and MRX was slightly modified from that published previously [27]. Briefly, wild-type Rad50 and Rad50-V1269M (50,100, and 200 nM), wild-type MRX and MRV1269MX (10,20, and 40 nM) were incubated with radiolabeled 70-bp dsDNA substrate (10 nM) in the reaction buffer (25 mM Tris-HCl, pH 7. 5,150 mM KCl, 2 mM MgCl2,1 mM DTT, 100 μg/ml BSA, 2 mM ATP) at 30°C for 10 min. The reaction mixtures were resolved in a 0. 3% agarose gel in SB buffer (10 mM NaOH, 40 mM boric acid, pH 8. 0) on ice. The gel was dried and then subject to phosphorimaging analysis. To examine Rif2 for DNA binding activity, the indicated amount of purified Rif2 was incubated with radiolabeled 100-bp dsDNA substrate (10 nM) in the reaction buffer at 30°C for 10 min, followed by the same analytical procedure described above. Flag-tagged Tel1 was incubated with MRX or MRV1269MX in 30 μl T buffer containing 100 mM KCl for 2 h on ice. The reaction was mixed gently with anti-FLAG-M2 agarose resin (10 μl) for 2 h to capture Flag-tagged Tel1 and associated MRX or MRV1269MX. After washing the resin three times with 200 μl T buffer, bound proteins were eluted with SDS-PAGE loading buffer and then subject to western blot analysis. To determine the effect of Rif2 on the interaction between Tel1 and MRV1269MX, Flag-tagged Tel1 was incubated with MRV1269MX in the absence or presence of Rif2, and then subjected to affinity pull-down as described above.

Many tumors contain mutations that confer defects in repairing DNA double-strand breaks (DSBs). In both yeast and mammals, the MRX/MRN complex (Mre11-Rad50-Xrs2 in yeast; Mre11-Rad50-Nbs1 in mammals) plays critical functions in repairing a DSB by either nonhomologous end joining (NHEJ) or homologous recombination (HR). Furthermore, it recruits the checkpoint kinase Tel1/ATM. Although ATM is considered to be a tumor suppressor, up-regulation of ATM signaling promotes chemoresistance, radioresistance and metastasis. For this reason, cancer therapies targeting ATM have been developed to increase the effectiveness of standard genotoxic treatments and/or to set up synthetic lethal approaches in cancers with DNA repair defects. We aimed to identify the precise role of ATM/Tel1 in these processes. By performing a synthetic phenotype screen, we identified a mutation (rad50-V1269M) altering the Rad50 subunit of the MRX complex, which sensitizes cells lacking Tel1 to genotoxic agents. Genetic and biochemical characterization of MRV1269MX protein complex revealed that Tel1 promotes MRX association at DSBs to allow proper MRX-DNA binding that is needed for DSB repair. The role of Tel1 in promoting MRX retention on DSBs is counteracted by Rif2, which can regulate MRX activity at DSBs by modulating ATP-dependent conformational changes in Rad50. Our finding that MRX dysfunctions can be synthetically lethal with Tel1 loss in the presence of genotoxic agents suggests that ATM inhibitors could be beneficial in patients whose tumors have defective MRN functions.

Abstract Introduction Results Discussion Materials and Methods

medicine and health sciences chemical compounds immunology biological cultures carbohydrates galactose organic compounds surgical and invasive medical procedures dna damage clinical medicine cell cultures dna hypersensitivity research and analysis methods chemistry surgical resection biochemistry hydrolysis organic chemistry nucleic acids clinical immunology genetics monosaccharides biology and life sciences dna repair physical sciences non-homologous end joining chemical reactions atp hydrolysis

2016

Tel1 and Rif2 Regulate MRX Functions in End-Tethering and Repair of DNA Double-Strand Breaks

There is increasing evidence suggesting that short telomeres and subsequent genomic instability contribute to malignant transformation. Telomere shortening has been described as a mechanism to explain genetic anticipation in dyskeratosis congenita and Li-Fraumeni syndrome. Since genetic anticipation has been observed in familial breast cancer, we aimed to study telomere length in familial breast cancer patients and hypothesized that genetic defects causing this disease would affect telomere maintenance resulting in shortened telomeres. Here, we first investigated age anticipation in mother-daughter pairs with breast cancer in 623 breast cancer families, classified as BRCA1, BRCA2, and BRCAX. Moreover, we analyzed telomere length in DNA from peripheral blood leukocytes by quantitative PCR in a set of 198 hereditary breast cancer patients, and compared them with 267 control samples and 71 sporadic breast cancer patients. Changes in telomere length in mother-daughter pairs from breast cancer families and controls were also evaluated to address differences through generations. We demonstrated that short telomeres characterize hereditary but not sporadic breast cancer. We have defined a group of BRCAX families with short telomeres, suggesting that telomere maintenance genes might be susceptibility genes for breast cancer. Significantly, we described that progressive telomere shortening is associated with earlier onset of breast cancer in successive generations of affected families. Our results provide evidence that telomere shortening is associated with earlier age of cancer onset in successive generations, suggesting that it might be a mechanism of genetic anticipation in hereditary breast cancer. Genetic anticipation is the observation of progressively earlier age of onset or an increase of severity of clinical features of a genetic disorder as it is passed on to the next generation. The molecular mechanisms underlying anticipation are largely unknown, but it has been typically associated to trinucleotide repeat expansions in several genetic diseases [1], [2]. In cancer, genetic anticipation has previously been described in several hereditary cancer syndromes, such as hereditary non-polyposis colorectal cancer (HNPCC) [3], [4], familial leukemia [5], Li-Fraumeni Syndrome[6]–[8] and also in familial breast and ovarian cancer [9]–[11]. Telomere shortening has been more recently described as another mechanism of anticipation, being associated with early onset and severity of disease in genetic disorders, such as dyskeratosis congenita [12], [13], a disease characterized by cutaneous abnormalities, bone marrow failure and an increased predisposition to cancer, and in the Li-Fraumeni Syndrome [7], [8]. Families with dyskeratosis congenita have mutations in genes in the telomerase or shelterin complex, causing reduced telomerase activity. Telomeres are nucleoprotein structures that protect the end of chromosomes. Telomeres shorten with each cell cycle and there is increasing evidence suggesting that short telomeres and subsequent genomic instability contribute to malignant transformation. In this way, data from several case-control studies have indicated that individuals with relatively short mean telomere lengths might have an increased risk for developing cancer [14], [15]. In particular, telomere length and risk of breast cancer have been evaluated in different studies, although disparities among the results did not allow final conclusions [16]–[19]. Inherited predisposition to breast cancer accounts for approximately 5% of all cases and is characterized by an autosomal dominant pattern of inheritance, young age of onset and bilateral breast cancer. Familial breast and ovarian cancer (FBOC) is associated with inherited mutations mainly in two genes, BRCA1 and BRCA2. Women who have inherited mutations in either one of these genes have a high risk of developing breast cancer, ovarian cancer, and several other types of cancer during their lifetimes. However, a large proportion of familial breast cancer is not caused by mutations in BRCA1 or BRCA2. These non-BRCA1/2 breast cancer families (referred to as BRCAX families) comprise a histopathologically heterogeneous group, further supporting their origin being from other genetic events [20]. Telomere length maintenance is a complex process controlled by a large number of different proteins, and in addition to telomere binding proteins, many other proteins commonly involved in DNA repair are also found at telomeric ends [21]. BRCA1 and BRCA2 genes are involved in repair of DNA double strand breaks. Inherited defects in these genes lead to chromosomal instability contributing to malignant cell transformation. Importantly, there is evidence that BRCA1 localized at telomeres and may regulate telomere length and stability [22]–[24]. In addition BRCA2 has been very recently described to be implicated in telomere replication [25]. Based on all these data we hypothesized that telomere shortening may be associated to age anticipation in hereditary breast cancer. In this study, we analyzed telomere length in DNA from peripheral blood leukocytes in familial breast cancer cases and evaluated generational changes in telomere length in mother-daughter pairs with the aim to investigate the role of telomere shortening and its potential implication as a mechanism of age anticipation in familial breast cancer. The occurrence of age anticipation in mother-daughter pairs with breast cancer was analyzed in 623 FBOC families, classified as BRCA1 (40 families), BRCA2 (52 families), and BRCAX (531 families). The distributions of age at diagnosis for mother and daughters showed a consistent shift to earlier ages in daughters (Figure 1). Evidence for anticipation comparing the age of breast cancer diagnosis in daughters and respective mothers from these families was found after t-test in the three genetic groups. Breast cancer was diagnosed at an average of 6. 8 years earlier in daughters in the BRCA1 families (p = 0. 002) (Table 1). In the BRCA2 and BRCAX groups, a more significant earlier age of diagnosis in daughters was found, being 12. 1 years in the BRCA2 (p = 2. 9×10−7) and 12. 3 years in the BRCAX families (p = 1. 5×10−55). Therefore, significant differences were found in the age of onset between mothers and daughters in the three groups, showing the BRCA2 and BRCAX groups stronger apparent anticipation effect than BRCA1 group. In order to evaluate the hypothesis that telomere shortening may be associated with the earlier onset of the disease, we investigated the mean relative telomere length of blood leukocytes in index cases from FBOC families, either BRCA1 (48 cases), BRCA2 (45 cases), or BRCAX (105 cases), and compared with the relative telomere length of a normal population of healthy women covering an age range between 23 and 70 years (267 samples) (Figure 2). Interestingly, telomere lengths in affected individuals from BRCA1 and BRCA2 families were significantly shorter than those in the control population after adjustment for age using the line of best fit from controls (p<0. 0001) (Figure 3). High risk BRCAX families showed a more heterogeneous distribution of telomere length, with cases of both short and long telomeres. Adjusting for age, this group also demonstrated significant differences to the controls (p = 0. 031) (Figure 3). We also compared the mean telomere length in blood leukocytes from a group of 71 sporadic breast cancer patients. Importantly, age-adjusted telomere length distribution in these cases did not differ from controls (p = 0. 133) (Figure 2 and Figure 3). Therefore, hereditary but not sporadic breast cancer seems to be characterized by short telomeres, primarily in BRCA1 and BRCA2 mutation carriers, but also in a subgroup of BRCAX. Since BRCA1 or BRCA2 mutations were found associated with short telomeres, we further explored whether the altered function of these genes would be causing a reduction in telomere length or whether short telomeres were the consequence of other genetic or environmental factors present in these families. We analyzed telomere length of 19 affected women carrying BRCA1/2 mutations (8 BRCA1 and 11 BRCA2) and 22 sisters from the same families (8 BRCA1 and 14 BRCA2) who did not inherit the mutations (Figure 4). Affected BRCA1/2 mutation carriers showed not only significantly shorter telomeres than the unrelated normal controls (p<0. 0001) but also versus the healthy sisters not carrying the mutation (p = 0. 034). Telomeres of these healthy sisters do not significantly differ from the normal control population (p = 0. 177) suggesting that short telomeres are not likely the result of a predisposing genetic background or environment, which was shared by mutation carrier and non-carrier sisters, but would rather be a consequence of the mutation in the BRCA1/2 genes. So, it seems that haploinsufficiency for BRCA1 or BRCA2 in heterozygous women contributes to progressive telomere shortening at a somatic and germline level, affecting the age of cancer onset in successive generations. Because in the BRCAX group the causing mutations are unknown, a similar study cannot be performed, but it is possible that at least a subgroup of BRCAX families characterized by shorter telomeres were associated to mutations in other genes with a role in telomere maintenance. To better characterize the subgroup of BRCAX families with short telomeres, we divided breast cancer cases into quartiles of telomere length, according to the telomere length distribution in control samples (Figure 5). A significant increase of the first quartile (shortest telomeres) representing 50–70% of the cases in the BRCA1, BRCA2, and BRCAX groups was observed (p = 0. 003, p<0. 0001, and p<0. 0001, respectively). In contrast, the proportion of cases in the first quartile significantly decreased in the sporadic breast cancer group compared to the controls (10% of cases, p = 0. 0026). These results suggested that familial breast cancer, BRCA1 and BRCA2, but also a subgroup of BRCAX, was characterized by short telomeres in peripheral blood cells. We further stratified BRCAX families based on the number of generations in which individuals with breast cancer appeared (Table 2). Thus, there were families in which the proband' s generation was the first one with affected individuals, and families in which, in addition to the proband' s generation, there were one or two additional generations with cancer. Interestingly, those BRCAX families with only one generation affected corresponded almost completely to the fourth quartile characterized by the longest telomeres (Table 2), suggesting that shortening of telomere length might be occurring in successive generations. We next investigated the relation between telomere length and genetic anticipation in breast cancer. We measured changes in telomere length in 19 mother-daughter pairs from FBOC families (3 BRCA1,1 BRCA2, and 15 BRCAX) who developed breast cancer (Figure 6) (Table 3) and compared them to 16 normal mother-daughter pairs. We additionally analyzed telomeres in 12 pairs of affected mothers from BRCA1/2 families and their respective daughters who were mutation carriers but did not develop cancer to date (Table 4). Telomere length was adjusted for age and we evaluated telomere differences between mothers and daughters in the three groups: both mothers and daughters affected, affected mothers and unaffected mutation-carrying daughters, and mother and daughter controls (Figure 7A). Interestingly, telomere length significantly decreased not only in affected daughters (p = 0. 00018) but also in unaffected daughters who were carriers of BRCA1/2 mutations (p = 0. 003), while the change between control mothers and daughters was not statistically significant (p = 0. 341). This indicates that telomere shortening was associated in these mother-daughter transmissions to the inheritance of a genetic mutation rather than with the disease. Looking at the difference in telomere length of mother-daughter pairs (Figure 7B), significantly larger differences in pairs from affected families were found compared to controls (p = 0. 003), with telomeres being shorter in daughters of breast cancer families compared with normal mother-daughter pairs. This decrease in telomere length in successive generations suggests that telomere length could explain age anticipation in familial breast cancer. Telomere dysfunction seems to underlie the development of a range of human genetic, degenerative, aging diseases and cancer [26]. Our results demonstrated that short telomeres in peripheral blood cells were a feature of hereditary breast cancer patients and that telomere shortening frequently occurred with successive generations in these families, suggesting that telomere shortening could be the mechanism to explain the phenomenon of age anticipation in this disease. Decreasing age of onset in families with hereditary breast cancer has been observed before [9]–[11]. Similar to what we have found, Paterson et al. [10] reported earlier age of diagnosis, of between 6 and 9 years, in successive generations of breast cancer families. Another study performed in a smaller number of BRCA1, BRCA2 and BRCAX families demonstrated that the mean maternal age at diagnosis in the BRCA1 group was significantly lower comparing to the BRCA2 or BRCAX groups, and no significant difference was found in the mean age at diagnosis between mothers and daughters in BRCA1 families [9]. In our study we detected lower anticipation effect in BRCA1 families comparing to BRCA2 or BRCAX. Since BRCA1 mutations predispose to breast cancer at an earlier age, it would be more difficult to have large differences between generations in BRCA1 families. However, there are not definitive conclusions about whether there exists a real anticipation phenomenon. We cannot exclude that there would be alternate explanations for the earlier age of diagnosis in daughters. This observation could be due to ascertainment bias of subjects and lead-time bias, as a result of early detection of cancers by extensive screening or surveillance programs in high risk families [27], [28]. More sophisticated statistical approaches and mechanistic studies are warranted to answer this complicated problem. To date there are studies for [10], [29], [30] and against [28] a real phenomenon of anticipation in breast cancer, but a final conclusion regarding anticipation is still an open matter that is complicated by the fact that there is no molecular mechanism to explain it. Importantly we provide data indicating that telomere shortening is a possible biological explanation for the complex phenomenon of anticipation in breast cancer. There are several studies trying to find association between short telomeres and risk of breast cancer that have reported contradictory results [16]–[19], [31], [32]. The fact that sporadic breast cancer showed normal telomere length distribution in our study agrees with previous results from case-control studies, focused on sporadic cases, indicating lack of association between telomere lengths in blood leukocytes with risk of breast cancer [31], [32]. Interestingly, one association study reported significant association with breast cancer risk in women under 50 years of age, but no association between telomere length and breast cancer in women 50 years of age or older [17]. Hereditary breast cancer, which we found characterized by short telomeres, typically occurs in women under 50 years. Then, it seems that hereditary breast cancer is associated with short telomeres, especially in BRCA1 and BRCA2 mutation carriers, as well as in a subgroup of BRCAX. It has been reported that telomere attrition may be affected by factors such as smoking, oxidative stress or obesity [33]–[35]. Recently a prospective case-control study in breast cancer suggested that telomere shortening mainly occurs after diagnosis, as an effect of chemotherapy or other aspects of disease progression [16]. However, the observed telomere shortening in unaffected BRCA mutation carrier daughters, as well as shorter telomere length in hereditary versus sporadic cases, although they followed similar therapeutic strategies, indicate that telomere shortening is largely influenced by genetic events. Nevertheless, the results are limited by the absence of data regarding timing of the sample draw, and the application of any chemotherapy. Our results showing that telomeres of affected women from BRCA1 and BRCA2 families were significantly shorter than normal controls suggest that these genes might be involved in telomere regulation. Similarly, we can speculate that other genes involved in telomere maintenance could also be susceptibility genes that explain at least part of the BRCAX families with short telomeres. Telomeres were shorter in affected mutation carriers versus non carriers and, moreover, we found that telomeres shortened in successive generations in affected families comparing with controls, suggesting that this is a plausible mechanism explaining the observed anticipation effect in familial breast cancer. We can speculate that mutations in BRCA1/2 or other genes induce faster telomere attrition during life time increasing the probability of genomic instability and the risk of developing breast cancer. Breast cancer would develop, first as a consequence of the inherited mutation, as this is the critical risk factor, and then the telomere length could modify the age at which the cancer would appear, as shorter telomeres at birth would reach dramatic genetic instability earlier that longer ones. On the other hand, telomere shortening could be a process mainly associated with the presence of mutations in BRCA1/2, or some other genes, regardless of the disease pathophysiology or age of presentation. Interestingly, we found four families in which daughters showed shorter telomeres than their mothers although the age of breast cancer was not anticipated with respect their mothers (Table 4), indicating that other genetic modifiers or environmental factors would be also determining the age of onset of breast cancer. Telomere length variations as well as different telomere erosion rates were observed within the different lymphocyte subsets [36], [37]. In our study we used unselected peripheral blood leukocytes to estimate telomere lengths and then it could be affected by cell-to-cell variations. Selection of specific lymphocyte subpopulation would provide a more refined knowledge of the role that telomere length could have in cancer risk assessment. The fact that telomere shortening has been associated with anticipation in other diseases, such as dyskeratosis congenita [13], as well as Li-Fraumeni Syndrome [7], suggest that telomere shortening may be responsible for genetic anticipation in a wide spectrum of genetic diseases. Our findings indicate that the study of telomere length would be of relevance in the clinical surveillance and design of appropriate screening tests for patients with familial breast cancer. Informed consent was obtained for all patients involved in this study and the research project has the approval of the ethics committee of our institution. Families used in this study were selected from the register of the Familial Cancer Consultation of the CNIO Human Genetics Group. All of them fulfilled the high risk criteria for genetic testing [38]. Index cases had been screened for mutations in BRCA1 and BRCA2 by a combination of DHPLC and direct sequencing as previously reported [39]. A total of 623 breast cancer high-risk families (40 BRCA1,66 BRCA2, and 531 families without mutations in BRCA1/2) which included 758 mother-daughter pairs affected with breast cancer were selected to analyze the anticipation effect in the age of onset. To avoid differences in the age of onset of different types of tumors occurring in these families, i. e. breast, ovary, and other tumors, only mother-daughter pairs who developed breast tumors were used to estimate the anticipation effect. Telomere length in familial breast cancer cases was analyzed using DNA extracted from peripheral blood leukocytes in index cases from a set of 198 Spanish breast cancer families corresponding to 48 BRCA1,45 BRCA2, and 105 BRCAX families. In these three groups the distribution of ages at which the telomere were analyzed was for BRCA1 a mean of 43 years (range, 18–61), mean 48 years (range, 21–71) for BRCA2 cases and for BRCAX cases a mean of 48 years (range, 25–70). These samples corresponded to women with familial breast cancer who met the high risk criteria and attended the Spanish National Cancer Centre family cancer clinics between 2002 and 2009. In addition, samples from 8 affected BRCA1 mutations carriers and sisters not carrying the mutation, and 11 samples from affected BRCA2 mutations carriers and 14 healthy sisters without mutation from these families were included to study the relation between telomere length and the presence of an inherited mutation under the same genetic background. DNA samples from 267 control women with a mean age of 46 years (range, 23 to 70 years) were also analyzed to compare normal telomere length distribution with that in breast cancer patients. Controls corresponded to Spanish healthy women without personal or familial antecedents of cancer, recruited at different Hospitals in Spain for different epidemiologic studies [40]. Age distribution of controls was homogeneous enough to demonstrate the expected decline of telomere length with age. Seventy-one peripheral blood samples from a group of women with sporadic breast cancer at ages ranging from 31 to 61, mean 53 years, were also analyzed. These sporadic cases were consecutive newly diagnosed breast cancer patients, without familial antecedents of breast cancer, recruited between 2006 and 2007 in the different Hospitals in Madrid. These cases were included in previous studies from our group [40], [41]. Genomic DNA was automatically extracted from peripheral blood mononuclear cells using the MagNA Pure LC 2. 0 System (Roche). Telomere length was measured using a quantitative PCR-based technique previously described [42], [43]. By this method telomere length is calculated as a ratio between telomere repeat copy number (T) and a single-copy gene, 36B4, copy number (S). Primers used to amplify telomere repeats and the 36B4 gene were described before [43]. DNA samples were amplified in a total reaction volume of 10 µl containing 1x Power SYBR Green PCR Master Mix (Applied Biosystems), 300 nM of primer Tel1,900 nM of primer Tel2, and 30 ng of DNA. For 36B4 reactions the concentrations of primers were 300 nM of 36B4u and 500 nM of 36B4d. All samples, for both telomere and 36B4 amplifications, were analyzed in triplicate using an ABI 7900HT thermal cycler, in 384-well format. A robot Biomek NXp (Beckman Coulter) was used to load DNA and PCR mixes into the 384 PCR plates. The thermal cycling profile was the same for both assays: 95°C incubation for 10 minutes followed by 35 cycles of 95°C for 15 seconds, 54°C for 2 minutes, and 72°C for 15 seconds. Each PCR reaction plate included two samples (DNA from the MBA-MD-436 cell line) to be used for inter-run calibration. DNA from the cell line MBA-MD-436 was used to construct a standard serial dilution series (1/4 dilutions starting from 50 ng) for PCR efficiency calculation. We observed that over 100 ng linearity is lost, and therefore samples were diluted to 10 to 30 ng for proper measurements. The amplification efficiencies (E) of each PCR were calculated from the slopes of the standard curves according to Eff = 10 (−1/slope). The efficiency was calculated for each plate, both for the telomere and for the 36B4 signal. PCR data was analyzed using the SDS 2. 2. 2 program. The threshold value was established in the initial part of the exponential phase of the amplification curves and the crossing of this line with the curve defines the threshold cycle value (Ct) for all samples. For each sample the relative concentration of both Telomere (T) and 36B4 (S) was calculated relative to the calibration sample and PCR efficiency to obtain the T/S ratio, as previously described [14], applying the following formula: Concordance among triplicates was checked, and the coefficient of variation was obtained for Telomere (average 0. 85%, range 0. 2–2. 8%) and 36B4 (average 0. 54%, range 0. 12–1. 5%). Reproducibility was tested by repeating two samples in all experiments. Good agreement between measurements were found throughout the experiments (r = 0. 86). To asses telomere length differences between control samples and hereditary and sporadic breast cancer cases, telomere length measurements were adjusted for age using the line of best fit for controls. Thus, the difference between the actual and the predicted value was calculated for each sample. Differences in age-adjusted telomere lengths were analyzed by bilateral t-tests. The Kolmogorov-Smirnov test was used to evaluate normality in telomere length of BRCA1/2 mutation carriers and their corresponding non-carrier sisters. As a normal distribution could not be assumed, a Mann-Whitney U test was applied to evaluate distribution differences. A similar analysis was done in order to assess generational differences in telomere length between control mother-daughter pairs and mother-daughter pairs from FBOC families. Statistical calculations were performed using SPSS version 17 (SPSS Inc, Chicago, Illinois). Nominal two-sided P-values less than 0. 05 were considered statistically significant. Due to limitations of previous information regarding telomere length in the population studied, a post hoc power analysis for comparisons of telomere length among the different groups was made. Power analysis for the test between controls and sporadic cases, which showed no significant differences in telomere length, was 63%.

The fact that accelerated telomere shortening accompanies different premature aging syndromes and seems to be associated with increased risk of cancer development prompted us to analyze the role of telomere length in hereditary breast cancer. In this study we found that telomeres of peripheral blood cells from familial breast cancer patients were significantly shorter than those from the control population. Women carrying a mutation in BRCA1 or BRCA2 genes, and a subset of BRCAX families, were characterized by short telomeres. We also demonstrated that genetic anticipation, the successive earlier age of onset of cancer, in these families was associated with a decrease of the telomere length in affected daughters compared to their mothers. This is the first study suggesting that telomere shortening may contribute to anticipation in breast cancer families and that analysis of telomere length in hereditary breast cancer may affect the design of surveillance programs for affected families.

Abstract Introduction Results Discussion Materials and Methods

oncology medicine cancer genetics breast tumors heredity genetics molecular genetics biology haploinsufficiency genetic testing cancers and neoplasms genetics and genomics human genetics

2011

Genetic Anticipation Is Associated with Telomere Shortening in Hereditary Breast Cancer

Enteric fevers, caused by the Salmonella enterica serovars Typhi (ST), Paratyphi A (PA) and Paratyphi B (PB), are life-threatening illnesses exhibiting very similar clinical symptoms but with distinct epidemiologies, geographical distributions and susceptibilities to antimicrobial treatment. Nevertheless, the mechanisms by which the host recognizes pathogens with high levels of homology, such as these bacterial serovars, remain poorly understood. Using a three-dimensional organotypic model of the human intestinal mucosa and PA, PB, and ST, we observed significant differences in the secretion patterns of pro-inflammatory cytokines and chemokines elicited by these serovars. These cytokines/chemokines were likely to be co-regulated and influenced the function of epithelial cells, such as the production of IL-8. We also found differing levels of polymorphonuclear leukocyte (PMN) migration among various infection conditions that either included or excluded lymphocytes and macrophages (Mϕ), strongly suggesting feedback mechanisms among these cells. Blocking experiments showed that IL-1β, IL-6, IL-8, TNF-α and CCL3 cytokines were involved in the differential regulation of migration patterns. We conclude that the crosstalk among the lymphocytes, Mϕ, PMN and epithelial cells is cytokine/chemokine-dependent and bacterial-serotype specific, and plays a pivotal role in orchestrating the functional efficiency of the innate cells and migratory characteristics of the leukocytes. Typhoid and paratyphoid fevers are known as enteric fevers and are caused by the intracellular Gram-negative bacteria Salmonella enterica serovars Typhi (ST), Paratyphi A (PA) and Paratyphi B (PB) [1–4]. Enteric fevers are rare in industrialized countries with most infections occurring in military personnel and travelers to endemic areas. The Centers for Disease Control and Prevention (CDC), in the United States, report approximately 400 laboratory-confirmed cases per year [5]. Nevertheless, ST and PA infections are a significant public health problem in the developing world [4,6–10]. Each year, 11. 9–20. 6 million new cases of typhoid fever occur in low- and middle-income countries and are associated with approximately 129,000–223,000 deaths [11–14]. These deaths happen mostly in Asia in children under five years of age [15]. Additionally, the emergence of multidrug-resistant strains of ST and PA has complicated the antimicrobial treatment of enteric fever and asymptomatic carriers [16–19]. To address this public health problem, there is an increased emphasis on sanitation measures, such as water and sewage treatment, and vaccination to fight these infections [4]. Interestingly, typhoid and paratyphoid fevers exhibit very similar clinical features [2,4, 20] but distinct epidemiologies, geographical distributions, and susceptibilities to antimicrobial treatment [21–23]. Although vaccines for PA and PB are not readily available, the existence of microbiological similarities among PA, PB, and ST, and the cross-reactivity elicited by the Ty21a typhoid vaccine against PA [24–27] and PB [27–29] support the feasibility of developing a Paratyphoid vaccine [30]. For example, the Center for Vaccine Development at the University of Maryland, Baltimore is currently evaluating a mucosally administered attenuated PA vaccine candidate [30,31]. In humans, the only reservoir for these infections, the disease spreads by the fecal-oral route via contaminated food and water [4,10]. ST, PA, and PB adhere to and invade the distal ileum epithelium and, subsequently, disseminate to cause enteric fevers. Intestinal epithelium and immune cells play a pivotal role in sensing and directing immune responses to maintain homeostasis [32,33]. The crosstalk among these cells is critical in regulating intestinal innate and subsequent adaptive immune responses against bacterial pathogens [32,33]. Thus, vaccination strategies to prevent enteric fevers, as well as therapeutic interventions to treat Salmonella infection, require detailed information on the early events of host responses to Salmonella. Yet, the role of crosstalk among innate cells at the site of infection, and how this crosstalk influences the host response to serovars that share a high degree of homology remains unexplored. Here, using a multicellular three-dimensional (3-D) organotypic model of the human intestinal mucosa composed of multiple cell types, which was developed and characterized by our group [34–39], and PA, PB, and ST, we observed significant differences in the secretion patterns of pro-inflammatory cytokines and chemokines elicited by these serovars. These cytokines/chemokines were likely to be co-regulated and influenced the function of epithelial cells, such as the production of IL-8. We also found differing levels of polymorphonuclear leukocyte (PMN) migration among the infection conditions that either included or excluded lymphocytes and macrophages (Mϕ), strongly suggesting feedback mechanisms among these cells. Blocking experiments showed that IL-1β, IL-6, IL-8, TNF-α and CCL3 cytokines were involved in these migratory differences. Thus, the crosstalk among the lymphocytes, Mϕ, PMN and epithelial cells was cytokine-dependent and bacterial-serotype specific, and likely to play a pivotal role in orchestrating the functional efficiency of innate cells and migratory characteristics of the leukocytes. To investigate how the host recognizes distinct Salmonella serovars that share a high degree of homology, we took advantage of a multicellular three-dimensional (3-D) organotypic model of the human intestinal mucosa, which was developed and characterized by our group [34–39], and three genetically similar serovars, namely PA, PB, and ST. The 3-D model was comprised of primary human lymphocytes, fibroblasts, endothelial cells, and a human epithelial cell line [34–39]. These epithelial cells consist of a human adenocarcinoma enterocyte cell line (i. e. , HCT-8) that was initially derived from the junction of the small and large bowel [40]. This 3-D model has unique characteristics with a close structural and functional resemblance to the human intestinal mucosa. In this organotypic 3-D model, the epithelial cell line behaves as a multipotent progenitor cell that gives rise to functional and highly differentiated cells from multiple lineages (i. e. , absorptive enterocyte, goblet, and M cells) [34–36,38]. Our focus was on the early interactions between bacteria and cells from the 3-D organotypic model. Cells were exposed (or not) to Salmonella serovars PA, PB, and ST. After 4 hours of incubation, the supernatants were collected and used to measure cytokines (i. e. , IL-1β, IL-6, IL-8, CCL3, and TNF-α). We found that exposure to any of the Salmonella strains, albeit at different levels, resulted in increases in cytokine secretion compared to negative controls (Fig 1). Interestingly, we observed different patterns of secretion in the cytokines elicited by each Salmonella strain. Exposure to PB induced a significantly higher secretion of IL-1β compared to that of cultures exposed to PA and ST (Fig 1). The exposure to PA prompted a significantly higher secretion of IL-6 and TNF-α compared to cultures exposed to PB and ST (Fig 1). The exposure to PA also triggered a significantly higher secretion of CCL3 compared to 3-D organotypic models exposed to PB, and a similar trend for ST was observed (Fig 1). In contrast, no significant differences in the secretion of IL-8 were observed among the strains (Fig 1). Moreover, we found a significant correlation between the secretion of IL-8, a cytokine predominantly secreted by intestinal epithelial cells (IECs), and CCL3, which are chemokines/cytokines predominantly produced by immune cells (Fig 2). We also observed a significant correlation between the secretion of CCL3, and either IL-6 or TNF-α, which are pro-inflammatory cytokines. Thus, the inflammatory effects of PA, PB and ST serovars might be mediated, at least in part, through the differential regulation of cytokines/chemokines secreted by epithelial and immune cells. It is well known that Mϕ play an essential role in the host response to Salmonella infection [3,41], and that the chemokine CCL3 is critical in the regulation and recruitment of leukocytes (i. e. , Mϕ and PMN) [42–44]. Moreover, Mϕ release and promote the secretion of pro-inflammatory cytokines, such as IL-1β, IL-6, and TNF-α, by other immune cells [45,46]. Thus, to evaluate the role of Mϕ and other immune cells in the 3-D organotypic model, we next performed “gain and loss” studies to define the importance of immune cells (i. e. , lymphocytes and Mϕ) (peripheral blood mononuclear cells, PBMC) in the production of cytokines. To this end, 3-D organotypic models were built without or with lymphocytes/monocytes-enriched PBMC. After ~17 days, the organotypic models were exposed, or not, to PA, PB or ST strains. After 4 hours of stimulation, the supernatants were collected and used to measure the secretion of IL-1β, IL-6, IL-8, CCL3, and TNF-α cytokines/chemokines. Regardless of the bacterial strain, the secretion of IL-1β and CCL3 was only observed in the supernatants from 3-D models containing immune cells (i. e. , PBMC) (Fig 3). We also observed that the secretion of IL-6 and TNF-α was significantly higher in supernatants from organotypic models containing PBMC than in supernatants from 3-D organotypic models built without PBMCs (Fig 3). Finally, exposure to PA and PB induced a significantly higher secretion of IL-8 in the supernatants from 3-D organotypic models containing immune cells as compared to supernatants from 3-D models built without PBMC (Fig 3). No significant difference was found for ST (Fig 3) (P = 0. 1762, ST with vs. ST without PBMC). Taken together, these results suggest that immune cells located at intestinal mucosa contribute substantially to the production of multiple chemokines/cytokines in responses to PA, PB, and ST infections. To characterize the role of Mϕ in the secretion of these cytokines, we depleted Mϕ from PBMC by using the adherence to agarose technique [47,48], and the Mφ-depleted PBMC were added during the construction of the 3-D organotypic models. Organotypic models built with whole PBMC (Total PBMC) were used as controls. Using this technique, monocyte depletion was >85% by flow cytometry analysis. A representative experiment is shown in Fig 4A, left 2 panels. After 17 days in culture, constructs from the 3-D models were collected, disaggregated by collagenase, and single cell suspensions used for surface marker staining (CD11b, CD14, CD19, CD45, and CD163). Flow cytometry analysis showed a reduction of 50–60% in Mϕ present in the models built with Mϕ-depleted PBMC compared to that of the models built with whole PBMC. A representative experiment is shown in Fig 4A, right 2 panels. Alternatively, or concomitantly, after 17 days in culture, the cells from the 3-D organotypic models were left untreated (none) or were exposed to either PA, PB or ST. After 4 hours, the constructs and supernatants were collected. Single cells were isolated from the constructs as described above. Flow cytometric analyses showed lower numbers of Mϕ (i. e. , CD45+CD14+CD19-CD11b+CD163+ cells) in the constructs exposed to Salmonella strains compared with the control constructs with media only (Fig 4B). Constructs exposed to PA and PB had higher decreases in Mϕ than did the constructs exposed to ST. We hypothesized that more Mϕ died in 3-D organotypic models exposed to PB and PA as compared to 3-D organotypic models exposed to ST. We then used supernatants from constructs exposed to the Salmonella serovars to measure the levels of IL-1β, IL-6, IL-8, TNF-α, and CCL3. We observed that regardless of the Salmonella strain, the secretion of IL-6 and TNF-α was higher in supernatants from organotypic models containing whole PBMC than in those from 3-D organotypic models built with Mϕ-depleted PBMC (Fig 4C). No changes in the secretion of IL-1β were observed among the strains when comparing supernatants from the 3-D models built with whole or Mϕ-depleted PBMC (Fig 4C). Interestingly, the secretion of CCL3 was higher in supernatants from models exposed to PA, but not to PB and ST, when comparing cultures with whole PBMC to cultures with Mϕ-depleted PBMC (Fig 4C). Thus, Mϕ were required in order to observe a predominant secretion of IL-6 and TNF-α in response to PA, PB and ST infection, and CCL3 in response to PA. To confirm that Mϕ were the source of the cytokines, we next measured the intracellular expression of IL-6, IL-8, TNF-α, and CCL3 by flow cytometry in cells isolated from 3D organotypic models to which PBMC were added. Although Mϕ constitutively expressed baseline levels of these cytokines (i. e. , media only), expression of IL-8 and TNF-α were higher both per cell (mean fluorescence intensity), and in frequency (%) in the cultures exposed to the Salmonella strains than to controls (media) (Fig 5). IL-6 and CCL3 expression, though present, did not or only marginally increased after exposure to Salmonella strains as compared to the control (Fig 5). These results were further confirmed via Mϕ derived from the human monocyte cell line U937 obtained using phorbol 12-myristate 13-acetate [49]. This process resulted in >90% of cells expressing CD14+CD163+CD11b+ markers, a phenotype characteristic of resident Mϕ and consistent with one expressed by the Mϕ in the organotypic model [50,51] (Fig 6A). For these experiments, U937-differentiated Mϕ were cultured with supernatants from 3-D organotypic models built with whole (Total) PBMC and exposed or not to either PA, PB, or ST strains. After 4 hours, cells were collected and used to measure viability by Trypan blue exclusion test, or to perform flow cytometric assays to analyze intracellular cytokine expression. Like for Mϕ isolated from the organotypic models, only IL-8 and TNF-α expression were markedly increased after exposure to Salmonella strains as compared to controls (media) (S1B–S1E Fig). To further explore the Mϕ functionality, we next performed experiments to evaluate the effects of the presence of Mϕ on cytokine/chemokine secretion and its impact on their migration. To this end, organotypic models built with whole or Mϕ-depleted PBMC were exposed or not to either PA, PB or ST strains. After 4 hours, the supernatants were collected and used to stimulate Mϕ migration over a trans-well system. Mϕ were differentiated as described above from the U937 cell line (Fig 6A). We found that the migration of Mϕ was higher in the presence of supernatants from models exposed to PB and ST than their migration in the presence of supernatants from models exposed to PA when the models were built with whole PBMC (Fig 6B & S2 Fig). Interestingly, the migrations observed when Mϕ were exposed to supernatants from models built with Mϕ-depleted PBMC and exposed to PA were increased, while the migrations when exposed to PB and ST supernatants were significantly decreased (Fig 6B). Thus, although one cannot assert that Mϕ present in the organotypic model are the only cells secreting chemokines, these results suggest that resident Mϕ might play a role in regulating the patterns of migration of circulating Mϕ into the local microenvironment. While soluble factors produced in response to PB and ST infections in cultures with Mϕ led to upregulation of Mϕ migration, soluble factors in response to PA infection mediated downregulation of Mϕ migration as compared to cultures with Mϕ-depleted PBMC. To determine whether IL-1β, IL-6, IL-8, TNF-α, or CCL3 were involved in the signaling of Mϕ, we depleted these cytokines/chemokines using monoclonal antibodies. Depletion of IL-1β, IL-6, IL-8, and TNF-α, but not CCL3, decreased the migration of Mϕ exposed to supernatants from ST-cultures (Fig 6C). Anti-IL-6, -IL-8 or -TNF-α antibodies also decreased the Mϕ chemotactic effect of supernatants obtained from the PB-exposed cultures (Fig 6C). Remarkably, blocking of TNF-α induced an increase in the Mϕ chemotactic activity of supernatants derived from the PA-exposed cultures (Fig 6C). Finally, blocking of CCL3 had only modest effects on Mϕ migration after exposure to supernatants obtained from ST- and PB-exposed cultures. These results suggest that the signaling of Mϕ migration is dependent on infection with particular Salmonella serovars. To further investigate the functionality of Mϕ, we examined their bactericidal ability by measuring the release of elastase and myeloperoxidase (MPO) after exposure to PA, PB, and ST strains. The production of elastase and MPO are part of the antimicrobial arsenal of Mϕ to fight infection. After 4 hours of infection, supernatants from the cultures with whole and Mϕ-depleted PBMC were collected and used to measure the release of elastase and MPO by ELISA. The production of elastase and MPO was similar between PA-derived supernatants from the organotypic models built with whole (total) and Mϕ-depleted PBMC (Fig 7). It is worth noting that the elastase levels were significantly higher or showed a significant trend in supernatants from the PB (P = <0. 0001) and ST (P = 0. 0785) cultures, when the models were built with whole as compared to the organotypic models built with Mϕ-depleted PBMC (Fig 7). Thus, Mϕ are more prone to produce elastase after exposure to PB and ST than after exposure to PA. We next evaluated cell viability in the presence or absence of Mϕ in the model. After 4 hours of infection, cell viability was evaluated by lactate dehydrogenase (LDH), a stable cytosolic enzyme that is released into cell supernatant upon cell lysis. Regardless of the presence of Mϕ, PB, and to lesser degree ST, infection induced the highest levels of cell killing. Interestingly, the levels of cell death in the PA cultures were higher in the organotypic models built with whole PBMC than in those built with Mϕ-depleted PBMC. Thus, while the levels of cell death in the PB and ST cultures encompassed multiple cell types (e. g. , epithelial cells and immune cells), PA killing appears to be mainly restricted to Mϕ. Flow cytometry analysis of cell viability, based on dye VIVID, showed that Mϕ were more susceptible to death after exposure to PB than after exposure to PA or ST (Fig 8B). Therefore, although PA killing affected more Mϕ, these killings were lower in magnitude than those observed for PB but more noticeable than those for ST. Since during the early stages of Salmonella infection, PMN are recruited by chemokines released by resident cells, such as Mϕ [52], we next assessed whether, in vitro, the factors secreted by Mϕ were capable of modulating Salmonella-driven PMN migration. To this end, PMN were purified by a standard dextran-500 gradient technique [53] and were used to measure chemotaxis after exposure to cell-free supernatants from organotypic models exposed, or not, to PA, PB, or ST. The PMN purity was ~90% (Fig 9A). We found differing levels of PMN migration between infection conditions of organotypic models built with whole or Mϕ-depleted PBMC. Notably, exposure of purified PMN to supernatants from the PA and PB, but not ST, cultures built with whole PBMC exhibit significant reductions in PMN migration (Fig 9B & S2 Fig) compared to the cultures built with Mϕ-depleted PBMC (Fig 9B). Interestingly, no significant changes in PMN migration were observed between cultures with whole and Mϕ-depleted PBMC after exposure to supernatants from organotypic models containing ST (Fig 9B). These results strongly suggest the existence of feedback mechanisms between Mϕ and PMN during Salmonella infections. Next, we performed blocking experiments to determine the impact of IL-1β, IL-6, IL-8, TNF-α, and CCL3 on PMN migration. To this end, organotypic models built with whole PBMC were exposed or not to PA, PB, or ST. After 4 hours, the supernatants were collected and used to stimulate PMN migration over a trans-well system. We found that depletion of IL-1β, IL-6, IL-8, TNF-α and CCL3 decreased the migration of PMN exposed to supernatants from ST-cultures (Fig 9C). Anti-IL-1β antibodies also decreased the PMN chemotactic effect of supernatants obtained from the PB-exposed cultures (Fig 9C). Besides, CCL3 showed a trend (P = 0. 067) of blocking PMN chemotaxis triggered by supernatants containing PB, without reaching statistical significance, which could be attributable to the concentrations of both CCL3 and anti-CCL3 antibodies in the supernatants. Regardless of the neutralizing antibodies used, PMN migration was not blocked after exposure to supernatants obtained from the PA-exposed cultures (Fig 9C). These in vitro observations prompted us to speculate the existence of synergic and/or antagonistic effects between the expression of cytokine/chemokines such as IL-6, IL-8, TNF-α and CCL3 that determine PMN migration following exposure to various Salmonella serovars. It is also possible that other cytokines/chemokines not evaluated in these studies play differential roles in the PMN migration, which we could not observe since we did not use the appropriate blocking mAbs. To study the association between PMN migration and their capacity to produce cytokines, we measured their intracellular expression of IL-6, IL-8, TNF-α, and CCL3 cytokines. Purified PMN were exposed to supernatants from PA, PB or ST cultures built with whole PBMC. After 4 hours, PMN were surface stained with CD3, CD14, CD19, CD45, CD163, and CD66c, and intracellularly with IL-6, IL-8, TNF-α and CCL3 mAbs and analyzed by flow cytometry. We found that PB induced higher levels of cytokines than either PA or ST (Fig 10). Although the frequency of PMN positive for IL-6, IL-8, TNF-α, and CCL3 is much lower than that for Mϕ, they might have a significant contribution to the overall production of IL-6, IL-8, TNF-α, and CCL3 during inflammation, since PMN are at higher frequency than Mϕ at sites of acute inflammation [54]. In conclusion, these results demonstrated that, as for Mϕ, PMN might be important producers of IL-6, IL-8, TNF-α, and CCL3 and their migration might require additional signals provided by bacteria or cytokines/chemokines produced by themselves or others to develop their functions. Because after PA, PB and ST exposure we observed substantial increases in IL-8 in the absence of immune cells, we next measured IL-8 expression in supernatants from the organotypic models built with whole (total) or Mϕ-depleted PBMC. After 4 hours of exposure to either PA, PB, or ST, the tissues were disaggregated and cells used to measure IL-8 intracellular staining by flow cytometric analysis. We confirmed the epithelial cell expression of IL-8 and its independence from the presence of Mϕ (Fig 11). Regardless of the presence of Mϕ in organotypic models, comparable increases in the levels of expression of IL-8 were observed when exposed to Salmonella over those observed in media control cultures (Fig 11). Because in Fig 3 we found that PA and PB induced higher secretion of IL-8 in the supernatants of organotypic models containing whole PBMC, as compared to supernatants of organotypic models built without PBMC, it is reasonable to speculate that cytokines secreted by immune cells modulate IL-8 secretion during inflammatory responses to PA and PB infections. This hypothesis is supported by previous studies showing that signals from lymphocytes are required for epithelial function [55–57]. It is well known that ligand activation of cytokine/chemokine receptors stimulates several pathways, including Toll-like receptor (TLR) and downstream signaling. Thus, we next investigated whether the differential responses described above could be the result of defined antibacterial gene signatures. A set of ~84 genes, including those responsible for TLR and downstream signaling of antibacterial responses, as well as the NOD-like receptor (NLR), apoptosis, inflammatory, and anti-microbial peptide and protein signaling, were detected by the Anti-microbial Responses RT2 Profiler PCR Array. After 4 hours of infection, the organotypic models were collected and used to measure gene expression by qRT-PCR. We performed unsupervised clustergrams displaying the hierarchical clustering of the dataset as a heat map, with dendrograms indicating co-regulated genes. Surprisingly, the clustering of the genes showed similar antibacterial signatures between PB and ST, whilst the signatures following exposure to PA exhibited unique patterns (Figs 12 and 13). Of note, temporal differences between gene expression and the release of cytokines into culture supernatants [58,59] might have been responsible for the lack of a tight concordance between these two measurements. Despite these temporal differences, gene clustering confirmed that the similarities and differences between PA, PB, and ST are related to the activation of different pathways that are Salmonella strain dependent. In the present studies, we demonstrated that by changing the conditions of the crosstalk between immune cells, it is possible to modulate both the production of pro-inflammatory cytokines and the recruitment of inflammatory cells (e. g. , Mϕ, PMNs) to the site of inflammation. Previous studies have described the critical role of Mϕ in Salmonella infections [60,61] but the data presented here are, to the best of our knowledge, the first demonstration of a direct role of epithelial cells, lymphocytes, Mϕ and PMN crosstalk in controlling their function during PA, PB and ST infections. Based on our observations, we postulate that innate cells alter the function of neighboring cells (including epithelial cells) in the gut microenvironment and that this phenomenon differs depending on individual Salmonella serovars. It is likely that when gut-resident Mϕ fail to contain the Salmonella infection they release cytokines/chemokines (e. g. , IL-6, IL-8, TNF-α, and CCL3) to attract more macrophages and PMN to the diseased area. Of note, several of these cytokines/chemokines are likely to be co-regulated since they are secreted simultaneously. It is well known that insults to the intestinal mucosa, including those resulting from bacterial infections, promote Mϕ and PMN infiltration to the affected site [62–64]. To cross the epithelium, PMN might cause transient increases in the epithelial barrier permeability by breaking junctional complexes, which the PMN actively reseal after transmigration [65,66]. Activated PMN will then engulf the bacteria, and, together with macrophages and epithelial cells, will mount a local bacterial defense and clearance response that will differ in function of the bacterial strain causing the insult. This rationale is consistent with our previous results employing a 3-D organotypic model similar to the one used in the present studies showing that different S. Typhi strains exhibiting high degrees of homology but with small variations in gene expression elicited dissimilar innate cell responses in the human intestinal mucosa [37], that may shape adaptive immune responses [32]. Since we observed that PB infection had a higher cytolytic effect on Mϕ compared to PA or ST infections, we propose that resident Mϕ infected with PB will die rapidly and be removed by Mϕ recruited to the site of inflammation. Upon clearance, the release of pro-inflammatory cytokines, such as IL-6, IL-8, and TNF-α, will decrease, reducing Mϕ trafficking to the site of infection without altering PMN migration. As shown by our blocking experiments, while neutralization of IL-6, IL-8, and TNF-α inhibited migration of Mϕ exposed to ST, it did not affect the migratory activity of PMN exposed to either PB or PA. We also observed that exposure of the PMN to supernatants from the PA and PB infected organotypic models built with whole PBMC exhibit significant reductions in PMN migration (Fig 9B) compared to the organotypic models built with Mϕ-depleted PBMC, indicating a major role of Mϕ in modulating PMN migration into the site of inflammation. Of note, we also found a direct correlation between IL-6 and TNF-α production. Current evidence suggests that TNF-α can both, depending on the experimental conditions, enhance or suppress the production of IL-6, a cytokine known to control PMN migration [67]. The results presented above are also supported by previous studies showing in a model of liver injury that a decline in IL-6 and TNF-α secretion is associated with PMN infiltration [68]. We also propose that Mϕ differentially increase their production of antimicrobial products (e. g. , elastase) and regulate PMN function upon exposure to Salmonella. This proposition is in agreement with previous studies showing that dying infected cells decorated with antimicrobial proteins, such as elastase, regulate Mϕ functions such as the production of TNF-α [52,69]. Thus, it is reasonable to speculate that while PA-infected Mϕ will decrease the cytokines/chemokines in the milieu; remaining viable Mϕ and PMN are likely to produce high amounts of CCL3, and perhaps other cytokines/chemokines, which will result in decreased migration of Mϕ but enhanced PMN migration. These assumptions are based on the PA major cytotoxic effect on Mϕ, and the data from the migration experiments. However, this phenomenon might not be as pronounced during ST infection, since, as for PB, ST did not elicit significant increases in the release of CCL3 into supernatants of models built with whole PBMC as compared with models built with Mϕ-depleted PBMC, suggesting that PA vs. PB & ST differ in their ability to evade the PMN-dependent host defense mechanism. Indeed, previous studies have shown that individuals infected with S. Typhi exhibited ileal mucosal hypertrophy caused primarily by Mϕ with PMN-poor exudates [70–73]. In this regard, one cannot exclude that the virulence-associated (Vi) capsular polysaccharide of ST as an impeding factor for bacterial-guided PMN chemotaxis. It has been shown that Vi of S. Typhi obstructs bacterial-guided PMN chemotaxis [74] by averting the phagocyte respiratory burst through inhibition of antibody-dependent complement activation [75]. Although PA, PB, and ST share a considerable genetic similarity, unlike ST, the Vi-antigen is not expressed by PA or PB strains [76]. Nonetheless, since the bacterial infection and PMN migration assays were performed in the absence of serum (e. g. , plain RPMI) to avoid complement activation and opsonization, cytokines/chemokines are likely to have played a pivotal role in the PMN migration observed in our studies. Indeed, as shown by our blocking experiments, PMN migration was more sensitive to the neutralization of cytokines after ST infection than after PA and PB infections. Chemokines such as CCL3 serve as PMN attractors to the site of infection [42], and have protective roles in Salmonella infections [42,77]. Thus, the differential oxidative environment and the antimicrobial responses (i. e. , cytokines, and pathways activated following engagement of their receptors) produced in response to Salmonella infection are all likely to enable differential host immune responses elicited by the various Salmonella serovars [78]. To our knowledge, this is the first study to directly examine the role of lymphocytes, Mϕ, PMN and epithelial cell crosstalk in the control of their function (i. e. , production of cytokine/chemokines and migration behavior) during infection with pathogens with high degrees of homology, such PA, PB and ST serovars. However, it should be noted that to simplify the experiments, only the heterogeneous preparation of lymphocytes, Mϕ, and PMN were used in this study. Thus, the role of cell subtypes (e. g. , M1 and M2 Mϕ) in the differential chemotaxis observed in this study remains to be explored. Another important consideration is that it is very likely that in addition to IL-6, IL-8, TNF-α, and CCL3, other cytokines/chemokines also play important roles in differentially regulating chemotaxis in the mucosal microenvironment. Future investigations including a larger panel of cytokines and chemokines and the role of various cell subsets are needed to fully elucidate the rules governing the crosstalk among various cell types following infections with ST, PA, and PB. Finally, it is not possible to exclude that some of the observations and differences highlighted in this study were specific to the individual strains studied in this manuscript. For example, different Salmonella strains with different levels of Vi expression, different virulence, or different lengths of the LPS chains might trigger different host responses. This hypothesis is supported to some extent by the results described in S2 Fig using the wild-type ST strain Ty2 and 2 attenuated ST strains, Ty21a, and CVD 915. Each of attenuated ST strains was derived from the parent strain Ty2 but possess small variations in gene expression (e. g. , Ty21a mutations in multiple genes, including galE, galT, alK, galP, rpoS, ilvD, rcsC, tviC, tviE and vexD [79], and CVD 915 with deletions in guaBA, interrupting the guanine biosynthesis pathway [80]). Different levels of Mϕ and neutrophil migration were observed among these different strains of ST. In sum, our data strongly suggest that PA, PB, and ST affect the early innate immune responses in the gut differently. We also show the importance of crosstalk among lymphocytes, Mϕ, PMNs, and epithelial cells, which is cytokine/chemokine-dependent, bacteria serotype-specific, and play a pivotal role in orchestrating the mucosal innate, and likely adaptive, host inflammatory immune response in the mucosal microenvironment. Blood samples were collected from volunteers who gave informed, signed consent for participation in the University of Maryland Institutional Review Board approved protocol authorizing the collection of blood specimens from healthy adult blood donors for in vitro studies (HP-00040025). The 3-D model setup and cultivation were performed as previously described [34–36,38]. Briefly, a multi-step process is required to build the model. First, primary endothelial cells (HUVEC cells, CRL-1459, ATCC, Manassas, VA), primary fibroblasts (CCD-18Co cells, CRL-1459, ATCC), and HCT-8 epithelial cell line (CCL-244, ATCC) were grown as a confluent monolayer in order to reach enough cells to construct the model. The second step consisted of preparing an extracellular matrix (ECM) composed mainly of collagen-I enriched with with other gut basement membrane proteins (i. e. , 10 μg/mL laminin (Sigma), 40 μg/mL collagen-IV (Sigma), 10 μg/mL fibronectin (BD Biosciences, San Jose, California, USA), 2 μg/mL heparin sulfate proteoglycan (Sigma). Then, fibroblasts and endothelial cells were embedded in the ECM, and the cell embedded-collagen transferred to the Rotating Wall Vessel (RWV) (Synthecon, Inc. , Houston, Texas, USA) bioreactor containing ~107 epithelial cells, and incubated at 37°C with 5% CO2. After 6 ± 1 and 13 ± 1 days, peripheral blood mononuclear cell (PBMC) were added to the system (~2 x 107 cells/vessel). For “gain and loss” studies to define the importance of immune cells (i. e. , lymphocytes and macrophages), models were set-up with or without PBMC. For “gain and loss” studies to define the importance of macrophages, these cells were removed from the PBMC by adhesion-mediated purification, for which PBMC were incubated for 2 hours on 2% gelatin-coated tissue culture flasks [47,48]. As for conventional assays, for “gain and loss” studies, whole or macrophage-depleted PBMCs were added into RWV vessels on days 6 ± 1 and 13 ± 1 after model set up. Isolates of wild-type PA strains CV223 (ATCC#9150) [25,26] and 01–0020 [81], PB strains CV23 (clinical isolate from Chile) [25,26] and 02–0303 [81], and ST strains Ty2 [37], and ISP1820 [34] and typhoid vaccine Ty21a [37,79] were streaked onto Luria-Bertani (LB) agar Lennox (Difco Laboratories, Detroit, MI) plates and allowed to incubate at 37°C and 5% CO2. Attenuated vaccine candidate ST strains CVD 915 [37,80] and CVD 908 [37,82] were grown on solid medium supplemented 1% Guanine or 0. 1% 2,3-Dihydroxybenzoic acid (DHB) (Sigma, St. Louis, MO), respectively. After overnight incubation, each strain suspension was diluted to obtain an optical density (OD) value of 0. 2, corresponding to a suspension of ~108 bacterial cells per mL [83]. Quantification of each strain was performed by λ-600 OD spectrophotometric analysis. Unless otherwise stated, experiments were performed using PA strain CV223, PB strain CV23, and ST strain Ty2. Infection of the 3-D model was performed as previously described [34,36]. Briefly, the vessels were removed from the RWV bioreactor and the constructs within were washed twice with RPMI to remove any residual of antibiotic and non-resident cells. The vessels were then refilled with RPMI. Except for the negative controls (media only), the appropriate bacterial suspension (approximately 100 multiplicity of infection (MOI) ) was then added to all vessels. The vessels were then returned to the RWV bioreactor and incubated at 37°C and 5% CO2 for up to 6 hours before the experiment was terminated, and supernatants and constructs were collected from each vessel for further analysis. Levels of elastase and myeloperoxidase (MPO) in culture supernatants were measured by using commercial NETosis and PMN Activity Myeloperoxidase Assay kits, respectively (Cayman Chemical, Ann Arbor, MI). The NETosis Assay kit employs a specific chromogenic substrate (N-methoxysuccinyl-Ala-Ala-Pro-Val p-nitroanilide), which is selectively cleaved by elastase. The rate of enzymatic hydrolysis of the substrate is followed by the increase in absorbance due to the release of 4-nitroaniline. The PMN Activity Myeloperoxidase Assay kit utilizes 3,3’, 5,5’-tetramethyl-benzidine (TMB) as a chromogenic substrate, which in the presence of MPO yields a blue color detectable by spectrophotometer. Levels of interleukin (IL) -1β, IL-6, IL-8, tumor necrosis factor (TNF) -α and Chemokine (C-C motif) ligand 3 (CCL3), also known as macrophage inflammatory protein 1-alpha (MIP-1α), were measured by using the Meso Scale Discovery (MSD, Gaithersburg, MD) multiplexed-assay. Supernatants were harvested 4 hours after Salmonella infection and kept at -20°C until assayed. In these studies, uninfected cells (medium only) were used as controls. ELISA and MSD assays were carried out following the manufacturer’s instructions. The level of sensitivity of elastase and MPO ELISAs were 0. 2 mU/ml and 28 ng/ml, respectively. The levels of sensitivity for the various cytokines measured by MSD ranged from 0. 3–2. 5 pg/ml The viability of the cells was assessed by quantifying the LDH release into the supernatant using a commercial kit (CytoTox 96; Promega, Madison, WI) as previously described [37]. Briefly, supernatants were harvested 4 hours after exposure to the different serovars and kept at -20°C until assayed. The LDH Positive Control was used to create a standard curve and interpolate the sample results to obtain the relative number of lysed cells. PMN were isolated as previously described with a few modifications [84]. Briefly, blood collected from healthy volunteers were diluted 1: 3 with 1x Phosphate Buffer Solution (PBS) and layered, up to 35 mL, over 15 mL of Ficoll-Paque density gradient media. This mixture was centrifuged for 30 minutes at 25°C allowing the formation of a denser precipitate of erythrocytes with overlying buffy coat of PMNs. After centrifugation, the upper layers (e. g. , plasma, PBMC, and Ficoll-Paque) were removed, and the remaining layer containing erythrocytes and PMNs resuspended in 1x PBS to the original blood volume before adding an equal volume of 6% Dextran-500 solution. After homogenization by inversion, the tubes were allowed to sediment. After 1 hour, the leukocyte-rich, erythrocyte-poor supernatant was aspirated and transferred into another 50mL conical tube. To lyse the remaining erythrocytes, cells were centrifuged at 25°C, the supernatant discarded, and the pelleted cells resuspended in 12 mL of ice-cold ddH2O for 20 seconds before adding 1. 2 mL of 10X PBS and diluting up to 50 mL with 1X PBS. This step was repeated a second time if needed. The purity of the cells was confirmed by flow cytometry by gating them based on their light scatter characteristics and specific lineage differentiation markers: CD11b+, CD11c+, CD14-, CD19-, CD45+. Analyses were performed in an LSR II flow cytometer (BD Biosciences) in the UMB Flow Cytometry and Mass Cytometry Core. PMN populations were >90% pure. Macrophages (Mϕ) were generated from the human monocyte cell line U937 (CRL-1593. 2, ATCC) using a phorbol 12-myristate 13-acetate (PMA) protocol [49]. Briefly, U937 monocytes were incubated with 6. 25 ng/ml [10nM] of PMA in RPMI 1640 (Gibco, Grand Island, New York) media supplemented with 100 U/mL penicillin, 100 μg/mL streptomycin, 50 μg/mL gentamicin, 2 mM L-glutamine, 1 mM sodium pyruvate, 10 mM HEPES (Gibco) buffer and 10% heat-inactivated fetal bovine (FBS) serum (R10). After 48 hours, culture flasks were washed twice with plain RPMI, and cells allowed to differentiate for another 24 hs in R10 media. Human PMN were isolated, as described above. Both PMN and macrophage migration experiments were performed using previously described protocols [53]. Briefly, a mixture was prepared by combining 800 μL of 5 mg/ml bovine collagen-I with 100 μL of 10x DMEM (Invitrogen, Camarillo, CA, USA). Next, NaOH was added to the mixture to attain neutral pH, as assessed by a phenol red color change. The mixture was then diluted with 1X Hanks’ buffered saline solution (HBSS) supplemented with 2% FCS and 10 mM HEPES to a final concentration of 4. 8 mg/mL. To establish an extracellular matrix that could facilitate PMN/macrophage anchoring and chemoattractant gradient formation, 70 μL of this collagen mixture was deposited into the well inserts of a 24-well plate (8 μm pore) (Corning, NY, USA) and allowed to gelify for 1 hour at 37°C and 5% CO2. After gelification was completed, the wells were filled, in triplicate, with 300 μL of supernatant from the appropriate RWV infection experiment diluted 1: 3 with RPMI. For PMN migration assays, positive control conditions consisted of 300 μL of a 4 ng/mL solution of N-formyl-methionine-leucine-phenylalanine (n-formyl-MLF) prepared in buffer A (PBS supplemented with 2% FCS and 10mM HEPES). For macrophage migration assays, positive control conditions consisted of 300 μL of a 100 ng/mL solution of CCL3 prepared in buffer A. Matrix-laden well inserts were then allowed to bathe in underlying supernatants for a 2 hour incubation period to facilitate gradient formation before buffer A was added to each insert and pre-warmed to 37°C in the incubator. Thus, 25 μL of a 1 x 107 cells/mL suspension of isolated PMNs or macrophages (~2. 5 x 105 cells) was added into each well-insert and incubated for a 3 (PMNs) or 4 hours (macrophages) at 37°C and 5% CO2. After incubation, the insert was removed, and PMN and macrophage migrations were visualized using a Nikon Eclipse TE2000-S inverted microscope (Nikon, Melville, NY, USA) under bright field setting and 10x objective lens. NIS-Elements BR software (Nikon) was used to take photographs, and quantification of cell migration was performed using ImageJ software (NIH). Cells were surface stained with anti-human monoclonal antibodies (mAbs) to CD11b (clone ICRF44), CD11c (clone B-ly6), CD14 (clone TuK4), CD45 (clone 2D1), CD66c (clone B6. 2/CD66), CD163 (clone GHI/61), IL-6 (clone MQ2-39C3), TNF-α (clone MAb11), (BD Pharmingen, San Diego, CA), CD19 (clone SJ25-C1), CCL3 (clone CR3M) (Invitrogen, Carlsbad, CA), IL-8 (clone E8N1), and CD326 (EpCAM, clone 9C4) (Biolegend, San Diego, CA). These mAbs were directly conjugated to the following fluorochromes: Fluorescein isothiocyanate (FITC), Phycoerythrin (PE), Peridinin chlorophyll protein (PerCP) -Cy5. 5, PE-Cy7, Energy Coupled Dye PE-Texas-Red conjugate (ECD), Pacific Blue, Brilliant Violet (BV) 570, BV605, BV650, Quantum dot (QD) 800, Alexa 647, allophycocyanin (APC) -Alexa 700, or APC-H7. After 4 hours of exposure to S. Typhi, PA or PB strains, the constructs were harvested and used to isolate cells by a 2-hour incubation with collagenase enzyme and additional mechanical agitation. Briefly, constructs were covered with 10 mg/ml (1%) of collagen/dispase (Roche, Indianapolis, IN) and vigorously re-suspended up and down with a transfer pipette. After a 30 minute incubation in a 37°C 5% CO2 incubator, an 18-G needle fitted on a 5-ml syringe was used to further disrupt the construct by passing the collagenase solution through the needle 3 times and then returning the tube to the 37°C 5% CO2 incubator. After an additional 30 minutes, the pieces were again vigorously resuspended up and down with a transfer pipette and filtered through a 40 μm filter to obtain single cells. For flow cytometric assays, single cells were stained with a dead-cell discriminator, violet fluorescent viability dye (ViViD, Invitrogen) [83], followed by the blocking of Fc-receptors with purified human IgG, surface staining and fixation and permeabilization with Fix & Perm cell buffers (Invitrogen, Carlsbad, CA) [83]. Cells were then stained intracellularly for IL-6, IL-8, CCL3, and TNF-α, and fixed with 1% formaldehyde. Data were analyzed by flow cytometry on an LSR-II instrument (BD Biosciences) and WinList v9. 0 (Verity Software House, Topsham, ME). Cells were gated based on their light scatter characteristics and specific lineage differentiation markers: CD45+CD163-CD66c+ for PMN, CD45+CD14+CD163+CD11b+ for macrophages, and CD45- EpCAM+ for epithelial cells. Flow cytometry experiments were performed at the Flow Cytometry and Mass Cytometry Core Facility of the University of Maryland School of Medicine Center for Innovative Biomedical Resources (CIBR), Baltimore, Maryland. Isolation of total cellular RNA was performed as previously described [36]. Briefly, total RNA was extracted using RNeasy Mini Kits (Qiagen). The RNA samples were then converted to cDNA and subjected to qPCR amplification using the QuantiTect SYBR Green Kit (Qiagen) on an ABI 7900HT Fast Real-Time PCR System ( (Applied Biosystems, Foster City, CA). Analyses of results were performed using the web-based GeneGlobe Data Analysis Center web-based Software (Qiagen) (https: //www. qiagen. com/us/shop/genes-and-pathways/data-analysis-center-overview-page/). The software automatically selected an optimal set of internal control/housekeeping/ normalization genes for the analysis from the available housekeeping gene panel (i. e. , ACTB, B2M, GAPDH, HPRT1, and RPLP0) on the PCR Array. The CT values for these genes were then geometrically averaged and used for the 2 (−Delta Delta Ct) calculations. The software also performs unsupervised clustergram displaying hierarchical clustering of the dataset as a heat map with dendrograms indicating co-regulated genes. Clustergrams are based on hierarchical clustering method that (1) assigns each gene to its own cluster (agglomerative), (2) joins the nearest clusters, and (3) re-estimate the distance between clusters. Experimental variables such as treatment do not guide or bias cluster building. To create a hierarchical cluster, the magnitude of gene expression is determined by calculating the 2–ΔCT for each individual gene and normalizing to the average 2–ΔCT of all genes across all arrays. A set of 84 genes was profiled including those responsible for Toll-Like Receptor (TLR) signaling (e. g. , TLR1, TLR4, TLR5 and TLR9), downstream signaling of antibacterial responses (e. g. , Nfκb1, NFKBIA, MAP2K1, and Jun), NOD-Like Receptor (NLR) signaling (e. g. , NIrp C4, NIrp1a, NIrp3, NOD1 and NOD2), apoptosis signaling (e. g. , Card6, CASP1, and CASP8), anti-microbial peptides and proteins (e. g. , Mop, Prtn3, Lyz, and Ltf), and inflammation (e. g. , IL-6, IL-1b, CCL3, and Myd88). All statistical tests were performed using Prism software (version 6. 0, GraphPad Software, La Jolla, CA). Comparisons between groups were performed using One-way ANOVA, with Geisser-Greenhouse corrections, with individual variances computed for each comparison. Correlations used the Pearson Product Moment tests. P values <0. 05 were considered significant.

Enteric fevers are acute illnesses caused mainly by the Salmonella enterica serovars Typhi (ST), Paratyphi A (PA) and Paratyphi B (PB). The incidence of enteric fevers is very low in industrialized countries, with most infections occurring in military personnel and individuals traveling to endemic areas. Nevertheless, worldwide, more than 20. 6 million cases of enteric fever occur annually in low- and middle-income countries and are associated with approximately 129,000–223,000 deaths. ST, PA, and PB exhibit very similar clinical symptoms but distinct epidemiologies, geographical distributions, and susceptibilities to antimicrobial treatment. However, the mechanisms by which the host recognizes pathogens with high levels of homology, such as these bacterial serovars, remain poorly understood. Using an in vitro model of the human intestinal mucosa, we found that the crosstalk among leukocytes—lymphocytes, Mϕ, and PMN- and epithelial cells is cytokine/chemokine-dependent, and bacterial-serotype specific and plays a pivotal role in orchestrating the functional efficiency of the innate cells and migratory characteristics of the leukocytes.

Abstract Introduction Results Discussion Methods

blood cells flow cytometry innate immune system medicine and health sciences immune physiology cytokines immune cells pathology and laboratory medicine pathogens immunology microbiology salmonellosis epithelial cells bacterial diseases developmental biology physiological processes enterobacteriaceae molecular development bacteria bacterial pathogens research and analysis methods infectious diseases spectrum analysis techniques white blood cells animal cells medical microbiology microbial pathogens biological tissue salmonella enterica salmonella spectrophotometry immune system cytophotometry cell biology anatomy physiology secretion epithelium biology and life sciences cellular types macrophages organisms

2019

Crosstalk between leukocytes triggers differential immune responses against Salmonella enterica serovars Typhi and Paratyphi

Histone chaperones facilitate assembly and disassembly of nucleosomes. Understanding the process of how histone chaperones associate and dissociate from the histones can help clarify their roles in chromosome metabolism. Some histone chaperones are intrinsically disordered proteins (IDPs). Recent studies of IDPs revealed that the recognition of the biomolecules is realized by the flexibility and dynamics, challenging the century-old structure-function paradigm. Here we investigate the binding between intrinsically disordered chaperone Chz1 and histone variant H2A. Z-H2B by developing a structure-based coarse-grained model, in which Debye-Hückel model is implemented for describing electrostatic interactions due to highly charged characteristic of Chz1 and H2A. Z-H2B. We find that major structural changes of Chz1 only occur after the rate-limiting electrostatic dominant transition state and Chz1 undergoes folding coupled binding through two parallel pathways. Interestingly, although the electrostatic interactions stabilize bound complex and facilitate the recognition at first stage, the rate for formation of the complex is not always accelerated due to slow escape of conformations with non-native electrostatic interactions at low salt concentrations. Our studies provide an ionic-strength-controlled binding/folding mechanism, leading to a cooperative mechanism of “local collapse or trapping” and “fly-casting” together and a new understanding of the roles of electrostatic interactions in IDPs' binding. Nucleosome, the fundamental repeating structural unit of chromatin, is comprised of two superhelical turns of DNA (base pairs) wound times around an octamer of histone proteins (H2A, H2B, H3, H4) or their variants [1]–[4]. Histone chaperones prevent histones from aggregating on DNA by blocking the DNA-binding sites on histones [5]–[7], and play essential roles in the assembly and disassembly of the nucleosome [8]–[11]. The histone proteins are highly positively charged and usually associated with their binding partners, such as DNA and histone chaperones, through electrostatic interactions [12]. However, little is known about the processes as how histone chaperones associate and dissociate from the histones, which could be closely related to how histone chaperones deliver the histones to the target molecules. Because of the oppositely charged characteristic between histone chaperones and histones, the electrostatic interactions rather than hydrophobic interactions are supposed to highly participate in these molecular events. Moreover, some histone chaperones are intrinsically disordered proteins (IDPs) [9], indicating that the association and dissociation are also coupled with folding and unfolding of polypeptide chains. The studies of IDPs have put forward a new dynamics-function paradigm for biomolecular recognition [13], [14]. Chz1 (159 amino acids) is the chaperone of histone variant H2A. Z-H2B. Its function involves the delivery of H2A. Z-H2B to the SWR1 complex that catalyzes the exchange of H2A-H2B in the canonical nucleosome with H2A. Z-H2B in an ATP-dependent manner [15]. Chz1 is an IDP and binds to H2A. Z-H2B using its middle region (residues 71–132), termed Chz. core [15]. Upon binding to H2A. Z-H2B, Chz. core forms two short helical structures at the N- and C-terminal regions and a long irregular loop in the middle (Figure 1) [16]. In contrast, the conformation of H2A. Z-H2B in the Chz. core-H2A. Z-H2B complex is essentially the same as the free H2A. Z-H2B [17]. The N-terminal region of Chz. core (residues 71–93) is largely negatively charged and interacts with the positively charged region in the H2A. Z-H2B while the region near the C-terminus has three positively charged arginine residues and interacts with several acidic residues in the H2A. Z-H2B. The bipolar charged Chz motif (residues 94–115) forms interactions with H2A. Z-H2B through complementary electrostatic forces. The NMR structure of Chz. core complexed with H2A. Z-H2B shows that the complex seems to be mainly stabilized through broad electrostatic rather than hydrophobic interactions. These structural features lead to the observation that Chz. core has a higher association rate than the diffusion limit, suggesting that the association process is accelerated by the electrostatic interactions [17]. To better understand the folding/binding of Chz. core to H2A. Z-H2B, we performed theoretical investigations on the underlying mechanisms from thermodynamic, kinetic and microscopic structural perspectives. We developed a structure-based coarse-grained model [18], [19] to simulate the formation of the Chz. core-H2A. Z-H2B complex. In particular, we implemented model to describe the electrostatic interactions. We observed two parallel binding/folding pathways in our simulations. By calculating the reaction rates, we found that the electrostatic interactions serve as the “steering forces” to facilitate the association, coincident with the NMR spectroscopy experiments. However, we found that the electrostatic interactions did not always accelerate the formation rate of the complex. Under low salt conditions, non-native electrostatic interactions transiently trapped Chz. core in the ensemble of collapsed structures slowed down folding/binding. It is worth noting that the Chz1 studied in our simulations only consider the Chz. core (residues 71–132), which is found to be responsible for the inter-chain interactions to stabilize the complex Chz1-H2A. Z-H2B [15]. It is consistent with the NMR experiment [17]. We plotted free energy along and (Figure 2) to illustrate how the binding/folding process happens. is the fraction of native contacts between H2A. Z-H2B and Chz. core, is the fraction of native contacts for folding of Chz. core. In unbound state, Chz. core comprises of a large number of unfolding conformations, consistent with a typical IDP. In contrast, the structure of H2A. Z-H2B remained folded and almost unchanged in both free and bound states. These structural features are in accordance with the NMR spectroscopy experiments, guaranteing the validity of our model. The free energy profile shows a typical 3-state binding transition with the first free energy barrier of at of, indicating that the initial recognition of Chz. core by the histone variant occurs very early. The second lower free energy barrier at separates the intermediate states and the native bound state. The highest free energy region with of 0. 06–0. 13 is taken as the initial binding transition state ensemble. In the initial transition state ensemble, some local regions of the conformations of Chz. core make native contacts with the histone variant while Chz. core remains largely disordered. This implies that significant binding happens when the Chz. core is partially disordered before complete folding. This is different from the conventional scenario of folding first with the structure formation of individual partners and then binding last. In our simulations, the coupled folding and binding of Chz. core occurs mainly after this transition state. To better characterize the structural ensemble of the first transition state and identify the key residues for the recognition between Chz. core and H2A. Z-H2B, we calculated the value for each residue in Chz. core. value has been used in protein folding for revealing the native interactions in the transition state [20]. The value here only counts native binding contacts and has a simple expression: . is the number of native contacts between Chz. core residue x and H2A. Z-H2B residues in the respective state. For value calculation, we selected all of the conformations with in the range of 0. 06–0. 13 from the simulation. We found that all values are smaller than 0. 5 (see Figure S1 in Text S1), indicating that none of the residues in Chz. core are well-ordered. In particular, the values at the C-terminal region (residues 116–132) approach to 0, suggesting that it has little native contacts with H2A. Z-H2B in the initial binding transition state. Since values lacking the non-native contacts may not yield an accurate interaction map of the transition states, we introduced a cut-off contact map to count non-native contacts and calculated the contact maps of both and, representing backbone–backbone and side chain–side chain interactions respectively. In the transition state, the N-terminal region and the acidic motif (residues 94–103) of Chz. core form wide contacts with the histones, while the C-terminal region has little inter-chain interactions (Figure 3A). Furthermore, Figure 3B shows that the N-helix (residues 81–93) and the acidic motif of Chz. core, carrying many negatively charged residues (E91 to D103), form wide electrostatic interactions with the positively charged residues: K89, K90 in H2B and R55, R57, K61 in H2A. Z. Meanwhile, the negatively charged region (E73 to D81) located at the N-terminal region of Chz. core forms electrostatic interactions with the positively charged region (R43 to K61) of H2A. Z. Notably, most of the contacts are referred to non-native contacts. Those non-native interactions can act as “steering forces” in the early binding process to facilitate the association [21]. Next, we calculated the probability of contact formation and the average number of contacts for each residue (Figure 3C–F). We found that the acidic motif and residue E91 of the N-helix of Chz1 have the largest number of contact residues, which are all negatively charged. In contrast, the “hot spot” residues in histone variant can be roughly divided into 3 regions: (1) residues from N88 to T100 in H2B; (2) residues from E109 to A131 in H2B and from Q29 to A33 in H2A. Z; (3) residues from Q38 to A63 in H2A. Z. By examining the contact map for the side chain electrostatic interactions, as residues in (1) and (3) of H2A. Z-H2B form abundant electrostatic interactions with Chz. core, it appears that association of those regions in H2A. Z-H2B are charge-oriented. On the other hands, the binding of Chz. core seems to start at N-terminal region and the acidic motif, which is highly charged. In conclusion, the electrostatic interactions appears to have strongly affected the transition state. To characterize the binding pathway of N-helix, C-helix and Chz motif of Chz. core in detail, we plotted a series of 2-D free energy landscape as a function of, , and (Figure 4). , , are the fractions of inter-chain native contacts between the regions of N-helix, C-helix, Chz motif in Chz. core and H2A. Z-H2B, respectively. The 3 regions of Chz. core bind to the histones through different patterns: The binding of N-helix takes off at (Figure 4A), when there is not much binding for other regions of Chz. core; the binding of C-helix is unique and can only occur at (Figure 4B), when N-helix or Chz motif have already certain degrees of binding; the free energy profile of binding of Chz motif shows no intermediate state (Figure 4C), indicating that the binding of Chz motif is highly coupled with the binding of the whole chain. The binding of the two helices to the histones are highly decoupled since there is no binding pathways along the diagonal line, suggesting there are two parallel binding pathways (Figure 4D). Therefore, we plotted the 3-D free energy landscape as a function of, , to investigate the binding pathways (Figure 5). We found that there are two binding intermediates, one for each pathway, as indicated by the minimum points on the free energy profile. By analyzing the equilibrium trajectories, we calculated the population of the two intermediates: (and) and (and) occupy and of the total population of intermediates, respectively. The results are likely due to the fact that the N-terminal region of Chz. core can form much more electrostatic interactions with H2A. Z-H2B than the C-terminal region does. So we can conclude that the long-range electrostatic interaction seems to be the driving force for the binding process, leading to an increased capture radius for searching the target. After the initial recognition, the partly bound intermediates are stabilized by the short-range electrostatic interactions. To gain further insights into the structures of the two intermediate states, we investigated the native contact map in the two intermediate ensembles (see Figure S2 in Text S1). We found that the folding and binding of Chz. core at both intermediate states are highly coupled. In, Chz. core binds to the histones by the N-terminal region and the acidic region of the Chz motif in their folded conformation. In, Chz. core binds to the histones by the folded C-helix and the Chz motif. It is interesting to point out that the conformations of the Chz motif in the two intermediates are not the same. The result implies that in, the Chz. core is more free and flexible. In addition, we found that the barrier height between intermediates, and the bound state are very low (and respectively). Such low barrier and the close value of between intermediate and bound state make the two parallel binding pathways mixed and the intermediate unobservable in Figure 4B. We also found that binding of Chz motif both occurs on the two parallel pathways, leading to only one binding pathway shown in Figure 4C. As our simulations are carried at a higher temperature near to the binding transition temperature, the intermediates observed in our simulation are not more populated than the unfolded or disassociated state under equilibrium conditions. Decreasing the temperature to the experimental temperature will bias the free energy basin to the bound state (see Figure S5 in Text S1) and the low barriers from intermediate state to bound state make the intermediates are not detectable in the NMR relaxation dispersion experiment [17]. Meanwhile, as the barrier height in the range of thermal motion () will be easily crossed, the intermediate and are not very thermodynamically stable and will overcome the barriers quickly to form the bound state in the binding process. In addition, the barriers of forming intermediate and from unbound state are and, indicating that rate-limiting steps for the two parallel binding pathway are forming the intermediates. To quantify the role of the electrostatic interactions in the binding process, we performed simulations at 7 different salt concentrations and a lower temperature than transition. By investigating the kinetic trajectories, we also observed the two parallel binding pathways consistent with the thermodynamic simulations. In addition, we found that the weight of the two parallel binding pathway is modulated by the salt concentrations (Figure 6A). The population of binding pathway increases when the salt concentration decreases, consistent with the conclusion that electrostatic interactions are the driving force for the binding of the N-terminal helix of Chz. core to the target. To explore how the electrostatic interactions affect the binding of N-helix, C-helix and the Chz motif, which have different charge distributions, we calculated the probability of the three regions to be the first to recognize the histones. We found that decreasing salt concentration leads to an increased probability for N-helix and decreased probability for the Chz motif and C-helix (Figure 6B). Because of the existence of the intermediate states, we dissected the association process into 4 steps: encounter, escape, evolve to intermediate states, and form the native state [22]. In the binding process, the two helices of Chz. core form different intermediates states, leading to different binding pathways with different binding rates (Figure 7A). All the 6 rates for describing the kinetics of the binding were calculated by using transition number (N) and mean passage time (MPT) in the binding trajectories with following equation (Figure 7B) [23]: In the first capture step, the electrostatic interactions as “steering effect” significantly accelerate the recognition rate and decelerate the dissociation rate. Then the binding is divided into two parallel pathways to form different intermediates. The rate of forming intermediate is slowed down, while the rate of forming intermediate significantly increases as the salt concentration decreases. Based on the structural analysis of the two intermediates, the interactions between Chz. core and the histones in are mostly electrostatic. Thus it is surprising that the increases as electrostatic interactions decrease and the value is smaller than by times. We found that there is a global structural rearrangement during the association from the free to the partly bound Chz. core (see subsection Collapse slows binding). The final step describes the evolution of the intermediate states to the bound states. For binding pathway, this evolution process corresponds to the binding of basic motif (residues 112–115) and the C-terminal region, these two regions are not highly controlled by electrostatic interactions, so the rate does not change with the different salt concentrations. On the other hand, on the binding pathway, the evolution process from intermediate to the bound state corresponds to the binding of N-terminal region of Chz. core, which is highly charged. So the rate increases as the salt concentration decreases. The results that is smaller than is coincident with the thermodynamic results, which shows a higher barrier to bound state for than at. In addition, we found and are much smaller than, , and, indicating that the the rate-limiting step are the evolution step from encounter step to intermediate state for the both binding pathways, consistent with the thermodynamic results. Although the kinetic simulations are performed at a lower temperature, this binding pattern is supposed not to be qualitatively changed from thermodynamic simulation [24]. In order to explain the abnormal relationship between and the salt concentrations, we investigated the unbound states of Chz. core by looking into the structural differences as a function of salt concentrations. We used the distance of specific group of residues and radius of gyration to detect the long-range interactions (Figure 8) since long-range contacts can exhibit a decrease in and as compared with the idealized random coil ensemble [25]. We found that residues 94–103 and residues 112–115, corresponding to the acidic motif and the basic motif, are close in space at low salt concentrations. The formation of this local tertiary compact structure is due to the non-native electrostatic interactions between oppositely charged residues located at the two ends of the Chz motif, which disassociate as the ionic strength decreases. In order to investigate how this local compact structure changes in the binding process, we plotted the evolution for the distance between the centroid of the acidic and basic motifs of Chz. core along and (Figure 9). and are used as reaction coordinates to describe the evolution step of the unbound states to the intermediate states on the and binding pathways respectively. We found that as the binding proceeds, the collapsed structure expands and becomes bound-like. However, this structural rearrangement appears in different steps on the two parallel binding pathways. On the binding pathway, remains unbound-like when N-helix starts binding and has an abrupt change in of 0. 1–0. 3 (Figure 9A), this local compact structure gets expanded and bound-like in the formation of the partly bound intermediates. Unraveling the collapsed region consumes time. As the salt concentration decreases, this region of Chz. core becomes more collapsed at free states, so decreases. In contrast, on the binding pathway, ascends acutely when increases (Figure 9B), implying that the collapsed region has folded to its final bound structure in the beginning of the recognition. As a result, the rate of evolution from encounter complex to intermediate is not affected by salt concentration. Thus, it is very interesting to see that electrostatic interactions always accelerate rates on the binding pathway. However, the electrostatic interactions decrease the rates on the binding pathway when they are strong but increase the rates when they are weak. In the thermodynamic and kinetic studies here, we provide a detailed binding-folding analysis on the formation of the Chz. core-H2A. Z-H2B complex with a structure-based coarse-grained model involving electrostatic interactions. We found that Chz. core, intrinsically unstructured in solution, folds upon binding to H2A. Z-H2B, consistent with the experimental observation [17]. The free energy profile shows Chz. core at the initial transition state remains unfolded resembling the unbound states, and then folds to the histone-bound structure through intermediate states as binding proceeds. Many IDPs seem to share such a common binding pattern, using the folded partner as a template to stabilize themselves rather than fold among themselves first [26]–[33]. Non-native interactions have been realized to play significant roles in protein folding and binding from experimental and simulation studies [26], [34]–[37]. The facilitating effect in association caused by non-native interactions are due to decreasing the transition barrier height [36], [37] or reducing the entropy on energy landscape at early stage [26]. In our studies, the contacts formed in initial binding transition states, are strongly oriented and positioned by the charge distribution, confirming that the electrostatic interaction is crucial in the recognition between the oppositely charged proteins [38]. Besides, the feature that the binding/folding pass through two parallel pathway is similar to that of pKID binding with KIX except for in the latter case, the dominant force is hydrophobic interaction [27], [28], [39], [40]. The close-range hydrophobic interactions are found to be the results of forming the intermediate states. Here, we demonstrated that the electrostatic interactions can also serve as the stabilizing forces in the forming of the partly bound complex [41], especially in binding of highly charged polypeptide chains. IDPs in general can not fold to compact globular conformations in aqueous solutions because of their low hydrophobicity [13], [42]–[44]. The flexible conformation of IDPs makes them highly susceptible to the non-native electrostatic interactions, which can have a dramatic effect on folding energy landscape [45]. Net charges in IDPs can modulate the conformational space by changing the residue distance and radius of gyration [46]. Recently, a single-molecule fluorescence resonance energy transfer (FRET) spectroscopy study shows that the low hydrophobicity and highly charged property can make IDPs expand or collapse depending on ionic strength and concentration of denaturant [47], [48]. Consistent with the IDPs' experiments, we successfully observed the collapsed structure of Chz. core formed in unbound states in our work. Importantly, the effects of collapsed ensemble caused by varying salt concentrations in Chz. core for binding kinetics are studied in details. From our studies, the electrostatic interactions do not always accelerate the association through the whole folding/binding process [22], [49]. Instead, they have discrete effects on the two parallel binding pathways. The role of electrostatic interactions in this association can be interpreted from two different aspects: inter-chain electrostatic interactions enlarge the radii for fly-casting effect and facilitate binding [50], [51] while stabilizing non-native intra-chain interactions at low salt concentration lead to the collapse of Chz. core to form local compact conformations and decrease the rate of binding. From energy landscape theory, the binding landscape is referred to as a funnel [52], [53]. At the beginning of binding, two detached chains are at the top of the funnel with large entropy. The electrostatic inter-chain interactions act as a steering force to reduce the entropy to facilitate the recognition as “fly-casting” effect [50], while the stabilizing intra-chain non-native electrostatic interactions in Chz. core cause kinetic traps on the energy landscape and lead to the slowing down of the association as “local collapse or trapping” effect. Thus, the binding rate of Chz. core to H2A. Z-H2B is controlled by the balance of native and non-native electrostatic interactions [54]. The correlation between disordered structure and charged sequence in unfolded proteins implies that this scheme is common in the process of IDPs' binding. In summary, we developed a structure based coarse-grained model that incorporates electrostatic interactions using model for studying protein-protein interactions, including IDPs. We used the model to investigate the folding/binding mechanism of Chz. core in the formation of the Chz. core-H2A. Z-H2B complex, revealing that electrostatic interactions can accelerate folding by steering the association but can also cause non-native interactions that slow down folding. The findings here provide a new understanding the role of electrostatic interactions in IDPs' binding. Our approach is applicable to the binding/folding of other IDPs to their targets and can be extended to include the association of disordered regions in some chromatin factors with the nucleosome that has a broadly distributed negatively charged surface on DNA. From energy landscape theory, the folding/binding energy landscape of proteins should be minimally frustrated and has a shape of funnel [52], [53], [55]. The proteins' native topology can determine the mechanism of folding and binding. The structure-based model has been used to study the folding of monomeric proteins and the binding of oligomers, and can successfully reproduce the experimental results [18], [19]. Plain structure based model only considers the interactions existing in native structure mapping a much smoother energy landscape to ensure the simulation achievable. In order to study the effect of electrostatic interactions on this system, we developed a modified coarse-grained structure based model in which each amino acid was modeled by two beads except for glycine. The first bead (named bead) belongs to the backbone of the protein chain, whereas another one (named bead) represents the side chain by its centroid and is responsible for the physicochemical properties of the amino acid. Especially, we introduced the charged characterization into our model to study the effect of electrostatic interactions on this system. The functional form of the forced field is given as a typical structure based model potential [56]. Where a few modifications are stated below: 1) the third term in this equation represents the chirality potential, which can maintain the correct chirality of the side chain. 2) The native contact map is built by Contacts of Structural Units (CSU) software [57]. An amino-acid-type dependent is used to describe the contacts between backbone and backbone [58]. The strength of each side chain–side chain interaction contributes the same weight to the potential. Backbone–side chain interaction is not introduced in the potential [59]. 3) The electrostatic interaction is represented by model, mimicking the effect of varying salt concentration: is the electric conversion factor; is the salt-dependent coefficient; is the Debye screening length which is directly affected by salt concentration; is dielectric constant and was set to 80 throughout the simulations. So the relationship between and salt concentration can be written explicitly: . The exact physical meaning of can be found here [35]. is the energy scaled coefficient which aims to make the total energy balanceable. In our work, the parameters derive from the original folding/binding studies [18], [60], namely, is the original MJ potential, is the mean value of the entire set of MJ weights in this protein system, is a variable which can modulate the range of energetic heterogeneity; in the present work, has been set to 1. 0 corresponding to the “flavored model” [61]. means that the contacts between side chains and side chains are weighted equally in the force field. In order to maintain the model energetic balanceable, we introduced two factors in front of dihedral energies and contact energies. , are chosen so that the ratio of backbone to side chain dihedral energy strength equals 2. 0; is set to make the ratio of total contact to total dihedral energy strength equals 2. 0. In our reduced model, only Arg and Lys have a positive point charge and Asp and Glu have a negative point charge. The charges are placed on the atoms mimicking the side chains. If the two side chains has already formed a salt bridge, its equals, so that its total energetic contribution will be similar to the other contacts [51]. In simulation, we set, in this case if, the DH potential for two opposite charges located at a distance of is equal to native contact energy. In order to study how the electrostatic interactions influence the thermodynamic properties of the system, we performed a group of constant temperature simulations. The simulations were conducted at started from 40 different configures either dimeric or dissociative expecting to observe the most transitions in a limited simulation time. The total simulations were running accumulating 396 binding transitions between unbound states and bound states to ensure the rationality for the thermodynamics analysis. The dynamics with electrostatic interactions was explored at a salt concentration of near to the physiological conditions. In order to study how the electrostatic interactions influence the kinetic properties of the system, we simulated 100 trajectories at each salt concentration started from varying dissociative configures with different initial velocities. The dissociative configures comprised of folded histone variant H2A. Z-H2B and unbound histone chaperone Chz. core, were extracted from high temperature simulations. These kinetic simulations were done at a series of dilute solution in the range of to guarantee the validity of the model. The temperature was set to 55 K (). The first passage time (FPT) of certain region in Chz. core is calculated when the fraction of native binding contacts exceeds 0. 8 at the first time, where “X” can be “N”, “Motif” and “C”, corresponding to the N-helix, Chz motif and C-helix of Chz. core, respectively. We calculated, corresponding to the mean passage time from unbound states to encounter states, from encounter states to unbound states, from encounter states to intermediate states and from intermediate states to bound states respectively, by averaging the 100 trajectories at each salt concentrations; and we also accumulated the corresponding numbers of transitions to calculate the 6 typical binding rates: [23].

Histone chaperones facilitate the assembly and disassembly of nucleosome by interacting with the corresponding histones or histone variants. As the biomolecules in nucleosome are highly charged, electrostatic interactions are particularly important in these processes. The experiments have explored that the histone chaperon Chz1 as an intrinsically disordered protein (IDP) can fold by binding to its histone variants H2A. Z-H2B. Here, we developed a molecular simulation program that treated electrostatic interactions with Debye-Hückel model to study the mechanism of the association. We found that the inter-chain electrostatic interactions facilitate the coupled folding and binding transitions, consistent with the kinetic experiments and microscopic structural perspectives. Furthermore, we show that the intra-chain electrostatic interactions collapse Chz1 and slow the binding rate. The collapsed structure in IDPs caused by intra-chain electrostatic interactions has been widely found in experiments and the effect in binding is well studied in our work. Our theoretical approach shed new light on the role of electrostatics on inter-chain and intra-chain interactions for IDPs' binding and is applicable to the binding/folding of other IDPs to their targets.

Abstract Introduction Results Discussion Materials and Methods

biomacromolecule-ligand interactions biophysic al simulations protein folding biophysics theory biology computational biology biophysics simulations biophysics

2012

Importance of Electrostatic Interactions in the Association of Intrinsically Disordered Histone Chaperone Chz1 and Histone H2A.Z-H2B

Numerous urban villages (UVs) and frequent infectious disease outbreaks are major environmental and public health concerns in highly urbanized regions, especially in developing countries. However, the spatial and quantitative associations between UVs and infections remain little understood on a fine scale. In this study, the relationships between reported dengue fever (DF) epidemics during 2012–2017, gross domestic product (GDP), the traffic system (road density, bus and/or subway stations), and UVs derived from high-resolution remotely sensed imagery in the central area of Guangzhou, were explored using geographically weighted regression (GWR) models based on a 1 km × 1 km grid scale. Accounting for 16. 53%–18. 07% of residential area and 16. 84%–18. 02% of population, UVs possessed 28. 55%–38. 24% of total reported DF cases in the core area of Guangzhou. The density of DF cases and the DF incidence rates in UVs were 1. 81–3. 13 and 1. 82–3. 06 times of that of normal construction land. Approximately 90% of the total cases were concentrated in the UVs and their buffering zones of radius ranged from 0 to 500 m. Significantly positive associations were observed between gridded DF incidence rates and UV area (r = 0. 33, P = 0. 000), the number of bus stops (r = 0. 49, P = 0. 000) and subway stations (r = 0. 27, P = 0. 000), and road density (r = 0. 39, P = 0. 000). About 60% of spatial variations in the gridded DF incidence rates were interpreted by the different variables of GDP, UVs, and bus stops integrated in GWR models. UVs likely acted as special transfer stations, receiving and/or exporting DF cases during epidemics. This work increases our understanding of the influences of UVs on vector-borne diseases in highly urbanized areas, supplying valuable clues to local authorities making targeted interventions for the prevention and control of DF epidemics. Dengue fever (DF) is a febrile illness caused by the dengue virus, which is further classified into four serotypes (Dengue virus 1–4), and transmitted by Aedes aegypti and Aedes albopictus mosquitoes [1]. Dengue is the most prevalent mosquito-borne viral infection of humans in the tropical and subtropical regions of the world. Approximately 2 to 4 billon people are at risk of contracting dengue virus every year, resulting in nearly 100 million confirmed cases and causing ongoing wide concern [2–5]. After the founding of the People’s Republic of China, DF was eliminated in mainland China. However, increased openness and movement across borders have resulted in a recent revival of this tropical infectious disease, which is an imported epidemic to China [6,7]. Approximately 94% of indigenous cases in mainland China were reported from Guangdong Province, and 83% of these cases were in Guangzhou City [8], following an unprecedented dengue outbreak in Guangzhou in 2014 that has attracted the attention of relevant researchers. Both domestic and foreign scholars have carried out a considerable amount of research into DF epidemics, including into factors affecting the spread and prevalence of the disease and the corresponding prevention and control measures [9–13]. These studies have shown that the population, transportation, and living environment have undergone tremendous changes due to rapid urbanization, which in turn has led to changing DF transmission characteristics. In addition to some important natural environmental factors (e. g. , temperature, precipitation) [9–11], social and economic factors, such as population distribution and density, land urbanization level, and road network density will have an important impact on the temporal and spatial patterns of DF epidemics [12,13]. Furthermore, the presence of infected people may accelerate the transmission of DF in regions with high population densities [14,15]. Informal urban settlements in China are described as urban villages (UVs), unique areas of high population density. Urban space has undergone dramatic transformation and reconstruction during China’s rapid urbanization, which has caused a large number of rural villages that were originally on the edge of the city to be gradually surrounded or semi-enclosed by urban land [16–20]. A lack of overall planning and scientific management of UVs has resulted in a large number of irregular buildings scattered in urban areas, with subsequent poor sanitation, lack of infrastructure and serious environmental pollution [21,22]. These characteristics of UVs, combined with their perennial humidity and relatively low temperature [23,24], provides an ideal living environment for the breeding of Aedes albopictus, the sole vector of dengue transmission in Guangzhou. However, the current quantitative relationship between UVs and DF epidemics has received very little attention. Moreover, different factors influencing the DF epidemic have a spatial scale effect. Most of the available research has been focused on analysis at a relatively large spatial scale, such as at regional and prefecture level [25–28], with a small number of studies gradually expanding to small scales, such as at county, township, and even community or regular grid scales [29,30]. However, these small spatial scales are often the final nodes where prevention and control measures can produce practical effects, and more research into the factors influencing DF and its prevention is required at this scale. Therefore, this study was based on high-resolution remotely sensed imagery extraction of UVs in the central areas of Guangzhou, using epidemiological statistical methods and spatial analysis to further analyze the spatial relationship between UVs, public transportation, road density, population density, gross domestic product (GDP) and the DF epidemic according to a 1 km×1 km grid scale. The aim was to provide effective guidance for relevant government departments making targeted prevention and control measures on the DF epidemics in urban regions with numerous UVs. The study area was located in the central areas of Guangzhou (113° 23' –113° 36' E, 23° 08' –23° 14' N) and included the four districts of Liwan, YueXiu, Haizhu, and Tianhe. The regional location is shown in Fig 1. The central area of the study was a highly urbanized area of Guangzhou, with an urbanization level of 100% and a total area of 279. 63km2. This area has a population of 5. 24 million permanent residents according to the 2017 Guangzhou Statistical Yearbook and is also the economic center of Guangzhou. In 2017, the GDP of the central area reached US$151. 73 billion [31]. The characteristics of its subtropical monsoon climate are obvious: warm and rainy, enough light and heat, an annual average temperature of 21–23°C, and an average annual precipitation of 1800 mm. These suitable natural and social environmental conditions are favorable to the growth of Aedes albopictus and to the transmission of DENV, making it a high-risk area for DF [32]. In addition, Guangzhou, in particular the central area, has a large number of UVs due to rapid urbanization over the past decades [33]. DF is a notifiable disease in China which means that, once diagnosed, cases must be reported to the web-based National Notifiable Disease Reporting Information System (NIDRIS) within 24 h [34]. The DF case information includes age, sex, address, and time of onset. The DF epidemic data for this study were obtained from the Guangzhou Center for Disease Control and Prevention, and included DF case data from 2012–2014 and from 2017. The targeted DF cases in our study included clinically diagnosed (based on clinical manifestations and epidemiologic exposure history) or laboratory-confirmed cases (“clinically diagnosed cases presenting with any of the following lab test results relating to DF: a 4-fold increase in specific IgG antibody titer, positive on a PCR test or viral isolation and identification test”). The address information of the confirmed cases, after desensitization, was used in conjunction with geocoding (http: //www. gpsspg. com/xGeocoding/) and coordinate deviation correction to produce case data for a spatial point layer using ArcGIS 10. 3 (ESRI, Redlands, CA, USA) software (Fig 2A). In 2014, DF cases in the Guangzhou region reached a peak, with a total of 36 344 cases reported, of which 18 350 were from the central area, accounting for 50. 49% of the entire Guangzhou city. Moreover, the ratio of the total DF cases in 2012–2017 to the population in 2015 was calculated so as to indicate the DF incidence rates during the study period on the 1 km × 1 km grid scale. With consideration of the high degree of urbanization of the study area, land use types for 2012 and 2017 was divided into five categories: normal construction land (NCL), UV, water, vegetation and unused land. NCL and UVs are collectively referred to herein as construction land (CL) (Fig 2B). A total of 206 sample points were randomly selected to verify the classification results for 2012 and 2017. The overall accuracy and the Kappa coefficients for 2012 and 2017 were 82. 67%, 0. 802 and 87. 40%, 0. 851, respectively. As far as the producer’s and user’s accuracy were concerned, the UVs in 2017 possessed slightly lower accuracies (87. 8% and 87. 8%) than those of water (88. 2% and 93. 8%), and roads (90. 0% and 87. 1%), although the omission and commission of UVs had been appropriately controlled by the texture selection procedure. In a word, the present extraction accuracy can meet the requirements for further analysis. Detailed information about the retrieval of land-use types can be found in an earlier study [24]. The public transport system (bus stops, subway stations) greatly facilitates the travel of people living in the central area. Population density and GDP data in 2015 were collected to indicate population and economic status. The public transport, population density, GDP and road network vector data in this study (Fig 2B and 2C) were all obtained from the Resources and Environment Science Data Center (RESDC, http: //www. resdc. cn). Road density was generated from road network vector data, including all roads in the central region of Guangzhou (highways, national ways, county roads, town roads, etc.), and it was the ratio of the length of road in each unit (grid) to the corresponding unit’s area. Spatial autocorrelation analyses are frequently utilized to explore the spatial patterns of incidence or mortality in terms of Moran’s I with z-score and/or p-value because of their high statistical power [35–37]. Moran’s I is produced by standardizing spatial autocovariance by the data variance using a measure of the connectivity of the data [38]. Generally, Moran’s I value ranges from −1 to 1 and a high positive Moran’s I value with larger z-score and/or appropriate p-value represents a tendency towards clustering, which means that adjacent units have similar incidence rates, whereas a low negative value indicates a tendency towards dispersal, which means that units with high incidence rates lie next to units with low incidence rates. In addition, the choice of spatial scale is the basis of spatial analysis. In many studies of infectious disease epidemiology, basic geographic units such as districts or townships/streets are often disturbed by changing administrative divisions, and the creation of regular spatial grids can effectively avoid this phenomenon [39]. With reference to our previous research work [29,30,39], a spatial gridded unit of 1 km × 1 km was used as the spatial unit in this study, and we analyzed the spatial autocorrelation degree of the DF epidemic at this grid scale. In view of the spatiotemporal heterogeneity of DF incidence rates, the DF epidemic may be affected by its potential influencing factors in different ways and to various degrees, which is appropriate to analyze using a geographically weighted regression (GWR) model. As an extension of the traditional multiple linear regression (i. e. , ordinary least square, OLS), a GWR model embeds the attributes’ spatial location into the regression parameter, yielding a local regression together with local estimates of regression coefficients[40]. The local estimation of the parameters with GWR is expressed by Eq (1) as below: yi=β0 (ui, vi) +∑k=1nβik (ui, vi) xik+εi (i=1,2, …, m) (1) where i = 1,2, …, m denotes the number of spatial units in the central area of Guangzhou; yi is the dependent variable (the DF incidence rates during 2012–2017) at location i; independent variable xik is the value of the k parameter at location i, xik referred to the value of an affecting factor k (such as land use types, GDP, and so on) at spatial unit i, which is specific for every spatial unit; ui, vi is the position coordinate of the sample point; β0 is the intercept; βik is the correlation coefficient for the independent predictor variable xik, which is to be estimated; and εi represents random error. During the GWR modeling, the most important parameter, named as bandwidth, that controls the degree of smoothing in the model was chosen by selecting the method of the corrected Akaike Information Criterion (AICc). Then, every spatial unit has a set of specific parameters to reflect the relationship between dengue fever incidence rate and influencing factors. In particular, the variance inflation factor (VIF) had also been employed in this study to test the collinearity among these independent variables integrated in the models, since these selected explanatory variables likely correlated with each other. Finally, all the parameters derived from both GWR and OLS will be compared in terms of the values of AICc, Sigma (i. e, residual standard deviation), VIF, and adjusted R2, on which the performance of these models could be evaluated. At the same time, the spatial autocorrelation analysis on the standardized residual (StdResid) values of these models was further employed to evaluate the explanatory performances (e. g. , spatial stability) of the OLS and GWR models. The Moran’s I values close to zero indicated that there is no spatial autocorrelation of the StdResid values, and the results would be more reliable and then recommended for subsequent analysis. By means of the exploratory regression tool, twenty-five OLS and GWR models with higher Adjusted R2 and lower values of AICc, Sigma, and VIF were recommended. Integrating various combinations of influencing factors in the OLS/GWR models with the lowest AICc and Sigma, the highest adjusted R2, and lower VIF than 7. 5, eleven univariate models and fourteen multivariate models were respectively conducted for the comparison between CL, UV/NCL, water, vegetation, public traffic, road density, population density and GDP grouped factors. In addition, we created a series of buffering zones with increasing radii based on the boundaries of the UVs, in which the incidence rates, the proportion of the DF cases, as well as its growth rates, in different years in each buffering zone were counted. Meanwhile, Pearson correlation analysis was applied to explore the relationships between DF incidences and all of the potential variables (UVs, population density, GDP, bus stops, subway stations, and road density) at the significance level of 0. 05 and 0. 01, by which some appropriate potential variables could be accordingly considered into the GWR models. All of the above spatial analysis and modeling were completed in ArcGIS 10. 3 software (ESRI, Redlands, CA, USA). Typical correlation analysis was achieved using SPSS 19. 0 (SPSS Inc. , Chicago, IL, USA). Land-use types across the central region, including Liwan, Haizhu, Yuexiu, and Tianhe districts in Guangzhou City, typically featured impervious surfaces (i. e. , NCL and UVs) according to their dominant area percentage (53. 80% in 2012 and 58. 12% in 2017) (S1 Table). Among these four central regions, in 2012, Haizhu District had the largest area of UV (10. 91 km2) and Yuexiu, Liwan, and Tianhe districts had 7. 98,4. 06, and 8. 80 km2 of UV. There were more than 450 UVs with areas varying from less than 0. 001 km2 to 0. 87 km2. As a result of urban renewal, the total area of UV decreased from 31. 75 km2 (2012) to 31. 38 km2 (2017), with a clear reduction in Liwan (about 0. 12 km2 in Xinglongfang and Dongjiao communities) and Tianhe (0. 25 km2 or so near Pingyun Square, the Second Xintang communities, and Xinxu communities) in particular, as shown in Fig 3. Since there were so fewer changes of UVs during 2012–2017, the data of land-use information in 2012 was chosen for subsequent analysis. In the region, the Pearl River fork zone across Yuexiu, Liwan, and Haizhu was the most typically spatially clustered with UVs. Dongpu Town in Tianhe and the central zone of Haizhu were the other two representative zones. Unused land and water area decreased by 16. 21 km2 and 1. 95 km2, respectively, between 2012 and 2017, while vegetation and NCL increased by 4. 03 km2 and 14. 50 km2, respectively. In summary, the central area of Guangzhou City mainly featured impervious surfaces, especially many widely distributed UVs. There were a total of 20 059 local DF cases reported in the central districts, which accounted for half of the total cases in the whole Guangzhou City during the study period. Meanwhile, the distribution of DF was spatially different across this typical region. The ratio of DF cases in each infected unit to the mean value of all the infected units (RDM) varied spatially on the 1 km × 1 km scale (close to the largest UV area) in 2012,2013,2014, and 2017. However, the units with high RDM were mainly located around the Pearl River fork between Yuexiu, Liwan, and Haizhu districts (Fig 4). Meanwhile, the DF epidemic during these four years was remarkably spatially clustered according to the spatial autocorrelation indices (0. 174 < Moran’s I < 0. 673,6. 398 < z-score < 16. 930, p-value < 0. 001; S2 Table). These results obviously showed that the DF epidemic in the central region was spatially featured on the grid scale. Between 2012 and 2017, DF patterns in the UVs differed from those of the NCL areas. As shown in Table 1,61. 76%– 71. 45% of total DF cases in the CL zones were distributed in the NCL zones with proportionally larger areas (81. 93% in 2012 and 83. 47% in 2017) across the central four districts. By comparison, the rest (28. 55%– 38. 24%) were distributed in UVs with proportionally smaller areas (18. 07% in 2012 and 16. 53% in 2017). Meanwhile, population density (persons per km2) in the UVs was slightly higher than that of NCL in 2012 and 2017, although the population size of NCL is 4. 55–4. 94 times that of the UVs. As a result, the density of DF cases and the DF incidence rates in UVs were respectively 1. 81–3. 13 and 1. 82–3. 06 times that of NCL between 2012 and 2017. It can be clearly seen that UVs possessed higher values of DF cases density, incidence rates, and population density in the central region of Guangzhou City. In other words, DF cases were more likely to be found in UV areas. The sizes of the DF epidemic were associated with the UVs’ area. In the UVs with recorded DF cases, the number of DF cases (the total in the study period) was strongly associated with acreage of these UVs (r = 0. 45, P = 0. 015), as shown in Table 2. Similarly, on the grid scale, the counts of DF cases were significantly correlated with the gridded UVs acreage (r = 0. 33, P = 0. 000) in the infected units. In addition, the regions surrounding UVs were obviously influenced by the DF epidemic in the UVs with DF cases. Along with the radius of buffers increasing, accumulated DF case count in regions including the UVs with DF cases and their surrounding buffering zones showed an ascending trend (Fig 5). Until the radius of the buffer zones was 500m, about 90% of the total DF cases were concentrated in these regions (i. e. , UVs and buffer zones). In comparison, newly included DF cases in the extended buffers per 50 m (i. e. , increasing slope) displayed clear decreasing trends, especially in the first two buffers (50 m and 100 m). This decline was alleviated in the 200 m buffers. Meanwhile, the incidence rates of DF in the buffering zones gradually decreased, although there was larger and larger population proportion due to the increasing buffering distances. These results illustrated that UVs posed an obvious aggregation effect on the DF epidemic across the central region in Guangzhou City. Advanced traffic conditions, especially public transportation systems (such as bus services and subway lines) facilitate contact among people living in UVs. DF case density in the units (n = 272) with either bus stops or subway stations was much higher (73. 21 cases per km2) than those without any bus stops or subway stations (7. 74 cases per km2) (n = 19). Moreover, as given in Table 2, the gridded DF incidence rates were significantly positively associated with UV area (r = 0. 33, P = 0. 000), number of bus stops (r = 0. 49, P = 0. 000) and subway stations (r = 0. 27, P = 0. 000), and road density (r = 0. 39, P = 0. 000). In comparison, the partial correlation coefficient between the gridded DF incidence rates and UV area individually decreased from 0. 33 to 0. 24 when traffic conditions were controlled for (Table 2). These partial correlation coefficients between DF incidence rates and traffic conditions (i. e. , the number of bus stops, subway stations, all stops, and road density) were slightly decreased to 0. 43,0. 27,0. 41, and 0. 38, respectively, when UV area was controlled for. Among them, bus stops were the most suitable indicator of traffic conditions because of their high correlation coefficients. These results indicated that the aggregation effects on the gridded DF epidemic across the central region were heavily influenced by the traffic system, especially the presence of bus stops. According to the adjusted R2, AICc and Sigma values (Table 3), spatial variations in the gridded DF epidemic in the central region of Guangzhou City were appropriately explained by the GWR/OLS models, which employed each influencing factor or their various combinations. In comparison, the comprehensive explanatory performance of the GWR models was much better than that of the OLS models due to the higher adjusted R2, lower AICc and Sigma values. About 46% or more spatial variation was interpreted by the univariate GWR models. Among the potential influencing factors derived from the univariate GWR models, bus stops and UVs possessed relatively higher adjusted R2, lower Sigma and AICc values. However, population density possessed a relatively lower adjusted R2, higher Sigma and AICc values. Similarly, the GWR model (Com 1) integrating bus stops and UVs performed the best among all of the bivariate models. When this model further integrated either GDP or population density that possessed relatively weaker explanatory ability, the performances were increased according to the rising adjusted R2 and declining values of AICc and Sigma in the GWR models (Com 9 and Com 10 in Table 3). In comparison, the other multivariate GWR models (Com 7,8, and 11–14) had slightly weaker performances. In particular, the weakest performance was observed in the model (Com 12) even though the best bivariate GWR model (Com 1) further integrated both GDP and population density. And the reliability of its results quickly decayed because the potential risk of collinearity (VIF) had increased from 1. 23 to 4. 82 (Table 3). In all of the models, the GWR model (Com 9) integrating UVs, bus stops, and GDP possessed the highest adjusted R2 (0. 59), the lowest AICc (5305. 02), the lowest Sigma (272. 80), and a lower VIF value (<1. 63). Moreover, according to the spatially random patterns of the StdResid values (Moran’s I = 0. 04, p = 0. 26, Z-score = 1. 12), the performance of this model was also spatially stable, although there were still a few units with absolute StdResid values > 2 (Fig 6). In terms of the influence of the three variables (GDP, UVs, and bus stops) on the spatial variation of DF infection, these variables were spatially differentiated across the central region (Fig 6). The presence of bus stops was positively associated with the DF epidemics in most of the units, especially the Liwan district. The influences of GDP displayed spatial disparities. They were positive effect in most areas of Tianhe and Haizhu districts and negative effect in Liwan, Yuexiu, and near the Pearl River fork of Haizhu district. In comparison, UVs tended to have a greater association with DF, with more units with relative higher local coefficient values in Liwan, Yuexiu, Haizhu, and Tianhe districts, although the association tended to be weaker (less than 200) in the central Haizhu where there were relatively fewer DF cases (Fig 4), lower road density, less population density, and sparser public transportation stations (Fig 2). These results suggested that UVs were the most important factor in the spatial variations in the DF epidemic across the central region of Guangzhou. Widely distributed urban settlements and serious DF epidemics are two major public concerns in Guangzhou City. In this study, we analyzed the spatial and quantitative relationship between the DF epidemic and UVs on a grid scale across the central region of Guangzhou. The interesting findings provide valuable clues to enable local environmental health authorities in targeted interventions in the prevention of this epidemic. The size and spatial heterogeneity of the DF epidemics were probably associated with numerous UVs widely distributed across the study region with developed public transportation (e. g. , many bus stops), good economic status, and a dense population. Previous studies across the entire joint Guangzhou-Foshan (GF) area have found that spatially clustered DF cases in this region are associated with its higher land urbanization level, population size, road density, and economic level (GDP per capita) [30,41]. Our study found that this central region typically featured by not only impervious surfaces (including NCL and UVs) but also spatially differentiated DF epidemics on the 1 km × 1 km grid scale. Meanwhile, there was higher density of DF cases and incidence rates in UV areas than in NCL areas, and the DF epidemic was significantly positively associated with UVs’ acreage both at the grid scale and the UV level. There are two possible reasons for this. First, UVs, as a type of informal urban settlement, provide Aedes albopictus mosquitoes with a suitable environment for survival and breeding, featuring slightly lower land surface temperature than NCL areas [24], especially in the summer months. Second, the denser population in UVs and its flowing traits were two core impetuses of UVs’ influences on the DF epidemic. In particular, UVs have a high density of low-cost accommodation rented by migrant workers from local house owners, which provided local citizens with a steady source of revenue and resulted in a large floating population [21,42]. This increases the probability of being bitten by mosquito vectors, causing rapid transmission of this disease. DF is also more frequent within a specific 200–300 m radius around UVs, which is probably determined by the mosquitoes’ maximum flying range of approximately 300 m [43,44]. Thus, it can be seen that widely distributed UVs have an important influence on the DF epidemic, not only within the specific UV areas but also within their surrounding zones, crucially associated with the severity and obvious spatial disparities in DF incidence across the central region in Guangzhou City. We cautiously suggest that both UVs and their surrounding zones should receive considerable focus during DF epidemics. A convenient public traffic system meets the commuting demands of local residents, including the floating population who reside in UVs but work outside of them. Earlier studies reported that traffic conditions impose important effects on DF transmission [13,41,45], similar to our findings that public transportation (the presence of bus stops in particular) was not only directly associated with the DF incidence rates on the grid scale, but also influenced the aggregation effect of UVs on the DF epidemic, especially in zones with numerous UVs. We cautiously speculate that DF cases in infected UVs or units (grids) may have been potential infection sources when they entered other zones via the public traffic system. In other words, these UVs or units/grids with infections presence likely acted as transfer stations (receiving and/or exporting DF cases) during DF transmission. In a sense, it can be supposed to some degree that the infected cases/patients would also act as the disease’s vector, since local residents are the other crucial element in the DF transmission and the flying distance of mosquitoes (Aedes albopictus) is far less than human mobility due to developed public transportation. Accordingly, we recommend that targeted and effective interventions should be implemented in zones with numerous UVs and public traffic stations across the central region in Guangzhou City during the DF epidemic. Use of generalized additive models has revealed that associations between DF infection and GDP were nonlinear at the township level across the Pearl River Delta [13]. Our earlier investigation found that GDP had a weak but spatially differentiated correlation with DF infection across the GF area [29]. We found similar in this study, employing GWR models at a fine spatial scale (1 km × 1 km) and taking three spatially differentiated variables (UVs and bus stops as special influencing factors, as well as GDP) into consideration. GDP had a clear protective effect in the west zones (including in Liwan, most of Yuexiu, and west Haizhu) where there were more serious DF epidemics and larger areas of UV. This effect is likely related to the UVs in these areas being surrounded by NCL, with a higher economic status and the better promotion of public health services (e. g. , education and publicity about hygiene) in these well-developed zones. On the contrary, GDP tended to be a risk factor for DF transmission in the east zones (i. e. , most of Tianhe and northeast Haizhu) where there were relatively small DF outbreaks and many construction sites (categorized as unused land in this study), especially in the district of Tianhe, which is experiencing rapid economic development and urban construction. However, whether the positive correlation between GDP and DF incidence rates was related to the wide distribution of unused land needs further investigation. Nevertheless, our findings are sufficiently reasonable and detailed to infer that the influence of economic status on DF transmission was spatially differentiated. We advise that the protective influences of GDP on DF infection in Liwan and Yuexiu districts are further investigated to explore how its protective effects can be expanded to Tianhe and Haizhu districts with their growing economies. There were some limitations to the study. First, many more important influential factors should be further explored and included in the GWR models to interpret much more spatial variation in the DF epidemic, since less than 60% was explained at present. In particular, an appropriate variable should be acquired to comprehensively reflect both public transportation and population density so as to further interpret the remained (40% or so) spatial variation in the current study. For this point, data derived from mobile devices, metro cards and/or bus cards could be used to capture information about mode of travel and the movement of local residents, both for confirmed and suspected DF cases. This is particularly relevant to the UVs, as the effects of the public transportation system and UVs on DF transmission could be further investigated and high intercept values (Fig 6) decreased. Second, reliable monitoring data on the vectors’ population or density in the study area should be continuously collected and then used fully for a further comprehensive analysis of the link between DF epidemic and all the influencing factors in the future. Finally, the time series of the DF case data and the spatiotemporally matched remote sensing images should be longer. This would enable better validation of the typical influence of UVs, as DF epidemics have periodically occurred in the central region of Guangzhou City, with rapid land urbanization, since the 2000s.

Due to the rapid urbanization of China, many villages in the urban fringe are enveloped by ever-expanding cities and become so-called urban villages (UVs). UVs are widely distributed in not only the Guangzhou core areas but also the other cities in the highly urbanized region of China (e. g. , Shenzhen, Wuhan). UVs are commonly featured by poor sanitation, overcrowding population, absent infrastructure, and some environmental pollution due to the development is neither authorized nor planned, resulting in a high environmental suitability for some vectors (e. g. , Aedes albopictus), as well as the vetor-borne diseases (i. e. , dengue fever) in these regions. In this study, we demonstrated that UVs may serve as transfer stations for the transmission of DF epidemic in the regions with developed transportation, higher GDP and dense population. This is manifested as that the rates of DF incidences were significantly positively associated with UV area. Furthermore, the density of DF cases and the DF incidence rates in UVs were 1. 81–3. 13 and 1. 82–3. 06 times of that of normal construction land and about 90% of the total DF cases were concentrated in 500m radius of UVs’ buffers. And the aggregation effects of UVs on this epidemic in the central region were obviously affected by public traffic conditions at the grid level. This study is the first quantitative analysis of the spatial relationship between UVs, public transportation, road density, population density, GDP and DF epidemics, which will provide a useful reference for accurately preventing and controlling DF epidemic in urban regions with numerous UVs.

Abstract Introduction Materials and methods Results Discussion

invertebrates medicine and health sciences ecology and environmental sciences engineering and technology transportation china infectious disease epidemiology spatial epidemiology geographical locations animals transportation infrastructure roads urban environments population biology insect vectors civil engineering infectious diseases epidemiology disease vectors insects arthropoda people and places population metrics mosquitoes eukaryota asia biology and life sciences species interactions population density organisms terrestrial environments

2019

Urban villages as transfer stations for dengue fever epidemic: A case study in the Guangzhou, China

Mechanical stability is a key feature in the regulation of structural scaffolding proteins and their functions. Despite the abundance of α-helical structures among the human proteome and their undisputed importance in health and disease, the fundamental principles of their behavior under mechanical load are poorly understood. Talin and α-catenin are two key molecules in focal adhesions and adherens junctions, respectively. In this study, we used a combination of atomistic steered molecular dynamics (SMD) simulations, polyprotein engineering, and single-molecule atomic force microscopy (smAFM) to investigate unfolding of these proteins. SMD simulations revealed that talin rod α-helix bundles as well as α-catenin α-helix domains unfold through stable 3-helix intermediates. While the 5-helix bundles were found to be mechanically stable, a second stable conformation corresponding to the 3-helix state was revealed. Mechanically weaker 4-helix bundles easily unfolded into a stable 3-helix conformation. The results of smAFM experiments were in agreement with the findings of the computational simulations. The disulfide clamp mutants, designed to protect the stable state, support the 3-helix intermediate model in both experimental and computational setups. As a result, multiple discrete unfolding intermediate states in the talin and α-catenin unfolding pathway were discovered. Better understanding of the mechanical unfolding mechanism of α-helix proteins is a key step towards comprehensive models describing the mechanoregulation of proteins. Protein activity can be modulated by mechanical cues in addition to chemical stimuli and ligand binding. Mechanical forces can induce conformational changes in the protein structure, that leads to either a switch between the functional states of the protein, or allows for multiple functions[1,2]. α-helix folds are highly abundant among the structural proteins located at the cell-cell and cell-ECM contacts [3–5], among the essential muscle costamere complexes[6], as well as among the structures interconnecting the cytoskeleton[7,8] and cellular organelles[9]. Despite the numerous studies on mechanotransduction and the mechanosensitivity of proteins, the mechanisms associated with forced protein unfolding and mechanosignaling are not well understood especially where α-helical proteins are concerned. One reason for such a lack of understanding might be that α-helices are, in general, mechanically weaker compared to β-strand folds[10] and are therefore challenging to study experimentally. The low mechanical stability of α-helical proteins requires sophisticated experimental methods capable of force measurement in the range of piconewtons[11]. Among the α-helical structural proteins, several distinct folds have been identified. Talin-like proteins contain 5- and 4-helix bundles[12]. Proteins of the catenin family contain a 4-helix conformation with long α-helices interconnecting two bundles[13]. Both of these multi-helical protein folds respond to mechanical load by a dissociation of the bundles leading to mechanoregulatory function. The spectrin fold is formed of a 3-helix conformation with a long α-helix connecting the neighboring domains, forming a rigid rod of interacting 3-helix bundles. The spectrin-like conformation accounts for structural reinforcement in the cellular scaffolds[3,4, 7]. Finally, single long α-helices and coils can also be found among structural α-helical proteins (PDB id 5KHT). Focal adhesions and adherens junctions are fundamental mechanosensitive structures through which cells communicate with the extracellular matrix and with their adjacent cells, respectively. The processes of focal adhesion formation and maturation are regulated by mechanical signals[14,15]. Similar to the role of focal adhesions in the cell-ECM connection, adherens junctions are essential for cell-cell contacts. Adherens junctions are associated with the cadherin super-family of transmembrane proteins, which are connected through catenin-rich protein complexes to the actin cytoskeleton. The cadherins bind to the cytoplasmic protein β-catenin, which in turn binds to the filamentous F-actin binding adaptor protein, α-catenin[16,17]. Talin is a large focal adhesion protein that contains an N-terminal head domain, which is responsible for integrin binding. The larger talin rod domain consists of amphipathic α-helices arranged into 13 four or five-helix bundles (R1–R13) and a single helix dimerization domain (DD) at the C-terminal end (Fig 1A). Talin provides a link between the ECM, via the talin head-integrin interaction, and the cytoskeleton through the binding of actin filaments at actin binding sites located in the rod domain. Furthermore, the talin rod comprises up to 11 buried vinculin binding sites (VBSs), distributed along its structure. These binding sites are exposed by partial or complete unfolding of different bundles[18,19] as a result of mechanical stretching. In this way mechanical load regulates talin function by exposing the buried binding sites for certain binding partners such as vinculin[20], while simultaneously, epitopes for other binding partners become inactivated. An example of such epitope inactivation would be the talin binding partner RIAM which binds only to folded talin domains[21]. Thus, conformational changes of talin under mechanical load regulate the recruitment and activation of talin-interacting proteins. Interestingly, talin dimerization is also regulated by mechanical force [22]. This property highlights talin as a key player in the transmission and sensing of mechanical signals between the extracellular matrix and cell cytoskeleton. These mechanical signals are central for a variety of cellular functions including spreading, migration, invasion and substrate sensing[23–25]. Similar to talin, α-catenin recruits vinculin, providing the mechanical connection between cell-cell adhesions and the cytoskeleton. α-catenin contains 5 α-helix domains: the dimerization domain (DD) functioning as β-catenin binding domain, three modulation domains I, II and III (MI, MII and MIII), and an F-actin binding domain (FABD) (Fig 1B). Vinculin binds the MI domain of α-catenin while the two adjacent domains (MII and MIII) inhibit vinculin binding to MI. It has been demonstrated that vinculin is recruited in a force-dependent manner to the cadherin/catenin complex upon force-dependent unfolding of α-catenin. This process resembles the binding of vinculin to the talin rod domain upon tension-dependent unfolding. This tension-dependent unfolding is thought to be central in cell-cell adhesion mechanosensing[9,26,27]. It has been previously hypothesized that the force-dependent unfolding of the vinculin binding domains of the talin rod and the α-catenin domains includes stable intermediate conformations[28–30]. However, this has never been previously studied in detail in computational simulation or in experimental setup suitable for analysis of single-molecule unfolding events. α-helical proteins are associated with key physiological and pathological processes in mechanobiology[31–33]. A number of diverse diseases from heart[34] and muscle[35], diseases of bone, vascular and nervous system, or skin[36] have been associated with mechanobiology. Hence, better understanding of their unfolding characteristics will open the possibility of answering a plethora of questions in biology and medicine. In this study, we investigate the molecular mechanisms of the unfolding of two α-helical proteins, talin and α-catenin, on an atomistic level by a combination of steered molecular dynamics (SMD) simulations and single-molecule atomic force microscopy (smAFM). This study elaborates in closer detail on observations made during our previous work described in Haining et al. [28]. Previously, we have concentrated on the mechanical sensitivity of the talin rod subdomains during the initial domain breaking. We have reported that the talin subdomains are similar, however not identical in their ability to withstand mechanical load. All the tested talin subdomains unfolded in AFM setup over a range of mechanical forces between 10 and 40 pN. During the SMD simulations we repeatedly observed a possible unfolding intermediate in the unfolding trajectories which we intended to investigate further. Therefore, in the light of our previous findings, we have now selected two talin subdomains on either end of the mechanical sensitivity scale for the current study; weak R3 and strong R9. Talin R9 is an exceptional subdomain responsible for talin autoinhibition while it does not contain VBS. For that reason, we have also included subdomain R11 to investigate the unfolding intermediate in the terms of VBS activation. We subjected two talin rod 5-helix bundles, R9 and R11, to end-to-end SMD stretching with constant velocity pulling simulation at 2 nm/ns. Previously, talin 5-helix bundles were more mechanically stable in our SMD simulations compared to 4-helix bundles. According to our previous study[28], R9 was one of the strongest rod bundles. Overall, unfolding of R9 and R11 showed two force peaks (Fig 2A), which correspond to breaking of the 5-helix bundle and 3-helix intermediate. This unfolding intermediate consists of core helices (H2–H4) after the dissociation of H1 and H5. For R9 the maximum unfolding forces detected in constant velocity pulling simulation at 2 nm/ns were 348 ± 24 pN and 349 ±39 pN (average force ± standard deviation). Unfolding peak forces for R11 were very similar to those for R9 (Fig 2A). Representative snapshots of the unfolding trajectories are shown in Fig 3, indicating changing of the bundle conformation during unfolding. To confirm the unfolding intermediate, we designed point mutations in the R9 bundle forming disulfide bonds (clamps) in order to block the unfolding of the 3-helix core. These clamped R9 mutants were subjected to end-to-end pulling in simulation and experimental setup. We prepared three constructs including L1698C and A1748C cysteine mutations that protect 3-helix core from N-terminus (N clamp), A1720C and A1779C mutations that prevent unfolding of the 3-helix core from C-terminus (C clamp), and R9 construct with both N- and C-terminal disulfide clamps (N/C clamps) (S1 Fig). All R9 clamp mutants showed very similar unfolding of 5-helix state compared to the wild-type R9 (Fig 2B). The unfolding of the 3-helix state was effectively blocked in the R9 equipped with N/C clamps, while the constructs with only one disulfide clamp allowed unfolding of either H2 (R9 with C clamp) or H4 (R9 with N clamp) of the 3-helix core, but did not compromise the stability of the 3-helix intermediate (Fig 2B). In additional experiments, we investigated talin R3 bundle and α-catenin modulation domains I to II (MI-MII), which are all 4-helix bundles. Talin R3 was easily unfolded to the 3-helix state by the separation of H4. The 3-helix intermediate (H1–H3) was more stable compared to the 4-helix bundle, the unfolding peak force for breaking the R3 3-helix state was 276 ± 20 pN (Fig 2E). Similarly, α-catenin was unfolded to 3-helix conformation by dissociation of H4 (four out of five simulations) in MII and H1 in MI (all five simulations). Further unfolding showed two force peaks for breaking the 3-helix state in MII (at 349 ± 33 pN) and MI (at 461 ± 68 pN) (Fig 2F). Collectively these data suggest that both 4- and 5-helix bundles unfold through stable 3-helix intermediate state. Furthermore, 5-helix bundles withstand mechanical load better than 4-helix domains, which are easily unfolded to the 3-helix state. Described constant velocity SMD simulations of individual talin rod bundles and α-catenin were run five times each. Unfolding force profiles showed that our results are well reproducible (S2 Fig) and allowed us to calculate average peak force and a standard deviation (S1 Table). To assess the effects of force penetration on the unfolding mechanisms in SMD simulations, we studied the unfolding mechanisms and the existence of the stable intermediates in linear protein chain consisting of two talin R9 monomers resembling the natural biological assembly of talin. We designed tandem construct possessing exactly the same mechanical stability, i. e. two talin R9 domains (R9WT−R9WT tandem). Furthermore, we analyzed the R9 tandems with disulfide clamps, protecting the 3-helix core from unfolding, in either first or second monomer, with respect to the fixed N-terminal and pulled C-terminal end. Thus, we prepared two tandem constructs with clamps, R9N/C clamps−R9WT and R9WT−R9N/C clamps, respectively. For R9WT−R9WT tandem, unfolding force showed four peaks, corresponding to the breaking of 5-helix states first, followed by dissociation of the 3-helix states in both α-helix bundles (Fig 2C). Because the pulling was applied to Cα atom of C-terminal residue, the second R9 in the tandem was closest to the point of pulling and unfolded to 3-helix state first. Both monomers of the R9WT−R9WT tandem unfolded to the 3-helix intermediate within approx. 30 ns of the SMD simulation with 2 nm/ns pulling velocity, and unfolded at approx. 50 ns. Both tandems containing one clamped monomer showed three unfolding force peaks (Fig 2D) lacking the peak corresponding to the breaking of the disulfide-clamped 3-helix structure. Indeed, the force penetration did affect the two different tandems with clamps resulting in different unfolding trajectories. For R9WT−R9N/C clamps tandem, both monomers had 3-helix conformations at ~ 30 ns, while for R9N/C clamps−R9WT, closest to the point of pulling monomer (R9WT) unfolded completely before R9N/C clamps molecule unfolded to 3-helix state (Fig 2D, Fig 3). The investigation of the unfolding of a tandem provided us with a tool of studying the unfolding principles of multiple domains. Furthermore, the use of disulfide clamps in the tandem construct protecting the stable state provided us with a comparison force trace and additional proof of an intermediate unfolding conformation in both mechanosensitive proteins. Altogether, these findings indicate that the unfolding force required for the unfolding of the 3-helix intermediate state is similar to that needed for the unfolding of 5-helix state. We utilized smAFM to characterize the unfolding patterns of R9 and R11 constructs and captured the 3-helix intermediate (Fig 4). Similarly to the SMD, we detected two unfolding events for each of the bundles, implying that unfolding occurs through a mechanically stable intermediate. Overall the bundle stability was higher in the case of R9 than R11, consistent with our previous results[28]. The distance between the two unfolding events is 20–25 nm, which is consistent with the collapse from 5 helices to 3 helices. Likewise, the distance from the second unfolding event to the HaloTag ruler of 25–35 nm is consistent with the subsequent collapse of the 3-helix intermediate. As such the smAFM data supports the picture derived from the SMD analysis: 5-helix bundles collapse via a stable 3-helix intermediate. We also tested the R9 tandem construct to examine if the 3-helix intermediate could be detectable within a model of a polyprotein (Fig 5A–5D). We detected 4 unfolding events, consistent with a pattern of two bundles unfolding via a stable intermediate. The distances between the first and second event (~20 nm), second and third (~30 nm), third and fourth (~20 nm) and fourth and HaloTag (~30 nm) imply that, contrary to the SMD results, one α-helix bundle collapsed completely through a 3-helix intermediate before the second α-helix domain started unfolding. However, it is difficult to be certain, given the error margin of the peak locations and the likely stochastic nature of the unfolding process. When the 3-helix disulfide clamp was introduced into one of the tandem R9 domains, we saw a reduction in the number of unfolding events from 4 to 3 (Fig 5E–5H). This, along with the reduction of the overall unfolding length from 105 nm to 85 nm, demonstrates that the disulfide clamping was able to protect the 3-helix intermediate of the R9 from mechanical unfolding. The forced unfolding of α-catenin modulation domains I to II (MI-MII) by AFM produced a pattern consistent with the SMD simulation (Fig 5I–5L). We detected two unfolding events with a maximum unfolding length of ~50 nm which implies that there is no unfolding event with respect to the reduction of both 4-helix bundles into 3-helix states. This is in agreement with herein presented SMD data and with our previous work reporting on the lower mechanical stability of the 4-helix bundles[28]. The two events observed with unfolding lengths of ~25 nm correspond to the mechanical unfolding of 3-helix intermediate states. For the comparison of the 3-helix state mechanical stability we selected two mechanically diverse talin rod bundles, i. e. the mechanically weaker R3 and the mechanically stronger R9 for more detailed analysis. Although constant velocity SMD simulations are an excellent tool comparing the results with AFM analysis, they are less sensitive for the assessment of intermediate states as compared to constant force simulations. Therefore, we subjected the R3 and R9 bundles to constant force pulling simulations where, after screening of suitable force regime, constant force ranging from 160 pN to 200 pN for R3, and from 200 pN to 300 pN for R9 (Fig 6) was used. In constant force SMD, 4-helix R3 was weak even at 160 pN and rapidly unfolded to 3-helix state (within ~ 3 ns). After the separation of H4, H3 was slowly sliding relative to H1 and H2 (from ~ 3 ns to ~ 23 ns). The 3-helix intermediate did not unfold at 160 pN in 40 ns time window (Fig 6A), however, it unfolded completely (at ~ 72 ns) in extended 160 pN simulation (S3A Fig). R3 was extended also with constant force at 170 pN, 180 pN and 200 pN. Although it was completely unfolded after ~ 18 ns, ~ 13 ns and ~ 11 ns respectively, the stable 3-helix intermediate was observed in all trajectories (Fig 6). For strong R9 bundle, we first applied constant force of 200 pN and observed only partial uncoiling of terminal helices (H1 and H5) within 40 ns time window (Fig 6B), yet the 5-helix state remained intact. However, R9 unfolded to the 3-helix state (at ~ 86 ns) in extended 200 pN simulation (S3B Fig). The application of constant force of 220 pN or higher resulted in gradual unfolding of the bundle. During the unfolding, we recognized two stable states (5-helix and 3-helix states). In order to compare mechanical stability of 5- and 3-helix states, we used the disrupted 5-helix state of R9 and subjected it to stretching with constant force of 200 pN. Although 3-helix intermediate was relatively stable, it slowly unfolded over time under 200 pN while 5-helix bundle resisted the unfolding under the same force magnitude. These results suggest that 3-helix state in R9 is a stable conformation. However, it is weaker as compared to the 5-helix state of R9. In more detail, the 3-helix state can be unfolded under lower force load once the 5-helix state of the R9 bundle is broken. On the other hand, the 3-helix intermediate state in R3 is the most stable conformation of the R3 bundle. Numerous studies concerning α-helical mechanosensitive proteins have provided information on mechanically regulated switches between diverse binding partners and their associated functions. Perhaps one of the best known examples of this mechanoregulated protein-protein interaction is the tandem talin-vinculin, where mechanical stress applied to talin rod exposes binding sites for vinculin[37,38]. Our observation of SMD trajectories for talin multidomain constructs, described in our previous work[28], revealed a possible stable 3-helix intermediate during the forced domain unfolding. Because of the complexity of the SMD and smAFM data we obtained during the multidomain construct unfolding, we were not able to identify previously the 3-helix intermediate among the force traces directly. However, simultaneous domain unfolding was recognized for bundles of similar mechanostability in smAFM[28]. In this study, we investigated the detailed unfolding mechanisms of α-helical talin rod bundles and α-catenin MI-MII domains to probe the presence of any intermediate or partial unfolded states. Our results show that the two studied proteins unfold through a stable 3-helix intermediate. Constant velocity pulling used in SMD and smAFM revealed, that the unfolding force profiles for the 5-helix rod bundles R9 and R11 have two peaks, which correspond to the breaking of the 5- and 3-helix states (Fig 2A). The talin 4-helix bundle R3 and α-catenin bundles MI-MII also unfolded through stable 3-helix intermediates (Fig 2E and 2F). In addition, this 3-helix state was recognized as the most mechanically stable conformation for the 4-helix domains (Fig 6A). Other studies have also provided indirect evidence of unfolding intermediates in alpha helical proteins. Investigations of the talin R3 subdomain have revealed a possible 3-helix intermediate capable of rapid or instant activation for vinculin binding. Specifically, the deletion of helix 4 of rod subdomain R3 (ΔR3H4) leads to super-active R3 localizing efficiently in cell-ECM contacts (S6 Fig). Rahikainen et al. , 2017[23] showed that one or two destabilizing mutations in R3 H1 were sufficient to facilitate bundle unfolding, increasing the activation of vinculin binding and resulting in a strong cellular phenotype. The phenotype of the further destabilized state modified with four mutations was comparable to the super-active R3 potentially indicating a 3-helix intermediate. Further evidence of a 3-helix state in R3 domain is found in a recent study by Baxter et al. [39]. A 3-helix open state has been recognized after the dissociation of H1 from the R3 bundle under high pressure conditions. Similar effects were observed even for the talin R1 bundle, where the deletion of H5 resulted in the exposure of the VBS located in H4 and an active conformation of R1[40]. The authors also suggest that the deletion of H5, resulting in a 4-helix partial bundle, causes a destabilization of the R1 domain leading to partial unfolding. This observation is in line with our results; we showed that the 4-helix fold is a fragile conformation which does not require excessive mechanical force to unfold to a stable 3-helix state (Fig 2E). Finally, even previous computational studies suggested that only partial unfolding of talin subdomains described by minimal protein extension is sufficient for VBS activation. In more detail, R1 VBS was activated through torsional conformational change of the hydrophobic core orientation within R1 subdomain during an extension of less than 2 Å [41]. Inspection of the molecular characteristics of 5- (4- in R3) and 3-helix states did not reveal any dominant differences between these assemblies in terms of interactions or packing. The hydrophobic interactions appear to be the main factor in maintaining both these states, as shown in S4 Fig. In the previous study[28], we proposed two conserved residues that are important for maintaining R9 5-helix bundle stability, namely Leu1668 in H1 and Met1803 in H5. Further studies including experimental investigation of subdomains carrying mutations targeting the 3-helix core fragment would be needed to evaluate the contributions of individual residues for the mechanical stability of the 3-helix intermediate state. Further unfolding of the 3-helix intermediate was observed in our experimental and simulation setup. Whether the complete unfolding of an α-helical domain takes place in vivo, or whether the 3-helix state is the final unfolding conformation remains unclear. Hints of both of these options can be found in the literature. As discussed earlier[40], S6 Fig, the deletion of terminal helix in R1 and R3 is sufficient for VBS activation and vinculin recruitment. Thus, we speculate that the 3-helix state is capable of vinculin binding. Vinculin binding to unfolded talin or α-catenin domains inhibits domain refolding under low mechanical load[20,27,42]. Simultaneously, vinculin binding to the 3-helix state bundle may protect it from complete unfolding[43]. Studies by Margadant et al. [43] show that the maximal length of talin is approx. 400 nm in living cells. This also supports the notion of partial unfolding even in the absence of vinculin. Interestingly, recently published work by Ringer et al. [11] revealed a force gradient across the talin rod domain. In the presence of vinculin, greater force was measured at the N-terminal end than at the C terminal end resulting in the bundle unfolding and activation for vinculin binding. As vinculin binds to activated talin and to actin, the force acting on the talin rod is divided and reduced towards the C-terminal end. We speculate that the reduced force might be insufficient to unfold the stable 5-helix subdomains located at the C-terminal end of the talin rod. However, here we showed that the intermediate resulting from 4-helix unfolding was mechanically weaker compared to the intermediate of a 5-helix bundle. Thus, the complete unfolding of 4-helix bundles at the N-terminal end of the talin rod may be possible. Based on the work by Yao et al. , we may assume that complete yet reversible unfolding of R3 domain takes place under low force load. It was shown than under 4. 8 pN of constant force load exerted on the full length talin, R3 occupies two distinct conformations with elongation of approx. 19 nm. However, which states these in fact are remains unclear [44]. We may hypothesize that only the end-to-end attachment to the pulling device and initial elongation under low force load is sufficient to collapse R3 into an activated 3-helix state. Such immediate conformational change would not necessarily result into an observable difference in the total end-to-end elongation compared to the R3 conformation in solution (S5 Fig) [41]. Similar elongation of approx. 19 nm between the 3-helix intermediate and the completely unfolded state was shown in our SMD results (Fig 6A). Here we also see that the 3-helix intermediate remained stable even though the end-to-end distance increased by 5 nm. This distance increase was caused by the uncoiling of H4. Based on our observations of the talin unfolding mechanisms, we propose a model of multidomain α-helical protein unfolding under mechanical load, shown in Fig 7. In the absence of mechanical force, α-helical bundles remain in a folded conformation, capable of binding their ligands such as RIAM (R2, R3, R8 and R11) and DLC1 (R8) in the case of talin[45]. At low mechanical load, soft α-helix bundles, namely 4-helix domains, unfold to stable 3-helix intermediates. Since the activation of vinculin binding sites (VBSs) requires the unfolding of talin bundles[14], the formation of 3-helix states suggests that VBSs located at the terminal helices become available for vinculin. At the same time, the partial unfolding of the bundles leads to a disturbance of the binding sites for other binding partners located on the bundle surface, which abrogates their interaction[21]. With increasing force, additional bundles collapse to a 3-helix conformation switching from the mechanoregulatory role to a structural reinforcement role, similar to that of spectrin. In other words, it is possible that talin reacts to a range of small mechanical forces by the dissociation of certain bundles leading to a change in binding to other proteins. Such mechanoregulation would take place until the bundles reach a stable 3-helix spectrin-like conformation. At this point, the talin protein would assume a structural reinforcement role. Finally, at a high force, completely unfolded bundles would lose the ability to support ligand binding as well as structural function[20]. Such a model enables rich mechanosignaling through talin. A recent study by the Barsukov group has proposed the talin protein as being a hub for several different binding partners[45] where the proposed 3-helix intermediate state could be an essential component of binding regulation. Moreover, it has been recently shown, that the mechanical load across talin is not homogeneous, providing further variation in the regulation of talin functions[11]. Since the talin rod experiences a force gradient, once vinculin is bound, the local stress may become modulated and insufficient to unfold the 3-helix state. The 3-helix state may thus represent abundant talin rod subdomain conformation in living cells[11,43]. Experimental work is essential to confirm and refine these models. While the activation of talin binding sites by mechanical force has been long studied, the detailed mechanism of the forced unfolding has not been previously discussed. The existence of a stable 3-helix intermediate may offer yet another level in the mechanoregulation process and in the cell’s response to mechanical stimuli. The existence of the unfolding intermediate also adds additional complexity to the assessment of the impact of mutations in the case of diseases. α-catenin truncating mutations have been detected in patients with hereditary gastric cancer[46] possibly increasing the disease susceptibility. Furthermore, α-catenin mutations have been directly associated with macular dystrophy[47]. The understanding of molecular mechanisms would shed light on the disease development and guide new treatment solutions. We may also speculate that the existence of a 3-helix intermediate, whether undergoing full unfolding or not, may provide an additional structural support. It is also possible that the 3-helix state functions as a molecular bumper reducing the impact of functional mutations present in the mechanosensitive protein. In other words, with additional level of mechanoregulation, the mutation effect on the cells behavior may be defused with only moderate effect on the cells fitness. Such a theory may be of importance in the case of talin which has been presented as a vital protein in cell and tissue biology. Yet, despite its important roles, only one mutation has been recognized as disease causing, in the talin-2 isoform located outside of the mechanosensitive region[48]. We show that α-helical proteins unfold via stable 3-helix intermediate states, representing biologically active states. smAFM and disulphide clamp mutations were used to confirm the models obtained with SMD. Our results suggest that talin is a central scaffolding hub in focal adhesions with multiple discrete unfolding states, acting as a sophisticated mechanosensor and an important regulatory switch. We further propose that the mechanical stability of α-helical domains as well as the mechanical stability of their unfolding intermediates should be considered when studying mechanoregulation models of α-helical proteins. The following structures from RCSB Protein Data Bank were used as the protein models for the individual talin rod subdomains: R3 (id 2L7A residues 796 to 909), R9 (id 2KBB) and R11 (id 3dYJ residues 1975 to 2140). Talin R9 tandems were constructed using PyMOL, by creating a peptide bond between the last residue of the first R9 monomer and the first residue of the second R9 monomer. α-catenin including MI and MII domains (id 4IGG residues 275 to 506) was used in our simulations. The point mutations introducing cysteine residues into the talin rod subdomains in order to form the disulphide bonds (clamps) preventing the unfolding of 3-helix state, were designed and mutated using PyMOL. MD and SMD simulations were performed using Gromacs ver 2016. 1[49,50] at the Sisu supercomputer, CSC, Finland. The CHARMM27 force field[51] and explicit TIP3P water model[52] in 0. 15 M KCl solution were used and the total charge of the system was adjusted by K+ and Cl- ions. The energy minimization of the system was performed in 10 000 steps using the steepest descent algorithm. The system was equilibrated in three phases using harmonic position restraints on all heavy atoms of the protein. The first phase of the equilibration was performed with NVT ensemble for 100 ps using the Berendsen weak coupling algorithm[53] to control the temperature of the system at 100 K. Integration time step of 2 fs was used in all the simulations. Following the NVT, the system was linearly heated from 100 to 310 K over 1 ns using an NPT ensemble at 1 atm of pressure. During this process, the Berendsen algorithm was used to control both temperature and pressure. For the final phase of equilibration and for all subsequent simulations, an NPT ensemble was maintained at 310 K using the V-rescale algorithm[54], and 1 atm as implemented in Gromacs 2016. 1. The temperature coupling was applied separately for the protein and the solution parts. Each system was equilibrated up to 30 ns, with subsequent monitoring of the root mean square deviations (RMSD) of Cα atoms, considering the first approx. 5 ns as relaxation step. Hence, snapshots at 5 ns were used as starting structures for SMD simulations. Pulling vector was set between Cα of the first and the last residue of the appropriate domain. The movement of Cα of N-terminal residue was restrained with harmonic potential, while Cα of C-terminal residue was subjected to the constant velocity or constant force pulling. The pressure control was turned off for the pulling dimension (z-axes) in all SMD simulations as described in our previous work[28]. The constant velocity pulling SMD simulations were performed at 2 nm / ns with the spring constant set to 1000 kJ/mol nm2. In the constant force pulling SMD simulations, different force regimes were applied, 160 pN, 170 pN, 180 pN and 200 pN for R3 and 200 pN, 220 pN, 230 pN, 250 pN and 300 pN for R9. The system size in SMD was 227 thousand atoms for R3, about 800 thousand atoms for R9, R11 and α-catenin MI-MII, and about 1. 2 million atoms for R9 tandems. Detailed composition of the systems used SMD simulations shown in S2 Table. The constructs and experimental procedure for the smAFM were similar to those described before[28]. The talin fragment polyprotein constructs, including flanking I27, were synthesized and cloned in to pFN18a. The polyproteins were expressed in E. coli BL21-CodonPlus (DE3) -RILP competent cells, using the T7 promoter present in the plasmid. Protein expression was induced with IPTG when the culture reached an OD600 nm of 0. 6. Cells were lysed by applying 0. 2 mg/ml lysozyme for 30 minutes at 25°C, followed by sonication with an Sonifier cell disruptor model SLPe (Branson Ultrasonics Corporation, USA) and clarification of the lysate using centrifugation. The clarified lysate was subjected to Ni-NTA affinity chromatography beads in a batch process. The proteins eluted with imidazole were analyzed for purity with SDS-PAGE and used at a final concentration between 1–10 ug/mL. Glass coverslips were functionalized with the chloroalkane ligand to HaloTag as previously described[28]. The glass coverslips were first cleaned using Helmanex III (1% in water), acetone, and ethanol washes. The surfaces were then prepped with O2 plasma cleaning for 15 min. Surfaces were then silanized using (3-aminopropyl) trimethoxysilane, diluted to 1% in ethanol. Surfaces were then washed with ethanol and then dried with N2. These amine-functionalized surfaces were then incubated with 10 mM succinimidyl-[ (N-maleimidopropionamido) tetracosaethylene glycol] ester (SMPEG24 –Thermo) diluted in 100 mM borax buffer (pH 8. 5) for 1 h. The final step involved incubating the surfaces overnight with 10 mMHaloTag Thiol O4 ligand in the same buffer. The surfaces were quenched with 50 mM 2-mercaptoethanol in water. We used a commercial AFS-1 from Luigs & Neumann, GmbH, based on a device developed at the Fernandez Lab, Columbia University[55]. The cantilevers used were gold-coated OBL-10 levers from Bruker. The spring constants varied between 4 and 10 pN/nm as measured by equipartition theorem with the appropriate adjustments for cantilever geometry[56,57]. Around 20 μL of protein solution was incubated on functionalized coverslips for 30 min prior to the experiments to allow for HaloTag binding. The cantilever was pressed into the surface with a force of ∼300 pN to bind the cantilever to the polyprotein. Force extension experiments were conducted at 400 nm/s retraction rate. Data analysis was carried out using Igor Pro (Wavemetrics). Unfolding peaks were identified by adjustable smoothing with a moving box average and then by searching for local maxima. The force of the peaks along with their unadjusted distance from the HaloTag benchmark was measured.

In order to migrate and survive, most cells need to be attached to their environment. Cells anchor to the extracellular matrix via transmembrane integrin, connecting it to contractile the cytoskeleton. Similarly, cell-cell contacts are formed via transmembrane cadherin, which also connects to the contractile cytoskeleton through scaffolding proteins. Examples of such proteins include talin and α-catenin, which connect integrin and cadherin respectively, to actin filaments of the cytoskeleton. Mechanical forces that are transmitted between the cell and its environment activate binding and regulate the functions of these scaffolding proteins at cell-extracellular matrix and cell-cell contacts. Functions of talin and α-catenin are tightly modulated by mechanical forces. The stretching of these proteins under mechanical load exposes buried binding sites for other partners, such as vinculin. We used steered molecular dynamics simulations and single-molecule atomic force microscopy to study how these proteins unfold under load. Our results suggest that α-helical talin and α-catenin unfold through stable 3-helix intermediates. These intermediates represent biologically active states, which may allow recruitment of other binding partners.

Abstract Introduction Results Discussion Materials and methods

chemical compounds focal adhesions organic compounds mutation developmental biology molecular development amino acids cellular structures and organelles cytoskeleton cysteine proteins adhesion molecules structural proteins chemistry biophysics physics sulfur containing amino acids biochemistry biochemical simulations point mutation cell biology organic chemistry genetics biology and life sciences physical sciences computational biology extracellular matrix proteins biophysical simulations

2018

Mechanical unfolding reveals stable 3-helix intermediates in talin and α-catenin

Methicillin-resistant Staphylococcus aureus (MRSA), particularly the USA300 strain, is a highly virulent pathogen responsible for an increasing number of skin and soft tissue infections globally. Furthermore, MRSA-induced soft tissue infections can rapidly progress into life-threatening conditions, such as sepsis and necrotizing fasciitis. The importance of neutrophils in these devastating soft tissue infections remains ambiguous, partly because of our incomplete understanding of their behaviour. Spinning disk confocal microscopy was used to visualize the behaviour of GR1-labelled neutrophils in subcutaneous tissue in response to GFP-expressing MRSA attached to a foreign particle (agarose bead). We observed significant directional neutrophil recruitment towards the S. aureus agarose bead but not a control agarose bead. A significant increase in neutrophil crawling within the capillaries surrounding the infectious nidus was noted, with impaired capillary perfusion in these vessels and increased parenchymal cell death. No neutrophils were able to emigrate from capillaries. The crawling within these capillaries was mediated by the β2 and α4 integrins and blocking these integrins 2 hours post infection eliminated neutrophil crawling, improved capillary perfusion, reduced cell death and reduced lesion size. Blocking prior to infection increased pathology. Neutrophil crawling within capillaries during MRSA soft tissue infections, while potentially contributing to walling off or preventing early dissemination of the pathogen, resulted in impaired perfusion and increased tissue injury with time. Staphylococcus aureus is a Gram-positive, facultatively anaerobic bacterium that poses considerable challenges to human health as a re-emerging pathogen in both hospital and community settings. As a commensal bacterium, approximately 50% of the general population carry S. aureus in the anterior nares [1]. Despite its commensal status, S. aureus is a serious pathogen, responsible for approximately 18,500 deaths per year in the United States, more than all deaths caused by AIDS, influenza, or viral hepatitis [2]. S. aureus infections, particularly those due to methicillin-resistant Staphylococcus aureus (MRSA) have been increasing in frequency in recent years, and now account for the majority of all clinical skin and soft tissue infections in the United States [3]. Importantly, these infections can cause serious complications, such as necrotizing fasciitis, necrotizing pneumonitis and sepsis [4]. A single MRSA strain, pulsotype USA300 is the dominant community acquired strain in North America [5], [6], [7], [8]. During S. aureus soft tissue infections, pattern recognition receptors such as NOD2 and TLR2, as well as complement fragments, induce signalling pathways that promote neutrophil recruitment critical for abscess formation and clearance of the bacteria [9]. The importance of neutrophils in S. aureus infections cannot be understated; neutrophils are the first to arrive at the local infectious nidus, migrate out of the vasculature, and attempt to eradicate the pathogen through an armamentarium of defenses that include oxidant production, as well as the release of proteases, defensins and various other toxins [10], [11]. Neutropenia leads to uncontrolled infection in mice, impaired healing, and increased likelihood of S. aureus dissemination that can lead to sepsis [12]. Additionally, neutrophil deficiencies (either genetic, or due to treatments such as chemotherapy or corticosteroids) make individuals highly susceptible to infection with S. aureus [11]. Paradoxically, these same defenses so critical to survival can also injure host tissues [13]–[15]. In fact, delayed neutropenia can actually provide some benefit to tissue repair associated with S. aureus soft tissue infections. Additionally, S. aureus can survive when phagocytosed by neutrophils [16] and the neutrophil may act as a “Trojan horse”, allowing the bacteria to disseminate from the point of infection and cause additional damage to the host [17]. Therefore, early neutrophil recruitment is critical to protect the host from the bacterial infection, but later neutrophil recruitment leads to additional bystander tissue damage, and may actually be a mechanism by which S. aureus enhances its virulence [18]. Neutrophil recruitment to a site of infection occurs exclusively from the post-capillary venules with no published reports of recruitment from other vascular structures such as arterioles or capillaries. The first step of the cascade subcutaneously is tethering and rolling, mediated largely by P- and E-selectin on endothelial cells binding with P-selectin glycoprotein ligand-1 (PSGL-1) on neutrophils [19]. This is followed by firm adhesion to the endothelium, typically mediated by the integrin LFA-1 [20]. Neutrophils then crawl inside the vessel, migrating along the vessel wall, usually perpendicular to or against blood flow via Mac-1 [20]. Although α4β1 (VLA-4) has also been reported to have a minor contribution in mouse neutrophils [21], in humans it appears to be upregulated and contributes primarily in severe infections such as sepsis [22]. Following adhesion and crawling, neutrophils emigrate predominantly via a junctional, paracellular pathway or at times transcellularly using integrins and intracellular adhesion molecules (ICAMs). In addition, platelet/endothelial cell adhesion molecule (PECAM-1, also known as CD31), junctional adhesion proteins (JAMs), and endothelial cell selective adhesion molecules (ESAM) play important roles in neutrophil emigration from the vasculature [23], [24]. This study made use of spinning disk intravital microscopy to visualize the behaviour of neutrophils in the first few hours following a localized nidus of S. aureus infection, introduced here as a small foreign inert particle, an agarose bead. The use of the agarose beads ensured that each mouse received a limited amount of bacteria to a very localized area that would best mimic the most common cause of S. aureus skin and soft tissue infection, namely a post puncture wound localized soft tissue infection. The approach unveiled a novel mechanism of neutrophil crawling within capillaries that we had not observed previously with intradermal injection of vast amounts of S. aureus disseminated over large areas of tissue. The random back and forth crawling of neutrophils within capillaries around the nidus of infection may occur in an attempt to prevent dissemination of bacteria through these vessels or to reduce pH in the area. Because we identified the molecular mechanisms of this capillary walk, we were able to inhibit this phenomenon and noted that this may also contribute to impaired capillary perfusion, increased cell death and increased lesion size of the classical open wound noted in patients with S. aureus infection. In order to visualize using spinning disk confocal microscopy, neutrophil recruitment to a localized S. aureus infection as might happen following a puncture wound with secondary infection, an agarose bead was inserted into the subcutaneous tissue layer, beneath the connective tissue of the skin via a fine needle to deliver a very small and reproducible amount of bacteria. The infected bead was easily visualized, due to the green fluorescent protein (GFP) -expressing bacteria and the sterile bead was visualized due to fluorescence nanoparticles (Figure 1a). This permitted us to examine the entire process of immune cell recruitment into the infected site, allowing for effective localization of the pathogen, and clear visualization of changes in neutrophil behaviour over time. Addition of the foreign particle with S. aureus was important as this has been shown to be more pathogenic [25]–[27], thus requiring fewer colony-forming units (CFUs) to induce an infection, and permitted optimal modelling of neutrophil responses to MRSA [28]. Since the size of bead linearly predicted the amount of bacteria delivered, we used beads 250–350 µm in diameter that delivered ∼106 bacteria. Sterile beads used as negative controls had no CFUs (Fig. 1b). Within the first few minutes, increased numbers of neutrophils could be seen rolling along the side of the vessels adjacent to infected but not non-infected beads. Many of these neutrophils adhered and emigrated. There were significantly more neutrophils emigrating as early as 1 hr after introduction of the beads that contained S. aureus when compared with sterile beads (p = 0. 031). This is quantified in Figure 1c, and illustrated in a series of panels in Figure 1d and Videos S1 and S2. As demonstrated in Video S1, many neutrophils emigrated outside of the vasculature (quantified in Figure 2a) towards the direction of the bead and migrated in that direction. In every experiment, we identified a subset of neutrophils that were dramatically deformed, elongated like sausages and crawled back and forth in a linear fashion over a distance of a few hundred microns surrounding the bead insertion site. PECAM-1 staining to delineate venules, capillaries, and arterioles in the skin revealed that this population of neutrophils was crawling in the smallest vascular structures, namely capillaries with diameters less than 10 µm (Figure 2b, Figure 2c and Video S3). This behaviour was not noted with sterile beads (Figure 2b). For these experiments, we used the RB6-8C5 anti-Ly6g antibody, which can label Ly6c (a molecule found on monocytes as well as neutrophils). Since monocytes but not neutrophils have previously been described to crawl in capillaries, 1A8, an antibody known to only label Ly6g and thus neutrophil specific [29], was also tested and confirmed that the crawling cells were indeed neutrophils (Supplementary Figure S1). The neutrophils could have been crawling on top of the capillaries using them as scaffolds; however z-stack imaging and 3D image reconstructions clearly demonstrated the neutrophils were inside and not outside the capillaries (Figure 2d). The neutrophils adhered directly in the mainstream of blood in capillaries, bypassing any rolling event. The neutrophils then immediately began crawling but no neutrophils were ever seen to emigrate from the capillaries in the twelve experiments assessed. In the S. aureus infected beads, there were approximately 7. 5 neutrophils crawling per 10 viewed capillaries although it was not unusual to see multiple neutrophils in one capillary such that approximately 35–40% of capillaries were laden with crawling neutrophils (Figure 2e, p = 0. 007) while sterile beads had fewer than one neutrophil per 10 capillaries. Since neutrophil crawling in capillaries has not been described previously, antibodies to LFA-1 and Mac-1 or to the common β subunit (CD18) of both molecules were used in an attempt to block this event. These are the two major integrins on neutrophils. Rather than pretreatment that could affect other parameters in this process, a very stringent approach of administering antibodies two hours after the infection when crawling was already at its peak was implemented. An antibody to CD18 blocked 60% of the capillary crawling (Figure 3a). Surprisingly the adhesion molecule thought to be dominant for crawling in venules, Mac-1, played no role in crawling in skin capillaries (Figure 3b) while LFA-1 antibody had a trend to reduced crawling (Figure 3c), only blocking the beta chain of the CD18 integrin reached significance (Figure 3a). The drop in recruitment we observed following the blocking of CD18 did not result in a complete inhibition of neutrophil recruitment to the capillaries; close to 40% of neutrophil recruitment remained unexplained. Inhibition of the integrin VLA-4 reduced more than 50% of the recruitment into the capillaries (Figure 3d). Tandem blockade with VLA-4 and the β2 integrin antibodies had additive inhibitory effects on the recruitment of neutrophils to the capillaries almost entirely ablating this event (p = 0. 0256 Figure 3d). Each antibody intervention was compared to its own IgG isotype control (Figure 3). We then sought to determine whether the common ligands for these integrins were binding to partner molecules in the capillaries. Our primary targets were ICAM-1 and VCAM-1. These molecules were also targeted because they are known to be expressed on endothelial cells during inflammatory conditions, and have been shown to be upregulated on endothelial cells in vitro after stimulation with components of the S. aureus cell wall [30]. Blocking ICAM-1 via anti-ICAM-1 antibodies resulted in a significant reduction in the number of neutrophils recruited to the capillaries (p = 0. 0246, Figure 3e). When VCAM-1 was blocked, there was also significant decrease in the number of recruited neutrophils (p = 0. 009, Figure 3e). However, blocking both VCAM-1 and ICAM-1 resulted in no significant differences in recruitment compared with either ICAM-1 or VCAM-1 alone (p = 0. 482, Figure 3e), suggesting that either CD18 or α4-integrin adheres to additional ligands besides VCAM-1 and ICAM-1. The profound deformation of neutrophils in the capillaries, suggested the potential for vessel occlusion. An intravenous injection of FITC-albumin was used to visualize perfusion through individual blood vessels [31], [32]. All vessels were first labelled with Alexa 647 conjugated to anti-CD31 antibodies (blue). Injection of FITC-albumin turned perfused vessels green (Figure 4a). Almost all of the capillaries in mice treated with sterile beads were perfused with virtually no occlusion of any vessels. Mice treated with S. aureus beads had ∼35–40% of the capillaries occluded a value significantly greater than sterile beads (Figure 4b). In general, no neutrophils were lodged inside perfused capillaries (white arrowheads, Figure 4a), and only capillaries, not venules nor arterioles were occluded. When all neutrophil sequestration in capillaries induced by S. aureus was prevented with anti-CD18 and anti-α4 antibodies, capillary occlusion was significantly reduced (Figure 4b). Capillary occlusion in the antibody-treated S. aureus mice was not significantly different from mice treated with the control sterile beads (Figure 4b) suggesting that neutrophil recruitment was causally related to vessel occlusion. Propidium iodide was used to investigate the degree of cell death in skin. It is worth noting that under non-inflammatory conditions there is always some basal cell death that was not increased by sterile beads (Figure 4c). There was increased cell death with S. aureus beads, as compared to sterile beads (Figure 4c). When mice were treated with blocking antibodies (anti-CD18 and anti-α4) there was a significant (Figure 4d) reduction in the number of dead cells, compared to isotype control treatment. S. aureus beads containing 1×106 CFU induced a lesion at 48 hours. Blocking neutrophil recruitment two hours after infection (anti-CD18 and anti-α4) reduced lesion size at 48 hours (Figure 5a). Importantly, blocking neutrophil recruitment two hours before infection resulted in increased lesion size compared to mice that only received S. aureus beads (Figure 5b), suggesting the initial recruitment of neutrophils is critical. Indeed, the tandem inhibition of both CD18 and anti-α4 integrins prevented all neutrophil recruitment to the infectious site, which included neutrophil adhesion (Figure 5c) and emigration in postcapillary venules (Figure 5d). However, administration of antibodies to CD18 and anti-α4 integrin two hours after infection did not affect the huge influx of neutrophils into the infectious nidus via postcapillary venules. Recent research has focused on the complex and often paradoxical role that the innate immune system plays in S. aureus infection [2], [11]. Neutrophil recruitment is considered critically important to eradicate S. aureus infections, since deficiencies in neutrophil function can impair the host' s ability to combat S. aureus infections, as demonstrated in both patients and experimental mouse models [33], [34]. However, neutrophil recruitment has also been shown to be highly cytotoxic, causing substantial bystander tissue damage in the process of controlling infections [13], [14]. In this study, we combined the use of spinning disk intravital microscopy and a novel model of S. aureus (MRSA USA 300) subcutaneous infection and visualized the complex interaction between neutrophils and this pathogen. Significant recruitment of neutrophils towards localized S. aureus infection was noted, despite the use of orders of magnitude fewer bacteria than previously reported. Although many molecules have been described to be released by and allow S. aureus to evade detection by neutrophils [35], [36], in this study very robust recruitment of neutrophils occurred. This perhaps highlights the ability of neutrophils to overcome these evasion mechanisms or highlights differences between in vivo and in vitro results. The latter may reflect the use of S. aureus that have upregulated their evasion mechanisms. Therefore, our model permitted systematic examination of bacteria localized around a foreign particle, and analysis of neutrophil behaviour around this nidus of infection. Although our model corroborates the results of other studies [11], [12], [37]–[40] that demonstrate that neutrophils are actively recruited to the site of S. aureus infection via postcapillary venules, we observed a very novel behaviour of neutrophils adhering and subsequently crawling inside the capillaries close to the S. aureus beads. The neutrophils were physically deformed taking the shape of the capillaries, and often moved to and fro inside the vessels. Three dimensional reconstruction confirmed that the neutrophils were inside the capillaries. Further evidence supporting that neutrophils were inside the capillaries was their direct inhibitory effect on perfusion of blood through the capillaries. This recruitment occurred in response to the S. aureus-infected bead, and not due to sham surgery or the bead alone. It is unlikely that the capillaries functioned as a thoroughfare to deliver neutrophils to the site of infection as no neutrophil was ever observed to emigrate out of these vessels. It is possible that neutrophils were recruited to capillaries to occlude perfusion of the infected tissue and thereby prevent any bacterial dissemination via the vasculature from the initial infectious nidus. The reduced perfusion could also reduce pH making the environment less conducive to survival of the bacteria. Alternatively, the neutrophil sequestration in capillaries could be a defense mechanism induced by S. aureus that limits the ability of neutrophils to infiltrate the tissue. Reduced perfusion of tissue could lead to more anaerobic conditions conducive to survival of the pathogen (a facultative anaerobe), and increase tissue damage [17]. Indeed, inhibition of neutrophil recruitment into capillaries resulted in improved perfusion, reduced cell death and significantly reduced lesion size. One complication with definitively establishing the importance of neutrophil recruitment to the capillaries was the associated inhibition of neutrophil recruitment from the venules. However, allowing neutrophils to infiltrate the tissue in significant numbers over the first 2 hours and then reversing neutrophil recruitment into capillaries reduced some of the pathogenesis associated with the S. aureus infection. Presumably, sufficient numbers of neutrophils were recruited to surround the infectious nidus and further neutrophil recruitment was unnecessary and perhaps even toxic. By contrast, preventing all recruitment of neutrophils by pretreating animals with the two anti-integrin antibodies caused greater tissue injury and more bacteria in blood, consistent with the observations by others [35], [36]. In the first few hours of infection, neutrophils are thus absolutely critical to limit bacterial dissemination. It also suggests that when unchecked by neutrophils, the bacteria can cause injury due to their release of many potent toxins. Herein, we demonstrate that in addition to surrounding the infectious nidus via emigration from venules, plugging surrounding capillaries very early might also contribute by preventing bacterial entry into the mainstream of blood. However, this latter event does cause hypoxia, cell death and increased lesion size, so eventually the occlusion of vessels causes pathophysiology and therapeutic intervention would be beneficial. The presence of α4 integrin on neutrophils is controversial [21], [22], [41] and thought to perhaps play a greater role in mouse than human. Although neutrophil recruitment to tissues like muscle, skin and brain are primarily via the CD18 integrin [20], [42], recruitment to tissues like liver or lung can occur independent of this β2-integrin. In addition, in both mouse and human, it has been shown that neutrophils can also use the α4 integrin VLA-4 in extreme conditions such as systemic infections associated with sepsis. Plasma from a septic human patient could induce the expression of α4 integrin on the surface of neutrophils from healthy patients [22]. In addition, this molecule induced functional adhesion to its ligand VCAM-1, although other ligands for α4 integrin also exist. In chronic adjuvant arthritis inflammation which was associated with a systemic vasculitis, α4 integrin was important in neutrophil recruitment, but VCAM-1 was not involved [43]. Herein, in a localized S. aureus infection, the recruitment to the capillaries was mediated by both the β2 integrins and the α4 integrin. Blocking either Mac1 or LFA-1 alone in our model did not have significant effects on neutrophil recruitment within the capillaries, suggesting that these subunits likely play overlapping roles in the capillaries, and both must be blocked in addition to α4 integrin in order to prevent neutrophil recruitment. Perhaps not surprisingly, a role for VCAM-1 was revealed for some of the neutrophil recruitment into capillaries as this molecule is expressed constitutively in murine skin endothelium [44]. The fact that ICAM-1 and VCAM-1 did not completely block recruitment to capillaries suggests that other molecules also are used by the integrins. This is not surprising since these integrins can adhere to many different ligands. With the discovery of neutrophil crawling in capillaries a number of new issues arise. First, how much capillary occlusion is necessary to cause tissue injury. Although 35–40% of capillaries were occluded in our study, it was impossible to exclude the possibility that injury also occurred due to the proteases and oxidants released by neutrophils that infiltrated the injury site via the post-capillary venules. It is also important to note that all capillary beds are different, raising the importance of imaging the skin when studying skin infections and imaging the liver when studying liver infections. Indeed, CD44 and not integrins are used by neutrophils in the sinusoids of the liver postinfection [45]. Moreover, capillaries of other organs may not have neutrophil crawling or capillary plugging. Finally the reason for why neutrophils crawl in capillaries is unclear. However millions of years of evolutionary pressure directing the fight between this common pathogen and the host may have evolved an important anti-microbial process or an important bacterial evasion mechanism that is still not entirely understood. In conclusion, in this study a novel neutrophil behaviour has been identified in response to subcutaneous infection due to a virulent strain of S. aureus. Using spinning disk confocal microscopy, we noted significant neutrophil recruitment into capillaries surrounding the infectious nidus. We determined that the molecules responsible for this behaviour were the β2 and α4 integrins, binding in part with ICAM-1 and VCAM-1, and causing occlusion of the capillary microvasculature. Blocking this recruitment at a delayed time point, reduced the malperfusion, cell death and lesion size that developed several days after infection with the S. aureus infected bead. As S. aureus becomes more resistant to antibiotics, understanding the mechanisms that underlie the pathogenesis of this infection will enhance the likelihood of non-antibiotic therapeutic intervention. C57BL6 male mice (Jackson, Bar Harbour), aged 6–8 weeks were used for all experiments. All animal protocols were submitted to the animal care committee of the University of Calgary under the protocol number AC12-0222. All animal protocols approved by the animal care committee of the University of Calgary and complied with the Canadian Animal Care guidelines. Green fluorescent protein (GFP) -expressing S. aureus was made from a previously isolated clinical strain USA300-2406, described previously [46]. Bacteria were grown in 5 ml of Brain Heart Infusion (BHI) media (Becton and Dickenson, Sparks, MD), and were incubated overnight at 37°C. GFP-expressing S. aureus (strain USA300-2406) was grown in 20 µg/ml chloramphenicol (EMD Biosciences, La Jolla, CA). Agarose beads were used to deliver bacteria on a foreign particle, based on an existing model of cystic fibrosis [47]. S. aureus were grown overnight in BHI (20 µg/ml chloramphenicol) at 37°C. The next morning, 5 ml of overnight media was mixed with 45 ml of fresh BHI (20 µg/ml chloramphenicol), and grown for a further two hours. S. aureus was then centrifuged at 2000 rpm for 10 minutes, and resuspended in 250 µl of 1× phosphate buffered saline (PBS). 10 µl of PBS containing bacteria were serially diluted and plated, to measure CFUs. The remaining PBS was then added to 2. 25 ml of liquid 1. 5% TSA agar. The TSA/PBS/S. aureus solution was then slowly injected into a mixture of 40 ml of mineral oil (Sigma-Aldrich, St Louis, MO) and 400 µl of Tween 20 (Sigma-Aldrich, St Louis, MO), which was gently stirred at 4°C, yielding spherical agarose beads embedded with S. aureus. After 15 minutes, the solution was centrifuged at 2000 rpm for 10 minutes. The mineral oil layer was removed, and beads were washed with PBS and resuspended, then spun again at 2000 rpm. This wash step was repeated three times. Beads were then washed in a 100 µl filter, and resuspended with PBS. Beads were stored at 4°C for up to 6 days. Plating of 1–6 day old beads on fresh agar showed no loss of CFU within this timeframe. For sterile beads (control), bacteria were replaced with 2 µl of Fluoresbrite plan yg 1. 0 micron microspheres (Polysciences, Warrington, PA) Images were analyzed by removing light collected from the 488 and 561 and 649 nanometer channels of the spinning disc confocal microscope. The contrast and brightness used to analyze data was held constant for analysis of each set of experiments. The number of neutrophils at 30,60,90, and 120 minutes was counted by using the point tool function of Volocity (Perkin-Elemer, Waltham, MA). For analysis of location of neutrophils, the 649 channel was used to examine the vasculature. Neutrophils that co-localized with CD31 labelled vessels were determined to be inside the capillaries if the vessels did not exceed 10 µm in width. Neutrophils both inside and outside the capillaries were counted using the point tool function of Volocity. Data was analyzed using the Students t-tests to compare two different conditions. When more than one comparison was made in the same graph, a bonferroni correction was used to correct for false positives. When three variables were all compared with one another in the same graph, a one-way analysis of variance (ANOVA) with a Bonferroni correction was used. All statistical analysis was performed using the statistical software GraphPad prism 4, version 4. 03 (GraphPad Software Inc. , La Jolla, CA).

Methicillin-resistant Staphylococcus aureus (MRSA) is a highly virulent pathogen responsible for a significant portion of skin and soft tissue infections throughout the world. We investigated the role of neutrophils in soft tissue infections, as these immune cells have been shown to be both essential for clearance of this pathogen but also for increasing tissue injury associated with S. aureus infections. We visualized the behaviour of neutrophils in the subcutaneous tissue following the introduction of a localized infectious stimulus. In addition to a profound neutrophil recruitment into the infectious nidus, significant neutrophil crawling in capillaries surrounding the region was also noted, a region of vasculature which has not previously been associated with neutrophil recruitment during infection. The neutrophils were not seen to emigrate from the capillaries but rather were retained in these vessels and maintained a crawling behaviour via β2 and α4 integrins. Blocking these integrins released the neutrophils from the capillaries, reinstituted capillary perfusion, and reduced the surrounding cell death leading to reduced lesion size following infection. Neutrophil crawling within capillaries during MRSA soft tissue infections, while potentially contributing to walling off or preventing dissemination of the pathogen, resulted in impaired perfusion and increased tissue injury.

Abstract Introduction Results Discussion Materials and Methods

infectious diseases biology and life sciences immunology medicine and health sciences

2014

Neutrophil Crawling in Capillaries; A Novel Immune Response to Staphylococcus aureus

Dysbiosis, or the imbalance in the structural and/or functional properties of the microbiome, is at the origin of important infectious inflammatory diseases such as inflammatory bowel disease (IBD) and periodontal disease. Periodontitis is a polymicrobial inflammatory disease that affects a large proportion of the world' s population and has been associated with a wide variety of systemic health conditions, such as diabetes, cardiovascular and respiratory diseases. Dysbiosis has been identified as a key element in the development of the disease. However, the precise mechanisms and environmental signals that lead to the initiation of dysbiosis in the human microbiome are largely unknown. In a series of previous in vivo studies using metatranscriptomic analysis of periodontitis and its progression we identified several functional signatures that were highly associated with the disease. Among them, potassium ion transport appeared to be key in the process of pathogenesis. To confirm its importance we performed a series of in vitro experiments, in which we demonstrated that potassium levels a increased the virulence of the oral community as a whole and at the same time altering the immune response of gingival epithelium, increasing the production of TNF-α and reducing the expression of IL-6 and the antimicrobial peptide human β-defensin 3 (hBD-3). These results indicate that levels of potassium in the periodontal pocket could be an important element in of dysbiosis in the oral microbiome. They are a starting point for the identification of key environmental signals that modify the behavior of the oral microbiome from a symbiotic community to a dysbiotic one. Dysbiosis, or the imbalance in the structural and/or functional properties of the microbiome, leads to the breakdown of host-microbe homeostasis, and has been associated with the pathogenesis of several important inflammatory diseases mediated by the activity of the microbial community, such as inflammatory bowel diseases (IBDs) [1,2] and periodontal diseases [3]. Periodontitis is a polymicrobial disease caused by the coordinated action of a complex microbial community, leading to inflammation and periods of active destruction of the tissues supporting the teeth. Periodontal inflammation has adverse impacts on a wide variety of systemic health conditions, such as diabetes, cardiovascular and respiratory diseases [4,5]. It is the sixth most prevalent disabling health condition in the world affecting approximately 750 million people worldwide [6]. It has been postulated that changes in the composition of subgingival biofilms could explain these periods of disease activity. In fact, a few studies have found differences in the levels of subgingival species when comparing progressing and non-progressing sites [7,8]. These studies also demonstrated considerable overlap in the composition of the microbial communities associated with progressing and non-progressing lesions, suggesting that the difference in the periodontal status of the sites could not be explained solely by differences in subgingival microbial composition. Recently, it has been proposed that certain organisms could act as' keystone-pathogens' that modulate the behavior of the oral microbial community, which becomes dysbiotic [9]. Indeed, we have previously reported that organisms not considered pathogens express large numbers of putative virulence factors during chronic severe periodontitis and disease progression [10,11]. However, the environmental signals that trigger this change in behavior of the community remain for the most part unknown. In two previous studies focused on the oral microbial metatranscriptome in health, disease and during periodontitis progression, gene ontology (GO) enrichment analysis showed that potassium ion transport was a key signature of microbial metabolic activities associated with disease [10,11]. In the present study we focus our interest on confirming our previous in vivo observations [11] on the potential role that potassium ion has as a signal that initiates changes in the oral microbiome, leading to dysbiosis of the microbial community. Potassium is the most abundant monovalent ion inside the cells. However, in healthy periodontal tissues potassium is present at low concentrations in the gingival crevicular fluid in contact with the oral biofilm [12–14]. A positive and statistically significant correlation has been found between the concentration of potassium in crevicular fluid and mean pocket depths [12], probably due to cell lysis of host cells. Here we performed a series of experiments to test the effects of potassium on plaque community gene expression, virulence, and inflammation and showed that levels of potassium ion act as an important environmental signal for microbial dysbiosis and epithelial response to the microbial challenge. Potassium ion (K+) transport has previously been identified as an important signature among the metabolic activities of the oral microbiome in periodontitis [10,11]. However, the exact mechanisms by which potassium exerts its activity as an environmental signal leading to microbial dysbiosis remain unknown. To test the effects of potassium on plaque community gene expression, plaque was collected from a healthy human volunteer, exposed to saliva with or without added potassium, and subjected to metatranscriptomic analysis. We used K+ concentrations akin to those found in gingival crevicular fluid in severe periodontitis [13,14]. After only 3 hours of incubation RNA was extracted for analysis. We thus identified the initial reaction of the community to higher levels of K+ in the environment. We detected between 73. 7 and 96. 2% of all genes in our libraries, which represents a high sequencing depth across all samples (S1 Fig). Phylogenetic assignment of the transcripts showed that several members of the community responded immediately to the presence of K+ in their surroundings (Fig 1). Transcripts were assigned to different taxa using Kraken and LEfSe was used to determine differentially transcriptionally active taxa. Among those that contribute a significantly higher fraction of transcripts to the metatranscriptome, we found organisms that previously have been associated with periodontal disease such as Leptotrichia spp. , Campylobacter spp. and Fusobacterium spp. and Prevotella spp. [15], but also organisms that have been considered to be associated with health such as Streptococcus spp. Nonetheless Streptococcus spp. have been also found in large numbers in periodontal disease [15], and we previously identified them as producing large numbers of putative virulence factors at early states of dysbiosis during periodontitis progression [11]. Genus Lautropia, which has been associated with health [16], was significantly less active in the presence of K+. However, other groups of microorganisms that have been considered to be associated with periodontal disease such as Corynebacterium and Campylobacter [15,16] were less active in the presence of high concentration of K+. To determine changes in metabolic activities in the whole community due to the increase in K+ concentration, we performed GO terms enrichment analysis. In order to mimic as much as possible the conditions present in the periodontal pocket during disease, we utilized levels of K+ of the same order as the ones found in severe periodontitis [12–14]. After only 3 hours of incubation, we observed at the community-wide level an over-representation of activities associated with disease, such as iron ion transport, oligopeptide transport, flagellum assembly and cobalamin biosynthesis (Fig 2a). Among the GO molecular functions over-represented in the presence of K+ there are some linked to proteolysis, we found metallo-exopeptidase activity and aminopeptidase activity (S1 Table). Protease activity is a well established player in the pathogenesis of periodontal disease [19,20]. Consistent with low overall environmental levels of K+ in the environment, we observed an over-representation of potassium transport activities in the oral microbiome incubated without K+ added (Fig 2b, S1 Table). We next determined whether the addition of K+ increased the synthesis of putative virulence factors. As a model we used hemolysins, which are recognized as potential virulence factors in a large number of anaerobic species [21]. In the case of whole dental plaque growing on plates with added K+, after 6 days incubation we observed hemolysis at all concentrations, with the 50mM concentration showing higher hemolytic activity, while lower concentrations and the control with no K+ added behaved similarly (Fig 3a). To show that the increase in hemolytic activity of the whole plaque at 50mM was due to changes in metabolic activities and not to changes in community composition, we characterized the microbial communities from the final plaque growing at the different concentrations of K+ from 3 different subjects (Fig 3b). The results of the phylogenetic composition based on 16s rRNA deep sequencing of those communities showed no differences between the communities growing at different concentrations of K+ and the control (Fig 3b and S2 Table). Additionally, we characterized the composition of the initial inoculum used in the experiments and the microbial communities from a periodontally healthy and from a sample with severe periodontitis. The initial inocula for all three patients analyzed was identical in total number of cells and after 6 days of incubation all plates from a single experiment had the same number of total cells growing (S3 Table). The microbial composition of the original inoculum from the different subjects was different as well as the final composition of the communities growing on plates (Fig 3b). However, those communities showed no differences between the communities growing at different concentrations of K+. Collectively, these results indicate that an increase in K+ in the environment leads to expression of genes associated with pathogenicity in the oral microbiome, which could be important in the process of dysbiosis, from commensal to pathogenic microbiome. We observed an up-regulation of hemolysins in 45 different species, with the majority belonging to genera Prevotella and Streptococcus (S4 Table). Among the species presenting high up-regulation of hemolysins were Prevotella nigrescens and Streptococcus mitis. Increased numbers of P. nigrescens have been associated with severity of periodontitis [22] and in previous work we showed that S. mitis expresses a high number of putative virulence factors in periodontitis [10,11]. To determine whether these genera increase hemolysin expression in response to potassium, we assayed hemolytic activity in culture supernatants of P. nigrescens and S. mitis. We observed an increase in hemolytic activity in the supernatant of P. nigrescens and S. mitis growing in liquid media at 0. 5mM and 5mM K+ added but an inhibitory effect at 50mM of K+ added to the media (Fig 4a and S4 Table). S. mitis had a lower hemolytic activity. S. mitis hemolysins tend to accumulate in the cytoplasm rather than in the extracellular environment [23]. These changes in hemolytic activity were not associated with different levels of growth. The final OD600 and CFU/mL of the different tubes used for analysis were not significantly different (S5 Table) indicating that activity was increased without a change in the total number of cells. P. nigrescens expresses β-hemolytic activity when grown on blood agar with a peak of hemolytic activity on the fifth day of incubation [24]. After 6 days of incubation we observed β-hemolytic activity at all concentrations of K+ but with lower activity at 50mM K+ in P. nigrescens (Fig 4b and 4c). Interestingly, as described above, the range where the effect occurs is well defined and once a certain concentration threshold is exceeded the effect is repressed. To test the effect of potassium on the host plaque interaction, we challenged a three-dimensional gingival multi-layered tissue model with cornified apical layers similar to in vivo gingival tissue (EpiGingival GIN-100, MatTek Corp.) with dental plaque. These three-dimensional tissue models have been used in a wide variety of studies and organs [25]. Tissue-engineered 3D culture systems of the oral mucosa provide an organizational complexity that lies between the culture of single cell types and organ cultures in vivo. We first confirmed that the histological morphology of the tissue used in the experiments was not altered by the different concentrations of K+ or the addition of bacteria. Haematoxylin-eosin stained sections showed a normal morphology of the gingival tissue models in both unchallenged and challenged tissues (S2 Fig). In the tissues that were challenged with dental plaque we observed that bacterial invasion was already occurring regardless of the addition of K+ (S3 Fig). Cytokines are important markers of inflammation. To test the effects of potassium on the inflammatory response to plaque, we measured cytokine production in the presence and absence of plaque and increasing concentrations of potassium. We observed that K+ had a major effect on the expression profiles of the cytokines assessed. As shown in Fig 5a, the profiles of expression clustered as a function of K+ concentration, regardless of the presence or absence of dental plaque interacting with the tissue. Two cytokines, IL-6 and TNF-α, presented significant differences in their levels of expression associated with the levels of K+ (S6 and S7 Tables). IL-6 showed higher levels of expression at 0mM, 5mM and 50mM of K+ than at 100mM of K+ while TNF-α was significantly up-regulated at 50mM and 100mM of K+ added (Fig 5). Most of the other cytokines analyzed (IFN-γ, IL-17A, IL-1β and IL-10) did not change their pattern of expression significantly either with different K+ concentrations or with the addition of bacteria to the system (S4 Fig). One of the cytokines, the anti-inflammatory IL-4, was not detected under any of the conditions studied. Interestingly, interaction analysis of plaque and K+ concentration showed that they indeed had an interacting effect on the values of TNF-α and IL-6 but not in the rest of cytokines values (S5 Fig). In case they did not interact we would expect a parallel plot for the lines as it was observed for rest of cytokines. To confirm that conclusion we fit a two-way ANOVA with an interaction term (see S1 Bioinformatic Analisys in Supplementary Information). Those results show evidence of significant interaction between plaque and potassium concentration in TNF-α and IL-6 response (S9 Table). Human β-defensin-3 (hBD-3) is widely expressed in the oral cavity and exerts strong antibacterial and immunomodulatory activities [26]. hBD-3 plays an important role in periodontitis [27] and it is reduced in individuals with severe disease [28]. More importantly, the appropriate expression of hBD-3 peptide may contribute to the maintenance of periodontal homeostasis, possibly through its antimicrobial effect and promotion of adaptive immune responses [29]. The three-dimensional tissue model used in these experiments expresses hBD-3 in all layers except the stratum corneum, expresses hBD-1 weakly only in the apical layers, and does not express hBD-2 at all. Using immunohistochemistry we assessed the effect that K+ and bacterial plaque had on the levels of expression of hBD-3. Fig 6a shows representative examples of the results. Expression of hBD-3 was observed in all layers including the apical areas of the tissue but was more intense on the basal layers (Fig 6aii and 6aiii). K+ had a major effect on hBD-3 expression. The intensity of the signal was normalized by the signal obtained by DAPI, which represents an estimate of the number of cells. The addition of K+ by itself inhibited the production of hBD-3 regardless of the presence of plaque. Individually, plaque and potassium each reduced hBD-3 production to similar levels. Combined, plaque and potassium had an additive effect, significantly reducing hBD-3 production more than either treatment alone. (Fig 6b). We compared the statistical significance of those differences using a non-parametric analysis (Kruskall-Wallis correcting for multiple comparisons) and found that all values shown in Fig 6 were significantly different, except for the results with no plaque plus 50mM K+ and plaque without K+ added, and plaque plus 50mM of K+ and no plaque plus 5mM of K+ (S8 Table). These results indicate that K+ exerts an inhibitory effect on the production of hBD-3, which would clearly weaken the antimicrobial response of the gingival tissue in response to bacterial challenge. ANOVA analysis revealed that there was not an interaction effect between plaque and potassium on hBD-3 production by the gingival epithelial (F = 0. 4148, p = 0. 661) (S10 Table, S6 Fig). The test for the effect of the presence of plaque shows a significant effect on the levels of hBD-3 (F = 31. 3052, p<0. 0001). Similarly, the test for the effect of potassium concentration (F = 23. 1869, p<0. 0001) indicates a significant effect on the levels of hBD-3. Our findings highlight the importance of ion potassium as a signal for dysbiosis in periodontitis. In the presence of high concentration of ion potassium we observed an increase in virulence of the whole microbial community, which agrees with our previous in vivo observations on severe periodontitis and progression of the disease. Moreover, we demonstrated the effect of potassium on the expression of virulence factors of isolated oral microorganisms including S. mitis, which is considered a commensal under normal conditions. Furthermore, pro-inflammatory cytokines were up-regulated as a response to the presence of K+ and bacteria and expression of the antimicrobial peptide hBD-3 was inhibited by both potassium and dental plaque. Future studies are needed to confirm our results on a periodontitis mouse model as well as to identify the mechanisms by which the oral biofilm senses the different levels of potassium in the environment To assess the effect that K+ has on the oral microbiome we performed metatranscriptome analysis of its effect on dental plaque. Dental plaque from a periodontally healthy subject sample was resuspended in his own saliva, vortexed for 30 seconds and split in 3mL aliquots, each placed in a well of a 6 well, flat bottom Corning Costar cell culture plate. To 3 of the well containing the saliva/plaque suspension we added KCl to a final K+ concentration of 50mM and the other 3 well containing saliva/plaque suspension were used as controls. The culture plate containing the samples was incubated at 37°C for 3 hours under anaerobic conditions. Cells were collected by centrifugation at 10,000 x g for 5 minutes and RNA was extracted immediately for further analysis. Detailed protocols for community RNA extraction, RNA amplification and Illumina Sequencing are described in Yost et al. [11]. Genomes of archaea and bacteria as well as their associated information were downloaded from the HOMD database server (http: //www. homd. org/), the PATRIC ftp server (https: //www. patricbrc. org/) [70] and the J. Craig Venter Institute (www. jcvi. org). A total of 524 genomes from 312 species of bacteria and 2 genomes from 1 archaea species were used in the analysis. Detailed explanation of genomes is reported in Yost et. al. [11]. Low-quality sequences were removed from the query files. Fast clipper and fastq quality filter from the Fastx-toolkit (http: //hannonlab. cshl. edu/fastx_toolkit/) were used to remove short sequences with quality score >20 in >80% of the sequence. Cleaned files were then aligned against the bacterial/archaeal database using bowtie2. We generated a. gff file to map hits to different regions in the genomes of our database. Read counts from the SAM files were obtained using bedtools multicov from bedtools [71]. Counts from the mRNA libraries were used to determine their phylogenetic composition. Phylogenetic profiles of the metatranscriptomes were obtained using the latest version of Kraken [17]. Phylogenetic profiles were used to identify significant differences between active communities under the different conditions studied. We performed linear discriminant analysis (LDA) effect size (LEfSe) as proposed by Segata et al. [18] with default settings except that LDA threshold was raised to 3 to increase the stringency of the analysis. To identify differentially expressed (DE) genes from the RNA libraries, we applied non-parametric tests to the normalized counts using NOISeqBio function of the R package' NOISeq' with' tmm' normalization, with batch and length correction and removing genes whose sum of hits across samples was lower than 10. We used a threshold value for significance of q = 0. 95, which is equivalent to a FDR adjusted p-value of 0. 05 [72]. To evaluate functional activities differentially represented we mapped the DE genes to Gene Ontology (GO) terms (http: //www. geneontology. org/). GO terms for the different ORFs were obtained from the PATRIC database (https: //www. patricbrc. org/). GO terms not present in the PATRIC database and whose annotation was obtained from the HOMD database or from the J. Craig Venter Institute were acquired using the program blast2GO under the default settings [73]. Enrichment analysis on these sets was performed using the R package' GOseq' , which accounts for biases due to over-detection of long and highly expressed transcripts [73]. Gene sets with ≤ 10 genes were excluded from analysis. We used the REVIGO web page [74] to summarize and remove redundant GO terms. Only GO terms with FDR adjusted p-value < 0. 05 in the' GOseq' analysis were used. We used the EpiGingival GIN-100 (MatTek Corp.) a multilayered tissue model with the apical layers cornified, similar to in vivo gingival tissue. The tissues used in these experiments had more than 10 layers of cell with 300,000 to 500,000 cells per tissue. After arrival, the tissues were maintained overnight in a humidified incubator at 37°C and in the presence of 5% CO2 in Dulbecco' s modified Eagle medium (DMEM) supplemented with 10% (vol/vol) fetal bovine serum (GIBCO/BRL) and 1% (vol/vol) penicillin-streptomycin (GIBCO/BRL). Next day the tissues were washed 3 times with PBS to remove any traces of antibiotics and were reinoculated with DMEM without K+ (USBiological Life Sciences D9800-15) or antibiotics. We challenged the tissues with different K+ concentrations (0,5, 50 and 100mM) and with bacteria from dental plaque and saliva. 4 different tissues were used for all concentrations. Supra and subgingival plaque from the same healthy volunteer used in all experiments was collected in saliva and diluted in DMEM -K to a McFarland standard #1 value (McFarland Standards Gibson Laboratories), which is equivalent to approximately 3 x 108CFU/mL. Based on the number of cells per tissue supplied by MatTek Corp. (see above) we inoculated with a multiplicity of infection (MOI) of 100, which had been used previously with success with other oral bacteria in invasion experiments [75]. At the end of the experiment the tissues were fixed with 4% formalin for 24 hours, paraffin embedded and cut into 5μm slices that were mounted on poly-lysine glass slides. Some slides were haematoxylin-eosin stained to check the integrity of the tissues. The rest were used for FISH analysis and immunohistochemistry as describe below. Cytokine levels from 3 biological replicates of the medium surrounding the tissue cultures under the conditions described above were determined using MILLIPLEX MAP Human Cytokine/Chemokine Magnetic Bead Panel-Immunology Multiplex Assay (EMD Millipore, Billerica, MA, USA). Seven cytokines: IFN-γ, IL-10, IL-17A, IL-4, IL-6, IL-1β and TNF-α were measured. Samples were thawed at 4°C prior to assay and kept on ice throughout the assay procedures. Manufacturers’ protocols were followed for all panels, with a general protocol as follows. Reagents were prepared as per kit instructions. Assay plates (96-well) were loaded with assay buffer, standards, samples, and beads and then covered and incubated on plate shaker (500 rpm) overnight at 4°C. After primary incubation, plates were washed twice and then detection antibody cocktail was added to all wells; the plates were covered and left to incubate at room temperature for 1 hour on plate shaker. After one hour incubation, streptavidin-phycoerythrin fluorescent reporter was added to all wells, plates were covered and incubated for 30 minutes at room temperature on plate shaker. Plates were then washed twice and beads were resuspended in wash buffer, placed on shaker for 5 minutes, and then read on Bio-Plex200 following manufacturers’ specifications and using Bio-Plex Manager software v6. 0. Mounted slides were deparaffinized using standard protocols. Antigen retrieval was performed in 10mM Na Citrate pH 6 buffer in a microwave with an initial cycle of 2 minutes at 80% power and a final cycle of 8 minutes at 40% power. Slides were blocked on blocking solution (2% goat serum in PBS) for 1 hour, washed three times with PBS and incubated overnight with Anti hBD-3 antibody L3-18b-E1 (Abcam, Cambridge, MA, USA) at 4°C. As a negative control we used as a primary antibody a polyclonal rabbit IgG whose serum was obtained from naive (non-immunized) rabbits (RD Systems, Minneapolis, MN, USA). After incubation with the primary antibody slides were washed three times in PBS and incubated with goat anti-Rabbit IgG (H+L) secondary Antibody, Alexa Fluor 594 conjugate (LifeTechnologies). We finally counter stained the slides with DAPI (100ng/mL) for 10 minutes before analysis. Tissues were dried and mounted with Prolong Gold anti-fade (Life Technologies). Images were captured using an Inverted Widefield Fluorescence—Zeiss Cell Observer Z using a 10x and 20x objectives and analyzed using Fiji Software [76]. The number of histological sections analyzed are indicated in Fig 6 legend. Sections for each condition tested came from a single three-dimensional multilayered gingival tissue model with cornified apical layers (EpiGingival, MatTek Corporation). Because of the low magnification used for analysis we analyzed one field per section to avoid fields with wrinkles or creases on the slide that could interfere with fluorescence measurements. Files with. czi extension were open in Fiji with ‘Autoscale’, ‘Split channels’ and ‘Color mode = colorized’ options marked. Brightness and contrast of the images were auto-adjusted, merged (channels ‘red’ and ‘blue’) and converted to RGB and analyzed using ‘Color histogram’. Results for the color histogram gives values of intensity for the different channels. We used the ratio of red fluorescence (Alexa Fluor 594 from hBD-3 expression) and blue fluorescence (DAPI from the cells' nuclei) as an estimate of the levels of expression of hBD-3 in the tissues. We performed FISH on the mounted slides with a universal probe for bacteria labeled with EUB probe (EUB388 5′-GCT GCC TCC CGT AGG AGT) [77] (Life Technologies). The hybridization oven was pre-warmed to 46°C and humidifying solution (20% formamide non-HiDi in water) was added to the hybridization chamber. Slides were placed in hybridization chamber and covered with 40μl of probe mixture (0. 9 M NaCl, 0. 02 M Tris pH 7. 5,0. 01% SDS, 20% Hi-Di formamide and 2pmol/μl) on top of whole mount. The hybridization chamber was sealed with parafilm and samples were incubated at 46°C for 3 hours. After hybridization in fume hood excess of hybridization solution from the slides was drained and slides were washed in 50 ml of pre-warmed wash buffer (215mM NaCl, 20mM Tris pH 7. 5,5mM EDTA) to 48°C for 15 minutes in the hybridization oven. Finally, slides were dipped in ice-cold water followed by a final dip in 100% ethanol at room temperature. The slides were air-dried and mounted in Prolong Gold antifade (LifeTechnologies) before being observed under the microscope as described above. DNA was extracted from the fresh plaque inoculum used in the plate hemolysis assays as well as from the cultures growing and showing hemolysis after 6 days of incubation as described below. DNA extraction was performed using Ultraclean Microbial DNA Isolation kit. (MoBio, Carlsbad, CA) following manufacturers’ specifications. We used HOMINGS (http: //homings. forsyth. org/index2. html) for species-level identification of oral bacteria using 341F (5' ATGATACGGCGACCACCGAGATCTACACTATGGTAATTGTCCTACGGGAGGCAGCAG) and 806R (5' CAAGCAGAAGACGGCATACGAGATNNNNNNNNNNNNAGTCAGTCAGCCGGACTACHVG GTWTCTAAT) primers to amplify the V3-V4 region of the 16s rRNA gene. The underlined stretch of 12N are designated barcode sequences. HOMINGS uses species-specific, 16S rRNA-based oligonucleotide' probes' , designed to target oral species, as a database in a BLAST program (' ProbeSeq' for HOMINGS) to identify the frequency of oral bacterial targets. Bacterial load was quantified by qPCR following the method described in Nadkarni et al. [78]. Total DNA was extracted as described above. Escherichia coli DNA was used as the standard for determining bacterial number by qPCR. Amplification and detection of DNA by real-time PCR were performed with iCycler (BioRad) using optical grade 96-well plates. Triplicate samples were used for the determination of DNA by qPCR. The PCR reaction was performed in a total volume of 20 μl using the TaqMan Universal PCR and Master Mix (Integrated DNA Technologies), containing 100 nM of each of the universal forward and reverse primers and the fluorogenic probe. Two different hemolysis assays were performed to assess the activity of individual organisms and whole dental plaque. The first was a plate hemolysis assay that gave a visual assessment of the hemolytic activity on agar plates to which different amounts of K+ were added. The second was a quantitative assay that measures release of hemoglobin from erythrocytes due to hemolysis. Agar hemolysis assays: we used P. nigrescens ATCC 33563 as a model organism. P. nigrescens was grown O/N in Schaedler Anaerobic Broth (Oxoid, Thermo Scientific, Lenexa, KS) and 10μl of the culture were spotted on horse blood TSBY plates with 0,0. 5,5 and 50 mM of K+ added. The plates were incubated at 37°C under anaerobic conditions and checked at 48 hours and 6 days. For the biofilm assay we used fresh collected dental plaque from the same healthy individual who was used for the rest of experiments and resuspended in 100μl Schaedler Broth (Oxoid, Thermo Scientific, Lenexa, KS). 10μl of the suspension were spotted on horse blood TSBY plates with 0,0. 5,5 and 50 mM of K+ added. The plates were incubated at 37°C under anaerobic conditions and checked at 48 hours and 6 days. Quantitative hemolysis assays: To quantitatively measure hemolytic activity we used supernatants from P. nigrescens ATCC 33563 and S. mitis NCTC 1226. We followed the protocol describe by Maltz and Graf [79] with minor modifications. Briefly, Schaedler Anaerobic Broth (Oxoid, Thermo Scientific, Lenexa, KS) diluted to 50% with water was pre-reduced in an anaerobic chamber for 48 h. Prior to pre-reduction the different concentrations of K+ were added (0,0. 5,5 and 50mM K+). P. nigrescens cells were inoculated from TSBY plates to OD600 of 0. 2 and grown O/N 37°C under anaerobic conditions. S. mitis was grown on Todd-Hewitt Yeast Broth (THY: Bacto Todd–Hewitt Broth supplemented with 0. 2% yeast extract) diluted to 50% with a minimal streptococci medium [80]. THY/MM media was pre-reduced in anaerobic chamber for 48 hrs. As mentioned above, prior to pre-reduction the different concentrations of K+ were added (0,0. 5,5 and 50mM K+). S. mitis cells from TSBY plate were resuspended in 1. 0 ml THY/MM and 100μl were added to tubes containing 3. 0 ml of the pre-reduced THY/MM to OD600 of 0. 2 and grown O/N at 37°C under anaerobic conditions. Horse red blood cells (Horse RBC; Northeast Laboratory Services) were washed 3 times in 1. 0 ml PBS and resuspended in PBS to final concentration 10% v/v. The O/N cultures were spun (5 min, 7,500 rpm) and 250 μl of culture supernatant was added to 250 μl of washed erythrocytes and incubated for 3h at 37°C. Horse RBC were also incubated with 250 μl PBS (negative control) or ultra pure dH2O (positive control). Reactions aliquots were centrifuged and 100 μl of each biological replicate were analyzed for hemolytic activity at OD540 with a BioTek Synergy HT plate reader. We used the R package' agricolae' to perform the non-parametric multiple comparison Kruskal-Wallis analysis on our results. Shapiro test analysis of our results showed that they did not follow a normal distribution. FDR adjusted p-value were obtained by setting the' p. adj' argument of the' kruskal' function as “fdr”. A cut-off value of 0. 05 was used to determine the significance of the results. We used a Two-way ANOVA interaction test in R to assess whether plaque and potassium had an additive or an interaction effect on levels of cytokines and hBD-3. The detailed protocol is described in the section ‘Two-way ANOVA interaction test in R’ in the S1 Bioinformatic Analysis file of the supplementary information.

Homeostasis of the human microbiome plays a key role in maintaining the healthy status of the human body. Changes in composition and function of the human microbiome (dysbiosis) are at the origin of important infectious inflammatory diseases such as inflammatory bowel disease (IBD) and periodontal disease. However, the environmental elements that trigger the development of dysbiotic diseases are largely unknown. In previous studies, using community-wide transcriptome analysis, we identified ion potassium transport as one of the most important functions in the pathogenesis of periodontitis and its progression. Here, we confirm with a series of in vitro experiments that potassium can act as an important signal in the dysbiotic process inducing pathogenesis in the oral microbiome and altering the host response in front of the microbial challenge that could lead to microbial immune subversion. Our study provides new insights into the important role that ion potassium plays a signal in oral dysbiosis during periodontitis.

Abstract Introduction Results Discussion Materials and methods

innate immune system medicine and health sciences immune physiology periodontal diseases cytokines microbiome pathology and laboratory medicine pathogens immunology microbiology genomic databases developmental biology genome analysis molecular development microbial genomics research and analysis methods potassium oral diseases oral medicine medical microbiology biological databases chemistry gene ontologies immune system periodontitis virulence factors physiology database and informatics methods genetics biology and life sciences physical sciences genomics computational biology chemical elements

2017

Potassium is a key signal in host-microbiome dysbiosis in periodontitis

Microorganisms rarely live isolated in their natural environments but rather function in consolidated and socializing communities. Despite the growing availability of high-throughput sequencing and metagenomic data, we still know very little about the metabolic contributions of individual microbial players within an ecological niche and the extent and directionality of interactions among them. This calls for development of efficient modeling frameworks to shed light on less understood aspects of metabolism in microbial communities. Here, we introduce OptCom, a comprehensive flux balance analysis framework for microbial communities, which relies on a multi-level and multi-objective optimization formulation to properly describe trade-offs between individual vs. community level fitness criteria. In contrast to earlier approaches that rely on a single objective function, here, we consider species-level fitness criteria for the inner problems while relying on community-level objective maximization for the outer problem. OptCom is general enough to capture any type of interactions (positive, negative or combinations thereof) and is capable of accommodating any number of microbial species (or guilds) involved. We applied OptCom to quantify the syntrophic association in a well-characterized two-species microbial system, assess the level of sub-optimal growth in phototrophic microbial mats, and elucidate the extent and direction of inter-species metabolite and electron transfer in a model microbial community. We also used OptCom to examine addition of a new member to an existing community. Our study demonstrates the importance of trade-offs between species- and community-level fitness driving forces and lays the foundation for metabolic-driven analysis of various types of interactions in multi-species microbial systems using genome-scale metabolic models. Solitary species are rarely found in natural environments as most microorganisms tend to function in concert in integrative and interactive units, (i. e. , communities). Natural microbial ecosystems drive global biogeochemical cycling of energy and carbon [1] and are involved in applications ranging from production of biofuels [2], [3], biodegradation and natural attenuation of pollutants [4], [5], [6], bacterially mediated wastewater treatment [7], [8] and many other biotechnology-related processes [9], [10]. The species within these ecosystems communicate through unidirectional or bidirectional exchange of biochemical cues. The interactions among the participants in a microbial community can be such that one or more population (s) benefit from the association (e. g. , through cooperation), some are negatively affected, (e. g. , by competing for limiting resources), or more often than not a combination of both. These inter-species interactions and their temporal changes in response to environmental stimuli are known to significantly affect the structure and function of microbial communities and play a pivotal role in species evolution [11], [12], [13], [14], [15], [16]. Recent advances in the use of high-throughput sequencing and whole-community analysis techniques such as meta-genomics and meta-transcriptomics promise to revolutionize the availability of genomic information [16], [17], [18]. Despite the growing availability of this high-throughput data, we still know very little about the metabolic contributions of individual microbial players within an ecological niche and the extent and directionality of metabolic interactions among them. This calls for development of efficient modeling frameworks to elucidate less understood aspects of metabolism in microbial communities. Spurred by recent advances in reconstruction and analysis of metabolic networks of individual microorganisms, a number of metabolic models of simple microbial consortia have been developed. Efforts in this direction started with the development of metabolic model for a mutualistic two-species microbial community [19]. The metabolic network of each microorganism was treated as a separate compartment in analogy to eukaryotic metabolic models [20], [21]. A third compartment was also added through which the two organisms can interact by exchanging metabolites. The same approach was employed for the metabolic modeling of another syntrophic association between Clostridium butyricum and Methanosarcina mazei [22]. Lewis et al [23] have also described a workflow for large-scale metabolic modeling of interactions between various cell lines in the human brain using compartments to represent different cells. Similarly, Bordbar et al [24] developed a multi-tissue type metabolic model for analysis of whole-body systems physiology. Alternatively, others proceeded to identify and model synthetic interactions among different mutants of the same species using genome-scale metabolic models. For example, Tzamali et al [25] computationally identified potential communities of non-lethal E. coli mutants using a graph-theoretic approach and analyzed them by extending dynamic flux balance analysis model of Varma and Palsson [26]. The same researchers have recently extended their study to describe the co-growth of different E. coli mutants on various carbon sources in a batch culture [27]. Wintermute and Silver [28] identified mutualistic relationships in pairs of auxotroph E. coli mutants. Each pair was modeled using an extended form of the minimization of metabolic adjustment (MOMA) hypothesis [29]. More recently, the concept of inducing synthetic microbial ecosystems not by genetic modifications but rather with environmental perturbations such as changing the growth medium was introduced [30]. All these studies aimed primarily at modeling communities where one or both species benefit from the association while none is negatively affected. The first study to characterize a negative interaction between two microorganisms using genome-scale metabolic models was published by Zhuang et al [31] where similar to [25], [27] an extension of the dynamic flux balance analysis [32] was employed to model the competition between Rhodoferax ferrireducens and Geobacter sulfurreducens in an anoxic subsurface environment. The same procedure was also employed in a study that characterized the metabolic interactions in a co-culture of Clostridium acetobutylicum and Clostridium cellulolyticum [33]. A wide range of methods beyond flux balance analysis have been used to model microbial communities [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45]. For example, Taffs et al [46] proposed three different approaches based on elementary mode analysis to model a microbial community containing three interacting guilds. Other studies have drawn from evolutionary game theory, nonlinear dynamics and the theory of stochastic processes to model ecological systems [39], [40], [43]. Despite these efforts, all existing methods for the flux balance analysis of microbial communities are based on optimization problems with a single objective function (related to individual species), which cannot always capture the multi-level nature of decision-making in microbial communities. For example, the flux balance analysis model described in [19] is applicable only to syntrophic associations, where the growth of both species is coupled through the transfer of a key metabolite. The dynamic flux balance analysis models introduced by Zhuang et al [31] and Tzamali et al [25], [27] rely on solving separate FBA problems for each individual species within each time interval. In all cases these methods cannot trade off the optimization of fitness of individual species versus the fitness function of the entire community. Therefore, there is still a need to develop an efficient modeling procedure to address this issue and to analyze and characterize microbial communities of increasing size with any combination of positive and/or negative interactions. Here, we introduce OptCom, a comprehensive flux balance analysis framework for microbial communities, which relies on a multi-level optimization description. In contrast to earlier approaches that rely on a single objective function, OptCom' s multi-level/objective structure enables properly assessing trade-offs between individual vs. community level fitness criteria. This modeling framework is general enough to capture any type of interactions (positive, negative or combination of both) for any number of species (or guilds) involved. In addition, OptCom is able to explain in vivo observations in terms of the levels of optimality of growth for each participant of the community. We first analyze a simple and well-determined microbial community involving a syntrophic association between D. vulgaris and M. maripaludis [19] to demonstrate the ability of OptCom in recapitulating known interactions. Next, OptCom is employed to model the more complex ecological system of the phototrophic microbial mats of Octopus and Mushroom Springs of Yellowstone National Park and compare our results with those obtained using elementary mode analysis [46]. OptCom identifies the level of sub-optimal growth of one of the guilds (SYN) in this community to benefit other community members and/or the entire population. Finally, we use OptCom to elucidate the extent and direction of inter-species metabolite transfers for a model microbial community [47], identifying the proportion of metabolic resources apportioned to different community members and predicting the relative contribution of hydrogen and ethanol as electron donors in the community. Addition of a new member to this community is also examined in this study. OptCom postulates a separate biomass maximization problem for each species as inner problems. The inner problems capture species-level fitness driving forces exemplified through the maximization of individual species' biomass production. If preferable, alternate objective function (e. g. , MOMA [29]) could be utilized in the inner stage to represent the cellular fitness criteria. Inter-species interactions are modeled with appropriate constraints in the outer problem representing the exchange of metabolites among different species. The inner problems are subsequently linked with the outer stage through inter-organism flow constraints and optimality criteria so as a community-level (e. g. , overall community biomass) objective function is optimized. Figure 1A schematically illustrates the proposed concept. OptCom is solved using the solution methods previously developed for bilevel programs [48], [49], [50], [51] (see Text S1 for details of the optimization formulation and solution). Note that since OptCom yields a (non-covex) bilinear optimization problem, all case studies presented in this paper were solved using the BARON solver [52], accessed through GAMS, to global optimality. It is important to note that OptCom can be readily modified to account for the case when one or more organisms show a form of cooperative behavior that benefits the whole population, but comes at the expense of growing at rates slower than the maximum possible [15], [53]. To quantify the deviation of community members from their optimal behavior, we introduce a metric called optimality level for each species k (i. e. , ck). The optimality level for each one of the microorganisms is quantified using a variation of OptCom which we refer to as descriptive. Descriptive OptCom incorporates all available experimental data for the entire community (e. g. , community biomass composition) as constraints in the outer problem and all data related to individual species as constraints in the respective inner problems while allowing the biomass flux of individual species to fall below (or rise above) the maxima () of the inner problems (see Figure 1B). We note that here the optimum biomass flux for each species () is community-specific as it is computed in the context of all microorganisms striving to grow at their maximum rate (using the formulation given in Figure 1A). An optimality level of less than one for a microorganism k implies that it grows sub-optimally at a rate equal to 100ck % of the maximum () to optimize a community-level fitness criterion while matching experimental observations. Alternatively, an optimality level of one implies that microorganism k grows exactly optimally at a rate equal to whereas a value greater than one indicates that it achieves a higher biomass production level than the community-specific maximum (i. e. , super-optimality) by depleting resources from one or more other community members. It is worth noting that super-optimality is achievable for a species only at the expense of sub-optimal behavior of at least one other member in the community. The identified combination of sub- and/or super-optimal behaviors of individual species is driven by the maximization of a community-level criterion (e. g. , maximize the total community biomass). OptCom can capture various types of interactions among members of a microbial community. Symbiotic interactions between two (or more) populations can be such that one or more species benefit from the association (i. e. , positive interaction), are negatively affected (i. e. , negative interactions), or combination of both. Mutualism, synergism and commensalism are examples of positive interactions, whereas parasitism and competition are examples of negative interactions. A pictorial representation of how these interactions can be captured within OptCom by appropriately restricting inter-organism metabolic flows is provided in Figure 2 (see Text S1 for implementation details). We first explore the capability of OptCom to model and analyze a relatively simple and well-characterized syntrophic association between two microorganisms, namely Desulfovibrio vulgaris Hildenborough and Methanococcus maripaludis. Syntrophy is a mutualistic relationship between two microorganisms, which together degrade an otherwise indigestible organic substrate. A prominent example of syntrophic interactions is interspecies hydrogen transfer, where the hydrogen produced by one of the species has to be consumed by the other to stimulate the growth of both microorganisms [54], [55], [56], [57]. In these communities degradation of a substrate by fermenting bacteria is energetically unfavorable as it carries out a reaction, which is endergonic under standard conditions. However, if this fermenting bacteria is coupled with a hydrogen scavenging partner such as methanogenic bacteria, the organic compound degrading reaction can proceed [58]. Methanogens use hydrogen and energy gained from the first reaction and reduce CO2 to methane [56], [58]. Here we focus on such a syntrophic association between Desulfovibrio vulgaris Hildenborough and Methano- coccus maripaludis S2, for which genomes-scale metabolic models as well as experimental growth data for the co-culture are available [19]. With lactate as the sole carbon source and in the absence of a suitable electron acceptor for the sulfate reducer, M. maripaludis provides favorable thermodynamic conditions for the growth of D. vulgaris by consuming hydrogen and maintaining its partial pressure low. Stoylar et al [19] modeled this microbial community as a multi-compartment metabolic network and employed FBA to identify community-level fluxes by maximizing the weighted sum of the biomass fluxes of two microorganisms. Here we examine the applicability of OptCom for modeling a more complex microbial community containing three interacting guilds, the phototrophic microbial mats of Octopus and Mushroom Springs of Yellowstone National Park (Wyoming, USA) [60]. The inhabitants of this community include unicellular cyanobacteria related to Synechococcus spp (SYN), filamentous anoxygenic phototrophs (FAP) related to Chloroflexus and Roseiflexus spp and sulfate-reducing bacteria (SRB) as well as other prokaryotes supported by the products of the photosynthetic bacteria [46], [60]. Diel (day-night) variations in metabolic activities of members of this community were observed before [61], [62], [63]. During the day when the mat is oxygenated cyanobacteria appear to be the main carbon fixer, consuming CO2 and producing storage products such as polyglucose as well as O2 as a by-product of photosynthesis. High levels of O2 relative to CO2 stimulate the production of glycolate. Glycolate is then used as a carbon and energy source by other community members such as photoheterotrophic FAP. At night, the mat becomes anoxic and cyanobacteria start to ferment the stored polyglucose into small organic acids such as acetate, propionate and H2. FAP can incorporate fermentation products photoheterotrophically while SRB oxidizes the fermentation products under anaerobic condition and produces sulfide [60], [64], [65], [66]. A schematic diagram representing the interactions in this community is given in [46]. This microbial community has been previously modeled and analyzed by Taffs et al [46] using a representative microorganism for each guild: Oxygenic photoautotrophs related to Synechococcus spp were chosen to represent the mat' s primary carbon and nitrogen fixers. FAP from the family Chloroflexaceae, were selected to represent metabolically versatile photoheterotrophs that capture light energy as phosphodiester bonds but require external reducing equivalents and carbon sources other than CO2. A SRB guild representative whose metabolic behavior was based on several well-studied sulfate-reducing bacteria was also included in the community model description [46]. The metabolic networks representing central carbon and energy metabolism for each guild were then constructed and three different modeling approaches based on the elementary mode analysis were employed to elucidate sustainable physiological properties of this community [46]. Here, we focus only on daylight metabolism (for which more experimental data is available) to assess the efficacy of OptCom in describing carbon and energy flows and the biomass ratio between guilds. In a recent study, Miller et al [47] established a model microbial community to better understand the trophic interactions in sub-surface anaerobic environments. This community was composed of three species including Clostridium cellulolyticum, Desulfovibrio vulgaris Hildenborough, and Geobacter sulfurreducens. Cellobiose was provided as the sole carbon and energy source for C. cellulolyticum whereas the growth of D. vulgaris and G. sulfurreducens were dependent on the fermentation by-products produced by C. cellulolyticum. D. vulgaris and G. sulfurreducens were supplemented with sulfate and fumarate, respectively, as electron-acceptors to avoid electron acceptor competition [47]. The experimental measurements for the biomass composition of the community showed that, as expected, C. cellulolyticum was the dominant member in the co-culture and confirmed the presence of D. vulgaris and G. sulfurreducens. It was, however, not possible to quantify experimentally the flow of shared metabolites among the community members as their concentrations were below the detection limits. Therefore, the authors proposed an approximate model of the carbon and electron flow based on some measurements of the three species community at steady-state, pure culture chemostat experiments and data from the literature [47]. Here, we model this microbial community by making use of the corresponding bacterial metabolic models and employ OptCom to elucidate the inter-species interactions. The metabolic models of C. cellulolyticum (i. e. , iFS431) and G. sulfurreducens were reconstructed by Salimi et al [33] and Mahadevan et al [69], respectively. A basic metabolic model of D. vulgaris containing 86 reactions was introduced by Stolyar et al [19], however, this model had only a compact representation of the central metabolism. For example, the model was not able to support growth in the presence of acetate or ethanol as the sole carbon source. Therefore, we expanded this model by adding new reactions from a first draft reconstructed model in the Model Seed [70] and the KEGG database [71] using the GrowMatch procedure [50] (see Text S1 for details). The updated model of D. vulgaris consists of 145 reactions and is capable of supporting growth on acetate as well as ethanol. This model is available in the supplementary material (Table S1). Here, we introduced OptCom, a comprehensive computational framework for the flux balance analysis of microbial communities using genome-scale metabolic models. We demonstrated that OptCom can be used for assessing the optimality level of growth for different members in a microbial community (i. e. , Descriptive mode) and subsequently making predictions regarding metabolic trafficking (i. e. , Predictive mode) given the identified optimality levels. Unlike earlier FBA-based modeling approaches that rely on a single objective function to describe the entire community [19], [30] or separate FBA problems for each microorganism [25], [27], [31], [33], OptCom integrates both species- and community-level fitness criteria into a multi-level/objective framework. This multi-level description allows for properly quantifying the trade-offs between selfish and altruistic driving forces in a microbial ecosystem. Species and community level fitness functions are quantified by maximizing the biomass formation for the respective entity. We note, however, that the physiology of microbial communities is highly context and environment dependent and a universal community-specific fitness criterion does not exist. Studies similar to those conducted for mono-cultures that examine and compare various presumed hypotheses on cellular objective function [82], [83], [84], [85], [86], [87] or algorithms that identify/test a relevant objective function using experimental flux data [88], [89] are needed in the context of multi-species systems. An important goal of studying natural and synthetic microbial communities is their targeted manipulation towards important biotechnological goals (e. g. , cellulose degradation, ethanol production, etc.). This has motivated researchers to construct simple synthetic microbial ecosystems, which are amenable to genetic and engineering interventions, for biotechnology- and bioenergy-related applications. As an example, Bizukojc et al [22], have proposed a co-culture composed of Clostridium butyricum and Methanosarcina mazei to relieve the inhibition of fermentation products and increase production of 1,3-propanediol (PDO) by Clostridium butyricum. Mixed cultures have been also established for overproduction of polyhydroxyalkanoates (PHA) [90], [91] and ethanol [92], [93], [94], [95], [96]. For example, Clostridium thermocellum, which is used for ethanol production, has been found to be capable of utilizing hexoses, but not pentose sugars generated from breakdown of cellulose and hemicellulose [96]. Therefore, cultivation of C. thermocellum with other thermophilic anaerobic bacteria capable of utilizing hexoses as well as pentose to produce ethanol (e. g. , Clostridium thermosaccharolyticum and Thermoanaerobacter ethanolicus) has been previously examined in vivo [92], [93], [94], [95], [96]. The multi-objective and multi-level structure of the OptCom procedure, introduced here, can help assess the metabolic capabilities of such synthetic ecosystems. Taking a step further, OptCom can be readily modified to identify the minimal number of direct interventions (i. e. , knock-up/down/outs) to the community leading to the elevated production of a desired compound (e. g. , by considering the overproduction of desired compound as the outer problem objective function), thus extending the applicability of strain design tools such as OptKnock [48], OptStrain [49], OptReg [97] and OptForce [98]. It is worth noting that a key bottleneck to the modeling and analysis of microbial communities is the paucity of genome-scale models for all participants in a complex microbial community. Overcoming this barrier would require the development of high-throughput metabolic reconstruction tools such as the Model Seed [70] resource. Given that microbial communities change with time (e. g. , day/night cycle) and also location (e. g. , nutrient gradients), approaches that would be able to capture temporal and spatial varying inter-species metabolic interactions are needed. For example, the separate FBA problems for each individual species in the dynamic flux balance analysis methods of Zhuang et al [31] and Tzamali et al [25], [27] can be integrated with OptCom to account for inter-species interactions and community-level fitness driving forces within each time interval.

Microorganisms rarely live isolated in their natural environments but rather function in consolidated and socializing communities. Despite the growing availability of experimental data, we still know very little about the metabolic contributions of individual species within an ecological niche and the extent and directionality of interactions among them. This calls for development of efficient modeling frameworks to shed light on less understood aspects of metabolism in microbial communities. Here, we introduce OptCom, a comprehensive mathematical framework for metabolic modeling and analysis of microbial communities, which relies on a multi-level/objective optimization formulation to properly describe trade-offs between individual vs. community level fitness criteria. OptCom is general enough to capture any type of interactions (positive, negative or combinations thereof) and is capable of accommodating any number of microbial species involved. We first demonstrate the capability of OptCom to quantify known metabolic interactions in a well-characterized microbial community. We next apply it to more complex communities to assess the optimality levels of growth for each microorganism, elucidate the extent and direction of inter-species metabolite transfers and examine addition of a new member to an existing community. Our study lays the foundation for metabolic-driven analysis of various types of interactions in multi-species microbial systems.

Abstract Introduction Methods Results Discussion

biology computational biology

2012

OptCom: A Multi-Level Optimization Framework for the Metabolic Modeling and Analysis of Microbial Communities

The intrinsic ability of cells to adapt to a wide range of environmental conditions is a fundamental process required for survival. Potassium is the most abundant cation in living cells and is required for essential cellular processes, including the regulation of cell volume, pH and protein synthesis. Yeast cells can grow from low micromolar to molar potassium concentrations and utilize sophisticated control mechanisms to keep the internal potassium concentration in a viable range. We developed a mathematical model for Saccharomyces cerevisiae to explore the complex interplay between biophysical forces and molecular regulation facilitating potassium homeostasis. By using a novel inference method (“the reverse tracking algorithm”) we predicted and then verified experimentally that the main regulators under conditions of potassium starvation are proton fluxes responding to changes of potassium concentrations. In contrast to the prevailing view, we show that regulation of the main potassium transport systems (Trk1,2 and Nha1) in the plasma membrane is not sufficient to achieve homeostasis. Potassium is an essential cation required for many cellular processes including the regulation of cell volume, intracellular pH, protein synthesis, activation of enzymes, and maintenance of the plasma membrane potential [1]–[4]. In their natural environment, most cell types have to accumulate intracellular potassium against a strong concentration gradient. Animal cells utilize the energy stored in ATP to directly pump potassium ions into the cell via the / ATPase. This enzyme is absent in most fungi and plants [2], which have developed alternative mechanisms to control the intracellular potassium concentration. Saccharomyces cerevisiae (S. c.) cells can grow in media with a potassium concentration ranging from to. Despite extensive knowledge about the identity and function of most potassium transporters in this organism [3], a systems level understanding of the interplay and regulation of the various transport pathways is still lacking. In S. c. , uptake of potassium across the plasma membrane is driven by the membrane potential, which itself is generated by proton pumping via the -ATPase, Pma1 [5], [6]. The high affinity and high velocity transporter, Trk1, is the major uptake system for potassium. The expression levels of the other Trk protein, Trk2, are low, compared to Trk1, and therefore considered of minor importance [7], [8]. A low affinity uptake observed by electrophysiological techniques in trk1,2 double mutants has been attributed to the putative calcium blocked channel Nsc1, though the gene responsible for this transport activity has not been found yet [9], [10]. Efflux of potassium is strongly pH-dependent and coupled to sodium toxicity. The antiporter Nha1 extrudes or ions in exchange for protons under acidic environmental conditions and contributes to the continuous cyclic flux of potassium ions across the plasma membrane and to pH regulation [11], [12]. It is only at higher external pH that potassium or sodium is actively extruded by the Ena1 ATPase [13]–[15]. Another potassium efflux system is the voltage gated channel, Tok1. Electrophysiological studies revealed that Tok1 opens at positive membrane potentials, which do not occur under normal physiological conditions [16]. Potassium is also stored in intracellular compartments, in particular in the vacuole. The effect of intracellular transport is, however, not sufficiently characterized yet [3], [17]. Besides protons, a number of other ions are associated with the transport of potassium. The anion bicarbonate was shown to be important for potassium accumulation [18]. Decarboxylation reactions produce carbon dioxide, which is quickly converted to carbonic acid (), by carbonic anhydrase. Carbonic acid can either diffuse freely across the cell membrane or dissociate into bicarbonate (), and protons. While protons can be extruded via Pma1, the permeability of bicarbonate is very low compared to that of carbonic acid. The resulting accumulation of bicarbonate provides the link to potassium homeostasis; the negative charges carried by bicarbonate can be balanced by potassium cations. In principle, other weak acids could contribute in a similar way to potassium accumulation, but our results below and previous investigations suggest that the bicarbonate reaction plays an important role [18]. Potassium transport is also related to ammonium toxicity [19]. Under low external potassium conditions, ammonium leaks into the cells, presumably via potassium transporters. Toxic concentrations of ammonium are counteracted by increased production and excretion of amino acids [19]. The maintenance of a minimal potassium concentration requires the orchestration of the different transport systems under the constraints of various thermodynamic forces. In this article, we use a mathematical model in conjunction with a novel inference algorithm (the reverse tracking algorithm) and model-driven experimentation to identify the key transport mechanisms that must be regulated under the conditions of potassium shortage. We show that the activation of the proton pump, Pma1, and the activation of the bicarbonate reaction sequence are the regulators of potassium homeostasis. We also show that potassium homeostasis is an example of non-perfect adaptation: The intracellular potassium concentration depends on the external potassium concentration and is only regulated to keep minimal levels of potassium required for survival. This is different from other homeostatic systems such as osmoregulation [20], where certain stationary systems characteristics perfectly adapt, irrespective of the external conditions. To study the response of S. c. cells to an abrupt decrease of external potassium, we performed potassium starvation experiments using and free media. Cells grown in non-limiting potassium (KCl) were washed with -free YNB medium (YNB without amino acids and ammonium sulphate, Formedium UK, CYN7505 plus 2% glucose, traces of KCl left: 15, hereafter referred to as Translucent -free medium [21]) and resuspended in the same medium [12]. The time course for changes in intracellular potassium concentrations for the wild type strain exhibits two different phases (Figure 1A). In the first hour of starvation there is a large net efflux of potassium indicated by the rapid decrease in the intracellular concentration. Loss of potassium slows down in the second phase and the internal concentration slowly approaches a new stationary state (Table 1). Although the cells cannot perfectly adapt to the large concentration gradients they are able to keep a certain amount of potassium required for survival (approx.). Interestingly, the second phase of potassium loss is slower for the trk1,2 double mutant than for the wild type (wt). This is surprising, because it is believed [3] that increased uptake of potassium via Trk1 even at very low external potassium concentrations is a major mechanism of potassium homeostasis. Thus, one would have expected the concentration of internal potassium in the trk1,2 mutant to be lower than in the wild type. The time course for the nha1 mutant is not significantly different from the trk1,2 mutant (see also Figure S7 in Text S1). Multiple signaling pathways modulate the activity of the various transport systems involved in potassium homeostasis [2]–[6], [14], [15], [22]–[24]. However, it is not entirely clear which of these signals are essential to achieve homeostasis and how they are acting under the constraints set by the thermodynamics of ion transport. To study these constraints, we developed a minimalistic mathematical model which incorporates the essential parts known to be important for potassium homeostasis. The model describes the dynamic coupling between the intracellular potassium concentration, internal pH (), carbon dioxide concentration, membrane voltage, and cell volume. A complete description of the equations and parameter values is given in the Materials and Methods section and derivations can be found in the Text S1. Here, only the basic model structure is given: (1) (2) (3) (4) (5) Equation (1) links the temporal change of the intracellular potassium concentration to the various potassium transport fluxes (Figure 1B). The model comprises the Trk1,2 system (abstracted as a single system,), the Nha1 antiporter (), and the Tok1 channel (). To mimic the joint contribution of other, mainly non-specific transport pathways for potassium (e. g. Nsc1) we added a potassium leak current to the model. The Ena1 ATPase is neglected because it is known to be inactive at the relatively low external pH used in the experiments [15]. The dynamics of carbon dioxide (Equation (2) ) is coupled to the transport fluxes of bicarbonate and carbonic acid. These transport rates are given in the Materials and Methods section (Equations (18–19) ) and a detailed derivation of the bicarbonate model [25] is given in the Text S1. Carbon dioxide is produced in various metabolic processes such as the TCA cycle or pyruvate decarboxylation. It is impossible to model all these processes explicitly, but we incorporate them in the effective metabolic carbon dioxide production flux. This flux is an input to the model and was initially assumed to be constant. The change in pH (Equation (3) ) per change in proton concentration is described by the buffering capacity. In principle, is a function of the internal pH, but due to the combined action of various buffering species [26] it can be approximated by a constant for a wide range of intracellular pH values. In addition to the proton fluxes via the -ATPase Pma1 () and the Nha1 antiporter () there are many other proton transport pathways in yeast. The corresponding net flux is subsumed in the proton leak current. The effective proton flux originating from the bicarbonate reaction sequence is given by the term, where is the pH-dependent fraction of dissociated carbon dioxide. The membrane potential (Equation (4) ) is modeled as a charge balance equation (, specific membrane capacitance; , Faraday constant; , surface area of the cell) [27]. We explicitly modeled the charges carried by potassium, total protons () and bicarbonate. The remaining net charges contributing to the membrane potential are subsumed in, which is determined by the initial conditions of the dynamic variables in the model. The cell volume (Equation (5) ) depends on the balance between internal osmotic pressure, external osmotic pressure and turgor pressure [28]. Ion transport processes change the intra- and extracellular solute concentrations and thus have an osmotic effect (Equations (24–26) ) in Materials and Methods). The resistance against volume changes is given by the hydraulic permeability parameter [29]. The concentration and voltage dependent kinetics of all transport systems were described by simple thermodynamic consistent relationships. The driving force for the transport fluxes of ions across the plasma membrane can be written as the difference of the membrane potential and the equilibrium potential. The equilibrium potential depends on the concentrations and stoichiometry of the ions transported, see Equations (12–14) in the Materials and Methods section. For the potassium fluxes in Equation (1) and the proton leak in Equation (3) we assumed linear relations (Ohm' s law) of the form between the driving force and the transport flux, or the corresponding electrical current, respectively. For the leak currents and we initially assumed constant conductivity parameters (Equations (9) and (11) in Materials and Methods). The conductivity of the transport proteins Trk1,2, Nha1 and Tok1 was modeled as a function of the membrane voltage, see Equations (6–8) in Materials and Methods. This minimalistic model captures the essential biophysical and thermodynamic constraints under which control of potassium homeostasis operates. Despite the simplicity of the model, the experimental data was not sufficient to uniquely identify all the parameters. We decided to use this flexibility to explore the parameter space for regions that are consistent with the data and performed extensive parameter scans and sensitivity analysis simulations. However, we were unable to identify a single parameter combination which reproduced the experimental time courses for the wild type strain observed in Figure 1A. In the model, all potassium inside the cell was rapidly and completely lost upon starvation (Figure S4A in Text S1). Based on our model simulations, this believed to be caused by a strong efflux via the Nha1 antiporter driven by the large concentration gradient across the plasma membrane. This model behavior is robust against various model variations, including the incorporation of an intracellular potassium storage mechanism that mimics the contribution of intracellular compartments to potassium retention. Thus, we conclude that further dynamic mechanisms counteracting the strong potassium gradient are essential for homeostasis. Importantly, the model described so far incorporates only the biophysics of transport but does not account for gene regulatory, signal transduction or metabolic events affecting the transporter activity. The fact that the minimal model is not able to reproduce the experimental time courses for potassium starvation means that there are some unmodeled dynamics that are not captured by the model. Under the working hypothesis that the model covers the major biophysical effects of potassium transport we assumed that there are additional regulatory responses to a shortage of potassium. Available knowledge [2], [3] and data is currently not sufficient to develop exhaustive models for the metabolic, signal transduction and gene regulatory responses to potassium starvation. It is not even clear which of the transporters or other components are activated or deactivated for the maintenance of homeostasis. In engineering terms [30], neither the regulators nor the signals triggering their action are sufficiently characterized. To overcome this limitation, we combined our minimal biophysical model with an inference algorithm for unmodeled dynamics. We assumed that the unknown regulatory events modulate the activity of the transport systems or other components in the model. Mathematically this means that a constant parameter in the model might in fact not be constant, but a function of time. For example, the maximum conductivity (see Equation 6) of the Trk1,2 transport system could be influenced by signal transduction events [3], [31] in response to low potassium. Any attempt to explicitly model this regulation by additional equations is hindered by insufficient knowledge of the structure and dynamics of the regulatory networks involved. However, one might recoin the question and ask: “Is there a function such that the given experimental time course of intracellular potassium and the time course predicted by the model are in sufficient agreement? ”. If such a function would exist we would regard the modulation of the Trk1,2 transporter as one potential regulatory mechanism and Trk1,2 as a potential regulator of potassium homeostasis. However, there might be another parameter (e. g.) associated with a transporter or another component in the model for which a time course exists such that experimental data can be reproduced. Our strategy was now to test different parameters and corresponding processes for being potential regulators, see Figure 2A. We define a transporter or any other component in the model to be a potential regulator if a tracking control signal exists which changes the activity of the component in such a way that the experimental time course and stationary data can be reproduced. We refer to this inference approach as the reverse tracking algorithm, a more detailed mathematical explanation is given in Materials and Methods. We used the reverse tracking algorithm to test the transporters Trk1,2, Ena1, Nha1, Tok1 and Pma1 and the activity of the bicarbonate reaction for being potential regulators of potassium homeostasis and then compared the predicted tracking control signals to experimental observations. There is no tracking control signal for the major uptake system Trk1,2; see Figure S1 in the Text S1. This is in contrast to the prevailing view that increased uptake of potassium via Trk1 is essential for potassium homeostasis under starvation conditions. The loss of potassium after starvation is slower in trk1,2 double mutants (see Figure 1A) than in wild type cells. It was experimentally observed [12] that these double mutants have a more negative membrane potential than wild type cells under starvation conditions and also when external potassium is plentiful. This stronger membrane potential (see also Figure S3 in the Text S1) counteracts the outwardly directed potassium concentration gradient and thus explains the higher potassium levels after starvation. Taken together, these results show that the uptake of potassium via Trk1,2 is not the primary mechanism to prevent excessive loss of potassium under starvation conditions. Although we found a tracking control signal for the Nha1 antiporter, we excluded it from our list of potential regulators based on two observations. First, as indicated in Figure 1A, the time course of potassium loss in nha1 mutants is slower than in the wild type and similar to the trk1,2 mutants. Secondly, it was demonstrated that the influence of Nha1 on the internal potassium concentrations decreases with time [11]. This is in contradiction to our predicted tracking signal (Figure S2 in Text S1), which is nonmonotonic in time. Similarly, the unspecific transport pathways (leak currents) were excluded, because it is not plausible that unspecific transporters are regulated for the specific purpose of potassium homeostasis. This is based on the well founded assumption that all potassium specific transporters are active under our experimental conditions are known [3] and included in the model. The non-specific cation uptake system NSC1 can be excluded, because our medium contains enough calcium to render NSC1 inactive [32]. The proton flux includes many co-transport mechanisms with nutrients and other molecules. It is thus unlikely, that one of these transport mechanisms is specifically regulated in response to potassium starvation. The remaining parts in our model are the Pma1 -ATPase and the bicarbonate reaction sequence. For both of them, the reverse tracking approach predicts a rapid burst of activity in response to the rapid removal of external potassium (Figure 2B and 2C). Activation of proton pumping by Pma1 (Figure 2B) hyperpolarizes the plasma membrane, which counteracts the large concentration gradient of potassium and thus limits potassium efflux. An increased reaction flux (see Figure 2C) through the bicarbonate system has a similar effect: The negative charges carried by bicarbonate increase the magnitude of the membrane potential and thereby compensate the potassium gradient. To test the prediction that Pma1 is activated after potassium starvation, we measured Pma1 activity from crude membrane preparations [33] using an in vitro method that has been extensively established as a faithful measure of in vivo Pma1 function [6], [33], [34]. Indeed, the activity measurements confirm the prediction of the reverse tracking algorithm that Pma1 activity increases rapidly (timescale of 10 minutes) and slowly declines during the first hours of potassium starvation (Figure 2D). Control experiments revealed that Pma1 protein levels do not change under these conditions. Moreover, we also observe, as predicted by the model, that the Pma1 activity is higher in the trk1,2 mutant than in the wild type strain throughout the time course of potassium starvation (Figures 2B and D). To further substantiate that the activation of Pma1 is essential for the response to low potassium, we measured growth for Pma1 mutants pma1–204 and pma1–205 [35] with decreased expression and ATPase activity (33 and 50% of wild type). Figure 3 shows that the ratio of the growth rates at 1 mM and 50 mM external potassium is much lower for the mutant strains than that of the wild type. These results are in line with the recent finding that the brp1 mutant, which is a PMA1 promotor deletion, that leads to decreased Pma1 protein levels, presents markedly decreased growth in low potassium medium and defective rubidium uptake [36]. The second prediction from the reverse tracking approach was an increased reaction flux for the bicarbonate system (Figure 2C). This prediction is supported experimentally by an increased mRNA expression of the NCE103 gene coding for carbonic anhydrase, the enzyme catalyzing the bicarbonate reaction (Figure 2E). This result was part of a genome-wide transcriptomic analysis, using DNA microarrays, of the response to potassium starvation (0–120 min) to be published elsewhere (Barreto et al. , submitted). It was shown earlier that protein and mRNA levels of carbonic anhydrase are highly correlated [37]. A qRT-PCR measurement confirmed the increase in NCE103 expression in wild type cells shifted to free medium. After 60 minutes of potassium starvation, the NCE103 mRNA levels increase more than four-fold (independent experiments). These results show that activation of both Pma1 and the bicarbonate reaction sequence are essential for the control of internal potassium concentrations. In non-starved cells the expression of NCE103 is higher for the trk1,2 double mutant than for the wild type (single dot in Figure 2E). A confirmatory semi-quantitative RT-PCR measurement using the same RNA sample as in the microarray experiment and one RNA sample from independent cultures yielded a mean expression ratio of (data points) for trk1,2 relative to the wild type. These results suggest a simple explanation for the reported hyperpolarization of the trk1,2 double mutant [12]: A high activity of the bicarbonate reaction sequence means that many protons and many bicarbonate ions are produced. Together with a more active proton pump (Figures 2B and D), this results in a more negative membrane potential that counteracts the outwardly directed potassium gradient. The consequence is a higher intracellular concentration of potassium (Figure 1A) in trk1,2 double mutants than wild type cells. Homeostatic control of a cellular function in response to a changing environment is often mediated by a negative feedback loop. A change in the input signal (e. g. the external potassium concentration) is counteracted by this feedback loop in order to keep an essential cellular quantity (e. g. the intracellular potassium concentration) in a range sufficient for the cell' s function. One particular type of feedback is integral control, where the control signal is the time integral of the difference between the reference and the actual quantity [30]. Integral control was observed for a number of cellular processes including bacterial chemotaxis [38], [39] and osmoregulation [20]. A characteristic property of integral control is perfect adaptation, where the steady state input is independent of the steady state output. For potassium this would mean, that the same intracellular potassium concentration (output) is approached irrespective of the extracellular potassium concentration (input). The activation of proton transport by Pma1 and the activation of the bicarbonate system counteracting low external potassium indicate the existence of a negative feedback loop. To further investigate this feedback, we have modified the potassium starvation experiment. As before, cells were grown at external potassium, but now resuspended in media with different external potassium concentrations. The potassium efflux and the stationary internal concentrations are different for the different external concentrations, which is also reflected by the model (Figure 4A). To test whether these stationary intracellular concentrations are characteristic for the external potassium, we grew cells overnight in media with different external potassium concentrations (Figure 4B). When external potassium is plentiful (), the internal concentration attains an upper limit of approx. . For low external potassium (), the internal concentration is proportional to the external and agrees with the stationary states of Figure 4A. These experiments show, that perfect adaptation by integral control is not a characteristic of potassium homeostasis for low external potassium. The molecular function and characteristics of this feedback have to be further explored. In summary, we found that direct regulation of potassium transport proteins is not sufficient for the maintenance of viable potassium levels inside the cell. Although the presence of Trk1,2 influences the dynamics of potassium loss under conditions of low potassium, the regulation of their activity is not the main regulatory process. Cells lacking these proteins have higher intracellular potassium concentrations and the loss of potassium after a rapid shift to low external potassium is slower than in wild type cells. The adaptation to low potassium requires a rapid modulation of proton fluxes as a rescue operation via the increased production of bicarbonate and the activation of the -ATPase Pma1 (Figure 5). The observation that the internal steady state potassium concentration is determined by the external concentration indicates, that potassium homeostasis is an example of non-perfect adaptation, excluding the existence of integral control. The detailed sensing and signaling mechanisms remain to be elucidated and currently we cannot distinguish whether changes in internal or external potassium are sensed directly or indirectly, e. g. , as changes of the membrane potential. Although we cannot completely rule out the possibility that other transport systems not considered in the model contribute to homeostasis, we have reason to believe that our model covers the dominant effects required for the maintenance of viable potassium levels under starvation conditions. All experiments were performed in the presence of calcium, which renders the activity of the calcium blocked non-selective cation pathway Nsc1 unlikely. In addition, non-specific transport of potassium is covered in the model by the leak current. The information about potassium storage in intracellular compartments in the literature is limited. To test the influence of intracellular potassium fluxes originating from an intracellular storage mechanism, we added a hypothetical compartment which can release potassium in response to starvation. This modification did not change the qualitative behavior of the model and was not sufficient to explain the slow efflux of potassium and the maintenance of sufficient intracellular potassium after starvation. Thus, we excluded this modification from the model. Many cation transporters are evolutionarily conserved in other yeast species and even in higher plants [1]–[3]. However, the current knowledge for these organisms is not as detailed. Considering the importance of ion homeostasis for some pathogenic yeasts [40] and for the growth and development of plants, the question of whether the regulation of proton fluxes plays a similar dominant role as in S. c. is an interesting starting point for future research. The development of dynamic mathematical models requires a compilation of all parts and processes which could potentially be important for a cellular mechanism under consideration. Other processes believed to be negligible are often lumped together in the parameter values of the model. The decision of which processes to incorporate or to neglect is often hampered by insufficient biological knowledge. Incorporating too many details is impractical and leads to overly complex models with many parameters and little predictive power. On the other extreme are simplistic models which potentially neglect important processes and cannot reproduce the experimental data. We believe that our strategy to start with such a minimal model and to infer unmodeled dynamics with a reverse tracking approach might be of broader interest in systems biology. The reverse tracking algorithm provides (i) candidate points of applications for regulatory signals not explicitly captured by the model and (ii) an estimate of the corresponding time dependent regulatory signal. We emphasize that these potential regulatory signals have to be checked for biological plausibility and have to be validated by experiments. It can be applied when the core model for the process of interest is “underfitted”, i. e. when it can not sufficiently reproduce the experimental data because other regulatory process influence the parameters in the model. Its main advantage is that it can be applied even when an explicit modeling of the processes generating these regulatory inputs is beyond reach. On the other hand, the algorithm can be used as a tool for prioritizing experiments. In combination with experiments, it also may also help to indicate which model extensions are most promising. The basic structure of the mathematical model is given by Equations (1–5) in the Results section. Here we report the details of the kinetic relationships. Parameter values, initial conditions and derivations are provided in the Text S1. In the following, and denote the Faraday constant, the gas constant and the temperature. Equations (1–5) have the form of a differential algebraic control system (28) with. Here, denotes the dynamical variables () and Equation (4) for the membrane potential corresponds to the algebraic equation. The scalar input function is given by the external potassium concentration. The solution of this system for given values of the parameters and a given input function is denoted by. Assume now, that we can observe of the components of experimentally. We collect the experimentally observable components in. This can be written as with a matrix with binary elements. For an experimentally observable variable the -th column of has a single entry and. A zero column with indicates that can not experimentally be observed and is thus excluded from. Assume further, that we have experimental data for certain time points in response to the known input function. Most parameter estimation techniques aim to minimize the squared errorover the parameter vector in order to bring the model prediction for a given input close to the experimental data. However, it might be the case that the minimum error is still too large so that the model cannot be regarded as a reasonable description of the data. This could mean that a dynamical process not explicitly accounted for renders at least one component of the parameter vector to be a time dependent function instead of being constant. The reverse tracking algorithm aims (i) to identify, which of the components of are potentially time dependent and (ii) to predict the time course which minimizes the error. Although the unmodeled dynamical process might effect more than one component, we consider for simplicity each component separately and solve the problem (29) for each component separately. Here, denotes the parameter vector with the l-th component excluded. We then regard as a potential regulatory input, if the problem (29) has a solution with a minimum error smaller than a predefined threshold: . There might be more than one potential regulatory input and the decision of which of these are real can only be made from biological considerations or from additional validation experiments. For example, it might be that has a huge magnitude or takes unrealistic values which could be used to exclude from the list of potential regulatory inputs. Mathematically, problem (29) is an optimal tracking problem, which often can be solved by a feedback control law [46]. This means that the function is updated according to the local error at time. For a scalar we found the integral controller [30] (30) to be a good solution. During a reverse tracking run, this equation is numerically integrated in parallel with the dynamic equations (28). Here, is a least squares spline fit to the experimental data points. Details and suitable parameter values for are provided in the Text S1. Details about the wildtype strain BY4741, the related trk1,2 mutant and the Translucent free medium can be found in [12], [21]. Cells were grown in Translucent -free medium supplemented with the indicated amount of KCl to an OD600 of 0. 4–0. 6. Intracellular potassium concentrations were measured by atomic emission spectrometry after extracting the cells with acid as previously described [12]. The time course of internal potassium was obtained by growing the cells in KCl, then cells were washed with Translucent -free medium (traces of KCl left:) and resuspended to the same free medium or containing the indicated KCl concentrations. Apart from the washing procedure the medium contains 2% glucose. Data for NCE103 expression changes upon potassium starvation was obtained in the context of a genome-wide transcriptomic analysis by DNA microarray (Barreto et al. , Manuscript submitted). Microarray data has been deposited at NCBI' s Gene Expression Omnibus [47] and are accessible through GEO Series accession numbers GSE24711 (trk1 trk2 data) and GSE24712 (time-course data). Briefly, wild-type strain BY4741 cells were grown in Translucent medium supplemented with 50 mM KCl to OD 0. 8. Cells were centrifuged and resuspended either in fresh Translucent medium with 50 mM KCl or without potassium. Samples (20 ml) were taken at 10,20,40,60 and 120 min by rapid filtration from 4 biological replicates. Total RNA was extracted by using the Ribo PureTM Yeast kit (Ambion) following the manufacturers instructions. cDNA was prepared and indirectly labeled with Cy3 and Cy5. Images with a resolution of 10 were analyzed with the GenePix Pro 6. 0 software (Molecular Devices). Microarray data was confirmed by qRT-PCR using independent RNA samples. To this end, 60 ng of RNA were amplified using oligonucleotides RT_ NCE103_5 (TCATTACCTGTCGCACTG) and RT_ NCE103_3 (CACAAAAGTTACCCCAAAA) and the QuantiTect SYBR Green PCR Kit (Quiagen). Cell cultures were grown at in Translucent YNB medium containing KCl to OD660 0. 6, then washed with Translucent - free medium and resuspended in the same medium without KCl. At the indicated times, cell samples were pelleted by centrifugation, resuspended in of fresh media (with KCl for t = 0 and without KCl for the remaining samples), incubated for 5 minutes and frozen in liquid nitrogen. For the crude membrane purification, of 3× extraction buffer (0. 3 M Tris-HCl pH 8. 0,180 mM KCl, 30 mM EDTA, 6 mM DTT and Protease Inhibitor Cocktail (Roche) ) was added to the thawed samples and cells were broken by vortexing in the presence of an equal volume of glass beads. of GTED20 buffer (20% glycerol, 10 mM Tris-HCl pH 7. 6,1 mM EDTA and 1 mM DTT) were added to the crude extract, which was then centrifuged 5 minutes at 2000 rpm. The supernatant was transferred to a new tube and centrifuged 20 minutes at 13000 rpm. The insoluble fraction was resuspended and homogenized in of GTED20. The total amount of protein present was estimated using the Bradford assay (BioRad). The amount of Pma1 present in this protein fraction was estimated by comparing the amount of Pma1 to a protein standard curve separated in SDS-PAGE gels stained with Coomassie Blue. In a microtiter plate, of total protein (which corresponds to of Pma1) were assayed in the presence and absence of a Pma1-specific inhibitor, dietilstylbestrol (DES, final concentration 0. 2 mM). The reaction was started by adding of the reaction buffer (50 mM MES-Tris pH 5. 7,5 mM MgSO4,50 mM, 5 mM Na Azide, 0. 3 mM Molybdate, 2 mM ATP) and the plate was incubated for 20 minutes at. The reaction was stopped by adding of detection solution (2% sulphuric acid, 0. 5% ammonium molybdate, 0. 5% SDS, 0. 1% ascorbic acid) and the color was allowed to develop for 5 minutes before reading the absorbance in microplate reader (BioRad) at 750 nm. Residual activity values in the presence of DES were subtracted from the absolute activity values to obtain the Pma1 activity measurements. The results represent the average of at least 4 measurements at each time point and essentially identical results were observed in two separate experiments. Measurements of Pma1 activity are expressed in mmol/min/g Pma1. Error bars represent the standard deviation.

Without potassium, all living cells will die; it has to be present in sufficient amounts for the proper function of most cell types. Disturbances in potassium levels in animal cells result in potentially fatal conditions and it is also an essential nutrient for plants and fungi. Cells have developed effective mechanisms for surviving under adverse environmental conditions of low external potassium. The question is how. Using the eukaryotic model organism, baker' s yeast (Saccharomyces cerevisiae), we modeled how potassium homeostasis takes place. This is because, through mathematical modeling and experimentation, we found that the electro-chemical forces regulating potassium concentrations are coupled to proton fluxes, which respond to external conditions in order to maintain a viable potassium level within the cells. Our results challenge the current understanding of potassium homeostasis in baker' s yeast, and could potentially be extended to other microorganisms, including non-conventional yeasts such as the pathogenic Candida albicans, and plant cells. In the future, the fundamental bases for this descriptive and predictive model might contribute to the development of new treatments for fungal infections, or developments in crop sciences.

Abstract Introduction Results Discussion Materials and Methods

systems biology biochemical simulations control theory mathematics biophysic al simulations applied mathematics regulatory networks biology computational biology metabolic networks signaling networks

2012

Potassium Starvation in Yeast: Mechanisms of Homeostasis Revealed by Mathematical Modeling

The study of chromosomal organization and segregation in a handful of bacteria has revealed surprising variety in the mechanisms mediating such fundamental processes. In this study, we further emphasized this diversity by revealing an original organization of the Pseudomonas aeruginosa chromosome. We analyzed the localization of 20 chromosomal markers and several components of the replication machinery in this important opportunistic γ-proteobacteria pathogen. This technique allowed us to show that the 6. 3 Mb unique circular chromosome of P. aeruginosa is globally oriented from the old pole of the cell to the division plane/new pole along the oriC-dif axis. The replication machinery is positioned at mid-cell, and the chromosomal loci from oriC to dif are moved sequentially to mid-cell prior to replication. The two chromosomal copies are subsequently segregated at their final subcellular destination in the two halves of the cell. We identified two regions in which markers localize at similar positions, suggesting a bias in the distribution of chromosomal regions in the cell. The first region encompasses 1. 4 Mb surrounding oriC, where loci are positioned around the 0. 2/0. 8 relative cell length upon segregation. The second region contains at least 800 kb surrounding dif, where loci show an extensive colocalization step following replication. We also showed that disrupting the ParABS system is very detrimental in P. aeruginosa. Possible mechanisms responsible for the coordinated chromosomal segregation process and for the presence of large distinctive regions are discussed. Members of the genus Pseudomonas (γ-proteobacteria) show remarkable metabolic and physiological diversity and versatility, enabling colonization of diverse terrestrial and aquatic habitats. These species are of great interest because of their importance in plant and human diseases and their growing potential in biotechnological applications. One of these bacteria, Pseudomonas aeruginosa, is an aquatic and soil bacterium that can infect a range of organisms, including plants, invertebrates and various mammals [1]. In humans, P. aeruginosa is an opportunistic pathogen that causes serious infections in immunocompromised patients, and it is the leading cause of morbidity in cystic fibrosis patients [2]. These infections are particularly challenging because of P. aeruginosa' s broad intrinsic antimicrobial resistance [3]. The PAO1 genome was the first P. aeruginosa genome to be sequenced [4]. It comprises 6. 3 Mbp encoding 5,570 genes. Its size results from genetic complexity rather than gene duplications, which may suggest that it adapts to colonize a diverse range of ecological niches [1], [4]. A comparison of five P. aeruginosa genomes revealed a relatively large set of 5021 conserved genes, which constitute the core genome. Beside this set, insertions containing blocks of strain-variable genes are found in a limited number of chromosomal locations, termed Regions of Genomic Plasticity (RGPs). The P. aeruginosa oriC sequence has been characterized and is adjacent to dnaA and dnaN [5], [6]. The dif site, a sequence that is required for chromosome dimer resolution by the XerCD recombinases upon activation by the DNA translocase FtsK, was identified opposite of oriC, whereas no system dedicated to replication termination (such as replication fork trap) has yet been characterized in P. aeruginosa. To fit inside bacterial cells, bacterial chromosomes need to be organized into a compact structure called the nucleoid. DNA compaction is thought to result from the interplay between macromolecular crowding, DNA supercoiling and the specific action of DNA binding proteins [7]. In all organisms in which this process has been examined, the nucleoid is oriented in a specific way inside the cell, which preserves the linear order of genes in the DNA [8]. This global orientation is longitudinal in the case of Bacillus subtilis, Caulobacter crescentus and Vibrio cholerae chromosomes and transversal in E. coli [9], [10]. In E. coli, large-scale organization in several domains is also present [11], [12]. The compaction of the chromosome must be compatible with various processes of DNA metabolism, such as gene expression and replication and the segregation of genetic information. Different hypotheses have been proposed to explain chromosomal segregation. For instance, studies in B. subtilis led to the development of the capture extrusion model, which suggests that the energy required for initial chromosomal segregation could come from the replication process that occurs at mid-cell in a static replication factory [13]. However, it was later shown that in E. coli, the two replication forks appear to follow chromosomal arms, rendering the capture extrusion model irrelevant for E. coli chromosomes [14], [15]. It was proposed instead that loss of cohesion between replicated origin regions could trigger global chromosomal movement and mediate chromosomal segregation [14]. It has also been proposed that entropic exclusion of replicated chromosomes might participate in the segregation process [16]. At the molecular level, a mitotic-like apparatus composed of a DNA binding protein (ParB) that binds to a specific sequence (parS) and a Walker-type ATPase (ParA) is present in a number of bacteria, including P. aeruginosa, but absent in E. coli. This apparatus is involved in chromosomal segregation in V. cholera, C. crescentus (where it is essential), B. subtilis and Streptococcus pneumonia [17]–[20]. It was also demonstrated in B. subtilis and S. pneumonia that the recruitment of bacterial condensins to the origin region by ParB-parS complex contributes to chromosomal segregation [20]–[22]. Two bacterial condensins have been identified in P. aeruginosa, and both the ParABS system and these two condensins appear to be important for chromosomal segregation, as the deletion of either of these factors leads to an increase in anucleated cells [23]–[25]. As a first step in the study of chromosomal organization and segregation in P. aeruginosa, we used a fluorescent microscopy approach to investigate chromosomal localization during growth. This approach allowed us to show that the global orientation of the P. aeruginosa chromosome is longitudinal and that the replication forks are mostly colocalized near mid-cell. Chromosomal loci appear to be relocated closer to mid-cell prior to replication, and the two replicated copies are then progressively segregated into opposite cell halves. This process is surprisingly different from what is observed in E. coli, another gamma-proteobacterium, but closer to what has been described for the gram-positive bacterium B. subtilis. Moreover, we could discern two distinctive regions in the P. aeruginosa chromosome, one surrounding oriC and the other surrounding the dif site. The origin region was defined by the fact that approximately 1. 4 Mbp of DNA centered on oriC segregate to the same position around the 0. 2/0. 8 relative cell length. The Ter region encompasses at least 800 kbp that remain close to the new pole of the cell before being relocated to near mid-cell prior to replication. Loci in the Ter region show a high level of colocalization following replication. Chromosomal arms linking these two regions are longitudinally distributed. These features allow us to propose P. aeruginosa chromosomal organization as an original model, combining longitudinal organization, as observed in C. crescentus, with large distinctive regions that might be suggestive of long-range organization, as observed in E. coli. We also provide evidence that the ParABS system plays a major role in chromosomal organization and segregation in P. aeruginosa. To study chromosomal organization in P. aeruginosa, we used a fluorescent microscopy approach. The intracellular position of chromosomal loci was visualized using two different reporter systems based on the binding of a fluorescently labeled protein to a target sequence inserted at twenty specific locations in the P. aeruginosa chromosome (see Figure 1A and Text S1). We used both the TetR-CFP chimera, which recognizes tetO arrays, and the yGFP-ParBpMT1 chimera, which binds to the parSpMT1 sequence [26], [27]. We created a plasmid allowing expression of both yGFP-parBpMT1 and tetR-CFP from an IPTG inducible promoter. The visualization of two chromosomal loci by fluorescent microscopy was thus possible upon growth in IPTG-containing medium. The use of either the parSpMT1 sequence or the tetO arrays to label a specific chromosomal locus gives similar results regarding foci number and positions inside the cell (data not shown). We tested different growth conditions and chose to perform extensive studies in minimal medium supplemented with citrate at 30 degrees. Under these growth conditions, the P. aeruginosa doubling time is approximately 45 to 50 minutes, with cell sizes ranging from 2 to 5 µm (mean size of approximately 2. 9 µm). We predicted, based on the number of foci corresponding to chromosomal loci or replisome proteins (see below), that most of the cells would contain a single replicating chromosome. Indeed, each chromosomal locus is visualized as one or two foci. Figure 1B shows the number of cells exhibiting one or two foci for each of the 20 positions. Loci close to oriC are mostly visualized as two foci (>90% of two-foci cells), indicating that they are replicated and segregated upon cell birth (focus duplication corresponds to the segregation of the two copies of a chromosomal locus). In contrast, loci located close to the dif site (presumably in the Ter region where the replication ends) are mostly visualized as one focus (>90% of one-focus cells), indicating that they are segregated immediately prior to cell division. For loci located in between, a linear increase in the proportion of one-focus cells (and therefore a decrease in the proportion of two-foci cells) is observed. Figure 1C shows the percentage of two-foci cells according to cell size for each position. This percentage indicates that the foci duplication process is sequential, proceeding from the origin of replication to the terminus. For each locus, the number of two-foci cells increases with cell size. The curves are remarkably parallel for loci close to oriC, and their slope decreases as the distance from oriC increases. Very interestingly, for positions close to the dif site (2,672-R, 2,957-R and 3,028-R on the right replichore, and 2,784-L and 3,090-L on the left replichore), the proportion of two-foci cells never exceeds 50%, even in larger cells. This finding indicates that the separation of the two copies of these loci occurs concomitantly with cell division in more than 50% of cells. We studied foci localization inside the cells. To orientate bacterial cells, we took advantage of the fact that loci located near dif exhibit a striking pattern: in one-focus cells, they are localized close to one pole (closer than the 0. 2 relative cell length) in small cells and close to mid-cell in larger cells. In two-foci cells, the 2 foci stay close to the division plane, suggesting that upon division, the pole where the dif region is localized is the new pole of the cell. We thus labeled two different loci: one close to dif, which we used as a marker of the new pole, and another elsewhere on the chromosome, whose position we were interested in. The data for each locus are presented in Figure S1, and the results are summarized in Figure 2. The relative positions of foci inside bacterial cells are represented for small cells (smaller than 2. 8 µm, Figure 2A), medium cells (between 2. 8 and 3. 5 µm, Figure 2B) and large cells (larger than 3. 5 µm, Figure 2C). The proportions of one-focus cells for each cell type are indicated in Table S3. Moreover, Figure S2 presents the same data differently: the relative positions of the foci in cells within 0. 2 µm size intervals are presented according to cell size, yielding curves that represent the segregation profiles for each chromosomal locus. From our results, we inferred the existence of two specific regions in the P. aeruginosa chromosome. The first region surrounds oriC. Indeed, in large cells (when most of the chromosome is replicated, before cell division), it is clearly observed that the two copies of the three loci closest to oriC on each replichore (loci 82-R, 327-R, 628-R and 92-L, 488-L, 851-L) are located at approximately the same position, close to the 0. 2/0. 8 relative cell length (blue, red and green markers, Figure 2C). Remarkably, eight of the ten parS sites found in P. aeruginosa are located in between these chromosomal loci (Figure 1A): four of them are very close to oriC (between 4 to 15kb from oriC), two are found between loci 327-R and 628-R, one is found between loci 92-L and 488-L, and one is found between loci 488-L and 851-L. From these data, we proposed that the three loci closer to oriC on each replichore (blue, red and green markers) define a distinctive region that we called the Ori region. Loci in this region segregate sequentially (Figure 1, Figure 2A and 2B) but reach the same final position inside the cell (around the 0. 2/0. 8 relative cell length). Loci closest to oriC are not the most polarly localized (the red and green markers are farther away from the new pole than the blue marker, Figure 2C), but they are precisely positioned at the 0. 2/0. 8 relative cell length. This finding is not expected from the position of the loci on the chromosomal map and is not consistent with a strictly longitudinal organization (as in C. crescentus chromosomal organization, for instance [28]). The second region that we discerned in the P. aeruginosa chromosome surrounds the dif site. Indeed, another striking pattern is observed for the four loci closer to dif (positions 2,672-R, 2,957-R, 3,090-L and 2,784-L). As indicated above, they are mostly visualized as a single focus, even in large cells (more than 75% of one-focus cells, Table S3 and Figure 1C). They are located close to the new pole (below the 0. 2 relative cell length) in small cells (Figure 2A); in medium cells, their position is still quite polar (Figure 2B and Figure S2), whereas they are located near mid-cell in large cells (Figure 2C and Figure S2). Because of their polar localization in small cells and the fact that the proportion of cells exhibiting two foci never exceeds 50%, even in large cells, we proposed that these 4 loci belong to another distinctive region that we called the Ter region. Chromosomal locus 3,028-R also belongs to this Ter region, but because it was used to orientate cells, it is not included in Figure 2. Once again, loci in this region segregate sequentially (Figure 1), but their positioning inside the cell is similar. Chromosomal loci located in between these Ori and Ter regions are spread between the 0. 7 and the 0. 2 relative cell length when they are visualized as a single focus and between the 0. 2/0. 8 relative cell length and mid-cell when they are visualized as two foci. This suggests a longitudinal organization of these parts of the P. aeruginosa chromosome. To confirm this hypothesis, we measured distances between pairs of loci. We observed that loci located at equivalent distances from oriC on different replichores are indeed more frequently colocalized than loci on the same replichore (Figure S3). Looking more specifically at one-focus cells, most chromosomal loci are relocated close to mid-cell (0. 5 relative cell length) before they are resolved into two elements (Figure 2 and Figure S2). This relocation could suggest a sequential repositioning of each chromosomal locus before replication. To investigate where replication takes place in P. aeruginosa, DNA polymerase was visualized in living cells using fusions between replisome components and green fluorescent protein (GFP). This approach has been successfully used in B. subtilis [29], C. crescentus [30] and E. coli [15] to study DNA polymerase localization. Chromosomal genes encoding DnaX (PA1532), HolB (PA2961) and HolA (PA3989) were replaced with genes encoding GFP tagged versions of these proteins (see Text S1 for details). We also engineered a Dronpa-tagged version of DnaX, as Dronpa is a fluorescent protein that has been reported not to cause aberrant foci formation [31]. The results using this DnaX-Dronpa fusion were quite similar to those obtained with DnaX-eGFP (Figure S5). In minimal medium supplemented with citrate, these GFP fusions were mostly observed as a single spot localized near mid-cell (Figure 3A and Figure S4A). Some cells had no visible focus (about 10%), and although we cannot exclude experimental faults, the highest proportion was observed in smaller cells or larger cells, which is consistent with replication starting immediately upon cell birth and ending before cell division. More than 90% of the cells with foci possessed only one visible focus, indicating that the two replication forks are mostly colocalized. However, a small number of cells presenting two GFP foci were observed for each cell size. In small cells, these foci were found close to mid-cell and relatively close to each other (distance <0. 35 of the relative cell length), which suggests that the two forks can separate during the replication process. In the largest cells, however, the two foci were localized far apart, around the 0. 2/0. 8 relative cell length. These foci coincide with the position of foci close to oriC, which could indicate that another round of replication is starting in a few cells, before division is achieved. Overall, these results show that replication forks are mostly colocalized near mid-cell in P. aeruginosa. Therefore, the repositioning of all chromosomal loci close to mid-cell prior to their duplication could indeed be linked to the replication process. We next wondered what would happen to chromosomal organization if the growth conditions were changed. In minimal medium supplemented with glucose and casamino acids, 2 to 4 copies of chromosomal loci close to oriC were observed (Figure S6), implying that a new round of replication starts before cell division more often in this medium than in citrate medium. The cells are slightly larger in this minimal medium than in minimal medium supplemented with citrate (Figure 3), and the doubling time is similar (approximately 45 minutes, data not shown). Observing DnaX-eGFP (as well as HolB-eGFP, Figure S4B, and DnaX-Dronpa, Figure S5B) revealed that in one-focus cells, the focus is localized close to mid-cell, as observed in minimal medium supplemented with citrate. The proportion of two-foci cells is higher in MM supplemented with glucose and casamino acids than in MM supplemented with citrate (approximately one third of cells with visible foci presented two foci, Figure 3B). The proportion of two-foci cells increased with cell size: a large majority of cells (approximately 65%) larger than 3. 5 µm exhibited two foci. Moreover, a clear difference was observed between the position of the two foci in cells smaller than approximately 3. 5 µm, in which the two foci were close together (distance <0. 35 of the relative cell length), and the position of the two foci in cells larger than approximately 3. 5 µm, where they were located near the one quarter/three quarter position in the cells. These results suggest that in cells smaller than approximately 3. 5 µm, a replication round is finishing near mid-cell, most often with the two forks colocalized. In contrast, in cells larger than approximately 3. 5 µm, another round of replication started on the two duplicated chromosomes, close to the mid-cell of the future daughter cells. The two replication forks that reproduce the same chromosome are also mostly colocalized. In contrast to loci close to oriC (which are present in two to four copies), intermediate chromosomal loci (1,812-L and 1,509-R) are observed as only one or two foci (Figure S6), which might suggest that cell division occurs before the replication forks reach these positions. Loci located near dif (3,090-L and 2,499-R) show the same localization pattern in minimal medium supplemented with glucose and casamino acids as in minimal medium supplemented with citrate: they are localized near the cell pole in small cells and near mid-cell in larger cells. A single focus near mid-cell can be observed in large cells despite the fact that the chromosome is fully replicated (as can be inferred from visualizing replisome proteins). When a locus in the Ori region and a locus in the Ter region are visualized in the same cells, the proportion of cells exhibiting four foci of the Ori locus and one focus of the Ter locus is twice the proportion of cells exhibiting four foci of the Ori locus and two focus of the Ter locus (data not shown). This finding suggests an extensive colocalization step following replication for the Ter region. Together, these results suggest that in minimal medium supplemented with glucose and casamino acids, the P. aeruginosa chromosome is organized similarly to how it is organized in minimal medium supplemented with citrate, except that two chromosomes are being replicated instead of one (see discussion). As a first step to study molecular mechanisms involved in P. aeruginosa chromosomal organization and segregation, we disrupted the ParABS system by deleting either the parA gene or the parB gene. Consistent with previously reported work [24], the ΔparA mutant showed an increase in its doubling time (approximately 100 minutes in minimal medium supplemented with citrate, as opposed to approximately 50 minutes for the wild-type strain, data not shown). In the genetic context we used, the ΔparB mutant showed the same growth defect, in contrast to previous observations reporting a growth rate only 5 to 10% lower than that of the wild type strain [25]. Both mutants produced more than 20% anucleated cells when grown in minimal medium supplemented with citrate (data not shown), suggesting a major defect in chromosomal segregation. To further investigate the impact of these mutations on chromosomal organization, we analyzed the positioning of chromosomal loci located in the Ter and Ori regions during growth in minimal medium supplemented with citrate. The results are shown in Figure 4. Consistent with the large number of anucleated cells, more than half the cells exhibited no visible foci for both the Ter and the Ori loci. Additionally, the proportion of cells containing a given number of foci in the Δpar mutants is strikingly different from the wild type strain (Figure 4A). For instance, only 15% of the cells have one visible Ter focus and two visible Ori foci in these mutants, compared to more than 80% of the wild type cells. Moreover, the localization of these foci inside the cells also completely changed (Figure 4B–4D). A strong segregation defect is observed for the Ori locus in both the ΔparA and the ΔparB mutants: the two copies of this locus are not segregated to the 0. 2/0. 8 relative cell length. Furthermore, the typical localization pattern of the Ter locus is completely lost in the ΔparA and ΔparB mutants, which indicates that the whole chromosomal organization is altered when the ParABS system is disrupted. The longitudinal organization of the P. aeruginosa chromosome is similar to chromosomal organization in C. crescentus. However, in C. crescentus, a linear correspondence exists between the position of any given chromosomal locus and its position inside the bacterial cell [28]; however, this is not always the case in P. aeruginosa for loci that belong to the Ori or Ter regions. Indeed, loci belonging to these regions are localized around the same position before and after segregation: loci from the Ori region are near the 0. 2/0. 8 relative cell length in large cells and loci from the Ter region are below the 0. 2 relative cell length in small cells. Large domains have only been described thus far in E. coli [11], [32], which exhibits a transversal organization of its chromosome: oriC and the Ter region are localized near mid-cell, while the replichores are localized in different halves of the cell [27], [33]. P. aeruginosa is thus the first bacterium in which a unique combination of properties could coexist: longitudinal positioning and the presence of large distinctive regions. A systematic study of the positioning of chromosomal loci in B. subtilis or V. cholera has yet to be published. In P. aeruginosa, the three loci closest to oriC on each replichore (encompassing approximately 1. 4 Mbp around oriC) appear to be localized at the same position upon segregation (See model, Figure 5). Interestingly, this region includes the eight parS sites located around oriC [34], suggesting a putative role of the ParABS system in organizing this specific region, in addition to its global role in the segregation of the P. aeruginosa chromosome. In C. crescentus, the most polar region of the chromosome contains two parS sites (which are located 8 kb away from oriC in the wild type chromosomal configuration). In P. aeruginosa, the whole Ori region is localized near the 0. 2/0. 8 relative cell length position. The two loci closest to oriC (82-R and 92-L) are precisely positioned, whereas loci 327-R, 628-R, 488-L and 851-L can be localized slightly more towards the cell pole (Figure 2C). Even if parS sites are dispersed in this Ori region, four of them are located between +4 kb and +15 kb from oriC, which could account for the precise positioning of the loci closest to oriC. Deletion or displacement of parS sites will be necessary to determine their role in the positioning and existence of the Ori region as we defined it. It is worth noting that in C. crescentus, sporulating B. subtilis and V. cholerae, oriC is more polarly localized than what we observed in P. aeruginosa. Proteins anchoring oriC to the cell pole via the ParABS system have been identified in these bacteria [35]–[38] and are not found in P. aeruginosa. The FimV protein, a homolog of HubP that is responsible for pole anchorage in V. cholerae, lacks the specific domain responsible for interacting with the ParA protein [38]. It is thus very unlikely that it plays a role in positioning the Ori region of P. aeruginosa at the 0. 2/0. 8 relative cell length position. The mechanisms responsible for this precise localization have yet to be identified. In B. subtilis, ParB (Spo0J) binds to parS and recruits the SMC complex, which plays a major role in organizing the origin region and promotes chromosomal segregation [21], [22]. Two condensins have been identified in P. aeruginosa, the SMC complex and the MksB complex, which could also be involved in chromosomal segregation [23]. The role of these proteins in organizing P. aeruginosa' s chromosome has yet to be carefully investigated, as well as their interplay with the ParABS system. We might imagine that the strong detrimental effect of the deletion of parB is linked to the inability of the condensins to be recruited to the Ori region, as observed in B. subtilis [21], [22]. However, the deletion of parA in P. aeruginosa is also very detrimental to the cell, in contrast to what is observed in B. subtilis. Considering the major impact on chromosomal organization and segregation of the impairment of the ParABS system in P. aeruginosa, the development of specific tools will be required to more specifically analyze the role of ParA and ParB in organizing the Ori region, as well as their interplay with Smc and MksB during chromosome segregation. Another striking observation in P. aeruginosa is that loci located in the approximately 800 kb surrounding the dif site show a characteristic localization during the cell cycle. We arbitrarily define the Ter region as containing these loci, which are positioned below the 0. 2 relative cell length in small cells and are sequentially repositioned near mid-cell prior to their duplication. Interestingly, loci in the Ter region are mostly visualized as one single focus, even when they are obviously already duplicated. This distinctive feature is clearly observed when cells are grown in minimal medium supplemented with glucose and casamino acids. In these conditions, large cells possess 2 complete chromosomes that are being replicated (see model, Figure 5), and nevertheless, loci close to dif are frequently visualized as 1 focus. In most cases, the separation of these Ter loci is concomitant with cell division and might require septum formation. Further analyses will be required to elucidate the nature of this Ter region and to identify processes that specifically control the segregation of loci belonging to that region. A specific domain surrounding the dif site was characterized in E. coli. This domain, called the Ter domain, was first identified using a cellular biological approach [11], [39]. Loci in this domain also present a long colocalization step at mid-cell following replication [14], [32], [39]. A specific factor involved in this colocalization step, called MatP, has been identified [40], [41]. MatP recognizes a specific sequence, matS, that is repeated 23 times in the Ter region of E. coli and interacts with the divisome. No MatP homolog is found in P. aeruginosa, and preliminary bioinformatic analysis did not identify a sequence characteristic of the Ter region of P. aeruginosa. Therefore, processes responsible for specifying the Ter region of the P. aeruginosa chromosome may rely on different molecular mechanisms. It is interesting to note that even if domains have not been characterized in C. crescentus and V. cholerae, one locus located near dif in these bacteria also remains colocalized at mid-cell after replication [42], [43]. This work also addressed the localization of replisomes near mid-cell in P. aeruginosa. This localization is reminiscent of that described in B. subtilis [29]. In C. crescentus, replisomes are also often colocalized, but they move from one pole to mid-cell during replication [30] following chromosomal organization. In E. coli, the two replication forks appear to follow chromosomal arms as they separate into the two halves of the cell [14], [15]. Based on our analysis of the localization of replisome proteins, it appears that replication lasts for most of the cell cycle when P. aeruginosa is grown in minimal medium supplemented with citrate (see model, Figure 5). In newborn cells, loci closest to oriC are being replicated and are found near mid-cell together with DNA polymerase. As cells grow, replisomes stay near mid-cell, where loci are successively relocated prior to their replication. This relocation may result from the replication process, which could be responsible for pulling DNA towards mid-cell. Overall, this study allowed us to give a precise description of chromosomal organization in P. aeruginosa. This organization is original, combining large distinctive regions and a longitudinal positioning. These results pave the way for additional work that will lead to the understanding of the mechanisms responsible for such organization, which will contribute to our appreciation of the wide diversity of mechanisms used by bacteria, even when the most fundamental processes are concerned. This work might also allow us to identify drugs that would interfere with fundamental processes such as chromosomal replication and segregation in this important pathogen. P. aeruginosa strain PAO1 was provided by Arne Rietsch (Case Western Reserve University). This PAO1 strain does not present the inversion described for the sequenced PAO1-UW subclone resulting from homologous recombination between the rrnA and rrnB loci, which are orientated in opposite directions and separated by 2. 2 Mbp [4]. This explains the discrepancies between PA numbers and positions on the chromosomal map, as gene annotation was done in the inversion containing strain. Chromosomal loci were called according to their position from oriC on each replichore. Details of plasmid and strain construction are provided in Text S1, Table S1 and Table S2. Strains were grown overnight in LB, diluted 300 times in minimal medium A supplemented with either 0. 25% citrate or 0. 12% casamino acids and 0. 5% glucose (when looking at chromosomal tags, 0. 5 mM IPTG was added to growth medium) until they reach an OD600 comprised between 0. 05 and 0. 1. Cells were then spread on agarose pads and immediately observed using a Leica DM6000 microscope, a coolsnap HQ CCD camera (Roper) and Metamorph software. Image analysis was performed using the MATLAB-based software MicrobeTracker Suite [44].

The processes of chromosomal disposition, replication, and segregation in bacteria have been characterized only in a handful of species, yet there is remarkable diversity in the ways such fundamental processes are managed. In this study, we analyzed the subcellular chromosomal organization of Pseudomonas aeruginosa, an important bacterial pathogen belonging to a large bacterial group involved in plant and human diseases. Most bacterial genomes are circular molecules, and DNA replication proceeds bidirectionally from a single origin to the opposite Ter region, where the replication forks meet. Analysis by fluorescence microscopy of 20 chromosomal markers and components of the replication machinery revealed that the 6. 3 Mb chromosome is globally oriented from the old pole of the cell to the division plane/new pole along the oriC-Ter axis. The replication machinery is positioned at mid-cell, and chromosomal loci from oriC to Ter are moved sequentially to mid-cell prior to replication. The two sister chromosomes are subsequently segregated at their final subcellular destination in the two halves of the cell. This study also identified two large regions in which several chromosomal loci show a biased localization pattern, suggesting that processes responsible for long-range chromosomal organization might exist in P. aeruginosa.

Abstract Introduction Results Discussion Materials and Methods

gram negative chromosome biology biology microbiology molecular cell biology bacterial pathogens

2013

Chromosomal Organization and Segregation in Pseudomonas aeruginosa

Cystic echinococcosis (CE) is a worldwide parasitic zoonosis caused by the larval stage of Echinococcus granulosus. Current chemotherapy against this disease is based on the administration of benzimidazoles (BZMs). However, BZM treatment has a low cure rate and causes several side effects. Therefore, new treatment options are needed. The antidiabetic drug glibenclamide (Glb) is a second-generation sulfonylurea receptor inhibitor that has been shown to be active against protozoan parasites. Hence, we assessed the in vitro and in vivo pharmacological effects of Glb against the larval stage of E. granulosus. The in vitro activity was concentration dependent on both protoscoleces and metacestodes. Moreover, Glb combined with the minimum effective concentration of albendazole sulfoxide (ABZSO) was demonstrated to have a greater effect on metacestodes in comparison with each drug alone. Likewise, there was a reduction in the cyst weight after oral administration of Glb to infected mice (5 mg/kg of body weight administered daily for a period of 8 weeks). However, in contrast to in vitro assays, no differences in effectiveness were found between Glb + albendazole (ABZ) combined treatment and Glb monotherapy. Our results also revealed mitochondrial membrane depolarization and an increase in intracellular Ca2+ levels in Glb-treated protoscoleces. In addition, the intracystic drug accumulation and our bioinformatic analysis using the available E. granulosus genome suggest the presence of genes encoding sulfonylurea transporters in the parasite. Our data clearly demonstrated an anti-echinococcal effect of Glb on E. granulosus larval stage. Further studies are needed in order to thoroughly investigate the mechanism involved in the therapeutic response of the parasite to this sulfonylurea. Cystic echinococcosis (CE) is among the most serious and life-threatening helminth infections in humans worldwide [1]. This disease is caused by the larval stage of the dog-tapeworm Echinococcus granulosus. Not only it does affect approximately 2–3 million people around the world but it causes substantial economic losses to the livestock industry [2]. Currently, CE chemotherapy involves the use of benzimidazoles (BZMs), with albendazole (ABZ) the most commonly used. However, this treatment option is not curative and it often leads to side effects [3]. Therefore, further research should focus on the development of alternative therapies for CE. This includes the use of combination treatments; firstly, to increase therapeutic effectiveness, and secondly, to delay the emergence of possible resistance [4]. Glibenclamide (Glb) is a diarylsulfonylurea that has been widely used in the clinic to treat type 2 diabetes mellitus [5]. Furthermore, a great number of other pharmacological properties have been reported to be associated to this drug, such as anti-cancer [6], anti-proliferative [7,8] and anti-inflammatory [9,10] activities. This drug has a low cost (less than USD 0. 10 per dose), it is non-toxic at doses commonly employed and it is readily available [11]. Glibenclamide has a high oral absorption but a low dissolution in gastric fluids and its pharmacokinetics depends on several transporters belonging to the solute carrier (SLC) and ABC superfamilies. Its uptake is mediated by the organic anion-transporting polypeptide (OATP, SLCO) family (preferentially by OATP2B1, OATP1B1 and OATP1B3). Its metabolism is through the cytochrome-P450 (CYP) -mediated oxidative pathways in the liver and its distribution and subsequent elimination is mediated by ABC transporters [12]. Glibenclamide exerts its mechanism of action by inhibiting ABC proteins with dissimilar functions, such as the sulfonylurea receptor–SURx, ABCC8–[13], which is associated with the pore-forming inwardly rectifying K+ channel–Kir6. X– (which together form the KATP channels), the cystic fibrosis transmembrane conductance regulator [14], the ABC1 transporter of immune cells [15], the P-glycoprotein (P-gp) [16] and the multidrug resistance-associated protein (MRP) (ABCC1) of cancer cells [17,18]. Moreover, Glb causes modulation of mitochondrial permeability by action on different ion channels [19–21]. In the present study, we assessed the in vitro and in vivo effects of Glb on the viability and growth of E. granulosus larval stage. Our data clearly demonstrated that the drug possesses an in vitro anti-echinococcal activity against both protoscoleces and metacestodes. In addition, the observed effect of the drug on the growth of hydatid cysts in mice leads to the consideration of a novel role of Glb in CE treatment. Glibenclamide (INN) was obtained from Sigma-Aldrich (USA), JC-1 from Thermo Fisher Scientific (USA) and ABZ and albendazole sulfoxide (ABZSO) were kindly provided by Dr. C. Salomon (National University of Rosario, Argentina). For in vitro assays, Glb and ABZSO were kept as a 100 mM and a 100 μM stock solution in dimethyl sulfoxide (DMSO), respectively, and added to the medium either separately or in combination. For in vivo experiments, oil solutions of Glb and ABZ (corn oil, Sigma-Aldrich) were prepared every 2 days from solid drug and maintained under refrigeration (3–5°C). Mice and bovine viscera were handled according to guidelines, management protocols and under the consent of the National Health Service and Food Quality (SENASA, Argentina), and in accordance with the 2011 revised form of The Guide for the Care and Use of Laboratory Animals published by the U. S. National Institutes of Health. The experimental protocols using parasite samples obtained from bovine viscera and infected mice with E. granulosus were evaluated and approved by the Animal Experimental Committee at the Faculty of Exact and Natural Sciences, Mar del Plata University (permit number: 2555-08-15). Protoscoleces were removed aseptically from hydatid cysts of infected cattle slaughtered in the Liminal abattoir (official number: 3879) located in the Southeast of Buenos Aires, Argentina, as part of the normal work of the abattoir. Viable and morphologically intact protoscoleces (n = 3,000) were cultured using medium 199 (Gibco) supplemented with glucose (4 mg/ml) and antibiotics (penicillin, streptomycin and gentamicin 100 μg/ml) in 24-well culture plates under normal atmospheric conditions as we described in detail previously [22]. Murine cysts (with diameters ranging between 3 and 10 mm) were obtained from the peritoneal cavities of CF-1 mice 5 months after intraperitoneal infection with protoscoleces. Then, from 10 to 20 E. granulosus murine cysts per replica were incubated in Leighton tubes under the same culture conditions as described for protoscoleces [23]. In vitro protoscolex treatments were performed with 0. 2,2 and 10 mM Glb for 20 days while in vitro metacestode treatments were performed with 10,50,100 and 200 μM Glb, 2. 5 μM ABZSO (equivalent to 0. 84 μg/ml), and the combination of 10,50,100 and 200 μM Glb plus 2. 5 μM ABZSO for 7 days [23]. Parasites incubated in culture medium containing DMSO were used as controls. In vitro protoscolex cultures were kept at 37°C with medium changes every 4 days. The protoscolex viability was determined every two days by the methylene blue exclusion test (at least 100 protoscoleces per replica were counted each time). The metacestode viability was assessed daily by trypan blue staining of detached germinal layers. Each experiment was performed in triplicate and repeated three times. All of the experiments were carried out until the viability of the control was lower than 90% or all treated parasites were dead. Control and Glb-treated protoscoleces (200 μM Glb for 24 h) were incubated with 10 mg/ml JC-1 dye for 30 min at room temperature. After incubation, parasites were washed with 20 mM HEPES buffer, pH 7. 2, and images were taken using a confocal microscope (Nikon Eclipse C1 Plus). The intensities of green (excitation/emission wavelength = 485/538 nm) and red (excitation/emission wavelength = 485/590 nm) fluorescence were analyzed for 20 individual protoscoleces from control and treated-samples. Images were analyzed using Image J software (NIH). The ratio of red to green fluorescence of JC-1 images was calculated using NIH Image J software (http: //rsb. info. nih. gov/ij/). Changes in intracellular-free Ca2+ concentration ([Ca2+]i) were fluorometrically monitored using Fluo3 acetoxymethylester (Fluo3-AM) probe [24]. Experiments were carried out with 5 x 103 protoscoleces incubated under control conditions or treated with 200 μM Glb for 2 h. Then, the parasites were incubated with a solution containing 10 μM Fluo3-AM, 1 mM CaCl2,0. 1% v/v pluronic F-127 and 2 mM probenecid (Sigma, USA) for 30 min at 4° C in the dark. Subsequently, the protoscoleces were washed thrice in 20 mM HEPES buffer, pH 7. 2 (without calcium) and the fluorescence was immediately registered with a spectrofluorimeter (model F-4500; Hitachi) every minute for 2 h. Excitation was provided by the 488 nm line of a krypton-argon laser and the emitted fluorescence was collected using band pass filters: 505–530 nm. Each experiment was individually corrected for autofluorescence. Protoscoleces were also subsequently imaged with an inverted confocal laser scanning microscope (Nikon, Confocal Microscope C1). A UV spectrophotometric method was used for the estimation of intracystic Glb concentrations, which is based on measurement of absorption at a maximum wavelength of 242 nm [25]. Hydatid cyst fluid was extracted from metacestodes incubated under control conditions or treated with Glb (10,50,100 and 200 μM) for 5 days. Then, 500 μl of hydatid liquid from each sample were mixed with 2 ml chloroform and centrifuged at 7500 xg for 1 min, the supernatants were discarded, and the absorbance of the chloroform phase was measured at 242 nm. A standard curve was prepared using a double spectrophotometer (Shimatzu-UV-100) and different concentrations of pure Glb dissolved in hydatid liquid, which obeyed Beer’s law in the range of 5–30 μg/ml. Since both Glb and atorvastatin (ATV) are transported by OATPs in humans, we also analyzed the ATV uptake in E. granulosus cysts. For that, cysts were incubated with ATV (10 and 50 μM) and the drug uptake was determined by the spectrophotometric method above described using an ATV (5–30 μg/ml) standard curve [26]. Healthy female CF-1 mice (30–35 g, 8 weeks old) supplied by the SENASA, Mar del Plata were acclimatized for one week before initiation of the experiment. Mice were infected by intraperitoneal infection with 1,000 protoscoleces in 0. 5 ml of medium 199 to produce experimental secondary hydatid disease [23]. The animals were maintained in standard polyethylene cages (five mice per cage), under controlled laboratory conditions (temperature 20±2°C, 12 hour light/12 hour dark with lights off at 8. 00 p. m. , 50±5% humidity). Food and water were provided ad libitum. Every 3 days, animals were placed into a clean cage with fresh sawdust. All the pharmacological treatments were performed by intragastric administration of a drug suspension (0. 3 ml/animal). At the end of experiments, mice were euthanized by cervical dislocation and previous anesthesia with ketamine–xylazine (50 mg/kg/mouse– 5 mg/kg/mouse). All efforts were made to minimize suffering. A minimum number of animals was used in each experiment. At necropsy, the peritoneal cavity was opened, the hydatid cysts were carefully recorded, and the weights were determined from each animal. The efficacy of treatments was calculated using the following formula: 100 x { (mean cyst weight of control group) – (mean cyst weight of treated group) }/ (mean cyst weight of control group). In addition, samples were processed for scanning electron microscopy (SEM) with a JEOL JSM-6460LV electron microscope as previously described [22]. At 2 months post-infection (p. i.), mice were randomly assigned into four groups of 10 animals each. Drugs were administered by oral gavage daily for 60 days as follows: control group (receiving corn oil as a placebo), ABZ at 5 mg/kg/day, Glb at 5 mg/kg/day, and a combination of ABZ (5 mg/kg/day) plus Glb (5 mg/kg/day). At the end of the treatment period, animals were euthanized and necropsy was carried out immediately thereafter. Given that Glb is a substrate for OATPs, BLASTp searches for OATP homologs in the E. granulosus genome database (http: //www. sanger. ac. uk/Projects/Echinococcus, [27]) were carried out using Mus musculus and Homo sapiens orthologs as queries. These data allowed the identification of two putative orthologous genes of OATP whose predicted open reading frame were analyzed. Orthologs were selected based on reciprocal best BLAST hits [28,29] on an E-value cut-off of 1xe-25 and on the presence of the characteristic domains in the deduced amino acid sequences. Sequence alignments were generated with the CLUSTALX software program and modeling of secondary structure of the putative receptor was obtained from the deduced primary structure using Gen-THREADER (http: //bioinf. cs. ucl. ac. uk/psipred/). The prediction of transmembrane regions was analyzed with TMHMM Server v. 2. 0 (http: //www. cbs. dtu. dk/services/TMHMM), SACS HMMTOP program (http: //www. sacs. ucsf. edu/cgi-bin/hmmtop. py) and TOPO2 (http: //www. sacs. ucsf. edu/cgi-bin/open-topo2. py). Data within experiments were compared and significance was determined using the student’s t test and the non-parametric Mann-Whitney test. All data were shown as arithmetic mean ± S. D. and p values are indicated in each assay. To investigate the in vitro effect of Glb on the viability of E. granulosus protoscoleces, the percentage of dead parasites was analyzed in response to various Glb concentrations. As shown in Fig 1A, Glb exposure led to a significant dose- and time-dependent decrease in the viability of protoscoleces. The mortality rate reached 100% during the treatment with 10 mM Glb at day 20, whereas parasites treated with 2 mM or 0. 2 mM Glb showed a mortality rate of 80% and 60%, respectively. Control parasites remained at least 95 ± 5. 0% viable during the complete experiments. In addition, SEM studies demonstrated the unaltered structure of control larvae and the drug-induced ultrastructural damage on treated parasites (Fig 1B). After 7 days of treatment with 10 mM Glb, the soma region was contracted (Fig 1Bb) and loss of hooks, shedding of microtriches and sucker deformation were observed (Fig 1Bb-c). Given that Glb could dissipate the mitochondrial membrane potential [21], we studied the mitochondrial functional status using the ΔΨm indicator JC-1 in Glb-treated protoscoleces. JC-1, a positively charged fluorescent compound, can penetrate mitochondria and change its color as a function of ΔΨm. It accumulates as aggregates with intense red fluorescence within the mitochondria when the ΔΨm is high, or remains as green monomers in the cytoplasm and the mitochondria when the ΔΨm is low [30]. Control and Glb-treated protoscoleces were examined by confocal microscopy for JC-1 fluorescence. Following 24h treatment with 200 μM Glb, the relative values of red/green JC-1 fluorescence ratios showed low dispersion. At this point, untreated protoscoleces showed a ratio of red to green fluorescence with a mean value of 2. 3 (Fig 2Aa-d and 2B), whereas Glb treated protoscoleces showed a lower mean ratio of around 0. 3 (Fig 2Ae-h and 2B). Glibenclamide treatment induced an increase in depolarized regions indicated by the disappearance of red fluorescence and the increase of green fluorescence (Fig 2Af, h). Membrane depolarization induced by Glb would result in the opening of voltage-gated Ca2+ channels, inducing changes in the levels of intracellular Ca2+ [20,31]. Based on this and on the depolarizing effect of Glb on mitochondria, [Ca2+]i were determined in protoscoleces treated with 200 μM drug. Glibenclamide exposure showed an increase of three-fold in free [Ca2+]i over a 2 h observation period, compared with the control (Fig 3). The anti-echinococcal activity of Glb was also tested in E. granulosus metacestodes maintained in vitro for 7 days. After incubation with 200 μM Glb, metacestodes presented detachment of the germinal layer in ~ 60% and 100% of the cysts at days 5 and 7, respectively (Fig 4A and 4B). Interestingly, the decrease in cyst viability with the combination of Glb and ABZSO was more pronounced, reaching 60% with 50 μM Glb + 2. 5 μM ABZSO and 90% with 200 μM Glb + 2. 5 μM ABZSO after 5 days of treatment (Fig 4A). Conversely, the viability of metacestodes incubated with 2. 5 μM ABZSO was 85% at day 5, whereas control metacestodes remained at least 90% viable throughout the experiment. Studies by SEM revealed that control metacestodes exhibit no ultrastructural alterations in parasite tissue, showing an intact germinal layer comprised of a multitude of different, morphologically intact, cell types (Fig 4Ca, b). In contrast, Glb-treated metacestodes revealed loss of the typical multicellular structure (Fig 4Cc, d). Furthermore, Glb concentration was measured in cysts incubated with 10,50,100 and 200 μM Glb using hydatid liquid from untreated cysts as negative control. Mean intracystic drug concentrations were 10 ± 3 μg/ml, 38 ± 5 μg/ml, 43 ± 6 μg/ml and 62± 4 μg/ml, respectively (Fig 4D). Thus, the drug was concentrated relative to the culture medium in the treatments in which there was no membrane detachment (10 and 50 μM Glb, equivalent to ~5 and ~25 μg/ml drug in the culture medium), but not in those in which there was membrane detachment (100 and 200 μM Glb, equivalent to ~50 and ~100 μg/ml drug in the culture medium). The intracystic drug accumulation may be suggesting the presence of Glb transporters in the parasite. In order to investigate this possibility, we analyzed the presence of OATPs in E. granulosus. Extensive BLASTp searches on the available parasite genome revealed two genes coding for members of the OATP family (GeneDB systematic names EgrG_000970200 and EgG_000345700). Due to the high identity of these predicted proteins (GenBank accession numbers CDS16984 and CDS22241), with vertebrate OATP orthologs, their genes were named Eg-oatp-1 and Eg-oatp-2. The genes encode a 1019 and a 1040-amino acid protein, respectively, both of which show a membrane topology in accordance with the prototype transporter, which includes 10±12 α-helical transmembrane domains (TMDs) and a large extracellular loop between TMDs IX and X [32,33]. The Eg-OATP-1 and Eg-OATP-2 sequences aligned with ~ 27% and ~ 26% identity with the Homo sapiens ortholog (GenBank accession number AAH41095), respectively (S1 Fig). Moreover, the transcriptional expression of both genes was confirmed in different parasite stages [34]. However, although the transcript corresponding to Eg-oatp-1 was detected as a single sequence (GenBank accession number: EUB57978. 1), the corresponding to Eg-oatp-2 was detected as four sequences (GenBank accession numbers: EUB58511, EUB55076. 1, EUB55077. 1 and EUB55078. 1). It also is noteworthy that the transcript of Eg-oatp-1 was annotated as NEDD8-activating enzyme E1 catalytic subunit. Additionally, we examined the possible role of Eg-OATPs in ATV transportation in metacestodes. For that, drug uptake was determined by the spectrophotometric method above described, since the maximum absorbance of ATV is also observed at ~242 nm [26]. Atorvastatin concentration was between 50 and 125 μg/ml in cysts treated with drug concentrations in the range of 10–50 μM (equivalent to 5. 6 and 28 μg/ml). Based on our in vitro results, we then examined the in vivo therapeutic effect of Glb and ABZ on the growth of E. granulosus larval stage in the murine CE infection model. To do this, protoscoleces were intraperitoneally injected in CF1 mice and treated 2 months later by oral administration of vehicle, ABZ (5 mg/kg/day), Glb (5 mg/kg/day) or the combination of Glb plus ABZ (5 mg/kg/day plus 5 mg/kg/day) over a period of 60 days. All infected animals in this study developed hydatid cysts in their abdominal cavity. At 4 months p. i. , every treatment from the therapeutic efficacy study (ABZ, Glb and ABZ plus Glb) resulted in a significant reduction (n = 10 p <0. 05) of the cyst weights compared to those obtained from untreated mice (2. 78 ± 0. 310 g) (Fig 5A). Cysts developed in mice belonging to the combined therapy group (0. 4 ± 0. 01 g for Glb plus ABZ treatment) weighed significantly less (p < 0. 05) than those from the group treated with ABZ alone (1. 08 ± 0. 040 g), but not than those from the group treated with Glb alone (0. 2 ± 0. 023 g) (Fig 5A). No adverse effects or weight change were observed in mice. In order to analyze the ultrastructural changes of cysts recovered from the different treatments, SEM studies were performed. Cysts from control mice at 4 months p. i. appeared turgid, with a massive amount of intact cells in germinal layers (Fig 5Ba, b). In contrast, metacestodes collected from ABZ-, Glb-, or Glb+ABZ-treated mice displayed a marked reduction in the amount of germinal cells (Fig 5Bc-h), with the changes more pronounced in the groups receiving either Glb alone or the combined treatment as compared with the group receiving ABZ alone. In an attempt to find new anti-echinococcosis drugs, the effectiveness of Glb against the larval stage of E. granulosus was evaluated because this drug has been shown to be active against other parasites. This drug prevented in vitro growth of P. falciparum by inhibiting the transport of low molecular weight solutes in infected human erythrocytes [35]. In addition, Glb decreased the viability of Leishmania sp. both in vitro and in vivo, and this effect seems to be related to its role on Ca2+ homeostasis [36–38]. In vivo effects of Glb would not depend on the reduction in glucose supply to the parasite, since under normoglycemic conditions, the chronic administration of the drug modified neither plasma insulin nor IGF-1 levels [6]. Additionally, the Glb acute administration (10 mg/day) did not alter endogenous glucose production [39]. In this report, we demonstrated that Glb kills E. granulosus protoscoleces and metacestodes in culture and reduces cystic weight in a murine secondary hydatidosis model. The drug toxicity mechanism could be related to the mitochondrial membrane depolarization and the increase of [Ca2+]i detected in the parasite. Glibenclamide reduced in vitro viability of protoscoleces and metacestodes in a dose- and time-dependent manner (Figs 1 and 4). It should be noted that the tegumental uptake system and tissue compartmentalization are decisive aspects affecting the access of anthelmintic molecules to target sites in helminth parasites [40]. Particularly for metacestodes, we used concentrations in the range reported in other in vitro studies with mammalian cells [41]. However, higher concentrations were required for a pharmacological effect of the drug on protoscoleces, as it has been previously reported [22,23,24,42]. The metabolism and the complexity of the histological structure of the protoscolex (differentiated into different tissues) compared to that of the metacestode (delimited by a thin germinal layer of parasite cells) may account for such a difference in drug susceptibility between both larval forms [23,43]. In comparison to BZMs, Glb has a rapid in vitro protoscolecide action. Regarding albendazole and ABZSO, they decrease protoscolex viability to 50% after 25 days of treatment [44,45]. However, when using 200 μM of Glb such decrease was achieved even before 20 days of incubation (Fig 1A). Furthermore, the effect of sulfonylurea (both alone and combined with ABZO) on the turgidity and collapse of metacestodes was evident earlier than in the case of ABZ combined with other drugs whose targets are ion channels, such as PZQ or ivermectin [45,46]. In these latter reports, the rate of collapsed cysts exceeded 70% only after 10 days of treatment, whereas with 200 μM Glb + 2. 5μM ABZSO approximately 90% of the cysts collapsed after 5 days of incubation (Fig 4). Similarly to ABZ, PZQ and ivermectin, Glb induced a contraction of the posterior region of the soma and a loss of microtriches in the scolex region in protoscoleces (Fig 1B) [44–46]. Moreover, loss of the characteristic multicellular appearance of the germinal layer was observed in metacestodes incubated with the drug (Fig 4C), as has been reported for modulating compounds of Ca+2 [42,47]. Additionally, the results of in vitro assays in the presence of low Glb concentrations (at which membrane detachment was not observed, Fig 4A) showed drug accumulation in the cysts, thus suggesting the presence of transporters involved in the uptake of Glb in the parasite (Fig 4D). Given that OATPs are involved in the uptake of the drug into different human tissues, they fit as candidate proteins [48]. In addition, the intracystic accumulation of ATV (this work) —a known substrate of OATPs—also suggests the presence of these transporters [49]. In this work, we identified two sequences that encode members of the OATP family of E. granulosus (Eg-oatp-1 and Eg-oatp-2), which conserve the characteristic topology of the prototype transporter (S1 Fig) and are expressed in different E. granulosus stages [34]. Since human OATPs mediate cellular uptake of a wide variety of endogenous amphipathic organic compounds (such as bile salts, steroid conjugates, certain oligopeptides and thyroid hormones) as well as certain drugs [50], it would be interesting to study the functional activity of the Eg-OATPs in the uptake of Glb and host molecules. In our in vivo experiment, Glb showed anti-echinococcal activity with a clear reduction in cyst weight compared with the unmedicated control mice (Fig 5). In these experiments, ABZ was used in combination with Glb as a strategy to enhance its therapeutic action against CE. However, the results indicate that Glb inhibited the growth of the parasite in a similar way to the combined treatment, with both treatments significantly more effective than the monotherapy with ABZ (Fig 5A). Likewise, the ultrastructure of the cysts extracted from treated mice with Glb and Glb + ABZ showed the damage in germinal layer with a higher absence of cells compared to those obtained from mice treated only with ABZ (Fig 5B). Therefore, unlike in vitro trials with ABZSO (Fig 4A), Glb + ABZ combined treatment was not more effective than the Glb monotherapy. In contrast to ABZ oral administration, which presents erratic absorption in the gastrointestinal tract, Glb is rapidly and completely absorbed (> 95%), reaching peak plasma levels (140–430 ng/ml) between 2 and 4 h [39,51,52]. Therefore, further research should focus on attempting to modify the ABZ formulations to highlight the potential synergistic parasiticidal effect of the Glb + ABZ combination suggested by our in vitro experiments. On the other hand, since the dose of Glb selected for in vivo tests (5 mg/kg/day) was ten times lower than the highest human dose recommended (20 mg/day, after considering the pharmacokinetic differences between humans -t1/2 ~ 8 h- and mice -t1/2 ~ 1 h-), the dose of Glb could also be adjusted, considering that the selected dose is ~ 600 times less than the 50% lethal dose for mice (3250 mg/kg) [11,38]. The amount of Glb dosed in our in vivo experiment would reach lower plasma levels than the drug concentrations used in the in vitro assays. The fact that Glb has a high level of plasma protein binding [53], could favor its solubility in plasma, resulting in a more powerful effect of the drug in vivo than in vitro. The high in vivo potency of Glb could also be explained by the pleiotropic effects of this drug previously described in rodent models of other human diseases [54]. Given that the binding of Glb to SURx produces the closure of KATP channels, reducing cellular potassium efflux and thus favouring membrane depolarization and the increase of [Ca2+]i [55,56], we analyzed the presence of the two subunits that constitute KATP channels in the E. granulosus genome. Although a Kir subunit could not be detected, it was determined that the parasite encodes an ABCC protein (EgrG_000592100 of 1998 amino acids) with 37–25% identity to the H. sapiens SUR regulatory subunit (Q09428 of 1581 amino acids). This protein is annotated as a multidrug resistance-associated protein (MRP), but the gene transcript is most similar to a canalicular multispecific organic anion transporter 2 (GenBank accession number: EUB57766) [27,34]. Therefore, the inhibitor effect of this drug on Echinococcus larval stage viability cannot be explained through the presence of a typical SUR in the parasite. However, it has been previously described that Glb can induce changes in membrane potential and calcium homeostasis through mechanisms independent of SUR [20,21]. In line with this evidence, our results demonstrated mitochondrial membrane depolarization and increase of [Ca2+]i in Glb-treated protoscoleces (Figs 2 and 3). These data allow us to suggest that Glb could induce changes in the membrane excitability and consequently mitotoxicity in this cestode [16,17]. Further studies should be carried out in order to characterize the action mechanism of Glb in Echinococcus sp. Since helminth infections are endemic in developing countries, we have explored the possibility of repositioning antidiabetic drugs such as metformin [23,57] and Glb (this work), given that these drugs attack energy-generating systems [19] interfering with the mitochondrial activity and the ATP generation of this parasite [58], with high safety for the normoglucemic host. Further studies are needed in order to thoroughly investigate the mechanism involved in the therapeutic response of the E. granulosus larval stage to treatment with Glb.

In this work we demonstrated the in vitro and in vivo efficacy of Glb against the larval stage of Echinococcus granulosus. At the cellular level, the drug triggered mitochondrial membrane depolarization and increased intracellular Ca2+ levels, thus affecting ATP generation in the parasite. In addition, since intracystic Glb concentrations were higher than those used in the external medium, we proposed that the drug might enter the cyst through cell surface transporters. The observed effect of the drug on the growth of hydatid cysts in mice leads to the consideration of a novel role of Glb in CE treatment. Therefore, our further studies will focus on the evaluation of ABZ formulations with enhanced bioavailability to achieve an improved in vivo anti-echinococcal effect using both drugs simultaneously.

Abstract Introduction Materials and methods Results Discussion

fluorescence imaging invertebrates medicine and health sciences cestodes helminths depolarization membrane potential tropical diseases electrophysiology parasitic diseases animals pharmaceutics neglected tropical diseases mitochondria bioenergetics cellular structures and organelles research and analysis methods mitochondrial membrane echinococcosis imaging techniques flatworms drug therapy biochemistry helminth infections eukaryota cell biology physiology biology and life sciences energy-producing organelles organisms

2017

Anthelminthic activity of glibenclamide on secondary cystic echinococcosis in mice

Sex chromosome dosage differences between females and males are a significant form of natural genetic variation in many species. Like many species with chromosomal sex determination, Drosophila females have two X chromosomes, while males have one X and one Y. Fusions of sex chromosomes with autosomes have occurred along the lineage leading to D. pseudoobscura and D. miranda. The resulting neo-sex chromosomes are gradually evolving the properties of sex chromosomes, and neo-X chromosomes are becoming targets for the molecular mechanisms that compensate for differences in X chromosome dose between sexes. We have previously shown that D. melanogaster possess at least two dosage compensation mechanisms: the well- characterized MSL-mediated dosage compensation active in most somatic tissues, and another system active during early embryogenesis prior to the onset of MSL-mediated dosage compensation. To better understand the developmental constraints on sex chromosome gene expression and evolution, we sequenced mRNA from individual male and female embryos of D. pseudoobscura and D. miranda, from ∼0. 5 to 8 hours of development. Autosomal expression levels are highly conserved between these species. But, unlike D. melanogaster, we observe a general lack of dosage compensation in D. pseudoobscura and D. miranda prior to the onset of MSL-mediated dosage compensation. Thus, either there has been a lineage-specific gain or loss in early dosage compensation mechanism (s) or increasing X chromosome dose may strain dosage compensation systems and make them less effective. The extent of female bias on the X chromosomes decreases through developmental time with the establishment of MSL-mediated dosage compensation, but may do so more slowly in D. miranda than D. pseudoobscura. These results also prompt a number of questions about whether species with more sex-linked genes have more sex-specific phenotypes, and how much transcript level variance is tolerable during critical stages of development. Differing dosage of sex chromosomes is one of the most significant forms of natural genetic variation that animals with genetic sex determination face. In Drosophila, like humans, females have two X chromosomes, while males have one X and one Y chromosome. Thus, roughly half of the population (males) is hemizygous for the entire X chromosome. Throughout evolutionary history, many different mechanisms have evolved to compensate transcription for this difference in sex chromosome dosage [1], [2]. Eutherian mammals transcriptionally inactivate one of the two X chromosomes in females, the nematode Caenorhabditis elegans downregulates gene expression from both X chromosomes in XX hermaphrodites, while Drosophila upregulate expression from the single X chromosome in males [3]–[5]. Thus, uncompensated sex chromosomal dosage is clearly a problem for many lineages with XY sex chromosomes, solvable in many different ways. Note that ZW systems, where females are the heterogametic sex, often lack chromosome-wide dosage compensation, though gene-specific compensation mechanisms have evolved in these systems [6], [7]. The canonical Drosophila dosage compensation mechanism involves the formation of the MSL protein complex (or dosage compensation complex; DCC), which consists of at least 5 proteins: male specific lethal- 1,2, and 3 (MSL-1, MSL-2, MSL-3), males absent on the first (MOF), and maleless (MLE), and two non-coding RNAs RNA on the X 1 and 2 (rox-1, rox-2). MOF is a histone acetylase that catalyzes the acetylation of lysine 16 of histone H4 (H4K16ac) across the X chromosome in males. The DCC binds the X chromosome at particular sites (known as high affinity or chromatin entry sites), and is thought to spread to adjacent actively transcribed genes. Through this process, the chromatin landscape of the X chromosome is altered, to produce a twofold upregulation of transcription of the single male X (see [8]–[10] for review). The establishment of MSL-mediated dosage compensation occurs after the onset of zygotic transcription. A handful of zygotic genes are transcribed prior to and during stage 4 (mitotic cycles 10–13, [11], [12]), but widespread zygotic activation of transcription occurs during mitotic cycle 14 (stage 5), at the time when the blastoderm is undergoing cellularization. The male-specific component of the DCC, msl-2, is not observed until late in stage 5 [13]–[15], and the earliest observation of the H4K16ac established by the DCC is at stage 9 [13], [14], leaving a gap of several hours of development between the onset of zygotic transcription and the establishment of MSL-mediated dosage compensation. As there are many important processes occurring during this period of development (such as mitotic cycling, segmentation along the anterior-posterior axis, the formation of germ layers along the dorsal-ventral axis, cellularization of the blastoderm, and gastrulation) but no canonical dosage compensation mechanism, we set out in a previous study to characterize gene expression in female and male Drosophila melanogaster embryos during this period of development [15]. In order to do so, we developed techniques to sequence mRNA from single embryos, allowing for precise staging and acquisition of sex-specific data. In that study, focusing on stages 3–5 (spanning the onset of zygotic transcription), we found that about half of zygotically expressed genes on the X chromosome had roughly equal transcript levels between female and male embryos. Thus, there was some form of incomplete early zygotic dosage compensation, by unknown mechanism (s), before the onset of MSL-mediated dosage compensation, consistent with earlier genetic studies on a single gene active during this period [16], [17]. This left many questions unanswered, including several evolutionary ones: Does early zygotic dosage compensation vary across Drosophila species? If so, how did early zygotic dosage compensation evolve? Is it dependent on gene content or age of the X chromosome? And, additionally, since our previous study in D. melanogaster ended at the end of stage 5, could we, by extending the timecourse, observe the onset of MSL-mediated dosage compensation? What would that look like? And how would it compare to early zygotic dosage compensation? The complex evolutionary history of sex chromosomes in Drosophila provides a basis for addressing many of these questions. Fusions of ancestral sex chromosomes to autosomes have happened numerous times in the evolutionary history of Drosophila, leading to the creation of new (neo) sex chromosomes [18]. As Drosophila males lack meiotic recombination, male-specific neo-sex chromosomes (neo-Ys) are completely sheltered from recombination and degenerate over time due to the decreased efficacy of natural selection on a non-recombining chromosome [19]. As genes on the neo-Y degenerate, their homologous copies on the neo-X become hemizygous in males and potential targets for dosage compensation. Here we focus on two closely related species, Drosophila pseudoobscura and Drosophila miranda, which carry neo-sex chromosomes of different evolutionary ages (Figure 1A). In the lineage leading to both D. pseudoobscura and D. miranda, the ancestral X chromosome (XL in these species) fused with an autosome (Muller element D, or 3L in D. melanogaster), to create chromosome XR, roughly 15 million years ago (MYA). XR has acquired many properties typical of an X chromosome including complete MSL-mediated dosage compensation [20], [21], whereas the non-recombining homolog of this chromosome is almost entirely degenerated and heterochromatic [22], as expected of a Y chromosome. Along the lineage leading to D. miranda, there has been an additional sex chromosome fusion [23] which arose ∼1 MYA [24], that of the Y chromosome to the autosome designated as chromosome 3 in D. pseudoobscura (Muller C, or 2R in D. melanogaster) [25]. This fusion formed a neo-Y that has now partially degenerated [25]–[29], with about half of the open reading frames (ORFs) of neo-Y genes disrupted by deletions, insertions, frameshift mutations, and premature stop codons [30]. Many neo-Y genes with these types of disruptions in ORFs likely fail to produce functional proteins, and many genes are transcriptionally silenced or expressed at a lower level from the neo-Y [30], and are thus truly hemizygous in males. At the same time, the neo-X of D. miranda, the formerly autosomal homolog of the neo-Y, is acquiring MSL-mediated dosage compensation [20], [31], [32], with about half of actively transcribed genes on the neo-X being targeted by the MSL complex [33]. To take advantage of this snapshot into sex chromosome and dosage compensation evolution, we generated a timecourse of transcript levels in female and male embryos in D. pseudoobscura and D. miranda, using mRNA sequencing (mRNA-Seq). We wanted to determine transcript levels during two important periods of embryonic development: the maternal to zygotic transition (MZT), when maternal transcripts begin to degrade and widespread zygotic transcription is initiated, and the onset of MSL-mediated dosage compensation (Figure 1B). Widespread activation of zygotic transcription occurs during stage 5, roughly 2. 5 hours into development in D. melanogaster. The exact point at which MSL-mediated dosage compensation is established in D. melanogaster is unclear, but the histone modification that is deposited by the MSL complex (H4K16ac) is not detectable until stage 9 by antibody staining in D. melanogaster [13], [14]. To sample both of these important events, we began our timecourse at the earliest stage readily distinguished by morphological features (stage 2), sampled through mitotic cycles four and five, and then again before, during and after stage 9, for a total of eight timepoints. Our single-embryo RNA sequencing approach was particularly beneficial here, as it allowed us to sample embryos from each species at precisely matched developmental stages based on morphology and not developmental time, which varies between species. It also allowed us to determine the sex of each embryo prior to sequencing and thereby select samples to generate sex-specific expression data. We chose eight stages with readily identifiable diagnostic morphological features (Figure 1B). We collected, dechorionated, and imaged live embryos from each species, and harvested embryos at the desired stages, extracting both DNA and RNA for subsequent analysis. We used the DNA to determine the sex of each embryo using a PCR-based assay, employing redundant primer sets to detect the presence of a Y chromosome or two X chromosomes in D. pseudoobscura, and primer sets to determine the presence of a neo-X vs. neo-Y in D. miranda (see Methods). We chose three female and three male embryos for each species, for each of the eight stages, for a total of 96 samples (see Table S1). We prepared single embryo mRNA sequencing libraries from RNA from each of the selected embryos, without amplification of input RNA (see Methods), and sequenced the libraries on an Illumina HiSeq 2000 DNA sequencer. We aligned mRNA-Seq reads to the D. pseudoobscura reference sequence (Flybase release 2. 25) or the D. miranda reference sequence [30] using Bowtie [34] and TopHat [35], and inferred transcript levels using Cufflinks [36]. We normalized transcript levels between samples so that the total inferred transcript levels of chromosomes 2 and 4 (which are autosomes in both species) were identical. Like in our previous study utilizing the single-embryo RNA-Seq technique, the data produced by this method are highly reproducible, with Spearman' s rank correlation coefficients between replicate samples (same stage, same sex) exceeding 0. 95. In order to distinguish transcripts of zygotic origin from those deposited in the egg by the mother, we analyzed embryos that were all hybrids between genetically distinct parental lines within each species. For D. pseudoobscura, all mothers were Flagstaff 14 and all fathers PP1134. For D. miranda, all mothers were MSH22 and all fathers SP138. We used genomic sequence data for each pair of lines to identify 90,347 single nucleotide polymorphism (SNPs) fixed between the parental lines, and covered by transcripts in our dataset in D. pseudoobscura (92% of expressed genes in our dataset have SNPs). Similarly, in D. miranda, we identified 31,189 SNPs fixed between parental lines (75% of expressed genes in our dataset have SNPs), not counting those that distinguish the neo-X from the neo-Y (both D. miranda genomes were female, so additional information was needed to compare the neo-X to the neo-Y). This is consistent with a reduced level of polymorphism in D. miranda [37], [38]. We also identified 14,060 SNPs in coding regions of genes that distinguish the neo-X from the neo-Y, covering 68% of expressed genes on this chromosome, similar to the genome-wide average (though genes on the neo-X/neo-Y that have SNPs have more divergent sites than those elsewhere, due to the divergence between the neo-sex chromosomes). For this study, we are primarily concerned with the zygotic transcript abundance for female and male embryos, as maternal mRNA deposition to female and male embryos does not differ. While mRNA-seq reads containing maternal alleles could be derived from either maternally deposited or zygotically transcribed RNAs, reads containing paternal alleles can only have come from zygotic transcription, and therefore can be used as a proxy for zygotic expression. We explored a number of different methods for defining which genes were zygotically expressed, all of which gave consistent results. Here, we use two definitions of zygotic expression for a gene. The first we primarily show (Figures 3,4, and 6) is based on having a high proportion (∼50%, see Methods) of reads that are paternally derived; this is determined using female transcript levels (as males have no paternal X). The allele-specific definition of zygotic expression is used to analyze total transcript level. The second definition of zygotic genes is based on a gene having very low total expression at early stages and high total expression at later stages (Figure 5). This second definition does not depend on the allele-specific data, and so we use this definition for analysis of allele-specific transcript level (see Methods for further information and explanation of method choice, and also figure legends for descriptions of definitions used in each figure). Both methods call zygotic genes separately in each species, and for the allele-specific method, genes are called separately for each stage. Transcript levels across all genes for this period of early development are highly conserved between D. pseudoobscura and D. miranda (Figure 2A), across both females and males (the same stage between species, Spearman' s rho of 0. 85–0. 92, as compared to a mean of 0. 95 for replicates of the same stage within either D. pseudoobscura or D. miranda, see Table S2). This is as expected for closely related species, due to a high level of conservation of transcript level between morphologically homologous stages between the species, and supports the accuracy of our embryo staging based on morphology. We used our allele-specific data to explore the activation of zygotic transcription in each species, using paternal reads as a proxy for zygotic transcription. Examining the average per gene proportion of zygotic reads (i. e. 2x fraction of allele specific reads that are paternal), we find that there is an offset in the timing of zygotic expression between the species (Figure 2B). D. miranda has significantly fewer zygotically derived transcripts per gene on average present from the beginning of zygotic transcription in stage 5, up to stage 8–9 or 10, where its zygotic transcript level equals D. pseudoobscura (see Table S3 for bootstrap confidence intervals). This pattern of lower levels of zygotic transcript per gene in D. miranda is species-specific, rather than chromosome-specific, as both autosomes and X chromosomes show the same fraction of zygotic transcripts within each species (Figure 2B). Could the differences we observe in onset of zygotic transcription between D. pseudoobscura and D. miranda affect subsequent analyses? To address this, we first asked whether the same morphological stage in both species is also the best comparison based on transcript level. We examined pairwise correlations of transcript levels between both sexes and species, for both the set of all genes, and for autosomal genes (Figure S1 and Table S4). Individuals of a particular sex and stage in a species are always highly correlated with the same stage, regardless of sex. This holds across species as well, though the correlations are slightly lower. In the majority of cases, the highest correlation is between individuals of the same stage. Occasionally, a highly developmentally similar neighboring stage has a statistically slightly higher correlation, but this is not systematic across comparisons. Thus, we believe that there are no systematic differences in development rate that are unaccounted for by our sampling, either between species, or between sexes within a species. This would indicate that the transcriptional delay between these two species is less than a single stage by our sampling. Additionally, in subsequent analyses, we will define genes as zygotic separately for each species, and using the allele-specific zygotic definition, per stage. While we cannot rule it out, these factors should minimize the contribution of the delay in zygotic transcription in D. miranda to subsequent analyses. At the onset of zygotic transcription, zygotic genes on the X chromosome are largely female biased. This female bias decreases over developmental time, and by stage 12, the X chromosomes are no more sex biased than the autosomes, indicating complete dosage compensation of zygotic genes (Figure 3A, Figure S2 for a different zygotic definition showing the same results). The onset of dosage compensation appears to be earlier in development in D. pseudoobscura than in D. miranda, though by stage 12, the two species reach equivalent levels of complete compensation (Figure 3A). The delay in onset of zygotic transcription in D. miranda may contribute to this, though we note that our zygotic genes are called in a stage and species-specific manner, minimizing the contribution of the delay to the compensation phenotype. As shown in Figure 3A, D. miranda has significantly more female-biased zygotic genes than D. pseudoobscura on XL and XR in nearly all developmental stages prior to stage 12 (See Table S5 and S6 for chi-squared test p-values for comparisons at various female-bias cutoffs). In early development, D. miranda has many zygotic genes on chromosomes XL and XR in the >2x female bias category, and significantly more than D. pseudoobscura until the last few stages, (Table S5). This is consistent with the view that the majority of zygotic genes in D. miranda are completely uncompensated at early stages, as then we would expect expression levels to be distributed reasonably symmetrically around 2x female bias. Sex-biased expression is much less prevalent on the autosomes, but D. miranda does have a significantly higher female bias on the autosomes for the early stages of zygotic transcription (stage 5, see Tables S7 and S8). After these early stages, D. miranda autosomes lose their sex-bias, and if anything, become slightly male-biased. The increase in X-linked female bias might be promoting the significant female-bias on autosomes during the early developmental stages. For example, an uncompensated X-linked transcription factor could upregulate autosomal targets in females. The gradual onset of dosage compensation could reflect the slow establishment of MSL-mediated dosage compensation chromosome-wide. Or, alternatively, it could be that the subset of genes expressed zygotically early in development are less likely to be dosage compensated throughout development. To examine this, we partitioned genes based on the stage at which they first meet our definition of being zygotically transcribed (see Methods). We then compared the extent of compensation of these different groups of genes at identical timepoints, and found that they had similar temporal patterns of dosage compensation (Figure 3B). This suggests that compensation status is a stage-specific, and not gene-specific property, and that by whatever mechanism compensation is occurring, the effect is genome wide. In addition to the mean behavior per gene, we visualized the sex bias for each gene (Figure S3) over development, which demonstrates the same overall pattern, where the female bias of early zygotic X-linked genes decreases in concert over developmental stages. How does this compare to our previous results for the pre-MSL early-zygotic period in D. melanogaster? In D. melanogaster roughly 20% of the genome is in sex chromosomes, in D. pseudoobscura it is 40% and 60% in D. miranda (though roughly half of the genes on the neo-X/Y are not truly hemizygous in males, as the neo-Y copies are likely still functional). When we compared the same developmental stage (late Stage 5) between D. miranda, D. pseudoobscura and D. melanogaster (using our earlier data described in [15]), D. melanogaster has the least female bias, and D. miranda the most (Figure 4, Figure S4 for different zygotic definition showing similar results). Comparing just the homologous X chromosome (X in D. melanogaster, XL in the other species), D. melanogaster is significantly less female-biased than either D. pseudoobscura or D. miranda (p<0. 002, Wilcoxon rank sum test). The comparison between D. pseudoobscura and D. miranda for this same chromosome and stage is borderline significant (p = 0. 05, Wilcoxon rank sum test), with D. miranda showing more female bias. The relatively low level of significance for this D. pseudoobscura and D. miranda comparison may be due to the relatively small number of genes on XL, as the XR comparison between D. pseudoobscura and D. miranda is significantly different (p = 2×10−8, Wilcoxon rank sum test) and the magnitudes of the differences in median are similar for XL and XR (1. 7∶1. 9x female bias for XL; 1. 7∶2x for XR). As the X we are comparing between all three species is the ancestral X chromosome of all Drosophila species, the lack of dosage compensation in D. pseudoobscura and D. miranda is not simply a product of insufficient time to evolve dosage compensation. To further explore the dynamics of dosage compensation onset, we examined transcript levels of zygotic genes (defined based on total expression levels, see Methods) in an allele-specific manner. In the early stages of expression for zygotic genes, during stage 5, we observe that both the single male X and each of the two female X chromosomes have similar average transcript levels, indicating no dosage compensation for these stages, in contrast to what we have previously found in D. melanogaster [15]. While D. melanogaster showed some dosage compensation from the beginning of zygotic transcription, the average zygotic gene in D. pseudoobscura and D. miranda has no dosage compensation for several stages after the onset of zygotic transcription. The allele-specific transcript abundances show the onset of dosage compensation, as the single male X chromosome begins to have significantly more zygotic transcripts per gene than each of the two female X chromosomes (Figure 5, Table S9; also see Figure S5, Table S10 for a different zygotic definition). By the end of this period of development, the male X is transcribed at twice the level of each female X in both species, indicating that dosage compensation has been fully established. This reduction in female bias over time is consistent with the predicted onset of MSL-mediated dosage compensation. We are likely observing the onset of zygotic transcription without dosage compensation followed by the onset of MSL-mediated dosage compensation in these species. There are differences in the timing of onset of dosage compensation in the two species. For an average zygotic gene, the single maternally derived X chromosome in males has a significantly higher transcript level than the maternal X in females (with the mean level for males falling outside the 95% bootstrap confidence interval for the female mean for both XL and XR) at stage 7 in D. pseudoobscura, whereas this is observed later in D. miranda, between stages 8 and 9 (Table S9, S10). Again, the delay in onset of zygotic transcription in D. miranda may contribute to this result, though we do not believe that the delay in zygotic transcription creates an offset equivalent to an entire stage between the species (see above). There are no significant differences between transcript abundances of autosomes by this same method. We had hoped to use our expression data and chromatin immunoprecipitation sequencing (ChIP-seq) data for MSL binding in D. miranda larvae [33], to observe the process of MSL-mediated dosage compensation being established in physical space along the chromosome. The MSL complex is thought to bind initially at high affinity or chromatin entry sites (HAS or CES; [39], [40]), and subsequently spread along the chromosome [10]. We first asked if compensated or uncompensated genes were closer together than we would expect by chance. There was no detectable signal of this, when we took into account that zygotic genes are themselves closer than expected (see Methods). We then examined genes based on their proximity to predicted HAS in D. miranda [33], and found that genes that were closer to HAS were no more likely to be compensated than those further away (see Methods), at any stage. We also examined the average H4K16ac enrichment per gene from ChIP-seq data in D. miranda larvae [33], and found that by stage 12, genes that were dosage compensated in the embryo were significantly more likely to be enriched for H4K16ac than non-compensated genes (with both sets falling outside the 95% bootstrap confidence intervals of the other category, see Table S11). This is consistent with genes compensated by stage 12 in our embryo data being targets of the MSL complex. Interestingly, at the earliest stage of widespread zygotic expression, mid-stage 5, we find that genes that are compensated in our data are significantly less likely to be enriched for H4K16ac in larvae than uncompensated genes (see Table S11, for bootstrap confidence intervals). This suggests that the small numbers of compensated genes found during this period are not compensated later by MSL, or are not being transcribed in the larval stage, and perhaps then have distinct compensation mechanisms. While we are able to observe the effects on gene expression before or after the onset of MSL-mediated dosage compensation, we are unable with this data to observe the process of establishing the MSL compensation mechanism (e. g. we do not find that genes that are compensated early during the establishment of MSL are nearer HAS sites). Perhaps we do not have appropriate time resolution of samples during this time period, or perhaps the genes targeted by MSL are largely different in embryos than they are in larvae. More mechanistic studies of the onset of MSL-mediated dosage compensation in a developmental context are needed. In examining zygotic X chromosomal gene expression in D. pseudoobscura and D. miranda, we can compare X chromosomes of three evolutionary ages: XL, the X ancestral to all Drosophila species; XR, which arose ∼15 MYA; and the neo-X in D. miranda, which arose 1–1. 5 MYA. Chromosome XR has been shown in previous studies to possess all the properties of an X chromosome, and in many ways it is indistinguishable from the ancestral X [20], [21], [33]. Our findings for transcript levels for both female and male embryos for the developmental periods both prior to and during the onset of MSL-mediated dosage compensation support this conclusion. The dosage compensation status of the neo-X chromosome is more complicated to assess, as the neo-Y chromosome still possesses functional copies of many genes (about half of the genes, [30]). When we combine reads from the neo-X and neo-Y (which we refer to as neo-XY) to count total transcripts in males for each gene, we find that it is better compensated at early stages than either of the older X chromosomes (Figures 3A, 3B, 4). This result is potentially due to retained transcription of neo-Y copies of genes. To distinguish whether the compensated genes on neo-XY are due to neo-Y transcription or due to a better-compensated neo-X, we again examined our allele-specific data to distinguish neo-Y reads from neo-X reads (see Methods). We find generally lower levels of transcripts from the neo-Y (Figure 5, dashed blue line) compared to the neo-X (Figure 5, solid blue line). Additionally, there is a striking pattern of the single neo-X in the male showing a higher mean transcript level than either neo-X in the female (Figure 5). The higher transcript level from the single male neo-X is similar in its temporal pattern to the compensation we see on the evolutionarily older X chromosomes. Thus, consistent with studies in larvae and adults [20], [31]–[33], the neo-X in D. miranda is to some degree dosage compensated at later stages, presumably through the onset of MSL-mediated compensation. When determining compensation status of a gene on the neo-XY, we must consider whether the neo-Y copy of the gene is functional. Transcripts are often produced whether the gene is capable of making a functional protein or not, in some cases then, total transcript level is not an accurate measure of functional dosage compensation. A previous study [30] classified genes on the neo-Y as having an intact or disrupted open reading frame (ORF). The ORF disruptions are due to frameshift mutations, premature stop codons, or sizable deletions, and thus make the neo-Y copy of the gene unlikely to make a functional protein. It is these genes that are likely to be, on the functional level, truly hemizygous in males, and thus in need of compensation. When we ask specifically about compensation in genes with or without intact ORFs, we find that both are equally well compensated, both early and late in embryogenesis (Figure 6A, Wilcoxon test p-values in Table S12). Thus, dosage compensation of any gene on the D. miranda neo-X in the embryo is generally uncorrelated with the neo-Y status of that gene (Figure 6A). This observation is consistent with findings from more mechanistic studies of the degradation and down-regulation of the neo-Y and acquisition of MSL dosage compensation on the neo-X, where the two processes appear to be independent from one another and primarily driven by the ancestral background chromatin state of the neo-sex chromosome [41]. This is apparent on a gene-by-gene level as well, for we observe genes with all combinations of predicted functionality, contribution of neo-Y transcription, and compensation status (Figure 6B). Were degradation of the neo-Y coordinated with the acquisition of dosage compensation, we would expect an overrepresentation of genes with disrupted ORFs amongst the compensated genes. Instead, we observe genes with disrupted ORFs who have not acquired dosage compensation, genes with intact ORFs who retain neo-Y transcripts who are precociously dosage compensated (as genes with functional neo-Y copies are not in need of dosage compensation), as well as every other combination (Figure 6B). Ultimately, to investigate how hemizygous genes in males on the neo-X are compensated in D. miranda, we must examine only neo-X transcript levels from genes with non-functional neo-Y ORFs. When we examine transcript levels from just the neo-X over developmental time (Figure 6A), we observe a significant difference between the neo-X and the two older X chromosomes (Wilcoxon test, almost all comparisons significant at p<0. 05, see Table S12). In the earliest stages of zygotic transcription, XL, XR, and the neo-X are similarly female biased in D. miranda; as early as stage 7, female bias begins to decrease in XL and XR relative to the neo-X. Through later stages, the neo-X decreases along a similar trajectory to XL and XR, but is always ∼0. 25x more female biased. This suggests that early zygotic expression is similarly compensated (i. e. a lack of compensation) between all of the D. miranda X chromosomes, but that the D. miranda neo-X has less effective (presumably MSL-mediated) dosage compensation at later stages. This is consistent with findings in larvae, where only about half as many genes are dosage compensated by MSL on the neo-X as compared to XL or XR [33]. In this study, we examine sex-specific transcript abundance across embryonic development, both before and after the onset of MSL-mediated dosage compensation. We find significant differences between species in the period prior to the onset of MSL-mediated compensation, where we had previously demonstrated in D. melanogaster that roughly half of zygotically expressed genes on the X chromosome are fully compensated. Conversely, for D. pseudoobscura and D. miranda, we find a general lack of compensation for X chromosomal genes during this pre-MSL period. We have also provided evidence for a delay in the onset of the later, more effective, MSL-mediated dosage compensation in D. miranda relative to D. pseudoobscura. The difference we have demonstrated in the effectiveness of compensation during the period of early zygotic expression is striking. X-linked genes in D. melanogaster genes have, on average, some degree of dosage compensation from the very beginning of zygotic expression, with the single male X having a higher transcript abundance than either single female X [15]. In contrast, genes in D. pseudoobscura and D. miranda male embryos have on average no dosage compensation until after gastrulation (or later in D. miranda), as the single male X has an average transcript level that is exactly the same as either one of the two female Xs, indicating that transcript level is entirely proportional to gene copy number (Figure 5). This indicates that either D. pseudoobscura and D. miranda lack a mechanism or some set of mechanisms that D. melanogaster possesses, or that the mechanism (s) are overwhelmed by the number of X-linked genes in the former two species. The average level of early zygotic dosage compensation we observe in D. melanogaster embryos [15] is consistent with studies on changes in dosage of autosomal genes [42]. In flies heterozygous for autosomal deficiencies of varying size, the mean transcript level of single copy genes is approximately 1. 5x (where 2x is the expression level of two copy autosomal genes), although there is considerable variance around this level for individual genes [43]–[46]. In D. melanogaster, we also found the female to male ratio for genes on the X chromosome for the early zygotic stages (Stage 5, mitotic cycle 14) was on average, 1. 5x [15]. This raises the possibility that both early zygotic X and autosomal aneuploidy dosage compensation might be due to a general mechanism that recognizes aneuploid genomic regions in D. melanogaster and compensates the expression of included genes, or that homeostatic mechanisms in some or all gene networks respond to abnormal levels of transcription by altering regulation of parts of the network [44], [46]. However, in this study, we observe an average female to male ratio of 1. 8x in D. pseudoobscura and nearly 2x in D. miranda for genes on XL and XR at these developmental stages, which is not consistent with the autosomal level of compensation for differences in gene dose observed in D. melanogaster. It is then possible that these species vary in their capacity to compensate for dosage differences in all stages or chromosomes, which could be tested by doing similar autosomal dose variation studies in species of the D. pseudoobscura lineage to those cited above for D. melanogaster. Or, it may be that the mechanism (s) responsible for X dosage compensation at the early zygotic stage of development is overwhelmed by the much larger number of genes that are present in a single copy in males of these species with many more X-linked genes. There is precedence for a limit of the number of genes that can be dosage compensated, also from the autosomal studies in D. melanogaster. In a classic 1972 paper, Lindsley et al. showed that D. melanogaster can tolerate single-copy deletions of roughly 1% of the genome (1. 5 Mb), and 3% is usually lethal [47]. The deficiencies described by [44], [48] are smaller than 1 Mb, and thus less than 1% of the genome. Additional studies have suggested that with increasing size of deletion, the additive effects of changing the dosage of an increasing number of individual genes results in the collapse of gene networks [44], [49], which could be responsible for the lethality in these animals. The X chromosome, at roughly 1/5 of the genome in D. melanogaster already exceeds the 3% viability threshold suggesting that the mechanism (s) conferring autosomal dosage compensation may not be the source of early zygotic dosage compensation in D. melanogaster. Additionally, these studies show that mechanism (s) that compensate for difference in gene dosage may have difficulty when the number of genes that are hemizygous increase, perhaps as with our species with larger numbers of X-linked genes. We have observed both a difference in early zygotic dosage compensation, and potentially a difference in timing of onset of MSL-mediated dosage compensation. Both findings are consistent with dosage compensatory mechanisms being overwhelmed in species where a larger percentage of the genome is held in sex chromosomes. Or, these findings may be explained by a lineage specific gain or loss of early zygotic dosage compensation mechanism (s). Additional studies of both the early zygotic stages and the onset of MSL-mediated compensation, with additional species of varying karyotype, are needed to distinguish between these possibilities. With transcript profiles of these stages from a number of additional species, we should be able to determine if there is a correlation between the effectiveness of compensation and the proportion of the genome held in sex chromosomes, or alternatively identify the point on the Drosophila phylogeny when the early zygotic dosage compensation phenotype was either gained or lost. Whatever the mechanistic basis of the difference in dosage compensation, additional studies are also needed to address the next obvious question: are there consequences of these differences in effectiveness of early zygotic dosage compensation and timing of the onset of MSL-mediated compensation? One could imagine several different outcomes. It could be that these (and other) species with less effective early dosage compensation or later onset of MSL-mediated dosage compensation will result in sex-specific phenotypic effects, and greater degrees of sexual dimorphism. Or, the inherent robustness of developmental systems will result in sex-specific differences in transcription having limited or no phenotypic effect on some or many developmental phenotypes. While many developmental processes have steps that are concentration-dependent, for example the number of transcription factor molecules in a particular space, it is exactly these kinds of processes that have been demonstrated to be the most robust to genetic, environmental, and stochastic variation. Understanding the developmental consequences of the variation in sex chromosome dosage will be critical to understanding how different mechanisms of dosage compensation evolve. Flies were raised on standard media at 18°C (preferred temperature of these species). Virgin females of the D. miranda MSH22 line were crossed to D. miranda SP138 males, and D. pseudoobscura Flagstaff 14 virgin females were crossed to D. pseudoobscura PP1134 males. Eggs were collected from these crosses, with mothers being between 5–10 days old, and numerous enough (hundreds) to minimize chances of multiple sample embryos coming from a single mother. After collection, eggs were dechorionated, placed on a slide in halocarbon oil, and visualized using light microscopy with a DIC filter on a Nikon Eclipse 80i microscope, with a Nikon DS-UI camera, and the NIS Elements F 2. 20 software. Morphological features were used to collect embryos from stages 2,4, mid stage 5, late stage 5, stage 7, the threshold between stages 8 and 9, stage 10, and stage 12, for a total of 8 stages. Embryos were then moved from the slide, cleaned from excess oil, and placed in a drop of TRIzol reagent (Invitrogen) within a minute or less of imaging. Embryos were then ruptured with a needle, allowed to dissolve, and moved to a tube with more TRIzol reagent, which was then frozen at −80C until extraction. RNA and DNA extraction from single embryos was done with TRIzol (Invitrogen) reagent according to manufacturers protocol, except with a higher volume of reagent relative to the amount of material (i. e. starting with 1 mL of TRIzol despite expecting very small amounts of DNA and RNA). Extracted DNA was amplified using the Illustra GenomiPhi V2 DNA Amplification Kit (GE Healthcare), and genotyping for sex was performed with PCR. D. pseudoobscura samples were genotyped by using Y specific primers described in Carvalho and Clark, 2005 [22], as well as from primers developed to distinguish X chromosomes in the two D. pseudoobscura lines (Flagstaff 14 and PP1134): pse_XX_L TGCTAAACATAATCAAGAGCGGCATCA pse_XX_R TTCCCAAGCTAGACGGACAACAGAAAC. For the D. miranda lines, we used two different sets of primers designed to distinguish the neo-X from the neo-Y, (mir_neoXY_1_L TGACTTACTGCTCACCATTGGCACACT, mir_neoXY_1_R TTGCTGGAATGAGTACGCATAGCCTCT; mir_neoXY_2_L GACCAGCATAAGTTCCCAAGGAGGAGT, mir_neoXY_2_R AATGACTGGGCAACTGTTTAACTTCA. From each species, 3 females and males were chosen for each of the 8 stages, resulting in 48 samples. The genotyping assay was imperfect, resulting in a few stages with differing numbers of males and females, the samples and their properties are listed in table S1. Extracted RNA was treated with TurboDNase (Ambion), mRNA-Seq library preparation was performed with Illumina TruSeq RNA kits, and samples were indexed to run 12 samples per sequencing lane, and sequenced on an Illumina HiSeq sequencer. Reads from each RNA-Seq sample were mapped to either the D. pseudoobscura genome (Flybase release 2. 25) or the D. miranda genome (http: //www. ncbi. nlm. nih. gov/genome/10915), using Bowtie and Tophat, and transcript abundances for annotated RNAs were called by Cufflinks. Data from each sample were normalized so that the total expression (reads per kb of sequence, per million mapped reads; RPKM) of autosomal genes (chromosomes 2 and 4) was constant. Analysis was also performed with normalization in D. pseudoobscura to all the autosomes in this species (chromosomes 2,3, and 4), as well as normalization to total read number rather than autosomal levels, all the normalization procedures we explored produced the same results relative to the X and neo-X chromosomes. SNPs were called from genomic DNA in both D. pseudoobscura lines and both D. miranda lines by aligning reads using Bowtie 2 (2. 0-beta5, [50]), and using both Samtools (0. 1. 18, [51]) and GATK' s Unified Genotyper [52], [53], and combining the SNPs called by these different methods using GATK. Called SNPs were kept only in the case where they were homozygous within both lines and differed between lines. Parameters were generally set to be permissive, as only SNPs that were covered and validated by RNA-Seq reads were used for allele-specific RNA-Seq analysis. SNPs between the neo-X and neo-Y chromosomes in D. miranda were obtained from aligning reads between neo-X and neo-Y with SOAPaligner (v2. 21), and calling SNPs with SOAPsnp (v1. 03), the SNPs were further filtered by the number of reads (>3 unique reads) supporting their existence. Methods for determining the predicted functionality of the neo-Y genes are detailed in [30]. To examine the percentage of genes with SNPs in our dataset, since only SNPs covered by reads were used, we first calculated the proportion of genes with at least one read (referred to in the Results as expressed genes), and determined the proportion of genes with SNPs compared to this total number of expressed genes. We identified all RNA-Seq reads overlapping SNPs that varied between strains, and counted the frequencies of reads that can be attributed to the maternal or paternal allele (using a custom python script). All maternal or paternal reads for each SNP per gene are combined, and divided by the number of SNPs per gene. All sequencing reads and processed data files used for analysis have been deposited in NCBI/GEO under accession number GSE53483 (http: //www. ncbi. nlm. nih. gov/geo/query/acc. cgi? acc=GSE53483). All analysis was performed in R (version 2. 15. 2, [54]). We used two definitions of zygotic genes, each determined for each species separately. One was based on the allele specific data (allele-specific definition), and determined which genes were zygotically expressed on a stage by stage basis, this required that the gene be above a minimum read level (>5 RPKM) and the paternal allele was greater than 40% of total transcript level (maternal+paternal reads), this is used in Figures 3,4 and 6. As males only had one allele for X chromosomal genes, we used the females to determine zygotic genes. We also used (but only show here in Figure 5) a definition of zygotic genes (transcript level definition) that simply required the gene to not have any maternal deposition (<5 RPKM at stage 2) and have read numbers >5 later in subsequent stages. Both of these methods had advantages, the allele specific definition is stage and species specific, and allowed for more genes to be counted as zygotic in later stages, as genes with maternally deposited mRNAs transitioned to zygotic transcription. The transcript level method allowed for more genes to be counted as it was based on total transcript level, and not necessary to have allele-specific data for that gene. We chose to use the transcription zygotic definition for Figure 5, because it avoids the circularity of conditioning an allele specific result on an allele-specific definition of zygotic activity. For Figure 2B, the percent of zygotic transcript was determined from the mean proportion of transcript attributable to the maternal and paternal allele per gene, depicted levels come from female embryos (due to the paternal allele on the X chromosome not being present in males). We determined the onset of transcription for individual genes (Figure 3B) by identifying the first stage they met our stage-specific definition of zygotic transcription. Female to male ratios of transcript abundance were determined for each developmental stage for those genes whose means across both females and males were above 5 RPKM. Figures 3,4 and 6, show female to male ratios for various chromosomes, groups of genes, stages, and species, for allele-specifically determined zygotic genes. Keeping these definitions constant produced a slightly different graph for D. melanogaster in Figure 4 than previously published [15]. To understand whether compensated genes were physically clustered on chromosomes, we calculated the mean distance from each compensated gene to the nearest compensated gene, and from each uncompensated gene to the nearest uncompensated gene. We compared these distances to a null distribution where we resampled zygotic genes at each timepoint, and similarly calculated the mean distance to the nearest compensated or uncompensated gene for each of the null replicates. Compensated or uncompensated genes are not on average closer together than what we would expect from a random sample of zygotic genes at any timepoint. It was necessary to draw the null distribution from zygotic genes, as zygotic genes are consistently closer together than expected by chance (compared to a null distribution resampled from all genes at each timepoint), this is observed on all chromosomes and across most stages. Using MSL high affinity binding site (HAS) data reported in [33], we tested for an association between compensation status and physical proximity to MSL high affinity sites. Over all chromosomes, we calculated the distance of each gene to nearest MSL HAS, and looked at mean sex ratio in lower quartile of the distance to the nearest MSL HAS compared to the upper three quartiles. The mean sex ratio in the lower quartile of the distributions of distances to the nearest MSL entry site looked no more compensated than the upper three quartiles (Wilcoxon test), in any stage. We partitioned our data into compensated and uncompensated and calculate the mean H4K16ac enrichment (data reported in [33]) over genes in each of these categories. In the earliest stage with zygotic expression, compensated genes on XL and the neo-X were significantly depauperate of H4K16ac binding, XR was not significant, possibly due to low sample size. In contrast, compensated genes at the last stage (stage 12) on, XL, XR, and the neo-X were marginally significantly associated with high H4K16ac binding. Thus, compensated genes in the early zygotic expression stages are not the same genes that are compensated by MSL in the larvae, and the genes compensated later in this period of development are targets for MSL-mediated compensation.

Many animals have sex-specific combinations of chromosomes. In humans, for example, females have two X chromosomes while males have one X and one Y. In most species with XX: XY systems, the Y chromosome is degenerate and gene-poor while the X encodes a large number of functional genes. A variety of systems have evolved to ensure that males with one X chromosome and females with two X chromosomes have the same gene expression level for X-linked genes. The vinegar fly D. melanogaster has at least two dosage compensation systems: one that acts early in development, and another active in later stages. In this paper, we determine expression levels for thousands of genes in male and female embryos at different developmental stages in two species, D. pseudoobscura and D. miranda, that have unusually large fractions of their genomes in X or X-like chromosomes. We show that dosage compensation is established slowly during embryogenesis, and that in these species, dosage compensation appears to be absent in early development. This may be due to a lineage-specific loss or gain of compensation mechanism, or possibly because the machinery of dosage compensation cannot effectively handle the increased demand in these species.

Abstract Introduction Results Discussion Methods

animal models genome expression analysis embryology developmental biology model organisms genome evolution molecular development gene expression genetics comparative genomics biology genomics evolutionary biology genomic evolution evolutionary genetics evolutionary developmental biology

2014

Sex-Specific Embryonic Gene Expression in Species with Newly Evolved Sex Chromosomes

Whether evolution is erratic due to random historical details, or is repeatedly directed along similar paths by certain constraints, remains unclear. Epistasis (i. e. non-additive interaction between mutations that affect fitness) is a mechanism that can contribute to both scenarios. Epistasis can constrain the type and order of selected mutations, but it can also make adaptive trajectories contingent upon the first random substitution. This effect is particularly strong under sign epistasis, when the sign of the fitness effects of a mutation depends on its genetic background. In the current study, we examine how epistatic interactions between mutations determine alternative evolutionary pathways, using in vitro evolution of the antibiotic resistance enzyme TEM-1 β-lactamase. First, we describe the diversity of adaptive pathways among replicate lines during evolution for resistance to a novel antibiotic (cefotaxime). Consistent with the prediction of epistatic constraints, most lines increased resistance by acquiring three mutations in a fixed order. However, a few lines deviated from this pattern. Next, to test whether negative interactions between alternative initial substitutions drive this divergence, alleles containing initial substitutions from the deviating lines were evolved under identical conditions. Indeed, these alternative initial substitutions consistently led to lower adaptive peaks, involving more and other substitutions than those observed in the common pathway. We found that a combination of decreased enzymatic activity and lower folding cooperativity underlies negative sign epistasis in the clash between key mutations in the common and deviating lines (Gly238Ser and Arg164Ser, respectively). Our results demonstrate that epistasis contributes to contingency in protein evolution by amplifying the selective consequences of random mutations. Understanding the factors that determine the outcome of evolution is a long-term goal of evolutionary biology. Epistasis (i. e. non-additive interactions between mutations that affect fitness) is one such determinant. Epistasis can affect the type and order of mutations that can be fixed by natural selection [1], [2]. As a result, it affects the dynamics of evolution, for instance by causing a mutation to have smaller fitness benefits in the presence than in the absence of other mutations [3], [4]. Under a specific type of epistasis called sign epistasis, interactions affect not only the size, but also the sign of the fitness effect of mutations [5]. Sign epistasis introduces particularly strong evolutionary constraints, and may create fitness landscapes with multiple adaptive peaks isolated by valleys of lower fitness [5], [6]. In such rugged fitness landscapes, epistasis can theoretically cause entire mutational pathways to be contingent on the chance occurrence of initial mutations due to a combination of two effects. First, once an initial mutation is fixed, epistasis prevents the selection of alternative initial substitutions. Next, it concurrently facilitates the selection of mutations that are beneficial in the background of this particular initial substitution, but that may be detrimental (or less beneficial) in the background of alternative initial substitutions. The predicted evolutionary constraints stemming from epistasis also depend on the available genetic variation. For example, a population that is stuck on a suboptimal adaptive peak can escape from this peak and cross the surrounding fitness valley when mutants carrying useful combinations of mutations can be produced. This may be expected to occur in large populations with a high mutation rate [7], or possibly when there is recombination [8], [9]. For asexual populations, the evolutionary constraints determined by epistasis are the most pronounced when the supply of mutations is low and mutations arise and go to fixation one at a time. In this case, even the smallest fitness valleys form obstacles for natural selection. To demonstrate how epistatic constraints affect evolution, one would ideally need to observe the full sequence of mutational events and subsequently test the evolutionary consequences of specific mutational interactions. While sequence divergence data may be used to study the role of epistasis [10], experimental evolution with microbes (where replicate lines evolve under identical conditions) offers a powerful experimental methodology. Such studies have shown instances of both divergent and parallel evolution, including changes in fitness, cell size and genotype [11]–[16]. A remarkable case of historical contingency was recently reported in a long-term evolution experiment with Escherichia coli, when investigators found that after more than 30,000 generations, multiple ‘potentiating’ mutations allowed one of twelve populations to evolve a key metabolic innovation [17]. Despite of such findings, identifying the exact order of mutations, and testing their individual and combined fitness effects has been challenging even with simple organisms. Here, we used in vitro evolution of an antibiotic resistance enzyme to study the effect of epistasis on the process of mutational pathway diversification. We used TEM-1 β-lactamase, an enzyme that catalyzes the hydrolysis of ampicillin, and studied its evolution towards increased activity against the semi-synthetic antibiotic cefotaxime. Briefly, we introduced random mutations using error-prone PCR and selected mutant alleles that mediate bacterial growth in a gradient of cefotaxime concentrations. After each round of evolution, a fixed mutant allele from each line was isolated from the highest antibiotic concentration in which bacterial growth was observed. This system offers the opportunity to study entire adaptive pathways at the level of phenotype (i. e. antibiotic resistance) and genotype. Previous studies used the same system to develop protocols for directed evolution [18], to investigate evolutionary scenarios [19], [20] and to explore the potential of evolution of resistance to a range of β-lactam antibiotics [21], [22]. Collectively, these studies showed the contribution of both unique and repetitive substitutions to TEM-1' s adaptation. They also identified a five-fold mutant (containing a promoter mutation and substitutions A42G, E104K, M182T and G238S) with an extremely high cefotaxime resistance [18], [23], [24]. Measurement of the resistance of constructed mutants carrying subsets of these mutations revealed substantial sign epistasis [24]–[26] and predicted the inaccessibility of most mutational pathways towards the five-fold mutant [24]. Notably, several other mutations in addition to the five mentioned above are capable of increasing cefotaxime resistance, independently or in combination with other substitutions [25], [26]. We were therefore interested in assessing whether evolution allowing all possible mutations would always lead to the fivefold mutant or whether alternative pathways to cefotaxime resistance exist, and to what extent epistasis plays a role in this. Twelve replicate lines of the TEM-1 gene were subjected to three rounds of error-prone PCR followed by selection for increased cefotaxime resistance. To broadly explore existing adaptive pathways, we varied the mutation rate (∼2 and ∼6 nucleotide substitutions per amplicon per PCR) and introduced a simplified form of recombination in half of the lines (see Materials and Methods), although neither of these two treatments appeared to affect the final level of adaptation, as measured by increases of the minimal inhibitory concentration (MIC) for cefotaxime (mutation rate: Mann-Whitney U = 12, two-tailed P = 0. 394; recombination: U = 12, P = 0. 394). After each round of evolution, a single clone was picked from the highest cefotaxime concentration that still permitted growth. We used this type of bottleneck selection via the fixation of a single clone [19], because it allowed us to follow the mutational pathway of the evolving genotypes. In addition, sequencing indicated that the evolved populations were dominated by a single clone (see Text S1). Plasmid from the isolated clone was purified and used as the template for the next round of evolution. Selected alleles were sequenced and assayed for increased cefotaxime resistance by measuring the MIC after cloning into the original vector and transformation into naïve bacterial cells. Figure 1A shows the changes in MIC for all lines, which levels off after the second round of evolution, suggesting the approach of one or more fitness peaks. This pattern is mirrored in the number of amino acid substitutions recorded per round for the twelve lines (30 substitutions after the first round, 29 after the second round, and 8 after the third, see Table S2). The amino acid substitutions show a remarkable pattern of parallel response. Seven of the twelve lines contained the same three substitutions, with G238S always occurring in the first round, often together with M182T, followed by E104K in the second round (see Figure 2A, mutation details and silent mutations for this experiment and subsequent in vitro evolution experiments can be found in Table S1). Since the promoter region was not allowed to evolve in our experiments, the main difference with the previously described adaptive peak (containing A42G, E104K, M182T and G238S and a promoter mutation) is the absence of A42G. There is evidence that A42G is a so-called ‘global suppressor’ substitution [27] and since many such substitutions with similar functional effects exist [28], [29], we suspect that in our experiments A42G was sometimes replaced by other global suppressor substitutions (e. g. H153L in line 1 of Figure 2A). We can not explain the absence of A42G in the lines that contain E104K, M182T and G238S after two rounds of evolution and remain unchanged after the third round (lines 5,9 and 10 in Figure 2A). However, we would like to point out that the combination of E104K, M182T, and G238S has occasionally been reported as the evolutionary end point in other studies [26], [30]. Overall, the striking pattern of parallel evolution is consistent with adaptive constraints from epistasis. Five lines (numbers 3,4, 7,11 and 12) deviate from this pattern, because they lack one or more of the three parallel substitutions and evolve to lower resistance levels (Mann-Whitney, U = 1, one-tailed P = 0. 003). We were interested in testing to what extent epistasis between different initial substitutions determined the mutational pathways taken. To test this effect of epistasis, we first had to identify the initial mutations of the common and deviating pathways. Substitution G238S was found in ten lines after the first round of evolution and is known to cause the greatest increase in cefotaxime resistance among all known single TEM-1 mutations [23]–[25]. We therefore screened the two lines that lack G238S (lines 3 and 4) for first-round adaptive substitutions with possible negative epistatic interactions with G238S, and found a likely candidate in both lines. Substitution R164S in line 3 has a positive effect on cefotaxime MIC, but shows a lower MIC when combined with G238S than both substitutions alone (see [25] and Table 1). Similarly, substitution A237T in line 4 increases activity on cefotaxime [31] and frequently occurs in clinical isolates, but never in combination with G238S (http: //www. lahey. org/studies/temtable. asp). In our experiments, the MIC of the double mutant A237T/G238S was equal to that of A237T alone and only slightly higher than that of TEM-1, but considerably lower than that of G238S, thus confirming negative epistasis (see Table S3). To test the predicted contingent role of these initial mutations, we ran another set of evolution experiments, this time starting with TEM-1 alleles carrying either R164S or A237T. We expected that these mutations would prevent the selection of mutations found in the common pathway, and instead facilitate the selection of other mutations. Five replicate lines of each allele were allowed to adapt to cefotaxime as before, using a mutation rate of ∼3 mutations per amplicon (see Text S2 and Table S4). Figure 1B shows the average change in resistance for all ten new lines, as well as the average for the twelve lines from the previous experiment. Again, MIC values levelled off after an initial rise, suggesting that the new lines also approach one or more adaptive peaks (see also Figure 3B). However, despite their head start due to the introduced adaptive mutation, after three rounds of evolution the resistance of these lines was significantly lower than that reached by the ten G238S-containing lines in the previous experiment (for both R164S and A237T lines: U = 3, one-tailed P<0. 01), while not significantly different from each other (U = 12, two-tailed P = 1). The phenotypic assays, therefore, suggest that R164S and A237T force evolution to take different adaptive trajectories than the common trajectory followed by the TEM-1 lines. The amino acid substitutions found in the new lines confirm this conclusion (see Figure 3A). First, despite their lower final level of resistance and lower average mutation rate, the five R164S lines accumulated more mutations than the ten G238S lines of the previous experiment (7. 2 mutations, without the introduced R164S, versus 4. 7 mutations including G238S, U = 4. 5, one-tailed P = 0. 008). Second, mutations in the background of R164S and A237T occurred at different positions, with relatively more mutations in the functionally important omega-loop (see Figure S1) compared to the ten lines carrying substitution G238S (Chi-square = 8. 80, d. f. = 1, P<0. 01), suggesting alternative routes towards the new enzyme functionality. Third, the presence of substitution R164S or A237T prevented the selection of G238S in all lines (Fisher' s exact test, P<0. 001), and often prevented the selection of other mutations repeatedly found in the G238S background, while they facilitated the selection of other mutations. For example, E104K is more often selected when G238S is present than when R164S (P = 0. 004) or A237T (P = 0. 001) are present, while E240K is more often found in the background of R164S than with G238S (P = 0. 004). Other parallel substitutions, such as M182T, show a weaker association (i. e. with G238S compared to R164S, P = 0. 036), and appear beneficial in more backgrounds. Together, the observed changes in phenotype and genotype show that the introduced substitutions are responsible for the pattern of parallel and divergent adaptation: they facilitate the selection of certain mutations, while preventing the selection of others. We have described the effect of epistasis between initial mutations on mutational trajectories. We were interested to see how pervasive such epistatic interactions were and whether they continue to play a role once the first mutational step in a mutational trajectory has been taken. With this in mind, our attention was drawn to line 7 of Figure 2A. Like lines 3 and 4, line 7 deviates from the other lines in Figure 2A in that it lacks two of the three main substitutions E104K, M182T, and G238S: it does contain G238S, but lacks both E104K and M182T. Because G238S is present in line 7 after the first round of evolution, we assumed that either A184V or R191H, the other two substitutions found after the first round, might be responsible for the atypical mutational pathway of this line. Because we had observed the combination of A184V and G238S in pilot experiments and because A184V is physically very close to M182T, we suspected that this substitution might be a functional equivalent of M182T. To investigate whether the double mutant A184V/G238S would prevent other substitutions of the G238S-based pathway from occurring, we constructed a TEM-1 allele carrying these two substitutions. The MIC of this double mutant was indeed higher than that of G238S by itself (see Table S3). Next, we subjected the A184V/G238S allele to three rounds of error-prone PCR and selection for increased cefotaxime resistance, again with fivefold replication, a mutation rate of on average ∼3 mutations per amplicon per error-prone PCR and without recombination. The amino acid substitutions and MIC-values recorded after each round of evolution and selection can be found in Figure 4A. Again, the MIC increases after the 1st and 2nd round, while it levels off in the 3rd round (Figure 4B). Despite of the head start of the two introduced mutations, final MIC levels were significantly lower than those of the seven lines in Figure 2A that contain mutations M182T and G238S (U = 0, one-tailed P = 0. 003). The presence of A184V in the background of G238S prevented the selection of M182T (Fisher' s exact test on all 13 lines of Figure 2A and Figure 4A that contain M182T or A184V in combination with G238S: P = 0. 001), but E104K was selected also in this background (P = 0. 07). More indications for sign epistatic interactions can be found in Figure 2, Figure 3, and Figure 4. For example, substitutions E104K and E240K can be found in most lines, but never co-occur in the same line. Similarly, five out of six lines that contain T265M lack M182T, and it has been speculated that both substitutions have a similar (i. e. stabilizing) function [32]. We lack statistical power to test more interactions due to their low incidence, but our examples show that sign epistasis in TEM-1 is pervasive and has adaptive consequences not only for the first, but also for later steps of the mutational pathway of this enzyme. Next, we were interested in testing the strength of the adaptive constraints resulting from sign epistasis directly by allowing alleles containing combinations of negatively interacting mutations to evolve. When evolution starts from an allele containing two negatively interacting mutations, can it alleviate this negative interaction by the addition of other mutations, or can it only proceed by the reversion of one of these mutations? Also, does the strength of sign epistasis affect the strength of the adaptive constraint, such that stronger sign epistasis limits the adaptive alternatives? To test this, the negatively interacting R164S/G238S double mutant, exhibiting a strong negative interaction (see MIC values for cefotaxime in Table 1), and A237T/G238S, exhibiting weaker epistasis (see Table S3), were created by site directed mutagenesis, and subjected to three rounds of directed evolution as before. The MIC trajectories of the five lines started by both double mutants show great similarity to those of the twelve TEM-1 lines in the first experiment (see Figure 1B), except that they have lower MIC-values after the first round of evolution (Figure 2B and Figure 5B; relative number of MIC doublings after round 1, U = 9, one-tailed P<0. 001). For the A237T/G238S double mutant, only one line reverted A237T after the second round, while the other four retained the introduced mutations at residues 237 and 238 (see Figure 5A, right half). Interestingly, the line that reverted A237T is the line that ended up having the highest MIC (see Figure 5B). Three of the lines that contained both A237T and G238S after three rounds of evolution did not acquire new substitutions in the third round. Lack of reversion is unlikely to be an effect of the mutagenesis protocol, since the a (t) → g (c) transition that can revert both A237T and G238S, is relatively frequent in the mutational spectrum of the mutagenic polymerase we applied. It could be that these lines have gathered mutations in round one and two that have neutralized the negative interaction between G238S and A237T and hence made the reversions T237A and S238G either neutral or detrimental. The combination of R164S and G238S seems to constrain evolution more severely, since all five lines eventually reverted R164S, and four lines did so directly after the first round of evolution (Figure 5A, left half). One line gathered other mutations and increased MIC during the first two rounds of evolution, and reverted R164S after the third round. In this line, in both first two rounds, at least one mutation was incorporated that is known to be beneficial in both the R164S and G238S backgrounds (E240K [33] and M182T [26], respectively). Yet, the reversion S164R in the third round, combined with other mutations, resulted in a large increase in resistance, while that of most lines with a first round reversion levelled off at this point (see Figure 5B). Thus, the level of selective constraint caused by epistasis varies for different combinations of mutations, and correlates with the strength of the negative epistatic interaction for these two cases. To gain insights into the biochemical basis of the sign epistatic interaction between R164S and G238S, we purified the double mutant, and the corresponding single mutants, and examined their kinetic parameters and stability (see Table 1 and Figures S2 and S3). On ampicillin, the single mutants and double mutants exhibit very similar catalytic efficiency (kcat/KM). However, the double mutant exhibited much lower kcat than both single mutants, in particular relative to R164S, thus accounting for the lower MIC on ampicillin at concentrations above the KM. The kinetic parameters of the double mutant on cefotaxime are essentially identical to that of the single mutant R164S, but 28-fold lower in terms of kcat/KM than G238S. These differences correlate with the differences in the resistance in vivo (Table 1), but do not account for the observation that the double mutant exhibits MIC values that are lower than the R164S mutant, and that are only slightly above the MIC of TEM-1 itself. Thus, regarding the kinetic parameters, we observed epistasis between mutations R164S and G238S with respect to cefotaxime hydrolysis: the R164S substitution eliminates the much more beneficial effects of G238S. However, the kinetic parameters do not account for the sign epistasis observed for the resistance levels in vivo. The origin of the latter is likely to be lower levels of functional enzyme, which may result from the lower kinetic stability of the double mutant. The Tm-values of all three mutants are similar, and are lower than that of TEM-1. Thus the thermostability of both single mutants seems comparable to that of the double mutant. However, the double mutant exhibits a cooperativity factor (m value) that is ∼2. 5-fold lower than that of the two single mutants. In fact, the fit of the double mutant to the standard two-state unfolding model [34] is poor (see Figure S2), also indicating that its folding follows a different path than that of wild-type TEM-1 and of the single mutants. Altogether, the similarity in kcat and KM values versus the lower MIC, and the perturbed folding parameters, suggest that sign epistasis relates to a combination of kinetic parameters that are much lower for the double mutant than for G238S on its own, and lower levels of properly folded and functional enzyme. We used laboratory evolution experiments with a model enzyme to study the topography of its fitness landscape by testing the involvement of epistasis in creating alternative mutational pathways. A previous reconstruction of the fitness landscape of TEM-1 β-lactamase for adaptation to cefotaxime based on MIC assays of all possible combinations of mutations A42G, E104K, M182T, G238S, and a promoter mutation suggested that, despite local ruggedness, it contains a single adaptive peak [24]. However, by allowing all possible mutations that may contribute to the evolution of cefotaxime resistance to be selected, we found that the fitness landscape contains more than a single adaptive peak. Our ability to monitor and manipulate the sequence of genetic changes allowed us to demonstrate that epistasis was at the basis of the landscape' s ruggedness. Epistasis caused one pathway to be used preferentially, while making the particular choice of pathways historically contingent upon the first mutational step. This first step therefore resembles the crucial choice of lanes before a junction of alternative evolutionary routes. Finally, by studying how the biochemical and biophysical parameters for a specific pair of mutations correlate with the level of fitness (here, antibiotic resistance), we found that sign epistasis resulted from a combination of negative interactions at the level of enzyme activity and reduced folding efficiency (kinetic stability). The epistatic constraints observed in our experiments depend not only on the topography of the fitness landscape, but also on the mutation supply rates of the evolving populations (i. e. population size x mutation rate). The conditions in our experiments were such that mutation supply rates were in the order of 106 (i. e. library sizes of ∼6×105 and mutation rates of ∼3 for most experiments) and multiple mutations were often selected in a single round of evolution (see Figure 2A). Assuming no mutational bias, simulations using the PEDEL program [35] indicate that all possible 2581 single mutants (i. e. 861 nucleotides×3 possible mutations) were present in our libraries, as well as up to 10% of all possible double mutants. That two of the twelve lines in our first experiment showed a deviating pattern of substitutions already after the first round of evolution, therefore, must be seen as a consequence of epistasis combined with clonal interference between genotypes carrying different sets of mutations. While epistatic constraints may be most severe during evolution by single-mutation selection (the so-called ‘weak-mutation, strong-selection’ regime), our results show that significant constraints are experienced also during evolution with much higher mutation supplies. Because of the high mutation supplies in our experiments, in some lines combinations of mutations that are individually neutral or deleterious but beneficial in combination may have been selected. The relative strength of the epistatic constraints experienced at different scales in genotype space will depend on the topography of the fitness landscape, and should be addressed in future work. How important are our observations on a single enzyme for real organisms? In our case where mutations affect a single enzyme, sign epistasis results from the biophysical constraints underlying functionality of a single protein [36], [37]. Although possibly less severe, similar constraints are likely to exist when mutations affect different genes and functions [5], [6]. Differences observed in the molecular changes in two bacteriophage populations that evolved in parallel, indicate that mutational stochasticity and mutational order may influence the pattern of adaptation [11]. A recent analysis of epistasis between individually deleterious mutations affecting different genes of the fungus Aspergillus niger confirms the significance of sign epistasis for entire organisms [8]. Such epistatic interactions can make evolution contingent by amplifying the effects of mutational chance events in finite populations, as demonstrated here. However, our results also show that even contingent evolution may have a relatively deterministic structure once the first step has been determined. Escherichia coli strain DH5αE (Invitrogen) was used as a host for all plasmids. Plasmid pACSE3 [19] was used as the vector for cloning and expressing TEM alleles. This ∼5. 1-kb plasmid contains a tetracycline-resistance marker and replicates at ∼10 copies per cell. LB-medium is 10g/L trypticase peptone, 5 g/L yeast extract and 10 g/L NaCl. LB-tetracycline medium is LB-medium containing 15 mg/L tetracycline and LB-kanamycin medium is LB-medium containing 50 mg/L kanamycin. Mueller Hinton (Merck) and Mueller Hinton II (BD) medium were prepared according to the manufacturers' instructions. SOC medium is 20 g/L bacto-tryptone and 5 g/L yeast extract with 10 mM NaCl, 2. 5 mM KCl, 10 mM MgCl2 and 20 mM glucose. Solid media contained 16 g/L agar. Plasmids were prepared from overnight LB-tetracycline cultures, and purified using a GenElute Plasmid Miniprep Kit (Sigma). TEM-1 (861 bp long) was amplified from pBR322 using the high-fidelity Pfu polymerase (Stratagene), sense primer P1 (GGGGGGTCATGAGTATTCAACATTTCCGTGTCG, BspHI site underlined, this enzyme cuts 1 bp upstream of TEM-1' s start codon) and antisense primer P2 (CCGAGCTCTTGGTCTGACAGTTACCAATGC, SacI site underlined, this enzyme cuts 17 bp downstream of TEM-1' s stop-codon), using the following temperature cycle: 30′ at 95°C, 30′ at 61°C and 90′ at 72°C for 30 cycles, followed by 72°C for 10 min. The resulting amplicon was digested with BspHI and SacI (New England Biolabs). Plasmid pACSE3 was digested with the same restriction enzymes and dephosphorylated with Calf Intestinal Phosphatase (New England Biolabs). Digested amplicons and vectors were purified using Sigma' s GenElute PCR Clean-up Kit, ligated using T4 DNA Ligase (New England Biolabs), and transformed into DH5αE by electroporation. Specific mutations were introduced into TEM-1 using the QuickChange site-directed mutagenesis kit (Stratagene). Note that in this protocol TEM-1' s promoter region is not part of the amplicon. Mutant TEM alleles were generated by introducing random mutations using the Genemorph I and II Mutagenesis Kits (Stratagene). Since the former kit was unexpectedly taken out of production and replaced by the latter, we carried out the initial experiment (Figure 2) using Genemorph I, but were forced to switch to Genemorph II for all other experiments. Since the error-prone polymerases in both kits differ only slightly in their mutational spectrum (see the Genemorph II manual, Stratagene), this switch has not significantly influenced the outcome of our experiments. Primers used for error-prone PCR were P3 (TCATCCGGCTCGTATAATGTGGA) and P4 (ACTCTCTTCCGGGCGCTATCAT), which flank the multiple cloning site of pACSE3. In order to make a broad exploration of the diversity of adaptive pathways, we varied the mutation rate (high rate in lines 1–6, and low rate in lines 7–12 of Figure 2A) and also introduced a simplified form of recombination in the initial experiment (in lines 4–6 and 9–12). The mutation rate was manipulated by varying the effective number of replication cycles by the application of different amounts of template DNA. Conditions were set to introduce on average ∼2 (low mutation rate) or ∼6 (high mutation rate) mutations per amplicon (0. 06 and 565 ng plasmid template, respectively). The resulting amplicons were digested with BspHI and SacI, ligated into pACSE3 and electroporated into DH5αE. After recovery for 90 min in SOC medium at 37°C, the cells were diluted in 500 mL LB-tetracycline. An aliquot was taken out directly after mixing and plated onto LB-tetracycline agar to determine the library size. The remainder of the culture was incubated at 37°C overnight to amplify the library. Effective library sizes varied between ∼104 and 106 transformants with an average of ∼6×105. Aliquots of the amplified libraries were stored in 10% glycerol at −80°C. In the first experiment, recombination between mutant TEM-1 alleles within a library was allowed in some lines (4–6 and 10–12) by digestion of the error-prone PCR amplicons with PvuI. TEM-1 has a single PvuI restriction site roughly in the middle of the sequence, allowing recombination via a single cross-over, as confirmed by the analysis of restriction markers. The truncated amplicons were ligated into pACSE3 via a single ligation step as described above. Expression of the TEM-1 allele in pACSE3 is under control of the pTAC promoter that is tightly regulated by the lac repressor, encoded by the lacI gene on pACSE3. Expression was induced by adding 50 µM isopropyl-β-D-thiogalactopyranoside (IPTG), which was shown to mimic natural expression of TEM-1 [19]. For selection, a series of bottles containing 50 mL Mueller-Hinton medium (Merck) was inoculated with cefotaxime (Sigma, stock solution in 0. 1 M NaPO4, pH 7. 0) concentrations in two-fold increments, ranging from 0. 0625 µg/mL, the MIC of wild-type TEM-1, to 1024 µg/mL. Each bottle was inoculated with a cell number approximately equal to 10 times the library size from the overnight amplified cultures. Cultures were incubated for 48 hours at 37°C. The culture that grew at the highest concentration of cefotaxime was plated on LB-tetracycline agar. The presumed clonal fixation of a single adaptive mutant in these 48 hour cultures [19] was confirmed by the sequence analysis of 10 randomly picked clones from a single culture (see Text S1). Therefore, the next day, a single colony was selected and grown overnight in LB-tetracycline. Plasmid from the overnight culture was isolated using GeneElute Plasmid Miniprep Kit (Sigma) and sequenced using BigDye (Perkin Elmer) or DYEnamic (AP-biotech) Terminator Cycle Sequence kits. The Minimal Inhibitory Concentration (MIC) was determined from 150 µl cultures at a titre of 105 cells/mL in Mueller Hinton II medium containing 50 µm IPTG. A 150 µl solution of twofold serial dilutions of antibiotic in MH II was added to these cultures. Cultures were grown for 24 hours at 37°C, growth was determined by visual inspection, and MIC was defined as the lowest concentration of antibiotic that completely prevents visible growth. To exclude phenotypic variation in bacteria, plasmid was isolated from all selected clones and re-transformed into isogenic E. coli DH5αE cells prior to each MIC-assay. Ampicillin MICs were determined without the addition of IPTG, since this turned out to mask differences between different mutants tested. Measurements were performed essentially as earlier described [38]. The equation used to fit the row thermal denaturation was: Where αN is the fluorescence of the native structure at T = 25°C, p is a slope of fluorescence change of the native state, αU is a fluorescence at T = 80°C, q is a slope of fluorescence change of the unfolded state, and mN–U is the slope of transition, RT = −582. The kinetic parameters kcat and KM were determined by non-linear regression fit to the Michaelis-Menten equation using the KaleidaGraph program (Synergy Software). Initial velocities were recorded with increasing substrate concentrations from 1/3 KM to 3 KM. For ampicillin, hydrolysis of the β-lactam ring was monitored by loss of absorbance at 232 nm (Δε = 600 M−1 cm−1), using a quartz plate (path of 0. 5 cm) in a microtitre plate reader (bioTeck). The hydrolysis of cefotaxime was monitored by loss of absorbance at 264 nm (Δε = 6360 M−1 cm−1). Because of the high initial absorbance, a quartz cell of 0. 1 cm pathway was used for measurements at ≥250 µM cefotaxime. The readings in lower concentrations were performed in quartz plate in a plate reader (path of 0. 5 cm). Since MIC values are measured on a discontinuous scale (with two-fold increases in antibiotic concentration), non-parametric tests on differences in median MIC estimates were used.

A long-term goal of evolutionary biology is to understand the factors that govern the outcome of evolution. Epistasis (i. e. the situation in which the fitness effect of a mutation depends on its genetic background) is one such factor. Epistasis not only affects the dynamics of evolution, it may also direct its outcome by affecting the type and order of selected mutations. This effect is particularly strong under sign epistasis, which occurs when the sign of a mutation' s fitness effect depends on its genetic background. Here, we demonstrate how epistasis causes divergence of mutational pathways of an antibiotic resistance enzyme, TEM-1 β-lactamase. First, we use in vitro mutagenesis followed by selection for cefotaxime resistance to demonstrate that alternative mutational pathways towards highly resistant variants exist in addition to the main pathway that was previously described. Next, to test whether negative interactions between alternative initial substitutions govern this diversification, we start identical evolution experiments with alleles containing initial substitutions from the deviating lines. These alleles consistently evolve to lower adaptive peaks and acquire different mutations than those in the main pathway. Our results demonstrate that sign epistasis between alternative initial substitutions may force evolution to follow different mutational pathways.

Abstract Introduction Results Discussion Materials and Methods

genetics and genomics/microbial evolution and genomics evolutionary biology/microbial evolution and genomics evolutionary biology/evolutionary and comparative genetics

2011

Initial Mutations Direct Alternative Pathways of Protein Evolution

Entomopathogenic fungi have been investigated as an alternative tool for controlling various insects, including triatomine vectors of the protozoan Trypanosoma cruzi, the etiological agent of Chagas disease. Here we tested the pathogenicity and virulence of ten isolates of the fungi Metarhizium spp. and Beauveria bassiana against Rhodnius prolixus and found all of the isolates to be virulent. We used two isolates (URPE-11 Metarhizium anisopliae and ENT-1 Beauveria bassiana) for further screening based on their prolific sporulation in vitro (an important property of fungal biopesticides). We characterized their virulences in a dose-response experiment and then examined virulence across a range of temperatures (21,23,27 and 30°C). We found isolate ENT-1 to maintain higher levels of virulence over these temperatures than URPE-11. We therefore used B. bassiana ENT-1 in the final experiment in which we examined the survival of insects parasitized with T. cruzi and then infected with this fungus (once again over a range of temperatures). Contrary to our expectations, the survival of insects challenged with the pathogenic fungus was greater when they had previously been infected with the parasite T. cruzi than when they had not (independent of temperature). We discuss these results in terms of aspects of the biologies of the three organisms. In practical terms, we concluded that, while we have fungal isolates of potential interest for development as biopesticides against R. prolixus, we have identified what could be a critical problem for this biological tool: the parasite T. cruzi appears to confer a measure of resistance to the insect against the potential biopesticide agent so use of this fungus as a biopesticide could lead to selection for vector competence. Insects of the subfamily Triatominae (Hemiptera: Reduviidae) are vectors of the protozoan parasite Trypanosoma cruzi, that is the causal agent of Chagas disease in Central and South America. This disease has a considerable medical and socioeconomic impact [1] with an estimated 7 to 8 million people affected by T. cruzi and approximately 12,000 deaths per year in the world [2,3]. The life cycle of T. cruzi is complex; the infection of mammals occurs when they come into contact with the infective metacyclic forms of the parasite that are eliminated with the feces of triatomines after feeding [4]. Interventions to manage triatomine vectors are based on their control using insecticides, particularly pyrethroids. However, insecticide resistance has been detected in some parts of South America, associated with ineffective treatments of deltamethrin against one of the most important vectors Triatoma infestans [5]. Isolates of entomopathogenic fungi have been reported to be highly infective to triatomines and potentially represent an alternative tool for controlling of Chagas disease vectors. These fungi can be highly infective to different insect life stages under laboratory conditions [6–9]. Selection of entomopathogenic fungi for use against insect pests requires consideration of factors such as the pathogen’s specificity, dose, host biology and environmental factors [10]. A highly virulent pathogen may require fewer propagules to cause the disease. However, fungal isolates with good persistence are more likely to come into contact with the target insect and so infect it [11]. The performance of fungi as biocontrol agents depends on environmental conditions as well as the behavioral responses of the insect targeted [12,13]. In this context, temperature could affect various biological parameters of triatomines [14,15] and also the development of the fungus inside the host [13]. Thus, it is of interest, in developing a fungal biocontrol agent, to have an understanding of how temperature affects the disease process. A final consideration, and one of crucial importance for arthropod vectors, is that the aim of biocontrol is not primarily to exterminate or even control the insect populations but to manage transmission of the disease to the human host, for example by blocking parasite transmission or reducing vector longevity [16–18]. With this in mind, the first objective of the current study is to test the pathogenicity and virulence of isolates of entomopathogenic fungi against a triatomine vector of Chagas disease. For this, we assessed ten isolates of Metarhizium spp. and Beauveria bassiana against Rhodnius prolixus and then assessed the virulence of two of these isolates in a dose-response experiment. Our second objective was to examine isolate virulence across a range of temperatures, as a next step in our screening of isolates. Our third objective was to investigate the survival of insects infected by fungus when previously infected with Trypanosoma cruzi. All experiments using live animals were performed in accordance to FIOCRUZ guidelines on animal experimentation and were approved by the Committee of Ethics of Animal Use-FIOCRUZ (L-058/08). Rhodnius prolixus were obtained from a colony maintained in the Vector Behaviour and Pathogen Interaction Group at the Centro de Pesquisas René Rachou (CPqRR), FIOCRUZ, MG, Brazil. Rhodnius prolixus were reared under controlled conditions of temperature (26±1°C) and relative humidity (65±10%). Insects were exposed to a natural cycle of illumination and allowed to feed weekly on chicken or mice. We used 10 isolates of Metarhizium spp. and Beauveria bassiana (Bals.) (see S1 Table). Seven of these (C66A, J54A, J60A, L60A, C76B, S71B, L46C) were obtained from soil samples from coffee crops in Viçosa, Minas Gerais, Brazil, using live baits of mealworm beetles Tenebrio molitor, a standard procedure in our laboratory to isolate insect-pathogenic fungi [19]. The isolates C66A, J54A, J60A and L60A were identified as Metarhizium robertsii. Two isolates were kindly provided by the Federal Rural University of Pernambuco-Brazil, URPE-11 isolated from Mahanarva posticata (Hemiptera: Cercopidae) and URPE-18 obtained from soil. Isolate ENT-1 was from an unidentified coleopteran host from the Mata de Paraíso, Viçosa, Minas Gerais. The isolate URPE-11 was identified molecularly through the Metarhizium barcode region, the final portion of the 5’ translation elongation factor, using the primers EF1T and EF2T [20]. The isolate was identified as Metarhizium anisopliae by sequence blast search at NCBI (http: //blast. ncbi. nlm. nih. gov/Blast. cgi) and alignment with other Metarhizium species at MEGA 6. 06 [21]. All fungi were reactivated through inoculation of larvae of T. molitor and reisolated from the mycosed insect on Petri dishes containing PDA medium (20% Potato, 2% Dextrose and 1. 5% Agar) and incubated at 25±1°C. Suspensions in sterile distilled water containing 0. 01% of Tween 20 were prepared for each isolate and inoculated on rice. The rice had been autoclaved for 15 min at 120°C in polypropylene bags. Once cooled, a 2 ml aliquot of conidial suspension was added to 200g of the rice. The bags were maintained in an incubator at 25±1°C for ten days to promote sporulation. Rice with spores was placed in Falcon tubes with sterile distilled water containing 0. 01% Tween 20. Suspensions were stirred for 3 min and filtered through sterile gauze so that the spores could be released. The suspensions were adjusted to standardized concentrations using a Neubauer hemocytometer, and were used immediately. Conidial viability was assessing by adding 100μl of the suspensions on to Petri dishes containing PDA medium at 25±1°C. Viability was assessed after 20h by checking the germination of 100 conidia under an optical microscope (at 400x magnification). Conidial germination was over 85% in all cases. We used first instar nymphs of R. prolixus of 2–3 days age for each fungal treatment. First instar nymphs were used in this experiment as they are expected to be more susceptible to fungal infection. As we are screening for pathogenicity, first instar nymphs can give a more rapid response for fungal pathogenicity to the insects. The nymphs were placed in Petri dishes (60x15mm) lined with filter paper containing 0. 2 ml of fungal suspension (1x108 spores/ml). Petri dishes were sealed with plastic film to maintain humidity and were maintained at 25±2°C, relative humidity of 80±5% and 12 h photophase. 48 h after exposure, the insects were transferred to new Petri dishes with untreated but humid filter paper. Insects were kept unfed in the Petri dishes until death. Dead insects were surface-sterilized (dipped in 70% ethanol then 2 min. in sodium hypochlorite then three washes in sterile distilled water) and were then placed in humidity chambers (within an incubator at 25±1°C) to promote fungal development and sporulation; hence confirming death by fungal infection. This procedure was done on the second day postmortem under a stereomicroscope (40x magnification). This experiment was done in a randomized block design, with 12 treatments (ten isolates and two controls) and eight replicates per treatment. Each replicate had five insects. Controls were only water plus 0. 01% Tween 20 (Control1) to check for cross-contamination or untreated filter paper to check for insect infections prior the experimental set-up (Control2). The methods used here for fungal inoculation and surface sterilization were also used in experiments 2,3 and 4. We used second instar R. prolixus nymphs, within 3–4 days post-moult, and exposed them to five conidial concentrations: 1x103,2x104,4x105,8x106,1. 6x108 spores/ml. Two isolates URPE-11 and ENT-1 were chosen based on their infectivity to R. prolixus (see results of Experiment 1 below) and high levels of sporulation on rice at 10 days (See S1 Appendix). The dilution series of both isolates were prepared in sterile distilled water with 0. 01% Tween 20 and conidia were counted with a Neubauer hemocytometer. This experiment was conducted in a randomized block design with 12 treatments and six replicates per treatment with five insects in each replicate kept together in a Petri dish. We used second instar R. prolixus nymphs at 3–4 days post-moult under four temperature regimes. We continued with the two isolates, URPE-11 and ENT-1, used in Experiment 2, at concentrations 1x103 and 2x104 spores/ml, respectively. These concentrations were selected as being borderline concentrations (based upon the results of Experiment 2 below) to allow the effects of temperature to be observed. Nymphs were kept unfed during the assays; inoculation was as in the previous experiments. The experimental design was as follow: insects were inoculated with one of the two fungal isolates or blank control (water plus 0. 01% Tween 20) and kept at 21,23,27 or 30°C until death. Each experimental set-up was replicated six times and five nymphs were used for each replicate kept together in a Petri dish. Here, the parasite Trypanosoma cruzi (CL strain, isolated from a triatomine bug Triatoma infestans) [19], was used to test its effect on the virulence of a single fungal isolate. For this, second instar nymphs of R. prolixus, within 3–4 days post-moult, were fed on inactivated rabbit blood (37°C/20min) with culture epimastigotes (parasite) added at a concentration of 1x107 parasites/ml. The control was only inactivated blood added with the same amount of culture medium used in the blood with contained parasites [22]. Upon feeding, insects were housed in a temperature-control chamber at 26°C until moulting to the third instar. Rhodnius prolixus infections were conducted as routinely performed in one of our laboratories. Parasites are passaged through triatomines and mice every six months to maintain the strain infectivity [23]. Briefly, nymphs are infected with culture epimastigotes through artificial feeding. One month after infection, these insects are fed and their urine, containing metacyclic trypomastigotes, is collected and inoculated into a Swiss mouse. Two weeks after inoculation parasites are recovered by cardiac puncture and used to perform a hemoculture. As this procedure ensures insect infection rates from 85 to 100% (See S2 Table), we relied on this experience to assure infection rather than using direct tests—the normal procedure for confirmation of infection by T. cruzi requires that they be squeezed, which is sufficiently traumatic that it would have invalidated our experiments. We used the third instar R. prolixus nymphs for the experiment, within 4–5 days post-moult and these were either parasitized or unparasitized by T. cruzi. Beauveria bassiana ENT-1 was used at a concentration of 2x104 spores/ml. This isolate was selected due to its maintenance of virulence over a broader range of temperatures than the M. anisopliae isolate (see results of Experiment 3 below). The inoculation was as above. We used a factorial experimental design 2 x 2 x 4, as follow: insects (n = 4) were parasitized by T. cruzi or not and inoculated with a fungus (above) or not (control: sterile distilled water plus 0. 01% Tween 20), and kept at 21,23,27 and 30°C until death. Each experimental set-up was replicated six times giving a total of 24 insects for each combination. Insects were kept unfed throughout the experiment. In all experiments, survival regression analyses were conducted in R software version 3. 0. 1 [24]. Data were analyzed by GLM with censored data with a Weibull distribution. Models were performed including fungal isolate, concentration, temperature and infection status (parasitized or unparasitized with T. cruzi) as independent variables. The function ‘frailty’ was used in the model to control the random effects, adding by blocks using Gamma distribution [25]. The models were carried out for all analyses and after exclusion of non-significant variables; the final model was accepted as the simplest model that was not significantly different from the full model. The mean survival time (lethal time = LT50) was calculated for each group for comparison between models by one-way analysis of variance (ANOVA) and the significance was observed using χ2 tests. Means were considered to be statistically different at P < 0. 05. All insects, whether or not they sporulated after death, were included in the survival analysis, since it is common for fungi to infect and kill an insect, but not sporulate successfully from the cadavers [12]. A similar example is the entomopathogenic bacterium Bacillus thuringiensis which is well-known to kill insects but which is extremely difficult to recover in any number from cadavers [26]. All R. prolixus nymphs died eight days post-inoculation with the fungal isolates (Fig 1); LT50 varied from 3. 7 to 5. 3 days. Insects in the two control groups survived for the same length of time as one another (χ2[01] = 2. 804, P = 0. 09) but considerably longer than the infected insects (7. 98±0. 09, χ2[10] = 589. 6, P<0. 0001); only 5% of control insects had died by the 17th day post infection. Comparisons among models showed that there were three groupings of isolates for which nymph survival times were statistically indistinguishable (P>0. 05). The group that killed insects most quickly (LT50 3. 85±0. 02 days) comprised Metarhizium sp. isolates C66A, L60A and L54A. The second group of isolates had an LT50 of 4. 02±0. 01 days, slower than the first group (χ2[15] = 1096. 1, P<0. 0001), and comprised Metarhizium spp. isolates J60A, URPE-11 and B. bassiana isolate URPE-18. This group in turn killed insects faster than the third grouping, of B. bassiana isolates ENT-1, C76B, L46C and S71B (LT50 4. 79±0. 01 days, χ2[13] = 1019. 4, P<0. 0001). Sporulation (as described above) was observed on 100% of dead insects for all Metarhizium spp. isolates and for B. bassiana isolates S71B, ENT-1 and C76B, whereas for B. bassiana isolates L46C and URPE-18, sporulation was 97. 5% on dead insects. No sporulation was observed from control insects. Second instar R. prolixus nymphs were inoculated with five concentrations of M. anisopliae URPE-11 and B. bassiana ENT-1 and were monitored for 20 days. At all but the lowest doses, M. anisopliae isolate URPE-11 caused 100% mortality by the 7th day post infection (Fig 2A), with mean survival times of 3. 96±0. 23 (mean±SE), 4. 4±0. 23,5. 13±0. 23,6. 06±0. 23 and 9. 96±0. 24 days at concentrations of 1. 6x108,8x106,4x105,2x104 and 1x103 spores/ml respectively. Survival curves of insects infected by fungi were significantly different from one another, these were different from controls (χ2[10] = 476. 0 for P<0. 0001) and all were significantly shorter than untreated controls (χ2[10] = 465. 84, P<0. 0001). With the exception of the two lowest doses, B. bassiana isolate ENT-1 caused 100% mortality by the 10th day (Fig 2B). Mean survival times were 5. 8±0. 23 (mean±SE), 6. 1±0. 23,7. 53±0. 23,10. 06±0. 23 and 13. 46±0. 23 days at concentrations of 1. 6x108,8x106,4x105,2x104 and 1x103 spores/ml respectively. Survival times from the two higher doses were statistically indistinguishable from one another (χ2[1] = 1. 87, P = 0. 17) while different from the three lowest doses and from controls (χ2[09] = 385. 57 for P<0. 0001). Sporulation was observed on 100% of dead insects at doses of 1. 6x108,8x106,4x105,2x104 spores/ml for the M. anisopliae treatment and on 84% of insects at the lowest dose (1x103 spores/ml). For B. bassiana, 97% of sporulation on dead insects was observed at doses of 1. 6x108,8x106,4x105 spores/ml 79% for 2x104 and 1x103 spores/ml. No sporulation was found for any control insects. For insects infected with M. anisopliae URPE-11, survival times generally decreased with increasing temperatures. At 30°C, mean survival time was 7. 2±0. 05 days. This was shorter than the survival times at 27 and 23°C (11. 43±0. 06 and 11. 03±0. 06 days respectively; χ2[11] = 223. 49 and χ2[11] = 233. 54 respectively, both P<0. 0001) and mortality was 100% by the 9th day. There was no difference in survival of insects at 27 and 23°C (χ2[1] = 1. 153, P = 0. 28). The survival times at 23 and 27°C were, in turn, shorter than survival at 21°C (13. 83±0. 08 days; χ2[10] = 256. 59, P<0. 0001) with only 50% mortality of insects by the 15th days (Fig 3A). All of these survival times were significantly lower than controls (χ2[10] = 251. 85 P<0. 0001). For insects infected with B. bassiana ENT-1, survival times also generally decreased with increasing temperatures. At 30 and 23°C the mean survival times were statistically indistinguishable from one another (7. 16±0. 05 and 7. 63±0. 05 days, respectively; χ2[1] = 1. 47, P = 0. 22) and mortality was 100% at 10 days (Fig 3B). These times were shorter than and statistically different from survival times at 21 and 27°C (10. 4±0. 03 and 11. 6±0. 06 days respectively; χ2[10] = 353. 49 and χ2[09] = 175. 56 respectively, both P<0. 0001). At 21°C, survival time was shorter than that at 27°C (χ2[10] = 374. 56, P<0. 0001). All of these survival times were significantly lower than controls (χ2[10] = 359. 12, P<0. 0001). Sporulation was observed on 100% of dead insects exposed at 21,23 and 27°C for both isolates. At 30°C, sporulation was observed on 87% of dead insects infected with URPE-11 and 93% on infected with ENT-1 isolate. No sporulation was found for any control insects. Trypanosoma cruzi infection generally prolonged the survival times of insects when these were subsequently infected with the entomopathogenic fungus B. bassiana (ENT-1). The initial (full) model was simplified to keep only significant interactions. In the final model, there was no three-way interaction (fungal infection x parasite infection x temperature) (χ2[3] = 4. 136, P = 0. 247). There were, however, two-way interactions between fungal infection and T. cruzi infection, between fungal infection and temperature and between T. cruzi infection and temperature (χ2[16] = 511. 24, P<0. 0001) (Fig 4). For the interaction between fungal infection and T. cruzi infection, the mean survival times of T. cruzi-parasitized versus unparasitized R. prolixus were lower when infected with B. bassiana (20. 4±1. 5 and 15. 5±1. 0 days respectively, χ2[6] = 384. 21, P <0. 0001) than when not infected with the fungus (51. 9±0. 6 and 53. 6±0. 9 days, respectively, χ2[6] = 5. 34, P = 0. 304). For the fungal infection x temperature interaction, the lower mean survival times were recorded for insects infected with B. bassiana at 23 and 30°C, there was a significant difference between survival at these two temperatures (10. 6±0. 6 versus 15. 5±0. 8 days respectively; χ2[10] = 455. 4, P<0. 0001). However there was no difference between survival times at 21 and 27°C (25. 2±2. 3 and 20. 5±2. 3 days respectively; χ2[1] = 1. 82, P = 0. 18). For insects not infected with the fungus B. bassiana, no differences in survival times were observed at 23 and 30°C (52. 2±1. 0 and 49. 5±1. 7 days respectively; χ2[1] = 1. 61, P = 0. 2), or between 21 and 27°C (55. 7±0. 1 and 53. 6±0. 8 days respectively; χ2[1] = 1. 06, P = 0. 3). However, these two pairs were different from one another (χ2[8] = 464. 19, P = 0. 02). For the T. cruzi x Temperature interaction, there were two groupings of statistically equivalent mean survival times: (1) insects parasitized and unparasitized with T. cruzi at 23 and 30°C plus unparasitized at 27°C (χ2[6] = 20. 68, P = 0. 25), and (2) insects parasitized and unparasitized with T. cruzi at 21°C plus parasitized at 27°C (χ2[3. 7] = 20. 19, P = 0. 54) Sporulation was observed on 100% of dead insects parasitized with T. cruzi and unparasitized at all temperatures, except for insects parasitized with T. cruzi at 30°C for which sporulation was observed on 92% of dead insects. No sporulation was found for any control insects. The sequence of fungal isolate Metarhizium anisopliae (URPE-11) is available at GenBank: accession number for the corresponding isolate is KX096871.

Entomopathogenic fungi have potential for use in biopesticides for control of vectors of medical importance. As they are biological agents, however, a range of factors may affect their efficacy for this purpose, including temperature and interactions with the vectored parasite. Little is known about how the infection of entomopathogenic fungi affect the interaction between vectors of Chagas disease and the protozoan Trypanosoma cruzi but in other systems it is known that the efficacy of fungal pathogens can be modulated by the presence of microorganisms within the insect target. Here we screened fungal isolates as candidates for triatomine control, using as a model Rhodnius prolixus, vector of Chagas disease. We included in our screening assays of the virulences of these fungi over a range of temperatures and in insects infected with T. cruzi. Temperature affected the virulence of the fungi, indicating that some strains may be better thermal generalists than others (and so more versatile as biocontrol agents). Concentrating now on a single fungal candidate (a strain of Beauveria bassiana, we found that insects already infected with T. cruzi resisted the fungus for longer than did insects uninfected with the parasite over most of the temperature range tested. These results indicate a potential problem for the use of these fungi as biocontrol tools, as their use could generate selection pressure on the insect for greater chances of acquiring T. cruzi.

Abstract Introduction Material and Methods Results Discussion

invertebrates medicine and health sciences pathology and laboratory medicine pathogens microbiology parasitic diseases parasitic protozoans animals developmental biology fungi nymphs protozoans fungal diseases insect vectors fungal pathogens infectious diseases mycology medical microbiology epidemiology microbial pathogens life cycles disease vectors insects arthropoda trypanosoma cruzi trypanosoma biology and life sciences organisms

2016

Screening of Fungi for Biological Control of a Triatomine Vector of Chagas Disease: Temperature and Trypanosome Infection as Factors

We determined female genome sizes using flow cytometry for 211 Drosophila melanogaster sequenced inbred strains from the Drosophila Genetic Reference Panel, and found significant conspecific and intrapopulation variation in genome size. We also compared several life history traits for 25 lines with large and 25 lines with small genomes in three thermal environments, and found that genome size as well as genome size by temperature interactions significantly correlated with survival to pupation and adulthood, time to pupation, female pupal mass, and female eclosion rates. Genome size accounted for up to 23% of the variation in developmental phenotypes, but the contribution of genome size to variation in life history traits was plastic and varied according to the thermal environment. Expression data implicate differences in metabolism that correspond to genome size variation. These results indicate that significant genome size variation exists within D. melanogaster and this variation may impact the evolutionary ecology of the species. Genome size variation accounts for a significant portion of life history variation in an environmentally dependent manner, suggesting that potential fitness effects associated with genome size variation also depend on environmental conditions. Genome size evolution is extensive and ubiquitous. However, the mechanisms by which this occurs are poorly understood and hotly debated, despite a wealth of information connecting genome size shifts to numerous phenotypes, lineages, and abiotic environments [1]–[16]. One critical component of this debate is whether selection can act on genome size, or if it is a neutrally evolving cellular character. Proponents of genome size evolution point to the association of genome size with cell size and rate of cell division, which impact phenotypes important to fitness [10], [11], [16]–[19]. For instance, it has been suggested that Drosophila species with longer development times tend to have larger genomes [10]. Similarly, “weedy” plant species have been hypothesized to have smaller genomes and short generation times [18]. Recently, a review of the genome size literature in numerous endothermic and ectothermic species has made a strong case that genome size evolution could play a role in temperature-size interactions, which could potentially explain adaptive variation in numerous species [16]. While the connection between genome size variation and phenotype is generally recognized for order of magnitude changes in genome size and for interspecific phenotype comparisons, there is little evidence that these effects act on the relatively small magnitude of variation in genome size expected within a species – especially in non-plants. Alternatively, neutral factors such as founder effects and random drift [15], [19] and changes in insertion/deletion balance [13] have been proposed as mechanisms for intraspecific changes in genome size. Consequently, conflicting theories of genome size evolution exist and neither camp has definitively documented the potential for selection rather than chance as a driving force. In order for selection to drive genome size evolution (either directly or indirectly), variation in genome size must occur within a species and be connected to a phenotype that impacts fitness. It is for this reason that repeated attempts have been made to observe conspecific variation in genome size [1]–[5]. Often they have been linked to phenotypic analyses in wild individuals. However, because the observed phenotypic variation is confounded with environmental variation and because it is difficult to achieve high levels of independently replicated genome size and phenotypic measures from wild populations; it has been difficult to develop a compelling case that genome size is associated with variation in fitness related traits. In addition, some of these studies may also be affected by environmental interactions with genome size measurement technology (e. g. anthocyanin in plants can bias genome size measures [20]). Accordingly, studies of wild individuals have not resolved this debate. One of the more compelling selection-based arguments in the recent genome size literature has linked nucleotypic effects (genome size is connected with replication rate and cell size) of genome size variation to thermal responses [16]. Many ectotherms follow the temperature-size rule, where body size increases as temperature decreases [21]. Drosophila species follow this rule [22] and also demonstrate population-level differences in size (across continents) that mirror this pattern, where strains derived from cooler environments are also larger than those from warmer environments [23]–[27]. Since this pattern has appeared on numerous continents, it is clear that larger size at higher latitudes/cooler temperatures is adaptive for Drosophila species. Drosophila body size is also correlated with numerous other life history traits in a manner that is not completely understood [28]–[31]. A better understanding of how thermal plasticity in body size, development time, and immature survival has evolved in Drosophila would shed light on the evolutionary ecology of the species. However, although the evolutionary history of the species is well studied at the phenotypic and genomic level, and interspecific observations of genome size-phenotype connections exist [10], genome size variation has not been considered in studies of the evolution of D. melanogaster. Here, we take a quantitative genetic approach to address the issue of conspecific genome size variation and its life history consequences. We ask how an environmental variable, temperature, interacts with genome size to affect D. melanogaster development. Previous studies have measured intraspecific variation, focusing on geographic-dependent variation in a small set of intraspecific populations [1]–[5]. However, no studies to date have addressed the effects of intraspecific genome size variation on life history traits in a way that enables the measurement of phenotype in multiple environments for a large number of individual conspecific genotypes. Recently, such studies have been proposed to investigate the role of genome size on thermal plasticity [16]. The availability of sequenced inbred strains from Raleigh, NC natural populations (the Drosophila melanogaster Genetic Reference Panel, DGRP, https: //www. hgsc. bcm. edu/content/drosophila-genetic-reference-panel [32], [33]) allows for replicated, accurate within-strain genome size estimates. In addition, the ease of measuring life history traits in these strains at different rearing temperatures makes D. melanogaster an ideal model organism for determining the relationships among genome size, temperature, and life history. Accordingly, we evaluated the extent of conspecific variation in genome size among 211 DGRP inbred strains, selected 50 lines representing the 25 of the largest and 25 smallest genomes, measured life history traits in all 50 of these lines, and asked if genome size variation correlates with variation in development-related life history traits or their environmental plasticity. We quantified genome size in females for 211 DGRP lines using flow cytometry (Table 1, Table S1). We found considerable variation in average genome size among these strains, with the average genome size per strain ranging from a minimum of 169. 7 Mbp and a maximum of 192. 8 Mbp. The overall average genome size was 175. 5 Mbp, which agrees well with the estimated genome size of 175 Mbp for the y w reference strain of D. melanogaster [34]. Further, the population appears to be biased toward the accumulation of large genomes (median = 175. 1; skew = 0. 5) [33]. Two of the strains, DGRP_378 and DGRP_554, were not included in the second release of the DGRP [33]. Interestingly, several of the large strains demonstrated instability in genome size, such that the addition of replicate measures did not reduce the within-strain standard error of genome size (Table 1). To further demonstrate intraspecific variation in genome size, we performed a flow cytometry analysis with co-processed samples of a line with small genome size (DGRP_208,169. 7 Mb) and a line with large genome size (DGRP_517,181 Mb) [35]. The histogram produced by co-processed females from these lines is shown in Figure S1. The co-preparations show separate fluorescence intensity peaks that differ in position precisely as expected from the genome size estimates (Table 1). Additional evidence of differences was provided by comparison of the proportion of under-replicated DNA in polytene tissues (Figure S2a, b) [36]. For the strains shown, DGRP_208 (169. 7 Mb) and DGRP_517 (181 Mb), 88% of the DNA is fully replicated (12% unreplicated) in the smaller genome, while 86. 2% of the DNA is fully replicated (13. 8% unreplicated) in the larger genome. The 1. 8% increase in the replicated sequences in the thorax represents 28% (3. 18 Mb) of the 11. 3 Mb difference between the strains; the remaining 72% (8. 14 Mb) is under-represented in thoracic tissues suggesting a role for both fully replicated and under-replicated sequences in genome size expansion. In order to take advantage of the observed variation in genome size among inbred strains and examine the life history effects of an increase or decrease of genome size, we reared 25 strains with large female genomes and 25 strains with small female genomes (Table 1) at three temperatures (20°C, 25°C, and 30°C) and scored life history traits for each strain at each temperature (Figures 1,2, S3; Table S1). A significance test across all genome size means of 211 strains derived from 1,052 measurements showed the 25 strains with the large genomes differed significantly from the 25 strains with the small genomes (Table 1) (t-test; P<0. 001). The life history traits of survival to pupation, minimum pupation time, female pupal mass, and female eclosion time varied significantly across strains and temperatures (Figures 1, S3; Table S1). Survival to pupation strongly correlated with survival to adulthood (r = 0. 975); therefore, we report only survival to pupation. We fitted linear mixed models to the developmental phenotypes that included fixed effects of genome size, temperature, and the interaction between the two factors; as well as random effects accounting for additive and non-additive strain effects and the interaction between strain and temperature. We found substantial variation in the effect of genome size on the plasticity of all non-survival traits (Table 2). The magnitude and/or the direction of the effects of genome size on these phenotypes were dependent on the temperature, as evident by the significant interactions between genome size and temperature (Figure 2; Table 2). The effects of temperature on all four phenotypes were highly significant (P = 0. 0013 for survival to pupation; P<0. 0001 for minimal pupation time, female pupal mass, and female eclosion time). We further tested the effect of genome size on the four phenotypes for the three temperatures separately (Table 3). As expected when there is no interaction between genome size and temperature, the effect of genome size on survival to pupation was similar across all three temperatures (Table 3). On the other hand, genome size only affected the other traits in specific thermal conditions (Table 3). We also evaluated and visualized the significant relationships between phenotype and genome size on each temperature via simple regression of phenotypic line means on genome size (Figure 2, Table 4). The general conclusions from regressions did not vary if outlier lines with extremely large genomes (Table 1) were removed; therefore, all data were included in the analyses. We estimated the proportion of phenotypic variation explained by genome size by comparing variance component estimates with or without genome size in the model for temperature/phenotype combinations where the effect of genome size was significant (Table 3). We found that genome size contributed between 6–23% of the total variation in survival to pupation (Table 3), 17% of the variation in minimum pupation time at 25°C, 14% of the variation in female pupal mass at 20°C, and 5% of the variation in female eclosion time at 20°C. Given that genome size appeared to influence development in an environment-dependent manner, we derived a basic measure of the degree of plasticity in each phenotype and performed regressions of plasticity against genome size (Figure 2E–G, Table 4). There appears to be a complex relationship between genome size and plasticity, such that large genomes are more plastic or less plastic than small genomes, depending on the phenotype. For example, minimum pupation time showed genome size-dependent plasticity where large genomes were more responsive to 20°C to 25°C thermal shifts, whereas small genomes were more responsive to 25°C to 30°C shifts. For the most genome size-sensitive phenotypes (e. g. survival to pupation) thermal plasticity was relatively independent of genome size. We further assessed correlations among all the phenotypes and genome size using a principal component (PC) analysis (Table S2). The first two PCs partitioned the data on the basis of genome size and accounted for 21% and 16%, respectively, of the total variation observed. The loadings of the first two PCs reflected the correlation of genome size with phenotype (genome size correlation to PC1 and PC2 was −0. 23 and 0. 18, respectively), and they correctly partitioned all but a few lines into large or small genome size groupings. Thus PC analyses upheld the general inferences obtained from the mixed model analysis. It is possible that genotype is confounded with genome size. For example, if co-adapted suites of traits are associated with specific chromosomes of different sizes, strains with small genome sizes may also have distinct genotypically correlated phenotypes. If this is the case, we expect lines within the large or small genome groups would be more closely related to each other than lines between the groups. Indeed, genome size is significantly correlated with inversion karyotypes in the DGRP, and lines with the same inversion karyotypes are slightly more related to each other [33]. However, inversions clearly do not completely explain genome size variation, accounting for only ∼0. 5 Mb of the variation in genome size [33]. To address the concern of relatedness among strains of atypical genome size, we evaluated the pair-wise genomic relatedness among lines. Relatedness between lines within the large and small genome size groups is not higher than that between groups, suggesting that the large and small genome lines form a genetically homogeneous pool rather than two separate clusters (Figure 3). This analysis, in combination with the fact that the aforementioned mixed models were designed to account for any confounding cryptic relationship among the lines, clearly suggests that there are correlations of genome size with life history traits that are independent of potential confounding genotypic effects at a broad genome-wide scale. Finally, to assess whether pleiotropic effects of QTLs affecting both genome size and the phenotypes could explain the observed correlation between genome size and life history traits, we tested the effect of genome size conditional on the top five genetic variants associated with genome size detected by a genome wide association study (GWAS) [33]. Although the inclusion of top GWAS hits diminished the significance of the association between genome size and life history traits (Table 3), this result is expected when there is a genuine association between genome size and life history traits. Inclusion of the top GWAS hits does not fully explain the effects of genome size on life history traits and actually lowered the P-values for genome size associations at some temperatures. Genome wide variation in gene expression has been evaluated using microarrays for a subset of the DGRP strains [37]. We assessed whether there is variation in gene expression between lines with small and large genomes. These observations can be used to guide further efforts to dissect mechanisms by which genome size can lead to phenotypic differences. Comparisons between microarray results of adult females of small genome (DGRP_208, DGRP_307, DGRP_313, DGRP_335, DGRP_360, DGRP_379, DGRP_555, DGRP_786, and DGRP_820), large genome (DGRP_362, DGRP_391, DGRP_517, DGRP_705), and more species-typical genome (DGRP_301, DGRP_303, DGRP_304, DGRP_306, DGRP_315, DGRP_324, DGRP_357, DGRP_358, DGRP_365, DGRP_375, DGRP_380, DGRP_399, DGRP_427, DGRP_437, DGRP_486, DGRP_514, DGRP_639, DGRP_707, DGRP_712, DGRP_714, DGRP_730, DGRP_732, DGRP_765, DGRP_774, DGRP_799, DGRP_852, DGRP_859) strains revealed 562 differentially expressed genes (Figure 4, Tables S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13). One hundred forty-nine genes were up-regulated in strains with small genomes (Figure 4; Table S3); 227 genes were up-regulated in strains with large genomes (Figure 4; Table S4). Strains with small genomes down-regulated 91 genes (Figure 4, Table S5) while strains with large genomes down-regulated 95 genes (Figure 4, Table S6). Gene ontology enrichment analyses revealed that strains with small genomes up-regulated genes related to metabolism, mitosis, egg development, translation, and salt transport (Tables S7, S9) and down-regulated genes related to development and enzymatic activity (Table S11). The up-regulated genes included ion binding genes that appear to be differentially regulated during exposure to thermal and chemical environments that affect oxidative stress [38]. Strains with large genomes up-regulated genes involved with development, metabolism, TOR signaling, and heme and ion binding (Tables S8, S10) while down-regulating primarily genes affecting gametogenesis (Tables S12, S13). Many of the enriched genes were expressed in the digestive system. This suggests, (in combination with the increased expression of metabolism and TOR signaling genes in strains with large genomes), that nutritional ecology plays an important role in these responses. Drosophila chromosomal inversions are classically understood to clinally segregate in an adaptive fashion [39], [40] and some of the phenotypes we studied can vary along clines [23]–[27]. Of the strains evaluated for gene expression, none of the large genomes contained known inversions (Table 5). In the expression comparison, inversions do not obviously explain differences in gene expression between large and small genomes. The role of natural selection in the evolution of genome size evolution is hotly debated. If natural selection directly or indirectly affects the evolution of genome size, genomes must vary conspecifically and be connected to adaptive phenotypes like life history traits. By measuring genome sizes of 211 inbred D. melanogaster strains derived from a single population, we document the presence of conspecific genome size variation, and the association of genome size with several life history traits in strains with the most extreme genome sizes. Only a few previous studies examined the correlation between genome size and organismal phenotypes [1], [5]. Here, we provide evidence of complex correlations between genome size and multiple life history traits in an experiment that affords greater resolution of genome size – phenotype connections than is possible with studies of wild individuals (Figures 1,2). A major conclusion of our study is that genome size appears to contribute a significant proportion of variation in life history traits in an environmentally dependent manner (Figures 1–2, Tables 2–4, S2). Genome size effects ranged from 5%–23% depending on the trait and temperature. These estimates were obtained after accounting for the additive and non-additive genotypic effects of strains. PC analyses uphold the general interpretations of the linear models and separate the phenotypic data based on genome size, with the first two principal components correlating with genome size. This study was not designed to infer the mechanisms or nature of the plastic responses, only to demonstrate their existence. More detailed studies investigating the details of this phenomenon are warranted. Future efforts should be targeted toward understanding the degree to which genome size effects are rooted in the “quality” versus “quantity” of the genome. While the reported results could be due to molecular changes in metabolism necessary to maintain a larger genome, these metabolic effects cannot currently be definitively disentangled from the fact that they could be associated with adaptive chromosomes of different sizes (such as the inversions on chromosome 3R in the small genomes). However, the fact that our analyses accounted for genomic levels of relatedness among the studied strains suggest that both genome size and genotypes of strains with the largest and smallest genome sizes contribute to variation in our target phenotypes. We assessed the effect of female genome size on female-specific (pupal mass and eclosion time) and non-sex-specific (pupal survival, minimum pupation time, and adult survival) traits. Thus, it is possible (depending on the mechanism of our observations) that females and males have divergent genome size-dependent phenotypic responses. It should be noted that, since females and males exhibit sexually dimorphic life history traits [41]–[44], which can have a different optimum for each sex, it will be interesting to assess whether dimorphism in genome size exists and if it is a mechanism by which the sexes can manage conflicts in life history trait optima. The DGRP consortium conducted a genome wide association study on genome size with the data produced by this project, along with a suite of complex quantitative traits [33]. Using linear mixed models, relatedness among individuals in genome-wide association studies is accounted for by estimating average levels of genomic similarity, before genetic associations with phenotype are identified [45]. Interestingly, variation in genome size is correlated with inversions in the DGRP, and correction for inversion karyotype associations resulted in the identification of several strong associations of genetic variants with genome size. When there is evidence of a genome size effect on a phenotype, the results of our work suggest that it may be appropriate to incorporate genome size into mapping efforts. Potential correction for genome size effects in mapping experiments may include using genome size as a cofactor (as observed in [45] for alleles of the frigida locus in Arabidopsis thaliana) and incorporating genome size as a correlated phenotype (as described in [46]). In theory, inbreeding should have just partitioned genome size variation among the strains of the DGRP, revealing genome size variation in a manner that allowed us to repeatedly sample genome size and phenotype from the same genotype. However, one could imagine that inbreeding might itself be a cause of genome size variation, which is a caveat that must be considered in this experiment. As a consideration to the strains analyzed, given the bias in strain maintenance (healthy strains were maintained preferentially), an inbreeding effect on genome size should be limited in its effect on fitness. In addition, if genome size shifted with the creation of the strains, it must be remembered that there were deviations from the average genome size in both directions. This would indicate support for Lynch' s proposal that genome size evolution is due to genetic drift [15], [19]. However, given our observation that variation in genome size is associated with several life history traits, we speculate that variation in genome size created by neutral processes may be reinforced in some instances by non-neutral forces. It is also possible that inbreeding could result in the fixation of alleles that pleiotropically affect life history traits and genome size. Such pleiotropic effects could drive an association between genome size and life history traits. Indeed, top GWAS hits of genome size variation explained some, but not all, of the association between genome size and life history traits (Table 3). However, this is a necessary statistical outcome even when the variation in the life history traits is entirely caused by genome size variation. In fact, in the event that genome size is causal for the life history traits, any QTL for genome size would appear to be pleiotropic for the life history traits. Whether the variation in life history traits is caused independently by the pleiotropic QTLs or by variation in genome size must be addressed by breaking the pleiotropic QTLs into independent ones, which may or may not be possible and is beyond the scope of the current study. In conclusion, we observed significant variation in genome sizes among sequenced D. melanogaster strains; and large and small genome sizes correlated with conspecific variation in life history traits. These results indicate that a portion of phenotypic variation may be due to genome size effects (potentially up to 23%, in a trait and environment dependent manner). What is even more interesting is that genome size variation appears to be associated with phenotypic plasticity in several traits, suggesting that the evolution of genome size may produce novel correlations among life history traits in a temperature-dependent manner. These observations support the recently proposed link between genome size and thermal plasticity [16] and advance our understanding of life history trait correlations. This research indicates that studies of genome size evolution can contribute to two major problems in biology: elucidating the genetic architecture of complex phenotypes and identifying mechanisms of life history trait evolution. We estimated genome size of 1,052 individual females from the 211 inbred D. melanogaster strains using flow cytometry, using D. virilis (1C = 328 Mb) as an internal standard. The final concentration of propidium iodide stain was 25 µg/mL [47]. In brief, samples were prepared from a single adult female head that was homogenized in Galbraith buffer using a Dounce tissue grinder and nylon mesh filtration. Samples were incubated at 4°C for approximately 30–60 minutes in 25 µg/mL propidium iodide. Flow cytometry measured 1,000 cell counts per unknown and control sample. Genome size of the unknown = GScontrol×PI−fluorunknown/PI-fluorcontrol where PI-fluor is the channel number of red propidium iodide (PI) fluorescence [47]. Mean genome size averages were compared using Proc GLM with Duncan multiple range tests in SPSS (SPSS Inc. Version 16. 0, Chicago, IL) and t-test comparisons to the population mean. Genome size differences between large and small genome strains were verified [20] by co-preparation of an individual from a high (DGRP_517) with one from a low (DGRP_208) genome size line. The extent of under-replication in polytene tissues of high and low genome size lines was scored using thoracic tissues prepared as described for genome size estimates [36]. In order to maximize the variation in genome size and the phenotypic variation, 25 strains with large female genomes (DGRP_21, DGRP_26, DGRP_28, DGRP_38, DGRP_40, DGRP_42, DGRP_45, DGRP_57, DGRP_69, DGRP_75, DGRP_88, DGRP_93, DGRP_101, DGRP_105, DGRP_138, DGRP_142, DGRP_153, DGRP_362, DGRP_391, DGRP_517, DGRP_705, DGRP_790, DGRP_819, DGRP_837, and DGRP_892) and 25 strains with small female genomes (DGRP_181, DGRP_195, DGRP_208, DGRP_237, DGRP_307, DGRP_313, DGRP_318, DGRP_321, DGRP_332, DGRP_335, DGRP_360, DGRP_377, DGRP_378, DGRP_379, DGRP_406, DGRP_440, DGRP_441, DGRP_554, DGRP_555, DGRP_595, DGRP_786, DGRP_787, DGRP_801, DGRP_820, and DGRP_884) [33] were chosen for phenotypic analysis. Male and female flies from these strains were passaged to perforated egg-laying bottles with a 35 mm grapefruit plate (10% grapefruit juice, 1% EtOH) and provided a small amount of yeast paste. Oviposition occurred at room temperature. Eggs were collected two hours after introduction of females. Seventy-five eggs were placed in vials containing Bloomington' s standard medium (The Bloomington Drosophila Stock Center, Indiana University; Table S14) for all experiments. Vials were placed in a 20°C, 25°C, or 30°C incubator under a 12-hr light∶dark cycle with 70% humidity. Ten replicate vials were set up for each strain at each temperature; three vials were used to measure pupal phenotypes (survival from egg to pupa, minimum pupation time, and female pupal mass) and three vials were used to measure adult phenotypes (survival from egg to adult, and female eclosion time). Survival to pupation or adulthood was calculated as the number of total pupae or adults produced, respectively, divided by 75, the number of eggs in each vial. Vials with high egg mortality, which was rare, were not used in calculating survivorship. Minimum pupation time was measured as the time elapsed from when eggs were placed into the vial until the emergence of the first pupal case at an 8 hour temporal resolution. A total of 50 females (10 per vial) per strain at each temperature were weighed individually to calculate female pupal mass. Eclosion was recorded at 8: 00 AM, 2: 00 PM, and 8: 00 PM each day to calculate the average eclosion time of each female in each vial. For each phenotype, the significance of genome size and temperature effects, as well as their interaction, was determined using the MIXED procedure in SAS. We first assessed the significance of genome size by temperature interaction by fitting the following model, y = μ+g+T+g: T+s+S+S: T+e, where y is the phenotype being modeled, μ is the overall mean, g is the fixed effect of genome size of the strain, T is the fixed effect of temperature on which the flies are raised, g: T is the interaction between genome size and temperature, s is the random additive genetic effect with the covariance matrix determined by the pair-wise genomic relationships between strains, S is the random strain effect which accounts for additional variation between strains, S: T is the interaction between strain and temperature, and e is the residual. When testing the effect of genome size on life history traits in the three temperatures separately, a reduced model was fitted, y = μ+g+s+S+e and the effect of genome size was tested by type III F test. We also tested the effect of genome size on life history traits conditional on the five most significant genetic variants associated with genome size variation in a GWAS (X_21136189_SNP, 3L_5383897_SNP, 2L_6541787_SNP, 2L_6035179_SNP, 3R_19140723_SNP) [33] by including their genotypes in the model as fix effects. Plasticity was scored three ways. First, we subtracted the mean of each phenotype from each strain at 30°C from the phenotype of that strain at 20°C. Second, we subtracted the mean of each phenotype from each strain at 25°C from the phenotype of that strain at 20°C. Finally, we subtracted the mean of each phenotype from each strain at 30°C from the phenotype of that strain at 25°C. This resulted in 50 measurements for each metric of pair-wise plasticity, which were regressed against genome size using Linear Regression in SPSS (SPSS Inc. Version 16. 0, Chicago, IL). All of the aforementioned measures were done with line means as genome size and phenotype were not scored in the same individuals. Finally, PC analyses were also performed using SAS software to assess correlations among the phenotypes and genome size. Pairwise relatedness was extracted from the second release of genomic data from the DGRP [33]. Genome-wide levels of relatedness were calculated for all of the strains in that project, including some that have not been identified for further analyses because they exhibited signatures of relatedness to others whose genomes had already been sequenced [48]. Reported here are the relevant levels of genomic relatedness among the strains of atypical genome size. These values were used to generate Figure 3. A previous study used microarray analysis to determine gene expression changes in a subset of the DGRP lines [37]. Briefly, RNA was extracted from two independent pools of 25 three to five day old flies per sex per line during the same two hour window each day. They were only evaluated in one environment. RNA extraction, labeling, and hybridization was randomized, and normalized values of gene expression were determined using median standardization [37]. We focused on the female microarrays. Of the 40 strains analyzed, nine had small genomes (DGRP_208, DGRP_307, DGRP_313, DGRP_335, DGRP_360, DGRP_379, DGRP_555, DGRP_786, DGRP_820), four had large genomes (DGRP_362, DGRP_391, DGRP_517, DGRP_705), and the remaining 27 had average-sized genomes (DGRP_301, DGRP_303, DGRP_304, DGRP_306, DGRP_315, DGRP_324, DGRP_357, DGRP_358, DGRP_365, DGRP_375, DGRP_380, DGRP_399, DGRP_427, DGRP_437, DGRP_486, DGRP_514, DGRP_639, DGRP_707, DGRP_712, DGRP_714, DGRP_730, DGRP_732, DGRP_765, DGRP_774, DGRP_799, DGRP_852, DGRP_859). We extracted expression values for each strain (averaged across each strain' s replicates) using the PM-MM algorithm of dChip (one nucleotide between the probe and target sequence is mismatched) [49]. We focused on genes with expression levels greater than 50. We used cyber-T Bayesian t-tests [50] (P<0. 05) and false discovery rate [51] (FDR<0. 05) analyses to determine significant changes in gene expression. Genes that were identified as being up-regulated in small genomes showed increased expression in strains with small genomes compared to 1) strains with average genomes and 2) strains with large genomes. Genes were deemed as down-regulated in small genomes when they were down-regulated in strains with small genomes compared to 1) strains with average genomes and 2) strains with large genomes. We followed similar criteria for up or down-regulated genes in large genomes. We assessed significant enrichment of gene ontology terms using DAVID Functional Annotation Tool [52], [53] and GO Finder [54]. Each gene list (up-regulated in small genomes, down-regulated in small genomes, up-regulated in large genomes, and down-regulated in large genomes) was compared independently to the D. melanogaster genome to assess enrichment (P<0. 05) of biological processes, cellular components, and molecular functions.

Genome size evolution is ubiquitous, and–even after decades of research–mysterious. There are two major classes of hypotheses regarding genome size evolution, those that attribute its causes to evolutionarily neutral processes and those that suggest a role for selection. Numerous correlations between genome size and fitness-related phenotypes have been documented, suggesting selection could play a role in genome size evolution. Unfortunately, many of the effects in those studies are confounded with factors that could provide alternative explanations. Here, we show that 211 inbred strains of Drosophila melanogaster exhibit abundant variation in genome size, which correlates with life history traits in a temperature-dependent manner. Gene expression analyses suggest a role for differences in metabolism between strains with large and small genomes. Thus, there is genetic variation in genome size within D. melanogaster, and this variation is connected to variation in environmentally dependent life history traits. These observations indicate that selection is indeed a potential mechanism by which genome size can evolve. Our results also suggest that higher levels of genetic architecture may explain some of the genetic contribution to biologically important complex traits and raise the possibility that nucleotide quantity can contribute to phenotype in addition to quality.

Abstract Introduction Results Discussion Materials and Methods

genome-wide association studies invertebrates population genetics quantitative traits animals animal models developmental biology drosophila melanogaster model organisms organism development genome analysis epigenetics drosophila chromatin research and analysis methods genomics chromosome biology insects arthropoda systems biology cell biology phenotypes natural selection heredity genetics biology and life sciences computational biology evolutionary biology evolutionary processes organisms complex traits

2014

Intrapopulation Genome Size Variation in D. melanogaster Reflects Life History Variation and Plasticity

Mycolactone is a lipid-like endotoxin synthesized by an environmental human pathogen, Mycobacterium ulcerans, the causal agent of Buruli ulcer disease. Mycolactone has pleiotropic effects on fundamental cellular processes (cell adhesion, cell death and inflammation). Various cellular targets of mycolactone have been identified and a literature survey revealed that most of these targets are membrane receptors residing in ordered plasma membrane nanodomains, within which their functionalities can be modulated. We investigated the capacity of mycolactone to interact with membranes, to evaluate its effects on membrane lipid organization following its diffusion across the cell membrane. We used Langmuir monolayers as a cell membrane model. Experiments were carried out with a lipid composition chosen to be as similar as possible to that of the plasma membrane. Mycolactone, which has surfactant properties, with an apparent saturation concentration of 1 μM, interacted with the membrane at very low concentrations (60 nM). The interaction of mycolactone with the membrane was mediated by the presence of cholesterol and, like detergents, mycolactone reshaped the membrane. In its monomeric form, this toxin modifies lipid segregation in the monolayer, strongly affecting the formation of ordered microdomains. These findings suggest that mycolactone disturbs lipid organization in the biological membranes it crosses, with potential effects on cell functions and signaling pathways. Microdomain remodeling may therefore underlie molecular events, accounting for the ability of mycolactone to attack multiple targets and providing new insight into a single unifying mechanism underlying the pleiotropic effects of this molecule. This membrane remodeling may act in synergy with the other known effects of mycolactone on its intracellular targets, potentiating these effects. Buruli ulcer (BU) is the third most common human mycobacterial infection in the world, after tuberculosis and leprosy [1,2]. BU is a neglected tropical disease of the skin and subcutaneous tissue caused by an environmental pathogen, Mycobacterium ulcerans (M. ulcerans). This disease, which can affect all age groups and both sexes, is commonest in West Africa and parts of Australia, but has been reported in over 30 countries worldwide [3,4]. These painless ulcers affect at least 5,000 patients per year and are thought to be heavily underreported [5]. Infection with M. ulcerans results in persistent severe necrosis with no acute inflammatory response. The ulcer begins as a painless nodule or papule on the skin, which, if left untreated, progresses to massive ulceration that may cover 15% of the skin of the patient, resulting in significant morbidity [5,6]. BU is not lethal, but patients may suffer lifelong disfigurement, functional impairment and disability unless the infection is recognized and treated at an early stage. M. ulcerans pathogenesis is mediated by a necrotizing immunosuppressive toxin, mycolactone (S1 Fig). This lipid-like polyketide macrolide has been identified as the main virulence factor produced by M. ulcerans and is responsible for the skin lesions and tissue necrosis [6,7]. After its production [8,9], this diffusible toxin is excreted in vesicles derived from the bacterial membrane and enriched in extracellular matrix, which acts as a reservoir of the toxin [10]. In vitro, mycolactone has been shown to localize in the cytosol of cultured murine fibroblasts, through non-saturable and non-competitive uptake in the presence of excess mycolactone [11,12]. Mycolactone has also been reported to accumulate in a time- and dose-dependent manner in the cytoplasm of human epithelial cells and lymphocytes, but not in the plasma or nuclear membranes of the cell [13]. These findings suggest that mycolactone can diffuse across cell membranes by non-cell-specific passive diffusion to reach its intracellular targets [11,14]. Mycolactone A/B (a 3: 2 ratio of Z-/E-isomers of the C4’-C-5’ bond in the long “Southern” polyketide side chains, S1 Fig), which is produced by the most virulent strains of M. ulcerans, has been shown to have pleiotropic effects on fundamental cellular processes, such as cell division, cell death and inflammation, depending on toxin dose and exposure time [14,15]. Exposure to pure mycolactone is cytotoxic for many cell lines, but the dose and exposure required for cell death are highly variable [15]. Early studies on cell lines suggested a role for mycolactone in cell-cycle arrest in the G1/G0 phase and apoptosis [7,16]. However, recent studies have suggested that anoïkis, due to cytoskeleton rearrangements, leading to changes in cell adhesion and detachment, is a much more likely mechanism of cell death in vivo [3,17]. By inducing changes to the cytoskeleton and disrupting tissue structure, this toxin compromises cell structure and homeostasis through the impairment of extracellular matrix biosynthesis [18]. In addition to its cytotoxicity, mycolactone has immunosuppressive activity, resulting in a lack of local inflammation despite extensive tissue damage, together with inhibition of the local immune response [19–23]. At low concentrations, this molecule has been found to be a powerful analgesic, due to its stimulatory effect on the angiotensin receptor [24]. These effects may account for the painlessness of BU lesions. The precise molecular mode of action of mycolactone in eukaryotic cells remains unclear, but a number of cellular targets have been identified. A literature survey revealed most of these targets to be membrane receptors residing in ordered plasma membrane nanodomains known to modulate the functionalities of membrane proteins [25,26]. Mycolactone can impair the migration of naïve T cells to peripheral lymph nodes [27], where they make contact with antigen-presenting cells during T-cell receptor activation. This alteration of T-cell homing is accompanied by a decrease in L-selectin receptor (CD62-L) levels. The downregulation of this receptor normally involves proteolytic cleavage upon stimulation, but the cleavage of L-selectin seems to involve membrane microdomains, which act as a signaling platform [28]. Similarly, the chemokine receptors involved in T-cell inflammatory responses also reside in membrane domains and, the depletion of cholesterol from membranes decreases chemokine binding and abolishes chemokine receptor signaling [29]. Another effect of mycolactone A/B is hyperactivation of the Src-family kinase, leading to the depletion of intracellular calcium and a downregulation of T-cell receptor (TCR) expression, limiting the T-cell response to stimulation and potentially contributing to apoptosis [3,14]. This hyperactivation is initiated by the relocalization of Lck in the microdomains of the plasma membrane, triggered by the action of the toxin [30]. Mycolactone has been reported to inhibit angiotensin II binding, in a dose-dependent manner, and to elicit signaling through human type 2 angiotensin II receptors (AT2Rs), leading to a potassium-dependent hyperpolarization of neurons, accounting for the painlessness of BU lesions [24]. AT2R, like AT1R, is a G protein-coupled receptor (GPCR). Microdomains (both lipid rafts and caveolae) have been reported to be involved in regulating GPCR signaling, by affecting both signaling selectivity and coupling efficacy [31,32]. Mycolactone has recently been shown to modulate Wiskott-Aldrich syndrome protein (WASP) and neural WASP (N-WASP), two members of a family of scaffold proteins that transduce various endogenous signals in dynamic remodeling of the actin cytoskeleton [17]. In immune cells, WASP regulates ordered lipid domain dynamics during immunological synapse formation, which involves clustering of the microdomains of the plasma membrane for optimal T-cell activation. WASP, which is recruited to lipid domains immediately after TCR stimulation, is required for the movements of these microdomains [33]. By disrupting WASP autoinhibition [17], mycolactone can hijack actin-nucleating factors, leading to uncontrolled activation of the ARP2/3-mediated assembly of actin, and a deregulation of lipid domain dynamics. Similarly, mycolactone provokes a disruption of the protein C anticoagulant pathway, with a depletion of thrombomodulin (TM) receptors at the surface of endothelial cells [34]. Nevertheless, the receptors of the protein C activation and activated protein C (APC) signaling pathways are colocalized in the lipid microdomains of endothelial cells [35,36]. Finally, it has recently been reported that mycolactone inhibits the function of the Sec61 translocon [37–39], a transmembrane channel located in the endoplasmic reticulum (ER) membrane [40]. This ubiquitous complex is responsible for cotranslational protein translocation, a universally conserved process in the biosynthesis of secretory and membrane proteins that operates for most of the 30–50% of mammalian proteins carrying a canonical signal peptide [41]. In investigations of transmembrane proteins (TNF), monotypic proteins (COX-2) and conventionally secreted proteins (IL-6), Hall et al. showed that mycolactone prevents ER protein translocation, with the proteins concerned being translated in the cytosol, where they are marked for rapid destruction by the proteasome. In this way, mycolactone causes a selective ~30% decrease in membrane-associated proteins and prevents the production of the vast majority of N-glycosylated proteins [37,38]. Cholesterol and sphingolipid levels are lower in the ER than in the plasma membrane and other organelles, but it has been suggested that ER membranes nevertheless contain lipid domains [42,43]. The fractionation of rough ER integral membrane proteins with 0. 18% Triton X-100 (similar to the treatment of cytoplasmic membranes with 1% Triton X-100, which has successfully revealed the presence of lipid domains in the cytoplasmic membrane) showed that the 0. 18% Triton X-100 fraction contained mostly ER-resident proteins, including, in particular, the Sec61alpha subunit [44], the central transmembrane component of the sec 61 ER translocon targeted by mycolactone [38,39,45]. Thus, mycolactone has diverse complex effects on a range of cells and tissues, and the underlying mechanism unifying its pleiotropic effects seems to be its action through microdomain-associated proteins [14]. In this study, we aimed to characterize in more detail the effects of pure mycolactone on biological membranes, focusing, in particular, on the effects of this toxin on microdomain segregation. Indeed, no molecular-scale description of the effects of this toxin on the cell plasma membrane before it reaches its cellular targets, most of which are located in the ordered plasma membrane nanodomains, has ever been reported. We investigated the capacity of mycolactone to interact with membranes and its effects on lipid organization when crossing the membrane, with several biophysical techniques, including Langmuir monolayers, which we used as an in vitro model of cell membranes, together with fluorescence and Brewster angle microscopy. Langmuir monolayers consist of supramolecular lipid films that form at an air-buffer interface. They can mimic biological membranes and are, thus, attractive membrane models, because the thermodynamic relationship between monolayer and bilayer membranes is direct, and monolayers overcome, independently of their lipid composition, the limitations associated with the regulation of lateral lipid packing encountered in model bilayer systems [46]. They are widely used in studies of peptide or membrane probe/lipid interactions [47–52], and in studies of membrane-protein association [53–61]. Brewster angle microscopy (BAM), which was used for the in situ characterization of Langmuir monolayers, provides additional information about membrane morphology and lipid organization at the air-water interface [62–64]. In our system, we used a lipid composition closely resembling that of the plasma membrane, including among others, 33% sphingomyelin (SM) and 19% cholesterol (Chol), which was considered to be a biologically normal concentration (natural membranes contain 5–50 mol% cholesterol [65,66]). Using this experimental approach, we demonstrated marked effects of mycolactone on membranes, and were able to visualize, for the first time, the capacity of this molecule to disrupt membranes at the molecular level. We characterized the surfactant properties of mycolactone, by evaluating its interfacial behavior at the air/buffer interface and in the absence of lipids. Experiments without lipids at the air/buffer interface can be used to determine: i) the concentration at which amphiphilic molecules saturate the lipid-free interface (i. e. , surface saturation concentration) and ii) the concentration range minimizing aggregation and, therefore, useful for experiments. It is widely accepted that the analytical concentrations to be injected into the subphase for subsequent molecule/lipid interaction analyses should be based on such pre-evaluations and lower than the surface saturation concentration, to prevent artifacts due to molecule aggregation [51,67–69]. The surface saturation concentration was determined by tensiometry [70,71]. Various mycolactone concentrations, from 60 nM to 6 μM, were injected into the PBS subphase. For each concentration, the adsorption of mycolactone at the air/buffer interface was monitored by continuous surface pressure measurement until the equilibrium value, πe, was reached. The curve of πe as a function of mycolactone concentration rapidly increased to reach a plateau at 34 mN/m (Fig 1). At this surface pressure, the interface was saturated with mycolactone molecules, regardless of the concentration of the toxin in the subphase. The surface saturation concentration of mycolactone was then determined at the start of the plateau, and was found to be 1 μM. Brewster angle microscopy (BAM) images recorded at πe with a final mycolactone concentration of 0. 6 μM (a), 1. 2 μM (b) or 6 μM (c) (Fig 1, Inset) confirmed the ability of this molecule to accumulate in a concentration-dependent manner at the air/PBS (pH 7. 4) interface, and to form a very thick film (3. 7 ± 0. 3 nm thick) at very high concentrations (6 μM). At a concentration of 0. 6 μM, below the apparent surface saturation concentration of 1 μM, mycolactone formed a homogeneous interfacial film, but with some bright nuclei also visible (spots, Fig 1, Inset a). These bright nuclei resulted from aggregate formation, as demonstrated by dynamic light scattering (DLS) for mycolactone solutions in PBS pH 7. 4 in S1 Appendix. Thus, mycolactone displayed surfactant properties at a nude interface. To prevent the association of molecules into aggregates in the next experiments, we used a low concentration of mycolactone, 60 nM. At this concentration, mycolactone interacts with lipids as a monomer (see S1 Appendix). The aim of this study was to analyze the interaction of mycolactone with biological membranes. We studied two membrane models: i) a monolayer consisting of a lipid mixture resembling that of the plasma membrane (given in mol%) [72–75]: 39% POPC, 33% SM, 9% POPE, 19% Chol (mixture 1), and ii) a monolayer with the same lipids but without cholesterol (given in mol%): 48% POPC, 41% SM, 11% POPE (mixture 2). Cholesterol is known to regulate lipid segregation in plasma membranes [25,26]. Mycolactone receptors have been reported to be located in ordered plasma membrane microdomains. We therefore investigated the effects of this particular membrane lipid on the ability of mycolactone to bind to membranes. We first studied the interfacial properties of the two monolayers alone, and the impact of cholesterol on lipid organization in particular, at 20 and 25°C. Whatever the temperature, the π-A isotherms of mixture 1 (Fig 2A) showed the monolayer to be in liquid-condensed (LC) phase throughout compression. The beginning of the steep rise started at a molecular area of 70 Å2, and the monolayer was compressed up to a lateral pressure of πcoll = 45 mN/m, corresponding to collapse. The molecular area at collapse, Acoll, was 31 Å2. This area was smaller than expected for two fatty acyl chains of phospholipids; the area per CH2 chain in a close-packed configuration is approximately 20 Å2 [76]. This discrepancy can be explained by the condensing effect of the cholesterol. The molecular area of a pure expanded monolayer of POPC (the major component of mixtures 1 and 2) at a lateral pressure of πcoll = 40 mN/m is ~40 Å2 at 20 or 25°C, consistent with the Tm value (-4°C) of POPC (S2 Fig). The addition of 19% cholesterol to the POPC monolayer, resulted in the same Acoll for the 81% POPC/19% cholesterol mixture at 25°C, but this area decreased to ~32 Å2 at 20°C. This suggests that the presence of 19% cholesterol lead to extensive condensation of the POPC monolayer in the liquid-expanded state (S2 Fig). Cholesterol has been shown to dehydrate lipid bilayers, resulting in lipid condensation [77]. The much lower level of condensation observed in the presence of mixture 1 (~37 Å2 at π = 40 mN/m) in terms of the area of POPC (~40 Å2) may be due to the presence of 9% POPE in mixture 1, at least partly preventing the condensing effect of cholesterol. BAM images recorded during compression revealed that the small lipid domains (shown in light gray) present at the start of compression (Fig 2A, image A, white arrows) increased in size and coalesced (Fig 2A, image B) to form a homogeneous interfacial film at the end of compression (Fig 2A, image C), regardless of temperature. This observation is consistent with the behavior of a condensed monolayer. Finally, the monolayer was homogeneous at 30 mN/m (Fig 2A, Image C), the lateral surface pressure reported for biological membranes [78]. A different pattern was observed for the isotherms of mixture 2 (Fig 2B), with the monolayers displaying a phase transition at both temperatures. Surface pressure began to increase at a higher molecular area, 98 Å2. BAM images taken at 20°C revealed the presence of holes (grayscale level identical to the background) in a continuous lighter phase (Fig 2B, image A, white arrows) for surface pressures below 3 mN/m. These holes gradually disappeared during compression until a short plateau was reached at about 5 mN/m. Beyond this point, the monolayer was homogeneous (Fig 2B, image B) and in a liquid-condensed state until collapse (πcoll = 45 mN/m; Acoll = 25 Å2). At 25°C, the phase transition, which could be attributed to the liquid-expanded/liquid-condensed (LE/LC) transition phase of monolayers incorporating SM [79,80], was attenuated. Consequently, the monolayer remained homogeneous (absence of holes at low surface pressures) throughout compression (Fig 2B, image C), until collapse (πcoll = 44 mN/m; Acoll = 30 Å2). In the absence of cholesterol, no lipid domains were observed in the monolayers. The apparent condensing effect observed for mixture 2 (S2 Fig) relative to the pure monolayer of POPC in the expanded state is due to the presence of 41% SM, a high-melting lipid (Tm = 41. 4°C). The shift in Acoll values observed when the temperature was increased from 20 to 25°C could be explained, in all cases, by the disordering effect of the higher temperature on acyl chain packing, tending to fluidize the monolayer. Superimposition of the isotherms recorded at 20 and 25°C (Fig 2C) highlighted the effect of cholesterol on the condensation state of the monolayer. At surface pressures below 10 mN/m, the isotherms of mixture 1 (with cholesterol) were shifted towards lower molecular areas than those of mixture 2 (without cholesterol). By contrast, at high surface pressures (above 25–30 mN/m), the isotherm of mixture 1 at 20°C was shifted towards larger areas than those of mixture 2 at the same temperature. This clear difference between the two mixtures was consistent with the modulation of membrane fluidity by cholesterol, through modification of the ordering of lipid acyl chains [81]: cholesterol tends to condense fluid phases (i. e. , it increases the lipid chain ordering of the liquid-crystalline disordered phase) and to fluidize condensed phases (i. e. , it decreases the lipid chain ordering of the solid-ordered phase) [82–85]. A comparison of the four isotherms also revealed that, in the absence of cholesterol at 20°C, the monolayer was extremely condensed. This condensation state may be directly due to the presence of 41% SM in mixture 2. Indeed, sphingolipids generally form a solid gel phase and are fluidized by sterols, which interact preferentially with them in the membrane [66,86]. Furthermore, the domains observed in mixture 1 (Fig 2A, image A) were probably characteristic of the liquid-ordered phase resulting from a ternary mixture of a high chain-melting lipid (like SM) and a low chain-melting lipid (like POPC) with cholesterol, and preferential interactions between Chol and SM [65,79,82,87–92]. We analyzed the interaction of mycolactone with monolayers at a working surface pressure of 30 mN/m, to mimic the lateral pressure of biological membranes [46,78]. As a control, and to decipher the effect of mycolactone more effectively, we checked the stability over time of the mixed monolayers in the presence of ethanol, the solvent used for mycolactone. For this purpose, we injected a volume of ethanol equivalent to that used for mycolactone solution (4. 45 μL) into the subphase underneath the stabilized monolayer at 30 mN/m. We then recorded changes in surface pressure over a period of about seven hours. At 20°C, the monolayers were highly stable, with only small surface pressure variations (± 2 mN/m) over time (S3 Fig). At 25°C, a greater variation of surface pressure was observed (from ‒2 to ‒5 mN/m), possibly due to subphase evaporation. We used BAM images for simultaneous characterization of the morphology and lipid organization of the mixed monolayers at 20°C (Figs 3A and 4A, rows a) and 25°C (Figs 3B and 4B, rows a). All the monolayers were homogeneous after one hour of relaxation, just before injection. After injection, the changes in monolayer morphology differed between temperatures and membrane lipid compositions. At 20°C, the mixed films displayed a phase segregation that differed according to the presence or absence of cholesterol. In the presence of cholesterol (Fig 3A, row a), lipid organization gradually changed, after about 3 h, with the formation of circular domains of an expanded fluid phase (dark phase) trapped within a more condensed phase (white phase). The same change in monolayer morphology was obtained without the injection of ethanol (S4 Fig, row a). This segregation, observed at 20°C, and leading to a new thermodynamic equilibrium with no loss of stability, could therefore be attributed to preferential interactions between cholesterol and the high-melting lipid SM in the mixed monolayer [65,82,83,87], with an expulsion of low-melting lipids such as POPC/POPE, resulting in the formation of round domains of fluid phase, as already reported for ternary mixtures of PC/SM/Chol [81,88,89]. In the absence of cholesterol, ordered domains appeared earlier, from the start of the experiment, and progressively grew in the form of “stars” (bright clusters, Fig 4A, row a). These domains resembled the condensed domains observed in the liquid-expanded/liquid-condensed (LE/LC) transition phase during the compression of a pure monolayer of SM on a PBS subphase (pH 7. 4) at 20°C (S5 Fig). These findings suggest that SM molecules retain their ability to segregate over time in the mixed monolayer, but only in the absence of cholesterol. Conversely, no segregation occurred at the higher temperature. Indeed, at 25°C, in the presence (mixture 1 –Fig 3B, row a) or absence (mixture 2 –Fig 4B, row a) of cholesterol, the two monolayers remained homogeneous throughout the entire experiment. We investigated the membrane-binding properties of mycolactone and evaluated the effect of this interaction on lipid organization in mixed films, by injecting a solution of mycolactone in ethanol into the subphase at a final concentration of 60 nM, beneath the monolayers of mixture 1 (with cholesterol) or mixture 2 (without cholesterol), compressed at an initial surface pressure πi of 30 mN/m. Upon injection, regardless of lipid composition and temperature, the interaction of mycolactone with the monolayer resulted in a rapid increase in surface pressure up to ~36 mN/m within the first 15–20 minutes (Fig 5). After a stabilization period of about 1–1. 5 h, π gradually decreased over time. The absence of cholesterol clearly did not affect the ability of mycolactone to penetrate into the monolayer; it simply delayed the decrease in surface pressure. BAM images were taken before and after mycolactone injection, throughout the adsorption period (Figs 3 and 4, rows b). In all cases, monolayers were homogeneous at the initial surface pressure of 30 mN/m. Mycolactone injection modified lipid segregation in the monolayers independently of temperature, but differently according to the presence or absence of cholesterol in the monolayers. For mixture 1 at 20°C (Fig 3A, row b), lipid organization changed rapidly over the first 15 min towards the formation of circular domains in a more condensed state (light gray phase), trapped within a less condensed phase (dark gray phase). This reorganization is essentially the opposite of the organization observed with the pure monolayer (Fig 3A, row a). A similar pattern was observed if the mycolactone was injected at the apparent saturation concentration of 1 μM (S4 Fig, row b). The time required for mycolactone to reverse the segregation pattern in membranes (15 minutes) corresponds to the time required for toxin penetration into the monolayer until stabilization. At 25°C (Fig 3B, row b), this segregation pattern occurred 3 h after injection, whereas no segregation was observed for the control monolayer at 25°C. Highly luminous structures (Fig 3, row b, t = 15 min or 85 minutes at 20°C and t = 30 min at 25°C), similar to those observed for mycolactone at the air/buffer interface (Fig 1, image b) were also observed. This feature indicated the presence of the toxin within the monolayer in the presence of cholesterol. For mixture 2 at 20°C, no star-shaped domains were visible, contrasting with observations for the monolayer alone. Very small domains (small bright dots) became visible much later, 4 h after mycolactone injection (Fig 4A, row b, white arrows). At 25°C, no significant change in the morphology of the monolayer relative to the control was observed upon mycolactone injection (Fig 4B). Again, only the presence of bright objects corresponding to mycolactone at different time points (Fig 4B, rows b) attested to the interaction of the toxin with the monolayer. In the absence of cholesterol, the presence of the toxin within the monolayer therefore prevented SM molecules from aggregating, thereby fluidizing the condensed and extremely rigid mixture 2 monomolecular film (Fig 2B). At 25°C, the monolayer was fluid enough to prevent SM aggregation, and this attenuated the potential fluidizing effect of the mycolactone. We analyzed the influence of lipid organization on the membrane-binding properties of the toxin further, by investigating the effect of initial surface pressure on the interaction of mycolactone with the monolayers. For this purpose, we monitored the maximal increase in surface pressure Δπmax immediately following toxin injection at various πi values, ranging from 5 to 30 mN/m. This relationship has been widely used to assess lipid-protein interactions and to distinguish between electrostatic and hydrophobic interactions [53,55,57,58,60,61,68,93]. The Δπmax = f (πi) plot shown in Fig 6 was used to evaluate the binding parameters of mycolactone on both types of Langmuir monolayers. Linear extrapolation to an increase in surface pressure of zero (Δπmax = 0) can be used to determine i) the maximum insertion pressure (MIP), reflecting the influence of initial lipid packing density on the ability of the molecule to penetrate into the monolayer, and ii) the synergy factor" a" [50,56,93,94]. This factor, first described by Salesse et al. [56,94], provides insight into the mechanisms governing the interaction with lipid monolayers. A positive a value indicates favorable interactions, as further demonstrated by MIP values exceeding the estimated membrane lateral pressure (~30 mN/m). A null synergy factor reveals a stationary state, with no favoring or disfavoring of membrane binding. A negative synergy factor indicates unfavorable binding to the monolayer, corresponding to a repulsion of the molecule as a function of the compactness of the monolayer. Here, MIP and a provided useful information about the effect of lipid composition on the ability of mycolactone to interact with membranes. For mixture 1, pressure variation profiles were similar at the two temperatures, with a linear decrease as a function of initial surface pressure πi. MIP and the a synergy factor were above 30–35 mN/m and positive, respectively, at both temperatures. These findings are consistent with strong insertion/penetration into the interfacial film and favorable interactions between mycolactone and the monolayer (Fig 6A and 6B). Furthermore, both MIP and a values were higher at 20°C (MIP = 45. 9 ± 2. 8 mN/m; a = 0. 58 ± 0. 02) than at 25°C (MIP = 38. 7 ± 1. 1 mN/m; a = 0. 32 ± 0. 02), suggesting that, in the presence of cholesterol, decreases in temperature leading to a rigidification of the monolayer may favor the interaction of mycolactone with the mixed film. By contrast, the curve profiles for mixture 2 differed considerably between temperatures. At 20°C (Fig 6C), the plot obtained was split into two distinct phases: an initial plateau, for which Δπmax remained constant at πi values below 17. 5 mN/m, with a synergy factor of 0. 93 ± 0. 09, and a second phase in which Δπmax decreased at πi values greater than 17. 5 mN/m, associated with an MIP value of 34. 8 ± 1. 6 mN/m and a synergy factor close to 0 (a = 0. 01 ± 0. 07). Such ‘biphasic’ behavior was recently reported by Hädicke and Blume for the binding of small cationic peptides to anionic phospholipid monolayers [95], and may be related to the physical state of the monolayer. As shown by these authors, incorporation, through hydrophobic interactions, into the loosely packed monolayer in the LE phase can lead to a constant Δπ value, depending on the nature of the lipids making up the monolayer. By contrast, in the LC phase, Δπ and πi display an inverse linear relationship, due to lipid condensation. For mixture 2, the monolayer displayed a LE/LC phase transition, as shown on the isotherms (Fig 2B), with the presence of holes in the loosely packed monolayer, as revealed by BAM (Fig 2B, image A). We therefore suggest that behavior similar to that proposed for small hydrophobic peptides may account for the unusual results obtained with mycolactone. Indeed, this toxin is also a small hydrophobic molecule (MW: 743. 021), and, when it penetrates into a loosely packed monolayer in the LE phase by hydrophobic interactions, it triggers no increase in surface pressure because the monolayer is too weakly compressed (loosely packed) and the molecular area of the toxin is too small to cause lipid condensation at the interface. In addition, mycolactone was probably able to fill the space, i. e. , the holes observed in the monolayer, due to its own surface activity, leading to an absence of surface pressure variation (Δπmax remained constant) as long as the monolayer was weakly compressed. Beyond 17. 5 mN/m, the monolayer was sufficiently tightly packed to attain its condensed state (observed on the isotherm Fig 2B), yielding a negative slope of the Δπmax = f (πi) plot (Fig 6C, part 2), with a synergy factor close to 0 (a = 0. 01 ± 0. 07). This value indicates that, even in a stationary state in which mycolactone was able to penetrate the monolayer at 20°C, no specific interactions (either favorable or unfavorable) occurred between mycolactone and lipids. The decrease in the ability of the molecule to penetrate the membrane was therefore entirely due to the physical condensation of the monolayer as a result of the increase in lipid packing density during compression [56,94]. At 25°C (Fig 6D), the curve profile and the MIP (41. 8 ± 2. 2 mN/m) were similar to those obtained for mixture 1 at the same temperature, but the a value (0. 45 ± 0. 02) was different. At the higher temperature (25°C vs. 20°C), the monolayer was more fluid, as revealed by the shift of the π-A isotherm towards larger molecular areas due to the disordering effect of the higher temperature on acyl chain packing (Fig 2B), and favorable interactions occurred between the toxin and the monolayer. As previously observed for π-A isotherms (Fig 2A and 2B), the effect of temperature on monolayer fluidity was more pronounced for mixture 2. This difference may account for the difference in synergy values. At 25°C, the interaction of mycolactone with monolayers seems to be governed by greater monolayer fluidity. However, the presence of cholesterol in the monolayer enabled the mycolactone to penetrate into a more condensed monolayer. Indeed, if mycolactone insertion were regulated solely by monolayer fluidity (as observed at 25°C), then its insertion should increase with temperature, which was found to be the case in the absence (Fig 6C and 6D), but not in the presence of cholesterol (Fig 6A and 6B). Conversely, the binding parameters (MIP and synergy) were highest at 20°C in the presence of cholesterol (Fig 6A). As the synergy factor measures sensitivity to lipid acyl chain packing, we can conclude that the insertion of mycolactone into the monolayer was favored by the presence of cholesterol at lower temperatures, which favored increases in monolayer rigidity. We investigated the effects of mycolactone on lipid segregation in the monolayer in the presence of cholesterol, by performing the same experiments (πi = 30 mN/m, 20°C) by fluorescence microscopy (FM), with mixture 1 labeled with TopFluor Cholesterol probe. This molecular probe can be used to study intracellular cholesterol dynamics, because its diffusion in the plasma membrane is free and unhindered [96]. In this context, the fluorescent domains observed were, thus, those containing the TopFluor Cholesterol molecule. In our study, the use of this fluorescent marker made it possible to track the localization of cholesterol in the monolayer and its distribution in domains. Lipid segregation in the control monolayer was observed 3 h after ethanol injection (4. 45 μL), with the appearance of circular dark domains trapped within a light phase (Fig 7, row a). Segregation occurred more rapidly in the presence of the toxin (in 1h15), but with a pattern opposite to that in the control, with the formation of circular fluorescent domains within a dark phase (Fig 7, row b). In ternary mixtures, SM is known to interact preferentially with cholesterol to form domains of liquid-ordered (Lo) phase, corresponding to a phase intermediate between the liquid-condensed phase (Lc) and the fluid liquid-expanded (Le) phase [91,92]. It can therefore be inferred from our measurements that the green fluorescent areas correspond to domains of liquid-ordered phase, whereas the dark areas correspond to domains of fluid phase [96]. Thus, these FM experiments yielded results identical to those obtained with BAM for the pure monolayer or after the injection of mycolactone (Fig 3A), in support of our conclusion. A similar correlation between the results of BAM and FM was reported in another recent study [79]. By modifying the interactions between SM molecules, and, probably, between SM and cholesterol, through physical insertion in the monolayer (the toxin impeded the segregation of SM in mixture 2 at 20°C without specific interaction, a = 0), mycolactone reversed the segregation of the Lo phase in the monolayer. Thus, mycolactone probably interacts preferentially with the Lo phase, which is intermediate between the highly condensed phase of SM and the fluid phase of POPC. This conclusion is consistent with the results obtained for the two mixtures at 20°C, and with the synergic interaction observed only in the presence of cholesterol (a>0 for mixture 1 at 20°C–Fig 6A). This preferential interaction with the Lo phase may also explain why the presence of cholesterol enhanced the penetration capacity of mycolactone at lower temperatures, which were associated with lower levels of fluidity (highest MIP and a value for mixture 1 at 20°C–Fig 6A). The adsorption kinetic (π-t) curves showed a progressive decrease in surface pressure after the injection of mycolactone, at both temperatures (Fig 5). This suggests that the toxin affects monolayer stability and may have a detergent-like effect. Indeed, detergents are amphiphilic molecules with surfactant properties that can solubilize lipids, depending on membrane phase and composition [97]. We tested this hypothesis, by performing interaction assays with a final concentration of 60 nM Tween 20 or Triton X-100 (i. e. , non-ionic detergents) at 20°C, with a monolayer of mixture 1 compressed at a πi of 30 mN/m. Tween 20 is known to solubilize lipid membranes regardless of their aggregation state [98], whereas Triton X-100 solubilizes the liquid-disordered (Ld) phase but not the liquid-ordered phase (Lo) [81,88,97,99]. The kinetic curves recorded were then compared with that obtained with mycolactone (Fig 8A). When Tween 20 was injected beneath the monolayer, the surface pressure π increased to a plateau value of about 34 mN/m, gradually decreasing thereafter (Fig 8A). Investigations of monolayer morphology by BAM (Fig 8C, row b) revealed that, upon interaction, lipid organization shifted towards the formation of circular condensed domains (white phase) trapped within a fluid phase (dark phase), as in the case of mycolactone (Fig 8C, row a). However, this lipid reorganization occurred 2h40 after detergent injection, later than for the toxin (Fig 8C, row b). The surface pressure π remained constant after Triton X-100 injection (Fig 8A). The absence of an effect on the surface pressure stability of mixture 1 was therefore compatible with an absence of Lo phase solubilization (a property of Triton X-100). However, BAM images revealed that Triton X-100 provoked the same pattern of lipid segregation as mycolactone (condensed domains in a fluid phase), but with the same time-shift (2h50) as for Tween 20 (Fig 8C, row c). Similar changes in the morphology of monolayers containing SM and cholesterol upon interaction with Triton X-100 have already been reported [100], and this detergent, which can induce Lo/Ld phase segregation in a typical raft-like ternary mixture, was recently described as a potent membrane-reshaping agent [97,99]. All these findings reveal, therefore, that the presence of 60 nM detergent or toxin in the subphase modifies lipid segregation in the POPC/SM/POPE/Chol monolayer in a manner opposite to that in the control. In these experiments, both the detergents and the toxin were injected into the subphase at the same final concentration (60 nM). However, the solubilizing action of a detergent depends on its critical micellar concentration (CMC) and on the detergent/membrane ratio at which it is used [101]. The CMC of Tween 20 is 50–60 μM [102], and that of Triton X-100 is 0. 2 mM, at 25°C [98,103]. For mycolactone, the apparent saturation concentration of 1 μM determined by tensiometry (Fig 1A) may be considered equivalent to an apparent CMC. Under these conditions, the two detergents and mycolactone may behave differently at an effective (active) concentration of 60 nM. Thus, to maintain a constant ratio between the effective concentration injected in the subphase and the CMC, we further investigated the effect of each detergent on monolayer stability with an “effective concentration/CMC ratio” of 0. 06 (i. e. , 60 nM divided by 1 μM, as for mycolactone). To respect this new experimental constraint, Tween 20 or Triton X-100, at final concentrations of 3. 6 μM and 12 μM, respectively, were injected in the subphase of the mixture 1 monolayer (Fig 8B). The use of these new detergent concentrations had no significant effect on interaction kinetics. The main difference concerned the formation of condensed domains, which occurred more rapidly at this constant “effective concentration/CMC ratio” of 0. 06 than following the injection of a 60 nM solution: 1h30 for Tween 20 (Fig 8C, row d) and 1h40 for Triton X-100 (Fig 8C, row e). However, these times remains longer than the 15 min for lipid segregation triggered by mycolactone with the same constant ratio of 0. 06 (Fig 8C, row a). At an “effective concentration/CMC ratio” of 0. 06, at which each molecule acts as a monomer, the same effects on lipid morphology were observed by BAM for Tween 20, Triton X-100 and mycolactone. Under these conditions, the toxin penetrated and destabilized the monolayer just like the detergents, but more efficiently, acting as a reshaping agent [97,99]. As shown in this study, mycolactone preferentially binds to monolayers containing cholesterol, and this interaction induces a destabilization of the Lo phase by fluidizing the monolayer, modifying the preferential interactions between SM and cholesterol, like a detergent, but not like Triton X-100 [88,97]. Similar results were recently reported for glycyrrhizin, a molecule of the saponin class extracted from plants and recognized as a natural detergent, which causes membrane perturbations after its migration toward SM/sterol-enriched membrane domains [79]. Mycolactones form a family of highly related macrocyclic polyketides identified as the primary virulence factors responsible for Buruli ulcer (BU), a neglected tropical disease of the skin and subcutaneous tissue caused by the environmental human pathogen Mycobacterium ulcerans [4]. Despite the wealth of research describing the pathogenic mechanism [14,34,37], there is still no molecular explanation of the necrosis seen in the ulcers, over and above cytopathic activity, and the immunomodulatory or analgesic properties of mycolactone [13,24]. The effects of the toxin on the cell plasma membranes they cross have never been described. In this study, we investigated, for the first time, the membrane-binding properties of mycolactone, with Langmuir monolayers, which were used as membrane models for the fine analysis of membrane binding kinetics. We chose to use this system because its experimental design is simple and it can be adapted for investigations of the molecular insertion properties of membranotopic molecules. We studied monolayers with a lipid composition of 39% POPC, 33% SM, 9% POPE and 19% cholesterol (% mol), which was considered representative of the plasma membrane [72–75]. These monolayers were compressed at an initial surface pressure (πi) of 30 mN/m, a value considered representative of the lateral pressure of biological membranes [46,78]. The results obtained provided new insight into the mechanism of membrane interaction and the effect of the toxin on membrane lipid organization. By studying the behavior of the pure toxin at an air/buffer interface, we found that mycolactone, like detergents, has surfactant properties, with an apparent surface saturation concentration of 1 μM, after which, the toxin is no longer in the monomer form (Fig 1). Experiments were conducted at a final concentration of 60 nM, to prevent artifacts during monolayer investigations. At this concentration, mycolactone interacts with the membrane as a monomer. The concentrations of mycolactone naturally present in the lesions during the course of the disease and leading to progressive ulceration are not always known. Determinations of the concentrations of mycolactone A/B in the various untreated pre-ulcerative nodules and plaques, ulcers and edematous lesions in M. ulcerans-infected human skin (biopsies) have revealed considerable variability [104]. The median concentration in all types of lesions or within the lesion itself (center or periphery) varied from 35 nM (i. e. , 26 ng/mL) for pre-ulcerative lesions or the periphery of the lesions, to 596 nM (i. e. , 443 ng/mL) in ulcers or 1. 2 μM (i. e. , 895 ng/mL) in edematous lesions. Mycolactone can also be detected in ulcer exudates obtained non-invasively from wound swabs, at concentrations of 67–270 nM (i. e. , 50–200 ng/mL) [105]. Finally, it has been shown that the toxin concentration rapidly increases following inoculation with M. ulcerans in a mouse model of disease, from 385 ± 142 nM (i. e. , 286 ± 105 ng/mL) on day 3, to 1. 28 ± 0. 29 μM (i. e. , 948 ± 215 ng/mL) on day 7 and 4. 85 ± 0. 63 μM (i. e. , 3603 ± 478 ng/mL) on day 62 [106]. Analyses of the ability of low biological concentrations (60 nM) of mycolactone to interact with the membrane provide information about the initial effects of the toxin on the plasma membrane, the first barrier that the toxin must cross to reach its intracellular targets. Presumably, these effects are progressively amplified during disease development, with the gradual increase in mycolactone concentration. Mycolactone can bind to membranes regardless of their lipid composition (Fig 5). However, the presence of cholesterol promotes toxin insertion into the monolayer (Fig 6), highlighting the key role of this sterol in the interaction of the toxin with the membrane. Cholesterol modulates membrane fluidity and influences the organization of other lipids by changing their ordering, available area and the formation of domains of characteristic composition [107]. We also found that mycolactone had a strong effect on lateral lipid segregation in the membrane and the formation of distinct domains in monolayers containing cholesterol (Figs 5 and 7). In the monolayer with a composition resembling that of the plasma membrane (mixture 1), preferential interactions between cholesterol (19%) and one high-melting lipid, SM (33%), which were mixed with the low-melting lipids POPC (39%) and POPE (9%) [65,90,108], led to Lo/Ld segregation (Figs 3A and 7) mimicking the lateral heterogeneity of cell membranes, with the coexistence of ordered and non-ordered lipid domains [66,79,81–83,87–89,92,109,110]. This lateral heterogeneity, with the coexistence of Ld and Lo phases, would compartmentalize cellular membranes and play a key role in the lateral segregation of various classes of membrane proteins to facilitate the various cellular functions and processes occurring at the membrane [81,92,108,111–113]. We show here that mycolactone acts as a reshaping agent at very low concentrations and that, in its monomer form, it disturbs lipid segregation in monolayers (Figs 3A, 7 and 8). A similar effect has been described for penetratin, a cell-penetrating peptide known to cross cell membranes. Penetratin recruits specific lipids locally for the formation of fluid membrane patches dispersed within ordered domains [114]. The reshaping induced by mycolactone may have a direct effect on the cellular functions modulated by lipid domains, by affecting the formation of these domains. This hypothesis is supported by the promotion of mycolactone/lipid interactions within the monolayer by cholesterol and by results concerning the membrane-fluidizing effect of the toxin preventing SM aggregation (in the absence of cholesterol, Fig 4A). Using a fluorescent derivative with a biological activity one tenth that of mycolactone, Snyder & Small showed, in 2003, that mycolactone was localized in the cytosol of murine fibroblasts cultured in vitro [11,12]. Similar results have been obtained with human epithelial cells and lymphocytes exposed to a 14C-labeled form of the toxin; the mycolactone accumulates in the cytoplasm, and not in cell plasma or nuclear membranes [13]. Overall, these results are consistent with non-cell-specific passive diffusion of the toxin through the plasma membrane to reach its intracellular targets. We show here that mycolactone, in its monomer form (i. e. , 60 nM final concentration), can modify membrane lipid organization by reversing lipid segregation. Thus, if it crosses the plasma membrane at concentrations below its apparent CMC, mycolactone may disturb the natural distribution of lipids in the membrane and the formation of lipid nanodomains, with consequences for metabolic pathways involving ordered membrane domains. As mentioned in the introduction, most of cellular targets of mycolactone are membrane protein receptors residing in ordered plasma membrane nanodomains, where their functionalities can be modulated. Mycolactone can bind and modulate the activity of two members of a family of scaffold proteins, Wiskott-Aldrich syndrome protein (WASP) and neural WASP (N-WASP), which transduce various endogenous signals through a dynamic remodeling of the actin cytoskeleton [17]. However, sphingolipid-cholesterol domains have been shown to be the preferred platforms for membrane-linked actin polymerization mediated by in situ phosphatidylinositol 4,5-bisphosphate (PIP2) synthesis and tyrosine kinase signaling through the WASP-ARP2/3 pathway (PIP2 stimulates de novo actin polymerization by activating the pathway involving WASP and the actin-related protein complex ARP2/3 [115]). Mycolactone binding to WASP involves a lysine-rich basic region (BR) [17] that has also been implicated in the activation of WASP/N-WASP by PIP2 in both the allosteric and the oligomerization domains [116]. The Lo phase reversion triggered by the membrane insertion of mycolactone may therefore play an important role in modulating the WASP-Arp 2/3 pathway. Could mycolactone replace PIP2 in the disorganized sphingolipid-cholesterol microdomains and bind to WASPs? Mycolactone also triggers a depletion of thrombomodulin (TM) receptors on the surface of endothelial cells, leading to disruption of the protein C anticoagulation pathway. This depletion has been observed in vitro in human dermal microvascular endothelial cells (HDMVECs) exposed to a very low dose (2 ng/mL i. e. , 2. 69 nM) of mycolactone and, in vivo, in the subcutaneous tissues of punch biopsies, and is strongly associated with the fibrin deposition commonly observed in BU skin lesions [34]. Nevertheless, TM receptors for protein C activation and activated protein C (APC) signaling pathways are colocalized in the lipid microdomains of endothelial cells [35,36]. This localization in the same domain is the key requirement for APC signaling pathways in endothelial cells [35,36]. Again, the change in lipid segregation, in addition to the observed depletion of TM receptors, due to the interaction of mycolactone with the plasma membrane during its passage into the cell, may disturb lipid microdomain formation in the membrane, thereby modifying the various signaling pathways requiring lipid membrane domains as signaling platforms [111]. Could the TM depletion observed in endothelial cells exposed to mycolactone [34] also be due to changes in lipid segregation in the plasma membrane, to which this receptor is targeted for functional activity? Finally, many of the pathogenic effects of mycolactone could potentially be explained by a blockade of protein translocation [14]. Nevertheless, no unifying molecular mechanism underlying the pleiotropic actions of mycolactone has yet been identified. It remains, unclear, for example, how mycolactone blocks ER translocation at the molecular level? Baron et al. recently showed that mycolactone targets the α subunit of the Sec61 translocon, thereby strongly blocking the production of secreted and integral membrane proteins [38]. However, it is now widely accepted that the signal sequence of secreted proteins is clipped off during translation by a signal peptide peptidase (SPP). After chaperone-assisted folding, the mature protein is then released into the lumen of the ER immediately after its synthesis [41]. However, it has been suggested that SPP cleavage may be regulated through the control of substrate entry into the microdomains of the membrane containing SPP [117,118]. During the fractionation of rough ER integral membrane proteins with 0. 18% Triton X-100, most ER-resident proteins, and the Sec61alpha subunit in particular, were found to be present in the microdomain-like fraction [44]. Little is known about the role of cholesterol in basic ER functions, but recent studies have clearly suggested that cholesterol may act with ER membrane proteins to regulate several important functions of the ER, including the folding, degradation, compartmentalization, and segregation of ER proteins, and sphingolipid biosynthesis [119]. McKenna et al. have provided biochemical evidence that mycolactone induces a conformational change in the transmembrane pore-forming Sec61alpha subunit of the translocon [39]. Hence, taking into account the potentially important contribution of cholesterol to the effect of mycolactone on membrane lipid segregation, we cannot exclude the possibility that the ER membrane may also be reshaped by mycolactone, abolishing the functions of the ER membrane-resident proteins. In the case of the Sec61 translocon, this hypothesis is consistent with the induction of a stabilized closed conformation of the Sec61alpha unit (forming the central gated protein-conducting channel across the ER membrane) [39,45] mediated by lipid redistribution in microdomains to facilitate the interaction of mycolactone close to the luminal plug of Sec61alpha, as recently suggested by Baron et al. [38]. The redistribution of lipid microdomains from the plasma membrane to mitochondria has recently been demonstrated in other contexts and diseases and is robust to this hypothesis [120]. The alteration of ER lipid microdomains initiates lipotoxicity in pancreatic β-cells, disturbing protein trafficking and initiating ER stress, thereby contributing to type 2 diabetes [121]. In the same way, the disruption of lipid microdomains stimulates phospholipase D activity in human lymphocytes, and this activation conveys antiproliferative signals in lymphoid cells, by impairing the transduction of mitogenic signals [122]. The gastrointestinal symptoms observed in patients suffering from Niemann-Pick type C disease are the consequence of changes in the composition of membrane lipid domains, resulting in impaired trafficking and an apical sorting of the major intestinal disaccharidases to the plasma membrane with a decrease in their functional capacities [123,124]. Edelfosine, an alkylphospholipid analog (APL) belonging to a family of synthetic antitumor compounds, induces apoptosis in several hematopoietic cancer cells, by targeting various subcellular structures at the membranes [125,126]. By recruiting death receptor and downstream apoptotic signaling molecules to ordered lipid domains, it displaces survival signaling molecules from these membrane domains. Edelfosine-induced apoptosis in solid tumor cells is mediated by an ER stress response and evidence has been obtained in vitro and in vivo to suggest that edelfosine treatment induces a redistribution of lipid domains from the plasma membrane to mitochondria, suggesting a raft-mediated link between the plasma membrane and mitochondria [120]. All these examples suggest that membrane reshaping can occur in different diseases, and that the disturbance of lipid segregation observed here with mycolactone is not an isolated case. In summary, all the cellular targets of mycolactone are membrane-bound proteins, and, with the exception of the Sec61 translocon, all are known to be regulated by ordered microdomains, which provide a platform for the assembly of signaling complexes and prevent cross-talk between pathways [25,110]. By disturbing lipid segregation in membranes containing cholesterol, mycolactone affects many cell functions and signaling pathways. This membrane remodeling may occur in synergy with the previously demonstrated effects of mycolactone on its intracellular targets, possibly even potentiating these effects. It is tempting to speculate that microdomain remodeling in membranes underlies the molecular events via which mycolactone affects multiple targets, but further studies are required to confirm this. Ultrapure water was obtained from a PURELAB option Q7 system (VEOLIA WATER STI, France). Phosphate-buffered saline (PBS, 2. 8 mM KCl, 140 mM NaCl and 10 mM phosphate, pH 7. 40 ± 0. 05 at 25°C) was prepared by dissolving tablets purchased from AppliChem GmbH (Darmstadt, Germany) in ultrapure water. We obtained 1-palmitoyl-2-oleoyl-sn-glycerophosphocholine (POPC), 1-palmitoyl-2-oleoyl-sn-glycerophosphoethanolamine (POPE), cholesterol (Chol) from ovine wool (≥98%) and 23- (dipyrrometheneboron difluoride) -24-norcholesterol or TopFluor Cholesterol from Avanti Polar Lipids (Alabaster, Alabama, USA). Sphingomyelin (SM) from chicken egg yolk (≥95%) was purchased from Sigma-Aldrich (Saint-Quentin Fallavier, France). All chemicals were used as received. The solvents were of analytical grade (Sigma-Aldrich, Saint-Quentin Fallavier, France). The lipid mixtures were prepared at a concentration of 1 mg/mL in chloroform (or chloroform/methanol, 9: 1 v/v, when containing SM) and stored at -20°C under argon to prevent lipid oxidation. Polyethylene glycol sorbitan monolaurate (Tween 20) and polyethylene glycol tert-octylphenyl ether (Triton X-100) were also purchased from Sigma-Aldrich (Saint-Quentin Fallavier, France). Mycolactones A/B were purified from M. ulcerans extracts as previously described [6,127]. Briefly, S4018, an African strain of Mycobacterium ulcerans obtained from a patient in Benin, was grown in Middlebrook 7H10 agar supplemented with oleic albumin dextrose catalase growth supplement. The bacteria were resuspended in chloroform-methanol (2: 1, v/v) and cell debris was removed by centrifugation. Folch extraction was performed by adding 0. 2 volumes of water. The organic phase was dried and phospholipids were precipitated with ice-cold acetone. The acetone-soluble lipids were loaded onto a thin layer chromatography plate and eluted with chloroform-methanol-water (90: 10: 1, v/v/v) as the mobile phase. The yellow band with a retention factor of 0. 23 was scraped off the plate, filtered, evaporated, resuspended in absolute ethanol and then stored in amber glass tubes in the dark. Its concentration was determined by measuring absorbance (λmax = 362 nm, log ε = 4. 29), and its purity (>98%) was evaluated with a Shimadzu Ultra-Fast Liquid Chromatograph (UFLC XR system with a CBM-20A controller, a CTO-10AS Prominence column oven, LC-20AB pumps, an SPD M20A diode array detector (Shimadzu, Japan) ) and a reverse C18 column (Zorbax 23 Eclipse XDB-C18,9. 4×250mm, Particle Size: 5 μm (Agilent, USA) ). Monolayers were prepared on a KSV 2000 Langmuir-Blodgett trough (3 multi-compartments, KSV NIMA, Biolin Scientific, Finland), with a symmetric compression system. The rectangular trough had a volume of 80 mL and a surface area of 119. 25 cm2. A Wilhelmy plate attached to an electronic microbalance was used to measure the surface pressure (π), with an accuracy of ± 0. 5 mN/m. The trough was cleaned with successive baths of dichloromethane, ethanol and ultrapure water, and filled with a filtered PBS solution. The subphase buffer was maintained at the desired temperature (20°C or 25°C) throughout the experiment, with an Ecoline RE106 low-temperature thermostat (LAUDA, Germany). It was not possible to work at a higher temperature, due to subphase evaporation, which can falsify surface pressure measurement during the run. Lipid mixtures in chloroform were gently spread at the air/liquid interface of the PBS subphase. The solvent was allowed to evaporate off for 15 minutes, and the monolayer was then slowly compressed by two mobile barriers at a constant rate of 0. 045 nm2. molecule-1. min-1 until an initial surface pressure (πi) of 5 to 30 mN/m was reached. A lag time of about 1 h was then applied to allow the monolayer to relax and stabilize. The surface area was then kept constant by stopping the movement of the mobile barriers. Mycolactone (1 mg/mL in ethanol) was injected (4. 45 μL) into the subphase just beneath the lipid monolayer at a final concentration of 60 nM. The changes in surface pressure induced by the interaction of mycolactone with the monolayer were recorded continually, as a function of time, with a computer-controlled Langmuir film balance KSV NIMA (Biolin Scientific, Finland), until the equilibrium surface pressure (πe) was reached, indicating the end of the adsorption process. All measurements were repeated at least three times for each set of conditions, with a satisfactory reproducibility, and the mean values are reported here. We used a monolayer with a lipid concentration closely resembling that of the plasma membrane, according to several authors [72–75]. This monolayer contained 39% phosphatidylcholine (POPC), 33% sphingomyelin (SM), 9% phosphatidylethanolamine (POPE) and 19% cholesterol (Chol) (in mol%). For evaluation of the influence of cholesterol on both the membrane-binding capacity and effects of mycolactone on phospholipid membrane organization, we also analyzed monolayers with a different composition devoid of cholesterol but with the molar ratios of the other lipids maintained. This second monolayer contained 48% POPC, 41% SM, and 11% POPE. The surface pressure increase (Δπ in mN/m) after the mycolactone injection corresponds to πe - πi. The curve of surface pressure increase (Δπ) as a function of time (t) recorded during the adsorption of mycolactone onto lipid monolayers corresponds to the adsorption kinetics of the molecule. The parameters characterizing the binding of mycolactone to different lipid membranes were further determined, as previously described [50,56,93,94]. Briefly, Δπ was plotted against different initial surface pressures πi to determine: i) the critical surface pressures πc, also known as the maximum insertion pressure (MIP), which is calculated by extrapolation of the linear regression line to the x-axis (Δπmax = 0) and, ii) the synergy factor, a, measured by adding 1 to the slope obtained from the linear regression of Δπ as a function of πi. The uncertainty on MIP and the synergy factor, a, were determined as previously described [50,94]. The uncertainty on MIP was calculated with a 95% confidence interval from the covariance of the experimental data for the linear regression [50]. The uncertainty on synergy was calculated as previously described [94]. These experimental errors were directly determined with the free binding parameters calculator software (http: //www. crchudequebec. ulaval. ca/BindingParametersCalculator) developed by Salesse’s group. Brewster angle microscopy (BAM) was used to characterize the lipid domain morphology of monolayers at the air/water interface [62,64]. Monolayer morphology was determined before and after mycolactone injection, with an EP3SW Brewster angle microscope (Accurion, Germany) equipped with a 532 nm laser, a polarizer, an analyzer and a CCD camera. BAM image size was 483 × 383 μm2. For ultrathin films, reflectance depends on both the thickness and refractive index of the monolayer. The different views of the interfacial film were reconstituted with EP3viewer BAM software (Accurion, Germany), based on the brightness of the BAM pictures. For a constant refractive index, reflectance is directly linked to the thickness of the interfacial film. Langmuir films were generated in a custom-built cylindrical Teflon trough with a quartz window, containing 25 mL of filtered buffer, connected to a peristaltic pump. The system was mounted on the stage of a Zeiss Observer Z1 microscope (Carl Zeiss Vision, Germany) for fluorescence microscopy (FM) experiments. Samples were prepared for FM by replacing 0. 5 mol% of the cholesterol with 0. 5 mol% of the sterol fluorescent probe, TopFluor Cholesterol [96]. The images were acquired at excitation and emission wavelengths of 495 and 507 nm, respectively. Images were processed and analyzed with dedicated Zeiss software (Axio Vision 4. 8). We used a Hamilton syringe to spread a few microliters of a 1 mg/mL phospholipid solution in chloroform onto the buffer subphase until the desired πi was reached. One hour later, after the solvent had evaporated and the lipid monolayer had stabilized at the desired initial surface pressure (πi), the mycolactone A/B solution was injected, with a Hamilton syringe, into the subphase through the lipid monolayer, with gentle stirring. During the time course of the experiment, changes in surface pressure (π) were also recorded simultaneously and continuously with a KSV NIMA computer-controlled Langmuir film balance.

Buruli ulcer is a necrotizing skin disease caused by an environmental mycobacterial pathogen. The pathogenesis of this neglected tropical disease involves the production of a toxin, mycolactone, which spreads through the tissues, away from the infecting organisms. Mycolactone has pleiotropic effects on fundamental cellular processes, resulting in pronounced cytotoxicity and immunosuppressive effects that together drive progressive ulceration. The molecular mechanisms underlying its cellular effects have been partly deciphered, but multiple cellular targets have been identified. A literature survey revealed that most of the identified targets of mycolactone are membrane receptors residing in particular domains of the plasma membrane. Despite its lipid-like nature, mycolactone has been shown to be intracellular, implying that it can cross the plasma membrane. We describe here a surprising membrane-reshaping effect of mycolactone due to effects on lipid domain formation. By reversing lateral lipid segregation, mycolactone may disrupt the formation of domains with well-established roles in the regulation of cellular signaling pathways. This remodeling of the cell plasma membrane may underlie the molecular events enabling mycolactone to attack multiple targets.

Abstract Introduction Results Discussion Materials and methods

medicine and health sciences toxins pathology and laboratory medicine toxic agents toxicology membrane proteins materials science surfactants cellular structures and organelles lipids geography detergents materials by attribute cartography isotherms cell membranes cholesterol biochemistry cell biology integral membrane proteins earth sciences biology and life sciences physical sciences

2018

The potent effect of mycolactone on lipid membranes

State-space and action representations form the building blocks of decision-making processes in the brain; states map external cues to the current situation of the agent whereas actions provide the set of motor commands from which the agent can choose to achieve specific goals. Although these factors differ across environments, it is currently unknown whether or how accurately state and action representations are acquired by the agent because previous experiments have typically provided this information a priori through instruction or pre-training. Here we studied how state and action representations adapt to reflect the structure of the world when such a priori knowledge is not available. We used a sequential decision-making task in rats in which they were required to pass through multiple states before reaching the goal, and for which the number of states and how they map onto external cues were unknown a priori. We found that, early in training, animals selected actions as if the task was not sequential and outcomes were the immediate consequence of the most proximal action. During the course of training, however, rats recovered the true structure of the environment and made decisions based on the expanded state-space, reflecting the multiple stages of the task. Similarly, we found that the set of actions expanded with training, although the emergence of new action sequences was sensitive to the experimental parameters and specifics of the training procedure. We conclude that the profile of choices shows a gradual shift from simple representations to more complex structures compatible with the structure of the world. In sequential decision-making tasks, an agent makes a series of choices and passes through several states before earning rewards. Making choices requires knowing the structure of the environment such as the available actions, the number of states of the environment and how they map on to external cues, i. e. , the state-space of the task. Within the lab this information is typically given to subjects in advance of the task either through instructions or pre-training, however, such a priori information is not usually made available to a decision-maker in natural environments, and the decision-making process must, therefore, involve (i) learning the correct state-space of the environment and (ii) acquiring new actions that are useful for earning reward in the task. Learning the state-space of the task is crucial in allowing the agent to navigate within the environment, and provides building blocks for various forms of reinforcement-learning algorithms in the brain [1,2]. This process involves considering different events and cues that occur after taking each action, and integrating them in order to recover how many states the task has and how they are related to external cues. Recent theoretical work provides a basis for this process in the context of classical conditioning and suggests that the underlying states used to represent the environment are dynamic; that animals are able to infer and learn new states of the environment based on their observations [3,4]. However, at present, there is no direct evidence for such adaptive state-space representations in decision-making situations. Actions are the other building block for reinforcement-learning algorithms, and refer, for example, to different motor commands that an agent can use to influence the state of the environment. Efficient decision-making relies on using actions at the appropriate scale; engaging decision-making at too fine-grained a level of motor movement will overwhelm this process with choice points. Indeed, evidence suggests that humans and other animals can create new actions in the form of action chunks or action sequences by concatenating simple actions together [5,6, 7,8, 2]. Such action sequences, known as ‘temporally extended actions’, can be thought of as new skills that expand the set of available actions and that are treated as single response units. By acquiring new action sequences, the selection process needs to be implemented only once at the initiation point of an action sequence instead of before each individual action and, in this way, adaptive representations of actions contribute to the scalability of the decision-making process. In the current study, using a sequential decision-making task in rats, we sought to investigate whether state-space and action representations adapt to the structure of the world. We used a two-stage decision-making task similar to a two-stage task previously used in human subjects and rodents [e. g. , 9,10,11,12], and show, without any explicit instructions about the structure of the task (which obviously cannot be provided to rats), that early in training, the rats made decisions based on the assumption that the state-space is simple and the environment is composed of a single stage whereas, later in training, they learned the true state-space reflecting the multi-stage structure of the environment and made decisions accordingly. We also found that concurrently with the expansion of the state-space, the set of actions also expanded and action sequences were added to the set of actions that the rats executed. This latter effect, however, was sensitive to the choice of experimental parameters and did not emerge in all of the test conditions. We conclude that decision making is adaptive and depends on acquiring, refining and updating both state-space and action representations over time. Importantly, although this may not seem an entirely unexpected result from an associative learning perspective [e. g. , 13], it required the development of an alternative hierarchical architecture to generate a description of the rats’ performance in RL terms. The stage 2 state that earned reward changed over time and, as such, subjects needed to use feedback from the previous trial to track which specific stage 2 state was rewarded so as to take the stage 1 action leading to that state. Given this situation, it should be expected that, if a reward is earned on the previous trial, the subjects will repeat the same stage 1 action on the next trial. Fig 3a shows the logarithm of odds ratio of staying on the same stage 1 action after earning a reward on the previous trial over the odds after earning no reward (across training sessions). Each bar in the graph represents a training session, and odds ratios were calculated using logistic regression analysis on the effect the reward had on staying on the same stage 1 action on the next trial (see Material and methods for details). The zero point on the y-axis in the figure shows the indifference point, i. e. , when the probability of staying on the same stage 1 action after earning reward or no reward is equal. As the figure shows, in early training sessions the rats failed to show a tendency to take the same stage 1 action after earning a reward on the previous trial and instead showed a tendency to switch to the other action (first five sessions; β = −0. 929 (CI: −1. 194, −0. 664), SE = 0. 135, p < 10−11). This sub-optimal behaviour can be explained with reference to the state-space and action representation that decisions were based on early in training; i. e. , subjects were initially unaware that the environment had two stages, treated it as a single stage environment, and therefore repeated the action taken immediately prior to reward delivery. For example, if they took ‘L’ at stage 1, and ‘R’ at stage 2 and earned reward, they repeated action ‘R’ at the beginning of the next trial. Although this looks like they switched to the other stage 1 action, they were clearly repeating the last rewarded stage 2 action, which should be expected if decisions are made as if the environment is composed of a single stage. Therefore, actions were not based on a two-stage representation, which would require that the subjects treat S1 and S2 as the outcomes of actions taken in S0; indeed, to the contrary, the data shows that the subjects acted as if their next action in S0 led to reward directly. This could be for two reasons: (1) Although S0 was visually distinct from S1 and S2, early in training it may not yet have been part of the state-space and therefore the outcomes of actions taken in S0 (which are S1/S2) were not differentiated from the outcomes of the actions taken in S1/S2 (which were reward/no-reward). From this perspective, a reward earned by taking an action in S1 or S2 was attributed to taking actions in the upcoming S0 and led the rats to repeat the same action in S0. Alternatively, (2) S0 was part of the state-space and was being treated differently from S1/S2, but the rats had yet to encode that ‘L’ leads to S2 and ‘R’ to S1. The former hypothesis relates to the state-space representation, whereas the latter relates to learning the “transition matrix” of the task, i. e. , state-action-state relationships. There are two points in favour of the first hypothesis. Firstly, if animals were confused about what will happen after taking stage 1 actions (i. e. , whether they will lead to S1 or S2), then we would expect the subjects to act randomly in S0, whereas, as the data indicate, the subject repeated the last rewarded action in S0 as if S0 was similar to the state in which the last action was taken. Secondly, animals typically learn action-outcome contingencies very rapidly. For example, in a simple instrumental conditioning experiment in which two levers lead to different (motivationally relevant) outcomes, such a food pellets, animals are able to learn the contingencies in a single training session [14], whereas here it took animals more than ten training sessions to take the correct actions in S0. Based on this finding, we interpret the effect as a consequence of the rats forming a simple state-space representation, i. e. , all the states are organised into a single stage early in training, which is shown as S0/S1/S2 in Table 1a. A final logically possible account can be formed based on the assumption that S0 is part of the state-space but S1 and S2 are not. This account can explain why animals do not treat S1 and S2 as the outcome of S0, but is inconsistent with the fact that the rats took different actions in these states and so were clearly able to discriminate between S1 and S2. Importantly, as Fig 3a shows, this pattern of choices reversed as the training progressed and the rats started to take the same stage 1 action that earned reward on the previous trial rather than repeating the action most proximal to reward (see S3 Fig for the behaviour of individual subject). Replications of this finding using other experimental parameters are provided in Supplementary Experiments 1-3 in (S5, S6 and S7 Figs and S2 Text). One explanation for this observation is that, at this point in training, the rats realised that the task had two-stages and, at that point, acquired the “correct” state-space of the task (Table 1b), where the correct representation refers to the Markov Decision Process underlying the task structure [note that defining the correct state-space is not trivial; see 15]. If this is true, however, then, during the course of training, the state-space used by the animals expanded from a simple representation (Table 1a) to a more complex representation consistent with the task state-space (Table 1b). Learning the state-space of the task is not the only way that the rats could have adapted to the two-stage structure of the environment; in this task reward can be earned either by executing ‘L’ at stage 1 and ‘R’ at stage 2, or by executing ‘R’ at stage 1 and ‘L’ at stage 2. As such, it is possible that animals chunked actions ‘L’ and ‘R’ to make action sequences; say, ‘L→R’, and ‘R→L’. Using these new actions, the rats could then repeat an action sequence on the next trial after earning a reward instead of merely repeating the action proximal to the reward, as early in training. If this is true, however, then the transition in the pattern of stage 1 actions shown in Fig 3a could have been driven by the rats acquiring action sequences rather then the state-space of the task. This assumption is also consistent with the fact that the delay between the first and second action (the rats’ reaction time) decreased as training progressed (Fig 3b), consistent with the formation of action chunks. In order to test the role of action sequences, we looked at the choices of the subjects in a probe session inserted at the end of the training sessions (last bar in Fig 3a), in which in a small portion of the trials transitioning between stage 1 actions and stage 2 states were switched (Fig 2c). For example, during training (non-probe sessions), after executing action ‘L’ subjects always ended up in state S2; however, the probe session included some rare transitions (20% of the trials), in which, after taking ‘L’, subjects ended up in state S1 instead of S2 [inspired by the two-stage task in 9]. As a consequence, in the probe session, after repeating the same stage 1 action subjects might end up in a different stage 2 state than on the previous trial, meaning that they would next take a different stage 2 action if they are selecting actions one by one. If, however, subjects are repeating the previously rewarded action sequence, we should expect them to repeat not only the first action, but also the second action even if they are now in a different stage 2 state [16,6, 10]. Fig 3c shows an example of this situation. Animals have earned reward from action sequence ‘L→R’ on the previous trial and have repeated action ‘L’ at stage 1 of the next trial, but on this trial have ended up in state S1 in which action ‘L’ should be taken. If, however, they take action ‘R’ this can be taken as a sign that they are repeating the whole action sequence rewarded on the previous trial. Fig 3d shows the probability of staying on the same stage 2 action on the trials in which the stage 2 state is different from that of the previous trial. As the figure shows, if the previous trial is rewarded (‘reward’ condition) and subjects stay on the same stage 1 action (‘stay’ condition) then there is a high chance that they will also repeat the same stage 2 action, indicating that they are repeating the whole previously rewarded action sequence. This is supported by a significant interaction between staying on the same stage 1 action and reward on the previous trial (β = 0. 494 (CI: 0. 055,0. 933), SE = 0. 224, p = 0. 027; see Table 2: stage 2 for the full analysis). Therefore, the pattern of choices at stage 2 is consistent with the suggestion that the subjects have expanded the initial set of actions, that previously only included actions ‘L’ and ‘R’ (Table 1a), to a more complex set that includes action sequences ‘L→R’ and ‘R→L’ (Table 1c). It is important to note that the emergence of such actions sequences depended on the experimental setting; as reported in the S5, S6 and S7 Figs and S2 Text, we did not find evidence of action sequences using particular experimental parameters; see Section ‘Choice of experimental parameters’ below. The analysis provided in the previous section showed that acquiring either the state-space representation or action sequences can explain the pattern of choices observed during the course of training (Fig 3a). Furthermore, the pattern of choices at stage 2 of the probe session provided evidence that the subjects are using action sequences. It remains open to question, therefore, whether the rats are exclusively solving the task using action sequences without relying on the state-space of the task (Table 1c), or are using both an expanded state-space and action representations for decision-making (Table 1d). To answer this question we looked at the pattern of choices at stage 1 of the probe session. As argued in the previous sections, if decisions are based on the true state-space of the task then we expect that, after earning reward on a trial, the same stage 1 action will be taken on the next trial. The same is not true for the trials with rare transitions in the probe session, however. This is because, if the reward was earned on a trial with a rare transition, the subjects should then switch to the other stage 1 action on the next trial if they are using their knowledge of the state-space of the task [9]. For example, imagine it is a trial with a rare transition and the rat, by taking ‘L’, is transferred to state S1 and earns reward. On the next trial, using the state-space of the task, the rat should switch to ‘R’ at stage 1 because ‘R’ is the stage 1 action that commonly (80% of time) leads to S1. As a consequence, staying on the same stage 1 action after earning reward depends both on the reward and the transition type on the previous trial. On the other hand, if the rats are exclusively using action sequences without relying on the state-space of the task (Table 1c), then staying on the same stage 1 action only requires that the previous trial was rewarded; the transition type of the previous trial should not have any effect. This is because earning reward by executing an action sequence will result in the same action sequence being repeated on the next trial (and so the same stage 1 action) irrespective of the transition type on the previous trial. Therefore, a main effect of reward on staying on the same stage 1 action in the next trial indicates that the subjects are using action sequences (Fig 3f) whereas, an interaction between reward and transition type on the previous trial indicates that the subjects are using the true state-space of the task (Fig 3g). Importantly, the results of stage 1 actions, presented in Fig 3e, clearly revealed a significant reward-transition interaction (Table 2: stage 1), indicating that the subjects were using the correct state-space of the task (Table 1b). In addition, the main effect of reward was also significant, which indicates that the subjects were also using action sequences (Table 1c). As such the pattern of choices indicates that the rats were using both action sequences and single actions guided by the true state-space of the task. Therefore, evidence from this study suggests that, as training progressed, the initially simple state-space and action representations (Table 2a) were expanded to align with the true structure of the task (Table 2d). Note that there are other explanations for the main effect of reward, other than using action sequences. For example, it could be the case that after experiencing a rare transition, subjects presumed that the relationship between stage 1 actions and stage 2 states had switched, which predicts a main effect of reward on staying on the same stage 1 action even if subjects were not using action sequences. Another explanation for the main effect of reward is based on the notion of ‘model-free’ actions. Intuitively, this implies that earning reward after taking an action increases the chance of repeating the action; i. e. , on this task, that reward increased the chance of taking the same stage 1 action on the next trial whether the experienced transition was common or rare [9]. Nevertheless, although these two accounts can predict a main effect of reward, they do not predict nor can they explain the effect observed on the stage 2 actions (as explained above in Section Adaptive action representation). As mentioned earlier, we did not find evidence for the operation of action sequences in all the range of experimental parameters that we tested and the probe tests that we conducted (as described below in section ‘Choice of experimental parameters’). For the conditions that we did not find evidence for the operation of action sequences, the main effect of reward mentioned above can be attributed either to the model-free effects—as if animals did not acquire action sequences at all [9]–, or alternatively to the interruption of actions sequences, i. e. , animals started action sequences, but because of the experimental parameters they managed to inhibit the execution of inappropriate sequences. Our results cannot distinguish between these two explanations, a point that we will expand on in the Discussion section. In the previous section, we argued that the reward-transition interaction is a sign the animals had acquired an adaptive state-space representation, which allowed them to learn the relationship between stage 1 actions and stage 2 states. However, recently, [17] argued that this form of reward-transition interaction in multistage decision-making can be explained if subjects have learned the ‘latent states’ of the task without relying on the relationship between stage 1 actions and stage 2 states. On this account, the rats simply learned a kind of rule: e. g. , whenever a reward is earned in S1, perform ‘R’ on the next trial (at stage 1), and whenever a reward is earned in S2, perform ‘L’ on the next trial. [17] argue that this process requires the subjects to expand their representation of the state-space by turning S0 into latent states S0S1 and S0S2, which encode which stage 2 state was rewarded on the previous trial and, therefore, their argument depends on the rats expanding their representation of the state-space to include new states. As a consequence, even under [17]’s account, the observed reward-transition interaction is evidence for an adaptive state-space representation, as we have argued. Furthermore, this account cannot explain the adaptive action representations that we observe; i. e. , the pattern of choices due to the formation of action sequences. This is because [17]’s account does not imply repeating the previously rewarded sequence of actions in S0 and S1/S2. There is another potential interpretation of the reward-transition interaction based on the potential for a local response bias induced by the reward function. Assume that, in a part of the probe session, actions taken in S1 are rewarded (and actions taken in S2 are not), and by trial and error the animal develops a tendency to take action ‘R’ more frequently than ‘L’ at S0; i. e. , the probability of staying on the same action when it is ‘R’ is higher than when it is ‘L’. As most of the common transitions after taking ‘R’ are rewarded (as they mostly lead to S1) and most of the rare transitions are non-rewarded (as they mostly lead to S2), there will be an effect of reward-transition interaction on the probability of staying on the same action, which looks like the animals are taking the structure of the world into account, while what they are doing is simply taking action ‘R’ more frequently. This issue was discussed in [10,18] and further analysed in [17] and one way to address it is to add a new predictor to the analysis of the effect of reward and transition on staying on the same stage 1 action. This new predictor encodes whether the previous stage 1 action was the best action, i. e. , it leads to the stage 2 state with the highest reward, which will absorb the effect of the reward-transition interaction if the interaction is just due to repeating the best action more frequently [18,17]. This analysis is presented in S5 Table, which shows that even in the presence of this predictor the effect of reward and the reward-transition interaction are still significant. As such, the reward-transition interaction is unlikely to be due to this form of response bias. Animals were given three probe sessions in total, and the results reported above were taken from the last of these tests which was the final experimental session (probe sessions are marked by an asterisk in Fig 3a). The full analysis of all the probe sessions is presented in see S4 Table. The structure of the probe sessions was identical to each other and also in terms of results; similar to the third probe session analysed above, the main effect of reward was significant in sessions one and two. However, unlike the last probe session, in the first two sessions, the rats did not show evidence that they were using action sequences (S4 Table: probe 1 and 2, stage 2 actions; reward-stay interaction; p-value>0. 1). A closer examination of these sessions revealed that, at this stage in the training, the rats were not discriminating between the stage 2 stimuli; i. e. , because the analysis of stage 2 only included trials in which the stage 2 state is different from the previous trial, we expected the probability of staying on the same stage 2 action to be generally low (as different actions are rewarded in the stage 2 states), which was not the case in the first two probe sessions (see S4 Table; p-value > 0. 05 for the intercept term at stage 2 actions in probe 1,2). As a consequence, under these conditions, staying on the wrong stage 2 action due to the performance of action sequences cannot be detected, because the rats are likely to take the incorrect action at stage 2 states even if they are not taking an action sequence (ceiling effect). One reason for this lack of discrimination is the potential for interference between the stage 1 and stage 2 actions; if the rats checked the magazine after taking the stage 1 action they may then have repeated the same stage 1 action instead of taking the correct stage 2 action. This would make it look like the animals were not discriminating between stage 2 stimuli. To address this issue we introduced the ‘strict sequences’ criterion for the next ten training sessions (Fig 3e) under which a trial was aborted if a rat entered the magazine between the stage 1 and stage 2 actions. After these ten training sessions the rats were given the third probe test, in which they showed they were able significantly to discriminate between the stage 2 states (see Table 2; p-value = 0. 001 for the intercept term at stage 2 actions). Note that the analysis presented in the previous and subsequent sections relates to this last probe session. In S5, S6 and S7 Figs, S2 Text we also present three supplemental experiments each of which used different parameters. Supplementary experiment 1, which is shown in S5 Fig, includes probe sessions throughout the training process and shows the development of choices. Supplementary experiments 2 and 3 are mostly similar to each other and provide a training protocol in which animals reliably exhibit the reward effect in their stage 1 actions. However, in none of these experiments were we able to observe the performance of action sequences, as indicated by the reward-same interaction in stage 2 actions (see S6 Table for the full analysis of supplementary experiment 1 and S8 Table for the full analysis of supplementary experiments 2,3). One main difference between these experiments and the experiment reported in the main paper is that, whereas in the main experiment the ITI was zero, in the supplementary experiments, the inter-trial interval was non-zero. In this latter condition, animals were often found to take actions during the ITI, which were not rewarded but which were very likely to interfere with the performance of action sequences once the next trial started. Using an ITI of zero addressed this issue. Lastly, as Fig 3a shows, there were some training sessions in which the contingency between stage 1 actions and stage 2 states was reversed. These training sessions were introduced to overcome the interference that could be produced when animals were first exposed to rare trails. We next sought to establish the computational model that best characterized the decision-making process used by the rats in this experiment. The modelling was focused on the probe session that we analysed in the previous sections (the final probe session). For this purpose, we compared different families of reinforcement-learning (RL) model to establish which provided a better explanation for the data (Table 3). The families compared included: (1) a non-hierarchical model-based RL family (MB) corresponding to Table 1b, which assumes that the subjects acquired the correct state-space of the task, but in which action sequences were not included in the set of actions; (2) a hierarchical RL family (H) corresponding to Table 1c, which assumes that the set of actions included only action sequences, but that decisions were not guided by the true state-space of the task; (3) a hierarchical model-based RL family (H-MB) corresponding to Table 1d, which assumes that the subjects were using single actions, action sequences and the true state-space representation for decision-making [10]; (4) a model-free RL family (MF) without action sequences but using the correct state-space (5) a model-free RL family with action sequences and single actions and correct stat-space of the task (H-MF), (6) a hybrid model-based RL and model-free RL family with only single actions and without action sequences using the correct state-space (MB-MF), and (7) a hybrid of model-based and model-free RL with both action sequences and single actions (H-MB-MF). Model-free RL has been previously used to characterise performance on a similar task [9], and here we used it as a baselines in mixture with model-based RL and hierarchical accounts. In total we considered 536 different models in which each family consisted of several members with different degrees of freedom (see S1 Text for details). We then calculated the negative log model-evidence for each model M given the choices of subjects, D (denoted by − log p (D|M) ). Table 3 shows the negative log model-evidence along with other properties of the best model for each family. The differences in log model-evidence (log-Bayes factor) between the best fitting model of the H-MB family and other families was greater than 5. In the Bayesian model comparison literature, log-Bayes factors greater than 3 are considered to be strong evidence [19]. Therefore, the above results provide strong evidence that the subjects were utilising H-MB to guide action selection compared to the other models. Fig 4 shows the negative log model-evidence for the best eight models in each family and shows that the best model in the H-MB family provides a better explanation of the data than any of the other families. We then simulated eight instances of the H-MB model of the task using the best fitting parameters for each subject (S2 Table) and analysed the stage 1 and stage 2 choices of the simulated model. Analysis of stage 1 choices (Fig 3i) revealed a significant main effect of reward (β = 0. 293 (CI: 0. 217,0. 369), SE = 0. 038, p < 0. 001), and a significant interaction between whether the previous trial was rewarded and the transition type of the previous trial (β = 0. 327 (CI: 0. 233,0. 422), SE = 0. 048, p < 0. 001). Analysis of stage 2 choices (Fig 3h), revealed a significant interaction between earning a reward on the previous trial and the likelihood of staying on the same stage 1 action (β = 0. 209 (CI: 0. 091,0. 327), SE = 0. 060, p < 0. 001). These results are, therefore, consistent with the behavioural results of our experiments using rats as subjects. The fact that the H-MB family provides a better fit than the H family implies that subjects were using the correct state-space of the task (the MB part), whereas the finding that the H-MB family were better than the MB family implies that subjects were using action sequences. See S8 Fig for the similar simulations using the best fitting model in other classes of models. See S1 Table for the negative log-model evidence of the best model in each family. The H-MB family also provided a better fit than baseline MB/MF models, however, this does not imply that some form of model-free RL is not working concurrently with a H-MB model. Indeed, one can imagine a model which includes both MB and MF components operating hierarchically over action sequences. Whether such a model provides a better fit of the data than H-MB cannot be addressed using the current task because it will require manipulating the value of the outcomes. The conclusions made earlier about learning the state-space and action sequences are orthogonal to the role that MF RL plays in these decisions, and therefore they are not affected by this limitation. Learning the value of different actions in various states of the environment is essential for decision-making in multi-stage environments. This learning process operates above the state-space and action representation and, therefore, the ability to (i) acquire the correct state-space of the task, and (ii) create new actions that are useful for solving the task, are important for efficient decision-making. Using a sequential decision-making task in rats, we provide direct evidence that, early in training, subjects make decisions based on simple state-space and action representations, but that, during the course of training, both state-space and action representations (under certain conditions) evolve and adapt to the structure of the environment. That is, we found that the rats responded initially as if the proximal response to reward was the only relevant action to earn that reward but gradually learned the interaction between the first lever press and the second lever press response within and across the states signalled by the discriminative stimuli and so acquired the multistage discrimination. Furthermore, we found evidence that the single lever press actions initially acquired by the rats later expanded to include sequences of lever presses and that, when these sequences were used, they tended to be used in a habitual manner by repeating previously rewarded sequences even when the stage 2 state was revalued. The ability to solve multi-stage decision-making tasks has been previously demonstrated in different species, however, unlike the current study, these demonstrations have either given the different stages of the task to the subjects [e. g. , 9], or have explicitly signalled the actions that should be taken at each stage [e. g. , 20], which remove the necessity for building multi-stage representations to solve the task. Similarly, the ability of animals to concatenate simple actions to execute action sequences has been established previously and here we extended these prior studies by showing that, during the course of learning, single actions turn into action sequences that are not only executed, but also are evaluated as a single response unit [13]. Similar findings have recently been reported demonstrating that lever press-magazine entry sequences are resistant to devaluation in rats [21]. A task similar to the two-stage task that we used here in rats has previously been employed to study different forms of decision-making processes in humans [9]. Although the experiments in those studies were composed of a single session, results indicated that the subjects were using an expanded state-space representation without needing to go through multiple training sessions. This is presumably due to the instructions and the cover story provided to the subjects, which informed them about the correct state-space representation. In terms of acquiring action sequences, using a similar task in humans we have previously shown that subjects engaged action sequences [10]. Again, however, we found they were able to do so without requiring multiple training sessions. Why such sequences should have formed so rapidly is a matter of conjecture but, as the task involved pressing keys on a keyboard, familiarity with similar response sequences could have supported sequence acquisition (especially as only two key presses were required to earn the reward). Based on these comparisons, the results of the current experiments point to the importance and complexity of learning state-space and action representations. As the profile of the rats’ choices indicates, they required a significant amount of training in order to learn the structure of the environment (10-40 sessions). This is while, in some instrumental conditioning settings, animals are able to learn the contingency between actions and states in two and sometimes in a single training session [14]. Uncovering the processes that determine the encoding of the state-space of the task and how this process interacts with that subserving instrumental conditioning will be an important step towards better understanding the learning mechanisms that mediate decision-making processes generally. The results of the computational modelling indicated that hierarchical model-based RL provides the best explanation for the rats’ choices. This model assumes that the subjects build an internal map of the environment which encodes both the specific outcomes of single actions and of action sequences. The validity of this assumption for single actions can be confirmed based on the results of the current experiment and previous studies [22] showing that subjects encode the specific outcome of each individual action, e. g. , taking ‘R’ leads to ‘S1’ and ‘L’ leads to ‘S2’. With regard to encoding the outcome of action sequences, although previous studies have indicated that the subjects specifically encode the outcome of each action sequence [13], we cannot assess whether subjects encoded outcome specific sequences in the current study because the value of the food outcome was not manipulated. As such, the results do not address the (model-based or model-free) nature of the controller mediating the evaluation of action sequences. Although the computational modelling analysis provided evidence consistent with hierarchical model-based RL, the results do not imply that all the behavioural trends in the data were captured by the model. In particular, although a wide range of models with different parameters were considered here, there were some differences between the pattern of stage 1 choices in the data shown in Fig 3 (e) and the simulations of the best model shown in Fig 3 (i). One way to address this issue is using recurrent neural networks (RNNs) instead of the RL family, which are more flexible and able to learn the details of the behavioural processes without relying on manually engineering the models [23]. Another limitation of the current work is that, although we provided evidence for the expansion of the state-space of the task, we did not provide any computational account for ‘how’ the states-space is acquired by the animals. This can potentially be addressed by approximating Q–values using a parametrised family of models able to adjust their predictions as the training progresses. Along the same lines, as we discussed in the previous sections, the emergence and detection of action sequences requires certain experimental conditions, such as a short ITI. Without these conditions actions at stage 1 are consistent with the operation of action sequences, but not actions at stage 2. One explanation for this effect is the possibility that action sequences were inhibited. For example, if during long ITIs the subjects go through extinction, it is unlikely that they keep performing the whole action sequence throughout the ITI and into the next trial. As such, although the first component of the action sequence at stage 1 is performed, the second component may be inhibited when it is inappropriate, making it harder to detect the performance of action sequences. Alternatively, this pattern of choices could also indicate the operation of another RL system, such as model-free RL, instead of interrupted sequences. Within this alternative framework, choices are a mixture of goal-directed actions (model-based), and model-free actions that are guided by their ‘cached’ [as opposed to their current values]. Our results are equivocal with respect to this interpretation, but since, in other conditions, there is positive evidence for action sequences, it is more parsimonious to interpret this result in terms of the inhibition of action sequences rather than as the output of an additional model-free system. The current task and the parameter set that we have assessed in these experiments (see also S2 Text) constitute an addition to the developing literature on multistage discrimination learning [e. g. , 17,12,11,24,25] and its relationship to action sequence learning in rats [13]. Importantly, this research demonstrates that one way in which rats, and potentially other animals including humans [10], learn complex multistage discriminations is not just by expanding the task space through shifts in attention across the perceptual feature dimensions of complex stimuli [26,27] but also by expanding the action repertoire from single actions to action sequences. The tendency to generate such sequences may have been aided by some important features of the current task; perhaps the most important of which was our attempt to minimize the impact of predictive cues in the first stage that could significantly interfere with the action-related predictions of the second stage state. However, a recent study [28] that replicated many of the effects reported by [10] with regard to the development of action sequences in a multistage discrimination task in human subjects nevertheless confounded stimulus and action predictions at the first stage as others have done [9]. However, the Adams and Cushman’s study differed from Dezfouli and Balleine’s in also reporting some evidence for a model-free component of performance over and above the model-based selection of habitual action sequences, which raises the possibility that evidence of model-free RL in first stage choices depended on using specific first stage stimuli that could provide stimulus values for the second stage states. That feature aside, however, the current task is a close analogue for the human 2 stage task generated by Daw and colleagues and particularly that of [10] and appears to produce very similar effects in both rodent and human subjects. Given its utility in animals, this task could, therefore, provide the opportunity to not only investigate the behavioural and psychological determinants of performance across species but, by utilising more direct neural manipulations, to establish the way in which the brain supports state and action learning in multistage discrimination tasks and particularly how model-based control of simple actions and habitual actions sequences is implemented in multistage discriminations. Generally, the interaction of a model-free controller with action sequence learning has not been evaluated at the neural level, although considerable evidence suggests that premotor and presupplementary motor regions are likely to play an important role [e. g. , 29]. This is particularly true given the evidence in animals that damage to the rodent homologue of premotor cortex reduces otherwise sequential action-outcome relations simple actions [13]. This account is strikingly different from other models within which goal-directed actions and habits and their computational model-based and model-free implementation have typically been seen as antagonistic processes for which some form of arbitrator is required to adjudicate competition between them. Arbitration has been investigated in a number of paradigms in humans and several investigators have found evidence of model-based model-free arbitration-related activity particularly in the inferior lateral prefrontal cortex [30,31]. In contrast, within hierarchical models, goal-directed actions and habit sequences do not compete, being two action strategies from which the goal-directed controller selects. Which of these is the more accurate statement of the way goal-directed and habitual actions interact is an empirical issue but other potential models of performance have generally suggested that collaboration rather than competition between action controllers more accurately captures their interactions [cf. 32]. Eight experimentally naive male Hooded Wistar rats served as subjects in this study. Data from all the subjects are included in the analyses. All animals were housed in groups of two or three and handled daily for one week before training. Training and testing took place in eight Med Associates operant chambers housed within sound- and light-resistant shells. The chambers were also equipped with a pellet dispenser that delivered one 45 mg pellet when activated (Bio-Serve). The chambers contained two retractable levers that could be inserted to the left and the right of the magazine. The chambers contained a white noise generator, a Sonalert that delivered a 3 kHz tone, and a solenoid that, when activated, delivered a 5 Hz clicker stimulus. All stimuli were adjusted to 80 dB in the presence of a background noise of 60 dB provided by a ventilation fan. A 3 W, 24 V house light mounted on the wall opposite the levers and magazine illuminated the chamber. Microcomputers equipped with MED-PC software (Med Associates) controlled the equipment and recorded responses. Animals were food deprived one week before starting behavioral procedures. They were fed sufficiently to maintain their weight at 90% of their free-feeding weight. The animals were fed after the training sessions each day and had free access to tap water whilst in their home cage. Each training session (except the magazine training sessions) started with insertion of the levers, and ended with their retraction. All procedures were approved by the University of Sydney Animal Ethics Committee. Rats were given two sessions of magazine training in which 30 grain pellets were delivered on a random time 60-s schedule (Fig 1: phase 1). Rats were then trained to lever press on a continuous reinforcement schedule with one session on the left lever and one session on the right lever each day for four days with the total number of outcomes each day limited to 60 per session (Fig 1). The total duration of each session was limited to 60 minutes (see S2 Fig for the average session duration). Next, rats were trained to discriminate the two stimuli (Fig 1: phase 3). Each session started with the presentation of a stimulus. The stimulus was presented until the rat performed an action (either pressing the left or right lever) after which the stimulus turned off. For one stimulus, taking the left action led to the reward, whereas for the other stimulus taking the right action led to reward. Levers and stimuli were counterbalanced across subjects. After an action was chosen, there was a 60-second inter-trial interval (ITI) after which the next trial started with the presentation of the next stimulus, again chosen randomly. The duration of each session was 90 minutes, with no limit on the maximum number of earned rewards. The stimuli were a constant or a blinking house light (5 Hz). The result of this phase is depicted in S4 Fig. The rats then received training on the two-stage task depicted in Fig 2b (maximum 60 outcomes in a session and maximum duration of a session was limited to 1 hour). Animals were trained on the two-stage task for 40 sessions. In the middle of, or at the end of these training sessions, they were given probe sessions, similar to the training sessions except that stage 1 actions led to stage 2 states in a probabilistic manner (Fig 2c). These sessions are indicated by ‘*’ in Fig 3a. After the first two training sessions, subjects then received ten more training sessions, and were then given a further probe test. The results reported in the Results section correspond to this last probe session. In the sessions marked with ‘#’ in Fig 3a the contingency between stage 1 actions and stage 2 states were reversed (‘L’ leads to S1 and ‘R’ to S2); these training sessions were followed by a session in which ‘L’ leads to S1 and ‘R’ to S2 in 20% of times, followed by normal training sessions, as Fig 3a shows. Finally, ‘strict sequence’ in Fig 3a refers to a session in which a trial was aborted if the animal entered the magazine between stage 1 and stage 2 actions. In all the training phases levers were present throughout the training session. See S3 Table for the total number of trials completed by each subject; see S7 and S9 Tables for the total number of trials completed by subject in the supplementary experiments. We used R [33] and lme4 packages [34] to perform a generalized linear mixed effects analysis. In all of the analyses, logistic regression was used and all the fixed effects (including intercepts) were treated as random effects varying across subjects. For analyses that included more than one session, random effects were assumed to vary across sessions and subjects in a nested manner. Confidence intervals (CI) of the estimates were calculated using the ‘confint’ method of lme4 package with the ‘Wald’ parameter. In the analyses of the stage 1 of non-probe sessions, we used a logistic regression analysis in which the independent predictor was whether the previous trial was rewarded (reward or no-reward), and the dependent variable was staying on the same stage 1 action. The p-value of this analysis was used in Fig 3a for colour-coding each bar, and the height of each bar represented the log odds ratio. The intercept term of this analysis is shown in S1 Fig. In the analyses of stage 1 of the probe sessions, the independent predictors were transition type of the previous trial (rare or common) and whether the previous trial was rewarded (reward or no-reward), whereas the dependent variable was staying on the same stage 1 action. The effects of interest were reward and the reward by transition-type interaction. In the analysis of stage 2 probe sessions, the independent variables were whether the stage 1 action was repeated (same stage 1), and whether the previous trial was rewarded. The dependent variable was staying on the same stage 2 action. The effect of interest was the interaction between the two independent variables. Note that only trials in which the stage 2 state was different from the stage 2 state of the previous trial were included in this analysis. In all the analyses, only trials in which subjects made a correct discrimination on the previous trial (‘R’ in S2, and ‘L’ in S1) were included (%71 of trials in the whole training period). This was for two reasons. Firstly, it was not clear how subjects learn from actions taken during incorrect discriminations, which were never rewarded. Secondly, as depicted in Fig 3c, for the analysis of adaptive action representation, we focused on the trials in which the stage 2 states was different from that of the previous trial. When executing action sequences, we expected the subject to take the same stage 2 action in the next trial if (i) they were rewarded in the previous trial and (ii) they take the same stage 1 action, but not otherwise (as we focused on consecutive trials with different stage 2 states). However, assume that the subject makes an incorrect discrimination in the previous trial, e. g. , it takes action ‘L’ at stage 1, moves to state S2 and takes action ‘L’ in that state, which is not rewarded since action ‘L’ in S2 is never rewarded. In the next trial, if the subject takes action ‘L’ again and ends up in state S1 (Fig 3c), there is a high chance that it will take ‘L’ again at stage 2, since ‘R’ is never rewarded in S1. Therefore, even if no reward was earned in the previous trial, there is a high chance that the subject will repeat the same stage 2 action in a different trial. This only happens in the condition that the subject made an incorrect discrimination in the previous trial, and in order to remove this interaction between the discrimination between actions at stage 2, and the analysis of action sequences, we only included the trials in which the subjects made correct discrimination in the previous trial. The analysis similar to the one presented in S4 Table without removing these trials is presented in S5 Table, which shows that the main statistical tests that we used to argue for adaptive state-space and action representations are statistically significant whether we include all of the trials or not. Reinforcement-learning models considered for behavioural analysis were similar to the hierarchical RL models provided in [10] and the model-based/model-free family provided in [9]. In addition to these families, we also considered a family of hierarchical models (corresponding to the H family) in which only action sequences were available at stage 1 (i. e. , single actions ‘L’ and ‘R’ were not available). In addition to the free-parameters mentioned in previous work, we added two new parameters here. The first free-parameter only applies to the hierarchical families (H, H-MB, H-MF, H-MB-MF), which represented the probability that the performance of action sequences is interrupted in the middle of the action sequence (i. e. , subjects only perform the first component of an action sequence, and select a new action at stage 2). The other free-parameter coded the tendency of animals to take the discriminative action at stage 2, irrespective of the value of each action (tendency to take ‘R’ in S2 and ‘L’ in S1). This free-parameter allowed the model to learn that one of the actions in each of the stage 2 states was never rewarded. Details of the computational models along with their mathematical descriptions are presented in S1 Text. For the purpose of model comparison, we generated different instances of each family of models with different degrees of freedom (see S1 Text for details). Model-evidence, reported in Table 3 and Fig 4 was calculated similar to [35].

Everyday decision-making tasks typically require taking multiple actions and passing through multiple states before reaching desired goals. Such states constitute the state-space of the task. Here we show that, contrary to current assumptions, the state-space is not static but rather expands during training as subjects discover new states that help them efficiently solve the task. Similarly, within the same task, we show that subjects initially only consider taking simple actions, but as training progresses the set of actions can expand to include useful action sequences that reach the goal directly by passing through multiple states. These results provide evidence that state-space and action representations are not static but are acquired and then adapted to reflect the structure of the world.

Abstract Introduction Results Discussion Methods and materials

learning decision making statistics sociology social sciences vertebrates neuroscience learning and memory animals mammals simulation and modeling animal models regression analysis model organisms cognitive psychology mathematics cognition experimental organism systems animal management animal performance research and analysis methods sequence analysis bioinformatics animal studies mathematical and statistical techniques communications mass media magazines agriculture psychology rodents eukaryota database and informatics methods biology and life sciences physical sciences cognitive science amniotes statistical methods organisms rats

2019

Learning the structure of the world: The adaptive nature of state-space and action representations in multi-stage decision-making

Intestinal cestodes are infecting millions of people and livestock worldwide, but treatment is mainly based on one drug: praziquantel. The identification of new anti-cestodal compounds is hampered by the lack of suitable screening assays. It is difficult, or even impossible, to evaluate drugs against adult cestodes in vitro due to the fact that these parasites cannot be cultured in microwell plates, and adult and larval stages in most cases represent different organisms in terms of size, morphology, and metabolic requirements. We here present an in vitro-drug screening assay based on Echinococcus multilocularis protoscoleces, which represent precursors of the scolex (hence the anterior part) of the adult tapeworm. This movement-based assay can serve as a model for an adult cestode screen. Protoscoleces are produced in large numbers in Mongolian gerbils and mice, their movement is measured and quantified by image analysis, and active compounds are directly assessed in terms of morphological effects. The use of the 384-well format minimizes the amount of parasites and compounds needed and allows rapid screening of a large number of chemicals. Standard drugs showed the expected dose-dependent effect on movement and morphology of the protoscoleces. Interestingly, praziquantel inhibited movement only partially within 12 h of treatment (at concentrations as high as 100 ppm) and did thus not act parasiticidal, which was also confirmed by trypan blue staining. Enantiomers of praziquantel showed a clear difference in their minimal inhibitory concentration in the motility assay and (R) - (-) -praziquantel was 185 times more active than (S) - (-) -praziquantel. One compound named MMV665807, which was obtained from the open access MMV (Medicines for Malaria Venture) Malaria box, strongly impaired motility and viability of protoscoleces. Corresponding morphological alterations were visualized by scanning electron microscopy, and demonstrated that this compound exhibits a mode of action clearly distinct from praziquantel. Thus, MMV665807 represents an interesting lead for further evaluation. Helminths are separated into the two major phyla of nematodes (roundworms) and platyhelminths (flatworms), including trematodes and cestodes, and they are important causes of disease in humans as well as animals. An estimated one billion people are infected with at least one helminth in developing countries of Africa, Asia and America [1]. Infection of livestock by helminths, small ruminants in particular, has an enormous economic impact on productivity in farming [2]. Despite the large number of infected individuals and enormous economic losses due to helminth infections in animals, there are still not many drugs registered for their treatment [1]. Present efforts ongoing to discover new anthelminthic drugs are focused on gastrointestinal nematodes, schistosomes and filariae [3], as they comprise the highest prevalences. However, most of the adult stages of trematodes, and all cestodes, are not being considered in the current drug screening efforts. Intestinal cestodes might be considered as parasites of lower relevance as they usually cause few clinical signs, but they are of high relevance as source of infection of diseases caused by the larval stages of these parasites [4,5]. For the treatment of nematodes, a variety of drugs are in use, and new ones have been introduced to the market recently. However, spread of resistance is a major problem in the veterinary sector [6]. For treatment of cestode and trematode infections, praziquantel (PZQ) is the drug of choice against most species [7]. For cestode-treatment, the alternatives available are epsiprantel that is exclusively applied in animals, and niclosamide, whose marketing status is currently discontinued [4,8]. PZQ is generally very well tolerated, even though it tastes bitter. It induces only mild adverse reactions, but rare events of allergy and hypersensitivity reaction have been described [7]. However, there is increasing evidence on resistance of schistosomes against PZQ [9] and treatment failures of PZQ against Taenia saginata are described as well [10]. A major problem is that PZQ is the only drug in use against many platyhelminths and mass drug administrations all over the globe might select for resistant platyhelminth strains in the future. In helminths, as compared to for example bacteria, resistance development takes more time as their generation time is much longer. Nevertheless, drug resistance is already a major problem for many diseases caused by nematodes and trematodes, and it will be only a question of time until resistance to PZQ spreads also to cestodes [11,12]. Therefore, it is of crucial importance to search for new anthelminthic drugs, including compounds against platyhelminths. Whole-organism screening is still the most widely accepted method for anti-parasitic drug discovery, despite the fact that target-based screening approaches have been widely introduced [3]. However, this approach is challenging as it relies on a profound knowledge of parasite life-stages and their biology, the availability of in vitro culture techniques, and reliable methods for compound efficacy assessment [3]. Caenorhabditis elegans is a frequently used model worm for whole-organism in vitro screenings, but over the last decades also other models have been successfully established. Classically, the viability and morphology of whole parasites upon treatment is assessed by light microscopy, which harbors drawbacks such as time-consuming and subjective evaluation procedures, and automatization is not possible. However, no specialized equipment is required [13]. Other assays include dyes that indicate viability or loss of viability such as trypan blue and eosin or even fluorescent stains, but nevertheless, these are low-throughput methods and they represent only indirect indicators of viability [14,15]. For objective larger-scale screenings, new methods were implemented over the last decades, such as MTT (3- (4,5-Dimethylthiazol-2-yl) -2,5-diphenyltetrazolium bromide) assay, alamar Blue assay or fluorescent labeling that allow an automated readout [3,14]. The observation that anthelminthics reduce larval motility in nematodes led to the development of a motility-based assay for assessing the effects of certain compounds [16]. Non-image-based methods to measure nematode motility include measurements of the fluctuations in electrical currents by xCELLigence [17] or isothermal microcalorimetry [18]. In addition, image-based methods have been introduced, such as the Parallel Worm Tracker [19], the WormAssay [20], the WormScan [21], the Worminator [22] or the whole-organism screening by Preston et al [23]. Adult cestodes have so far been excluded from in vitro screening, since for most of these parasites in vitro culture methods have not been established, or they are too large to be cultured in tissue culture devices suitable for screening assays. Adult E. multilocularis tapeworms live in the small intestine of final hosts, such as foxes and dogs, and they release infectious eggs in the faeces of these hosts. Upon ingestion of eggs, intermediate hosts, such as rodents and small mammals, get infected with the parasite and a multivesicular metacestode will develop mainly in their livers. After 2–4 months, brood capsules with protoscoleces will form within these metacestodes. Once a final host feeds upon an intermediate hosts, protoscoleces get ingested as well, and they will develop into adult tapeworms in the intestine of these hosts [24]. The fox tapeworm Echinococcus multilocularis has become an important model for the study of cestodes, since the genome has been sequenced and corresponding data is publically available, including also the close relative E. granulosus [25,26], advanced molecular tools have been developed for the study of the disease-causing metacestode stage [27,28], and metacestode in vitro drug screening assays have been implemented [29–31]. However, it is known that drugs with activity against larval stages are not necessarily active against adult stages and vice versa [3,14]. Albendazole is the drug of choice against alveolar echinococcosis caused by E. multilocularis metacestodes, but the drug is ineffective against adults. On the other hand, PZQ is the most widely used drug against adult cestodes, but it is not active against metacestodes. Adult E. multilocularis worms have not been studied extensively, due to high risk of infection for the experimenter and lack of suitable laboratory models. There are a number of studies on protoscolicidal substances for the treatment of E. granulosus infections. However, none of these have considered protoscoleces as a potential model for adult tapeworms. Protoscoleces are easily generated and purified in large numbers, they are relatively small (150–350 μM in length), move actively and they represent precursors of adult tapeworms. Thus, we describe here an in vitro drug screening assay that is easy to perform, inexpensive, and which is based on the semi-automated, quantitative assessment of E. multilocularis protoscolex motility. In addition, we also compare the method of motility assessment to the classical trypan blue staining method. E. multilocularis protoscoleces were extracted under sterile conditions from E. multilocularis metacestodes kindly provided from strain maintenance surplus by Dr. R. Fiechter (permission number ZH139/2015, Institute of Parasitology, Zürich, Switzerland). The parasite originated from naturally infected monkeys from the German primate center, and they had been under in vivo passage in Mongolian gerbils for up to five years. The protocol described by Brehm et al was applied to purify protoscoleces from metacestodes with minor changes [32]. In short, the metacestode material was pressed through a tea sieve, washed out with PBS and further broken mechanically by vigorous shaking in a QIAGEN tissue lyzer for 10 minutes (8 shakes per second). The resulting suspension was passed through a sieve with 250 μM mesh size. The flow through was then passed through a second sieve with 50 μM mesh size and thoroughly washed with PBS. The protoscoleces that were collected in the 50 μM sieve were transferred into a 50 mL tube and washed repeatedly with PBS until all remaining vesicle tissue was removed and the protoscoleces sedimented fast. For further purification, the protoscoleces were given into a petri dish (14 cm diameter) containing 70 mL DMEM with 10% FCS. The dish was moved in circles so the protoscoleces could be collected in the centre of the plate and they were again washed in PBS before being immediately subjected to activation (see below). The standard procedures for evagination of protoscoleces are based on incubation in pepsin (0. 5 mg/mL), pH 2. 0 for 3 hours at 37°C [33–35], or pepsin (0. 05%), pH 2. 0 for 30 minutes and subsequent incubation in 0. 2% Na-taurocholate for 3 hours at 37°C [36,37]. In order to simplify this method for a subsequent motility-based screening, we incubated freshly extracted protoscoleces also for 3 hours at various concentrations of DMSO (0,1, 2. 5,5, 10, and 20%) in DMEM, including 10% FCS. All incubations were done in triplicates. Subsequently, the protoscoleces were washed in PBS and were allowed to recover overnight in DMEM with 10% FCS at 37°C and 5% CO2. Thereafter, the numbers of invaginated and evaginated protoscoleces were counted manually under the light microscope within one field of vision (40 x magnification, > 170 protoscoleces per view) and the percentage of evaginated protoscoleces was determined. Averages and standard deviations were calculated in Microsoft Office Excel 2010. The experiment was repeated three times. Based on the results obtained above, a standardized activation protocol was established: a maximum of 25’000 protoscoleces per well were seeded in a 6-well pate in DMEM containing 10% FCS and 10% DMSO, at 37°C, and 5% CO2 for 3 hours. Thereafter, the protoscoleces were washed in PBS twice at room temperature and incubated in DMEM with 10% FCS and antibiotics (100 U/mL penicillin, 100 μg/mL streptomycin) at 37°C, and 5% CO2, overnight. For all subsequent manipulations, pipettes and tubes were pre-rinsed with FCS in order to avoid sticking of protoscoleces to the tubing walls. Photomicrographs of protoscoleces were taken in a live cell imaging system (Nikon TE2000E microscope connected to a Hamatsu ORCA ER camera) with the software NIS Elements Version 4. 40 and the additional module JOBS (JOBS program given in S1 File). For motility measurement, two separate images were taken of each well at a 10 seconds interval and at 40 times magnification. Motility was assessed at various time points (1,6, 12,18, and 24 h) after addition of compounds. The motility index for each well, resulting from differences in pixels within the 10 seconds interval, was calculated with a pixel grey value threshold of 230 in ImageJ version 1. 49 (the respective Macro including details of calculation is given in S2 File). Averages of movement indices (excluding the minimal and maximal values of each of the six replicas) and respective standard deviations were calculated in Microsoft Office Excel 2010. In order to check the correlation of number and movement of protoscoleces in the 384-well format, various numbers of protoscoleces (1 to 58) were tested. Protoscoleces were seeded in a total of 20 μL of DMEM without phenol red, including 10% FCS and 1% DMSO, in a 384-well plate (order nb. 788095–128, Greiner bio-one, via Huberlab, Aesch, Switzerland) and sealed by a pressure sensitive seal (order nb. 7676–070, Greiner bio-one, via Huberlab). The total motility corresponding to each number of protoscoleces was assessed as described above, but without normalization and the correlation was determined in R (version 3. 3. 0). To determine the optimal temperature for the motility assay, the same general protocol was used as in the number to motility ratio experiment (see above): 20–30 protoscoleces per well were seeded in a total of 20 μL of DMEM without phenol red, including 10% FCS and 1% DMSO. Automated seeding was performed by application of a peristaltic pump (multidrop combi, Thermo Fisher Scientific, Reinach, Switzerland) equipped with a standard size cassette (tubing inner diameter 1. 3 mm, tip inner diameter 0. 5 mm) at low speed. After incubation for 1 hour at various temperatures (25,30,37, and 41°C), corresponding motilities of 10 replicas were assessed as described above. The average value of absolute movement and corresponding standard deviations, and Wilcoxon rank-sum test for determination of p-values, were calculated in R. The experiment was repeated three times. Several experiments were performed to measure the effects of different compounds (each in six replicas) on the motility of protoscoleces. In an initial experiment, freshly activated protoscoleces (activated by incubation in 0 (DMEM control) to 20% DMSO, pepsin, or pepsin and Na-taurocholate, see above) were tested for their motility as described in this section. In an additional DMSO control experiment, various concentrations of DMSO (0,0. 1,0. 3,1, 3, and 10%) were incubated with a total number of 20–30 protoscoleces per well in DMEM (without phenol red, containing 10% FCS) for 12 h. Therefore, respective DMSO dilutions were added first to each well and protoscoleces were distributed using the peristaltic pump with the standard size cassette at low speed. The plates were then sealed by pressure sensitive seal and incubated in the incubation chamber of the Nikon live imaging system at 37°C and the motility assessed as described above. For incubation of activated protoscoleces with various drugs, the compounds were pre-distributed in 5 μL aliquots into 384-well plates as 4x stocks in DMEM (without phenol red, containing 10% FCS and 4% DMSO). To measure the effects of different enantiomers of praziquantel (PZQ), PZQ racemate, (R) - (-) -PZQ and (S) - (-) -PZQ were added to final concentrations of 100 to 0. 0006 ppm in a 1: 3 dilution series. Further standard compounds (see Fig 1) with known activity (niclosamide [4], nitazoxanide [38]) and known inactivity (albendazole and monepantel), as well as the compound MMV665807 from the open-access malaria box [31], were prepared to final concentrations of 100 to 0. 4 ppm in a 1: 3 dilution series. Wells with 4% DMSO in DMEM (without phenol red, 10% FCS) served as negative controls. Subsequently 20–30 protoscoleces per well were added in a total of 15 μL DMEM (without phenol red, containing 10% FCS), in order to reach a final DMSO concentration of 1%. They were distributed using a peristaltic pump. Motility was assessed as described above and expressed as percentage of the DMSO control in order to normalize for parasite batch-variations. The experiment with standard compounds was repeated 3 times. The minimal inhibitory concentration (MIC) was determined by calculating the critical concentration where the slope of the dose-response curve changed significantly according to students T-test (p-value < 0. 05) in Microsoft Excel 2010. To evaluate different protoscolex-activation protocols with regards to sensitivity to standard drugs, we compared also the movement of protoscoleces activated with 10% DMSO, pepsin only, or pepsin and Na-taurocholate, and incubated them with the standard compounds as mentioned above. In order to compare the effects of the standard drugs as assessed by motility assay also to protoscolex viability, trypan blue staining of protoscoleces was performed. The protoscoleces were extracted and activated as described above. After overnight recovery in DMEM including 10% FCS, the protoscoleces were distributed into 96-well plates of approximately 100 protoscoleces in DMEM without phenol red containing 10% FCS. Subsequently, standard drugs (PZQ, niclosamide, nitazoxanide, albendazole, monepantel and DMSO) and the compound MMV665807 were added to final concentrations of 100 to 0. 4 ppm in a 1: 3 dilution series with a final DMSO concentration of 1% and in triplicates. The protoscoleces were then incubated with drugs for 18 h at 37°C and 5% CO2. For viability staining, trypan blue was added to a final concentration of 0. 5% trypan blue during 5 minutes at room temperature. Thereafter, the protoscoleces were washed once in PBS and pictures were taken of each well at 40 times magnification. At last, the trypan blue-stained protoscoleces and the non-stained protoscoleces were counted manually for each picture and the resulting average percentage of viabilities and standard deviations were calculated in Microsoft Excel 2010. This experiment was repeated three times. In order to compare the relative motility of protoscoleces treated with the above mentioned standard drugs to the relative viability of protoscoleces as assessed by trypan blue staining, linear exponential regression was applied in R (version 3. 3. 2, function lm) for each drug. To compare the morphological effects induced by different activation protocols, or by the drugs PZQ and MMV665807 in more detail, SEM was performed as described by Hemphill and Croft [39]. To compare the morphology of differently activated protoscoleces (DMEM only, 10% DMSO, pepsin, or pepsin and Na-taurocholate), protocols were applied as described above, and protoscoleces were allowed to recover overnight in DMEM including 10% FCS at 37°C, 5% CO2. Thereafter, 500 protoscoleces per condition were washed in 0. 1 M cacodylate buffer (pH 7. 3) and fixed for 2 hours at room temperature in 2 mL glutaraldehyde (2% in 0. 1 M cacodylate buffer), before being further treated as described below. To analyze the drug-induced effects on the morphology of protoscoleces, 500 DMSO-activated protoscoleces per well were incubated in a 96 round well plate with 1% DMSO, PZQ (100,10,1 and 0. 1 ppm, in 1% DMSO) or MMV665807 (100,10,1 and 0. 1 ppm, in 1% DMSO) in DMEM without phenol red and 10% FCS for 18 hours at 37°C and 5% CO2. Thereafter the protoscoleces were washed in 0. 1 M cacodylate buffer (pH 7. 3) and fixed for 2 hours at room temperature in 2 mL glutaraldehyde (2% in 0. 1 M cacodylate buffer). Following three washes in 1 mL of cacodylate buffer (0. 1 M, pH 7. 3), samples were postfixed in 1 mL osmium tetroxide (2% in 0. 1 M cacodylate buffer) during 2 hours, washed twice in water, and specimens were dehydrated in increasing concentrations of ethanol (30,50,70,90, and 3x 100%), and subsequently 1 mL hexamethyldisilazane (HMDS) was added and incubated for 2 minutes. After removal of the HMDS, protoscoleces were resuspended in another 50 μL of HMDS, and were spotted onto a glass coverslip. After evaporation at room temperature, the fixed protoscoleces samples were sputter coated with gold and inspected in a Hitachi scanning electron microscope S-3000 N operating at 25 kV. All figures were prepared in R (version 3. 3. 2) and formatting as well as compiling was done in Adobe Illustrator (2015. 1. 0). The compound structures in Fig 1 were depicted by MolView V2. 4. Different concentrations of DMSO were applied in addition to the standard method (using pepsin, or pepsin and Na-taurocholate), in order to improve the protocol for evagination and activation of motility of protoscoleces. The isolation procedure itself already led to evagination of 69. 1 ± 6. 1% of protoscoleces (Fig 2A). Incubation in 20% DMSO for 3 hours yielded the highest (92. 3 ± 1. 8%) evagination efficiency, while the presence of 1 to 5% DMSO seemed to inhibit rather than promote evagination (Fig 2A). Motility was most pronounced upon incubation in 10% DMSO (2. 1 ± 0. 1 x 104 changed pixels), and superior to the standard pepsin (0. 7 ± 0. 2 x 104 changed pixels) or pepsin/Na-taurocholate (1. 3 ± 0. 1 x 104 changed pixels) methods Fig 2B. Upon activation in 20% DMSO, motility was drastically reduced (0. 8 ± 0. 3 x 104 changed pixels, Fig 2B). In Fig 2C, SEM micrographs of differently activated protoscoleces are depicted, and they show that the morphology was not affected by the here presented activation methods. A good discrimination of moving and non-moving parasites is crucial for the motility-based drug-screening applied here. Therefore, we concluded to induce evagination and activation by incubation in 10% DMSO for 3 h in all subsequent experiments. As all tested drug formulations were dissolved in DMSO, we investigated whether the presence of DMSO would affect the motility of protoscoleces. As shown in Fig 3A, protocoleces incubated in the presence of up to 3% DMSO did not exhibit significantly reduced motility, while protoscoleces incubated in 10% DMSO or higher for 12 h showed largely impaired movement. For practicability of subsequent assays, a final concentration of 1% DMSO was defined for all subsequent drug-testing experiments. Fig 3B shows the correlation between protoscolex number and movement (r = 0. 86, R2 = 0. 75). For further assays, a representative number of 20–30 protoscoleces per well was chosen, as with this number wells are not overfilled with parasites and individual parasites can be discriminated. In order to determine the optimal temperature for the assay, comparative runs with protoscoleces incubated for 1 hour in 1% DMSO at different temperatures (25,30,37, and 41°C) were performed. As shown in Fig 3C, incubation at a physiological temperature of 37°C yielded the highest motility. For a first validation of the test, the standard drug in use (PZQ) as well as its R- and S-enantiomers were tested in the protoscolex motility assay. As shown in Fig 4A and S1 Fig, neither the racemic mixture of PZQ, nor its enantiomers (R) - (-) -PZQ or (S) - (-) -PZQ led to complete inhibition of motility at concentrations up to 100 ppm and up to 24 h of incubation time. After 1 h, drug-induced effects did not yet reach their maximum, but as shown in S1 Fig, after 12 h the maximum effects were largely reached. Therefore, in all subsequent experiments, 12 h drug incubations were applied. Based on the drug-response curves of 12 h of drug-incubation, the MICs were determined: (R) - (-) -PZQ and the racemic mixture of PZQ had a MIC of 0. 02 ppm (0. 06 μM), whereas (S) - (-) -PZQ had a MIC of 3. 7 ppm (11. 8 μM) (Fig 1). Corresponding micrographs showing morphological changes induced by each drug at 0. 02 ppm after 12 h of drug incubation are depicted in Fig 4B. Movies for comparison of effects of PZQ as compared to DMSO are shown in S1 Movie. Additional reference compounds with known activity against adult cestodes or protoscoleces (niclosamide and nitazoxanide) and without activity against adult cestodes or protoscoleces (albendazole and monepantel) were selected, in order to further validate the protoscolex motility assay. Also for the use of these standard compounds, maximal drug-effects were reached after 12 h of incubation on (see S2 Fig). Therefore, results corresponding to the 12 h time points are provided in Fig 5. One exception is nitazoxanide that, interestingly, lost its activity slightly over time. Niclosamide and nitazoxanide reduced the motility of protoscoleces in a dose- and time-dependent manner (Fig 5A–5E, S2 Fig). Morphological effects induced at a drug concentration of 33. 3 ppm are shown in Fig 5A–5E. Corresponding MICs are given in Fig 1. Both albendazole and monepantel did not inhibit the motility of parasites at any of the concentrations tested, except some slight reduction in motility at 100 ppm. We also compared the above described standard drugs against protoscoleces that were activated by 10% DMSO, pepsin, or pepsin and Na-taurocholate. As shown in S3 Fig, protoscoleces followed the same drug-responses independent of which activation method was used. However, differences between active and inactive drugs were highest when protoscoleces were activated with DMSO, as visible in the absolute movement data (S3 Fig). In addition, a compound previously described to exhibit parasiticidal activity against the metacestode stage of E. multilocularis, MMV665807 [31], was assessed for its motility-reducing activity on protoscoleces. As shown in Fig 5F (and S2 Fig), the drug induced a strong inhibition of motility with a MIC of 3. 7 ppm (11. 7 μM) after 12 h of drug incubation. Corresponding morphological effects (at 33. 3 ppm) are visualized in Fig 5F and a representative movie is provided in S1 Movie. For comparative reasons, all drugs described above were also assessed by trypan blue staining, which is the standard method of protoscolex viability testing (Fig 6A). The viability of protoscoleces was dose-dependently lowered upon incubation with niclosamide, MMV665807 and nitazoxanide as shown in Fig 6A. The drugs albendazole and monepantel had only slight effects on the viability of protoscoleces. The drug currently used for therapy, PZQ, as well as its enantiomers, showed only minor effects on the viability of protoscoleces at all tested concentrations. The correlation between motility and viability followed an exponential correlation curve for all active drugs except PZQ (Fig 6B and S1 Table for functions and R2 values). Interestingly, the motility of protoscoleces had already been completely inhibited, when viability was still at 50%, which highlights that a reduced motility does not necessarily indicate a reduction or loss of viability. PZQ and its enantiomers did not follow an exponential correlation curve, as they never killed the protoscoleces (Fig 6B). The standard drug PZQ and the newly identified MMV665807 were also compared regarding their effects on the morphology of protoscoleces. Control protoscoleces that were incubated in 1% DMSO showed the characteristic structures of body and head with rostellum and 4 muscular suckers. The protoscolex surface was comprised of microtriches (Fig 7A). All PZQ-treated protoscoleces showed the same effects, regardless of the tested concentration (100 to 0. 1 ppm): they were contracted, especially in the neck region, and started forming blebs all over the body. The microtriches, suckers and the rostellum were not visually affected by PZQ (Fig 7B). Treatment with a concentration of 100 and 10 ppm of MMV665807 (Fig 7C) also led to contraction of the protoscolex body, but to a lower extent compared to PZQ. In addition, the microtriches and the tissue mesh covering the rostellum were both lost and therefore the hooks were not present anymore. The suckers were not visibly affected by MMV665807. At 1 ppm, MMV665807 showed still some of these effects, but at 0. 1 ppm, all protoscoleces were visually indistinguishable from the control sample. These morphological findings further confirmed the loss of activity of MMV665807 at concentrations lower than 3. 7 ppm (Fig 5). The present paper describes for the first time an in vitro-based screening test that will allow researchers to evaluate compounds for activity against adult cestodes. In contrast to most other species, E. multilocularis multiplies asexually in its intermediate hosts. Therefore, the generation of parasite material for this test is easy, as metacestode-containing protoscoleces are usually available in excess in laboratories maintaining E. multilocularis in gerbils. The purification of protoscoleces from metacestodes is simple and straight forward and allows high numbers of protoscoleces to be purified from a batch of metacestode material originating from one euthanized animal. In this assay, motility is used as a phenotypic readout of worm activity, which is often, but not always, coincidental with viability. A reduction of motility to zero could theoretically be seen in a worm that is still alive. However, a viable worm that cannot move, or is severely limited in movement, will be expelled from the gastrointestinal tract [40–42]. Therefore, we consider motility as a suitable readout for the screening of new drugs against adult cestodes. We induced evagination and motility by incubation in DMSO, and did not follow the standard pepsin or pepsin/bile acid methods. DMSO-activation resulted in higher resolution of different motility rates, did not alter the drug-response, did not induce any visible damage to the parasite, and, from the technical point of view, DMSO activation is easy and highly reproducible, since no pH adjustments are necessary. In addition, we also compared the quantified readouts of the motility assay to the subjective and laborious trypan blue viability staining, and we found an exponential correlation between the methods. The lag phase of this correlation curve highlights that inhibition of motility does not necessarily lead to loss of viability. Described targets of PZQ are voltage-gated calcium-channels and adenosine receptors, both leading to an influx of calcium ions that induces paralysis in the worm [7]. Thus, reduction of protoscolex movement by PZQ-treatment was expected. Our tests revealed that drugs with known anthelmintic activity (PZQ, niclosamide and nitazoxanide) were active and reduced motility of protoscoleces, and those known to be ineffective against adult Echinococcus (albendazole and monepantel) were not. For PZQ we determined a MIC of 0. 02 ppm, which is in line with previous observations on Echinococcus protoscoleces [43,44] as well as a variety on various adult cestodes [45,46]. Niclosamide was described to be active against Hymenolepis nana adults at 0. 1 ppm within 30 minutes [46], and we observed a slightly higher MIC of 3. 7 ppm against E. multilocularis protoscoleces. Nitazoxanide showed a MIC of 33. 3 ppm after 12 h against E. multilocularis protoscoleces, which is comparable to results found earlier against E. granulosus protoscoleces (5 ppm after 3 days [47]), as well as the general activity of nitazoxanide against a variety of nematodes with MICs ranging between 10 to 100 ppm [48,49]. Treatment with PZQ led only to a partial paralysis of E. multilocularis protoscoleces. This observation has, to the best of our knowledge, been unknown so far. Viability assessment by trypan blue further confirmed this finding, as PZQ reduced the viability only slightly. Interestingly, after one hour of drug-incubation, PZQ rather increased parasite motility, which could be a first sign of stress response towards the drug. Further, the motility assay allowed also to discriminate between the activity of PZQ enantiomers, and (R) - (-) -PZQ was confirmed as the more active enantiomer, as previously reported for Schistosoma [50]. However, it cannot be concluded whether the (S) - (-) -PZQ has some slight intrinsic activity, or whether this activity simply resulted from impurities in the enantiomer preparation (purity stated by the manufacturer ≥ 95%). In addition, the electron microscopical assessments revealed clear morphological alterations in protoscoleces treated with PZQ. These changes were largely in line with previously described effects of the drug when applied on adult Echinococcus worms, as also in protoscoleces immediate contraction, neck shortening and bleb formation were observed [7,51]. In contrast to descriptions for adult worms, hooks were only partially lost and the suckers did not expand into a convex shape in the present study [51]. One interesting new compound identified by this novel assay: MMV665807, a derivative of niclosamide that is provided within the MMV malaria box [52]. MMV665807 showed a MIC in the same range as niclosamide, and it reduced the viability of protoscoleces to the extent that is comparable to niclosamide. Future in vivo confirmatory studies, such as in the Hymenolepis mouse model, will show, whether the compound exhibits also good in vivo anti-cestode activity. Electron microscopical assessment of protoscoleces treated with MMV665807 showed a clear difference to the changes observed by PZQ, which implies that another mode of action is involved. The mode of action of MMV665807 against E. multilocularis is currently under investigation. Motility assessment by comparison of change in pixels of two single pictures over a 10 seconds interval was chosen, as such a simplified setup reduces the computational power and analytical complexity needed for data analysis. Previous publications on other motility-based tests of helminths used to employ video-based analyses (e. g. [22]), but due to their analytical complexity they are not easily transferable to higher throughput assays [13]. An approach that could be of interest for increased throughput is the cheaper model described by Marcellino et al [20], where a whole test plate is measured at once. However, in this assay only 24-well plates were applied, as the filarial worms tested with this approach were too large to allow for smaller culture devices. Another approach, based on a real-time monitoring device from Roche, was used for Haemonchus contortus, Strongyloides ratti, hookworms and blood flukes and it allows entire plate assessments as well [17]. However, this device is expensive and so far also restricted to the 96-well format. The more simple Wormscan is relatively cheap, but restricted to the 12-well format [21]. Microfluidic-based platforms for screening of anthelminthics and resistance allow the in-depth and real-time study of the response of worms to drugs, and are therefore of high interest for the study of selected compounds [53]. However, none of the mentioned assays was shown to be applicable for the screening of adult cestodes. Another study by Camicia et al. (2013) showed that E. granulosus protoscolex motility can be monitored in the worm tracker method developed for C. elegans by Simonetta et al. [54], and they also showed that the activity of the neurotransmitter inhibitor citalopram could be detected by this system [55]. Any further application of this system on Echinococcus protoscoleces has, to the best of our knowledge, not been published and the test has also not been applied as a cestode-screening system. An alternative approach that could be used for larger-scale screening of drugs against adult cestodes, and anthelmintics in general, would be stem cell-based. For E. multilocularis, such an assay would theoretically be feasible, as stem cell cultivation [28] and drug testing on stem cells [31] has already been implemented. However, compared to the simplicity of protoscolex generation and purification, as well as the presence of intact parasite structures in the protoscolex-based approach, we consider the whole-organism protoscolex screening to be more suitable. In conclusion, the screening assay described in this paper is based on protoscoleces of E. multilocularis. Protoscoleces are sufficiently small to allow testing in 384-well format with multiple parasites per well and the setup is inexpensive. Furthermore, it can basically be carried out with any microscope that allows digital images to be taken, thus no highly specialized equipment is needed. The motility-based assay will allow objective and medium-throughput screening of substances against cestodes, and at the same time enables researchers to visualize morphological effects. As such, the motility-based assay will be further applied for the testing of drug libraries, novel compound classes, and for the in-depth characterization of MMV665807, a new compound of interest with respect to activity against adult cestodes.

Tapeworms (cestodes) are a medically important group of helminths that infect humans and animals all around the globe. The clinical signs caused by intestinal infection with adult cestodes are mostly mild, in contrast to the more severe disease symptoms inflicted by infection with the tissue-dwelling larval stages of the same species. Praziquantel is the main drug in use against intestinal cestode infections. Development of resistance and treatment failures have been reported in trematodes, and are expected to become a problem in the future also in the case of cestode infections. Therefore, new treatment options against intestinal helminths are needed. To date, there is no in vitro-based whole-organism screening assay available that allows screening of candidate drugs with potential activity against adult cestodes. We established and characterized of a screening assay in 384-well format, which serves as a model for adult stage parasites by using Echinococcus multilocularis protoscoleces and their loss of motility as a read-out. This novel assay showed that drugs with known activity against adult cestodes inhibited motility of protoscoleces. The movement-based assay identified MMV665807 as a novel compound with profound activity against protoscoleces, and potentially also adult cestodes. Light- and electron microscopical assessments of protoscoleces treated with praziquantel and MMV665807 point towards different modes of action of the two drugs.

Abstract Introduction Materials and methods Results Discussion

invertebrates medicine and health sciences stereoisomers chemical compounds cestodes helminths enzymes enzymology parasitic diseases animals drug screening isomerism nematode infections pharmaceutics microscopy pharmacology pepsins research and analysis methods isomers proteins enantiomers flatworms scanning electron microscopy chemistry biochemistry helminth infections hydrolases stereochemistry electron microscopy biology and life sciences physical sciences drug therapy organisms

2017

Development of a movement-based in vitro screening assay for the identification of new anti-cestodal compounds

The yeast Fps1 protein is an aquaglyceroporin that functions as the major facilitator of glycerol transport in response to changes in extracellular osmolarity. Although the High Osmolarity Glycerol pathway is thought to have a function in at least basal control of Fps1 activity, its mode of regulation is not understood. We describe the identification of a pair of positive regulators of the Fps1 glycerol channel, Rgc1 (Ypr115w) and Rgc2 (Ask10). An rgc1/2Δ mutant experiences cell wall stress that results from osmotic pressure associated with hyper-accumulation of glycerol. Accumulation of glycerol in the rgc1/2Δ mutant results from a defect in Fps1 activity as evidenced by suppression of the defect through Fps1 overexpression, failure to release glycerol upon hypo-osmotic shock, and resistance to arsenite, a toxic metalloid that enters the cell through Fps1. Regulation of Fps1 by Rgc1/2 appears to be indirect; however, evidence is presented supporting the view that Rgc1/2 regulate Fps1 channel activity, rather than its expression, folding, or localization. Rgc2 was phosphorylated in response to stresses that lead to regulation of Fps1. This stress-induced phosphorylation was partially dependent on the Hog1 MAPK. Hog1 was also required for basal phosphorylation of Rgc2, suggesting a mechanism by which Hog1 may regulate Fps1 indirectly. Under conditions of high osmolarity stress, many fungal species, including Saccharomyces cerevisiae, maintain osmotic equilibrium by producing and retaining high concentrations of glycerol as a compatible solute [1], [2]. Intracellular glycerol concentration is regulated in S. cerevisiae in part by the plasma membrane aquaglyceroporin, Fps1 [3]–[5]. Increased external osmolarity induces Fps1 closure, whereas decreased osmolarity causes channel opening, both within seconds of the change in external osmolarity [5]. This channel is required for survival of a hypo-osmotic shock when yeast cells have to rapidly export glycerol to prevent bursting [3], [5], and is required for controlling turgor pressure during fusion of mating yeast cells [6]. The pathway responsible for regulation of Fps1 in response to changes in osmolarity has not been delineated, but appears to involve the Hog1 (High Osmolarity Glycerol response) MAP kinase [5], [7], [8]. Hog1 is activated in response to hyper-osmotic stress to mediate the biosynthesis of glycerol and perhaps its retention as well through inhibition of Fps1 channel activity. Although a hog1Δ mutant displays an elevated rate of glycerol uptake in the absence of osmotic stress, it is not impaired for Fps1 closure in response to hyper-osmotic stress [5], suggesting that Hog1 regulates the basal activity of Fps1. Fps1 is regulated not only in response to changes in external osmolarity, but also by exposure to acetic acid [9], and in response to trivalent metalloids (e. g. arsenite and antimonite) [10], [11]. Both acetic acid and metalloids enter the cell through Fps1 and induce Hog1 activation. Fps1 is down-regulated by acetic acid treatment through ubiquitin-mediated endocytosis, which is triggered by its phosphorylation by Hog1 on Thr231 and Ser537 [9]. By contrast, metalloids down-regulate both the expression of FPS1 and its channel activity [10]. We describe the identification of a pair of paralogous S. cerevisiae proteins, Ask10 and Ypr115w that are positive regulators of glycerol efflux through Fps1. The ASK10 and YPR115w genes encode members of a family of pleckstrin homology (PH) domain proteins in yeast that includes Slm1 and Slm2 [12]. The Ask10 protein shares 41% sequence identity with its paralog Ypr115w. Although PH domains are known to bind phosphatidylinositides [13], the PH domains of Ask10 and Ypr115w are interrupted by long insertions, prompting the suggestion that they bind different ligands [12], or even serve as protein-binding domains [14]. The ASK10 gene was suggested previously to play a role in cell wall biogenesis through its identification in a genetic screen for activators of the Skn7 transcriptional regulator (Activator of SKN7) [15], which has been reported to influence cell wall assembly and cell wall stress signaling [16]–[19]. Additionally, Ask10 has been reported to be a component of the Srb/Mediator complex of RNA polymerase II [20], which is required for repression of several stress responsive genes [21], [22]. In this context, Ask10 was implicated in oxidative stress-induced destruction of the Srb11 C-type cyclin [20]. There are no reports on the function of YPR115w, or on the consequences of mutations in both Ask10 and Ypr115w. In this study, we describe the behavior of an ask10Δ ypr115wΔ mutant, finding that it displays a cell lysis defect that results from hyper-accumulation of glycerol. We find further that a defect in the function of the Fps1 glycerol channel is responsible for the ask10Δ ypr115wΔ phenotype. For this reason, we have given the name RGC1 (for Regulator of the Glycerol Channel) to YPR115w and suggest RGC2 as an alternate name for ASK10. Because the fungal kingdom is replete with members of this family of proteins, but they are not represented in animal cells, Rgc1/2 orthologs represent potentially attractive antifungal drug targets. We constructed a double deletion mutant of RGC1 and RGC2 to test its susceptibility to cell wall stress. The double rgc1/2Δ mutant, but not the single mutants, displayed a temperature-sensitive growth defect (37°C; Figure 1A) accompanied by cell lysis, as judged by the presence of non-refractile “ghosts. ” This result is in contrast to that reported by Cohen et al. [20], who found that deletion of ASK10 (RGC2) alone conferred a temperature-sensitive phenotype in the same strain background. The growth defect of the rgc1/2Δ mutant was suppressed by inclusion of sorbitol in the medium for osmotic support (Figure 1A), indicating that cell lysis is the cause of the terminal mutant phenotype. To determine if the PH domain of Rgc2 was important for its function, we tested two C-terminal truncation mutants of RGC2 for their ability to complement the rgc1/2Δ mutant cell lysis defect. The rgc2 (1–720) allele, which is missing the C-terminal 426 residues, but retains the PH domain, complemented the double mutant when over-expressed (Figure 1B). By contrast, the rgc2 (1–420) allele, which lacks the PH domain, failed to complement the double mutant. Neither allele complemented the mutant when expressed at low level from the chromosome (data not shown). This reveals that the C-terminal 426 residues are not critical to the function of Rgc2, and suggests that the PH domain contributes to its function. Mutants that display osmotic-remedial cell lysis are typically compromised for cell wall biogenesis. To test this, we measured the rate of cell lysis of the rgc1/2Δ mutant by digestion of the cell wall with zymolyase, a wall degrading enzyme. Surprisingly, this mutant did not lyse more rapidly than the wild-type strain, but displayed slower than normal lysis kinetics (Figure 2A), suggesting that it produces a fortified cell wall. The single rgc1Δ and rgc2Δ mutants were slightly more resistant to zymolyase than was wild-type. A mutant that produces a fortified cell wall, but is nevertheless susceptible to cell lysis upon imposition of a cell wall stress may be interpreted to be under constitutive cell wall stress. We tested this by measuring the transcriptional output of the cell wall integrity (CWI) pathway. The rgc1/2Δ mutant was strongly activated for transcription of a PRM5-lacZ reporter (Figure 2B), which is under the control of the Mpk1 MAP kinase and the Rlm1 transcription factor [23]. This mutant also displayed constitutively active Mpk1, as judged by the phosphorylation state of this MAP kinase (Figure 2C). These results confirm that the rgc1/2Δ mutant experiences severe cell wall stress, to which it responds by fortifying the cell wall, and also explains its lysis defect in response to additional cell wall stress at high temperature. In further support of this conclusion, we found that the rgc1/2Δ mutant is sensitized to growth inhibition by caspofungin (Figure 2D), an antifungal drug that interferes with cell wall biosynthesis by inhibiting β-glucan synthase activity [24]. Caspofungin treatment prevents the fortification of the cell wall that is essential to the survival of this mutant. To understand the cause of the cell wall stress in the rgc1/2Δ mutant, we conducted a dosage suppressor screen for high-copy number plasmids that conferred growth at 37°C. A single class of strong suppressor was identified as the FPS1 gene (Figure 3A). FPS1 encodes an aquaglyceroporin that is the major facilitator of glycerol uptake and efflux in yeast [3], [5]. This plasma membrane channel protein also mediates uptake of toxic metalloids, such as arsenite and antimonite [10], [11]. One interpretation of the suppression result is that the rgc1/2Δ mutant experiences abnormally high turgor pressure from accumulation of glycerol, which yeast cells use as a compatible solute for osmoregulation. Measurement of intracellular glycerol concentration confirmed that the rgc1/2Δ mutant has a 5. 9-fold higher glycerol level than wild-type cells under normal growth conditions, a value that is approximately half that of an fps1Δ mutant and approximately equal to that of wild-type cells exposed to hyper-osmotic shock (Figure 3B). To determine if excess intracellular glycerol is responsible for the phenotypic defects of this mutant, we blocked glycerol biosynthesis at the first committed and rate limiting step, glycerol-3-phosphate dehydrogenase (GPD) [25]–[27]. GPD is encoded by the paralogous genes GPD1 and GPD2. Deletion of either GPD1 or GPD2 alone did not suppress the lysis defect of the rgc1/2Δ mutant, but blocking glycerol biosynthesis completely by deletion of both GPD1 and GPD2 allowed growth at 37°C (Figure 3C), confirming that glycerol accumulation is responsible for the cell lysis defect. This also provides an explanation for the fortified cell wall of the rgc1/2Δ mutant as a response to the stress of abnormally high turgor pressure. Consistent with this interpretation, the gpd1/2Δ mutations relieved the cell wall stress signaling observed in the rgc1/2Δ mutant (Figure 2B and 2C). Finally, the gpd1/2Δ mutations relieved the caspofungin sensitivity of the rgc1/2Δ mutant (Figure 2D). We considered two possible explanations for the hyper-accumulation of glycerol in the rgc1/2Δ mutant – the mutant either produces excess glycerol, or it is impaired for glycerol efflux through Fps1. These hypotheses generate different predictions for the impact of the rgc1/2Δ mutations on the phenotype of an fps1Δ mutant. If the rgc1/2Δ mutant produces excess glycerol, this should exacerbate the lysis defect of an fps1Δ mutant, which is blocked for glycerol efflux. By contrast, if the rgc1/2Δ mutant is blocked for glycerol efflux through Fps1, loss of the glycerol channel should not result in an additive defect. Figure 3D shows that an fps1Δ mutant displays a temperature-sensitive growth defect that is slightly more severe than that of the rgc1/2Δ mutant, with a semi-permissive growth temperature of 34. 5°C. The fps1Δ mutant growth defect at elevated temperature is also the result of cell lysis (data not shown). Significantly, the rgc1/2Δ fps1Δ triple mutant behaves identically to the fps1Δ mutant (Figure 3D), supporting the hypothesis that the rgc1/2Δ mutant is impaired for glycerol efflux through Fps1. To test this directly, we measured export of glycerol from cells exposed to a hypo-osmotic shock, a condition that would induce glycerol efflux through Fps1. We found that glycerol was released from wild-type cells, but not from the rgc1/2Δ mutant or the fps1Δ mutant (Figure 3E), supporting the conclusion that Rgc1 and Rgc2 regulate glycerol efflux through Fps1. The varied initial content of 14C-labeled glycerol among these mutants is a consequence of differential glycerol loading, reflecting the importance of Fps1 for glycerol influx as well as efflux. Finally, strong overexpression of RGC2 failed to suppress the temperature-sensitivity of the fps1Δ mutant (data not shown), thus establishing an epistatic relationship that places RGC1 and RGC2 above FPS1 in a common pathway for glycerol efflux. To explore the mechanism by which Rgc1/2 regulate Fps1, we first examined the Fps1 protein level in an rgc1/2Δ mutant. Despite the observed defect in glycerol efflux of the rgc1/2Δ mutant, this mutant maintains strongly elevated Fps1 protein levels as compared to wild-type (increased approximately 10-fold; Figure 4A), suggesting that it attempts to compensate for impaired Fps1 function by increasing the number of channel proteins. The increase in Fps1 protein is a consequence of elevated glycerol concentration resulting from the rgc1/2Δ mutation, because the Fps1 level was reduced in an rgc1/2Δ gpd1/2Δ mutant (Figure 4A), which is blocked for glycerol production. The increased steady-state level of Fps1 in the rgc1/2Δ mutant is not the result of transcriptional induction, because FPS1 was expressed under the control of a heterologous promoter (MET25). This conclusion was supported by the finding that expression from an FPS1-lacZ reporter was not altered in the rgc1/2Δ mutant (Figure 4B). An even greater increase in Fps1 protein level in the rgc1/2Δ compared to wild-type (approximately 20-fold) was observed when FPS1 was expressed from its native promoter on a multi-copy plasmid (Figure 4C). Evidently, ectopic overexpression of FPS1 suppresses the rgc1/2Δ lysis defect by assisting the cell in its efforts to enhance glycerol efflux through an impaired channel. Under these conditions the cells retain more than 20-fold higher levels of Fps1 protein than wild-type cells (the comparison in Figure 4C was to wild-type cells also expressing FPS1 from a multi-copy plasmid). Therefore, we conclude that Fps1 channels in the rgc1/2Δ mutant retain less than 5% of normal activity. To determine the cause of the increased steady-state level of Fps1 in the rgc1/2Δ mutant, we conducted a test of Fps1 stability. Fps1 levels were followed in cells in which FPS1 transcription was shut down with the simultaneous inhibition of protein synthesis. We found that Fps1 was stabilized in the rgc1/2Δ mutant relative to wild-type cells (Figure S1). Therefore, we conclude that increased intracellular glycerol in the rgc1/2Δ mutant, which is caused by a deficiency in Fps1 function, induces an increase in the level of weakly functional Fps1 through protein stabilization. We conclude further that, because the rgc1/2Δ mutant does not display diminished Fps1 levels, Rgc1/2 positively regulate Fps1 function by a mechanism other than increased protein level. Fps1 migrates as a doublet as a consequence of phosphorylation [11], although the responsible protein kinase has not been identified. It is interesting to note that the more slowly migrating band (the phosphorylated form) predominates in the rgc1/2Δ mutant (Figure 4A). Both Rgc1 and Rgc2 have been reported to reside in the cytoplasm [28]. If these proteins function as activators of the Fps1 glycerol channel, they might be expected to interact with Fps1 at the plasma membrane. We examined the intracellular localization of Rgc2-GFP2 in response to hypo-osmotic shock, conditions in which the Fps1 channel must be opened to allow glycerol efflux. Figure 5A shows that under unstressed conditions, Rgc2-GFP2 displays diffuse cytoplasmic localization, but very rapidly aggregates into punctate spots that appear near the cell surface in response to hypo-osmotic shock. These spots dissipate over a period of approximately 45 seconds (Figure 5B). Fps1 has been reported to reside in punctate spots at the plasma membrane [5]. Therefore, we asked if Rgc2-GFP2 co-localizes with Fps1-tdTomato in response to hypo-osmotic shock. Figure 5C shows that these spots do not co-localize. Other efforts to detect physical interaction between Rgc2 and Fps1 (e. g. co-precipitation and two-hybrid analyses; data not shown) failed to provide such evidence. Additionally, the number, location, and intensity of Fps1 punctate spots do not appear to be altered in an rgc1/2Δ mutant (Figure S2). This last result is difficult to understand considering that the mutant retains much more Fps1. It is possible that the fluorescent protein is preferentially cleaved from the stabilized Fps1 and digested in the vacuole. Nevertheless, the Fps1 we can detect in the rgc1/2Δ mutant appears to reside in the same location as in wild-type cells. These results, taken in the aggregate, suggest that regulation of Fps1 by Rgc1/2 is at the level of channel activity, rather than channel expression or localization. Fps1 is a multi-pass plasma membrane protein with cytoplasmic N-terminal and C-terminal extensions that are inhibitory to channel function [5], [29], [30]. Truncation of these extensions results in constitutively open forms of the Fps1 channel. To explore the dependence of open channel character of Fps1 mutants on Rgc1/2 function, we tested their ability to allow xylitol uptake. A gpd1/2Δ mutant is very sensitive to high external osmolarity, because it cannot produce glycerol to re-establish osmotic balance. However, open channel fps1 mutants suppress this defect when hyper-osmotic shock is induced by 1M xylitol, which enters the cell only through unregulated Fps1 to restore osmotic balance [30]. Although a gpd1/2Δ mutant expressing wild-type FPS1 grew very poorly in the presence of xylitol, two Fps1 open channel mutants, one with an N-terminal truncation (fps1-Δ1, produces Fps1Δ12–231) [5], the other with a C-terminal truncation (fps1-C 1 produces Fps1Δ534–650) [30], conferred growth on xylitol to a gpd1/2Δ mutant even in the absence of RGC1/2 (Figure 6). This result indicates that the open channel mutants of Fps1 obviate the requirement for Rgc1/2 for Fps1 function, and support the conclusion that Fps1 is properly folded and localized independently of Rgc1/2 function. The toxic metalloids arsenite and antimonite enter yeast cells through the Fps1 channel [10], [11]. An fps1Δ mutant is therefore resistant to toxicity of these metalloids. As a further test of the role of Rgc1 and Rgc2 in the regulation of Fps1, we examined the sensitivity of mutants in these genes to arsenite. Wild-type cells were sensitive to growth inhibition by 5 mM arsenite, but both the rgc1Δ and rgc2Δ mutants were resistant to this treatment (Figure 7A). Moreover, the rgc1/2Δ double mutant was resistant to 10 mM arsenite, consistent with the additive nature of Rgc1 and Rgc2 function. These results further support the conclusion that Rgc1/2 function is required to open Fps1. Thorsen et al. [11] demonstrated that the Hog1 MAP kinase is activated in response to arsenite treatment and that Hog1 is required for control of basal Fps1 channel activity. A hog1Δ mutant was shown to display increased arsenite uptake and hyper-sensitivity to arsenite toxicity, both phenotypes being blocked by an fps1Δ mutation. Therefore, to place Hog1 within the Rgc1/2 – Fps1 pathway, we tested an rgc1/2Δ hog1Δ triple mutant for arsenite sensitivity. Like the rgc1/2Δ mutant, the rgc1/2Δ hog1Δ mutant was resistant to arsenite toxicity (Figure 7B). Suppression of the hog1Δ arsenite hyper-sensitivity defect by the rgc1/2Δ mutations indicated that Fps1 is closed in the triple mutant. These results suggest that Hog1 promotes Fps1 closure by inhibiting the action of Rgc1/2. The order of function of these pathway components was supported by the observation that the cell lysis defect of the rgc1/2Δ mutant was not suppressed by the hog1Δ mutation (data not shown). Because epistasis analysis revealed that Hog1 acts upstream of Rgc1 and Rgc2 to oppose their function, we asked if Rgc2 becomes phosphorylated in response to stresses that lead to the opening or closing of the Fps1 channel. Cells expressing C-terminally His-tagged Rgc2 were subjected to hypo-osmotic shock, hyper-osmotic shock (with sorbitol), or arsenite treatment. Rgc2 displayed mobility shifts on SDS-PAGE in response to all three of these stresses (Figure 8A), presumably reflecting post-translational modifications. The treatments that result in Fps1 closure (arsenite and hyper-osmotic shock) induced the greatest shifts, but hypo-osmotic shock, which induces Fps1 opening, also caused a detectable band-shift. In fact, multiple bands were detectable even in Rgc2 from unstressed cells. To determine if these mobility shifts were dependent upon Hog1, we examined Rgc2 mobility in a hog1Δ mutant. The absence of Hog1 did not prevent the stress-induced Rgc2 band-shifts, but in all cases reduced the extent of shift (Figure 8A). Rgc2 from unstressed cells also displayed increased mobility in a hog1Δ mutant (Figure 8B), suggesting that Rgc2 sustains a basal level of Hog1-dependent phosphorylation. This experiment also revealed the existence of additional modifications in response to these stresses that are Hog1-independent. To determine if these additional modifications were phosphorylations, we subjected Rgc2 isolated from stressed cells to protein phosphatase treatment. For all three stresses, phosphatase treatment collapsed the Rgc2 band to the same level as phosphatase treated, unstressed Rgc2 (Figure 8C). We conclude that although basal phosphorylation of Rgc2 is Hog1-dependent, other protein kinases are responsible for the hyper-phosphorylation observed in response to Fps1-regulating stresses. It has been demonstrated that in the absence of Hog1, hyper-osmotic stress activates the Fus3 and Kss1 MAP kinases through inappropriate cross-talk [31]. Therefore, to determine if the Rgc2 band-shift observed in response to high osmolarity in the absence of Hog1 was due to such cross-talk, we tested a hog1Δ ste11Δ double mutant, which is blocked for activation of Fus3 and Kss1. The mobility shift observed for Rgc2 in this mutant was indistinguishable from that of the hog1Δ mutant (Figure S3), indicating that these MAP kinases are not responsible for the hyper-osmotic stress-induced phosphorylation. We identified a pair of paralogous genes, RGC1 (Regulator of the Glycerol Channel; YPR115w) and RGC2 (ASK10), that function as positive regulators of Fps1. The studies described reveal that loss of function of both RGC1/2 results in cell wall stress that is caused by excess turgor pressure associated with elevated intracellular glycerol concentration. The increase in glycerol is the consequence of impaired Fps1 function. We found that the increased turgor pressure experienced by the rgc1/2Δ mutant provokes the cell to activate the CWI signaling pathway and to fortify the cell wall. Nevertheless, imposing additional cell wall stress on this mutant induced cell lysis, a defect that was suppressed by blocking glycerol synthesis. In this regard, it is interesting to note that blocking the function of the glycerol channel activators also sensitized cells to caspofungin, an antifungal agent that acts by inhibiting cell wall biosynthesis [24]. Evidently, preventing the cells from responding to their internally imposed cell wall stress is lethal. Therefore, Rgc1/2 might be suitable antifungal targets for combination therapy with caspofungin. The mechanism by which Rgc1/2 regulate Fps1 remains unclear. Although there is some evidence that Rgc2 (Ask10) can act as a transcriptional regulator (see below), we did not find that Rgc1/2 control Fps1 transcription. We were not able to detect direct interaction between Rgc2 and the Fps1 channel. However, the findings that Fps1 localizes to the plasma membrane in the presence or absence of Rgc1/2 and that constitutive mutants of Fps1 retain their open channel character independently of Rgc1/2 suggests that these proteins regulate Fps1 through its activity, rather than at an earlier step, such as protein folding, or proper localization. Rgc1/2 control of Fps1 folding or localization would be expected to impact the function of open channel mutants as well as the wild-type. Fps1 is unusual in its possession of extensions at both its cytoplasmic N-terminus and C-terminus that play a role in regulating Fps1 channel activity [29], [30]. These extensions have been suggested to function as flaps that restrict the flow of glycerol through the channel. However, the mechanism by which they respond to changes in extracellular osmolarity remains largely unknown. The HOG pathway is activated in response to hyper-osmotic stress [8]. Hog1, the stress-activated MAP kinase at the base of this pathway plays a poorly-defined role in the regulation of Fps1. A hog1Δ mutant exhibits a glycerol uptake rate that is approximately 3-fold-higher than that of wild-type cells [5], [11]. However, this mutant is not impaired for Fps1 closure in response to hyper-osmotic stress [5], suggesting that Hog1 regulates the basal activity of Fps1 (i. e. in the absence of osmostress), but not the osmotic stress-induced closure. Basal inhibition of Fps1 by Hog1 may result from phosphorylation at Thr231, which resides within the N-terminal extension, because Hog1 can phosphorylate this site in vitro [11], and mutation of Thr231 to Ala results in constitutive Fps1 activity [11], [29]. In addition to glycerol, the toxic metalloid arsenite enters the cell through the Fps1 glycerol channel [10]. Loss of Fps1 function confers resistance to arsenite, whereas loss of Hog1 function results in an increase in the rate of arsenite uptake through Fps1 and consequent hyper-sensitivity to the metalloid [11]. We found that null mutations in RGC1/2 also conferred resistance to arsenite, consistent with the conclusion that Rgc1 and Rgc2 are important for Fps1 channel activity. The rgc1/2Δ mutations suppressed the arsenite hyper-sensitivity of a hog1Δ mutation. In fact, loss of RGC1/2 function was completely epistatic to the hog1Δ mutation with regard to arsenite sensitivity, suggesting that Hog1 exerts its negative effect on Fps1 channel function by inhibiting Rgc1 and Rgc2. We found that Rgc2 undergoes phosphorylation-induced band-shifts in response to various Fps1-regulatory stresses (i. e. hypo- and hyper-osmotic shock, and arsenite stress). These phosphorylations were partially dependent on Hog1, as intermediate shifts were observed in a hog1Δ mutant. Rgc2 also appears to undergo basal phosphorylation that is Hog1-dependent. The PhosphoPep database (part of the Saccharomyces Genome Database) [32] identifies 5 phosphorylation sites on Rgc1 and 10 in Rgc2 from unstressed cells. However, only one of these sites in Rgc2 (Thr808), and none in Rgc1 reside at potential Hog1 phosphorylation motifs (S/TP), suggesting that the observed Hog1-basal phosphorylation of Rgc2 is largely, or entirely indirect. It is also possible that Hog1 inhibits basal Fps1 activity both directly, through phosphorylation of Thr231, and indirectly through phosphorylation of Rgc1/2. In any case, it is clear that other protein kinases contribute to the regulation of Rgc2 (and probably Rgc1), and consequently Fps1, in response to various stresses. These results establish a regulatory pathway from Hog1 to Rgc1/2 to Fps1, in which Rgc1 and Rgc2 are positive regulators of Fps1 channel activity and Hog1 inhibits Fps1 through inhibition of Rgc1/2. Although the interaction between these proteins and Hog1 may be direct, the phosphorylation sites on Rgc1 and Rgc2 remain to be identified. It is possible that Rgc1/2 are multifunctional proteins. Overexpression of Ask10 was reported to enhance growth of a strain in which histidine production was under the control of (lexAop) -HIS3 reporter driven by a LexA-Skn7 fusion [15]. However, ASK10 overexpression failed to drive a similarly regulated (lexAop) -lacZ reporter. This was in contrast to the behavior of MID2, another gene identified in this screen that activated both reporters [18], raising the possibility that Ask10 does not activate Skn7-mediated transcription. A second report, by Cohen et al. [20], suggested that Ask10 participates in the oxidative stress-induced destruction of Srb11, a C-type cyclin that is part of the Mediator complex of RNA polymerase II. These investigators identified Ask10 in a two-hybrid screen for Srb11-interacting proteins. They further demonstrated that, like Srb11 and its cyclin-dependent kinase (Srb10), Ask10 is a component of the RNA polymerase II holoenzyme. We do not know how the function of Rgc1/2 as regulators of Fps1 might relate to their reported roles in stress-activated transcription. Rgc1 and Rgc2 are large proteins (120kD and 127kDa, respectively), and our immunoblot analysis of Rgc2 suggests that its regulation in response to different stresses that regulate Fps1 is complex. The unstressed and stressed forms of Rgc2 all migrate as several distinct bands. We have shown that these bands represent a variety of phosphorylated states of Rgc2. Although identities of many of the phosphorylation sites are not known, numerous Rgc1 and Rgc2 phosphorylation sites have been identified in response to DNA damage stress. Albuquerque et al. [33] identified 17 phosphorylation sites in Rgc1 and 20 in Rgc2 in response to treatment with the DNA alkylating agent, MMS. Additionally, as noted above, numerous basal phosphorylation sites in Rgc1 and Rgc2 are reported the PhosphoPep database [32]. Only a few of these sites overlap with those found in MMS-treated cells. Finally, Cohen et al. [20] found that Rgc2 (Ask10) is phosphorylated in response to oxidative stress induced by hydrogen peroxide. These authors reported that the redundant MAPK kinases of the Cell Wall Integrity (CWI) signaling pathway (Mkk1 and Mkk2) were responsible for this modification. Oddly, however, none of the four MAP kinases in yeast were found to be involved [20]. We revisited this result, finding that none of the kinases within the CWI MAPK cascade (including Mkk1/2) were required for the oxidative stress-induced phosphorylation of Rgc2 (Figure S4). Rgc1/2 may function to integrate multiple stress signals, only some of which are known to control Fps1 channel activity. The regulation of Rgc1/2 by phosphorylation in response to different stresses appears to be complex. Moreover, these proteins may have additional functions that have yet to be identified. The S. cerevisiae strains used in this study are listed in Table 1. Yeast cultures were grown in YPD (1% Bacto yeast extract, 2% Bacto Peptone, 2% glucose) or SD (0. 67% Yeast nitrogen base, 2% glucose) supplemented with the appropriate nutrients to select for plasmids. Yeast strains bearing multiple deleted genes were constructed by genetic crosses, followed by PCR-based detection of the deleted alleles. Diploid strains were used for most experiments, because the cell lysis phenotypes were more pronounced in diploids than in haploids, and also because diploids have a reduced tendency to acquire suppressor mutations. Three different genomic clones of FPS1 were isolated from a high-copy genomic library in pRS202 (gift of P. Hieter) as suppressors of the temperature-sensitivity of a rgc1/2Δ mutant. The screen was conducted in the rgc1/2Δ mutant (DL3209) by plating transformations directly at 37°C. Plasmids were isolated from colonies arising after 3 days. A total of approximately 10,000 transformants were subjected to selection (as judged by low-temperature plating). This was calculated, based on an average insert size of 6 kb, to be approximately 5 genome-equivalents. Deletion analysis of one of these plasmids (p2165) confirmed that FPS1 was responsible for the suppression activity. Two reporter plasmids for different transcriptional outputs were used in this study. One reporter, PRM5 (−994 to +1) -lacZ (p1366) responds to the cell wall stress transcription factor, Rlm1 [23]. The other, FPS1 (−933 to −57) -CYC1-lacZ (p2213), was constructed by PCR amplification of the 5′ non-coding region of FPS1 using primers with Xho1 (upstream primer) and Sph1 (downstream primer) sites for cloning into the Xho1 and Sph1 sites of pLG178 (p904) [34]. This placed the regulatory sequences for FPS1 upstream of the basal CYC1 promoter linked to lacZ. The entire FPS1 gene was amplified by PCR from genomic yeast DNA (EG123 strain background) using a pair of primers 650 bp 5′ to the start codon and 500 bp 3′ to the stop codon. The primers were designed with a Not1 site (5′ primer) and a Sal1 site (3′ primer) for subcloning into pRS316 [35] to produce pRS316-FPS1 (p2833). Open channel mutant fps1-Δ1 in a multi-copy vector (YEp195-fps1-Δ1; p2496) was the gift of M. Mollapour. Open-channel mutant fps1-C1 (YEp181-fps1-C1-myc; p2829) was the gift of S. Hohmann. The FPS1 gene, fused with a C-terminal Flag epitope, was expressed under the control of the MET25 promoter. The FPS1 coding sequence amplified from genomic DNA (EG123) with an XbaI site immediately 5′ to the initiation codon and a HindIII site immediately 3′ to the final codon and inserted into pRS426-MET25P-FLAG (p2186) so as to fuse the C-terminus with the Flag coding sequence, yielding MET25P-FPS1-FLAG (p2492). The YEpmyc181-FPS1 plasmid (p2184) was the gift of S. Hohmann). The FPS1 gene was tagged at its C-terminus with tdTomato (red fluorescence) [36] and expressed under the control of its own promoter in two steps. First, the tdTomato coding sequence was subcloned from pRSET-B [tdTomato] (gift of R. Tsien) into pRS316 at the BamHI and EcoRI sites, yielding p2487. Next, the FPS1 gene was amplified (omitting the endogenous stop codon) from genomic DNA (EG123) and inserted into p2487 using NotI and SpeI sites designed into the primers. This fused the FPS1 reading frame with tdTomato, yielding pRS316-FPS1-tdTomato (p2489). The RGC2 gene was tagged at its C-terminus with 6xHis and expressed under the control of the MET25 promoter. The RGC2 coding sequence was amplified by PCR from genomic yeast DNA using primers that included XbaI and XhoI sites and cloned behind the MET25 promoter in pUT36 (p2415) [37] to yield pUT36-MET25P-RGC2-HIS6 (p2501). His-tagged C-terminal truncations of Rgc2 were also expressed under the control the MET25 promoter. The first 1260 base pairs (amino acids 1–420) or 2160 base pairs (amino acids 1–720) of RGC2 were amplified from genomic DNA (wild-type strain EG123) by PCR using a forward primer that contained an XbaI site immediately 5′ to the start codon and reverse primers that introduced a 6xHis tag followed by a stop codon and an XhoI site. The two regions were inserted into pUT36, resulting in pUT36-MET25P-rgc2 (1–420) -His6 (p2808) and pUT36-MET25P-rgc2 (1–720) -His6 (p2809). The RGC2 coding sequence was tagged at its C-terminus with two tandem copies of GFP and expressed under the control of the MET25 promoter in three steps. In the first step, the RGC2 promoter and coding sequence (omitting the endogenous stop codon) was amplified by PCR and inserted into the Not1 and Sma1 sites of pRS315[GPF] (p1164) [38] to yield pRS315-RGC2-GFP (p2478). In the second step, RGC2-GFP was amplified by PCR from p2478 and inserted in the same way into pRS315[GFP], to yield RGC2-GFP2 (p2479). In the final step, the RGC2-GFP2 coding sequence only was amplified by PCR and inserted into pRS414-MET25P (p976) using Spe1 and EcoRV sites designed into the primers. This yielded pRS414-MET25P-RGC2-GFP2 (p2481). The RGC1 gene with 800 bp of upstream sequence was amplified by PCR from genomic EG123 DNA and using a forward primer that introduced a NotI site and a reverse primer that introduced a SalI site and cloned into centromeric vector pRS313 [35], yielding pRS313-RGC1 (p2627). FPS1 and RGC1/2 constructs were validated by DNA sequence analysis and all were tested for functionality of these proteins by complementation of the cell lysis defects associated with an fps1Δ mutant or an rgc1/2Δ mutant, respectively. Zymolyase sensitivity was carried out as described previously [39]. Promoter-lacZ expression experiments for determination of cell wall stress were carried out as described previously [40], with methods for β-galactosidase assays described in Zhao et al. [41]. Intracellular glycerol concentrations were measured in whole cells grown in YPD and centrifuged briefly to remove the culture supernatant. Enzymatic assays for glycerol were carried out using a kit from Boehringer Mannheim and normalized to A600 of the initial culture. Efflux measurements of 14C-glycerol were carried out as described by Tamas et al. [5]. Briefly, cells from log-phase cultures (30 ml) grown in YPD were washed in ice-cold MES buffer (10 mM MES, pH 6. 0), resuspended in 1 ml ice-cold labeling buffer solution (10 mM MES buffer + 300 mM [14C]glycerol) and incubated for 1 hour at 30°C to load cells with labeled glycerol. Cells were then diluted 10-fold in ice-cold MES buffer to induce hypo-osmotic shock. Aliquots of cells were filtered onto Whatman GFB 25 mm discs at various time points, and washed with MES buffer. Radioactivity of dried filters was measured by a scintillation counter. For detection of total Mpk1 and activated Mpk1, protein samples (20 µg) were separated by SDS-PAGE (7. 5% gels) followed by immunoblot analysis. Total Mpk1 was detected with rabbit polyclonal antibodies from Santa Cruz Biotechnologies. Activated Mpk1 was detected with rabbit polyclonal α-phospho-p44/p42 MAPK (Thr202/Tyr204) antibodies (New England Biolabs). Both primary antibodies were used at a dilution of 1∶2000. Secondary donkey anti-rabbit antibodies (GE Healthcare) were used at a dilution of 1∶5000. For detection of Fps1-Flag, protein samples (4 µg) were separated by SDS-PAGE (7. 5% gels) followed by immunoblot analysis using mouse monoclonal α-FLAG antibody (M2; Sigma) at a dilution of 1∶10,000. For detection of Fps1-Myc, protein samples (25 µg) were separated by SDS-PAGE (7. 5% gels) followed by immunoblot analysis using mouse monoclonal α-Myc antibody (9E10; BabCo) at a dilution of 1∶10,000. For detection of Rgc2-His6, protein samples (16 µg) were separated by SDS-PAGE (7. 5% gels) followed by immunoblot analysis using mouse monoclonal α-tetra-HIS antibody (Qiagen) at a dilution of 1∶5000. Secondary antibodies (goat anti-mouse; Amersham) were used at a dilution of 1∶5000. For protein phosphatase treatment of Rgc2-His6, Nickel NTA agarose (Qiagen) was used to precipitate Rgc2-His6 from protein extracts (100 µg) prior to treatment with calf intestinal phosphatase (CIP; Promega) with, or without phosphatase inhibitor (10 mM Na3VO4) for 1 hour at 37°C. Precipitates were processed for immunoblot detection of Rgc2-His6. Diploid cells transformed with plasmids that express Rgc2-GFP2 with out without Fps1-tdTomato were grown in selective medium and visualized by fluorescence microscopy using a Zeiss Axioplan II with a 100x objective and fitted with a GFP and RFP filter. For hypo-osmotic shock experiments, log-phase cultures (1 ml) were centrifuged briefly to pellet cells, which were resuspended in 0. 5 ml distilled water for 20 seconds to impose hypo-osmotic shock, followed by the addition of 0. 5 ml 20 mM NaN3,20 mM NaF, 20 mM Tris buffer to block further membrane transport [42] and set on ice for 20 seconds. Samples were centrifuged briefly to concentrate cells and mounted for microscopy. The membrane transport inhibitors were omitted from the time-course experiment.

When challenged by changes in extracellular osmolarity, many fungal species regulate their intracellular glycerol concentration to modulate their internal osmotic pressure. Maintenance of osmotic homeostasis prevents either cellular collapse under hyper-osmotic stress or cell rupture under hypo-osmotic stress. In baker' s yeast, the Fps1 glycerol channel functions as the main vent for glycerol. Proper regulation of Fps1 is critical to the maintenance of osmotic homeostasis. In this study, we identify a pair of proteins (Rgc1 and Rgc2) that function as positive regulators of Fps1 activity. Their absence results in hyper-accumulation of glycerol and consequent cell lysis due to impaired Fps1 channel activity. Additionally, we found that these glycerol channel regulators function between the Hog1 (High Osmolarity Glycerol response) signaling kinase and Fps1, defining a signaling pathway for control of glycerol efflux. Because members of the Rgc1/2 family are found among pathogenic fungal species, but not in humans, they represent potentially attractive targets for antifungal drug development.

Abstract Introduction Results Discussion Materials and Methods

biochemistry/cell signaling and trafficking structures cell biology/cell signaling

2009

Identification of Positive Regulators of the Yeast Fps1 Glycerol Channel

Histone deacetylase (HDAC) 4 is a transcriptional repressor that contains a glutamine-rich domain. We hypothesised that it may be involved in the molecular pathogenesis of Huntington' s disease (HD), a protein-folding neurodegenerative disorder caused by an aggregation-prone polyglutamine expansion in the huntingtin protein. We found that HDAC4 associates with huntingtin in a polyglutamine-length-dependent manner and co-localises with cytoplasmic inclusions. We show that HDAC4 reduction delayed cytoplasmic aggregate formation, restored Bdnf transcript levels, and rescued neuronal and cortico-striatal synaptic function in HD mouse models. This was accompanied by an improvement in motor coordination, neurological phenotypes, and increased lifespan. Surprisingly, HDAC4 reduction had no effect on global transcriptional dysfunction and did not modulate nuclear huntingtin aggregation. Our results define a crucial role for the cytoplasmic aggregation process in the molecular pathology of HD. HDAC4 reduction presents a novel strategy for targeting huntingtin aggregation, which may be amenable to small-molecule therapeutics. Huntington' s disease (HD) is a progressive, inherited neurological disorder characterized by severe motor, cognitive, behavioural, and physiological dysfunction for which there is no effective disease-modifying treatment [1]. The disease is caused by the expansion of a CAG repeat to more than 35 CAGs within exon 1 of the HTT gene. At the molecular level, mutant huntingtin (HTT) containing an expanded polyQ stretch has a propensity to self-aggregate to produce a wide-range of oligomeric species and insoluble aggregates and exerts a gain of toxic function through aberrant protein–protein interactions [2]. Therefore, as with other neurodegenerative diseases such as Alzheimer' s disease, Parkinson' s disease, and the prion diseases, the polyglutamine (polyQ) disorders including HD are associated with the accumulation of misfolded proteins leading to neuronal dysfunction and cell death. Transcriptional dysregulation is part of the complex molecular pathogenesis of HD, to which abnormal histone acetylation and chromatin remodelling may contribute [3]. The imbalance in histone acetylation was proposed to be caused by the inactivation of histone acetyltransferases, which led to the pursuit of histone deacetylases (HDACs) as HD therepeutic targets [4], [5]. There are 11 mammalian Zn2+-dependent HDACs divided into three groups based on structural and functional similarities: class I (HDACs: 1,2, 3,8), class IIa (HDACs: 4,5, 7,9), class IIb (HDACs: 6,10), and HDAC11 as class IV [6]. Initial genetic and pharmacological studies performed in flies, worms, and HD mouse models have suggested that HDAC inhibitors may have a significant therapeutic potential [4], [5]. The preclinical evaluation of the HDAC inhibitor suberoylanilide hydroxamic acid (SAHA) demonstrated a dramatic improvement in the motor impairment that develops in the R6/2 HD mouse model [7]. Initially, SAHA was shown to inhibit class I and II HDACs at nanomolar concentrations, although it is predominantly a class I inhibitor [8]. More recently, SAHA was shown to lead to the degradation of HDACs 4 and 5 via RANBP2-mediated proteasome degradation in cancer cell lines [9]. Following on from this, we demonstrated that in addition to its deacetylase activity and the known effect on decreasing Hdac7 mRNA levels [10], SAHA treatment results in a reduction in HDAC2 and HDAC4 in brain regions of both WT and R6/2 mice, without affecting their transcript levels in vivo. This was associated with a reduction in aggregate load and the restoration of cortical Bdnf transcript levels in R6/2 mice [11]. It is well-established that HDAC4 acts as a transcriptional repressor that shuttles between the nucleus and cytoplasm. Phosphorylated HDAC4 is retained in the cytoplasm through its association with 14-3-3 proteins [12]. The N-terminal region of HDAC4 contains a MEF2 binding site and represses the transcription of MEF2-dependent genes important in the regulation of neuronal cell death [13]. In this context, MEF2 can act as a neuronal survival factor, and its inhibition has been linked to the death of neurons in several cell culture systems [14]. Crystallization of the N-terminal domain of HDAC4 suggested that HDAC4 might have the propensity to self-aggregate through its glutamine-rich domains, consistent with cell-culture studies [15]. Interestingly, regions containing high glutamine content in proteins have been observed to facilitate interactions with other glutamine-rich proteins, leading to the spontaneous assembly of insoluble toxic amyloid-like structures in mammalian cells [16]. In this study, we identified a novel mechanism by which HDACs can modify HD pathogenesis in vivo and found that HDAC4 reduction delays the HTT aggregation process. We demonstrated that HDAC4 associates with mutant exon 1 and full-length HTT in vivo in a polyQ-length-dependent manner and co-localizes with cytoplasmic inclusions in the brains of HD mouse models. HDAC4 knock-down inhibited aggregate formation in both the R6/2 (N-terminal fragment) and HdhQ150 (full-length knock-in) HD mouse models. This delay in aggregation occurred in the cytoplasm, consistent with the subcellular localisation of HDAC4 in mouse brain. We found no evidence for HDAC4 translocation to the nucleus during disease progression, and HDAC4 knock-down had no effect on HTT aggregation in the nucleus and no impact on global transcriptional dysregulation. HDAC4 reduction led to a marked restoration of the membrane properties of medium spiny neurons (MSNs) and of corticostriatal synaptic transmission. This was associated with an improvement in neurological phenotypes and extended survival. These data provide a clear demonstration that cytoplasmic pathogenic mechanisms contribute to HD-related neurodegenerative phenotypes and identify HDAC4 as a therapeutic target for HD. In order to investigate whether HDAC4 is involved in the molecular pathogenesis of HD, we used a genetic approach to reduce HDAC4 levels in both the R6/2 and HdhQ150 knock-in HD mouse models. R6/2 mice are transgenic for a mutated N-terminal exon 1 HTT fragment [17]. The HdhQ150 mice have an expanded CAG repeat knocked in to the mouse huntingtin gene (Htt) [18], [19], which is partially mis-spliced with the result that these mice express mutant versions of both an exon 1 HTT and a full-length HTT protein [20]. Because Hdac4 knock-out (Hdac4KO) mice die in early postnatal life [21], the HD mutation could not be transferred onto an Hdac4 null background. Therefore, we crossed males for each of the HD mouse models to Hdac4HET females (Figure 1A). Analysis of the progeny indicated that Hdac4 mRNA levels were decreased to approximately 50% in both Hdac4HET and double-mutant mice in both crosses (Figure 1B). Since HDAC4 functions as a transcriptional corepressor [22], it was important to check whether Hdac4 knock-down regulated the expression of the R6/2 transgene, as this would be expected to modulate the onset and progression of disease in R6/2 mice. Therefore, we used Taqman qPCR to demonstrate that exon 1 HTT mRNA was not altered in the cortex (Figure 1C), cerebellum, nor striatum (Figure S1A) of Dble: : R6/2 mice. Similarly, we showed that endogenous Htt levels were unchanged as a consequence of HDAC4 reduction in both R6/2 and HdhQ150 mice (Figures 1D and S1B). In addition, given that CAG repeat length is linked to aggregation kinetics and disease progression, we ensured that the CAG repeats were maintained at comparable levels throughout the course of this study (Table S1). Alteration of HDAC4 levels has been shown to modulate Hdac9 in muscle cells [23] and Hdac5 in primary mouse hepatocytes [24]. Therefore, we used Taqman qPCR to show that Hdac4 knock-down did not affect the levels of the other 10 Hdacs in brain regions of mice that did or did not express the HD mutation (Figures 1E and S1C and S1D). Bioinformatic predictions of HDAC4 structure suggested that HDAC4 has an N-terminal coil–coil domain within which it possesses short polyQ tracts that might convey an increased propensity for amyloid formation [15] as was confirmed in cultured cells [13]. Hence, we hypothesized that HDAC4 might exhibit pro-aggregation properties in HD mouse models. In order to investigate the molecular consequences of HDAC4 knock-down in HD mice, we employed the Seprion ligand ELISA to quantify aggregate load [25] and time-resolved Förster resonance energy transfer (TR-FRET) to measure soluble mutant HTT levels [26]. TR-FRET detects a FRET signal between two appropriately labelled antibodies. In this case, 2B7 (epitope: 1–17 amino acids of HTT) is paired with MW1 (epitope: polyQ in nonaggregated HTT). In R6/2 mice, the level of soluble exon 1 HTT decreases with disease progression as a consequence of its aggregation. The Seprion ELISA revealed that HDAC4 knock-down reduced the aggregate load in the cortex (Figure 2A), brain stem, hippocampus, and cerebellum (Figure S2A) of Dble: : R6/2 mice at 4 and 9 wk but that this effect had diminished by 15 wk of age. Accordingly, TR-FRET demonstrated that reduced HDAC4 levels led to an increase in soluble exon 1 HTT in the cortex (Figure 2B), brain stem, hippocampus, and cerebellum (Figure S2B) of Dble: : R6/2 mice at 4 and 9 but not at 15 wk of age. This shift in the ratio between soluble and aggregated exon 1 HTT levels can be visualised on the western blots in Figure 2D. We performed Seprion ELISA to determine whether similar results could be obtained in the HdhQ150 knock-in mice. A significant reduction in the aggregate load was observed in the striatum, cortex and cerebellum of Dble: : HdhQ150 mice at 6 and 10 mo of age (Figure 2C). Taken together, these data show that HDAC4 knock-down significantly reduced aggregate load and increased levels of soluble mutant HTT in HD mouse models, reflecting a delay in the aggregation process. We used Hdac4KO P3 brain tissue to confirm that the commercially available antibodies Sigma (DM-15), Santa Cruz (H-92), and Cell Signalling (CS2072) detected an HDAC4 specific signal (Figure S2C and unpublished data). On western blotting of nuclear and cytoplasmic fractions of mouse brain, we found that only trace amounts of HDAC4 could be detected in the nuclear fraction (Figure 2E). Therefore, to investigate whether the reduction in aggregation occurred in the cytoplasm, as would be consistent with the presence of HDAC4, we perfomed cellular fractionation on 4 wk and 9 wk brain tissue. We then resolved detergent insoluble high-molecular weight aggregates from the nuclear and cytoplasmic lysates by agarose gel electrophoresis (AGERA), prepared western blots, and performed immunodetection with the S830 antibody (epitope: exon 1 HTT). We found that HDAC4 knock-down reduced the aggregate load in the cytoplasm but not in the nucleus of Dble: : R6/2 mice at both 4 (Figure S2D) and 9 (Figure 2F) wk of age. Consistent with this, we found that the cytoplasmic steady-state levels of HDAC4 were significantly reduced in Dble: : R6/2 mice as compared to R6/2 at both 4 (Figure S2E) and 9 (Figure 2G) wk of age. The purity of the cellular fractions was validated by immunoblotting with α-tubulin and histone H3 antibodies (Figures 2F and S2D). The R6/2 colony was maintained by backrossing to (CBA/Ca×C57BL/6J) F1 mice and the Hdac4 knock-out mice had been bred to the same F1 background for more than six generations. We know from having bred R6/2 mice for 99 generations and from multiple experiments that the differential segregation of CBA/Ca and C57BL/6J alleles has no effect on HD-related phenotypes in R6/2 mice [10], [27]–[33]. However, the Hdac4 null allele had been created on a 129S mouse strain background, and it is inevitable that even after backcrossing to (CBA/Ca×C57BL/6J) F1 mice for multiple generations, the Hdac4 null allele would be retained in a genomic region of 129S DNA that had not been removed by recombination. Therefore, it was possible that the observed effects might be due to genetic variation in the 129S Hdac4-linked haplotype rather than through a reduction in HDAC4. To rule out this scenario, we identified an Hdac4-linked single nucleotide polymorphism (SNP) that was polymorphic between 129S and both the C57BL/6J and CBA/Ca strain backgrounds. We crossed R6/2 mice to (129S×C57BL/6J) F1s and identified R6/2 progeny that either did not carry or were heterozygous for the 129S SNP (n = 7/genotype). The heterozygous mice contained the 129S haplotype with a wild-type Hdac4 allele. Seprion ELISA on 9-wk-old cortex showed that the 129S Hdac4-linked haplotype did not modify aggregate load in R6/2 mice (Figure S2F), confirming the role for HDAC4 in aggregate reduction. To further understand the nature of the reduction in aggregate load by HDAC4 knock-down, we reasoned that HDAC4 might associate with HTT. Hence, we employed an in vitro GST pull-down assay and found that HDAC4 interacted specifically with exon 1 HTT containing a 53 polyQ tract but not with its 20 polyQ counterpart (Figure 3A). To determine whether HDAC4 associates with endogenous HTT, we immunoprecipitated HTT (2B7, epitope 1–17) or HDAC4 (DM-15) from brain lysates of 8-wk-old WT (7Q), HdhQ150 heterozygous, and HdhQ150 homozygous mice and immunoblotted with the MW1 (soluble mutant HTT), MAB2166 (soluble wild type and mutant HTT), and H-92 (HDAC4) antibodies. We found that mutant but not wild-type HTT co-immunoprecipitated with HDAC4 (Figure 3B). To investigate the effect of polyQ length on this interaction, we repeated the experiment with lysates from 8-wk-old heterozygous knock-in mice carrying polyQ tracts of 20 (HdhQ20) or 80 (HdhQ80). We found that HDAC4 could immunoprecipitate HTT containing 80 but not 20 glutamines (Figure 3C), consistent with the in vitro pull-down data. The sequence of HDAC4 is very similar to the class IIa member HDAC5. Therefore, to investigate the specificity of these interactions, we repeated the in vitro and in vivo immunprecipitations with an antibody specific to HDAC5. Although there was a weak interaction between exon 1 HTT and HDAC5 by in vitro pull-down (Figure 3A), this was not specific to mutant HTT, as was the case for HDAC4, and HDAC5 failed to immunoprecipitate HTT from brain lysates (Figure 3D). To further explore this association between HDAC4 and mutant HTT, we performed immunohistochemistry to determine whether HDAC4 co-localized with nuclear and/or cytoplasmic inclusions. For this purpose, we validated a number of commercially available antibodies and found CS2072 to be specific for HDAC4 by fluorescent immunolabelling as it gave no signal on HDAC4KO P3 brain sections (Figure S3A). Consistent with our western blot results, HDAC4 was localised to the cytoplasm appearing as a punctate pattern in adult brains (Figures 3E and S3B and S3C). This cytoplasmic localisation of HDAC4 is supported by its co-localisation with the synaptic markers synaptophysin and PSD95 (Figure S3C). Confocal microscopy showed that the S830 HTT antibody detects huntingtin aggregates in R6/2 and HdhQ150 brains and that HDAC4 co-localised with some but not all cytoplasmic inclusions (Figures 3E and S3B) in both cases. Taken together, our data indicate that HDAC4 associates with soluble mutant HTT in vivo and co-localizes with cytoplasmic inclusions in all brain regions studied. Transcriptional dysregulation is a well-documented molecular characteristic of HD pathogenesis. A comparative study of the striatal transcription profiles of seven mouse models and HD post mortem brains showed that the dysregulated signature in R6/2 and HdhQ150 models was highy comparable and in both cases more closely replicated that observed in human HD tissue than that of the other mouse models [34]. HDAC inhibitors were initially pursued as a therapy for HD because of their potential for reversing these transcriptional changes. Therefore, we performed Affymetrix microarray profiling of 9- and 15-wk cortex for WT, Hdac4Het, R6/2, and Dble: : R6/2 mice to assess whether HDAC4 knock-down might rescue the global transcriptional dysregulation that occurs in R6/2 mice. As expected the cortical expression profile was profoundly changed between WT and R6/2 mice by 9 wk of age and was further dysregulated at 15 wk (Figure 4A). However, comparison of the R6/2 and Dble: : R6/2 profiles indicated that only a very small number of probe sets were predicted to be differentially expressed at statistically significant levels (Figure 4A). This suggested that reduction in HDAC4 had not served to rescue transcriptional dyregulation. We used Taqman qPCR to validate the predicted changes in gene expression between R6/2 and Dble: : R6/2 cortex. We were only able to detect statistically different expression levels of small effect size for Secis and Casc4, and in both cases this was in the opposite direction of that predicted by the arrays (Figure 4B). Dysregulation of Bdnf promoter transcripts is a well-characterised hallmark of disease progression in HD [35], and restoration of Bdnf levels has been shown to correlate with phenotypic improvements in HD mouse models. As Bdnf probe sets were not represented on the arrays, we used Taqman qPCR to measure the levels of multiple Bdnf promoter trancripts as well as the coding exon (Bdnf-b) in cortex at 15 wk of age. We found that HDAC4 reduction increased Bdnf-b levels in WT cortex and almost restored the R6/2 dysregulated Bdnf transcripts to WT levels in Dble: : R6/2 mice (Figure 4C). MSNs in symptomatic R6/2 mice show pronounced morphological abnormalities, including dendritic shrinkage and spine loss. Largely consistent with these anatomical changes, at a behaviorally symptomatic age, R6/2 MSNs display a marked increase in membrane resistance, depolarization of the resting membrane potential (RMP), and an increased intrinsic excitability in response to current injection [36]. These phenotypes indicate that the R6/2 MSNs are hyperexcitable relative to MSNs in WT animals. In addition, symptomatic R6/2 mice display a progressive impairment in corticostriatal connectivity [37]. In combination, this reduction of cortical input, coupled with the abnormal excitability of the MSNs within the R6/2 striatum, will have serious consequences for appropriate striatal information processing and resultant basal ganglia output. These features likely contribute and in part underlie the impaired behavioral function exhibited by these mice. We determined the extent of functional improvement in MSNs from Dble: : R6/2 as compared to R6/2 mice at 7–8 and 12 wk of age. As previously published, R6/2 MSNs exhibited a higher membrane resistance than those from WT and Hdac4HET mice at both ages, which was restored to WT levels in Dble: : R6/2 mice (Figure 5A, E). While R6/2 MSNs at 7–8 wk of age were not significantly depolarized (Figure 5B), by 12 wk the RMP was depolarized by approximately 3 mV relative to WT MSNs, and this was rescued in the Dble: : R6/2 mice (Figure 5F). Despite previous reports of reduced rheobasic current (minimum current injection required to elicit an action potential) in R6/2 mice [36], this was a modest phenomenon in our hands and the reduction of HDAC4 in the Dble: : R6/2 mice had no effect (Figure 5C, G). Action potential amplitude was, however, significantly reduced in R6/2 compared to WT and Hdac4HET at both ages and was fully restored in the Dble: : R6/2 mice (Figure 5D, H). To assess an improvement in corticostriatal transmission, glutamatergic excitatory postsynaptic currents (EPSCs) were evoked by stimulating layer V cortical afferents innervating the striatum. In both 7–8 and 12 wk age groups, R6/2 mice showed significant reduction in EPSC amplitude for any given stimulus intensity applied. There was no difference in evoked EPSCs per genotype between the two age groups, and the data were therefore pooled (n = 28–32 neurons per genotype; Figure 5I). In the Dble: : R6/2 mice, a significant restoration of evoked EPSC amplitude compared to R6/2 was noted. To further delineate the locus of this improvement, a paired-pulse stimulation paradigm (20 ms interstimulus interval) was employed to specifically assess glutamate release probability. R6/2 corticostriatal synapses displayed higher paired-pulse ratios than WT synapses, and this was significant in the 12-wk dataset, indicative of impaired glutamate release in this age group. This was fully restored in the Dble: : R6/2 mice (Figure 5J). In agreement with the reduction in evoked glutamate release in R6/2 corticostriatal synapses, quantal glutamate release within the striatum, which can arise from release at both thalamostriatal and corticostriatal presynaptic terminals, was severely depressed in the 7–8-wk-old mice [mean frequency of mEPSCs in MSNs from WT = 2. 3±0. 25 Hz (n = 12), Hdac4HET = 1. 87±0. 38 Hz (n = 11), R6/2 mice = 0. 13±0. 03 Hz (n = 11) ]. The Dble: : R6/2 mice showed a profound rescue in this phenotype, with a mean frequency of mEPSCs of 1. 1±0. 14 Hz (n = 12) (significance against R6/2 frequency assessed by Kolmogirov–Smirnoff analysis p<0. 0001; Figure 5K, L). There was no change in the amplitude of mEPSCs in R6/2 compared to WT mice, suggesting that postsynaptic 2-amino-3- (5-methyl-3-oxo-1,2-oxazole-4-yl) propanoic acid (AMPA) receptor function was not impaired in the HD model, and did not contribute to the impairment in glutamatergic transmission. mEPSC amplitude was unchanged in the Hdac4HET or Dble: R6/2 animals relative to WT or R6/2 mice (Figure 5M). In conclusion, the combined restoration of the membrane properties of MSNs from the Dble: R6/2 mice alongside the improvement in glutamatergic cortical input to the striatum would be expected to significantly improve striatal information processing, normalize aberrant basal ganglia output, and result in an improvement in behavioral function. In order to evaluate whether the molecular and electrophysiological changes that had been detected in the Dble: : R6/2 mice might ameliorate HD-related behavioural and physiological phenotypes, we employed a set of quantitative, well-characterised tests. Mice were well matched for CAG repeat length (Table S1), and phenotypic parameters were measured from 4 to 15 wk of age in WT, Hdac4HET, R6/2, and Dble: : R6/2 mice. All analyses were performed blind to genotype, and in all cases, the progression of R6/2 phenotypes was consistent with previous reports. Rotarod performance is a sensitive indicator of balance and motor coordination that has been reliably shown to decline in R6/2 mice. In line with previous results, R6/2 rotarod performance was impaired by 8 wk (p<0. 001) and deteriorated further with age (Figure 6A). HDAC4 knock-down had no impact on the performance of WT mice. However, it significantly improved R6/2 rotarod performance at all ages and delayed the progression of rotarod impairment by 1 mo to the extent that 12-wk-old Dble: : R6/2 mice performed as well as 8-wk-old R6/2 mice (Figure 6A). At 14 wk of age, close to end stage disease, the deterioration in the appearance of the Dble: : R6/2 mice was considerably less marked than that of R6/2. To document this, we performed a modified SHIRPA analysis [38] and measured 16 phenotypic parameters (Table S2), for which nine gave a positive score in R6/2 as compared to WT mice. In all cases, the appearance of these phenotyes was improved in the Dble: : R6/2 mice when compared to R6/2 (Figure 6B). In particular, piloerection and tremor could not be detected in Dble: : R6/2 mice, and hunched back and body tone were vastly improved (Figure 6B). The phenotypic improvements are evident in Videos S1 and S2, and in the light of these, it was surprising that we were unabe to detect an any marked decrease in body weight loss (Figure 6D). The dramatic improvement in the appearance of the mice led us to assess whether HDAC4 knock-down had pro-survival effects, and we found that it extended the lifespan of R6/2 mice by approximately 20%, p = 0. 0004 (log-rank test) (Figure 6C). Brain weight was measured for cohorts of mice that were culled at 8,12, and 15 wk of age, and as previously described, there was a progressive decrease in the weight of R6/2 brains as compared to WT. HDAC4 knock-down resulted in a very slight but statistically significant increase in brain weight at 9 and 12 but not at 15 wk of age (Figure 6E). There is mounting support for the use of HDAC inhibitors in the treatment of a wide range of brain disorders, predominantly aimed at pathological alterations in the brain transcriptome. HDAC4 is a transcriptional repressor that is normally retained in the cytoplasm, but localises to the nucleus upon de-phosphorylation [39], [40]. In line with previous reports [41], we found that HDAC4 was located in the cytoplasm in mouse brain and showed that it did not relocate to the nucleus during disease progression. We demonstrated that HDAC4 associates with mutant HTT in a polyQ-dependent manner in vivo, consistent with an association that occurs between the polyQ stretch in HTT and the Q-rich domain of HDAC4. In line with these observations, we found that HDAC4 co-localised with cytoplasmic inclusions in both R6/2 and knock-in HD mouse models. The genetic knock-down of HDAC4 led to a significant delay in the cytoplasmic aggregation process and led to a significant restoration of synaptic function. At a physiological level, knock-down of HDAC4 extended lifespan and partially restored motor coordination and other neurological phenotypes. This suggests that a cytoplasm-based pathophysiological mechanism contributes to key aspects of neurodegenerative phenotype observed in HD. A cytoplasmic mechanism of action for HDAC4-mediated beneficial effects was unexpected. In general, HDACs have been pursued as therapeutic targets because of their impact on epigenetics and transcription. HDAC4 is known to repress MEF2 in muscle [40], and it has been proposed that HDAC4 binds to HDAC3 to activate its deacetylase domain [24]. However, we found no impact on global transcriptional dysregulation upon HDAC4 knock-down in R6/2 mice, consistent with a predominantly cytoplasmic localisation of HDAC4. Surprisingly, the absence of HDAC4 in knock-out postnatal brain tissue had little effect on the brain transcriptome [42]. In fact, it has been shown that HDAC4 does not function as a lysine-deacetylase [43], and consistent with this, we found that HDAC4 knock-out had no effect on global acetylation in brain in vivo [42]. Our demonstration that the dysregulation of Bdnf promoter transcripts was alleviated in the double mutant mice is consistent with a cytoplasmic-based mechanism of action. Bdnf expression is repressed by RE1 silencing transcription factor (REST), which is retained in the cytoplasm in a complex containing HTT and which translocates to the nucleus in response to HD pathology [44]. The cytoplasmic aggregation process initiates prior to a reduction in Bdnf transcripts in R6/2 and other HD mouse models, and therefore it is not surprising that a delay in this cytoplasmic pathology might in turn result in a delay in Bdnf dysregulation. Our demonstration that a delay in the cytoplasmic aggregation process has beneficial consequences is supported by the published correlation between the appearance of neuropil aggregates and disease progression [45] and is consistent with previous predictions [46]. In the human HD post mortem brain, neuropil aggregates are much more common than nuclear inclusions, present in a large numbers, and potentially associated with onset of clinical symptoms [47]. Our data suggest that HDAC4 might modulate HTT aggregation through a direct interaction serving to template or nucleate soluble HTT. Alternatively, given that HDAC4 may self-aggregate, it could influence HTT aggregation indirectly through perturbation of proteostasis networks [48]. The delay in HTT aggregation afforded by a reduction in HDAC4 was most pronounced in presymptomatic and early-stage disease and diminished with disease progression, presumably reflecting changes in aggregation kinetics. This association between phenotypic improvements and a shift from aggregated to soluble mutant exon 1 HTT is consistent with our previously published in vivo studies [49], [50]. Our data provide no information as to the aggregate species that is toxic, or as to whether all species of aggregates have detrimental consequences, but only indicate that shifting the equilibrium toward soluble HTT is beneficial in vivo. The improvement in synaptic function as a consequence of HDAC4 knock-down was not related to a restoration in the expression level of dysregulated synaptic transcripts. Instead, it may act through reducing cytoplasmic aggregation as neuropil aggregates have been shown to inhibit axonal transport, synaptic function, and glutamate release in HD fly models [51]. The restoration of corticostriatal synaptic function demonstrated that the reduction of HDAC4 has functional consequences in the brain. However, in this study, HDAC4 was ubiquitously knocked down, and as HD has a peripheral component to its pathophysiology [52], it is conceivable that the reduction of HDAC4 also had beneficial consequences in tissues other than the brain. Given that HDAC4 has well-established functions in muscle, that muscle atrophy is a major symptom of HD, and that HDAC4 has been linked to disease progression in an ALS mouse model [53], we are currently investigating the extent to which HDAC4 reduction in muscle might contribute to the improved HD phenotypes. The administration of HDAC inhibitors has been shown to improve disease phenotypes in a wide range of HD models [5]. To better understand which HDACs, when inhibited, are most responsible for these beneficial consequences, we embarked on a series of genetic manipulations in HD mouse models. In this article we show that the genetic knock-down of HDAC4 delayed cytoplasmic aggregation, improved synaptic function, and improved disease phenotypes. In contrast, the genetic knock-down of HDAC3 [27] and the class IIa members HDAC7 [10], HDAC5, and HDAC9 (our unpublished data) had no effect on R6/2 phenotypes. Strikingly, we recently showed that administration of SAHA caused a reduction in HDAC2 and HDAC4 at the protein but not RNA level in some R6/2 brain regions and that this correlated with a reduction in aggregation and a restoration of cortical Bdnf transcripts [11]. Therefore, we speculate that the beneficial effects of SAHA were at least in part transmitted through the down-regulation of HDAC4 via a mechanism not related to its enzyme activity. Perhaps the best validated therapeutic target for HD is the HTT protein, and the reduction of HTT through gene silencing is being developed using both antisense oligonucleotides and RNAi [54]–[56]. However, delivery to the brain is a major challenge for these approaches, and as HTT has many essential functions, the potential liability of decreasing HTT to a detrimental level cannot be ignored. We have shown that reduction of HDAC4 shifts the ratio from aggregated to soluble HTT and therefore acts directly on the HD mutation. Our demonstration that HDAC4 levels can be decreased through the administration of a small brain-penetrant molecule (SAHA) is extremely promising as more selective inhibitors (e. g. , specific to HDAC4 or class IIa enzymes) may have similar effects, making it possible to target HTT aggregation with a small molecule. Finally, these findings may have wider implications as HDAC4 is a component of Lewy Bodies in Parkinson' s disease brains [57] and administration of SAHA improved the synaptic plasticity and learning behaviour in an Alzheimer disease model [58]. All animal work was approved by the local ethics committees and was performed in accordance with UK Home Office regulations or the Swiss Law (Kantonales Veterinäramt Basel-Stadt). Hemizygous R6/2 mice were bred by backcrossing R6/2 males to (CBA×C57BL/6) F1 females (B6CBAF1/OlaHsd, Harlan Olac, Bicester, UK). Similarly, the Hdac4 knock-out colony [21] was maintained by backcrossing heterozygous males to B6CBAF1/OlaHsd females. HdhQ150 homozygous mice on a (CBA×C57BL/6) F1 background were obtained by intercrossing HdhQ150 heterozygous CBA/Ca and C57BL/6J congenic lines as described previously [19]. The HdhQ20 and HdhQ80 mice were from CHDI colonies at The Jackson Laboratory (Bar Harbor, ME) and maintained on a C57BL/6/J background. 129S2/SvHsd females were from Harlan Olac. The cross between HdhQ150 and Hdac4HET mice, both on a C57BL/6 background, was performed at Novartis. All animals had unlimited access to food and water, were subject to a 12-h light/dark cycle, and housing conditions and environmental enrichment were as previously described [59]. The R6/2 colony was kept on breeding chow (Special Diet Services, Witham, UK). Genomic DNA was isolated from an ear-punch. R6/2 and HdhQ150 mice were genotyped by PCR, and the CAG repeat length was measured as previously described [25]. PCR conditions for genotyping Hdac4 knock-out mice were for WT band: Fw: CTTGTTGAGAACAAACTCCTGCAGCT, Rw: AGCCCTACACTAGTGTGTGTTACACA; for Hdac4 mutant band: Fw: AGCCCTACACTAGTGTGTGTTACACA, Neo Rw: CCATGGATCCTGAGACTGGGG. Cycling conditions were 4 min at 95°C, 35× (30 s at 95°C; 45 s at 60°C; 2 min at 72°C), 10 min at 72°C using Taq polymerase and buffer from Promega. The HdhQ20, HdhQ80 mice [60] were genotyped as described [61] using the Hotstart polymerase (Thermoscientific). Dissected tissues were snap frozen in liquid nitrogen and stored at −80°C until further analysis. All behavioural tests were performed as previously described, and the data were analysed by repeated measures general linear model ANOVA with the Greenhouse Geisser post hoc test using SPSS software [59]. Pair-wise statistical comparisons were corrected for multiple comparisons using Bonferroni post hoc test in SPSS. Survival was assessed blind to genotype, and mice were euthanized when they reached end-stage disease. The data are presented as Kaplan–Meier cumulative survival functions and statistically analysed by the log-rank test. Total RNA was extracted with the mini-RNA kit accordingly to manufacturer instructions (Qiagen). Reverse transcription (RT) was performed using MMLV superscript reverse transcriptase (Invitrogen) and random hexamers (Operon), and all Taqman-qPCR reactions were performed using the Chromo4 Real-Time PCR Detector (BioRad) as described [35]. Expression level of the gene-of-interest was normalised to the geometric mean of three endogenous housekeeping genes (Primer Design) as described [35]. The primer and probe sequences are detailed in Table S3. For the Affymetrix arrays, biotinylated cRNAs were prepared from 200 ng total RNA using the GeneChip 3′ IVT Express Kit (Affymetrix) following the manufacturer' s instructions. cRNA (15 µg) was hybridized to GeneChip Mouse Genome 430 version 2. 0 Arrays (Affymetrix) and processed, stained, and scanned according to the manufacturer' s recommendations. The quality of input RNAs and cRNAs was verified with the Bioanalyzer 2100 (Agilent Technologies) before use. Microarray quality control was performed using the software package provided on RACE [62]. Chips with a median normalized unscaled standard error greater than 1. 05 were excluded. Affymetrix annotations (version 3. 0) were used for probeset-to-gene assignments. Two-tailed t test was performed to assess the differences in gene expression between groups for each genotype (WT n = 8; R6/2 n = 9; Hdac4HET n = 8; Dble n = 9). Corrections for multiple testing were performed using the false discovery rate (FDR) according to Benjamini and Hochberg [63] with a significance threshold of p<0. 05. The array datasets can be found at NCBI GEO accession number GSE38237. All primary and secondary antibodies used in this study are presented in Table S4. Preparation of protein lysates and western blotting were as described previously [11]. In general, 20 µg protein lysate was fractionated on 10% SDS-PAGE gels. Aggregates were captured in Seprion ligand-coated plates (Microsens) and detected using the S830 sheep polyclonal or MW8 mouse monoclonal antibodies as described [25]. Sample preparation and the TR-FRET assay were performed as previously described [26]. Nuclear and cytoplasmic fractions were prepared as previously described [64], and their purity was determined by immunoblotting with antibodies to anti-histone H3 and α-tubulin. The AGERA assay was performed as described [65]. In general 100 µg of nuclear or cytoplasmic fractions, isolated from whole snap frozen brains, were loaded in nonreducing Laemmli buffer onto 1. 5% agarose gels supplemented with 0. 1% SDS and run at 3 V/cm followed by western blotting and immunodetection with anti-HTT (S830) antibodies. The high molecular weight protein marker was from Invitrogen. Co-immunoprecipitation was performed as previously described [66]. Briefly, protein lysates were prepared from whole brains in HEPES buffer and incubated with protein-G Dynabeads (Invitrogen) overnight at 4°C on a rotating platform. The full-length mouse Hdac4 gene in pCMV6 was obtained from Origene. This was amplified by PCR using high fidelity polymerase (Roche) and subsequently cloned into pCR2-Topo (Invitrogen) accordingly to the manufacturer' s instructions. The detailed protocol used for GST pull-downs is available in the Text S1. For immunohistochemical studies, brains were frozen in isopentane at −50°C and stored at −80°C until further analysis. We cut 10–15 µm sections using a cryostat (Bright instruments), air dried and immersed them in 4% PFA in PBS for 15 min, and washed them for 3×5 min in 0. 1% PBS-Triton X-100. Blocking was achieved by incubation with 5% BSA-C (Aurion) in 0. 1% PBS-Triton X-100 for at least 30 min at RT. Immunolabelling with primary antibodies in 0. 1% PBS-Triton X-100,1% BSA-C (HDAC4, S830) was completed by overnight incubation in a humidity box at 4°C. Sections were washed 3× in PBS, incubated for 60 min at RT in a dark box with the appropriate combinations of secondary antibodies diluted in PBS, washed 3× in PBS, and counterstained with DAPI (Invitrogen). Sections were mounted in Vectashield mounting medium (Vector Laboratories). Sections were examined using the Leica TCS SP4 laser scanning confocal microscope and analysed with Leica Application Suite (LAS) v5 (Leica Microsystems, Heidelberg, Germany). Detailed procedures for acute striatal slice preparation, patch-clamp recordings, and the isolation of miniature excitatory postsynaptic currents (mEPSCs), along with the appropriate statistical analysis, can be found in Text S1. All data were analysed with Microsoft Office Excel and two-tailed Student' s t test or as otherwise stated.

Huntington' s disease (HD) is a late-onset neurodegenerative disorder caused by protein-folding defects in the huntingtin protein. Mutations in huntingtin can result in extra-long tracts of the amino acid glutamine, resulting in aberrant interactions with other proteins and also causing huntingtin proteins to self-associate and -aggregate. The pathology of HD is therefore associated with nuclear and cytoplasmic aggregates. HDAC4 is a histone deacetylase protein traditionally associated with roles in transcription repression. The HDAC4 protein contains a glutamine-rich domain and in this work we find that HDAC4 associates with huntingtin in a polyglutamine-length-dependent manner and that these proteins co-localise in cytoplasmic inclusions. Importantly, reducing HDAC4 levels delays cytoplasmic aggregate formation and rescues neuronal and cortico-striatal synaptic function in mouse models of HD. In addition, we observe improvements in motor coordination and neurological phenotypes, as well as increased lifespan in these mice. Nuclear huntingin aggregates or transcription regulation, however, remained unaffected when HDAC4 levels were reduced to enable these effects. Our results thus provide valuable insight into separating cytoplasmic and nuclear pathologies, and define a crucial role for cytoplasmic aggregations in HD progression. HDAC4 reduction presents a novel strategy for alleviating the toxicity of huntingtin protein aggregation, thereby influencing the molecular pathology of Huntington' s disease. As there are currently no disease-modifying therapeutics available for Huntington' s disease, we hope that this HDAC4-mediated regulation may be amenable to small-molecule therapeutics.

Abstract Introduction Results Discussion Materials and Methods

2013

HDAC4 Reduction: A Novel Therapeutic Strategy to Target Cytoplasmic Huntingtin and Ameliorate Neurodegeneration

Human T-cell Immunoglobulin and Mucin-domain containing proteins (TIM1,3, and 4) specifically bind phosphatidylserine (PS). TIM1 has been proposed to serve as a cellular receptor for hepatitis A virus and Ebola virus and as an entry factor for dengue virus. Here we show that TIM1 promotes infection of retroviruses and virus-like particles (VLPs) pseudotyped with a range of viral entry proteins, in particular those from the filovirus, flavivirus, New World arenavirus and alphavirus families. TIM1 also robustly enhanced the infection of replication-competent viruses from the same families, including dengue, Tacaribe, Sindbis and Ross River viruses. All interactions between TIM1 and pseudoviruses or VLPs were PS-mediated, as demonstrated with liposome blocking and TIM1 mutagenesis experiments. In addition, other PS-binding proteins, such as Axl and TIM4, promoted infection similarly to TIM1. Finally, the blocking of PS receptors on macrophages inhibited the entry of Ebola VLPs, suggesting that PS receptors can contribute to infection in physiologically relevant cells. Notably, infection mediated by the entry proteins of Lassa fever virus, influenza A virus and SARS coronavirus was largely unaffected by TIM1 expression. Taken together our data show that TIM1 and related PS-binding proteins promote infection of diverse families of enveloped viruses, and may therefore be useful targets for broad-spectrum antiviral therapies. The entry of enveloped viruses is a multi-stage process. Following attachment, some viruses fuse to cells at the plasma membrane, whereas others are internalized through various endocytic routes and, primed by low pH or compartment-resident factors, fuse at the endo/lysosomal membranes. Viruses attach to the cell surface through the binding of their entry glycoproteins (GPs) to specific receptors/coreceptors and also through less specific interactions with various attachment factors [1]. While the distinction between attachment factors and bona fide receptors has not always been carefully made, receptors typically involve necessary, specific, and high-affinity interactions that can prime the viral entry protein for subsequent fusion steps. Attachment factors, in contrast, are typically interchangeable, involve less specific interactions, and serve primarily to localize the virus to its receptor (s) and other cofactors necessary for fusion. A recent study reported that human TIM1 (hTIM1), a protein previously implicated as a receptor for the non-enveloped hepatitis A virus [2]–[4], also functioned as a receptor for the enveloped viruses Ebola (EBOV) and Marburg (MARV) [5]. This observation added hTIM1 to the long list of filovirus entry factors, which include β1-integrins [6], [7], the folic acid receptor alpha, which was later disputed [8]–[10], the TAM receptors Axl, Mer and Tyro [11], various C-type lectins [12]–[14] and the intracellular receptor Niemann-Pick C1 (NPC1) [15]–[17]. hTIM1 was identified by correlating gene expression patterns of 60 cancer cell lines with their permissiveness to EBOV entry [5]. The TIM protein family is composed of three members in humans (hTIM1,3, and 4) and eight in mice (mTIM1-8) that are implicated in the regulation of innate and adaptive immune responses [18]. Based on expression, functional and structural data hTIM1,3, and 4 are considered direct orthologs of mTIM1,3, and 4, respectively [19], [20]. The ectodomain of TIM proteins includes an N-terminal variable immunoglobulin-like (IgV) domain and a stalk-like mucin domain that varies in length and O-glycosylation [18]. Importantly, the IgV domains of all hTIM proteins contain a high-affinity binding site for PS, a phospholipid constituent of eukaryotic membranes [21], [22]. Generally present on the cytosolic side of the plasma membrane lipid bilayer, PS flips to the outer leaflet upon the onset of apoptosis, where it acts as a so-called “eat-me” signal for professional phagocytes (macrophages and dendritic cells) as well as non-professional phagocytes (e. g. , epithelial cells) [23]–[25]. Consistent with a PS receptor function, one important role of TIM proteins is to initiate PS-mediated engulfment of apoptotic cells and debris [21], [22], [26]. Although this role may be more prominent for TIM3 and TIM4, which are expressed on dendritic cell and macrophage sub-populations [21], [22], [26], [27], TIM1 is known to be expressed on various epithelial cells [5], [28], which can also assume phagocytic roles. TIM1 and TIM3 are further expressed on subsets of activated T-cells, where they act as costimulatory and coinhibitory molecules, respectively [29]–[31]. Several recent findings suggest that negatively-charged phospholipids like PS might play a role in mediating virus entry. PS was shown to be exposed on the membranes of various enveloped viruses, including Pichinde virus, vesicular stomatitis virus (VSV) and the intracellular mature virion form of Vaccinia virus [32], [33]. This is likely a common feature of most enveloped viruses, as virus-infected cells were shown to overexpress PS on their plasma membranes [32]–[34]. In addition, the entry of lentiviral pseudoviruses bearing the GPs of Sindbis (SINV), Ross River (RRV) and Baculo virus was enhanced in a PS-dependent manner by the TAM receptor tyrosine kinase Axl [35]. Axl has also been shown to promote EBOV and MARV entry [11]. Finally, an antibody targeting anionic phospholipids effectively rescued rodents from lethal challenges by either Pichinde virus or mouse cytomegalovirus, demonstrating in vivo contribution of anionic phospholipids to the infectivity of these viruses [33]. Because hTIM1 binds PS with high affinity [21] and various viruses contain PS on their virion surfaces [32], [33], we explored the possibility that the reported hTIM1-mediated EBOV entry [5] is PS dependent. Furthermore, using pseudoviruses, VLPs, and infectious viruses bearing the entry proteins of 19 viruses from 7 different families, we investigated the generality of PS-receptor usage and its underlying mechanisms. We observed that hTIM1 promoted infection by a range of viruses, including members of the filovirus, flavivirus, alphavirus and New World arenavirus families, and that this enhancement required the PS-binding activity of hTIM1. We further demonstrated that additional PS receptors, such as hTIM4 and hAxl enhanced the entry of most hTIM1-using pseudoviruses, and that the efficiency of this enhancement largely correlated with that observed with hTIM1. Finally, we explore and discuss the molecular mechanisms that contribute to the differential efficiency of viral PS receptor usage. Collectively our data suggest that hTIM1 and related proteins function as attachment factors for a full range of enveloped viruses. HEK293T (human embryonic kidney), Huh7 (human hepatocarcinoma), 3T3 (murine embryonic fibroblast) and MDCK (canine kidney) cells were grown at 37°C in DMEM supplemented with 10% fetal bovine serum (FBS), 1 mM sodium pyruvate and 1% penicillin/streptomycin (P/S). NPC1−/− and NPC1−/−mNPC1 CHO cells (also referred to as M12 and wt8, respectively [36]) were kept at 37°C in DMEM/F12 with 5% FBS and 1% P/S. These cells were gifts from Daniel S. Ory at Washington University School of Medicine. C6/36 Aedes albopictus mosquito cells were maintained at 28°C in DMEM containing 10% FBS, 1 mM sodium pyruvate and 1% P/S. Coding sequences of hTIM1, hTIM3 and hTIM4 (without signal peptides) were PCR amplified from human T-cell or macrophage cDNA libraries and cloned into the retroviral expression vector pQCXIX (BD Biosciences) downstream of the signal peptide of mouse angiotensin-converting enzyme 2 (ACE2) and a myc-tag. Their sequences were deposited to the Genbank under accession numbers JX049978, JX049979 and JX049980, respectively. hAxl cDNA (clone 5205825, Invitrogen) was purchased and similarly cloned into pQCXIX with an N-terminal myc-tag. The PS-binding deficient variant of hTIM1 (AA-hTIM1) was created by site-directed mutagenesis. It carries W112A and F113A mutations based on the previously-described equivalent mutations in other TIM proteins [19], [21]. The stalk-truncated Δ131-221 hTIM1 and Δ197-287 hTIM1 variants were obtained using deletion mutagenesis. The plasmids encoding N-terminally myc-tagged human ACE2 (hACE2) and civet cat ACE2 were previously described [37]. Also previously described were the expression plasmids encoding the entry glycoprotein precursors of Zaire EBOV (Mayinga) and Lake Victoria MARV (Musoke), both lacking the mucin domains, Amapari virus (AMAV, BeAn 7063), Tacaribe virus (TCRV), Junín virus (JUNV, MC2), Machupo virus (MACV, Carvallo), Lassa fever virus (LASV, Josiah), lymphocytic choriomeningitis virus (LCMV, Armstrong), Chikungunya virus (CHKV, 37997), Eastern Equine Encephalitis virus (EEEV, FL91-4697), influenza A virus (FLUAV, H7: Rostock, N1: Puerto Rico), VSV (Indiana), SARS coronavirus (SARS-CoV; Tor2, GD and SZ) and West Nile virus (WNV, lineage 1, NY99) [37]–[43]. The precursor of the Oliveros virus (OLIV) entry protein was synthesized by GenScript based on Genbank ID AAC54654. 1 and was cloned into pCAGGS. The expression of various receptors was assessed with monoclonal antibodies and detected using the Accuri C6 flow cytometer (BD Biosciences). To measure endogenous hTIM1 expression, cells were stained with mouse anti-hTIM1 antibody (clone 219211, R&D Systems) or purified mouse Fc (mFc) as a negative control. Primary antibody binding was detected with a phycoerythrin (PE) -conjugated goat anti-mouse antibody (Jackson ImmunoResearch Laboratories). To assess endogenous mouse TIM1 (mTIM1) expression, cells were stained with anti-mTIM1-PE, a PE-conjugated rat anti-mTIM1 antibody (clone RMT1-4, BioLegend) or with anti-mIFNγ-PE, a PE-conjugated rat anti-mouse interferon gamma antibody (BioLegend) used as negative control. Cells transfected or transduced to express exogenous receptors were stained with various antibodies. While the antibody used to detect wildtype (wt) hTIM1 and the AA-hTIM1 variant was the anti-hTIM1 antibody described above, that used in the hTIM1 stalk truncation experiment was anti-hTIM1 antibody 3D1, which specifically recognizes the IgV head domain of hTIM1 [21]. Expression of N-terminally myc-tagged hAxl, hTIM3/4, hACE2 and civet cat ACE2 was assessed using anti-myc antibody 9E10. Pseudoviruses bearing various viral entry proteins were produced in 293T cells as described [39] by calcium-phosphate transfection of a retroviral vector pQCXIX (BD Biosciences) encoding eGFP together with two other plasmids, separately encoding a viral entry protein and the Moloney murine leukemia virus (MLV) gag and pol. For FLUAV H7N1, an additional plasmid encoding N1 neuraminidase (Puerto Rico) was co-transfected [41]. Pseudovirus-containing culture supernatants were harvested at 32–34 h post-transfection, filtered through a 0. 45 µm PES membrane, stored at 4°C, and used within 2 weeks. WNV VLPs were produced and harvested in the same way after co-transfecting a plasmid encoding the WNV structural proteins (capsid and entry glycoproteins prM and E) and a WNV replicon encoding the non-structural proteins NS1-5 and GFP [43]. For infection, cells were plated on poly-lysine-coated (293T) or uncoated 48-well plates and incubated at 37°C with pseudovirus- or VLP-containing supernatants diluted to yield comparable levels of infection. In order for virus entry enhancement not to be limited by virus titers, cells were generally incubated with virus supernatants for less than 1 hour, unless mentioned otherwise. Supernatants were then replaced with fresh medium, incubated for one (293T) or two (3T3, Huh7 and NPC1−/−) days to allow for eGFP reporter expression and infection levels were assessed by measuring GFP fluorescence with the Accuri C6 flow cytometer (BD Biosciences). Independent experiments were performed with independent pseudovirus and VLP preparations. In addition, all pseudoviruses and VLPs used in a given experiment were produced in parallel. Lyophilized infectious TCRV (TRVL 11573), passaged in suckling mice and Vero cells, as well as RRV (T-48) and SINV (Ar-339), both passaged in suckling mice, were purchased from ATCC, and resuspended in PBS according to the instruction. Type 2 infectious dengue virus (DENV, New Guinea C) was obtained by passaging in C6/36 Aedes albopictus mosquito cells. Virus-containing culture supernatants were 0. 45 µm-filtered, stored at −80°C and virus titers determined using plaque assays in baby hamster kidney cells as described [44]. Infectious FLUAV (H1N1, A/PR/8/34) was propagated in MDCK cells [45], 0. 45 µm-filtered and virus titers assessed as tissue culture infectious dose 50 (TCID50) in cultured human macrophages using CellTiter-Glo One solution (Promega). For infection, cells were incubated for 1–6 h at 37°C with viruses serially diluted in DMEM containing 10% FBS, washed and supplemented with fresh medium. At indicated days post-infection, cells were detached by trypsinization, washed, fixed with 1% formaldehyde in PBS and permeabilized with 0. 1% saponin in PBS containing 2% goat serum. Cells were then stained with immune ascitic fluids (ATCC) for TCRV, RRV and SINV, anti-FLUAV antibody C111 (Clontech), or anti-DENV antibody 2H2 (Millipore) [46] followed by an Alexa 649- or PE-conjugated goat anti-mIgG antibody (Jackson ImmunoResearch Laboratories) and analyzed by flow cytometry. VP40-GFP VLPs were produced in 293T cells by co-transfecting a pCAGGS-based plasmid encoding a previously described GFP-fusion version of the EBOV VP40 matrix protein [47], a gift from Christopher F. Basler at Mount Sinai School of Medicine, with a plasmid encoding mucin-domain-deleted EBOV GP at 3∶1 ratio. To make VP40-GFP VLPs bearing no entry protein, the VP40-GFP plasmid was similarly cotransfected with an empty plasmid. VLP-containing culture supernatants were harvested at 36 h post-transfection and cell debris removed by two consecutive centrifugations at 900 g. The presence of fluorescent filovirus-like particles in the supernatants was confirmed by fluorescence microscopy. MLVgag-GFP pseudovirions were produced similarly as described for the non-fluorescent pseudoviruses, except that 25% of the MLV gag-pol plasmid DNA was replaced with plasmid DNA encoding a MLV gag-GFP fusion protein [48], a gift from Walther Mothes at Yale University School of Medicine. Also, pQCXIX-eGFP was replaced with the same vector encoding a non-fluorescent protein. Virus supernatants were harvested at 34–36 h post-transfection, 0. 45 µm-filtered, purified by ultracentrifugation (SW40Ti rotor, 70' 000 g for 2 h at 10°C) and resuspended in a small volume of DMEM containing 10% FBS. To assess VLP/pseudovirion internalizaton, cells were incubated for 2–6 h at 37°C with VP40-GFP VLPs, with MLVgag-GFP virions normalized for MLV reverse-transcriptase (RT) activity or with mock supernatants obtained from cells transfected with a plasmid expressing eGFP alone. Uninternalized VLPs and virions were removed with two 1 min acid-washes (200 mM glycine, 150 mM NaCl, pH 3. 0) followed by trypsinization at 37°C for 15 min. Internalization of VLPs or pseudovirions was assessed by flow cytometry. VLPs made with EBOV VP40 matrix proteins fused to β-lactamase (Bla) and bearing EBOV GP, LASV GP or no GP were produced as described above for VP40-GFP VLPs. The VP40-Bla fusion construct was a gift from Paul Bates at University of Pennsylvania [8]. Naive peritoneal macrophages were obtained from wildtype BALB/cBYJ mice. Briefly, peritoneal cavity cells were plated in 12-well plates at 106 per well and incubated for 1 h at room temperature to let macrophages adhere. After removing non-adherent cells by washing twice in PBS/2% FBS, macrophages were incubated overnight at 37°C in RPMI containing 10% FBS, 1% P/S and 50 µM β-mercaptoethanol and infected the following day for 2 h at 37°C with VP40-Bla VLPs. Infected cells were detached by scraping in trypsin/EDTA, washed and loaded with the Bla substrate CCF2-AM, as previously described [49]. The conversion of substrate by cytoplasmic esterases and Bla, which reflects VP40-Bla VLP entry, was detected using the LSR II flow cytometer (BD Biosciences). 1,2-diacyl-sn-glycero-3-phospho-L-serine (PS), 1,2-diacyl-sn-glycero-3-phosphocholine (PC) and 1,2-diacyl-sn-glycero-3-phosphoethanolamine (PE) were purchased from Sigma and resuspended at 10 mg/ml in chloroform. Aliquots of PC alone or PC mixed with PS or PE at 1∶1 molar ratios were dried under argon and then in a SpeedVac for 1 h, followed by hydration in PBS at a concentration of 1 mM total lipids. Liposomes were made by sonicating these milky lipid suspensions to clarity, stored at 4°C, and used within a week. For both entry and internalization blocking assays, liposome preparations were diluted in the appropriate medium containing 10% FBS and preincubated with cells for 20–30 min at room temperature. Pseudoviruses or VLPs were then added and culture plates shifted to 37°C for infection. Independent experiments were performed with independent liposome preparations. Relevant SwissProt accession numbers are Q96D42 (hTIM1), Q8TDQ0 (hTIM3), Q96H15 (hTIM4), P30530 (hAxl), Q9BYF1 (hACE2), P02786 (hTfR1) and O15118 (NPC1). We first tested the specificity of viral use of hTIM1 in two cell lines that express little or no endogenous TIM1: human 293T and murine 3T3 cells (Figs. 1A, B). Cells engineered to overexpress hTIM1 or hACE2, a control receptor, were infected with a panel of 14 MLV pseudoviruses bearing the GPs of the filoviruses EBOV and MARV, the arenaviruses LASV, LCMV, AMAV, TCRV, JUNV, MACV and OLIV, the alphaviruses CHKV and EEEV, the orthomyxovirus FLUAV (H7N1), the rhabdovirus VSV or the coronavirus SARS-CoV. In addition, cells were infected with VLPs bearing the entry proteins of WNV, a member of the flavivirus family. As shown in Figures 1C and S1, relative to control cells, many pseudoviruses infected hTIM1-expressing 293T cells more efficiently. The entry of EBOV, AMAV, TCRV and EEEV pseudoviruses as well as WNV VLPs was strongly increased by hTIM1 (over 15 fold), that of CHKV considerably (8 fold) and that of MARV, JUNV, MACV and VSV moderately (2–5 fold). The entry of the remaining pseudoviruses tested - LASV, LCMV, OLIV, H7N1 and SARS-CoV - was increased by less than two-fold, with LASV, H7N1 and SARS-CoV being the least affected. Unlike in 293T cells, hTIM1 overexpression in 3T3 cells had a less dramatic impact on viral entry (Figs. 1D and S1): Only WNV VLPs showed a strong increase in entry in hTIM1-expressing 3T3 cells relative to control 3T3 cells, while EBOV, AMAV, TCRV, JUNV and MACV pseudoviruses showed a moderate increase. In both experiments virus titers were not limiting for non-hTIM1-using pseudoviruses, since longer infection times yielded much higher levels of entry in the control cells (Figs. S1B, D). Together these data suggest that hTIM1 supports the entry of a wide range of pseudoviruses, in particular those for which no high-affinity cell surface receptor has been identified. In addition, the effect of hTIM1 is dependent on the cellular background in which the experiment is performed. To further assess the role of TIM1, we tested the ability of an anti-hTIM1 antibody to inhibit infection. As shown in Figure 1E in TIM1-expressing 293T cells the mouse monoclonal anti-hTIM1 antibody 3D1 [21] inhibited the entry of each pseudovirus to the same extent as its entry had been enhanced by hTIM1. Similar efficient inhibition was observed in Huh7 cells, a human cell line with high endogenous hTIM1 expression (Figs. 1F, G): Anti-hTIM1 antibody 3D1 inhibited the entry of EBOV, AMAV, TCRV and EEEV pseudoviruses by over 70% at a 10 nM concentration, indicating that hTIM1 serves as a major entry factor for these viruses in Huh7 cells. JUNV pseudovirus entry was also considerably inhibited (by 60%), suggesting that hTIM1 can contribute to the entry of this virus in Huh7 cells, although JUNV predominantly uses human transferrin receptor 1 (hTfR1) in other cells [42], [50], [51]. In contrast, the other pseudoviruses tested were only moderately affected by the presence of anti-hTIM1 3D1. While results of these entry blocking experiments are overall consistent with the gain-of-entry experiments in 293T cells (Fig. 1C), there are a few exceptions. For instance, MARV and CHKV pseudovirus entry was blocked less efficiently than expected in Huh7 cells (Fig. 1G) and Huh7 cells were not efficiently infected by WNV VLPs (data not shown). We then confirmed the role of hTIM1 in virus infection, using replication-competent viruses. To circumvent the need for BSL3 or 4 laboratories, required for infectious MACV, JUNV, CHKV, EEEV or WNV, we chose TCRV to represent the arenavirus family, RRV and SINV to represent the alphavirus family and DENV to represent the flavivirus family. In addition, FLUAV H1N1 was used as a negative control. When hTIM1-293T cells and the control hACE2-293T cells were infected with serially diluted viruses, infection levels were markedly enhanced in hTIM1-293T cells across the board, except for H1N1 (Fig. 2), which is consistent with the pseudovirus entry data shown in Figure 1. Of note, the magnitude of infection enhancement was generally greater at low virus titers, implying that TIM1 may play a more important role when virus titers are limiting, for example at the initial phases of infection. This initial advantage conferred by hTIM1 was further amplified during the first few cycles of replication (Fig. S2). As a surrogate for replication-competent EBOV we used EBOV VP40-based VLPs [8] bearing the wildtype EBOV entry protein, and confirmed that hTIM1 efficiently enhances their entry (data not shown). hTIM1 is a receptor involved in the PS-mediated uptake of apoptotic cells [21]. To test the hypothesis that hTIM1 may broadly increase viral entry because it binds PS on viral membranes rather than to specific viral glycoproteins, we compared viral usage of a hTIM1 variant defective in binding PS with that of wt hTIM1. The mutant variant, hereafter referred to as AA-hTIM1, has two mutations in the PS-binding pocket that are known to nearly abrogate PS binding [19], [21]. Indeed, when these receptors were overexpressed in 293T cells we found that AA-hTIM1 was unable to enhance the entry of any pseudovirus tested (Fig. 3A) despite efficient AA-hTIM1 expression (Fig. 3B). This indicates that viral hTIM1 usage is PS-dependent. To further assess the role of PS, we tested whether various liposomes are able to block hTIM1-mediated viral entry (Fig. 3C). Consistent with a PS-dependent mechanism, liposomes consisting of 50% PS and 50% PC, but not those consisting of PC alone, efficiently blocked the entry of all hTIM1-using pseudoviruses into hTIM1-expressing 293T cells at 3 µM concentration. In contrast, the entry of LASV and H7N1 pseudoviruses, whose entry is not enhanced by hTIM1, were not affected by either liposomes. Unexpectedly, PE-containing liposomes were also able to inhibit hTIM1-medated viral entry (Fig. S3). This observation raises the possibility that PE, another marker of apoptotic cells [52], which shares structural similarities with PS but is not negatively charged at physiological pH, may also play a role in viral hTIM1 usage. PS dependency of viral hTIM1 usage implies that virions lacking any viral entry protein should also bind to and internalize into the intracellular compartments of hTIM1-expressing cells. To test this we took advantage of GFP-fused matrix proteins [47], [48] that allow, when incorporated into the virions, the detection of prefusion stages of viral entry. As shown in Figures 4A and B, when 293T cells expressing hTIM1, AA-hTIM1 or a control receptor were infected with EBOV VP40-matrix-based VLPs (VP40-GFP VLPs), those bearing no GPs were readily internalized by hTIM1, provided that the TIM1 PS-binding domain was functional. Although EBOV GP-bearing VP40-GFP VLPs appeared to be internalized more efficiently than those lacking GP, this bias is most likely due to the fact that the latter are released 3 to 5 times less efficiently from the producer cells [53], [54]. Consistent with this explanation, when the internalization experiment was repeated with RT-activity normalized MLVgag-GFP virions, we obtained even higher internalization efficiencies for GP-free than EBOV GP-bearing virions (Figs. 4C, D). Thus, results presented in Figure 4 demonstrate that virions bind to and internalize via hTIM1 in a manner that is independent of specific viral entry proteins, but instead is dependent on components of the viral membrane. We next tested whether the observation that GP-free MLVgag-GFP virions are readily internalized by hTIM1 could be extended to the same virions bearing the GPs of LASV, H7N1 and SARS-CoV, which had been unaffected by hTIM1 expression in the entry assays that rely on a post-fusion readout (Figs. 1 and S1). As shown in Figure 5A, the internalization of H7N1 and SARS MLVgag-GFP virions, normalized for RT-activity, considerably increased in the presence of hTIM1, albeit less than that of EBOV and GP-free virions. We cannot, however, exclude the possibility that the SARS and H7N1 MLVgag-GFP virions that internalized via hTIM1, are those carrying fewer entry proteins on the their surface. Nonetheless, this internalization was blocked by PS-containing liposomes, but not by those consisting of only PC (Fig. 5B). These findings indicate that hTIM1 promotes the PS-dependent internalization of H7N1 and SARS-CoV virions without leading to productive infection (compare with Figs. 1C, 6A and S1E). In contrast, internalization of LASV MLVgag-GFP virions was only minimally increased by hTIM1 (Fig. 5A) and was not blocked by PS-containing liposomes (Fig. 5B), suggesting that the molecular mechanism responsible for the lack of hTIM1-mediated entry for LASV is distinct from those of H7N1 and SARS-CoV. As suggested by Morizono et al. [35], PS receptor usage by viruses may be influenced by their affinity for other cell surface receptors. For instance, LASV internalization via alpha dystroglycan, its primary receptor, may be too efficient for PS receptors to compete with. We tested this receptor-affinity hypothesis using the GPs from various SARS-CoV isolates with differing affinities for the same receptor. Tor2, isolated from the major SARS-CoV outbreak, GD from a minor one, and SZ, from a reservoir species civet cat, respectively show high, moderate and low affinity to hACE2 [37]. As shown in Figure 6A, when 293T cells expressing hACE2, with or without hTIM1, were infected with pseudoviruses bearing these GPs, infection levels reached by these three pseudoviruses corresponded to their reported affinities to hACE2. However, no TIM1-mediated entry increase was observed with any of these SARS-CoV GPs, while the entry of the control pseudovirus (TCRV) was substantially enhanced. These results indicate that receptor affinity cannot explain the inability of hTIM1 to promote infection in the case of SARS-CoV. To further test the receptor-affinity hypothesis we assessed whether the blocking of hTfR1, a high-affinity receptor for MACV [42], might promote MACV' s TIM1 usage. However, as shown in Figure 6B, the entry of MACV pseudovirus into TIM1-expressing 293T cells was as strongly inhibited by the anti-hTfR1 antibody ch128. 1 [51] as in control cells. Thus, limiting the availability of the high affinity receptor does not increase TIM1 usage. Since a number of human PS receptors have been described [25], we sought to determine if PS receptors other than hTIM1 have a similarly broad impact on viral entry. For example, hAxl was recently shown to enhance the infection mediated by the entry proteins of SINV, RRV and Baculovirus in a PS-dependent manner by binding the serum proteins Gas-6 and Protein S, which in turn bind PS displayed on these viruses' membranes [35]. As shown in Figure 7A, the infection of 293T cells expressing exogenous hAxl with the panel of pseudoviruses and VLPs used in Figure 1 yielded a pattern almost identical to that observed with hTIM1. One exception, however, was that WNV VLP entry was not much enhanced by hAxl, indicating that Gas-6 and Protein S from FBS might bind PS differently compared to TIM proteins. We then tested whether hTIM3 and 4, also shown to be PS receptors [21], [22], similarly enhance viral entry. Again, 293T cells expressing hTIM3, hTIM4 or a control receptor were infected with various pseudoviruses and WNV VLPs (Figs. 7B, C). While hTIM4 expression resulted in a pattern of entry enhancement similar to that of hTIM1, hTIM3 showed only moderate support of TCRV pseudovirus and WNV VLP entry. The mechanisms underlying inefficient viral TIM3 usage seem to be complex (see Fig. S4 and Supplementary Text S1). Collectively, these data indicate that different PS receptors tend to enhance infection of the same viruses - although not every virus uses every PS receptor - and suggest that PS receptors other than TIMs and Axl are also likely to increase the entry of a wide range of viruses using a common mechanism. Experimental infections in animal models have shown that macrophages are early targets for the replication of several viruses [55], [56]. We therefore assessed the contribution of PS receptors to viral entry in mouse peritoneal macrophages, which are known to express TIM4 [21] and other PS receptors. To be able to detect viral entry instantaneously, we used VLPs consisting of EBOV VP40 matrix proteins fused to β-lactamase (VP40-Bla VLPs [8]) for infection. As shown in Figure 7D, the entry of VP40-Bla VLPs bearing EBOV GP was inhibited by 45% in the presence of PS-containing liposomes at 10 µM, reflecting as expected that other host cell molecules also play a role in virus infection. In contrast, the entry of the same VLPs bearing LASV GP was only little inhibited (by 8%). The blocking effect was specific for PS, as liposomes consisting of only PC did not inhibit either VLPs. These data are consistent with the notion that PS receptors can help potentiate viral infection in macrophages. EBOV and MARV entry was recently shown to critically depend on NPC1, a cholesterol-transporting protein located in the endo/lysosomal compartment [15]–[17]. To test whether hTIM1 usage by EBOV and MARV is dependent on NPC1, we used NPC1-null CHO cells (NPC1−/−), as well as NPC1-null CHO cells engineered to overexpress mouse NPC1 (NPC1−/−mNPC1), which supports EBVO entry [36]. As expected based on our earlier results (Fig. 1), EBOV pseudovirus entry, and to a lesser extent MARV entry, in NPC1−/−mNPC1 cells was increased in the presence of hTIM1 (right panels of Figs. 8A, B). In contrast, the previously reported non-permissiveness of NPC1−/− cells to EBOV and MARV [15] was not circumvented by hTIM1 overexpression (left panels of Figs. 8A, B). These data indicate that hTIM1 expression cannot overcome NPC1 dependence, confirm the role of NPC1 as a filovirus entry factor and suggest that hTIM1 itself may best be regarded as an attachment factor. Our results, summarized in Figure 9, demonstrate that hTIM1 is an efficient attachment factor for a range of enveloped viruses, and imply that hTIM1 promotes infection by associating with PS on the virions. PS dependency of TIM1 is supported by several independent lines of evidence. First, all pseudoviruses capable of using hTIM1 can also be enhanced by at least one other PS receptor, e. g. , hTIM4 or hAxl (Fig. 7). Second, a functional, hTIM1 PS-binding domain was an absolute prerequisite for viral hTIM1 usage (Fig. 3). Finally, the interaction between hTIM1 and GP-free virions was sufficient to elicit attachment and internalization, and this internalization was blocked by PS-containing liposomes (Figs. 4 and 5). Our data are noteworthy and surprising in a few respects. First, the finding that the entry efficiency of a large number of viruses can be enhanced by TIM1 or related receptors suggests that PS receptors may play a more prominent role in viral entry than previously appreciated. This may appear difficult to reconcile with structural studies reporting that the membranes of flavi- and alphaviruses are occluded by tightly-arranged viral glycoproteins. However, flavivirus virions often incorporate immature glycoproteins [57], which could lead to gaps in the glycoprotein coat through which PS becomes accessible. Our findings are consistent with another recent report for a role of TIM proteins in flavivirus entry [58]. Second, as described below, the importance of PS receptors in viral entry is underscored by the fact that several viruses are known to initiate infections in PS receptor-rich cells. Finally, because the exposure of PS on virions is likely a common feature of enveloped viruses [33], it is perhaps most surprising that the entry of a number of viruses remains unaffected by TIM1 expression. There are several plausible explanations for why not every PS receptor supports the entry of every enveloped virus (Fig. 9). First, PS receptor usage could be affected by the endocytic routes of bona fide virus receptors. For example, the six arenaviruses used in Figure 1C can be divided into three groups with respect to TIM1 use: LASV and LCMV, which use alpha-dystroglycan as their cellular receptor [59] only weakly utilize TIM1; MACV and JUNV whose receptor is TfR1 [42], [50] use TIM1 with moderate efficiency; and TCRV and AMAV, which lack a known human receptor, utilize TIM1 with exceptionally high efficiency. For TIM1 to enhance infection, the endocytic pathways of TIM1-mediated internalization probably need to coincide with those of the primary receptors of these viruses. The finding that hTIM1 use by MACV is dependent on the presence of hTfR1 (Fig. 6B) is consistent with this hypothesis. However, the same explanation may not fully describe the clear differences in TIM1 usage among EBOV, MARV, H7N1 and SARS-CoV (Figs. 1 and S1), all of which need access to late endosomes/lysosomes for productive infection. A second possibility, suggested by Morizono and colleagues for hAxl, is that high-affinity receptors may render hTIM1 use ineffective for some viruses [35]. Although appealing, this receptor-affinity hypothesis cannot explain the absence of TIM1 usage by the SARS-CoV SZ isolate with low affinity to hACE2 or the finding that MACV' s hTIM1 use was not enhanced upon blockage of the hTfR1-mediated entry route (Fig. 6). A final possibility is that steric hinderance by individual viral entry proteins may affect TIM1-mediated enhancement. Specifically, long or densely packed entry proteins, which are usually covered with glycans, may prevent PS receptors from reaching PS on the viral membrane. Consistent with this hypothesis, truncated hTIM1 variants and hTIM3, which has a much shorter mucin stalk than hTIM1, were used less efficiently than wt hTIM1 by several pseudoviruses (Figs. 7 and S4). The efficiency with which viruses utilize PS receptors also appears to be dependent on the cellular background. For example, we show that overexpression of hTIM1 had a lesser effect on viral entry in 3T3 compared to 293T cells (Fig. 1). This difference may be due to cell-specific expression of various endogenous PS receptors. Consistent with this explanation, 3T3 cells express Axl [60], [61], while 293T cells do not [11], [35]. In addition, WNV VLPs, which do not efficiently utilize Axl (Fig. 7A), were the only VLP/pseudovirus showing strong TIM1-mediated entry enhancement in 3T3 cells (Fig. 1D). This example emphasizes that results obtained with specific cell lines need to be generalized with caution. One important question concerning viral PS receptor usage is whether PS receptors are sufficient for productive infection or whether virus-specific receptors are still required. We showed for several pseudoviruses that TIM1-mediated entry depended on the availability of their cellular receptors (Figs. 6B and 8), indicating that TIM1 functions solely as an attachment factor. For instance, TIM1 was unable to enhance the infection of EBOV and MARV pseudoviruses in the absence of the intracellular filovirus entry factor NPC1 (Fig. 8). Also, blocking the accessibility of MACV pseudovirus to its receptor hTfR1 lowered MACV TIM1 usage (Fig. 6B). On the other hand, some viruses were reported to fuse with receptor-free liposomes at low pH [62]–[64], raising the possibility that in a few specific cases PS receptor-mediated internalization may be sufficient for productive infection. Nevertheless, studies using liposomes that are unnaturally enriched with specific lipid components may not accurately represent physiological conditions. PS receptors form a complementary, widely expressed network of receptors that is characterized by functional rather than structural conservation. These particular features of PS receptors likely contribute to their exploitation by viruses as attachment factors. Notably, several PS receptors, including TIM4, are highly expressed on mammalian macrophages and dendritic cells. These cells play critical roles in the initial stages of infection of filoviruses and flaviviruses in particular [55], [56], [65], and PS receptors may thus play correspondingly important roles in establishing these infections. PS receptors may be more important still for viruses like DENV, WNV, EEEV and CHKV, which are borne by insect vectors. The role of PS as apoptotic marker is conserved in insects [66], and compared to mammalian cells the membranes of insect cells are generally enriched with PE [67], [68], which binds several PS receptors. Thus mosquito-delivered virions may especially benefit from PS receptor-mediated enhancement of infection. In conclusion, our results indicate that hTIM1, hTIM4, hAxl and potentially other PS-binding receptors can enhance the entry of a number of highly divergent viruses. As demonstrated for hTIM1, the enhancement conferred by all of these receptors is likely PS dependent and does not require any viral entry protein. Accordingly, these proteins cannot properly be described as viral receptors, although the nature of the viral entry protein clearly impacts the relevance of PS receptors to infection. In some cases, PS receptors may play critical roles in establishing or maintaining an in vivo infection, which could affect disease severity. Thus our results support the proposal of Soares et al. , demonstrated for Pichinde virus and mouse cytomegalovirus [33], that therapeutic strategies targeting PS and other anionic phospholipids may be broadly effective against a wide range of viruses.

To infect cells, enveloped viruses typically utilize cellular receptors, which mediate specific, high-affinity interactions with the viral entry protein and prime the entry protein for subsequent steps in the viral entry process. Viral entry is also enhanced by attachment factors. Although less specific than receptors, attachment factors can alter the course of infection and thus severity of viral disease by increasing the infection efficiency of specific target cells. Here we observed that TIM proteins, a group of proteins that promote phagocytosis of apoptotic cells, can dramatically enhance the entry of a number of viruses, including Ebola, West Nile and dengue viruses, whereas they have little effect on the entry of other viruses. The inability of a virus to use TIM proteins may be due to the presence of an abundant, high-affinity receptor (Lassa fever virus), or because the TIM proteins direct virions to a non-productive internalization pathway (SARS coronavirus, influenza A virus). Mechanistically, TIM proteins appear to interact with enveloped viruses and apoptotic cells similarly by binding phosphatidylserine residues exposed on the viral and cellular membranes. Collectively our studies show that TIM proteins are attachment factors that can substantially improve the infection efficiency of a number of pathogenic viruses.

Abstract Introduction Materials and Methods Results Discussion

viral attachment viral entry viral transmission and infection virology biology microbiology

2013

TIM-family Proteins Promote Infection of Multiple Enveloped Viruses through Virion-associated Phosphatidylserine

Productive HIV infection of CD4+ T cells leads to a caspase-independent cell death pathway associated with lysosomal membrane permeabilization (LMP) and cathepsin release, resulting in mitochondrial outer membrane permeabilization (MOMP). Herein, we demonstrate that HIV infection induces damage-regulated autophagy modulator (DRAM) expression in a p53-dependent manner. Knocking down the expression of DRAM and p53 genes with specific siRNAs inhibited autophagy and LMP. However, inhibition of Atg5 and Beclin genes that prevents autophagy had a minor effect on LMP and cell death. The knock down of DRAM gene inhibited cytochrome C release, MOMP and cell death. However, knocking down DRAM, we increased viral infection and production. Our study shows for the first time the involvement of DRAM in host-pathogen interactions, which may represent a mechanism of defense via the elimination of infected cells. Several proteolytic processes are involved in programmed cell death. Lysosomal membrane permeabilization (LMP) and mitochondrial outer membrane permeabilization (MOMP) have been identified as major events triggering programmed cell death. Thus, in a number of models, lysosomal destabilization plays an early and important role in cell death [1], [2]. Lysosomes are acidic organelles that contain numerous acid hydrolases capable of digesting macromolecules of the cell. Upon LMP, the cathepsins are released to the cytosol, where they can initiate the intrinsic apoptotic pathway. This process is mediated in part by the proteolytic activation of the pro-apoptotic molecules, Bid and Bax, resulting in MOMP and cytochrome C release [3]. Thereafter, the release of cytochrome C causes the activation of effector caspases and triggers a caspase-dependent apoptotic pathway. However, lysosome leakage can also induce a caspase-independent non-apoptotic cell death pathway [4]. Thus, lysosomal hydrolases and proteases are acting as initiators and effectors of programmed cell death. CD4+ T cells productively infected with HIV-1 die through a caspase-independent death pathway [5], [6], [7], [8], [9]. Treatment of productively infected CD4+ T cells with the reverse transcriptase inhibitor DDI prevents programmed cell death [5], [7], [10]. The death of HIV-infected CD4+ T cells is associated with the limited permeabilization of lysosomes and lysosomal efflux of cathepsins to the cytosol [10]. Cathepsin D induces conformational change of Bax and its insertion into the OMM promotes the release of cytochrome C [10]. LMP is induced by both the X4 and R5 laboratory strains and by HIV-1 isolates from infected patients. Thus, the permeabilization of lysosomes precedes that of mitochondria and represents an early commitment to cell death in HIV-infected CD4+ T cells [10]. Activation of the tumor suppressor p53 can trigger a primary lysosomal destabilization [11], [12]. Induction of its proapoptotic target genes in virally infected cells has been considered as an altruistic suicide mechanism that limits viral infection. Thus, many viruses, including simian virus 40 (SV40), human papilloma virus (HPV) and adenoviruses (Ad), have evolved mechanisms to prevent p53 responses [13], whereas active p53 was detected with several other types of viruses, such as vesicular stomatitis virus (VSV), Newcastle disease virus (NDV) [14] and human immunodeficiency virus (HIV) [15]. However, the mechanism by which p53 mediates LMP is so far unknown. Lysosomes are also important by their ability to regulate the terminal steps of autophagy [16], [17], [18]. Autophagy is an evolutionarily conserved process first defined genetically in yeast [19], [20]. The primary function of autophagy in most cell types is thought to be an adaptive response to starvation, and is essential for cell survival by degrading proteins and organelles damaged during oxidative stress. In some cellular settings, it can serve as a cell death pathway by itself [21] or in collaboration with apoptosis [22], although its role in this regard is still debated. The cross-talk between apoptosis and autophagy is therefore quite complex, and sometimes contradictory. It has been suggested that autophagy is involved in HIV viral replication [23], in the death of uninfected CD4+ T cells following the interaction of the HIV envelope glycoprotein and its co-receptor CXCR4 [24]. On the opposite, HIV infection inhibits autophagy in macrophages, dendritic cells [25], [26], [27] or even in CD4+ T cell lines [28]. The damage-regulated autophagy modulator (DRAM), a lysosomal protein, has been reported to link p53 to autophagy [29]. It shows a high degree of conservation throughout evolution. Interestingly, its expression was reported to be lower in certain tumors, suggesting it plays a role in human pathology. However, whether DRAM links p53 and LMP in the context of host-pathogens relationships has never been addressed, to the best of our knowledge. Our results highlight the major role played by DRAM in the regulation of LMP and autophagy in HIV-infected CD4+ T cells downstream from p53 activation. A specific siRNA blocking DRAM protein expression inhibited LMP and prevented the death of CD4+ T cells, which leads to a higher number of infected cells. We propose that the ancestral DRAM protein represents a mechanism of self-defense involved in the elimination of microbe-infected cells and constitutes a critical aspect of antiviral immunity. Based on our previous results [7], [10], we infected purified primary CD4+ T cells with HIV-1LAI for 12 h at a MOI of 0. 01, then activated the cells with ConA and IL-2. The number of infected cells (intracellularly stained with anti-HIV-Gag antibody) peaked five days after infection (Figure S1A, B), and a concomitant increase in lysosomal destabilization, as demonstrated by the release of cathepsin D (B and L, not shown), was observed (Figure S1C). Thus, lysosomes are rapidly permeabilized in primary CD4+ T lymphocytes infected with HIV-1, resulting in the release of cathepsins into the cytosol [10]. We then assessed the expression of DRAM in primary CD4+ T cells infected with HIV-1. We detected more DRAM protein at 31 kDa by western blots at day five in HIV-infected CD4+ T cells than in non-infected cells (Figure 1A, B). Consistently with previous results [15], [30], we found by western blot that p53 in HIV-1-infected primary CD4+ T cells is phosphorylated on serine 15 (P-p53) and increases concomitantly with the increase of viral replication (Figure S1D). By cell fractionation, we found an accumulation of p53 within the nucleus (Figure S1E). Fluorescent microscopic analysis of P-p53 showed that the percentages of CD4+ T cells expressing P-p53 increase (Figure S1F) and P- p53 occurred in HIV-infected primary CD4+ T cells (Gag+) but not in non-infected cells (Gag−) (Figure S1G). More than 80% of the CD4+ T cells expressing P-p53 were positive for Gag antigen. We then assessed mRNA levels of DRAM in HIV-infected primary CD4+ T cells versus non-infected cells. Quantitative gene expression analysis with real-time RT-PCR showed that DRAM expression was increased on days 4 and 5 post-infection in HIV-infected CD4+ T cells (Figure 1C). We also found an up-regulation of the mRNA levels of p53-inducible genes such as Bax, p21, and HDM2 (data not shown). Altogether, these results indicate that HIV infection in primary CD4+ T cells causes p53 transcriptional activation associated with increased expression of DRAM. In order to confirm the link between p53 activation and DRAM expression, we used specific siRNAs targeting DRAM and p53 mRNA. As expected, the inhibition of DRAM protein production had no effect on p53 expression, whereas the siRNA targeting p53 resulted in lower levels of both the p53 and DRAM proteins in HIV-infected primary CD4+ T cells (Figure 1D, E and F). Interestingly, DRAM (in red) forms aggregates in Gag+ cells (in green) (Figure 1F). Confocal microscopy revealed the colocalization of DRAM and the lysosome-associated membrane glycoprotein 2 (LAMP2) that confirms its lysosomale localization (Figure S2). Quantification of DRAM and LAMP2 expression using ImageJ software indicated that the level of DRAM is increased in HIV-infected CD4+ T cells but not of LAMP2. Altogether, our results demonstrated for the first time that HIV-1 induces DRAM expression, which is linked to p53. Next, we examined autophagy-related ultrastructures in CD4+ T infected by HIV. Electron microscopic analyses of HIV-infected primary CD4+ T cells with budding HIV viruses (arrow heads) showed that cells contained large numbers of vacuoles with double-membrane structures (arrows) (Figure S3A, B). These vacuoles were not observed in non-infected cells (Figure S3, panel Aa). The presence of cytoplasmic material within these double-membrane structures represents autophagosomes (large arrows), the first autophagic-related structures to be produced (Figure S3, panel Ca, b). We also observed lysosomes located near autophagosomes (Figure S3, panel Cb). Microphotographs also show autophagolysosomes (dashed arrows) resulting from the fusion of lysosomes with autophagosomes associated with degradation of the sequestered content, in HIV-infected CD4+ T cells (Figure S3, panel Cc). In the latter, autophagolysosomes were more frequently detectable than autophagosome structures (Figure S3D). Moreover, in CD4+ T cells, in contrary to the situation reported for differentiated macrophages [25] or monocytes infected with HIV at day 5 (Figure S3E), we detected no viral particles in autophagosome structures. The identification of several genes encoding proteins responsible for the execution of autophagy has facilitated the detection and manipulation of the autophagy pathway (see reviews [16], [18]. LC3 is a protein marker reliably associated with autophagosomes during autophagy [31] although its localization in phagosomes after TLR stimulation has also been reported [32]. We investigated LC3 status, to identify the machinery involved in autophagy at the biochemical level. Detection of both LC3-I and LC3-II revealed that the ratio of LC3-II/LC3-I was higher in HIV-infected CD4+ T cells than in non-infected CD4+ T cells (Figure 2A). In order to examine the relationship between Gag+ target cells and LC3 proteins, we analyzed the formation of LC3 puncta by confocal microscopy. Consistent with immunoblotting results, larger numbers of CD4+ T cells with punctate LC3 were found in HIV-infected cultures (Gag+) than in non-infected ones (Figure 2B, C). Moreover, we compared LC3 staining in uninfected bystander cells (Gag−) and HIV-1 infected CD4+ T cells (Gag+, green) found within infected samples five days after infection and found that the formation of LC3 puncta was mostly confined to Gag+ target cells (Figure 2D). We next analyzed the early events associated with the autophagy pathway. The initiation of autophagy involves a complex of Beclin 1 and PIK3C3, whereas Atg5 (30 kDa) is required for autophagosome precursor synthesis. Atg5 forms a complex with Atg12 (the Atg5-Atg12 complex), which has a molecular weight of 64 kDa and participates in the autophagosome membrane elongation. Consistent with the accumulation of LC3-II in HIV-infected CD4+ T cells, on day 5 post-infection, Beclin 1 protein level was higher in HIV-infected CD4+ T cells than in uninfected CD4+ T cells (Figure 3A, B). We also found that the expression of Beclin mRNA is significantly increased on day 5 post-infection (Figure 3C), whereas the expression of DRAM mRNA increases at day 4 (Figure 1C). Moreover, the Atg5 protein level was higher in HIV-infected CD4+ T cells than in non-infected ones, and a band at 64 kDa band corresponding to the Atg5/Atg12 complex was detected (Figure 3A, B). Using small interfering RNAs (siRNAs) directed against the mRNAs for BECLIN1 and ATG5, LC3-II protein levels were lower, as expected, in HIV-1-infected CD4+ T cells than in control siRNA (mock) -transfected cells, as shown by western blotting and fluorescence microscopy (Figure S4A, B). Altogether, these results support the hypothesis that HIV infection of primary CD4+ T cells induces autophagy. It has been reported that depletion of LAMP2 by siRNA leads to the accumulation of autophagic vacuoles [33], [34]. Thus, the accumulation of autophagic vacuoles in HIV-infected CD4 T cells could be the consequence of LAMP depletion. However, confocal microscopy of LAMP2 in HIV-infected cells revealed no major difference with uninfected CD4+ T cells (Figure S2). Now, the appearance of autophagosomes can result from an increase in their induction or, since virtually all cells have a basal autophagic rate, from a block of their turnover. We used Pepstatin A (PA) to inhibit lysosomal proteases in order to influence the lysosome-autophagy pathway and block the flux of autophagy. We detected increased protein levels of LC3-II in HIV-infected primary CD4+ T cells treated with PA (Figure 3D). One of the best-characterized substrates of selective autophagy is p62 (sequestosome 1/SQSTM1). The p62 protein binds to the autophagy regulator Atg8/LC3 and is incorporated into the autophagosome. Lysosomal degradation of autophagosomes leads to a decrease in p62 levels during autophagy [35], [36]; conversely, impairment of autophagy is accompanied by the accumulation of p62 [37]. Thus, we measured the expression of p62 as a marker of the terminal step in the autophagy pathway. We found that the amount of p62 decreased in HIV-infected CD4+ T cells (Figure 3E). These results show that autophagy results more from induction of autophagosomes rather than from reduced autophagy flux in HIV-infected CD4+ T cells. Finally, we investigated whether knocking down DRAM and p53 expressions inhibits autophagy in HIV-infected CD4+ T cells. Immunoblotting showed that the inhibition of DRAM and p53 protein production decreased the formation of LC3-II (Figure 3F). Moreover, staining for LC3 remained punctuate in Gag+ CD4+ T cells treated with control siRNA (mock), whereas lower levels of puncta were displayed after treatment with siRNA targeting DRAM or p53 (Figure 3G). Altogether these results demonstrated that HIV infection of primary CD4+ T cells mediates autophagy in a DRAM/p53 dependent pathway. Because we have previously showed that HIV mediates LMP, we investigated whether the knockdown of DRAM and p53 affected LMP. First, to monitor lysosomal destabilisation, CD4+ T cells isolated from HIV-1-infected cultures at day 5 were loaded with FITC-conjugated dextran of 40-kDa, and after a 2-h chase, the cells were visualized by laser scanning confocal microscopy (Figure 4A, B). The redistribution of the FITC-dextran molecules was observed in Gag+ cells (diffuse staining), but not in Gag− cells (Figure 4A). Thus, a diffuse staining pattern for the 40-kDa FITC-dextran molecules was observed in 60% of the Gag+ cells (≥200 cells were examined) (Figure 4B), which is indicative of lysosomal efflux. By inhibiting the expression of DRAM and p53 using siRNAs, the 40-kDa molecules were almost totally confined to punctate structures, consistent with exclusive lysosomal localization (Figure 4A, B). We then assessed by confocal microscopy whether the inhibition of p53 and DRAM prevented cathepsin D release in the cytosol. By modifying the levels of p53 and DRAM proteins with specific siRNAs, we showed a significant decrease of cathepsin D release in cytosol in Gag+ target cells (Figure 5A, B). Digitonin-based subcellular fractionation and immunoblot analysis was used to visualize cathepsin D release. Our results showed higher amount of cathepsin D in the cytosol of HIV-1 CD4 T cells in comparison to uninfected cells. In presence of specific siRNAs against p53 and DRAM, cathepsin D release is reduced (Figure 5C). Anti-Lamp-1 was used to verify the absence of lysosomal contamination in the cytosolic fraction. We previously reported that cathepsin D release from lysosomes is an early event resulting in mitochondrial destabilization and cytochrome C release in HIV-1-infected CD4+ T cells [10]. This led us to investigate whether the knockdown of DRAM and p53 affected MOMP. Our results clearly show that siRNAs specific for DRAM and p53 inhibited cytochrome C release in Gag+ as assessed by fluorescence microscopy (Figure 5A, B) and by immunoblot (Figure 5C). Moreover, MOMP (ΔΨm) and cell death (PI+) assessed by flow cytometry using DioC6 probe (Figure 6A, B) and using propidium iodide (Figure 6C, D), respectively, confirms a preventive effect of specific siRNA for DRAM and p53. We also evaluated whether the inhibition the Beclin 1 and Atg5 gene products impacts on LMP. In fact, as demonstrated by the diffuse staining of cathepsin D in Gag+ cells, inhibition of BECLIN1 and ATG5 expression by specific siRNAs had no major effect on LMP (Figure S5A, B) and cell death (Figure S5C). Because it has been proposed that HIV mediates death of bystander CD4 T cells through autophagy [24], we incubated quiescent CD4 T cells in the presence of HIV and ddI. At days 4 and 5, we analyzed mitochondrial depolarization (ΔΨm), cell death (PI+) and viral replication (Gag detection) by flow cytometry (Figure S6). In the absence of viral replication (less than 2% of the CD4+ T cells are Gag+) the cells do not undergo death [7], [10]. We also demonstrated by western blot the absence of DRAM augmentation and the absence of LC3-II in bystander cells in contrast to what happen in productively infected CD4+ T cells (Figure S6). HIV-1 accessory proteins such as the viral protein R (Vpr) or nef have been associated with T cell death. Thus, Vpr causes G2 arrest and apoptosis in cycling cells [38], [39]. Therefore, we assessed whether Vpr modulated LMP. We infected purified CD4+ T cells with either wild type or a Vpr-defective NL4-3 virus. Our results showed that the release of cathepsin D and cytochrome C is similar in the presence or absence of Vpr, and that inhibition of DRAM using specific siRNA prevents LMP (cathepsin D release, Figure S7B, C) and MOMP (cytochrome c release, Figure S7B). Thus, Vpr encoded by HIV is dispensable to trigger LMP in primary CD4+ T cells. Another viral protein, Nef, induces in CD4+ T lymphocytes the accumulation of lysosomes [40] and a limited lysosomal permeabilization [10]. Therefore, we assessed whether the inhibition of DRAM and p53 prevents Nef-mediated LMP and MOMP [10]. Whereas overexpression of Nef induced cathepsin D and cytochrome C releases, in contrast to Nef antisense (Figure S8A, B), siRNA against DRAM or p53 did not impact LMP (Figure S8A, B) and MOMP (Figure S8C) mediated by Nef. However, we cannot exclude that residual DRAM is enough to induce LMP. Because LMP is upstream from MOMP and cell death, we evaluated the consequence of DRAM inhibition on viral infection. Thus, we evaluated the numbers of infected cells (Gag+) in the culture as previously described [10]. The siRNAs targeting p53 and DRAM clearly resulted in the presence of greater number of HIV-infected CD4+ T cells than control siRNA (mock) (Figure 6E). This is consistent with the levels of HIV DNA measured (data not shown). Thus, the knock-down of DRAM and p53 protein levels by specific siRNAs resulted in numbers of infected cells three to four times greater than the control. We also measured by ELISA in the supernatants the p24 production in the presence or absence of DRAM. Our results revealed that viral production is increased following DRAM knock-down as compared to the control (Figure 6F). Taken together our results demonstrate the major role for DRAM in regulating LMP and cell death in HIV-infected cells. Many different viruses induce lysosomal damage and kill the cells in which they replicate [41], [42], [43], [44]. Our results provide new insight in virus-cell interaction, showing a clear relationship between p53 activation, DRAM and LMP in a model of viral replication-mediated cell death. The data presented here demonstrate for the first time that DRAM controls LMP; the depletion of p53 and DRAM indeed prevents LMP and cell death in HIV-infected CD4+ T cells. Thus, this provides a rationale for the previous observation that the tumor suppressor p53 can trigger a lysosomal destabilization that contributes to cell death [11], [12]. These results reinforce the concept that productive HIV infection is associated with a caspase-independent cell death pathway associated with early lysosomal destabilization. The discovery of the role of DRAM during HIV infection identifies this molecule as a new regulator of host cell-pathogen interactions, contributing to the control of viral infection. This may represent a process of altruistic cell suicide developed by multicellular organisms to defend themselves against microbial infections. Lysosomes are permeabilized in CD4+ T lymphocytes productively infected with HIV-1, resulting in the early release of cathepsins into the cytosol. The released cathepsin D acts upstream from the conformational change in Bax and MOMP [10]. On the contrary, bystander cells exposed to HIV do not express higher levels of DRAM and LC3, in agreement with idea that viral replication induces a death signal [5], [6], [7], [10]. Accumulating evidence indicates that lysosomes function as death signal integrators in response to a wide variety of death stimuli [1]. We demonstrated that the inhibition of DRAM by specific siRNA prevents cathepsin D release, demonstrating for the first time that DRAM is critical for LMP in the context of host-pathogen interaction. We found higher levels of both DRAM mRNA and protein in HIV-infected CD4+ T cells than in non-infected cells. However, it was previously shown that the overexpression of DRAM was not enough for inducing autophagy and cell death [29]. This initial observation suggests that additional partners and/or translational modifications are probably required for DRAM-mediated LMP in HIV-infected CD4+ T cells. We found higher amounts of DRAM that form puncta in Gag+ cells, as visualized by fluorescence microscopy. Bax and Bak, the gatekeepers that induce MOMP, also form aggregates. Unfortunately, attempts at immunoprecipitating DRAM in primary CD4+ T cells infected by HIV using commercial antibodies to identify partners have been unsuccessful so far. Therefore, further analysis is necessary to specify the mechanism by which DRAM destabilizes lysosome membranes in primary CD4+ T cells. Our results demonstrated that inhibition of p53 by specific siRNA prevent DRAM expression and autophagy. The infection of T-cell lines and primary CD4+ T cells with HIV was initially reported to be associated with stronger expression of pro-apoptotic genes, such as those encoding Bax, p21 and MDM2 [45], [46]. CD4+ T cells productively infected with HIV display phosphorylation of p53 on serine 15 leading to the accumulation of p53 within the nucleus in agreement with other observations [15], [30]. However, in these models the role of p53 was not directly addressed. Here, we demonstrated using siRNAs that inhibition of p53 prevents LMP and programmed cell death in productively infected CD4+ T cells. Viral accessory protein Vpr has been proposed to induce p53 phosphorylation [38], and it causes G2 arrest and apoptosis via ATR [39]. However, resting CD4+ T cells infected by HIV and stimulated with mitogens accumulate predominantly in the G0/G1 phase of the cell cycle at day 5 [7], [10], [47]. Consistently with the observation in cell cycle in primary CD4+ T cells, our data indicate that Vpr is dispensable for LMP. Other candidates encoded by HIV such as the gp120 envelope glycoprotein [48] have been proposed to induce p53 phosphorylation. However, these proteins in primary CD4+ T cells are dispensable for HIV infection-mediated cell death [9], [47] and no syncytia are observed in the culture [7], [10], [47]. Moreover, in bystander CD4+ T cells, DRAM and LC3-II are not increased. Nef is in itself an important pathogenic factor in HIV-1 infection. In severe combined immunodeficient (SCID) –hu (thymus/liver) mice, Nef is pathogenic, as evidenced by thymocyte depletion [49]. It has also been reported that Nef can induce in CD4+ T lymphocytes the accumulation of lysosomes [40] and a limited lysosomal permeabilization [10]. Here, we found that DRAM or p53 siRNA has no preventive effect on LMP-mediated by Nef. In agreement with the absence of a p53 effect, overexpression of Nef is not associated with p53 activation (data not shown). Previous observations have indicated that Nef, instead of activating p53, inhibits it [50], [51]. Nef is a critical factor that enhances virus replication in vitro in primary CD4+ T cells and is clearly associated with AIDS [52], [53], [54], [55], [56]. Thus, it is conceivable that Nef indirectly acts as a cofactor in the onset of the permeabilization of lysosomes by favoring viral replication [10]. Activation of p53 – the guardian of the genome – has been reported as a sensor detecting pathogen replication in order to eliminate infected cells [14]. Therefore, prompt induction of LMP and apoptosis of virally-infected cells via p53/DRAM activation will be beneficial for the host as an altruistic cell suicide to limit virus dissemination. We demonstrated that DRAM regulated autophagy in virally-infected CD4+ T cells downstream from active p53. Thus, whereas DRAM was initially described as regulating autophagy in cancer [29], we extended here its role in the context of host-pathogen interactions. Within HIV-infected CD4+ T cells, we have observed (i) ultrastructural autophagy-associated structures, such as autophagosomes and autophagolysosomes; (ii) higher levels of Beclin 1, which is essential for the induction of autophagy; (iii) Atg5-Atg12 complexes, which are considered essential for membrane elongation, (iv) LC3-II aggregates in p24+ cells, involved in the formation of the autophagosome membrane, and (v) the degradation of p62. These results suggest that HIV mediates autophagy flux in primary CD4+ T cells. It has been suggested that HIV infection inhibits starvation-induced or rapamycin-induced autophagy in CD4 T cell lines [28]. Thus, using primary CD4 T cells and in the absence of additional stimuli to trigger autophagy, we demonstrated that HIV infection increased autophagy instead of inhibiting it. In macrophages infected with HIV, the virus inhibits autophagy flux through Nef, leading to the accumulation of autophagic vacuoles [25]. Thus, although autophagy is observed in both cellular models, the mechanisms are distinct. Indeed, macrophages are resistant to apoptosis, contrary to infected CD4+ T cells. Autophagy is suspected of participating in the elimination of damaged organelles and preserving cell integrity from the accumulation of abnormal proteins [21]. Thus, autophagy could be induced to limit damage induced by disrupted lysosomes. The clearance of ruptured lysosomes by autophagy has been previously shown [57]. Our results demonstrate in primary CD4+ T cells that siRNAs blocking the expression of the Atg5 and Beclin 1 proteins have no effect on LMP and cathepsin D release. Therefore, the induction of autophagy is not directly responsible for the lethality of HIV-infected CD4+ T cells. Viral protein clearance requires intact lysosomes to remove protein aggregates and limit cell death [58]. Herein, by preserving LMP by knocking down DRAM, we prevented cell death mediated by HIV infection. Interestingly, we found greater viral infection and production. Altogether, these data demonstrate that although DRAM is controlling both autophagy and LMP, only this latter process is essential for survival of the infected cells. Viral replication is thought to directly induce CD4 T-cell death during the acute phase of HIV infection, particularly in the intestine [59], [60], [61], [62]. This suggests that an absence of DRAM could be favorable for viral dissemination and persistence of virally-infected CD4+ T-cells in HIV-infected individuals. Pharmacological intervention to modulate DRAM in HIV-infected CD4+ T cells may thus help to eliminate viral reservoirs and delay development of clinical AIDS. Our results demonstrate for the first time that the destabilization of lysosomes is induced by DRAM and is an early event in the commitment to cell death contributing in the control of viral infection. PBMC were isolated from the peripheral blood of anonymous healthy volunteers. All blood donors were informed and agreed to a written consent prior to blood donation in accordance with the guidelines of the Etablissement Français du Sang. CD4+ T cells were obtained by negative selection with a CD4 T-cell Isolation kit (Miltenyi Biotec). The CD4+ T-cell preparation was at least 98% pure. Monocytes (5%) were added to the purified cells, to ensure full T-cell activation. The cells were incubated with HIV-1LAI for 12 h at a multiplicity of infection (MOI) of 0. 01, and activated with 1 µg/ml ConA (Sigma-Aldrich) and 100 units/ml recombinant human IL-2 (Roussel-Uclaf, France), as previously described [7], [10]. The virus was produced from CEM-LTRgfp that allows to monitoring viral replication [63]. HIV-1LAI was recovered from supernatant at the peak of replication. Virus stocks were prepared by transfection of 293T cells with plasmids of Wt and Vpr-defective NL4-3 (kindly provided by S. Benichou). HIV replication was assessed by monitoring the Gag+ cells by flow cytometry using anti-HIV-Gag (KC57-PE, Beckman-Coulter) and by measuring Gag-p24 release in supernatants by an Innotest HIV antigen enzyme-linked immunosorbent assay (ELISA) kit (Ingen). The number of live infected CD4+ T cells was assessed by flow cytometry as previously described [7], [10], [64], [65]. Dead cells were detected using 1 µM of propidium iodide (PI) from Molecular Probes and Annexin V-FITC from Beckman Coulter. To evaluate changes in the inner mitochondrial transmembrane potential ΔΨm, cells were stained for 15 min at 37°C with 40 nM of the potential-sensitive fluorescent dye DiOC6 (3. 3′-diethyloxacarbocyanine) from Molecular Probes. The reagents used for immunofluorescence studies were: rabbit polyclonal antibodies recognizing anti-MAP LC3 (H-50) purchased from Santa Cruz, anti phospho-p53 (Ser 15) antibodies purchased from Cell Signaling Technology, anti-DRAM antibodies purchased from φProSci, anti-cathepsin D antibodies from Zymed Laboratories, anti-p53 mAb (DO-1) purchased from Santa Cruz, anti-Lamp-2 mAb from Calbiochem, and a sheep anti-cytochrome c antiserum from Sigma. Intracellular Gag antigen was assessed by flow cytometry after fixation and permeabilization of the cells (Intraprep permeabilization reagent, Coulter), which were then stained with FITC- or RD1-labeled mAb against p24gag antigen (KC-57, Beckman coulter). Otherwise, the cells were fixed by incubation with 1% paraformaldehyde, span on glass slides, washed with PBS, and permeabilized by incubation with 0. 05% Triton X-100. The cells were washed and incubated with the antibodies indicated in PBS supplemented with 0. 5% BSA and 2% FCS. The cells were stained with an Alexa-conjugated secondary antibody (Molecular Probes). Nuclei were counterstained for 5 minutes with 5 µM DAPI (Molecular Probes). The cells were examined by conventional or confocal fluorescence microscopy (Zeiss Microsystems). For the formation of LC3-II aggregates, cells with more than 6±2 vesicles were considered positive as previously described [31]. To monitor lysosomal destabilization, CD4+ T cells were incubated for 2 h with 5 mg/ml FITC-dextran of 40-kDa (Sigma) as previously described [10]. After a 2-h chase period, the cells were stained with anti-p24 mAb (Gag+) and examined by laser scanning confocal microscopy. Pellets of 1×106 CD4+ T cells were either directly resuspended in Laemmli buffer containing 2% SDS and 10% 2-ME and boiled for 5 minutes, or lysed in Nonidet P-40 buffer (1% NP-40,50 mM Tris-HCl (pH 7. 4), 150 mM NaCl) supplemented with protease inhibitors. Pepstatin was purchased from Sigma. Cytosolic and nuclear fractions were obtained by extraction with the NE-PER kit (Nuclear and Cytoplasmic Extraction Reagents from PIERCE). Lysates were then subjected to electrophoresis in NUPAGE 4–20% polyacrylamide gels (Invitrogene). The proteins were transferred to polyvinylidene difluoride membranes (Amersham Bioscience) and then incubated with primary antibodies and with horseradish peroxidase-coupled secondary reagents (Amersham Biosciences). The primary antibodies used for western blotting were: rabbit antisera against Beclin 1 (H-300, Santa Cruz), phospho-p53 (Ser 15) (Cell Signaling Technology), DRAM (Stressgen), Atg5 (Novus) and tubulin (Santa-Cruz); mouse mAbs against p53 (DO-1, Santa Cruz), Cathepsin D (BD Transduction Laboratories), Cytochrome c (Pharmingen), p62 (Cell Signaling Technology), Lamp-1 (BD Transduction Laboratories), lamin B (Ab-1, Oncogene Research Products Calbiochem) and actin (Millipore). Rabbit antisera against MAP LC3 was purchased from MBL. Productive HIV infection was visualized by western blotting that allows detection of the presence of viral antigens in cell extracts. The immunoblots were incubated with sera obtained from a pool of HIV-infected patients, and then revealed with horseradish peroxidase-linked goat anti-human secondary antibodies (Amersham Biosciences). The blots were then developed by enhanced chemiluminescence methods (ECL+ from GE Healthcare), photographed with a CCD camera (GBOX, SYNGENE), and the optical densities measured and normalized with respect to the loading control (tubulin or Hsp60 for mitochondrial fractions). It must be noted that we have tested all commercial antibodies directed against DRAM for the immunoprecipitation assay; none of them are functional. Cytosolic and heavy membrane fractions were generated from 107 cells, using a selective digitonin-based permeabilization and subcellular-fractionation technique as previously described [7], [10]. In brief, CD4 T lymphocytes were washed with cold PBS and then suspended in a lysis buffer (250 mM sucrose, 20 mM Hepes, 5 mM MgCl2,10 mM KCl, 1 mM EDTA, 1 mM EGTA, pH 7. 4, supplemented with protease inhibitors purchased from Roche). The digitonin concentration was 35 µg/ml. After 5 min, the cells were centrifuged and the supernatant was removed (cytosolic fraction). The remaining pellet was resuspended in lysis buffer (150 mM NaCl, 1. 0% NP-40,0. 5% deoxycholate, 0. 1% SDS, 50 mM Tris-HCl, pH 8. 0) and incubated 30 min. The supernatant comprising the membrane fraction was retained after centrifugation (30 min at 15000 g). Predesigned small interfering RNA (siRNA) molecules targeting p53, DRAM, BECLIN 1 and ATG5 were synthesized by Dharmacon. Scrambled controls were also used. Gene expression was silenced by the small interfering RNA (siRNA) technique [10], using duplexes of 21-nucleotide siRNAs with two 3′-overhanging TT residues (Proligo). The sense strand of the siRNA used to silence the BECLIN 1 gene was CAGTTTGGCACAATCAATATT, that of ATG5 gene seq 1 was GCAACTCTGGATGGGATTGTT and that of seq 2 was CATCTGAGCTACCCGGATATT, whereas that of the DRAM gene was CCACAGAAATCAATGGTGATT. For p53 we used a smart pool. Purified resting CD4+ T cells were transfected, by electroporation with siRNAs (0. 75 µM/4×106 cells), mediated by the Nucleofection system (Amaxa). Cells were allowed to rest for 16 hours, exposed to HIV-1 cultured for an additional 12 h and then stimulated with Con A and IL-2. We also used the autophagy inhibitor 3-methyladenine in some experiments, but the results obtained were unconclusive, due to a strong toxicity of this drug in primary cultures of human cells. Plasmids carrying the Nef gene (from the LAI isolate) in a sense (Nef-WT) and antisense (Nef-AS), under the control of the cytomegalovirus promoter, were kindly provided by O. Schwartz (55). CD4+ T cells were first transfected with siRNA directed against DRAM and p53, and after overnight culture were restimulated with ConA and IL-2 for 4 days. Thereafter, the cells were transfected with either Nef-WT or Nef-AS using the Nucleofection system (Amaxa). After 24 h, the cells were washed and fixed for flow cytometry and confocal microscopy. RNAs were isolated from uninfected and HIV-1 infected CD4+ T-cells at days 3,4 and 5 post-infection. cDNA synthesis was performed using the AffinityScript QPCR cDNA Synthesis Kit (Stratagene, Agilent Technologies) with 1 µg of total RNA and random primers. The resulting RT product was expanded using the Brilliant II SYBR Green QPCR Master Mix (Stratagene, Agilent Technologies) and specific primers for DRAM (forward: 5′-AGACTCCATCTTTTCACCCAAA-3′, reverse: 5′-GCTCTTCACCTTTCAAGCCTAA-3′), Beclin-1 (forward: 5′-AAGACAGAGCGATGGTAG-3′, reverse: 5′-CTGGGCTGTGGTAAGTAA-3′) and the housekeeping gene ribosomal RNA S14 subunit (forward: 5′-GGCAGACCGAGATGAATCCTCA-3′, reverse: 5′-CAGGTCCAGGGGTCTTGGTCC-3′). Detection was performed with the Mx3000P QPCR System (Stratagene, Agilent Technologies). Threshold cycle (Ct) values were obtained for each gene at the different time points using the instrument software. Differences in the levels of gene expression over time were determined for each condition by relative quantification using the Delta Ct method. Pellets of uninfected or infected CD4+ T cells were fixed by incubation for 1 h in phosphate buffer pH 7. 2 supplemented with 1. 6% glutaraldehyde and were then post-fixed by incubation for 2 h in 0. 1 M phosphate buffer supplemented with 1% osmium tetroxide. Pieces of cell pellet were washed for five minutes in water and then dehydrated in a series of increasing concentrations of ethanol before embedding in Epon 812 [66]. Ultrathin sections were cut and stained with 4% uranyl acetate and lead citrate. They were then examined under a ZEISS 902 electron microscope, at 80 KV, or under a FEI Technaï 12 microscope at 80 KV. Data are reported as means ± SEM. The significance of differences was assessed by Student' s t test (Prism software) with p<0. 05 considered significant.

Lysosomes are acidic organelles capable of digesting macromolecules and regulating autophagy. In the context of host-pathogen interactions, productive viral infections are associated with lysosome membrane permeabilization (LMP) and programmed cell death (PCD). At a molecular level, the tumor suppressor protein 53 (p53), which is a key player in the detection of DNA damage, acts also as a sensor of pathogen replication. Activation of p53 has been considered to be an altruistic cell suicide mechanism that limits viral infection. Here, we provide new evidence that damage-regulated autophagy modulator (DRAM), a p53 target gene, regulates both LMP and PCD of HIV-infected CD4 T cells. Whereas the inhibition of DRAM or p53 prevents autophagy in infected cells, the inhibition of the autophagy machinery has a minor role in this context. As a consequence, the silencing of DRAM leads to increased HIV viral infection. This is the first report describing the role of DRAM in the context of host-pathogen interaction. Whereas it is to the advantage of the pathogens to preserve their hosts and thus facilitate their multiplication and dissemination, hosts have developed altruistic cellular processes to defend themself and limit the spread of the infectious agent in multicellular organisms. We propose that the ancestral DRAM protein represents a mechanism of self-defense, inducing elimination of infected cells through LMP.

Abstract Introduction Results Discussion Methods

biology

2013

DRAM Triggers Lysosomal Membrane Permeabilization and Cell Death in CD4+ T Cells Infected with HIV

Chromatin insulators are eukaryotic genome elements that upon binding of specific proteins display barrier and/or enhancer-blocking activity. Although several insulators have been described throughout various metazoans, much less is known about proteins that mediate their functions. This article deals with the identification and functional characterization in Paracentrotus lividus of COMPASS-like (CMPl), a novel echinoderm insulator binding protein. Phylogenetic analysis shows that the CMPl factor, encoded by the alternative spliced Cmp/Cmpl transcript, is the founder of a novel ambulacrarian-specific family of Homeodomain proteins containing the Compass domain. Specific association of CMPl with the boxB cis-element of the sns5 chromatin insulator is demonstrated by using a yeast one-hybrid system, and further corroborated by ChIP-qPCR and trans-activation assays in developing sea urchin embryos. The sns5 insulator lies within the early histone gene cluster, basically between the H2A enhancer and H1 promoter. To assess the functional role of CMPl within this locus, we challenged the activity of CMPl by two distinct experimental strategies. First we expressed in the developing embryo a chimeric protein, containing the DNA-binding domain of CMPl, which efficiently compete with the endogenous CMPl for the binding to the boxB sequence. Second, to titrate the embryonic CMPl protein, we microinjected an affinity-purified CMPl antibody. In both the experimental assays we congruently observed the loss of the enhancer-blocking function of sns5, as indicated by the specific increase of the H1 expression level. Furthermore, microinjection of the CMPl antiserum in combination with a synthetic mRNA encoding a forced repressor of the H2A enhancer-bound MBF1 factor restores the normal H1 mRNA abundance. Altogether, these results strongly support the conclusion that the recruitment of CMPl on sns5 is required for buffering the H1 promoter from the H2A enhancer activity, and this, in turn, accounts for the different level of accumulation of early linker and nucleosomal transcripts. Chromatin insulators are specialized DNA elements that upon binding of specific proteins display barrier and/or directional enhancer-blocking activity. The analysis of the genome-wide localization of insulator binding proteins (IBPs) in vertebrates and Drosophila suggests that insulators partition the eukaryotic genome in autonomous functional domains by promoting the formation of physical loop structures and/or mediate tethering of the chromatin fiber to structural elements within the nucleus [1], [2]. In vertebrates, CCCTC-binding factor (CTCF) is the only IBP that has been well characterized. Mechanistically, CTCF and its associated co-factors, most notably cohesin, are important in establishing long range chromatin interaction [3], [4]. This is illustrated by the CTCF-dependent intra- and inter-chromosomal interaction necessary for allele specific transcription within the mouse β-globin locus and at the imprinting control region in the H19/Igf2 locus [5]–[7]. Similarly, upon binding near the ins and syt8 promoters, located more that 300 kb away, CTCF stabilizes their interaction and affects gene expression at the human insulin locus [8]. Distinct families of insulators, defined by the IBPs necessary for their activity, have been described in drosophila. The best characterized IBPs are Zeste-white5 (Zw5) and Boundary Element Associated Factor 32 (BEAF-32), that bind to the first identified enhancer-blocking insulators scs and scs′ [9], [10], Suppressor of Hair-wing [Su (Hw) ] of the gypsy retrotransposon [11], and dCTCF [12]. The functions of all Drosophila insulators converge as chromatin organizer into that of CTCF in vertebrates. Zw5 and BEAF-32 interact with each other to generate a chromosomal loop that include the 87A7 hsp70 locus [13]. Su (Hw) and dCTCF colocalize at several insulator bodies of diploid nuclei, but not in polytene chromosomes, with the Centrosomal Protein 190 (CP190) which is necessary for both insulator body formation and enhancer-blocking activity [14], [15]. BEAF-32 has also been shown to recruit CP190 to specific DNA sites [16], suggesting that loop formation mediated by CP190 might be a common mechanism for insulator function in drosophila. A DNA element displaying features common to other chromatin insulators has been found at the 3′ end of the sea urchin P. lividus H2A gene, within the tandem repeat of the early histone unit. As reported, the 462 bp sns5 fragment is required for regulation of histone gene expression in the early embryo as well as for H2A silencing at gastrula stage [17], [18]. A physically separable sns fragment of 265 bp, displaying directional enhancer-blocking function in both sea urchin and mammalian cells [19]–[21], was previously identified in sns5. Most importantly, sns5, but not the enhancer-blocker sns, placed in flanking location of a γ-retrovirus vector prevents position effect variegation, improves transgene expression at randomly integration sites in erythroid cells, and by binding erythroid and ubiquitous transcription factors modifies nucleosomal histones to maintain a euchromatic state at the provirus locus [22]. Four protein binding sites have been identified by DNaseI footprinting in the sns5 element, namely -A, -B, -CT, and -D box, all required for the enhancer-blocking and silencing functions, and none of them resemble the CTCF binding-site consensus sequence [23]. Also the ArsI element, the only other insulator so far characterized in sea urchins, does not belong to the CTCF type [24]. It follows that the identification of sea urchin IBPs is of some importance to unravel the mechanism of action of insulators in chromosome organization and gene expression in this species. There is at least an additional reason to identify sns5 IBPs, that is, the mechanism of function of sns5 can be studied within the natural histone gene context. We have in fact presented compelling evidence that its role is to attenuate the H2A enhancer in the interaction with the downstream H1 promoter in order to assure the different level of accumulation of nucleosomal and linker transcripts during sea urchin embryogenesis [25]. In this paper, we describe the identification and functional characterization of a novel homeodomain-containing IBP encoded by the Compass/Compass-like locus that is exclusive to the ambulacrarians. To identify the trans-acting factors that interact with the sns5 insulator in P. lividus, we used a yeast one-hybrid genetic assay [26]. Briefly, a cDNA library of N-terminal fusions to the GAL4 activation domain was screened using as bait a yeast strain bearing a stably integrated pentamer of the boxB cis-element upstream of both the HIS3 and lacZ reporter genes. From this screening we isolated a ∼2. 2 kb cDNA clone encoding a predicted protein of 396 amino acids, which contains a Compass domain followed by an atypical Homeodomain at the C-terminus (Figure 1A). The former domain is shared exclusively among members of the SATB and COMPASS (CMP) protein families [27], [28]. SATB proteins possess an atypical Homeodomain with phenylalanine, instead of tryptophan, at the 48th residue and a single glycine insertion between the first and second helices, whereas CMP proteins contain two atypical Homeodomains with a ten amino-acid insertion between the second and third helices (Figure 1B) [28]. Differently from the above described proteins, the sea urchin predicted protein exhibits a unique atypical Homeodomain bearing an eleven amino acid long insertion between helices II and III (Figure 1B). For these reasons, we have named this newly identified factor COMPASS-like (CMPl). By blasting the public databases with the sequence coding for the Homeodomain of the P. lividus CMPl protein, we show that the above mentioned differences are completely conserved in orthologs of various sea urchin species and in the hemichordate Saccoglossus kowalevskii (Figure 1B). Such a high degree of conservation suggests that these proteins play important role (s) in echinoderms and hemichordates, altogether forming the Ambulacraria group of deuterostome metazoans [29]. To clarify the phylogenetic relationship between SATB, CMP and CMPl, we built a neighbor-joining tree using set of Homeodomain sequences from various metazoans. As expected, in this analysis orthologs of the SATB family, which have only been identified in vertebrates [30], [31], comprised a monophyletic clade (Figure 1C). Orthologs of the CMP family, which instead have been described only in invertebrates [31], also formed a clade. Importantly, the ambulacrarian CMPl sequences formed a distinct clade supported by a high bootstrap value, suggesting that they constitute a novel family of proteins. In spite of extensive searches in the currently available databases of several metazoans, additional CMPl orthologs were not identified, indicating that the CMPl family probably exists only in ambulacrarians. In order to obtain the nucleotide sequence of the Cmpl gene, we BLAST-searched the P. lividus genome database (whole genome shotgun assembly v1. 0, http: //octopus. obs-vlfr. fr/blast/oursin/blast_oursin. php) using the Cmpl full cDNA sequence as a query. Several overlapping scaffolds and contigs were isolated (Supplementary Table S1), from which the overall sequence was derived. The gene structure was inferred by aligning the genome sequence with that of the Cmpl cDNA and by the use of the Genscan software. We further coupled this analysis to the screening of the available P. lividus EST resources. By this approach we retrieved several hits of different size (Supplementary Figure S1). Collectively, these cDNAs harbor a nearly identical 5′-UTR and utilize the same translation initiation sequence, but only one of them almost entirely matched to Cmpl. Intriguingly, four of the remaining cDNAs appeared instead larger and highly divergent at the 3′-side compared to the query sequence (Supplementary Figure S1). We noticed that this fragment actually maps within the Cmpl gene, being partitioned in a couple of additional exons, namely e5 and e6, which are spliced out in the Cmpl mRNA being mutually exclusive with respect to e7–9 (Figure 2A and B). To assess the conservation of the Cmpl locus across ambulacrarians, we extended the BLAST searches to the public genomic databases of the Strongylocentrotus purpuratus and Lytechinus variegatus sea urchins, and to that of S. kowalevskii. From each of the mentioned database, a single genomic scaffold was retrieved (Figure 2A and Supplementary Table S1). Of importance, by phylogenetic footprinting performed by comparison of nucleotide sequences with the VISTA software, we established that the genomic organization of the P. lividus Cmpl locus is fully conserved in the two evolutionarily distant sea urchins, as well as in the hemichordate (Figure 2A). Furthermore, the retrieving of a single Cmpl hit from the fully completed genome sequence of S. purpuratus leads us to presume that Cmpl is most probably a single copy gene in sea urchins. Sequence analysis revealed that, as expected, the protein encoded by the largest splice variant was identical to CMPl for 233 amino acids at the N-terminal side, including the Compass domain, but strongly diverged in the C-terminal region. Most notable is the presence of two atypical Homeodomains, with an insertion of ten amino acids between helices II and III (Figure 2C and Figure 1B). On the bases of these findings, coupled to the phylogenetic analysis based on Homeodomain sequences (Figure 1B and C), we designated this protein as the sea urchin CMP ortholog. Therefore, and unexpectedly, the genetic information for CMPl and CMP proteins partially overlaps in representative genomes of both the ambulacrarian taxa. We then looked by qPCR at the time-course of accumulation of the two splice forms, utilizing primers that distinguish them. As shown in Figure 2D, both transcripts are maternally stored in the unfertilized egg and present at all stages of development. However, Cmpl mRNA is accumulated in the embryo at about a three- to ten-fold lower level than is the Cmp mRNA. After fertilization, Cmpl transcript abundance declines throughout the very early cleavage (up to morula stage), followed by a slight and steady increase until the prism stage. At this time, a later sharp burst in the message prevalence is detected through the pluteus larva. The Cmp transcript is the most abundant and it is accumulated in the embryo following three main phases of expression. Just after fertilization, the mRNA level rapidly raises to peak at the 16-cell stage. A second increase in transcript level occurs approximately from the morula stage, to peak as the pre-hatching blastula is approached. The terminal phase of mRNA accumulation begins at the prism stage, by which time a dramatic climb in the transcript abundance is observed. Thus, these results clearly established distinct temporal expression patterns for the alternative splice products of the Cmp/Cmpl locus. Altogether, our findings indicate that the genomic organization of the Cmp/Cmpl locus is evolutionary conserved across ambulacrarians, and that the mRNAs generated by the alternative spliced Cmp/Cmpl transcript exhibit distinct temporal expression profile in the sea urchin embryo. To ascertain the binding activity of CMPl to the sns5 chromatin in sea urchin embryos, we performed quantitative ChIP assays. To this end, we expressed different portions of the CMPl protein in E. coli. As the fragment corresponding to the N-terminal 98–270 amino acid residues gave the maximum yield of the protein in a soluble form, we have generated a polyclonal antibody against this peptide. Being the first 134 residues of this peptide shared by CMPl and CMP, we predicted that the anti-CMPl antibody should rather be able to react with both proteins. Indeed, in western blot assay, the antibody recognized two distinct protein bands at roughly 40 and 90 kDa in sea urchin nuclear extracts at morula and gastrula stages (Figure 3A). These molecular weights were congruent with those predicted for CMPl and CMP proteins, respectively, whereas no reaction occurred with the pre-immune serum (Figure 3A). Chromatin containing the sns5 region was consistently precipitated by the affinity-purified anti-CMPl antibody, in samples obtained from cultures of embryos at morula and gastrula stages (Figure 3B; see Materials and Methods). As a negative control, we selected two additional genes, hbox12 [32] and otp [33], [34], that do not share significant sequence similarity with sns5 in their promoters. As expected, both genes were clearly negative to CMPl occupancy from the same ChIP preparations (Figure 3B). Furthermore, only negligible amounts of sns5 sequences were precipitated from chromatin of both developmental stages by the unrelated antiserum from the same host species against the Otx regulator [32], used as control. Overall, these results point out the specific and constitutive association of CMPl to sns5 sequence in the native chromatin. However, as the antibody effectively recognizes epitopes common to both CMPl and CMP, these experiments may represent the full impact of both proteins on the sns5 chromatin. We addressed this question by performing a in vivo trans-activation assay. To this purpose, pentamer of the boxB cis-element was introduced, in both orientations, upstream of the H3 minimal promoter in the pH3-GFP vector, to obtain the 5×boxB-pH3-GFP reporter constructs (Figure 3C). As effectors we used synthetic mRNAs encoding for a forced transcription activator, in which the activation domain of the viral VP16 protein was joined to the DNA binding domain of either CMPl or CMP. Each transgene, alone or in combination with a chimeric effector mRNA, was then microinjected into sea urchin zygotes, embryos were allowed to develop and scored for GFP expression. As shown in Figure 3D, the control pH3-GFP vector and the 5×boxB-pH3-GFP constructs were not expressed in the absence of effectors in all of the injected embryos (n>300). As expected, and in agreement with the one-hybrid and ChIP results, a significant fraction of embryos (52%, n>300) injected with the reporter construct along with VP16-CMPl mRNA exhibited patches of clonal GFP-expression, irrespectively of the orientation of the cis-acting element on the transgene. By contrast, expression of the reporter was barely detectable in a minor fraction of embryos (5%, n>300) co-injected with equal amounts of the VP16-CMP mRNA (Figure 3D). qPCR measurements further confirmed that GFP expression was weakly evoked in these specimen, with respect to the VP16-CMPl co-injected embryos (Figure 3E). Altogether, these results support the contention that most, if not all, of the boxB binding sequences specifically recruits CMPl in vivo. Further insights were obtained by examining the specificity of binding of CMPl versus CMP to the boxB element in the natural chromatin context of sns5, within the histone gene cluster. The DNA replication-dependent sea urchin early histone genes are organized in a single large cluster made up of almost 2000 tandem repeats of the 5′-H2B-H3-H2A-H1-H4-3′ unit [35]. Coordinate transcription of these genes is limited to the cleavage and reaches its maximum at the morula stage. The M30 cis-regulatory sequence, upstream the H2A promoter, upon binding of the MBF1 activator displays a bidirectional enhancer activity [25], [36]. Remarkably, as we previously shown [25], the H1 promoter is shielded by the M30 enhancer activity by the sns5 insulator, which is located at the 3′end of the H2A transcription unit (Figure 4A). Sea urchin zygotes were microinjected with either the VP16-CMPl or the control VP16-CMP synthetic transcripts. Then, the expression of the H3, H2A and H1 genes was analyzed by qPCR at the morula stage. As expected, the injection of the VP16-CMP transcript had no detectable effect on histone genes expression (Figure 4B). Likewise, the mRNA levels of H3 and H2A did not show relevant change following the injection of the VP16-CMPl transcript. By contrast, the number of molecules of H1 mRNA was more than double in embryos expressing VP16-CMPl (Figure 4B). The most obvious explanation for the enhancement of H1 expression is that VP16-CMPl acted as a transcriptional activator on the H1 promoter. Alternatively, VP16-CMPl, by competing with the binding of the endogenous CMPl protein, impaired the enhancer-blocking activity of the sns5 element, thus allowing the H2A enhancer to act on the H1 promoter. This hypothesis is in line with our previous in vivo competition assays showing that inhibition of the sns5 led to up-regulation of only the H1 gene [25]. In conclusion, whatever is the mechanism, these experiments, as well as ChIP and trans-activation assays, strongly suggest that CMPl, but not CMP, associates to the boxB site in vivo. The finding that CMPl occupies the sns5 chromatin provided an opportunity to examine the function of an IBP in its natural gene context. To challenge CMPl activity, increasing amounts of the affinity-purified anti-CMPl antibodies, or control antibodies against the H2A enhancer binding factor MBF1, were injected into the sea urchin zygotes and histone gene expression analyzed by qPCR at the morula stage. In these experiments, injection of anti-MBF1 provoked a dose-dependent negative effect on the expression of the nucleosomal H2A and H3, but not the linker H1, genes (Figure 4C), excluding a unspecific effect of the injection. Also, it should be noted that H2A was more strongly affected compared to H3. These results are in agreement with those previously obtained by the in vivo inhibition of the H2A enhancer [25]. In strict accordance with previous findings [25 and this paper], both H2A and H3 mRNAs did not vary their abundance upon injection of different doses of anti-CMPl (Figure 4D), while the number of H1 mRNA molecules increased proportionally with the augmentation of the CMPl antiserum (Figure 4D). Taken together, these findings strongly point a role of the CMPl factor in mediating the sns5 insulator function by binding to the boxB cis-element. Furthermore, we predicted that the block of the H2A enhancer function might counteract the rise of H1 expression due to inhibition of CMPl binding to sns5. Indeed, the results showed in Figure 4E fulfilled this assumption, as co-injection of dnMBF1 mRNA, the dominant-negative version of MBF1 [25], along with saturating amounts of anti-CMPl caused a significant drop in the prevalence of H1 transcripts. Remarkably, in these embryos the number of mRNA molecules for the linker histone was comparable to that of the control uninjected embryos, confirming the independence of H1 transcription from the MBF1/enhancer positive input. Once again, the mRNA abundance of H2A and H3 (although to a lesser extent than H2A) decreased with the microinjection of dnMBF1, irrespectively of the anti-CMPl presence (Figure 4E). On the basis of these results, we conclude that recruiting of the CMPl factor to the boxB cis-element is critical for the enhancer-blocking activity of the sns5 insulator during the early embryogenesis of the sea urchin. Previous studies in the sea urchin model led to the identification of several candidate proteins (such as ISWI, Sin3A, PARP1, and more) that are recruited on the ArsI insulator [40]. Although all these factors are probably part of protein complexes required for enhancer-blocking activity of ArsI, none of them directly binds the insulator DNA sequence and therefore should not be considered a sensu stricto IBP. Here we have described the identification and functional characterization of CMPl which, to our knowledge, represents the first IBP of a non-model organism. Our molecular analyses indicate that although Cmpl is a single copy gene in the sea urchins, at least two distinct transcripts exist, due to alternative RNA splicing. They encode the CMPl and CMP proteins that are identical in their N-terminal sequences, where the Compass domain is located. Such a domain is followed by one or two atypical Homeodomains, respectively in CMPl and CMP. The sea urchin CMP and CMPl proteins belong to distinct families. Indeed, the former, and likely the most abundant, protein displays features of the COMPASS family, containing two 48W-type Homeodomains, each embedding a decapeptide insertion between helices II and III. Although displaying a 48W signature, the single Homeodomain of CMPl exhibits instead an unusual K/R-rich insertion of eleven residues which is not found in any other known Homeodomain protein but is conserved in sea urchins and S. kowalevskii. As for most of the Drosophila IBPs, apparently, there are no sequence homologs of CMPl in vertebrates. Thus CMPls constitute an additional, previously not described, ambulacrarian-specific family of proteins within those containing both the Compass- and Homeo-domain. Although the CMP protein is rather conserved among invertebrates, our phylogenetic analysis strongly suggests that hemichordates and echinoderms share a unique genomic organization at the Cmp/Cmpl locus. Consequently, the CMPl protein is found neither in chordates nor in protostomes. The monophyly of Ambulacraria is supported by three morphological characters: a trimeric arrangement of the adult coeloms, an axial complex with hydropore, and a dipleurula larva with neotroch [41]. At the molecular level, monophyly is also supported by 18S rDNA gene analyses [41]–[44], a unique mitochondrial gene code [45]–[46], the presence of three distinct posterior Hox genes [47], and now the possession of the Cmp/Cmpl locus. The functional significance of the emergence and loss of CMPl, respectively in ambulacrarians and chordates, is not clear at the moment. On the other hand, the Cmp gene is thought to have a highly flexible behavior during evolution. According to a current model, Cmp genes had emerged from a common ancestor with the POU class homeobox genes, acquiring the insertions in the COMPASS- and Homeo-domain [31]. In the lineage to vertebrates, SATB genes emerged and the genomic structure changed after the divergence of the amphioxus and vertebrate ancestors. SATB gene may have arisen from the Cmp gene by domain shuffling between Cmp and Onecut genes and eventually the Cmp gene was lost from the ancestral vertebrate [31]. In light of this scenario, the genomic configuration of the ambulacrarian Cmp/Cmpl locus could be emerged by similar mechanisms. Last, but not least, many insulator proteins are known to have multiple functions, and a wider functional analysis of the possible CMPl functional contribution to the sea urchin embryogenesis, outside the sns5 region, should help in the elucidation of the matter. In any case, this study provides an additional step towards an understanding of the evolution of Cmp, Cmpl and SATB genes in lower deuterostomes. The specific binding of CMPl to the boxB cis-element of the sns5 chromatin insulator is substantiated by several lines of evidence. First, a cDNA encoding the CMPl protein was recovered following a yeast one-hybrid assay, using the boxB sequence as bait. Second, constitutive occupancy of the sns5 chromatin by the CMPl protein was demonstrated by ChIP assay, using a CMPl antiserum. Third, specific interaction between the CMPl Homeodomain and the boxB element was further verified by a trans-activation assay in which boxB was placed upstream of the GFP reporter and the resulting transgene injected together with an mRNA encoding the CMPl Homeodomain fused to VP16. Fourth and most important, the expression of the VP16-CMPl chimeric protein, by competing with the endogenous CMPl, acts as either a trans-activator of the H1 promoter or as inhibitor of the enhancer-blocking activity of sns5, or both. Whatever is the mechanism, such a result definitively proved the specific association of the CMPl Homeodomain with the sns5 native chromatin. Intriguingly, this result clearly indicates that the Homeodomain alone is sufficient for CMPl effective DNA binding, in apparent contrast to the Compass domain-mediated oligomerization required for SATB1 to bind specific DNA sequences [48]. Although the Compass domain has been initially assimilated to a PDZ-like domain, a recent structural study indicated that it rather resembles a classic ubiquitin domain, even in the absence of sequence homology [49]. According to this study, the Compass domain mediates homo-tetramerization of SATB1 in solution. In particular, two dimers are joined together through multiple hydrogen bonds and hydrophobic interactions within their interfaces. Among these reciprocal interactions, the 97E98F162H residues are necessary for the formation of a hemi-dimer, and the 136K137W138N triad is important for the formation of the other dimer. A sequence alignment of Compass domain across species shows that these residues are all highly conserved among SATB proteins (Supplementary Figure S2), indicating that the Compass domain may have similar biological functions in vertebrates. By contrast, residues at positions 97,98, and 162 are divergent in ambulacrarian sequences, and the 136K137W138N tripeptide exhibits an inverted orientation in the primary sequence (Supplementary Figure S2). The observed changes in key residues of the Compass domain could explain the different behaviour in DNA binding activity between CMPl and SATB1. Expression of a CMP Homoeodomain-VP16 chimera in the developing embryo only modestly affected GFP expression in the trans-activation assay, and failed to prejudice sns5 enhancer-blocking function. Altogether, these findings lead us to conclude that no, or maybe very weak, interaction occurs between CMP and the boxB cis-element. It is known that the transcription factor LFB1/HNF1 contains a twenty-one amino-acids insertion between helices II and III which forms a large extra-loop that does not affect the overall structure of the Homeodomain [50]. On this basis, we speculate that the different-size insertions in CMPl and CMP Homeodomains do not participate in DNA interactions. Rather, we reckon that substitution of specific amino acids in Homeodomain sequences may account for differences in DNA binding affinity. Indeed, the Homeodomain of CMPl has a certain sequence similarity with those of CMP (58% and 66%, respectively; Figure 1B) but, notably, significant differences are detectable even in the helix III, which is known to be responsible for the discrimination of binding sequences [51]–[52]. The identification of CMPl in the sea urchin is of some importance to unravel the mechanism of action of insulators in chromosome organization and gene expression in this species. A principal criticism on studying insulators is that, often, the assays are performed by using artificial constructs in a chromosome context different than that of theirs, and it is known that the enhancer/promoter combination influences the activity [53]. In sharp contrast, our experiments provide a clear demonstration of the CMPl-dependent anti-enhancer function of the sns5 insulator in its natural chromatin context. Indeed, we have shown that the inhibition of the CMPl/boxB interaction, either by expressing the VP16-CMPl chimera or by titrating the CMPl factor through injection of specific antibodies, allowed an up-regulation of only the endogenous H1 gene. Most importantly, impairment of the H2A enhancer activity by expressing the forced repressor dnMBF1 restores the normal level of H1 transcripts in embryos in which CMPl is titrated by saturating amounts of the specific antiserum. Altogether, the results presented in this article strongly suggest that the recruitment of the CMPl protein on the sns5 insulator is absolutely required for buffering the H1 promoter from the H2A enhancer activity, and this, in turn, accounts for the different level of accumulation of early nucleosomal and linker transcripts. We have previously published several manuscripts analyzing the properties of the sns5 insulator in mammalian cells [20]–[22]. The finding that CMPl is an ambulacrarian-specific factor raises the paradoxical question of how sns5 works as an insulator in cells in which CMPl is absent. For completeness, it should be also remarked that neither the sns5 sequence does exist in the normal vertebrate genome but, if introduced by means of specific vectors, it is bound by several nuclear factors that are normally not recruited in sea urchin cells [21], [22]. In particular, through EMSA and ChIP assays we have demonstrated the occupancy of the boxB element by the Oct1 Homeodomain-containing regulator [21], [22]. In light of this evidence, an explanation to the above mentioned paradox could be that the unconventional recruitment of Oct1 on sns5 in mammalian cells somehow compensates for the lack of CMPl. A pertinent study in transgenic plants documents the enhancer-blocking activity of BEAD-1, a CTCF-dependent human-derived insulator [54], [55]. As reported above, no functional equivalents of CTCF factor and correspondent binding sites have been identified in plants. However, a large number of zinc-finger factors exhibit at least some degree of similarity at the amino acid level with the zinc-fingers of the vertebrate CTCF proteins [56] and may provide a similar function. In any case, the fact that insulators retain their activity in a unnatural host organism implies that at least a proportion of the insulator machinery in eukaryotes may be evolutionarily conserved. poly (A) + RNA was extracted from total RNA of P. lividus embryos at the gastrula stage and cDNA library was made by using the Matchmaker One-Hybrid System kit (Clontech). Briefly, poly (A) + RNA was reverse transcribed and the cDNA products were inserted into a shuttle vector, pGAD10, containing the GAL4 activation domain. Transformation of E. coli DH5α cells yielded >6×105 independent colonies. Pentamer of the 38 bp boxB sequence was used as the bait to select DNA-binding domains encoded in the library. The bait was inserted into pHISi-1 and pLacZi reporter vectors, and the recombinant plasmids were introduced sequentially into the genome of the yeast strain YM4271. Transformants were tested for growth on medium lacking histidine (SD/−His) in the presence of increasing concentrations of 3-aminotriazol (3-AT). Cells whose growth was inhibited by 5 mM 3-AT were selected as the host for the library screen. Transformation with the library was carried out by using LiCl-PEG, and transformants grown on SD/−His and SD/−Leu selective medium were tested for β-galactosidase activity. Plasmids from positive yeast clones were isolated by homogenization with glass beads and then individually transferred into DH5α cells for amplification. To eliminate false positives, these plasmids were separately introduced into yeast cells containing either the boxB bait or the p53 binding site, and transformants were tested for β-galactosidase activity. In this assay, a single plasmid conferred expression only in the boxB host. Sequence analysis revealed that such a plasmid harbored a ∼2. 2 kb cDNA insert encoding for CMPl (Genbank accession number: KF421245). BLASTP, BLASTX, and TBLASTN searches in public sequence databases (http: //blast. hgsc. bcm. tmc. edu/blast. hgsc; http: //www. molgen. mpg. de/~ag_seaurchin) with P. lividus CMPl and CMP as queries were performed and yielded candidates for many phyla. In some cases, a CMP coding region was deduced by Genscan analysis of genomic contigs. Protein domain architecture was defined via use of the Pfam (http: //pfam. sanger. ac. uk) and SMART (http: //smart. embl-heidelberg. de) databases. Multiple sequence alignments were generated using ClustalW, the output file was formatted using BioEdit, and neighbor-joining tree was constructed using the MEGA software package (http: //www. megasoftware. net). The reliability of branching points was inferred by bootstrapping using 1000 replicates. Genomic sequence comparisons for phylogenetic footprinting were performed with the VISTA platform (http: //genome. lbl. gov/vista/index. shtml), using window size varied between 50 and 100 bp, with 70% and 50% conservation respectively for sea urchins and S. kowalevskii. A DNA fragment corresponding to amino acids 98–270 of CMPl was subcloned into the pGEX-4T-1 expression vector to create a glutathione-S-transferase (GST) fusion protein. This protein was affinity purified from bacterial extracts and then used to generate a rabbit polyclonal antibody (Eurogentec, BE). After serum preincubation with a sepharose-4B column bound to the GST protein, the anti-CMPl antibody was purified by sepharose-4B affinity columns bound to the GST-CMPl protein. The specificity of the antibody was assayed by western blot detection against GST-CMPl and Thioredoxin-CMPl proteins. ChIP experiments were performed essentially as described in [32], with minor modifications. Briefly, formaldehyde cross-linked sea urchin embryos at morula and gastrula stages were incubated in cell lysis buffer (10 mM HEPES pH 8. 0 and 85 mM KCl, containing the following protease inhibitors: 0. 5% NP40,1 µg/ml leupeptin, 1 µg/ml aprotinin, 1 mM PMSF), for 10 min on ice. Nuclei were then resuspended in nuclear lysis buffer (50 mM Tris-HCl pH 8. 1,10 mM EDTA and 1% SDS, containing the same protease inhibitors as in cell lysis buffer) and incubated on ice for 10 min. Chromatin was sonicated using a Bandelin Sonopuls ultrasonic homogenizer to an average fragment size of 150 to 500 bp, as determined by agarose gel electrophoresis. To reduce non-specific background, the samples were diluted into five volumes of ChIP dilution buffer (16. 7 mM Tris-HCl pH 8. 1,167 mM NaCl, 0. 01% SDS, 1. 1% Triton X-100,1. 2 mM EDTA, plus proteinase inhibitors) and incubated with 100 µl of a salmon sperm DNA/protein A-sepharose slurry for 1 h at 4°C, with mixing. Ten percent of chromatin was withdrawn (Input) and processed as the immunoprecipitated chromatin. Aliquots of chromatin containing 25 µg of DNA were incubated in the absence of antibodies (as a negative control) or either with the anti-CMPl or the anti-Otx serum overnight at 4°C. The immune complexes were adsorbed to protein A-sepharose beads, which were sequentially washed with a low salt buffer (0. 1% SDS, 1% Triton X-100,2 mM EDTA, 20 mM Tris-HCl pH 8. 1,150 mM NaCl), a high salt buffer (0. 1% SDS, 1% Triton X-100,2 mM EDTA, 20 mM Tris-HCl pH 8. 1,500 mM NaCl), a LiCl buffer (0. 25 M LiCl, 1% NP40,1% deoxycholate, 1 mM EDTA, 10 mM Tris-HCl pH 8. 0) and twice in 1×TE buffer (10 mM Tris-HCl, 0. 1 mM EDTA pH 8. 0). The immune complexes were eluted with the elution buffer (1% SDS, 0. 1 M NaHCO3), digested with RNase at 37°C and treated with proteinase K in 0. 3 M NaCl at 65°C for 4 h to reverse the cross-links. DNA from chromatin samples was extracted with phenol/chloroform, precipitated with ethanol and dissolved in 50 µl of water. DNA samples were then quantified by readings in a Qubit Fluorometer (Invitrogen) using the Quant-iT dsDNA HS assay kit (Invitrogen). The enrichment of histone regulatory sequences in 100 pg aliquots of genomic DNA purified from the precipitated chromatin fractions was examined by qPCR as described previously [25], [32]. qPCR experiments were performed from two different batches and all reactions were run in triplicate on the 7300 Real-Time PCR system (Applied Biosystems) using SYBR Green detection chemistry. ROX was used as a measure of background fluorescence and, at the end of the amplification reactions, a ‘melting-curve analysis’ was run to confirm the homogeneity of all amplicons. Calculations from qPCR raw data were performed by the RQ Study software version 1. 2. 3 (Applied Biosystems), using the comparative Ct method. Primer efficiencies (i. e. , the amplification factors for each cycle) were found to exceed 1. 9. In every experiment, a no-template control was included for each primers set. Primers used in this study are listed in Supplementary Table S2. The amounts of Cmp/Cmpl and histone gene transcription in control and injected embryos were evaluated as follows. Total RNA from batches of 150 embryos at the desired stage was extracted by using the Power SYBR Green Cells-to-CT kit (Ambion) and reverse transcribed following the manufacturer' s recommendations. The resulting cDNA sample was further diluted and the equivalent amount corresponding to one embryo was used as template for qPCR analysis, using the primers indicated in Supplementary Table S2. A cytochrome oxidase or the mbf1 mRNA, which are known to be expressed at a constant level during development [25], [32], were used to normalize all data, in order to account for fluctuations among different preparations. The pH3-GFP DNA plasmid was constructed as follows. A PCR fragment harboring the H3 minimal promoter (spanning from −62 to +60 with respect to the transcription start site) was inserted in the plasmid polylinker of a pGL3 vector containing the GFP coding sequence. The 5×boxB-pH3-GFP reporter constructs were obtained by shotgun cloning of ligated double-stranded oligonucleotides, bearing the boxB cis-regulatory sequence, upstream of the H3 promoter of the pH3-GFP plasmid. The VP16-CMPl and VP16-CMP effector constructs were obtained by fusing the DNA-binding domain coding sequences of either CMPl or CMP to those of the VP16 activation domain cloned in the CS2+nls expression vector. All DNA clones were checked by sequencing. Capped mRNAs were synthesized from the linearized pCS2-constructs using the mMessage-mMachine kit (Ambion). Purified RNAs were resuspended at 0. 5 mg/ml and 2 pl were then microinjected either alone or in combination with the linearized pH3-GFP and 5×boxB-pH3-GFP constructs. Microinjection was conducted as previously described [57]–[58]. In the trans-activation assay, more than 100 injected embryos per experiment were scored for GFP expression at the mesenchyme blastula stage and each experiment was repeated three times with different batches of eggs. As a control of the expression of the injected transgenes, we used an actively transcribed GFP construct driven by the full promoter of the hbox12 gene [32]. Images were captured with a Leica DC300F digital camera. As for in vivo titration assays with antibodies, affinity purified rabbit polyclonal IgG reacting with either CMPl or MBF1 were diluted to a final concentration of 12. 5–100 ng/pl in ultrapure water containing 30% glycerol, and eventually injected into sea urchin zygotes.

Mounting evidence in several model organisms collectively demonstrates a role for the DNA-protein complexes known as chromatin insulators in orchestrating the functional domain organization of the eukaryotic genome. Several DNA elements displaying features of insulators, viz barrier and/or directional enhancer-blocking activity, have been identified in yeast, Drosophila, sea urchin, vertebrates and plants; however, proteins that bind these DNA sequences eliciting insulator activities are far less known. Here we identify a novel protein, COMPASS-like (CMPl), which is expressed exclusively by the ambulacrarian group of metazoans and interacts directly with the sea urchin sns5 insulator. Sns5 lies within the early histone gene cluster, basically between the H2A enhancer and H1 promoter, where it acts buffering the H1 promoter from the H2A enhancer influence. Intriguingly, we find that CMPl role is absolutely required for the sns5 activity, therefore imposing the different level of accumulation of the linker and nucleosomal transcripts. Overall, our findings add an interesting and novel facet to the chromatin insulator field, highlighting the surprisingly low evolutionary conservation of trans-acting factors binding to chromatin insulators. This opens the possibility that multiple lineage-specific factors modulate chromatin organization in different metazoans.

Abstract Introduction Results Discussion Materials and Methods

2013

The Compass-like Locus, Exclusive to the Ambulacrarians, Encodes a Chromatin Insulator Binding Protein in the Sea Urchin Embryo

Cocktails of drugs can be more effective than single drugs, because they can potentially work at lower doses and avoid resistance. However, it is impossible to test all drug cocktails drawn from a large set of drugs because of the huge number of combinations. To overcome this combinatorial explosion problem, one can sample a relatively small number of combinations and use a model to predict the rest. Recently, Zimmer and Katzir et al. presented a model that accurately predicted the effects of cocktails at all doses based on measuring pairs of drugs. This model requires measuring each pair at several different doses and uses interpolation to reduce experimental noise. However, often, it is not possible to measure each pair at multiple doses (for example, in scarce patient-derived tumor material or in large screens). Here, we ask whether measurements at only a single dose can also predict high-order drug cocktails. To address this, we present a fully factorial experimental dataset on all drug cocktails built of 6 chemotherapy drugs on 2 cancer cell lines. We develop a formula that uses only pair measurements at a single dose to predict much of the variation up to 6-drug cocktails in the present data, outperforming commonly used Bliss independence and regression approaches. This model, called the pairs model, is an extension of the Bliss independence model to pairs: For M drugs, it equals the product of all pair effects to the power 1/ (M−1). The pairs model also shows good agreement with previously published data on antibiotic triplets and quadruplets. The present model can only predict combinations at the same doses in which the pairs were measured and is not able to predict effects at other doses. This study indicates that pair-based approaches might be able to usefully predict and prioritize high-order combinations, even in large screens or when material for testing is limited. Cancer and antibiotic treatments face the problem of drug resistance. Cancer drugs also face problems of efficacy at the low doses needed to tolerate side effects [1–5]. One strategy to overcome these challenges is cocktails of several drugs [5–9]. Cocktails overcame the challenges of resistance and efficacy in diseases such as HIV [10–12] and are used in diverse medical contexts. Effective cocktails can, in principle, be designed for each individual patient [13]. Currently, there are approximately 1,000 available compounds to treat cancer [14,15]. Testing all combinations at all doses is impossible, because the number of experiments grows exponentially with the number of drugs and doses. Hence, very effective cocktails may be hidden in this vast space of possible combinations [1,6, 16,17], as recently demonstrated by Horn et al. in an extensive study of colorectal cancer [5]. Current approaches to overcome the combinatorial explosion problem of drug combinations use mathematical models to predict the effects of combinations based on a small number of measurements [18–20]. The commonly used Bliss model is a reasonable first approximation but does not include synergy and antagonism effects. Machine learning approaches have been proposed, but the large datasets needed to train these models are still not widely available [21–26]. The Isserlis model proposed by Wood et al. showed excellent results on antibiotic combinations of 3 and 4 drugs [27–29]. Additional studies considered the interactions of multiple drugs using first- and second-order terms for drug effects [30–38]. Recently, Zimmer and Katzir et al. suggested a model that accurately predicted the multidose response of triplets and quadruplets of antibiotics and cancer drugs [29]. This model requires measurements of each pair of drugs at a few dose combinations. It uses this multidose information to fit a smooth drug response curve for all doses, in which each drug changes the effective dose of the other drugs. This model greatly reduces the number of experiments needed in order to scan the space of drug cocktails at multiple doses. Data at multiple doses required for the multidose model of [29] is not always available. This fact is most pressing when the measurements are made on rare materials whose limited quantity allows only a few tests, such as patient-derived material in individualized medicine applications [39–42] or in expensive high-throughput screens [43–45]. There is therefore need for a model that uses measurements at a single dose in order to predict the effects of high-order combinations (such a model is expected to work only at the measured doses and no other doses). In particular, previous models were tested only up to triplets and quadruplets of drugs. Experiments on higher-order combinations are hence of interest [5]. To address this, we tested all combinations of 6 chemotherapy drugs at a single dose on 2 cell lines (a fully factorial design). We find that synergy and antagonism are cell-line dependent and are usually consistent with the synergy/antagonism of the pairs that make up each combination. We developed a simple model for cocktails that is insensitive to experimental noise and that uses only measurements on drug pairs at a single dose. In addition to the 6-drug combinations, we further tested the model on previous fully-factorial datasets of 3 chemotherapy drugs at 8 doses [29] and of 3 or 4 antibiotics [28], totaling 1,392 additional triplets and 248 quadruplets. The pairs model predicts well the effect of these cocktails, with the limitation that it can only predict effects at the same dose at which the pairs were measured. We selected 6 cytotoxic drugs from several clinically employed drug families with different mechanisms of action (Table 1). We chose drugs that are used in combination in some clinical settings, such as an alkylating agent with a microtubule poison. For each drug, we measured cell survival after 48 h using the neutral red assay. A second survival assay, MTT, gave very similar results (S1 Fig). Each measurement was repeated on at least 6 biological repeats, in triplicate. We repeated this for 2 human cancer cell lines from different cancers and genetic backgrounds, H1299 and HeLa. The non-small-cell lung carcinoma line H1299 was derived from metastatic lymph node from a male patient and characterized by partial homozygous deletion in the p53 gene, resulting in lack of expression of the p53 protein [46]. HeLa is a cervical cancer cell line. HeLa cells express p53 at low levels and were derived from a primary tumor from a female patient [47]. We first measured single-drug dose response curves, which were well described by Hill functions with moderate cooperativity (Table 1). For each drug, we chose a dose that shows about 80% survival, lethal dose 20% (LD20; we chose a dose of high survival in order to have sensitive measurements when combining 6 of the drugs). We then measured all 63 combinations of the six drugs: 6 singles, 15 pairs, 20 triplets, 15 quadruplets, 6 quintuplets, and the sextuplet. In the case of HeLa cells, 17 of the combinations had large variations between repeats and were not included. The results for all measured drug combinations are shown in Table 2 (and S1 Table), as well as interactions and their confidence intervals based on bootstrapping the biological repeats. We measured the interactions between the drugs by comparing the effects of the combination to Bliss independence. The effect of drug i, gi, is the fractional reduction in cell survival. In other contexts, gi is the fractional reduction in cell growth rate [28,29,48] or other measures of drug impact. Bliss independence is a model in which the combination effect is the product of the effects of each individual drug gBliss1. . k=g1g2…gk (1) We quantified the interaction by the logarithmic deviation from the Bliss model, I = log (1+g1. . k − gBliss1. . k) [49] (for other approaches to calculate synergy, see [5]). Negative numbers indicate synergy, where drugs kill more cells than Bliss independence, and positive numbers indicate antagonism. We computed 95% confidence intervals for each interaction term by bootstrapping over the biological repeats of each measurement. We consider interaction terms as non-zero when I = 0 is not included in their 95% confidence interval. We find that 30% of the combinations are synergistic for HeLa cells and 44% for H1299 (S2 Fig). The most synergistic combination for H1299 was Cisplatin, Carboplatin, and Nocodazole (CisPt+CbPt+NCZ), with I = −0. 36. For HeLa, the most synergistic combination was Camptothecin, Carboplatin, and Etoposide (CPT+CbPt+Etopo) with I = −0. 14. We compared the synergy/antagonism of each combination between the 2 cell lines. Fig 1 (and S1 Table) shows each combination plotted by its interaction in HeLa versus H1299. Overall, there is only a moderate correspondence between the interactions in the 2 cell lines (R = 0. 16). The higher the combination order, the smaller the correlation between the two cell lines: Two-drug interactions (circles) tend to be more similar between the 2 cell lines (R = 0. 5) than triplets (triangles, R = 0. 1) and quadruplets (squares, R = 0. 08). This indicates that cell-line-specific predictions are needed, especially for the high-order cocktails in this sample, although care is needed with this interpretation due to small number effects and noise. To address the possibility of using pairs to model the cocktails, we asked whether the synergy/antagonism sign of pairs in a combination is informative with regards to the overall synergy/antagonism sign of the cocktail in a given cell line. For this purpose, we compared the interaction (I) values of each combination of drugs to the interaction of its constituent pairs. The results for triplets are shown in Fig 2 (and S1 Table). An indication of high-order interactions is when the signs of the pair interactions do not correspond to the interaction sign of the combination. Such high-order interactions would make it difficult to use pairs to model higher-order combinations, especially if the interaction sign of all pairs is opposite to that of the combination. We find that for HeLa cells all of the cocktails (of 3–6 drugs) that are antagonistic had at least 1 antagonistic pair (16/16), and all the synergistic cocktails had at least 1 synergistic pair (10/10). For H1299, all of the antagonistic cocktails had at least 1 antagonistic pair (7/7), and all but 3 of the synergistic combinations had at least 1 synergistic pair (19/22). The 3 exceptions are all triplets, underlined in Fig 2, and may be candidates for exploring third-order interactions. Taking all data together, the synergy/antagonism sign of the combinations is different from all of the pairs only rarely (6% overall) in this sample, suggesting that drug pairs may carry useful information about the cocktail effects [5,28,29]. We next asked to what extent data on drug pairs can improve on the Bliss formula to predict the effect of cocktails. This requires mathematical formula to properly utilize pair data. There are several existing approaches for such pair-based predictions. A widely used machine learning approach is based on logarithmic regression [21–26]. For triplets, regression yields gRegression123 = g12g23g13/g1g2g3, and for quadruplets gRegression1234=∏gij/∏gi2 [29]. A second approach employs the Isserlis formula introduced by Wood et al. based on maximum entropy considerations [28]. For triplets, the Isserlis formula is gIsserlis123 = g1g23 + g2g13+g3g12 − 2g1g2g3, and for quadruplets, gIsserlis1234 = g12g34 + g13g24 + g14g23 − 2g1g2g3g4. The Isserlis formula showed excellent agreement with data on antibiotic triplets and quadruplets [28]. The regression and Isserlis formulas have the desirable mathematical property that if all pairs are Bliss (that is, if for all pairs gij = gi gj), then the predicted cocktail is also Bliss g123. . k = g1g2g3 …gk. We call this property Bliss conservation. We sought to test additional models that use single drugs and drug pairs as inputs, measured at a single dose. We therefore sought a family of models that generalize the Bliss and regression formulas. We choose the family of log-linear combinations of single and pair measurements, which smoothly interpolates between Bliss and regression. For triplets, we tested g123 = (g12g23g13) α (g1g2g3) β, where α and β are parameters. To preserve the Bliss conservation property requires β = 1 − 2α. Generalizing to M drugs, we have g1…M= (∏gij) α (∏gi) β with β = 1 − (M − 1) α. The Bliss formula is obtained when α = 0 and the regression formula when α = 1. We scanned the value of the parameter α, setting β = 1 − (M − 1) α to preserve Bliss conservation, and compared the R2 values for the fully factorial datasets described above. We find that a simple formula shows nearly maximal R2 values and outperforms Bliss, Isserlis, and regression (see next section) (Fig 3, S1 Data). This formula includes only pair data (β = 0, α = 1/ (M − 1) ), and we hence name it the pairs model: gpairs1…M= (∏gij) 1/ (M−1) (2) Thus, for triplets, the pairs model is the square root of the product of the 3 pair effects. One feature of the pairs model is that it is less sensitive to experimental noise than most other models in this class, because it uses only data for pairs; other models use both pair and single drug data, increasing the number of variables and hence the sensitivity to noise. Assuming independent multiplicative experimental noise for each measurement with standard deviation σ, the Bliss formula has total experimental noise of Mσ, the regression formula has larger noise of σM (M−1) 2+M (M−2) 2, and the pairs formula has noise of only σM/2 (M−1). For triplets (M = 3), for example, these noise terms are 3=1. 7,6=2. 5, and 3/4=0. 9 times σ for Bliss, regression, and pairs, respectively. The pairs model is expected to be most useful when data is noisy. We plot the agreement of different formulas to the present dataset in Fig 4 and S1 Data. The Isserlis and regression formulas are far from the data, as evidenced by their negative R2 values R2 = −0. 79 and R2 = −12. 1, respectively (negative R2 indicates lack of accuracy for the data mean since R2=1−∑i (yi−fi) 2/∑i (yi−y¯i) 2 for data yi and prediction fi). Bliss independence has R2 = 0. 29. The pairs model improves this to R2 = 0. 54. This value of R2 is reasonable given the experimental noise in these measurements, which adds up when considering combinations of 4 to 6 drugs. Also, the higher the number of drugs, the more the potential for high-order effects above pairs, which cannot be captured by the present model. We tested the pairs model also on previously published data for 1,360 antibiotic triplets and quadruplets by Wood et al. [28] The pairs model shows good fit to this data (R2 = 0. 78), comparable to the Isserlis formula (R2 = 0. 88). In this dataset, errors may be smaller than the present dataset (S3 and S5 Figs). This antibiotic data are extensive enough to ask how well the models can rank the combinations in terms of efficacy. Efficacy ranking is of interest if one needs to prioritize potential cocktails based on measuring the pairs. We find that the pairs model shows 85% accuracy in identifying the top 10% most effective triplets (that is, triplets with lowest bacterial growth rate), compared to 75% accuracy in the Isserlis model, 22% for Bliss, and 10% for random (S6 Fig). The regression model shows worse accuracy than random. The pairs model also describes the multidose cancer drug-triplet dataset by Zimmer et al. [29], with R2 = 0. 7 compared to R2 = −0. 29, −2. 03, and 0. 58 for Bliss, regression, and Isserlis formulae, respectively (S4 and S5 Figs). The model of [29] that uses multiple doses outperforms the pairs model (reaching R2 = 0. 82). We presented a fully factorial experiment on the effect of combinations of 6 chemotherapy drugs on viability of 2 cancer cell lines. The combinations showed varying degrees of synergy or antagonism. The synergy/antagonism of each combination was mostly consistent with that of the drug pairs that made up the combination. This led us to test models that predict combinations using only data on pairs and single drugs at a single dose per drug. We screened a class of log-linear models to develop a model for combinations based on pairs called the pairs model. In the pairs model, the effect of a combination of M drugs is equal to the product of the pair effects to the power 1/ (M − 1). The pairs model improves on the predictions of the commonly used regression and Bliss models. In addition to the 6 cancer drug combinations measured here, we also tested the pairs model on an additional 1,640 combinations form previous studies: 280 cocktails of 3 cancer drugs at 8 doses [29], 1,112 cocktails of 3 antibiotics, and 248 quadruplets of antibiotics [28]. The model predicts these combinations reasonably well. The model only works at the measured doses and is not able to predict effects of combinations at doses in which the pairs were not measured. The synergy and antagonism of some of the combinations showed cell-line-specific effects; for example, the quadruplet CPT, CisPt, NCZ, and Etoposide is synergistic in H1299 but antagonistic in HeLa cells. A few combinations, however, showed synergy/antagonism that is consistent between the 2 cell lines tested. For instance, the pair MG132 and Etoposide is antagonistic in both cell lines, and the quadruplet CPT, CisPt, CbPt, and Etoposide is synergistic in both lines. If such cocktails turn out to be generally synergistic on a large number of cell lines and patient-derived samples, they may be a promising direction for therapy. The experimental dataset presented here is limited, and these findings must be tested on a broader number of cell lines and at multiple doses, as exemplified by the recent study by Horn et al. [5] on combinations of up to 6 drugs on multiple colorectal cancer cell lines. The ability to use single and pair measurements to predict the effects of high-order combinations is a way to overcome the problem of combinatorial explosion. When measurements at several doses are available for pairs, the recent dose model of Zimmer and Katzir et al. provides excellent predictions for triplets and quadruplets of drugs as a function of dose [29]. That model uses multiple dose measurements to define response surfaces and therefore reduces the effect of measurement noise. In addition, the dose model provides interpolation of the effects of drug cocktails at different doses and therefore can be used to scan the combinatorial space of drug doses to find the most synergistic cocktails. The present study addresses the case where only a single dose is measured and suggests the pairs model as an improvement over Bliss independence and regression, especially in the presence of experimental noise. The pair model estimates the effect of cocktails based on the measured pairs, but it cannot provide estimates for doses for which the pairs were not measured. This single-dose scenario may be relevant to tests of drug combinations in large screens or on rare patient-derived material for personalized medicine applications [39–42]. H1299 is a non-small-cell lung carcinoma cell line (clone 310806pl1H11 LMNA) described in Cohen et al. 2008. HeLa S3 cells were obtained from the American Type Culture Collection. Drugs were treated as described in Geva-Zatorsky et al. 2011 [50]. Cisplatinum (P4394 Sigma) was dissolved in DMSO (hybri-max, D2650 Sigma), giving a stock solution of 25 mM; Nocodazole (M1404, Sigma) was dissolved in DMSO, giving a stock solution of 10 mM; Camptothecin (CPT, C9911 Sigma) and Etoposide (E1383 Sigma) were dissolved in DMSO, giving a stock solution of 0. 3 mM; Carboplatin (C2538 Sigma) was dissolved in DDW, giving a stock solution of 500 mM; and MG132 (C2211 Sigma) was dissolved in DMSO, giving a stock solution of 0. 32 mM. In each experiment, each drug was diluted to the desired concentration in transparent growth medium (RPMI 1640,0. 05% Penicillin-Streptomycin antibiotics, 10% FCS, with L-Glutamine, lacking riboflavin and phenol red, Bet Haemek, Biological Industries, catalog number 06-1100-26-1A). Normal transparent growth medium was replaced by the diluted drug solution [50].

Drug cocktails are a promising strategy for diseases such as cancer and infections, because cocktails can be more effective than individual drugs and can overcome problems of drug resistance. However, finding the best cocktail comprising a given set of drugs is challenging because the number of experiments needed is huge and grows exponentially with the number of drugs. This problem is exacerbated when the experiments are expensive and the material for testing is rare. Here, we present a way to address this challenge using a mathematical formula, called the pairs model, that requires relatively few experimental tests in order to estimate the effects of cocktails and to predict which cocktail is most effective. The formula does well on experimental data generated in this study using combinations of between 3 and 6 anticancer drugs, as well as on existing data that use combinations of antibiotics.

Abstract Introduction Results Discussion Materials and methods

antimicrobials medicine and health sciences methods and resources hela cells dose prediction methods drugs biological cultures microbiology drug screening pharmaceutics antibiotics cell cultures pharmacology research and analysis methods cell lines chemotherapy microbial control biology and life sciences cultured tumor cells drug therapy drug interactions drug information

2017

Prediction of drug cocktail effects when the number of measurements is limited

The identification of regulatory elements from different cell types is necessary for understanding the mechanisms controlling cell type–specific and housekeeping gene expression. Mapping DNaseI hypersensitive (HS) sites is an accurate method for identifying the location of functional regulatory elements. We used a high throughput method called DNase-chip to identify 3,904 DNaseI HS sites from six cell types across 1% of the human genome. A significant number (22%) of DNaseI HS sites from each cell type are ubiquitously present among all cell types studied. Surprisingly, nearly all of these ubiquitous DNaseI HS sites correspond to either promoters or insulator elements: 86% of them are located near annotated transcription start sites and 10% are bound by CTCF, a protein with known enhancer-blocking insulator activity. We also identified a large number of DNaseI HS sites that are cell type specific (only present in one cell type); these regions are enriched for enhancer elements and correlate with cell type–specific gene expression as well as cell type–specific histone modifications. Finally, we found that approximately 8% of the genome overlaps a DNaseI HS site in at least one the six cell lines studied, indicating that a significant percentage of the genome is potentially functional. Biological processes such as proliferation, apoptosis, differentiation, development, and aging require carefully orchestrated spatial and temporal gene expression [1,2]. To understand the molecular mechanisms that underlie global transcriptional regulation, it is essential to identify all the DNA regulatory elements in the human genome. Three methods, DNaseI hypersensitive site (HS) mapping, chromatin immunoprecipitation followed by hybridization to tiled arrays (ChIP-chip), and expression arrays identify gene regulatory elements in different ways. DNaseI HS sites identify regions of open chromatin, which encompass all different types of regulatory elements, including promoters, enhancers, silencers, insulators, and locus control regions (LCR) [3]. However, DNaseI HS mapping does not directly reveal the transcription factor (s) that bind within each DNaseI HS site. ChIP-chip directly identifies the global locations of regulatory factors [4–6], but this method can only be used to study known factors and requires high quality ChIP-grade antibodies. In addition, expression arrays detect genes that are expressed in certain cell types, but do not provide information regarding the factors that cause the cell type–specific expression. Therefore, to completely understand how chromatin structure ultimately regulates gene expression, a multi-pronged integrated experimental approach using all three methods is needed. We previously used DNase-chip to identify DNaseI HS sites from two cell types across the 1% of the human genome identified by the ENCODE consortium [4]. DNase-chip is a method that works by capturing DNase digested ends, labeling, and hybridizing the material to tiled microarrays. This method is highly sensitive and specific when used to identify valid DNaseI HS sites. To identify the regulatory elements that control cell type–specific and housekeeping gene expression, we have now performed DNase-chip on the same 1% of the genome from six diverse human cell types: CD4+ T cells, GM06990 (B lymphoblastoid), K562 (erythroleukemia), H9 (undifferentiated embryonic stem cell), IMR90 (fetal lung fibroblast), and HeLa S3 (cervical carcinoma). In this study, we find that approximately 22% of all DNaseI HS sites from each cell type are ubiquitously present in all six cell types, while the remainder are a mixture of cell type specific (only present in one cell type) or common (present in two to five cell types). To identify the regulatory roles of these DNaseI HS sites, we performed computational analyses to integrate the DNase data with the ChIP-chip data for two distinct enhancer-binding proteins, one insulator-binding protein, and five histone modifications, as well as expression data from the same six cell lines. The majority (86%) of ubiquitous DNaseI HS sites are within 2 kb of a transcription start site (TSS). Surprisingly, of the remaining ubiquitous HS sites that are distal to TSS, the majority (70%) are bound by CTCF, a factor with known enhancer-blocking activity [7], suggesting that a major role of ubiquitously modified chromatin is to prevent misregulation by local enhancers. In contrast, cell type–specific HS sites are correlated with known enhancer elements [8] and histone-modified regions in a cell type–specific manner. Cell type–specific DNaseI HS sites also contain overrepresented sequence motifs that are biologically relevant and often map near the TSS of genes that exhibit cell type–specific expression. Collectively, these results show that ubiquitous chromatin structures are predominantly associated with promoters and insulators while enhancers tend to associate with cell type–specific chromatin structures. For each cell type, DNase-chip data was generated using three concentrations of DNase on each of three biological replicates (See Figure S1 for correlation plots). Averaged data from all replicates (Figure 1A) was used for subsequent analyses, because we have previously shown that averaging data from replicate datasets generates higher sensitivity and specificity [4]. Similar numbers of DNaseI HS sites were identified from each cell type, indicating data consistency (Table 1). To determine specificity for each cell line, we determined the overlap of DNase signal from previously reported “gold standard” negative sets of DNaseI HS sites for CD4+ T cells and GM06990 cell lines using real time PCR [4], and calculated >92% specificity for all six cell lines (Table 1). As a second measure of specificity, we determined the numbers of significant signals that are detected in two ENCODE regions (ENr112 and ENr313) that are depleted for TSS, DNaseI HS sites, active histone modifications, and ChIP-chip signals. Significant signals that map within these two regions are considered likely false positives. For each cell line, only a few significant signals were observed in these two regions, which also indicates high specificity (Table 1). We have previously shown that sensitivity of DNase-chip experiments from CD4+ T cells and GM06990 was >86% [4]. To assess the sensitivity of these additional cell lines, we examined five well-characterized DNaseI HS sites that make up the globin locus control region [9,10]. We robustly detect all five DNaseI HS LCR sites in K562 cells, as well as the well-characterized 3′ DNaseI HS site [11] (Figure 1B). In addition, results from all six cell lines show a significant enrichment for TSS and CpG islands, one of the hallmarks of active chromatin (Table 1 and unpublished data). Together, these results indicate that the sensitivity and specificity in the four newly studied cell lines are consistent with those in CD4+ T and GM06990 cells. All DNase-chip data described here is publicly available on the University of California Santa Cruz (UCSC) genome browser [12] (http: //genome. ucsc. edu). For the 222 proximal DNaseI HS sites (<2 kb from TSS) that are ubiquitous in all six cell lines, 78% overlap recently published ChIP-chip data specific to basal promoter factors (RNA PolII and TAF1) [15], enhancers (p300 and TRAP220) [8], or the insulator factor CTCF [16] (Figure 3A and 3B). The majority (81%) of DNaseI HS sites bound by p300 or TRAP220 also bind Pol II or TAF1. However, only 22% DNaseI HS sites bound by CTCF are also bound by TAF1 or Pol II, suggesting that CTCF binding to the promoter decreases the likelihood of binding by other factors examined. For the ubiquitous proximal DNaseI HS sites that do not overlap known promoter factors, 53% overlap with the ChIP hits of H3K4me3, a histone modification mark for active promoters (Figure 3A) [17]. We present DNase-chip data from six cell lines and classify the DNaseI HS sites into cell type–specific, common (found in more than one but not all cell lines), and ubiquitous categories. Only 22% of all DNaseI HS sites are ubiquitous in all cell lines, indicating that the majority of gene regulatory elements are involved in cell type–specific function. The identification of ubiquitous DNaseI HS sites provides clues to the function of housekeeping chromatin structures that are maintained in most cell types. We detected 259 such ubiquitous sites in the ENCODE regions. Approximately 86% of ubiquitous DNaseI HS sites are proximal to TSS and map to basal transcription factor binding sites, indicating that these regions function as housekeeping promoters. The majority of ubiquitous distal DNaseI HS sites bind to CTCF, a protein with known enhancer-blocking insulator activity [7], indicating that CTCF is involved in stable chromatin structure and gene expression maintenance across many cell types. Because most ubiquitous sites bind to either basal transcription machinery or CTCF, we conclude that ubiquitous DNaseI HS sites function primarily as promoters and insulators, but not enhancers. Cell type–specific DNase HS sites, however, are more enriched for protein binding sites with known enhancer activity, cell type–specific histone modifications, and cell type–specific gene expression. Although our DNaseI HS site data was limited to 1% of the human genome, the integration over multiple data types allowed us to conclude that we have uncovered many different types of functional regulatory elements. In the future, as additional cell types are analyzed using whole genome DNase-chip, we will be able to better characterize DNaseI HS sites that are truly cell type specific, as well as those that are shared between cell types of similar lineages. We expect that genome-wide analysis from additional cell types, under different cellular conditions, or at different developmental stages, will provide for more powerful de novo motif discovery, similar to our identification of CTCF, for identifying and characterizing unknown factors that regulate temporal and spatial gene expression. Our integrated approach combines the strengths of four high-throughput technologies (DNase-chip, ChIP-chip, expression array, and motif discovery). DNase-chip can identify all types of regulatory elements in a single experiment and integration with other datasets has allowed us to delineate the functions of subsets of DNaseI HS sites. This approach will be increasingly more powerful as more high-throughput datasets become available and will be an important part of ensuring that no regulatory element is missed. Nonetheless, our analysis is missing an important component—we cannot identify the target gene (s) of a DNaseI HS site. Technologies such as chromosome conformation capture carbon copy (5C) [35] are ideal for detecting large numbers of long-range interactions between genomic elements. Since 5C works best by anchoring to known regulatory elements, DNaseI HS sites identified in our study can be used to significantly reduce the search space. DNaseI HS sites can be used as a general tool for evaluating future ChIP-chip datasets that have been performed on only one of the cell types described here, to determine whether those factors bind genomic DNA in a cell type–specific or ubiquitous manner. This is illustrated for the p300 ChIP-chip data in Figure S9, which shows that the percentages of p300 binding (performed in HeLa cells) are highest for HeLa-specific distal DNaseI HS sites. Other examples of cell line-specific marks are H3K4me1 and H3K4me2 (Figure S9). In contrast, H3K4me3, H3ac, H4ac, and CTCF show less cell line specificity (Figure S10). Our DNaseI HS data can also be used to help identify unknown transcription factors binding and unknown histone modification patterns. For example, only 60% of proximal DNaseI HS sites overlap with the five histone modifications we examined in this study. Future studies will be needed to identify the histone modification (s) that are associated with these regions. While DNaseI HS sites from each cell type cover approximately only 2%–3% of the genome, the combined DNase data from six cell types covers roughly 8% of the genome. Since the actual functional regulatory sequences (i. e. , protein binding sites) may make up a fraction of each DNase HS site, the actual percentage of functional DNA may be smaller. As we have not detected a significant decrease in the number of new DNaseI HS sites identified with the addition of each cell type, this indicates that a large percentage of the genome may be functional in all possible cell types, disease states, and responses to external stimuli. Whole genome identification of all DNaseI HS sites using DNase-chip [4] or DNase-sequencing [36] methods will play a key role in identifying and ultimately understanding the function of all functional noncoding DNA sequences. DNase-chip was performed as previously described [4]. Briefly, intact nuclei were digested with optimized amounts of DNase. DNase digested ends were blunted, ligated to biotinylated linkers, sonicated, and enriched on a streptavidin column. Sheared ends were blunted and ligated to nonbiotinylated linkers. DNase-enriched material was amplified by linker-mediated PCR, labeled, and hybridized to NimbleGen ENCODE arrays. Randomly sheared DNA was used as a reference control. For each cell type, DNase-chip material was generated from three biological replicates and three different DNase concentrations (total of nine hybridizations per cell type). Raw ratio data from each cell type was averaged and significant signals were identified using ACME (p-value = 0. 001). All DNase-chip data is publicly available on the UCSC genome browser [12] (http: //genome. ucsc. edu). Human ES cell line H9 [37] (WiCell Research Institute; National Institutes of Health Code WA09) was cultured on a feeder layer of mitotically inactivated mouse embryo fibroblasts in medium consisting of DMEM-F12 supplemented with 20% KSR (Invitrogen, http: //www. invitrogen. com), 5 ng/ml FGF2 (R&D Systems, http: //www. rndsystems. com/), 2mM L-glutamine, 0. 1mM 2-mercaptoethanol, and 1× nonessential amino acids. For analysis, hES cell colonies were separated away from the feeder layer and processed for DNaseI hypersensitive site mapping. The undifferentiated state of the cultures was determined by morphology, immunohistochemistry, and Affymetrix expression array (http: //www. affymetrix. com) analysis. Total RNA was extracted from CD4+ T cells, GM06990, HeLa S3, K562, and H9 undifferentiated stem cells using Trizol (Invitrogen). RNA was analyzed by Bioanlyzer to confirm high-quality 18s and 28s ribosomal bands (Agilent, http: //www. home. agilent. com/), labeled, and hybridized to Affymetrix U133 Plus 2. 0 arrays. The expression data for IMR90 was publicly available (http: //licr-renlab. ucsd. edu/download. html). All data was normalized together using RMA [38] through the BioConductor project' s Affymetrix package (http: //www. bioconductor. org). Only genes expressed in the ENCODE regions were used for the analysis. Gene expression was categorized as expressed or not expressed by the Affymetrix A/P call. The enhancer blocking assay was performed as described previously [7]. Briefly, the β-globin DNaseI HS2 site, which is a known enhancer element [39], was cloned upstream of a NeoR gene. Putative insulators were cloned between the enhancer and NeoR gene. The previously described chicken insulator was used a positive control [40]. DNaseI HS sites proximal or distal to TSS that also overlapped CTCF binding sites were cloned into the enhancer block vector. All plasmids were purified from three independent bacterial cultures, linearized, and each DNA prep was electroporated independently into K562 cells. Each electroporation was plated in triplicate (total of nine experiments per plasmid). The next day, cells were transferred to soft agar media containing G418. After 16 days, plates were scanned and colonies were counted. Publicly available ChIP-chip data of CTCF, p300, RNA Pol II, TRAP220 was obtained from http: //licr-renlab. ucsd. edu/download. html. ChIP-chip data for five histone modifications were obtained from the UCSC genome browser (http: //genome. ucsc. edu). The coordinates for TSS were obtained from the ENCODE pilot study [14] (TSS set ABCDE defined in supplement 3. 5 therein). These data were mapped onto the DNaseI HS sites in each cell line based on their overlapping coordinates. To determine whether distal DNaseI HS sites are still statistically near genes, we binned all DNaseI HS sites according to their distances to the closest TSS. We computed an enrichment score for each bin, defined as the ratio between the number of DNaseI HS sites and the number of all possible positions in the ENCODE regions in the same distance bin. MEME [18] with the “-zoop” option was used to identify the CTCF sites in the ubiquitous distal DNaseI HS sites. Clover [24] was used to identify motifs overrepresented in cell type–specific DNaseI HS sites at each distance category: proximal (<2 kb), distal (between 2 kb and 10 kb) and far distal (>10 kb). Two background sets were used (union of ChIP-chip hits and random dinucleotide shuffling of input sequences). Overrepresented motifs (p-values < 0. 01) from the TRANSFAC database were reported. Overlapping motifs were reported as groups.

There are many different types of gene regulatory elements that control gene expression. Identifying the location of these regulatory elements in the genome, as well as understanding how exactly they control gene expression in different cell types, has been a major challenge. Here, we use a relatively new strategy to identify all gene regulatory elements within a select 1% of the human genome from six diverse human cell types. We find that only 22% of gene regulatory elements are shared among all cell types studied. Among these, 86% are located near annotated transcription start sites and 10% are bound by CTCF, a protein with known enhancer-blocking insulator activity. The gene regulatory elements that are found to be cell type specific are highly correlated with cell type–specific gene expression as well as cell type–specific chromatin modifications. This indicates that we have made a significant step toward understanding why some genes are expressed in all different cell types within the human body, and why others are only expressed in certain cell types.

Abstract Introduction Results Discussion Materials and Methods

homo (human) genetics and genomics

2007

Identification and Characterization of Cell Type–Specific and Ubiquitous Chromatin Regulatory Structures in the Human Genome

Trypanosoma vivax, one of the leading parasites responsible for Animal African Trypanosomosis (Nagana), is generally cyclically transmitted by Glossina spp. but in areas devoid of the tsetse flies in Africa or in Latin American countries is mechanically transmitted across vertebrate hosts by other haematophagous insects, including tabanids. We followed on from our recent studies on the maintenance of this parasite in vivo and in vitro, and its genetic manipulation, by constructing a West African IL1392 T. vivax strain that stably expresses firefly luciferase and is fully virulent for immunocompetent mice. We report here on a study where murine infection with this strain was monitored in vivo using a non-invasive method. Study findings fully support the use of this strain in the assessment of parasite dynamics in vivo since a strong correlation was found between whole body light emission measured over the course of the infection and parasitemia determined microscopically. In addition, parasitemia and survival rates were very similar for mice infected by the intraperitoneal and sub-cutaneous routes, except for a longer prepatent period following sub-cutaneous inoculation with the parasite. Our results clearly show that when administered by the subcutaneous route, the parasite is retained few days in the skin close to the inoculation site where it multiplies before passing into the bloodstream. Ex vivo bioluminescence analyses of organs isolated from infected mice corroborated our previous histopathological observations with parasite infiltration into spleen, liver and lungs. Finally, our study reinforces previous observations on the presence of the parasite in the central nervous system and consequently the brain commitment in the very late phases of the experimental infection. Animal African trypanosomosis (AAT) is a major protozoan disease due to trypanosomes. The disease which is endemic in Africa is mainly caused by Trypanosoma vivax, T. congolense and T. b. brucei. T. vivax accounts for up to half of all AAT prevalence in West Africa where it is considered to be the major pathogen that together with T. congolense causes 3 million cattle deaths annually [1]–[3]. Furthermore, T. vivax but also T. equiperdum and T. evansi trigger different pathologies (Nagana, Dourine and Surra, respectively) and are species that have spread to South America. Globalization and livestock trade between countries, coupled with the lack of rapid diagnostic tools and the transport of infected animals to non-endemic areas have a huge impact on agriculture and, consequently, on the economy of breeding and endurance. One of the specificities of T. vivax compared to other animal trypanosomes (i. e. T. brucei spp and T. congolense) is its ability to be transmitted not only by Glossina spp. (tsetse) flies but also by other biting flies of the Tabanidae and Muscidae families that can mechanically transmit the parasite among mammalian hosts [4], [5]. It is noteworthy that Glossina spp. are the only vectors in which T. vivax is able to multiply and pursue its differentiation into metacyclic forms. In contrast, T. vivax is unable to grow or multiply in other insects that can only mechanically transmit the parasite. Regardless of the natural type of transmission (cyclical or mechanical), T. vivax is inoculated in the subcutaneous tissue and the infective forms join the bloodstream via the lymphatic system. After one or more parasitemia peaks, the animals generally show neurological disorders in late phases of infection and perish [6], [7]. Ruminants and equines infected with T. vivax show a range of tissue damage and the diversity of the pathognomonic signs and the severity of the disease frequently correlate with the degree to which the host shows resistance (“tolerance”) or susceptibility to the parasite. Few studies have been conducted to compare the infective process following a bite by tsetse or tabanids, or experimental infections by intraperitoneal or subcutaneous inoculation routes [8]. In efforts to overcome the problems encountered when studying T. vivax infection and pathology in the field, we recently developed murine models that deliver sustained and reproducible infections which successfully mimic the parasitological, histological and pathological features of the infection and closely resemble those observed in cattle trypanosomosis [9], [10]. For instance, histopathological examinations performed throughout the infective process showed many necrotic foci in lymphoid and non-lymphoid organs with extravasated blood cells and trypanosomes in hemorrhagic spots. Most importantly, the infection resulted in multifocal lesions in the central nervous system along with vasogenic edema and damaged blood vessels characteristic of the late-stage ischemic necrosis caused by the wild-type strain. Although the presence of trypanosomes in the meningeal blood vessels at these late stages was suggestive of blood-brain barrier crossing and invasion of cells and parasites into the brain parenchyma [9], our knowledge of the invasive characteristics of T. vivax, its tissue tropism, the temporal course of its invasion and the crucial question of the permeabilization of the blood brain barrier is still incomplete. In order to address some of these questions and supplement the conventional anatomic pathology examinations conducted during studies of the infectious process, we took full advantage of the latest advances made in T. vivax genetic manipulation [11] and engineered a parasite strain that stably expresses firefly luciferase. Here we report on the in vitro and in vivo characterization of the T. vivax luciferase strain and the validation of real-time biophotonic detection systems employed to study the propagation of this parasite in vivo. We determined method limits of detection and linearity ranges to better correlate mouse parasitemia with luminescence measured in vivo, and analyzed the course of the infection and parasite tissue distribution over time. Finally, we compared infection dynamics and organ commitment after subcutaneous and intraperitoneal inoculations with the parasite. Our results confirmed the usefulness of real-time biophotonic analysis in the study and monitoring of the T. vivax infectious process in vivo. Irrespective of the causes that have conducted each mouse to perish during early phases of infection, such as anemia, hyperparasitemia or organ failure, our data provide important evidence that at long-term trypanosomes attain the central nervous system of all the animals which have showed a extended survival just some days before death. All mice were housed in our animal care facility in compliance with European animal welfare regulations. Institut Pasteur is a member of Committee #1 of the Comité Régional d' Ethique pour l' Expérimentation Animale (CREEA), Ile de France. Animal housing conditions and the procedures used in the work described herein were approved by the “Direction des Transports et de la Protection du Public, Sous-Direction de la Protection Sanitaire et de l' Environnement, Police Sanitaire des Animaux” under number B 75-15-28, in accordance with the Ethics Charter of animal experimentation that includes appropriate procedures to minimize pain and animal suffering. PM is authorized to perform experiments on vertebrate animals (license #75-846 issued by the Paris Department of Veterinary Services, DDSV) and is responsible for all the experiments conducted personally or under her supervision as governed by the laws and regulations relating to the protection of animals. Trypanosoma (Dutonella) vivax IL 1392 was originally derived from the Zaria Y486 Nigerian isolate [12]. These parasites have recently been characterized and are maintained in the laboratory by continuous passages in mice, as previously described in detail [9]. Seven to ten week-old male Swiss Outbred mice (CD-1, RJOrl: SWISS) (Janvier, France) were used in all experiments. They were injected intraperitoneally or sub-cutaneously with bloodstream forms of T. vivax (102 parasites/mouse). Parasitemia was determined as previously described [9]. All animal work was conducted in accordance with relevant national and international guidelines (see above). A luciferase assay kit (Roche Molecular Biochemicals; Mannhein, Germany) was used to monitor luciferase expression. Serial dilutions of parasite suspensions were washed in PBS and pellets were suspended in 150 µl of cell lysis buffer. The lysates were then transferred into white, 96-well microplates (Dynex Technologies, Chantilly, France). Light emission was initiated by adding the luciferin-containing reagent, in accordance with manufacturer instructions. The plates were immediately transferred to the luminometer (Berthold XS3 LB960; Thoiry, France) and light emission was measured for 0. 1 s. Luminescence was expressed in Relative Light Units (RLU). Mice were inoculated intraperitoneally with luciferin (D-Luciferin potassium salt, Xenogen, California), the luciferase substrate, at a dose of 150 mg/kg before any bioluminescence measurements were made. They were anaesthetized in a 2. 5% isoflurane atmosphere (Aerane, Baxter SA, Maurepas, France) for 5 minutes and kept in the imaging chamber for analysis. Emitted photons were acquired for 1 minute by a charge couple device (CCD) camera (IVIS Imaging System Lumina, Caliper, Villepinte, France) set in high resolution (medium binning) mode. The analysis was then performed after defining a region of interest (ROI). The same ROI was used for all animals and all time points. Total photons emitted from the image of each mouse were quantified using Living Image software (Xenogen Corporation, Almeda, California), and results were expressed as number of photons/sec/ROI. The methods used to engineer this T. vivax strain that stably expresses firefly luciferase (TvLrDNA-luc) have been described elsewhere [11]. The infective forms of these recombinant parasites maintain their infectivity in immunocompetent mouse strains and show the same parasitemia profiles over time and result in similar levels of mortality as wild type (WT) T. vivax. Luciferase expression levels in non infective epimastigote axenic forms (EPI) and in infective bloodstream trypomastigote (BSF) forms of TvLrDNA-luc were compared by measuring the luciferase activity of equivalent numbers of parasites purified from axenic cultures or mouse blood, respectively. Serial dilutions of EPI and BSF were washed, lysed and the extract supernatants assayed in parallel for in vitro luciferase activity by measuring relative light emission (RLU) initiated by adding luciferin substrate. Figure 1A illustrates the linearity of the RLU results over more than 3 logs for both EPI and BSF TvLrDNA-luc extract supernatants. Since no bioluminescence was detected in the WT EPI and BSF parasites included in the assays (<50 RLU), these results indicate that this light emission is specific to bioluminescent (luciferase-expressing) parasites. Limits of detection were about 300 EPI and 8000 BSF using the bioluminescence assay. EPI clearly gave 7 to 10 fold the luciferase activity of purified BSF. To check that BSF serial passages in vivo do not result in any loss of the construction carrying the luciferase and the resistance marker genes, TvLrDNA-luc parasite strain was maintained in vivo without drug pressure for 3,7 or 15 sequential passages and compared for light emission. Figure 1B shows that light emission was comparable whatever the number of passages in vivo. The results obtained confirmed that the differences observed between EPI and BSF were not due to any in vivo loss of the construction. These differences therefore may reflect dissimilar stabilities of the enzyme at different temperatures or, like for other trypanosomatids [13], may stem from the distinct morphometries of EPI and BSF which are compatible with their size, nucleic acids and protein contents. Altogether, plasmid integration in the ribosomal region of T. vivax was shown to be stable over at least 15 consecutive passages, corresponding to 15 weeks. Parasite dissemination and disease progression in a T. vivax-infected mouse model previously studied in the laboratory, was followed by using sensitive, non-invasive optical imaging to track TvLrDNA-luc parasite strain in live mice. The bioluminescent signal obtained in vivo with TvLrDNA-luc parasites was validated by considering criteria such as background spontaneous signals obtained from whole-body images of mice infected with WT parasites and optimal time period between substrate administration to live animals and image capture. Firstly, a group of mice were infected with WT parasites, injected with D-Luciferin and subsequently exposed to photon detection under the IVIS Lumina Imaging System (IVIS) to determine background emissions across the entire body. This resulted in 106 ph. /s being considered as the background level for further in vivo experiments using the TvLrDNA-luc parasite strain (not shown). Secondly, another group of mice were infected with TvLrDNA-luc parasite strain and analyzed once the infection had resulted in moderate parasitemia (106 parasites/mL). Total body light emission was determined under the IVIS at time points 1,3, 5,10,15 and 20 minutes after D-Luciferin injection. As can be seen in Figure 2A, light detection was maximal between 5 and 10 minutes after substrate injection and did not show any major variations up to 20 minutes after the injection. Further in vivo measurements were then made 10 minutes after the D-Luciferin injection. Lastly, we checked whether or not the light emitted correlated with the T. vivax infectious process in vivo. To do this, we determined whether or not the parasitemia observed microscopically correlated with the bioluminescence measured over the whole animal. Parasites were counted under a light microscope in five microliters of blood harvested individually from the tail vein of mice infected with 102 TvLrDNA-luc parasites, and parasitemia was expressed as number of parasites per mL of blood. Mice were immediately injected with D-Luciferin and submitted to whole-body imaging. An extensive and increasing light emission was observed during the course of the infection, as shown in Figure 2B. Total bioluminescence increased in the course of the infection and in line with the parasite count obtained optically, reaching ph. /s levels that were more than 1000 fold the background level with parasitemia of 108 parasites/mL. The exhaustive plotting of bioluminescence versus parasitemia depicted in Figure 2B represents 35 individual measurements obtained in a group of 15 mice and shows a close correlation between the 2 parameters, as confirmed by a high Spearman rank correlation coefficient of 0. 9365. These findings validated the TvLrDNA-luc bioluminescent strain and the baseline imaging parameters necessary to analyze and monitor T. vivax infection and disease progression in vivo. In order to gain a clearer insight into disease progression, and in particular determine whether T. vivax multiplication is confined to the vascular compartment, we compared the parasite dissemination after infection by two different routes. The conventional intraperitoneal (IP) route commonly used for mouse experimental infections was compared to the subcutaneous (SC) inoculation route that closely resembles the natural infections, cyclic or mechanically conveyed by the insects. The course of the resulting infection together with parasitemia and survival rates were therefore studied in mice infected subcutaneously or intraperitoneally with 102 TvLrDNA-luc BSF parasites. As can be seen in Figures 3A and 3B, no substantial differences were observed for parasitemia between the two groups. As expected, a straightforward correlation was found between the numbers of parasites determined optically and the signals resulting from bioluminescent parasites. The only difference found between the groups was the length of the prepatent period preceding the microscopically-detectable parasitemia (104 parasites per ml of blood). As shown in Figure 3B, mice infected by the subcutaneous route showed a longer prepatent period (9 to 10 days) and consequently a more delayed onset parasitemia than mice inoculated by the intraperitoneal route that presented detectable parasites 5 days after infection (Figure 3A). Similar data were obtained after experimental infections with T. congolense, as reported previously [8]. With the exception of this time lag, parasite multiplication was seen to follow the same kinetics with both routes of infection and parasitemias were invariably similar after day 15 of infection. Accordingly, survival rates during infection were not significantly different between mice injected IP or SC, with 30% of the mice dying by day 15 p. i. , 40% between days 20 and 22 and the remaining dying by day 28 post-infection (Figure 3C). Interestingly, while detectable light emission in IP-infected mice invariably correlated with parasite appearance in peripheral blood, we noted that light emission was already detectable in SC-infected mice in the prepatent period. In efforts to investigate this phenomenon, we infected a group of mice with 102 TvLrDNA-luc by the SC route and monitored bioluminescence every day during the prepatent period. Light emissions were seen to increase between days 8 and 9 post-infection, suggesting that the parasite load was increasing but remained below the limit of detection of the microscopy visualization technique (<104/mL). By the time parasitemia had become detectable (day 9–10), the mice showed a bright spot on the right lateral flank close to the injection site (Figure 4A) that gradually increased thereafter (Figure 4B). The mice were sacrificed on day 10 for gross anatomy and a representative mouse is shown in Figure 4C. As can be seen, the light emission is confined to the skin near the SC inoculation site. The increase in light emissions in this area and the very circumscribed foci of photons shows that the infection initially develops in situ and that parasite multiplication takes place in the skin close to the injection site before parasites reach the bloodstream. T. vivax infection was followed in vivo by inoculating new groups of mice by the IP route with 102 TvLrDNA-luc BSF and following the infection by biphotonic analysis. Mouse parasitemias were measured individually both microscopically and by light emission, and as described here above, the two techniques gave comparable results. Groups of at least 3 mice were analyzed at each time point and bioluminescence recorded individually. Light became detectable 5 days after infection and at this point was 4 fold background levels in non infected control mice. These observations correlated with very low parasitemias (1–2×104 parasites/mL), as determined microscopically. Figure 5A shows the results obtained with one infected mouse representative of a group of 3 mice examined by time of infection as compared to a uninfected control during the study period of 4 weeks. The first foci observed were located at the muzzle and the inguinal regions. Once parasitemia increased (105–106 parasites/mL), photons were detected along the entire body, with hotspots corresponding to spleen, lungs and liver. Attempts were made to accurately define the dynamics of parasite dissemination by segmenting the images obtained for each animal into several areas corresponding to whole body (R), head (R1), thoracic region (R2), abdomen (R3), inguinal (R4) and testis areas (R5) (Figure 5B). The bioluminescence detected from day 10 for each of these defined areas was up to 10000 fold background levels (Figure 5C) for some areas. No apparent parasite infiltration/retention was seen in any particular region, supporting the notion that the development of T. vivax is confined to the vascular compartment. In a further set of experiments we compared the distribution of T. vivax at key time points in the infectious process by ex vivo examination of the main organs affected after injection of the TvLrDNA-luc strain. Groups of 3 mice per time point were sacrificed and the light emitted by spleen, lungs, liver and brain was promptly recorded in the presence of excess of D-Luciferin. As can be seen in Figure 6A and regardless the individual variation in the level of bioluminescence observed inside the group, the spleen was affected soonest after infection and constituted one of the first sites of parasitic retention (day 10 p. i. , peaking by day 15 p. i.), as shown by at least 1000 fold greater light emission than in uninfected mice. Photon emission increases were recorded for all the organs tested after day 10, with elevated levels in liver and in particular lungs (Figures 6B and 6C). At about day 20 p. i. , the luminescence in the lungs of infected mice accounted for up to 15% of the total signal recorded for the entire body, compared with 1% in lungs harvested from non-infected mice. Likewise, while no surprising light emission is apparent in the hearts after 10 days of infection (Figure 6C arrows), a significant rise in photons per second (1,4×107±6×106 ph. /s, not shown) is recorded for the mouse hearts to attain more than 1000 times the background levels for the organ by day 28 of infection. Use of bioluminescent T. vivax in vivo also allowed the validation of previous data which showed that the parasite may cross the blood brain vessels and lodge into the brain parenchyma [6], [14]. Indeed the bioluminescence signal from the brains of the animals that resisted longer the hyperparasitemia peak (20–30% survival by day 20, see Figure 3C), increases substantially from day 20 (Figure 7) but it is only visible after 25 days p. i. in localized light emission foci, just some days preceding death. T. vivax is one of the leading parasites responsible for AAT, or Nagana, that still ranks among the most neglected diseases. One of its main particularities that can explain its capacity to emerge in areas free from tsetse flies is its ability to also be transmitted mechanically by a broad spectra of haematophagous insects [5], [15]–[17]. We have previously developed experimental models of T. vivax infection using mice infected with the ILRAD1392 reference strain [9]. Immunobiological and immunophysiopathological analyses confirmed that these models are reliable and consistent with all the relevant characteristics of the animal disease [9], [10]. Furthermore, we have also developed robust methods for parasite growth and differentiation in axenic cultures, paving the way to appropriate conditions for the first genetic manipulation of T. vivax [11]. In the study reported herein we generated an ILRAD1392 strain that stably expresses firefly luciferase (TvLrDNA-luc). Then, in detailed studies using this bioluminescent parasite, we ascertained that it has the same virulence in immunocompetent mouse models and behaves in the same manner as WT parasites. We established that these TvLrDNA-luc mutant parasites can be used successfully to i) monitor in vivo the infectious processes triggered by T. vivax and ii) evaluate organ infiltration by these parasites in vivo and ex-vivo. To the best of our knowledge, the study reported herein is the first to use a systematic imaging method to study the experimental model of T. vivax infection in vivo and the first to demonstrate the presence of the parasite in the brain by simple bioluminescent signal. The use of bioluminescent parasites is a strategy of choice for investigating and following infectious processes in vivo [18], [19]. The approach has been used successfully to analyze infections caused by Plasmodium berghei, Leishmania major, Toxoplasma gondii, Trypanosoma cruzi and T. brucei [13], [20]–[28]. Here, an imaging system is used to quantify the light emitted by transfected cells - and in particular by microorganisms constitutively expressing the luciferase reporter gene - and thus monitor the infectious process in vivo without animal sacrifice. The technique is widely used for instance to screen new active compounds with the intention of discovering novel chemotherapies [20], [22], [29]–[31]. We decided to use the TvLrDNA-luc strain to follow the dynamics of T. vivax infection in vivo from the start to the end of the infectious process. We compared the results obtained with two different routes of infection, i. e. the conventional experimental intraperitoneal (IP) route and subcutaneous inoculation that mimics natural transmission of the parasite by the vector. The only difference observed between these routes was that the subcutaneous injection resulted in a more prolonged prepatent period and in parasite multiplication in situ for some days before it reached the bloodstream. These observations are fully consistent with data previously obtained mainly with T. congolense and T. evansi where it was shown that the inoculation of metacyclics into the animal' s hypodermis either by insect bite or syringe resulted in the development of a local inflammatory reaction, called inoculation chancre [32]–[35]. This was followed by parasite multiplication in the skin, as shown by conventional histopathology, and their migration into the bloodstream. In our study, we did not observe any inflammatory reaction at the injection site. This discrepancy could be due to the nature of the parasitic form injected since it has been previously reported that T. congolense bloodstream forms, at least in low numbers, are unable to induce a metacyclics-like inflammatory reaction [36]. Although it is generally recognized that African trypanosomes migrate from the skin into the blood via the lymph system, our results did not demonstrate whether or not parasite multiplication in this compartment contributes to the infection spreading or to the orientation of the immune response. However, the light emissions we measured clearly demonstrated that growing parasites were accumulating close to the inoculation site. The results we obtained with bioluminescent T. vivax are fully consistent with our previous reports [10] and confirm that the parasite spreads across the spleen and liver compartments. It is interesting to note that lung infiltration by the parasite is difficult or impossible to observe by immunohistopathology, contrasting with our present observations. But, considering that the bioluminescent reaction is dependent on the O2 concentration [37], we cannot exclude that the substantial signal in the lungs during the late phases of the infection partially results from the abundance of O2 in this organ. Our previous report [9] has revealed the presence of innumerous parasites in the ventricular cavities of the heart. The present data showed a considerable and unfailing increase of luminescence throughout the period of study which attains its maximum between days 15 and 20 of infection when 40% of the mice die. These observations are suggestive of a congestive heart failure-inducing death, consistent with that reported for infected cattle [38], [39]. Noteworthy, the progressive increase of bioluminescence observed in testis (R5) is suggestive that parasites can pass through Sertoli cell barrier during infection and thus contribute to reproductive disorders in the seminiferous tubules, as already suggested in reports with host infected with T. vivax and T. b. brucei [27], [40]–[42] Our observations together with earlier histopathological studies [9] nevertheless suggest that the parasite reaches the brain tissues during late (encephalic?) phases of the infection for those mice that survive longer the hyperparasitemia occurrence. The light emitted by the brain increased slightly up till day 20 post-infection (<400 fold the background) then rose substantially by day 25, reaching sufficient levels (up to 2000 fold the background) to provide a picture of parenchymal infiltration. These observations corroborate previous reports showing molecular and histopathological data on the detection of T. vivax both in the cerebrospinal fluid and the nervous tissue parenchyma of goats [14]. Correspondingly, at late stages of T. b. brucei infection in rat and mouse models, the parasite actively migrate out of the cerebral blood vessels, cross the endothelial basement membrane, the perivascular space and the parenchymal membrane to invade the brain parenchyma, with no signs of plasma protein leakage into the brain [43]. These results were indicative that parasites had penetrated the parenchyma through the blood brain barrier rather than from circumventricular organs or through the cerebrospinal fluid (for a review, see [44]). Our present data could not clarify if the bioluminescent signal results from intra or extra vascular parasites. Using PCR of CSF extracts and histopathology of the brain may give a better picture of this question (ongoing experiments). Altogether, the results reported herein strongly support use of the TvLrDNA-luc strain for detailed in vivo studies of the infectious process triggered by T. vivax. In particular, our data showed that the TvLrDNA-luc strain is highly appropriate to ascertain the evolution of the infection and the mechanisms involved in the progression of the disease. A more in-depth comprehension of the strategies set in place by the parasite to persist inside the host could open up perspectives for the development of a new therapeutic strategy against AAT. Our data also validate the use of bioluminescent T. vivax in high throughput drug screening strategies.

Very little work has been performed on Trypanosoma vivax for decades, but the recent development of murine infection models and axenic cultures has enabled the genetic manipulation of this parasite and has opened the door to a more in-depth understanding of its biology and the infectious process that leads to animal trypanosomosis. We report herein the characterization of a luciferase-expressing strain that can be used to follow parasite dynamics in vivo in real time using a non-invasive method. Regardless of the inoculation parasite route and some minor differences concerning the length of the prepatent period of infection following the subcutaneous injection of the parasites, we highlight the general commitment of the organs triggered by the infection and particularly the presence of the parasite in the brain at late phases of disease. The study presented herein provides new insights into the interaction between T. vivax and its mammalian host and assesses new tools for in vivo drug screening.

Abstract Introduction Materials and Methods Results Discussion

medicine infectious diseases neglected tropical diseases infectious disease modeling parasitic diseases veterinary science

2013

Non-Invasive In Vivo Study of the Trypanosoma vivax Infectious Process Consolidates the Brain Commitment in Late Infections

Type III secretion systems (T3SSs) are essential virulence factors of numerous bacterial pathogens. Upon host cell contact the T3SS machinery—also named injectisome—assembles a pore complex/translocon within host cell membranes that serves as an entry gate for the bacterial effectors. Whether and how translocons are physically connected to injectisome needles, whether their phenotype is related to the level of effector translocation and which target cell factors trigger their formation have remained unclear. We employed the superresolution fluorescence microscopy techniques Stimulated Emission Depletion (STED) and Structured Illumination Microscopy (SIM) as well as immunogold electron microscopy to visualize Y. enterocolitica translocons during infection of different target cell types. Thereby we were able to resolve translocon and needle complex proteins within the same injectisomes and demonstrate that these fully assembled injectisomes are generated in a prevacuole, a PI (4,5) P2 enriched host cell compartment inaccessible to large extracellular proteins like antibodies. Furthermore, the operable translocons were produced by the yersiniae to a much larger degree in macrophages (up to 25% of bacteria) than in HeLa cells (2% of bacteria). However, when the Rho GTPase Rac1 was activated in the HeLa cells, uptake of the yersiniae into the prevacuole, translocon formation and effector translocation were strongly enhanced reaching the same levels as in macrophages. Our findings indicate that operable T3SS translocons can be visualized as part of fully assembled injectisomes with superresolution fluorescence microscopy techniques. By using this technology, we provide novel information about the spatiotemporal organization of T3SS translocons and their regulation by host cell factors. Bacterial type III secretion systems (T3SSs) are molecular machines also termed injectisomes that translocate proteins of bacterial origin (i. e. effectors) into host cells. T3SSs are essential virulence factors of numerous human, animal and plant pathogens including Chlamydia, Pseudomonas, EPEC and EHEC, Salmonella, Shigella and Yersinia [1,2]. Based on sequence identity among structural components nine T3SS families were classified [3]. Whereas the assembly process, structure and function of the T3SSs are highly conserved, the biochemical activities of the translocated effectors often are multifaceted and reflect the infection strategies of the individual pathogens [4]. Because of their uniqueness in bacteria on one hand and central role for bacterial pathogenicity on the other hand T3SSs have been considered as targets for novel antiinfective strategies [5–8]. In addition, the ability of T3SSs to inject immunogenic proteins into immune cells has been exploited for experimental vaccination strategies [9]. Based on topology and function injectisomes can be separated into different parts: i) the sorting platform on the cytoplasmic side of the injectisome is a protein assembly thought to control targeting and secretion of the T3SS substrates [10]; ii) the basal body including the export apparatus spans the inner and outer bacterial membranes [11]; iii) the 30–70 nm long needle filament is built by a single multimerized protein and together with the basal body forms the needle complex; iv) the tip complex consists of a hydrophilic protein that caps the needle filament, mediates binding of the translocators to the needle tip and regulates formation of the translocon; v) the translocon consists of two hydrophobic translocator proteins that upon host cell contact form a pore in the host cell membrane serving as a regulated entry gate for the bacterial effectors [1,2, 7,12–15]. The assembly process of the injectisome starts with formation of the basal body whose components are exported by the bacterial Sec system and is followed by export of the needle proteins as early T3SS substrates. The tube-like needle then allows passage of the tip complex and translocon proteins, which are intermediate substrates. Bacterial effector proteins, representing the late T3SS substrates, are thought to be translocated into host cells through a conduit formed by needle complex and translocon. Although electron microscopy, crystallography and biophysical techniques have provided a high resolution picture of the assembly and architecture of needle complex and sorting platform [1,5, 16,17], major properties of the translocon such as its composition, exact localization—i. e. attached to or separated from the needle tip—or regulation have long remained elusive or controversial [18]. A recent cryo electron tomography study clearly indicates that the Salmonella translocon is connected to the injectisome needle and in parallel embedded in the host cell membrane whereby it protrudes towards the host cell cytoplasm. In this study the Salmonella translocon has a diameter of ~13. 5 nm and a thickness of 8 nm. The part of the translocon protruding into the target cell creates an indentation which may contain the inner opening through which the effectors enter the cell [19]. All investigated T3SSs express two hydrophobic translocators, a major translocator harboring two and a minor translocator harboring one transmembrane domain [14,20]. The translocators are inserted into the host cell membrane where they form a heteromultimeric pore complex [21–23]. Numerous studies suggested that both translocators are required for a functional pore complex [14,20]. Although the inner opening of the pore complex could so far not be visualized in situ, the pore opening was estimated by in vitro reconstitution, osmoprotection and dextran release assays to have an approximate diameter of 2–4 nm [20–22,24–28]. Reconstitution in liposomes suggested that the P. aeruginosa translocators PopB and PopD, which are highly homologous to Yersinia YopB and YopD, form a hexadecameric 8: 8 complex [29]. A pore complex transferred by Y. enterocolitica into erythrocyte membranes displayed a molecular weight of 500–700 kDa [23]. Of note, considering the approximate molecular weights of 44 kDa for YopB and 34 kDa for YopD, an 8: 8 complex of YopB and YopD would have an expected molecular weight of around 624 kDa. A previous report challenged the paradigm that effector translocation by Yersinia pseudotuberculosis proceeds through a continuous conduit from the bacterium directly into the target cell. Rather, it was proposed that isolated effectors located on the surface of the bacteria can translocate into target cells with the help of a separate and even an unrelated Salmonella T3SS [18]. It has become clear that effector translocation as well as isolated pore activities of T3SSs, such as disruption of cells and increased permeability of cell membranes, are regulated by diverse host factors including actin, Rho proteins, cholesterol, sphingolipids, coatomers, clathrin and exocyst [30–33]. In Yersinia, filamentous (f) -actin disruption and Rho GTPase inhibitors block translocation of effector Yops and YopB/D pore activity, whereas activation of Rho GTPases enhances effector Yop translocation [34–38]. In addition, in a feedback mechanism pathogenic yersiniae can control T3SS function through their own effectors, i. e. by modulating Rho protein activity in host cells [30]. In this work we performed high resolution fluorescence and immunogold electron microscopy to visualize translocons of Y. enterocolitica during host cell infection. Thereby we deciphered that translocons are connected to the remaining T3SS, that their overall number is changed dependent on the level of effector translocation and that they are formed in a specific host cell compartment. During infection with pathogenic yersiniae the translocators YopB and YopD are inserted into host cell membranes where they form the translocon [21–23]. To visualize the Yersinia translocators YopB and YopD we produced specific rabbit and rat polyclonal antibodies (S1A, S1B and S1F Fig for antibody specificity). In lysates of Y. enterocolitica WA-314 (wild type; Table 1) grown at 27°C, YopB and YopD could not be detected by immunoblot. However, YopB and YopD proteins became detectable in bacteria grown at 37°C in high Ca2+ medium (non-secretion condition) and their levels further increased in bacteria grown in low Ca2+ medium (secretion condition; Fig 1A). It is well accepted that expression of the Yersinia T3SS genes is switched on at 37°C and further enhanced by depletion of Ca2+ from the growth medium. The latter phenomenon is associated with a massive secretion of intermediate and late T3SS substrates and has been named the low Ca2+ response [42–44]. YopB and YopD were immunofluorescence-stained in Yersinia wild type at secretion condition using a procedure that does not permeabilize the bacterial inner and outer membranes (Material and methods) [45,46]. By confocal microscopy intense YopB and YopD fluorescence signals were seen along the bacterial circumference in essentially 100% of the bacteria (Fig 1B and 1C and S1C and S1D Fig). In comparison, essentially no YopB/YopD immunofluorescence signals were found in bacteria at non-secretion condition and in bacteria at secretion condition which were treated with proteinase K (PK) before staining (Fig 1B and 1C and S1C and S1D Fig). When cells were permeabilized with 2% sodium dodecyl sulfate (SDS) before immunostaining, essentially all bacteria at non-secretion and secretion conditions displayed YopB and YopD signals (Fig 1B and 1C and S1E Fig). In this case the YopB/YopD signals filled the whole bacterial cell rather than just the cell periphery, indicating that also intrabacterial pools of the proteins were stained. Accordingly, treatment with proteinase K before permeabilization and staining of the bacteria did not alter the intracellular YopB/D signal (Fig 1B and S1E Fig). Total YopB signals were considerably higher in bacteria at secretion than at non-secretion condition, consistent with the immunoblot data (Fig 1A and 1B; S1E Fig). To visualize YopB and YopD on the bacterial surface with higher spatial resolution, we employed stimulated emission depletion (STED) microscopy [47]. STED microscopy increases resolution of fluorescence signals to approximately 30–80 nm under the condition used (lateral resolution at 100% 2-D STED). The YopB and YopD fluorescence signals recorded with STED microscopy appeared sharper and confined to a narrow band encompassing the bacterial periphery when compared to the signals obtained with confocal microscopy (Fig 1D). Co-immunostaining revealed a considerable but not complete colocalization of YopB and YopD on the bacterial surface as demonstrated by merge of YopB and YopD 3D-STED images (single planes; resolution: lateral 80–90 nm and axial approximately 100 nm) and by superimposed intensity plots of the YopB and YopD signals on the bacterial surface (Fig 1D). 3D reconstructions of YopB- and YopD z-stacks revealed that the bacterial surface is widely covered with both of these translocators, which inadvertently will lead to colocalization between these proteins (Fig 1D). We also tested the appearance of YopD in a yopB mutant strain, YopB in a yopD mutant strain as well as YopB in a lcrV (tip complex) mutant strain (S1G Fig). In all of these cases the rather widespread translocator coating on the bacterial surface looked similar. This data suggests that under secretion condition the translocators on the Yersinia surface are likely not associated with injectisomes. We conclude that during the low Ca2+-response the secreted translocators YopB and YopD localize on the Yersinia cell surface but do not display a specific (co) localization pattern when investigated with high resolution fluorescence microscopy. In order to visualize operational translocons, YopB and YopD were immunostained during Y. enterocolitica infection of HeLa cells and primary human macrophages. Confocal micrographs revealed that YopB and YopD concentrate in distinct patches in host cell associated wild type bacteria (Fig 2A and S2A Fig). This clustered appearance as imaged by confocal microscopy was in clear contrast to the more uniform YopB/YopD distribution in secreting bacteria (Fig 1B and 1D). Time course experiments demonstrated that the fraction of wild type bacteria that stained positive for YopB in HeLa cells reached only about 2% at 60 min post infection (Fig 2B). By comparison, in human macrophages, which represent more physiological target cells of pathogenic yersiniae [36,48,49], the fraction of YopB positive bacteria amounted to around 25% at 20 min post infection (Fig 2B). We next investigated the YopB signals in HeLa cells using confocal and STED microscopy in parallel. In strain WA-314ΔYopE (Table 1), which was well suited for this analysis because it displayed a much higher percentage of YopB positive bacteria than wild type (see below), single patches in the confocal recordings could regularly be resolved into 3 distinct spots with the STED technology (Fig 2C). In the mean around 11 YopB positive patches and 33 YopB positive spots were detected per bacterium when investigated with confocal and STED microscopy, respectively (Fig 2C). For a more comprehensive evaluation of the organization of the YopB spots on the bacterial surface they were recorded in 3D-STED mode and subjected to image analysis. Spot detection and segmentation as well as cluster analysis revealed that the YopB spots are regularly organized in clusters with on average 2. 8 ± 1 spots per cluster. The mean distance between the spots in one cluster was 127 ± 42 nm and the mean distance between individual clusters was 713 ± 121 nm (mean ± S. D. , n = 64; Methods). To visualize the distribution of all identified clusters on a single bacterial cell, superresolution 3D images were prepared in which each segmented cluster is represented by a different color and the bacterium is viewed from different angles (Fig 2D, S2B Fig and S1 Movie; Methods). We reasoned that if the YopB and YopD spots detected by STED microscopy reflect translocons associated with the injectisome, YopB and YopD should colocalize with each other as well as with the tip complex protein LcrV, which is essential for linking YopB/YopD to the needle tip [50,51]. YopB and YopD showed a complete colocalization in host cell associated bacteria, which was best documented when colocalizing points or intensity plots of YopB and YopD fluorescence on the bacterial surface were determined (Fig 2E and 2F). Furthermore, STED images of YopB and LcrV co-immunostaining indicated that essentially all YopB signals are associated with LcrV signals (Fig 2E). The LcrV staining was less bright than the YopB staining which may result from the lower number of LcrV molecules per injectisome [52–54]. Finally, in a lcrV mutant (Table 1) YopB and YopD did essentially not colocalize, confirming the relevance of LcrV for organizing YopB and YopD in translocons (S2C Fig). To further support the notion that the YopB/YopD fluorescence signals in the cell associated bacteria represent translocons attached to needle tips, we investigated the spatial coupling of YopB/D to the basal body component YscD at high resolution. Considering the published data on Yersinia basal body, needle and tip complex length as well recent work in Salmonella, the N-terminal domain of YscD—or of its homologues—is located in approximately 100 nm distance from the needle tip (Fig 3A) [11,55–58]. For this experiment HeLa cells were infected with Y. enterocolitica strain E40 GFP-YscD (Table 1) [10] expressing a N-terminal EGFP-fusion of YscD and immunostained for YopB or YopD. Because GFP-YscD did not produce a high enough fluorescence signal for STED microscopy, the alternative high resolution microscopy technique structured illumination microscopy (3D-SIM, resolution to ~100 nm in x-y plane) [59,60] was employed to visualize GFP-YscD and YopB/D in parallel. SIM revealed single YopB or YopD dots that could clearly be separated from each other and from neighboring patches of GFP-YscD (Fig 3B and S3A Fig). A fluorescence intensity plot of a representative YopB/GFP-YscD pair indicated a distance of 90 nm between the fluorescence maxima (Fig 3B). A more comprehensive nearest neighbor analysis indicated distances between the GFP-YscD patches and YopB/D dots of around 110 nm (109 ± 4 nm; n = 424 pairs evaluated; Methods; Fig 3C). Having successfully resolved fluorescence signals from translocon and basal body proteins that are located in distant parts of the same injectisomes, we next co-immunostained the needle protein YscF with YopB in strain E40 GFP-YscD. YscF forms the needle that connects translocon and basal body in injectisomes (Fig 3A). SIM revealed tripartite complexes made up of YopB, GFP-YscD and YscF, whereby the fluorescence signal for YscF was sandwiched between the YopB and YscD signals (Fig 3D). Thus, by high resolution SIM we were able to resolve three proteins located in different parts of Yersinia injectisomes. We next aimed to visualize YopD and GFP-YscD embedded in their native bacterial and host cellular environment. Transmission electron microscopy (TEM) of ultrathin sections combined with immunolabelling allows high resolution localization of proteins within their cellular context [61,62]. Electron-dense protein-A/gold particles of different sizes (usually 5–15 nm) additionally permit to localize different proteins in the same sections [63]. We infected Rac1Q61L expressing HeLa cells, in which the expression of Yersinia injectisomes is strongly enhanced (see below), with strain E40 GFP-YscD and immunolabelled YopD or GFP-YscD with 10 nm gold particles. The labelling density of the 10 nm gold particles marking YopD was much higher in the bacterial cell (cytoplasm, periplasm and inner and outer bacterial membranes; relative labelling index (RLI): 2. 73) than in the extrabacterial area (RLI: 0. 42) (252 gold particles evaluated in 36 sections on 2 grids; Fig 3E and S1A Table). Thus, the observed distribution of the 10 nm gold particles reflects the expected YopD localization in section staining, which includes the injectisome and the intrabacterial pools (S1C and S1E Fig). We next aimed to assess if the extrabacterially located 10 nm gold particles could represent YopD in translocons at the tip of injectisome needles. We assumed that in this case the gold particles should be located within a range of 90 nm from the bacterial outer membrane which we defined as injectisome range, reflecting the cumulative dimensions of the injectisome needle (~60 nm), tip complex (~8 nm) and antibody/protein A complex used for immunogold staining (~20 nm; Fig 3E). Distances less than 90 nm may occur depending on the geometry and spatial orientation of the type III secretion machines in the 2D analysis. YopD labelling within the injectisome range was significantly enriched (RLI: 2. 16) compared to the remaining host cell area (RLI: 0. 79) (80 gold particles evaluated in 36 sections on 2 grids; Fig 3E and S1B Table) suggesting that it represents translocons associated with injectisomes. The labelling density of the 10 nm gold particles marking GFP-YscD was highest in the area between the bacterial inner and outer membranes (RLI: 4. 44) followed by the bacterial cytoplasm (RLI: 2. 38) and was only minor in the extrabacterial space (RLI: 0. 52) (580 gold particles evaluated in 41 sections on 2 grids; Fig 3E and S1C Table). This distribution reflects the expected localization of GFP-YscD in assembled basal bodies in the inner bacterial membrane and also surprisingly suggests a cytoplasmic GFP-YscD pool. Because YscD is a membrane protein and should be inserted into the bacterial membrane in co-translational manner, we presently cannot explain the nature of the cytoplasmic YscD protein and whether it might be related to the fact that we used GFP-fused YscD. We finally employed co-immunogold staining to detect GFP-YscD (15 nm gold particles) and YopD (10 nm gold particles) within the same injectisomes. Considering the dimensions of the injectisome (length of ~100 nm; Fig 3A) and the two antibody/protein A complexes (length of 2 x 20 nm = 40 nm), we assumed that 10 nm gold particles (YopD label) that lie extrabacterially and in an at most ~140 nm distance from 15 nm gold particles (YscD label), that themselves are located between the bacterial inner and outer membranes, belong to the same fully assembled injectisome. Consistent with this notion we repeatedly identified configurations of 10 nm and 15 nm gold particles fulfilling these premises (58 sections on 3 grids evaluated; Fig 3F). Thus, the observed distributions of the 10 nm and 15 nm gold particles in bacteria surrounded by host cells reflect the expected locations of YopD and GFP-YscD at the tip and basal body, respectively, of injectisomes. In summary, we conclude that the Yersinia translocons can be visualized in fully assembled injectisomes by superresolution fluorescence microscopy (SIM and STED) and immunogold TEM techniques, the latter method also suggesting that translocons are located in a specific cellular compartment. The visualization of operable translocons in host cells prompted us to test how translocon number or distribution correlates with the level of effector translocation. To enhance Yop effector translocation into HeLa cells, cells were infected with the YopE-deficient strain WA-314ΔYopE or cells expressing the constitutively active Rho GTP-binding protein Rac1Q61L were infected with wild type Yersinia. Under these conditions Yop-translocation rates increase 5-10-fold [38] which is a consequence of the elevated Rac activity in the host cells [38,64,65]. In the case of WA-314ΔYopE the elevated Rac activity is caused by the diminished Rac inhibition that is normally imposed by the Rho GTPase-activating protein YopE [66,67]. Notably, the fraction of YopB positive bacteria increased from around 2% in wild type infected cells to 30% in WA-314ΔYopE infected cells and also reached around 30% in the Rac1Q61L expressing and wild type infected cells at 60 min post infection (Fig 4A). As an alternative method to detect host membrane inserted translocons we employed a digitonin-based release assay. Digitonin extracts host cell associated Yops but not intrabacterial Yops and has hitherto been used to assay effector-Yop translocation [68]. Consistent with an increased deposition of translocons in the infected cells, the amounts of YopB and YopD extracted by digitonin were increased in the WA-314ΔYopE infected cells as well as in the wild type infected and Rac1Q61L expressing cells when compared to the respective controls (Fig 4B). The extracted amounts of YopB and YopD correlated with the extracted amount of YopH, the latter serving as a measure for effector-Yop translocation (Fig 4B). In comparison, the levels of YopB, YopD and HSP60 in the pellet fraction (representing the bacterial protein pool) were similar in all conditions. Thus, in infected HeLa cells stimulation of Yop translocation is associated with a large increase in the number of bacteria forming translocons which causes enhanced translocon incorporation into cell membranes. We next tested whether the number of translocons per bacterium, as identified by YopB fluorescence spots in STED images, was also altered under conditions of increased Yop translocation. In strain WA-314ΔYopE the number of translocons per bacterium was nearly twice as high as that in wild type upon infection of HeLa cells (Fig 4C). Furthermore, in macrophages wild type bacteria showed a significantly higher number of translocons than in HeLa cells reaching similar values as WA-314ΔYopE in HeLa cells (Fig 4C). There was no difference in the number of translocons formed between wild type and WA-314ΔYopE in macrophages (Fig 4C) excluding that the WA-314ΔYopE strain intrinsically produces more translocons. Interestingly, in macrophages there was also no difference in the fraction of translocon positive bacteria between wild type and WA-314ΔYopE (Fig 4D). These results indicate that host factors not only regulate the number of bacteria producing translocons and thereby the level of translocons within cell membranes but also can affect the number of translocons/injectisomes per bacterium. They also indicate that macrophages can trigger pathogenic yersiniae much more effectively than HeLa cells to produce translocons. Of note, upon activation of Rac HeLa cells acquire the same potency as macrophages in stimulating translocon formation whereas translocon formation in the already highly effective macrophages appears not to be affected (Figs 2B and 4D). The TEM images showed translocons in yersiniae enclosed by host cell membranes. This prompted us to test whether formation of translocons is triggered in a specific host cell compartment. Previously it was described that during cell invasion avirulent yersiniae enter a precompartment in which they are accessible to externally administered small proteins or compounds (MW 50 kDa) but not to antibodies (MW around 150 kDa, [69,70]). This compartment was named a prevacuole and shown to be enriched in the phospholipid PI (4,5) P2 [69]. When HeLa cells were infected with biotinylated wild type Y. enterocolitica for 60 min and bacterial accessibility to externally administered streptavidin (MW 53 kDa) or Yersinia specific antibodies was assessed by fluorescence staining, three different staining patterns were found. The bacteria were either accessible to antibodies and streptavidin (denominated outside), inaccessible to antibodies but accessible to streptavidin (intermediary) or inaccessible to both, antibodies and streptavidin (inside; Fig 5A). Quantitative analysis showed that about 70% of the YopB positive bacteria were located in the intermediary compartment and around 25% in the inside compartment but only a negligible fraction (less than 5%) was present in the outside localization at 60 min post infection (Fig 5A). To find out in which macrophage compartment translocons are formed, we assayed YopB fluorescence signals of bacteria during their passage into the intermediary and inside compartments (see S5A Fig for staining procedure and representative images). For this macrophages prepared from four different donors were infected with wild type Y. enterocolitica and investigated during a 60 min time period. The bacteria had entered the intermediary compartment already at 5 min of infection (Fig 5B) and in all but one preparation of macrophages (donor 4, Fig 5B) the fraction of bacteria that localized to the intermediary compartment remained stable between 20 to 60 min post infection. At 60 min post infection 20 to 40% of the cell associated bacteria still resided in the intermediary compartments (Fig 5B). Notably, the fraction of bacteria that further progressed to the inside compartment differed widely among the different macrophage preparations. It amounted to 60–80% in macrophages from donors 1 and 2 and was about 5% in macrophages from donors 3 and 4 at 60min post infection (Fig 5B). Thus, in macrophages from some individuals only a minimal fraction of wild type Yersinia transits from the intermediary to the inside compartment, whereas in macrophages from other donors the bacteria readily proceed to the inside compartment. In a preparation of macrophages in which the bacteria mostly ended up in the inside compartment (similar to macrophages from donor 1, Fig 5B) it was first verified that bacteria in the outside location do not show YopB staining like already seen in HeLa cells (no YopB positive outside bacteria in 50 macrophages from two experiments investigated). At 5–10 min post infection essentially all YopB positive bacteria were found in the intermediary compartment and thereafter the fraction of YopB positive bacteria decreased in the intermediary compartment and in parallel increased in the inside compartment (Fig 5C). These results clearly indicate that translocons are formed in the intermediary compartment of macrophages and then are carried on to the inside compartment. That the translocon proteins in the intermediary compartment are accessible to externally added proteins was confirmed with a modified digitonin lysis assay. Proteinase K (MW 29 kDa) was added to Yersinia WA-314 or WA-314ΔYopE infected HeLa cells and was then neutralized with phenylmethylsulfonyl fluoride (PMSF) prior to digitonin lysis and Western blot analysis of the cells. Under these conditions YopB and YopD were largely degraded whereas the effector YopH and actin remained unchanged (Fig 5F). It was verified in control experiments that with the proteinase K amounts employed Yops can principally be degraded and that addition of PMSF abrogates proteinase K activity (S4A Fig, Methods). The prevacuole formed during invasion of avirulent Y. pseudotuberculosis into COS1 cells was characterized by accumulation of the PI (4,5) P2 sensor PLCδ-PH-GFP [69]. Accumulation of PLCδ-PH-GFP also marked the intermediary compartment in wild type infected HeLa cells (Fig 5D) and accumulated around translocon positive wild type bacteria in human macrophages (S5B Fig). STED recordings (z-planes) of co-immunostained YopB and PLCδ-PH-GFP in wild type infected HeLa cells clearly indicated insertion of YopB and thus translocons in the PLCδ-PH-GFP enriched prevacuole membrane (Fig 5E). We finally hypothesized that the strong enhancement of translocon formation in HeLa cells upon Rac1 activation may be due to the capability of Rac to stimulate bacterial uptake into the intermediary prevacuolar compartment [71–73]. In fact, in HeLa cells overexpressing myc-Rac1Q61L around 60% of wild type bacteria became inaccessible to antibodies compared to around 10% in control cells after 60 min of infection (Fig 5G). This resulted in a 6-fold higher number of YopB positive bacteria both, in the intermediary and inside compartments of myc-Rac1Q61L overexpressing cells when compared to controls (Fig 5H). Altogether we conclude from this set of experiments that the formation of Yersinia translocons is triggered in a PI (4,5) P2 enriched permissive cell compartment, which is protected from large extracellular proteins like antibodies. Primary human macrophages readily internalize the bacteria in this permissive compartment, which is most certainly the reason why they so effectively stimulate translocon formation. In comparison, epithelial cells like HeLa cells possess a low intrinsic activity to internalize the bacteria in the permissive compartment. However, uptake of the bacteria into the permissive compartment, translocon formation and effector translocation can be dramatically stimulated to values reached in primary macrophages by activation of the Rho GTP binding protein Rac1. The two hydrophobic translocators present in most T3SSs and the pore complex/translocon that these proteins form in host cell membranes are particularly difficult to investigate. This is amongst others due to the highly elaborate transit of the translocators from the bacterial interior, where they have to be in a soluble form, through the T3SS needle, when they are in an unfolded state, up to their dynamic interaction with the needle tip. The tip complex is supposed to orchestrate integration of the translocators into the host cell membrane, a process that presumably is accompanied by refolding and heteromultimeric assembly of the proteins. Because it is localized at the interface of the bacterial T3SS and the target cell membrane, the translocon is unavoidably controlled by host cell factors that determine the composition and function of cell membranes. A recent super resolution fluorescence microscopy study of Salmonella Typhimurium without host cell contact described clusters of the basal body protein PrgH, a Yersinia YscD analogon, with a width of 46 nm [55]. The 12 PrgH clusters found on average per Salmonella cell were shown to represent individual needle complexes whereby the mean distance between the PrgH fluorescent signal and the signal of the tip complex protein SipD, a Yersinia LcrV analogon, was determined to be 101 nm [55]. Cryo-Electron Tomography (Cryo-ET) analysis of Y. enterocolitica (mini) cells also provided an estimation of the number and organization of needle complexes expressed without host cell contact. In tomograms of single Y. enterocolitica cells 6. 2 injectisomes were detected on average whereby it was calculated that the employed technique underestimates the total number of injectisomes by a factor of 2–3. Cryo-ET also demonstrated that separate fluorescence signals of Yersinia needle complexes seen in the confocal microscope contained in the mean 2. 5 injectisomes organized in clusters. Within these clusters the injectisomes were about 100 nm apart whereas more randomly distributed injectisomes showed distances of about 400 nm [74]. These numbers are in good agreement with the 18–33 translocons implying fully assembled injectisomes identified by superresolution fluorescence microscopy in Y. enterocolitica. They also comply well with the average distance of 127 nm between individual translocons in clusters as well as with the mean distance of 716 nm measured between the clusters. Our data further suggest that the number of operable translocons on the bacteria increases in parallel to effector translocation and is significantly higher in macrophages than in epithelial cells. In fluorescence patches of translocator proteins visualized with confocal microscopy we were able to resolve on average 3 separate translocator spots by STED, corresponding well to the 2–3 injectisomes found with Cryo-ET in fluorescence clusters of injectisome components [74]. Taken together, the previously reported numbers of injectisomes in secreting bacterial cells, their dimensions and distances among each other is highly concordant with the respective features of the Yersinia translocons described here. We show here that YopB and YopD secreted without cell contact rather uniformly cover the bacterial surface even in a lcrV/tip complex mutant, rendering it unlikely that these translocators are associated with the needle or are functionally relevant. Pathogenic yersiniae have the unique property to secrete massive amounts of Yops including translocators during the low Ca2+ response and presumably because of their hydrophobicity a fraction of these collapses back onto the bacterial cell surface as was suggested previously [43]. In this study we employed high resolution STED and SIM fluorescence microscopy to resolve translocon, needle and basal body proteins of Yersinia injectisomes during cell infection and come to the conclusion that Yersinia injectisomes form a continuous conduit from the bacterial to the target cell cytoplasm. However, our findings do not exclude the additional existence of translocons operating independently of the remaining injectisome as was proposed recently [18]. An elegant Cryo electron tomography study recently also demonstrated that the Salmonella translocon of minicells is connected to the needle and at the same time embedded in the host cell membrane [19]. High resolution fluorescence techniques provide the basis for future live imaging studies. Using these techniques we could characterize here the host cell compartment that promotes translocon formation. Our study thereby provides an explanation for the reported stimulatory effects of Rho activators on one hand and the inhibitory effect of Rho deactivators like YopE on the other hand on effector translocation by Yersinia [37,38,64–67]. On the basis of previous work and the findings presented here we propose the following scenario. Before cell infection yersiniae growing at 37°C produce incomplete injectisomes with tip complex but not translocators attached. The translocator proteins at this stage reside already in the bacterial cytoplasm. In the prevacuole the injectisome needle senses a host cell factor which triggers secretion of the translocators followed by their association with the needle tip and integration into the host cell membrane. Activation of Rac1 promotes uptake of the bacteria into the prevacuole and thereby enhances translocon formation and effector translocation. Vice versa, deactivation of Rac by the translocated effector YopE inhibits further uptake of the bacteria into the prevacuole and thereby acts as a negative feedback regulator of translocation. Amongst others it will be very interesting to decipher in future studies the nature of the host cell factor that stimulates translocator secretion, the molecular mechanisms of host cell sensing by the injectisome and how signal transduction from the needle tip to the bacterial interior proceeds. All standard laboratory chemicals and supplies were purchased from Roth (Karlsruhe, Germany), Sigma-Aldrich (Steinheim, Germany) or Merck (Hohenbrunn, Germany) unless indicated otherwise. The following plasmids were described previously: PLCδ1-PH-GFP was provided by T. Balla (National Institutes of Health, Bethesda, MD). The myc-Rac1Q61L plasmid was kindly provided by Dr. Pontus Aspenström (Uppsala University, Uppsala, Sweden) and pRK5myc was purchased from Clontech. Polyclonal rabbit anti-YopB (aa 1–168) and anti-YopD (aa 150–287) as well as rat anti-YopB (aa1-168) and anti-YscF antibodies were produced by immunization of the animals with the respective purified GST-fused proteins (animal research project A10a 675). For immunofluorescence staining, sera were affinity purified by binding either to the suitable GST-fused recombinant antigens bound to glutathione beads or to antigens released by Y. enterocolitica WA-314 that were run on SDS-polyacrylamide gel electrophoresis (PAGE) and blotted onto polyvinylidine fluoride (PVDF) membranes (Immobilon-P, Millipore, Schwalbach, Germany). Anti-LcrV, anti-YopH and anti-PepC [75] rabbit polyclonal sera were a gift of Jürgen Heesemann (Max von Pettenkofer-Institute, Munich, Germany). Primary antibodies and their sources were: rabbit polyclonal anti-Y. enterocolitica O: 8 (Sifin, Berlin, Germany); rabbit polyclonal anti-calnexin (Enzo, Lörrach, Germany); rabbit polyclonal myc (Cell Signaling, Cambridge, UK); mouse monoclonal anti-actin (Millipore, Schwalbach, Germany); mouse monoclonal anti-HSP60 and anti-streptavidin-Cy5 (ThermoFisherScientific, Waltham, USA); biotin conjugated goat polyclonal anti-GFP (Rockland, Limerick, USA). Secondary anti-IgG antibodies and their sources were: Alexa488 chicken anti-rabbit and goat anti-rat, Alexa568 goat anti-rabbit and goat anti-rat, Alexa647 goat anti-rabbit, Alexa594 chicken anti-rat (Molecular Probes, Karlsruhe, Germany). AbberiorStar580 donkey anti-rabbit, AbberiorStarRed donkey anti-rabbit and goat anti-rat, AbberiorStar635P goat anti-rabbit (Abberior, Göttingen, Germany). Rabbit polyclonal anti-biotin (Rockland, Limerick, USA). Protein A gold was purchased from G. Posthuma (University Medical Center Utrecht, Netherlands). Horseradish peroxidase linked sheep anti-mouse, donkey anti-rabbit and goat anti-rat (GE Healthcare, Chicago, USA). Statistical analyses were performed with GraphPad Prism 6 (La Jolla, CA, USA) using two-tailed t-test or one way-Anova with uncorrected Fisher’s LSD. Data was tested for normal distribution with a D’Agostino-Person normality test. The source of the Yersinia strains used here and the generation of Yersinia mutants is described in Table 1 and in the Methods section (see below). Approval for the analysis of anonymized blood donations (WF-015/12) was obtained by the Ethical Committee of the Ärztekammer Hamburg (Germany). Y. enterocolitica wild type strain WA-314 was a gift of Jürgen Heesemann (Max von Pettenkofer Institute, Munich, Germany) and described elsewhere [40]. Y. enterocolitica mutants WA-314ΔYopB, WA-314ΔYopD were generated as described previously [41]. Briefly, mutants were constructed by replacing the coding region of yopB and yopD by a kanamycin resistance cassette in the pYV plasmid. Correct replacement of the respective yop genes by the resistance cassettes was verified by PCR and SDS-PAGE of secreted Yop proteins and Western blotting. To rule out any unwanted recombination in the chromosome due to the action of Redα and Redβ, the mutated plasmids were transferred to the pYV-cured strain WA-C. E40ΔLcrV was generated by allelic exchange, replacing the WT gene on the virulence plasmid by the mutated version, as described previously [76]. HeLa cells (ACC#57, DSMZ-German Collection of Microorganisms and Cell Cultures) were cultured at 37°C and 5% CO2 in DMEM (Invitrogen, GIBCO, Darmstadt, Germany) supplemented with 10% FCS. For infection with bacteria, HeLa cells were seeded in 6 well plates (3x105 cells per well) or on glass coverslips (12mm, No. 1. 5H for high resolution, Marienfeld GmbH, Lauda-Königshafen, Germany) at a density of 5x104. HeLa cells were transfected with turbofect (Thermo Fisher Scientific, Waltham, Massachusetts, USA) for 8-16h according to the manufacturer’s protocol. Human peripheral blood monocytes were isolated from heparinized blood as described previously [77]. Monocytes/Macrophages were cultured in RPMI1640 (Invitrogen) containing 20% heterologous human serum for 7 days with medium changes every three days. For immunostaining, 1x105 macrophages were seeded on coverslips (12 mm, No. 1. 5H, Marienfeld GmbH) one day prior to infection. Macrophages were transfected with the Neon Transfection System (Invitrogen) with 5 μg DNA per 106 cells (1000 V, 40 ms, 2 pulses). Yersinia were grown in Luria Bertani (LB) broth (supplemented with required antibiotics and diaminopimelic acid as stated in Table 1) at 27°C overnight and then diluted 1: 20 in fresh LB broth, followed by cultivation at 37°C for 1. 5 h to induce expression of the T3SS (non-secretion condition in Fig 1 and S1 Fig). These cultures were also used for cell infection experiments (see below). For analysis of in-vitro Yop secretion, EGTA was added to the growth medium (Ca++-depletion), followed by another 2 h of incubation at 37°C, as described before [78]. Bacteria were then centrifuged and resuspended in ice-cold PBS (secretion condition in Fig 1 and S1 Fig). To degrade the secreted proteins adhering to the bacterial surface, bacteria were incubated in PK solution (500 μg/ml in PBS) at RT for 10 min, followed by incubation with 4 mM PMSF in PBS to inactivate PK (secretion + PK condition in Fig 1 and S1 Fig). For immunostaining, bacteria were then attached to gelatin (0. 2%) coated coverslips and fixed with 4% para-formaldehyde (PFA; Electron Microscopy Science, Hatfield, USA) for 5 min. Bacterial samples were then treated with either 0. 1% Triton X-100 in PBS for non-permeabilizing conditions or with 2% SDS (w/v) in PBS for permeabilizing conditions (Fig 1 and S1 Fig). For cell infection, bacteria were centrifuged, resuspended in ice-cold PBS and added to target cells at a defined multiplicity of infection (MOI), as specified in the figure captions. Bacteria were then centrifuged at 200 x g for 2 min onto the target cells to synchronize the bacterial attachment. Biotinylation of bacteria for cell infection experiments was performed with EZ-Link Sulfo-NHS-SS-Biotin (Thermo Fisher Scientific), as described previously [69]. Cell-associated bacteria were fixed with 4% PFA in PBS for 5 min and permeabilized with 0. 1% Triton X-100 (w/v) in PBS for 10 min. Cell-free bacteria were fixed with 4% PFA in PBS for 5 min and then treated with either 0. 1% Triton X-100 in PBS for non-permeabilizing conditions or with 2% SDS (w/v) in PBS for permeabilizing conditions (see Fig 1 and S1 Fig). Unspecific binding sites were blocked with 3% bovine serum albumin (BSA, w/v) in PBS for at least 30 min. Samples were then incubated with a 1: 100 dilution of the indicated primary antibody for 1 h, washed three times with PBS and incubated with a 1: 200 dilution of the suitable fluorophore-coupled secondary antibody for 45 min. Both, primary and secondary antibodies were applied in PBS supplemented with 3% BSA. Fluorophore-coupled phalloidin (1: 200, Invitrogen) and 4' , 6-diamidino-2-phenylindole (DAPI; 300 nM, Invitrogen) were added to the secondary antibody staining solution as indicated. Colocalization studies using STED microscopy were performed with Abberior-StarRed and AlexaFluor-594 labelled secondary antibodies. For staining of biotinylated yersiniae Cy5-conjugated streptavidin (strep-Cy5; 1: 100, Thermo Fisher Scientific) was added to the primary antibody staining solution. Coverslips were mounted in MOWIOL (Calbiochem, Darmstadt, Germany), ProLond Diamond (Thermo Fisher Scientific) or Abberior mount liquid antifade (Abberior). Fixed samples were analyzed with confocal laser scanning microscopes (Leica TCS SP5 or SP8) equipped with a 63x, NA1. 4 oil immersion objective and Leica LAS AF or LAS X SP8 software (Leica Microsystems, Wetzlar, Germany) were used for acquisition, respectively. STED and corresponding confocal microscopy were carried out in sequential line scanning mode using two Abberior STED setups. The first was based on an Olympus IX microscope body and made use of 100x NA 1. 4 oil immersion objective for fluorescence excitation and detection. The second setup, used for colocalization studies, was based on a Nikon Ti-E microscope body with perfect focus system and employed for excitation and detection of the fluorescence signal a 60x (NA 1. 4) P-Apo oil immersion objective. Two pulsed lasers were used for excitation at 561 and 640 nm and near-infrared pulsed laser (775 nm) for depletion. The detected fluorescence signal was directed through a variable sized pinhole (set to match 1 Airy at 640 nm) and detected by novel state of the art avalanche photo diodes APDs with appropriate filter settings for Cy3 (605–635 nm) and Cy5 (615–755 nm). Images were recorded with a dwell time of 10 μs and the voxel size was set to be 20x20x150 nm for 2D-STED or 40x40x40 nm for 3D-STED. The acquisitions were carried out in time gating mode i. e. with a time gating width of 8ns and a delay of 781ps (Cy3) and 935ps (Cy5). 3D-STED images were acquired with 80% 3D donut. 3D-STED z-stacks were background subtracted and colocalization events were quantified using the ImageJ plugin JACoP. STED spots of 3D-STED images were quantified with Imaris v6. 1. 1. (Bitplane, Zürich, CH). After baseline subtraction, each channel was analyzed individually with settings adjusted to confocal or STED. Background subtraction was applied in order to detect single spots. An average spot was measured (diameter of largest sphere 0. 3–0. 45 μm for confocal and 0. 11–0. 24 μm for STED) and the Gaussian filter was adjusted to the diameter of the largest sphere. Local maxima were filtered by size and quality of spots, which is defined by the intensity at the center of the spot. Image deconvolution was performed using the deconvolution analysis tool of the Abberior inspector acquisition software (applies only for GFP staining in Fig 5E). The iterative Richardson-Lucy approach was used. The algorithm was stopped after 30 iterations. As STED-PSF estimate the 3D-STED stack of an individual 20 nm diameter fluorescence nano sphere was used. STED spot detection and segmentation was done with Imaris (v6. 1. 1) on 3D-STED images as described above. Segmentation analysis provided information about volume and position of single STED spots which was then evaluated by a cluster analysis in Matlab (S1 MatLab script, written by AF; Version 9. 2, The MathWorks Inc. , Natick, USA). In brief, the radius of each spot was calculated through its volume and the resulting sphere was placed at the position of the center of mass of the segmented volume. The spot size was defined as the full width at half maximum (FWHM) of a fluorescence profile (resolution ~100 nm). Distances between the centers of mass were calculated and categorized into clusters. The threshold for spots included in a cluster was defined by the sum of the radiuses of two spheres. For visualization of STED clusters, they were projected in different colors in a super-resolution 3D image. We used 3D structured illumination microscopy to visualize the GFP-tagged basal body component YscD together with an antibody staining against the pore proteins YopB/D. Cells were imaged with a CFI Apochromat TIRF 100x Oil / NA 1. 49 objective (Nikon, Tokio, Japan) on a Nikon N-SIM E equipped with a Ti eclipse inverted microscope (Nikon). Images were acquired using NIS Elements software steering a LU-N3-SIM 488/561/640 laser unit, an Orca flash 4. 0 LT (Hamamatsu Photonics, Hamamatsu, Japan) sCMOS or Ultra EM-CCD DU-897 (Andor Technology, Belfast, Northern Ireland) camera, a Piezo z drive (Mad city labs, Wisconsin, USA), a N-SIM motorized quad band filter combined with N-SIM 488 and 561 bandpass emission filters using laser lines 488 and 561 at 100% output power and adjustable exposure times of 300–800 msec. Z-stacks were acquired at 200 nm step size, covering about 1. 6–2. 2 μm. Reconstruction was performed with the stack reconstruction tool (Nikon, NIS-Elements) using default parameters. Particle detection was done with the ImageJ plugin TrackMate v3. 5. 1 [79] followed by a nearest neighborhood analysis in Matlab (Version 9. 2). In brief, single z-planes of 3D 2-color SIM images were used for analysis. Distinct spots of GFP-YscD and YopB/D were detected with Trackmate using an estimated blob diameter of 0. 3 micron for both channels, individually set thresholds and activated median filter. The resulting x/y coordinates were exported into xml files and imported into Matlab by parseXML (github. com/samuellab). The euclidean nearest neighbors of GFP-YscD spots for every YopB/D spot were detected with the function Knnsearch and the resulting distances were plotted. Scripts (written by JB) can be found in S2 MatLab script. For TEM analysis, HeLa cells were transfected with myc-Rac1Q61L for 16 h and infected with E40 GFP-YscD at a MOI of 50 for 60 min. Samples were washed with ice-cold PBS and then fixed with a double-strength fixative made of 4% PFA (w/v) with 0. 2% glutaraldehyde (GA) in PB. Fixative was replaced after 10 min with fresh single strength fixative (2% PFA with 0. 1% GA in PB for 20 min) at RT. Fixation was followed by three wash steps with PBS at RT. Samples were then warmed up at 37°C and coated with 1% gelatin. Sedentary cells were harvested carefully and spun down in the same fixative. Suspended cells were embedded in 12% (w/v) gelatin in PBS at 37°C for 5 min and the pellet was solidified on ice. Small blocks were cut and infiltrated in 2. 3 M sucrose overnight. Thereafter the blocks were mounted on specimen holders and frozen by immersing them in liquid nitrogen. Ultrathin sections (70 nm) were cut and labeled according to Slot and Geuze [63]. Briefly, sections were collected on Carbon-Formvar-coated nickel grids (Science Services GmbH, München, Germany). Anti-GFP-biotin (1: 300) was recognized with secondary anti-biotin (1: 10 000) and 15 nm large protein A gold; anti-YopD (1: 50) with 10 nm large protein A gold in single and double labelling experiments. Sections were examined in an EM902 (Zeiss, Oberkochen, Germany). Pictures were taken with a TRS 2K digital camera (A. Tröndle, Moorenweis, Germany) at 30. 000x magnification. Labelling intensities in randomly selected images were quantified as described previously [80,81]. Briefly, compartments of interest were defined for each antigen (Fig 3E; YscD: bacterial cytoplasm, periplasm and bacterial inner and outer membranes and host cell; YopD: bacterial cytoplasm, injectisome range, host cell). Labelling intensities were then analyzed by counting colloidal gold particles per compartment. Superimposed test-point lattices were used to estimate the compartment area and to count chance encounters with compartments. Labelling densities (gold/μm2), expected golds (ne) and relative labelling indices (RLI) with partial chi-squared values were then calculated from observed and expected gold distributions to determine the preferentially labelled compartments [81]. For a combined analysis of volume and surface occupying compartments an acceptance zone adjacent to membranes was defined and this zone was treated as a profile area [82]. Released effector (YopH) and translocator proteins (YopB/D) were analyzed as described previously [68]. In brief, HeLa cells were infected with a MOI of 100 for 60 min and subsequently washed with PBS to remove non-adherent bacteria. To remove extracellular effectors, cells were treated with PK (500 mg/ml in PBS) for 20 min at room temperature. Prior to cell lysis, protease activity was blocked by the addition of PMSF (4 mM in PBS). HeLa cells were lysed by the addition of digitonin (0. 5% w/v in PBS) at room temperature for 20 min, with repeated vortexing. Cell debris and attached bacteria were separated from the lysate containing the released effectors by centrifugation. The resulting supernatants and pellets were analyzed by SDS-PAGE, transferred to a PVDF membrane (Immobilon-P, Millipore), and analyzed by Western blot using antisera against YopH, YopB and YopD and antibodies against actin, calnexin and myc-tag.

Many human, animal and plant pathogenic bacteria employ a molecular machine termed injectisome to inject their toxins into host cells. Because injectisomes are crucial for these bacteria’s infectious potential they have been considered as targets for antiinfective drugs. Injectisomes are highly similar between the different bacterial pathogens and most of their overall structure is well established at the molecular level. However, only little information is available for a central part of the injectisome named the translocon. This pore-like assembly integrates into host cell membranes and thereby serves as an entry gate for the bacterial toxins. We used state of the art fluorescence microscopy to watch translocons of the diarrheagenic pathogen Yersinia enterocolitica during infection of human host cells. Thereby we could for the first time—with fluorescence microscopy—visualize translocons connected to other parts of the injectisome. Furthermore, because translocons mark functional injectisomes we could obtain evidence that injectisomes only become active for secretion of translocators when the bacteria are almost completely enclosed by host cells. These findings provide a novel view on the organization and regulation of bacterial translocons and may thus open up new strategies to block the function of infectious bacteria.

Abstract Introduction Results Discussion Material and methods

blood cells medicine and health sciences immune cells pathology and laboratory medicine viral transmission and infection hela cells pathogens biological cultures immunology microbiology light microscopy microscopy yersinia cell cultures cellular structures and organelles bacteria bacterial pathogens research and analysis methods specimen preparation and treatment staining white blood cells animal cells medical microbiology yersinia enterocolitica fluorescence microscopy microbial pathogens cell lines cell membranes cell staining host cells cell biology virology biology and life sciences cellular types cultured tumor cells macrophages organisms

2018

Visualization of translocons in Yersinia type III protein secretion machines during host cell infection

Since emerging in Saint Martin in 2013, chikungunya virus (CHIKV), an alphavirus transmitted by the Aedes aegypti mosquito, has infected approximately two million individuals in the Americas, with over 500,000 reported cases in the Dominican Republic (DR). CHIKV-infected patients typically present with a febrile syndrome including polyarthritis/polyarthralgia, and a macropapular rash, similar to those infected with dengue and Zika viruses, and malaria. Nevertheless, many Dominican cases are unconfirmed due to the unavailability and high cost of laboratory testing and the absence of specific treatment for CHIKV infection. To obtain a more accurate representation of chikungunya fever (CHIKF) clinical signs and symptoms, and confirm the viral lineage responsible for the DR CHIKV outbreak, we tested 194 serum samples for CHIKV RNA and IgM antibodies from patients seen in a hospital in La Romana, DR using quantitative RT-PCR and IgM capture ELISA, and performed retrospective chart reviews. RNA and antibodies were detected in 49% and 24. 7% of participants, respectively. Sequencing revealed that the CHIKV strain responsible for the La Romana outbreak belonged to the Asian/American lineage and grouped phylogenetically with recent Mexican and Trinidadian isolates. Our study shows that, while CHIKV-infected individuals were infrequently diagnosed with CHIKF, uninfected patients were never falsely diagnosed with CHIKF. Participants testing positive for CHIKV RNA were more likely to present with arthralgia, although it was reported in just 20. 0% of CHIKF+ individuals. High percentages of respiratory (19. 6%) signs and symptoms, especially among children, were noted, though it was not possible to determine whether individuals infected with CHIKV were co-infected with other pathogens. These results suggest that CHIKV may have been underdiagnosed during this outbreak, and that CHIKF should be included in differential diagnoses of diverse undifferentiated febrile syndromes in the Americas. Chikungunya virus (CHIKV), a mosquito-borne virus in the family Togaviridae, genus Alphavirus, was first isolated in 1952 from a patient on the Makonde plateau that is now part of Tanzania. It derives its name from the Makonde term kungunyala, meaning roughly, “that which bends up” [1]. Urban chikungunya fever (CHIKF) outbreaks were first described in the late 1950s in Asia, transmitted by the mosquito vector Aedes aegypti. In 2005, CHIKV emerged again from enzootic circulation in Africa and spread to the Indian Ocean Basin, India and other Asian countries and also made incursions into Italy and France, which reported local transmission for the first time in Europe [2–6]. This unprecedented spread was partly credited to the ability of the CHIKV strains within the new Indian Ocean lineage (IOL) to adapt through E1 and E2 envelope glycoprotein substitutions to a new vector species, Ae. albopictus, which itself spread from Asia to many parts of the world since 1985 [7–9]. Then, in 2013, a CHIKV strain belonging to the same lineage that had been circulating in Asia at least since the 1950s (Asian lineage) was introduced into the Americas, first detected on the Caribbean island of St. Martin before spreading through South and Central America, including Mexico [10,11]. Local transmission was reported in the U. S. in Florida and Texas after the arrival of infected travelers [12,13]. To date, this New World introduction has resulted in over 1. 9 million suspected cases of CHIKF in North, Central, and South America as well as the Caribbean Islands [Pan American Health Organization (PAHO) data]. An independent introduction of an East, Central, and South Africa (ECSA) -derived enzootic CHIKV strain from Africa was also reported in Brazil in 2014 [14]. Strains from all CHIKV lineages can cause explosive outbreaks of CHIKF, which is characterized by a generalized febrile syndrome typically including high fever, maculopapular rash, muscle and joint aches, headache, and malaise, which progresses to severe polyarthritis/polyarthralgia. Although CHIKF is generally self-limiting and sometimes resolves within a month of onset, up to 30–70% of CHIKF patients experience chronic and/or recurring arthritis lasting months to years [4]. Polyarthritis and polyarthralgia are hallmarks of CHIKF, and can range between 80% and 97% of patients reporting these symptoms during past epidemics [2,3, 15–18]. However, data from New World CHIKV infections remain limited with few reports examining in detail patient signs and symptoms. Some studies suggest that infection with New World/Asian CHIKV lineage strains results in arthritic outcomes similar to those observed in previous epidemics, but with only limited data available for long-term joint sequelae [19]. Among New World CHIKF outbreaks, the Dominican Republic has had the most suspected cases, with over 500,000 reported since 2014 [PAHO data] (Fig 1). Here, we describe an outbreak in the southeastern city of La Romana. In February, 2014, the Dominican Republic’s first suspected cases were reported in Nigua, a town in San Cristóbal Province, southwest of the capital. Suspected cases were defined by the Dominican Ministerio de Salud Pública (MSP) by acute febrile syndrome and polyarthralgia, though many patients also presented cervical, supraclavicular, and inguinal adenomegalies and facial, vulva and scrotal edema. It was hypothesized that CHIKV entered the country through Bajos de Haina, a port city located 2 km from Nigua. On April 3,2014, the U. S. Centers for Disease Control and Prevention (CDC) confirmed CHIKV infection by detecting CHIKV-specific IgM antibodies in a patient’s blood [10]. Nationally, cases continued to rise, peaking between mid-July and mid-August with up to 45,000 new cases each week. After administering brief questionnaires in major cities, the MSP estimated attack rates ranging from 40% of the population-at-risk in Higüey to 81% in Azua de Compostela. In La Romana, up to 89% of households interviewed were suspected to have been affected by CHIKV as of August, 2014 [20]. Clinicians and patients reported a high fever and arthralgia in the wrists and ankles, the latter which lasted up to six months in middle-aged and elderly female patients. Nevertheless, little published information describes the rate of CHIKV seropositivity or CHIKF symptoms in the La Romana population. Here, we report a case series of CHIKF patients in La Romana from June-August, 2014 during the CHIKF outbreak, following diagnostic tests of patient sera and retrospective chart reviews of signs and symptoms as well as clinical data associated with these patients, and genetic characterization of CHIKV isolates from patients. Between June and August, 2014, serum collected for complete blood counts (CBC) was collected from discarded diagnostic blood samples of patients attended by the department of emergency medicine at Hospital el Buen Samaritano (HBS). The criteria for serum collection in HBS included patients for whom a complete blood count (CBC) was performed. A retrospective chart review was performed to collect patient data. Patient age, gender, symptoms, and CBC results were collected retrospectively wherever possible. For patients who were admitted into the hospital, a physical examination and medical history were recorded, including a history of the present illness and clinical and familial antecedents. All patient samples and data were assigned institution-specific identification numbers to ensure patient anonymity. Neither patient names nor clinic-assigned laboratory numbers were used, and patient age was used in lieu of birthdate, eliminating all potential identifying information from the study without compromising data integrity. All patient data were deidentified and handled under University of Texas Medical Branch Institutional Review Board Protocol #15–0265. RNA was first isolated from serum samples using the ZR-96 Viral RNA Kit (Zymo Research, Orange, CA, USA) according to the manufacturer’s protocol. Quantitative reverse transcription PCR (RT-qPCR) was then performed using the TaqMan RNA-Ct 1-step Kit (Applied Biosystems, San Francisco, CA, USA) and previously described primers [21]. Serum samples were screened for anti-CHIKV IgM antibodies by enzyme-linked immunosorbent assay (ELISA) as previously described [22] using the CHIKjj Detect MAC-ELISA kit (InBios, Inc. , Seattle, WA), which was validated by the CDC [23]. All samples were tested in duplicate and any inconclusive samples were retested. African green monkey kidney cells (Vero; American Type Cell Culture, Bethesda, MD) were grown to 90–100% confluency in 12-well plates. Serum was diluted in Dulbecco' s mimimal essential medium (DMEM) (supplemented with 5% FBS and from a series of 10-fold dilutions, medium was removed from plates, and 100uL of serum dilution was plated per well. After 60 minutes incubation, an overlay composed of DMEM and 0. 2% agarose was added and incubated for 24–48 hours. Plates were then fixed with 10% formaldehyde for one hour before staining with crystal violet, and plaques counted. Ten serum samples obtained during the CHIKV RT-qPCRs with low RT-qPCR Ct values were submitted for Illumina HiSeq sequencing, without passaging, as previously described [19]. Viral genomes were assembled using the Abyss software [24]. Assembled contigs were checked using bowtie2 to align reads to the contigs followed by visualization using the integrative genomics viewer [25]. Genomic sequences were manually aligned with those representing all three genotypes downloaded from Genbank using Se-Al (http: //tree. bio. ed. ac. uk/software/seal/), and non-coding sequences were removed from the alignment, resulting in a common length of 11,241 nt. The final data set comprised of 85 complete open reading frame sequences from 26 countries isolated during 1953–2014. A Bayesian maximum clade credibility (MCC) phylogeny was inferred using the GTR+G4 nucleotide substitution model with BEAST version 1. 8. 2 [26]. This study is primarily observational as it followed a series of CHIKF cases, and thus most data were reported in raw form. Contingency tables for admitted vs. outpatients were constructed with outpatients in the first row and hospitalized in the second, and those with symptoms/parameter in the first column and without in the second; relative risk analysis was then performed using Medcalc (MedCalc Software, Ostend, Belgium). Continuous variables were first tested for normalcy, normalized as needed, and then means were analyzed by either T-test or one-way ANOVA using SPSS software. Regression analyses were employed to ascertain correlation between CT value and symptom frequency using SPSS software. A total of 194 serum samples, collected between July and August, 2014, were included in the study; at the time of sample receipt, scientists were blinded to disease state of the patients sampled. Of those, 95 (49%) were positive for CHIKV RNA by RT-qPCR, 48 (24. 7%) were positive for acute CHIKV antibodies by IgM ELISA, and 2 samples were positive for both (1%; Table 1). In total, 145/194 (74. 7%) sera tested positive for recent CHIKV infection. CT values for RNA-positive samples ranged between 34 and 17. 39 (S1 Fig), and plaque assays on RNA-positive samples revealed infectious titers ranging between 2. 9x105 plaque forming units/mL to below the limit of detection of 1x102 PFU/mL. Sequencing and phylogenetic analysis confirmed that the CHIKV isolates obtained from the DR were closely related to other isolates collected from Caribbean outbreaks, stemming from an Asian lineage strain (S2 Fig). After samples underwent diagnostic testing, de-identified patient data were analyzed for demographic characteristics such as gender, age, and hospital admission (Table 2). As inclusion criteria were not used to regulate sample submission, these data are strictly observational and are presented to describe the general clinical picture associated with this particular sample set. Only RNA-positive samples were included in these analyses because IgM can persist up to several months after infection, thus not necessarily indicating active infection. In total, demographic data were available for 75 CHIKF (+) (CHIKV RNA-positive) and 41 CHIKF (-) (both RNA- and IgM-negative) patients. More male samples were represented for both CHIKF (+) and CHIKF (-) patient groups. A broad age range of both CHIKF (+) and CHIKF (-) patients was represented, between 13 days and 85 years, with the majority of cases involving children between the ages of 6 and 14 years (Fig 2). The mean time from fever onset to clinic visit ranged from 2–7 days, with an average of 3. 8 days for CHIKF (+) patients and 4. 0 days for CHIKF (-) patients. Viremia was detectable up to 7 days post-onset of illness in CHIKF (+) patients. Patients in the CHIKF (-) group had a higher rate of hospitalization, with 18. 7% of CHIKF (+) patients and 26. 8% of CHIKF (-) patients requiring admission. The mean age and time of symptom onset (before visiting the clinic) were not significantly different between admitted and outpatient CHIKF (+) and CHIKF (-) groups (ANOVA with Tukey’s post-hoc, 0. 27<p<0. 93; Table A in S1 Tables), nor did gender significantly affect the relative risk (RR) of being hospitalized for either group. In addition to demographic data, specific sign and symptom data were matched to samples (Table 3). These data were available for 48 CHIKF (+) patients and 19 CHIKF (-) patients. The average ages for patients with available symptom data were 16. 5±16. 4 years for CHIKF (+) patients and 29. 6±23. 3 years for CHIKF (-) patients. More symptom data were available for admitted patients, as hospital admission rates for patients with available symptom data were 31. 2% for CHIKF (+) and 57. 9% for CHIKF (-) patients; however, similar to demographic data, no demographic trait significantly affected the risk of hospitalization (Table B in S1 Tables). The most common feature of both CHIKF (+) and CHIKF (-) patients was fever (91. 7% and 63. 2%, respectively) averaging 39. 4°C for CHIKF (+) patients, while the average fever for CHIKF (-) patients was slightly lower at 38. 8°C. Complaints of arthralgia and myalgia were surprisingly low for CHIKF (+) patients at 20. 0% and 13. 3%, respectively. Despite the low frequency of these particular symptoms, they may still be considered of diagnostic value, as arthralgia and myalgia appeared to be CHIKV-specific, with no patients in the CHIKF (-) group exhibiting these symptoms. Several preexisting conditions were noted, the most prominent being pregnancy of greater than 12 weeks and hypertension However, because these occurred so infrequently, the effect of risk on hospital admission was not assessed. Other signs and symptoms less commonly associated with CHIKV infection can be broadly categorized as gastrointestinal, respiratory, and neurological. About 15% of CHIKF (+) patients presented with gastrointestinal signs and symptoms, including diarrhea, nausea, and gastroenteritis. A surprising 19. 6% of CHIKF (+) patients presented with respiratory signs and symptoms. Most CHIKF (+) patients exhibiting gastrointestinal signs and symptoms were below the age of 15; patients exhibiting respiratory signs and symptoms were also largely under the age of 15 (Fig 3). No sign or symptom correlated to level of viremia, as determined by regression analysis of CT bin against patient data (0. 32<p<0. 98; S3 Fig). Given the high rate of hospitalization among CHIKF (+) patients relative to past outbreaks, the relative risk of presenting with specific signs and symptoms among admitted patients was assessed (Table C in S1 Tables). Firstly, while the mean age of admitted CHIKF (+) patients was lower than CHIKF (+) outpatients, this difference was not significant (ANOVA with Tukey’s post-hoc, p = 0. 55); a similar observation was made for CHIKF (-) patients (ANOVA with Tukey’s post-hoc, p = 0. 74). While fever and myalgia were not specifically associated with either admitted nor outpatient CHIKF (+) patients, admitted CHIKF (+) patients were 4. 7 times more likely to present with arthralgia (95%CI = 1. 03,21. 06), 12. 7 times more likely to present with headache (95%CI = 1. 78,90. 18), 6. 3 times more likely to present with dehydration (95%CI = 2. 42,16. 67), 6. 9 times more likely to present with gastrointestinal signs and symptoms (95%CI = 1. 53,30. 84), and 9. 6 times more likely to present with respiratory signs or symptoms (95%CI = 2. 31,40. 05). Temperature was not significantly different between admitted and outpatient CHIKF (+) patients (ANOVA with Tukey’s post-hoc, p = 0. 94), nor was time of symptom onset (ANOVA with Tukey’s post-hoc, p = 0. 99). While no signs or symptoms imparted a significant risk of hospitalization among CHIKF (-) individuals, this finding may reflect the small number of patients in the CHIKF (-) group (n = 11 and n = 8 for admitted and outpatient groups, respectively) rather than the biological significance of respiratory and gastrointestinal features. Time of symptom onset was not significantly different between admitted and outpatient CHIKF (-) patients (p = 0. 99). Initial diagnoses were made based on clinical findings (Fig 4). Only 6% of CHIKF (+) cases were clinically diagnosed as such, likely due to the absence of joint symptoms normally associated with CHIKF and possibly a lack of knowledge about CHIKF by some health care providers. CHIKF (+) patients were more likely to be diagnosed with idiopathic febrile syndrome, dengue fever, or pneumonia. No CHIKF (-) patients were misdiagnosed with CHIKF. Diagnoses classified under “other” included meningitis, pregnancy, asthma complications, and trauma. Some diagnoses for “generalized febrile syndrome” were inferred from the prescription of fever reducing agents, namely metamizole. Pediatric (age<21) blood cell reference values were derived from standard hematology references [27], while adult reference values were provided by the laboratory at the Fundación Hospital General el Buen Samaritano. White blood cell (WBC) counts were generally unremarkable for both CHIKF (+) and CHIKF (-) groups, with the average for most age groups falling within normal ranges albeit with large variation; this also made statistical comparison of means impractical. Median values for complete WBC and lymphocytes were generally lower in CHIKF (+) patients than CHIKV (-) patients (Table 4). For CHIKF (+) patients: complete WBC ranged between 1. 6–16. 3x 103 cells/μL; neutrophils ranged between 0. 2–11. 9x103 cells/μL; lymphocytes ranged between 0. 3–8. 4x103 cells/μL; and platelets ranged between 91-789x103 cells/μL. CHIKF (-) patients showed similar ranges to CHIKF (+) patients: complete WBC ranged between 1. 1–16. 3x103 cells/μL; neutrophils ranged between 0. 8–12. 7x103 cells/μL; lymphocytes ranged between 0. 2–9. 0x103 cells/μL; and platelets ranged between 103-487x103 cells/μL. While most values fell within normal ranges for whole WBC, neutrophils, and platelets, many CHIKF (+) patients presented with varying degrees of lymphopenia when compared to reference values (Fig 5). Lymphopenia was significantly associated with relative level of viremia in CHIKF (+) patients, with the percent of patients presenting with lymphopenia decreasing with decreasing level of viral RNA; thrombocytopenia was not significantly associated with the level of viremia (linear regression with ANOVA, p = 0. 006 and p = 0. 081, respectively; S3 Fig), However, basal lymphocyte counts are highly specific to individuals, so it is possible that some of the deviant values fell within normal limits for some patients. Our study succeeded in identifying unique clinical and laboratory factors present during the outbreak of CHIKF in the Dominican Republic in 2014. These data demonstrate new clinical characteristics that may be valuable for diagnostic and surveillance considerations in other areas affected by CHIKV strains closely related to the Asian strain introduced to the Caribbean. However, as an outbreak investigation, our study had several limitations including an inherent selection bias in that patients came from a single hospital in La Romana, which is not necessarily representative of a random sample of the Dominican population. The selection was further biased by the fact that RT-qPCR and IGM ELISA for CHIKV were run on all patients subjected to a CBC test. Reporting data, therefore, were sparse and inconsistent, and included patients who may or may not have experienced febrile symptoms. Since historical and physical exam data were not standardized, extracting them from clinical records created an information bias that included the possibility of various measurement errors (vital signs, interview information, etc.). Finally, there appeared to be an information bias toward hospitalized patients, particularly in the CHIKV (-) group, as percent hospitalization increased with increasing level of information (i. e. , symptoms were more likely to be recorded and/or records were more likely to be stored appropriately if a patient was admitted to the hospital). This is also a bias inherent to utilizing emergency clinic samples, as patients with severe illness will more likely visit the emergency clinic than those with minor (or no) symptoms, and thus a greater rate of hospital admissions will be detected in this particular patient population regardless of disease state. While this complicated our ability to interpret risk analyses when comparing CHIKF (+) to CHIKF (-) individuals, it did not necessarily affect our ability to detect symptoms in CHIKF (+) patients. Roughly 30% of the CHIKF (+) patients with symptom data were admitted to HBS; therefore roughly 30% of our population exhibited the most severe CHIKF symptoms (while 70% exhibited slight to moderate symptoms which did not necessitate hospital admission); even among the most severe CHIKF manifestations, only 30% of CHIKF (+) patients presented with arthralgia (compared to just 12% among the CHIKF (+) outpatient group). Furthermore, the failure to associate any sign or symptom (except lymphopenia) with the CT value indicates that viremia was not an accurate predictor of disease severity. Along with the absence of fever in some patients with low to moderate CT values, this suggests viremia in the absence of signs and symptoms as reported by Simmons et al, who found many blood donation pools to be strongly CHIKV RNA (+) [46]. Thus, information bias toward admitted patients and its complicating effect on comparing CHIKF (+) patients to the general CHIKF (-) population probably contributes to an overestimation of CHIKF symptoms among CHIKF (+) individuals.

Chikungunya virus (CHIKV) is known for its ability to cause explosive outbreaks of flu-like illness followed by severe joint pain and swelling, which can persist for months to several years. We tested patient serum of both suspected and unsuspected chikungunya fever (CHIKF) cases collected at an emergency clinic in La Romana, Dominican Republic for markers of current or recent CHIKV infection, and showed through next generation sequencing that the causative CHIKV strain is closely related to other isolates from the Americas. After matching clinical outcomes with diagnostic data, we found that relatively few CHIKF patients presented with the joint symptoms typically associated with CHIKV infection in the past, which likely contributed to a notable frequency of CHIKF misdiagnosis. Other deviations from Old World CHIKF outbreaks were also discovered, including shifts in affected age groups and in the frequency of respiratory signs and symptoms. These data are particularly important for improving future surveillance endeavors, as well as the diagnosis and treatment of CHIKF in the Americas.

Abstract Introduction Methods Results Discussion

medicine and health sciences pathology and laboratory medicine togaviruses chikungunya infection respiratory infections pathogens tropical diseases microbiology alphaviruses pulmonology viruses chikungunya virus rna viruses signs and symptoms neglected tropical diseases infectious diseases medical microbiology microbial pathogens critical care and emergency medicine arthralgia pain management diagnostic medicine fevers viral pathogens biology and life sciences viral diseases organisms

2016

Molecular Virologic and Clinical Characteristics of a Chikungunya Fever Outbreak in La Romana, Dominican Republic, 2014

In light of increased co-prescription of multiple drugs, the ability to discern and predict drug-drug interactions (DDI) has become crucial to guarantee the safety of patients undergoing treatment with multiple drugs. However, information on DDI profiles is incomplete and the experimental determination of DDIs is labor-intensive and time-consuming. Although previous studies have explored various feature spaces for in silico screening of interacting drug pairs, their use of conventional cross-validation prevents them from achieving generalizable performance on drug pairs where neither drug is seen during training. Here we demonstrate for the first time targets of adversely interacting drug pairs are significantly more likely to have synergistic genetic interactions than non-interacting drug pairs. Leveraging genetic interaction features and a novel training scheme, we construct a gradient boosting-based classifier that achieves robust DDI prediction even for drugs whose interaction profiles are completely unseen during training. We demonstrate that in addition to classification power—including the prediction of 432 novel DDIs—our genetic interaction approach offers interpretability by providing plausible mechanistic insights into the mode of action of DDIs. Drug-drug interactions (DDIs) refer to the unexpected pharmacologic or clinical responses due to the co-administration of two or more drugs [1]. With the simultaneous use of multiple drugs becoming increasingly prevalent, DDIs have emerged as a severe patient safety concern over recent years [2]. According to The Center for Disease Control and Prevention (CDC), the percentage of Americans taking three or more prescription drugs in the past 30 days increased from 11. 8% in 1988–1994 to 21. 5% in 2011–2014, and the occurrence of polypharmacy, defined as the concurrent use of five or more drugs, increased from 4. 0% to 10. 9% within the same time period [3,4]. Polypharmacy is especially common among elderly people, affecting 42. 2% of Americans aged 65 years and older, exposing them to a higher risk of adverse DDIs. Indeed, DDIs were estimated to be responsible for 4. 8% of hospitalization in the elderly, a 8. 4-fold increase compared to the general population [5]. Overall, DDIs contribute to up to 30% of all adverse drug events (ADEs) [6] and account for about 74,000 emergency room visits and 195,000 hospitalizations each year in the United States alone [3]. Therefore, it has become a medical imperative to identify and predict interacting drug pairs that lead to adverse effects. In order to facilitate identification of interacting drug pairs, a number of in vitro and in vivo methods have been developed. For example, drug pharmacokinetic parameters and drug metabolism information collected from in vitro pharmacology experiments and in vivo clinical trials can be used to predict interacting drug pairs [7,8]. However, these methods are labor-intensive and time-consuming, and are thus not scalable to all unannotated drug pairs [9]. In the past decade, machine learning-based in silico approaches have become a new direction for predicting DDIs by leveraging the large amount of biological and phenotypic data of drugs available. The advantage of machine learning-based approaches lies in their ability to perform large-scale DDI prediction in a short time frame. So far, various features have been explored for building DDI prediction models, including similarity-based features and network-based features, among others. Similarity-based features characterize the similarity of the two drugs at question in terms of chemical structure, side effect profile, indication, target sequence, target docking, ATC group, etc. [10–24]. Network-based features exploit the topological properties of the drug-drug interaction network or the protein-protein interaction network, which relates to DDIs through drug-target associations [16,25–27]. While these methods have yielded important information about DDIs, few methods to date have been able to provide insight into the molecular mechanisms of drug-drug interactions. To this end, in this study, we employ the genetic interaction between genes that encode the targets of two drugs as a novel feature for predicting interacting drug pairs that cause adverse drug reactions. We show that targets of adversely interacting drugs tend to have more synergistic genetic interactions than targets of non-interacting drugs. Exploiting this finding, we apply a machine learning framework (S1 Fig) and build a gradient boosting-based classifier for adverse DDI prediction by integrating genetic interaction and three widely used features–indication similarity, side effect similarity and target similarity. We show that our model provides accurate DDI prediction even for pairs of drugs whose interaction profiles are completely unseen during training. Furthermore, we find that excluding the genetic interaction features significantly decreases the performance of our model. Through genetic interactions, our method provides insight into the mode of action of drugs that lead to adverse combinatory effects. In order to explore the separating power of various features to distinguish adversely interacting drug pairs from non-interacting drug pairs, we constructed a high-confidence set of adversely interacting drug pairs from all DDIs labeled “the risk or severity of adverse effects can be increased” in DrugBank [28] (S1 Table). This resulted in a set of 117,045 adversely interacting drug pairs involving 2,261 drugs. 2,195,023 non-interacting drug pairs were generated by taking all other combinations of these drugs before removing any drug pair that has been reported in DrugBank, TWOSIDES [29] or a complete dataset of DDIs compiled from a variety of sources [30]. Furthermore, we required that all features, including indication similarity, side effect similarity, target sequence similarity and genetic interaction, should be available for each drug pair. After this filtering step, 1,113 adversely interacting drug pairs and 11,313 non-interacting drug pairs involving 262 drugs remained. Interacting and non-interacting drug pairs exhibit different distributions in terms of the four groups of properties that we investigated. Indications and side effects of drugs were mapped to four levels of the MedDRA hierarchy [31] (Fig 1A). At every level, adversely interacting drugs are associated with significantly more similar side effects as well as indications than non-interacting drugs (Fig 1B and 1C, S2A and S2B Fig, S2 Table). On another front, target similarity was calculated by aligning the sequences of the protein targets with the Smith-Waterman algorithm [32]. Since a drug may have multiple protein targets, aggregation was performed by taking the minimum, mean, median or maximum alignment score for each drug pair (Fig 1D). As expected, the maximum, mean and median target similarity between targets of adversely interacting drug pairs are significantly higher than those of non-interacting drug pairs (Fig 1E, S2 Table). Interestingly, interacting drug pairs manifest a significantly lower minimum target similarity than non-interacting drug pairs (Fig 1E, S2 Table). This could be due to the fact that interacting drugs possess a higher number of protein targets combined, thereby having a higher chance of targeting vastly different targets (S3A Fig). These results establish indication similarity, side effect similarity and target similarity as informative predictors of adverse DDIs. Genetic interaction refers to deviation from the expected phenotype when two genes are simultaneously mutated [33]. In short, the genetic interaction score quantifies the extent to which the fitness of a double mutant carrying mutations on two genes deviates from what is expected from the fitness defects of the corresponding single mutants. A negative score indicates synergistic genetic interaction, where the double mutant exhibits a fitness defect that is more extreme than expected from single mutants, while a positive score suggests buffering genetic interaction, where the double mutant exhibits a greater fitness than expected [34]. Since binding of drugs modulates the function of their targets, the genetic interaction between protein targets of two drugs might be associated with their joint effects. On this account, we investigated whether targets of adversely interacting drugs and targets of non-interacting drugs display divergent genetic interaction profiles. For each pair of drugs, we mapped their protein targets to the corresponding yeast homologs and obtained genetic interaction scores between the yeast genes from a global yeast genetic interaction network [35]. When the minimum, mean, median or maximum genetic interaction score was taken for targets of each drug pair, adversely interacting drugs showed significantly lower scores than non-interacting drugs irrespective of the aggregation function applied (Fig 1F, S2C Fig). This trend can be recapitulated using two recently published human genetic interaction datasets (S3B and S3C Fig). Furthermore, genetic interaction provides complementary information that is not captured by target similarity, indication similarity, or side effect similarity, as seen from their poor correlation (S4 Fig). Therefore, genetic interaction profiles of drug targets provide new information as a predictor of adverse DDIs. To divide drug pairs into a training set and a test set for building a machine learning model, most previous studies randomly split their data with a specified ratio [10,16,17,19,22,23,36,37], without considering the fact that drugs appearing in both sets may carry extra information about their interaction propensity. Considering the scenario of predicting interactions of drugs without prior information about their interaction profiles, this splitting scheme becomes inappropriate. To address this problem, we draw on a method that partitions drug pairs based on drugs [14,20,21]. All drugs in our constructed dataset were randomly split into “training drugs” and “test drugs” with a ratio of 2: 1. The training set consists of all drug pairs where both drugs are “training drugs” and the test set comprises all drug pairs where both drugs are “test drugs” (Fig 2A). As a result, 475 interacting drug pairs and 4,802 non-interacting drug pairs involving 175 drugs went into the training set; 131 interacting drug pairs and 1,322 non-interacting drug pairs involving 87 drugs went into the test set. To build a more interpretable model and speed up the training process, we applied a feature selection method known as group minimax concave penalty (MCP) [38] that has been previously employed on biological datasets [39]. This resulted in a final group of 11 features whose value distributions were all significantly different between adversely interacting drugs and non-interacting drugs (Fig 1B, 1E and 1F). An extreme gradient boosting (XGBoost) classifier [40] was then built because of its speed and outstanding performance in data science competitions. We optimized hyperparameters of the classifier using the tree-structured Parzen Estimator (TPE) approach [41], which has been shown to drastically improve the performance in a recent study predicting protein-protein interaction interfaces [42]. Notably, instead of doing cross-validation, we adopted the same drug-based splitting scheme on the training set for hold-out validation (Fig 2A). This enables the model to be best tuned for predicting interacting drug pairs without any prior information about the interaction profiles of the drugs involved. Indeed, a previous report by Liu et al. showed that classifier performance dropped significantly when evaluated on a test set consisted of pairs of drugs completely unseen in the training set if conventional cross-validation was performed [21], and this flaw in the generalizability of cross-validation performance has been shown to be true in general for pair-input data [43]. Our novel training strategy resulted in an average area under the receiver operating characteristic curve (AUROC) of 0. 727 and an average area under the precision-recall curve (AUPR) of 0. 326 over 1,000 trials of hold-out validation on the training set (Fig 2B–2D). When evaluated on the test set, our classifier achieved an AUROC of 0. 689 (Fig 2D and 2E) and an AUPR of 0. 280 (Fig 2D and 2F), demonstrating the robustness of our model. As shown in Table 1, our classifier attained a precision of 100% on the top 10 predictions, and a precision of 65% on the top 20 predictions (Table 1). Since there is no gold-standard set of non-interacting drugs, it is plausible that our non-interacting drug pairs might actually contain adverse DDIs. Not surprisingly, some non-interacting drug pairs with the high predicted probabilities can be found with evidence supporting their possible adverse interactions. For example, the drug pair with a non-interacting label with the highest predicted interacting probability in the test set, liothyronine and tretinoin, has been indicated to potentially cause intracranial pressure increase and a higher risk of pseudotumor cerebri when taken together [44]. Furthermore, diazoxide and spironolactone, predicted with an interacting probability of 0. 846, have been reported to induce asthma, cardice hypertrophy and pulmonary edema according to FDA reports when co-administrated [45]. In order to showcase the competitiveness of the XGBoost algorithm, we implemented a number of alternative classification algorithms including support vector machine (SVM), random forest and the standard gradient boosting algorithm and performed the same prediction task using exactly the same dataset and features. We found that XGBoost achieved better or comparable performance than the other algorithms (S3 Table). Furthermore, XGBoost is substantially faster than its closest contenders in terms of performance, gradient boosting and random forest. These results highlight the advantage of XGBoost over other algorithms in both predictive performance and speed. To further demonstrate the efficacy of our method, we compared it against a previously published similarity-based method for DDI prediction [18] using our training and test sets. Our method exhibited a substantial advantage both in training and on the test set (S3 Table). To demonstrate the utility of our method, we obtained 5,039 drug pairs involving 295 drugs that had not been used for training and testing (S6 Fig). After refitting our model on all 12,426 drug pairs that were used to develop our method, we predicted 432 novel DDIs (S4 Table). Remarkably, out of the top 20 newly predicted adversely interacting drug pairs, 9 can be verified in the TWOSIDES database (Table 2), manifesting the reliability of our method. We investigated the contribution of genetic interaction features to classifier performance by building and tuning a new model without them. Excluding genetic interaction features significantly decreases classifier performance when either AUROC or AUPR is examined (P < 10−20 for both AUROC and AUPR, two-sided Welch’s t-test). More interestingly, the performance drop is not as profound when other groups of features are excluded (Fig 2B–2D). Furthermore, prediction with genetic interaction features alone rendered significantly better performance than prediction with target similarity features alone (P < 10−20 for both AUROC and AUPR, two-sided Welch’s t-test, S3 Table). These results establish genetic interaction as an important feature in our model for predicting DDIs, providing complementary information that other features cannot capture. More importantly, genetic interaction can help us generate plausible mechanistic explanations for drug-drug interactions. For example, mesalazine and dexamethasone, both of which are anti-inflammatory drugs, are a pair of drugs in the test set that have been labeled as adversely interacting. Mesalazine can target the IKBKB protein, whereas dexamethasone can target NOS2, which plays important roles in nitric oxide signaling. In yeast, double knockout of ATG1 and TAH18, the respective yeast homologs of IKBKB and NOS2, exhibits a more negative impact on cell viability than expected from single knockout phenotypes [35]. In human, IKBKB can phosphorylate the NF-κB inhibitor and activate NF-κB [46], which is a family of transcription factors involved in inflammation and immunity. Notably, the transcription of NOS2 is induced by NF-κB activity [47]. Mesalazine has been shown to inhibit IKBKB, thereby inhibiting the activation of NF-κB, while dexamethasone is a negative modulator of NOS2. A previous study has reported that dexamethasone can decrease NOS2 translation and facilitate NOS2 degradation in rat [48] (Fig 3A). The combined use of mesalazine and dexamethasone may largely reduce the amount of NOS2, potentially affecting neurotransmission, antimicrobial and antitumoral activities. As another example, arsenic trioxide and mexiletine are a pair of drugs not labelled as adversely interacting in DrugBank, but predicted by our model to interact with high probability. As a chemotherapy drug for acute promyelocytic leukemia (APL), arsenic trioxide has been reported to decrease the activity of a serine/threonine-protein kinase AKT1 [49]. On the other side, mexiletine is a sodium channel blocker that has also been used as part of a prophylactic therapy to treat APL patients to reduce cardiac complications [50]. PKC1, the yeast homolog of AKT1, exhibits strong synergistic interaction with CCH1 [35,51], which is the homolog of SCN5A, the gene encoding the sodium channel NAv1. 5 targeted by mexiletine. In human, the transcription of SCN5A is repressed by FOXO1, whose transcriptional repression activity is in turn inactivated by AKT1-dependent phosphorylation [52] (Fig 3B). Therefore, the simultaneous inhibition of AKT1 and the sodium channel by the two drugs may reduce sodium influx in cardiac cells to a greater extent, potentially causing undesired adverse effects. Indeed, this pair of drugs is reported by TWOSIDES as interacting, providing additional supporting evidence to their adverse interaction. In the past decade, many methods have been developed for predicting DDIs based on various types of features. In this study, we have incorporated a novel feature, namely genetic interaction, to build a gradient boosting-based model for fast and accurate adverse DDI prediction. We have shown that our classifier can robustly predict drug-drug interactions even for drugs whose interaction profiles are completely unseen during training. Furthermore, we have predicted 432 novel DDIs, with additional evidence supporting our top predictions, demonstrating the usefulness of our approach. Most previous efforts of predicting DDIs suffer from an inability to make predictions for newly developed drugs due to train-test split based on drug pairs rather than drugs [10,16,17,19,22,23,36,37]. Three studies attempted to address this problem by dividing the entire dataset based on drugs [14,20,21]. However, they failed to do so during the training phase, resulting in an inflated performance on the training set. We have followed the drug-based train-test splitting scheme and have adopted a hold-out validation approach to avoid using overlapping drug sets for fitting the model and evaluating its performance. By doing so, we have achieved robust performance on the training set and the test set, which establishes the ability of our method to predict new DDIs for drugs whose interaction profiles are completely unknown. By examining genetic interactions, our method provides mechanistic insights into how two drugs may interact in a detrimental fashion. The combined modulatory effect resulted from binding of two drugs to their respective targets might underlie adverse DDIs, and genetic interaction gives valuable information about the nature of such combined effect. Indeed, we have observed that genetic interaction features are indispensable to our classifier performance. Notably, target sequence similarity features and genetic similarity features capture conceptually different mechanisms by which DDIs can occur. While the former can capture dosage effects where two drugs target same or similar genes, as exemplified by prolonged QT interval caused by concomitant administration of terfenadine and ketoconazole, both of which are strong CYP3A4 inhibitors [53], the latter captures DDIs resulting from drug pairs targeting genes with an epistatic relationship. For example, asthma patients receiving leukotriene-modifying drugs often show attenuated response to β2-agonists, including albuterol. This drug-drug interaction has been implicated to be associated with the epistasis between ALOX5AP and LTA4H [54]. Nevertheless, our work is limited by the lack of a global human genetic interaction network. As a surrogate for human genetic interactions, genetic interactions of yeast homologs were used in this study. Fortunately, large-scale human genetic interaction studies are coming into sight. Using a recently published dataset of human genetic interactions in K562 cells encompassing 222,784 gene pairs [55], we have found that the distribution of human genetic interaction scores vary significantly between adversely interacting drugs and non-interacting drugs (S3B Fig). Notably, the same trends could be recapitulated with a smaller dataset of genetic interactions [56] in the HEK293T cell line, demonstrating the generalizability of genetic interactions across different cell contexts (S3C Fig), although certain genetic interactions can exist in a cell type-dependent manner. For example, interactions between cancer driver genes are frequently specific to the cancer type [57]. In addition to DDI prediction, a similar machine learning method leveraging genetic interaction features can potentially be developed for predicting beneficial drug combinations. Indeed, current combination therapy for cancers have typically been developed to induce synthetic lethal genetic interactions in cancer cells [58,59]. While there have been some efforts aimed at predicting synergistic drug effects [60,61] or directly predicting drug combinations for disease therapy, especially cancer treatment [62–64], incorporating cell type-specific genetic interaction data from the matching cell type can be crucial for developing combination therapies that specifically target certain cell types. With the continuous advancement of technologies for probing human genetic interactions including CRISPR interference, we anticipate that more comprehensive maps of human genetic interactions for multiple cell lineages will become available in the near future, which could illuminate predictions of adverse DDIs and beneficial drug combinations to a larger extent. We obtained DDI data from DrugBank (version 5. 0. 10) [28]. Among the 5 major interaction categories in DrugBank (S1 Table), we only considered the first category as they were clearly defined as adverse DDIs. Non-interacting drug pairs were constructed by taking all other combinations using the same set of drugs, removing drug pairs also appearing in other categories in DrugBank, TWOSIDES [29], or a complete dataset of DDIs [30] compiled from a number of sources. This minimizes the chance of having actual adverse DDIs in the non-interacting set given the absence of a gold standard set of non-interacting drug pairs. From DrugBank, we also collected human protein targets of drugs and their sequences. Side effects were obtained from SIDER 4. 1 [65] and OFFSIDES [29]. Both databases use UMLS concept IDs as their side effect identifiers. However, as reported by Zhang et al. [20], some side effect terms are similar, and synonyms could cause biases when calculating side effect similarity. To solve this problem, we obtained mapping from UMLS concept IDs to MedDRA concept IDs from the 2017AB release of UMLS [66]. Furthermore, we obtained the full MedDRA hierarchy from MedDRA (version 21. 0) [31]. This allowed us to map UMLS concept IDs to different levels (PT, HLT, HLGT and SOC) of the MedDRA hierarchy. Similar to side effect data, indications of drugs were acquired from SIDER 4. 1 [65] and mapped to the same 4 levels of the MedDRA hierarchy. For genetic interactions, we obtained yeast genetic interactions from Costanzo et al. [35]. We first filtered all genetic interactions by a p-value cutoff of 0. 05 and aggregated the scores of all combinations of alleles of each yeast gene pair by applying the arithmetic mean. Drug targets in the form of UniProt IDs were mapped to gene names by UniProt [67] and these human genes were mapped to their yeast homologs via SGD YeastMine [68]. For human gene pairs mapped to multiple yeast gene pairs, we obtained a single score for each human gene pair by applying the arithmetic mean. For a drug pair (A, B), four groups of features were calculated (Fig 1A and 1D): indication similarity scores between A and B, side effect similarity scores between A and B, target sequence similarity scores between targets of drug A and targets of drug B, and genetic interaction scores between targets of drug A and targets of drug B. Indications and side effects of drugs were mapped to 4 different levels of the MedDRA hierarchy as described above. At each level, indication similarity was calculated by taking the Jaccard index between the respective indication vectors of drug A and drug B (Fig 1A). Similarly, side effect similarity was calculated by applying the same measure on the side effect vectors at the 4 different MedDRA hierarchy levels (Fig 1A). For genetic interactions, since each drug can have multiple targets, we obtained a single score for each drug pair by aggregating the genetic interaction scores of all their corresponding target pairs using 4 different functions, namely taking the minimum, mean, median or maximum (Fig 1D). Similarly, the same 4 functions were used for constructing target similarity features, which were calculated from the target sequences with the Smith-Waterman algorithm using the scikit-bio Python library. The raw scores were normalized as described in Bleakley et al. [69]. Overall, 16 features belonging to 4 feature groups were constructed. Only drug pairs with all features available were considered when building the machine learning model. All drugs were randomly split into “training drugs” and “test drugs” with a 2: 1 ratio. The training set consisted of all drug pairs where both drugs were “training drugs” and the test set consisted of all drug pairs where both drugs were “test drugs” (Fig 2A, S5 Table). We constrained the fraction of adversely interacting drug pairs in the training set and that in the test set to be fairly balanced. To obtain the optimal feature combination, we calculated all features for the training set and applied group minimax concave penalty (MCP) [38] with the ‘grpreg’ R package with default parameters. All subsequent training was done using this optimal set of features. The gradient boosting-based algorithm XGBoost [40] was used in this study. To find the best combination of hyperparameters for the XGBoost classifier, the tree-structured Parzen estimator (TPE) approach [41] was adopted. Because of the drug-based approach by which we split our dataset into training and test sets, we applied the same splitting scheme on the training set multiple times to obtain training seti and validation seti instead of simply using cross-validation. Each split on the training set can be seen as a hold-out validation, as we used training seti to fit the model and validated model performance on validation seti. We selected one minus the average AUPR of 50 trials of hold-out validation as the loss function to minimize for TPE, and we ran TPE for 2,000 iterations to obtain set of hyperparameters that minimized the loss function for our XGBoost classifier (S5 Fig). After finding the optimal set of hyperparameters, we fit the model on the complete training data. Model performance on training set was evaluated by 1,000 runs of hold-out validation on the training set. For each hold-out validation, we fitted the model on training seti and obtained AUROC and AUPR. We averaged AUROC and AUPR over 1,000 runs of hold-out validation as measurements of the performance of the model. Approximate ROC curve and precision-recall curve (Fig 2B and 2C) were plotted by averaging the 1,000 ROC curves and 1,000 precision-recall curves respectively at every thousandth of a point on the x-axis. In order to evaluate the ability of the classifier to identify drug-drug interactions between drugs whose interaction profiles were completely unknown during training, the model was evaluated on the test set which had no overlap with the training set in terms of the drugs involved. Predictions were ranked according to their raw prediction scores to produce the ROC curve and the precision-recall curve. To make novel adverse DDI predictions, we examined all combinations of drugs that appeared in DrugBank, excluding drug pairs where both drugs were involved in the first category of DDIs (S6 Fig), which we used for building the machine learning model. We then predicted 6,690 drug pairs involving 336 drugs for which all features could be calculated using the classifier retrained on the whole dataset. The probability cutoff that produced the maximum averaged F1 score over 1,000 runs of hold-out validation on the training set was chosen for determining new DDI predictions.

Adverse drug-drug interactions are adverse side effects caused by taking two or more drugs together. As co-prescription of multiple drugs becomes an increasingly prevalent practice, affecting 42. 2% of Americans over 65 years old, adverse drug-drug interactions have become a serious safety concern, accounting for over 74,000 emergency room visits and 195,000 hospitalizations each year in the United States alone. Since experimental determination of adverse drug-drug interactions is labor-intensive and time-consuming, various machine learning-based computational approaches have been developed for predicting drug-drug interactions. Considering the fact that drugs effect through binding and modulating the function of their targets, we have explored whether drug-drug interactions can be predicted from the genetic interaction between the gene targets of two drugs, which characterizes the unexpected fitness effect when two genes are simultaneously knocked out. Furthermore, we have built a fast and robust classifier that achieves accurate prediction of adverse drug-drug interactions by incorporating genetic interaction and several other types of widely used features. Our analyses suggest that genetic interaction is an important feature for our prediction model, and that it provides mechanistic insight into the mode of action of drugs leading to drug-drug interactions.

Abstract Introduction Results Discussion Methods

computer and information sciences medicine and health sciences machine learning drug-drug interactions drug research and development artificial intelligence gene identification and analysis genetics adverse reactions pharmacology biology and life sciences genetic interactions drug interactions drug information drug screening

2019

Leveraging genetic interactions for adverse drug-drug interaction prediction

The cerebellum is a brain structure which has been traditionally devoted to supervised learning. According to this theory, plasticity at the Parallel Fiber (PF) to Purkinje Cell (PC) synapses is guided by the Climbing fibers (CF), which encode an ‘error signal’. Purkinje cells have thus been modeled as perceptrons, learning input/output binary associations. At maximal capacity, a perceptron with excitatory weights expresses a large fraction of zero-weight synapses, in agreement with experimental findings. However, numerous experiments indicate that the firing rate of Purkinje cells varies in an analog, not binary, manner. In this paper, we study the perceptron with analog inputs and outputs. We show that the optimal input has a sparse binary distribution, in good agreement with the burst firing of the Granule cells. In addition, we show that the weight distribution consists of a large fraction of silent synapses, as in previously studied binary perceptron models, and as seen experimentally. Purkinje cells (PCs) are the only outputs of the cerebellar cortex, a brain structure involved in motor learning. They receive a very large number (150,000) of excitatory synaptic inputs from Granule Cells (GCs) through parallel fibers (PFs), and a single very strong input from the inferior olive through climbing fibers (CFs). Single PCs have long been considered as a neurobiological implementation of a perceptron [1], [2], the simplest feedforward network endowed with supervised learning [3], since CFs are thought to provide PCs with an error signal [4]. A perceptron learns associations between input patterns and a binary output that are imposed to it. Learning is due to synaptic modifications, under the control of an error signal. The learning capabilities of perceptrons have been extensively studied for unbiased [5], [6] as well as biased patterns [6], and for unconstrained synapses [5], [6]. In real neurons, synapses are either excitatory (glutamatergic synapses), or inhibitory (GABAergic synapses), depending on the identity of the pre-synaptic neurons (except during early development, when GABAergic synapses are initially excitatory and then become inhibitory). A multitude of experiments characterizing synaptic plasticity have shown that the strength, but not the sign, of a synapse can be modified by patterns of neuronal activity. This has led to the study of perceptrons with sign-constrained weights [7], [8], [9], [10]. In particular, Brunel et al. [10] showed that when synaptic weights are constrained to be excitatory (positive or zero), a perceptron at maximal capacity has a distribution of synaptic weights with two components: a finite fraction of zero-weight (‘silent’) synapses; and a truncated Gaussian distribution for the rest of the synapses. They further showed that this distribution is in striking agreement with experimental data [10]. Numerous experiments show however that in the course of specific motor tasks, the firing rate of Purkinje cell varies in an analog, not binary, fashion [11], [12], [13], [14]. We therefore set out to investigate the capacity and distribution of synaptic weights of a perceptron storing associations between analog inputs and outputs. More precisely, each input or output unit can take an analog value drawn from a distribution with a given mean and variance. We show that the optimal input distribution matches the firing pattern of the Granule cells, and weight distribution at maximal capacity reproduces the experimental Parallel Fiber to Purkinje cell synaptic weight distribution. The perceptron consists of inputs and one output. Both inputs and outputs take continuous values. We require this perceptron to learn a set of prescribed random input-output associations, where the inputs (,) are drawn randomly and independently from a distribution, with mean and standard deviation while the target outputs are drawn randomly and independently from a distribution with mean and standard deviation. Note that since and represent firing rates of input and output cells, respectively, they must be non-negative quantities. In particular, , represent the mean firing rates of granule/Purkinje cell, respectively. The output of the perceptron when a pattern is presented in input is given by (1) where is a monotonically increasing static transfer function (f-I curve), are the synaptic weights from input, represents inhibitory inputs that cancel the leading order term in so that the argument of is of order 1. In Purkinje cells, these inhibitory inputs are provided by interneurons of the molecular layer. The goal of perceptron learning is to find a set of synaptic weights for which for all. We focus for simplicity on a linear transfer function, but our results can be applied to arbitrary invertible transfer functions. Indeed, the problem of learning associations () in a perceptron with an arbitrary invertible transfer function is equivalent to the problem of learning () in a linear perceptron. All the results derived in this paper can then be applied to a perceptron with transfer function, except that and are now defined to be the two first moments of. In the large limit the probability of finding a set of weights that satisfies for all is expected to be 1 if is below a critical value, while it is 0 when [15]. is therefore the number of associations that can be learned per synapse, and is commonly used as a measure of storage capacity. This storage capacity can be computed analytically using the replica method (see Methods) [6], [16], [17], [10], [15]. The capacity is given by (2) is given by the equation (3), , and depends on the statistics of the associations as (4) Therefore, the maximal capacity only depends on a single parameter, which is a function of the statistics of the patterns that need to be learned. This dependence is shown in Fig. 1A. It shows that the capacity is exactly equal to 0. 5 when, while it decreases monotonically as increases. If the number of patterns to be learned exceeds the maximal capacity, the mean squared error becomes strictly positive. It can also be computed using the replica method (see Methods, Eq. (17) ). Unsurprisingly, it increases monotonically with, as shown in Fig. 1B which shows the result of the analytical calculation, as well as numerical simulations. If uncorrelated noise is added to the perceptron, the total mean squared error is the sum of the error without noise (Eq. 17) and the variance of the uncorrelated noise. In the simulations, inputs and outputs are drawn from an exponential distribution. The weight update at each presentation is the standard perceptron one, i. e. (5) where is the learning rate. is set to zero if application of the update leads to a negative weight. This corresponds to a gradient descent of a cost function proportional to, in the closed orthant. This learning rule is in qualitative agreement with experimental data on synaptic plasticity in GC to PC synapses [18], . In Purkinje cells, the error signal is thought to be conveyed by climbing fiber (CF) activation. Two protocols have been shown to be effective in eliciting long-term plasticity. Pairing GC with and CF activation leads to Long-Term Depression (LTD) of the synapse, while Long-Term Potentiation (LTP) is induced by stimulating the GC alone (see Fig. 3AB of [19] for details). Writing climbing fiber activation as, we see that Eq. (5) is recovered if one chooses, which captures the two experimental protocols described above. The distribution of synaptic weights at maximal capacity can also be computed using the replica method (see [10] for details of the calculation). It turns out that the distribution obeys exactly the same equation as in the binary perceptron, i. e. (6) where (7) and is the average synaptic weight. In particular the fraction of zero weight synapses is. Interestingly, there is a very simple relationship between capacity and fraction of silent synapses, , that holds for any value of. The fraction of silent synapses is shown as a function of in Fig. 2A. It shows that when, and increases monotonically with. The full distribution of weights is shown in Fig. 2B, together with the results of a numerical simulation (see parameters in the caption of Fig. 2B). The theoretical distribution of synaptic weights is in good agreement with experimental measurements of the efficacy of a large set of GC to PC synapses, using paired recordings in vitro (see Fig. 6A of [10] for details) [20], [21], [10]. Above maximal capacity, , the distribution of synaptic weights is still given by Eq. (6), but the fraction of zero weight synapses decreases monotonically with, and goes to zero in the large limit (see Fig. 2C). In that limit the distribution becomes increasingly close to a Gaussian distribution peaked around a positive value, with a width that tends to zero in the large limit. To maximize storage capacity, should be as small as possible. We first ask which distribution of inputs maximize capacity. From Eq. (4), it is clear that to maximize capacity, should be as small as possible, while should be as large as possible. Since is a distribution of firing rates, it must be bounded between 0 and a maximal firing rate. The distribution of a bounded variable that maximizes the variance with a fixed mean is a binary distribution. Thus, we predict that to optimize capacity, patterns of activity in the Granule cell layer should be sparse (to ensure is small), but active cells should be active close to their maximal firing rates. Interestingly, this is in striking agreement with available data [22], [19], [23] showing that (i) Granule cells have very sparse activity in vivo (average firing rates of 0. 5 Hz [22]) (ii) they can respond with brief, high frequency bursts of action potentials to sensory inputs (with an average frequency of 77 Hz within the burst, and maximal frequencies up to 250 Hz, see e. g. Fig. 3 of [22]). The next question is which distribution of output firing rates optimizes the capacity. Eq. (4) makes it clear the capacity is optimized for. In this limit however, all input patterns lead to exactly the same output, and the Purkinje cell output contains no information on which input was presented. This is of course not a desirable outcome, and suggests the capacity is not the correct measure to maximize in this case. We therefore turn to the Shannon mutual information between the Purkinje cell output and its inputs as a more suitable measure. In the presence of additive Gaussian noise of zero mean and standard deviation, this is simply the mutual information of a Gaussian channel with a signal-to-noise ratio, i. e. bits per pattern (see e. g. [24]). The total information in bits per synapse is therefore. The information is zero when, and reaches a maximum for a finite value of, which depends on both the noise standard deviation and. Fig. 3A shows the information as a function of, for different values of, for. It shows that the optimal value of increases approximately linearly with for large (see Fig. 3B). In this paper, we have considered an analog firing rate model for a Purkinje cell with plastic excitatory weights, and derived both its maximal capacity and the distribution of weights at maximal capacity. We showed that the capacity is of the same order as in a binary perceptron model. The distribution of synaptic weights of the analog perceptron is composed at maximal capacity of two parts: a large fraction () of silent synapses and a truncated Gaussian. It has exactly the same shape as in several other models: a standard binary perceptron [10], and a bistable perceptron [25]. This distribution is in quantitative agreement with a combination of electron microscopy and electrophysiological data in adult rat slices [20], [21], [10]. Furthermore, a gradient descent learning rule leading to maximal capacity bears strong similarities with synaptic plasticity experiments: LTD when PF and CF are coactivated, LTP when PF fires alone (i. e. CF below baseline, thus) [18], [19]. We found that in order to maximize the capacity, the input variance should be as large as possible. We argue that GCs in vivo are close to such an optimal distribution, since they fire high-frequency bursts at very low rates [22], [19], [23]. Furthermore, GC bursts have been found in some experiments to be critical to induce plasticity in PF to PC synapse [26]. Indeed, no plasticity is induced in those protocols with a single GC spike. Secondly, lower variance in the output also increases the capacity, but at a cost of losing information contained in the output, in the presence of noise. For a given variance of the noise, there is an optimal variance of the output that maximizes the information contained in the output. The model we have studied here is essentially equivalent to the ADALINE (Adaptive Linear Neuron) model [27], whose storage capacity, in the absence of constraints on synaptic weights, is equal to 1. The result can be easily intuitively understood by the fact that when, there are exactly N linear equations to solve, Eq. 1, with N unknowns, (see e. g. [15]). We have shown here that the constraints that all synaptic weights should be positive or zero leads to a capacity which is decreased by a factor 2 or more, depending on the value of. This decrease in capacity is similar to what is observed in the standard perceptron with excitatory synapses [7], [8], [9], [10]. Note that learning associations with constrained weights is similar conceptually to non-negative matrix factorization [28], [29]. Generalizations of such models in the temporal domain (the so-called adaptive filter models) have been proposed to describe learning in the cerebellar cortex [30], [31], [32], [33]. It would be of interest to investigate capacity and distribution of synaptic weights of such models. In this paper, we have focused on a single plasticity site, the GC to PC synapse. Many other sites of plasticity are known to exist in the cerebellum [18]. Future studies are needed to clarify the impact of these additional sites of plasticity on the learning capabilities of this structure. The replica method involves calculating the average logarithm of the volume of the space of weights satisfying all constraints given by Eq. (1) [6]. To compute the average logarithm, one uses the replica trick: replicas of the system are introduced, one computeswhere represents an average over the patterns, and is a replica index. This calculation is done using a standard procedure. After introducing integral representations for the delta functions, one averages over the distribution of the patterns. One then introduces order parameters (8) (9) (10) together with conjugate parameters, and. We then use a replica-symmetric ansatz (all the order parameters are taken to be independent of replica index), perform the limit and obtain (11) (12) where in the Equation for (Eq. (12) ), the two first lines are identical to the binary perceptron with excitatory weights [10], while the last line is specific to the analog perceptron. In the large limit, the integral in Eq. (11) is dominated by the extremum of. The typical values of all order parameters are then obtained by the resulting saddle point equations, setting the derivatives of with respect to all order parameters to zero. The maximal capacity is obtained in the limit, for which the volume vanishes. This limit yields Eqs. (2,4). Following [34], we introduce a cost function which is given by the sum of the squared error for all patterns, (13) and compute its minimum over the space of weights. This is done introducing a partition function, (14) where is an inverse temperature, and computing using the replica method. The mean squared error is then given by (15) To perform this calculation, a new parameter has to be introduced, (16) which will remain finite when in the limit, . The mean squared error is then given by (17) where (18) (19) (20) When, diverges to infinity, , and Eqs. (19,20) reduce to Eqs. (2,3).

Learning properties of neuronal networks have been extensively studied using methods from statistical physics. However, most of these studies ignore a fundamental constraint in networks of real neurons: synapses are either excitatory or inhibitory, and cannot change sign during learning. Here, we characterize the optimal storage properties of an analog perceptron with excitatory synapses, as a simplified model for cerebellar Purkinje cells. The information storage capacity is shown to be optimized when inputs have a sparse binary distribution, while the weight distribution at maximal capacity consists of a large amount of zero-weight synapses. Both features are in agreement with electrophysiological data.

Abstract Introduction Results Discussion Methods

physics computer science mathematics biology

2013

Optimal Properties of Analog Perceptrons with Excitatory Weights

Kenya and Uganda have reported different Human African Trypanosomiasis incidences in the past more than three decades, with the latter recording more cases. This cross-sectional study assessed the demographic characteristics, tsetse and trypanosomiasis control practices, socio-economic and cultural risk factors influencing Trypanosoma brucei rhodesiense (T. b. r.) infection in Teso and Busia Districts, Western Kenya and Tororo and Busia Districts, Southeast Uganda. A conceptual framework was postulated to explain interactions of various socio-economic, cultural and tsetse control factors that predispose individuals and populations to HAT. A cross-sectional household survey was conducted between April and October 2008. Four administrative districts reporting T. b. r and lying adjacent to each other at the international boundary of Kenya and Uganda were purposely selected. Household data collection was carried out in two villages that had experienced HAT and one other village that had no reported HAT case from 1977 to 2008 in each district. A structured questionnaire was administered to 384 randomly selected household heads or their representatives in each country. The percent of respondents giving a specific answer was reported. Secondary data was also obtained on socio-economic and political issues in both countries. Inadequate knowledge on the disease cycle and intervention measures contributed considerable barriers to HAT, and more so in Uganda than in Kenya. Gender-associated socio-cultural practices greatly predisposed individuals to HAT. Pesticides-based crop husbandry in the 1970' s reportedly reduced vector population while vegetation of coffee and banana' s and livestock husbandry directly increased occurrence of HAT. Livestock husbandry practices in the villages were strong predictors of HAT incidence. The residents in Kenya (6. 7%) applied chemoprophylaxis and chemotherapeutic controls against trypanosomiasis to a larger extent than Uganda (2. 1%). Knowledge on tsetse and its control methods, culture, farming practice, demographic and socio-economic variables explained occurrence of HAT better than landscape features. Human African Trypanosomiasis (HAT) or Sleeping sickness caused by Trypanosma brucei protozoa and transmitted by tsetse fly (Glossina spp) vector is found only in Sub-Saharan Africa, within 36 countries lying between latitudes 14° North and to 29° South. An annual incidence ranging from 50,000 and 70,000 of HAT was been reported for the past 50 years until 2009 when new cases per annum dropped to below 10,000 [1] which further reduced to 6,743 cases in 2011 [2]. This decline was achieved through concerted campaigns led by World Health Organization (WHO), and many nongovernmental organizations [3]. HAT is caused by Trypanosoma brucei rhodesiense (T. b. r) and Trypanosma brucei gambiense (T. b. g.), with the former being commonly found in Eastern and Southern Africa, while the latter is in Central and West Africa. T. b. r causes acute illness with untreated cases dying within three to five months of infection while T. b. g. causes chronic infection lasting several years [4]. T. b. r form of HAT is endemic in our study area [5], [6]. The main biophysical determinants of transmission are the presence of the tsetse fly and mammalian reservoir hosts. In addition, social determinants of the health (SDH) including income, education, occupation, gender, race/ethnicity, culture and other factors may have a potential to influence the outcome of HAT [7], [8]. HAT remains an endemic and a neglected tropical diseases (NTDs) in poor Sub-Saharan regions where prevailing political, social, cultural, economic and physical environment do not allow for formulation of appropriate interventions strategies [9] for vector and disease control. Uganda has reported higher cases of T. b. r form of HAT in the past over 3 decades compared to Kenya. It has been reported that the nature and duration of the contact which could either be personal (intimate) or impersonal (casual) determines the intensity of transmission to humans [10], [11]. Thus understanding land use and land cover characteristics, tsetse and their infection rates with T. b. r. , animal reservoirs, lifestyle activities such as livestock keeping practices, cattle numbers, livestock marketing, human activities, grazing and watering points which influenced incidence of HAT in western Kenya and southeastern Uganda was necessary. Other concomitant factors such cultural practices also contributed to HAT incidence. Here, we describe interactions between socio-economics, culture, tsetse control methods and environmental issues and T. b. r. infection at Kenya and Uganda transboundary. We also examined the application of conventional and non-conventional tsetse control strategies over time and existing landscape features at homesteads and how such transformations influenced HAT occurrence at the transboundary of Kenya and Uganda. The study hypothesis was that land use, economic, socio-cultural, livestock keeping practices, livestock marketing, tsetse and trypanosomiasis control practices influenced HAT dynamics in affected villages of Western Kenya and Southeast Uganda. The study site was located in Kenya (Ke) and Uganda (Ug) transboundary in an area comprising four districts namely Tororo (Ug), Busia (Ug), Busia (Ke) and Teso (Ke). Busia (Ke) and Teso (Ke) Districts (within Busia County) are located in Western Kenya between latitude 0° and 0°45′ North and longitude 33°54′ east and 34°25′east [12], [13]. Tororo (Ug) and Busia (Ug) Districts are located in Southeastern Uganda and lie between latitude 0° and 0°45′ North and longitude 34° and 34°15′ East [14]–[17]. The tsetse vectors present are Glossina pallidipes and Glossina fuscipes fuscipes. The study protocol and the use of informed oral consent were approved by the Board of Graduate Studies, Moi University, Kenya. Also permission was sought from Districts administration through the village chiefs and village elders before the interviews in Western Kenya and Southeast Uganda. Interviews using pre-tested structured questionnaires were conducted systematically to randomly selected household heads or their representative which was either a spouse or older child of above 30 years. Most of the respondents were illiterate or semi-literate and could not read or write, therefore oral consent was sought from all the participants for uniformity. After obtaining verbal informed oral consent the research assistant interviewed the respondent and if the respondent declined another household was sought. All interviewed persons provided informed oral consent witnessed by an administrative head (the chief or village elder) who also acted as the village guide. The verbal oral consent was documented by the respondents providing an affirmative tick mark in the provided acceptance space in the questionnaires and each sampled household was geo-referenced. The study was a cross-sectional household survey conducted between April to October 2008. The four districts were purposely selected on the basis of them being foci for T. b. r. type of HAT in both countries and that they lie adjacent to each other at the international boundary of Kenya and Uganda. Then sub-locations/sub-counties from each district with relatively higher reports of HAT cases were purposively selected. The sampling unit was the village; therefore all the villages within the sub-locations and sub-counties constituted the sampling frame. Household data collection was conducted in 12 villages comprising 3 villages from each of the 4 study districts namely; Teso (Ke), Busia (Ke), Tororo (Ug), and Busia (Ug). In both countries 2 of the villages in each district had experienced HAT (HAT affected villages) and the other one village had no reported HAT case (unaffected villages) from 1977 to 2008 (fig. 1). The study stations comprised centers of the village which was either school, church or trading center representing the dispersal point from where systematic sampling was used to identify the households. In order to conduct a detailed analysis of risk factors at the household level, 8 villages that had reported high number of HAT cases ranging from 3 to above 100 HAT cases from 1977 to 2008 were selected. The hospital records of HAT affected villages in both Kenyan and Ugandan from 1977 to 2008 kept and stored by the Trypanosomiasis Research Centre (TRC) in Kikuyu, Kenya, and the National Livestock Health Research Institute (NALIRI) in Tororo, Uganda were used. All the HAT affected villages were mapped and control villages (unaffected villages) points were marked randomly in locations within the study area. The coordinates of the marked point was uploaded in a GPS machine and the village was traced to its actual position on the actual ground. The name of the village was obtained through government administrative officers and the local residents. The authors acknowledge that some villages may not have reported HAT cases due to misdiagnosis, therefore not referred to HAT referral hospitals in both countries [18]. The study stations comprised centers of the village which was either school, church or trading center representing the dispersal point from where systematic sampling was used to identify the households. Interviews were conducted randomly to selected household heads or their representatives. The questionnaire was first designed in English and then translated and pre-tested in local languages well understood by the participants. The languages of the respondents were Teso, Luhyas, Luo, and Samia in Western Kenya and Teso, Adhola, Nyole, Mugwe, Musoga and Samia in Southeast Uganda. Ten enumerators were recruited from the various tribes in the study districts in each country. Every third household was sampled. In each country 384 households were sampled. This was a retrospective study and elderly individuals above 30 years were interviewed. If the household head or their representative who was either a spouse or child was less than 30 years, the household was skipped and the immediate third household was sampled. A child in this paper refers to a boy or daughter who were not married and living in their parents homestead or did not have their own households at the time of the interview. The respondents were given information about the objectives of the study using the local language of the participants' preference. A minimum of 63 respondents from each of the selected 6 villages in each country were interviewed. The sample size formula was statistically determined using Rasosoft program [19]. The sample size n and margin of error E were given by: Where N was the population size, r is the fraction of sample collected, and Z (c/100) was the critical value for the confidence level c in this study 95% confidence level was used therefore, the minimum sample size required was 384 from each country. The official human population in Western Kenya study districts were 552,099 in 1999 [12], [13], [20] while in Southeast Uganda it was estimated at 887,345 in 2002 population census [16], [17]. The geographic coordinates of all homesteads in these villages were mapped using a hand-held Garmin Global Positioning System (GPS). The questions focused on various sub-themes like the socio-demographic characteristic of the respondent, gender, age, tribe, knowledge and perception on tsetse and trypanosomiasis, historical tsetse and trypanosomiasis control methods, livestock keeping practices, land size, land use patterns, occupation, human dwellings, cultural and behavioral practices, other family practices related to HAT transmission and perceived effectiveness of available control programs. The questionnaires were prepared in English and verbally translated into local language during the interview time. The percent of respondents giving a specific answer was reported. Secondary data through literature was also obtained on socio-economic and political issues in both countries. The authors acknowledge that in many retrospective studies, the quality of the information obtained can be affected by recall bias. In the present study, to minimize the inaccuracy resulting from errors in recall, information obtained directly from the respondents was verified by interviewing their spouses and elder children above 30 years. A conceptual framework was postulated to explain interactions of various socio-economic, cultural, biological and physical factors that predispose individuals and populations to HAT (fig. 2) at Kenya and Uganda transboundary. Both qualitative and quantitative data collection methods were used in this largely descriptive study. Historical human activities aimed directly or indirectly at tsetse control were collected from the households in Uganda and Kenya transboundary. The data were entered into Excel 2003 and transferred to Statistical Package for Social Science (SPSS) version 14. 0 version for Windows for further statistical analyses. The characteristics of the households in the two countries comprising Kenya and Uganda were described using tables of frequencies. Demographic characteristics such as gender, age, tribe, level of education knowledge on tsetse and trypanosomiasis and their control methods, livestock keeping practices, land size, land use patterns and occupation were compared statistically. Comparisons of the two countries and correlation among the variables were analyzed using Pearson' s Chi-squared tests. The relationship between land use patterns in HAT affected and unaffected villages on the occurrence of diseases was investigated through Analysis of variance (ANOVA) [40] tests. Estimation of relative HAT risk was made at 95% confidence level (CL) and set at a significance level of 5% to correlate the strength of HAT and the variable of interest in the two countries, HAT affected and unaffected villages. The demographic factors studied included household heads' age, education, ethnicity, gender and occupation. The mean age of the study population in Teso and Busia Districts, Western Kenya was 59. 0, SE ±2. 7 years while in Busia and Tororo Districts, Southeast Uganda it was 52. 6, SE±1. 9 years. In Teso and Busia Districts, Western Kenya the main ethnic group that were interviewed in the study villages were Teso (50. 3%) and Luhya (consisting of Samia, Marachi, Wanyole, and Bukusu sub-tribes) (47. 3%). At Busia (Ug) and Tororo (Ug) Districts, Southeast Uganda the inhabitant' s tribes of respondents interviewed significantly varied with the major ethnic groups being Samia, Nyole, Adhola and Teso contributing 30. 8%, 29. 0%, 17. 8% and 13. 1% respectively. It should be noted that the Teso in the two countries are same ethnicity while the Samia and Nyole of Uganda are similar to the Luhya' s in Kenya with regards to ethnicity. The level of primary, secondary, tertiary and no formal education was 25. 1%, 15. 4%, 3. 2% and 14. 2%; and 18. 6%, 8. 1% 1. 2% and 14. 2%, in Western Kenya and Southeast Uganda, respectively (fig. 3). The respondents' main economic activities were farming, business/trading, employment as a public servant and retired for the old persons who solely depended on pension or relatives at 79. 7%, 7. 7%, 3. 5%, and 5. 6% for Kenya and 82. 2%, 9. 3%, 3. 6% and 2. 8% for Uganda, respectively. The level of knowledge regarding causes of HAT was high and differences according to the households interviewed in the two countries were minimal. The cause of HAT was well known in both Southeast Uganda and Western Kenya study districts. Household interviews showed that most people were able to associate the presence of tsetse with HAT. Respondents in Teso (Ke) and Busia (Ke) Districts, Western Kenya who knew tsetse flies as the cause of HAT during the survey were 97. 9% (n = 376) and the other 2. 1% associated it to witchcraft, bushes and mosquitoes. At Busia (Ug) and Tororo (Ug) Districts, Southeast Uganda 82. 8% (n = 317) of the respondents knew the cause of HAT was due to tsetse flies, 4. 6% that did not know what causes the disease, while 3. 7% associated HAT to various reasons like too much rainfall, blood transfusion, bushes and alcohol. In both countries it was generally reported that the male adults (28. 4%) were at higher risk of contracting the disease than female adults (22. 9%). The respondents (34. 6%) reported that all gender were at risk. The socio-economic activities that contributed to the resident' s exposure to HAT vectors in Kenya were herding (51. 8%), bathing at the river (14. 2%), fishing (10. 6%) and other activities had combined contribution of less than 10% (Table 1). In Uganda the important activities that exposed individuals to HAT risk were herding (31. 1%), location of homestead in bushy area (12. 6%), and bathing in the river (10. 3%) (Table 1). The study respondents' historical knowledge on tsetse control methods differed significantly (χ 2 = 60. 818; df 27, P<0. 001) in the two countries with Western Kenya reporting 62. 4% and 37. 6% in Southeast Uganda. The selected HAT affected and unaffected villages also differed significantly (χ 2 = 25. 691; df 9, P<0. 001) reporting 65. 9% and 34. 1% respectively. Sixty one percent of the respondents in Busia (Ke) and Teso (Ke) Districts, Western Kenya knew at least one of the conventional control methods during the interview (Table 2). Of these 34. 4%, 21. 5% and 0. 8% knew traps, bush clearing and ground spraying, respectively for tsetse control. The other known control methods for both tsetse and trypanosomiasis were live bait technology (0. 8%) and animal treatment (0. 9%). HAT risk was avoided by not bathing in the river and drainage of stagnant water reporting 0. 9% and 0. 4%, respectively. The recent methods used in the selected villages in Western Kenya were bush clearing, traps and live bait technology contributing 47. 4%, and 4. 6%, and 3. 1%, respectively (Table 3). The live bait technology acted both as a control for tsetse and trypanosomiasis. Also 6. 7% of the respondent' s used animal treatment to control trypanosomiasis. There was twofold use of traps in HAT affected villages (5%) compared to unaffected villages (2. 5%). Similarly bush clearing was twofold in HAT affected villages (51%). However, chemotherapeutic interventions were less varied between the HAT affected villages (5. 8%) and unaffected villages (4. 2%). Thirty nine point two percent of the respondents in Busia (Ug) and Tororo (Ug) Districts, Southeast Uganda knew at least a tsetse control method during the interview (Table 2). Of these 30. 6% and 2. 5% knew of traps and bush clearing, respectively. Other methods were bush burning, hand catching and ground spraying contributing 0. 4%, 0. 4%, and 0. 4%, respectively. Other historical methods for HAT control included human medical treatment, putting on white and long sleeved clothes and avoidance of bushy areas contributed 2. 1%, 0. 4%, and 0. 4% respectively. The respondents also reported keeping animals away from the homestead (0. 4%) and animal treatment (0. 4%) contributed to HAT control. The recent methods practiced in Busia (Ug) and Tororo (Ug) Districts, Southeast Uganda for tsetse and HAT control were traps, bush clearing, avoiding bathing in rivers reporting 17. 0%, 13. 9% and 0. 5% respectively (Table 3). Animal treatment (2. 1%) and live bait technology (1. 5%) reported as one of the recent methods being used to control HAT. Within the Southeast Uganda study area use of traps in both the HAT affected villages and unaffected villages were reported at 28. 4% and 16. 2%, respectively. Also bush clearing and livestock treatment within HAT affected villages reported 28. 4% and 1. 4% respectively while in not affected villages bush clearing and animal treatment reported 5. 4% and 4. 1%, respectively. Cleansing rituals, followed by circumcision were cited by the respondents as the most important risk factors in both countries. Other activities associated with the disease occurrence were similar in the transboundary and they included exhumation of the dead, appeasing spirits, rain making and marriage where the bride and her age mates wait in the bushes to be offered gifts before being presented to the groom' s home (Table 4). The males were considerably more predisposed than the females in all age groups and HAT risk increased with age. The mean land size in Busia (Ke) and Teso (Ke) Districts, Western Kenya was reported to be 2. 7 acres. Majority of the respondents in Busia (Ke) and Teso (Ke) Districts in both HAT affected villages (Diseased) and unaffected villages (Not diseased) owned land ranging from 1–5 acres (51. 8%) to 6 to 10 (28. 7%) acres. The others (19. 5%) had < 1 or >10 acres per household. The recent main crops grown in Busia (Ke) and Teso (Ke) Districts, Western Kenya by the respondents were maize (40. 8%), cassava (30. 3%), finger millet (5. 6%) and sorghum (4. 9%) (Table 5). Generally acreage of maize and cassava has been increasing, though it experienced some decline in 2000s due to diseases such as cassava mosaic virus attack leading to fewer acres under the crop. According to the respondents, crops such as cotton were grown in 1970s and 1980s but ceased due to lack of markets and unavailability of seeds. The other crops have been almost constant standing crops in the fields within the study area over the study period. Other crops grown by few farmers were soya beans, beans, groundnuts, sesame, yams, pumpkins, sugarcane, vegetables and fruits such as oranges and pineapples. Perennial crops which included bananas and cassava that can act as peri-domestic tsetse habitats were grown by 33. 8% of the respondents in the year 2006. In Tororo (Ug) and Busia (Ug) Districts, Southeast Uganda, most respondents in both HAT affected villages and unaffected villages owned land from 1–5 acres (72. 2%) and 6 to 10 acres (14. 0%). At Southeast Uganda, the mean land size that each household occupied was 2. 19 acres. Majority of respondents grow maize (37. 1%), cassava (19. 0%), finger millet (21. 0%) and sweet potatoes (6. 7%) (Table 5). Maize and cassava husbandry has been increasing from 1970s to date. Perennial crops such as cassava and bananas which can act as peri-domestic tsetse habitats were grown by 20. 9% of the respondents in the year 2006. Cotton farming accompanied by intensive use of agrochemicals was practiced in 1970s and 1980 but declined in 1990s to late 2000s due to unsuccessful market performance and withdrawal of agriculture support policies. Cotton growing in unaffected villages was higher compared to HAT affected villages. The rest of the crops were almost constant standing crops in the fields. Other crops grown were soya beans, beans, groundnuts, sesame, vegetables and fruits such as oranges and pineapples. In 1970s crop farming (Figure 4) significantly differed (F [41] = 17. 490; p<0. 05) between with HAT affected villages and unaffected villages in Tororo and Busia Districts, Southeast Uganda. HAT affected villages had more types of crops including cassava and bananas cultivated in the respondents' fields than unaffected villages which reported more millet, beans and crops using pesticides such as cotton. Eighty percent (n = 608) of the total households in the selected villages in both countries owned livestock. The total livestock reared in the sampled study villages in Western Kenya were 1,416 and 2,781 in Southeast Uganda, respectively. Most respondents in Kenya (99. 9%) and Uganda (67. 3%) owned livestock albeit the total population of livestock in Uganda was twice that of Kenya. The villages recording higher cases of HAT had correspondingly higher number of animal and in general the cattle population has drastically reduced due to nagana (cattle trypanosomiasis) especially in Uganda. In Western Kenya the respondents mainly tethered their livestock (78. 5%), free grazing (19. 3%), practiced both tethering and zero grazing (0. 7%) while 73. 3%, 16. 7% and 3. 3% respectively were reported in Southeast Uganda. However, during the dry seasons cattle were driven to open grasslands along the rivers and in the swampy area. The communities in the study area sheltered their animals in close proximity to their household. Most goats and sheep were sheltered within the family households such as their kitchens. Sixty five percent of the respondents in Western Kenya and 35% in Southeast Uganda generally treated their livestock. The most commonly used trypanocides was Veriben (33. 0%), and the rest were Noviduim, Berenil and Samorin at low percentage of (0. 9%) each and the rest did not treat their livestock. The low numbers in application of chemotherapy and chemoprophylaxis was due to fact that most trypanosomiasis drugs were expensive for the farmers to afford. Respondents in Western Kenya frequently used these drugs (72. 7%) compared to Southeast Uganda (27. 3%) citing drug cost as the impediment factor and also knowledge on the benefits of the treatment to human health. It was noted that other diseases such as tick-borne diseases and worms strongly constrained livestock husbandry at the Kenya and Uganda transboundary. The respondents mainly watered their livestock from twelve noon to three o' clock in the evening (90. 2%) and from nine to eleven o' clock (6. 1%) (fig. 5). Five major watering livestock methods were identified namely, rivers and streams, boreholes, wells, piped water, protected spring and fetched water from rivers. In Kenya, the rivers or stream was the most preferred while in Uganda the boreholes and wells were better preferred. At the HAT affected villages it was found that threefold more respondent fetched river water compared to unaffected villages. It was also observed that wildlife, livestock and human shared watering points in both countries. Livestock markets were not well established in Southeast Uganda compared to Western Kenya. In Busia (Ug) and Tororo (Ug) Districts in Southeast Uganda, farmers bought livestock from far markets and kept them in their homesteads for local re-selling within their homesteads. The major livestock markets for the respondents (32. 7%, n = 251) in both countries were Funyula, Amukura (21. 1%), Angurai (13. 7%) in Kenya. The other markets were Pasindi (4. 5%), Busaba (3. 5%), Akapa (3. 0%), Busolwe (2. 5%) and the rest of the markets were visited by less than 2% of the respondents. Residents in the study area bought livestock from either country. In addition, some few respondents in both countries traveled to other districts such as Kumi (3. 6%), Soroti Districts (1. 8%) in Uganda and Bungoma District (0. 5%) in Kenya to purchase livestock. The outcome of WHO' s committee on the social determinants of health has given an impetus to the consideration of social factors as crucial determinants of diseases [7], [42]. HAT, a neglected tropical disease presents special technological-based intervention challenges which are complicated with intricate socio-cultural issues. Basically, the transmission of HAT requires interaction of the human host, trypanosome, animal reservoirs, tsetse vectors, abiotic, and biotic environment. The major tribe of the respondents in Teso (Ke) and Busia (Ke) Districts study villages was Ateso (50. 3%), followed by the Abaluhya (47. 3%) tribe, other tribes such as were low (0. 4%) as indicated in the results. In Busia (Ug) and Tororo (Ug) Districts selected villages main tribes were Abaluhya (59. 8%), Adhola (Luo) (17. 8%) and Ateso (13. 7%). A number of factors combine to explain the higher occurrence of HAT in Uganda than Kenya. Kenya had more respondents with primary, secondary and tertiary level of education compared their Ugandan counterparts in the selected villages. There were proportionally a higher number of farmers in Southeast Uganda compared to Western Kenya study villages. The results from this cross-sectional study further showed that tsetse flies were recognized by majority of respondents as important vectors of HAT in the study districts. However, some respondents did not know the vectors that transmit HAT especially in Southeast Uganda, while others mentioned vectors not specifically related to HAT (e. g. mosquitoes). Basic knowledge and understanding of the main biology, habitat, hosts and control methods of the main tsetse vectors in Southeast Uganda (Glossina fuscipes fuscipes) and Western Kenya (Glossina pallidipes) is a prerequisite for sound management of the fly population. In Western Kenya more respondents (61%) knew more about tsetse and trypanosomiasis control methods compared to residents of Busia and Tororo Districts' , Southeast Uganda (39. 2%) therefore vector and trypanosomiasis control may be more in Kenya than in Uganda. Compared to Uganda, Kenya has had more application of bush clearing and live bait technology interventions in addition to chemoprophylaxis and chemotherapeutic measures over the years as reported in this study. Previously, it has been shown that farmers exposed to trypanosomiasis messages significantly had higher knowledge than those in the control areas or those not exposed to the messages [41], [43]. Busia (Ke) and Teso (Ke) Districts in Busia County, Western Kenya have particularly had concerted efforts to increase farmers' awareness of trypanosomiasis through initiating community education by use of posters and drama [41]. The respondents reported that male adults (28. 4%) were at higher risk of contracting the disease followed by female adults (22. 9%), and the children had low tsetse interactions due to their daily activities which corresponded with the actual analyzed gender HAT risk as was documented in this study finding. Herding livestock contributed the highest risk to tsetse interaction reporting 51. 8% and 31. 1% in Teso and Busia Districts in Busia County, Western Kenya and Busia and Tororo Districts, Southeast Uganda respectively. Social-cultural activities including bathing at the rivers and stream, fetching firewood and fishing; and occurrence of conducive woody vegetation especially in Uganda were important contributing factors to HAT occurrence in consonant with previous studies [10], [37]–[39], [41], [44]. In Teso and Busia Districts in Busia County, Western Kenya and Busia and Tororo Districts, Southeast Uganda, the most risky cultural activities were cleansing rituals contributing 45. 5% and 58. 0% respectively. Southeast Uganda compared to Western Kenya had higher HAT risk due to circumcision explained by the different ethnic composition of the study area in which circumcision rite was practiced by the Luhia communities such as Bagisu unlike in Kenya where they have been assimilated by the neighboring communities, therefore not practicing the rite with the exception of the Luhia Bukusu sub-tribe. The males, who engaged more in circumcision and cleansing rituals, and herding of animals which are performed in tsetse conducive habitats and for long durations, were more at risk in concord with known fact about the influence of human behavior on the epidemiology of a number of pathogens [39], [45]. Children had lower risks for the same reason. Farming and herding were perceived as the most risk occupation while ritual cleansing in agreement with Kokwaro et al [10] and circumcision ranked highly as risk factors of HAT acquisition. Although male circumcision was not practiced majority of residents (Luo, Adhola, Teso and Samia tribes) respondents perceived it as a cause of increased HAT risk. Land use and land cover has immense influence on the occurrence of the tsetse flies and wildlife carriers of trypanosomiasis [29], [30]. The reason for this difference in LULC cover in a region of similar climatic zone can be explained by the past differences in management of politics, economies and tsetse and trypanosomiasis control efforts of the two states. The density of vector population and proportion of the infectious vector determines the vector load and therefore the potential per capita risk burden on host [46]. The type of agriculture especially application of pesticides have strongly influenced HAT occurrence in the study area [41], [47]–[49]. Notably, cotton and tobacco farming areas had lower reported cases of HAT during the peak periods of the crops' farming (1970' s), a trend that changed when, especially, cotton was abandoned and the farms invaded by Lantana camara bushes [50]. This coincided with HAT epidemic from 1976 to the early 1990s in Uganda. However, the presence of cassava as a crop type around the homestead has been significantly associated with HAT [39]. According to reports in 2002 Busia (Ug) District in Uganda had 37. 9% and 55. 5% of households growing maize and cassava, respectively [51]. During the dry seasons cattle are driven along the rivers and in the swampy area to graze which may increase the risk of livestock trypanosomiasis and human HAT due the close human-livestock-tsetse contact. Southeast Uganda also reported high livestock numbers compared to Western Kenya hence more HAT risk in the latter [6] explained by more tsetse fly catches in the traps [33], [34]. This has also been further supported by studies that demonstrate a positive spatial correlation of total cattle population and HAT occurrence in Western Kenya [6]. Zymodemes from domestic animals such as the cow and the pig have been found to be identical to those in man [52]. Pigs and cattle in the study area are known carriers of Trypanosoma brucei brucei and human infective T. b. r and the infection in domestic animals was not necessarily associated with signs of disease [31], [32], [35], [53]–[55]. Application of chemotherapeutic and chemoprophylaxis was hampered by the costs of drug and was reported to be low at 6. 7% and 2. 1% for Kenya and Uganda, respectively. Pest control targeting another important disease vector, the tick, has concomitantly controlled the HAT vectors. Successful installation of public dips and later increased private initiatives to install dips or spray livestock with acaricides ranging from dichlorodiphenyltrichloroethane (DDT), dieldrin and cypermethrin to organophosphates to amitraz in both countries in the 1980s and 1990 saw corresponding reduction in tsetse population. Water sources for both livestock and domestic use may influence interaction among vectors and hosts. Other studies have reported that watering sites as major transmission foci of HAT [56]. Bathing or drawing water in the rivers and streams influenced HAT acquisition due to increased vector-human-wildlife-livestock interactions as reported by the respondents. If the tsetse fly bites an infected wildlife or domestic livestock HAT reservoir, it will transmit the disease to human beings during vector-human-wildlife –domestic animals interactions at water points. However, more studies needs to be carried out at the local scale at the transboundary to determine the impacts of watering management practices on HAT infection. Livestock mobility has escalated in volume and speed between and within countries as the human population increased. Long distance trade of livestock facilitates the geographic redistribution of disease hosts and pathogens especially in Busia (Ug) and Tororo (Ug) Districts, Southeast Uganda where few markets for selling livestock existed than in Busia (Ke) and Teso (Ke) Districts in Busia County, Western Kenya with many established livestock markets. Cattle husbandry practices and marketing may affect the spread of HAT disease as reported in the study findings which showed that cattle increased HAT prevalence. Also the livestock congregation in markets (Kenya) and homestead (Uganda) may lead to more dissemination than isolated or less connected foci as farmers hold infected animals hosting human infective parasites. Occasional sale of animals across the boundary of the countries may lead transfer of HAT reservoirs between the countries, a possible explanation for the isolated case of HAT in 2007 in Kenya at an area that lacked conducive environment for tsetse fly. During conflicts or difficult socio-economic and political times, vector control, active screening and treatment of potential HAT livestock reservoirs in order to eliminate circulating parasites are not undertaken [57]. Also farming activities which removes tsetse habitats has not been intensified in Uganda (29. 4%) due to the civil unrest compared to Kenya (58. 2%) [44]. In Western Kenya there have been continuous efforts to eradicate tsetse in the past three decades. Kenya has had a well-established socio-economic unit at KARI-TRC (formerly KETRI) which interacts with the communities in order to create awareness and transfer of knowledge and technology aimed at controlling HAT. At KARI-TRC the research teams apply the multipronged approach for tsetse and trypanosomiasis control where biological, economic, socio-cultural and environmental issues are considered in addition to engaging community participation. HAT has consequently been reduced through the targeted multi-disciplinary approach by KETRI, Kenya government, relevant stakeholders and donors. It is no longer considered a significant public health problem. However in Southeast Uganda, T. b. rhodesiense epidemics have been associated with civil strife [5] when collapse of essential services such as health, veterinary and vector control have occurred. In the past decade the concerted efforts of the Ugandan government and donor programs such as Farming In Tsetse Controlled Areas (FITCA) program, Pan African Tsetse and Trypanosomiasis Eradication Campaign (PATTEC) in conjunction with other stakeholders in old HAT endemic foci reduced HAT cases to minimal numbers. However, HAT has spread to virgin foci away from the traditional areas in Southeast Uganda. This study found that the differences in the social behavior of the communities who are similar in many respects but have experienced differences in national socio-politics influenced tsetse and trypanosomiasis control policy formulation and action. This explained the higher number of HAT cases in Uganda than Kenya. Otherwise, the communities across the borders are similar with regards to cultural practices, climatic and environmental living conditions, income, and literacy (measured as acquisition of basic education). HAT occurrence was found to be directly related to contacts between the study population and tsetse habitats so that socio-economic and cultural activities which entailed closer proximity to the tsetse enhanced the disease (Table 1) [10]. This study also demonstrated that increased cattle and pigs enhanced HAT occurrence as indicated by other studies. In addition lack of established livestock markets in, Southeast Uganda where cattle traders held their commodities (mostly untreated) within their homesteads for long periods before being re-sold exacerbated HAT risks compared to Kenya. Hence this study encourages multi-stakeholder participation in tsetse and trypanosomiasis control of both concerned communities, ministries of livestock and health to effectively manage HAT. Continuous and systematic approach to tsetse and trypanosomiasis control eliminates HAT as demonstrated by Kenya. Kenya has had much less occurrence of HAT through the application of simple methods such as habitat destruction, treatment and live bait technology using synthetic pyrethroids reduced or eliminated tsetse, circulating parasites in both the vectors and animal carriers. We conclude that control and prevention of HAT disease requires adequate knowledge of interactions among factors such as human behavior, the environment, and the life cycles of parasites. Therefore, there is need to educate communities to bring changes on their belief, socio-economic and cultural practices to protect themselves from tsetse bites using available effective and long term sustainable tsetse and trypanosomiasis control methods. There is need for national health services to include tsetse and trypanosomiasis programs for systematic and continuous control efforts targeting the vector and the parasites causing disease. This study recommends that a place with similar culture, tribe, climate, physical features, historical can have considerable difference in epidemiology of disease that was attributed to the socio-economic and political differences. Therefore socio-political and economic environment play a big in the management of neglected tropical diseases.

The prevention and control of Human African Trypanosomiasis depends on application of knowledge and appropriate technologies to modify tsetse ecology by community members and relevant stakeholders. In our study Ugandan districts reported more T. b. r. than the neighboring districts in Kenya in the past 30–50 years. Historical, political, social and economic factors have influenced the ecology of HAT vectors and its prevalence in both Western Kenya and Southeast Uganda. The villagers' participation in different conventional and traditional methods of tsetse control influenced HAT occurrence and distribution in the study area. Cattle husbandry practices and marketing may affect the spread of HAT disease as reported in Western Kenya and Southeast Uganda. Land use had immense influence on the occurrence of the tsetse flies and wildlife carriers of trypanosomiasis. The modification of vegetation cover through bush clearing reduced habitats suitability to tsetse flies. The respondents' occupation and gender roles played a role in influencing the interaction between humans and tsetse flies. Human African trypanosomiasis could be controlled effectively by modifying both physical and socio-political factors that affect the interaction of tsetse flies and human beings.

Abstract Introduction Materials and Methods Results Discussion

social and behavioral sciences veterinary science

2013

Socio-Economic and Cultural Determinants of Human African Trypanosomiasis at the Kenya – Uganda Transboundary

The advent of Next-Generation Sequencing (NGS) technologies has opened new perspectives in deciphering the genetic mechanisms underlying complex diseases. Nowadays, the amount of genomic data is massive and substantial efforts and new tools are required to unveil the information hidden in the data. The Genomic Data Commons (GDC) Data Portal is a platform that contains different genomic studies including the ones from The Cancer Genome Atlas (TCGA) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiatives, accounting for more than 40 tumor types originating from nearly 30000 patients. Such platforms, although very attractive, must make sure the stored data are easily accessible and adequately harmonized. Moreover, they have the primary focus on the data storage in a unique place, and they do not provide a comprehensive toolkit for analyses and interpretation of the data. To fulfill this urgent need, comprehensive but easily accessible computational methods for integrative analyses of genomic data that do not renounce a robust statistical and theoretical framework are required. In this context, the R/Bioconductor package TCGAbiolinks was developed, offering a variety of bioinformatics functionalities. Here we introduce new features and enhancements of TCGAbiolinks in terms of i) more accurate and flexible pipelines for differential expression analyses, ii) different methods for tumor purity estimation and filtering, iii) integration of normal samples from other platforms iv) support for other genomics datasets, exemplified here by the TARGET data. Evidence has shown that accounting for tumor purity is essential in the study of tumorigenesis, as these factors promote confounding behavior regarding differential expression analysis. With this in mind, we implemented these filtering procedures in TCGAbiolinks. Moreover, a limitation of some of the TCGA datasets is the unavailability or paucity of corresponding normal samples. We thus integrated into TCGAbiolinks the possibility to use normal samples from the Genotype-Tissue Expression (GTEx) project, which is another large-scale repository cataloging gene expression from healthy individuals. The new functionalities are available in the TCGAbiolinks version 2. 8 and higher released in Bioconductor version 3. 7. Cancer is among the leading causes of mortality worldwide. It is a complex disease where multiple different mechanisms are at play all at once. This complexity also arises from the fact that cancer is extremely heterogeneous and can exist in distinct forms where each cancer type or subtype can be characterized by different molecular profiles with possible consequences on treatment and prognosis for the patient [1,2]. Advances in next-generation sequencing are currently making a massive amount of data available via the profiling of samples from cancer patients [3–7]. In this context, numerous large-scale studies have been conducted using state-of-the-art genome analysis technologies. One of the most important examples is The Cancer Genome Atlas (TCGA), which started in 2006 as a pilot project aiming to collect and conduct analyses on an unprecedented amount of clinical and molecular data including over 33 tumor types spanning over 11,000 patients. This project has subsequently generated more than 2. 5 petabytes of publicly available data over the past decade [8,9]. Publicly funded by The National Institute of Health (NIH), TCGA has made numerous discoveries regarding genomic and epigenomic alterations that are candidate drivers for cancer development. This was achieved through the creation of an" atlas" and by applying large-scale genome-wide sequencing and multidimensional analyses. These efforts have significantly contributed to high-quality oncology studies, either led by the TCGA research network or other independent researchers [10], which recently culminated in 27 original publications from the Pan-Cancer TCGA initiative [11]. In 2016, TCGA was moved under the umbrella of the broader repository Genomic Data Commons (GDC) Data Portal [12] together with other studies. TCGA offers two versions of public data: legacy and harmonized. The legacy data is an unmodified collection of data that was previously maintained by the Data Coordinating Center (DCC) using GRCh36 (hg18) and GRCh37 (hg19) as genome reference assemblies. On the other hand, the harmonized version provides data that has been fully harmonized using GRCh38 (hg38) as a reference genome available through the GDC portal. Many tools have been developed to interface with TCGA data [13–25] and to help with the aggregation, pre- and post-processing of the datasets. Among them, TCGAbiolinks was developed as an R/Bioconductor package to address the challenges of comprehensive analyses of TCGA data [19,20,26]. Software packages such as TCGAbiolinks regularly require enhancements and revisions in light of new biological or methodological evidence from the literature or new computational requirements imposed by the platforms where the data are stored. For example, it is well-recognized that the tumor microenvironment also includes non-cancerous cells of which a large proportion are immune cells or cells that support blood vessels and other normal cells [27,28]. These components can ultimately alter the outcome of genomic analyses and the biological interpretation of the results. Recently, an extensive effort was made to systematically quantify tumor purity with a variety of diverse methods integrated into a consensus approach across TCGA cancer types [29], which the tools for analyses of TCGA data should employ. Other cancer genomic initiatives have been following the TCGA model, such as Therapeutically Applicable Research to Generate Effective Treatments (TARGET), which is an NCI-funded project conducting a large-scale study that seeks to unravel novel therapeutic targets, biomarkers, and drug targets in childhood cancers by comprehensive molecular characterization and understanding of the genomic landscape in pediatric malignancies [30]. Comprehensive support for the analyses of different genomic datasets with the same workflow is thus essential for both reproducibility and harmonization of the results. Lastly, it is common practice to use adjacent tissue showing normal characteristics at a macroscopic or histological level as a control. This advantageous practice concerning time-efficiency and reduction of patient-specific bias is based on the assumption that these samples are truly normal. Nevertheless, a tissue that is in the vicinity of or adjacent to a highly genetically abnormal tumor is likely to show cancer-related molecular aberrations [31], biasing the comparison. Moreover, circulating biomolecules, originating from cancer cells, can be taken in by the surrounding normal-like cells and alter their gene expression and processes. TCGA includes non-tumor samples from the same cancer participants. Furthermore, the pool of TCGA normal samples is often limited or lacking in TCGA projects. In this context, initiatives such as Recount [32], Recount2 [33] and RNASEQDB [34] where TCGA data were integrated with normal healthy samples from the Genotype-Tissue Expression (GTEx) project [35] have the potential to boost the comparative analyses especially for those TCGA datasets where normal samples are underrepresented or unavailable. In light of recent discoveries on the impact of tumor purity quantification on the samples under investigation [29], the need for a more substantial amount of normal samples [33], as well as the implementation of robust and statistically sound workflows for differential expression analyses [36,37] and exploration of potential sources of batch effects [38], we present new key features and enhancements that we implemented in TCGAbiolinks version 2. 8 and higher. For the sake of clarity, we will briefly introduce the main functions of TCGAbiolinks that are extensively discussed in the original publication and a recently published workflow [19,20]. We advise referring directly to these publications and to the vignette on Bioconductor for more details about the basic functionalities. The data retrieval is handled by the three main TCGAbiolinks functions: GDCquery, GDCdownload and GDCprepare and allows the user to interface with three main platforms: i) TCGA, ii) TARGET and, iii) The Cancer Genome Characterization Initiative (CGCI) (https: //ocg. cancer. gov/programs/cgci). TCGAbiolinks also allows the user to interface with different -omics data including genomics and transcriptomics, clinical and pathological data, information on drug treatments, and subtypes. GDCprepare allows the user to prepare the gene expression data for downstream analyses. This step is done by restructuring the data into a SummarizedExperiment (SE) object [39] that is easily manageable and integrable with other R/Bioconductor packages or just as a dataframe for other forms of data manipulation, which the user can operate even decoupled from the TCGAbiolinks package. Moreover, TCGAbiolinks offers the option to apply normalization methods with the function TCGAanalyze_Normalization adopting the EDASeq protocol [40], to apply between-lane normalization to adjust for distributional differences between samples or within-lane normalization (to account for differences in GC content and gene length). To guide result interpretation, the TCGAvisualize function allows the user to generate the plots required for a comprehensive view of the analyzed data using mostly the ggplot2 package that has incremental layer options (such as principal component analysis, pathway enrichment analysis etc.) [41]. We extended TCGAbiolinks with new functionalities and methods that could boost the analyses of genomic data while at the same time not necessarily limiting these functionalities to just the TCGA initiative. TCGAbiolinks was initially conceived to interact with TCGA data, but the same workflow could be in principle extended to other datasets if the functions to handle their differences in formats and data availability are properly handled. Thus, we worked to support the SE format for other GDC datasets, such as the ones from the TARGET consortium which is included in TCGAbiolinks version 2. 8. The SE object provides the advantage of collecting clinical information on the samples (such as patient gender, age and treatments) and on genes (ENSEMBL and ENTREZ IDs). One of the major problems in the study of genomic data is that they are often stored in unconnected silos which can lead to the of stalling of advancements in the analyses [42]. The design of the GDCprepare function of TCGAbiolinks thus nicely fulfills the need for standardized and harmonized ways to process data from different genomics initiatives which could find common storage in the GDC portal. Moreover, we provide the possibility to integrate data from external sources and carry out joint analyses with the GDC dataset (see the new TCGAbatch_correction function below). High-throughput sequencing and other -omics experiments are subject to unwanted sources of variability due to the presence of hidden variables and heterogeneity. Samples are processed through different protocols, depending on the practices followed by each independent laboratory, involving time factors and multiple people orchestrating the genomic experiments. Known as batch effects, these sources of heterogeneity can have severe impacts on the results by statistically or biologically compromising the validity of the research [38,43,44]. Here, we created the TCGAbatch_Correction function to address and correct for different potential sources of batch effects linked to TCGA gene expression data using the sva package in R [38]. The sva package provides a framework for removing artifacts either by (i) estimating surrogate variables that introduce unwanted variability into high-throughput, high-dimensional datasets or (ii) using the ComBat function that employs an empirical Bayesian framework to remove batch effects related to known sources [44]. Modeling for known batch effects significantly helps to improve results by stabilizing error rates and reducing dependence on surrogates. In this context, TCGAbatch_Correction takes GDC gene expression data as input, extracts all the needed metadata by parsing barcodes, corrects for a user-specified batch factor, and also adjusts for any selected cofactor. In cases where the investigator is not interested in correcting for batch effects with ComBat or this step is discouraged for the downstream analyses, the voom (an acronym for variance modeling at the observational level) transformation can be applied to carry out normal-based statistics on RNA-Seq gene counts [36] (see below). The TCGAbatch_Correction function also generates plots to compare the parametric estimates for the distribution of batch effects across genes and their kernel estimates. Moreover, the so-called Q-Q plots can be produced showing the empirical data of ranked batch effects on each gene compared to their parametric estimate. Before applying batch effect corrections, one should investigate if there is any evidence of extreme differences between the kernel and the parametric estimates. Such differences can show up as bimodality or severe skewness and are due to the inability of the parametric estimation to pick up the empirical kernel behavior (an example is provided in the case study on breast cancer below and is discussed in Fig 1). Additionally, to make TCGA data useful in a broader context, we included the possibility of integrating data from external sources or unpublished data in the context of publicly available datasets such as the ones in the GDC portal. To reach this goal, we have provided the possibility within the TCGAbatch_Correction function to integrate gene expression data from external sources (e. g GEO or unpublished datasets) and obtain a merged dataframe that can be used for further analysis within the TCGAbiolinks pipeline such as differential expression analysis. Nevertheless, we recommend the user to proceed with extreme caution with regards to the downstream analyses and to include the proper steps for batch corrections and harmonization of the data when they come from different sources. It is also important to rely on data that have been collected with the same technique and possibly the same instrument. We provide an example for illustrative purposes only to handle the integration of datasets from external sources with TCGA data. The TCGAbatch_Correction function can be used to correct the integrated data for a common batch factor. In this example, we integrated the TCGA Lung Adenocarcinoma (LUAD) with the GEO dataset GSE60052 [45] where RNA-seq data are available for 79 samples from Small Cell Lung Cancer (SCLC) tissues and 7 normal controls. We restricted our analysis to only tumor samples in both datasets since there were no clear annotations for the normal samples on the GEO dataset. We queried, downloaded, and pre-processed the TCGA-LUAD data according to the workflow used in case study 1 and 2 (see below). We log2-transformed the TCGA data to make them comparable with the GEO data, which were released as log-transformed values. We decided to correct the data according to the year when the sample was taken since it is the only factor in common and a suitable candidate to correct for technical variability in this example. We retrieved the sample year from the downloaded TCGA clinical data using the GDCquery_clinic function. The GEO clinical data has been released as supplementary material to the original publication (Table S1 in [45]). In particular, we selected all the tumor samples taken from 2010 to 2012 (three batches in total) in both datasets. We also ensured that more than one sample was available for each batch. The tumor samples which fulfilled the chosen batch criterion were 50 and 21 in TCGA and GEO, respectively. Since TCGA includes 17400 and GEO 15711 genes, we selected only the features in common (15711) by converting the TCGA Ensembl IDs to gene names using the information stored in the SummarizedExperiment object, retrieved through the rowData function. We then merged the two datasets and created the corresponding batch information. This information was then provided as input to the TCGAbatch_Correction function to produce the integrated year-corrected matrix. The script to reproduce this example is available in the GitHub repository associated to this publication (https: //github. com/ELELAB/TCGAbiolinks_examples). We would like to stress the fact that this is just an example to show how the function works. In a real case study, the best course of action would be to process the external (GEO) data and the TCGA data through the same pipeline, starting from the external raw data and calculating the read count as it is done in the harmonized or legacy version of the TCGA data, depending on the dataset of interest for the comparison. Although each cancer is believed to be a single disease, advances in the genomic field now indicate that each cancer type is much more heterogeneous than previously thought and that different subtypes can be identified. Bioinformatics applied to genomics data can enable a molecular understanding of the tumors across different cancer subtypes. Instead of binning all cases and patients into a single category, differentiating the intrinsic subtypes of each cancer has provided efficient, targeted, treatment strategies and prognoses. Cancer subtypes can be defined according to histology or molecular profiles. Tables with general annotations from the TCGA publications on classifications of the patients are provided by the TCGAquery_subtype function [19]. However, the format of these data is not so easy to navigate or integrate within other functions. For this reason, we designed a new function TCGA_MolecularSubtype to retrieve information on manually curated molecular subtypes for a total of 24 cancer types (Table 1). Collectively, we have molecular subtype annotations for 7734 individuals. The function also allows fetching of the subtype information not only for each cancer type, but also for each TCGA barcode (i. e. for each individual sample). The information used to classify cancer subtypes is the one used (and most recently published) by the Pan-Cancer works from the TCGA consortium (http: //bioinformaticsfmrp. github. io/TCGAbiolinks/subtypes. html#pancanceratlas_subtypes: _curated_molecular_subtypes). As an alternative, there is also the PanCancerAtlas_subtypes function. These new functions have the advantage that the data are manually curated from each TCGA cancer type marker paper and are thus up to date when a new paper from the TCGA research network is published and reported in https: //gdc. cancer. gov/about-data/publications. Recently, we showed the advantage of using these functions to have a curated matrix in one single place for all of the subtypes. In particular, it has been applied to identify associations between molecular subtypes and the stemness index [46] and the immune subtypes [47] of TCGA samples. The tumor microenvironment encompases cellular and non-cellular units that play a critical role in the initiation, progression, and metastasis of the tumor [27,29,48–50]. An important concept to remember from the TME definition is that tumor purity is described as the proportion of carcinoma cells in a tumor sample. In previous times, tumor purity used to be estimated through visual inspection with the assistance of a pathologist and by image analysis. Nowadays, with the advent of computational methods and the use of genomic features such as somatic mutations, DNA methylation, and somatic copy-number variation (CNV), it is feasible to estimate tumor purity [27]. To account for tumor purity in the TCGAbiolinks workflow, we designed the TCGAtumor_purity function that filters data according to one of the following five methods: i) ESTIMATE (Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data) [49]; ii) ABSOLUTE to infer tumor purity from the analysis of somatic DNA aberrations [50]; iii) LUMP (Leukocytes Unmethylation) that uses the average of 44 detected non-methylated immune-specific CpG site; iv) IHC, that uses hematoxylin- and eosin–stained slides, provided by the Nationwide Children’s Hospital Biospecimen Core Resource, which are processed using image analysis techniques to generate a tumor purity estimate; v) Consensus measurement of Purity Estimation (CPE), a consensus estimate from the four methods mentioned above [29]. CPE is calculated as the median purity level after normalization of the values from the four methods and correcting for the means and standard deviations and it is the default option of the TCGAtumor_purity function. We revised and expanded the pre-existing TCGAbiolinks function TCGAanalyze_DEA that performs differential expression analysis (DEA) by calling the commonly used R package, edgeR [37]. In the former version of TCGAbiolinks, only a pairwise approach (for example, control versus case) was applied to a matrix of count data and samples to extract differentially expressed genes (DEGs). More specifically, the former TCGAanalyze_DEA function implemented two options: (i) the exactTest framework for a simple pairwise comparison or (ii) the GLM (Generalized Linear Model) where a user faces a more complex experimental design involving multiple factors. However, in the latter case, the design of the function allowed the user to provide arguments only for case and control thereby being incompatible with multifactor experiments, for which GLM methods are particularly suited [51]. We thus implemented a different design to improve the functionality of TCGAanalyze_DEA by providing the ability to analyze RNA-Seq data in a more general and comprehensive way. The user is now able to apply edgeR with a more sophisticated design matrix and to use the limma-voom method, an emerging gold standard for RNA-Seq data [52]. Furthermore, modeling multifactor experiments and correcting for batch effects related to TCGA samples is now an option in the updated version of TCGAanalyze_DEA. The new arguments for the function allow to use different sources of batch effects in the design matrix, such as the plates, the TSS (Tissue Source Site), the year in which the sample was taken and the patient factor in the cases of paired normal and tumor samples. Moreover, an option is provided to apply two different pipelines to the study of paired or unpaired samples, namely limma-voom and limma-trend pipelines. A contrast formula is provided to determine coefficients and design contrasts in a customized way, as well as the possibility to model a multifactor experimental design. In particular, the model formula for the edgeR pipeline is designed so that the intercept is set to 0 when there are multiple conditions (such as the molecular subtypes) or contrasts to be explored, following the recommendation of edgeR developers. The function returns two types of objects: i) a table with DEGs containing logFC, logCPM, p-value, and FDR corrected p-values in cases of pairwise comparison for each gene, and/or ii) a list object containing multiple tables for DEGs according to each contrast specified in the contrast. formula argument. The Recount project was created as an online resource that comprises gene count matrices built from 8 billion reads using 475 samples gathered from 18 published studies [32]. This atlas of RNA-Seq count matrices improves the process of data acquisition and allows cross-study comparisons since all of the count matrices were produced from one single pipeline reducing batch effects and promoting alternative normalization. Recount was then extended to Recount2 consisting of more than 4. 4 trillion reads using 70,603 human RNA-seq samples from the Sequence Read Archive (SRA), GTEx, and TCGA that were uniformly processed, quantified with Rail-RNA [51], and included in the recent Recount2 interface [33]. For this reason, TCGAquery_recount2 queries GTEx and TCGA data for all tissues available in the Recount2 platform, providing the user with the flexibility to decide which tissue source to use for the calculations. TCGAquery_recount2 integrates normal samples from GTEx and normal samples from TCGA. If the user wants to use GTEx alone as a source of normal samples, an ad hoc curation of the dataset will be needed before applying the functions for pre-processing of the data and downstream analyses with TCGAbiolinks. Below, we illustrate two case studies as an example of the usage of the new functions and the interpretation of their results. The TCGA Breast Invasive Carcinoma (BRCA) dataset is the ideal case study to illustrate the new functionalities of TCGAbiolinks (see Fig 2 for a workflow illustrating this case study and the new functions). We carried out the query, download and pre-processing of the TCGA-BRCA RNA-Seq data through the GDC portal with a variation of the workflow suggested for the previous versions of the TCGAbiolinks software (see the script reported in https: //github. com/ELELAB/TCGAbiolinks_examples). As an example, out of a possible 1222 BRCA samples available in the GDC portal, we restricted our analysis to 100 tumor (TP) samples and 100 normal (NT) samples respectively. We constructed the SE object as the starting structure displaying information for both genes and samples with gene expression tables of HTSeq-based counts from reads harmonized and aligned to hg38 genome assembly. Afterwards, we applied an Array Array Intensity correlation (AAIC) to pinpoint samples with low correlation (0. 6 threshold for this study) using TCGAanalyze_Preprocessing, which generates a count matrix ready to be used as input for the downstream analysis pipeline. In addition, we normalized the gene counts for GC-content using TCGAanalyze_Normalization adopting EDASeq protocol incorporated with TCGAbiolinks. An exploratory data analysis (EDA) step is now possible within TCGAbiolinks to help to understand the quality of the data and to identify possible anomalies or cofounder effects. This can be done by estimating the presence of batch effects through the plots provided by the ComBat function, as described above. We can call the TCGAbatch_Correction function on a log2 transformed instance of the count matrix. For the sake of clarity, we used batch correction on TSS as a cofounder factor along with accounting for one covariate (cancer versus normal) and only two batches were retained. The results are reported in Fig 1. According to the standard defined by the TCGA consortium, 60% tumor purity is the recommended threshold for analyses [29]. Thus, we applied a filtering step using the TCGAtumor_purity function of TCGAbiolinks whereby tumor samples that show a purity of less than 60% median CPE are discarded from the analysis. As a result, a total of 26 samples were discarded with the goal of reducing the confounding effect of tumor purity on genomic analyses. We then applied the new TCGAanalyze_DEA function to exploit the power of generalized linear models beyond the control versus case scheme. As an illustrative case, we queried the PAM50 classification [52] for each of the samples through TCGA_MolecularSubtype. We identified 86 samples with information on subtypes. The output is then provided to the DEA method so the customizable contrast. formula argument can contain the formula for designing the contrasts. Beforehand, the data is normalized for GC-content, as explained above. As a final step, quantile filtering is applied with a cutoff of 25%, as suggested by the original TCGAbiolinks workflow. Within the TCGAanalyze_DEA function, it is also possible to perform a voom transformation of the count data, as detailed above. In Fig 3A, we show the results of the new implementation of the TCGAanalyze_DEA function as a volcano plot. The genes with highest logFC are shown (using logFC higher or lower than 6 in absolute value as a cutoff). We then compared these results to the ones produced using DEA as implemented in edgeR within the TCGAanalyze_DEA (see volcano plot in Fig 3B). We calculated the correlation between the top 500 DE genes identified by the two methods (Fig 3C) which resulted in a Pearson Correlation Coefficient higher than 0. 9. We then quantitatively compared the results of the two methods calculating the intersect with UpSetR [53] (Fig 3D). The two methods are in good agreement showing 1629 and 1365 down- and up-regulated genes in common, which account for approximately 90% of the total DE genes. With both methods we identified up-regulated matrix metalloproteinases (such as MMP11 and MMP13) which are a class of enzyme known to be involved in cancer invasion and metastasis and have been linked to breast cancer outcomes [54]. We also identified different collagen proteins (such as COL10A1 and COL11A1) that are up-regulated in luminal versus normal breast cancer samples. Those proteins are important for the composition of the extracellular matrix (ECM). Changes in the amount or composition of the ECM have been considered a hallmark of tumor development [55]. COL11A1 and COL10A1 have recently been proposed as markers to discriminate between breast cancer and healthy tissues and could be helpful in the diagnosis of suspicious breast nodules [56]. One issue that can be encountered when planning DEA of TCGA data is the fact that some projects on the GDC portal do not contain normal control samples for the comparison with the tumor samples. As explained previously, it is now possible to query data from the Recount2 platform to increase the pool of normal samples and apply the DEA pipelines of TCGAbiolinks (see Fig 4A for a workflow). For this case study, we used the TCGA Uterine Carcinosarcoma (UCS) dataset to illustrate this application. We queried, downloaded, and pre-processed the data using a similar workflow to our previous case study, and then GTEx healthy uterine tissues were used as a source of normal samples for DEA. Concerning the type of count data queried, it was similarly harmonized HTSeq counts and aligned to the hg38 genome assembly (see the script reported in https: //github. com/ELELAB/TCGAbiolinks_examples). We used the TCGAquery_recount2 function to download tumor and normal uterine samples from the Recount2 platform as Ranged Summarized Experiment (RSE) objects. Before engaging in DEA, one should keep in mind that the Recount2 resource contains reads, some of them soft-clipped, aligned to Gencode version 25 hg38 using the splice-aware Rail-RNA aligner. Moreover, the RSE shows coverage counts instead of standard read count matrices. Since most methods are adapted to read count matrices, there are some highly recommended transformations to perform before commencing with DEA. The user should extract sample metadata from RSE objects regarding read length and mapped read counts to pre-process the data. If one provides a target library size (40 million reads by default), coverage counts can be scaled to read counts usable for classic DEA methods according to Eq (1) (possibly with the need to round the counts since the result might not be of an integer type). The denominator is the sum of the coverage for all base-pairs of the genome which can be replaced by the Area under Curve (AUC) [57]. It is possible to use the function scale_counts from the recount package. After that, we merged the two prepared gene count matrices, normalized for GC-content and applied the quantile filtering with a 25% cut-off. The data were then loaded into the TCGAanalyze_DEA function for comparison of normal samples versus cancer samples using the limma-voom pipeline. Two volcano plots depicting the top up- and down-regulated genes are shown in Fig 4B and 4C, respectively. As an example, we identified the up-regulated gene ADAM28 in the UCS tumor samples when compared to the normal ones (logFC = 3. 13, thus not shown in Fig 4B). ADAM28 belongs to the ADAM family of disintegrins and metalloproteinases which are involved in important biological events such as cell adhesion, fusion, migration and membrane protein shedding and proteolysis. They are often overexpressed in tumors and contribute to the promotion of cell growth and invasion [58]. Among the top up-regulated genes in UCS, we also identified other key players in cell adhesion such as the cadherin CDH1 [58] shown in Fig 4B. The functions illustrated in this manuscript are now available in version 2. 8 of TCGAbiolinks on Bioconductor version 3. 7 (https: //bioconductor. org/packages/release/bioc/html/TCGAbiolinks. html), as well as through the two Github repositories (https: //github. com/ELELAB/TCGAbiolinks and https: //github. com/BioinformaticsFMRP/TCGAbiolinks/). In addition, we provide daily scientific advice to the Github community within the ‘issues’ forum (https: //github. com/BioinformaticsFMRP/TCGAbiolinks/issues) to solve both software bugs and to provide new functionalities needed or requested by the Github community. This forum is also a place where TCGAbiolinks users can share and discuss their experience with their analyses with our team and/or other Github users. The newly developed functions will for the first time allow users to fully appreciate the effect of using genuinely healthy samples or normal tumor-adjacent samples as a control as well as the benefits of correcting for the tumor purity of the samples. We provide a more robust and comprehensive workflow to carry out differential expression analysis with two different methods and a customizable design matrix, as well as the capability to handle batch corrections. Overall, this will provide the community with the possibility to use the same framework for vital analyses such as the benchmarking of differential expression methods. (https: //bioconductor. org/packages/release/bioc/vignettes/TCGAbiolinks/inst/doc/extension. html).

The advent of Next-Generation Sequencing (NGS) technologies has been generating a massive amount of data which require continuous efforts in developing and maintain computational tool for data analyses. The Genomic Data Commons (GDC) Data Portal is a platform that contains different cancer genomic studies. Such platforms have often the primary focus on the data storage and they do not provide a comprehensive toolkit for analyses. To fulfil this urgent need, comprehensive but accessible computational protocols that do not renounce a robust statistical framework are thus required. In this context, we here present the new functions of the R/Bioconductor package TCGAbiolinks to improve the discovery of differentially expressed genes in cancer and tumor (sub) types, include the estimate of tumor purity and tumor infiltrations, use normal samples from other platforms and support more broadly other genomics datasets.

Abstract Introduction Results

cancer genomics medicine and health sciences carcinomas cancer risk factors cancers and neoplasms basic cancer research endometrial carcinoma gastrointestinal tumors liver diseases oncology gastroenterology and hepatology genome analysis head and neck tumors medical risk factors head and neck squamous cell carcinoma epidemiology gene expression gynecological tumors hepatocellular carcinoma head and neck cancers genetic causes of cancer genetics biology and life sciences squamous cell carcinomas genomics computational biology genomic medicine

2019

New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx

Genetic exchange by a process of genome-segment ‘reassortment’ represents an important mechanism for evolutionary change in all viruses with segmented genomes, yet in many cases a detailed understanding of its frequency and biological consequences is lacking. We provide a comprehensive assessment of reassortment in bluetongue virus (BTV), a globally important insect-borne pathogen of livestock, during recent outbreaks in Europe. Full-genome sequences were generated and analysed for over 150 isolates belonging to the different BTV serotypes that have emerged in the region over the last 5 decades. Based on this novel dataset we confirm that reassortment is a frequent process that plays an important and on-going role in evolution of the virus. We found evidence for reassortment in all ten segments without a significant bias towards any particular segment. However, we observed biases in the relative frequency at which particular segments were associated with each other during reassortment. This points to selective constraints possibly caused by functional relationships between individual proteins or genome segments and genome-wide epistatic interactions. Sites under positive selection were more likely to undergo amino acid changes in newly reassorted viruses, providing additional evidence for adaptive dynamics as a consequence of reassortment. We show that the live attenuated vaccines recently used in Europe have repeatedly reassorted with field strains, contributing to their genotypic, and potentially phenotypic, variability. The high degree of plasticity seen in the BTV genome in terms of segment origin suggests that current classification schemes that are based primarily on serotype, which is determined by only a single genome segment, are inadequate. Our work highlights the need for a better understanding of the mechanisms and epidemiological consequences of reassortment in BTV, as well as other segmented RNA viruses. Reassortment is an important evolutionary process in segmented RNA viruses that can occur when two viruses (of the same species) co-infect a single host cell [1]. This provides an opportunity for their genome segments to be exchanged and packaged together into progeny viruses that are therefore genetically distinct from their parental virus strains. By combining potentially divergent genetic material, reassortment can quickly generate novel virus phenotypes, with potentially dramatic biological consequences, including an altered ability for immune escape, changes in host or vector range, changes in transmissibility and altered virulence or pathogenicity [2–6]. Although some taxa, such as influenza viruses, have received considerable attention in this respect, our understanding of reassortment, including its natural rate, evolutionary and epidemiological consequences, still remains relatively poor for most segmented viruses [1]. Members of the genus Orbivirus, within the family Reoviridae, have ten genome segments. Bluetongue virus is the prototype species of the genus and includes several viruses which are the causative agents of bluetongue (BT), a major disease of wild and domestic ruminants [7,8]. BTV is an arthropod-borne virus spread between its mammalian hosts primarily by competent species of biting midges (Culicoides spp.). It can sometimes also be transmitted via an oral route, or vertically in sheep and cattle, and some serotypes may be transmitted horizontally by direct contact [9]. The ten linear segments of double-stranded RNA (dsRNA) that comprise the BTV genome are identified as genome-segment 1 to genome-segment 10 (Seg-1 to Seg-10) in order of decreasing size. Collectively the BTV genome segments encode seven structural (VP1-VP7) and four non-structural proteins (NS1-NS4) that are expressed during virus replication in vertebrate or insect cells [10,11]. The highly variable outer-capsid protein VP2 (encoded by Seg-2) is of particular relevance because it determines the virus-serotype, which is important for selection of appropriate vaccines and represents an important component of current strain identification for BTV [12–15]. To date, 27 distinct serotypes have been characterised. In addition, BTV are further distinguished into different ‘topotypes’, including the major “eastern” (e) and “western” (w) groups, as well as several additional groups and sub-groups [16]. There is a considerable body of prior work, mainly from North America and Australia, demonstrating that BTV reassortment occurs in the field and that it can take place in the Culicoides vector as well as the ruminant host [17–20]. The earliest of these studies were not based on sequencing [21–25] and thus could not provide exhaustive information about the segments involved. Subsequent work based on sequence data confirmed earlier findings but focussed on specific segments and often involved partial sequences [26–29]. More recently, sequence data for all ten genome segments for representative isolates of the ten BTV serotypes isolated in Australia produced evidence of frequent reassortment at the genomic scale [30,31] based on a relatively small number of isolates (<30). While collectively, these data highlight the global importance of BTV reassortment [32], the lack of appropriately large genomic data sets has so far precluded a quantitative assessment of reassortment in terms of its frequency and evolutionary implications. Our recent work involving European strains of BTV-1 and BTV-8 (both western topotype) has demonstrated that all segments can reassort in infected tissue cultures and that these strains show few constraints limiting or preventing particular reassortment combinations from arising and being viable [15]. However, we found evidence that certain reassortants occur more frequently than expected by chance, suggesting that these combinations are either favoured by selection or because some genome-segments are more likely to be packaged together. Overall, these in vitro studies raise the question of how conditions in vivo, where additional constraints are imposed by antiviral immune responses and need for the virus to replicate in both the vertebrate host and the arthropod vector, restrict the diversity of reassortants arising and circulating in the field. Bluetongue virus has been documented on every continent except Antarctica. Before 1998, BTV outbreaks in Europe tended to be localised, caused by a single serotype and were usually limited to a few years duration. Examples include outbreaks in Cyprus (1943; BTV-3 and BTV-4), Turkey (1944–1947; BTV-4), the Iberian Peninsula (1956–1960; BTV-10) and Greece (1979–1980; BTV-4) [33]. However, since 1998, Europe has experienced multiple incursions caused by different BTV serotypes, as well as different topotypes and lineages within individual serotypes. This expansion in the distribution of BTV is thought to reflect a range of environmental factors, including changes in climate, range expansion of Culicoides vector species, global transport networks, and increased trade and travel [34,35]. Bluetongue incursions into Europe have occurred via several distinct routes: strains of BTV-1 (eastern topotype: e), BTV-4 (western topotype: w), BTV-9 (e) and BTV-16 (e) have all invaded the eastern Mediterranean, possibly via Turkey. Other strains of BTV-1 (w), BTV-2 (w), BTV-4 (w) and BTV-9 (w) have entered western Europe from northern Africa, either via Sicily, Italy and the western Mediterranean islands or into the Iberian peninsula from Morocco. In 2006, a strain of BTV-8 (w) [NET2006/01], (that was related to BTV-8 from Nigeria [NIG1992/07]) was detected in the Netherlands. This represented the start of the first recorded BT outbreak in Northern Europe and indicated a route of entry to the region directly from sub-Saharan Africa that did not involve step-wise progression through southern Europe or the Mediterranean region. Finally, multiple live vaccine strains (including BTV-2w, BTV-4w, BTV-9w, BTV-16e) were also used to combat disease outbreaks in southern Europe, resulting in local transmission and spread of these viruses. Subsequently BTV-6 (w), BTV-11 (w) and BTV14 (w) which are all closely related to BTV vaccine strains, were also detected in northern Europe, although their route of entry is unclear. In addition with the development of better detection and identification methods, two novel serotypes of BTV have been identified in Europe (BTV-25 and BTV-27), although it is unclear if they represent new introductions, or already existed in the region for long periods [36,37]. Information based on partial genome sequences from a limited numbers of BTV field strains, indicated that reassortment between some strains had occurred (e. g. for BTV-16 (e) in Italy 2002 or BTV-1 (w) and BTV-4 (w) in Sardinia 2012 [38–40]). Modified live-attenuated vaccines (MLVs) were only used in a few of the European countries that experienced BTV outbreaks since the 1940s. This includes Italy, where vaccines against BTV-2,4, 9 and 16 were deployed, France (Corsica) where vaccines against BTV-2,4 and 16 were used, and Spain and Portugal where vaccines against BTV-4 were used [41]. These MLVs were derived from strains isolated in South Africa, Pakistan or Australia. BTV strains that are closely related to MLVs have been shown to spread in the field, including BTV-2 [42], BTV-6 [43], BTV-11 [44] and BTV-16 [45]. In addition, MLVs are commonly used in North African and Middle Eastern countries, many of which report regular outbreaks caused by a variety of different BTV serotypes. In this study, we have investigated the role of reassortment in BTV evolution following the virus’ multiple recent incursions into Europe. While Europe was considered to be free from Bluetongue from 1980 to 1998, multiple BTV strains have co-circulated since 1998, all of which were characterised soon after introduction. Combined with the use of MLVs, some of which have also been transmitted in the field, this has generated a unique research opportunity to study BTV reassortment in vivo and to examine its effect on the evolutionary and epidemiological dynamics of BTV. We have conducted sequence analyses of >150 full BTV genomes, including multiple European field samples collected as far back as the 1950’s. This has allowed us to i) estimate the frequency of reassortment following recent European colonisations; ii) test for non-random associations of specific genome segments during reassortment; iii) examine whether reassortment triggers selectional responses in the BTV genome; iv) determine the involvement and role of MLVs in reassortment. This study uses full genome sequences that we have generated from 116 BTV isolates from Europe and the Mediterranean region and Africa countries (collected during 1958–2012) as well as 4 monovalent BTV vaccine strains, in addition to sequences of 26 BTV reference strains and other isolates and vaccines available in GenBank at the time of this study. In total, consensus sequences for individual genome segments of 178 BTV isolates for Seg-2 and Seg-6 and from 163 BTV isolates for all other segments were included in our analyses. Details of the date and location of sample collection, host species, and passage history are shown in S1 Table along with GenBank Accession numbers. Based on the most parsimonious reconstruction of segments onto Seg-2, it is clear that reassortment among BTV strains is a common event, resulting in highly admixed genomes (Fig 1). Using molecular clock calibrations, we find that the majority of reassortment events detected in our data set occurred after the mid-1990’s, coinciding with emergence of new strains and serotypes in Europe, as well as with the use of different live vaccine strains that have been applied, or have been circulating in the region (Fig 2). Up until 1998, BTV-4 was the only serotype known to be present in Europe over multiple years (Cyprus 1964–2011), although BTV-3 was previously detected in 1958, and BTV-10 caused a single outbreak in the Iberian peninsula between 1956 and 1960. According to the estimated dates associated with the corresponding tree nodes, nine reassortants emerged between 1993 and 1998 so prior to serotypes other than BTV-4 becoming established in Europe. This suggests that additional strains may have been already present during that period but escaped detection. In light of these results, we use 1993 as our lower temporal cut-off point for quantifying reassortment in Europe. Since 1993, a total of 49 reassortment events were detected in European lineages, providing an average rate of 0. 05 per genome per year. This compares to an estimated overall evolutionary rate of 3. 84 substitutions per genome per year (S2 Table) that is broadly consistent with rates previously reported for BTV [31,32]. For nine highly passaged viruses analysed (>40 passages), the original isolation dates were within the confidence limits of the ‘estimated dates’ for most samples. Based on all isolates, the number of passages did not significantly correlate with the difference in time between the estimate and the actual date of isolation, suggesting that passage in cell cultures made a negligible contribution to their molecular evolution. The frequency at which segments were involved in reassortment was variable, ranging from a single instance (Seg-2 and Seg-6) up to eleven (Seg-1). However, observed frequencies largely fell within the 95% CI of the simulated data, indicating no particular bias in terms of some segments reassorting more or less frequently than expected (Fig 3). The number of genome-segments reassorting around the same time also varied. In most cases, a single segment reassorted, but up to seven segments were exchanged at the same node in a European lineage of BTV-6 relative to Seg-2 (Figs 4–7), S1 Fig). For five serotypes or topotypes, no reassortment events were detectable within Europe (S2–S6 Figs). Taking variation in reassortment frequency into account, genome segments were found to be reassorting non-randomly overall (observed C = 0. 36955, expected C = 0. 50718, p = 0. 0125). However, none of the individual segment pairs showed significant associations after controlling the false discovery rate (Table 1). The multi-dimensional scaling plot shows that the times to most recent common ancestor (tmrca) are broadly consistent between Seg-1, Seg-3, Seg-4, Seg-5, Seg-8, Seg-9 (Fig 8). By contrast, the tmrca of Seg-2, Seg-6, Seg-7 and Seg-10 do not overlap with any of the other segments, although Seg-2 and Seg-6 are in close proximity in the two-dimensional space. This suggests that in vivo, a core set of segments (Seg-1, Seg-3, Seg-4, Seg-5, Seg-8 and Seg-9) are less permissive to be broken up by reassortment than a second set comprised of Seg-2, Seg-6, Seg-7 and Seg-10. As found in previous studies [31,32], purifying selection was by far the most dominant regime within the BTV genome with average dN/dS ratios ranging between 0. 02 and 0. 33 across the eleven open reading frames (Table 2). There was no relationship between the amount of selective constraint a segment experiences, as measured by its dN/dS ratio, and the frequency at which it reassorts (R2 = 0. 074, p = 0. 417). Small numbers of sites identified as positively selected by both the FEL and FUBAR methods were found in Seg-1, Seg-2, Seg-5, Seg-8, with the strongest signal of repeated adaptive change coming from Seg-9 (the gene encoding VP6 and NS4) where ten sites, all within VP6, were identified (Table 3). Although evidence for positive selection had been found for Seg-2, Seg-5 and Seg-9 before [32], the three codon sites previously implicated were different from the ones identified here. For Seg-9, we reconstructed changes in the ten positively selected residues along the phylogeny to determine whether non-synonymous changes took place more frequently at nodes where reassortment events had occurred. Out of the 169 nodes post-1955 assigned to European lineages in the Seg-9 tree, 8 had selection and reassortment, 15 had reassortment only, 13 had selection only and 133 had neither. According to these results, amino acid changes consistent with positive selection were about four times as common on nodes with reassortment than on nodes without (Fisher’s exact test, one-tailed, p = 0. 002). Confirming the same pattern for the post-1993 data set was not possible, due to the number of nodes with reassortment being too small for meaningful analysis in this case. To test whether reassortants are selected against over time, we compared reassortment frequency at internal and tip nodes of the phylogeny but found a similar proportion of internal nodes with reassortment (7 out of 84) compared to tip nodes (11 out of 121, Fishers exact test, p = 0. 530). To investigate whether live-attenuated vaccines have contributed to reassortment, we determined the distance between each vaccine strain and its closest genetic relative among the European field isolates. This produced repeated evidence for the reassortment and acquisition of segments from live vaccines (Table 4). For example, Seg-7 of the Spanish BTV-2 isolate [SPA2005/01], which was sampled at a time when live vaccines were being extensively used in a number of North African countries, is identical to that of a BTV-4 vaccine strain used in Southern Europe (0% uncorrected p-distance) [49]. Another three field strains were found to contain between two and six segments that closely matched known vaccine strains (Table 4). In this study, we provide a systematic assessment of BTV reassortment under field conditions that generates novel insights into the frequency, evolutionary constraints and consequences of this process. While previous studies have generated evidence of BTV reassortment, they were largely based on small, sub-genomic data sets, precluding specific insights into the frequency and patterns of association between segments. Our data confirm that genetic exchange by reassortment is a common and widespread phenomenon that, despite a time scale of only a few decades, has had a major impact on the genomic composition of European BTV strains. All lineages we sampled had undergone reassortment in their recent evolutionary past and in many cases this has taken place after viruses arrived in Europe. Given that our threshold for detection was chosen conservatively, the true extent of reasortment is likely to be even greater than documented here. While we don’t suggest that these findings are unique to Europe, our regionally focussed approach provided a particularly clear view of this process that would be hard to gain from studying areas of the world where BTV has been endemic for a long time. As a result of reassortment, the genomes of BTV field strains are genetically highly variable, including substantial heterogeneity within the same serotype. The frequency with which reassortment takes place, appears to be largely driven by opportunity, suggesting a lack of any fundamental barriers keeping different BTV strains from exchanging genetic material during natural transmission. Indeed, our findings suggest that genetic ‘mixing’ is the norm whenever multiple strains co-circulate, mirroring observations from previous in vitro experiments [15]. However, natural reassortment does not appear to be a random process in BTV, consistent with findings from other segmented viruses [50–54]. While we found no significant bias in terms of individual segments or segment pairs being more likely to reassort than others, we detected a signal of non-random associations between segments during reassortment overall. This was also reflected in the MDS plot (Fig 8), depicting correlations in the time to most recent common ancestor between segments: Seg-7, Seg-10 failed to show any clear association with other segments and there was only weak evidence for a connection between Seg-2 and Seg-6. This suggests that these four segments experience few restrictions to being placed into different genomic backgrounds, implying protein functions that involve generalised interactions with proteins encoded by other genome segments. This finding is particularly noteworthy for Seg-2, which encodes the outer-capsid protein VP2 determining BTV serotype, since it suggests that serotype may be largely uncoupled from the phenotypic variation determined by other parts of the genome. Although the outer capsid proteins VP2 and VP5 (encoded by Seg-6) and the core surface protein VP7 (encoded by Seg-7) all interact extensively, it is thought that these interactions are not highly specific [55], consistent with the weak association found here. In contrast, the remaining genome-segments (Seg-1, Seg-3, Seg-4, Seg-5, Seg-8, Seg-9) exhibit times to most recent common ancestor that are consistent among each other, suggesting physical, or biochemical interactions between their encoded proteins and consequently epistatic interactions that result in stronger evolutionary links. Some of these interactions are known: for example, VP1 (RNA-dependent RNA polymerase encoded by Seg-1), VP4 (capping enzyme including methyltransferase, encoded by Seg-4) and VP6 (RNA-dependent ATPase and helicase, encoded by Seg-9) form the viral replication complex and therefore it is reasonable that they need to coevolve in order to function optimally [56–59]. These viral enzymatic proteins are enclosed by layers of VP3 (subcore, encoded by Seg-3). The structural integrity of the core is essential for efficient transcriptional activity to be maintained [58,60]. In other cases however, the interactions suggested by our results are less well understood. Overall, these findings are broadly consistent with those reported recently for a smaller genomic data set from Australia, which also found Seg-7 and Seg-10 to evolve completely independently of the rest of the BTV genome, whereas all remaining segments showed at least some phylogenetic evidence of association with other segments. As in our work, for Seg-2 (VP2) this association was limited to Seg-6 (VP5). The observed reassortment patterns indicate that under natural conditions, some genome segment combinations must be deleterious, causing these reassortants to be removed by purifying selection. This contrasts with findings from previous in vitro work, in which we were able to generate viable viruses representing all possible genome-segment combinations between two different BTV strains (BTV-1 (w) and BTV-8 (w) ) by replacing segments one at a time [15]. Subsequent experiments with these reassortants further revealed no obvious phenotypic differences, neither with respect to in vitro growth kinetics nor for pathogenicity in an in vivo mouse model. However, more recent reverse genetics studies involving more distantly related BTV strains shows some combinations of reassortants cannot always be achieved at least using current methodology [61]. The notion that reassortment has fitness consequences for viruses circulating in the field is further supported by our findings regarding positive selection. We found evidence for adaptive evolution in a number of sites across the BTV genome, with the strongest evidence seen for ten sites in Seg-9, coding for VP6 and NS4. Following reassortment, amino acid changes were more common in these sites than would be expected by chance. This suggests that newly reassorted viruses are often under novel selective pressure, leading to adaptive genetic change that reflects the interplay of the proteins and/or RNAs derived from distinct parental origins. Our results point to physical or functional interactions between NS4, and/or VP6 and proteins encoded by other segments, consistent with the observation of Seg-9 maintaining close associations with most of the other segments (Fig 8). These findings also imply that a large proportion of reassortment changes must be detrimental, creating virus phenotypes with reduced fitness that are quickly removed from the population. They also indicate that genetic drift and shift are not entirely independent mechanisms but may be linked through functional constraints and changes in the individual RNAs and proteins that can either restrict or accommodate specific combinations of genome segments. Further experimental work, combining reverse genetics approaches with studies in animal models or arthropod vectors, will be needed to better understand this aspect of BTV biology. We show that the live-attenuated BTV vaccine strains used in Europe over the past decades have repeatedly contributed segments to circulating ‘field’ strains, as suggested by earlier reports [38,43] and as seen on other continents [27]. Our data, representing a large set of full, European-wide genomes, demonstrates further cases of reassortment with live vaccines and shows that these events so far have involved all but two BTV segments (Seg-8 and Seg-9). The ability to detect evidence for reassortment between vaccine and field strains also implies that the frequency and transmission of progeny virus strains had risen to sufficiently high levels to be readily detected. This could reflect the widespread use of live-attenuated vaccines in certain periods and geographical locations, but could also indicate that the emerging reassortant strain accrues a fitness benefit relative to its parental strains and other reassortants from the same co-infection. Regardless of whether this is true, our results demonstrate that live BTV vaccines contribute to the genotypic and phenotypic variability of naturally circulating strains. This needs to be considered during the design and implementation of control strategies. Our findings also have significant implications for BTV nomenclature and surveillance. Given the frequency at which reassortment occurs, Seg-2 (which determines virus serotype) can frequently become disassociated from the other genome segments within a specific virus lineage. Identifying BTV strains by serotype alone therefore reflects only 10% of the genome segments and does not reveal the potentially high level of genetic and phenotypic heterogeneity that exists within individual BTV serotypes. However, serotype remains an important indicator of strain relationships in antibody based neutralisation assays, and informs the choice of vaccine strain for control strategies and interventions. More generally, our work underscores the need for a nomenclature system for BTV and potentially other orbiviruses that reflects the entire genome rather than just one segment. With this in mind, using a system such as the one used in the orbivirus reference collection (www. reoviridae. org/dsRNA_virus_proteins/ReoID/BTV-isolates. htm) where virus isolates are identified individually by year, country of origin and isolate number, may be useful, so that differences between strains can be accurately recorded. Comparing our inferred reassortment rate of around 0. 05 per year to those of other segmented virus, such as influenza virus [51], is difficult because of methodological differences between studies. For example, we defined reassortment as segments changing their phylogenetic association from clade to another, with clades being at least 5% divergent. This would have ignored any events at finer genetic scales as illustrated by some of the field strains showing evidence of admixture with vaccine strains, which for some segments would not have been picked up by our conservative threshold. Regardless of the specific rate, reassortment is likely to be a major driver of genotypic and phenotypic change in BTV and might be more important in this respect than nucleotide substitutions. Comparative analyses of reassortment patterns and rates seen in different segmented RNA viruses, based on standardised approaches, would be useful and provide broader insights into the role of reassortment in virus evolution. While our data revealed some of the general patterns, constraints and adaptive consequences of reassortment, many open questions remain. For example, although genetic exchange can take place in both vertebrate hosts and insect vectors [17,18], the relative contributions of these potential ‘mixing vessels’ to generating the observed patterns require further study. This includes importance of the individual insect as a genetic bottleneck fixing new variants (both point mutations and reassortants) within the virus population through founder effects and genetic drift [62,63]. We further hypothesise that the insect vector, which so far has been difficult to study experimentally, plays a critical role in determining the fitness of novel reassortant viruses. Similarly, it remains unclear to what extent reassortment might have facilitated faster invasion of novel host and vector communities in Europe, a question of much applied relevance that we are currently examining. Increasing our understanding of the biological mechanisms as well as the population-level consequences of BTV reassortment will be critical to improve our ability to prevent and control the global spread of this important livestock disease. Complete genomes were sequenced from 120 BTV virus isolates, from Europe and the Mediterranean region and African countries, collected during 1958–2012. These included 116 field isolates of BTV types 1,2, 3,4, 8,9, 14,15 and 16, as well as four live monovalent BTV vaccine strains of types 1,2, 9, and 16 (S1 Table). Each of these virus isolates is included in the Orbivirus Reference Collection at The Pirbright Institute (www. reoviridae. org/dsRNA_virus_proteins/ReoID/BTV-isolates. htm) and is identified here by the corresponding reference number. Total RNA was extracted from infected cell culture supernatants using the QIAamp Viral RNA Mini Kit (Qiagen) or Direct-zol RNA MiniPrep (Zymo Research) as per manufacturer' s protocol. Identification and typing of BTV isolates was done by serogroup-specific real-time RT-PCR assays, targeting Seg-1 and Seg-10, and by specific conventional [64] and real-time RT-PCRs (available from Laboratoire Service International [LSI], Lissieu, France) targeting Seg-2. dsRNA was extracted from pellets of BTV infected cells using Trizol reagent (Invitrogen) as per manufacturer’s instructions for full-length cDNA synthesis. Viral RNA was analysed by agarose gel electrophoresis and used for whole genome sequencing. While in principle some field isolates containing mixed infections could have resulted in reassortment during cell passage, this would have become evident during typing of the isolate (for Seg-2) or by the detection of mixed sequences (below), since strains were not plague-cloned and usually passaged only a few times. Mixed infections are therefore unlikely to have impacted our dataset. The coding region of each genome-segment was aligned, according to the protein sequence, then converted to codon alignment using ‘PAL2NAL’ [69]. The 3’ and 5’ UTR regions were aligned using ‘Clustal Omega’ [70] and concatenated with the codon alignment for phylogenetic analysis. The isolate’s age was estimated following Shapiro et al. 2011 [71] if one of the following applied: information about original sample date was missing (11 isolates); virus had been heavily passaged (>40 passages, 9 isolates); the number of passages was unknown (2 ‘old’ samples). Bayesian phylogenetic trees were estimated in ‘BEAST’ 1. 7 [72] using a GTR+G+I model for the 5’ and 3’ UTRs and the SDR06 codon substitution model for the coding sequence [73]. Two Monte Carlo Markov chains were run for the number of generations needed for stationary distribution to be maintained after convergence (8 x 108 generations sampled every 10000th generation). We used Tracer v1. 6 [74] to visualize the posterior distribution for each parameter and obtain an estimate of the effective sample size (ESS). We assumed a run had converged if the ESS of all the parameters was above 100 when the two chains were combined. The trees from both chains were combined after removal of the initial 10% burn-in and resampled to provide approximately 10,000 trees. The maximum clade credibility (MCC) tree with mean node heights was produced from these trees using the auxiliary program TreeAnnotator, included in the BEAST package. Cluster Picker [46] was used to define monophyletic clades on the MCC trees with posterior probabilities of 0. 9 and a genetic distance threshold for clusters of 5%. The assignment to these clusters was subsequently used for character mapping of the clusters. Estimated phylogenies showed no evidence of clustering by host species. The ancestral “cluster” state for each segment was summarized for each node of the maximum clade credibility tree over a distribution of Bayesian trees from Seg-2 using the parsimony reconstruction method in Mesquite [75]. In order to be accepted as a reassortment event, the majority of trees needed to contain that state as a uniquely best solution, according to the parsimony reconstruction. Because we are using conservatively defined clusters as our basis, our approach can only detect reassortment events involving sufficiently divergent lineages. In order to ensure that cluster switching was attributable to reassortment, as opposed to increased divergence due to continuous evolution, all reassortment events were confirmed visually on the phylogeny. The date of each state change for each segment was subsequently extracted from the maximum clade credibility tree to determine the timing of reassortment events for nodes that had an ancestral node post-1900. The location of reassortment events was determined by reconstructing the character state (intra-Europe/extra-Europe) in the same way as above. Unless indicated otherwise, our analysis focussed on lineages that were assigned to Europe and that were sampled post-1998, as our sampling outside this spatial and temporal window was limited. The number of reassortments per genome was calculated by dividing the total number of reassortment events in European lineages by the total branch lengths of European lineages scaled in time, using the BEAST consensus tree. To obtain an estimate of the number of reassortment events involving Seg-2, the ancestral state reconstruction was repeated for Seg-2 onto the maximum clade credibility tree for each of the other segments using a distribution of Bayesian trees. For illustrative purposes, we present results mapped onto Seg-6, as it had a small number of reassortment events itself. We used the ETE python tool to produce the phylogenetic figures [76]. To compare the segment phylogenies, multidimensional scaling plots were used to determine the tree-to-tree variation in branch lengths following the approach of Bahl [77]. Five hundred trees sampled from the MCMC chain for each segment were used to determine the time to most recent common ancestor (tmrca) for any pair of European taxa sampled within the same year (for years from 1998 to 2012). The correlation coefficient of tmrca estimates across all pairwise comparisons of trees was calculated and from it the tree-to-tree distance was estimated. The matrix of tree-to-tree distances was then plotted in two dimensions using multi-dimensional scaling. We used simulations test whether the observed frequency at which segments were found to reassort differs from random expectations. Based on the observed number of reassortments, we draw samples of the same size from a vector of ten states with replacement and sorted the resulting sample by frequency. The mean and 95% range of frequencies encountered was calculated based on 10,000 replicates. The checkerboard score was calculated from the matrix of genome-segment associations to test for the non-random co-segregation of individual segments. The C-score is compared to a null distribution of 1000 random matrices of the same size maintaining the number of reassortments per node but irrespective of the segment using the R packages ‘vegan’ [78] and ‘bipartite’ [79]. We further tested for significant positive and negative associations between segment pairs by determining Spearman’s rank correlation and corrected for multiple testing by controlling the false discovery rate. To elucidate the potential role that vaccination strains may have played in reassortment, the distance from all European vaccine strains to their nearest sister clade among the European field strains was determined across all genome-segments. After initially using an uncorrected p-distance of 0. 3% as a cut-off to classify a field isolate as ‘close’ to a vaccine strain, we determined in each case the number of substitution differences between field and vaccine strain. To test whether increased selection occurs following reassortment, site-specific selection was estimated for all genes using the ‘fixed-effects likelihood’ (FEL) method [47] and the ‘fast unconstrained Bayesian approximation’ (FUBAR) method [48]. Sites under significant positive selection according to both methods (FEL: p-value <0. 05; FUBAR: posterior probability >0. 90) were mapped onto the appropriate gene tree, to determine the nodes where amino acid changes have occurred. We then tested whether reassortment had co-occurred on the same nodes more frequently than expected by chance. Mean dN/dS ratios for each segment were estimated using ‘Single Likelihood Ancestor Counting’ (SLAC) [47]. All selection analyses were performed in Datamonkey (http: //www. datamonkey. org/). All sequence data generated in this study have been deposited in GenBank (S1 Table, accession numbers KP820860—KP822064).

Segmented viruses have genomes that are separated into multiple segments, comparable to chromosomes in higher organisms. When two segmented viruses of the same species infect the same cell, their progeny may incorporate segments picked up from the “parental” viruses. This process is called “reassortment” and represents an important way for segmented viruses to evolve. Whereas reassortment has received a lot of attention in certain segmented viruses, especially influenza A, its frequency and biological consequences remain poorly understood for most of the others. Here, we present a comprehensive analysis of the reassortment patterns in bluetongue virus, an important pathogen of livestock, during its repeated emergence in Europe in recent decades. We confirm earlier reports that reassortment is common and can involve segments derived from live vaccines used to control outbreaks. However, the mixing of viral genomes is not strictly random and reassortment is commonly followed by novel adaptive changes in the progeny virus. This points to important functional links (paired associations) between certain segments. Our findings have important implications for the classification and control of segmented viruses and generate new insights and hypotheses about the biological interactions among different parts of the bluetongue virus genome.

Abstract Introduction Results Discussion Materials and Methods

2015

Widespread Reassortment Shapes the Evolution and Epidemiology of Bluetongue Virus following European Invasion

Cystine-knot miniproteins (knottins) are promising molecular scaffolds for protein engineering applications. Members of the knottin family have multiple loops capable of displaying conformationally constrained polypeptides for molecular recognition. While previous studies have illustrated the potential of engineering knottins with modified loop sequences, a thorough exploration into the tolerated loop lengths and sequence space of a knottin scaffold has not been performed. In this work, we used the Ecballium elaterium trypsin inhibitor II (EETI) as a model member of the knottin family and constructed libraries of EETI loop-substituted variants with diversity in both amino acid sequence and loop length. Using yeast surface display, we isolated properly folded EETI loop-substituted clones and applied sequence analysis tools to assess the tolerated diversity of both amino acid sequence and loop length. In addition, we used covariance analysis to study the relationships between individual positions in the substituted loops, based on the expectation that correlated amino acid substitutions will occur between interacting residue pairs. We then used the results of our sequence and covariance analyses to successfully predict loop sequences that facilitated proper folding of the knottin when substituted into EETI loop 3. The sequence trends we observed in properly folded EETI loop-substituted clones will be useful for guiding future protein engineering efforts with this knottin scaffold. Furthermore, our findings demonstrate that the combination of directed evolution with sequence and covariance analyses can be a powerful tool for rational protein engineering. Protein-protein interactions govern many biological processes in the cell, often with high affinity and specificity. Such interactions are typically mediated by a relatively small portion of the protein, while the remainder of the molecule serves as a framework to ensure the proper presentation of the binding epitopes. Many naturally-occurring proteins with diverse functions are based on common protein frameworks; for example, the immunoglobulin fold is a widespread structural motif found in antibodies, enzymes, and receptors. These common protein frameworks, or molecular scaffolds, can be engineered for novel properties, such as altered molecular recognition [1], increased stability [2], or improved expression levels [3], through the incorporation or evolution of functional epitopes. Ideally, molecular scaffolds should have high intrinsic conformational stabilities and be structurally tolerant of sequence modifications, including insertions, deletions, or substitutions. While antibodies are the most developed class of molecular scaffold, their application is limited in many cases by their large size, complex fold, cost-intensive manufacturing, and complicated patent considerations [4], [5]. Thus, in the past decade there has been much effort toward developing non-antibody scaffolds with enhanced structural robustness, ease of modification, and cost-efficient production. Examples of such alternative molecular scaffolds include: fibronectin, protein A, ankyrin repeat proteins, lipocalins, thioredoxin, ribose-binding proteins, protease inhibitors, PDZ domains, and knottins (reviewed in [4]–[7]). These alternative molecular scaffolds have been engineered for applications in biochemical assays [8], separation technologies [9], and diagnostics and therapeutics [4], [10]. Directed evolution of a protein scaffold for new molecular recognition properties is often achieved by screening focused libraries and isolating clones that bind to a target with high affinity. Prior to screening, a library of protein variants is created by replacing one or more existing loops or domains with new sequences in which the amino acids are randomized at a few or all positions. In some examples, such as the thioredoxin aptamer, a single loop has been substituted [11], while in other cases, such as the 10th domain of fibronectin, as many as three loops have been engineered [12]. One major limitation of this approach is that substitution of entire loops or functional domains may lead to misfolding or loss of structural integrity [13]. In addition, while some new loop sequences represented in the library will lead to properly folded and functional proteins, other loop sequences may not be tolerated and will lead to misfolded, aggregated, or otherwise inactive proteins. Moreover, specific residues may be preferred in certain positions while forbidden in others, or the presence of a specific residue in one position may dictate the presence of another specific residue at a nearby position. In addition to positional amino acid preferences, the length of the substituted loop sequence may also be critical for the structural integrity of the protein [14]. For example, steric or torsional constraints may prohibit substituting a loop with a peptide of shorter length, while substitution with a longer peptide may be highly destabilizing due to entropic factors. A better understanding of the tolerated loop lengths and compositional parameters of a protein would be helpful for evaluating its utility as a scaffold; such insight would allow for the creation of optimal focused libraries and the prediction of admissible sequence modifications that lead to correct protein folding. Here, we describe a comprehensive study on the tolerance of scaffold loop substitution with different sequences and loop lengths using a small, highly structured polypeptide, the Ecballium elaterium trypsin inhibitor II (EETI, UniprotKB/Swiss-Prot P12071, Figure 1A). Further, our work applies the findings from the study of EETI loop tolerance to the prediction of artificial, loop-substituted knottin sequences that yield properly folded proteins. This novel approach toward interrogating functional tolerance in a predictive manner is useful not only for the EETI scaffold, but also for the creation of optimally-designed libraries of scaffold proteins in general. EETI belongs to the cystine-knot (knottin) family of proteins [15], a class of small polypeptides (typically 20–60 amino acids) that possess several advantageous characteristics for their development as molecular scaffolds [7]. Knottins contain three disulfide bonds interwoven into a molecular ‘knot’ that constrain loop regions to a core of anti-parallel β-sheets. The unique topology of the knottin fold imparts high chemical and thermal stability [16] and resistance to proteolysis [17], which are important for biotechnology and biomedical applications. Moreover, knottins can be chemically synthesized and folded in vitro [18] or produced recombinantly in various expression systems [19]–[22]. As a prototypical member of the knottin family, the folding pathway and structure of EETI have been well studied [23]–[25]. EETI is composed of 28 amino acids with three disulfide-constrained loops: loop 1 (the trypsin binding loop, residues 3–8), loop 2 (residues 10–14), and loop 3 (residues 22–26) (Figure 1A). Although the inhibition of trypsin is mediated exclusively through binding of EETI loop 1 (Figure 1B), disruption of any of the three disulfide bonds will abolish the EETI-trypsin interaction [26]. EETI has been the subject of previous mutagenesis studies aimed at investigating its protein fold [26] or altering its binding specificity through introducing diversity into loop 1[27]–[29]. While these previous studies support the potential for using EETI as a molecular scaffold, substitution of loops 2 and 3 has not been well explored, and the tolerated sequence space for these loops is unknown. Here we present a novel combinatorial and computational approach for interrogating functional tolerance of a molecular scaffold in a predictive manner. We created libraries of EETI mutants where loop 2 or loop 3 was substituted with randomized sequences of varying lengths, and used high-throughput screening to identify clones that were properly folded based on their ability to bind to trypsin (Figure 1). We then performed a detailed bioinformatics analysis on the sequences of isolated trypsin-binding mutants, and used this information to successfully predict new loop sequences that led to properly folded EETI variants. Wild-type EETI (EETIwt) was displayed on the yeast cell surface as an N-terminal protein fusion to the yeast Aga2p agglutinin subunit [30]. A schematic of the yeast display platform is shown in Figure 2A. We measured cell surface expression levels of the EETIwt fusion protein by flow cytometry, after staining yeast cells with a primary antibody against a C-terminal cMyc epitope tag followed by the addition of a fluorescently-labeled secondary antibody. Next, we used fluorescently-labeled trypsin, the native binding partner of EETIwt, to assay whether yeast-displayed EETIwt was properly folded and functional [26] (Figure 2). We showed that EETIwt was well-expressed on the yeast surface and that fluorescently-labeled trypsin bound specifically to yeast-displayed EETIwt with an approximate equilibrium binding constant of 25 nM (data not shown). Next, in order to explore the tolerance of the EETI scaffold for different loop sizes and amino acid compositions, we created yeast-displayed loop-substituted libraries in which a single cysteine-flanked loop of EETI was substituted with randomized amino acid sequences of varying lengths (Figure 1A). Libraries were generated by overlap extension PCR using oligonucleotides with degenerate NNS codons (where N = A, T, G, C and S = G or C), which encode for all 20 amino acids and only the TAG stop codon. We generated six libraries in total: two libraries of EETI loop 2 variants with substitution lengths of 7 amino acids (EL2-7) and 9 amino acids (EL2-9), and four libraries of EETI loop 3 variants with substitution lengths of 6 amino acids (EL3-6), 7 amino acids (EL3-7), 8 amino acids (EL3-8), and 9 amino acids (EL3-9). We did not mutate EETI loop 1, which is responsible for binding to trypsin, but instead used it as a handle to probe the structural integrity of the EETI loop-substituted clones (Figure 1B). To create the libraries, mutant DNA was electroporated into the Saccharomyces cerevisiae EBY100 strain along with linearized yeast-display plasmid as previously described [31]. By performing dilution plating, we estimated that the sizes of the loop-substituted libraries ranged from 5×106–1×107 transformants each. Previous studies showed that trypsin binding can be used as a convenient handle to examine formation of the correct pairings of disulfide-bonded cystine residues in EETI [26]. Therefore, we screened each of the EETI loop-substituted libraries for clones that were both displayed on the yeast cell surface (as detected by antibodies against the C-terminal cMyc epitope tag) and properly folded (as determined by their ability to bind fluorescently-labeled trypsin) using dual-color fluorescence-activated cell sorting (FACS) (Figure 2). We performed multiple rounds of FACS on each yeast-displayed library, each time collecting the 1–2% of clones that were the best displayed and exhibited the highest levels of trypsin-binding. The sorts were performed with this stringency in order to enrich each library to near wild-type EETI trypsin binding levels while maintaining as large a diversity of sequences as possible. After four rounds of sorting, a pool of clones showing moderate to wild-type levels of trypsin binding had been isolated from each library (Figure 2B and C). We sequenced at least 50 clones from each of the six original libraries to confirm that the substituted loops were of the correct lengths and had diverse amino acid compositions (Dataset S1). The amino acid frequencies of the loop-substituted regions were generally similar to those expected from a degenerate NNS codon library (Figure 3A). To obtain an analytical measurement of the diversity within each of the loop-substituted libraries, we applied the method of Makowski & Soares [32] to the sequences of clones from each original library. Using the population diversity (POPDIV) algorithm, we determined that the functional diversities (i. e. the percentage of possible library members of a population that are present, adjusted for differences in copy numbers of the members) of the EETI loop-substituted libraries ranged from 3%–11%, corresponding to effective library sizes of 106–1010. These functional diversity values are roughly as expected for randomly generated libraries, due to differences in amino acid frequencies resulting from inherent biases in the genetic code [32]. Next, to obtain information on the amino acid composition of properly folded EETI loop-substituted proteins, we sequenced at least 50 clones from each of the libraries after enrichment for trypsin binders (Dataset S1). We examined the sequences from the enriched libraries to determine whether their functional diversities had changed over the course of enrichment for properly folded clones. Using the POPDIV algorithm [32], we found that the functional diversities of the enriched libraries had decreased 10–300 fold. Not surprisingly, libraries that showed the greatest trypsin binding levels after FACS (Figure 2C) also had the greatest decreases in diversity. The amino acid compositions of the enriched library populations also differed from their original, unsorted counterparts (Figure 3B and C). Notably, the frequency of glycine increased in all enriched EETI loop-substituted libraries compared to the starting libraries and cysteine virtually disappeared from all trypsin-binding clones, except in the EL3-7 library (see below) (Figure 3B and C). To quantitatively assess the positional diversities along the lengths of the loop-substituted peptides, we applied the amino acid sequence diversity (DIVAA) algorithm [33] to the sequences of enriched trypsin-binding clones. We found that EETI loop 2-substituted clones isolated from both EL2-7 and EL2-9 libraries were moderately tolerant of substitution across all loop positions, with average diversity scores of 0. 3±0. 1 and 0. 4±0. 1 for loop lengths of seven and nine amino acids, respectively (Figure 4). To put this into context, a score of 0. 05 indicates complete conservation of a single amino acid and a score of 1. 0 indicates the presence of all amino acids in equal proportions. Glycine comprised approximately 25–30% of all amino acids in EETI loop 2-substituted trypsin-binding clones at all but the second loop position. On average, EETI loop 2-substituted sequences of both 7- and 9-amino acids contained approximately 2 glycine residues per clone. Proline residues, which commonly populate turn segments, predominated in the second position of EETI loop 2-substituted variants (Figure 4). The overall diversities of EETI loop 3-substituted clones were slightly higher than those of loop 2-substituted clones, with average diversity scores ranging from 0. 4±0. 1 (for EL3-6 clones) to 0. 5±0. 2 (for EL3-7 and EL3-8 clones) (Figure 5). The enriched EL3-9 library had an intermediate average diversity score of 0. 4±0. 2, owing to the high conservation of the final two loop positions (Figure 5). Although the average diversity scores of enriched loop 2- and loop 3-substituted libraries were similar, the amino acid variability for specific positions (positional diversity) in loop 3-substituted clones had a larger range than that observed in loop 2-substituted clones. We found that the greatest levels of diversity occurred in the middle positions of the substituted sequences of loop 3 variants while the first, penultimate, and final positions had the lowest diversities (Figure 5). Nearly half of all trypsin-binding EETI loop 3-substituted clones contained sequences that began with one of three preferred residues: asparagine, arginine, or aspartate (Figure 5). The most common amino acids for the penultimate and final positions of EETI loop 3 were glycine and tyrosine, respectively; nearly a quarter of all loop 3 substituted sequences from enriched clones ended in a glycine-tyrosine doublet. We observed the aforementioned trends in trypsin-binding EETI loop 3-substituted clones across all loop lengths. EETI loop 3 was tolerant of substitution with 6-, 8-, and 9-amino acid sequences, but surprisingly did not appear to be tolerant of a 7-amino acid loop. Roughly half of the enriched clones from the EL3-7 library contained substituted loops whose length deviated from the designed length of seven amino acids, despite the apparent absence of clones with incorrect loop lengths in sequences identified from the original library. We hypothesized that clones of incorrect loop lengths likely arose from infrequent impurities in the degenerate oligonucleotide or arose during the gene assembly process. To determine whether the trend for isolating clones of the incorrect lengths resulted from low library quality or from preference of the EETI scaffold, we constructed a second EL3-7 library using a highly purified degenerate oligonucleotide. After sorting the new EL3-7 library for trypsin binding as before, we again sequenced the enriched clones, but now found that they were all of the correct length. However, approximately 34% of the loop sequences contained an internal cysteine residue, potentially shortening the actual loop length from the intended seven amino acids to between four and six amino acids (Dataset S1). When these cysteine-containing loop sequences were disregarded, the remaining EL3-7 clones displayed sequence patterns in agreement with clones isolated from other EETI loop 3-substituted libraries, but had a higher frequency of proline residues. The enriched EL3-9 clones were chosen as a model group for further exploration of the EETI knottin scaffold because their trypsin-binding affinities were closest to that of EETIwt (Figure 2C). We performed covariance analysis on the substituted loop sequences of the enriched EL3-9 clones to determine whether the amino acid preferences at one loop position influenced the amino acid preferences at a second loop position. A comprehensive set of commonly used scoring functions [34] containing the following covariance algorithms was employed: Observed Minus Expected Squared (OMES) [35], Mutual Information (MI) [36], Statistical Coupling Analysis (SCA) [37], McLachlan Based Substitution Correlation (McBASC) [38], [39], and Explicit Likelihood of Subset Co-variation (ELSC) [40]. To determine background levels of covariance scores for each algorithm, we calculated the mean covariance and standard deviation across all positional pairs in the substituted loops of sequences from the unsorted EL3-9 library (Dataset S2). We then used the resulting background values to convert the covariance scores for positional pairs in enriched EL3-9 clones (Dataset S3) to standardized scores (z-scores). The MI scoring function failed to give scores detectable over background noise, as the maximum z-score returned by this algorithm for any covarying pair was less than 0. 1. The SCA algorithm identified the most covarying pairs. However, SCA-identified covarying pairs mainly contained highly conserved loop positions (i. e. positions 8 & 9), inhibiting the identification of correlated amino acids within coupled positions. For this reason, covarying pairs identified by SCA were discarded. The ELSC scoring function identified only a single covarying pair; this pair was redundant with results from the OMES algorithm. Therefore, only covariance scores calculated using the OMES and McBASC algorithms were considered for further analysis. Setting the covariance threshold to pairs with a standardized score greater than 2 (indicating a score two standard deviations above the background mean, significance threshold p<0. 025) identified four covarying pairs of loop positions: positions 1 and 7, positions 2 and 4, positions 2 and 9, and positions 5 and 6 (Dataset S3). However, there was minimal agreement in the results from the three covariance algorithms used. Of the four covarying pairs, only one (positions 2 and 4) was predicted by more than one algorithm. To identify predictive residues at these coupled positions, we manually analyzed each covarying position to uncover frequently occurring correlated amino acid pairs. Our analyses revealed multiple pairs of correlated amino acids (Table 1) at three of the four covarying loop positions (Figure 6). While several of the correlated amino acids were mutually predictive, other pairs displayed uni-directional predictivities. Due to the high level of conservation at loop position 9, we were unable to find correlated pairs of amino acids at positions 2 and 9 whose paired frequencies differed significantly from their occurrence rates in the overall population, so this covarying pair was excluded from further analysis. We next used the results of our covariance analysis to predict sequences of nine-amino acid peptides that could be substituted into loop 3 of EETI without disrupting the knottin fold. Since loop positions three, eight, and nine were not predicted by covariance analysis, we constrained these residues using other observed sequence patterns. EETI loop 3 positions eight and nine were fixed to a glycine-tyrosine doublet because this combination of residues was present at the final two loop positions in 52% of enriched EL3-9 clones. EETI loop 3 position three was set to either asparagine or threonine, since these two amino acids frequently occurred in sequences containing a glycine-tyrosine doublet. We constrained the remaining loop positions with the identified covariance patterns and correlated pairs of amino acids (Figure 6 and Table 1). By exhaustively combining all possible pairs of covarying residues, we predicted a total of 420 tolerated loop sequences for substitution into EETI loop 3 (Dataset S4). Although the predicted sequences were generated by combining pairs of amino acids observed in the enriched EL3-9 library, none of the predicted sequences were identical to any of the enriched EL3-9 clones. We then ranked our predicted peptide sequences according to the number of common motifs each shared with the loop 3 sequences of enriched clones from the EL3-9 library (Table S1). Here, we define a common motif as a discontinuous three-amino acid sequence pattern uncovered from analysis of enriched EL3-9 clones, not including the XXN/TXXXXGY motifs used to generate the predicted sequences. Because the common motifs used for ranking the predicted sequences all contained a C-terminal tyrosine or glycine-tyrosine pair, inclusion of these motifs allowed us to consider potential sequence preferences associated with the C-terminal positions of loop 3 whose conservation levels were too high to be detected by covariance analysis. We hypothesized that predicted peptides containing the greatest number of common motifs would be the least likely to disrupt the EETI fold. We found that 40 of the 420 predicted sequences contained four or more common motifs (Dataset S5) and 134 of the sequences contained three common motifs (Dataset S6). We aligned and grouped the 174 motif-filtered predicted sequences using ClustalW [41] and then chose a representative clone at random from each of the resulting fifteen subgroups to be tested for its ability to bind trypsin (Figure 7A and B). In addition to ranking our predicted clones by incorporation of common motifs, we used a scoring function based on a modified BLOSUM62 amino acid substitution matrix [42] to rank the predictions according to their similarities to clones isolated from the enriched EL3-9 library. To test the lower limits of our predictive capability, we used ClustalW to align predicted clones whose similarity scores were in the lowest 10% and selected the clone with the lowest similarity score from each subgroup to be tested for its trypsin-binding ability (Figure 7C). Finally, for comparison, we used the ExPASy RandSeq tool to generate random, nine-amino acid peptides with frequencies typical of NNS codons for substitution into EETI loop 3. We prepared twenty-five yeast display plasmids containing representative EETI loop 3-substituted clones that were predicted to be properly folded, and hence bind trypsin: fifteen motif-filtered sequences and ten least-similar sequences (Table S2). In addition, we also constructed twenty-five clones where EETI loop 3 was replaced by a randomly-generated nine-amino acid sequence (Table S2). Plasmids containing DNA encoding for predicted and randomly-generated clones were transformed into yeast and induced for expression on the yeast cell surface as described above. Next, we analyzed the predicted and randomly-generated EETI loop 3 clones by dual-color flow cytometry for yeast cell surface expression and binding to fluorescent trypsin, indicating retention of the knottin fold (Figure 8). Remarkably, we found that all motif-filtered predicted clones bound trypsin. Of the motif-filtered predicted clones, two (p1 and p9) showed trypsin binding levels roughly 1. 5-fold higher than that of EETIwt and one (p3) showed trypsin binding levels approximately 70% that of EETIwt; these differences in binding were statistically significant, as determined by single-factor ANOVA (p<0. 05). The remaining 12 motif-filtered predicted clones bound trypsin at levels comparable to that of EETIwt. Overall, the least-similar predicted clones also bound trypsin, but not as well as the motif-filtered clones, as expected. The small differences in trypsin binding levels of six of the least-similar predicted clones as compared to EETIwt were deemed statistically insignificant, based on single-factor ANOVA (p<0. 05). Although two of the least-similar predicted clones (p20 and p25) bound trypsin at levels approximately 30% that of EETIwt, they still bound trypsin at levels that were statistically significant over the highest-binding randomly-generated clone, as determined by single-factor ANOVA (p<0. 02). The majority of randomly-generated clones showed trypsin binding levels comparable to that of a negative control, the potato carboxypeptidase inhibitor knottin, which does not bind trypsin (<5% of the wild-type EETI trypsin binding level). Even the best randomly-generated clone only bound trypsin at 16% of the wild-type EETI trypsin binding level. The successful prediction of nine-amino acid sequences that can be substituted into EETI loop 3 without disrupting the knottin fold highlights the potential of this combinatorial approach for rational protein engineering applications. The trypsin binding loop of EETI (loop 1) has previously been engineered for novel binding functionalities [15], [27]–[29], [43]. Although prior studies suggested that EETI loops 2 and 3 might be amenable to mutagenesis [24], [26], the potential for using these loops to confer stability or new recognition properties has not been previously investigated. In this study, we surveyed the tolerated sequence and loop length diversity of the EETI knottin to assess its utility as a scaffold for protein engineering applications. We displayed EETI loop-substituted variants on the surface of yeast and determined whether they retained the knottin fold by assaying their ability to bind trypsin. In the process, we developed a new method for understanding tolerated diversity based on isolating properly folded protein variants from highly diverse libraries, followed by comprehensive sequence analysis of the isolated clones. EETI was chosen as an optimal knottin scaffold for this study for a number of reasons. First, EETI has two solvent-exposed loops that are not involved in trypsin binding and do not have steric or electrostatic interactions with the trypsin-binding loop, making them well-suited for loop substitution [24]. Second, trypsin binding can be used to assess proper protein folding since removal of any of the three disulfide bonds in EETI (e. g. by cysteine to serine mutations) prevents formation of the cystine-knot and abolishes trypsin binding [26]. Third, the crystal structures of EETI variants previously selected for binding to trypsin depict wild-type-like knottin structures [24]. These studies provide significant support that only properly folded EETI variants will retain trypsin-binding capabilities, while variants that deviate from the knottin fold will be unable to bind trypsin. Taken together, the interaction between the N-terminal loop of EETI and trypsin provided a conformational handle for probing the structural effects of extended mutations on the remaining two EETI loops. Moreover, EETI has already been shown to be amenable to yeast surface display [28], as well as other directed evolution platforms including Escherichia coli cell surface display [26], [27], and mRNA display [44]. Yeast surface display has proven to be a robust platform for screening combinatorial libraries of proteins with disulfide bonds and complex folds, including antibodies [45], cell surface receptors [46], growth factors [47], and knottins [22], [28]. Additionally, yeast surface display allows for quantitative library screening using FACS, which enabled knottin protein expression levels to be correlated with trypsin binding levels with single-cell resolution. Such normalization allowed us to isolate clones that possessed the highest fraction of properly folded knottins over clones that displayed a large amount of misfolded proteins with only a small trypsin-binding subset. The quality control mechanisms of the yeast secretory pathway should prevent misfolded or incompletely folded proteins from being displayed on the cell surface [48], [49]. However, others have reported limitations in the ability of the yeast quality control system to differentiate between properly folded and unfolded variants of proteins with high thermal stabilities [50]. Therefore, it is possible that EETI loop-substituted clones that were expressed on yeast but did not bind trypsin were in an improperly folded state, such as the EETI two-disulfide intermediate [25]. Such clones may be sufficiently stable to escape the quality control machinery of the yeast secretory pathway. Alternatively, it is possible that some of the loop-substituted sequences introduced structural perturbations that were propagated along the polypeptide backbone, preventing the proper interaction of EETI loop 1 with trypsin despite retention of the native topology of the knottin fold. We designed EETI libraries where loop 2 or loop 3 was substituted with randomized sequences to determine whether the knottin fold was affected by sequence modifications. In addition, we simultaneously tested the effects of varying loop size on the knottin fold by replacing the loops with randomized peptides of different lengths. For these studies, we explored only loop lengths longer than those naturally occurring in the EETI knottin. Our choice of loop lengths was influenced by several factors: 1) the utility of the knottin scaffold for protein engineering applications relies on the ability to evolve novel molecular recognition epitopes, and high-affinity interactions typically require binding interfaces larger than the native loop lengths of EETI (4–5 residues), and 2) although loop lengths of 7–17 amino acids have previously been grafted in place of the EETI trypsin binding loop [27], [29], loops of longer lengths are less structurally constrained and the entropic binding advantage of presenting the loop on a scaffold is mitigated. Hence, we limited the loop lengths tested to between six and nine amino acids. In some previous examples of knottin engineering, a sequence of interest was grafted into a constrained knottin loop on the premise that the new sequence would be structurally tolerated [17], [29], [43]. This assumption of tolerance was based on the lack of sequence homology that exists between proteins that share the knottin fold. We found a moderate degree of functional diversity amongst properly folded EETI loop-substituted clones (0. 01%–0. 9%, corresponding to enriched populations of 105–107), but not complete tolerance. In addition, sequences of properly folded EETI loop-substituted clones were largely composed of residues that would likely maintain the local secondary structure of the native EETI knottin fold. In wild-type EETI, loop 2 (KQDSD) composes a loop and short alpha helix that lies directly after the first beta sheet. It was therefore not surprising that the preferred amino acid residues found to populate loop 2 in properly folded EETI mutants were glycine, proline, and serine; these residues typically disrupt secondary structures and would therefore be conducive to the formation of a loop. Amino acids that preferentially adopt alpha helical structures were the next most abundant: alanine, leucine, and arginine collectively accounted for 35% of the remaining residues in properly folded EETI loop 2-substituted clones. The wild-type EETI loop 3 sequence (GPNGF) lies within a loop region and the beginning of the third anti-parallel beta sheet in the native structure. Similarly, we observed that properly folded EETI loop 3-substituted clones contained amino acids at the beginning of their loop sequences that favor the formation of a loop, such as asparagine, glycine, proline, and serine. The preferred glycine-tyrosine doublet found at the C-terminus of EETI loop 3 clones enriched for trypsin binding is similar to the glycine-phenylalanine sequence at the same location in the native loop; both doublets would be effective initiators of a beta sheet secondary structure. It is interesting to note that while histidine, threonine, tryptophan, and tyrosine are not present in the wild-type EETI sequence, each appeared in the sequences of loop-substituted clones, indicating there is not an intrinsic structural bias against these amino acids. While the observed sequence trends were found to be specific to the location of the substituted loop, they were largely independent of loop length. The overall tolerance of the EETI knottin fold to a variety of substitution lengths in loop 2 and loop 3 is an important attribute for its potential as a molecular scaffold. Interestingly, the high tolerance of variations in loop length was greater than what might be anticipated from analysis of natural loop lengths across members of the knottin family. Over 40% of naturally-occurring knottins have a loop 2 length of five amino acids, while less than 10% and less than 5% have loop 2 lengths of seven and nine amino acids, respectively [51]. There is an intriguing parallel between observed loop 3 lengths in naturally-occurring knottins and the tolerated loop lengths we observed. The most common loop 3 lengths of knottins are four (more than 25%), six (nearly 20%), or ten (nearly 15%) amino acids, while loop lengths of seven or eight amino acids each occur in less than 5% of the knottin family [51]. This is congruous to our findings here: a loop 3 substitution length of seven amino acids was found unfavorable and EETI loop 3-substituted clones with loop lengths of six or nine amino acids were enriched to high levels of trypsin binding after FACS. We used sequence covariance analysis to identify inter-residue couplings at positions along the length of properly folded EETI loop 3-substituted clones. Covariance analysis can be applied to proteins to highlight structurally or functionally important residues and has previously been employed for a myriad of purposes, including revealing inter-residue contacts within protein structures [38], [52], protein folding pathways [53], energetic coupling pathways [37], communication pathways of allosteric proteins [35], and correlated mutations involved in drug-resistance [54]. In addition, covariance analysis has been used to predict protein structure [39], predict protein-protein interactions [55], and guide protein docking experiments [56]. Although we performed covariance analysis on the loop 3-substituted sequences of trypsin-binding EETI mutants using several different algorithms [34], only the results from the OMES [35] and McBASC [38], [39] scoring functions were used to generate sequence predictions. The lack of agreement among results calculated with the various covariance algorithms likely stems from differences in their sensitivities to amino acid conservation levels. Indeed, the OMES and McBASC scoring functions have been reported to have similar sensitivities to background levels of conservation, resulting in similar scoring performances for covariance analysis on sets of Pfam protein families [34]. The sensitivity of our covariance and correlated mutation analyses was limited by the quantity of sequencing data available (52 and 56 sequences for the enriched and starting EL3-9 libraries, respectively). Because of the small sample size of our sequencing data, we were able to detect only the strongest trends over background noise; we anticipate that there exist other, more subtle connectivities and less prevalent correlated pairs of amino acid residues within the substituted loop regions that would be revealed with a larger data set. However, it is notable that even a small dataset was able to predict hundreds of functionally tolerated sequences. We applied the results of our covariance analysis to predict nine-amino acid sequences for substitution into EETI loop 3 that lead to proper folding of the protein. The exhaustive combination of all possible correlated mutations at three covarying positional pairs and three constant loop positions afforded 420 predicted clones. Although the rational design of a tolerated loop sequence was aided by the stability of the knottin fold, there are 15 possible ways to form three disulfide bonds from six cysteine residues, and folding and oxidation must occur in coordination to result in a cystine-knot topology [7]. The task of designing sequences for substitution into EETI loop 3 was further complicated by the important role of this loop in the folding of the knottin. The residues of EETI loop 3 initiate the folding of the knottin structure by forming a beta-turn that is responsible for facilitating the association of the anti-parallel beta sheets prior to disulfide bond formation [23], [25]; furthermore, mutations to EETI loop 3 have been observed to result in misfolded by-products [26]. The results of our studies, in which EETI loop 3 clones that properly fold and bind to trypsin were predicted, demonstrate that covariance and correlated mutation analysis can be successfully used for rational protein engineering. Moreover, trends identified by sequence and covariance analyses provide guidelines for introducing diversity into knottin loop regions when minimal structural disruption is desired, for example, when performing directed evolution experiments. Pál and colleagues previously used phage display and covariance analysis to design pacifastin protease inhibitors with altered binding specificities for trypsin [57]. In another study, Ranganathan and colleagues used covariance analysis to computationally predict artificial sequences of properly folded and functional WW domain proteins [58], [59]. These studies suggested that a combination of amino acid conservation and covariance analysis is necessary and sufficient to inform successful protein design. This finding is corroborated in our studies; we used both conserved amino acids and covariance analysis with correlated mutations to design EETI loop 3 variants that adopt the cystine-knot topology. While both of these previous studies were based on genetic information from naturally-occurring proteins, our study used sequences isolated from a naive library of loop-randomized protein variants. This distinction extends the applicability of covariance analysis for protein design to include sequences that differ significantly from their naturally-occurring counterparts not only in amino acid composition, but also in sequence length. Indeed, our approach successfully predicted artificial EETI-based knottin proteins with substituted loop sequences 1. 5–2 fold longer than those found in the most closely related, naturally-occurring knottin family members. Further, the covariance analysis of clones isolated from a naive loop-substituted library permits the exploration of greater diversity than analysis of a family of naturally-occurring or naturally-derived proteins, potentially resulting in predicted artificial protein sequences with greater diversity. Simultaneous maintenance of this diversity and compliance with minimal structural requirements is essential for engineering novel characteristics (e. g. molecular recognition) into scaffold proteins while ensuring that the protein structure is not compromised. Upon analysis of the knottin database [51], [60], we found loop sequences in naturally-occurring knottins that were homologous to those of clones from the enriched EL3-9 library. Such homologous sequences were found in several serine protease inhibitor knottins, which are functionally related to EETI. Surprisingly, we also found natural loop sequences that shared at least 50% homology with those of enriched EL3-9 clones from knottin families functionally unrelated to EETI, including conotoxin, spider toxin, plant toxin, and scorpion toxin (Table S3). Further, the C-terminal glycine-tyrosine doublet present in enriched EL3-9 clones was also found in the sequences of many naturally-occurring knottins belonging to the fungi, insect antimicrobial, plant antimicrobial, and trematoda families (Table S3). The observation that sequences similar to those of our enriched EETI loop 3-substituted clones exist in the loops of naturally-occurring knottins that are functionally unrelated to EETI suggests that the covariance patterns we identified here may be useful for engineering other members of the knottin family. In summary, this work has shown the feasibility of using yeast-displayed libraries of EETI loop-substituted proteins to investigate the structural tolerances of a knottin fold in a predictive manner. Since combinatorial library sizes are limited by host transformation efficiencies and rational design thus far has been met with limited success, a set of guidelines for biasing starting libraries is valuable. This is especially true for the knottin scaffold, whose advantageous characteristics (e. g. high thermal stability and resistance to proteolysis) are dependent on the correct disulfide-bonded topologies. Finally, this work demonstrates the potential of using directed evolution platforms in combination with covariance analysis to guide future efforts in engineering functional proteins whose sequences differ from their naturally-occurring counterparts not only in amino acid composition, but also in sequence length. YPD media contained 20 g/L dextrose, 20 g/L peptone, and 10 g/L yeast extract. Selective SD-CAA media contained 20 g/L dextrose, 6. 7 g/L yeast nitrogen base without amino acids, 5. 4 g/L Na2HPO4,8. 6 g/L NaH2PO4⋅H2O, and 5 g/L Bacto casamino acids. Selective SD-CAA plates were of the same composition as the liquid media except with the addition of 182 g/L sorbitol and 15 g/L agar. SG-CAA media was identical to the SD-CAA media except dextrose was replaced with galactose. PBSA was composed of phosphate buffered saline containing 1 g/L bovine serum albumin. Lyophilized trypsin was purchased from Sigma Aldrich and fluorescently labeled with Alexa Fluor 488 tetrafluorophenyl ester (Invitrogen). Mouse anti-cMyc antibody was purchased from Covance and goat anti-mouse antibody conjugated to R-phycoerythrin was purchased from Sigma Aldrich. EETI loop-substituted knottin libraries were constructed by overlap extension PCR using KOD polymerase (Novagen) in the presence of 1 M betaine and 3% dimethylsulfoxide. Oligonucleotides were designed using yeast-optimized codons, with randomized loop positions encoded by the degenerate NNS codon. Assembled PCR products were amplified with Pfx50 polymerase (Invitrogen) using forward and reverse oligonucleotides with 45 bp homology upstream or downstream of the NheI and BamHI restriction sites, respectively, in the pCT yeast display vector [31]. The pCT yeast display vector was digested with NheI and BamHI restriction enzymes (New England Biolabs) and treated with calf intestinal phosphatase (New England Biolabs). Amplified PCR products of the correct lengths and linearized pCT vector backbone were separated by electrophoresis on a 2% agarose gel and purified using a QIAquick Gel Extraction Kit (Qiagen). EETI loop-substituted DNA inserts (10–15 µg) and linearized pCT vector (1–1. 5 µg) were transformed into EBY100 yeast [30] by electroporation [31] at a ratio of 10∶1. After electroporation, yeast were allowed to recover in YPD at 30°C for 1 h with shaking prior to transfer to selective growth media. Libraries were propagated in selective SD-CAA media and induced for protein expression in SG-CAA media at 30°C. Library sizes were estimated by plating serial dilutions onto selective SD-CAA agar plates and colony counting. Four rounds of FACS were performed on each EETI loop-substituted library to obtain an enriched pool of trypsin-binding clones. Libraries of yeast clones induced for protein expression were suspended in PBSA with mouse anti-cMyc monoclonal antibody (1∶50 dilution) and incubated for 1 h at room temperature. Yeast were pelleted by centrifugation at 4,000 rpm for 5 min, the supernatant was aspirated, and cells were washed with ice-cold PBSA. Washed yeast libraries were resuspended in PBSA with goat anti-mouse R-phycoerythrin secondary antibody (1∶25 dilution) and Alexa 488-labeled trypsin and incubated on ice in the dark for 30 min. Yeast libraries were washed as before and screened by dual-color FACS for mutants that were both displayed on the yeast surface and bound to trypsin using a Becton Dickinson FACSVantage SE instrument (Stanford FACS Core Facility) and CellQuest software (Becton Dickinson). Collected library clones were propagated in SD-CAA media with penicillin-streptomycin (400 µg/mL), induced for protein expression in SG-CAA media and subjected to three additional rounds of sorting. For the first round of sorting, approximately 2×107 yeast were sorted from each library, and at least 10 times the number of collected yeast were sorted in each subsequent round to decrease the probability of losing unique clones. Sort stringency was increased by gradually decreasing the concentration of Alexa488-labeled trypsin from 200 nM in the first round to 25 nM in the fourth round. Plasmid DNA from the original and FACS-enriched libraries were recovered from the yeast using a Zymoprep kit (Zymo Research) and then transformed into XL1-blue supercompetent E. coli (Strategene). Transformed E. coli were incubated in SOC media (Invitrogen) at 37°C for 1 h with shaking before plating on LB agar plates with ampicillin (100 µg/mL). At least 50 unique clones from each of the original and enriched EETI loop-substituted libraries were recovered and sequenced. Elim Biopharmaceuticals (Hayward, CA) and MCLAB (South San Francisco, CA) performed plasmid sequencing services. Only clones without truncations and with loops of the correct target length were included in the analysis below. Library sequences were analyzed using programs available on the RELIC bioinformatics server [61]. The AAFREQ program was used to calculate overall and positional frequencies of each amino acid residue in the substituted loop regions. POPDIV [32] was used to estimate the diversity within the original and sorted EETI loop-substituted library populations. The DIVAA program [33] was used to quantify the tolerated diversity at each position of the randomized loops of EETI loop-substituted library clones, both in the original and enriched libraries. The MOTIF1 and MOTIF2 programs [61] were used to identify continuous and discontinuous motifs, respectively, within the loop regions of EL3-9 clones isolated from the fourth round of FACS. Substituted loop sequences from the original and enriched EL3-9 libraries were aligned using ClustalW2 [41] with default parameters, except the penalty for opening a gap was set to 100 instead of 15. Covariance analysis was performed on the aligned sequences to identify coupled loop positions. The analysis was conducted using the OMES, ELSC, MI, SCA, and McBASC algorithms as previously described [34]. Covariance analysis with each of the algorithms was first performed on sequences from the original, unsorted EL3-9 library, which served as a negative control for covariance since it was designed to have randomized loops. To quantify the average background covariance score inherent to each method, we calculated the average scores and standard deviations obtained using each of the algorithms across all positional loop pairs in the unsorted EL3-9 library. Covariance analysis was then performed on the aligned sequences from the enriched EL3-9 library. The resulting covariance scores for each positional pair were converted into z-scores using the average and standard deviation values obtained for the original library with each corresponding algorithm. Positional pairs with z-scores greater than or equal to 2 were individually analyzed for common correlated mutations in amino acid residues. Those positional amino acid pairs (i, j) whose frequency was at least 50% greater as a matched pair than their individual frequencies in the sorted population were used to predict tolerated loop sequences. To minimize error introduced by small sample sizes, only those residues that populated the predictor position in at least 10% of the sequences were considered for analysis. Further, predicted amino acids were only considered if they occurred at least twice in the general population. Predicted peptide sequences were generated for substitution into EETI loop 3 by combining the trends uncovered in the sequence and covariance analyses of the enriched EL3-9 trypsin-binding clones. The third position of EETI loop 3 was set to either asparagine or threonine based on frequently-occurring motifs and positions 8 and 9 were set to the observed glycine-tyrosine consensus sequence. The remaining positions of EETI loop 3 were predicted according to the results from covariance analysis. All possible combinations of correlated amino acid pairs were used to generate 420 clones that we predicted would retain binding to trypsin. The predicted clones were then filtered based on their inclusion of common motifs observed in the EL3-9 sorted library. Predicted clones whose loop 3 sequences contained 3 or 4 common motifs were aligned using ClustalW2 [41] and 15 clones representative of the predicted sequence space were chosen at random for testing of their trypsin-binding abilities. Additionally, the 420 predicted clones were ranked according to their similarities (calculated based on a modified BLOSUM62 matrix) to clones from the enriched EL3-9 library using the FASTAskan program [42] from the RELIC bioinformatics server [61]. Predicted clones whose similarity scores were in the lowest 10% were aligned with ClustalW2 and the 10 lowest ranked clones representative of the sequence space were selected for testing. Additionally, we used the RandSeq tool (http: //ca. expasy. org/tools/randseq. html) from the ExPASy server to generate 25 EETI clones whose loop 3 sequences were replaced with randomized, nine-amino acid sequences. For this purpose, amino acid compositions were set according to the frequencies expected from degenerate NNS codons. The 25 predicted EETI loop 3 clones and the 25 randomly-generated EETI loop 3 clones were constructed by PCR with overlapping primers, digested with NheI and BamHI restriction enzymes, and ligated into linearized pCT vector with T4 DNA ligase (New England Biolabs). Ligated pCT-EETI loop 3 predicted and randomly-generated plasmids were transformed into XL1-blue supercompetent E. coli (Stratagene) for plasmid miniprep and sequencing (MCLAB). Clones of the correct sequences were transformed into S. cerevisiae strain EBY100 yeast by electroporation and grown on selective SD-CAA agar plates. For each EETI clone, three individual yeast colonies were selected from the corresponding transformation plate, propagated in selective SD-CAA media, and induced for protein expression in SG-CAA media at 30°C. Thus, triplicate samples of yeast-displayed EETI loop 3 clones were analyzed by dual-color flow cytometry for protein expression and trypsin binding at 25 nM as described above.

The use of engineered proteins in medicine and biotechnology has surged in recent years. An emerging approach for developing novel proteins is to use a naturally-occurring protein as a molecular framework, or scaffold, wherein amino acid mutations are introduced to elicit new properties, such as the ability to recognize a specific target molecule. Successful protein engineering with this strategy requires a dependable and customizable scaffold that tolerates modifications without compromising structure. An important consideration for scaffold utility is whether existing loops can be replaced with loops of different lengths and amino acid sequences without disrupting the protein framework. This paper offers a rigorous study of the effects of modifying the exposed loops of Ecballium elaterium trypsin inhibitor II (EETI), a member of a family of promising scaffold proteins called knottins. Through our work, we identified sequence patterns of modified EETI loops that are structurally tolerated. Using bioinformatics tools, we established molecular guidelines for designing peptides for substitution into EETI and successfully predicted loop-substituted EETI variants that retain the correct protein fold. This study provides a basis for understanding the versatility of the knottin scaffold as a protein engineering platform and can be applied for predictive interrogation of other scaffold proteins.

Abstract Introduction Results Discussion Materials and Methods

biochemistry/molecular evolution computational biology/macromolecular structure analysis molecular biology/molecular evolution biochemistry/protein chemistry biochemistry/bioinformatics biochemistry/biomacromolecule-ligand interactions biotechnology/bioengineering computational biology/macromolecular sequence analysis biotechnology/protein chemistry and proteomics chemical biology/protein chemistry and proteomics biochemistry/drug discovery chemical biology/directed molecular evolution

2009

Interrogating and Predicting Tolerated Sequence Diversity in Protein Folds: Application to E. elaterium Trypsin Inhibitor-II Cystine-Knot Miniprotein

In this paper, we present a combined theoretical and experimental study of the propagation of calcium signals in multicellular structures composed of human endothelial cells. We consider multicellular structures composed of a single chain of cells as well as a chain of cells with a side branch, namely a “T” structure. In the experiments, we investigate the result of applying mechano-stimulation to induce signaling in the form of calcium waves along the chain and the effect of single and dual stimulation of the multicellular structure. The experimental results provide evidence of an effect of architecture on the propagation of calcium waves. Simulations based on a model of calcium-induced calcium release and cell-to-cell diffusion through gap junctions shows that the propagation of calcium waves is dependent upon the competition between intracellular calcium regulation and architecture-dependent intercellular diffusion. Multi-level organization and dynamics is a hallmark of most biological systems. This is particularly true in tissues in which single cells are organized into multicellular structures, which are further assembled into complex tissue and organs. For example, endothelial cells are assembled into multicellular tubes (i. e. vessels) which are connected to each other to form a branched vascular tree system. Molecular signals are initiated and/or processed at the endothelial cell level yet influence overall tree behavior and vice-versa [1]. Central to the proper behavior in these biological systems is cross-level interdependence. To date, limited studies of signaling in multicellular networks have demonstrated that the architecture of multi-cellular systems have a significant impact on the behavior of individual cells as well as their emerging collective behavior. Over the past decade, questions concerning the system behavior of cellular structures have received increasing attention. For instance, there is strong evidence that the branching architecture of the mammary gland is a major regulator of normal epithelial cell signaling and function [2], [3]. Normal organ architecture can suppress tumor formation and prevent malignant phenotypes even in grossly abnormal cells [4]. Tissue engineering in its attempt to construct functional tissues faces the challenge of arranging cells (e. g. scaffolding via decellularization of allograph tissue) in a three-dimensional configuration with architecture analogous to the native tissue to support proper spatial and temporal molecular signaling necessary to sustain appropriate development and function [5]. Also, downstream and upstream signal conduction between endothelial cells along the walls of vessels plays an important role in microcirculatory function, vascular network remodeling, vasculogenesis, and neovascularization [6]. A particularly relevant aspect to tissue engineering is the emerging behavior of a multicellular architecture in which cell-level functions, such as intracellular communication, integrate with multicellular architectures through local cell-to-cell interactions. Central to this problem is that cellular networks inherently combine dynamical and structural complexity. Early progress on modeling coupled dynamical systems was limited to space-independent coupling or regular network topologies. Further progress to circumvent the difficulty of modeling associated with the combined complexity of the dynamics and of the architecture was achieved by taking a complementary approach where the dynamics of the network nodes is set aside and the emphasis is placed on the complexity of the network architecture [7]. Accordingly, linear solutions of calcium reaction/diffusion models of multicellular architectures composed of networks of chains of cells with grafted side branches have shown that calcium wave propagation differs in ordered or disordered architectures [8], [9]. Similar effects have also been encountered in chains of endothelial cells with non-linear intracellular calcium dynamics [10]. To evaluate the effects of multilevel architectures on biological signal behavior, we modeled calcium-signal propagation in networks of endothelial cells experimentally and computationally. The vasculature is an ideal system for evaluating multi-scale behavior given the relatively simple but multi-ordered organization of the cells and tissues. Here, the behavior of a calcium wave moving along branched chains of endothelial cells was simulated using experimentally observed parameters in the computation. While there are numerous stimuli that can initiate calcium waves in endothelial cells, we utilized the mechanical stimulation of a single endothelial cell as the wave initiator to minimize confounding issues related to multiple upstream and downstream effects intrinsic to diffusible (i. e. pharmacological) signals. Furthermore, mechanical forces play important roles in endothelial function in vivo [11]. The theoretical aspect leverages progress in modeling of the dynamics of complex networks and in microengineering of multicellular structures to generate new knowledge concerning multicellular architectures. Our study is based on networks of human umbilical vein endothelial cells (HUVEC) (ATCC CRL-1730) in which intercellular calcium wave propagation is primarily dominated by gap junction [12]. Since we are interested in the behavior of networks of endothelial cells composed of one-dimensional chains of cells and networks of chains of cells, a reaction/diffusion model is developed to gain insight into the architecture-dependence of calcium wave propagation. For the sake of simplicity, we only consider the dynamic of intracellular calcium and assume the intercellular Ca2+ is transported between cells by diffusion through gap junctions. In this section we consider the behavior of a finite chain of endothelial cells among which a single cell in the chain is subjected to mechanical stimulation to initiate a calcium impulse, due to the intracellular increase in calcium concentration. We now consider the behavior of a chain of cells subjected to dual mechano-stimulation. The stimulations are applied simultaneously on two cells separated by a short distance. In light of an average distance of propagation of a calcium pulse of approximately 4. 7 cells, this distance is chosen so that one could expect possible overlap of the signals emanating from the two stimulated cells in the region separating them. The growth of “T” structures formed by surface-patterning perpendicular single chains of cells does not permit the formation of cellular junctions composed of a single cell. Typically, many cells aggregate at the junction of the three branches forming a cell cluster (see Figure 8 and Figure 9). In section “Single Chain-Dual Stimulation: Experiments” we have demonstrated that two calcium waves cannot cross when propagating toward each other in a chain of endothelial cells. We consider, here, the dual-stimulation of a “T” structure with stimulations located in two separate branches. We address the question of the interaction of the two calcium pulses in the junction area. In this paper, we study experimentally the propagation of calcium waves in different multicellular structures composed of human umbilical vein endothelial cells (HUVEC). The fabrication of cell-chain based multicellular chain structures relies on organizing multiple cells into specific configurations via selective plasma surface functionalization, which guides cellular attachment. Calcium waves are actuated via mechano-stimulation of selected cells. Calcium wave propagation is characterized by time-resolved fluorescence microscopy. The experimental observations are complemented by modeling and simulation of calcium wave propagation using a diffusion/reaction model. The model of intracellular calcium dynamics is non-linear and mimics the IP3-induced calcium release and calcium induced calcium release (CICR). In order to capture the essence of cross-level interactions in calcium signal propagation in multicellular architectures, we only consider a single component model of CICR. This model is different from previous CICR models, which consisted of multiple coupled non-linear differential equations describing the kinetics of IP3/Ca2+ pumping, release and activation [25], [26]. Nevertheless, the model is capable of capturing most essential features of calcium wave propagation in HUVEC observed in the experiment. Cell-to-cell interactions are described in this paper via intercellular diffusion through gap junctions. Experimental observation of calcium waves induced by a single mechano-stimulation and propagating along a chain of endothelial cells is used to calibrate the model. Experiments and simulations of chains of cells subjected to dual stimulation (i. e. simultaneous stimulation of two different cells) show that two calcium waves cannot cross each other due to the refractory stage of endothelial cells. The study of more complex multicellular structures utilized “T” structures, which are composed of three side branches joining at a junction. The junction is comprised of cell clusters. In this case, we observe experimentally that when a single cell in one of the side braches is stimulated, the calcium signal does not propagate beyond the junction area. However, when two mechano-stimulations are simultaneously applied on separate branches the calcium signal can propagate through the junction area and beyond well into the third unstimulated side branch of the “T” structure. A computational model of a “T” structure, which includes a cell cluster at the junction, shows the importance of intracellular calcium dynamics and intercellular diffusion in determining the propagation behavior of calcium waves. In particular, the organization of cells in the junction determines the existence of multiple paths for intercellular diffusion, which may affect the accumulation of cytosolic calcium and subsequently the ability of cells to undergo CICR. In summary, this work demonstrates that the propagation of calcium waves is dependent upon the architecture of multicellular structures. This dependence is due to the competition between intracellular calcium reaction and diffusion, which is affected by the topology through cell connectivity via gap junctions.

Calcium wave signal has been found in a wide variety of cell types. Over the last years, a large number of calcium experiments have shown that calcium signal is not only an intracellular regulator but is also able to be transmitted to surrounding cells as intercellular signal. This paper focuses on the development of an approach with complementary integration of theoretical and experimental methods for studying the multi-level interactions in multicellular architectures and their effect on collective cell dynamic behavior. We describe new types of higher-order (across structure) behaviors arising from lower-order (within cells) phenomena, and make predictions concerning the mechanisms underlying the dynamics of multicellular biological systems. The theoretical approach describes numerically the dynamics of non-linear behavior of calcium-based signaling in model networks of cells. Microengineered, geometrically constrained networks of human umbilical vein endothelial cells (HUVEC) serve as platforms to arbitrate the theoretical predictions in terms of the effect of network topology on the spatiotemporal characteristics of emerging calcium signals.

Abstract Introduction Methods Results Discussion

materials science biology engineering

2012

Calcium Wave Propagation in Networks of Endothelial Cells: Model-based Theoretical and Experimental Study

The health of the honeybee and, indirectly, global crop production are threatened by several biotic and abiotic factors, which play a poorly defined role in the induction of widespread colony losses. Recent descriptive studies suggest that colony losses are often related to the interaction between pathogens and other stress factors, including parasites. Through an integrated analysis of the population and molecular changes associated with the collapse of honeybee colonies infested by the parasitic mite Varroa destructor, we show that this parasite can de-stabilise the within-host dynamics of Deformed wing virus (DWV), transforming a cryptic and vertically transmitted virus into a rapidly replicating killer, which attains lethal levels late in the season. The de-stabilisation of DWV infection is associated with an immunosuppression syndrome, characterized by a strong down-regulation of the transcription factor NF-κB. The centrality of NF-κB in host responses to a range of environmental challenges suggests that this transcription factor can act as a common currency underlying colony collapse that may be triggered by different causes. Our results offer an integrated account for the multifactorial origin of honeybee losses and a new framework for assessing, and possibly mitigating, the impact of environmental challenges on honeybee health. In the last few years, large-scale losses of honeybees (Apis mellifera L.) have been recorded all over the world [1]. A poorly understood syndrome, called Colony Collapse Disorder (CCD), reported in the United States of America since 2006 [2], has attracted the attention of both the scientific community and the public opinion [3], [4]. However, elevated winter colony losses, not related to CCD, have been reported in most regions of the northern hemisphere [5] and, even in the USA, CCD seems to be just one of the many causes of colony losses [6]. Several possible causes have been claimed for colony losses but there is now a general consensus about the fact that many factors are likely involved [7]. Whatever the origin, this problem has caused great concern due to the importance of honeybees as pollinators of many crops, which represent a significant and increasing proportion of human diet [8], [9]. Unfortunately, despite the considerable research efforts devoted to the study of the problem, the causes of widespread colony losses still remain poorly understood from a functional point of view, although pathogens seem to play a key-role [7], [10]. Several lines of direct and indirect evidence for the involvement of existing and emerging parasites and pathogens have been provided [11]–[17]. Recent studies suggest that, more generally, the collapse of honeybee colonies involves an interaction between pathogens and other stress factors, including the parasitic mite Varroa destructor Anderson & Trueman [2], [18], [19]. V. destructor is a widespread and economically important parasite of A. mellifera [20], [21], which can transmit pathogenic viruses, often associated with colony collapse [14], [16], [19], [22], [23], and determine a host immunosuppression syndrome not fully characterized at the molecular level [24]–[26]. Even though the possible role of the Varroa mite in colony losses is supported by a wealth of data [7], [17], [19], [27], and its active vectoring of bee viruses is demonstrated [28], the functional details of this dangerous association still remain poorly defined [21]. In particular, the association with Deformed wing virus (DWV) appears particularly interesting due to the increasing body of evidence about the role of this virus in bee colony losses [14]–[16], [19]. DWV is a positive strand RNA virus that can be vertically transmitted through the germ-line, causing covert infections in honeybee populations [29]. Available data suggest that DWV copy control can be undermined by concurrent infestation with V. destructor, leading to damaging overt infection [29]. However, although significant contributions have been provided [30]–[32], the mechanism of this interaction remains unclear. Multi-parasite within-host interactions are receiving increasing attention [33] in order to achieve a better comprehension of the structure, dynamics and pathogenic significance of parasite communities [34]. Unfortunately, the descriptive nature of most studies carried out so far on honeybees has not allowed a detailed functional representation of the complex network of biotic interactions underpinning the decline of honeybee colonies. The present study aims at filling this gap, by dissecting at the population and molecular level the major changes that underlie the colony collapse associated with V. destructor infestation, in order to describe both the mechanistic basis and dynamical properties of the biotic interactions that are involved. To address these issues we adopted an approach based on the comparative analysis of bee colonies exposed to different infestation levels of V. destructor, while maintained in the same environmental conditions. This allowed to accurately monitor the major changes occurring over time in the colonies and to shed light on the most crucial components involved in the decline and eventual collapse; laboratory experiments, carried out under strictly controlled conditions, complemented our field study. The results allow us to define and analyse a novel dynamical model to describe the complex interactions between bees, pathogens and parasites and other stress factors, providing a new predictive framework for the study of the impact of diverse environmental stress factors on honeybee health. In an isolated location we set up two experimental apiaries, one of which received conventional acaricide treatments to control mite infestation (low infested colonies: LIC), while the other was left untreated, to monitor the effects of an increasing mite population (highly infested colonies: HIC). A decline of bee population was observed in all colonies along the Summer, although a marked acceleration of the process was noted in HIC late in the season so that, at the end of October, a significant reduction of bee population was observed in such colonies (U = 0, n1 = 6, n2 = 5: P<0. 01; Figure 1A). Two highly infested colonies collapsed by the end of Autumn, whereas the remaining ones did so by the following Spring. Bee mortality, as determined from the number of dead bees recovered in front of the hives and bee population, was abruptly and significantly raised at the end of the season in HIC (U = 0, n1 = 6, n2 = 5: P<0. 01; Figure 1B). After a steady increase over time, the HIC mite population reached its highest level at the end of the season, whereas acaricide treatments kept it under control in the LIC (Figure 2A). A metagenomic analysis of bee samples collected in October from all experimental hives revealed the presence of a few common symbionts [11], [35] (Table S1). With regard to non-viral pathogens, Nosema ceranae, linked to colony losses in Spain [12], occurred at similar rates both in LIC and HIC (Table S1). A survey by RT-PCR of the most common pathogenic bee viruses [22], carried out on bee samples collected monthly from the experimental hives, revealed the widespread presence of Black queen cell virus (BQCV), Deformed wing virus (DWV) and Sacbrood virus (SBV) only. Both BQCV and SBV prevalence fluctuated and overall declined along the season (Figure S1); in contrast, DWV prevalence increased over time and, in September, approached 100% in all experimental hives (Figure 2B), similar to reports in other studies [19], [36], [37]. Quantitative Real-Time RT-PCR analysis of DWV infected bees collected in October from the experimental hives showed that the number of viral genome copies was significantly higher in honeybees from HIC (U = 46, n1 = 14, n2 = 11: P≤0. 05; Figure 2C), and that the significant increase of bee mortality recorded concurrently (Figure 1B) was associated with higher viral loads in infested colonies, exceeding 1×1015 genome copies per bee. The increase of viral load associated with intense Varroa infestation and its lethal impact were further corroborated by laboratory experiments. A significant increase of DWV genome copies in artificially mite-infested honeybee larvae was triggered by Varroa feeding (H = 12. 46, df = 2: P<0. 01; Figure 3A). Moreover, the level of viral infection alone markedly influenced the survival of honeybees. In fact, injection of different dilutions of bee lysates obtained from individuals showing deformed wings resulted in rates of bee mortality significantly higher than in controls (M = 5. 645 and 7. 442 for lower and higher concentration respectively: P<0. 001) and positively related to the lysate concentration used (M = 2. 564: P<0. 01; Figure 3B). This effect can be considered the result of different levels of DWV injected. In fact, this virus was exclusively present in the lysate of symptomatic bees, it was absent in control extracts, while both experimental lysates contained BQCV and were Nosema-free (data not shown). To shed light on the alterations of the honeybee immune system associated with Varroa-induced viral replication, we applied RNA-seq technology to perform a transcriptomic analysis of adult bees collected from each experimental colony in October, when the concurrent viral outbreak and the bee mortality peak were observed. An immunosuppressive effect was evident in bees collected from HIC, which was characterized by a significant down-regulation of 19 immune genes [38]. The most pronounced effects were observed on signalling molecules (e. g. dorsal-1A, a member of the protein family NF-κB, and serine proteases), while a relatively lower degree of down-regulation was recorded for those involved in recognition of non-self (e. g. AmSCR, scavenger receptors B5 and B7, C-type lectin 8) and for a few components of immune signalling pathways (e. g. Hem, Tak1, SOCS) (Table 1, Table S2). However, this immunosuppressive syndrome was associated with a significant up-regulation of 6 immune genes, encoding both recognition (PGRP-S2, NimC2, Eater-like) and signalling (serine proteases) molecules (Table 1, Table S2), part of them playing a role in phagocytosis. The differential expression of the genes which showed the most evident alteration of their transcriptional profile was confirmed by means of a Real-Time RT-PCR analysis of bee samples collected from the same colonies (Figure S2); in particular, the absolute quantification of dorsal-1A, the most down-regulated gene, with potential impact on several immune and stress responses, confirmed the strong reduction of transcript level observed late in the season in HIC (U = 2, n1 = 5, n2 = 5: P<0. 05; Figure S3). To tentatively assess the respective contribution of Varroa mites and DWV in the induction of the observed immunosuppression, we measured the transcriptional level of dorsal-1A in bees, either infected or not by DWV, as affected by infestation of Varroa mites in vitro. No significant differences in the level of the dorsal-1A transcript were induced by mite feeding in lab reared bees that resulted DWV-free at the end of the experiment; conversely the expression level of dorsal-1A in lab reared bees infected by DWV was significantly lower than in the case of virus-free individuals, irrespective of their exposure to mite infestation (F = 26. 79, df = 1: P<0. 001; Figure 4). This result indirectly indicates that the virus may play an important role in the observed transcriptional down-regulation of dorsal-1A, which could be considered part of the virulence strategy adopted by DWV to overcome one of the central components of the antiviral immunity in insects [39]–[45]. To corroborate this hypothesis, we assessed whether dorsal-1A transcript abundance can affect viral infection in honeybees, by using RNA interference (RNAi) and measuring the resulting effects on viral load. We observed a significant suppression of dorsal-1A transcription in bees ingesting the corresponding dsRNA (H = 7. 00, df = 1: P = 0. 008; Figure 5A), along with a concurrent significant increase of DWV genome copies (H = 9. 61, df = 1: P = 0. 002; Figure 5B). This result demonstrates that a reduction of NF-κB availability promotes viral replication, and supports the hypothesis that this transcription factor is an important component of the antiviral response in honeybees. Moreover, it indirectly indicates that any stress factor triggering responses mediated by NF-κB can compete for the use of this transcription factor and promote viral replication. To explore the dynamical properties of our proposed pathogen-parasite interaction, we constructed and analysed a series of simple dynamical models of DWV copy number, mediated by a shared immune currency that can in turn be modified by the presence of virus and other stressors such as mite feeding. Under a default chronic infection model (with a constant rate of immuno-excitation) [46], we see a stable intermediate viral set-point. If, in contrast, we have constant immuno-suppression, then a purely aggressive viral dynamic results, with all successful infections leading to explosive growth. The simplest model consistent with the observed bistable copy number control (low, cryptic or high, overt infection) requires that the immunosuppressive effect of DWV displays some form of threshold function with increasing copy number. Given this assumption, Figure 6 and the corresponding analysis highlights that in the absence of any additional immunological strain on the host (such as mite feeding), DWV can be effectively regulated to low copy number, so long as DWV is kept below a high and critical threshold. However, any factor that depletes the immune system will cause a gradual increase in the stable set-point until a critical transition occurs and uncontrolled viral replication ensues. The sudden transition to explosive viral growth results directly from the non-linear immuno-suppressive behaviour of DWV, potentially allowing the virus to function as an opportunistic pathogen, sensing and exploiting host weakness with escalating immuno-suppression and explosive growth. A steady decline of bee population during Summer, after the peak of nectar importation, is a common event under temperate climatic conditions, often followed by population collapse in untreated bee colonies exposed to increasing mite infestations [21]. However, the data reported here show that the decline of highly infested colonies is characterized by a sharp acceleration occurring at the end of the Summer. The time course of mite infestation did not mirror the sudden increase in bee mortality, suggesting that other mortality factors, interacting with the Varroa mite, were likely involved but neither the metagenomic analysis nor the molecular survey of bee viruses revealed any relevant qualitative difference between HIC, that collapsed at the end of the season, and LIC, that survived in good condition. Instead, significant quantitative differences in DWV genome copies were found associated with different mite infestation levels, with bees from HIC bearing viral loads 103 fold higher than bees exposed to a lower mite pressure, as reported also by other authors [19]. The field data were further corroborated by laboratory experiments showing that mite feeding triggers viral replication in bees, which show a mortality rate that is positively associated with the viral load. The negative effects of the association between the Varroa mite and DWV have been widely investigated and many interesting details of this dangerous interaction have been revealed [19], [23], [24], [30]–[32], [47]–[50]. Experimental data on the impact of this interaction on honeybee health and colony stability are currently being expanded, largely on the basis of correlation studies, which allow a thorough analysis of the factors involved [14], [15], [19]. The present study builds upon this growing background information, by providing experimental evidence on the mechanistic details of this virus-mite association, trying to shed light on the functional link between V. destructor infestation, DWV abundance and bee mortality. The transcriptomic analysis of adult bees, collected from each experimental colony in October, when viral replication rate was high, evidenced a severe alteration of the transcriptional profile of several immune genes. This immune syndrome was largely suppressive, with the majority of genes showing negative regulation in bees from HIC. In particular, the marked transcriptional down-regulation of a member of the NF-κB gene family indicates that the pathogen-parasite interaction can interfere with a number of immune responses regulated by this transcription factor, such as the synthesis of antimicrobial peptides, clotting, melanisation and antiviral defences [38]–[45]. Furthermore, the marked impact on some serine proteases seems to reinforce the virus-mite effect on humoral components of the immune response. Even though we have limited information on the role of these enzymes in honeybee immunity [38], [51], we can reasonably assume, on the basis of studies on other model insects, that the down-regulation of these genes may well impair the enzymatic cascades leading to the activation of melanisation and clotting responses [52], [53], as well as other immune pathways that remains to be further characterized. This immunosuppressive effect seems to be largely driven by viral replication, since we have observed that Varroa feeding alone does not seem to influence the expression level of dorsal-1A, the most down-regulated gene that we used as an indicator of immunosuppression, in our in vitro infestations with mites on bee larvae, either bearing or not a DWV infection. This finding is further corroborated by a recent genome-wide analysis of the transcriptional profile in bees infested by the Varroa mite, but not infected by viruses, which evidenced a clear differential expression, with respect to control uninfested bees, only for genes involved in metabolic processes and nerve signalling [54]. Collectively, these experimental evidences indicate that DWV may use a conditional virulence strategy which disrupts NF-κB immune signalling. However, a more direct experimental approach is needed to assess the impact of DWV infection on the bee immunosuppression syndrome and to follow its dynamic changes with the progression of viral infection. The significant increase of DWV genome copies in response to dorsal-1A knockout by RNAi shows that this gene plays a crucial role in the antiviral immune response controlling DWV replication, and corroborates the hypothesis that DWV adopts a conditional virulence strategy partly based on the transcriptional down-regulation of this NF-κB family member. Many viruses target this key-molecule, which is central in the orchestration of the complex network of responses to infection and, more generally, to environmental stress [55]–[57]. However, the present case seems to be different, as, unlike other viruses infecting vertebrates [56] or invertebrates [57], DWV would exert a transcriptional down-regulation, which results in a reduced level of NF-κB transcripts. This suggests that any bee antiviral immune response relying on this transcription factor is reduced, but not strongly suppressed, as happens in more aggressive viral pathogens, which are able to interfere with NF-κB, either directly or indirectly, by targeting upstream events that control NF-κB activation [56]–[59]. Therefore, the delicate balance of covert DWV infections could be disrupted by any stress factor that activates a response triggered by NF-κB. In other words, the limited availability of this transcription factor seems to be sufficient to maintain under control the DWV infection, which, however, may undergo intense replication if NF-κB is substantially subtracted by any other pathway activated by acute responses to stress factors. In insects, wounding activates NF-κB dependent clotting and melanisation [39]; therefore, the bee reaction to Varroa feeding wounds is expected to use the already limited cellular pool of this transcription factor in DWV infected individuals, and consequently can promote an intense viral replication, which can be further aggravated by the injection of additional virus particles. In this framework, the observed viral replication triggered by injection of bacteria in DWV infected bees, rather than to be exclusively considered a consequence of a wide antimicrobial immunosuppression induced by mite feeding [24], could be partly reinterpreted as a possible effect of the competitive use of this transcription factor involved in multiple immune responses [38]–[45], and available at reduced level in infected bees. Indeed, we have observed that the immunosuppression syndrome is unexpectedly characterized by the up-regulation of a limited number of genes (Eater-like, NimC2, serine proteases), that are mostly involved in bacterial phagocytosis [60]. The up-regulation of Eater-like has also been reported in bees infected by IAPV [61]. This experimental evidence suggests that a complete suppression of the bee antimicrobial response is not a stringent functional requirement of the complex co-evolutionary process among bees, DWV and V. destructor. The DWV-mediated immunosuppression of NF-κB signalling may provide significant benefits to the vector mite, because it reinforces the disruption of immune reactions activated by feeding wounds and salivary components [62]. This could be particularly relevant to the Varroa mite since both the invading mite and its offspring feed through the same hole made in the honeybee cuticle, at the beginning of the pupal stage, by the mother mite [63]: clotting and melanization could severely impair mite feeding activity. We reasonably speculate that the Varroa-DWV association can be interpreted as a mutualistic symbiosis in its early stages. A similar, but more ancient, evolutionary pattern can be observed in some parasitoids of lepidopteran larvae, which are associated with immunosuppressive viral symbionts in the family Polydnaviridae [64]. The ancestor of bracoviruses, members of the polydnavirus family, is a host pathogen of the Nudivirus group, closely related to baculoviruses, which was domesticated by the wasp to its own benefit [65]. The “alliance” of parasitic organisms with the viral pathogens of the host seems to be an effective strategy also for some insects attacking plants. The tight association between stylet feeding insects and viral plant pathogens provides a good example of how these latter can be used for suppressing the plant defense response against them. Indeed, it has been recently demonstrated that the Cucumber mosaic virus (CMV) encodes a protein that disrupts the plant antiviral mechanisms, and, at the same time, blocks defense pathways active against aphids [66]. These are just a few examples of the multifaceted viral mutualistic symbioses, which have played an important role in life evolution, by allowing a more effective exploitation of hostile ecological niches [67]. In order to investigate the dynamical properties of our system, we built and analysed a series of dynamical models capturing differing assumptions on the interactions between virus, host and additional stressors (e. g. mite infestation), and contrasted the model behavior with our observed results. This methodology allowed us to conclude in favour of a threshold immune-suppression model for DWV, which would allow the virus to function as an opportunistic pathogen, able to switch in response to host condition from a stable, cryptic state to aggressive exploitation. This opportunistic strategy is highly reminiscent of the condition-dependent behaviour of temperate phage viruses, which are able to switch between cryptic vertical transmission and aggressive horizontal transmission, as a function of the stress level (SOS response) in their bacterial host [68]. Clearly, the mechanisms underlying condition-dependent host exploitation are vastly different between phage lambda and DWV, however the selective contexts contain analogies: aggressive exploitation and increased horizontal transmission is likely to be more favourable when current host condition dips below a critical limit - broader biological examples of rats leaving a sinking ship. This novel dynamical framework builds on our experimental results and offers predictions for future work. Specifically, not only V. destructor but other stressors competing for immune resources have the potential to destabilise DWV dynamics by tipping DWV copy number above its control threshold into its aggressive exploitation regime. The key immune currency identified by our transcriptome analysis is a member of NF-κB gene family. This gene family not only plays a central role in insect immunity [69], but is also involved in intricate cross-talks with a number of physiological and stress response pathways, conserved across different organisms [55], which are often reciprocally tuned to allow optimal energy allocation between metabolism and immune response, as recently demonstrated in Drosophila [70]; the observed induction of DWV replication in bees exposed to cold stress [50] seems to lend further support to this hypothesis. Therefore, different stress factors impacting immunity and metabolism may compete for the use of NF-κB cellular pools, already reduced by the parasite-pathogen association, promoting intense viral replication in bees harbouring silent infections and subsequent colony collapse (Figure 7). The considerable diversity of stress factors that can interfere with the immune system may partly account for the variety of putative causal agents invoked so far to explain honeybee colony losses, that do not seem to be univocally linked to a specific causative agent. Two apiaries, made of six colonies each, were set up at the end of April in an isolated location of the Prealps (Porzus, Udine, Italy; 46°11′N, 13°20′E), 1. 6 km apart from each other. Previous studies indicated that the local bee population consists of hybrids between A. mellifera ligustica and A. m. carnica [71], [72]. Queens were local and naturally inseminated; hives were all treated the preceding year with acaricides, in order to have very low starting populations of the parasitic mite V. destructor at the beginning of the experiment. In one apiary mite populations were kept under control during the experiment by treating the hives with prophylactic acaricides (the colonies of this apiary are referred to as “low infested colonies” (LIC) in the text). A thymol-based product in tablets (ApiLife Var) was used, from mid-August to mid-September, in presence of brood; at the end of October, two treatments were carried out with oxalic acid, in absence of brood (5 ml of a solution of 30 g of oxalic acid in 1 l of deionized water were sprayed on both sides of each comb of the hive). In the other apiary no acaricidal treatments were carried out (the colonies of this apiary are referred to as “highly infested colonies” (HIC) in the text). In August, one hive in this apiary succumbed because the queen became drone layer, and was not further considered. Re-infestation can strongly affect the population dynamics of the mite if highly infested, weak colonies are robbed by low infested, strong colonies located in the vicinities [73]. Therefore, we adopted an experimental design in which treatments (high and low infestation) were applied to hives belonging to two different apiaries located at a distance such that the environmental conditions were the same but robbing was prevented. As regards as possible factors affecting the independency of hives, with similar infestation rates, belonging to the same apiary (e. g. worker drifiting), available data suggest that they should not affect significantly any of the variables considered in the field study [74]. This experimental design was conceived to allow a very detailed and direct analysis of the potential collapse-inducing factors, under uniform experimental conditions. Severe logistical constraints precluded the use of multiple apiaries per treatment in the field experiment; however, the laboratory experiments had a replicated design and confirmed the central field observations. Bee population in the experimental hives was estimated approximately once a month, from May to October, by counting the number of full or partial “sixth of frames” covered by bees in each hive at sunset and calculating the overall bee population, on the basis of the correlation which indicates that one fully covered sixth of comb corresponds to 253 adult bees [75]. The number of brood cells was estimated using the same method, taking into account that one sixth of frame of brood cells corresponds to 728 worker brood cells. On the same sampling dates, starting in June, the infestation of adult bees and brood by Varroa mites was estimated. The first was assessed on a sample of about 1,000 bees, collected from a frame located in the central part of the hive. Sampled bees were transferred into a flask, covered with 35∶65 ethanol∶water, and hand-shaken for about 5 minutes. Then, bees were recovered by filtration, the liquid phase was filtered again with a convenient sieve to collect the mites and reused for rinsing the bees until no mites were found in three consecutive washes. Infestation level was expressed as number of mites per adult bee. To assess brood infestation, one piece of brood comb (10×10 cm) was collected from each colony and 50 sealed cells from each side were opened and inspected for the presence of mites. Only dark-red mites were considered, to exclude any offspring produced by founder mites. Infestation was expressed as number of mites per cell. Mite infestation was calculated using the following formula: [ (adult bees infestation×bee population) + (brood infestation×brood cells) ]×1,000/ (bee population+brood cells). Dead bees found in cages placed in front of the colonies were counted on weekly basis, from May to October. Bee mortality on each sampling occasion was calculated by averaging the number of dead bees in the time interval elapsed since the last sampling date; this value was then referred to the mean bee population in that period, obtained by considering the initial and final bee population and then multiplied by 1,000. Samples of 10 bees were collected in October, from each LIC and HIC colony (n = 6 and n = 5 for the two groups respectively), ground in liquid nitrogen and immediately used to extract total RNA, using Tri-Reagent (Ambion Inc.). These RNA samples were processed using the TruSeq mRNA-seq sample prep kit (Illumina, Inc. , CA, USA) starting from 2 micrograms of total RNA. Briefly, poly-A containing mRNA molecules were isolated using poly-T oligo-attached magnetic beads using two rounds of purification. During the second elution of the poly-A RNA, the RNA is also fragmented and primed for cDNA synthesis. Then standard blunt-ending plus add ‘A’ was performed and Illumina adapters with indexes (from 1 to 12) were ligated to the ends of the cDNA fragments. After ligation reaction, separation of not ligated adapters and size selection in the range 500–600 bp was performed on 2% low-range agarose gel. Samples were amplified by PCR to selectively enrich those DNA fragments in the library having adapter molecules at both ends. Pools of 3–4 samples were loaded on cBot, to create clusters and sequenced at ultra-high throughput [76] on HiSeq2000 (Illumina Inc.). One lane for 12 samples was run obtaining 23–36 millions of pair-end reads per colony, 100 bp long. Sequences from each colony were quality trimmed by CLC (modified-Mott trimming algorithm, trim using quality score 0. 03) and mapped on Amel 4. 0 bee genome reference sequence using CLC Genomics Workbench (CLCBio, Denmark). The un-aligned reads (about 20% of total reads) were de novo assembled using the same software. Contigs were compared to the non-redundant sequence databases at NCBI (http: //www. ncbi. nlm. nih. gov), using BLASTX (protein homology). BLASTX alignment hits with e-values larger than 1×10−5, scores lower than 100 and percentage identity lower than 50% were filtered; isolated assignments (i. e. taxa hit by one sequence only) were discarded. Custom applications written in Perl were used to parse the results. Raw pair-end sequences used for metagenome survey are available at: https: //services. appliedgenomics. org/sequences-export/193-Nazzi_et_al/; password: rawdata). To get a description of the microorganisms associated to the bees under study, taking into account all taxa whose presence was not just sporadic, we considered only those represented in at least two colonies of either group of hives. The resulting list was then filtered against available data on honeybee symbionts from previous reports [11], [35], [77]–[79] retaining only taxa whose presence in honeybees had already been demonstrated. Total RNA was isolated from individual bees by using Trizol Reagent (Invitrogen, Carlsbad, CA), according to the manufacturers' instructions. The concentration and the purity of total RNA was determined using a spectrophotometer (Nanodrop ND100, Thermo Scientific Inc.). Virus presence was assessed by conventional RT-PCR as described elsewhere [80] using the primer pairs reported in Table S3. The quantification of DWV genome copies in individual bees was performed by SYBR-Green Real-Time Quantitative RT-PCR. The amplification conditions and reaction mixture were the same as conventional RT-PCR, using QuantiFast SYBR Green RT-PCR Kit (Quiagen, Hilden, Germany). The titers of DWV were determined by relating the CT values of unknown samples to an established standard curve, according to the absolute quantification method previously reported [81]. The standard curve was established by plotting the logarithm of seven 10-fold dilutions of a starting solution containing 21. 9 ng of plasmid DNA (TOPO TA Cloning) with DWV insert (from 21. 9 ng to 21. 9 fg), against the corresponding threshold value (CT) as the average of three repetitions. The PCR efficiency (E = 107. 5%) was calculated based on the slope and coefficient of correlation (R2) of the standard curve, according to the following formula: E = 10 (−1/slope) −1 (Slope = −3. 155, Y-intercept = 41. 84, R2 = 0. 999). This experiment was designed to assess the impact of Varroa mite feeding on DWV replication in honeybees. Bees and mites used in this and the following laboratory experiments came from A. mellifera colonies maintained in Udine (northeastern Italy). Previous studies indicated that the local bee population consists of hybrids between A. m. ligustica and A. m. carnica [71], [72]. The mites and last instar bee larvae were collected from brood cells capped in the preceding 15 h obtained as follows. In the evening of the day preceding the experiment the capped brood cells of a comb were marked. The following morning the comb was transferred to the lab and unmarked cells, that had been capped overnight, were manually unsealed. The comb was then placed in an incubator at 35°C, 75% R. H. where larvae, either infested or not, spontaneously emerged. Last instar bee larvae were transferred into gelatin capsules (Agar Scientific ltd. , 6. 5 mm diameter) with 1 or 3 mites, and maintained at 35°C, 75% R. H. for 12 days [82]; Varroa-free larvae were used as controls (Figure S4). After 1,6 and 12 days, 5 bees for each infestation level were sampled to determine the total number of DWV genome copies, as described above. This experiment was designed to assess the longevity of adult bees emerging from larvae that received an injection of different numbers of DWV genome copies. The artificial infection with DWV of last instar bee larvae, collected as described above, was performed by injecting 2 µl of a lysate of symptomatic bees, at two different dilutions, using a Hamilton syringe equipped with a 30 gauge needle. Five bees with crippled wings, that is the typical symptom of DWV infection, collected in mite-infested colonies, were frozen in liquid nitrogen, crushed with a pestle in a mortar and suspended in 5 ml of phosphate buffer solution, pH 7. 4. After centrifugation (3,000 rpm/min for 30 min at 4°C), the supernatant was transferred into sealed tubes and stored at −20°C until use [83]. The extract of five healthy bees was prepared in the same way and used for control injections. The lysates obtained as above were tested for seven honeybee viruses and two fungi species by conventional RT-PCR as described elsewhere [80] using the primer pairs reported in Table S3. The number of DWV genomic copies in the samples was assessed by Real-Time Quantitative PCR, as described above. The two adopted dilutions (10−3 and 10−5) in PBS allowed the delivery of an estimated number of DWV genome copies of 1. 66×103 and 16. 6, respectively. Following injection, bee larvae were confined into gelatin capsules, as described above, and maintained in an incubator at 35°C, 75% R. H. . After 12 days, when adult bees were fully developed, gelatin capsules were opened and the experimental bees transferred to an aerated plastic cage (18. 5×10. 5×8. 5 cm), maintained in an incubator, at the same condition indicated above, and fed ad libitum with sugar candy (Apifonda) and water. The number of bees with deformed wings was recorded and dead bees were daily counted and removed. The experiment was replicated 3 times, across April–May, using 25–30 bees per replicate of each treatment. The proportion of symptomatic bees among those treated as above confirmed the effective infection by this method (Figure S5). The same RNAs used for metagenomic analysis were analyzed in terms of gene expression. The standard mRNA sample prep from Illumina was used to produce 36 bp long tags, about 25–30 millions per sample. CLC-Bio Genomics Workbench software (CLC Bio, Denmark) was used to calculate gene expression levels based on Mortazavi et al. approach [84]. A table reporting the data used for subsequent analysis can be found at: https: //services. appliedgenomics. org/sequences-export/193-Nazzi_et_al/ (username: nazzi_et_al; password: rawdata). Differential expression of six selected genes was confirmed by means of Quantitative Real-Time RT-PCR using the primer pairs reported in Table S3. Relative gene expression data were analyzed using the 2−ΔΔCT method [85]. To assess that the amplification efficiencies of the target and reference gene (β-actin) were approximately equal, the amplification of six five-fold dilution of total RNA sample (from 1,000 ng to 0. 32 ng per reaction) were analysed; in all cases the efficiency plot for log input RNA versus ΔCT had a slope lower than 0. 1 (Dorsal = 0. 089; cSP33 = 0. 019; SPH51 = 0. 025; Eater-like = 0. 064; NimC2 = 0. 035; PGRP-S2 = 0. 048). The calibrator was the LIC group. Three estimates of the ΔΔCT of each gene were obtained from independent analyses; for each analysis, one pool of three bees from each colony of both groups was used. The differential expression of dorsal-1A, the most down-regulated gene with potential impact on several immune and stress responses, was also confirmed by absolute quantification; in this case, one pool of three bees from five colonies of both groups was analysed. The standard curve was established by plotting the logarithm of nine 10-fold dilutions of a starting solution containing 127. 4 ng of plasmid DNA (TOPO TA Cloning) with dorsal-1A insert (from 127. 4 ng to 1. 3 fg), against the corresponding threshold value (CT) as the average of three repetitions. The PCR efficiency (E = 93. 2%) was calculated based on the slope and coefficient of correlation (R2) of the standard curve, according to the following formula: E = 10 (−1/slope) −1 (Slope = −3. 495, Y-intercept = 46. 19, R2 = 0. 996). In order to assess the role of the Varroa mite and DWV in the transcriptional down-regulation of dorsal-1A, we measured the impact of mite feeding on the expression level of this gene in virus-free bee pupae and pupae testing positive for DWV. Honeybee pupae, either uninfested or infested by one mite, were prepared as described above, then, after 12 days, they were processed for Quantitative Real-Time RT-PCR, to evaluate the expression of dorsal-1A and DWV infection rate in infected bees. To increase the chances of sampling DWV-free bees, the experiment was carried out on three dates in early Spring, when, according to the data shown in Figure 2B, the prevalence of infection is low, and repeated twice later in the year, when most bees test positive for DWV. Thus virus free and virus infected bees had to be collected on different times; however, a regression analysis revealed no significant effect of time on Dorsal expression in virus free bees. Double-stranded honeybee dorsal-1A (A. mellifera Dorsal variant A, mRNA, GI: 58585243,2389 bp) was prepared using MEGAscript RNAi kit (Ambion), following the manufacturer' s standard protocol. The target sequence was PCR amplified with specific primers, carrying a 5′ tail of the T7 promoter at both ends and used as template for T7-depended in-vitro transcription. Primers used were: F-5′-TAATACGACTCACTATAGGGAGACAATCCAGCACTTATTC-3′; R-5′-TAATACGACTCACTATAGGGAGCCTGAATAGTGTTATTAGC-3′. The reaction product was subjected to DNase digestion, purified and the final preparation was dissolved in nuclease free-water. Individual frames were removed from the colony and stored in an incubator overnight, at 34°C, 90% R. H. . Emerging bees were maintained as groups of 30 individuals in sterile boxes, as described by Evans et al. [86]. Experimental bees were fed daily with 2 ml of a 50% sucrose/protein solution, containing 50 µg of dsRNA of dorsal-1A, while controls were fed with a similar solution, containing a dsRNA of mGFP6 (Green Fluorescent Protein), obtained as described above. Samples of 5 bees were collected at the beginning of the experiment, to assess the starting level of scored parameters, and after 48 and 96 hours of exposure to the dsRNA feeding solution. Samples were stored at a −80°C, until use for RNA extraction. The transcription level of dorsal-1A and the number of DWV genome copies were determined by SYBR-Green Real-Time Quantitative RT-PCR, as described above. Comparisons between treated and untreated colonies, for bee population, bee mortality, Varroa mite infestation and gene expression values resulting from RNA-seq, were carried out using the non-parametric Mann-Whitney test. In all cases, the number of replicates in each group correspond to the number of colonies, that was 6 for the low infested group (LIC) and 5 for the highly infested one (HIC). To compare both the mortality rates and the infestations in the two groups of colonies while controlling for the correlation among repeated observations on the same colony over time, a model for longitudinal data was estimated; in this case a total of 44 observations, deriving from 11 colonies, observed 4 times each, were considered. A between groups regression panel model pointed out a significant effect of the indicator variables (bee mortality and mite infestation respectively) for HIC (bee mortality: estimated coefficient 3. 045, P = 0. 005; mites/1,000 bees: estimated coefficient 116. 968, P = 0. 005). The proportion of DWV infected bees, out of the total analyzed in LIC and HIC, was compared using the Fisher Exact Solution test. In this case, 5 bees per group and per date were used for the analysis. The number of DWV genome copies in individual honeybees, from LIC and HIC, was compared with the non-parametric Mann-Whitney test. In this case, 6 and 13 bees from LIC and HIC, respectively, were used in September, while, in October, 14 and 11 bees were considered for the same experimental categories. Data from the experiment on the effect of Varroa mite feeding on viral replication in honeybee larvae were analyzed using the Scheirer-Ray-Hare extension of the Kruskal-Wallis test. Data from 5 bees per infestation level per time after the beginning of the experiment were used. Comparison of survival rates following injection of bee body lysates were carried out using the logrank test without continuity correction; in this case, 25–30 bees per group were used in each of the 3 replicates. Data on gene expression in virus free and virus infected bees either infested or not by the Varroa mite were compared with the GLM procedure after log tranforming data; 9 uninfested and 9 infested virus-free bees, 10 uninfested and 10 infested virus infected bees were used for the analysis; the software Minitab was used. In RNAi experiments, gene expression and viral replication in bees fed with dsRNA of dorsal-1A or dsRNA of Green Fluorescent Protein, as a control, were compared using the Scheirer-Ray-Hare extension of the Kruskal-Wallis test. Five bees per each time per treatment were used in the analysis. A characteristic of DWV infections in unstressed hosts is the ability of the virus to persist in a cryptic state, and to be stably transmitted vertically [29]. We use the existence of a stable state of chronic infection to base our dynamical model on a ‘predator-prey like system’ [46], as described by the following equations for viral copy number (V) and shared immune currency (I), (S1) (S2) These equations (identical to equations 1 and 3 in [46]) describe the within-host growth of a pathogen population V and its controlling immunological counterpart I. The maximal rate of pathogen replication is r, which is countervailed by a rate of immunological control cI. The dynamics of I are shaped by an intrinsic production rate a, a rate of decay u and an activation rate bV (activation by the pathogen population). A stability analysis of equations S1, S2 using standard techniques [87] and assuming all parameters are positive, reveals that whenever pathogens are able to invade a naïve host (when r>ca/u) then their density V will tend to a single stable equilibrium at. A key characteristic of the interaction between DWV and its host is some degree of immuno-suppression (Table 1). The simplest modification of equation S2 to allow for immuno-suppression is to consider the negative space of the ‘immune-activation’ parameter b. If b is negative, then increasing pathogen density V will act to reduce the immunological control variable I, with potentially de-stabilising consequences. Accordingly, a stability analysis now reveals that whenever pathogens can invade a host (same condition as above), their density will always increase without bounds, thus we have an obligately virulent pathogen that will grow and consume any host that they are able to establish within. We now turn to our threshold suppression model. We again assume that the dynamics of I are modified by an interaction with the pathogen population V, however we now assume that the sign of the interaction (immuno-stimulatory or immuno-suppressive) will depend on the magnitude of V. Specifically, we assume that at low densities the pathogen is a net activator of immunological activity, whereas at high densities (whenever V>b/s) the pathogen becomes immuno-suppressive, with b/s controlling the threshold point between the two regimes. These assumptions give the following revised equation for the dynamics of I (S3) To clarify presentation, we first normalize the system (S1, S3) to reduce the parameter dimensions. Specifically, we rescale the units of time to the maximal growth rate of the virus (t′ = rt), the units of viral density to the density that halts immune proliferation (V′ = Vs/b) and the units of immune density to the density that halts viral proliferation (I′ = Ic/r). Applying these normalizations to equations (S1, S3) lead to the following equations (S4) (S5) Note that the full system (S1, S3) can be recovered from (S4, S5) by rescaling the units and replacing parameters as follows: , , . A stability analysis of the system (S4, S5) reveals equivalent invasion conditions (1>x/y) but following invasion the virus can either tend to a stable equilibrium at (solid line in Figure 6), or grow without limit if V is above an unstable equilibrium at (dashed line in Figure 6) or if non-viral immunological depletion is sufficiently high (if y>x+z/4, to right of intersection in Figure 6). For low y (y<x+z/4), the stable equilibrium is an increasing function of y and the unstable equilibrium is a decreasing function of y.

Honeybees are of capital importance for humans since crop production significantly depends upon pollination by these insects. In recent years, widespread collapses of honeybee colonies have been reported throughout the world; unfortunately, despite intense research efforts, the causal agents of such losses are not yet identified, although parasites seem to play a key-role. We combined molecular, field-longitudinal and theoretical approaches to describe the mechanistic basis and dynamical properties of collapse-causing interactions within the multi-parasite community infecting the honeybees. We found that the parasitic mite Varroa destructor can de-stabilise the within-host dynamics of Deformed wing virus (DWV), transforming a cryptic and vertically transmitted virus into a rapidly replicating killer. The de-stabilisation of DWV infection results from a widespread immunosuppression characterized by a strong down-regulation of a member of the gene family NF-κB. This gene family not only plays a central role in insect immunity, but is also involved in intricate cross-talks with a number of physiological and stress response pathways. This suggests that different stress factors may alter the critical balance between viral pathogens and host-defences, promoting intense viral replication in bees harbouring silent infections and subsequent colony collapse. The model we propose can potentially explain the multifactorial origin of bee losses.

Abstract Introduction Results Discussion Materials and Methods

veterinary pathology pathology pest control biology zoology veterinary science parasitology agriculture

2012

Synergistic Parasite-Pathogen Interactions Mediated by Host Immunity Can Drive the Collapse of Honeybee Colonies

Vaccines may help reduce the growing incidence of fungal infections in immune-suppressed patients. We have found that, even in the absence of CD4+ T-cell help, vaccine-induced CD8+ T cells persist and confer resistance against Blastomyces dermatitidis and Histoplasma capsulatum. Type 1 cytokines contribute to that resistance, but they also are dispensable. Although the role of T helper 17 cells in immunity to fungi is debated, IL-17 producing CD8+ T cells (Tc17 cells) have not been investigated. Here, we show that Tc17 cells are indispensable in antifungal vaccine immunity in hosts lacking CD4+ T cells. Tc17 cells are induced upon vaccination, recruited to the lung on pulmonary infection, and act non-redundantly in mediating protection in a manner that requires neutrophils. Tc17 cells did not influence type I immunity, nor did the lack of IL-12 signaling augment Tc17 cells, indicating a distinct lineage and function. IL-6 was required for Tc17 differentiation and immunity, but IL-1R1 and Dectin-1 signaling was unexpectedly dispensable. Tc17 cells expressed surface CXCR3 and CCR6, but only the latter was essential in recruitment to the lung. Although IL-17 producing T cells are believed to be short-lived, effector Tc17 cells expressed low levels of KLRG1 and high levels of the transcription factor TCF-1, predicting their long-term survival and stem-cell like behavior. Our work has implications for designing vaccines against fungal infections in immune suppressed patients. The incidence of invasive fungal infections in immune-compromised hosts has skyrocketed. These patients often have diminished or dysfunctional CD4+ T cells rendering them susceptible to fungal infections caused by Candida, Aspergillus, Cryptococcus, Histoplasma and Pneumocystis [1]. Thus, it would be advantageous to harness residual immunity against fungal infections in this setting. Regrettably, there are no vaccines to prevent or treat primary or opportunistic fungal infections. Substantial progress has been made in identifying the components of innate and adaptive immunity that control mucosal and systemic fungal infections [2]–[8]. The cytokine IL-17 helps defend against mucosal infections, including those due to fungi. The product is needed for control of mucosal and cutaneous candidiasis in mice [9]. Genetic mutations in IL-17 production or signaling lead to increased susceptibility to mucocutaneous candidiasis in humans [10]. Likewise, neutralization of IL-17 enhances susceptibility to Aspergillus pneumonia in mice [11]. T helper (Th) 17 cells, one source of IL-17, play a prominent role in fungal infections [2], [12], [13]. Although there is controversy about the beneficial and harmful roles of IL-17 and Th17 [14]–[18], we have shown that Th17 cells are pivotal in vaccine resistance against three systemic mycoses [13]. In another study, in immune deficient mice lacking CD4+ T cells, vaccination against Blastomyces and Histoplasma elicited CD8+ T-cell immunity and resistance [19]. There, type 1 cytokines contributed significantly to resistance mediated by CD8+ T cells. However, individual type 1 cytokines were dispensable and compensated without any loss of resistance. The role of IL-17 producing CD8+ T cells (Tc17) was not explored in that study and it is unknown whether Tc17 cells can be induced without CD4+ T-cell help or mediate protective immunity upon fungal vaccination. Although the role of Tc17 cells in fungal infections remains unexplored, recent work shed some light on Tc17 cells during other infections. Tc17 cells lacking granzyme B have been associated with enhanced progression of SIV infection in macaques [20]. In another viral model, Tc17 cells protected against lethal influenza infection [21]. Lastly, Tc17 cells were shown to mount immunity against vaccinia virus infection by acquiring cytotoxic ability [22]. Studies of CD4+ T cells have provided insight into how T cells differentiate into IL-17 producing cells. Naïve CD4+ T cells in mice differentiate into Th17 cells with cues from IL-6 and TGFβ [23]. Differentiated Th17 cells are amplified and sustained by IL-21 and IL-23, respectively [24]. The absence of IL-6 may not abolish Th17 differentiation since IL-21, like IL-6, also can activate Stat3 [23], [25] to induce the expression of RORγt, thereby resulting in the production of IL-17 family members [26]. Additionally, pattern recognition receptor (PRR) signals augment Th17 differentiation. Dectin-1 signals suppress Th1 differentiation and promote a Th17 phenotype [27], [28]. Other C-type lectins and TLRs can augment Th17 cells [29]. The cytokine IL-1 also plays a vital role in inducing and maintaining Th17 cells [30], [31]. Th17 cells are not induced upon antigen challenge in mice lacking IL-1 [32], and IL-1 signaling in non-T cells leads to production of IL-23, which is required for maintenance of Th17 cells [31], [33]. Activation of PRR pathways also leads to production of IL-1β, which, alone or together with IL-6, IL-21 and TGFβ, enhances Th17 development [31]. To the best of our knowledge, the requisite elements of Tc17 cell differentiation during fungal infection have not been studied. Herein, we asked whether and how Tc17 cells are induced by fungal vaccination, and studied the functional role of these cells in vaccine immunity. Since we found a vital role for Tc17 cells, we investigated the PRRs and products that induce their differentiation. We also analyzed the features that might be predictive of their long-term survival and stem-cell likeness in view of our and other' s recent findings [34] suggesting that IL-17 producing T cells may be longer lived than previously believed [35]. We report here that polyclonal and antigen-specific Tc17 cells are induced by fungal vaccination in the absence of CD4+ T-cell help. Tc17 cells were indispensable in vaccine immunity against lethal pulmonary fungal infection. Tc17 cells did not influence Tc1 cells, nor did the loss of IFN-γ producing CD8+ T cells abate the vaccine resistance mediated by Tc17 cells. Vaccine control of lethal fungal infection was dependent on neutrophils, and was linked functionally with CD8+ T-cell derived IL-17. Tc17 cells expressed both chemokine receptors CCR6 and CXCR3, but recruitment of the cells into the lung was mediated by CCR6. Vaccine-induced Tc17 cells and immunity to infection surprisingly did not require Dectin-1 or IL-1 receptor signaling, but did require IL-6. Although IL-17 producing CD4+ T cells are generally thought to be short-lived, Tc17 cells demonstrated a phenotype with markers that are predictive of long-term survival, multi-potency and stem-cell likeness. Given that CD4+ T cells help in initiating the inflammation and activation of DCs needed for efficient priming of CD8+ T cells, we asked whether Tc17 cells are primed after vaccination without CD4+ T-cell help and can be recalled into the lung upon lethal pulmonary infection. The draining lymph nodes (dLNs) and spleens were analyzed for differentiation and expansion of Tc17 cells in mice that had been vaccinated with Blastomyces in the presence or absence of CD4+ T cells. After vaccination, the frequency and total number of Tc17 cells were much higher in CD4 T-cell depleted mice compared to CD4+ T-cell sufficient mice (p≤0. 05; Fig. 1A). To determine whether Tc17 cells expand in an antigen-specific manner in response to the fungal vaccine, we immunized mice with a novel recombinant vaccine strain of Blastomyces that expresses the model ovalbumin epitope SIINFEKL, along with adoptive transfer of OT-I cells into the mice [36]. Nearly 2% of the OT-I cells in the spleen of vaccinated CD4+ T-cell depleted mice were IL-17 positive (Fig. 1B). In vitro co-culture of OT-I cells similarly showed IL-17 production specifically in response to the OVA expressing vaccine strain (data not shown). To validate that Tc17 cells respond specifically to intrinsic yeast antigens, we re-stimulated dLN cells with yeast-loaded bone-marrow dendritic cells (BMDCs). CD8 T cells from vaccinated mice responded specifically to yeast antigen and were positive for IL-17A (Fig. 1B). We also analyzed the recall response of Tc17 cells in vaccinated mice. Tc17 cells were recruited into the lung after pulmonary challenge irrespective of CD4+ T-cell help (Fig. 1C & D). In fact, greater numbers of Tc17 cells were recruited in mice vaccinated in the absence of CD4+ T cells. Tc17 cells recruited to the lung responded in a Blastomcyes antigen-specific manner (Fig. 1C & D). In experiments using Histoplasma for vaccination, we found that Tc17 cells also were induced in response to that yeast, and recalled to the lung upon pulmonary challenge (Fig. S1A & B). Collectively, these data suggest that fungal-specific Tc17 cells can be induced upon fungal vaccination and recalled into the lung independent of CD4+ T-cell help. The induction of a large number of Tc17 cells following fungal vaccination and their trafficking to the lung after experimental challenge led us to explore their functional role in vaccine resistance. We used three approaches. First, we neutralized IL-17A with monoclonal antibody (mAb) during the efferent phase of the immune response following pulmonary infection. Vaccinated CD4+ T-cell depleted mice that received anti-IL-17A mAb had >1 log more lung CFU after challenge than mice that got rat IgG control (Fig. 2A). In a second approach, we used recombinant adenovirus secreting soluble IL-17 receptor to neutralize circulating IL-17A in vaccinated mice depleted of CD4+ T cells. Mice that received the soluble IL-17 receptor had nearly 3 logs more lung CFU after challenge than mice that got the control adenovirus expressing luciferase (Fig. 2B). Thus, IL-17A is required during the efferent phase of vaccine immunity mice lacking CD4+ T-cells. In a third approach, to study the roles of IL-17 and IL-17A/IL-17R signaling, we investigated vaccine resistance in IL-17 receptor A (IL17RA) knock out mice. Mouse T cells produce only IL-17A and IL-17F [37], [38], which both signal through IL-17RA. Thus, even if IL17RA−/− mice develop vaccine-induced Tc17 and Tc1 cells, IL-17 could not signal via its receptor to mediate effector functions. Here, vaccinated IL-17RA−/− mice depleted of CD4+ T cells had ≈2 logs more CFU in their lungs after challenge than did vaccinated wild-type (WT) mice (Fig. 2C). Thus, signaling through the IL-17A receptor is essential for controlling fungal pneumonia in vaccinated mice depleted of CD4+ T cells. Moreover, impaired IL-17A signaling reduced vaccine resistance, even in the presence of Tc1 cells. We have reported plasticity in anti-fungal vaccine immunity, with type 1 cytokines such as IFN-γ, TNF-α and GM-CSF contributing to resistance, but each having dispensable and compensatory roles for one another. We explored the obligate role of IL-17 using IL-17A−/− mice depleted of CD4+ T cells. Vaccinated IL-17A−/− mice had >3 logs more lung CFU after challenge vs. WT mice (Fig. 2D). Thus, Tc17 cells have an obligate, indispensable role in vaccine immunity in CD4+ T-cell deficient hosts. It is possible that non-T cells are a source of IL-17 and resistance in vaccinated mice. We analyzed the lung cells of vaccinated CD4 depleted mice following pulmonary challenge. ∼82% (2. 76/3. 36%) of IL-17A+ cells were CD8+ T cells (Fig. S2). Thus, CD8+ T cells are the main cellular source of IL-17 in lung cells of vaccinated CD4 depleted mice. Recent studies have shown cross-regulation of type 17 and type 1 immunity; the latter being augmented by the former during experimental infection with Mycobacterium tuberculosis or Francicella tulerensis [39], [40]. We studied cross-regulation of Tc1 by Tc17 cells in our vaccine model by analyzing CD8+ T cell responses in vaccinated IL-17A−/− and IL-17RA−/− mice after pulmonary challenge. The frequency of IFN-γ- and TNF-α-producing CD8+ T cells was not reduced in vaccinated IL-17A−/− mice depleted of CD4+ T cells (Fig. 3A & data not shown). The frequency of IFN-γ producing cells was actually higher in vaccinated IL-17RA−/− mice vs. WT controls (Fig. 3B). Overall, the numbers of Tc1 cells recruited to the lungs of vaccinated CD4+ T-cell depleted mice were maintained in IL-17A−/− and IL-17RA−/− mice vs. WT controls (Fig. 3C & D). Collectively, these data suggest that Tc17 cells are distinct and function independently of Tc1 cells in vaccine immunity to fungi. Tc17 cells neither reduced nor augmented type I immunity. IL-12 signaling polarizes the T cell response towards type 1 immunity and down modulates type 17 responses. Hence, lack of IL-12p35 enhances Th17 responses in A. fumigatus infection in mice [27]. We asked whether inhibiting type 1 responses have any impact on type 17 responses and vaccine-induced fungal immunity. We assayed resistance in vaccinated IL-12 receptor beta 2 (IL-12Rβ2) knock out mice depleted of CD4+ T cells. Surprisingly, loss of IL-12 signaling did not perturb vaccine immunity (Fig. 4A), since lung CFU values after challenge were similar in knockout and WT mice. Interestingly, abrogation of IL-12 signaling significantly enhanced resistance against pulmonary infection in unvaccinated knockout mice compared to WT controls (Fig. 4A). We investigated how loss of IL-12 signaling influenced the recall responses of Tc1 and Tc17 cells to the lungs after pulmonary infection. Vaccinated IL-12Rβ2−/− mice lacking CD4+ T cells had significantly less IFN-γ transcript and fewer numbers of IFN-γ producing CD8+ T cells in the lungs than WT controls (Fig. 4B; and data not shown). However, loss of IL-12 signaling did not affect lung transcript expression of IL-17A or IL-4, nor the recruited numbers of IL-17A, GM-CSF or TNFα –producing CD8+ T cells (Fig. 4B; data not shown). Collectively our data suggest that lack of IL-12 signaling in CD4+ T-cell deficient hosts impairs Tc1 (IFN-γ) responses, but this neither skews Tc17 responses nor alters vaccine resistance mediated by these cells against lethal pulmonary blastomycosis. Th17 immunity promotes infiltration and activation of neutrophils to sites of infection. We studied the mode of action of Tc17 cells and tested whether neutrophils promote Tc17 vaccine immunity against Blastomyces. To address this, we used IL-17RA−/− mice in which IL-17A signaling is abolished on responding cells, including neutrophils, and analyzed LFA-1+ neutrophils in the BAL fluid in vaccinated CD4+ T-cell depleted mice after challenge. The frequency of LFA-1+ neutrophils recruited to the lungs was significantly higher in vaccinated WT mice compared to vaccinated IL-17RA−/− mice or unvaccinated controls (Fig. 5A). To functionally test the role of neutrophils in vaccine resistance, we used monoclonal antibody to selectively deplete Ly6G+ neutrophils during the effector phase or recall response to pulmonary infection. Vaccinated WT mice that were depleted of neutrophils had 37-fold more lung CFU than WT controls given rat IgG (Fig. 5B). Unvaccinated WT mice depleted of neutrophils also had higher lung CFU values than control littermates that got rat IgG – approximately 17-fold (Fig. 5B). Thus, a significant component of the resistance mediated by neutrophils was attributable to vaccination in WT mice. In contrast, in IL-17RA−/− mice, depletion of neutrophils had a negligible effect on lung CFUs in vaccinated mice (and also unvaccinated mice). Thus, IL-17 signaling is essential for the infiltration and activation of neutrophils in vaccinated mice depleted of CD4+ T cells. These data suggest that Tc17 cells employ neutrophils as a mode of action in mediating vaccine immunity against fungal infection. We considered that neutrophils recruited in response to IL-17 in vaccinated mice might be responsible for exuberant immunity and excessive damage. To address this issue, we analyzed lung histopathology (Fig. S3). Vaccinated mice deficient in IL-17 signaling had extensive lung inflammation after infection, whereas vaccinated wild-type mice had the least inflamed lungs. IL-17A−/− mice gave similar results (data not shown). Thus, IL-17 was associated with better control of the infection and less inflamed lungs rather than more inflammation in this model. We investigated elements that regulate the differentiation of Tc17 cells that mediate anti-fungal vaccine immunity. In many fungal infection models, Dectin-1 and IL-1 are instrumental for the induction of Th17 cells [2]. In a recent study [41], IL-1 was found to be indispensable during differentiation of Th17 cells, whereas IL-6 was required at some but not all compartments. We looked into these pathways since little is known about the differentiation of anti-fungal Tc17 cells. The differentiation of Tc17 cells was unimpaired in vaccinated Dectin-1−/− and IL1-R1−/− mice depleted of CD4+ T cells as compared to vaccinated WT littermates. The numbers of Tc17 cells in the lung during recall responses were comparable among these groups (Fig. S4A & B, and S5A). Similarly, vaccinated Dectin-1−/− and IL-1R1−/− mice depleted of CD4+ T cells were as resistant to pulmonary infection as vaccinated WT littermates. Each group had lung CFU values nearly 6 logs lower than unvaccinated controls (Fig. 6A & B). In contrast, vaccinated IL-6−/− mice depleted of CD4+ T cells harbored ≈3 logs more lung CFU after challenge compared to vaccinated WT mice, even though vaccinated IL-6−/− mice were significantly more resistant than unvaccinated WT littermates (Fig. 6C). The numbers of Tc17 cells detected in the dLNs and spleen of vaccinated mice and in their lungs after challenge were significantly lower in IL-6−/− mice as compared to WT mice (Fig. 6D & S5B). Thus, IL-6 was essential for the induction of Tc17 cells and anti-fungal vaccine immunity, whereas both Dectin-1 and IL-1R1, were each dispensable for the induction of Tc17 cells and vaccine-induced resistance in CD4+ T-cell depleted mice. We have found that the chemokine receptor, CXCR3 mediates the recruitment of vaccine-induced anti-fungal IFN-γ producing CD8+ T cells [36]. The chemokine receptor CCR6 is thought to be preferentially associated with Th17 cells [42], but its expression and function on Tc17 cells has not been studied. We examined the expression of chemokine receptors on vaccine induced Tc17 cells and their function in recruiting these cells into the lung. Following vaccination and analysis of cells in dLNs, nearly 70% of Tc17 cells expressed either CXCR3 or CCR6, whereas IFN-γ producing CD8+ T cells preferentially expressed CXCR3 (Fig. 7A). Among Tc17 cells that were recruited to the lungs of vaccinated mice after infection, the expression of CCR6 (84%) was much higher than that of CXCR3 (49%) (Fig. 7B). In view of co-expression of these receptors on a substantial number of Tc17 cells, we tested their functional role in recruitment to the lung. Antibody blocking of the CXCR3 receptor during recall did not affect recruitment of Tc17 cells (data not shown), although it did affect the recall of IFN-γ producing CD8+ T cells [36]. To test the role of CCR6 on Tc17 cells, we neutralized its chemokine ligand CCL20 during the effector phase (or recall response) after infection. Recruitment of Tc17 cells was sharply reduced in mice that received α-CCL20 as compared to control rat IgG (Fig. 7C). Treatment with α-CCL20 did not perturb the infiltration of IFN-γ+ or IL-2+ CD8+ T cells. Interestingly, TNF-α-producing CD8+ T cells were also reduced in mice treated with α-CCL20, but the reduction in double positive cells (TNFα+IL-17+) could not account for the reduction of total TNFα+ CD8+ T cells (Fig. 7C & data not shown). Thus, CCR6 mediates the recruitment of Tc17 cells to the lung during recall after pulmonary fungal infection. Th17 cells reportedly survive poorly after Listeria infection due to their inability to maintain CD27 expression [35]. However, we recently found that Tc17 and Tc1 cells can be recalled into the lungs even after 6 months of rest in vaccinated CD4+ T-cell deficient hosts, suggesting that they can persist [36]. Consistent with our findings, a recent study found that, irrespective of CD27 expression, Th17 cells can exhibit stem-cell like features and survive for long periods in a manner that correlates with expression of the stem-cell like transcription factor TCF-. 1 [34]. We therefore assessed the surface and transcriptional profile of Tc17 cells, while also contrasting it with Tc1 cells. After fungal vaccination, Tc17 cells are mainly CD43hi (∼93%), ∼70% are CD27hi and most are also CD62Llo (∼80%) (Fig. 8A). In sharp contrast, IFN-γ+ Tc1 cells are chiefly CD43lo (∼7%), CD27hi (∼98%), and CD62Lhi. Tc17 cells expressed the lineage-specific transcription factor RORγt and showed a phenotypic profile of memory precursors i. e. KLRG1lo and TCF-1hi (Fig. 8B & 8C, and S6). Expression of the T17 prototypic transcription factor RORγt correlated strongly with the expression of TCF-1 (Fig. 8C; r = 0. 80). These data suggest that fungal vaccine-induced Tc17 cells are full effector cells, but portray a stem cell-like phenotype that has been observed in memory T-cell precursors [34]. Our study shows that Tc17 cells can be induced irrespective of CD4+ T cell help upon fungal vaccination. Like Th17 cells, Tc17 cells are non-redundant in mediating fungal resistance [13]. We used four different approaches to eliminate IL-17 or its activity in vaccinated CD4 depleted mice: neutralizing antibody against IL-17A or soluble IL-17A receptor during recall responses and IL-17A and IL-17RA knockout mice. All indicated an unequivocal and distinct role for IL-17A in vaccine resistance in CD4 depleted mice. Our data do not formally prove that Tc17 cells mediate vaccine resistance in this model, since it is impossible to selectively deplete Tc17 cells while leaving behind other Il-17 producing lymphoid cells. Yet, the fact that nearly 85% of the IL-17A producing cells in the lung of vaccinated CD4-depleted mice are CD8+ T cells, and that depletion of CD8+ T cells in this model eliminates vaccine resistance [19] supports our conclusion that Tc17 cells are non-redundant and indispensable for vaccine resistance in this model. In a M. tuberculosis infection model, Th17 cells mediated resistance by recruiting anti-bacterial Th1 cells into the lungs. However, our studies with IL-17A and IL-17A receptor knockout mice showed that the recruitment of functional anti-fungal Tc1 cells was not impaired in this setting. Yet, the fungal clearance was dramatically blunted in the absence of Tc17 cells, indicating the unique and indispensable role of Tc17 cells in anti-fungal resistance. We did not see significant numbers of dual type I and type 17 cytokine-producing T cells during recall responses on pulmonary infection (data not shown). Our observations are in line with the recent study in a Klebsiella infection model where Th17 cells conferred distinct anti-microbial function independent of Th1 immunity [43]. Our previous work showed that Tc1 immunity in CD4+ T cell deficient hosts was pivotal for vaccine-induced resistance against blastomycosis and histoplasmosis [19]. The type 1 cytokines IFN-γ, TNFα and GM-CSF played critical and overlapping roles in the clearance of pulmonary fungal infection. Our current study adds the additional cytokine IL-17A produced from CD8+ T cells. In fact, when IL-12 signaling is nullified, vaccine-induced resistance was intact even though there were reduced numbers of IFN-γ producing cells. Unlike the situation of CD4+ T-cell immunity in A. fumigatus infection [27], we found that a lack of IL-12 signaling did not abate or enhance Tc17 cells, indicating minimal cross-talk between the two pathways - Tc1 and Tc17 - in our model of anti-fungal vaccine immunity. Moreover, Tc17 cells mediate anti-fungal vaccine immunity independently of Tc1 cells. Tc17 immunity did not influence Tc1 immunity, and vice versa, suggesting the unique, indispensable role of Tc17 cells for fungal resistance in the absence of CD4+ T cells. Although the anti-microbial actions of IL-17A are under investigation, several studies have reported its role in recruiting and activating neutrophils [44]. Here, we showed that Tc17 cells likely play a critical role in activating neutrophils. Depletion of neutrophils during the efferent/recall response reduced fungal clearance and this effect was dependent on IL-17A signaling since there was no effect of depletion on fungal resistance in IL-17RA knockout mice. Dectin-1 promotes anti-fungal defense by inducing Th17 cells in the setting of infection with C. albicans, A. fumigatus, and P. carinii [11], [45], [46]. Dectin-1 can promote the induction of Th17 cells by inhibiting Th1 differentiation [27]. Our work shows that Dectin-1 is dispensable for eliciting Tc17 cells and promoting vaccine resistance to Blastomyces in CD4+ T-cell deficient hosts. Similarly, IL-1R1, which has been shown to be essential for the induction of Th17 cells in different microbial or non-microbial models, was found here to be dispensable for inducing Tc17 and controlling fungal infection in vaccinated CD4+ T-cell deficient hosts. Conversely, we found that a classical inducer of the Th17 lineage, IL-6, was essential in induction of Tc17 cells. It is noteworthy that Dectin-1 activation leads to production of IL-1, which in turn can induce and amplify production of IL-6 [28], [47], [48]. Our work indicated that even though IL-6 is required for induction of Tc17, its production was independent of Dectin-1 or IL-1 receptor signaling suggesting the involvement of different pathogen-recognition receptor (s). Further studies may reveal distinct or overlapping functions of different PRRs in the induction of IL-6. Th17 cells express the chemokine receptor CCR6, which promotes their trafficking to mucosal surfaces [42], [49]. Although T cells may co-express different chemokine receptors, CCR6 is distinctly expressed on Th17 cells [42], [50] due to the upstream influence of the transcription factor RORγt. Conversely, CXCR3 regulates the trafficking of type 1 cytokine producing cells [51]. Surprisingly, we observed that Tc17 cells expressed both CXCR3 and CCR6 in the dLNs and after recall to the lungs [52]. We did not see dual expression of IFN-γ and IL-17A in these Tc17 cells. Nevertheless, CCL20 neutralization and CXCR3 blocking experiments showed that CCR6 but not CXCR3 is critical in mobilizing Tc17 cells from lymphoid organs to the lungs for vaccine resistance against fungal pneumonia. This feature of dual chemokine receptor expression marks an additional layer of Tc17 differentiation following fungal vaccination. Dual expression may be due to Tc17 plasticity during the initial differentiation stages after vaccination or a stochastic phenomenon of these T cells. CD4+ T cell help was dispensable for the induction of Tc17 cells and their recall to the lung. This finding may have implications for designing anti-fungal vaccines targeted to immune-compromised patients. Although CD8+ memory immunity against bacteria and viruses wanes quickly in the absence of CD4+ T cell help [53], we have found that vaccine induced anti-fungal CD8+ T cells do acquire long-term memory and can be maintained in the absence of CD4+ T cell help [36]. While antigen-specific Th17 cells have been thought to be short lived [35], and exhibit plasticity toward a Th1 phenotype [54], a recent study found that Th17 cells evince stem-cell likeness and multi-potency in cytokine production [34]. Such Th17 cells displayed a unique transcriptional signature allowing them, despite plasticity and conversion toward a Th1 phenotype, to retain their ability to produce type 17 cytokines and reject tumors more effectively than distinct Th1 lineage cells. Here, we found that Tc17 cells were indispensible in vaccine resistance to lethal fungal infection in mice rested for a month after vaccination. Indeed, in a recent study [36], we found that Tc17 cells persisted 6 months after vaccination in CD4+ deficient hosts and could be recalled to the lungs after challenge (though we did not test their role in resistance). In the current study, we extend those findings by demonstrating that the phenotype and transcription factor profile of these Tc17 cells after vaccination, even in the absence of CD4+ T cells, show features that portend long-term persistence and stem-cell likeness. Herein, vaccine induced effector Tc17 cells were distinct in their surface phenotype as compared to effector Tc1 cells. They were chiefly CD43hi and CD62Llo. The role of CD43 on Tc17 cells has not been studied, but other work has shown that it promotes expansion, contraction and tissue trafficking [55], [56]. CD43 expression on CD8+ T cells has been associated with enhanced clonal burst size during the expansion phase and increased tissue trafficking, suggesting its positive role during the early phase of an immune response [47], [57]. During later phases, it potentiates apoptosis of CD8+ T cells, down-regulating immune responses and immunopathology [55]. Thus, CD43 on Tc17 cells after fungal vaccination may promote tissue trafficking to the lungs or tone down immunopathology associated with IL-17A production. Although we have shown that Tc17 cells are maintained in vaccinated mice [36], we did not assess CD43 expression on those cells. CD43 may be down regulated on these persistent cells, or alternatively, CD43 function may differ on persistent anti-fungal Tc17 cells versus anti-viral Tc1 cells [58]. Vaccine induced Tc17 were fully differentiated effectors, but not terminally differentiated as indicated by their phenotype: CD44hi, CD43hi, CD62Llo, KLRG-1lo and TCF-1hi. Only ∼20% of Tc17 cells were CD62Lhi denoting them as central memory cells. The remaining 80% of Tc17 cells were CD62Llo yet also KLRG-1lo and TCF-1hi suggesting they have the propensity to become memory cells [34], [59], [60]. Although, we did not look at the long-term fate of these cells, an important question is whether they remain as effector memory cells and traffic to peripheral tissues or convert into a central memory phenotype during the memory phase. TCF-1 can repress T17 lineage development by directly binding the IL-17A locus [61], [62]. Here, we showed that RORγt expression in Tc17 cells was strongly correlated with the expression of TCF-1. It is possible that the quality or strength of the fungal vaccine signal for Tc17 cell differentiation was sufficient to convert them to fully but not terminally differentiated effector cells without down-regulating TCF-1, and to overcome TCF-1 inhibition of IL-17A expression. The quality and quantity of TCR signals regulating TCF-1 require further investigation. Understanding the features that foster the long-term survival and function of Tc17 cells are important for developing vaccine strategies that prevent fatal fungal disease in immune-compromised patients, where Tc17 cells are known to exert and indispensable role in protective immunity. All animal procedures were performed in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. Care was taken to minimize animal suffering. The work was done with the approval of the IACUC of the University of Wisconsin-Madison. Wild type C57BL/6 mice were obtained from the National Cancer Institute. Breeder pairs of IL17ra−/− and IL17a−/− mice were provided by Amgen and Jay Kolls (University of Pittsburgh, Pittsburgh, Pennsylvania, USA), respectively. Breeder pairs of Il12rb2−/− B6. 129S1-Il12rβ2tm1Jm/J (stock 3248); Il1r1−/− B6. 129S7-Il1r1tm1Imx/J (stock 003245); B6. 129S2-Il6tm1Kopf/J (stock 002650); C57BL/6-Tg (TcrαTcrβ) 1100Mjb/J (stock 003831) mice (referred to as OT-I mice in this paper); B6. 129S2-Cd4tm1Mak/J (stock 002663); and T lymphocyte–specific Thy 1. 1 allele-carrying congenic B6 strain B6. PL-Thy1a/Cy (stock 000406) were purchased from Jackson Laboratories. OT-I Tg mice were bred on Thy1. 1 congenic mice to generate Thy1. 1+ OT-I Tg mice. Dectin-1−/− mice were a kind gift from Dr. Gordon Brown (University of Aberdeen, Scotland). All mice were 7–8 weeks of age at the time of experiments. Mice were housed and cared for according to strict guidelines of the University of Wisconsin Animal Care Committee, who approved all aspects of this work. The wild-type virulent strain of Blastomyces dermatitidis is American Type Culture Collection (ATCC) 26199 and was obtained from ATCC. The isogenic, attenuated mutant lacking BAD1, designated strain #55, was used for vaccination. Isolates of B. dermatitidis were maintained as yeast on Middlebrook 7H10 agar with oleic acid-albumin complex (Sigma-Aldrich) at 39°C. For OT-I responses, recombinant strain #55 carrying the OT-I epitope, ovalbumin SIINFEKL, was used for vaccination and was maintained as isolates of strain #55 as described [36]. In some of the experiments, H. capsulatum strain G21B was used, which was maintained on Histoplama Macrophage Medium (HMM) plates. OT-I cells from lymph nodes and spleens of OT-I Tg mice were purified using CD8+ T-cell negative enrichment magnetic beads kit (BD Biosciences). A total of 1×106 naïve OT-I cells were adoptively transferred to naïve congenic mice by the intravenous (i. v.) route. Mice were vaccinated with 105–106 yeast of attenuated B. dermatitidis (#55 strain) by the subcutaneous (s. c.) route at each of two sites, dorsally and at the base of the tail. For challenge studies, mice were infected intratracheally (i. t.) with ∼2×103 yeast of the isogenic wild-type strain of B. dermatitidis, ATCC 26199. To assess yeast burden, lungs were homogenized before plating on brain heart infusion (BHI; Difco) agar. For OT-I T cell studies, mice were vaccinated with 106–107yeast of recombinant vaccine strain #55 yeast expressing OVA SIINFEKL. All experimental mice, unless stated, were depleted of CD4+ T-cells with monoclonal antibody GK1. 5 (Biovest International Inc. /NCCC, MN) using a weekly dose of 100 µg given by the i. v. route. The efficiency of depletion of CD4+ T cells was ≥99% as measured by flow cytometry [36]; ≈5% of depleted cells are Thy1. 2 negative. Lymphocytes from the draining lymph nodes (dLNs), spleen and lung were obtained after homogenization and lysis of RBCs. Cells were re-stimulated with anti-CD3 (clone 145-2C11; 0. 1 µg/ml) and anti-CD28 (clone 37. 51; 1 µg/ml) in the presence of Golgi-Stop for 5 hrs at 37°C. Following incubation, cells were washed and surface stained with anti-Thy1. 1, anti-CD8α and anti-CD44 antibodies. In some experiments, cells were also stained with anti-CD27, anti-CD43 (1B11), anti-KLRG-1 and anti-CD62L antibodies. Cells were washed to remove unbound antibodies and were fixed and permeabilized using a Cytofix/Cytoperm kit. Cells were then stained for intracellular cytokines using anti-IFN-γ, anti-IL-17A, anti-TNF-α and anti-IL-2 antibodies. For staining of chemokine receptors, anti-CCR6 and anti-CXCR3 antibodies were added during surface staining. All antibodies and staining reagents were obtained from BD Biosciences except for the anti-CXCR3 antibody, which was obtained from Biolegend. Cells were analyzed by flow cytometry. ∼2×106 bone marrow derived dendritic cells (BMDCs) were incubated with ∼2×106 CFU of heat-killed vaccine yeast overnight at 37°C. On the following day, ∼1×106 dLNs cells or lung cells were added to the culture along with Golgi stop and incubated for an additional 5 hrs. Cells were subjected to surface and intracellular staining before flow cytometric analysis. To neutralize soluble IL-17A, mice were given 100 µg of anti-IL-17A mAb by the i. v. route on days 0,2 and 4 after pulmonary infection as described [13], which efficiently neutralizes IL-17 [63]. To neutralize IL-17 with the soluble receptor, mice were infected with recombinant adenovirus expressing soluble IL-17RA at a dose of 2–4×109 pfu on days −3 and −1 (i. v. route) and on day 0 (i. t. route at the time of Blastomyces infection). As a control, we used adenovirus AdLuc expressing luciferase (provided by Jay Kolls and propagated by the Vector Core lab at the University of Michigan, Ann Arbor, MI). For neutralization of chemokine CCL20, mice were given ∼90 µg of α-mouse CCL20 mAb (R&D Systems) by the i. v. route. A total of 10 ml of BAL fluid was harvested by repetitive instillation of 1 ml cold PBS plus 0. 05% EDTA via the i. t. route. Cells were washed and re-suspended in FACS buffer and surface stained with Violet Live/Dead stain (Molecular Probes/Invitrogen), Ly6G-APC (clone 1A8), LFA1-PE, CD11b-PECy7, Ly6G-APC, and 7/4-biotin with streptavidin PerCPCy5. 5 (BD Bioscience). Cells were kept on ice throughout the procedure. Cells were fixed and analyzed by flow cytometry. Vaccinated and unvaccinated mice were injected by the i. v. route with 100 µg of anti-mouse Ly6G mAb (clone 1A8; BioXCell) on days 0,2, and 4. Depletion (99% efficient) was confirmed by FACS analysis of cells in the lung homogenate. As a control, mice were given similar amounts of rat IgG antibody (Sigma-Aldrich). Cells from dLNs were harvested, stimulated with anti-CD3 and anti-CD28 at 37°C for 5 hrs in the presence of Golgi-stop. In some experiments, TAPI-2 was added to inhibit shedding of CD62L [64]. Following incubation, cells were surface stained in FACS buffer. Cells were then fixed/permeabilized using Phosflow Lyse/Fix buffer and Phosflow Perm/Wash buffer I (BD Biosciences), blocked with buffer containing normal goat sera and stained with rabbit anti-mouse TCF-1, anti-IL-17A, anti-IFNγ and RORγt. As a control for antibody staining, we blocked the staining antibody with the peptide used to generate TCF-1 antibody (Cell Signaling Technology). After washing, cells were stained with goat anti-rabbit secondary antibody. Cells were washed and analyzed by flow cytometry. Statistical significance of differences in fungal lung CFU was measured by the non-parametric Mann-Whitney test. All other statistical analysis was performed using a two-tailed unpaired Student t test. A two-tailed P value of ≤0. 05 was considered statistically significant.

Systemic fungal infections have emerged as a public health problem, especially for patients with suppressed immunity. At present, there are no vaccines against fungi, partly because it is hard to elicit strong immunity in immune suppressed patients. We have found however that residual elements of T cell immunity can be harnessed by vaccination even in immune suppressed hosts. We show here that immune suppressed mice lacking T helper cells can still be vaccinated successfully against lethal fungal pneumonia. A population of T cytotoxic IL-17-producing cells (Tc17 cells) is instrumental and indispensible in vaccine protection. We describe here mechanisms that explain how these cells are induced to mature in Tc17 cells, persist for long periods in the body providing “immune memory”, recruit to the site of infection, and clear the tissue of fungi. Our work sheds new light on potent T cells that can be harnessed by vaccine strategies against fungal infection in vulnerable patients.

Abstract Introduction Results Discussion Methods

medicine immune cells histoplasmosis immunology microbiology adaptive immunity immune defense fungal diseases immunizations infectious diseases mycology mycosis infections blastomycosis t cells biology yeast immunity systemic mycoses

2012

Tc17 Cells Mediate Vaccine Immunity against Lethal Fungal Pneumonia in Immune Deficient Hosts Lacking CD4+ T Cells

CgPdr1p is a Candida glabrata Zn (2) -Cys (6) transcription factor involved in the regulation of the ABC-transporter genes CgCDR1, CgCDR2, and CgSNQ2, which are mediators of azole resistance. Single-point mutations in CgPDR1 are known to increase the expression of at least CgCDR1 and CgCDR2 and thus to contribute to azole resistance of clinical isolates. In this study, we investigated the incidence of CgPDR1 mutations in a large collection of clinical isolates and tested their relevance, not only to azole resistance in vitro and in vivo, but also to virulence. The comparison of CgPDR1 alleles from azole-susceptible and azole-resistant matched isolates enabled the identification of 57 amino acid substitutions, each positioned in distinct CgPDR1 alleles. These substitutions, which could be grouped into three different “hot spots, ” were gain of function (GOF) mutations since they conferred hyperactivity to CgPdr1p revealed by constitutive high expression of ABC-transporter genes. Interestingly, the major transporters involved in azole resistance (CgCDR1, CgCDR2, and CgSNQ2) were not always coordinately expressed in presence of specific CgPDR1 GOF mutations, thus suggesting that these are rather trans-acting elements (GOF in CgPDR1) than cis-acting elements (promoters) that lead to azole resistance by upregulating specific combinations of ABC-transporter genes. Moreover, C. glabrata isolates complemented with CgPDR1 hyperactive alleles were not only more virulent in mice than those with wild type alleles, but they also gained fitness in the same animal model. The presence of CgPDR1 hyperactive alleles also contributed to fluconazole treatment failure in the mouse model. In conclusion, this study shows for the first time that CgPDR1 mutations are not only responsible for in vitro/in vivo azole resistance but that they can also confer a selective advantage under host conditions. Candida glabrata has recently emerged as the second most common cause of invasive candidiasis, and there are increasing numbers of reports showing its important role in mucosal or bloodstream infections [1], [2]. Systemic infections due to C. glabrata are characterized by a high mortality rate, and they are difficult to treat due to the intrinsically low susceptibility of this species to azole drugs, especially to fluconazole [3]. In addition, C. glabrata easily develops fluconazole resistance in response to drug exposure during patient treatment [4]–[6]. Azole antifungals target the cytochrome P-450 lanosterol 14-α demethylase, encoded by ERG11. Resistance of yeast clinical isolates to azole antifungal agents can result from either overexpression or mutations in ERG11. Alternatively, the cells can fail to accumulate azole antifungal agents due to enhanced drug efflux, a consequence of transcriptional activation of drug efflux pumps (for review, see [7]). At least two families of multidrug transporters, the ABC (ATP-binding cassette) transporter family and the major facilitator superfamily (MFS), are involved in azole resistance. In C. glabrata, the constitutive upregulated expression of ABC-transporter genes CgCDR1 and, to a lesser extent, CgCDR2 (also known as PDH1) plays a dominant role in azole resistance [4], [8]–[11]. Each of these genes can be upregulated in C. glabrata clinical isolates and disruption of CgCDR1 or CgCDR1/CgCDR2 leads to hypersusceptibility to fluconazole, cycloheximide, and chloramphenicol [4], [9], [10], [12], [13]. The expression of CgCDR genes is regulated by a single Zn (2) -Cys (6) transcription factor, CgPdr1p, an homologue of S. cerevisiae Pdr1p/Pdr3p [11]. CgPDR1 deletion leads to a loss of CgCDR1 and CgCDR2 regulation and to a sharp increase in azole susceptibility [14]. Due to the presence of PDRE (pleiotropic drug response element) sequences in the CgCDR1 and CgCDR2 promoters, CgPdr1p acts probably by binding to these regulatory elements as Pdr1p and Pdr3p in S. cerevisiae. CgPDR1 contains a PDRE in its promoter suggesting an auto-regulation of its transcription. Consistent with this observation, upregulation of CgCDR1 and CgCDR2 in azole-resistant strains is correlated with an increase of CgPDR1 expression [14], [15]. CgPDR1 is also essential in azole resistance caused by mitochondrial dysfunction in C. glabrata petite mutants. Since enhanced CgPDR1 expression is observed in some petite mutants, it has been proposed that CgPDR1 regulates its own expression in response to mitochondrial dysfunction [15]. CgPdr1p acts as nuclear receptor by directly binding to diverse drugs and xenobiotics, such as azoles, to activate expression of efflux pumps genes resulting in multidrug resistance [16]. The activation domain of CgPdr1p binds directly to the Mediator co-activator subunit CgGal11p in a xenobiotic-dependent manner in order to activate transcription of target genes [16]. Two studies have identified three separate amino acid substitutions (W297S, F575L, P927L) in CgPdr1p of azole-resistant strains that are responsible for constitutive high expression of CgCDR1, CgCDR2 and CgPDR1 itself [14], [15]. Recently, another Pdr1p-regulated ABC-transporter gene, CgSNQ2, was shown to participate to azole resistance of C. glabrata clinical isolates [17]. In this study, a fourth CgPdr1p amino substitution, P822L, was identified. Interestingly, the P822L substitution is responsible for the constitutive overexpression of CgSNQ2, but has no effect on the expression of CgCDR1 and CgCDR2 [17]. In the present study, we investigated the incidence of CgPDR1 mutations in a large collection of clinical isolates. Because no study has yet addressed whether the presence of CgPDR1 mutations is correlated with fitness costs in C. glabrata, we engineered isogenic strains with individual CgPDR1 mutations and tested their virulence in two different animal models. The strains that acquired in vitro azole resistance were used to test the in vivo response to fluconazole. We observed a high diversity among CgPDR1 alleles and identified 57 distinct single amino acid substitutions, which may confer hyperactivity to CgPdr1p in order to mediate high expression of ABC transporter genes. Although CgCDR1, CgCDR2 and CgSNQ2 are all regulated by CgPdr1p, they are not always coordinately expressed in azole-resistant isolates indicating that ABC transporter genes were differentially regulated depending on the mutation present on the CgPDR1 allele. Finally, the identified amino acid substitutions in CgPdr1p enhanced virulence and led to fluconazole treatment failure in mouse models. Taken together our data demonstrate a high variability in CgPDR1 mutations, which themselves have differentiated effects on target genes including ABC-transporters and probably on yet unidentified virulence factors. The incidence of CgPDR1 mutations was investigated in a collection of C. glabrata clinical isolates (n = 122) consisting of 30 groups of sequential isolates (n = 66). Each group contained at least one azole-susceptible (MIC fluconazole≤16 µg/ml) and one azole-resistant (MIC fluconazole≥32 µg/ml) isolates, which were shown to be highly related by genotyping methods (Cg6 and Cg12 repetition probes or MLST) (data not shown). There were 36 azole-resistant isolates among the groups of related isolates. The 56 remaining isolates were unrelated (41 azole-resistant and 15 azole-susceptible, Table S1). In this collection, azole-resistant isolates upregulated at least one of the ABC-transporter genes including CgCDR1, CgCDR2 or CgSNQ2 (see Figure 1 for ABC-transporter genes expression levels measured by real-time RT-PCR in groups of isolates and Figure S1 for CgCdr1p and CgCdr2p levels determined by western blot). CgPDR1 from each isolate was cloned and sequenced. To determine nucleotide polymorphisms, the 122 sequences were aligned and showed 66 non-synonymous nucleotide substitutions among a total of 70 distinct CgPDR1 alleles. By comparison of CgPDR1 alleles from azole-susceptible and azole-resistant isolates, we identified 12 different alleles recovered only from azole-susceptible isolates containing combinations of eight different mutations and 58 different alleles specific for azole-resistant isolates with 58 distinct mutations. These mutations yielded 57 single amino acid substitutions (Table S2) located at 50 locations along the protein and encompassing three distinct protein domains: i) the region similar to the transcriptional inhibitory domain of Pdr1p from S. cerevisiae, ii) the middle homology region (MHR) and iii) a putative transcriptional activation domain (Figure 2). Eleven distinct amino acids substitutions were found repeatedly in azole-resistant resistant isolates (six found twice and five found three times) and several substitutions occurred at the same position in six different cases (Table S2). Overall, CgPdr1p unique substitutions were found in 46 distinct azole-resistant isolates. This suggests that saturation of CgPDR1 mutations in azole-resistant isolates may be still not reached. Indeed, a parallel and independent CgPDR1 sequence analysis of ten azole-resistant C. glabrata isolates still revealed one unknown mutation and two additional distinct mutations on one of the 51 nucleotide positions identified in this study (O. Bader, unpublished). We hypothesized that CgPDR1 mutations may confer enhanced activity (or hyperactivity) to CgPdr1p leading to increased expression of the CgCDR and/or CgSNQ2 genes. Azole resistance was generally correlated with the occurrence of mutations in CgPDR1, except in four azole-resistant isolates (DSY717, DSY2282, DSY2325 and BPY41). Mitochondrial dysfunction is one of the possible mechanism by which azole resistance can occur during azole treatment of patients [18]. Indeed, three isolates (DSY2282, DSY2325 and BPY41) displayed altered mitochondrial respiratory capacity as deduced from their inability to grow on medium containing glycerol as sole carbon source and from their defects in mitochondrial DNA (Figure S2). Although CgCDR1 was upregulated in the remaining isolate DSY717, no CgPDR1 mutation could be detected. We are currently investigating azole resistance in this isolate and these data will be reported elsewhere. Single point mutations in CgPDR1 have been shown to increase the expression of both CgCDR genes and CgPDR1, thus contributing to azole resistance of clinical isolates [14], [15]. To evaluate whether the expression level of CgPDR1 is important for the upregulation of CgCDR genes in our isolates, the CgPDR1 mRNA levels of 21 matched pairs of azole-susceptible and azole-resistant isolates (listed in Table 1) were quantified by slot blot analysis and real-time RT-PCR (Figure 3). The comparison between CgPDR1 expression levels from azole-susceptible and azole-resistant matched isolates yielded comparable results as judged by similar relative increase of CgPDR1 expression obtained with the two methods (Figure 3). We concluded from these results that CgPDR1 upregulation was not correlating with azole resistance since CgPDR1 was overexpressed up to two-fold in some resistant isolates (DSY2268, DSY565, DSY756) as compared to their matched azole-susceptible isolate, whereas it was similarly expressed in others (DSY2257, DSY2277, DSY2271) (Figure 3). To determine whether the two-fold increase in CgPDR1 expression observed in some isolates was sufficient to induce high levels of CgCdr1p and CgCdr2p and thus azole resistance, CgPDR1 alleles from an azole-susceptible and an azole-resistant matched isolate (DSY2235 and DSY2234, respectively) were cloned into the CEN-ARS plasmid pCgACU-5 [19] and expressed in a strain lacking CgPDR1. The CgPDR1 alleles from DSY2235 and DSY2234 only differ by the amino acid substitution T588A. Expression of CgPDR1 alleles from the episomal plasmid resulted in a three- to four-fold increase of CgPDR1 mRNA in the revertant strains as compared to the clinical isolates (Figure 4A), but no significant change was observed in CgCdr1p and CgCdr2p levels and in azole susceptibility (Figure 4B and 4C). Similar results were obtained by overexpressing other mutated CgPDR1 alleles (data not shown), indicating that the slight increase in CgPDR1 expression observed in some resistant isolates could not account for azole resistance. The identified CgPDR1 mutations were next investigated for their ability to confer hyperactivity to CgPdr1p by mediating high expression of ABC-transporter genes resulting in azole resistance. For this purpose, CgPDR1 was first inactivated in a pair of azole-susceptible and azole-resistant isolates (DSY562 and DSY565, respectively) and reintroduced at the CgPDR1 genomic locus by homologous recombination in the obtained pdr1Δ strains. CgPDR1 alleles from these two isolates only differ by the amino acid substitution L280F in the putative inhibitory domain of CgPdr1p. Disruption of CgPDR1 in both azole-susceptible and azole-resistant isolates led to a drastic increase of azole susceptibility and to the complete downregulation of both CgCDR genes (Figure 5A and 5B), thus confirming the involvement of CgPdr1p in azole resistance. Each DSY562 and DSY565 pdr1Δ mutant received the CgPDR1 wild type or mutated alleles. Expression of CgCDR1, CgCDR2 and CgSNQ2 in reconstituted strains was restored to similar levels than those of the original clinical isolates (Figure 5B). Expression of the CgPDR1 allele containing the L280F substitution was sufficient to confer CgCdr1p and CgCdr2p constitutive high expression and thus azole resistance in C. glabrata independently on the strain genetic background (Figure 5C). Selected CgPDR1 alleles from eight other pairs of isolates (Table 1) were reintroduced at the CgPDR1 genomic locus in an azole-susceptible background lacking CgPDR1. CgPDR1 alleles from each pair of isolates only differ by a point mutation leading to a single amino acid substitution in either the inhibitory domain, the MHR or the activation domain of CgPdr1p. These mutations, which were specific for azole-resistant isolates, restored azole resistance in a pdr1Δ mutant (fluconazole MICs from 64–128 µg/ml, Figure 6A). Since only alleles containing these mutations conferred CgCDR1 constitutive high expression (from 4- to 150-fold expression increase, Figure 6B), these mutations could be assigned as GOF mutations. Moreover, single amino acid substitutions in either the inhibitory domain, the MHR or the activation domain could confer drug resistance. Once again, altered CgPDR1 expression could not account for azole resistance, since the CgPDR1 mRNA levels were similar between the clinical strains and the revertant strains expressing their corresponding CgPDR1 alleles (Figure S3). To determine the effect of distinct mutated CgPDR1 alleles on ABC-transporter genes expression, mRNA levels of CgCDR1, CgCDR2 and CgSNQ2 were determined by real-time RT-PCR in 21 matched pairs of azole-susceptible and azole-resistant isolates clinical isolates listed in Table 1 (Figure 1). As previously observed by Torelli et al. [17], CgCDR1, CgCDR2 and CgSNQ2 were not always coordinately expressed in azole-resistant isolates. Among them, CgSNQ2 showed in general the lowest expression variations. Taking a significant upregulation between two isolates as a threshold of two-fold expression difference, only nine isolates were above this value. CgSNQ2 was more upregulated (8. 5-fold) than the other ABC transporters only in DSY2277 (Figure 1). Upregulation of CgCDR1 and CgCDR2 in azole-resistant isolates followed similar patterns but without reaching statistical significance (data not shown). In some cases, significant upregulation (from 13- to 170-fold) of only CgCDR1 was measured (DSY2268, DSY2273, DSY754, DSY2315, DSY565 and DSY2746). In two cases (DSY2254 and DSY2234), CgCDR2 upregulation outreached that of CgCDR1 (Figure 1). Since the three ABC-transporter genes are regulated by CgPDR1, their uncoordinated expression might be explained by either differences in their promoter sequences or by differences in the transcriptional capacity of CgPdr1p. To avoid differences due to strain genetic backgrounds, pairs of CgPDR1 alleles were reintroduced in the same background (DSY562 pdr1Δ). mRNA levels of CgCDR1, CgCDR2 and CgSNQ2 were measured by real-time RT-PCR in the revertant strains (Figure 6B) and compared with those obtained in the clinical isolates (Figure 1). For example, the presence of the Y584C substitution (from CgPDR1 of DSY754) led to CgCDR1 upregulation only (17- and 12-fold expression increase, Figure 1 and Figure 6B), whereas the presence of the T588A substitution (from CgPDR1 of DSY2234) resulted in high mRNA levels of both CgCDR genes (4- and 8-fold expression increase for CgCDR1,23- and 30-fold increase for CgCDR2, Figure 1 and Figure 6B). Finally the presence of the P822L substitution (from CgPDR1 of BPY55) in a pdr1Δ mutant resulted in the upregulation of CgSNQ2 only (15-fold, Figure 6B), which is consistent with previous observation made in this clinical isolate [17]. These results as well as those obtained with other engineered isolates were overall consistent with expression profiles from clinical isolates containing the same CgPDR1 alleles (Figure 1) and thus indicate that ABC-transporter genes might be differentially regulated depending on the CgPDR1 GOF mutation and independently on the strain background. The C. glabrata isolates analysed in this study are of clinical origin. These isolates may have adapted to the host conditions in order to cause disease. Moreover, azole therapy in these patients selected for azole-resistant isolates and ultimately led to treatment failure. One interesting and unresolved question is whether CgPDR1 mutations responsible for high transporter expression have an impact on virulence and fitness of C. glabrata under host conditions. We therefore first measured tissue fungal burdens in animal models using tail-vein injections in groups of immuno-competent and immuno-suppressed mice according to previously established protocols [20]–[22]. Fungal loads in kidneys, spleen and liver of all mice were measured seven days after infection. We next performed virulence assays by measuring mice survival after infection with several C. glabrata isolates to challenge correlations between virulence and possible differences in tissue fungal loads. Using first DSY562 and DSY565 in immuno-competent or immuno-suppressed mice (Figure 7A and 7B), our results showed that fungal loads in kidneys were significantly increased in both mouse models infected with DSY565 as compared to DSY562 (P<0. 0001 and P = 0. 0007, respectively; see details in Table S3). The same trend was observed in fungal loads of spleen and liver (Figure S4). Higher fungal loads in tissues of mice infected with the azole-resistant isolate DSY565 correlated with a significant increased virulence as compared to DSY562 (Figure 8A). Taken together, these results demonstrate that the azole-resistant isolate (DSY565) was more virulent than its azole-susceptible parent, DSY562. Since DSY562 and DSY565 differ at least by the presence of the L280F substitution in CgPDR1, it was tempting to test whether this substitution was responsible for this behavior. We therefore replaced the mutated allele with the CgPDR1 wild type allele in DSY565 (named as DSY565 pdr1Δ-PDR1; SFY118) and also replaced the CgPDR1 wild type allele with the mutated allele in DSY562 (named as DSY562 pdr1Δ-L280F; SFY115). CgPDR1 alleles were also reconstituted in their original backgrounds (DSY562 pdr1Δ-PDR1; SFY114 and DSY565 pdr1Δ-L280F; SFY119). Mice infected with DSY562 pdr1Δ-L280F or DSY565 pdr1Δ-L280F had significant higher fungal loads in their kidneys as compared to DSY565 pdr1Δ-PDR1 or DSY562 pdr1Δ-PDR1 (Figure 7A, P<0. 0001). The same was observed in immuno-suppressed mice (Figure 7B, P<0. 0001). No significant changes in kidneys fungal loads occurred between immuno-competent mice infected with DSY562 and DSY562 pdr1Δ-PDR1 (P = 0. 07) or with DSY562 pdr1Δ-PDR1 and DSY565 pdr1Δ-PDR1 (P = 0. 62). Virulence assays with the same strains confirmed the association of increased virulence with increased fungal loads of infected tissues (Figure 8A). Thus, increased virulence was associated with the presence of a GOF mutation in CgPDR1 independently on the strain background. Introducing other mutations in the background of DSY562 mimicked results of fungal tissue burdens and of virulence assays obtained with the first tested CgPDR1 mutant allele (see results obtained with DSY562 pdr1Δ-T588A, -Q1083Q, -P822L and DSY562 pdr1Δ-P822L, Figure 7 and Figure 8B) and thus one can predict that increased virulence may be caused by any of the GOF mutations so far identified in CgPDR1. Interestingly, the absence of CgPDR1 in DSY562 (DSY562 pdr1Δ; SFY92) did not significantly alter fungal tissue burdens and virulence in animal models (Figure 7 and Figure 8A). This strongly suggests that it is rather the presence of a GOF mutation than the presence of CgPDR1 that directly affects virulence. This assumption is also based on fitness tests performed in vitro and in vivo with two selected isolates, each containing a wild type (DSY562 pdr1Δ-PDR1; SFY114) or a mutated CgPDR1 allele (DSY562 pdr1Δ-L280F; SFY115). We observed that a GOF mutation had no selective advantage in vitro for C. glabrata over a susceptible parent isolate since both strains cultivated over 24 h at a 1∶1 population ratio remained in equivalent population proportions (Figure 9A). It is only in vivo that the azole-resistant population displayed a selective advantage over susceptible isolates. After inoculating mice with a 1∶1 population ratio, a progressive disappearance of the susceptible population was visible over the time lapse of the animal experiment (Figure 9B). These results demonstrate that a GOF in CgPDR1 is associated with a gain in fitness in vivo even in the absence of drug selection. Since DSY565 is resistant to azoles in vitro, we expected little changes between tissue burden of mice infected with this clinical isolate in fluconazole-treated or -untreated conditions. The data obtained after measurement of colony forming units (CFU) from tissues of treated versus untreated animals infected with this strain confirmed this hypothesis only in spleen and liver (Figure S5, P = 0. 39 and 0. 82), while partially in kidneys (Figure 10, P = 0. 04). These results contrasted with those obtained with the azole-susceptible isolate DSY562: a sharp and significant decrease of fungal load was observed in all organs when comparing treated versus untreated animals (P<0. 0001–0. 03). The absence of CgPDR1 in both DSY562 and DSY565 backgrounds resulted in this experimental setting in a three-log decrease of fungal loads when animal were treated with fluconazole. This result could be expected from in vitro susceptibility data that yielded for these mutants the lowest fluconazole MIC values (Figure 5C). This suggests that CgPDR1 is essential for the response of C. glabrata to fluconazole challenge in vivo. Reconstituting wild type isolates from DSY562 and DSY565 backgrounds with a CgPDR1 wild type allele (DSY562 pdr1Δ-PDR1: SFY114; DSY565 pdr1Δ-PDR1: SFY118) gave in terms of fluconazole efficacy similar results to those obtained with the starting clinical isolate DSY562 (Figure 10). With the exception of DSY562 pdr1Δ-T588A (P = 0. 02), the reconstitution of GOF mutations in CgPDR1 in both DSY562 (DSY562 pdr1Δ–Q1083Q, -P822L) and DSY565 (DSY565 pdr1Δ-L280F, -P822L) gave by CFU counts in kidneys no significant differences between fluconazole-treated and untreated animals. However, CFU counts of spleen and liver of animals infected with DSY562 pdr1Δ-T588A were not significantly different from untreated animals upon fluconazole treatment (P = 0. 28 and 0. 16), thus suggesting that this GOF was still responsible for treatment failure. Taken together, our results are in agreement with the idea that high fluconazole MIC values are mirrored by treatment failure in this animal model. Moreover, our results demonstrate the critical role of CgPDR1 for the adequate response of C. glabrata to fluconazole challenge. On the other hand, our results also highlight that CgPDR1 GOF mutations alone are responsible for fluconazole treatment failure in the experimental model tested here. Nine distinct mutations in CgPDR1 alleles, three in each of the “hot spots” domains, conferred constitutive upregulation of ABC-transporter genes when expressed in an azole-susceptible background and thus can be considered as true GOF mutations responsible for azole resistance in C. glabrata. The 48 other mutations are also likely GOF mutations since they are present only in alleles isolated from azole-resistant strains upregulating ABC-transporter genes and the localization of these mutations along the protein is similar to GOF mutations described in the S. cerevisiae homologs Pdr1p/Pdr3p. Most GOF mutations in PDR3 map to the first two motifs of the inhibitory domain and to the transcriptional activation domain [23]. In contrast, PDR1 mutations are scattered throughout the entire protein with some “hot spots” at the C-terminus [24]. Given the high diversity of GOF mutations found in CgPDR1, it is unlikely that all mutations affect similarly the transcriptional activity of CgPdr1p that is responsible for ABC-transporter genes overexpression. Hence, GOF mutations in the inhibitory domain might impair transcriptional inhibition and those in the transcriptional activation might induce hyperactivation as proposed for PDR3 mutations in S. cerevisiae. The effect of some of the identified GOF mutations might be enlightened by the recent finding that CgPdr1p acts as nuclear receptor by directly binding to azoles in order to activate expression of efflux pumps genes [16]. For instance, the xenobiotic binding domain of S. cerevisiae Pdr1p has been mapped between amino acids 352 and 543, and this domain corresponds by sequence alignment to the MHR region of CgPdr1p. GOF mutations in the MHR might bypass the requirement of xenobiotic binding that is otherwise necessary to activate the transcription of efflux pumps genes. Determining whether CgPDR1 alleles containing mutations in the MHR domain could still be induced by azoles may verify this hypothesis. Once bound to azoles, CgPdr1p forms a complex with the Mediator co-activator subunit CgGal11p though the KIX motif to activate transcription of target genes. The CgPdr1p KIX-binding domain has been mapped between amino acids 1074 to 1104 [16]. Interestingly, we identified nine different GOF mutations within this short motif, suggesting that GOF mutations might modify CgGal11p recruitment in absence of drug binding. It is now well established that S. cerevisiae Pdr1p and Pdr3p act through cis acting sites present in the promoters of target genes. The consensus motif is named PDRE (for pleiotropic drug resistance element) and is present in several ABC-transporter gene promoters such as PDR5, SNQ2 and YOR1 [25]. In C. glabrata, a genome-wide study identified genes upregulated by a CgPDR1 GOF mutant and, by analysis of the promoters, the sequence 5′-TCC (GA) (CT) GAA-3′ was identified as a strong candidate for C. glabrata PDRE. This sequence is found in the promoters of CgCDR1, CgCDR2 and CgSNQ2, suggesting that CgPdr1p binds directly to PDRE sequences to regulate transcription of target genes [14], [15], [17]. Surprisingly, our results show that CgCDR1, CgCDR2 and CgSNQ2 are not coordinately expressed in azole-resistant isolates. Moreover, the selective upregulation of efflux pump genes is dependent on the CgPDR1 GOF alleles, since expression of distinct mutated alleles in the same genetic background restored the ABC-transporter gene expression of the parental clinical strains from which the alleles were isolated. This observation highlights still underscored and novel functions of CgPDR1 on the regulation of target genes. One possible explanation for these differentiated effects could be the localization of the mutations that may alter the transcriptional activity of CgPdr1p. However, data presented here indicate that mutations within the same domain do not yield similar ABC-transporter expression patterns. For example, two distinct mutations in the KIX domain, E1083Q (in DSY530) and D1082G (in DSY2254), gave selective upregulation of CgCDR1 alone or both CgCDR1 and CgCDR2, respectively. On the other hand, differences in the number and/or sequence of the PDRE present in the efflux pump promoters could influence CgPdr1p regulatory activity. Alternatively, the function of CgPdr1p might be subjected to regulation by other transcription factors. Irrespective of the precise molecular mechanism involved, our results strongly suggest that CgPDR1 GOF mutations have differentiated effects on target genes including the major ABC-transporters involved in azole resistance. Microarray experiments performed with individual CgPDR1 mutations would help determining whether other target genes are differentially expressed and promoter analysis may provide clues to uncover this mechanism. The presence of a PDRE sequence in the promoter of CgPDR1 as well as the constitutive high expression of CgPDR1 in some azole-resistant isolates suggested an auto-regulation similarly to PDR3 [14], [15], [26]. Moreover, enhanced CgPDR1 expression was also observed in some petite mutants [15]. It has thus been proposed that CgPDR1 may regulate its own expression leading to azole resistance. However, results presented here suggest that CgPDR1 upregulation is restricted to a limited number of azole-resistant isolates and does not correlate with the presence of GOF mutations. The increase in CgPDR1 expression observed in our strains is not sufficient per se to induce azole resistance. This observation is consistent with a recent study demonstrating that CgPDR1 expression is poorly correlated with azole resistance in C. glabrata [27]. Even though our experimental approaches were different from published studies, especially with respect to the engineering of mutant and revertant strains, the regulation of CgPDR1 needs to be further investigated. During the last decades, there has been a significant increase in the appearance of resistance in C. glabrata as a result of an increased use of azoles combined with the exceptional ability of this yeast species to develop resistance. In bacteria, antimicrobial resistance is often associated with fitness costs and thus results in a competitive disadvantage against otherwise drug-susceptible bacteria within the host [28]. Restoration of fitness in drug-resistant bacteria is often associated with the emergence of compensatory mutations [29]. A few examples illustrate that mutations in genes involved in antibiotic resistance can be beneficial in the fitness of pathogenic bacteria [30], [31]. Implicit in this reasoning is that antifungal resistance may have fitness costs in fungi and thus may result in a counter-selection against resistant strains without drug pressure. Whether resistance similarly exerts a relevant fitness cost that is associated with diminished virulence in fungi is still debated [28]. One study has addressed this question in C. albicans by testing the virulence of azole-resistant isolates compared to their azole-susceptible parental isolate [32]. The authors concluded that no direct relationship exists between the development of azole resistance and virulence. In vitro studies showed that individual C. albicans colonies subcultured in fluconazole-containing medium can follow individual emergence of azole resistance mechanisms. Some of these trajectories can be associated with an immediate decrease in fitness (measured by reduced growth rate). However fitness is restored by further cultivation by still unknown compensatory mechanisms [33]. Other studies suggest that once the selective pressure eases, the fungal-resistant strains will disappear [34]–[36]. Testing the interplay between C. glabrata and the host requires a validated animal model. Existing studies have mostly used intravenous injection of C. glabrata at varying doses (107 to 108 cells per mouse) with different immune system status (immuno-competent and immuno-suppressed) and in different mice backgrounds [37], [38]. Depending on the initial infection doses and the mice genetic background, not only CFU counts in target organs (kidneys, spleen and liver) differed by several logs (from 105 to 108 CFU per g infected tissue) over the duration of the experiments but also mice survival was varying. As compared to C. albicans, C. glabrata was consistently much less pathogenic. In our study, we have followed protocols established recently [17], [21] and used different immune status regimen to enable valid conclusions on the relationship between azole resistance and virulence. Our findings that C. glabrata resistant strains are both more virulent in mice and less susceptible to azoles in vitro/in vivo as compared to wild type isolates strongly suggest a gain in fitness for the resistant isolates. As shown in this study (Figure 9B), the azole-resistant population did effectively take over the azole-susceptible isolates in the absence of drug selection. We showed that it is the presence of GOF mutations rather than the presence of CgPDR1 that accounts for increased virulence. On the opposite to known C. glabrata mutants (such as ace2Δ) for which hypervirulence was associated with formation of pseudo-hyphae [39], the morphology of azole-resistant isolates tested here was not altered and therefore GOF mutations in CgPDR1 are likely to account for the observed phenotype. Hypervirulence has been also observed in other fungal species such as Cryptococcus neoformans where perturbation in cAMP signalling by inactivation of PKR1 (encoding the PKA regulatory subunit) resulted in capsule overproduction [40]. Even though other C. glabrata infection models remain to be tested, this is to our knowledge the first example of in vivo acquired mutations in a fungal gene with a positive impact on in vivo fitness. This highlights a need of carefully monitoring drug resistance of C. glabrata in infected patients, since increased virulence observed here may also have a negative impact on the outcome of the disease. In C. glabrata, the gain in fitness may have favoured an important proportion of azole-resistant C. glabrata isolates with ABC transporter upregulation [4], [13]. This is in contrast with studies on molecular epidemiology of drug resistance performed with other fungal pathogens, where resistance develops by more diverse resistance mechanisms, thus raising the question whether the gain of fitness mediated by CgPDR1 is a feature unique for C. glabrata. It will be therefore interesting to test whether transcriptional activators of drug resistance genes have similar effects on virulence and fitness in other important fungal pathogens. How CgPDR1 GOF mutations may affect virulence is still unknown. Nevertheless, published microarray experiments comparing a wild type C. glabrata isolates with a resistant strain expressing a mutated form of CgPdr1p may help identifying genes having a putative role in the virulence on the basis of differentially expressed genes [14]. Even though the number of differentially regulated genes is high in this study, several genes involved in stress response, resistance to DNA damage and cell wall structure are upregulated in the azole-resistant isolate, all of which may contribute individually or in combination to modulate the virulence of C. glabrata. Further studies are therefore necessary to address the involvement of these genes in virulence. Our results demonstrate that CgPDR1 GOF mutations alone are responsible for fluconazole treatment failure in a murine model. In most cases, animals infected with isolates containing GOF mutations were not responding to fluconazole treatment on the basis of CFU counts in specific organs (Figure 10 and Figure S5). Fluconazole is not considered as a therapeutic option for C. glabrata infections in human but served here as a compound to test the effect of GOF mutations on treatment outcome. Our results showed that a C. glabrata isolate with a low azole MIC can still respond to fluconazole treatment. A reduction of C. glabrata CFU counts in the range observed in the present study in azole-treated animals (approximately 1. 5 logs) is not consistently reported in experimental C. glabrata candidiasis [41]–[44]. These variations probably reflect the technical difficulties behind the establishment of a disease by C. glabrata in different animal models but could also be the result of inappropriate drug regimen. Having determined conditions necessary to establish a C. glabrata infection and to respond to drug treatment by a corresponding drug dosage, our study could demonstrate that in vitro fluconazole resistance was well correlated with in vivo resistance. Because GOF mutations in CgPDR1 are also responsible for increased virulence, drug treatment faces the challenges of both higher fungal loads and acquired resistance. Under these conditions, the failure of drug treatment is more likely. Besides the role of CgPDR1 GOF mutations in azole treatment failure, CgPDR1 was shown to play a critical role for the response of C. glabrata during fluconazole treatment as observed by the sharp decrease in fungal tissue burden after treatment in animals infected with mutants lacking CgPDR1. Together with the recent discovery that CgPDR1 possesses functional domains used as drug ligands and therefore potential sites for inhibitors [16], [45], our data suggest that inhibition of CgPDR1 could shortcut fitness and potentiate azole therapy. Future studies are therefore necessary to explore this possibility. The C. glabrata strains used in this study are listed in Table S1. One hundred twenty two clinical isolates of C. glabrata and were recovered from different specimens (e. g. blood, urine, vagina, sputum) of patients. Strains with the prefix “BPY” were collected at the Università Cattolica del Sacro Cuore, Rome, Italy. Strains with the prefix “DSY” were collected at the University Hospital Center, Lausanne (Switzerland) except strains DSY1166, DSY1169, DSY1174, DSY1176, DSY1180, DSY1185 that were provided by from Nippon Roche Research (Kanagawa, Japan). Strains DSY2724, DSY2725, DSY2726, DSY2731, DSY2746 were obtained from J. -P. Bouchara at the Centre Hospitalier d' Angers (France) and strains DSY2769, DSY2770 from M. -E. Bougnoux at the Necker Hospital (Paris, France). Strains were stored in 20% glycerol stocks at −80°C and cultured on either YPD (1% yeast extract, 2% peptone, 2% glucose) or minimal medium YNB (0. 67% yeast nitrogen base plus 2% glucose) at 30°C when necessary. For solid media, 2% agar was added. YNB with appropriate amino acids and bases was used as a selective medium after transformation of yeast strains [10]. YEPG (1% yeast extract, 2% peptone, 3% glycerol, 1% ethanol) agar plates was used to test C. glabrata strains for petite growth phenotype. YPD agar plates containing nourseothricin (clonNAT, Werner BioAgents) at 200 µg ml−1 were used as a selective medium for growth of yeast transformant strains. Drugs were obtained from the following sources: fluconazole (Pfizer), itraconazole and ketoconazole (Janssen). Escherichia coli DH5α was used as a host for plasmid construction and propagation. DH5α was grown in Luria-Bertani broth or on Luria-Bertani agar plates supplemented with ampicillin (0. 1 mg ml−1) when required. The C. glabrata strains were tested for azole susceptibility with the broth microdilution method described in the CLSI (formerly NCCLS) document M27-A2 (National Committee for Clinical Laboratory Standards, 2002). Briefly, aliquots of 1. 5 (±1. 0) ×103 cells ml−1 were distributed to wells of a microtitre plate in RPMI 1640 containing 2% glucose and incubated at 35°C for 48 h. Endpoint readings were recorded with an automatic plate reader (Multiskan Ascent, Thermo) and the lowest azole concentration that reduced growth to 50% of that of the drug-free control was defined as the MIC. Susceptibility to fluconazole of C. glabrata strains was tested qualitatively by spotting serial dilutions of overnight-grown yeast broth cultures onto YPD agar plates with different drug concentrations, as described previously [10]. After incubation at 30°C for 48 h, yeast spots were visualized onto plate surfaces. C. glabrata genomic DNA was used as a template to amplify by PCR CgPDR1 using the primers CgPDR1-EcoRI (5′-ATACCAGAATTCGGTCTCCGCTACAGGTTATA-3′) and CgPDR1-BamHI (5′-AAGTTTGGATCCAACGTTGTTGAGAAGGTATT-3′). The resulting PCR product was sequenced with the BigDye Terminator v1. 1 Cycle Sequencing Kit (Applied Biosystems) according to the manufacturer protocol. C. glabrata cell extracts for immunoblotting were prepared by an alkaline extraction procedure as described previously [46]. Detection of CgCdr1p and CgCdr2p was performed as described previously [9]. Signals were revealed by exposure to Amersham Hyperfilm MP films (GE Healthcare). Total RNA was extracted from log phase cultures with an RNeasy Protect Mini kit (Qiagen) by a process involving mechanical disruption of the cells with glass beads and an RNase-free DNase treatment step as previously described [13]. Expression of the CgCDR1, CgCDR2 and CgSNQ2 genes was quantitatively assessed with real-time RT-PCR in an i-Cycler iQ system (Bio-Rad). All primers and probes [13] were designed with Beacon Designer 2 (version 2. 06) software (Premier Biosoft International) and synthesized by MWG Biotech (Ebersberg, Germany). RT-PCRs were carried out as previously described [13]. Each reaction was run in triplicate on three separate occasions. For relative quantification of the target genes, each set of primer pairs and the Taqman probes were used in combination with the primers and probe specific for the CgACT1 reference gene in separate reactions [17]. Changes (n-fold) in gene expression relative to that of DSY562 (azole-susceptible control isolate) were determined from CgACT1-normalized expression levels. A two-fold increase in the expression level of each gene was arbitrarily considered as significant [17]. Expression of CgPDR1 in related azole-susceptible and azole-resistant clinical isolates was determined by real-time RT-PCR in an ABI Prism 7000 (Applied Biosystems). Each reaction was run in triplicate on three separate occasions. CgPDR1 quantification was performed using the primers CgPDR1-for (5′-AGCCTTGCCGATAGTCATAC-3′) and CgPDR1-rev (5′-AAGGTCAGGGCATACTTCAG-3′) using the QuantiTect SYBR Green PCR Kit (Qiagen). CgPDR1 alleles sequenced in this work can be found under GenBank accession numbers FJ550215 to FJ550284. CgPDR1 expression was normalized by CgACT1 expression levels using the specific primers CgACT1-for (5′-TTCCAGCCTTCTACGTTTCC-3′) and CgACT1-rev (5′-TCTACCAGCAAGGTCGATTC-3′). Small-scale isolation of RNA from C. glabrata was performed as previously described [10]. Five µg of denaturated RNA was transferred under vacuum onto GeneScreen Plus membranes (Perkin Elmer) using the slot-blotter MINIFOLD II (Schleicher & Schuell). Membranes were washed in 2× SSC and dried during 2 h at 80°C under vacuum. Probes were labelled by random priming with [α-32P]dATP using the Mega Labeling kit (GE Healthcare) according to the instructions of the manufacturer. Radioactive signals were revealed by exposure to Amersham Hyperfilm MP films (GE Healthcare). Signals obtained in blotted membranes were quantified by counting of radioactivity with the help of a Typhoon Trio (GE Healthcare). The CgPDR1 probe corresponds to the entire open reading frame and was generated by PCR using primers CgPDR1-SphI (5′-GCGCAAAGCATGCATG CAAACATTAGAAACTACA-3′) and CgPDR1-10 (5′-TCCTTAAGCCCGATAAGG-3′). The CgACT1 probe was used as an internal standard and was generated by PCR using primers CgACT1F (5′-TTGACAACGGTTCCGGTATG-3′) and CgACT1R (5′- CCGCATTCCG TAGTTCTAAG-3′). To express CgPDR1 alleles in an azole-susceptible strain, the CgPDR1 ORF flanked by 1. 2 kb-upstream and 0. 5 kb-downstream regions was amplified by PCR from DSY2235 and DSY2234 genomic DNA using the primers CgPDR1-EcoRI and CgPDR1-BamHI (see above) and inserted into pCgACU-5 to yield pSF18 and pSF19, respectively. These plasmids were used to transform SFY53 (DSY562 pdr1Δ) to obtain SFY72 and SFY73, respectively. pSF2, in which the SAT1 flipper is flanked by CgPDR1 upstream and downstream sequences, was used for the disruption of CgPDR1 in DSY562 and DSY565 [17]. This plasmid was linearized by digestion with KpnI and SacI and transformed into DSY562 and DSY565 to obtain after selection of transformants on nourseothricin-containing YPD plates the CgPDR1 deletion strains SFY92 and SFY93, respectively. For CgPDR1 replacement in SFY92 and SFY94, FLP-mediated excision of the SAT1 cassette was first induced by growing the cells for 4 h at 30°C in YCB-BSA medium (23. 4 g l−1 yeast carbon base and 4 g l−1 bovine serum albumin; pH 4. 0). One hundred to 200 colonies were plated onto YPD plates containing nourseothricin (15 µg ml−1) and grown for 48 h at 30°C to obtain nourseothricin-sensitive strains SFY93 and SFY95, respectively. The revertant strains generated in this study were obtained by transformation of the pdr1Δ mutants SFY93 and SFY95 with linearized plasmids containing the SAT1 marker and the PCR-amplified CgPDR1 open reading frames of the strains listed in Table 1 as described previously [17]. Briefly, the complete CgPDR1 ORF flanked by 500 bp was amplified by PCR from genomic DNA of the first eight strain pairs of Table 1 using primers CgPDR1-KpnI (5′-GCAAAGGTACCCGTTGATCATTATAATTGTGGGTAAA-3′) and 3′UTR-PDR1-SacI (5′-GCGCAAAGAGCTCGAGTTACAGACGACCAACGTGTCG-3′) and inserted into pBluescript II SK (+). These plasmids were amplified by PCR using the primers CgPDR1-EcoRI inv (5′-GCGCAAAGAATTCGTTGAGAAGGTATTAAGAACTTC-3′) and 3′UTR-PDR1-NotI (5′-GCGCAAAGCGGCCGCTACCGAAAGTTCGGTAAATCTAGG-3′). The resulting PCR products were digested by EcoRI and NotI and ligated to a 1. 8 kb EcoRI/NotI fragment containing SAT1 from pSFS1. The resulting plasmids were linearized by KpnI and SacI and transformed into SFY93 and SFY95 to obtain, after selection of transformants on nourseothricin-containing YPD plates, the CgPDR1 revertant strains listed in Table S1. Female BALB/c mice (20 to 25 g; Harlan Italy S. r. l) were housed in filter-top cages with free access to food and water, and were used for all in vivo experiments with the approval by the institutional Animal Use Committee (Università Cattolica del Sacro Cuore, Rome Roma, Italy). To establish C. glabrata infection, mice were injected into their lateral vein with saline suspensions of the C. glabrata strains (each in a volume of 200 µl). In virulence studies, a group of ten mice was established for each yeast strain. In a first experiment series, immuno-competent or immuno-suppressed mice were inoculated with 4×107 colony-forming units (CFU) of the yeasts [21]. Mice were rendered neutropenic by intraperitoneal administration of cyclophosphamide (200 mg kg−1 of body weight per day) three days before challenge and on the day of infection. After seven days, mice were sacrificed by use of CO2 inhalation, and target organs (liver, spleen and kidney) were excised aseptically, weighted individually and homogenized in sterile saline by using a Stomacher 80 device (Pbi International) for 120 s at high speed. Organ homogenates were diluted and plated onto YPD. Colonies were counted after two days of incubation at 30°C, and the numbers of CFU g−1 of organ were calculated. In a second experiment, mice were rendered neutropenic as above described and were injected with 7×107 CFUs of the strains [22]. Mice were monitored with twice-daily inspections and those that appeared moribund or in pain were sacrificed by use of CO2 inhalation. In fluconazole treatment studies, two groups of tenmice, one for drug treatment and one for control, were established with each strain. Neutropenia was induced as above described on days −4, +1 and +4 post-infection [20]. Mice were injected with 4×107 CFUs [20] of the strains and were sacrificed one day after the end of therapy to assess organ fungal burden (see above). Mice received daily intraperitoneal injections of 100 mg kg−1 fluconazole diluted in saline [41] and the treatment was initiated 24 h after challenge and continued through post-infection day 7. CFU counts were analysed with non-parametric Wilocoxon Rank sum tests, while mean survival times were compared among groups by using the long-rank test. A P-value of less than 0. 05 was considered to be significant. All relevant P- values calculated in this study are listed in Table S3. Strains SFY114 and SFY115 were grown overnight in YEPD and diluted to a density of 5×106 cells ml−1. Equal volumes of each culture were mixed together and cultures were grown under constant agitation at 30°C for 24 h. Growth of SFY114, SFY115 and the co-culture was determined at 2 h, 4 h, 8 h and 24 h by measuring the absorbance at 540 nm and by plating diluted samples of the cultures onto YEPD agar plates. Since SFY115 is able to grow on high concentrations of fluconazole in contrast to SFY114, the relative proportion of both strains in the co-culture was determined by replicating colonies onto YEPD agar containing 30 µg−1 ml of fluconazole. After incubation at 30°C for 48 h, colonies on YEPD plates and on plates containing fluconazole were counted. For in vivo fitness assays, cultures of strains SFY114 and SFY115 were diluted to a density of 4×107 CFUs and these suspensions were used to infect three groups of mice (four per group). Two groups of mice were infected with SFY114 and SFY115, respectively, and the third group with both strains mixed at a ration of 1∶1. At two, four and seven days post-infection, mice were sacrificed and kidneys homogenates were obtained (see above). Diluted samples from these homogenates were plated onto YEPD. Colonies grown after two days of incubation at 30°C were replicated onto YEPD plates containing fluconazole (30 µg−1 ml) to determine the relative proportion of both strains as above-described.

Candida glabrata is a yeast causing several diseases in humans and especially in immuno-compromised people. C. glabrata infections are treated with antifungal agents, however the use of some agents, for example azoles, is associated with the development of resistance. In this yeast species, azole resistance is mediated almost exclusively by ATP Binding Cassette (ABC) multidrug transporters. Their overexpression results in enhanced efflux of azoles and thus generates resistance. Regulation of ABC transporters is therefore of pivotal importance to understanding azole resistance. In C. glabrata, the expression of ABC transporters is mediated by a zinc finger transcription factor called CgPDR1. Gain of function (GOF) mutations in CgPDR1 result in high ABC transporter expression. In this study, we investigated the occurrence of GOF mutations in a large collection of azole-resistant isolates and found a high variety of mutations localized in three distinct domains of CgPDR1. We found that these mutations are not only associated with resistance but also enhanced virulence and fitness of C. glabrata in animal models. Our study provides for the first time evidence that mutations causing antifungal resistance can also provide a selective advantage under host conditions and thus highlights the need of carefully monitoring resistance in this pathogen.

Abstract Introduction Results Discussion Materials and Methods

microbiology/cellular microbiology and pathogenesis microbiology/medical microbiology

2009

Gain of Function Mutations in CgPDR1 of Candida glabrata Not Only Mediate Antifungal Resistance but Also Enhance Virulence

Yeast that naturally exhaust their glucose source can enter a quiescent state that is characterized by reduced cell size, and high cell density, stress tolerance and longevity. The transition to quiescence involves highly asymmetric cell divisions, dramatic reprogramming of transcription and global changes in chromatin structure and chromosome topology. Cells enter quiescence from G1 and we find that there is a positive correlation between the length of G1 and the yield of quiescent cells. The Swi4 and Swi6 transcription factors, which form the SBF transcription complex and promote the G1 to S transition in cycling cells, are also critical for the transition to quiescence. Swi6 forms a second complex with Mbp1 (MBF), which is not required for quiescence. These are the functional analogues of the E2F complexes of higher eukaryotes. Loss of the RB analogue, Whi5, and the related protein Srl3/Whi7, delays G1 arrest, but it also delays recovery from quiescence. Two MBF- and SBF-Associated proteins have been identified that have little effect on SBF or MBF activity in cycling cells. We show that these two related proteins, Msa1 and Msa2, are specifically required for the transition to quiescence. Like the E2F complexes that are quiescence-specific, Msa1 and Msa2 are required to repress the transcription of many SBF target genes, including SWI4, the CLN2 cyclin and histones, specifically after glucose is exhausted from the media. They also activate transcription of many MBF target genes. msa1msa2 cells fail to G1 arrest and rapidly lose viability upon glucose exhaustion. msa1msa2 mutants that survive this transition are very large, but they attain the same thermo-tolerance and longevity of wild type quiescent cells. This indicates that Msa1 and Msa2 are required for successful transition to quiescence, but not for the maintenance of that state. The need to stop proliferation and remain in a protected quiescent state is universally conserved and is just as important to yeast as it is to human cells. Failure to enter, or unscheduled exit from quiescence results in uncontrolled proliferation and cancer in humans, and death in unicellular organisms [1]. Most cells enter quiescence from G1. As such, there must be regulators in G1 cells capable of recognizing stop signals when they arise and provoking a stable but reversible halt to S phase. The regulatory strategy that controls the G1 to S transition in cycling cells is well understood and its basic framework is highly conserved from yeast to humans [2]. Studies of yeast have provided many insights into this process, but little is known about the cell cycle regulators that give rise to quiescent yeast cells. We have identified a pair of related transcription factors that play a critical role in halting the cell cycle in G1, specifically during the transition to quiescence. Like the highly conserved quiescence-specific complexes of higher eukaryotes [3–5], these factors repress transcripts that promote the G1 to S transition and enable yeast cells to enter the quiescent state. In rapidly growing yeast cells, as in higher cells, the G1 to S transition is tightly controlled by two consecutive waves of cyclin expression. Cln3 is expressed at the M/G1 boundary and initiates the transition by binding and activating the cyclin-dependent kinase (Cdk). The critical target of Cln3/Cdk is Whi5, which represses SBF. SBF is a transcription factor complex that includes Swi6 and its DNA binding partner Swi4. Cln3 phosphorylates and releases Whi5 from the complex, thus enabling SBF to activate late G1-specific transcription of the G1 cyclins CLN1 and CLN2 and other genes that promote the G1 to S transition [6–8]. The G1 cyclin/Cdk complexes then phosphorylate Sic1 and target it for degradation. Once Sic1 is degraded, the B type cyclin/Cdk complexes that are bound and inhibited by Sic1 are released, allowing them to phosphorylate and activate the DNA replication machinery and S phase ensues. Swi6 also associates with a second DNA binding protein, Mbp1, which is related to Swi4 and binds to a similar but distinct DNA sequence [9]. This complex, referred to as MBF, also confers late-G1 specific transcription on many genes involved in DNA replication and repair. These genes are regulated by Nrm1-dependent negative feedback [10]. Nrm1, itself a late-G1 transcript, accumulates in S phase, binds MBF complexes and represses transcription through S and G2/M. This wave of late G1 transcription is critical for the timing and fidelity of DNA replication. If the G1 to S transition is accelerated by ectopic expression of Swi4 or the G1 cyclin Cln2, there are checkpoint proteins, including Mec1 and Rad53, that detect replication stress and become essential for delaying S phase and promoting DNA repair [11,12]. This is in part accomplished by the direct phosphorylation of Nrm1 by Rad53, which releases it from the MBF complex and allows DNA replication and repair genes to be activated [13]. The transition from logarithmic growth to quiescence involves a stable but reversible cell cycle arrest in G1. Our previous studies have shown that this transition begins with a lengthening of G1, which is initiated before the diauxic shift (DS), when all the glucose has been taken up from the media [14]. The cell divisions that follow are highly asymmetric and the physical growth of those cells slows, resulting in a dramatic shift in the cell size of the population [15]. To explore the mechanism of this stable but reversible G1 arrest associated with quiescence, we have assessed the roles of known regulators of the G1 to S transition in rapidly growing cells. In wild type cells, Rad53 plays a role in the transition to quiescence, and it becomes essential if the G1 to S transition is driven by Cln3 overproduction. A second checkpoint gene, Rad9, is not required during this transition. Rad9 responds to DNA damage, while Rad53 responds to both DNA damage and replicative stress. This indicates that the latter, replicative stress, is the likely signal for Rad53 activation during the transition to quiescence [14]. The G1 arrest is maintained in post-diauxic cells by Xbp1, which is induced to high levels and represses CLN3 along with over 800 other genes [14]. Xbp1 recruits the histone deacetylase, Rpd3, which plays a unique and prominent role in the transcriptional repression that takes place in quiescent cells [16]. Rpd3 is targeted to at least half the budding yeast promoters, where it affects global nucleosome repositioning, histone deacetylation and a 30-fold global repression of transcription [16]. In this paper, we report the role of two proteins, Msa1 and Msa2 in the early transcriptional regulation that promotes G1 arrest and the transition to quiescence. Msa1 and Msa2 are two related proteins that were identified by mass spectrometry to be associated with SBF and MBF complexes [17]. Mutations in these proteins have mild phenotypes in rapidly growing cells [17–19], but we find that Msa1 and Msa2 are both important during the transition to quiescence. Each single mutant survives this transition, but the msa1msa2 double mutant fails to G1 arrest and loses viability rapidly. When paired with rad53-21, we observe a G1 arrest defect with the single mutants, especially msa2. These data indicate that Msa1 and Msa2 are both important regulators that promote G1 arrest during the transition to quiescence and cells rely on the Rad53 checkpoint function when either protein is missing. We have carried out RNA deep sequencing with the single and double msa mutants as cells transition from log phase to quiescence and find that they have significant impact on the expression of both MBF and SBF target genes, specifically in post-diauxic cells. In many cases, both Msa1 and Msa2 are required to repress SBF targets and activate MBF targets, and their effects are not additive. This suggests that they both play critical roles in the regulation of these late G1-specific transcripts. In other contexts, either Msa protein is sufficient to perform their regulatory function in post-DS cells. Chromatin immunoprecipitation (ChIP) of candidate targets show binding of both Msa1 and Msa2 to their targets, and show stronger binding in post-DS cells. The post-diauxic regulation of these genes by Msa1 and Msa2 is likely to be important for a normal transition from proliferation to quiescence. The SBF transcription factor drives the transcription of many genes in late G1, which play important activatory roles in the G1 to S transition. Hence, it would be a likely target of negative regulation as cells enter the stable G1 arrest associated with quiescence. If so, loss of Swi4 or Swi6 activity might promote the transition to quiescence as we’ve seen with loss of Cln3 (Table 1). However, we find that both swi4 and swi6 mutants suffer significant loss of viability as they are grown from logarithmic (log) phase to stationary phase (SP). Though not considered an essential gene, swi6 mutants grow very slowly (Fig 1B). Only 60% of the cells are viable during the logarithmic phase of growth and that drops to 20% after seven days (Fig 1C). SWI4 is an essential gene in the W303 strain, but that lethality is suppressed by SSD1 [21]. SSD1swi4 cells show a similar slow growth and loss of viability pattern. Both swi6 and swi4 mutants undergo the diauxic shift (DS) late, at about half the cell number that their respective wild type cells undergo the DS (Fig 1B). However, the optical densities of these mutant cultures are comparable to wild type at the DS (OD550 5 to 6), indicating that cell mass is the key variable for the timing of this transition. Both mutants are larger and more heterogeneous than wild type cells based on light scattering (S1 Fig). Dead cells predominate, based on dye exclusion (Fig 1C) and the accumulation of cell debris on the left margin of their flow cytometry profiles (S1 Fig). These data indicate that normal growth control in response to nutrient limitation requires the activities of both of these key regulators of G1. Wild type cells that have entered quiescence can be purified based on their density in percoll gradients [20]. No such high density cells can be purified from swi6 cultures (yellow dot in Fig 1A and 1D). This indicates that Swi6 is critical for the transition to quiescence. In contrast, about half the swi4 SSD1 cells become dense, but these cells are three times the size of wild type Q cells (Fig 1E) and they include both live and dead cells (Fig 1F). The live high density swi4 cells suffer a further three-fold loss of viability over the course of an 80 day incubation in water compared to wild type cells which drop very little (Fig 1F). The high density swi4 cells also recover very slowly upon re-feeding (Fig 1G). We conclude that the longevity of the dense swi4 cells is compromised. By all these criteria, swi6 and swi4 mutants are defective in both the log phase of growth and the transition into and out of quiescence. We have also assayed mutants of other known components of the G1 transcription complexes. The DNA binding component of MBF, Mbp1, is not required for G1 arrest (S1 Fig), viability (Fig 1B and 1C) or for Q cell production (Table 1). Stb1, a component of both SBF and MBF [22–25], is also not required for Q cell production (Table 1). Cells lacking Whi5, which binds and inhibits SBF [6,7, 25], undergo more cell divisions than wild type (Fig 2A) and they significantly delay, but finally achieve 80% G1 arrest after 48 hours of growth (Fig 2B). The whi5 mutant produces almost wild type levels of Q cells (Fig 2C), and these cells are identical in size to wild type (Fig 2D). The whi5 Q cells also have a comparable, if not somewhat longer life span (Fig 2E). This indicates that this SBF repressor plays a role in achieving efficient G1 arrest, but it has no detectable role in the maintenance of quiescent cells. However whi5 Q cells show a 30 minute delay in recovery from the quiescent state (Fig 2F). This is the opposite of what is seen with G1 cells purified by elutriation, where whi5 accelerates the transition to S phase and produces smaller cells [6,7]. These observations suggest that the late G1-specific SBF transcription complex of Swi4 and Swi6 plays a critical role in the transition to quiescence, but that its regulation as cells enter and exit quiescence may involve novel partners other than Whi5. The whole genome duplication that S. cerevisiae underwent [26] gave rise to a Whi5-related protein, which was originally identified as a high copy suppressor of rad53 lethality (SRL3 [27].) More recently, Srl3 was shown to bind to SBF in response to DNA damage [28] and to regulate the nuclear localization of Cln3 [29]. SRL3 transcription is induced by DNA damage and many other forms of stress [30–32]. This led us to determine whether Srl3 (also known as Whi7) plays a redundant role with Whi5 in the transition to quiescence. Fig 2 shows that loss of Srl3 causes a modest further delay of G1 arrest, but only when Whi5 is also missing. Its most striking phenotype is the delay of budding as srl3 and srl3whi5 Q cells re-enter the cell cycle upon re-feeding. These observations led us to consider two other known components of SBF and MBF transcription complexes. Msa1 and Msa2 are also related proteins that arose from the whole genome duplication. They were initially found by tandem affinity purification and multidimensional protein identification technology (MudPIT) to be associated with both SBF and MBF [17]. Msa1 was also identified as a high copy suppressor of three temperature-sensitive DNA replication mutants [18]. Genome-wide transcript analyses of rapidly growing cells indicated that Msa1 has both an activating and a repressing role at a small and diverse set of target genes during the log phase of growth [18]. The fifty genes identified in that study that are both bound and regulated by Msa1 are mostly involved in glucose metabolism, cell wall organization or ribosomal structure. MSA1 is an ECB-driven transcript that peaks at the M/G1 boundary, like CLN3 [33,34]. Msa1 binds to both SBF and MBF-regulated promoters and has a modest impact on the timing of late G1 transcription and budding in log phase cells [17]. This suggests that Msa1 performs an activatory function at these promoters during the log phase of growth. However, excess Msa1 leads cells to accumulate in G1 and S phase [35], suggesting that Msa1 either represses cell cycle progression directly, or that its presence in excess is activating the DNA damage or replication stress checkpoint. Msa1 also binds to Dbf4, which is the regulatory subunit of the Cdc7/Dbf4 kinase required for DNA replication and for activation of the replication stress checkpoint [36]. More recently, it was shown that the Hog1 kinase phosphorylates Msa1 during osmotic stress, and may play a role in delaying S phase under these conditions [19]. We have not observed a Hog1-dependent effect on the production of quiescent cells (Table 1). The Msa2 protein sequence is highly conserved compared to that of Msa1. Despite the tight cross-species conservation of Msa2, almost nothing is known about its role in cells. Like, Msa1, it associates with SBF and MBF [17]. MSA2 is an MBF target [37], which is transcribed in late G1, and induced by DNA damage and other forms of stress [13]. Msa1 and Msa2 have also been found to form a distinct activatory complex with Ste12 and Tec1 on the FLO11 and MSB1 promoters [38]. These genes are involved in cell adhesion and pseudohyphal development and the msa1msa2 double mutant is adhesion-defective. This led the authors to conclude that the Msa proteins may play a role in coordinating cell division with development. Our data indicate that both Msa1 and Msa2 are critical for cell division arrest and growth arrest as cells transition to quiescence. In prototrophic W303, loss of either Msa1 or Msa2 or both has no effect on the fraction of time the cells spend in G1 when they are growing logarithmically. The fraction of log phase cells that are in G1 in the single and double msa mutants is comparable to wild type (Table 1 and S1 Fig). However, as these cultures increase in cell number, the double mutant stops dividing at about half the cell density of wild type cultures (Fig 3A). With wild type cells, the percent of cells in G1 triples as they approach the diauxic shift [14] and Fig 3B). The single msa mutants are slightly delayed if at all in this response compared to wild type cells. In contrast, the msa1msa2 double mutant shows a slow accumulation of G1 cells that plateaus at 60% after 18 hours of growth (Fig 3B). After seven days of growth, the msa1msa2 cells show very low heterogeneous DNA content (Fig 4A) and most of the cells are dead (Fig 4B). Not surprisingly, the msa1msa2 Q cell yield is also low (red dot in Fig 1A). The failure of the double mutant to arrest in G1 and its loss of viability over this time course suggests that Msa1 and Msa2 play redundant roles in halting cell cycle progression specifically during the transition to quiescence. However, in checkpoint-deficient cells, carrying the rad53-21 mutation [39], Msa1 and Msa2 are both required for efficient G1 arrest in response to nutrient consumption (Fig 3C) We have previously shown that the Rad53-mediated replication stress checkpoint plays a role during the transition to quiescence [14]. Just as in rapidly cycling cells [11,12,40], Rad53 function is essential for restraining cells in G1 and achieving quiescence when the transition to S is driven prematurely by excess Cln3 [14] and Table 1). If the Msa proteins are also important for G1 arrest, we expected that their absence would also exacerbate the rad53-21 phenotype, and this is exactly what we observe. As noted previously [14], checkpoint-deficient rad53-21 cells do not achieve the full G1 arrest observed with wild type cells after 48 hours of growth (Fig 3C). The additional loss of either Msa1 or Msa2 mutants has a more extreme phenotype. These double mutants are almost as defective in cell cycle arrest after 48 hours of growth as the msa1msa2 mutant (Fig 3C). This indicates that when Rad53 is not present to reinforce the arrest, Msa1 and Msa2 are both required to efficiently halt cell cycle progression. msa2rad53-21 has the most extreme defect. It is an unstable strain and we were unable to construct the msa1msa2rad53 triple mutant. However, in contrast to msa1msa2, most of the msa1rad53-21 and msa2rad53-21 cells achieve G1 arrest after seven days in culture (Fig 4A) and retain viability (Fig 4B). Our previous work shows that shortly after the diauxic shift, wild type cells undergo a dramatic shift in cell size, due to a highly asymmetric cell division [15]. Fig 4C shows the cell size distribution of wild type cells during logarithmic growth compared to that of cells after the diauxic shift (18hr) and after seven days in culture. The asymmetric cell division of wild type cells gives rise to daughters that are about 14 femtoliters (fL) in volume. These cells slowly increase in volume to about 20 fL and never attain the 40–60 fL volume observed in log phase cultures. To see if the Msa proteins are required for this asymmetric cell division, we plotted the modal cell size of the single and double mutants as they grew from log to stationary phase (Fig 4D). All five strains undergo asymmetric cell division, but the msa2rad53-21 and msa1msa2 cells continue to enlarge. msa1rad53-21 has an intermediate phenotype. Interestingly, the msa2rad53-21 cells increase to the same size as msa1msa2 cells, but do not lose viability over this time course. They also produce nearly wild type levels of Q cells (Table 1). This is correlated with and may be explained by the fact that the majority of the msa1rad53 and msa2rad53 cells eventually attain G1 arrest after seven days of growth (Fig 4A). Despite their large size, the viable cells in G1 purify as Q cells from those seven day cultures (Figs 4A and 5D) We conclude that Msa1 and Msa2 both contribute to efficient cell division arrest and cell growth arrest as nutrients become limiting. Rad53 checkpoint function provides critical backup in restraining cell cycle progression and growth when either Msa protein is absent. However, Msa1 and Msa2 have critical overlapping roles in achieving full G1 arrest and maintaining viability during the transition to quiescence. To see if the survivors of this transition achieve a protective quiescent state, we purified the high density cells from a seven day old culture of msa1, msa2 and msa1msa2. Fig 5A and 5B compare the size of these cells from the starting log phase culture and the purified high density Q cells. Both msa mutants are smaller than wild type cells during logarithmic growth, but the msa1msa2 cells are clearly larger and more heterogeneous than the single mutants. The small size of the single mutants could indicate that they spend less time in G1, but this is not born out by their flow cytometry profiles, which look like wild type (S1 Fig). The msa1msa2 cells also have a flow cytometry profile very similar to the wild type profile during logarithmic growth (Fig 4A). This suggests that their overlapping function is not important during rapid growth. After seven days of growth to stationary phase, we obtained wild type yields of high-density cells from the msa1 and msa2 mutants (61 ±3% and 57 ±2% respectively.) However, the double mutant produces a lower, more variable yield of 34 ±6% (Table 1). The dense msa1msa2 cells are also very large (Fig 5B), consistent with their inability to cease cell growth (Fig 4D). Despite their large size, one third of the msa1msa2 cells achieve the density characteristic of Q cells. To see if these high-density cells attained other features of quiescent cells, we tested their thermo-tolerance (Fig 5C) and their longevity (Fig 5D). The dense fraction of msa1msa2 cells is comprised of 75% viable cells (Fig 5D), compared to only 30% viable cells found in the seven day old cultures (Fig 4B). Interestingly, these high-density cells have the same thermo-tolerance and longevity of wild type quiescent cells (Fig 5C and 5D.) We conclude that Msa1 and Msa2 are critical for the efficient transition into quiescence, but the cells that survive this transition achieve at least some of the protective features of Q cells. Another feature of quiescent cells is their rapid and synchronous return to the cell cycle upon re-feeding [20]. Fig 5E shows the typical 90 minute delay, followed by a highly synchronous cell division, that we observe when Q cells are transferred from water to rich media. The single msa mutants are clearly delayed, and the msa1msa2 cells lag considerably longer. The starting population of msa1msa2 Q cells is about 15% budded, but these budded cells are likely dead, based on their phase dark appearance in the microscope and their failure to progress. The unbudded population begins to bud after 135 minutes. These delays show that both Msa1 and Msa2 are important for an efficient transition out of quiescence, but they are not required for this transition because eventually nearly all the live cells bud. The failure of the msa1msa2 mutant to arrest in G1 and the association of these proteins with both SBF and MBF led us to ask if known MBF and/or SBF targets were deregulated in these mutants as they transition from log phase to quiescence. As discovered previously [18], msa1 and msa2 mutants have minimal impact on transcription during the log phase of growth. However, they have a significantly greater influence on transcription after the diauxic shift (S2 Fig). To see if MBF and SBF targets are affected, we looked for known MBF and/or SBF targets [37], which were negatively or positively affected by msa1, msa2 or the double mutant. Using a cutoff of 1. 8-fold, we found that about half the known SBF and MBF targets were affected by msa1 or msa2. Fig 6 shows the levels of these MBF and SBF target transcripts in the msa single and double mutants in log phase cells and in cells that have undergone the diauxic shift, expressed as a ratio of mutant over wild type. With the exception of YOX1 and MNN1, none of these transcripts are significantly affected by the msa mutants during the log phase of growth. However, after the diauxic shift, we find that MBF targets, and targets of both MBF and SBF are primarily under-represented, and those that are only SBF targets are primarily elevated in the mutants. We also find that the impact of msa1 and msa2 is similar at these promoters and their effects are not additive. This indicates that both Msa1 and Msa2 are required to regulate these MBF and SBF targets in post-diauxic cells. The fact that loss of both Msa proteins has about the same effect at these targets as loss of either one suggests that both Msa proteins are critical components of the same pathways that confer this regulation. It is also worth noting that while only about half the known targets of these late G1-specific transcription factors meet the 1. 8-fold threshold in either single mutant, many others are affected in the same way but to a lesser extent (S1 Table). We predict that other transcriptional regulators, mRNA stability or mRNA sequestration are likely to be variables that complicate the extent of de-regulation we observe. The set of MBF targets whose activation in post-diauxic cells require both Msa1 and Msa2 include SLD2 and a number of other genes involved in DNA replication. In fact, 22 of the 54 MBF targets (p value = 10−9) most repressed in the msa single mutants are involved in DNA metabolism and 18 are involved in DNA repair (p value = 10−10.) This may help explain why galactose-induced overproduction of Msa1 suppresses sld2 and some other DNA replication defects [18]. SLD2 is also an MSA2 activated gene, but Msa2 over-expression does not suppress sld2. This asymmetry could be explained if MSA2, itself an MBF target, is also over-expressed upon galactose-induction of Msa1. This would result in high levels of both Msa proteins, which would activate SLD2 and other DNA replication genes. In contrast, high levels of Msa2 would not be expected to induce high levels of Msa1, and since both are required for activation of these replication genes, high Msa2 alone would not have the same suppressing effect. The next most enriched class of MBF targets that are activated by both Msa1 and Msa2 are genes involved in sister chromatid cohesion (SMC1, SMC3, IRR1, PDS5, MCD1 and CSM3.) It is known that genome-wide cohesion occurs in response to a single double strand break [42,43]. Moreover, this break-induced cohesion prevents the loss of the unbroken chromosomes, indicating that it serves a purpose beyond repair of the single double strand break [43]. The co-activation of cohesion genes as cells enter quiescence brings up the intriguing possibility that cohesion may protect and/or compact the genome in quiescent cells. It has recently been shown that quiescent cells have uniquely compact chromatin in which all the telomeres are in a tight cluster in the center of the nucleus [44,45]. At the SBF target promoters, where both Msa1 and Msa2 are required for the repression, the histone transcripts are among the most affected. All eight core histone transcripts are highly elevated in the msa mutants (Fig 6 and S1 Table). HHT1 is not pictured in Fig 6 because it missed the cutoff for being an assigned SBF target [37]. In addition, the linker histone HHO1 and the H2A variant HTZ1, both barely missed our cutoff for inclusion on Fig 6, each being about 1. 7-fold elevated above wild type in post-diauxic msa2 cells. There are 29 targets that meet or exceed the 1. 7-fold mRNA level increase and 10 of them are histones. The only other histone, CSE4, which is centromere-specific, is unaffected. Histone expression is tightly controlled by multiple mechanisms that are not entirely understood despite decades of investigation [46]. To confirm the role of SBF in transcription of these genes, we assayed the HTA1 promoter activity through the cell cycle in swi4, mbp1 and swi4mbp1 cells. S3 Fig shows that Swi4 contributes to the cell cycle-specific activation of this promoter and Mbp1 has much less effect. Our data indicate that Msa1 and Msa2 both make independent contributions to the post-diauxic regulation of many MBF and SBF targets. Loss of these activities is not additive, suggesting that they participate in the same pathways of regulation at these promoters. However, the extreme phenotype is only observed in the double mutant. This suggests some redundancy of function. Redundancy is also suggested by the sequence similarity between Msa1 and Msa2. To address this question, we looked across the genome for transcripts that were mis-regulated in the msa1msa2 double mutant, but not mis-regulated to the same extent in either single mutant in the post-diauxic time point. Table 2 lists all the transcripts across the genome that are mis-regulated 1. 7 fold or more in msa1msa2 but less so in msa1 or msa2 after the diauxic shift. Most of these transcripts also show additivity, in that the single mutants mis-regulate in the same way, but to a lesser extent. This can be readily seen in the bar graph in S4 Fig. What is striking from this genome-wide survey is that of the 47 transcripts most down-regulated in the double mutant, one-quarter are already known MBF and/or SBF targets (Table 2). This further supports the view that regulating MBF and SBF activities are the critical functions of Msa1 and Msa2 in post-diauxic cells. Overall, eleven of the 47 genes are cell cycle genes, and all but three of these are involved in chromosome segregation and/or the establishment of polarity in the cell division process. Others affect a diversity of processes. There is a smaller set of transcripts that are elevated specifically in the double mutant (Table 3). Here again, the single mutants typically also elevate the transcript levels, but to a lesser extent. None of these transcripts are known MBF and/or SBF targets, nor do they show significant enrichment for cell cycle regulation. Rather, seven of these 30 genes respond to stress, and four are meiosis-specific. Another four genes (TIR1,3, and 4 and DAN1) are specifically expressed during, and required for anaerobic growth [48,49]. Three of the most elevated transcripts are involved in pyrimidine biosynthesis. There is no reason to think that these are direct targets of Msa1 or Msa2. Rather we suspect that failure to properly initiate the transition to quiescence indirectly results in the ectopic expression of genes involved in other developmental pathways. This ectopic expression could contribute, indirectly, to the loss of viability of the double mutant. It is unclear which of the mis-regulated transcripts listed in Table 2 and/or 3 might be responsible for the loss of viability observed in the double mutant. Indeed, there is no reason to assume that a single member of either class is causing loss of viability in the msa1msa2 mutant. For example, seven of the down-regulated genes are essential for viability (bold in Table 2) and the reduced expression of any or all of these genes could be deleterious. Included among the up-regulated transcripts is an uncharacterized gene (YLR162W), which is known to cause growth arrest and apoptosis when over-expressed [50]. The down-regulation of MBF targets in the absence of Msa activity could be due to direct binding and activation by the Msa/MBF complex, or it could be due to indirect effects on other regulators. We note that NRM1 is slightly elevated and RAD53 is substantially down-regulated in the msa mutants (S1 Table). Rad53 is known to phosphorylate and release the negative regulator Nrm1 from MBF complexes in the presence of DNA damage [13,51]. Similarly, the high levels of SBF target transcripts in the msa mutants may be due to direct repression by the Msa/SBF complex, or it could reflect indirect effects on other regulators that are mis-expressed in the Msa mutants. For example, Swi4, the DNA binding component of SBF is up-regulated in the msa mutants (Fig 6). To see if Msa regulation of these targets is direct or indirect, we assayed binding of Msa1 and Msa2 to a set of SBF and MBF targets by chromatin immunoprecipitation. Fig 7A shows a survey of Msa binding to thirteen of these genes, including eleven from Fig 6 and two from Table 2 (TOS6 and CSI2.) We see robust binding signals for both Msa proteins on SBF targets (S) and on promoters containing both SBF and MBF binding sites (B). Binding is weaker to the MBF targets (M). In most cases, the binding signals are higher in the post-diauxic time points (16 and 24 hours.) To confirm the relatively weak binding to MBF targets, we asked if the binding was dependent upon Mbp1, which is the DNA binding component of MBF [9]. Fig 7B shows that the binding observed at three MBF target promoters in post-diauxic cells is Mbp1-dependent. Fig 7C shows that the binding of Msa1 does not depend on the presence of Msa2, or vice versa, at both MBF and SBF targets. These data are most consistent with the direct binding of Msa1 and Msa2 to both classes of late G1-specific promoters. The Msa-dependent regulation of these transcripts is likely to be important for preventing entry into S phase as cells respond to a waning nutrient supply and enter quiescence. The transition from proliferation to quiescence involves a stable but reversible cell cycle arrest in G1. It follows that the transcriptional regulators that drive the G1 to S transition have to either be eliminated or reprogrammed. Perhaps because of the need for rapid reversibility of this arrest, budding yeast utilize their E2F-like complex of Swi4 and Swi6 (SBF) as a platform for a novel form of regulation that involves Msa1 and Msa2 and is initiated as glucose levels drop. The regulation conferred by Msa1 and Msa2 is critical for the cell cycle arrest, cell growth arrest and viability of cells as they transition to quiescence. Other known SBF regulators (Stb1, Whi5, Srl3) do not play a significant role in the transition into quiescence, but Whi5 and the related Srl3 protein seem to accelerate the reversal of quiescence when nutritional conditions improve. This is opposite their roles as negative regulators in exponentially growing cells [6,7, 29]. One possible explanation is that Whi5 (and/or Srl3) may displace the Msa proteins from SBF as an early step in the recovery phase. Such an exchange would maintain repression of SBF targets, but would make their activation responsive to the increased cyclin levels that accompany the transition to S phase. The Msa1 and Msa2 proteins were identified by two very different strategies. The MudPIT analysis used to identify Whi5 and Nrm1 as components of the SBF and MBF complexes [7] also identified two related proteins that were named MBF- and SBF- associated (Msa) proteins [17]. Msa2 had been shown to interact with Swi6, the common component of SBF and MBF [52], and both MSA genes were known to be transcribed in a cell cycle-specific manner. MSA1 (YOR066W) is transcribed at the M/G1 boundary in a Yox1/Mcm1-dependent manner [34,53], and MSA2 is a late G1-specific transcript [54]. Both proteins are also expressed only in G1 and they undergo cell cycle-specific modifications in growing cells [17,18]. Msa1 is among the handful of proteins whose nuclear localization is regulated by cyclin-dependent kinase activity and is G1-specific, like Whi5 [55]. Both proteins bind to SBF and MBF target promoters, specifically during G1 in cycling cells, and that binding is Swi4- and Mbp1-dependent, respectively [17]. Msa1 was also identified as a high copy suppressor of three temperature sensitive DNA replication mutants: drc1-1/sld2, dbp11-1 and pol2-12 [18]. Interestingly, Msa1 over-expression had a deleterious effect on other DNA replication genes (cdc6-1 and cdc7-1) and other cell cycle regulators (cdc28 and cdc14-1.) Being aware of the previous study showing the interaction of Msa1 with the SBF and MBF transcription complexes [17], these authors carried out genome-wide chromatin immunoprecipitations and transcript microarrays to identify Msa1 targets. They found 50 genes that were both bound and regulated by Msa1 in cycling cells. These genes affect all aspects of cell growth, but showed no clear connection to DNA replication. This left the mystery of Msa1’s role in DNA replication unresolved. They did identify about 60 MBF and/or SBF targets as binding sites for Msa1, but most were not Msa1 regulated in cycling cells, just as we observe. Our study has shown that there are many DNA replication genes that are activated by Msa proteins, including SLD2, that could explain the suppression of DNA replication defects, but this only occurs in post-diauxic cells. One thing that these studies, as well as the study implicating Msa1 in osmoregulation of the cell cycle [19], have in common is the relatively mild phenotypes observed for the msa single and double mutants in cycling cells. Altering the levels of Msa1 causes modest changes in the timing of late G1-specific transcription [17], and the initiation of S phase [18]. Further, no synergistic effects in the msa1msa2 double mutant were reported in cycling cells. We observe similarly mild phenotypes for these mutants during the log phase of growth. This is surprising, considering the many layers of regulation that are exerted upon these proteins in cycling cells, and the critical roles their putative targets play in the G1 to S transition. However, we observe strong deleterious effects of these mutants after the diauxic shift, when cells are preparing to shift from proliferation to quiescence. The Msa proteins are critical during this transition, and one clear effect that they have is in the reprogramming of SBF and MBF activity. Our data suggest that this reprogramming is important for entry into and recovery from quiescence. We know that the Msa proteins are produced, localized to the nucleus and have the capacity to bind SBF and MBF targets specifically during the G1 phase of every cell cycle. However, only after the cells receive a signal of nutrient limitation are the Msa proteins able to influence the activity of most of these transcription complexes. We propose that the purpose of this tight G1-specific regulation in cycling cells is to ensure that these proteins are present, in the G1 nucleus, to respond immediately to these environmental signal (s), to modulate late G1 transcription and to promote G1 arrest and cell growth arrest. In this way, cells in other phases of the cell cycle would continue to progress and only the G1 cells would initiate cell cycle arrest. This may also explain why there is a correlation between the length of G1 in cycling cells and the ability to enter the quiescent state (Fig 1A). The majority of the Msa1- and Msa2-dependent regulation we observe fits into one of two patterns. In many cases, loss of either Msa1 or Msa2 disrupts regulation, and loss of both is not additive, indicating that both Msa proteins are required in the same pathway of regulation. In other cases, we see maximum deregulation in the double mutant, which suggests some redundancy. However, in most of these cases, both of the single mutants also de-regulate but to a lesser extent. Though there are exceptions (Tables 2 and 3), the bulk of the evidence suggests that both Msa proteins are required at most promoters. Consistent with this, if we eliminate Rad53 checkpoint function, we see deleterious effects of the single msa1 or msa2 mutants that are qualitatively similar but less severe than that of the msa1msa2 mutant. We conclude that Msa1 and Msa2 have undergone substantial functional divergence, but there is a set of critical targets at which either Msa protein can regulate to a sufficient extent to promote survival during the transition to quiescence. We have shown that Mbp1 is required for Msa binding at three MBF target sites, but deletion of Mbp1 does not interfere with the transition to quiescence (Fig 1B and Table 1). In contrast, the principle SBF components: Swi4 and Swi6, are required for a normal transition to quiescence. This makes it most likely that the critical targets of Msa regulation that promote G1 arrest are among the SBF targets that they regulate. However, more complicated scenarios are possible. Swi4 and Mbp1 have similar DNA binding domains and similar binding sites [2], and there are instances in which an SBF binding site in G1 becomes an MBF binding site in S phase [56]. These and other complexities make it difficult to guess which of these transcripts could play a critical role in promoting a stable G1 arrest and thereby be responsible for the loss of viability of the double mutant. Further work will be required to determine how Msa1 and Msa2 activity is modulated by nutritional cues, how they achieve this regulation and which of their direct or indirect targets are responsible for the G1 arrest that occurs as cells transition to quiescence. Despite the lack of physical similarity at the protein sequence level, there are striking parallels between the transcriptional regulation that promotes the G1 to S transition in yeast and mammalian cells [2]. Like SBF and MBF, there are E2F protein complexes that activate transcription in G1 and promote S phase. These complexes are inactive in early G1 due to the binding of repressors (Whi5 and RB), which recruit histone deacetylases to their target genes. In both cases, activation requires removal of the repressors by cyclin-dependent kinases. This enables them to induce transcription of their target genes, many of which are also conserved (e. g. cyclins, replication proteins and histones.) With this work, we extend this conservation of strategy to the transcriptional regulation that promotes the transition from G1 to quiescence. In higher eukaryotes, entry into quiescence depends on the formation of novel E2F complexes that serve to repress these same target genes [4]. This so-called DREAM complex of DP, RB-like, E2F and MuvB was first identified in worms and flies [3,57] and later found to perform a similar function in human cells [4]. It is assembled on E2F target genes to repress transcription in cells entering quiescence and disruption of these complexes drives cells back into the cell cycle [5]. Msa1 and Msa2 perform a similar function, by binding SBF and MBF complexes and reprogramming their activities. They are not required in cycling cells, but they are critical for the transition to quiescence. In their absence, cells fail to arrest in G1 and lose viability. Interestingly, Msa1and Msa2 do not significantly affect the longevity of cells that successfully enter quiescence, but they are required for efficient entry to and exit from quiescence. It will be interesting to determine how cell cycle re-entry from quiescence differs from the G1 to S transition in cycling cells and which of their targets are rate limiting for this transition. All yeast strains used in this study are isogenic with BY6500, the prototrophic version of W303 [58], unless otherwise indicated. Strain numbers are provided in Table 1 or in figure legends. The 5xCLN3 was created by integrating four additional copies of CLN3 at different marker loci [14] with the integrating vectors, pRS303-306 [59]. The W303 SSD1 was created as described [58]. All the deletions were made using the Longtine deletion vectors [60] unless otherwise indicated. The checkpoint deficient rad53-21 mutant [39] was crossed with the prototrophic W303 (BY6500) and then crossed with the deletion strains as listed in Table 1. The myc-tagged strains were constructed using pFA6a-13Myc-KanMX6 [60]. Viability was monitored by Live/Dead FungaLight (Invitrogen, Grand Island, NY) and colony formation as described. Cell size and cell number was measured on a Z2 Beckman Coulter Counter (Beckman Coulter, Brea, CA.) Growth assays were all carried out in triplicate at 30°C in rich media with 2% glucose (YEPD) with 200 rpm aeration on platform shakers. Growth from log to stationary phase (log to SP) was followed by starting two equivalent 25 ml cultures at an OD600 of. 02, ten hours apart, from the same culture maintained in log phase. The first culture is sampled at 8,10,12,24,28 and 48 hours. The second culture is used for the 14,16,18,20 and 38 hour time points. At each time point, samples for cell count, cell size, and flow cytometry were taken. The diauxic shift was determined by the absence of glucose in the media. Glucose levels were determined using glucose detection strips (GLU 300, Precision Labs, Inc. West Chester, OH.) To follow seven days of growth, cultures were inoculated as above. Samples were taken for the zero time point 5 hours after inoculation, then daily, for assaying cell number, viability and colony forming units. Number of trials averaged for these figures is shown in parentheses. Quiescent cells were purified after seven days of growth as described above, by centrifugation through a percoll gradient [15,20]. Typically 200 OD600 units of cells are loaded onto a 25 ml gradient and Q yield is calculated as the percentage of OD600 units that sediment to the bottom 9 ml of the 25 ml gradient. The high density Q cells are washed and maintained in water. Longevity of the Q cells was monitored in triplicate from 13 ml suspensions of Q cells in water inoculated to an OD600 of 1. 0 and incubated with aeration at 30°C. We see no acidification of the water after 300 days of incubation under these conditions with wild type cells. To monitor longevity in the non-dividing state, samples are taken from these Q cell suspensions at two week intervals for cell count, cell size, cell viability and colony forming units. Before sampling, these suspensions are weighed and water is added to replace loss due to evaporation. Thermo-tolerance of the high density Q cell fraction was assayed in triplicate starting with Q cells in water at an OD600 of 1. 0. 50μl of these cells were transferred to a. 5 ml PCR tube and incubated for 10 minutes at the specified temperature. These were chilled, diluted and plated for colony forming units. Q cell re-entry into the cell cycle was followed in triplicate by transferring 10 OD600 units of Q cells in one ml into 25 ml of YEPD, sampling at 15 minute intervals and counting percent of budded cells from a total of 200 cells for each time point. Number of trials averaged for these figures is shown in parentheses. Flow cytometry was carried out as in [15]. DNA content was quantified by staining with Sytox Green and the percent of cells in G1 was determined using the cell cycle module of FlowJo V9. 6. 4. As we have shown [15], cells transitioning to the quiescent state undergo asymmetric cell divisions and fortify their cell walls. These events give rise to heterogeneity in the flow cytometry profile. In particular, the G1 peak splits into three peaks, which must be added together to obtain the total number of cells in G1 (Fig 4A and S1 Fig). Our plots all report the percent of live cells that are in G1. Dead cells and cell debris, which accumulate in swi4, swi6, msa1msa2, and msa2rad53 cultures pile up on the left margin of the DNA fluorescence histograms. Number of trials averaged for these figures is shown in parentheses. RNA sampling, collection and paired end Next-Generation RNA sequencing was carried out as described [14]. mRNA expression levels following polyA selection were assayed by using the HiSeq 2500 next generation sequencing system from Illumina [61] in the Fred Hutchinson Cancer Research Center Genomics Core Facility. Sequences were aligned to the reference genome W303 using the Tophat2 application [62], then counted with HTSeq [63]. Differential expression between samples was measured using the DESeq package from Bioconductor [64]. Ratios of expression in mutant versus wild type were then computed from the normalized read counts. Two biological replicates were generated and averaged for this analysis. These data for all SBF and MBF targets is provided as (S1 Table). Cells carrying Msa1 or Msa2 tagged with the myc epitope or non-tagged controls were collected from log phase cells, or from cells that had passed the diauxic shift as indicated by the lack of glucose in the media. Proteins were cross-linked to DNA as described [65]. Frozen cell pellets were resuspended in lysis buffer (50 mM HEPES pH 7. 6,140 mM NaCl, 1% Triton X-100,0. 1% Na deoxycholate, 1mM EDTA, 1 mM PMSF, 1 μg/ml aprotinin, leupeptin, and pepstatin A). Cells were broken with glass beads in a Mini Beadbeater-8 (BioSpec Products, Bartlesville, OK) three times for 30 seconds on the Homogenize setting. After a 15-min centrifugation the supernatant was discarded and the pellet (chromatin fraction) was resuspended in the initial volume of lysis buffer. The DNA was fragmented to ∼500 base pairs with a Sonifier Cell Disrupter (Heat-Systems-Ultrasonics, Inc. , Plainview, NY), sonicating at setting 3 for 10 seconds 5 times with a one minute ice rest between sets. After clarification, immunoprecipitation was performed with 3 × 109 cells of chromatin, the monoclonal anti-c-MYC antibody 9E10 (Roche Applied Science, Indianapolis, IN) and protein A sepharose CL-4B beads (GE Healthcare, Pittsburg, PA) rolling overnight at 4°C. Immune complexes were washed twice with 1 ml of lysis buffer, 1 ml of lysis buffer with 250 mM NaCl, 1 ml of ChIP wash buffer (10 mM Tris pH 8. 0,250 mM LiCl, 0. 75% NP-40,0. 75% Na deoxycholate, 1 mM EDTA), and 1 ml of Tris-EDTA. DNA-protein cross-linking was reversed in 100 μl 1% SDS/Tris-EDTA at 65°C overnight. DNA was cleaned up with 50μg RNase A at 37°C for one hour then 300μg Proteinase K at 50°C for one hour. DNA was purified on Purelink PCR Purification columns (Invitrogen, Grand Island, NY) according to the manufacturer' s instructions. PCR reactions (5 min 95°C, 26 times [1 min 94°C, 1 min 55°C, 1 min 72°C], 10 min 72°C, hold 4°C) were performed using HotStarTaq Plus DNA Polymerase (QIAGEN, Hilden, Germany) on 1 μl of 1/1000 eluted input (chromatin) and 1 μl of eluted immunoprecipitation. Sequences of the primers used to detect binding are available upon request. PCR fragments were separated on a 2% agarose gel and visualized by ethidium bromide. All the demultiplexed FASTQ RNA sequence files are available from the National Center for Biotechnology Sequence Read Archive from accession SRP068917.

In spite of the many differences between yeast and humans, the basic strategies that regulate the cell division cycle are fundamentally conserved. In this study, we extend these parallels to include a common strategy by which cells transition from proliferation to quiescence. The decision to divide is made in the G1 phase of the cell cycle. During G1, the genes that drive DNA replication are repressed by the E2F/RB complex. When a signal to divide is received, RB is removed and the complex is activated. When cells commit to a long term, but reversible G1 arrest, or quiescence, they express a novel E2F/RB-like complex, which promotes and maintains a stable repressive state. Yeast cells contain a functional analog of E2F/RB, called SBF/Whi5, which is activated by a similar mechanism in proliferating yeast cells. In this study, we identify two novel components of the SBF/Whi5 complex whose activity is specific to the transition to quiescence. These factors, Msa1 and Msa2, repress SBF targets and are required for the long term, but reversible G1 arrest that is critical for achieving a quiescent state.

Abstract Introduction Results Discussion Materials and Methods

g1 phase gene regulation cell cycle and cell division regulatory proteins cell processes dna-binding proteins dna transcription regulator genes dna replication transcription factors gene types dna synthesis phase proteins gene expression biochemistry cell biology nucleic acids genetics biology and life sciences

2016

Msa1 and Msa2 Modulate G1-Specific Transcription to Promote G1 Arrest and the Transition to Quiescence in Budding Yeast

Many bacteria carry two or more chromosome-like replicons. This occurs in pathogens such as Vibrio cholerea and Brucella abortis as well as in many N2-fixing plant symbionts including all isolates of the alfalfa root-nodule bacteria Sinorhizobium meliloti. Understanding the evolution and role of this multipartite genome organization will provide significant insight into these important organisms; yet this knowledge remains incomplete, in part, because technical challenges of large-scale genome manipulations have limited experimental analyses. The distinct evolutionary histories and characteristics of the three replicons that constitute the S. meliloti genome (the chromosome (3. 65 Mb), pSymA megaplasmid (1. 35 Mb), and pSymB chromid (1. 68 Mb) ) makes this a good model to examine this topic. We transferred essential genes from pSymB into the chromosome, and constructed strains that lack pSymB as well as both pSymA and pSymB. This is the largest reduction (45. 4%, 3. 04 megabases, 2866 genes) of a prokaryotic genome to date and the first removal of an essential chromid. Strikingly, strains lacking pSymA and pSymB (ΔpSymAB) lost the ability to utilize 55 of 74 carbon sources and various sources of nitrogen, phosphorous and sulfur, yet the ΔpSymAB strain grew well in minimal salts media and in sterile soil. This suggests that the core chromosome is sufficient for growth in a bulk soil environment and that the pSymA and pSymB replicons carry genes with more specialized functions such as growth in the rhizosphere and interaction with the plant. These experimental data support a generalized evolutionary model, in which non-chromosomal replicons primarily carry genes with more specialized functions. These large secondary replicons increase the organism' s niche range, which offsets their metabolic burden on the cell (e. g. pSymA). Subsequent co-evolution with the chromosome then leads to the formation of a chromid through the acquisition of functions core to all niches (e. g. pSymB). While most bacterial genomes have only a single chromosome, many are more complex and consist of two or more large replicons. Depending on their characteristics, these replicons are classified as a chromosome (largest replicon containing most of the core genes), megaplasmid (laterally acquired with a plasmid origin of replication and lacking core genes), or a chromid (displays characteristics of both chromosomes and megaplasmids) [1]. While this genome organization is most commonly found in the proteobacteria, it is by no means limited to this class [2]. Interestingly, multipartite genomes are prevalent among plant symbionts (eg. Sinorhizobium and Rhizobium species) and plant and animal pathogens (eg. Agrobacterium, Vibrio, Burkholderia, and Brucella) [1], [2]. As such, understanding the general role and evolution of these accessory replicons may provide vital insight into the biology of these organisms and possible strategies to promote or suppress these interactions. The potential advantages of multipartite genomes imply that this genome architecture is not simply an evolutionary peculiarity. For example, the division of a genome may decrease the time required for genome replication, potentially allowing more rapid growth. Indeed, multipartite genomes are larger on average [1] and some of the fastest replicating species have divided genomes [3]. However, each replicon within a divided genome is not of equal size [3] and there is no correlation between genome size and maximal growth rate [4]. Alternatively, multipartite genomes may provide a method of controlling gene dosage and thus expression, as in Vibrio species [3], [5]. This can consequently result in weaker purifying selection and greater rates of evolution on the smaller replicon, as observed in Vibrio and Burkholderia [6], [7]. However, this does not hold true for slow-replicating species with a divided genome [5]. A third hypothesis is that multipartite genomes allow for additional genome expansion once the chromosome reaches its maximal size [8]. Yet, some species with multipartite genomes have primary chromosomes smaller than 2. 5 Mb, while some species with a single chromosome have genomes greater than 9 Mb [9], [10]. Moreover, Brucella species generally have two chromosome-like replicons, except for Brucella suis biovar 3, which has a single chromosome equivalent in size to the total of both replicons in related strains due to integration of one replicon into the other [11], [12]. While all three of the ideas discussed above may help promote the maintenance of a divided genome architecture once established, the observations inconsistent with each suggest they are unlikely to be general driving forces for multipartite genome evolution. An alternative hypothesis is that multipartite genomes allow for the functional division of genes onto separate replicons [13]. Several lines of evidence are consistent with this idea: uneven COG distribution between each replicon such as in Burkholderia xenovorans [7] and Rhizobium etli [14], replicon-dependent evolution in Sinorhizobium meliloti [15], and replicon-dependent gene regulation in Vibrio cholerae [16] and S. meliloti [17]. Furthermore, an association exists between the presence of a divided genome and an interaction with a host organism [18]. This hypothesis implies that secondary replicons are over-represented in cellular processes specific to host interaction, which, if true, should focus the genetic analyses of these processes; however, the acceptance of this idea is limited due to a paucity of experimental support [1]. S. meliloti is a N2-fixing endosymbiont of legumes, and inhabits diverse environments including bulk soil, the rhizosphere, and the legume root nodule. It is an interesting organism to study the evolution of multipartite genomes as the large 6. 7 megabase (Mb) genome of the model strain Rm1021 (and the highly related strain, Rm2011) is divided into a chromosome (∼3. 7 Mb), an evolutionarily old and conserved chromid (pSymB; ∼1. 7 Mb), and an evolutionarily recent and variable megaplasmid (pSymA; ∼1. 4 Mb) [19]–[21]. Each of these is present in all wild-type isolates [20], [21], and there is no evidence that pSymA or pSymB are naturally lost by S. meliloti. This indicates that each replicon is a stable and indispensible part of the genome in the natural environment. Both pSymA and pSymB encode major pathways of interaction with the plant symbiont and the environment: exopolysaccharide biosynthesis and many ABC transporters are encoded by pSymB [22], and the nodulation and nitrogen fixation genes are present on pSymA [23]. The complete removal of pSymA has been described [24], and we now report the removal of pSymB and the construction of a strain lacking both pSymA and pSymB. This reduced genome provides a novel platform to facilitate forward genetic studies of rhizobium and bacterium-plant interactions, and we employed it here to experimentally test hypotheses surrounding the evolution and role of multipartite genomes. Optimal growth of the ΔpSymAB strain on complex LB or TY media required cobalt [30] and calcium supplementation, while growth on minimal M9 medium is best with thiamine [37] and iron [38] addition. The affect of calcium could possibly be related to the loss of exopolysaccharide loci on pSymB. No additional nutritional requirements were identified, which was unexpected as the genome sequence indicated the asparagine biosynthesis genes to be located on pSymB [19]. Below we describe the growth of S. meliloti in sterile bulk soil, and interestingly, we observed that growth of the ΔpSymB and ΔpSymAB strains in this soil did not require thiamine supplementation. This indicates that thiamine biosynthesis, the sole nutrient whose biosynthesis is pSymB-dependent, is not required for growth in S. meliloti' s natural environment, although thiamine concentrations may limit growth in the rhizosphere [39]. Thus, very few fundamental genes are located on these replicons. Growth profiles of each strain were examined in complex and minimal media (Figures 1A, 1B, S1) by monitoring the change in OD600. The light scattering properties of all strains were the same as in soil mesocosm experiments described below, a 10−4 dilution of cell suspensions with an OD600 value of 1 repeatedly resulted in viable counts of 4×103 CFU gm−1 of soil for each strain, indicating that the CFU/OD600 in the inoculum was 2×109 for all strains (Figure 1C, 2A, 3B). Removal of both replicons led to a surprisingly small growth deficit in minimal medium, with the ΔpSymAB strain showing only a 1. 37-fold slower growth rate than that of the wild type. However, a striking pattern emerged when the effect of the removal of pSymA and pSymB was examined independently: loss of the evolutionarily older pSymB resulted in a 1. 6-fold slower growth rate, while loss of the evolutionarily younger pSymA led to a 1. 18-fold increase in growth rate. Qualitatively similar exponential phase dynamics are observed in complex media, although a large decrease in stationary phase density is observed when the cells lack pSymB. Others have observed a fitness improvement following the loss of large replicons, such as a megaplasmid from Agrobacterium tumefaciens [40] or large virulence plasmids from pathogenic Escherichia coli [41]. Thus, it appears a general characteristic for large replicons is to be metabolically expensive, and that their maintenance indicates they must provide a fitness advantage to the cell not necessarily evident during laboratory growth; the symbiotic nodulation and N2-fixation loci on pSymA would provide such a fitness benefit. While we also expect pSymB to impose a metabolic burden on growing cells, we postulate the loss of pSymB resulted in a decreased growth rate because of the acquisition of core genes (by core genes, we mean genes that encode products that are either essential for survival or are involved in central bacterial processes) on pSymB from the chromosome (eg. bacA, minCDE, bdhA). It has been shown that gene transfer occurs from the primary chromosome to secondary chromosomes and chromids [8], [27]; indeed, 25–30% of genes located on pSymB that are also present in the related species A. tumefaciens are located on the A. tumefaciens circular (primary) chromosome [42]. This suggests that since their divergence, there has been significant gene transfer between the primary chromosome and secondary replicons in S. meliloti and A. tumefaciens. Furthermore, a bioinformatics approach indicated that in Rhizobium etli there is a correlation between the evolutionary age of a replicon and the level of functional integration with the chromosome [14]. Thus, while gene transfer from the chromosome to pSymA presumably occurs as well, the young evolutionary age of pSymA has so far precluded a significant accumulation of core genes. The decreased stationary phase density of strains lacking pSymB (Figure 1B) prompted an examination of the metabolic capacity of these cells. Accordingly, wild-type S. meliloti and the cured derivatives were examined for the ability to grow (increase in OD600) with various sources of carbon, nitrogen, phosphorus, and sulfur. Wild-type S. meliloti grew on 73 carbon, 55 nitrogen, 53 phosphorus, and 20 sulfur sources (Table 1), and the removal of pSymA and particularly pSymB greatly decreased this potential (Table 1, Data sets S1, S2, S3, S4). This was most evident in carbon metabolism, as 50 of 73 carbon sources required pSymB and/or pSymA (Table 2) to be effectively utilized. As pSymA and pSymB account for 45% of the genome (20% and 25%, respectively), if the carbon transport and metabolic genes were randomly distributed throughout the genome, only 45% of the carbon sources metabolized by the wild type (equivalent 33 of the 73) should be dependent on these replicon. Thus, the data show that carbon utilization loci are over-represented on the non-chromosomal replicons (50 vs 33). Moreover, pSymB is essential for the metabolism of twice the expected number of carbon sources (36 vs 18), which is consistent with the prevalence of predicted solute ABC transporters on pSymB [22], [26], [27], [43]–[61]. Additionally, nitrogen and sulfur transport/metabolism is significantly enhanced by the presence of pSymB, although to a lesser extent than that of carbon metabolism, while phosphorus transport/metabolism is largely dependent on the chromosome (Table 1, Data sets S2, S3, S4). To investigate the environmental significance of pSymA and pSymB, we developed a sterile soil mesocosm system to study the growth of wild-type S. meliloti and the cured derivatives (Figure 1C) (see materials and methods). In this system, the exponential growth dynamics of each strain were qualitatively similar to that in minimal medium; the loss of pSymA resulted in faster growth, the loss of pSymB impaired growth, and the removal of both resulted in an intermediate phenotype. Additionally, strains lacking pSymB showed a decreased stationary phase cell density similar to that observed in complex medium and consistent with their decreased metabolic capacity. To identify the region (s) responsible for the growth defect associated with the removal of pSymB, a library of 14 strains in which defined regions of pSymB were deleted (representing >90% of pSymB) [28] was screened for growth in soil. None of the pSymB deletion strains showed a significant change in exponential growth dynamics, and only the loss of the two regions identified as B116 (pSymB nucleotide (nt) position 1,256,503 to 1,307,752) and B122 (nt 1,529,711–1,572,422) showed a significant and reproducible reduction in the stationary phase density in soil (Figure 2A). To investigate whether carbon availability was a growth-limiting factor in the soil, 15 mM glucose was added to stationary phase soil cultures of the wild type, ΔpSymAB strain, and strains with deletions of either the B116 or B122 regions. Viable cell counts following 3 and 5 days of incubation showed that glucose stimulated growth of all four strains, whereas no growth stimulation was observed following supplementation with nitrogen, phosphorus, and sulfur (Figure 2B). Thus, the availability of a usable carbon source appears to be a major factor limiting stationary phase growth for all strains in the soil mesocosms. The deletion of B116 results in a 2 fold decrease in viable cell density in bulk soil, and the removal of B122 results in a 5–25 fold reduction (Figure 2A). While we have not confirmed which genes within these regions are responsible for the observed phenotype, we note that the B122 region includes genes (bhbA-D) involved in metabolism of the carbon storage compound poly-3-hydroxybutyrate [62], while half of the B116 region spans a DNA fragment known to have translocated to pSymB from the chromosome in a S. meliloti ancestor [27]. As the stationary phase defect associated with the loss of both B116 and B122 is related to decreased carbon metabolic abilities (Figure 2B), it is reasonable to assume a multiplicative effect if both B116 and B122 are removed simultaneously, which would be a 10–50 fold decrease in stationary phase density. In fact, this is highly consistent with the observed stationary phase reduction of the ΔpSymB and ΔpSymAB strains (Figure 1C). Thus, we propose that the stationary phase defect associated with the removal of pSymB may be attributed predominately, if not entirely, to the loss of genes within these two regions. In summary, the growth rate of S. meliloti in soil appears to be positively impacted by the removal of pSymA, but negatively impacted by the removal of pSymB, likely for the reasons discussed previously (see ‘effects on growth’). On the other hand, the evidence shows that few pSymA- or pSymB-encoded metabolic capabilities are biologically necessary during growth of S. meliloti in sterile bulk soil. Thus, we wondered what evolutionary pressures maintain these metabolic capabilities. Slater et al. [8] presented strong bioinformatics evidence suggesting the common ancestor of the Rhizobiales order contained a single chromosome, and that this species captured a repABC plasmid (which they referred to as the ITR) that has evolved into secondary chromosomes or chromids in many modern day Rhizobiales (eg. pSymB in S. meliloti, and the second chromosome of Agrobacterium species). The presence of exopolysaccharide biosynthetic genes, which facilitates a strong plant-microbe interaction [18], on pSymB [22] and the second chromosome of Agrobacterium species [8] suggest that these genes may have originated on the ITR. Furthermore, phylogenetic studies have concluded that the evolution of an association with plants was associated with a large increase in solute, and particularly sugar, transporters [63], [64]. Indeed, S. meliloti is capable of using a broad range of carbon sources for growth, and these functions are significantly over-represented on pSymB. Consequently, we suspect that an early plasmid derived from the ITR allowed improved colonization of the rhizosphere, leading to a selection for new genes specific to growth in this novel niche. Unlike plasmids, large rearrangements of bacterial chromosomes are generally selected against [8], [65], thus the subsequent genome expansion occurred primarily with the ITR-derived plasmid, resulting in a replicon specialized for growth in the rhizosphere. While the fitness advantage provided by pSymB in the rhizosphere was not directly assessed here, we note that many of the carbon sources unable to support growth of the ΔpSymAB strain are indeed present in the rhizosphere (e. g. organic acids, galactosides, and several polyols and sugars [66]–[68] (Table 2). In natural environments, microorganisms are found as mixed populations and compete with each other for available resources. We therefore wished to examine whether pSymA and pSymB influences the competitive fitness of S. meliloti. Interestingly, in an agar plate assay, growth of the wild-type S. meliloti was found to inhibit the growth of strains lacking pSymA (Figures 3A, S2), but not the ΔpSymB strain (Figure S2). While such inhibition was observed previously [69], the nature of the inhibition was not identified. To identify loci responsible for this phenotype, we analyzed a library of strains, in which defined regions of pSymA were deleted [28], for inhibition by the wild type. This screen identified a 64 kilobase region (A133) whose loss confers sensitivity to the inhibition by the wild type. This region encodes siderophore biosynthetic (rhbA-F) and uptake (rhtA, rhtX) genes [70], [71], and subsequent mutant analysis revealed that simply disrupting the siderophore uptake genes conferred sensitivity to inhibition by the wild type (Figure S2), while disrupting the biosynthetic genes in the wild-type background precluded inhibition of the ΔpSymAB strain (Figure 3A). Furthermore, no inhibition was observed in the presence of excess iron (Figure 3A). Taken together, these analyses revealed that inhibition was mediated through the siderophore sequestering environmental iron from the ΔpSymA or ΔpSymAB strains (Figures 3A, S2). The effect of this siderophore during soil growth was examined through co-inoculation of the wild type and the ΔpSymA strain in the same soil mesocosm (Figure 3B). Consistent with carbon being the growth-limiting nutrient and available iron being in excess, the presence of the wild type did not impact the growth of the ΔpSymA strain, and the ΔpSymA strain easily outcompeted the wild type. In line with this result, Loper and Henkels [72] previously reported that the Pseudomonas fluorescens siderophore was not expressed during growth in bulk soil. However, the synthesis/uptake of a siderophore may impact fitness in the rhizosphere [72] and possibly affect symbiosis [70]. In addition to intra-species competition, inter-species competition is a major fitness determinant. We assessed the growth of the ΔpSymAB strain in the presence of three competing species: Pseudomonas syringae, Streptomyces ceolicolor, and a soil-isolated Aspergillus species (Figure 4). The early exponential growth of the S. meliloti strain was not adversely impacted by any of the competing species, and the ΔpSymAB strain was able to establish a stable population in the presence of these species over the course of the 26-day assay. However, we observed that the maximum cell density attained by the ΔpSymAB strain was decreased ∼10–20 fold when co-inoculated with a competitor, which may be attributed to inter-species competition for common nutrients and energy sources. As a whole, our data nonetheless suggest that neither pSymA nor pSymB are required for S. meliloti to effectively establish a long-term population or compete for resources with other species, and their loss does not render S. meliloti susceptible to killing by these species. There are two general scenarios for the evolution of multipartite genome evolution. The schism hypothesis suggests that second chromosomes or chromids result from the split of an ancestral chromosome into two [18]. This has been suggested to have occurred in Rhodobacter sphaeroides [73]. Alternatively, the plasmid hypothesis suggests chromids result from the capture of a megaplasmid that subsequently acquires core genes from the chromosome [18]. The often-observed bias for essential genes to be located on one chromosome suggests that the plasmid hypothesis is more generally applicable [18], and evidence suggests that the plasmid hypothesis is true in the case of Vibrio, Agrobacterium, Rhizobium, and Sinorhizobium, among others [2], [8], [13], [18], [21]. Several hypotheses exist about the function of multipartite genomes, and the evolution of multipartite genomes through the plasmid hypothesis; however, little experimental evidence has previously been reported to support these ideas. The presence of two replicons with distinct evolutionary histories (ie. pSymA was a much more recent addition to the genome than pSymB) and characteristics (ie. megaplasmid vs chromid), and the presence of strains lacking one or both of these replicons makes S. meliloti an ideal system in which to experimentally develop a model describing the evolution of multipartite genomes. In the proposed model (Figure 5), as a first step a host cell captures a plasmid that encodes genetic determinants allowing the cell to occupy a novel niche. Inhabiting this new environment puts an evolutionary pressure on the cell to obtain additional genetic material that provides a fitness benefit unique to this location. As genetic rearrangements of bacterial chromosomes are generally associated with a fitness cost [65], this new genetic material is disproportionately acquired by the plasmid, resulting in a plasmid specialized for a specific niche. As plasmids are mobile elements, this enrichment of niche-specific traits is advantageous as it would promote plasmid retention following transfer to a new unichromosomal organism. From the host' s view, while this plasmid is valuable in the new niche, its specialized nature means it provides little advantage in the original environment and is in fact a fitness burden due to its metabolic load. In S. meliloti, pSymA represents an example of a plasmid that encodes functions essential to a specialized niche (forming N2-fixing root nodules with legumes) and yet imposes a fitness cost to cells growing in the species original environment (bulk soil). Thus, strains lacking pSymA grow more rapidly and outcompete wild-type S. meliloti in bulk soil (Figures 1,3B), although ΔpSymA strains are unable to form root nodules [23], [38] and growth of the ΔpSymA strains may be inhibited by the wild type in specific environments (Figure 3A). Over time, random translocations from the chromosome to a resident plasmid would result in the formation of a chromid, leading to an evolutionary pressure to maintain the chromid in all environments, including the species original niche where the loss of the replicon would otherwise be favored. pSymB has had a long association with the S. meliloti lineage and during this time has acquired core elements from the chromosome [8], [27], [42]. Loss of this replicon adversely affects the growth of S. meliloti in bulk soil (Figure 1C) despite the reduced metabolic demand of no longer maintaining the chromid. However, the many metabolic functions dependent on pSymB largely appear to not be necessary for growth in bulk soil, and may be more relevant during growth in the rhizosphere, consistent with a niche-specialized role of this replicon. Overall, the phenotypic data reported here support a model where environmental specialization is a general driving force for multipartite genome evolution, with secondary replicons being enriched for functions unique to the new environment. Indeed, previous comparative genomics analysis [1], [8], [15] presented evidence consistent with many of the core postulates of this model that were derived through experimental examination. While this model addresses the evolution and primary role of secondary replicons, it is still unclear as to why this genome architecture persists, and why secondary replicons do not integrate into the chromosome. Integration has been postulated to have occurred in Mesorhizobium and Bradyrhizobium [8], which carry a single large chromosome, despite having similar lifestyles to Sinorhizobium and Rhizobium, which have divided genomes. While it is possible that the presence of a divided genome is an evolutionarily transient event, this seems unlikely. As discussed in the introduction, several advantages have been ascribed to the presence of a multipartite genome that may promote its maintenance. Indeed, S. meliloti strains that carry all three replicons recombined into one show a growth defect [74], illustrating how genome structure and not just gene content affects the cell' s phenotype. There may also be constraints on the ability of a chromid or megaplasmid to recombine into the chromosome. The origin and terminus of replication separate bacterial chromosomes into subdivisions that tend to be equal in size. Large insertions, such as the integration of a secondary replicon into the primary chromosome, would disrupt this balance and thus be unfavourable [75]. Additionally, it has been suggested that there is an upper size limit of bacterial chromosomes, which could potentially preclude the integration of a large replicon into the chromosome [8]. Finally, plasmids, being mobile elements, can move into naïve cells, leading to further propagation of their DNA. As such, the fitness of the plasmid would be reduced following recombination into the main chromosome. Complex media included LB (10 gm/L tryptone, 5 gm/L yeast extract, 5 gm/L sodium chloride), LBmc (LB with 2. 5 mM MgSO4 2. 5 mM CaCl2), and TY (5 gm/L tryptone, 2. 5 gm/L yeast extract, 10 mM CaCl2). For growth of S. meliloti, complex media was supplemented with 2 µM CoCl2. Minimal media included M9 (41 mM Na2HPO4,22 mM KH2PO4,8. 6 mM NaCl, 18. 7 mM NH4Cl, 4. 1 µM biotin, 42 nM CoCl2,1 mM MgSO4,0. 25 mM CaCl2,38 µM FeCl3,5 µM thiamine-HCl, 10 mM sucrose) and a 4-morpholinepropanesulfonic acid (MOPS) buffered medium (M9 with the phosphate buffer replaced with 40 mM MOPS and 20 mM KOH, with 2 mM KH2PO4). For the phenotype macroarray analysis, cultures were grown in M9 medium for the carbon and sulfur analyses, while strains where grown in MOPS medium for the nitrogen and phosphorus analyses. Additionally, the concentration of biotin was reduced to 40 nM for the analysis of sulfur metabolism. Unless stated otherwise, antibiotics were added to the following concentrations (µg/mL) for S. meliloti (E. coli), when appropriate: streptomycin 200 (N/A), spectinomycin 100 (100), tetracycline 5 (5), gentamicin 60 (20), neomycin 200 (N/A), kanamycin N/A (25), and chloramphenicol N/A (5). Antibiotic concentrations were halved for liquid media. S. meliloti was grown at 30°C and E. coli was grown at 37°C. Common genetic techniques and manipulations were performed as previously described [76], [79], [80]. Overnight cultures were washed, resuspended, and diluted in fresh media. 150 µL of diluted cultures (OD600∼0. 05, measured with a 1 cm wavelength) were inoculated into 96-well plates, with each strain done in triplicate. The edges of the 96-well plates were taped to prevent moisture loss and the 96-well plates were incubated in a Tecan Safire for 48 hours at 30°C (+/−1°C) with shaking. OD600 measurements were taken every 15 minutes. A Perl script was written to calculate averages, standard deviations, and generation times. Phenotype macroarrays were performed in Biolog plates (PM1, PM2A, PM3B, PM4A). Overnight cultures were washed, resuspended, and starved overnight in media free of the appropriate nutrient. Starved cultures were washed, resuspended, and diluted in fresh media, then 100 µL was inoculated into each well of the Biolog plates. Plates were incubated at 30°C for 5–7 days in a SteadyShake 757 Benchtop Incubator Shaker (Amerex Instruments, Inc.), with OD600 readings taken every 12–24 hours. In 2007, a 40 kg soil sample was obtained from an alfalfa field within a dairy farm near Guelph, Ontario, Canada, which does not apply pesticides, fertilizers, or herbicides. Large materials were manually removed, and following 9 days of drying, the soil was passed through a sieve to remove fragments larger than 2 mm. The soil was heat sealed in polyethylene freezer bags (FoodSaver; Jarden Corporation) as 100–300 gm samples. Soil samples were subjected to γ-irradiation (using 6°Co as a source) at the McMaster University Nuclear Reactor with a final dosage of 25. 0 kGy (over a period of 54. 3 hrs). As subsequent testing revealed the soil was not sterile, a second round of γ–irradiation at a final dosage of 42. 3 kGy was performed, and stored at −20°C until use. However, as a Dienococcus species still remained viable, soil samples were autoclaved once (123°C; 17 psig; 20 minutes) within a few days of beginning each growth assay. A chemical analysis of the soil was performed by the University of Guelph Laboratory Services Agricultural and Food Laboratory (Guelph, Ontario, Canada), and the results are presented in Table S2. 47. 62 gm (40 gm dry weight) of γ–irradiated soil was added to 500 mL screw-capped glass bottles (Gibco), autoclaved, and allowed to cool. Within a few days, S. meliloti strains were grown in LBmc or TY and cells were washed once with 0. 85% NaCl and three times with de-ionized, autoclaved water (ddH2O). Cells were resupended in ddH2O, adjusted to an OD600 of 1 and serial diluted to 10−4, which equals approximately 2×105 CFU/mL. 1 mL of this dilution, together with an additional 1. 38 mL ddH2O, was added to each mesocosm. The resulting mesocosm contained 50 gm soil (40 gm dry weight a 20% moisture (wt/vol) ), and ∼4×103 CFU gm−1. Soil mesocosms were incubated at room temperature (22°C+/−2°C) in the dark, and soil moisture content was maintained by the addition of ddH2O every one to two weeks (at the rate of 48 µL per day). To determine cell density, 0. 62 gm samples were removed from each mesocosm into a 2 mL Eppendorf tube in a sterile environment. 1 mL of 0. 85% NaCl was added to each tube and cells were re-suspended with vigorous vortexing. Soil particles were pelleted by vortexing for 1 minute at 60 g. The supernatant was serial diluted and plated on LB or LBmc to determine CFU gm−1. Throughout co-inoculation experiments, when plating for CFU gm−1, dilutions were plated on non-selective and selective media. When wild-type S. meliloti and the ΔpSymA strain were co-inoculated, each strain was inoculated to ∼2×103 CFU gm−1, and strains were differentiated based on growth with 10 mM trigonelline as the sole carbon source as only the wild type will grow. For co-inoculation of S. meliloti with P. syringae, S. meliloti was inoculated to ∼2×103 CFU gm−1 while P. syringae was inoculated to ∼8×102 CFU gm−1, and CFU gm−1 was determined by plating on LB with streptomycin (S. meliloti) or LB with 20 µg/mL rifampicin (P. syringae). When co-inoculated with S. ceolicolor, S. meliloti was inoculated to ∼2×103 CFU gm−1 while S. ceolicolor was inoculated with 2×103 spores gm−1, and S. meliloti was selected for with streptomycin. Co-inoculation with Aspergillus was initiated with ∼4×103 CFU gm−1 of S. meliloti and ∼8×102 spores gm−1 of Aspergillus, and CFU gm−1 of S. meliloti was determined on LB with 100 µg/mL cycloheximide. A 0. 45 gm sample of non-sterilized soil used for the soil growth assays was vigorously vortexed in 1 mL 0. 85% saline, and dilutions were plated on YPD medium (10 gm/L yeast extract, 20 gm/L peptone, 20 gm/L dextrose, 15 gm/L) with 50 µg/mL chloramphenicol. Based on morphology, an Aspergillus species was identified and streak purified on YPD. The Aspergillus was sporulated on LCA medium [81] for eight days at 30°C, and spores were re-suspended in PBS (0. 8% NaCl, 0. 02% KCl, 0. 144% Na2HPO4,0. 024% KH2PO4) with 100 µg/mL streptomycin. Spores/mL were determined by counting spores with a hemacytometer. This assay was performed essentially as described previously [69], [82]. Strains being tested for bacteriocin production were stabbed into TY agar plates and incubated at 30°C overnight. The next day, surface growth of the producer was largely removed using a sterile toothpick. Overnight cultures of strains being tested for bacteriocin sensitivity were diluted to an OD600∼0. 01 in TY, and 1 mL was mixed with 5 mL of TY with 240 µg/mL streptomycin or spectinomycin and 0. 6% agar (giving a final concentration of Sm200 or Sp200 and 0. 5% agar), and all 6 mL were poured onto the plates with the stabbed producers. The inclusion of streptomycin or spectinomycin was to prevent the producer from growing into the soft agar overlay. Plates were incubated at 30°C for two nights, following which zones of clearance were identified. When applicable, 150 µM FeCl3 was added to the soft agar overlay. Previous work has indicated that there are only two single copy essential genes outside of the chromosome (engA and tRNAarg), both located on pSymB [27], [28]. Previously, these two essential genes were integrated into the chromosome [27]. However, this integration included a neomycin resistance marker, and we wished to use neomycin as a selective marker during the process of removing pSymB. Thus, it was necessary to begin by constructing a neomycin sensitive integration of the essential genes into the chromosome. Based on how the integration was performed, there are two possible orientations of the genes following integration (Figures S3A, S3B), and using PCR we determined that the construct integrated as seen in Figure S3B (RmP2686). Using the same procedure as previously followed [27], we isolated a second strain with the orientation illustrated in Figure S3A (RmP2711). The insertion in RmP2711 was transduced into a metH: : Tn5-B20 strain selecting for spectinomycin resistance; metH is located ∼5 kilobases upstream of the engA/tRNA insertion site (Figure S3C). The resulting strain was the recipient in a transduction with a phage lysate prepared from RmP2686 (Figure S3D). Colonies were selected for based on a MetH+ phenotype on minimal medium and screened for Nm resistance. Following the isolation of a neomycin sensitive colony, PCR was used to confirm that the genetic organization of the insertion was as expected (Figure S3E). The insertion in this final strain, RmP2719, is stable and is neomycin sensitive. The two-gene operon, smb21127/smb21128 (pSymB nt: 766,498–767,430), functions as an active toxin-antitoxin locus, although it is possible to delete this system with a low frequency [28]. Therefore, the deletion ΔB180 (pSymB nt: 635,940 nt–869,645) was transduced into S. meliloti strain Rm2011, selecting for neomycin resistance. Subsequently, the chromosomal integration of engA and tRNAarg was transduced into this strain, selecting for spectinomycin resistance. The resulting strain, RmP3005 carries the essential pSymB genes on the chromosome as well as a 234 kilobase deletion that removed the only known active toxin-antitoxin system on this replicon. The replication and segregation machinery of pSymB is encoded by the repABC operon [29]. An incompatibility factor, incA, is encoded within the repB and repC intergenic region; thus, pSymB cannot be stably co-inherited with another replicon carrying an exact copy of incA [29]. Thus, pTH1414 (pOT1 carrying the pSymB incA region) [29] was introduced into S. meliloti RmP3005, and streptomycin/gentamicin resistant colonies were selected for on LB supplemented with 2 µM cobalt chloride to compensate for the loss of the major S. meliloti cobalt uptake ABC transporter (CbtJKL), which is pSymB-encoded [30]. Recovered colonies were streak purified, and initially the inability to amplify six pSymB fragments using PCR was evidence that pSymB was indeed lost. One colony was inoculated in LBmc broth, serial diluted and plated on LB, and colonies were screened for loss of pTH1414 by patching for gentamicin sensitivity. A gentamicin sensitive colony was purified and stored as S. meliloti RmP3009. In order to construct the strain lacking both pSymA and pSymB, the same procedure was followed as above, with two modifications. The starting strain lacked pSymA [24]. Additionally, the chromosomal integration of engA and tRNAarg from S. meliloti RmP2719 was transduced into this strain prior to the transduction of ΔB180. Following the removal of pSymB using incompatibility, and the subsequent loss of pTH1414, the resulting strain was frozen as S. meliloti RmP2917, which lacks both pSymA and pSymB.

Rhizobia are free-living bacteria of agricultural and environmental importance that form root-nodules on leguminous plants and provide these plants with fixed nitrogen. Many of the rhizobia have a multipartite genome, as do several plant and animal pathogens. All isolates of the alfalfa symbiont, Sinorhizobium meliloti, carry three large replicons, the chromosome (∼3. 7 Mb), pSymA megaplasmid (∼1. 4 Mb), and pSymB chromid (∼1. 7 Mb). To gain insight into the role and evolutionary history of these replicons, we have ‘reversed evolution’ by constructing a S. meliloti strain consisting solely of the chromosome and lacking the pSymB chromid and pSymA megaplasmid. As the resulting strain was viable, we could perform a detailed phenotypic analysis and these data provided significant insight into the biology and metabolism of S. meliloti. The data lend direct experimental evidence in understanding the evolution and role of the multipartite genome. Specifically the large secondary replicons increase the organism' s niche range, and this advantage offsets the metabolic burden of these replicons on the cell. Additionally, the single-chromosome strain offers a useful platform to facilitate future forward genetic approaches to understanding and manipulating the symbiosis and plant-microbe interactions.

Abstract Introduction Results/Discussion Materials and Methods

biotechnology ecology and environmental sciences genetics synthetic biology biology and life sciences soil science microbiology evolutionary biology agriculture

2014

Examination of Prokaryotic Multipartite Genome Evolution through Experimental Genome Reduction

The broadly neutralizing HIV-1 antibody 2F5 recognizes an epitope in the gp41 membrane proximal external region (MPER). The MPER adopts a helical conformation as free peptide, as post-fusogenic forms of gp41, and when bound to the 4E10 monoclonal antibody (Mab). However, when bound to 2F5, the epitope is an extended-loop. The antibody-peptide structure reveals binding between the heavy and light chains with most the long, hydrophobic CDRH3 not contacting peptide. However, mutagenesis identifies this loop as critical for binding, neutralization and for putative hydrophobic membrane interactions. Here, we examined length requirements of the 2F5 CDRH3 and plasticity regarding binding and neutralization. We generated 2F5 variants possessing either longer or shorter CDRH3s and assessed function. The CDRH3 tolerated elongations and reductions up to four residues, displaying a range of binding affinities and retaining some neutralizing capacity. 2F5 antibody variants selective recognition of conformationally distinctive MPER probes suggests a new role for the CDRH3 loop in destabilizing the helical MPER. Binding and neutralization were enhanced by targeted tryptophan substitutions recapitulating fully the activities of the wild-type 2F5 antibody in a shorter CDRH3 variant. MPER alanine scanning revealed binding contacts of this variant downstream of the 2F5 core epitope, into the 4E10 epitope region. This variant displayed increased reactivity to cardiolipin-beta-2-glycoprotein. Tyrosine replacements maintained neutralization while eliminating cardiolipin-beta-2-glycoprotein interaction. The data suggest a new mechanism of action, important for vaccine design, in which the 2F5 CDRH3 contacts and destabilizes the MPER helix downstream of its core epitope to allow induction of the extended-loop conformation. The membrane proximal external region (MPER) of the HIV-1 envelope transmembrane glycoprotein, gp41, is the target of the two broadly neutralizing monoclonal antibodies (Mabs) 2F5 and 4E10 [1], [2] (Figure 1A). Although the exact mechanism of neutralization of these two antibodies is not yet determined, it is likely that they interfere with the process of fusion of the viral-to-target cell membranes. Recent data from our group indicate that the epitopes of the MPER antibodies are not exposed on most HIV-1 primary isolates and become accessible after engagement of the viral spike with receptors on the target cell surface [3]. To bring about membrane fusion, trimeric gp41 has to undergo large conformational changes, beginning with a structurally undefined native state interacting with the non-covalently associated exterior envelope glycoprotein gp120, to an energetically favored six-helix bundle conformation. Briefly, cellular receptors interacting with gp120 induce conformational changes in both gp120 and gp41. These conformation changes lead to the insertion of the gp41 N-terminal fusion peptide into the target cell membrane, eventually permitting fusion of the two membranes as gp41 collapses into the six-helix bundle state, mediating entry of HIV genetic information into the target cell. To mediate this process, the envelope glycoproteins must possess an inherent capacity for conformational change, and consistent with this requirement, the MPER region is well recognized for its conformational diversity. Multiple structural studies have highlighted its tendency to adopt flexible helical secondary structures as free peptides in solution [4], [5], [6], [7], [8], in the context of oligomeric post-fusogenic and intermediate gp41 forms [9], [10], [11] and when bound to the Mab 4E10 [12] or the non-neutralizing Mab 13H11 [13]. In contrast, when bound to the 2F5 Mab [14], [15] or the non-neutralizing Mab 11F10 [16] the MPER adopts an extended loop conformation, again attesting to its conformational flexibility. The 2F5 Mab structure in complex with its cognate peptide reveals that the peptide is bound in a cleft between the heavy and light chains making no contacts with most of the unusually long 22 amino acids (aa) CDRH3 loop [14], [15] (Figure 1B). Extensive studies of the CDRH3 corroborate its importance for both binding and neutralization activities of the 2F5 Mab, especially the hydrophobic residues present at the apex of the loop [17], [18], [19]. However, the exact role of the 2F5 CDHR3 in regards to how this region precisely contributes to binding and neutralization mediated by the 2F5 Mab remains undefined. The 2F5 Mab affinity for its epitope is enhanced when the epitope is presented in a lipid context, suggesting that, perhaps, the highly hydrophobic CDRH3 makes contact with the viral membrane [14], [20], [21]. Indeed, it was recently demonstrated that alterations in hydrophobicity at the tip of the CDRH3 were directly correlated with 2F5-mediated neutralization capacity [19]. In the present study, to further investigate the role of the 2F5 CDRH3 in binding, putative membrane interactions and neutralization mechanisms, we first made structure-based alterations in the length of the CDRH3 and assessed their impact on Mab function. We then increased the hydrophobicity and aromatic character of the resulting mutant CDRH3s by targeted tryptophan (W) and tyrosine (Y) substitutions. We measured binding affinities of the mutant antibodies for selected gp41 epitope probes by bio-layer light interferometry and we assayed their neutralization activity against a panel of HIV-1 strains of varying sensitivity. We found that the antibody 2F5 CDRH3 loop can sustain elongations of up to 26 residues in length and reductions to 18 residues in length and still maintain nanomolar range binding affinity to its cognate epitope peptide and retain some neutralizing activity. Targeted W and Y substitutions of the length-altered CDRH3s generally enhanced both the mutant antibodies' binding affinities and their neutralization activity to levels approaching or exceeding those of wt 2F5. These data indicate that a more diverse range of 2F5-like antibodies might be elicited by MPER-based candidate immunogens and yet retain HIV-1 neutralization capacity. We found a direct correlation between the antibody affinity constant for the MPER peptide and the inhibitory concentration (IC50) of HIV-1 neutralization that suggests a new property of the 2F5 CDRH3 loop that may be additional to and independent of its suggested membrane interaction. From these data, we propose an alternative model in which the CDHR3 loop destabilizes the helical secondary structure of the MPER in the context of the functional Env spike, acting downstream of direct core epitope contacts, allowing the antibody to induce the extended loop peptide-bound conformation observed in the Mab-peptide crystal structure. Finally, we demonstrate a direct interaction of both the wild-type (wt) 2F5 CDRH3 and the truncated, W-containing variant CDRH3s with elements of the downstream 4E10 region by alanine scanning mutagenesis of the MPER peptide. These data have direct and important implications for using MPER residues outside of the 2F5 core epitope for attempts to elicit broadly neutralizing 2F5-like antibodies as part of broadly effective vaccine. The crystal structure of the 2F5 antibody in complex with its gp41 peptide epitope reveals the peptide embedded in a cleft between the heavy and light chains of the antibody in an extended-loop conformation [14], [15]. The unusually long (22 aa) 2F5 CDRH3 minimally contacts the antibody-bound peptide, however, this region is critical for 2F5 binding and neutralization (Figure 1B). Our primary goal in the present study was to investigate the length requirements of the 2F5 CDRH3 in regards to these functional capacities, in part because antibodies requiring a long CDRH3 to bind and neutralize virus might be difficult to elicit by vaccination. Previous studies demonstrate that ablation of the CDRH3 results in the complete loss of neutralization capacity. Another study revealed the importance of the residues at the apex of the loop (100ALFGV100D) for 2F5 neutralizing activity [17], [18], [19]. Therefore, for the CDRH3 length alterations we left residues at the tip of the loop unaltered and modified only the residues adjacent to each side of this apex (98PTT100 and 100EPIA100G) (Figure 1B and Figure 2, top panel). We then reduced or elongated the CDRH3 loop by removing or adding two residues at a time, one from each side of the conserved apex residues. Residues were removed or inserted in this balanced manner in an attempt to preserve the local secondary structure of the CDRH3 loop. Since the precise identification of the D gene segment that is used to comprise the 2F5 CDHR3 is uncertain, we did not make modifications based upon the potential genetic elements encoding this region of the Mab. The modifications that we did make resulted in variant CDRH3s with lengths ranging from 16 to 26 aa. We called the resulting mutant 2F5 antibodies r2, r4 and r6 for reductions of the length of the loop of 2,4 and 6 residues, respectively. For the elongated CDRH3s, we added glycine residues, two at a time, at each side of the tip (GG100ALFGV100DGG) and we named the antibodies e2 and e4, for elongation of 2 and 4 residues, respectively (Figure 2, top panel). The variant antibodies were produced by co-transfection of 293F mammalian cells with plasmids encoding the mutated heavy chains and the wt 2F5 light chain as described in the Methods section. To characterize binding kinetics of wt 2F5 and the CDRH3 length-altered 2F5 variant antibodies, we performed a bio-layer light interferometry kinetic binding analysis (Octet), similar in principle to the widely used surface plasmon resonance technology. We immobilized the immunoglobulin molecules on an anti-human IgG Fc-specific sensor and analyzed the MPER probes as free analyte in aqueous solution, completely free of a lipid bilayer. Based on their differential propensity to present the 2F5 epitope in a helical conformation, or not, three variant MPER probes were selected for this analysis. One version was an MPER peptide comprised of the complete wt sequence, encompassing both the 2F5 epitope and the contiguous 4E10 epitope, and containing a poly-lysine tail at the C-terminus to increase solubility [4], [5], [6], [7]. A second version of the MPER peptide that we refer to as “linked peptide” was generated to contain a poly-glycine linker separating the 2F5 epitope from the 4E10 epitope (Table 1). We chose to insert glycine residues to potentially disrupt the continuous helical conformation spanning from the 2F5 epitope to the 4E10 epitope observed in the MPER peptide under several crystallographic and NMR conditions. Glycine residues have high conformational flexibility and thus have poor helix-forming propensity due to entropic penalties. The circular dichroism (CD) spectra for both the wt MPER peptide and the linked peptide were consistent with a high content of alpha-helix, displaying positive bands at 192 nm and negative bands at 208 nm and 222 nm. The values for the negative bands were weaker for the linked peptide containing the glycine insert, suggesting, as expected, a lower alpha-helical content for this peptide compared to the wt MPER peptide (Figure 3A). Finally, we assessed binding in the ES2 2F5-epitope scaffold context which presents the 2F5 epitope exclusively in the antibody-bound, extended-loop conformation. This latter state is confirmed by crystallography [16]. As expected, the wt 2F5 antibody displayed high affinity interactions with all three target analytes, with similar affinity constants (KD) in the 1 to 3 nM range (Table 1 and Figure 3B). In contrast, the CDRH3 length-altered antibodies displayed higher affinities for the ES2 scaffold protein than for either of the MPER peptides. Generally, the on-rate constant for the antibody recognition of the ES2 scaffold was 3- to 20-fold faster compared to antibody recognition of either of the other unconstrained MPER peptides (Table 1 and Figure 3C; For a complete set of binding curves see supplementary figures S1 and S2). The faster on-rate for ES2 is likely reflective of the fact that the variant 2F5 antibodies do not have to induce the extended-loop epitope conformation presented by this scaffold. Induction is likely necessary when starting with the helical conformation present in the unconstrained MPER peptides. Likely due to the impact of the conformational fixation, the highest affinity interactions of the mutant antibodies were observed for ES2 scaffold recognition. In these cases, the dissociation constants (KD) ranged from 2 to 40 nM, not much different from the values obtained for the wt 2F5 antibody. In contrast, the affinities for the MPER peptide were markedly lower, ranging from 90 nM to 460 nM. Slightly higher affinities were observed for the linked peptide, in an intermediate range from 26 nM to 125 nM (Table 1 and Figure 3B). These results are consistent with previous data that suggest that the antibody 2F5 CDRH3 plays a prominent role in the interaction with the MPER despite the fact that the antibody-peptide crystal structure reveals no contacts between most of the CDRH3 loop and the MPER peptide. This interaction likely happens before the antibody locks the 2F5 epitope into the extended loop conformation and is therefore not required when the Mabs interact with the ES2 scaffold that displays a preformed, extended-loop conformation, consistent with the faster on-rates in this context. Also note that the measurements presented here were performed in aqueous solution, indicating that a lipid environment is not required to detect such differences. We next tested the antibodies capacity to neutralize HIV in a single-cycle infectivity assay using Env-pseudotyped virus and TZM-bl target cells. We selected a panel of viruses ranging from the highly sensitive HIV strains (HXBc2, MN and the HIV-2/HIV-1 MPER chimeric virus, 7312a-C3) to more resistant strains (JR-FL and JRCSF) and the HIV-2 strain 7312a as a negative control. The mutant 2F5 antibodies r2, r4 and e2 showed similar neutralization activity against the sensitive strains with 50% inhibitory concentrations (IC50) ranging from 12 µg/ml to 181 µg/ml, which were several orders of magnitude higher concentrations of the variant Mabs relative to those of wt 2F5. The variant 2F5 Mabs with the larger CDRH3 alterations, e4 and r6, displayed no neutralization activity against the strains tested. The 2F5 mutant antibody r2 was able to neutralize the more resistant HIV variants, but with values up to two orders of magnitude higher than those of the wt 2F5; ie, the r2 IC50 for JR-FL was 111 µg/ml and 98 µg/ml for JR-CSF whereas wt 2F5 values were 1. 36 and 2. 15, respectively (Figure 2, top panel). Given the weak neutralization activity displayed by the CDRH3 length-altered antibodies, we next investigated if a V100DW mutation, previously shown to increase the neutralization activity of the wt 2F5 antibody [19], would also imbue the same effect on the CDRH3 length-altered Mabs. We generated a second series of mutant antibodies by substituting the V100D residue at the apex of the loop for a W and named the resulting antibodies r2W, r4W, r6W, e2W and e4W (Figure 2, middle panel). For example, r6W, denotes a reduction of 6 residues in the length of the CDRH3 and possessing the V100DW mutation. We then analyzed the antibody neutralization capacity against our original panel of pseudoviruses. The W substitution improved the neutralization activity for all variant 2F5 Mabs as much as 20-fold (Figure 2, middle panel). In the case of e4W, the added W was sufficient to convert the completely non-neutralizing antibody, e4, into a neutralizing antibody. Note that the V100DW substitution endowed the CDRH3 length-altered antibodies the capacity to neutralize the more resistant viruses of our panel, with the exception of r6W. That the antibody r6W did not exhibit any neutralization activity suggests that either the additional residues removed from the loop (P98 and A100G) are directly involved in neutralizing capacity or that the r6 CDRH3 was compromised in some indirect manner, perhaps by perturbation of local CDRH3 structural integrity. Given that the single V100DW mutation demonstrated such a positive increase in neutralizing activity, we selected the loop-shortened r4W antibody, to introduce further W substitutions into the CDRH3. We added a second W substitution at three different positions within the apex of the loop: A100GW, G100CW and L100AW to generate antibodies r4W2a, r4W2b and r4W2c, respectively (Figure 2, middle panel and Figure 4). These W substitutions further increased the neutralization activity of these antibodies to levels just below the wt 2F5 antibody (Figure 2, middle panel). To potentially yet further increase neutralization activity, we selected r4W2a, our most potent mutant Mab, and added a third tryptophan at position L100AW, to generate r4W3 (Figure 2, middle panel and Figure 4). This additional W substitution fully reconstituted the neutralization activity of r4W3 relative to 2F5 wt against most viruses and, in most cases, r4W3 was slightly more potent than wt 2F5 (Figure 2 middle and bottom panels). The data indicated that the 2F5 CDRH3 can tolerate some plasticity in both terms of length and, as previously shown, hydrophobicity and yet still neutralize several representative HIV-1 isolates. To better differentiate between either hydrophobicity or aromaticity as being critical for the activities of the CDRH3-shortened, W-containing 2F5 Mabs, we next changed two of the critical W residues to Y residues, since Y residues are still aromatic but less hydrophobic (Figure 2, middle panel and Figure 4). An additional consideration for these substitutions was the possibility that the W substitutions might imbue the CDRH3-shortened 2F5 Mabs with a hydrophobic nature and make them poly-reactive, replacement by the polar Y residue, which still possesses an aromatic ring in its side chain, might generate less poly-reactive 2F5 variants. Substitutions of two Y residues in place of the W residues resulted in a slight decrease in neutralization potency however this Mab variant still potently neutralized a broad panel of primary isolates displaying varying degrees of sensitivity to the 2F5 neutralizing Mab (r4WY2; Figure 2, bottom panel). These data suggest that the aromatic side chains of the substituted residues are contributing significantly to the binding energy responsible for neutralization activity of these length-altered antibodies; however, it appears that the bulkier and more hydrophobic side chain of the W residues better enhanced the neutralizing capacity of the mutant antibodies. Since the targeted W and Y CDRH3 substitutions generally increased the HIV neutralization activity of the mutant antibodies, we sought to determine if this improvement was also reflected in their binding properties. We performed kinetic binding measurements on the modified antibodies using the MPER peptide as analyte, since, in our initial binding analysis, higher affinity for this probe was associated with neutralization activity (Table 1 and Figure 2, top panel). With the exception of r6W, all antibody mutants carrying one (r2W, r4W, e2W and e4W), two (r4W2a, r4W2b and r4W2c) or three (r4W3) W substitutions displayed incremental increases in binding affinity (KD) to the MPER peptide, relative to their counterparts lacking the tryptophan substitutions (Figure 5A and Figure S3 and S4). The comparison of the mean KD values of the antibodies carrying W substitutions with the mean of their counterpart antibodies was statistically significant (p = 0. 0008) when measured on a two-tailed paired t test (Figure 5B). Similar high binding affinities were observed for the variants containing the two W/Y substitutions (r4WY, r4WY2) (Figure 5A). The increases in binding affinities for the W-containing variants was mostly due to increases in the Mab on-rates of binding to the MPER peptide (p = 0. 03), while there was not a statistically significant difference in the off-rates (p = 0. 1) (Figure 5B). These data demonstrate that increasing the hydrophobic or aromatic character of the length-altered CDRH3 loop facilitates recognition of the helical MPER peptide. We interpret the data to mean that the enhanced binding of the MPER by the W or Y substitutions results mostly from an improvement in the recognition of the epitope (faster on-rate) rather than an improvement in the stabilization of the interaction (no difference in off-rate). Additionally, when we compared the MPER peptide binding data with the antibodies' neutralization function, we found a statistically significant correlation (Pearson r = 0. 695, p = 0. 005) between neutralization activity (measured as the IC50) and the ability of the antibodies to recognize the MPER peptide (measured as the KD). However, there was no correlation when comparing the antibodies' neutralizing capacity with their ability to recognize the ES2 scaffold (Figure 5C and Figure S5). Taken together, the data suggest a new mode of interaction of the 2F5 antibody with its gp41 epitope in which the highly hydrophobic and/or aromatic CDRH3 loop destabilizes the helical secondary structure of the MPER by interacting with MPER residues outside the 2F5 epitope core to allow other elements of the 2F5 antibody to then induce the extended loop antibody-bound conformation observed in the crystal structure (Figure 6). These data indicate that the hydrophobic character or aromatic nature of the length-altered CDRH3 loop facilitates recognition of the MPER either as a monomeric protomer of the functional trimer, or within the putative MPER triple helix, to mediate neutralization and, importantly, in the absence of lipids. The fact that both binding of the MPER peptide and HIV neutralization are enhanced by the W/Y substitutions suggests that perhaps pi stacking of the phenyl rings with the W/F side chains of the helical MPER (i. e. W670, W672, F673 or W678) might contribute substantially to this functional enhancement, along with some degree of hydrophobicity. To examine if the wt 2F5 antibody might interact with residues downstream of its core epitope, we analyzed 2F5 binding to MPER peptides harboring L669A, W670A, N671A, W672A and F673A substitutions. These residues are immediately C-terminal of the ELKWAS 2F5 core epitope. To this alanine scanning analysis, we added the r4 mutant series (r4, r4W, r4W2a and r4W3) displaying shorter CDRH3 loops and neutralization activities ranging from the very weak neutralizer, r4, to the most potent neutralizing 2F5 variant, r4W3. We reasoned that, if antibody contacts are made C-terminal of the ELKWAS core epitope, the A substitutions might also affect the binding of the r4 family antibodies and perhaps reveal specific residues that permit these 2F5 variant to regain their neutralizing potency. As a control, we also included the Mab 4E10 whose core epitope is contained within the region scanned by the A substitutions and should be sensitive to the 671–673 changes. Consistent with direct contacts at these residues, we observed decreased 4E10 affinity of the Ala mutant peptides 671/672/673 (Figure 6C and Figure S6). The Mab 2F5 presented decreased binding affinities for the A-substituted peptides in positions 669 through 672 suggesting weak contacts with this region, also noted previously [22]. Consistent with effects observed for the wt 2F5 antibody, the more potent neutralizers r4W2a and r4W3, with two and three W residues in their CDRH3 respectively, showed decrease binding affinities at all positions of peptides included in this alanine scan, including F673A, for which 2F5 displayed no sensitivity. These data suggest that the HCDR3-truncated, but W-rich CDRH3s, likely make hydrophobic interactions with residues 669–673 to regain their potent neutralizing capacity. In contrast, for the weak neutralizer r4, the A substitutions had the opposite effect, especially when the bulkier W and F side chains of residues 670,672 and 673 were mutated to A, suggesting that perhaps these residues are important for the stabilization of the MPER in a conformation only accessible to neutralizing antibodies. The A substitutions had no substantial effect in the peptide binding of the moderately neutralizing antibody, r4W, with the exception of a small decrease in affinity at position 669 (Figure 6C and Figure S6). Both the Mabs 2F5, and especially 4E10, have been reported to be poly-specific, binding to a set of human molecules, including CL [23], [24], [25]. Here, we selected three binding assays to measure the effect that our multiple CDRH3 alterations might have in the poly-reactive nature of the resulting 2F5 variants. Based on a previous study [25], we selected two antibody concentrations (5 and 1 µg/ml) and assessed binding to CL alone, CL: B2G1 complexes and to B2G1 (also known as Apolipoprotein H), alone. Antibodies that recognize the CL: B2G1 are associated with pathogenic autoimmunity [26]. Consistent with the previous study assessing CL binding, at 5 µg/ml, both 2F5 and 4E10 bind to CL, with 4E10 displaying the strongest reactivity, whereas at 1 µg/ml the antibody 2F5 is negative for CL binding, while 4E10 remained positive (Figure 7 and Figure S7). In addition to 4E10 and 2F5, we also selected the non-neutralizing 11F10 mouse monoclonal antibody, a 2F5-epitope binding antibody that does not possess a long hydrophobic CDRH3 and does not react with CL [16], [27], [28]. The antibody 4E10 was positive at both concentrations in the CL alone and the CL: B2G1 complex assays, however, negative in the B2G1 assay. The antibody 2F5 reacted only with CL alone at the 5 µg/ml concentration and was not reactive with the CL: B2G1 complex or the B2G1 alone. 11F10 antibody was negative in all assays. Our original alterations in the length of the CDRH3, both reductions (r2, r4 and r6) and elongations (e2 and e4), resulted in antibodies that weakly bound CL at 5 µg/ml and generated a signal that was weaker than the binding observed for the antibody 2F5 (Figure 7). The mutant antibodies carrying one tryptophan substitution (r2W, r4W, r6W, e2W and e4W) reacted with CL at a level comparable with that of the 2F5 antibody, with the exception of e4W, which was non-reactive. Interestingly, the e4W antibody which exemplifies an MPER-directed neutralizing antibody, exhibited no reactivity to CL or CL: B2G1. The tryptophan substitutions, two or three, (r4W2a, r4W2b, r4W2c and r4W3) strongly enhanced CL reactivity, surpassing the levels of the wt 2F5 antibody and matching that of the Mab 4E10 (Figure 7 and Figure S6), but were generally lower on CL: B2G1 complexes. Generally, it appears that the antibodies that displayed stronger binding to CL also possessed higher neutralization activity. The data suggest that the antibody 2F5 CDRH3 loop, and its hydrophobic or aromatic character, is responsible for the relatively weak CL binding, and, perhaps as well, its poly-reactive nature. Finally, the Y-substituted r4WY2 showed no reactivity to CL: B2G1 nor B2G1 at the 5 µg/ml, suggesting that MPER directed neutralizing antibodies may not possess the signature of bona fide auto-reactive Mabs while maintaining HIV neutralization activity. The binding sites of HIV-1 broadly neutralizing antibodies (bnAbs) reveal sites of accessibility on the viral spike and present pathways for immunogen design to penetrate evolved barriers that successfully exclude access by most host-generated antibodies. Sequence analysis and structural characterization of these bnAbs reveal that several of these bnAbs possess unusually long and often protruding CDRH3s [12], [14], [29], [30], [31]. Antibodies with such long CDRH3 loop lengths might be difficult to elicit via vaccination, based in part on their relative infrequency due to limits imposed by D gene starting sequences and N nucleotide addition, but also due to possible counter selection imposed by self-reactivity encountered during transitional B cell stages from the bone marrow to the peripheral naïve repertoire. Generally, the CDRH3 is intimately involved in the binding to antigen and often modifications in this loop result in loss of binding and, relevant to this study, neutralization capacity. In the case of the antibody 2F5, the apex of CDRH3 does not directly contact the gp41 peptide epitope [14], [15], however, this CDRH3 region is essential for neutralization [17], [18], [19]. Binding and neutralization of HIV primary isolates by 2F5 likely occur following receptor engagement which exposes the MPER, but under certain conditions the spike can be inactivated in a receptor-independent manner, resulting in a slow dissociation of gp120 from gp41 [32]. Viral accessory proteins, such as Nef, also affect MPER accessibility and 2F5/410 neutralization potency [33]. In this study, we generated 2F5 Mab CDRH3 variants with both shorter and longer CDRH3 loops that still displayed a range of binding and neutralization activities. When these CDR loop length alterations were combined with aromatic residues on the flanks of the CDRH3, high affinity binding, in the absence of lipid, and neutralization activity equivalent to that of wt 2F5 was observed. These data suggest that the 2F5 CDRH3 interacts with aromatic MPER side chains outside of the 2F5 core epitope. That loop shortening still permits such aromatic residue-dependent interactions, and that that binding effects to MPER peptide can be observed in aqueous solution, argue against the model proposed previously, and recently, that the long 2F5 CDRH3 dips into the viral membrane to extract its epitope [14], [21]. In addition, the CDRH3 analysis presented here better defines the range of antibodies targeting the 2F5 gp41 MPER that might be able to neutralize HIV-1. Specifically, we show that variations in the length of the CDRH3 can be tolerated in terms of high affinity binding and neutralization equivalent to wt, provided that the W and Y residues are present at the CDRH3 apex. The W substitutions generally increased the antibody neutralizing capacity of all mutants except for the shortest r6W CDRH3, suggesting that hydrophobic character or the aromatic nature of the W residues may play a more prominent role than its length in regards to neutralization. Encouragingly, Y residues could also restore neutralizing activity of CDRH3-shortened 2F5 variants, increasing the range of antibodies that, if elicited, might still efficiently recognize the HIV-1 MPER. Restoration of binding and neutralization to wt 2F5 levels could be accomplished by targeted substitutions of aromatic residues on the flanks of the CDRH3 apex. Variability in both the 2F5 CDRH3 length and sequence broadens the spectrum of antibodies targeting the 2F5 epitope that can be permitted, revealing the possibility that antibodies elicited via vaccination, displaying more frequently generated short CDRH3 loops, can effectively function as neutralizing antibodies to this region. Although much is known about the 2F5 antibody, its mode of interaction with the HIV-1 Env spike, such as putative CDRH3 contacts with the viral membrane or as suggested here, within the HIV-1 trimeric spike itself, are not yet fully elucidated. Our alterations within the 2F5 CDRH3 showed that interactions with peptide were facilitated by the W residues substituted in the length-altered CDHR3 loops and that such modifications greatly enhanced Mab neutralization activity. Aromatic residues such as W and Y are overrepresented in antibody-combining sites, perhaps due to evolutionary selection. Biochemically this might be advantageous to imbue the Mab a capacity to stack their aromatic side chains, producing cooperative binding effects to antigen [34], [35], [36]. Hydrophobic interactions can account for large contributions to binding energies in protein-protein interactions and for 2F5, hydrophobicity of the CDRH3 appears critical for neutralization. The lack of a hydrophobic CDRH3 might in part account why antibodies generated to the structurally defined 2F5 epitope-peptide alone, such as the 11F10 antibody, lack neutralization activity despite displaying high-affinity binding to the 2F5-epitope-peptide [16], [27]. In the case of the CDRH3-altered Mabs presented here, the neutralization-enhancing W substitutions, which also increased antibody affinity for the MPER peptide, perhaps by interacting with MPER hydrophobic/aromatic residues immediately downstream of the 2F5 core epitope into the 4E10 epitope region (residues 669–673, see Figure 4 and Figure 6). This model is supported by the alanine scanning binding analysis performed in the 669–673 region of the MPER peptide. We would suggest that for wt 2F5, weaker contacts are made with these MPER residues as well, and perhaps even further downstream due to its longer CDRH3 length. Consistent with this model, a published crystal structure of a trimeric MPER construct reveals a parallel triple-stranded coiled held together by a stabilizing hydrophobic core, composed mostly of aromatic residues, in which the epitopes of antibodies 2F5 and 4E10 are occluded from antibody recognition of this conformation [10]. Recent cryo-electron microscopy studies show that both envelope glycoproteins gp120 and gp41 experience torsional movements after engagement of the viral receptor and co-receptor [37], [38], which may then expose the 2F5 and 4E10 epitopes for antibody recognition by these neutralizing Mabs [3]. Considering the structural information provided in the aforementioned studies, and the mutagenic, binding and neutralization data presented here, it is plausible to speculate that the 2F5 CDRH3 may interact with the hydrophobic residues located within a single protomer of the MPER or in between the MPER helices in the hydrophobic interior stalk of the trimeric spike. This positioning of the CDRH3 loop would permit the substituted W residues to interact with other aromatic residues abundant in the MPER, generating π-stacking interactions to confer additional binding energy. Curiously, the positioning of the second W in the CDRH3 loop (as in antibodies r4W2a-b-c) had an impact in their neutralization activity; namely, the A100GW and G100CW were more favorable than the L100AW substitution. By inspection of the crystal structure of the 2F5 antibody in complex with its cognate peptide epitope, one can envision that the side chains of W residues at positions A100G and G100C would lay orthogonally and adjacent to the main chain axis of the 2F5-bound peptide, positioning the loop residues C-terminal of the 2F5 epitope and proximal to the 4E10 epitope. The binding kinetic analysis presented here showed a statistically significant correlation between the antibody binding affinity for the MPER peptide, driven mainly by an increase on the on-rate constant, and the HIV-1 neutralization activity of the antibody as determined by its IC50 value. That is, the faster is the on-rate of the antibody for the peptide, the more potent the antibody is in regards to HIV-1 neutralization assay. Taken together, we postulate that the antibody 2F5 CDRH3 loop interacts with the helical MPER, disturbing its helicity and allowing the antibody to induce the extended loop peptide-bound conformation into the Mab cleft observed in the antibody-peptide complex crystal structure. It also appears that interaction with the MPER by the CDRH3 mutants and wt 2F5 is downstream of the 2F5 core epitope and implies that more of the MPER should be included in vaccine candidates designed to re-elicit 2F5-like antibodies. The model that the MPER has helical propensity does give rise to the question of how was 2F5 initially elicited? We would suggest that, following receptor engagement, this region has evolved to undergo the conformational changes required for fusion and entry and that it has a natural tendency to sample multiple conformations during this process, both helical and beta turn. We would speculate that the B cell displaying 2F5 as its B cell receptor may have trapped the beta turn conformation, and the soluble antibody that we study has the property of being able to extract this conformation from the native Env, an apparently rare property for antibodies elicited against the MPER. The CL binding assay showed that some variant Mabs displaying the highest neutralization activity, possessing the W substitutions, also presented the strongest reactivity to CL but not to B2GI. Recognition of B2GI itself or to CL-B2GI complexes and less so to CL alone, is a pattern that is associated with pathogenic autoreactive antibodies. Since most the variant Mabs possessing the Ws, despite binding to CL, do not well recognize the complex, concerns that these antibodies might be auto-reactive and be deleted from the naïve repertoire are reduced. Encouragingly, we demonstrate that Y residues in lieu of W substitutions could be accommodated in the loop length-altered antibodies and diminished or abolished CL reactivity with minimal effects in neutralization capacity. In sum total, the data presented here provide hope for vaccine efforts to re-elicit 2F5-like Mabs since our CDRH3 modifications on the 2F5 antibody illustrate that many 2F5-related variants can still bind the MPER and, most importantly, potently neutralize HIV-1 primary isolates. The ES2 protein was expressed in 293F cells and purified from filtered supernatant by affinity chromatography using the wt 2F5 Mab. MPER peptides were synthesized with a poly-lysine tail at the C-terminus to facilitate solubility, Genscript (Piscataway, NJ). The plasmids encoding the wt 2F5 Mab were generated by de novo synthesis (Genscript) of the heavy and light chain genes and subsequently cloned into the CMVR mammalian expression vector. The Mabs containing the length-altered CDRH3 loops were generated from the wt 2F5 heavy chain plasmid by site-directed mutagenesis. The primers for the site-directed mutagenesis PCR reactions were synthesized by IDT (Integrated DNA Technologies, Coralville IA). All antibodies were generated by co-transfecting 293F cells with a unique mutant heavy chain and the 2F5 wt light chain encoding plasmids, at a 3∶2 ratio. Supernatants were collected 5 days after transfection, filtered, and run over a protein A chromatography column to purify the immunoglobulin molecules from the supernatant. The isolated antibodies were buffered exchanged to PBS pH 7. 4 using the Amicon (30 kD) ultra-centrifugation filtering device from Millipore and stored at −80°C. CD measurements were performed using an Aviv model 420 spectrometer. Measurements were recorded at 25°C at 2 nm/min scan rate. The peptide concentration was 10 µM in H2O, and spectra were collected with a 0. 1 cm path-length in the far-UV region (280 nm-185 nm). For Bio-layer interferometry, the Mabs were immobilized on anti-human IgG Fc biosensors (ForteBio, incorporated). The biosensors were hydrated in PBS pH 7. 4,0. 2% Tween 20 for 10 minutes prior to starting the assay. Briefly, using an Octet Red instrument (ForteBio, incorporated) hydrated biosensors were immersed for 60 seconds at 1000 rpm in individual wells of a 96 well black microplate (Greiner bio-one) containing 200 µL of solution with a concentration of 10 µg/mL of antibody in PBS pH 7. 4,0. 2% Tween 20. After a 60 second wash in PBS pH 7. 4,0. 2% Tween 20 at 1000 rpm, bio-sensors with captured antibody were immersed in the analyte-containing well for 60 seconds at 1000 rpm to allow association of ligand and analyte. Analyte concentrations were two-fold serially diluted within the following range of concentrations 500 nM down to 7. 81 nM. The analytes used were: the 2F5-epitope-scaffold ES2 protein, a wt MPER peptide (EQELLELDKWASLWNWFDITKWLWYIKKKKGSKKK) and a MPER linker peptide (EQELLELDKWASLGGGGSGGWNWFDITKWLWYIKKKKGSKKK) where the 2F5 epitope is separated from the 4E10 epitope by non-helix forming multi-glycine linker. Dissociations were allowed for 60 seconds at 1000 rpm by immersing complexes on the biosensor onto wells containing PBS pH 7. 4,0. 2% Tween 20. Six peptides of the same length (35aa) were used for this alanine scanning analysis with sequences based on the wt MPER peptide used in the light interferometry experiment (EQELLELDKWAS669LWNWF673DITKWLWYIKKKKGSKKK). Residues 669–673, located C-terminal of the 2F5 epitope core ELDKWAS, were individually mutated to alanine. The binding measurements were done using light interferometry (ForteBio) where the antibodies 2F5,4E10, r4, r4W, r4W2a and r4W3 were immobilized on the tip sensor and the peptides were in aqueous solution. The measurements were carried out using specifications detailed in the prior section. Binding affinity constants (KD, kon and koff) were calculated for all antibodies with the Ala-substituted peptides and the wt MPER peptide. We then calculated fold-changes of these parameters for each antibody and plotted the fold-increase or fold-decrease in KD as a positive or negative bar, respectively. The assay was performed in a 96-well microtiter plate with 20,000 TZM-bl cells distributed into each well. Titration of the Mabs were done in a separate plate and pre-incubated with virus for 1 hour at 37°C, then added to the target cells. Approximately 48 hours after addition of virus to the target cells to allow infection, the cells were lysed, and RLU were measured using white solid OptiPlates-96F plates (PerkinElmer, Boston, MA) and a Veritas Luminometer (model 1420-061; PerkinElmer) that injects luciferase assay substrate (Promega) into each well. Pseudoviruses were prepared by cotransfecting 293T cells with an Env expression plasmid containing a full-length gp160 env gene and an env-deficient HIV-1 backbone vector (pSG3ΔEnv). Pseudotyped virus-containing culture supernatants were harvested two days after transfection and stored at −80°C. For neutralization assays, each pseudotyped virus stock was diluted to a level that produced approximately 100,000 to 500,000 RLU. The CL binding assay was carried out in an ELISA format with a kit purchased from Alpha Diagnostic International, TX (CL) and Genesis Diagnostics, NH (CL+ß-2-glycoprotein I complex and ß-2-glycoprotein I). All experimental antibodies were diluted to either 1 µg/mL or 5 µg/mL utilizing a provided diluent containing bovine serum antigen (BSA) and goat serum to reduce background non-specific binding. Samples were run in duplicate at both antibody concentrations in a 96 well plate coated with purified CL antigen, or CL saturated with beta-2-glycoprotein I, or beta-2glycoprotein alone. After one hour incubation at RT, the antibodies were washed five times to remove unbound antibody. Secondary anti-human IgG Fc HRP-conjugated secondary antibodies were allowed to bind for 30 minutes, followed by five washes. Then, chromogenic substrate (TMB) was added and color was allowed to develop for 10 minutes. The reaction was terminated by adding a stopping solution (0. 25 M H2SO4) and absorbance was then measured at 450 nm. Statistical analyses were performed using GraphPad Prism version 5. 0 (GraphPad Softwared Inc.). For the comparison of the antibodies' binding kinetic parameters to the MPER peptide (KD, koff and kon) in Figure 3A, before and after the substitution of W residues, we used a paired t test. The data passed a D' Agostino & Pearson omnibus normality test. The alpha tests level was set a 0. 05, where n = 9 pairs and the tests were two-tailed. For the KD comparison the p = 0. 0008 and the R2 was 0. 7714, for the kon comparison the p = 0. 0308 and the R2 was 0. 4611, and for the koff comparison the p = 0. 1094 and the R2 = 0. 2885. To determine if the Antibody Inhibitory Concentration (IC50 µg/mL) correlated with the antibody binding to the MPER peptide measured as KD values as in Figure 3C, we applied a two-tailed Pearson correlation analysis to both sets of experimentally determined values. The results of this test show a Pearson r = 0. 695, a p-value = 0. 0052 and R2 = 0. 483.

Host antibodies raised in response to acute viral infection are often protective to second exposure. However, in the less frequent examples of chronic infection, in which the virus actively replicates for prolonged periods, host immunity can impact on viral characteristics by applying selective pressures upon progeny. Such a dynamic process is exemplified by the extremely variable and pathogenic human immunodeficiency virus type 1 (HIV-1). Relatively infrequently, antibodies are elicited during infection that can neutralize a diverse array of this malleable pathogen. Hence, studies which elucidate such antibodies are elevated in importance if the pathogen causes human suffering, yet no vaccine exists. Here, we describe a new property of the broadly neutralizing antibody, 2F5, which is directed to a conserved region of the HIV-1 surface protein near the lipid membrane. Through mutagenesis of the antibody and subsequent functional analysis, we present data that suggest a model in which the antibody first binds downstream of its known core epitope in a two-step process not directly involving the lipid membrane. Such studies may better elucidate the not yet defined details of virus-to-cell fusion by which viral DNA enters host target cells. Additionally, such analysis reveals 2F5 binding specificities, important for future vaccine designs.

Abstract Introduction Results Discussion Materials and Methods

immunodeficiency viruses virology immunology biology microbiology host-pathogen interaction immune response immunoglobulins

2012

Structure-guided Alterations of the gp41-directed HIV-1 Broadly Neutralizing Antibody 2F5 Reveal New Properties Regarding its Neutralizing Function

Genomic location can inform on potential function and recruitment signals for chromatin-associated proteins. High mobility group (Hmg) proteins are of similar size as histones with Hmga1 and Hmga2 being particularly abundant in replicating normal tissues and in cancerous cells. While several roles for Hmga proteins have been proposed we lack a comprehensive description of their genomic location as a function of chromatin, DNA sequence and functional domains. Here we report such a characterization in mouse embryonic stem cells in which we introduce biotin-tagged constructs of wild-type and DNA-binding domain mutants. Comparative analysis of the genome-wide distribution of Hmga proteins reveals pervasive binding, a feature that critically depends on a functional DNA-binding domain and which is shared by both Hmga proteins. Assessment of the underlying queues instructive for this binding modality identifies AT richness, defined as high frequency of A or T bases, as the major criterion for local binding. Additionally, we show that other chromatin states such as those linked to cis-regulatory regions have little impact on Hmga binding both in stem and differentiated cells. As a consequence, Hmga proteins are preferentially found at AT-rich regions such as constitutively heterochromatic regions but are absent from enhancers and promoters arguing for a limited role in regulating individual genes. In line with this model, we show that genetic deletion of Hmga proteins in stem cells causes limited transcriptional effects and that binding is conserved in neuronal progenitors. Overall our comparative study describing the in vivo binding modality of Hmga1 and Hmga2 identifies the proteins’ preference for AT-rich DNA genome-wide and argues against a suggested function of Hmga at regulatory regions. Instead we discover pervasive binding with enrichment at regions of higher AT content irrespective of local variation in chromatin modifications. With the advent of genomics techniques, the understanding of the many roles of histone proteins and their modifications has increased rapidly [1,2]. However, comparably little attention has been given to the second most abundant class of nuclear proteins after histones [3], the high mobility group proteins [4]. Initially described as small proteins (< 30 KDa) associated with chromatin [5], they were named after their fast mobility in polyacrylamide gels. In mouse and humans, high mobility group proteins are highly conserved and have been divided into 3 families (A, B, N) based on their different structural features [6]. The two members of the A group, Hmga1 and Hmga2, are ~100 amino acids (AA) long intrinsically disordered proteins, which possess 3 DNA-binding domains (DBD) and a short acidic tail [7]. The DBDs are constituted by short stretches of positively charged amino-acids that contact the minor groove of the DNA [8]. Compared to Hmga1, Hmga2 harbors a smaller linker between the first and the second DBD and a longer AA sequence between the third DBD and the acidic tail. Nevertheless, within the 3 DBDs there is high conservation between both proteins with 74% identity and 15% similarity (S1A Fig). Hmga proteins are robustly expressed during embryonic development and in rapidly replicating cells (such as hematopoietic lineages) [9,10] but have been found misregulated and/or truncated in a number of cancers [11–14]. Whereas expression of Hmga1 is upregulated in hematopoietic malignancies [15], Hmga2 overexpression has been associated with malignant epithelial tumors [16,17]. The Hmga2 gene has also been linked to rearrangements, mostly in benign tumors of mesenchymal origin [14]. However, while increasing evidence indicates that deregulation and rearrangements of Hmga proteins are present both in malignant and benign neoplasia, Hmga overexpression also seems to sensitize cancerous cells to various genotoxic agents [6]. From a functional perspective, Hmga proteins have been mainly implicated in regulating chromatin architecture through direct interaction with histone proteins or with the transcriptional machinery [10]. Additional mechanisms for a role in transcriptional control range from stabilization of enhancer-associated protein complexes through displacement of positioned nucleosomes (for the activation of IFN-beta and IL-2Ralpha genes) [18,19], to competition with histone H1 [20], to direct interaction with the mediator complex [21] and histone chaperones [22]. In light of the disparate physiological functions described, many mechanisms have been proposed that link tumorigenesis and malignant transformation with Hmga misregulation [23]. Such functional models make predictions on the chromosomal location of Hmga proteins and suggest preferential binding to regulatory regions. Attempts to determine genomic localization have however remained limited. The Hmga1 protein was discovered due to its ability to bind in vitro to a primate major satellite sequence [24] and its DBD was called AT-hook since DNA sequences protected from footprinting were rich in A or T nucleotides. Many studies replicated Hmga1-2 binding to AT-rich DNA (reviewed in [20]), however such studies mainly focused on single loci experiments and were mostly conducted in vitro. Regarding the in vivo genome-wide binding determination of Hmga proteins, data is only available for Hmga2. In the first in vivo determination of Hmga2 binding preference, the identified motif (consensus of 49 sequences) was a simple repetition of W nucleotides (either A or T) [25]. This is in contrast with a low-throughput SELEX assessment of Hmga2 affinity, which resulted in a high-information-content DNA logo [26]. In another study, a ChIP-chip experiment was performed in the MKN28 gastrinoma cell line overexpressing Hmga2 [27], yet binding preferences were not discussed in this work. Similarly, a recently published Hmga2 ChIP-seq study in mouse embryonic fibroblasts [28] did not comment on sequence specificity while reporting promoter-centered enrichments. This pattern however, warrants caution as promoters are sites of open chromatin, which frequently causes an intrinsic bias in ChIP-seq experiments [29]. We reasoned that a more thorough understanding of the mechanism adopted in vivo for DNA and chromatin recognition by Hmga proteins will help shed light on the many, partially opposing functions that have been described. Here we use an antibody-free ChIP-seq approach to investigate the location of Hmga1-2 proteins genome-wide. We adopted a flexible cellular system in mouse embryonic stem cells that allows stereotyped expression of different protein constructs. This reveals widespread DNA-binding throughout the genome, with a preference for DNA with high AT content. Interestingly this binding appears neither affected by chromatin state, nor linked to regulatory regions and is conserved upon neuronal differentiation, underscoring the robustness of the primary sequence readout. Accordingly regions that show a compositional bias towards AT, like heterochromatin found at major satellites [30], show on average high occupancy of both Hmga proteins. We aimed to investigate the in vivo DNA and chromatin-binding preferences of Hmga1 and Hmga2 proteins. Given the discordant results obtained using antibodies [26,28], we decided to determine their genomic location with our previously established RAMBiO approach, a biotin-tagging protocol that combines stringent ChIP washes with controlled transgene expression and the possibility to measure genomic binding in stem and differentiated cells [31,32]. Importantly, the approach also allows an assessment of the binding preferences of functional mutants under identical experimental conditions, either a priori or after analysis of the wild-type (WT) data (Fig 1A). We designed recombination constructs for the main isoforms of Hmga1 and Hmga2, respective DBD-mutants and a GFP control, each flanked by inverted lox sites to enable site-specific targeting by the Cre recombinase. Utilizing this comprehensive sample set enabled unambiguous assessment of whether genomic location was a reflection of genuine DNA binding. As DBD-mutants we generated Hmga variants that are mutated at the conserved arginines of the central Arg-Gly-Arg motif of the DBDs (S1A Fig), previously shown to be important for DNA binding in vivo [33]. As a control for unspecific interaction with DNA we utilized monomeric GFP, which is known to diffuse freely in the cellular volume [34]. After transfection, individual cell clones of mouse embryonic stem cells (ESC) were isolated and characterized for targeted integration at a previously utilized chromosomal location that confers stable and homogeneous expression [32]. RNA-seq expression profiling showed that Hmga1 is expressed to levels comparable to a master transcription factor (i. e. Sox2 in ESC) and that Hmga2 is not expressed at the stem cell stage (Fig 1B, top). However, Hmga2 expression increases during differentiation towards a neuronal lineage (Fig 1B, top) and the Hmga2 protein can be detected at the neuronal progenitor stage (S1B Fig). Protein quantification by Western Blotting revealed that the tagged Hmga1 is expressed at levels comparable to endogenous Hmga1 (Fig 1B, bottom). Tagged Hmga2 is expressed similarly to Hmga1 after insertion into the same genomic locus and under the same promoter (S1C Fig). Importantly, in our cellular system, introduction of either bioHmga1 or bioHmga2 (unless otherwise specified, abbreviated as Hmga1 and Hmga2 from hereon) did not result in any apparent change in clonogenic potential (S1D Fig). Hmga1 and Hmga2 DBD-mutants were similarly quantified and levels were comparable to the respective tagged WT proteins (S1E and S1F Fig). Next we determined the subcellular localization of Hmga1-2 proteins by immunofluorescence. In line with previous observations [14,35], we detected enrichment at DAPI-dense foci for both the biotin-tagged proteins and endogenous Hmga1 (Fig 1C, middle and top set respectively). Colocalization of the signal was also present between the tagged and the endogenous Hmga1, pointing to a complete functional equivalence (S1G Fig). Tagged monomeric GFP was distributing throughout the cell volume (Fig 1C, bottom set), as previously described for monomeric GFP [34]. In light of the correct subcellular localization of the biotinylated proteins we proceeded with bioChIP. After pull-down, a considerable amount of DNA was retrieved for both Hmga proteins (up to 1/500 of the total amount of DNA subjected to chromatin IP), potentially hinting at a high intrinsic affinity for chromatin. Interestingly, we noted that for DBD-mutants much less DNA was recovered (down to 1/10’000 of the total amount of DNA subjected to chromatin IP), indicating that mutations in the DBD indeed compromised the ability to bind chromatin. In order to stringently account for systematic biases, we generated paired input controls for each IP condition. Additionally, we used similar numbers of PCR cycles for both IP and input chromatin during library preparation in order to minimize biases arising from different rounds of exponential amplification [36]. Subsequent next-generation sequencing and input normalization revealed good reproducibility but, upon visual inspection, a lack of focal sites of binding as previously observed for binders of low complexity motifs such as MBD proteins [32] or DNA methyltransferases [31]. Systematic analysis of Hmga binding behaviour thus requires direct comparison of binding between WT proteins and DBD-mutant to rigorously identify sequence or chromatin features that direct binding. As a first step, we performed principal component analysis (PCA) on log2 enrichments of IP over input for two replicates of WT proteins, DBD-mutants, and a GFP sample, calculated over 1kb tiling windows along the genome. Strikingly, the first principal component (PC1) accounts for almost 50% of the total variance in the data (Fig 1D) while none of the other principal components explain more than 10%, suggesting that the main signal in the data is contained in PC1. In agreement with a direct DNA-binding modality of Hmga proteins, the PC1 scores revealed a clear separation between WT Hmga proteins and GFP or DBD-mutant proteins which was reproducible among replicates (Fig 1E). To directly link PC1 to physical variables, we contrasted the PC1 loading to marks of chromatin states and genomic features (S1H Fig). This revealed that, while the PC1 loading was only moderately related to chromatin features (|R| ≤ 0. 43), it was highly correlated (R = 0. 86) to AT content, a metric of DNA compositional bias (Fig 1F). AT content is simply 1—GC-content and can be directly calculated as the percentage of A and T nucleotides over regions of DNA, in this case and throughout the manuscript (unless otherwise specified) 1-kb genomic windows. This result suggests that AT content alone can instruct functional Hmga1-2 binding. To further explore the link to AT content, we directly performed hierarchical clustering of input normalized data from all samples and AT content. This identifies one cluster which groups WT samples and their replicates together with AT content, and a second cluster consisting of the DBD-mutant and GFP control samples (Fig 2A). The robustness of this observation was explicitly confirmed by a repetition of the bioChIP-seq experiments using different buffers for cell lysis and SAV precipitation (replicates c in Fig 2A and S2A Fig). Since at megabase scale, the genome shows clear structures in terms of AT content due to the presence of isochores [37,38], Hmga1-2 differential binding is best visually appreciated by plotting enrichments over an entire chromosome (Fig 2B). This reveals broad regions of high and low enrichment that are shared between Hmga1 and Hmga2 (Fig 2B, in purple and green), consistent between replicates (Fig 2B and S2B Fig) and that follow AT content. Interestingly, the GFP signal, albeit noisy, resembles the binding profile of the DNA-binding mutants of Hmga1-2 (Fig 2A and 2B, yellow track). Importantly, interaction of the DBD-mutants with DNA is increased at regions where Hmga enrichment is low (lower tracks in Fig 2B) providing direct support for the notion that Hmga1-2 binding is specific. Additionally, since only the DNA-binding domain was altered in the mutants, this data further argues that no other protein domain contributes to genomic binding. Together, these extensive controls show that binding of Hmga proteins in vivo is variable between different genomic regions but dependent on a functional DNA-binding domain. To further dissect the nature of the AT dependence, we tested whether specific AT-rich motifs were preferentially bound by Hmga1 and Hmga2 proteins. As the absence of focal binding prevents a motif finding-based analysis, we instead, using ridge regression, modelled signal dependence in 1kb tiling windows as a function of nucleotide frequencies of increasing complexity (mono-, di-, tri- and tetranucleotides, see Materials and methods and S3A Fig for details). The inferred coefficients for the mononucleotide model again confirm the importance of A or T nucleotides (S3B Fig). Interestingly, the improvement in predictive power obtained by accounting for higher-order sequence combinations compared to the mono-nucleotide model is only modest (Fig 2C and S3C–S3E Fig). Similarly, longer stretches of As and/or Ts appear not to create binding sites that are more strongly bound than predicted by the mononucleotide preference (S4A and S4B Fig). Next we asked if local DNA shape, which varies based on combinations of neighbouring bases [39], can improve the mononucleotide model. At the resolution allowed by our study, including DNA shape leads to only minor improvements in predictive power suggesting limited influence on binding (S4C Fig). We thus propose that binding of Hmga proteins to genomic DNA occurs as a function of AT content alone and is not noticeably increased when specific DNA-sequence motifs are present. This binding behaviour directly accounts for the lack of focal enrichments of Hmga proteins since As and Ts are inherently abundant in DNA even though they vary in frequency. This is readily illustrated at CpG islands where AT content drops sharply and on average by ~20%. This coincides with a local decrease in Hmga binding (S2C Fig). Since Hmga1 and 2 are independently regulated in our model system and expressed with different tissue specificity [40,41], we next asked for potential variation in the strength of AT-dependence between proteins, which could indicate non-redundancy of their function. For this and for all subsequent analyses, we further normalized the log2 WT enrichment values by subtracting the log2 enrichment values of the respective DBD-mutant (see Materials and methods). We reasoned that this metric would be more accurate for a thorough description of Hmga1 and Hmga2 DNA-binding activity since it accounts for sequencing bias and unspecific binding. This comparison reiterates the positive correlation between binding and AT content for both proteins and a stronger AT-dependence for Hmga2 (Fig 2D). Despite this small difference, AT-dependence appears similar for both proteins and thus functional differences, if any, should probably be ascribed to different interaction partners and not to differences in the DNA-binding readout. Motivated by this AT dependence we determined the binding to extremely AT-rich repeats (roughly 80% AT) that are of sufficient length (at least 300 bps, similar to ChIP assay resolution) and still mappable to specific sites. A subgroup of (TA) n simple repeats fulfills these criteria and these indeed show strong binding (Fig 2E), and are in several cases even visible as peaks at the single locus level (S2D–S2F Fig). This again argues that AT content is an important contributor to Hmga binding. Local differences in chromatin, through DNA methylation, nucleosome compaction or histone modifications, can modulate the readout of a genomic sequence [42–44]. We therefore investigated the relation of Hmga1-2 to DNA and chromatin features other than AT content. From the results of the PCA (S1H Fig) one might expect low correlations for any such features. Indeed, by contrasting binding to chromatin marks and factors, no relevant correlation manifests itself genome-wide, except for an anti-correlation with euchromatic marks (Fig 3A). This dependence however is of small magnitude within all replicates (S5A and S5B Fig; for a summary plot including all Hmga1-2 samples, see S5C Fig). To further dissect the nature of this anticorrelation, we focused on specific regions in the genome known to undergo extensive chromatin remodeling. Many euchromatic histone modifications are set in an activity-dependent manner, in particular within the promoters of transcribed regions [45,46]. We therefore focused next on promoters and divided them based on activity level (see Material and methods). This revealed no major differences in the levels of Hmga1-2 binding between active and inactive promoters (Fig 3B). Since promoter regions differ largely in their sequence composition, we explored how Hmga binding relates to chromatin in the context of local sequence by further stratifying promoters into CpG islands and CpG-poor promoters. However, in both cases, we again did not observe a major difference in Hmga binding between active and inactive promoters (S5D Fig). Notably, enrichments at CpG island promoters were lower than at non-CpG island promoters in line with the fact that the latter are richer in AT [47]. The experiments discussed so far highlight that Hmga1-2 proteins bind to DNA in a DBD-dependent manner and that binding correlates genome-wide with AT-richness but not with specific chromatin marks. In order to test if this behavior is not limited to stem cells and to test differential chromatin recruitment, we collected Hmga binding data in a different cell type. The utilized stem cells can be readily differentiated to neuronal progenitors (NP) for which we and others have already generated a variety of epigenomic maps [48–51]. In the used differentiation paradigm, not only do these cells change function, identity and transcriptome, but they also become post-mitotic [52]. Furthermore differentiation entails loss of pluripotency, which has been argued to be characterized by unique global chromatin changes [53]. Notably Hmga2 is endogenously expressed in neuronal progenitor cells providing another rationale to monitor Hmga1-2 binding in these cells. Upon neuronal differentiation we performed bioChIP followed by sequencing for both proteins and calculated enrichments over DBD-mutants at genomic tiling windows. As in the case of ES cells, hierarchical clustering results in a cluster grouping WT samples with AT content and a second cluster consisting of all DBD-mutants (S5E Fig). Indeed, similarity in binding as compared to ESC can be appreciated visually by inspecting the binding pattern along an entire chromosome (Fig 3D). The absence of reproducible differences between ESC and NP replicates extends this observation and argues against a consistent role for chromatin in modulating DNA binding (S5F Fig). The good correlation values between samples and AT content confirm similar binding preferences at the majority of sites (Fig 3C and S5G Fig). Thus the dynamic and well-documented changes in chromatin that occur during loss of pluripotency, gain of neuronal identity and exit from the cell cycle [49,54,55] show limited effect on genomic location of Hmga proteins, which remains effectively a function of DNA sequence. Having established that binding of Hmga1-2 is not influenced by sites of open chromatin or presence of local histone marks, we set out to characterize in more detail Hmga1-2 targets in the genome. First, we specifically profiled binding over distal regulatory regions since it was previously reported that Hmga proteins bind subsets of these [20]. Fig 4A and 4B illustrate enrichment values for Hmga1-2 at regions of low DNA methylation (LMRs) in ESC and NP, which we have previously shown to represent distal regulatory regions [48]. We do not observe any enrichment in binding of Hmga1 nor Hmga2 at these sites (Fig 4A and 4B). At LMR centers, a different profile for Hmga1 as compared to Hmga2 can be observed (Fig 4A and 4B). However, this does not seem to be a robust feature as the difference is absent in a second replicate (S6A Fig). This lack of binding is not a feature of enhancer definition or activity. If we use other chromatin features that mark either primed (only H3K4 monomethylation) or active enhancers (both H3K27 acetylation and H3K4 monomethylation) [56] we observe a similar lack of binding (S6B and S6C Fig). Since Hmga1-2 binding seems largely invariant between cell types and at low resolution appears to cover broad regions defined by high AT content, we next asked whether enriched regions coincide with broad chromosomal features that are known to be largely invariant. These include constitutive heterochromatin, which is characterized by low histone acetylation, high H3K9me2 and high cytosine methylation [57]. Additionally, these regions tend to replicate late during the S-phase of the cell cycle [58]. Importantly with respect to our work, constitutive heterochromatic regions show higher than average AT content [59], due to high prevalence of major and minor satellite repeats and transposon integration events [60]. Due to their large size, such regions are best observed at the chromosomal scale. We accordingly determined Hmga1-2 enrichments over 10 kb windows when comparing them to hallmarks of heterochromatin (Fig 4C). While Hmga1-2 binding was only weakly correlated to H3K9me2, it displayed good correlations with replication timing [61] and the presence of LaminA [62], with LaminA showing the highest correlation (Fig 4D). LaminA locates to the inner nuclear membrane and is a well-known nuclear organizer of heterochromatin [63]. Importantly, even though in all replicates Hmga binding is well correlated to heterochromatic marks such as LaminA, it clearly displays the largest correlation to AT content (S7A and S7B Fig), suggesting that sequence composition rather than heterochromatic marks determines Hmga binding. As Hmga binding appears domain-like at a large scale, we wondered whether binding is simply a function of the particular genomic distribution of AT-rich DNA or whether some form of spreading could play a role. Towards this goal we built a linear model that predicts binding over a 1kb window based not only on the AT content of the window in question, but also its immediately neighbouring windows (Materials and methods). Importantly, the correlation of a window’s AT content to the AT content of its flanking windows is not above 0. 6 (S7C and S7D Fig), thus containing sufficient additional potentially predictive information. The model fit results in coefficients that are high only for the central window, suggesting that surrounding windows play little role in explaining Hmga binding in the central segment (Fig 4E). This is confirmed by the lack of an improvement in predictive power compared to a model that only includes the AT content of the central window (S7E Fig). Given that Hmga proteins have been shown to bind mouse major satellite DNA in vitro [64,65] and show strong signal at centromeric heterochromatin in the nucleus (Fig 1C and [66]) we also systematically investigated the binding to different classes of repeats (S8A–S8C Fig). This indeed reveals preferential binding to a subset of repeats. However, this differential binding is mainly a function of AT richness as we observed for the non-repetitive part of the genome. Importantly this analysis confirms the previous observations of Hmga binding to major satellites (S8C Fig). In summary, these findings reinforce our observation that Hmga proteins bind the genome preferentially at regions of higher AT content, which, as a consequence of genome evolution, tend to overlap with large, heterochromatic domains. As we do not detect a dependence of binding on sequence composition of the surrounding regions at the kilobase scale, Hmga1-2 binding to AT-rich DNA thus appears to be determined locally, whilst the organization of mammalian genomes in isochores [37,38] explains the appearance of broad regions of enrichment at Mb resolution. Our results thus far reveal that Hmga binding to the genome mainly occurs outside of regulatory regions. This is somewhat in contrast to previous in vitro and in vivo studies that suggested that Hmga1 functions as a co-activator by stabilizing the pre-initiation complex or the enhanceosome, an enhancer-associated protein complex contacting active promoters [18,19,21]. In an attempt to further test the role of Hmga1 on global transcriptional regulation in ESC, we generated a cell line that lacks Hmga1 protein using CRISPR-based mutagenesis (S9A and S9B Fig). Collaterally, this KO cell line further allowed us to test whether binding of bioHmga is different in the presence or absence of the endogenous protein. To this end, we reintroduced either Hmga1 or Hmga2 proteins in the KO background (S9B and S9C Fig), with Hmga1 protein expression restored to levels comparable to WT, and repeated bioChIP experiments. These experiments revealed a genome-wide distribution that was superimposable to Hmga1 and Hmga2 binding in presence of endogenous Hmga1 (S9D Fig). Accordingly, the correlation with AT content was also captured (S9E and S9F Fig) and no residual difference in binding could be detected with respect to the experiments performed in the WT background (S9G Fig). Together these data argue that observations made in the WT background reflect the genuine binding preference of Hmga proteins. Next we determined the global effect of loss of Hmga function. Since Hmga2 is not expressed in mouse ESC this analysis was limited to Hmga1 KO cells, which consequently do not contain any Hmga protein. We observe in ESC a lack of significant changes in colony formation ability and morphology (Fig 5A and S10A Fig), cell cycle distribution (Fig 5B and S10B Fig) and cell proliferation (Fig 5C and S10C Fig). Additionally, we do not observe alterations in the karyotype (S10D Fig). In line with the limited phenotypic differences, global transcriptome analysis of total RNA identifies only 3 genes significantly altered upon loss of Hmga1 (Fig 5D and S11 Fig). Importantly these genes do not include Hmga2 (Fig 5D and S12 Fig), pointing to lack of a shared regulation. Similar to the absence of transcriptional deregulation at the gene level, we did not observe deregulation of repeat elements (Fig 5E and S14 Fig), which could have been potential targets due to the colocalization of Hmga1-2 enriched regions and heterochromatin. Additionally, ESC lacking Hmga differentiate normally upon neurogenic stimuli (S15A and S15C Fig), and heterochromatin organization does not appear affected at the post-mitotic neuronal stage (S15D Fig), as it is not in stem cells (S1G Fig). Given the lack of detectable protein products in the Hmga frameshift mutant and matching changes in RNA abundance and structure (S13A–S13C Fig), it seemed unlikely that the lack of transcriptional response could be due to traces of aberrant Hmga1 protein. Nevertheless, we additionally generated a cell line where the entire Hmga1 gene is deleted (S16 Fig). Similarly, this line shows almost no transcriptional changes (S17 and S18 Figs). In summary, it appears that the very limited transcriptional effects upon loss of Hmga function are compatible with our protein location data (preferential binding outside of regulatory and gene-dense regions) and provide no clear evidence for a role for Hmga1 in influencing the output of a particular set of genes. In order to shed light on the in vivo binding preferences of Hmga1 and Hmga2, we applied RAMBiO [32] to mouse ESC and NPs. This approach enabled a rigorous and genome-wide assessment of the preferred in vivo DNA and chromatin substrate of Hmga proteins in both cell types. The observed absence of focal binding of Hmga proteins is backed up by several controls and normalization steps to account for potential contributions of biases that are frequently observed in genome-wide ChIP experiments [29,36]. This exemplifies an inherent problem of ChIP of chromatin components or DNA-binding proteins with low complexity motifs that might bind to any open region in chromatin as compared to transcription factors that bind to more complex and thus less frequently occurring motifs. Here we first performed input normalization, and assessed the impact of mutations in key residues of the DBDs in order to be able to account for such biases. PCA analysis showed that a single principal component was able to explain almost half of the total variance and this first principal component was strongly correlated to AT content. Visualization of the actual data highlighted reproducible binding between replicates and loss of this binding upon mutation of the DNA-binding domain. These controls were necessary to convincingly expose that the genome-wide distribution of Hmga1-2 is a direct function of DBD affinity for DNA. The dependence on AT content was further confirmed by a direct comparison of Hmga1-2 enrichments to AT content. For both proteins the majority of the genome contains a sufficient density of A or T nucleotides to elicit a response in terms of binding. However, the binding to a highly abundant sequence feature directly explains the overall lack of focal binding and thus precludes the use of algorithms for peak detection. This is highly reminiscent of our previous experience with proteins reading or writing methylated CpGs [31,32]. In turn this required a regional analysis as a function of AT content. Our findings reveal similar AT-dependence for both proteins and a comparable range of enrichments. However, Hmga1 shows higher noise levels in AT readout, pointing to either lower affinity for DNA or higher sensitivity to chromatin cues. The latter however seems unlikely given the absence of genome-wide correlations with features other than AT content. Together these results argue that genomic binding of Hmga1 and Hmga2 in stem cells is entirely encoded in the respective and highly similar DBDs. To further evaluate to which extent DNA sequence was the sole determinant of binding, we asked whether a different chromatin environment was able to modulate affinity for AT-rich DNA, either at subsets of regions in the same cell-type or genome-wide in a differentiated post-mitotic cell. In both cases, enrichments were not modulated by the different chromatin states. Importantly, this finding argues that the Hmga1 and Hmga2 binding modality is conserved in other cell-types and cellular states, provided that a functional DBD is expressed. While it seems unlikely, we cannot exclude that Hmga might show varying binding behaviors in other cell types, e. g. due to post-translational modifications [67,68], which might account for the inconsistent observations in the literature. Regardless, our results elucidate the nature of the long-ago proposed preference of Hmga1-2 for AT-rich DNA, namely we show that specific binding occurs throughout the genome over a continuum of affinities with the exception of sites where A and/or T bases are rare such as in CpG islands. Proteins that share a similar dependence on low complexity DNA motifs are proteins of the MBD protein family, the majority of which display a linear relationship between binding and density of methylated CGs [32]. From a biochemical perspective, mechanisms of AT recognition could be read out of base or shape. Indeed the pattern of hydrogen bond donors and acceptors in the minor groove does not allow a discrimination of A: T and T: A nor G: C and C: G base pairs [69]. Thus minor groove binders like Hmga proteins may directly recognize degenerate sequences of the type Wn or W-rich (where W stands for A or T nucleotides, IUPAC nomenclature). Alternatively, Hmga proteins may recognize specific DNA shapes. AT-rich sequences are indeed often associated with altered minor groove shapes and in particular A-tract, ApT and ApA (TpT) sequences induce narrowing of the minor groove [70]. In such instances, arginine-mediated recognition of the enhanced negative electrostatic potential offers a mechanism for sequence-specific readout from DNA shape. In vitro studies however tend to support the base readout mechanism [71], which is also in line with our observation that including DNA shape results only in a minor improvement over the simple mononucleotide model. Hmga was also suggested to increase IFN-beta enhanceosome assembly through DNA bending [72] even though it is not part of the enhanceosome structure [73]. Given the limits in resolution of our approach and the activity profile of enhancers in ESCs and NPs we cannot discriminate which of these mechanisms is preponderant in vivo. It is known that regions of higher AT content can overlap with heterochromatic DNA. Accordingly, in ESC, regions of higher Hmga1-2 enrichment are broad, replicate late in S-phase and correlate weakly with methylation of lysine 9 of histone H3, all of which are known hallmarks of heterochromatin. Another feature of heterochromatin is binding to the nuclear periphery, which can be assessed through the quantification of the interaction with LaminA, a protein localizing at the inner nuclear membrane. Indeed LaminA displays the second highest correlation with Hmga protein enrichment (after AT content), opening the possibility that Hmga1-2 might be involved in the sub-nuclear localization of heterochromatin since peripheral domains are maintained in a lamin-independent fashion in ESC [62]. It is tempting to speculate that Hmga proteins might function in nuclear organization and that this might only become obvious in terminally differentiated cells and account for the organismal phenotype of loss of Hmga [74]. Furthermore, Hmga might have specific functions in DNA replication, repair or in the organization of the epigenome [4] that we have not tested in detail in our study. While we cannot formally exclude a potential functional compensation by other Hmg proteins, this seems unlikely given that Hmgb and Hmgn proteins are structurally unrelated, with different binding domains and location [75,76]. As heterochromatic regions tend to be gene-poor, Hmga1-2 binding appears to be depleted in regions of high gene density and indeed shows no enrichments at active promoters or distal regulatory regions. This observation is in contradiction to the recently reported enrichment of Hmga2 at gene regulatory regions, albeit observed in a different model system [28]. While we can only speculate about the nature of this difference, we note that Singh et al. did not control for potential biases towards open chromatin by comparing to input chromatin or tested DNA-binding mutants. Regardless, our location data suggests that Hmga proteins are pervasively distributed across the genome and that they do not function as direct regulators of transcription. In agreement with this model, we observe very limited transcriptional effects when deleting Hmga1 in ESC. Taken together, these results challenge the notion of a central role for the Hmga family of proteins in transcriptional regulation [77]. Instead our findings are more compatible with a recent observation that connects human Hmga1 with genome organization via proper positioning of chromosomal domains [78]. The RAMBiO approach has previously been described [32]. Here below is a summary of the relevant procedures adopted in this work. ESC (159 background, which is a mixed 129Sv-C57Bl/6), were cultivated on feeder cells or 0. 2% gelatine coated dishes. ESC growth medium consisted of DMEM (Invitrogen) supplemented with 15% fetal calf serum (Invitrogen), 13 nonessential amino acids (Invitrogen), 1 mM L-glutamine, LIF, and 0. 001% beta-mercaptoethanol. Multipotent Pax6-positive radial glial neuronal progenitors were obtained as described previously [49,52]. For construct design cDNAs were amplified from a random hexamer reverse transcription cDNA library (Superscript III, Invitrogen) generated from RNeasy extracted total RNA (QIAGEN, 74104) and sequence verified or alternatively ordered for gene synthesis. Mutations in the DBD of Hmga1-2 where targeted to the core RGR motif of the three AT-hooks. Basic and bulky arginine residues were substituted with small and polar cysteines. Mutations targeting the same residues were previously shown to impair DNA binding in vitro and in vivo [11,79,80]. The amino-acid sequence of the proteins investigated in this study is available in S1 Text. Constructs were then cloned into pL1-CAG-bio-MCS-polyA-1L. The two inverted L1 Lox sites allowed CRE-mediated integration into a unique genomic site. Gancyclovir (6 μM) resistant clones were selected and tested for direction of the integration through junction-PCR. The parental cell line expresses BirA-V5 biotin ligase under the CAG-promoter, which leads to stable biotinylation of the tagged protein throughout differentiation [32]. Protein expression was assessed by transcriptomics (reported in Fig 1B are read counts per kilobase and million mapped reads obtained from cufflinks v2. 0. 2 [81] output of Tophat [82] aligned (standard parameters, against Mus_musculus. NCBIM37. 67. gtf) data from GSM687305 and unpublished neuronal progenitors RNA-seq data) and western blotting (WB) on whole cell extracts (TNN extraction buffer: 50nM Tris pH 7. 5,250mM NaCl, 0. 2mM Na3VO4 0. 5% NP-40,1mM dithiothreitol and protease inhibitors) blotting with specific antibodies (see below) or Streptavidin-HRP (Pierce). For visualizing protein subcellular localization, cell suspensions were placed on poly-L-lysine for 10 minutes, fixed for 10 min in 3% PFA and permeabilized in 0. 1% NaCitrate and 0. 1% Triton X-100. After 30 min blocking with 0. 1% Tween20,3% BSA (w/v) and 10% normal goat serum in PBS, detection was performed with Streptavidin-AF568 (ThermoFisher) or primary antibodies over night at 4°C using a Z1 (Zeiss) epifluorescence microscope. Nuclear-enriched cell preparations were obtained as follows: cell pellets from 1 confluent 10 cm plate were washed twice in cold PBS, lysed in nuclear extraction buffer (n. e. b.) A (20 mM HEPES KOH pH7. 5,10 mM KCl, 1 mM EDTA, 0. 2% NP40,10% glycerol), washed once with n. e. b. A, homogenized 10X with Dounce homogenizer, resuspended in n. e. b. B (20 mM HEPES KOH pH7. 5,10 mM KCl, 1 mM EDTA, 350 mM NaCl, 20% glycerol), homogenized 10X with Dounce homogenizer and cleared supernatant was saved for analysis. All buffers were cooled to 4°C and supplemented with 1mM dithiothreitol and protease inhibitors before use. Primary antibodies used were Lamin B1 Santa Cruz (C-20) or Abcam (ab16048), Hmga1 Active Motif (39615) and Hmga2 R&D Systems (AF3184). BD Pharmingen BrdU FITC Flow Kit (BD Biosciences) was used for cell-cycle profiling, whereas CellTrace Violet Cell Proliferation Kit (Thermo Fisher) was used for assaying cell proliferation capability. In both cases, manufacturers’ protocols were followed and data was acquired with a LSR II Flow Cytometer (BD Biosciences). KO strategy relied on introducing frame-shift mutations in the coding sequence of Hmga1 via CRISPR-Cas9 induced indels. We targeted an intron-exon junction in the Hmga1 gene in order to avoid off-targets caused by the presence of pseudogenes [83]. Tools used for CRISPR-Cas9 guide design were http: //crispr. mit. edu and http: //www. e-crisp. org/E-CRISP/, which led to the selection of the guide GTCCCCTAGGAGGCTCACCC. A pX330 plasmid expressing CRISPR-Cas9 and guide RNA together with a reporter expressing Puromycin-2A-mCherry were co-transfected in ESC. On the following day, Puromycin (2 μg/mL) was added and cells were kept under selective media overnight. Media was refreshed the next day and after single cell plating, clones were isolated. After PCR amplification of a 700 bp region centered on the CRISPR guide, indels were analyzed by Sanger sequencing. WB with a specific antibody was performed to confirm absence of the targeted protein. For the full-deletion cell line, the same approach was used as for obtaining the KO cells, with the simultaneous addition of GTGAGTCTGGGGGAGATGCA (5’ UTR) and GAAGTTAGCCTTGTCAGGAT (3’ UTR) sgRNAs. Primers used for the screening PCRs described in S16 Fig were: internal (CTTGAGTGACAGTTCTCCCCAGG and GGGCCAGGGGTTAAAACATAAGG), external (AAGTGGGTGGAGCCAACATC and TGCCCTTGCCCTAAGGTAG) and control region (Hmgb1 locus, GTGTTCTCCTTACTATATGAC and GTAGTGATATACTGTGCAAAG). BioChIP experiments were performed as described [32] except that after crosslink reversal DNA was purified with QIAquick PCR Purification Kit (QIAGEN, #28104). Briefly cells were fixed for 10 minutes with 1% Formaldehyde at room temperature and incubated for 10 min on ice in the presence of 125mM Glycine. Cells were harvested and treated for 10 min with 10mM EDTA, 10mM TRIS, 0. 5mM EGTA and 0. 25% Triton X-100 and 10 min in 1mM EDTA, 10mM TRIS, 0. 5mM EGTA and 200mM NaCl with subsequent nuclear lysis in 50mM HEPES, 1mM EDTA, 1% Triton X-100,0. 1% deoxycholate, 0. 1% SDS and 150 mM NaCl. DNA was purified with Qiagen columns for PCR Purification. Crosslinked chromatin was subjected to sonication in a Bioruptor instrument (Diagenode). ProteinA (Invitrogen) pre-cleared chromatin was either saved as input or incubated with blocked (1%CFSG, 100ng tRNA) Streptavidin-M280 (Invitrogen) magnetic beads over night at 4°C. Beads were washed and treated with RNaseA for 30 min at 37°C, Proteinase K for 3 hours at 55°C, then de-crosslinked over night at 65°C. For replicate C of Hmga1, cross-linked cell pellets were resuspended in 50 mM Hepes-KOH (pH 7. 5), 140 mM NaCl, 1 mM EDTA, 10% Glycerol, 0. 5% NP-40,0. 25% Triton X-100 for 10 min on ice (membrane lysis). Nuclei were collected by centrifugation and resuspended in 10 mM Tris-HCl (pH 8. 0), 1 mM EDTA, 200 mM NaCl, 0. 5 mM EGTA for 10 min RT (removal of detergents). Nuclei were collected by centrifugation and resuspended in 10 mM Tris-HCl (pH 8. 0), 1 mM EDTA, 0. 1% Deoxycholate, 200 mM NaCl, 0. 25% N-Lauroylsarcosin, 0. 5 mM EGTA. Crosslinked chromatin was subjected to sonication in a Bioruptor instrument (Diagenode). Triton X-100 to 1% final concentration was added before SAV-IP. Subsequent steps were performed as for the other replicates. Libraries of extracted DNA from the IP and input (50 μl of IP) fraction were prepared according to the manufacturer’s protocol using either the NEBNext ChIP-Seq Library Prep Master Mix Set for Illumina (New England BioLabs, #E6240) or the NEBNext Ultra DNA Library Preparation Kit (New England Biolabs, #E7370L). DNA was measured using NanoDrop 3300 Fluorospectrometer (Witec AG) and Qubit dsDNA HS Assay Kit (ThermoFisher). Size-selection was performed using Agencourt AMPure XP beads (Beckman Coulter, # A63880) before PCR amplification with NEBNext Multiplex Oligos for Illumina (New England BioLabs, #E7335). PCR amplification was performed for 6 to 12 cycles using indexed primer and cycling conditions according to Illumina recommendations. Adapter-ligated and amplified DNA was purified using AMPure XP beads. Before pooling, the size distribution was checked on an Agilent Bioanalyzer 2100 using Agilent High Sensitivity DNA kit (Agilent technologies, #5067–4626). For RNA-seq, two micrograms of total RNA was used from at least two independent cultures harvested on different days. RNA was isolated with the RNeasy mini kit (Qiagen) with on-column DNA digestion and ribosomal RNA was depleted using the Ribo-Zero rRNA removal kit (Epicentre). Strand-specific RNA-seq libraries were prepared from rRNA-depleted samples using the ScriptSeq v2 protocol (Epicentre) following producer’s instructions. Up to 7 samples with different barcodes were mixed at equimolar ratios per pool. Sequencing was performed on an Illumina HiSeq 2500 machine (50 bp read length, single-end, according to Illumina standards).

We investigated the chromosomal location of a group of highly abundant nuclear proteins. Our genome-wide results for Hmga1 and Hmga2 reveal a unique binding modality indicating preference for DNA rich in A or T bases in vivo. Importantly this preferential binding to AT-rich sequences occurs throughout the genome irrespectively of other local chromatin features. Genomic location and loss of function experiments challenge the view that Hmga proteins act as local modulators of transcriptional regulation but rather argue for a role as structural components of chromatin.

Abstract Introduction Results Discussion Materials and methods

neuronal differentiation dna-binding proteins cell differentiation developmental biology dna replication sequence motif analysis epigenetics dna mammalian genomics dna methylation chromatin research and analysis methods sequence analysis genomic signal processing chromosome biology proteins bioinformatics gene expression chromatin modification dna modification animal genomics biochemistry signal transduction cell biology nucleic acids database and informatics methods genetics biology and life sciences genomics cell signaling

2017

Binding of high mobility group A proteins to the mammalian genome occurs as a function of AT-content

Over 1. 2 million people are blind from trachomatous trichiasis (TT). Lid rotation surgery is the mainstay of treatment, but recurrence rates can be high. We investigated the outcomes (recurrence rates and other complications) of posterior lamellar tarsal rotation (PLTR) surgery, one of the two most widely practised TT procedures in endemic settings. We conducted a two-year follow-up study of 1300 participants who had PLTR surgery, conducted by one of five TT nurse surgeons. None had previously undergone TT surgery. All participants received a detailed trachoma eye examination at baseline and 6,12,18 and 24 months post-operatively. The study investigated the recurrence rates, other complications and factors associated with recurrence. Recurrence occurred in 207/635 (32. 6%) and 108/641 (16. 9%) of participants with pre-operative major (>5 trichiatic lashes) and minor (<5 lashes) TT respectively. Of the 315 recurrences, 42/315 (3. 3% overall) had >5 lashes (major recurrence). Recurrence was greatest in the first six months after surgery: 172 cases (55%) occurring in this period. Recurrence was associated with major TT pre-operatively (OR 2. 39,95% CI 1. 83–3. 11), pre-operative entropic lashes compared to misdirected/metaplastic lashes (OR 1. 99,95% CI 1. 23–3. 20), age over 40 years (OR 1. 59,95% CI 1. 14–2. 20) and specific surgeons (surgeon recurrence risk range: 18%–53%). Granuloma occurred in 69 (5. 7%) and notching in 156 (13. 0%). Risk of recurrence is high despite high volume, highly trained surgeons. However, the vast majority are minor recurrences, which may not have significant corneal or visual consequences. Inter-surgeon variation in recurrence is concerning; surgical technique, training and immediate post-operative lid position require further investigation. Blindness from trachoma is the end result of progressive scarring of the conjunctiva driven by Chlamydia trachomatis. The major risk factor of blinding corneal opacification (CO) is trichiasis (TT), the in turning of the eyelashes. TT traumatises the delicate epithelium of the cornea, rendering it vulnerable to secondary infection. TT encompasses a range of eyelid and eyelash abnormalities from a few peripheral in turned lashes to the entire upper eyelid pulled inwards by scarring (entropion). TT can also occur without entropion, from metaplastic or misdirected lashes [1]. Recent global estimate suggested that in 2008 there were 8. 2 million people living with trichiasis. Surgical treatment for TT is a key component of the SAFE strategy for trachoma control, directly reducing the risk of blindness [2]. Several different surgical procedures have been used to correct upper lid TT, some of which have been compared in randomized trials [3]–[6]. The technique of Bilamellar Tarsal Rotation (BLTR) has the lowest recurrence risk. However, the widely used Posterior Lamellar Tarsal Rotation (PLTR or ‘Trabut’ procedure) was not included in these comparisons. The World Health Organisation (WHO) advocates either BLTR or PLTR surgery for TT [7]. Both procedures involve a horizontal tarsotomy combined with everting sutures to rotate the inferior portion of the upper lid outwards [3]. TT surgery can prevent or reduce progression of corneal opacity, improve vision and relieve pain [6], [8], [9]. However, surgical outcomes are variable. Most studies of post-operative TT recurrence reports rates of 20%–40% at one-year, ranging from 7. 4% at one year to 62% at three years [5], [6], [8], [10]–[22]. Recurrence is generally subdivided into early (before six months) and late (after six months). Early recurrence is probably attributable to a number of factors including the severity of the preoperative disease, the type and quality of the surgery, and post-operative wound healing events [8], [23], [24]. Substantial inter-surgeon variation of TT recurrence rates has been reported [8], [23]. After six months there is a steady accumulation of recurrence that probably results from progressive scarring disease [8], [22]. Serious surgical complications are rare in TT surgery. However, complications such as granuloma and lid contour abnormalities (notching) occur relatively frequently [23]. Granulomas are pedunculated masses of inflammatory tissue ranging in size from a few millimetres to over a centimetre. Larger granulomas can obscure the visual axis, and all except the smallest require surgical removal. The reported frequency of granulomas ranges from 0% to 14% [6], [10], [11], [16], [23], [25]–[28]. Lid notching, a focal overcorrection of the lid caused by irregular suture tension or an irregular tarsal incision is cosmetically unsatisfactory and may be associated with lagophthalmos, putting the cornea at risk [29]. During the course of two recently reported randomised controlled trials conducted in Ethiopia we recruited 1300 individuals with the full spectrum of TT type and severity, who received PLTR surgery with silk sutures, and were followed up for two year [30], [31]. This represents the largest data set to date on the results of PLTR surgery (recurrence risks, vision and other outcomes), and provides an opportunity to investigate outcomes in relation to the type and position of the trichiatic lashes. The National Health Research Ethics Review Committee of the Ethiopian Ministry of Science and Technology, the London School of Hygiene & Tropical Medicine Ethics Committee and the Emory University Institutional Review Board approved this study. Informed consent was taken at the time of enrolment. The research adhered to the tenets of The Declaration of Helsinki. All participants gave written informed consent to take part in the study. Two previously reported randomised controlled trials of the management of TT were conducted in Ethiopia from 2008 to 2010 [30], [31]. Each trial recruited 1300 individuals aged 18 years or older with previously unoperated TT: in each trial one arm comprised participants undergoing TT surgery with silk sutures. For the purpose of these studies, TT was defined as one or more lashes touching the eye or clear evidence of epilation (broken/re-growing lashes), without another obvious cause for the trichiasis, such as trauma, malignancy, involutional changes or severe blepharitis. Exclusion criteria were previous eyelid surgery and self-reported pregnancy. Participants presented during TT surgical treatment campaigns in rural villages in the West Gojjam Zone, Amhara Regional State, which were advertised in local markets, churches and schools. Additionally, health extension workers from every sub-district (kebele) across West Gojjam were trained to recognize trichiasis. They visited each village in their kebele to identify potential participants. In the first trial, individuals with major TT (>5 lashes touching the eye) were randomly allocated to PLTR surgery with either silk or polyglactan (vicryl) sutures. In the second trial individuals with minor TT (<6 lashes touching) were randomly allocated to either PLTR surgery with silk sutures or repeated epilation. In individuals with bilateral TT, one eye was randomly designated (sequentially selected from a blocked randomly generated list of right and left eyes) as the study eye and included in the analysis, although both eyes were treated. In both trials, participants were allocated to surgeons sequentially. Surgeons were not permitted to select specific participants and participants were not allocated according to severity. The group described in this report is comprised of all the individuals who were randomly allocated to the PLTR with silk suture arms of the two studies. They represent the full spectrum of TT disease (both major and minor TT) and received exactly the same surgical intervention performed by the same group of surgeons. Participants were examined immediately before surgery and again at 6,12,18 and 24 months. The methods used have been described in detail [30], [31]. Briefly, LogMAR visual acuity was measured in each eye. Participants' weight and height were measured in order to calculate the body mass index (BMI). Both eyes were examined for signs of trachoma using 2. 5× magnification loupes and a bright torch. Baseline, one-year and two-year examinations were by a single ophthalmologist (SNR) and the six and 18-month examinations were by a single ophthalmic nurse (EH). The examiners were standardised to each other. Lashes touching the eye were counted (‘lash burden’) and sub-divided into the part of the eye contacted when looking straight ahead (corneal or peripheral (lateral or medial conjunctiva) and subdivided by the type of trichiatic lash (entropic, misdirected or metaplastic). Clinical evidence of epilation was defined as the presence of broken or newly growing lashes, or areas of absent lashes. In the absence of epilation, eyes with <6 trichiatic lashes were designated as minor TT and those with >5 lashes as major TT. In the presence of epilation, a clinical judgement was made of the number of epilated lashes, by assessing regrowing lash stubs that were pointing towards the globe; if the total trichiatic lashes+epilated lashes was <6, the lid was designated as having minor TT and >5 as major TT. Upper lid entropion was graded using a previously described system [1]. Corneal scarring was classified based on a modified WHO FPC grading system [32], [33]. Corneal opacity was graded in the field and with high-resolution digital photographs (Nikon D300, Nikon 105 mm macro lens). The eyelid was everted and the location of the muco-cutaneous junction (MCJ) graded [1]. Following surgery the presence/absence of notching and granuloma were noted. Notching was defined as central overcorrection of the lid such as to cause a clear deviation in contour of the lid margin. This would correspond to either moderate or severe lid contour abnormalities in a recently published grading system [34]. Surgery was performed under local anaesthesia using the technique previously described [30], [31]. Post-operatively, the operated eye was padded for a day and then tetracycline eye ointment was self-administered twice a day for two weeks. Five nurses, who had previously been trained in and were regularly performing PLTR surgery, performed the surgery. They were selected as the best surgeons from a larger group of 10, during a two-day standardisation workshop. The PLTR techniques of the five nurses were carefully observed and standardised to ensure that all performed the operation in the same way. The intra-operative quality of surgery was periodically reviewed during the course of the trials. Participants were seen at 7–10 days post-operatively, at which point silk sutures were removed. The presence of trichiasis and other complications were noted and treated as needed. Any individual who had five or more lashes at any follow-up examination was offered repeat surgery to be performed by a senior surgeon, within a few weeks of the follow-up assessment. Individuals in whom other ophthalmic pathology (e. g. cataract) was detected were referred to the regional ophthalmic services. The primary outcome measure was trichiasis recurrence defined as either (1) one or more eyelashes touching the eye or (2) clinical evidence of epilation. Secondary outcome measures were surgical complications, entropion and conjunctivalisation. Data were double entered into an Access (Microsoft) database and transferred to Stata 11 (StataCorp, College Station, TX). For participants who had bilateral surgery only the randomly designated ‘study eye’ was included in the analysis. The cumulative incidence of failure in each six-month block of follow-up was calculated using the Kaplan-Meier method. The associations of binary outcomes with exposures were assessed using logistic regression to estimate odds ratios (OR) and 95% confidence intervals (CI). Variables associated with the outcome on univariate analyses (p<0. 2) were retained in the multivariable model. The p-values for the association between categorical variables and specific outcomes were calculated using likelihood ratios. For visual acuities of counting fingers or less, logMAR values were attributed: counting fingers: 2. 0, hand movements: 2. 5, perception of light: 3. 0, no perception of light: 3. 5. Overall, recurrence occurred in 315/1276 (24. 7%) study eyes. Recurrence was more frequent in participants with pre-operative major TT (32. 6%) compared to minor TT (16. 9%): OR 2. 39,95%CI 1. 83–3. 11, p<0. 005 (Figure 1 and Table 2). This association was found at each follow-up (Table 2). Within both TT groups, the risk of recurrence was much higher during the first six-month period compared to all subsequent periods (p<0. 0001; Figure 1 and Table 2), with 58. 0% and 48. 1% of recurrences accruing during this initial period in major TT and minor TT participants, respectively. Thirty-eight participants had recurrence at the 6 months follow-up, but not at any subsequent timepoint, of whom ten had repeat TT surgery. Overall, there was a significant reduction in mean lash burden between baseline (4. 66 lashes) and 24 months (0. 29 lashes; t-test p<0. 0005) (Table 2). Amongst people with recurrence who were examined at 24 months, 14/198 (7. 1%) of those with baseline major TT had more trichiasis at 24 months compared to baseline, compared with 2/107 (1. 9%) of those with baseline minor TT (Table 2). There were similar risks of recurrence in right and left eyes in both groups (Table 2). Individual surgeon' s recurrence rates ranged from 17. 7% to 52. 6%. The pre-operative severity of cases operated by the different surgeons varied to a degree (X2: p = 0. 035), as did their post-operative under-correction rates (X2 = 0. 003) (Table 3). Their risk of recurrence was not affected by the variation in case mix (Table 4). There was no significant difference between the recurrence rate in the first 20 surgeries conducted by each surgeon (32/100,32%) and the last 20 surgeries (29/100,29%, p = 0. 42). Multivariable logistic regression modelling identified increased baseline TT severity, the presence of entropic lashes (compared with misdirected or metaplastic lashes without entropion), specific surgeons (No. 2 and No. 5) and older participant age as independent risk factors for recurrence (Table 4). Early (noted by the 7–10 day suture removal follow-up) and later (seen at any subsequent follow-up) post-operative complications and their association with recurrent trichiasis are presented in Table 5. Over three-quarters (23/30,77%) of individuals noted to have trichiasis at the 7–10 day follow-up had recurrent trichiasis at a subsequent follow-up (OR: 10. 3,95% C. I. : 4. 33–24. 23, p<0. 001) (Table 6). Post-operative granuloma (OR: 0. 39,95% C. I. : 0. 19–0. 83, p = 0. 014) and notching (OR: 0. 44,95% C. I. : 0. 28–0. 72, p = 0. 001) were both significantly associated with lower recurrence rates (Table 6). Univariate and multivariable associations for developing a granuloma and lid notching are shown in Tables 7. Surgeons (No. 1 and No. 4) who had the lowest recurrence rates also had significantly higher rates of granuloma and notching. There was no association between granuloma formation and visible suture fragments being left in the lid (X2: p = 0. 495), gender (X2: p = 0. 239) or younger (<41 years) age (X2: p = 0. 41) Surgery successfully corrected entropion: 1148 (93. 5%) participants at 12 months and 1126 (92. 1%) at two years had no entropion, compared to 327 (25. 2%) at baseline (Table 8). Surgery reduced the entropion grade in 886/918 (97%) (Paired t-test: p<0. 0001). Entropion grade worsened in three participants. In the 1213 participants with conjunctivalization of the lid margin at baseline, an improvement was seen in 699 (58%) (paired t-test: p<0. 0001) and worsening in 29 individuals (Fig. 1a and b). Recurrence was twice as frequent in individuals with major TT pre-operatively. Furthermore, pre-operative entropic trichiasis (rather than misdirected or metaplastic) was an independent risk factor for recurrence. More severe pre-operative trichiasis is consistently a major risk factor for recurrent TT [8], [11], [15], [17]–[20], [22], [35], [36]. Such individuals generally have more conjunctival scarring and may have horizontal or vertical lid shortening. The lid surgery is technically more challenging as the anterior and posterior lamellae are more difficult to dissect and post-operatively there may be strong contractile forces pulling the lid back to an entropic position. These cases, who are at higher risk of sight threatening disease, should be treated by more experienced surgeons and have enhanced follow-up to detect recurrence. Interestingly, metaplastic lashes, even in the absence of entropion, appear to be ‘cured’ by surgery. It is unclear whether they cease to grow, or whether they are simply rotated far enough away from the globe. In our study only the PLTR procedure was used and gave recurrence rates in the middle of the reported range for this procedure: 12% to 55%, with reported follow-up periods of between 3 months and four years [8], [10], [20]–[22], [26], [37], [38]. One randomised trial has compared the PLTR and BLTR procedures and found similar outcomes [10]. However, ophthalmologists performed all the surgery, follow-up was only three months and sample size insufficient to address the question. The range of outcomes in these different studies suggests that a comparative trial of PLTR and BLTR is required under more representative operational conditions to determine if one procedure is superior, particularly for more severe cases. The surgeons in this study were selected for their surgical ability and given additional training. Their technique was intermittently observed. Nevertheless, two surgeons had significantly higher recurrence rates than the best performing surgeon. Surgeon No. 5, who had the highest recurrence rate, did operate on a higher proportion of major TT cases than the other surgeons, but remained an independent risk factor for recurrence after adjusting for baseline TT severity. Inter-surgeon variability has previously been highlighted as a concern in trachoma surgery with one study finding recurrence rates ranging from 0–83% between surgeons [8]. Several factors may contribute to variable outcomes. Firstly, surgical training varies in quality and number of cases performed [39]. Secondly, supervision and refresher training is often sporadic and of variable quality and content, with many surgeons operating entirely independently [39], [40]. Thirdly, surgical volume may be low which may lead to loss of surgical skills. In cataract surgery, for example, higher volume is associated with better outcomes [41]. The WHO advises that a minimum of 10 TT procedures per month should be conducted [7]. Studies from Ethiopia and Tanzania found few high volume surgeons, with the vast majority of TT surgeons perform few cases [39], [42]. In our study surgeon 5 did perform less procedures than the other surgeons as she was dismissed for disciplinary matters mid-way through the trial. However, she still conducted over 150 procedures during the trials from which this study emanates, so low surgical volume is unlikely to explain the variation. Finally, despite attempts to standardise, subtle residual variation in technical ability and technique probably remain. For example, short incisions have been associated with increased recurrence following BLTR surgery (crude OR: 3. 58,95% C. I. : 1. 39–9. 23) [23]. The immediate post-operative lid position warrants further investigation: if this is predictive of outcome, immediate revision could be undertaken. In programmatic settings, if individual surgeons are underperforming this needs to be addressed. Ideally, they would receive refresher training and be reassessed. Unfortunately, TT surgical audit is rarely conducted, so poor performance is probably frequently missed. Notching is focal external rotation or irregularity of part of the lid usually caused by excessive suture tension. Some authors include notching within a broader category of ‘lid contour abnormalities’ [11]. Large notches may cause lagophthalmos and disruption of the tear film, leading to corneal exposure. Notching can be cosmetically unsightly, in contrast to general overcorrection which is less noticeable. Other studies have reported notches in 6–30% for PLTR surgery and 0–14% in BLTR surgery [11], [21], [23], [27], [37]. The association between notching and reduced recurrence is not surprising, as notching usually reflects a degree of overcorrection. Notching occurred more frequently in older and less well nourished people (lower BMI), which may reflect age and nutrition-related reduction in tarsal plate rigidity, leading to a more pliable eyelid. Granulomas usually develop at the incision site within weeks of surgery. They require excision when they are large. In ophthalmic surgery they have been described following tarsal rotation and chalazion surgery and found to be associated with residual suture fragments, male gender and younger age [23], [43]. Here we report an association between granulomas and a lower recurrence rate and increased baseline papillary inflammation. Granulomas do not usually develop following surgery that tightly closes the incision site. In tarsal rotation surgery, the everting sutures hold the lid in an out-turned position, which may slightly part the edges of the incision from where granulomas develop. With greater external rotation, the posterior incision is less well opposed, leading to more granulation tissue formation. Granulomas may therefore be an inevitable consequence of tarsal rotation surgery with a good degree of eversion. This study has a number of limitations that potentially constrain the generalisation of the conclusions. It is possible that the results are better than those achieved under routine operational conditions. The five surgeons were selected for their technical skill, received additional training and supervision and performed relatively large volume surgery. They are, therefore, not truly representative of many ‘field’ TT surgeons, who typically perform few cases, have limited training and supervision [39], [42]. Participants were not randomly assigned to a surgeon, however, the risk of selection bias was low, as participants were allocated on a “first-come-first-served” as surgeons became available. Finally, only one operation type, the PLTR, was used for all cases. Set against these limitations, this study has a number of strengths. Firstly, we report the results of a large number of operations performed in a standardised manner. Secondly, follow-up rates are high despite the inaccessibility of many participants; reducing follow-up bias. Finally, participants were representative of the spectrum of TT disease in the wider population of TT patients in Ethiopia, which remains the country with the highest prevalence of TT in the world. Recurrence rates were comparable to previous studies. Baseline disease severity and inter-surgeon variation are major determinants of recurrent disease. However, PLTR surgery successfully corrected most entropion and much of the recurrence was minor, which may not represent a significant risk for most patients. The inter-surgeon variation in recurrence rates is concerning. Further research is needed to ascertain whether recurrence can be predicted immediately after surgery, and whether it can be ameliorated.

Trachoma is the most common infectious cause of blindness worldwide. It causes trichiasis (inturning of the eyelashes to touch the eye), which can cause visual loss. Trachomatous trichiasis (TT) affects over eight million people, 1. 2 of whom live in Ethiopia – the most affected country worldwide. Surgery is the mainstay of treatment for TT. However, results of surgery in the field are often very mixed. We investigated the surgical outcomes of one of the two most widely used surgical techniques (posterior lamellar rotation), in 1300 individuals in the Amhara Region of Ethiopia. We found that recurrence occurred frequently: 315/1276 (24. 7%) participants. However, recurrence was rarely severe (greater than 5 lashes): 42 participants (3. 3%). Recurrence occurred much more frequently in participants who had severe pre-operative disease and with specific surgeons. The high recurrence rates and inter-surgeon variation is concerning. Further research will be required to investigate factors such as surgical technique, surgeon training and immediate post-operative lid position, in order to improve surgical outcomes.

Abstract Introduction Methods Results Discussion

ophthalmology medicine trachoma global health

2013

The Outcome of Trachomatous Trichiasis Surgery in Ethiopia: Risk Factors for Recurrence

Protein-protein interactions play an important role in all biological processes. However, the principles underlying these interactions are only beginning to be understood. Ubiquitin is a small signalling protein that is covalently attached to different proteins to mark them for degradation, regulate transport and other functions. As such, it interacts with and is recognised by a multitude of other proteins. We have conducted molecular dynamics simulations of ubiquitin in complex with 11 different binding partners on a microsecond timescale and compared them with ensembles of unbound ubiquitin to investigate the principles of their interaction and determine the influence of complex formation on the dynamic properties of this protein. Along the main mode of fluctuation of ubiquitin, binding in most cases reduces the conformational space available to ubiquitin to a subspace of that covered by unbound ubiquitin. This behaviour can be well explained using the model of conformational selection. For lower amplitude collective modes, a spectrum of zero to almost complete coverage of bound by unbound ensembles was observed. The significant differences between bound and unbound structures are exclusively situated at the binding interface. Overall, the findings correspond neither to a complete conformational selection nor induced fit scenario. Instead, we introduce a model of conformational restriction, extension and shift, which describes the full range of observed effects. Protein-protein interactions are crucial in most biological processes, yet the principles governing the conformational effects of these interactions are still poorly understood. X-ray structures of protein complexes provide a wealth of high resolution structural information but reflect a static snapshot of the structure, leaving the mechanism of complex formation and dynamics in the complex unaddressed. In addition, compared with the growing number of experimentally determined structures of unbound proteins, there is only a small number of known structures of protein complexes. Computational methods are being developed to derive complex conformations from unbound structures, but this remains a challenging and highly non-trivial task [1]. With the increase in computational power, flexibility has been introduced in the computational methods, and shows promising results [2]. Two different models have been suggested to explain the conformational differences observed experimentally between bound and unbound proteins. The induced fit model [3] postulates that after the formation of a preliminary “encounter complex”, the interaction between the binding partners induces conformational changes into the complex structures. The conformational selection model [4]–[6] takes into consideration the inherent flexibility of proteins. According to this model, unbound proteins can with a certain probability sample the same conformations as observed when bound. In this model, changes in the free energy landscape of the protein due to interactions in the complex shift the conformational density towards the complex structure upon binding. More recent studies [7], [8] have indicated that elements of both models play a role in protein binding with an initial conformational selection step followed by induced fit rearrangements [9]. A good model system to investigate the conformational effects of complex formation is ubiquitin with its binding partners. Ubiquitin is a 76 residue protein that plays an important role in metabolic pathways, as the ubiquitination (covalent attachment of ubiquitin to a lysine side chain of a protein) can, among other functions, control the degradation or regulate transport of this protein. In this function, ubiquitin is recognised by and interacts with a multitude of other proteins. Lange et al. [10] found evidence for conformational selection, showing low root mean square (rms) differences between NMR solution structures of isolated ubiquitin and x-ray structures of ubiquitin in complexes. Wlodarski and Zagrovic [8] found indications for “residual induced fit” by performing statistical analysis on the atomic detail of the same structures. It has recently been shown [11] however, that the observed differences between the experimental bound structures and a molecular dynamics (MD) ensemble of unbound ubiquitin decrease with an increasing number of snapshots considered from the simulation ensemble, indicating that indeed conformational selection largely suffices to explaining the conformational heterogeneity of ubiquitin in different complexes. Thus far most studies have focused on static snapshots of ubiquitin complexes in comparison to solution ensemble of unbound ubiquitin. Here, based on several experimental structures of ubiquitin in different complexes [12]–[22], we have performed and analysed MD simulations of ubiquitin interacting with different binding partners, thereby finally taking into account the flexibility the proteins display in the bound state. It has been shown [23] that MD simulations of unbound ubiquitin agree quantitatively with solution NMR data. Statistical evaluation of simulations of ubiquitin both in the presence and the absence of a binding partner indicates conformational selection to be the appropriate model for complex formation when considering the dominant backbone dynamics, while some localised differences between bound and unbound ensembles can be found near the binding interface. To investigate the effect of binding on the backbone dynamics of ubiquitin, a principal component analysis (PCA) of the backbone atoms of residues 1–70 of the ubiquitin chain was performed. It reveals a functionally relevant “pincer mode” in the first eigenvector (Figure 1, previously described in [10]), that has direct influence on the geometry of the “hydrophobic patch”, a group of three hydrophobic residues (Leu 8, Ile 44 and Val 70) that are involved in most binding interfaces of ubiquitin with other proteins (Figure S1). A simulation of unbound ubiquitin (Figure 2 “1ubi”) spans a conformational space similar to that covered by a large number of known experimental structures from both X-ray and NMR experiments (see also Figure S2). Like the unbound simulation ensemble, also simulations of bound ubiquitin show considerable conformational variety and in fact show a conformational entropy similar to unbound simulations (Figure S3, estimated according to [27]). However, while the dynamics of bound ubiquitin ensembles are considerable, specific restrictions can be observed in most of the 11 complexes when considering the main backbone dynamic modes (Figure 2). All bound trajectories sample a subspace of that spanned by the unbound trajectory. The first two eigenvectors displayed here cover about of the total variance (Figure S4), and are the only ones for which significant differences between bound and unbound ensembles could be observed (Figures 2, S5, S6, S7). In all but one of the bound ensembles, the free energy profile along the “pincer mode” appears to have changed to shift the equilibrium towards either side of the conformational range (Figure 2). While in most cases the shift is partial and most of the conformational space still is sampled (albeit with a lower probability on one side), some trajectories can be described as purely “open” (the ensembles based on the PDB structures 1xd3 and 2fif) or “closed” (ensembles based on PDB structures 1nbf and 2ibi). Besides the obvious exception of the ensemble 1ubi based on an unbound ubiquitin structure, only one ensemble of bound ubiquitin (2hth) shows a distribution very similar to the unbound reference ensemble and therefore does not indicate restriction of the ubiquitin dynamics in the complex. Figure 3 shows a possible explanation for the restriction in both the open and closed states in two of the complexes. Ubiquitin bound to HAUSP (the binding partner in complex 1nbf) resides in a cavity that restricts its conformation in the closed state. In the open conformation, clashes would occur between residues Leu-8 and Thr-9 of ubiquitin and Ser-353 and Met-407 of HAUSP. In the complex of ubiquitin and UCH-L3 (complex 1xd3), residues Leu-8 and Thr-9 reside in a cavity of UCH-L3 when ubiquitin is in the open conformation. In the closed conformation, a clash between these residues and Leu-220 of the binding partner would occur which precludes these conformations. The C-terminal tail of ubiquitin, comprising residues 71–76, shows high flexibility in the unbound and most of the bound ensembles to a degree that some parts of it are fully resolved only in four of the eleven experimental structures used for simulation setup (PDB codes 1nbf, 1s1q, 1ubi and 2g45) with three experimental structures (PDB codes 1uzx, 1xd3 and 2ibi) missing only the last residue. Four of these structures (1nbf, 1xd3,2g45 and 2ibi) are the only ones in this study that show a significantly stronger restriction of dynamics if the C-terminal residues are included in the analysis (Figure S8). Besides this, the dynamic behaviour of the ubiquitin tail seems to be rather unstructured. Hence, like in other studies [10], [11] we focus on the analysis of ubiquitin dynamics to residues 1–70 as we have done in the PCA and will do in the following analysis, where inclusion of the C-terminal residues also does not qualitatively change the results while significantly increasing estimated uncertainties (Figure S9). The principal component analysis indicates conformational overlap between bound and unbound ensembles on the level of the dominant collective backbone degrees of freedom. However, PCA as a method is not aimed at discrimination, especially if the amplitude of the differences is small compared to the variation within the ensembles. It is well possible that differences between the ensembles on a more local level are not detected by PCA. To determine differences between multidimensional ensembles, partial least squares discrimination analysis (PLS-DA, cf. Materials and Methods) has been found to be more effective than PCA [28]. Indeed, using this method, models can be found to almost completely distinguish some of the bound ensembles from the unbound reference ensemble The magnitude of these differences is however significantly smaller than that of the main fluctuation modes of ubiquitin (compare length-scales in Figures 2 and 4). PLS-DA distinguishes between ensembles both on a global as well as on a local level. Even the systematic difference between two ensembles in e. g. a single side chain rotamer will result in a zero overlap. While both bound and unbound control ensembles are fully covered by the unbound reference ensemble along the main mode of ubiquitin dynamics (Figure 5 A), the coverage of the bound ensembles after PLS-DA on the backbone atoms of residues 1–70 (Figure 5 B) is found to be significantly lower. When also considering all non-hydrogen side chain atoms (Figure 5 C), several bound ensembles are no longer covered by the unbound reference ensemble. To validate the significance of the observed differences, the same method has been applied to calculate the coverage of unbound control ensembles by the unbound reference ensemble. It was found to be significantly higher (i. e. almost full), as expected. The observed differences correlate well () with number of ubiquitin atoms involved in binding (i. e. with a distance of less than from the binding partner, Figure 5 D). Hence a more extensive binding interface correlates with more significant differences to the unbound state. To localise differences between bound and unbound ensembles, individual PLS-DA calculations were performed on the conformations of each residue (including side chains) of ubiquitin separately after fitting the backbone of the whole chain. Only a small number of residues for each complex ensemble show an overlap with the unbound reference ensemble which is significantly below and none of them shows an overlap below, Most of the unbound control ensembles show almost complete () overlap with the reference ensemble. The observed differences due to binding interactions are local, as all of the residues found to change their conformation are located near the binding partner (Figure 6). Again, in none of the cases, a complete distinction between bound and unbound ensembles could be found. Even for the residue displaying the smallest overlap between bound and unbound ensembles (residue His68 in ensemble 1nbf chain C) a small fraction of bound structures can be found in the same conformational region as the unbound ones (Figure 7). We compared ensembles of ubiquitin structures from molecular dynamics simulations with and without binding partners aimed at a detailed investigation of the conformational effects of protein binding. The main collective mode of fluctuation found in unbound ubiquitin is the “pincer mode” which strongly influences the shape of the binding surface (Figure 1). It has been indicated [10] that the flexibility of this mode is essential for ubiquitin to interact with a large number of different binding partners. Indeed, this mode is characteristically affected by binding, as all but one of the bound ensembles show a significant shift or restriction of conformational density, while still the whole range of flexibility of unbound ubiquitin is required to accommodate all observed bound states. Since all bound ensembles are completely covered by the unbound ensemble along the pincer mode, the conformational selection model is applicable for this aspect of binding. Employing the partial least squares discrimination analysis method, that specifically aims at identifying differences between ensembles, low amplitude difference modes between bound and unbound ubiquitin ensembles were identified. The observation of the unbound protein displaying the bound state conformation is often considered indicative of conformational selection ([6], [10], [11], [29]). We observed a significant fraction of the unbound ubiquitin ensemble showing a strong similarity (especially in the main pincer mode) to the conformations of bound ubiquitin. This is consistent with a conformational selection binding scenario, while the differences between bound and unbound ensembles on the local level indicate residual induced fit effects as have been introduced in recent binding models [7]–[9]. It is still possible that a portion of the binding events occurs according to an induced fit scenario. An alternative classification of the binding process is based on the inclusion of binding kinetics [30]–[32]. As we have concentrated our analysis on the comparison of bound and unbound states rather than on association and dissociation events, a kinetic approach is beyond the scope of this paper. An aspect not considered in recently discussed binding models [7]–[9] is the dynamic nature of bound proteins. Earlier work [33] already indicated that binding does not necessarily decrease the conformational entropy of proteins. We have also found that the dynamics of the bound ubiquitin ensembles are on a similar scale as those of unbound ubiquitin (Figures 2, S3). In general, two effects of binding on the conformational space of the protein can be expected (Figure 8). Conformations accessible to the unbound protein can be prohibited by interactions (Figure 3) with the binding partner (conformational restriction) while conformations that were energetically unfavourable to the unbound protein can become accessible due to favourable interactions with the binding partner (conformational extension). These two effects are not mutually exclusive and indeed in most cases we observe a combination of both effects in the binding behaviour of ubiquitin. In the most extreme cases, all conformations accessible to the unbound protein are restricted, with all the conformations in the complex being the effect of conformational extension. This “conformational shift” corresponds best to the induced fit binding model. In the case of conformational extension, changes of the energy landscape due to binding allow the protein to access conformations that are energetically unfavourable in the absence of the binding partner. While not generally considered, conformational extension is well compatible with the conformational selection model of binding, as the binding process itself can well take place in the overlap between the bound and unbound states. Most complexes considered in this study can be described by the scenario of conformational extension combined with conformational restriction, showing a significant overlap between bound and unbound ensembles. Interestingly, also for those complex with near-zero overall overlap, substantial overlap is found between the bound and unbound states on the level of individual residues. Hence, for these complexes, each residue samples states in the unbound state that are found in the bound state, but the probability to find all contact residues in a complex compatible state simultaneously approaches zero for these complexes, resulting in zero overall overlap. The consideration of conformational ensembles is a common feature of modern computational protein docking approaches to account for conformational changes due to binding [2], [34]. Our results suggest that while native conformational ensembles are likely to yield good binding conformations on a global scale, small-scale structural adaptions at the binding interface seem to occur that are specifically caused by interactions with the binding partner. From the Protein Data Bank (PDB, [24]), eleven structures of ubiquitin in complex with a binding partner and two structures of unbound ubiquitin were selected (see table 1 for PDB codes and references). To avoid unspecific interactions, structures containing more than one complex were separated before simulation. Simulations were performed using GROMACS 4 [35]. In accordance with recent evaluations of simulation setups ([23] and [36]) the ffamber port [37] of the amber99sb force field [38], particle-mesh Ewald electrostatics [39], [40] were employed with fourth order interpolation, a maximum grid spacing of and a cutoff of 0. 9 nm. Water was modelled using the SPC/E water model [41]. A twin-range van der Waals cut-off (0. 9/1. 4 nm) was used. Both protein and solvent where separately held at a temperature of 300 K using the v-rescale algorithm [42] () and pressure coupled at 1 bar using the Berendsen algorithm [43] (). A time step was achieved by using Lincs bond constraints [44], SETTLE [45] constraints on water and virtual sites [46]. After a steepest descent energy minimisation and a 1 ns equilibration using position restraints on the protein, 10 production runs of 100 ns each were performed for each ensemble, using random starting velocities. Simulation snapshots were taken every for analysis (this seems to be more than sufficient as a sampling returns about the same general results as can be seen in Figure S10). For each simulation of bound ubiquitin, an unbound control simulation from the same starting structure of ubiquitin was performed without the binding partner. To allow for relaxation of structural differences, the first 10 ns of the production run was not included in the analysis. An unbound reference ensemble was created from simulation trajectories based on the unbound x-ray structures 1UBI and 1UBQ and these unbound control trajectories. Ensembles based on similar structures (i. e. from starting structures from the same PDB entry) were not used in comparisons with either bound or control ensembles. Principal component analysis [47]–[49] has been performed on a structural ensemble consisting of structures (snapshots every) from the 17 bound and 20 unbound simulation ensembles simulated for this study. PCAs based on only unbound or only unbound simulation ensembles resulted in very similar eigenvectors (Figures S11 and S12). The backbone atoms of residues 1–70 of ubiquitin have been used for both fitting and analysis resulting in 630 degrees of freedom. All ensembles have been projected on the first eight eigenvectors found in this analysis (fig. 2, S5, S6, S7). Partial least squares regression (PLS) can be used to find a linear model to calculate an external parameter from protein structures. By defining a label of which structures belongs to which class (in this case denoting structures from unbound ensembles and denoting structures from bound ones) as this external parameter, PLS can be used to calculate a model which describes differences between these two classes of structures provided such a difference exists. The resulting linear model yields a difference vector similar to a PCA eigenvector. If a structural difference between the classes exist, the projection of structures onto this difference vector will make it possible to assign a structure to one or the other class. If it is not possible to completely distinguish structures belonging to the two different classes, the model will still produce the best possible distinction, allowing quantification of the remaining overlap between bound and unbound ensembles. For this, both ensembles are projected onto the difference vector and histograms of the projections are calculated (fig. 4). The PLS-DA algorithm used in this study produces a model that maximised the difference of the projection of two structures from different classes (bound vs. unbound) while minimising the difference between structures from the same class. Consequently, if more than one structural mode can be used to distinguish the two classes, the resulting model will not necessarily represent both of them, especially if one would result in stronger variation within the classes. While the method can be used to determine whether or not a full distinction between bound and unbound ensembles can be found, additional steps are necessary to fully characterise the structural differences. For this, PLS-DA was performed on sub-groups of atoms (i. e. the backbone as well as each residue including side chain individually) after fitting of the ensemble on the backbone atoms. Helland' s Algorithm [50] as implemented by Denham [51] was used to perform the partial least squares discrimination analysis (PLS-DA) on the simulation ensembles. PLS performs a regression on a basis that is optimised to correlate with the external parameter. Choosing a high dimensional basis generally improves the quality of the model on the training data but can decrease its predictive power due to overfitting. For this, the combined structures of the bound and unbound ensemble were divided into a model building set (containing half of both ensembles) and a test set (containing the other half of each ensemble). Comparing model quality for both training and test set (Figure S13) shows both correlations to reach a plateau for dimensions and no overfitting effects, so a ten dimensional basis was used in all PLS-DA calculations. For comparison, both ensembles were sorted into the same set of 100 bins spanning their combined range. The overlap of one ensemble by the other is defined as the normalised sum of the products of the number of structures for each bin. Coverage of one ensemble by another is defined as the fraction of structures from the first ensemble in bins containing a minimum number (50) of structures from the other ensemble. The stationary bootstrap algorithm [52] was used to estimate the uncertainty of overlaps and coverage.

Due to their importance in biological processes, the investigation of protein-protein interactions is of great interest. Experimental structures of protein complexes provide a wealth of information but are limited to a static picture of bound proteins. Ubiquitin is a signalling protein that interacts with a wide variety of different binding partners. We have used molecular dynamics simulations to compare the dynamic behaviour of bound and unbound ubiquitin in complex with different binding partners. Our observations suggest that the conformations accessible to bound ubiquitin, while often restricted in comparison to unbound ubiquitin, still occupy a subspace of the conformational space as those of unbound ubiquitin. This corresponds to the “conformational selection” binding model. Only on a local level near the binding interface, differences between bound and unbound structures were found in specific regions of the bound ensemble. To account for the different types of behaviour observed, we extend the currently available binding models by distinguishing conformational restriction, extension and shift in the description of binding effects on conformational ensembles.

Abstract Introduction Results Discussion Materials and Methods

biochemistry protein interactions molecular dynamics proteins chemistry biology computational chemistry

2012

Ubiquitin Dynamics in Complexes Reveal Molecular Recognition Mechanisms Beyond Induced Fit and Conformational Selection

The Lutzomyia longipalpis complex has a wide but discontinuous distribution in Latin America, extending throughout the Neotropical realm between Mexico and northern Argentina and Uruguay. In the Americas, this sandfly is the main vector of Leishmania infantum, the parasite responsible for Visceral Leishmaniasis (VL). The Lu. longipalpis complex consists of at least four sibling species, however, there is no current consensus on the number of haplogroups, or on their divergence. Particularly in Argentina, there have been few genetic analyses of Lu. longipalpis, despite its southern expansion and recent colonization of urban environments. The aim of this study was to analyze the genetic diversity and structure of Lu. longipalpis from Argentina, and to integrate these data to re-evaluate the phylogeography of the Lu. longipalpis complex using mitochondrial markers at a Latin American scale. Genetic diversity was estimated from six sites in Argentina, using a fragment of the ND4 and the 3´ extreme of the cyt b genes. Greatest genetic diversity was found in Tartagal, Santo Tomé and San Ignacio. There was high genetic differentiation of Lu. longipalpis in Argentina using both markers: ND4 (FST = 0. 452, p < 0. 0001), cyt b (FST = 0. 201, p < 0. 0001). Genetic and spatial Geneland analyses reveal the existence of two primary genetic clusters in Argentina, cluster 1: Tartagal, Santo Tomé, and San Ignacio; cluster 2: Puerto Iguazú, Clorinda, and Corrientes city. Phylogeographic analyses using ND4 and cyt b gene sequences available in GenBank from diverse geographic sites suggest greater divergence than previously reported. At least eight haplogroups (three of these identified in Argentina), each separated by multiple mutational steps using the ND4, are differentiated across the Neotropical realm. The divergence of the Lu. longipalpis complex from its most recent common ancestor (MRCA) was estimated to have occurred 0. 70 MYA (95% HPD interval = 0. 48–0. 99 MYA). This study provides new evidence supporting two Lu. longipalpis genetic clusters and three of the total eight haplogroups circulating in Argentina. There was a high level of phylogeographic divergence among the eight haplogroups of the Lu. longipalpis complex across the Neotropical realm. These findings suggest the need to analyze vector competence, among other parameters intrinsic to a zoonosis, according to vector haplogroup, and to consider these in the design and surveillance of vector and transmission control strategies. Visceral leishmaniasis (VL) is a parasitic disease caused in the American continent, by Leishmania infantum (syn. Le. chagasi). VL has an estimated global incidence of 500,000 cases and 59,000 deaths per year [1]. Human cases of VL in Latin America are reported in 12 countries, while Argentina reports an expanding epidemiological scenario [2]. The first autochthonous VL cases in both humans and canines in Argentina were reported in 2006, in the city of Posadas, Misiones [3]. Currently, the country has reported 106 human cases of VL, with 7. 7% mortality, between 2006 and 2016 [4]. Misiones is the province with most human VL cases; the age group with highest incidence is children 0–15 yrs [5]. In Argentina, the main vector of Le. infantum is Lutzomyia longipalpis (Lutz & Neiva) [3]. This phlebotomine was first reported in 1951 in Candelaria, and later in 2000, in Corpus, both in Misiones [6]; there were no VL cases reported at either time. The first autochthonous VL human cases with parasite presence confirmed were reported from Posadas, in 2006 [3]. At present, the vector has dispersed to southern Argentina while also expanding in the north. Currently, Lu. longipalpis is also reported in Corrientes, Entre Ríos, Chaco, Formosa, and Salta provinces [5,7–10]. Lutzyomia longipalpis is currently considered a species complex, despite a broad yet discontinuous distribution within the Neotropical realm, from Mexico to the north of Argentina and Uruguay [9,11]. Recent studies using ecological niche models report that anticipated distributional shifts of Lu. longipalpis vary by region, although greater projected landscape fragmentation and anthropic modifications will not significantly affect model projections [12,13]. This sandfly is adapted to a variety of habitats in tropical regions, from rocky, arid, semi-arid, to very humid, and forested [11,14]. Throughout the species´ distribution, different patterns of genetic divergence and evidence for cryptic species have been reported [15–20]. Mangabeira [21] was the first to evidence ecological and morphological differences between populations of Lu. longipalpis collected in Pará and Ceará states, Brazil. These males had one pair of pale tergal spots on abdominal tergite IV (phenotype 1S), and another additional pair on tergite III (phenotype 2S). Other studies in Lu. longipalpis have reported sympatry of the differentiated phenotypes [22,23], isoenzyme variability [19,23–26], differences in sex pheromones [27–29], variation in the salivary peptide, maxadilan [30,31], differences in male copulation songs [29], wing morphometric variation [24], and genetic variability and divergence using multiple genetic markers [16,18,19,28,29,32–34]. Mitochondrial genes are considered good tools for population genetics and phylogeography due to their abundance, little or no recombination and a haploid mode of inheritance [35]. Fragments of the nicotinamide dinucleotide dehydrogenase subunit 4 (ND4) and the 3' region of cytochrome b (cyt b) are highly variable at the inter and intra-specific level in phlebotomine sandflies [16,36–39]. They have been successfully used to analyze genetic diversity, population genetics, and phylogeography of Lu. longipalpis [16,20,37,40–42], as well as in other New World sandfly species e. g. Lu. cruciata (Coquillett) [43], Lu. anduzei (Rozeboom) [44], Lu. olmeca olmeca (Vargas & Díaz-Nájera) [45] and old world sandfly species e. g. Phlebotomus papatasi (Scopoli) [38,39], Phlebotomus ariasi (Tonnoir) [46] and Sergentomyia (Sintonius) clydei (Sinton) [47]. Genetic diversity, genetic differentiation, and sandfly speciation have been associated with multiple factors, such as latitude or altitude, distance between populations, habitat modifications, anthropogenic landscape fragmentation, vegetation type, geographic barriers (rivers, mountains), host communities, and host species turnover. These factors reduce sandfly dispersal capacity thereby giving rise to isolated populations, loss of genetic diversity, and increasing differentiation among populations [20,39–41,43,44,46,48–51]. There are few studies on the genetic status of Lu. longipalpis in Argentina, although it is clear that the species is expanding southward and is colonizing urban environments [52]. Salomón et al. [33] using the “per” gene, found high genetic differentiation between Lu. longipalpis from Posadas (Misiones province, Argentina) and the north and southeast regions of Brazil, suggesting they might represent another sibling species. In contrast, males from Argentina secrete the same male pheromone as those from Paraguay [53] and other populations from Brazil [27,29,54]. It is therefore important to be able to identify and differentiate haplogroups, since they may differ in importance as diseases vectors. Haplogroups may differ in terms of vector competence, vector capacity, or other epidemiological aspects [55], or the clinical expression of disease, based on maxadilan salivary content [31]. Although at least four sibling species have been proposed for the Lu. longipalpis complex [16,18,19,56,57], there is insufficient evidence currently for a general consensus on the exact number of haplogroups, or their divergence. The aim of this study was to analyze the genetic diversity and structure of Lu. longipalpis from Argentina, and to integrate these data to re-evaluate the phylogeography of the Lu. longipalpis complex using mitochondrial markers at a Latin America scale. Lutzomyia longipalpis was collected in six sites in Argentina by the Leishmaniasis Research Network (REDILA). Four of these localities have reported human VL cases (Puerto Iguazú, San Ignacio, Santo Tomé, and Corrientes city), one has reported only canine cases (Clorinda), and one has not reported any VL cases to date (Tartagal) (Fig 1, S1 Table). Clorinda and Corrientes city are located in the humid Chaco ecoregion, which has a warm subtropical climate. Puerto Iguazú and San Ignacio are located in the Paranense forest ecoregion with a humid subtropical climate, and Santo Tomé is found in grassland and forest ecoregion also in a humid subtropical climate. Tartagal is located in the Yunga forest ecoregion with a subhumid warm climate [58]. Sandfly collections were carried out using REDILA-BL light traps [59], and specimens were preserved in 90% ethanol and transported to the laboratory. The last three segments of males were dissected for taxonomic identification, while the rest of the insects were used for molecular analyses. The dissected segments were clarified with lacto-phenol and mounted on slides, and identified using optical microscopy using the Galati taxonomic key [60]. Genomic DNA was extracted from individual sandflies using DNA Puriprep-S kit Highway (Inbio, Argentina), according to manufacturer’s instructions. The ND4 fragment was amplified using primers reported by Soto et al. [16]. PCR reactions were carried out in 50 μl volumes, containing 25 μl of Gotaq green Master Mix (Promega, USA), 50 pmol/μl of each primer, 2. 5 mM of MgCl2 and 50 ng of template DNA. Amplification conditions were: 95°C for 5 min, followed by 35 cycles of 95°C (30 sec), 48°C (45 sec), 72°C (1 min) and a final elongation step at 72°C for 10 min. The 3´end of the cyt b fragment was amplified using primers and amplification conditions reported by Hodgkinson et al. [40,41] and the PCR was performed according to Pech May et al. [43]. PCR products were separated using 1. 5% agarose gel electrophoresis, stained with 0. 5 μg/ml Sybr Green (Invitrogen, USA), and visualized under UV light. PCR products were purified by HW DNA Puriprep-GP kit (Inbio, Argentina), and sequenced using both forward and reverse primers in a CEQ 2000XL automated sequencer (Beckman Coulter, USA). Forward and reverse sequences from all samples were used to generate consensus sequences and were manually aligned and edited using MEGA v. 7 [62]. All haplotypes were deposited in GenBank (accession numbers for the ND4 fragment: MH166358- MH166392; accession numbers for the cyt b fragment: MH166339- MH166356). Intra-population and global genetic diversity was analyzed using the number of mutations (η), the number of segregating sites (S), the number of unique sites (Su), the mean number of pairwise differences (k), haplotype number (Nh), haplotype diversity (Hd), nucleotide diversity (π), and the nucleotide polymorphism index (θ), using DnaSP v. 5. 10 [63]. Neutrality tests based on Tajima’s D and Fu’s Fs [64,65] were calculated based on segregating sites, using the same software. In addition, the mismatch distribution test was also analyzed using ARLEQUIN v. 3. 5 [66]. The goodness-of-fit of the observed data to a simulated expansion model was tested using both Harpending’s raggedness index (r) [67] and the sum of squares deviations (SSD), using 10,000 replicates. Molecular variance (AMOVA) was used to evaluate population genetic differentiation, using 10,000 random permutations in ARLEQUIN v. 3. 5. The p values of pairwise FST were adjusted using the Holm-Bonferroni sequential correction “p´” [68,69]. Association between genetic and geographic distance was analyzed using a Mantel test [70], implemented in a trial version of XLTAT (https: //www. xlstat. com). Linear geographic distances between sites were estimated using QGIS v. 2. 6. 1 [61]. ND4 and cyt b fragments were concatenated and analyzed using GENELAND package in R to infer the number of Lu. longipalpis genetic clusters among Argentinian sites [71–74]. This program uses a spatial statistical model and Markov chain Monte Carlo sampling with GPS coordinates to estimate the number of populations or genetic clusters (K). Preliminarily, we estimated the number of K from 1 to 10, using 10 million MCMC iterations and 1,000 thinnings. Five independent runs with fixed “K” were run (to avoid ghost populations), assuming an uncorrelated allelic frequency and spatial model. For each run, the posterior probability (PP) of subpopulation membership was computed for each pixel of the spatial domain (100 x 100 pixels), using a burn-in of 1,000 iterations. Variation among and within populations of genetic clusters was analyzed using 10,000 random permutations for the AMOVA in ARLEQUIN v. 3. 5. Unique Lu. longipalpis haplotypes generated herein from Argentina, ND4 gene sequences from GenBank (AF293027—AF293054; AY870836—AY870863 [16,42]), as well as cyt b gene sequences from GenBank (AF448542; AF468979—AF468999; AF480170—AF480181; EF107662—EF107666; HM030727 [20,37,40,41]) were used in analyses (Fig 1, S1 Table). The relationship among haplotypes using ND4 and cyt b sequences was evaluated by constructing independent median-joining haplotype networks implemented in Network v. 4. 6 [75]. The jModeltest v. 2 [76] was used to select the best-fitting model of evolution using the Bayesian Information Criterion (BIC). The Bayesian inference (BI) was generated using Mr. Bayes v. 3. 2 [77]. Posterior probabilities of phylogenetic trees were estimated using 10 million generations (sampled every 1,000 generations) and four Metropolis-coupled Markov chain Monte Carlo (MCMC) to allow adequate time and mixing for convergence. The first 25% of sampled trees were considered as burn in. The consensus tree was visualized using FigTree v. 1. 4. Sequences of Migonemyia migonei (access numbers, ND4: MH166393, cyt b: MH166357) and Phlebotomus papatasi (access number, ND4 and cyt b: KR349298) were used as outgroups. Divergence times among haplogroups were estimated using BEAST v. 1. 8. 4 [78]. We calibrated using the divergence rate reported by Esseghir et al. [36], and corrected as suggested by Ho et al. [79], assuming 0. 105 substitution/site/million years. An uncorrelated lognormal relaxed-clock model was used to allow rate variation among branches and the coalescent exponential growth option. Convergence of the MCMC chains was checked using TRACER v. 1. 6, with 200 as the minimum effective sample size. The length of the runs were: a) 100 million generations for the ND4 fragment, and b) 500 million generations for the cyt b fragment; sampling was every 10,000 and 50,000 generations, respectively. The first 10% of the samples were discarded as burn-in and a maximum clade credibility tree, reflecting divergence times and their 95% highest posterior densities, was estimated using TreeAnotator of the BEAST package and visualized using FigTree v. 1. 4. Nucleotide divergence (Da) for the haplogroups was estimated based on the number of net nucleotide substitutions, using the Jukes and Cantor (JC) correction [80,81] with DnaSP v. 5. 1. 0. The pairwise FST comparison between haplogroups was performed using 10,000 random permutations in ARLEQUIN v. 3. 5. The p values were adjusted using a Holm-Bonferroni sequential correction “p´” [68,69]. The single sequence from Venezuela was not included in these latter analyses. Seventy-three specimens of Lu. longipalpis were sequenced from six Argentinian sites using a 618 bp fragment of the ND4 gene (Table 1A). This fragment had 68 polymorphic nucleotides (11%) and the A-T composition was 73. 3%. A total of 35 haplotypes were identified, with a range of 4–14 haplotypes per site. Globally, haplotype diversity was high (Hd ± SD = 0. 858 ± 0. 039), while nucleotide diversity and polymorphism indices were low (π ± SD = 0. 014 ± 0. 001 and θ ± SD = 0. 022 ± 0. 002, respectively). Populations from San Ignacio, Santo Tomé, and Tartagal had highest genetic diversity (Table 1A). The global neutrality tests were, in general, not significant, except for Corrientes city, which was negative and significant for both tests (Tajima D = -1. 897; Fs = -3. 724), consistent with population expansion. This result agrees with the mismatch distributions which are unimodal (SSD = 0. 0003, p = 0. 979; r = 0. 019, p = 0. 992). Population expansion based on the mismatch distribution was also found in Puerto Iguazú (SSD = 0. 02, p = 0. 43; r = 0. 089, p = 0. 705) and San Ignacio (SSD = 0. 098, p = 0. 28; r = 0. 217, p = 0. 273). In the latter site, the distribution was multimodal, and hence few individuals shared each haplotype. In contrast, in Tartagal with no indication of population expansion (SSD = 0. 038, p = 0. 007; r = 0. 062, p = 0. 04), there was a multimodal distribution indicating demographic equilibrium. Analyses for Santo Tomé and Clorinda were inconclusive (SSD = 0. 966, p = 0; r = 0. 051, p = 1; SSD = 0. 297, p = 0. 049; r = 1. 11, p = 0. 057, respectively) (S1 Fig). The global genetic differentiation of Lu. longipalpis from Argentina using the ND4 fragment is high (FST = 0. 452, p < 0. 0001), although most of the genetic differentiation was within populations (54. 8%). Greatest genetic difference was between San Ignacio and Corrientes city, with an FST of 0. 86 (p < 0. 0001) (Table 2). There was no evidence for genetic isolation associated with geographic distance (r = 0. 24, p = 0. 37). Seventy-six specimens of Lu. longipalpis were sequenced from all six sites in Argentina, using the 261 bp fragment of the cyt b gene (Table 1B). This fragment had 20 polymorphic nucleotides (7. 66%), and the A-T composition was 76. 3%. The cyt b fragment identified 18 haplotypes, ranging from 2 to 12 per site. Globally, haplotype diversity was high (Hd ± SD = 0. 772 ± 0. 036), similar to that for the ND4 fragment, as were low nucleotide diversity and nucleotide polymorphism index (π ± SD = 0. 008 ± 0. 0009 and θ ± SD = 0. 015 ± 0. 003, respectively). San Ignacio, Santo Tomé and Tartagal had highest cyt b diversity (Table 1B), although the global neutrality test was not significant. The Tajima test was significant for Corrientes city, but with a positive value (D = 2. 083), probably due to the low number of haplotypes, suggesting bottleneck events. The mismatch analysis for this site was inconclusive (SSD = 0. 196, p = 0. 145, r = 0. 767, p = 0. 016). In contrast, in Tartagal, Fu’s Fs was negative and significant (Fs = -5. 598; p < 0. 0001), consistent with population expansion or selective sweep/hitch-hiking. This latter also agrees with the mismatch distribution analysis (SSD = 0. 012, p = 0. 481; r = 0. 033, p = 0. 761). Population expansions based on mismatch distributions were also present in Clorinda (SSD = 0. 021, p = 0. 655; r = 0. 25, p = 0. 93), San Ignacio (SSD = 0. 089, p = 0. 405; r = 0. 204, p = 0. 74), and Santo Tomé (SSD = 0. 037, p = 0. 302; r = 0. 135, p = 0. 228). Despite the previous for San Ignacio and Santo Tomé, they had multimodal distributions with pronounced peaks, suggesting that few individuals share each haplotype. There were inconclusive results from Puerto Iguazú (SSD = 0. 202, p = 0. 14; r = 0. 783, p = 0. 034) (S2 Fig). There was high genetic differentiation (FST = 0. 201, p < 0. 0001), principally within populations (79. 84%). Greatest genetic difference was between San Ignacio and Corrientes city (FST = 0. 605, p < 0. 0001) (Table 2). Again, there was no evidence for genetic isolation associated with geographic distance (r = 0. 49, p = 0. 06). Geneland analysis (integrating genetic and spatial information) revealed two main genetic clusters (PP = 0. 9, K = 2). Each cluster included three sites: Cluster 1 with Tartagal, Santo Tomé, and San Ignacio, and Cluster 2 with Puerto Iguazú, Clorinda, and Corrientes city (Fig 2). According to the AMOVA analysis, differences between the geographic clusters explained 30. 16%, whereas inter- and intrapopulation differences explained 12. 13% (p < 0. 0009) and 57. 72% (p < 0. 0001) of the variation, respectively. A total of 91 haplotypes of Lu. longipalpis reported from seven Latin American countries were included in ND4 phylogeographic analyses (Guatemala, Honduras, Costa Rica, Venezuela, Colombia, Brazil and Argentina) (S1 Table). The Median-joining network identifies at least eight haplogroups, each separated by various mutational steps (BI results were also taken into account for the definition of these haplogroups, Figs 3 and 4). In the Ar1 haplogroup there are 12 haplotypes corresponding to San Ignacio, Santo Tomé and Tartagal specimens, which coincide with Cluster 1 from Geneland analyses. In the Ar-Bra haplogroup, H7 (from both Tartagal and Santo Tomé) and H22 (from Santo Tomé) are grouped with haplotypes from Jacobina and Lapinha in Brazil; there are at least two mutational steps between these haplotypes. The Ar2 haplogroup only includes haplotypes from Argentinian sites, H2 being the most prevalent and shared by all six sites from this study. Haplotypes H15, H20, and H30 are separated by at least 17 mutational steps from the Bra haplogroup. This latter haplogroup includes haplotypes exclusively from Sobral and Pernambuco, Brazil. The haplogroup from Central American (CA) (Honduras, Guatemala, and Costa Rica) is more related to the Bra haplogroup than to the two Colombian haplogroups (Col1 and Col2). The Ven haplogroup (only H53) is more related to haplotypes within the Col2 haplogroup. The two Colombian haplogroups (Col 1 with haplotypes from Giron and Col2 with haplotypes from Neiva and El Callejón) are separated by at least 71 mutational steps (Fig 3, for details of haplotypes see S1 Table). The BI for ND4 phylogeographic analysis was constructed using the HKY + G model as the most appropriate for the data (-lnL = 2913. 8833; BIC = 7035. 9464) with gamma of 0. 1510. The tree revealed eight haplogroups with maximum support for two major clades (PP = 1. 0; Fig 4). One of the major clades is composed of Ar1 (Argentina), Ar-Bra (Argentina-Brazil), Ar2 (Argentina), and Bra (Brazil) haplogroups. The other major clade includes haplogroups from CA (Guatemala, Honduras and Costa Rica), and the three northern South America haplogroups: Col1 (Colombia), Col2 (Colombia), and Ven (Venezuela). Divergence of the Lu. longipalpis complex from the most recent common ancestor (MRCA) was 0. 70 MYA (95% HPD interval = 0. 48–0. 99 MYA). Divergence of the clade that includes Ar1, Ar-Bra, Ar2 and Bra haplogroups is estimated at 0. 36 MYA (95% HPD interval = 0. 23–0. 53 MYA), and that of the other major clade that includes CA, Col1, Col2, and Ven, 0. 58 MYA (95% HPD interval = 0. 38–0. 81 MYA. The MRCA of Argentinian populations is estimated at 0. 22 MYA (95% HPD interval = 0. 14–0. 32 MYA). Net nucleotide substitutions (Da) between haplogroups ranged from 1. 2% to 7. 5%, while the pairwise FST between haplogroups was high, with genetic differentiation ranging from 0. 468 to 0. 966 (Table 3). Phylogeographic analyses using the cyt b fragment included 58 haplotypes of Lu. longipalpis from Venezuela, Brazil, and Argentina (S1 Table). The Median-joining network, had few mutational steps separating haplotypes, identifying five haplogroups (BI was also taken into account for haplogroup definition; Figs 5 and 6). The Bra haplogroup has a difference of two mutational steps with H46/Tartagal/Argentina (Ar1 haplogroup), while the Ar1 haplogroup has haplotypes only from Tartagal, Santo Tomé, and San Ignacio. The Ar2 haplogroup has cyt b haplotypes from all six Argentinian sites, the most frequent haplotype being H39 (present in five sites), although this has only a two mutational step difference with H5 (Brazil). The Ar-Bra haplogroup has haplotypes from Tartagal, Santo Tomé and from Juazeiro (Brazil). The single haplotype from Altagracia de Orituco, Venezuela, has a 9 mutational step difference with other frequent haplotypes shared with four Argentinian populations (H40) (Fig 5). The BI from cyt b sequences was constructed using the HKY + G model as the most appropriate for the data (-lnL = 1073. 7679; BIC = 2793. 0201) with gamma 0. 1910. Two major clades were identified, with PP ranging from 0. 6 to 1. 0: a) one with the Ar1, Ar2, Ar-Bra and Bra haplogroups, and b) the clade with the single haplotype from Venezuela. One haplotype from Juazeiro (H5/Brazil) grouped with Argentinian haplotypes into the Ar-Bra haplogroup (Fig 6). Divergence of the complete Lu. longipalpis complex from the MRCA was estimated at 0. 45 MYA using the cyt b fragment (95% HPD interval = 0. 22–0. 83 MYA). The MRCA for the Argentinian and Brazilian haplogroups was estimated at 0. 28 MYA (95% HPD interval = 0. 15–0. 45 MYA). Net nucleotide substitutions (Da) between haplogroups varied from 0. 4%–4. 7%, while the pairwise FST between haplogroups was high, genetic differentiation ranging from 0. 381 to 0. 521 (Table 4). The two mitochondrial fragments from the ND4 and cyt b genes identified high overall haplotype diversity, but relatively low nucleotide diversity and nucleotide polymorphism for Lu. longipalpis in Argentinian populations. Similar indices for the ND4 gene were reported by Soto et al. [16] in Lu. longipalpis populations from Honduras, Central America (Honduras + Guatemala) and Colombia. Likewise, Coutinho-Abreu et al. [20] using the 3´ of the cyt b gene from nine Brazilian Lu. longipalpis populations reported similarly low nucleotide diversity, but higher haplotype diversity. Variability in Lu. longipalpis is similar to that within other species having wide distributions such as P. papatasi in the Old World using the ND4 gene [38]. A greater number of ND4 haplotypes were found (Nh = 35), than that for the 3´ of cyt b gene (Nh = 18) in the present study. The populations with highest genetic diversity, using both markers, were Tartagal, Santo Tomé, and San Ignacio. Previous studies have demonstrated that in general, older established populations have higher genetic diversity, which could be related in part to a relatively constant population size [82]. Neutrality indices were not significant for the three populations, which coincides with little change in population size. However, Tartagal had contradictory results, since the cyt b neutrality test (Fs = -5. 598) and its mismatch distribution were consistent with an excess of low-frequency haplotypes, both characteristic of relatively recent population expansion, or of selective sweep/hitch-hiking [83]. In contrast, neutrality tests for the ND4 in Tartagal were not significant and the mismatch distribution was multimodal, which is consistent with demographic equilibrium [84]. Both fragment mismatch distributions from Santo Tomé and San Ignacio indicate that few individuals share each haplotype, which along with high haplotype diversity and a large number of unique haplotypes, may indicate that their populations were historically established. In comparison, Corrientes city, Puerto Iguazú, and Clorinda had lower genetic diversity, which may be the result of a reduced effective population size and capacity for dispersal (causing loss of genetic diversity and increase of differentiation among the populations). These two features would favor genetic drift, as was proposed for Lu. cruciata [43], Lu. anduzei [44], and Lu. olmeca olmeca [45]. Additionally, populations with low haplotype and nucleotide diversity may have recently experienced prolonged or severe bottlenecks [85]. Results from both gene fragments are contradictory from Corrientes city, the neutrality test with cyt b suggesting an important population size reduction (D = 2. 083, p < 0. 05, and positive Fs), but the ND4 indicating population expansion (significant and negative Fs and unimodal mismatch distribution). A greater sampling effort will be required for Puerto Iguazú, Corrientes city, and Clorinda, from which too few haplotypes were identified for analyses and comparisons. These populations should be continuously monitored to analyze the impacts of environmental factors (abiotic, biotic, fragmentation) and healthcare or agricultural interventions (insecticides) over time. Landscape modification or fragmentation caused by anthropogenic actions (e. g. agricultural practices, deforestation) has been shown to be associated with the loss of genetic diversity in Lu. cruciata [43], Lu. gomezi [51], Lu. anduzei [44], and P. papatasi [39]. Argentinian Lu. longipalpis populations have high global genetic differentiation, with both markers, perhaps associated with the species´ reduced dispersal capacity. Sandfly dispersal depends on several factors, including average flight distance, wind speed and variation, and distance to resources [86]. It also depends on landscape heterogeneity (abiotic and biotic conditions), which influence female host, resting, mating, and egg laying site selection [87]. Phlebotomines are generally poor fliers, with movement restricted to short, flight-assisted hopping. Lu. longipalpis flight range is from 1 m to 500 m, but no more than 1,000 m around their breeding sites [86]. This flight range limitation would lead to rapid local population differentiation, due to genetic drift or to population fragmentation [43,44,51]. In Argentina, two specific Lu. longipalpis genetic clusters were identified. Given shared haplotypes in Tartagal and Santo Tomé, cluster 1 may have occurred via a colonization event from Brazil (Ar-Bra haplogroup) or from the Bolivia-Brazil-Paraguay Gran Chaco eco-region, which is associated with sporadic cases of rural human VL [6]. Curiously, there are no sequences of Lu. longipalpis from these latter countries in GenBank. In the case of Santo Tomé, the most likely route of entry to Argentina is São Borja, Brazil, where an outbreak of VL was reported in 2008 [88]. A similar situation could have occurred with rapid dispersal of Lu. longipalpis-VL and urban colonization in Campo Grande (Mato Grosso do Sul-Brazil). Subsequent colonization may have occurred via Paraguay and the Argentinian border areas of Clorinda and Posadas. In the present study, we detected genetic similarities among the populations of Clorinda, Corrientes city and Puerto Iguazú (these three populations share several haplotypes and low genetic differentiation), which is in agreement with rapid invasive scenarios of urban Lu. longipalpis-VL [52,89]. Bayesian inference using the cyt b gene was not well supported, probably due to its low substitution rate [44]. However, too few haplogroups may have been analyzed (Argentinian, Brazilian and Venezuelan), and hence analysis of a larger sample set is recommended. The ND4 gene inference, in contrast, strongly supports monophyly for the Lu. longipalpis complex. Currently, the exact number of sibling species in the complex remains tentative, due to its broad distribution and absence of sufficient analyses. Soto et al. [16] identified four clades using the ND4 gene (Brazil, Central America, and laboratory colony population from Colombia and Venezuela), and similarly, Arrivillaga et al. [18,19] using the COI gene also identified four clades (Laran: Venezuela; cis-Andean: Venezuela, Colombia, northern Brazil; trans-Andean: Venezuela, Colombia, Central America; and Brazilian: Brazil). Analyses of the ND4 sequences from the present study, in addition to those reported by Soto et al. [16] and Sonoda [42], identified eight haplogroups: Ar1 (Argentina), Ar2 (Argentina), Ar-Bra (Argentina, Brazil), Bra (Brazil), CA (Guatemala, Honduras, Costa Rica), Col1 (Colombia), Col2 (Colombia) and Ven (Venezuela). The latter haplogroup is interpreted with caution since only one sequence was analyzed despite the MRCA support with 1. 0 PP. These eight haplogroups are highly distant by a minimum of several mutational steps, and they have high pairwise FST and nucleotide divergence, suggesting negligible gene exchange, similar to that suggested in a previous study [16]. This high genetic differentiation among haplogroups may potentially be due to vicariance, and/or climatic tolerance limits, in addition to low dispersal capacity. Geographic and/or climatic barriers have already been associated with diversification not just for Lu. longipalpis [16,20,42], but also for Lu. whitmani [48,49], Lu. cruciata [43], Lu. gomezi [51], Lu. umbratillis [90], and P. papatasi [91]. The present study provides evidence that there are at least three Lu. longipalpis haplogroups in Argentina. Salomón et al. [33] were the first to provide evidence that Argentinian populations may be sibling species to those reported from the northeast and southeast of Brazil [29,92,93]. The present study documents significantly high genetic differentiation between the Argentinian and Brazilian haplogroups, despite the Ar-Bra haplogroup, which groups haplotypes from Argentina (Tartagal and Santo Tomé) and Brazil (Jacobina, Lapinha caves and Juazeiro). This genetic difference contrasts with similar complex-level compounds produced by Lu. longipalpis from Posadas, Argentina [33], Asunción, Paraguay [53], and many populations from Brazil including Lapinha [54]. Divergence of the Lu. longipalpis complex from its MRCA occurred approximately 0. 70 MYA, and resulted in two principal clades, one located east and south of the Amazon basin, giving rise to the principal South American (SA) haplogroups (Brazil and Argentina), and another north from northern South America (NSA), through the Mesoamerican corridor (CA) to Mexico. These two clades are similar to those reported by Arrivillaga et al. [18,19], although in contrast to those reported by Soto et al. [16]. Divergence times indicate that this latter cluster from Mesoamerica and NSA, was the first to diverge (0. 58 MYA), significantly earlier than the cluster from east and south SA (0. 36 MYA). Secondary divergence between the Col2 and the Ven haplogroups (0. 25 MYA) and between Ar2 and Ar-Bra/Ar1 haplogroups (0. 22 MYA), occurred on similar time scales. Diversification of all haplogroups occurred after the middle Pleistocene, probably during inter-glacial periods, when landscape fragmentation probably provoked diversification hotspots which conserved high diversity [13]. Extreme climate changes that occurred in the Pleistocene forced adaptation/selection of most biota, including phlebotomine sandflies, to resulting biotic and abiotic conditions [94]. Sandflies may have been subjected to local cycles of dry and cold periods, taking refuge and adapting to the more permanent humid resting fragments, which allowed these haplogroups and diversity to evolve [13,95]. Indeed, plant species’ distribution shifts resulting from climate variation during the Pleistocene, have also been associated with changes in Lu. longipalpis complex diversification [18,95,96]. Arrivillaga et al. , [18,19] suggest that divergence was probably a result of vicariance events that occurred throughout the late Pliocene and Pleistocene (e. g. Andean orogeny). However, recent analyses indicate a highly conserved geographic coverage of the ecological niche of Lu. longipalpis from the Last Glacial Maximum (LGM) in northern Mesoamerica (88. 1%), the species complex´ northern limit [13]. Hence, genetic diversity is highest in the most temporally conserved landscapes. This hypothesis can now be analyzed between and within the major Lu longipalpis haplogroup clades. This is the first report on the genetic diversity of Lu. longipalpis from Argentinian populations, which has high genetic differentiation, two genetic clusters, and three haplogroups. Phylogeographic results provide evidence for a high level of divergence among the eight haplogroups identified for the Lu. longipalpis complex using ND4. Finally, the findings represent only the first stage of future studies required to include a more balanced sampling across Lu. longipalpis distributions and a greater number of samples not only within Argentina, but in all continental subregions. Multilocus genetic analyses will also be required in order to more completely understand evolutionary processes in this important vector species complex, and the impact of environmental change on vector transmission risk of VL. The understanding of the biological and evolutionary aspects of this species complex at the micro and macro-evolutionary levels are central to understanding the interplay between vector capacity, vectorial competence for different Leishmania parasites, urban colonization potential (micro-environment adaptation), demographic aspects of Leishmania transmission, and the clinical expression of disease [30,55].

The Lutzomyia longipalpis complex has a wide but discontinuous distribution in Latin America, extending throughout the Neotropical realm between Mexico and northern Argentina and Uruguay. In the Americas, this sandfly is the main vector of Leishmania infantum, the parasite responsible for Visceral Leishmaniasis (VL). The Lu. longipalpis complex is composed of at least four sibling species, although there is no current consensus on the number of haplogroups, or their divergence. In Argentina, little is known about the complex population structure. Therefore, the aim of this study was to analyze the diversity and genetic structure of Lu. longipalpis from Argentina and subsequently to analyze the complex phylogeography at a Latin American scale, using two mitochondrial markers. Greatest genetic diversity was found in Tartagal, Santo Tomé and San Ignacio. Two genetic clusters and three Lu. longipalpis haplogroups were identified from the six sites in Argentina. Phylogeographic analyzes using ND4 and cyt b gene sequences and those from across the Neotropical realm registered in GenBank, suggest greater divergence than previously reported. At least eight haplogroups are differentiated using the ND4, each separated by multiple mutational steps. Lu. longipalpis complex divergence from its most recent common ancestor (MRCA) was estimated at mid Pleistocene, 0. 70 MYA (95% HPD interval = 0. 48–0. 99 MYA).

Abstract Introduction Methods Results Discussion

biogeography ecology and environmental sciences population genetics geographical locations argentina genetic mapping population biology haplogroups research and analysis methods sequence analysis ecological metrics geography south america bioinformatics phylogeography species diversity brazil people and places haplotypes dna sequence analysis ecology heredity earth sciences database and informatics methods genetics biology and life sciences evolutionary biology

2018

Genetic diversity, phylogeography and molecular clock of the Lutzomyia longipalpis complex (Diptera: Psychodidae)

Claudins constitute the major component of tight junctions and regulate paracellular permeability of epithelia. Claudin-10 occurs in two major isoforms that form paracellular channels with ion selectivity. We report on two families segregating an autosomal recessive disorder characterized by generalized anhidrosis, severe heat intolerance and mild kidney failure. All affected individuals carry a rare homozygous missense mutation c. 144C>G, p. (N48K) specific for the claudin-10b isoform. Immunostaining of sweat glands from patients suggested that the disease is associated with reduced levels of claudin-10b in the plasma membranes and in canaliculi of the secretory portion. Expression of claudin-10b N48K in a 3D cell model of sweat secretion indicated perturbed paracellular Na+ transport. Analysis of paracellular permeability revealed that claudin-10b N48K maintained cation over anion selectivity but with a reduced general ion conductance. Furthermore, freeze fracture electron microscopy showed that claudin-10b N48K was associated with impaired tight junction strand formation and altered cis-oligomer formation. These data suggest that claudin-10b N48K causes anhidrosis and our findings are consistent with a combined effect from perturbed TJ function and increased degradation of claudin-10b N48K in the sweat glands. Furthermore, affected individuals present with Mg2+ retention, secondary hyperparathyroidism and mild kidney failure that suggest a disturbed reabsorption of cations in the kidneys. These renal-derived features recapitulate several phenotypic aspects detected in mice with kidney specific loss of both claudin-10 isoforms. Our study adds to the spectrum of phenotypes caused by tight junction proteins and demonstrates a pivotal role for claudin-10b in maintaining paracellular Na+ permeability for sweat production and kidney function. Epithelial and endothelial cells constitute sheets that divide organs into functional compartments. Homeostasis of different organ and body compartments are dependent on epithelial cells and their paracellular barrier that prevents solutes and water from leaking between the cells. The tissue specific paracellular barrier properties are determined by the protein composition of tight junctions (TJs) [1]. Some TJs form truly impermeable barriers whereas others contain paracellular channels for selective exchange of small ions between compartments. The selectivity is largely determined by the expression of specific members of the claudin protein family [2,3]. In mammals, the claudin protein family comprises 27 members consisting of small, highly conserved transmembrane proteins with four transmembrane helices and two extracellular loops [2]. The first extracellular loop contributes to ion selectivity in channel-forming claudins [4] and the second extracellular loop is of importance for claudin-claudin interactions [5,6]. The critical roles for claudin proteins in development and homeostasis are documented by mouse models as well as by some rare human diseases. Early lethality or specific phenotypes have been identified in mice targeted for the genes encoding claudin 1,2, 4,5, 7,10,11,15,16 18, and 19, respectively [7]. In humans, variants in the CLDN16 and CLDN19 genes are associated with hypomagnesemia, hypercalciuria and nephrocalcinosis (OMIM #248250 and OMIM #248190), CLDN14 mutations causes a form of autosomal recessive deafness (OMIM #614035) and CLDN1 mutations have been described in rare patients with ichthyosis, leukocyte vacuoles, alopecia, and sclerosing cholangitis (ILVASC; OMIM#607626) [8–12]. CLDN10 encodes two distinct isoforms that differ in their amino terminal transmembrane helix and the first extracellular loop [13]. Expression of isoform 10a is restricted to the kidney and uterus while isoform 10b is ubiquitously expressed. Claudin-10a forms an anion-selective channel whereas claudin-10b forms a water-impermeable cation-selective channel with preference for Na+ [13,14]. In mice, loss of claudin-10b in the distal segments of the nephron has been shown to cause impaired Na+ permeability as well as increased Ca2+ and Mg2+ resorption that lead to hypermagnesemia and nephrocalcinosis [15]. However, the effect of impaired claudin-10 function in humans has remained unknown. Here, we report on a homozygous CLDN10b variant c. 144C>G, p. (N48K), in 13 individuals from two kindreds presenting with anhidrosis, alacrima (inability to produce tears), xerostomia (dry mouth) and kidney failure associated with hypermagnesemia. We demonstrate that the claudin-10b N48K variant has pathogenic consequences, since it alters paracellular Na+ transport in a model for sweat secretion, claudin oligomerization tight junction formation at cell-cell contacts, electrophysiological properties of epithelial monolayers and the amount of claudin-10b in the cell membrane. Together, our data reveal mechanisms caused by impaired claudin-10b function and its phenotypic consequences. Two distantly related Pakistani kindreds segregating heat intolerance and generalized anhidrosis from birth were identified. Altogether 13 affected individuals were ascertained and several loops of consanguinity suggested an autosomal recessive mode of inheritance (Fig 1A). The anhidrosis was associated with inability to produce tears (alacrima) and dry mouth (xerostomia) in all 13 individuals. In addition, several affected members suffered from recurrent kidney pain due to nephrolithiasis with onset in adolescence (individuals 4,7, 12 and 14). Heat intolerance was assessed by exposure to heat during 20 minutes (ind. 4 and ind. 11) and resulted in a rapidly increased body temperature from 37°C to 39. 6°C when compared to gender- and age-matched control individuals (Fig 1B). The increased skin temperature was accompanied with an increase in heart rate from 106 bpm to 170 bpm. Perspiration was sparse or absent in patients when using the starch-iod test applied on different body parts consistent with generalized anhidrosis (S1 Fig). Analysis of serum electrolytes revealed increased Mg2+ levels but no other overt abnormalities (Table 1). An abnormal renal reabsorption of cations was reflected in urine spot samples that showed low concentrations of Mg2+ as well as Ca2+ in six affected individuals. Parathyroid hormone (PTH) was analyzed in two affected individuals and revealed a two- and three-fold increase, respectively, when compared to normal levels. The same two individuals had reduced 25-hydroxy vitamin D levels. In combination, these observations suggested secondary hyperparathyroidism and kidney damage and further supported by an eGFR in the lower normal range (Table 1). In contrast, creatinine, urea and bicarbonate in serum were normal (n = 4) as well as the 24h urine production (n = 2; 1400ml and 1450ml, respectively). Computer tomography (CT) scans of kidneys (n = 2) were normal. Pancreatic function was assessed by analysis of amylase and lipase levels that turned out normal (n = 2). Lung functions were investigated by spirometry and turned out normal (n = 2; S1 Table). Autozygosity mapping of the family identified a homozygous region of 235 consecutive SNPs spanning a 2 Mb region on chromosome 13q32. Fine mapping using microsatellite markers confirmed homozygosity and linkage analysis resulted in a maximum two-point logarithm of odds (LOD) score of 4. 25 (Fig 1A). We enriched genomic DNA spanning the candidate region on chromosome 13q32 (average fold enrichment x386) from one affected family member. Variant detection using v2. 1 of the LifeScope Software (Life Technologies) revealed only two homozygous missense variants in the linked homozygous region: A c. 144C>G, p. (N48K) in CLDN10b (NM_006984. 4) and a c. 982T>G, p. (S328A) in UGGT2 (NM_020121. 3), respectively. The c. 982T>G variant in UGGT2 was annotated as a SNP in dbSNP132 (rs816142, mean allele frequency 0. 14; ExAC) and without predicted severe impact on function. The CLDN10b gene variant c. 144C>G results in the change of an uncharged asparagine into a positively charged lysine at amino acid position 48 in the first extracellular loop of the protein (Fig 1C). The N48 residue is part of the conserved claudin consensus motif (W-G/NLW-C-C) and the substitution was predicted to have a severe impact on claudin-10b function. The c. 144C>G transition was found in a homozygous state in all affected family members and in a heterozygous state in unaffected parents. Furthermore, the variant was excluded on 600 control chromosomes from Pakistan and it was not present in the ExAC database (http: //exac. broadinstitute. org/) suggesting this variant to be very rare [16]. The finding of a homozygous missense variant in the CLDN10b gene suggested altered paracellular ion permeability as a mechanism of disease. Anhidrosis was an early and prominent symptom of the disease and hence we performed histological investigations of sweat glands in forearm punch biopsies of two affected individuals. The morphology and number of sweat glands appeared normal and immunohistochemical analysis of claudin-10b, the single claudin-10 isoform expressed in sweat glands, revealed strong signals in cells of the secretory portions without visible differences between patients and healthy controls. Immunofluorescence staining of sweat glands revealed that claudin-10b is localized in the periphery of cells from the secretory portions (corresponding to the clear cells), most likely in the basal membrane infoldings, as well as in the plasma membranes lining the canaliculi and, to a lesser extent, the lumen. Claudin-10b staining co-localized with that of the “sealing” claudin-1 and claudin-3 (S2A and S2B Fig). Furthermore, co-staining of claudin-10b and the TJ protein occludin confirmed an overlap in membranes lining the canaliculi and the lumen. In addition, occludin co-localized with the TJ protein ZO1 in both canalicular and luminal membranes (S2C and S2D Fig). Compared to healthy subjects, the claudin-10b staining in sweat glands of two affected individuals (ind. 4 and ind. 15) showed staining in membranes facing the lumen, but was otherwise mainly intracellular and dramatically reduced in the canaliculi (Fig 1D and 1E). The pattern suggested impaired association of claudin-10b to TJ and an intracellular accumulation, possibly as degraded products, in vesicles. In contrast, immunofluorescence staining of the TJ protein occludin did not reveal any differences in distribution when comparing sweat glands from patient to those of healthy controls (Fig 1D and 1E). To mimic sweat secretion in a model system we cultured MDCK-C7 cells in Matrigel. Under these conditions, MDCK-C7 cells form three-dimensional cysts (apical side towards the lumen). As demonstrated by Bagnat et al. 2007, lumen formation and expansion in MDCK cells depends on transcellular Cl- secretion that is accompanied by paracellular Na+ movement. The resulting osmotic gradient drives water into the cyst lumen. Cyst lumen diameters were shown to increase, when cells were transfected with the cation channel-forming zebrafish claudin-15 [17]. We therefore hypothesized that the presence of the cation channel forming claudin-10b should similarly enhance fluid secretion. As shown in Fig 2A–2C, this is indeed the case: claudin-10b expression in MDCK-C7 cells resulted in an increase in the mean lumen diameter (untransfected control, 33. 5 ± 2. 6 μm, n = 10 different z-stacks [total of 151 cysts]; claudin-10b WT clone #3,70. 7 ± 4. 7 μm, n = 9 [140], p < 0. 01; claudin-10b WT clone #39,70. 6 ± 5. 6 μm, n = 11 [187], p < 0. 01, student’s t-test with Bonferroni-Holm correction, mean ± SEM). Claudin-10b N48K transfected MDCK-C7 cells on the other hand, formed cysts that showed considerably less lumen expansion (claudin-10b N48K clone #21,52. 68 ± 1. 7 μm, n = 7[113], p < 0. 01 vs control, p < 0. 01 vs WT claudin-10b #39; claudin-10b N48K clone #5,48. 5 ± 4. 4 μm, n = 9[167], p < 0. 05 vs control, p < 0. 01 vs WT claudin-10b #3). Both WT and N48K claudin-10b resided in the TJ in 2D as well as 3D cultures (Fig 2D and 2E), however, the mutated variant showed a distribution that suggested an increased intracellular accumulation (Fig 2D). Claudin-10b acts as a paracellular cation channel that is expressed in multiple tissues. To determine a possible effect of the claudin-10b N48K variant on ion permeability we generated MDCK-C7 cells stably expressing the mutated and the WT proteins. Starting 21 days after transfection we measured dilution potentials at weekly intervals. Initially, the dilution potentials, and thus the ratio for Na+ and Cl- permeabilities (PNa/PCl), were similar for cell layers expressing claudin-10b WT and claudin-10b N48K, but considerably increased when compared to control cell layers containing an empty vector. However, whereas dilution potentials of claudin-10b WT transfected cell layers remained stable over several passages, dilution potentials of cell layers expressing claudin-10b N48K progressively decreased (Fig 3A), signifying a time-dependent reduction in permeability ratio PNa/PCl. Transepithelial resistance (TER) was considerably reduced in claudin-10b WT expressing cell layers, as expected for a channel-containing tight junction (Fig 3B). In contrast, the TER reduction in cell layers expressing claudin-10b N48K was less pronounced and similar to cell layers with a weak expression of claudin-10b WT. Relative permeabilities for the monovalent cations Li, Na, K, Rb and Cs in claudin-10b N48K-expressing cell layers followed Eisenman sequences, resembling cell layers with weak expression of claudin-10b WT (Fig 3C) [14]. These observations strongly suggest a reduced selectivity for Na+ over other cations mediated by the p. N48K variant, or a reduced amount of claudin-10b within the tight junction. To clarify if increased degradation of claudin-10b N48K may contribute to the reduced Na+ selectivity, we analyzed the turn-over of the protein in HEK293 cells stably expressing claudin-10b fused to CFP or YFP. For claudin-10b N48K, a higher proportion of cells showed cytosolic CFP/YFP fluorescence, when compared to that of claudin-10b WT (S3A and S3B Fig). This indicates that presence of p. N48K increases cleavage of the CFP/YFP moiety from the fusion protein. Furthermore, Western blot analyses of cell lysates revealed more cleaved products for claudin-10b N48K when compared to claudin-10b WT (S3C and S3D Fig). These data suggest that p. N48K enhances the degradation of claudin-10b in addition to the effect on Na+ selectivity. Since claudin-10b is a TJ protein we sought to further investigate the effect of the p. N48K mutation on claudin-10b-mediated formation of TJs despite apparently normal immunofluorescence staining of occludin in sweat glands of patients. To this end, we expressed claudin-10b in HEK293 cells without endogenous TJs [5]. Formation and reconstitution of TJs after transfection with either YFP-claudin-10b WT or N48K, respectively, were analyzed by freeze fracture electron microscopy (EM). Stable expression of YFP-claudin-10b WT resulted in the formation of typical epithelial TJs with complex meshwork of continuous and branched TJ strands (Fig 4A). Smooth strands with continuity of intramembranous particles were detected on the protoplasmic fracture face (P-face) of the plasma membrane. In contrast, stable expression of YFP-claudin-10b N48K resulted in very few TJ strands and meshwork with a much lower complexity (Fig 4B). In addition, the strands consisted of separated intramembranous particles and partly two-dimensional particle arrays were observed. After transient expression, similar results were obtained as for stable expression: Transfection of YFP-claudin-10b WT resulted in extended meshwork of continuous TJ strands (Fig 4C), whereas expression of YFP-claudin-10b N48K gave rise to a sparser TJ meshwork with discontinuous and beaded intramembranous particles (particle-type strands) on the P-face (Fig 4D). These data suggest that p. N48K inhibits the formation of continuous-type TJ strands by claudin-10b. To corroborate our findings, we analyzed the capability of claudin-10b for homophilic trans-interaction. We performed a cellular contact enrichment assay in which trans-interaction of claudins is measured from the selective enrichment of the construct of interest at contacts between two claudin-expressing cells [5]. We observed that both YFP-claudin-10b WT and N48K localized primarily to the plasma membrane of HEK293 cells (Fig 4E and 4F). Cells transiently expressing YFP-claudin-10b WT showed a strong contact enrichment indicating trans-interactions whereas cells expressing YFP-claudin-10b N48K showed no contact enrichment (Fig 4G). Similar results were obtained after stable expression of the two fusion proteins (Fig 4H). However, detection of the contact enrichment was more intricate in cells with stable expression when compared to the transient expression due to fragmented enrichments of the claudins at cell contacts. Hence, we used co-cultures of HEK293 cells expressing either YFP- or CFP-fusion proteins [18]. Accordingly, we quantified the enrichment of YFP and CFP that co-localized at the mixed cell contacts to discriminate between trans-interacting claudins and other potential local claudin enrichments in the plasma membrane (S4 Fig). The enrichment at contacts with co-localization of CFP and YFP was significantly lower for claudin-10b N48K than for WT (Fig 4H). Furthermore, similar differences between claudin-10b WT and claudin-10b N48K were obtained for transiently expressed fusion proteins with C-terminal GFP-tag (S5 Fig). Together, the data suggest that the p. N48K mutation does not prevent targeting of full length claudin-10b to the plasma membrane but does inhibit claudin-10b trans-interaction. Claudins assemble both in trans (between opposing membranes) and in cis (within one membrane) to form paracellular ion channels or barriers. To test whether the p. N48K mutation affects cis-oligomerization of claudin-10b we employed a fluorescence resonance energy transfer (FRET) assay on HEK293 and MDCK-C7 cells expressing CFP- and YFP-tagged claudin-10b [5,19]. In contrast to HEK293, MDCK-C7 cells contain endogenous claudins and form TJs. In both cell types the maximum FRET efficiency was significantly higher for YFP-claudin-10b N48K/CFP-claudin-10b N48K than for YFP-claudin-10b WT/CFP-claudin-10b WT. In addition, after co-transfection in HEK293 cells, maximum FRET efficiency for both YFP- claudin-10b N48K/CFP-claudin-10b N48K and YFP-claudin-10b WT/CFP-claudin-10b WT were significantly higher than that for YFP- claudin-10b N48K /CFP-claudin-10b WT (Fig 5). Hence, the p. N48K mutation affects, but does not prevent, cis-oligomerization of claudin-10b. Together, the microscopic analysis supports that p. N48K reduces formation of claudin-10b based tight junction strands by affecting cis-interaction and by inhibition of trans-interaction. We generated a 3D homology model of claudin-10b using the crystal structure of murine claudin-15 (PBD ID: 47P9; 52% amino acid sequence identity to human claudin-10b) as template. The N48 residue is part of the consensus motif of claudins (W-G/NLW-C-C), where only claudin-15 and claudin-10b contain an asparagine (N) instead of a glycine (G). The claudin-10b model (Fig 6A) shows a fold that is very similar to the fold of the claudin-15 structure with a left-handed, four transmembrane helix bundles and a β-sheet connecting the extracellular loops (ECL) one and two [6]. Strikingly, residue N47 in the claudin-15 structure and the corresponding residue N48 in the claudin-10b model, seem to form hydrogen bonds bridging the backbone of consensus motif residues (L49 and W50 of claudin-10b) with the backbone (T27 of claudin-10b) at the transition of transmembrane helix one and ECL1 (Fig 6B and 6C). In the model, these conserved bridging interactions are disrupted by the replacement of asparagine for lysine (Fig 6D). Furthermore, a potential electrostatic interaction between residues D28 and K51 within claudin-10b could be disturbed in claudin-10b by the replacement of the uncharged for the positively charged side chain at position 48 (Fig 6B and 6D). Together, the 3D modeling suggests that the p. N48K mutation alters the claudin-10b structure at the membrane-ECL1 transition around the claudin consensus motif. It is plausible that these intra-molecular alterations have an indirect effect on claudin-10b oligomerization. Such indirect effects of p. N48K on oligomerization are further supported by the fact that the corresponding N47 residue of claudin-15 is not part of an intermolecular interface in a polymer model reported previously [20]. Our work demonstrates that a homozygous missense mutation in the CLDN10b gene, encoding the TJ protein claudin-10b, is responsible for a phenotype characterized by generalized anhidrosis, xerostomia, alacrima and kidney damage. The missense mutation p. N48K is located in the first extracellular loop (ECL1) that distinguishes the ubiquitously expressed isoform 10b from the kidney specific isoform 10a. The loop determines the opposing electrophysiological properties of the two proteins: Isoform 10b is selective for cations and isoform 10a for anions. Notably, the N48 residue in claudin-10b is included in the consensus motif shared by all mammalian claudins. Evidence for the pathogenic nature of the claudin-10b p. N48K was obtained by a combination of in vitro experiments. To model the effect of claudin-10b N48K on sweat production we cultured MDCK-C7 cell in matrigel to produce cysts. The expansion of cysts is driven by an osmotic gradient caused by transcellular Cl- secretion and paracellular Na+ transport that, in a similar way, drive sweat production. However, expression of claudin-10b N48K caused a reduced lumen expansion when compared to cysts expressing claudin-10b WT. This observation suggests that the mutation reduces overall Na+ conductance of the cysts that may be brought about by a reduction in single cell conductance or by a reduction of the number of paracellular channels. A reduction in single channel conductance cannot be excluded although dilution and bionic potential measurements indicate that claudin-10b N48K is still able to form charge and size-selective channels. However, claudin-10b N48K showed a tendency for intracellular accumulation in MDCK cells and in sweat glands of our patients. Thus, our observations suggest that the anhidrotic phenotype is not exclusively caused by the partial loss of function mediated by junctional claudin-10b N48K but also to its reduced incorporation into TJs. Since claudin-10b forms TJ strands and paracellular ion-channel we sought to investigate the capability of the mutated protein for TJ strand formation by ultrastructural analysis. The p. N48K substitution of claudin-10b was associated with fewer TJ strands arranged in a less complex meshwork and in contrast to claudin-10b WT, the strands formed by claudin-10b N48K consisted of separated intra-membranous particle arrays similar to those found for claudin-2, claudin-5 and claudin-3/claudin-5 and claudin-10a/claudin-10b chimeric mutants [21–23]. It has been suggested that these ultrastructural changes are related to altered claudin subtype-specific oligomerization properties [22]. The claudins interact in trans as well as in cis and we show an aberrant assembly mediated by the p. N48K substitution in cis accompanied by a marked decrease in trans-interactions at cell-cell contacts. Thus, the combined data strongly suggest that p. N48K alters both trans- and cis-interactions resulting in perturbed TJ formation. However, TJ formation is not fully prevented by the N48K mutation. Furthermore, in a 3D model based on the crystal structure of the highly homologous murine claudin-15, the N48 residue is predicted to form hydrogen bonds bridging the NLW-motif in the ECL1 with the transition between trans-membrane helix one and the ECL1. These interactions seem to be disrupted by replacing the polar asparagine for the longer and positively charged lysine. The modeling and the fact that the corresponding N47 residue of claudin-15 is not part of an intermolecular interface of a previously reported claudin-15 polymer model suggests that p. N48K indirectly affects oligomerization of claudin-10b by altering intramolecular interactions [20]. In the kidneys, an important proportion of Na+ reabsorption takes place within the ascending limb (TAL) of Henle’s loop through both transcellular and paracellular transport. The concerted action of apical and basolateral ion transporters generates a transepithelial voltage that drives the reabsorption of both Ca2+ and Mg2+. Within the kidney, claudin-10a is expressed exclusively in cortical proximal tubulus segments of the nephron, whereas claudin-10b is highly expressed in the medulla where 50% of the reabsorbed Na+ takes the paracellular route. Interestingly, ablation of the claudin-10b isoform in mouse kidney results in hypermagnesemia due to an increase in renal Mg2+ reabsorption. The absence of claudin-10b in the TAL results in elevated transepithelial resistance, an increased transepithelial voltage and consequently, in an increased driving force for the paracellular reabsorption of cations [15]. Additionally, absence of claudin-10b in TAL tubules resulted in increased paracellular permeability for divalent cations, possibly due to increased expression of claudin-16 and claudin-19. However, absence of claudin-10b or presence of claudin-10b N48K should not affect the claudin 16/19 pore directly since claudin-10b does not physically interact with either claudin-16 or claudin-19 [24]. The mouse model further revealed that the reduced paracellular reabsorption of Na+ in the TAL did not result in sodium loss. Given the observations in the mouse model and the elevated levels of Mg2+ in serum of our patients we hypothesized that claudin-10b p. N48K disturbs the cation permeability and in particular the paracellular Na+ transport. Indeed, we observed that the p. N48K mutation was associated with a reduced selectivity for Na+ over Cl- and with a preserved transepithelial resistance in MDCK-C7 cell layers. Still, claudin-10b N48K retained the ability to interact with the hydration shell of monovalent cations Li, Na, K, Rb and Cs as judged from the higher Eisenman sequence. Furthermore, the reduction in PNa/PCl for the mutated protein was time dependent in culture and similar to that for clones with a weak expression of claudin-10b WT. These observations suggest that the paracellular channels formed by claudin-10b p. N48K have a subnormal Na+ permeability and that the number of channels is greatly reduced. In addition, the observed increased proteolytic cleavage of mutated YFP- and CFP- claudin-10b and the staining of sweat glands in our patients suggest that the N48K mutation leads to increased degradation of claudin-10b that contributes to the reduced formation of Na+ channels. The active transcellular transport of Cl- in the sweat glands as well as in the TAL generates a transepitheleal voltage and a driving force for paracellular cation transport. In the kidney, the severely reduced Na+ conductivity caused by the N48K mutation is thus a likely contributing mechanism for the increased Mg2+ and Ca2+ reabsorption that results in kidney damage in our family as shown by reduced eGFR values, reduced levels of 25-hydroxy vitamin D and increased PTH levels. Furthermore, the increased reabsorption of Mg2+ that is associated with claudin-10b p. N48K is consistent with the kidney specific lack of claudin-10 in mice showing decreased Na+ permeability in the TAL accompanied by increased reabsorption of Mg2+. Interestingly, the increased Mg2+ mediated by claudin-10b p. N48K contrasts with the Mg2+ wasting observed in patients who carry claudin-16 or claudin-19 mutations. In combination, these findings highlight the complex renal mechanisms mediated by claudins to maintain cation homeostasis. Taken together, our data support that claudin-10b N48K causes a disturbed relative overall permeability for cations that results in increased reabsorption of Mg2+ and hypermagnesemia. In contrast to the kidney specific isoform claudin-10a, the claudin-10b isoform is ubiquitously expressed and presumably of importance for paracellular Na+ transport in multiple organs. In our family, affected members presented with heat intolerance due to anhidrosis, and alacrima as first symptoms in early childhood. Sweat production is mediated by IP3 acting as an intracellular messenger and the release of Ca2+ that opens Cl- channels to the glandular lumen and thus activates transcellular Cl- secretion energized by the basolateral Na+K+2Cl- symporter [25]. The resulting electrochemical gradient drives paracellular Na+ flux that, together with AQP5 mediated water flux leads to a net secretion of a largely isotonic NaCl solution into the secretory portions of the glandular lumen [26,27]. Our IHC analyses show claudin-10b staining in cells of the secretory portion of normal sweat glands with intense staining lining the canaliculi. This is consistent with a role for claudin-10b in the paracellular Na+ flux into the lumen. Compared to healthy individuals, the immunohistochemical staining of sweat glands in affected individuals showed a pronounced reduction of claudin-10b N48K in the cell peripheries and canaliculi. In sum, our data suggests that the likely mechanism behind abolished sweat production in our patients is a reduced incorporation of claudin-10b N48K into TJs mediated by altered trans- and cis-interaction properties accompanied by increased degradation of the mutated protein. Accordingly, the plausible explanation for alacrima and xerostomia is a reduced paracellular Na+ transport in the lacrimal and salivary glands, respectively, leading to a perturbed secretion of NaCl and water into the lumen of secretory portions [28]. In conclusion, we show that a mutation in the claudin-10b isoform results in abolished or reduced sweat production as well as a relative shift in cation resorption in the kidneys that leads to kidney damage. The affected organs contain epithelia in which transepithelial transport of NaCl is paralleled by paracellular transport of Na+ that is impaired by claudin-10b N48K. The combined findings expand our knowledge on the role of claudin-10b and the complex functional networks of claudins that may be useful in identifying the genetic basis for additional phenotypes caused by altered paracellular ion transport. The kindred examined in this study were referred to the Health Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Faisalabad, Pakistan, because of severe heat intolerance and anhidrosis. The patients were exposed to heat (45°C, 45% humidity) and perspiration was measured using starch-iodine together with healthy control individuals. It became evident that several family members also suffered from renal insufficiency. Blood and urine samples were obtained from available family members and punch skin biopsies were taken from two affected individuals. Consanguinity was ascertained over several generations and the affected individuals were related through five loops suggesting autosomal recessive inheritance (Fig 1A). The study was carried out under a protocol approved by the ethical committee of the National Institute of Biotechnology and Genetic Engineering (NIBGE), Faisalabad, Pakistan, and the Regional Ethical Committee of Uppsala, Sweden. Informed consent was obtained from all study participants or their legal guardians. SNP genotyping was performed on DNA samples from three affected family members (using the GeneChip Mapping 250K array (Affymetrix) according to the manufacturer’s protocol. Homozygosity mapping and sorting of genomic regions were performed as described previously with the dedicated software AutoSNPa [29]. Two point LOD scores were calculated for microsatellite markers using the MLINK program of LINKAGE computer package [30]. A custom enrichment design covering 6M base pairs (hg19 chr13: 93278935–99228090, NimbleGen Sequence Capture Microarrays, Roche) was used to enrich for the linked region on chromosome 13. Sequencing of the enriched region was performed using the Illumina HiSeq system and variant detection was performed using v2. 1 of the LifeScope Software (Life Technologies). Prediction of possible impact on protein function was performed using PolyPhen-2 analysis [31]. Variant allele frequencies were assessed using the Exome Aggregation Consortium (ExAC) database (Cambridge, MA (URL: http: //exac. broadinstitute. org) accessed June 2016). Exon 1 of isoform b of the CLDN10 gene was analyzed for the identified variant by bi-directional sequencing of genomic DNA from all available family-members using the primers: ATC AAG GAA GGA GGG CTG AG (sense) and: AGA CGC CCG TGG AGT CGG TA (antisense). Histological analysis of skin biopsies was performed after hematoxylin and eosin (H&E) staining. Immunofluorescence staining of claudin-1, claudin-3, claudin-10, occludin and ZO-1 α (Invitrogen, San Francisco, California rb-α-claudin-1, rb-α-claudin-3, m-α-occludin, rb-α-ZO-1, m-α-claudin-10) were added in blocking solution at a dilution of 1: 100 and sections were incubated over night at 4°C. Secondary antibodies (Jackson ImmunoResearch, Newmarket, UK, Cy2 Fab gt-α-m-IgG, Cy5 Fab gt-α-rb-IgG) were applied at a dilution of 1: 600 (at least 30 min at room temperature). Fluorescence images were obtained with a LSM (Zeiss LSM780, Jena, Germany). HEK293 cells were transiently or stably transfected and MDCK-C7 cells were stably transfected with WT or mutated CLDN10b vectors containing a puromycin or neomycin resistance using PEI (Polyethylenimine, Sigma-Aldrich). Transiently transfected HEK293 cells were used for confocal laser microscopy after 24 or 48 hours. For stable transfection, MDCK-C7 cells were treated with puromycin (10 μg/ml, Sigma-Aldrich) or G418 (1000 μg/ml, Biochrom, Berlin, Germany), respectively. After 2 weeks, G418 resistant clones were picked with cloning-cylinders and the cells were further cultured with 600 μg/ml G418. Puromycin-resistant MDCK-C7 cells were pooled after 7 days, grown for further 10 days, seeded onto Millicell cell culture inserts (pore size 0. 45 μm, effective area 0. 6 cm2; Millicell-HA) and grown for further 5 days before they were mounted in an Ussing chamber. In contrast, HEK293 cells were treated with 600 U/ml G418 (Biochrom, Berlin, Germany) for 4 weeks and the resistant cells were further cultured in the presence of 150 U/ml G418. Cells were trypsinized to stimulate proliferation. On the following day, cells were trypsinated again and 104 cells were seeded into 100 μl BD Matrigel (BD Biosciences, Heidelberg, Germany) per Lab Tek well (Lab Tek II Chambered Coverglass; Nunc). After 5 days, the cysts were fixed with 4% PFA in PBS (1 hour shaking at room temperature). Subsequently, the cysts were treated with a permeabilization solution (0. 5% Triton X-100,0. 25% Saponin and 25 mM Glycine, pH 8. 0, in PBS; 6 hours shaking at room temperature). For immunostaining of claudin-10, the cysts were incubated with rabbit anti-Cldn 10 (1: 150, Assay bio Tech, Sunnyvale, CA, USA) overnight at 4°C. Cysts staining for diameter determination was achieved by incubation with Alexa Fluor 594 Phalloidin (1: 450, Invitrogen) and DAPI (1: 500, Roche) overnight at 4°C. All cysts were washed with permeabilization solution (6 hours shaking at room temperature) and the solution was changed every half hour. Cysts intended for diameter determination were subsequently covered with ProTaqs Mount Fluor (Biocyc, Luckenwalde, Germany). Dyed Cldn10-cysts were further incubated with Phalloidin-Dy-647P1 (1: 1000, Dyomics GmbH, Jena, Germany), anti-rabbit IgG Alexa Fluor 488 (1: 400, Molecular Probes), and DAPI (1: 500) overnight at 4°C. Dyed Cldn10-cysts were washed with permeabilization solution (6 hours shaking at room temperature; solution changed every half hour). The cysts were subsequently covered with ProTaqs Mount Fluor. Fluorescence images were obtained with a LSM (Zeiss LSM780, Jena, Germany). Z-stack images of cysts stained with DAPI and phalloidin were recorded by confocal laser scanning microscopy (Zeiss LSM780, x20). Within each stack, the layer with the largest diameter of each individual cyst was identified, the diameter marked with a straight line and the length of each line determined, using the Zeiss ZEN software. Mean diameters of each stack were calculated and averaged to obtain mean ± SEM for each cell clone. For size distribution histograms, individual cyst diameters were sorted into size intervals (width 15 μm), and the relative frequency was calculated by the ratio of cyst number per interval divided by total number of cysts of each cell clone. For visualization of the shift in the distribution, normal distributions were calculated for each histogram. For freeze fracturing, HEK293 cells were washed twice with PBS with MgCl2 and CaCl2 (Sigma-Aldrich), fixed with phosphate-buffered 2. 5% glutaraldehyde (Sigma-Aldrich) for 2 hours at room temperature. Cells were washed with PBS and stored in 0. 1% glutaraldehyde in PBS at 4°C. Electron microscopy was performed similar as described before [32]. For claudin trans-interaction analysis, HEK293 cells, a cell line devoid of TJs, was used. As a measure for claudin-10b trans-interaction, enrichment of the transfected YFP-/CFP-claudin-10b constructs at contacts between two claudin-expressing cells was analyzed similar as described previously [5,22]. Briefly, one or two days after transient transfection or replating stable lines, cells were transferred to Hanks' Balanced Salt Solution (HBSS) pH 7. 4 with Ca2+, Mg2+, glucose, sodium bicarbonate, without phenol red (Thermo Scientific) and examined with a LSM 780 system (Carl Zeiss, Jena, Germany). Randomly chosen cells were analyzed using the ZEN software (Carl Zeiss, Jena, Germany) and YFP intensity profiles of confocal images. The enrichment factor (EF) was calculated as the intensity of the YFP signal at contact between two claudin-expressing cells divided by the sum of the intensities at contact between these two claudin-expressing cells and neighboring non-expressing cells. EF >1 indicates enrichment [5]. For analysis of the cis-interaction between claudin-10b constructs along the plasma membrane of one cell, FRET analysis was performed. HEK293 were co-transfected with two plasmids encoding a CFP-claudin-10b (mutated or WT) and an YFP-claudin-10b (mutated or WT) fusion protein, respectively and analyzed at cell-cell contacts as described previously [19]. Before and after acceptor bleaching, CFP and YFP intensity was detected. Since the FRET efficiency EF depends on the acceptor/donor ratio, EF was plotted as a function of YFP/CFP for each acceptor/donor pair [33,34]. Signals were calibrated using an YFP-CFP tandem protein, so that equal YFP and CFP intensities denote equal amounts of these proteins. Curve fitting and data analysis to obtain the average FRET efficiency were carried out as described previously [19]. HEK293 cells were washed with ice cold PBS (with Ca2+ and Mg2+), lysed on ice with 1% (v/v) Trition X-100 in PBS containing EDTA-free protease inhibitor cocktail (Roche, Mannheim, Germany) and centrifuged at 10. 000 x g (5 min, 4°C). Supernatants were mixed with Laemmli buffer, boiled and loaded on 10% SDS gels, transferred on PVDF membranes. The CFP/YFP-fusion proteins were detected using mouse anti-GFP (JL-8, Takara Clontech, Saint-Germain-en-Laye, France) and HRP-coupled (Jackson Immunoresearch) anti-mouse antibodies. The protomer model of human claudin-10b was generated using the crystal structure of murine claudin-15 (PDB ID: 4P79) as template and Swissmodel (http: //swissmodel. expasy. org) [6,20,35,36]. Model quality was estimated employing the QMEAN server (http: //swissmodel. expasy. org/qmean) [37]. In addition, Modeller was used and resulted in a similar model [38]. Images of the structures and models were generated using PyMOL (version 1. 5. 0. 4 Schrödinger, LLC). Cell culture inserts were mounted into Ussing chambers and the water-jacketed gas lifts on both sides were filled with 10 ml of a bath solution containing 119 mM NaCl, 21 mM NaHCO3,5. 4 mM KCl, 1. 2 mM CaCl2,1 mM MgSO4,3 mM HEPES, and 10 mM D (+) -glucose. The solution was constantly bubbled with 95% O2 and 5% CO2, to ensure a pH value of 7. 4 at 37°C. After equilibration, 5 ml of the apical or basolateral solution were replaced by a bath solution which, instead of 119 mM NaCl, contained 238 mM mannitol. The resulting voltage step (`dilution potential' ) was converted into the permeability ratio, PNa/PCl, as described previously [14]. Eisenman sequences were determined analogously by using solutions containing 119 mM XCl (LiCl, KCl, RbCl, or CsCl, respectively), instead of NaCl. PX/PNa was calculated from the resulting ‘bi-ionic potentials’ and the PNa/PCl obtaind from the dilution potential measurements. Complete equations are described by Günzel et al [14].

Claudins are tight junction proteins forming paracellular barriers that are critical for normal development and homeostasis. The tissue specific paracellular barrier properties are determined by the protein composition of tight junctions that regulates the permeability of solutes and water between different compartments of the body. We show, for the first time, that a mutation in claudin-10b, forming paracellular cation channels in different tissues, causes perturbed Na+ selectivity through altered tight junction formation and function as well as increased degradation of the protein. The mutation is associated with the inability to sweat (anhidrosis) and heat intolerance as well as abnormal cation reabsorption, hypermagnesemia and kidney damage. Our combined findings show that the claudin-10b-mediated paracellular Na+ transport is required for normal sweat production and for the regulation of cation homeostasis in the kidneys.

Abstract Introduction Results Discussion Materials and methods

skin cell physiology medicine and health sciences sweat glands integumentary system membrane staining ions junctional complexes permeability tight junctions membrane proteins materials science kidneys cations cellular structures and organelles physical chemistry research and analysis methods specimen preparation and treatment staining chemistry exocrine glands cell membranes anatomy cell biology biology and life sciences renal system physical sciences material properties

2017

Altered paracellular cation permeability due to a rare CLDN10B variant causes anhidrosis and kidney damage

This paper addresses the problem of providing mathematical conditions that allow one to ensure that biological networks, such as transcriptional systems, can be globally entrained to external periodic inputs. Despite appearing obvious at first, this is by no means a generic property of nonlinear dynamical systems. Through the use of contraction theory, a powerful tool from dynamical systems theory, it is shown that certain systems driven by external periodic signals have the property that all their solutions converge to a fixed limit cycle. General results are proved, and the properties are verified in the specific cases of models of transcriptional systems as well as constructs of interest in synthetic biology. A self-contained exposition of all needed results is given in the paper. We consider in this paper systems of ordinary differential equations, generally time-dependent: (1) defined for and, where is a subset of. It will be assumed that is differentiable on, and that, as well as the Jacobian of with respect to, denoted as, are both continuous in. In applications of the theory, it is often the case that will be a closed set, for example given by non-negativity constraints on variables as well as linear equalities representing mass-conservation laws. For a non-open set, differentiability in means that the vector field can be extended as a differentiable function to some open set which includes, and the continuity hypotheses with respect to hold on this open set. We denote by the value of the solution at time of the differential equation (1) with initial value. It is implicit in the notation that (“forward invariance” of the state set). This solution is in principle defined only on some interval, but we will assume that is defined for all. Conditions which guarantee such a “forward-completeness” property are often satisfied in biological applications, for example whenever the set is closed and bounded, or whenever the vector field is bounded. (See Appendix C in [21] for more discussion, as well as [22] for a characterization of the forward completeness property.) Under the stated assumptions, the function is jointly differentiable in all its arguments (this is a standard fact on well-posedness of differential equations, see for example Appendix C in [21]). We recall (see for instance [23]) that, given a vector norm on Euclidean space (), with its induced matrix norm, the associated matrix measure is defined as the directional derivative of the matrix norm, that is, For example, if is the standard Euclidean 2-norm, then is the maximum eigenvalue of the symmetric part of. As we shall see, however, different norms will be useful for our applications. Matrix measures are also known as “logarithmic norms”, a concept independently introduced by Germund Dahlquist and Sergei Lozinskii in 1959, [24], [25]. The limit is known to exist, and the convergence is monotonic, see [24], [26]. We will say that system (1) is infinitesimally contracting on a convex set if there exists some norm in, with associated matrix measure such that, for some constant, (2) Let us discuss informally (rigorous proofs are given later) the motivation for this concept. Since by assumption is continuously differentiable, the following exact differential relation can be obtained from (1): (3) where, as before, denotes the Jacobian of the vector field, as a function of and, and where denotes a small change in states and “” means, evaluated along a trajectory. (In mechanics, as in [27], is called “virtual displacement”, and formally it may be thought of as a linear tangent differential form, differentiable with respect to time.) Consider now two neighboring trajectories of (1), evolving in, and the virtual displacements between them. Note that (3) can be thought of as a linear time-varying dynamical system of the form: once that is thought of as a fixed function of time. Hence, an upper bound for the magnitude of its solutions can be obtained by means of the Coppel inequality [28], yielding: (4) where is the matrix measure of the system Jacobian induced by the norm being considered on the states and. Using (4) and (2), we have thatThus, trajectories starting from infinitesimally close initial conditions converge exponentially towards each other. In what follows we will refer to as contraction (or convergence) rate. The key theoretical result about contracting systems links infinitesimal and global contractivity, and is stated below. This result can be traced, under different technical assumptions, to e. g. [6], [13], [12], [11]. Theorem 1. Suppose that is a convex subset of and that is infinitesimally contracting with contraction rate. Then, for every two solutions and of (1), it holds that: (5) In other words, infinitesimal contractivity implies global contractivity. In the Materials and Methods section, we provide a self-contained proof of Theorem 1. In fact, the result is shown there in a generalized form, in which convexity is replaced by a weaker constraint on the geometry of the space. In actual applications, often one is given a system which depends implicitly on the time, , by means of a continuous function, i. e. systems dynamics are represented by. In this case, (where is some subset of), represents an external input. It is important to observe that the contractivity property does not require any prior information about this external input. In fact, since does not depend on the system state variables, when checking the property, it may be viewed as a constant parameter, . Thus, if contractivity of holds uniformly, then it will also hold for. Given a number, we will say that system (1) is -periodic if it holds thatNotice that the system is -periodic, if the external input, , is itself a periodic function of period. The following is the basic theoretical result about periodic orbits that will be used in the paper. A proof may be found in [6], Sec. 3. 7. vi. Theorem 2. Suppose that: Then, there is a unique periodic solution of (1) of period and, for every solution, it holds that as. In the Materials and Methods section of this paper, we provide a self-contained proof of Theorem 2, in a generalized form which does not require convexity. As a first example to illustrate the application of the concepts introduced so far, we choose a simple bimolecular reaction, in which a molecule of and one of can reversibly combine to produce a molecule of. This system can be modeled by the following set of differential equations: (6) where we are using to denote the concentration of and so forth. The system evolves in the positive orthant of. Solutions satisfy (stoichiometry) constraints: (7) for some constants and. We will assume that one or both of the “kinetic constants” are time-varying, with period. Such a situation arises when the' s depend on concentrations of additional enzymes, which are available in large amounts compared to the concentrations of, but whose concentrations are periodically varying. The only assumption will be that and for all. Because of the conservation laws (7), we may restrict our study to the equation for. Once that all solutions of this equation are shown to globally converge to a periodic orbit, the same will follow for and. We have that: (8) Because and, this system is studied on the subset of defined by. The equation can be rewritten as: (9) Differentiation with respect to of the right-hand side in the above system yields this () Jacobian: (10) Since we know that and, it follows thatfor. Using any norm (this example is in dimension one) we have that. So (6) is contracting and, by means of Theorem 2, solutions will globally converge to a unique solution of period (notice that such a solution depends on system parameters). Figure 1 shows the behavior of the dynamical system (9), using two different values of. Notice that the asymptotic behavior of the system depends on the particular choice of the biochemical parameters being used. Furthermore, it is worth noticing here that the higher the value of, the faster will be the convergence to the attractor. We study a general externally-driven transcriptional module. We assume that the rate of production of a transcription factor is proportional to the value of a time dependent input function, and is subject to degradation and/or dilution at a linear rate. (Later, we generalize the model to also allow nonlinear degradation as well.) The signal might be an external input, or it might represent the concentration of an enzyme or of a second messenger that activates. In turn, drives a downstream transcriptional module by binding to a promoter (or substrate), denoted by with concentration. The binding reaction of with is reversible and given by: where is the complex protein-promoter, and the binding and dissociation rates are and respectively. As the promoter is not subject to decay, its total concentration, , is conserved, so that the following conservation relation holds: (11) We wish to study the behavior of solutions of the system that couples and, and specifically to show that, when the input is periodic with period, this coupled system has the property that all solutions converge to some globally attracting limit cycle whose period is also. Such transcriptional modules are ubiquitous in biology, natural as well as synthetic, and their behavior was recently studied in [29] in the context of “retroactivity” (impedance or load) effects. If we think of as the concentration of a protein that is a transcription factor for, and we ignore fast mRNA dynamics, such a system can be schematically represented as in Figure 2, which is adapted from [29]. Notice that here does not need to be the concentration of a transcriptional activator of for our results to hold. The results will be valid for any mathematical model for the concentrations, , of and, of (the concentration of is conserved) of the form: (12) An objective in this paper is, thus, to show that, when is a periodic input, all solutions of system (12) converge to a (unique) limit cycle (Figure 3). The key tool in this analysis is to show that uniform contractivity holds. Since in this example the input appears additively, uniform contractivity is simply the requirement that the unforced system () is contractive. Thus, the main step will be to establish the following technical result, see the Material and Methods: Proposition 1. The systemwhere (13) for all, and, , , and are arbitrary positive constants, is contracting. Appealing to Theorem 2, we then have the following immediate Corollary: Proposition 2. For any given nonnegative periodic input of period, all solutions of system (12) converge exponentially to a periodic solution of period. In the following sections, we introduce a matrix measure that will help establish contractivity, and we prove Proposition 1. We will also discuss several extensions of this result, allowing the consideration of multiple driven subsystems as well as more general nonlinear systems with a similar structure. (A graphical algorithm to prove contraction of generic networks of nonlinear systems can also be found in [18] where this transcriptional module is also studied.) We will use Theorem 2. The Jacobian matrix to be studied is: (14) As matrix measure, we will use the measure induced by the vector norm, where is a suitable nonsingular matrix. More specifically, we will pick diagonal: (15) where and are two positive numbers to be appropriately chosen depending on the parameters defining the system. It follows from general facts about matrix measures that (16) where is the measure associated to the norm and is explicitly given by the following formula: (17) Observe that, if the entries of are negative, then asking that amounts to a column diagonal dominance condition. (The above formula is for real matrices. If complex matrices would be considered, then the term should be replaced by its real part.) Thus, the first step in computing is to calculate: (18) Using (17), we obtain: (19) Note that we are not interested in calculating the exact value for the above measure, but just in ensuring that it is negative. To guarantee that, the following two conditions must hold: (20) (21) Thus, the problem becomes that of checking if there exists an appropriate range of values for, that satisfy (20) and (21) simultaneously. The left hand side of (21) can be written as: (22) which is negative if and only if. In particular, in this case we have: The idea is now to ensure negativity of (20) by using appropriate values for and which fulfill the above constraint. Recall that the term because of the choice of the state space (this quantity represents a concentration). Thus, the left hand side of (20) becomes (23) The next step is to choose appropriately and (without violating the constraint). Imposing, , (23) becomes (24) Then, we have to choose an appropriate value for in order to make the above quantity uniformly negative. In particular, (24) is uniformly negative if and only if (25) We can now choosewith. In this case, (24) becomesThus, choosing and, with, we have. Furthermore, the contraction rate, is given by: Notice that depends on both system parameters and on the elements, , i. e. it depends on the particular metric chosen to prove contraction. This completes the proof of the Proposition. In this Section, we discuss various generalizations that use the same proof technique. We introduced above a methodology for checking if a given transcriptional module can be entrained to some periodic input. The aim of this section is to show that our methodology can serve as an effective tool for designing synthetic biological circuits that are entrained to some desired external input. In particular, we will consider the synthetic biological oscillator known as the Repressilator [31], for which an additional coupling module has been recently proposed in [32]. A numerical investigation of the synchronization of a network of non-identical Repressilators was independently reported in [33]. We will show that our results can be used to isolate a set of biochemical parameters for which one can guarantee the entrainment to any external periodic signal of this synthetic biological circuit. In what follows, we will use the equations presented in [32] to model the Repressilator and the additional coupling model. We will make use of the following definition: Definition 1. Let be any positive real number. A subset is -reachable if, for any two points and in there is some continuously differentiable curve such that: For convex sets, we may pick, so and we can take. Thus, convex sets are -reachable, and it is easy to show that the converse holds as well. Notice that a set is -reachable for some if and only if the length of the geodesic (smooth) path (parametrized by arc length), connecting any two points and in, is bounded by some multiple of the Euclidean norm, . Indeed, re-parametrizing to a path defined on, we have: Since in finite dimensional spaces all the norms are equivalent, then it is possible to obtain a suitable for Definition 1. Remark 1. The notion of -reachable set is weaker than that of convex set. Nonetheless, in Theorem 5, we will prove that trajectories of a smooth system, evolving on a -reachable set, converge towards each other, even if is not convex. This additional generality allows one to establish contracting behavior for systems evolving on phase spaces exhibiting “obstacles”, as are frequently encountered in path-planning problems, for example. A mathematical example of a set with obstacles follows. Example 1. Consider the two dimensional set, , defined by the following constraints: Clearly, is a non-convex subset of. We claim that is -reachable, for any positive real number. Indeed, given any two points and in, there are two possibilities: either the segment connecting and is in, or it intersects the unit circle. In the first case, we can simply pick the segment as a curve (). In the second case, one can consider a straight segment that is modified by taking the shortest perimeter route around the circle; the length of the perimeter path is at most times the length of the omitted segment. (In order to obtain a differentiable, instead of merely a piecewise-differentiable, path, an arbitrarily small increase in is needed.) We now prove the main result on contracting systems, i. e. Theorem 1, under the hypotheses that the set, i. e. the set on which the system evolves, is -reachable. Theorem 5. Suppose that is a -reachable subset of and that is infinitesimally contracting with contraction rate. Then, for every two solutions and it holds that: (68) Proof. Given any two points and in, pick a smooth curve, such that and. Let, that is, the solution of system (1) rooted in, . Since and are continuously differentiable, also is continuously differentiable in both arguments. We defineIt follows thatNow, so, we have: (69) where. Using Coppel' s inequality [28], yields (70), , and. Notice the Fundamental Theorem of Calculus, we can writeHence, we obtainNow, using (70), the above inequality becomes: The Theorem is then proved. Proof of Theorem 1. The proof follows trivially from Theorem 5, after having noticed that in the convex case, we may assume. In this Section we assume that the vector field is -periodic and prove Theorem 2. Before starting with the proof of Theorem 2 we make the following: Remark 2. Periodicity implies that the initial time is only relevant modulo. More precisely: (71) Indeed, let, , and consider the function, for. So, where the last equality follows by -periodicity of. Since, it follows by uniqueness of solutions that, which is (71). As a corollary, we also have that (72) where the first equality follows from the semigroup property of solutions (see e. g. [21]), and the second one from (71) applied to instead of. Define nowwhere. The following Lemma will be useful in what follows. Lemma 1. for all and. Proof. We will prove the Lemma by recursion. In particular, the statement is true by definition when. Inductively, assuming it true for, we have: as wanted. Theorem 6. Suppose that: Then, there is an unique periodic solution of (1) having period. Furthermore, every solution, such that, converges to, i. e. as. Proof. Observe that is a contraction with factor: for all, as a consequence of Theorem 5. The set is a closed subset of and hence complete as a metric space with respect to the distance induced by the norm being considered. Thus, by the contraction mapping theorem, there is a (unique) fixed point of. Let. Since, is a periodic orbit of period. Moreover, again by Theorem 5, we have that. Uniqueness is clear, since two different periodic orbits would be disjoint compact subsets, and hence at positive distance from each other, contradicting convergence. This completes the proof. Proof of Theorem 2. It will suffice to note that the assumption in Theorem 6 is automatically satisfied when the set is convex (i. e.) and the system is infinitesimally contracting. Notice that, even in the non-convex case, the assumption can be ignored, if we are willing to assert only the existence (and global convergence to) a unique periodic orbit, with some period for some integer. Indeed, the vector field is also -periodic for any integer. Picking large enough so that, we have the conclusion that such an orbit exists, applying Theorem 6. In order to show that cascades of contracting systems remain contracting, it is enough to show this, inductively, for a cascade of two systems. Consider a system of the following form: where and for all (and are two -reachable sets). We write the Jacobian of with respect to as, the Jacobian of with respect to as, and the Jacobian of with respect to as, We assume the following: We claim that, under these assumptions, the complete system is infinitesimally contracting. More precisely, pick any two positive numbers and such thatand letWe will show that, where is the full Jacobian: (73) with respect to the matrix measure induced by the following norm in: Sincefor all and, we have that, for all and: where from now on we drop subscripts for norms. Pick now any and a unit vector (which depends on) such that. Such a vector exists by the definition of induced matrix norm, and we note that, by the definition of the norm in the product space. Therefore: where the last inequality is a consequence of the fact that for any nonnegative numbers with (convex combination of the' s). Now taking limits as, we conclude thatas desired. The general principle that we apply to prove entrainment of a population of Repressilators is as follows. Assume that the cascade system (74) with being an exogenous input, satisfies the contractivity assumptions of the above Section. Then, consider the interconnection of identical systems which interact through the variable as follows: (75) Suppose that is a solution of (75) defined for all, for some input. Then, we have the synchronization condition: , as. Indeed, we only need to observe that every pair is a solution of (74) with the same input Furthermore, if is a -periodic function, the interconnected dynamical systems synchronize onto a -periodic trajectory. The above principle can be immediately applied to prove that synchronization onto a -periodic orbit is attained for the Repressilator circuits composing network (67) (see also [19]). Specifically, let and; we have that is a solution of (67). We notice that any pair is a solution of the following cascade system (76) Thus, as shown above, contraction of (76) implies synchronization of (67). Differentiation of (76) yields the Jacobian matrix (77) where and denote the partial derivatives of decreasing and increasing Hill functions with respect to the state variable of interest and, . Note that the Jacobian matrix has the structure of a cascade, i. e. with: , . Thus, to prove contraction of the virtual system (76) it suffices to prove that there exist two matrix measures, and such that: where. Clearly, since is a positive real parameter, the second condition above is satisfied (with being any matrix measure). Now, notice that matrix has the same form as the Jacobian matrix of the Repressilator circuit (56). Hence, if the parameters of the Repressilator are chosen so that they satisfy (66), then there exist a set of positive real parameters, , such that (that is, the first condition above is also satisfied with). Thus, we can conclude that (76) is contracting. Furthermore, all the trajectories of the virtual system converge towards a -periodic solution (see Theorem 6). This in turn implies that all the trajectories of network (67) converge towards the same -periodic solution. That is, all the nodes of (67) synchronize onto a periodic orbit of period. In [5] there is given an example of a system with the following property: when the external signal is constant, all solutions converge to a steady state; however, when, solutions become chaotic. (Obviously, this system is not contracting.) The equations are as follows: where and. Figure 14 shows typical solutions of this system with a periodic and constant input respectively. The function “rand” was used in MATLAB to produce random values in the range. We have presented a systematic methodology to derive conditions for various types of biochemical systems to be globally entrained to periodic inputs. For concreteness, we focused mainly on transcriptional systems, which constitute basic building blocks for more complex biochemical systems. However, the results that we obtained are of more generality. To illustrate this generality, and to emphasize the use of our techniques in synthetic biology design, we discussed as well the entrainment of a Repressilator circuit in a parameter regime in which endogenous oscillations to not occur, as well as the synchronization of a network of Repressilators. These latter examples serve to illustrate the power of the tools even when a large amount of feedback is present. Our key tool is the use of contraction theory, which we believe should be recognized as an important component of the “toolkit” of systems biology. In all cases conditions are derived by proving that the module of interest is contracting under appropriate generic assumptions on its parameters. A surprising fact is that, for these applications, and contrary to many engineering applications, norms other than Euclidean, and associated matrix measures, must be considered. Of course, more than one norm may be appropriate for a given problem: for example we can pick different' s in our weighted norms, and each such choice gives rise to a different estimate of convergence rates. This is entirely analogous to the use of Lyapunov functions in classical stability analysis: different Lyapunov functions provide different estimates. Ultimately, and as with any other method for the analysis of nonlinear systems, such as the classical tool of Lyapunov functions, finding the “right” norm is more of an art than a science. A substantial amount of trial and error, intuition, and numerical experimentation may be needed in order to come up with an appropriate norm, and experience with a set of already-studied systems (such as the ones studied here) should prove invaluable in guiding the search.

The activities of living organisms are governed by complex sets of biochemical reactions. Often, entrainment to certain external signals helps control the timing and sequencing of reactions. An important open problem is to understand the onset of entrainment and under what conditions it can be ensured in the presence of uncertainties, noise, and environmental variations. In this paper, we focus mainly on transcriptional systems, modeled by Ordinary Differential Equations. These are basic building blocks for more complex biochemical systems. However, the results that we obtain are of more generality. To illustrate this generality, and to emphasize the use of our techniques in synthetic biology, we discuss the entrainment of a Repressilator circuit and the synchronization of a network of Repressilators. We answer the following two questions: 1) What are the dynamical mechanisms that ensure the entrainment to periodic inputs in transcriptional modules? 2) Starting from natural systems, what properties can be used to design novel synthetic biological circuits that can be entrained? For some biological systems which are always “in contact” with a continuously changing environment, entrainment may be a “desired” property. Thus, answering the above two questions is of fundamental importance. While entrainment may appear obvious at first thought, it is not a generic property of nonlinear dynamical systems. The main result of our paper shows that, even if the transcriptional modules are modeled by nonlinear ODEs, they can be entrained by any (positive) periodic signal. Surprisingly, such a property is preserved if the system parameters are varied: entrainment is obtained independently of the particular biochemical conditions. We prove that combinations of the above transcriptional module also show the same property. Finally, we show how the developed tools can be applied to design synthetic biochemical systems guaranteed to exhibit entrainment.

Abstract Introduction Results Materials and Methods Discussion

mathematics computational biology/synthetic biology computational biology/systems biology

2010

Global Entrainment of Transcriptional Systems to Periodic Inputs

Influenza viruses (IV) cause pneumonia in humans with progression to lung failure and fatal outcome. Dysregulated release of cytokines including type I interferons (IFNs) has been attributed a crucial role in immune-mediated pulmonary injury during severe IV infection. Using ex vivo and in vivo IV infection models, we demonstrate that alveolar macrophage (AM) -expressed IFN-β significantly contributes to IV-induced alveolar epithelial cell (AEC) injury by autocrine induction of the pro-apoptotic factor TNF-related apoptosis-inducing ligand (TRAIL). Of note, TRAIL was highly upregulated in and released from AM of patients with pandemic H1N1 IV-induced acute lung injury. Elucidating the cell-specific underlying signalling pathways revealed that IV infection induced IFN-β release in AM in a protein kinase R- (PKR-) and NF-κB-dependent way. Bone marrow chimeric mice lacking these signalling mediators in resident and lung-recruited AM and mice subjected to alveolar neutralization of IFN-β and TRAIL displayed reduced alveolar epithelial cell apoptosis and attenuated lung injury during severe IV pneumonia. Together, we demonstrate that macrophage-released type I IFNs, apart from their well-known anti-viral properties, contribute to IV-induced AEC damage and lung injury by autocrine induction of the pro-apoptotic factor TRAIL. Our data suggest that therapeutic targeting of the macrophage IFN-β-TRAIL axis might represent a promising strategy to attenuate IV-induced acute lung injury. Influenza viruses (IV) can cause primary viral pneumonia in humans with rapid progression to lung failure and fatal outcome [1]. Histopathologic and clinical features of IV-induced lung injury in humans resemble those of other forms of ARDS (acute respiratory distress syndrome), characterized by apoptotic and necrotic alveolar epithelial cell (AEC) damage, loss of alveolar barrier function and severe hypoxemia [1]–[3]. Intrapulmonary expression of pro-apoptotic ligands such as Fas ligand (FasL) or TRAIL has recently been ascribed a key role in induction of epithelial damage during progression to ARDS [4]–[6]. As soon as the infection spreads from the upper to the lower respiratory tract, AEC and resident AM become primary targets for IV infection [3], [7], [8]. Severe IV pneumonia is characterized by an exaggerated alveolar expression of inflammatory mediators and a dysbalance of pro- and anti-inflammatory cytokines causing pulmonary injury, and AM have been attributed a substantial role herein [9]–[11]. However, the key players and molecular mechanisms of the cellular cross-talk during induction of epithelial injury in severe IV pneumonia and the inflammatory mediators involved are still poorly defined. As therapeutic modulation of these pathways offers the potential advantage of exerting less-selective pressure on viral populations than established antiviral drugs, a detailed understanding of the cell-specific inflammatory responses and subsequently induced pathways of epithelial injury in the context of IV infection is of particular importance. Type I IFN are rapidly expressed in various myeloid and parenchymal cell types following viral infection of the lung [12], [13]. They engage a unique heterodimeric IFN-α receptor (IFNAR) in an autocrine or paracrine way to induce the transcription of various interferon-stimulated genes such as the double-stranded (ds) RNA-dependent protein kinase R (PKR) via the Jak/STAT pathway to restrict viral spread [14], [15]. Moreover, type I IFN signalling was recently ascribed a role in functional differentiation of myeloid cell subsets during IV infection, including monocytes, CD8+ T cells, neutrophils and dendritic cells [16]–[18]. Apart from these well-known anti-viral effects, evidence also hints at pathogenic roles for type I IFNs during viral infection. Plenty of pro-inflammatory cytokines and chemokines are induced downstream of IFNAR signalling which may contribute to IV pathogenesis, and disease onset correlates directly with local respiratory production of IFN-α in humans [19]. Thus, type I IFNs may play dual roles in viral clearance and tissue-damaging inflammation. In the present study we elucidate a previously undefined role of AM-expressed IFN-β in inducing apoptotic AEC damage by expression and release of the pro-apoptotic factor TRAIL upon ex vivo and in vivo IV infection. Detailed study of the signalling pathways involved in this cellular cross-talk revealed that macrophage IFN-β was released upon IV infection in a PKR- and NF-κB-dependent way and induced autocrine, IFNAR-dependent induction of TRAIL. Macrophage-specific blockade of any of these signalling steps abrogated TRAIL expression in resident and lung-recruited AM, reduced AEC apoptosis and attenuated IV-induced lung injury. These findings highlight type I IFNs as key players in IV-induced pathogenesis and provide new insights into the mechanisms of inflammatory macrophage-epithelial cross-talk in IV-induced lung injury with potential therapeutic implications. To evaluate the role of type I IFN in IV-induced AEC damage and lung injury, we first quantified type I IFN levels in bronchoalveolar lavage fluid (BALF) during the time course of A/PR8 infection in wildtype (wt) mice. As shown in Fig. 1A (left), IFN-α and -β were both released into the air spaces in response to A/PR8 infection. Corresponding A/PR8 titers in BALF are provided (Fig. 1A, right). To test whether endogenous type I IFNs contributed to the severe lung injury observed in IV-infected mice we analysed AEC injury after alveolar deposition of a neutralizing anti-IFN antibody (Ab). As demonstrated in Fig. 1B, AEC apoptosis was significantly reduced to similar levels when either IFN-α or IFN-β or both were blocked at d5 pi. In turn, alveolar injury at d7 pi was further increased when recombinant IFN-β was applied to the airways of A/PR8-infected mice at d5 pi, as shown in Fig. 1C (left; AEC apoptosis quantified by AnnexinV binding to AEC; right, alveolar protein leakage). Of note, lung viral loads at d7 pi remained unchanged regardless of whether endogenous IFN-β was blocked or recombinant IFN-β was applied intratracheally at d5 pi (data not shown). It is well established that AEC and AM are primary targets for different IV strains in the alveolar compartment of the lung [20]. To determine the predominant source of IV-induced alveolar IFN-β, we next infected primary murine AEC and AM ex vivo with A/PR8 and quantified peak IFN-β levels in the culture supernatants. Of note, both AEC and AM were infected by A/PR8 at different MOIs ex vivo as determined by immunolabelling of viral nucleoprotein (Fig. S1A). In contrast to AEC, A/PR8 infection of AM was found to be abortive as we could not detect infectious virions in the supernatants of AM cultures. As demonstrated in Fig. 1D, murine AM nevertheless released significantly higher IFN-β levels within 24 h compared to murine AEC in response to infection with live, but not heat-inactivated A/PR8, and this was similarly found when we compared human AM and AEC (Fig. S1B). IFN-β expression robustly occurred in ex vivo IV-infected murine AM in a dose- and IV strain-dependent manner (Fig. 1E, S2A), and likewise in human AM (Fig. S2B). To further dissect the mechanisms by which macrophage IFN-β increased AEC apoptosis, we treated non-infected AEC with IFN-β ex vivo and determined AEC apoptosis induction. Interestingly, recombinant IFN-β did not increase apoptotic damage in infected mono-cultured AEC, however, AEC apoptosis was highly increased upon IFN-β treatment when AEC were co-cultured with AM (Fig. 1F). Together, these data indicate that macrophage IFN-β induces apoptotic AEC damage upon IV infection and suggest a second, IFN-β-inducible pro-apoptotic macrophage mediator to be involved. Several pro-apoptotic factors are known to induce AEC apoptosis during pathogen-related acute lung injury, among which TRAIL and FasL have been reported to be involved [4], [5], [21]. We therefore determined whether TRAIL or FasL were upregulated in murine AM upon ex vivo A/PR8 infection. Whereas FasL gene and protein expression were less induced (Fig. S3), TRAIL mRNA was highly upregulated in a dose- and time-dependent manner after A/PR8 infection in AM, with peak TRAIL expression found at 16 h pi at an MOI of 1 (Fig. 1G). Furthermore, TRAIL mRNA was induced after ex vivo infection with different IV strains in murine AM (Fig. S2C) and, similarly, in human AM (Fig. S2D). Importantly, TRAIL was increased in alveolar macrophages of hospitalized patients with pandemic H1N1 (pH1N1) -induced ARDS as compared to patients with non-viral ARDS or healthy control individuals, both on mRNA and surface expression level (Fig. 2A, B). Moreover, soluble TRAIL (sTRAIL) levels were significantly higher in BALF of pH1N1-ARDS patients compared to patients suffering from bacterial pneumonia (Fig. 2C), suggesting activation of a TRAIL induction pathway specific for viral infection. Of note, AEC did not increase TRAIL gene expression in response to A/PR8 infection to levels observed in murine AM (Fig. 1H and Fig. S4). Given the findings that peak TRAIL expression coincided with peak IFN-β expression at 16 h pi in murine AM (Fig. 1E, G) and that TRAIL and IFN-β showed similar IV strain-specific induction patterns in both murine and human AM (Fig. S2), we speculated that macrophage TRAIL expression was dependent on autocrine IFN-β-induced signaling. Indeed, when we stimulated non-infected murine AM with recombinant IFN-β, we detected a time- and dose-dependent increase of TRAIL mRNA (Fig. 3A) and significantly enhanced cell surface TRAIL expression after 24 h of IFN-β stimulation, which was comparable to A/PR8-induced expression (Fig. 3B). Finally, blockade of IFN-β-induced signalling via the type I IFN receptor (IFNAR) nearly abolished A/PR8-induced TRAIL mRNA upregulation in AM regardless of the MOI applied, as demonstrated by either addition of neutralizing anti-IFN-β Ab to the culture medium, use of ifnar−/− AM, or application of an inhibitor of the IFNAR downstream mediators, Jak and STAT (Fig. 3C). Collectively, these data demonstrate that macrophage-released IFN-β induces expression of the pro-apoptotic factor TRAIL in AM upon IV infection in an autocrine fashion. Several pattern recognition receptors and downstream signalling events were previously shown to be involved in type I IFN induction in IV-infected epithelial cells [22], [23]. However, the cell-specific mechanisms of IV recognition and subsequent IFN-β release in primary AM are far less defined. Given our previous results on protein kinase R (PKR) as important inflammatory signal transducer to NF-κB-mediated transcriptional activity in primary alveolar mouse macrophages [24], we questioned whether the IV-induced type I IFN response in AM similarly depended on PKR, known to directly associate with viral RNA in IV-infected epithelial cells [25]. Indeed, ex vivo A/PR8 infection of murine AM resulted in phosphorylation of PKR, most prominent at 2–4 h pi (Fig. 4A). Moreover, the p65 NF-κB subunit was substantially activated at 4–8 h pi in response to A/PR8 infection in wt but not pkr−/− AM (Fig. 4B). Of note, the infection-triggered activation or expression of transcription factors IRF3 and IRF7, respectively, known to induce IV-dependent type I IFN responses in epithelial cells [23], [26], did not differ in A/PR8-infected wt and pkr−/− AM (Fig. 4C). To test whether the IV-induced type I IFN response in AM depended on the PKR-NF-κB axis, we quantified IFN-β release from A/PR8-infected wt vs. pkr−/− AM or in wt AM after NF-κB inhibition. As shown in Fig. 4D, both PKR-deficiency and treatment with a selective IκB kinase (IKK) phosphorylation inhibitor abrogated IFN-β release in A/PR8-infected AM, indicating that IV-induced macrophage IFN-β expression required signal transduction via both PKR and NF-κB. Finally, to address the significance of this signalling pathway for IV-induced TRAIL expression in AM, we stimulated wt or pkr−/− AM with recombinant IFN-β or infected them with A/PR8 in presence or absence of the IKK inhibitor with or without neutralizing anti-IFN-β Ab. As expected, TRAIL gene expression was significantly induced upon IFN-β stimulation in both wt and pkr−/− AM, regardless of NF-κB inhibition (Fig. 4E, left gray graphs). However, when AM were A/PR8-infected, TRAIL gene induction depended on presence of PKR and activation of NF-κB. Macrophage IFN-β blockade in addition to NF-κB inhibition did not further decrease TRAIL expression significantly (Fig. 4E, right black graphs). Altogether, these data demonstrate that IV-infection of AM induces a PKR-NF-κB-dependent autocrine IFN-β signaling loop resulting in expression of the pro-apoptotic ligand TRAIL in IV-infected AM. To determine whether IV-induced AM-expressed IFN-β might additionally promote TRAIL-induced signalling on receptor level, we next quantified AEC TRAIL receptor (DR5, death receptor 5) expression in mono-cultured AEC. As shown in Fig. 5, DR5 was MOI-dependently upregulated in both murine and human AEC upon ex vivo A/PR8 infection on gene expression level (Fig. 5A). Of note, A/PR8-infected (IV nucleoprotein+, NP+) AEC showed significantly increased DR5 surface expression compared to non-infected (NP−) AEC within the same culture (Fig. 5B). Interestingly, stimulation of AEC with IFN-β did not impact on DR5 gene expression (Fig. 5C), and DR5 surface expression on AEC did not differ between wt and ifnar−/− mice in vivo (Fig. 5D), suggesting that macrophage IFN-β induces target cell apoptosis rather by increased macrophage expression of death receptor ligands than by affecting DR5 levels on AEC. Nonetheless, IV-infection sensitizes AEC for TRAIL-mediated killing through IFN-β-independent DR5 upregulation. To address whether epithelial IV-infection was required for macrophage TRAIL-induced apoptosis induction, mock- or A/PR8-infected AECs were co-cultured with infected AM in the presence of anti-TRAIL or control IgG isotype Ab. AEC apoptosis was induced in both mock- and A/PR8-infected co-cultured AEC, however, apoptosis levels in infected AEC, expressing increased levels of DR5 (Fig. 5A and B) exceeded those of mock-infected AEC. Addition of neutralizing anti-TRAIL Ab significantly reduced AEC apoptosis induction in both mock- or A/PR8-infected AEC (Fig. 6A). To address whether the macrophage-specific signalling events mediating IV-induced TRAIL expression indeed promoted AEC apoptosis, we mock- or A/PR8-infected wt, pkr−/−, ifnar−/−, or trail−/− AM for 24 h, co-cultured them with AEC for further 48 h and then analysed the proportion of AnnexinV+ AEC. AEC apoptosis rates were not increased upon co-culture with mock-infected wt, pkr−/−, ifnar−/−, or trail−/− AM, compared to uninfected, mono-cultured AEC (8. 3±1. 8%, data not shown). However, co-culture with infected wt AM strongly induced apoptosis in AEC, and this was significantly reduced when AEC were co-cultured with infected pkr−/−, ifnar−/−, or trail−/− AM (Fig. 6B). Similar data were obtained when expression of cleaved caspase-3 was analysed by western blot in co-cultured AEC (Fig. 6C). Correspondingly, levels of sTRAIL in supernatants were significantly increased upon infection of wt, but not pkr−/−, ifnar−/−, or trail−/− AM (Fig. 6D). Finally, use of blocking DR5 Ab and of AEC derived from DR5-deficient mice in co-cultures revealed that AM-mediated AEC apoptosis induction was dependent on the epithelial TRAIL receptor DR5 (Fig. 6E). Collectively, these data demonstrate that PKR-IFN-β/IFNAR-dependent macrophage TRAIL- induces apoptotic cell death via DR5 in both non-infected and IV-infected AEC ex vivo. To investigate whether IFN-β induced pro-apoptotic AM-AEC cross-talk operates also during in vivo IV infection, A/PR8-infected wt mice were intratracheally treated with IFN-β at d5 pi. As shown in Fig. 7A, sTRAIL concentrations in BALF at d7 pi were significantly increased compared to vehicle-treated or mock-infected mice. IFN-β-induced (both endogenous and exogenously applied IFN-β) AEC apoptosis and alveolar protein leakage were attenuated upon intratracheal treatment with an anti-TRAIL Ab (Fig. 7B, C). To address the role of PKR activation and subsequent type I IFN release in macrophage TRAIL-induced epithelial cell damage, we next created bone marrow chimeric mice of wt recipient phenotype which were transplanted wt, pkr−/−, ifnar−/−, or trail−/− BM cells. Chimeric mice displayed >90% of circulating and tissue-resident macrophages of donor phenotype at 12w post BMT (Fig. S5), and were subjected to A/PR8-infection as outlined in Fig. 8A. Quantification of sTRAIL in BALF revealed induction in A/PR8-infected wt BM transplanted mice, whereas sTRAIL concentrations were significantly reduced in infected chimeric mice with pkr−/−, ifnar−/− or trail−/− AM (Fig. 8B). Likewise, the proportions of mTRAIL expressing alveolar resident (AM) and recruited exudate (ExMac) macrophages ranged at ∼15% in wt BM transplanted mice, and were widely reduced when resident or exudate macrophages were PKR-, or IFNAR-deficient (Fig. 8C, flow cytometric gating strategies for AM and ExMac depicted in Fig. S6). Notably, mTRAIL was not significantly expressed on macrophages of mock-infected mice or on further myeloid lung-recruited leukocyte populations in BALF (neutrophils; data not shown) or lung homogenates (CD11b+ dendritic cells, CD103+ dendritic cells; data not shown) in the different groups of chimeric mice after A/PR8 infection. Correspondingly, the fractions of apoptotic AEC, increased in A/PR8-infected compared to mock-infected wt BM transplanted mice, were reduced in mice transplanted pkr−/−, ifnar−/− or trail−/− BM, as demonstrated by flow cytometric quantification of AnnexinV+ AEC or levels of cleaved caspase-3 in AEC isolated from mock- or A/PR8-infected chimeric mice (Fig. 8D, E, respectively). Minor differences in between the AnnexinV- and cleaved caspase 3-based AEC apoptosis quantification might result from the analyses of different signalling events within the apoptotic cascade. Of note, viral clearance was not affected in mice lacking leukocyte TRAIL or IFNAR, however, PKR expression in leukocytes was required for effective antiviral host defense as demonstrated by quantification of A/PR8 titers in BALF at d7 pi (Fig. S7). Finally, macrophage TRAIL deficiency was associated with attenuated IV-induced lung injury and reduced morbidity as shown by alveolar albumin leakage at d7 pi (Fig. 8F) and body weight loss in the time course pi (Fig. 8G). Together, these data reveal that TRAIL expression is induced in a PKR- and type I IFN-dependent way in AM upon A/PR8 infection in vivo which significantly contributes to IV-induced lung injury. IV pneumonia is characterized by infection of distal lung epithelial cells and resident macrophages and frequently progresses to acute lung injury/ARDS with poor outcome. Rapid onset of effective host defense strategies is therefore crucial for recovery from IV-induced lung damage. On the other side, an exaggerated inflammatory response will add substantial tissue injury to virus-induced cytopathic damage. This study elucidates a previously unrecognized pathway of cytokine-mediated epithelial injury and highlights the IV-infected alveolar macrophage as key player in this scenario. We demonstrate that IFN-β, induced in IV-infected AM through a PKR-NF-κB-dependent pathway, induces expression and release of the pro-apoptotic ligand TRAIL by autocrine IFNAR activation, and thereby significantly contributes to IV-induced apoptotic tissue injury (Fig. 9). Although it becomes increasingly evident that influenza viral 5′-triphosphate-linked RNA in epithelial cells is primarily recognized by RIG-I (RNA helicases retinoic acid inducible gene-I) [22], the pathways of IV-dependent type I IFN induction in primary AM have not been addressed in this respect. Given that RIG-I-like helicases signal through the downstream adapter MAVS (mitochondrial antiviral signaling, also called IPS-1) in epithelial IV infection, which mediates IRF3/7-dependent production of type I IFNs [23], we analysed whether these transcriptional effectors were required for IFN-β production in primary AM. However, we did not detect increased expression or activation of IRF7 or IRF3, respectively, upon A/PR8 infection in wt or pkr−/− AM. In fact, our data for the first time demonstrate that, in AM, PKR signalling and subsequent transcriptional activity of NF-κB are indispensable for type I IFN release upon IV infection, suggesting a macrophage-specific way of signal transduction and adding to a previous study highlighting NF-κB as key regulator of the cellular type I IFN response towards a highly pathogenic IV [27]. The importance of macrophage PKR in the context of IV infection is highlighted by the fact that myeloid PKR-deficiency results in ineffective viral clearance at d7 pi (Fig. S7). The double-stranded (ds) RNA-dependent kinase PKR is a well-characterized component of the antiviral response present in non-stimulated cells at basal levels, and further upregulated by type I IFN [15], [28]. PKR is activated by autophosphorylation upon binding to viral RNP (ribonucleoprotein) complexes and phosphorylates the protein synthesis eukaryotic initiation factor 2a (eIF-2a), resulting in rapid inhibition of translation to limit virion production [25], [29]. More recently, PKR was shown to control transcriptional activation of the NF-κB pathway, independently of its kinase activity, in murine AM and other cells [24], [30]. Whether binding of viral RNPs is required for NF-κB activation or whether PKR rather transduces IV-induced upstream signals in AM is under our current investigation. Type I IFNs are critical components of the host' s innate antiviral response [31], and compounds that trigger this response are clinically in use to treat different viral infections. Accumulating evidence suggests that engagement of the type I IFN response prior to infection may be a therapeutic strategy to control IV infection in different animal models [32], [33]. Furthermore, mice genetically deficient in type I IFN signalling had inefficient IV clearance [16], [34], and ifnar−/− mice displayed a 50% increased mortality rate compared to wt litters in response to A/PR8 infection (data not shown). Together with our data obtained from the ifnar−/− BM chimeric mouse model, these findings suggests a minor role of myeloid as opposed to lung parenchymal IFNAR signalling in antiviral host defense, as ifnar−/− chimeric mice clear IV to a similar extent as control mice (Fig. S7). Our data reveal that AM are a substantial source of IFN-β in the IV-infected lung. Neutralization of alveolar IFN-β and/or -α at d5 pi attenuated AEC injury at d7 pi suggesting that blockade of one of the type I IFNs at the IFNAR receptor is sufficient to strongly reduce AEC apoptosis. Concomitantly, alveolar deposition of recombinant IFN-β at d5 pi increased apoptotic AEC injury in IV-infected lungs, whereas IFN-β pretreatment of mice 24 h prior to infection significantly reduced viral spread and concomitant alveolar protein leakage (data not shown). These data add to the aforementioned studies demonstrating potent anti-viral effects of IFN-β applied before or early in the infection course and suggest that a prolonged or exaggerated type I IFN response at later stages, when virus is virtually cleared from the lungs, might be an important amplifier of a detrimental inflammatory response in IV pneumonia. A major determinant of this interferon-induced detrimental host response was found to be the potent apoptosis-inducing ligand TRAIL. TRAIL is a well-characterized component of the host' s anti-tumour immune response which was thought to exert its pro-apoptotic effects only towards malignant cells [35]. Only recently, we and others demonstrated a role of TRAIL-mediated apoptosis in epithelial injury upon viral pneumonia in mice and humans [5], [6]. However the cell-specific signalling events which regulate TRAIL expression in AM remained elusive. Previous reports indicated that IFN-β-stimulated dendritic cells engage TRAIL to mediate tumour cell killing [36] and suggested TRAIL expression in peripheral blood mononuclear cells to be IFN-dependent [37]. In line with these studies, our data reveal that TRAIL gene expression is induced by interferon via autocrine IFNAR signalling in AM upon ex vivo and in vivo IV infection. Importantly, IV strains of different pathogenicity induced TRAIL gene expression in murine AM to different extents. The mouse-adapted A/PR8, known to be highly pathogenic in mice, caused an ∼800-fold, and the highly pathogenic avian H5N1 IV A/Thailand/KAN-1/04, which causes severe pneumonia in mice [38], induced an ∼200-fold peak increase in TRAIL expression, whereas infection with the lower pathogenic ×31 stimulated by only ∼8-fold. Likewise, patterns of TRAIL mRNA induction resembled those of IFN-β gene expression. These findings suggest a relation between the extent of TRAIL-mediated apoptotic AEC injury and the pathogenic potential of the respective IV strain. Thus, induction of the type I IFN-TRAIL axis may be considered as novel determinant of IV pathogenicity. Of note, given our findings that AM show various levels of permisseveness for different IV strains (abortive infection of H1N1 IV, high replication rates of highly pathogenic IV, not shown), the extent by which this pathway contributes to lung injury upon infection with IV other than A/PR8 remains to be defined. TRAIL is expressed in a variety of immune cells such as T cells, NK cells, resident tissue macrophages, dendritic cells and circulating monocytes [39]. Previous studies demonstrated that antibody-mediated inhibition of TRAIL signalling resulted in reduced viral clearance in a mouse model of IV pneumonia [40], and that CD8+ T cells utilize TRAIL to kill IV-infected alveolar epithelial cells in vivo [41], suggesting IV-induced TRAIL as important executor of cytotoxic T lymphocyte (CTL) responses. Although our data obtained from the BM chimeric mouse model suggest a minor role of leukocyte-expressed TRAIL in IV clearance, as mice transplanted trail−/− BM completely cleared IV at d7 pi (Fig. S7), our findings do not exclude that, apart from FasL or Perforin, antigen-specific CTLs engage TRAIL to specifically kill infected IV target cells. In contrast, macrophage TRAIL-mediated epithelial cell killing was not restricted to IV-infected, DR5 high-expressing AEC, but as well affected non-infected AEC with low level DR5 surface expression (Fig. 5,6). This suggests that TRAIL released from AM in high amounts upon type I IFN stimulation causes substantial collateral damage to the (uninfected) epithelial barrier, especially when macrophages are present in the airspaces in high numbers, as observed during IV pneumonia [6]. In conclusion, our data reveal a novel role of type I IFNs in induction of apoptotic alveolar epithelial injury and highlight the IV-infected macrophage as central player. Identification of the signalling steps involved in this IFN-β-dependent injurious macrophage-epithelial cross-talk provides new potential targets for therapeutic strategies to attenuate lung injury without compromising the anti-viral immune response during severe IV pneumonia. All animal experiments were conducted according to the legal regulations of the German Animal Welfare Act (Tierschutzgesetz) and approved by the regional authorities of the State of Hesse (Regierungspräsidium Giessen; reference numbers 64/2007,09/2009,39/2011). All experiments were performed under ketamine/xylazine anesthesia, and all efforts were made to minimize suffering of infected animals. Human lung tissue was obtained from patients who underwent lobectomy after informed written consent (Departments of Pathology and Surgery, Justus-Liebig-University, Giessen). BALF samples derived from the Universities of Giessen and Marburg (UGMLC) biobank. Use of human lung tissue and BALF samples was approved by the University of Giessen Ethics Committee. C57BL/6 wt mice were purchased from Charles River Laboratories. trail−/− mice [35] were provided by Amgen Inc. (Thousand Oaks, CA, USA). pkr−/− [42] were a kind gift from J. Pavlovic (Institute of Medical Virology, University of Zurich, Switzerland). U. Kalinke provided ifnar−/− mice [43] that under sponsorship of the Paul-Ehrlich-Institut, Langen, Germany, had been backcrossed to B6N20. Dr5−/− mice were a kind gift from T. Mak (Campbell Family Institute for Breast Cancer Research, Department of Medical Biophysics, University of Toronto, Canada) [44]. B6. SJL-Ptprca mice expressing the CD45. 1 alloantigen (Ly5. 1 PTP) on circulating leukocytes (C57BL/6 genetic background) were obtained from The Jackson Laboratory. Mice were bred under specific pathogen-free conditions. Human lung tissue was obtained from lobectomy specimen distal from tumors. BALF material derived from the Universities of Giessen and Marburg Lung Center (UGMLC) biobank. BALF samples from patients who were subjected to bronchoscopy for diagnostic purposes but displayed normal BALF cellularity and differential leukocyte counts (i. e. >90% resident alveolar macrophages) were used for isolation of resident alveolar macrophages for in vitro infection experiments or as control samples. pH1N1-BALF samples were collected from patients admitted to the Intensive Care Unit of the Department of Internal Medicine II, UGLC between Dec. 2009 and Jan. 2011 with RT-PCR-confirmed (BALF) pandemic H1N1 infection (bacterial infection was excluded in BALF material). All patients (n = 6, age 42±15 y) required mechanical ventilation due to ALI/ARDS and were subjected to BAL for diagnostic reasons on the day of admission. BALF material from patients who were mechanically ventilated due to non-viral (i. e. bacterial pneumonia- or sepsis-induced) ARDS and samples from patients who were subjected to BAL for diagnostic reasons at the day of admission and revealed bacterial pneumonia (n = 8) were additionally included. The following anti-mouse mAbs/secondary reagents were used for flow cytometry and immunofluorescence: CD31-FITC (BD Pharmingen, Heidelberg, Germany), CD45-APC-Cy7 (BD Pharmingen, Heidelberg, Germany), EpCam-annexin V-Alexa Fluor 647 (Invitrogen, Karlsruhe, Germany), TRAIL (Biolegend, Uithoorn, Netherlands), IgG2a Isotype (Biolegend, Uithoorn, Netherlands), donkey anti-rat IgG Alexa Fluor 488 (Invitrogen, Karlsruhe, Germany), goat anti-rat IgG-PE (Serotec, Düsseldorf, Germany), mouse-anti influenza NP (Meridian life science, Asbach, Germany), anti-mouse DR5 (R&D, Wiesbaden, Germany), goat anti-mouse IgG Alexa Fluor 647 (Invitrogen, Karlsruhe, Germany), goat anti-mouse IgG Alexa Fluor 555 (Invitrogen, Karlsruhe, Germany), donkey anti-rat IgG Alexa Fluor 488 (Invitrogen, Karlsruhe, Germany), CD45. 1-FITC (BD Pharmingen, Heidelberg, Germany), CD45. 1-PE (BD Pharmingen, Heidelberg, Germany), CD45. 2-APC-Cy7 (Biolegend, Uithoorn, Netherlands), SiglecF-PE (BD Pharmingen, Heidelberg, Germany), CD11c-PE-Cy5. 5 (Invitrogen, Karlsruhe, Germany), GR1-PE-Cy7 (Biolegend, Uithoorn, Netherlands), CD3ε-APC (Biolegend, Uithoorn, Netherlands), CD11c-APC (Biolegend, Uithoorn, Netherlands), MHCII-FITC (BD Pharmingen, Heidelberg, Germany), CD103-PE-Cy5. 5 (Biolegend, Uithoorn, Netherlands), CD11b-PE-Cy7 (Biolegend, Uithoorn, Netherlands), F4/80-APC (Invitrogen, Karlsruhe, Germany), B220-PE-Cy7 (BD Pharmingen, Heidelberg, Germany). Anti-human TRAIL-PE and the corresponding isotype IgG1κ-PE were from BD Pharmingen (Heidelberg, Germany). For western blot analysis the following anti-mouse antibodies were used: Anti-β-Actin (Biolegend, Uithoorn, Netherlands), anti-IRF3 (Santa Cruz, Heidelberg, Germany), anti-phospho IRF3 (Cell Signalling, Frankfurt a. M. , Germany), anti-IRF7 (Santa Cruz, Heidelberg, Germany), anti-PKR (Abcam, Cambridge, UK), anti-phospho PKR (Abcam, Cambridge, UK), anti-cleaved Caspase-3 (Cell Signalling, Frankfurt a. M. , Germany) and secondary anti-rabbit IgG-HRP antibody (Cell signalling, Frankfurt a. M. , Germany). Polyclonal anti-IFN-β Ab (PBL, Herford, Germany) was used for ex vivo neutralization at a concentration of 180 IU/ml. For in vivo neutralization 10. 000 IU/70 µl of anti-IFN-α or anti IFN-β Ab (PBL, Herford, Germany) were intratracheally administered if not otherwise indicated. The inhibitory anti-TRAIL antibody or corresponding IgG control (both Biolegend, Uithoorn, Netherlands) were used ex vivo at a concentration of 500 ng/ml and 150 µg were intraperitoneally injected at day 3 and 5 post infection. 10. 000 IU/70 µl recombinant murine IFN-β (PBL, Herford, Germany) diluted in 0. 1%BSA or 0. 1%BSA alone were applied intratracheally or added to the cell culture supernatants at the given concentrations. Recombinant murine TRAIL (R&D, Wiesbaden, Germany, 100 pg/ml) and Staurosporin (Sigma, Deisenhofen, Germany, 2 µM) were used for in vitro experiments. Murine AEC were isolated by the method developed by Corti and colleagues [45] as outlined [46]. 2. 0×105 AEC/well were seeded in 24 well plates (BD Biosciences, Heidelberg, Germany). For co-culture experiments, 3. 0×105AEC were seeded on the bottom side of 8 µm pore size transwells (BD Biosciences, Heidelberg, Germany) [47]. The cells were kept in DMEM supplemented with HEPES, L-glutamine, 10% FCS and antibiotics for 5 d. Human AEC were isolated as described previously [48] and were kept in HAM' s F12 medium (Biochrom, Berlin, Germany) supplemented with 10% FCS and antibiotics for 5 d. The purity of murine and human AEC was assessed using anti-CD45, anti-CD326/EpCam and anti-pro-SP-C antibodies (Biolegend, Uithoorn, Netherlands; Millipore, Eschborn, Germany). Cell suspensions with a purity >90% were used for further experiments. Murine resident alveolar macrophages were isolated by bronchoalveolar lavage (BALF) from lungs of wt or gene-deficient mice as described [47], cultivated in RPMI 1640 containing 2% FCS, L-glutamine and antibiotics and were left to adhere for 2 h before further treatment. In selected experiments, protease inhibitor cocktail (1∶400 dilution, Sigma, Deisenhofen, Germany) was added to the culture medium directly after infection or macrophages were treated with Jak/Stat Inhibitor I (1500 nM in DMSO, Calbiochem, Darmstadt, Germany) or Bay 11-7082 (25 µM in DMSO, Calbiochem, Darmstadt, Germany) or DMSO alone was added 1 h prior to and after infection to the cell culture medium. In co-culture experiments 2. 5×105 previously infected or mock-infected murine resident alveolar macrophages/well were combined with murine alveolar epithelial cells seeded on the bottom side of transwells and were co-cultivated for 48 h as previously described [47]. Human resident alveolar macrophages were purified from BALF of patients who were subjected to bronchoscopy for diagnostic purposes but revealed no abnormalities in BALF cellularity or differential leukocyte counts in BALF and seeded at a density of 2. 5×105 cells/well or 1. 25×105cells/well in a 24- or 48-well plate, respectively. Alveolar macrophages from pandH1N1 or control patients (5. 0×105 BALF cells/well) were seeded in 12 well plates, kept in RPMI 1640 containing 50% FCS, L-glutamine and antibiotics and left to adhere for 40 min. Cells were washed several times to remove non-adherent lymphocytes and neutrophils. Macrophage purity was assessed using overall morphological criteria, including differences in cell size and shape which was always >90%. The following IV strains were used for ex vivo infection: A/PR/8/34 (H1N1) (A/PR8), A/X-31 (H3N2) (×31), highly pathogenic avian H5N1 virus isolated from a human fatal case A/Thailand/KAN-1/04 (A/Thai) [49], the swine-origin pandemic H1N1 virus A/Hamburg/5/09 (A/Ham) and seasonal H1N1 virus A/Memphis/14/96-M (A/Mem) [50]. Heat inactivation was performed at 56°C for 30 minutes. Cells were infected (MOI = 1, unless otherwise indicated) in a total volume of 75 µl in 48 well plates or 100 µl in 24well plates diluted in PBS+/+ supplemented with 0. 1% BSA (Sigma, Deisenhofen, Germany) and antibiotics for 1 h. After removal of the inoculum the respective culture medium supplemented with 0. 1% BSA (Sigma, Deisenhofen, Germany), 2 µg/ml Trypsin-TPCK (Worthington, Troisdorf, Germany) and antibiotics was added and cells were incubated at 37°C and 5% CO2 for 16–24 h. For further processing, cells were trypsinized with Trypsin/EDTA (PAA, Cölbe, Germany) for FACS analyses or lysed for real-time PCR or western blot. Mice were intratracheally inoculated with 350–500 pfu A/PR8 diluted in sterile PBS−/− in a total volume of 70 µl or mock infected with PBS−/− alone. Mice were sacrificed at the indicated time points and blood and BALF were obtained and handled as previously described [6]. BALF cells were counted with a hemocytometer and quantification of leukocyte subsets was performed by differential cell counts of Pappenheim-stained cytocentrifuge preparations using overall morphological criteria, including differences in cell size and shape of nuclei. Alveolar leakage was analyzed by the lung permeability assay by i. v. injection of FITC-labelled albumin (Sigma, Deisenhofen, Germany) and detection of FITC-fluorescence in serum and BALF, as previously described [6] or by detection of total protein concentrations in BALF by a commercially available spectrophotometric assay (BCA assay, Biorad, München, Germany). For analysis of AEC apoptosis, lungs were digested by intratracheal application of Dispase and processed as outlined previously [6]. BM cells were isolated under sterile conditions from the tibias and femurs of wt C57BL/6, pkr−/−, ifnar−/− or trail−/− donor mice as previously described after total body irradiation (6 Gy) [6]. As controls for bone marrow engraftment, wt C57BL/6 BM cells (expressing the CD45. 2 alloantigen) were transplanted into CD45. 1 alloantigen-expressing C57BL/6 mice during each transplantation experiment and the proportion of donor CD45. 2-expressing leukocyte populations in blood, BALF and lung homogenate were analyzed by flow cytometry. The proportion of donor circulating and lung-resident myeloid cells was regularly >90% as shown by FACS analyses of the proportions of CD45. 2+ vs. CD45. 2+ cells. Chimeric mice were housed under specific pathogen free conditions for 12 weeks before PR/8 infection. After cell lysis in RLT buffer (Qiagen, Hilden, Germany), RNA was isolated using RNeasy mini kit (Qiagen, Hilden, Germany) according to the manufacturer' s instructions. For cDNA synthesis, reagents and incubation steps were performed as outlined previously [46]. Reactions were performed in a Step one plus Sequence detection system (Applied Biosystems, Foster City, CA) using Sybr green as fluorogenic probe in 25 µl reactions containing 5 µl cDNA sample, Platinum Sybr Green qPCR supermix (Invitrogen, Karlsruhe, Germany) and 45 pmol of forward and reverse primer. The following forward and reverse primers were used: Murine actin forward, 5′-ACCCTAAGGCCAACCGTGGC-3′, reverse 5′-CAGAGGCATACAGGGACAGCA-3′; human Actin forward, 5′-CTGGGAGTGGGTGGAGGC-3′, reverse 5′-TCAACTGGTCtCAAGTCAGTG-3′; murine TRAIL forward, 5′-GAAGACCTCAGAAAGTGGC-3′ and reverse 5′-GACCAGCTCTCCATTCTTA-3′; human TRAIL forward 5′-GAGGTTGCAGTGGTGAGA-3′, reverse 5′-CCCCTGCTGGCAAGTCAA-3′; murine IFN-beta 5′-ACGTCTCCTGGATGAACTCCA-3′ and reverse 5′-CAGTTGAGGACATCTCCCACG-3′; human IFN-β forward, 5′-CAGCAATTTTCAGTGTCAGAAGC-3′, reverse 5′-TCATCCTGTCCTTGAGGCAGT-3′; murine DR5 forward 5′-AAGTGTGTCTCCAAAACGG-3′, and reverse 5′-AATGCACAGAGTTCGCACT-3′; human DR5 forward 5′-GGTTCCAGCAAATGAAGGTG-3′ and reverse 5′-GAGTCAAAGGGCACCAAGTC-3′. Relative gene abundance to housekeeping gene was calculated as a ΔCt value, with ΔCt = Ct reference−Ct target, and data are presented as ΔCT or fold expression (2ΔΔCt) values in between treatment groups. Cultured cells or AEC isolated from A/PR8 infected mice were lysed in lysis buffer (20 mM Tris-HCL, 150 mM NaCl, 1 mM Na2EDTA, 1 mM EGTA, 0. 5% NP-40,2 mM sodium orthovanadate and protease inhibitor cocktail; Roche, Mannheim, Germany) and the protein content was determined using a commercially available spectrophotometric assay (BCA assay, Biorad, München, Germany). Separation of proteins was resolved on a SDS-PAGE and transferred onto Hybond PVDF-membrane (GE healthcare; München, Germany). Membranes were blocked and incubated with the primary and secondary antibodies. The bands were detected using the enhanced chemiluminescent western blotting system (GE healthcare, München, Germany). Virus titers in BALF were determined using plaque assay in Madin-Darby canine kidney cells. In brief, duplicate cell monolayers grown in 6-well plates were incubated with 1 ml BALF dilution for 1 h at room temperature and covered with 1. 25% Avicel semi-solid overlay medium containing 2 µg/ml Trypsin-TPCK (Worthington, Troisdorf, Germany) for 48 h. Cells were fixed with 4%PFA in PBS, permeabilized with 1%Triton X-100, incubated with diluted primary anti-NP (Meridian Life Science, Asbach, Germany) and secondary HRP-conjugated anti-mouse antibody (Santa Cruz, Heidelberg, Germany) for 45 min, respectively, and stained with True Blue (KPL, Gaithersburg, MD, USA). Plaques were counted using a light microscope (DM 2500, Leica Microsystems, Wetzlar, Germany). 1–5×105 cells were washed in FACS buffer (PBS−/−, 7. 4% EDTA, 0. 5% FCS, pH 7. 2) and fixed in 1%PFA/PBS−/− or resuspended in annexin V staining buffer (10 mM HEPES, 140 mM NaCl and 2. 5 mM CaCl2), incubated with the respective primary and secondary antibodies for 15 min at 4°C, and flow cytometric analysis was performed using a FACSCanto flow cytometer (BD Biosciences, Heidelberg, Germany) and a FACS Diva Software. Cells cultured in chamber slides were fixed for 5 min in a mixture of methanol∶acetone (1∶1) and incubated for 30 min in 3% BSA in PBS. The cells were stained for 2 h with the primary and after washing for 2 h with the secondary antibody. After a final washing step the cells were covered with a DAPI-containing mounting medium (Vectashield; Vector Labs, Eching, Germany) and left to dry overnight. Samples were analysed with a Leica DM 2000 light microscope using a Leica digital imaging software (Wetzlar, Germany). Murine and human IFN-α, IFN-β, TRAIL and FasL levels were analyzed from cell culture supernatants, murine or human BALF or supernatants of homogenized lungs using commercially available ELISA Kits (R&D Systems, Wiesbaden, Germany; PBL, Herford, Germany; USCN, Basel, Switzerland) according to the manufacturer' s instructions. Activated NF-κB was quantified using a commercially available TransAM Assay p65 (active motif, La Hulpe, Belgium) according to the manufacturer' s instructions. Briefly, the TransAM assay quantifies activated p65 binding to an immobilized consensus-binding site oligo after addition of nuclear extracts. A primary antibody specific for an epitope on the bound and active form of p65 is then added followed by subsequent incubation with secondary antibody and developing solution for colorimetric quantification. All data are given as mean ± standard deviation. Statistical significance between two groups was estimated using the two-tailed paired or the unpaired student' s t-test for paired or unpaired samples, respectively. For comparison of>two groups with each other one-way ANOVA and post-hoc Tukey-HSD was applied. Significancies were calculated with the SPSS for Windows software program. A value of p<0. 05 was considered as significant.

Acute lung injury induced by influenza virus (IV) infection has been linked to an unbalanced release of pro-inflammatory cytokines including type I interferons (IFN) causing immune-mediated organ damage. Using ex vivo and in vivo IV infection models, we demonstrate that alveolar macrophage-expressed IFN-β induces alveolar epithelial cell injury by autocrine induction of the pro-apoptotic TNF-related apoptosis-inducing ligand (TRAIL). Elucidating the cell-specific underlying signalling pathways revealed that IV-induced IFN-β release from alveolar macrophages (AM) strictly depended on protein kinase R- (PKR-) and NF-κB-signalling. Autocrine activation via the macrophage type I IFN receptor (IFNAR) resulted in increased expression and release of TRAIL which caused apoptosis of IV-infected and non-infected alveolar epithelial cells and promoted alveolar barrier dysfunction as demonstrated in ex vivo co-cultures and in bone marrow chimeric mouse models in vivo. Importantly, we found TRAIL highly upregulated in and released from AM of hospitalized patients with pandemic H1N1-induced lung failure. Therapeutic targeting of the macrophage IFN-β-TRAIL axis might therefore represent a promising strategy to attenuate IV-induced acute lung injury.

Abstract Introduction Results Discussion Materials and Methods

medicine immunopathology infectious diseases clinical immunology immunology respiratory medicine critical care and emergency medicine pulmonology immune response

2013

Macrophage-expressed IFN-β Contributes to Apoptotic Alveolar Epithelial Cell Injury in Severe Influenza Virus Pneumonia

Amebic liver abscess (ALA) is a focal destruction of liver tissue due to infection by the protozoan parasite Entamoeba histolytica (E. histolytica). Host tissue damage is attributed mainly to parasite pathogenicity factors, but massive early accumulation of mononuclear cells, including neutrophils, inflammatory monocytes and macrophages, at the site of infection raises the question of whether these cells also contribute to tissue damage. Using highly selective depletion strategies and cell-specific knockout mice, the relative contribution of innate immune cell populations to liver destruction during amebic infection was investigated. Neutrophils were not required for amebic infection nor did they appear to be substantially involved in tissue damage. In contrast, Kupffer cells and inflammatory monocytes contributed substantially to liver destruction during ALA, and tissue damage was mediated primarily by TNFα. These data indicate that besides direct antiparasitic drugs, modulating innate immune responses may potentially be beneficial in limiting ALA pathogenesis. Entamoeba histolytica (E. histolytica) is a protozoan parasite that colonizes the human gut. Infection is typically asymptomatic; however, in about 10% of cases, E. histolytica trophozoites penetrate into the gut tissue and cause hemorrhagic colitis or spread to the liver and induce amebic liver abscesses (ALA), a progressive focal destruction of liver tissue. Invasive amebiasis is estimated to constitute approximately 50 million cases annually worldwide [1]. Over the past several decades, most studies of ALA focused on parasite-specific pathogenicity factors such as the D-galactosamine-inhibitable (Gal/GalNAc) adherence lectin, the pore forming peptides (amebapores), and cysteine peptidases, as causative agents in the penetration of host tissue and induction of invasive disease [2]–[4]. However, homologues of a majority of the genes that are assumed to be essential for pathogenicity are also present in the non-pathogenic species, E. dispar, which is genetically very closely related to E. histolytica but does not cause clinical symptoms [5]. Beside parasite-specific effector molecules, there is accumulating evidence that host-mediated mechanisms also contribute to disease progression in the liver. For example, adult males are more susceptible to ALA, despite the fact that infection with E. histolytica is more prevalent in women and children [6]. In addition, histological analysis of liver sections from human ALA patients, as well as from ALA rodent models, consistently shows massive accumulation of inflammatory cells, primarily neutrophils, and macrophages, within the abscess [7]–[9]. While these immune cells represent the first line of defense against microorganisms, such an overwhelming immune response and the antimicrobial factors released by inflammatory cells could damage the host tissues as well [10], [11]. Neutrophils are terminally differentiated cells characterized by surface expression of Ly6G [12]. They are rapidly recruited to sites of injury or infection, where they generate and release reactive oxygen intermediates (ROI) and proteolytic enzymes directed at killing and phagocytosis of pathogens [13]. Subsequently, neutrophils undergo cell death, which potentially increases the amount of cytotoxic molecules at the site of infection [10]. Resident macrophages in the liver, termed Kupffer cells, also contribute to host antimicrobial defenses. However, in animal models of hepatotoxic liver injury, Kupffer cells also exhibit tissue-destructive potential [14]. Recent reports suggest that there are two subpopulations of Kupffer cells that can be differentiated by phenotype and function [15]. All Kupffer cells express the macrophage-restricted glycoprotein F4/80 [16]; however, subsets can be further characterized by the expression of CD11b, a C3b receptor present on the surface of monocytes and macrophages [17], or CD68, also known as macrosialin [18]. CD11b+ cells mainly produce cytokines and show weak cytolytic activity. By contrast, CD68+ cells exhibit phagocytic and cytotoxic activity via production of reactive oxygen species [19] and superoxide [20]. A heterogeneous CD11b+ monocyte population has been identified that expresses C-C chemokine receptor 2 (CCR2) and also shows high-level cell surface expression of Ly6C (Ly6ChiCCR2+). Secretion of C-C chemokine ligand 2 (CCL2) by injured or inflamed tissue cells induces migration of these Ly6ChiCCR2+ monocytes from the bone marrow to the site of infection, where they are involved in the immune defense responses against pathogenic microorganisms [21]. Activated Ly6ChiCCR2+ monocytes exhibit strong antimicrobial activity and promote pro-inflammatory immune responses [22]. In particular, in the liver, Ly6ChiCCR2+ monocytes give rise to TNFα- and iNOS-producing dendritic cells (TipDCs), inflammatory macrophages, and inflammatory DCs [22]. A number of models of hepatotoxicity show that CCR2−/− knockout mice are protected from liver injury, indicating the tissue destructive potential of Ly6ChiCCR2+ inflammatory monocytes [23]–[26]. The aim of the present study was to investigate the contribution of neutrophils, resident Kupffer cells, and Ly6Chi monocytes to liver injury in ALA using an immune competent mouse model for ALA [9]. The recruitment dynamics of these three immune cell subsets was investigated by immunohistochemistry and flow cytometry. The effects of selective cell depletion and neutralization on abscess development were monitored by magnetic resonance imaging (MRI). Here we showed for the first time that not parasite-derived hepatotoxic substances but TNFα released by Kupffer cells and Ly6Chi monocytes is critical for tissue damaging effects during ALA development. To determine the sites of neutrophil and liver macrophage infiltration within the liver abscess during ALA development, liver tissue sections were obtained over several days following infection with E. histolytica and analyzed by histology. Hematoxylin & Eosin (H&E) and periodic acid-Schiff (PAS) staining of paraffin-embedded samples was used to visualize host cells and amebic trophozoites, respectively, within the abscess lesion (Figure 1). Discrete infiltrates of predominately mononuclear cells (Figure 1A, Day 1) that co-localized with amebic trophozoites (Figure 1B, Day 1) were evident on Day 1 post-infection. Immunohistochemical staining demonstrated that most of the infiltrated cells were neutrophils (Figure 1C, Day 1), with a few macrophages (Figure 1D, Day 1). By Day 3 post-infection, the cellular infiltrate in the abscess had increased and become more organized (Figure 1A, Day 3). Mononuclear cells were located within the center of the abscess surrounding the trophozoites and at the periphery (Figure 1A and B, Day 3). Mononuclear cells within the abscess were identified as neutrophils (Figure 1C, Day 3), while macrophages accumulated predominantly at the boundary of the abscess (Figure 1D, Day 3). By Day 5 post-infection, H&E staining revealed a denser, but reduced, cellular infiltrate in the center of the abscess (Figure 1A, Day 5). The immune cell infiltrate was no longer dominated by neutrophils (Figure 1C, Day 5), but by F4/80-positive macrophages (Figure 1D, day 5). These results clearly demonstrate the differential migration and localization of neutrophils and macrophages during abscess formation relative to the location of amebic trophozoites within the liver tissue, which might suggest distinct functions of these myeloid immune cells for ALA-related hepatopathogenesis. Since neutrophils appeared to be the predominant cell type in the immune cell infiltrates during the first three days after infection, we analyzed total RNA isolated from abscessed liver tissue by quantitative real-time-PCR (qPCR) to determine the mRNA expression kinetics of C-C chemokine ligand 3 (CCL3), also known as macrophage inflammatory protein-1α (MIP-1α), a chemokine that participates in the recruitment of neutrophils. As shown in Figure 2A, CCL3 expression was already elevated by 6 h post-infection compared with that in uninfected (naïve) liver tissue (P<0. 03). CCL3 mRNA expression was also slightly increased in liver tissue from sham-operated animals that were injected with amebic culture medium alone. To further quantify neutrophil migration into the liver during ALA, liver leukocytes isolated from the abscessed liver region (abscess), an unaffected (healthy) part of the liver, and from the livers of sham-operated mice (sham) were analyzed by flow cytometry. Neutrophils were identified as CD11b+, Ly6C+ and Ly6G+ cells [22] (Figure 2B). The proportion of neutrophils in the abscessed liver tissue was highest on Day 1 one and then decreased over time up to Day 5 post-infection. These results were consistent with the immunohistochemistry results. Additionally, there was a slight increase in the proportion of neutrophils in the non-abscessed part of the affected liver lobe that also decreased over time (Figure 2B). To investigate the role of neutrophils in abscess formation, mice were subjected to immune depletion using anti-Ly6G and anti-GR1 (Ly6G+- and Ly6C+-reactive) antibodies [27] prior to intrahepatic amebic infection. Depletion of neutrophils (Ly6G+CD11b+) and Ly6C+ monocytes (Ly6ChiCD11b+) was confirmed by flow cytometry (Figure 2C). Depletion of Ly6G+CD11b+ cells was greatest one day after antibody treatment with either anti-GR1 (P<0. 01) or anti-Ly6G (P<0. 01) antibodies. Treatment with either antibody resulted in reduced numbers of Ly6G+CD11b+ cells up to six days post-depletion (Figure 2C, 1). Ly6ChiCD11b+ monocyte numbers were significantly reduced after a single treatment with anti-GR1 but not with Ly6G on Day 1,2 and 4 post depletion. However, six days post depletion, the blood counts of Ly6C+ monocytes raised indicative for a reduction in anti-GR1 antibody level (Figure 2C, 2). Two days after antibody depletion, mice were infected intra-hepatically with E. histolytica trophozoites. T2-weighted MRI spin echo analysis of infected livers enabled 3-dimensional analysis of liver lesions during the course of abscess development and quantification of abscess size (Figure 2D). There was a slight reduction in abscess size following neutrophil depletion using the anti-Ly6G antibody, but this result was not statistically significant. By contrast, depletion with anti-GR1 antibody resulted in a significant decrease in the size of the liver abscess as early as Day 3 post-infection (Figure 2E). Thus, Ly6C+ mononuclear cells, but not Ly6G+ neutrophils, appear to be critical cell mediators of tissue destruction during ALA formation. To narrow down the cell subset involved in liver tissue damage from the heterogeneous population of mononuclear phagocytes, we monitored the recruitment of three distinct macrophage subsets to the site of the developing abscess. Mononuclear cell subsets were identified based on differential expression of the surface markers CD11b, F4/80, Ly6C and Ly6G [28]. Resident Kupffer cells in the liver were defined as CD11bloF4/80hiLy6G− cells, whereas CD11bhiF4/80loLy6G− macrophages represented a transient inflammatory stage from blood monocyte to tissue macrophage (transient Kupffer cells). This latter subpopulation was further differentiated based on expression of the monocyte marker Ly6C. Gradient-purified liver leukocytes were isolated from the abscess region, from an unaffected region of the same liver lobe, and from livers of naïve mice, and the mononuclear subpopulations were assessed by flow cytometry (Figure 3A). Following infection with E. histolytica, the population of CD11bloF4/80hi macrophages, representing resident Kupffer cells, remained stable and did not differ between infected and naïve mice (Figure 3A, subset 1). By contrast, CD11bhiF4/80lo cells, representing transient Kupffer cells, were more abundant in the abscess on Day 1 post-infection compared with adjacent healthy tissue or naïve liver tissue (P<0. 05) (Figure 3A, subset 2). Of these, 30% also expressed a high level of Ly6C, indicating that they were derived from monocytes. During the course of ALA development, the subset of liver cells expressing high levels of Ly6C was lost, indicating the differentiation of monocytes into liver macrophages (Figure 3B). Resident Kupffer cells in the liver can exert hepatotoxic effects via expression of pro-inflammatory cytokines such as TNFα and IL-1β, as well as effector molecules such as NO [11]. To investigate the contribution of Kupffer cells to host tissue destruction during ALA, mice were subjected to cell depletion using clodronate [29]. Clodronate treatment significantly reduced the proportion of resident Kupffer cells (CD11b+F4/80hi) [28] in the liver (Figure 4A, subset 1). By contrast, transient, inflammatory monocyte-derived Kupffer cells (CD11bhiF4/80lo) were unaffected by clodronate treatment (Figure 4A, subset 2). Kinoshita et al. defined an “activated”, tissue destructive Kupffer cell population by the expression of CD68 (CD68+CD11b−F4/80+). We found that these cells were also diminished in the livers of infected clodronate-treated mice (Figure 4B, 1), whereas the proportion of non-activated (CD68− CD11b+F4/80+) Kupffer cells increased (Figure 4B, 2) [15]. Similarly, immunohistochemical staining of liver sections from clodronate-treated mice revealed a complete loss of F4/80+ Kupffer cells (Figure 4C). Moreover, there was an increase in Ly6C-expressing, CD11b+Ly6G− monocytes in the liver and blood of clodronate-treated mice compared with control animals (Figure 4D). Neutrophils were unaffected by clodronate treatment; moreover, there was a significant increase in the relative numbers of these cells in the abscess and healthy liver tissue (data not shown). ALA formation in infected mice was monitored by MRI from Day 3 to Day 7 post-infection. There was a significant reduction in abscess volume in clodronate-treated mice compared with that in control mice treated with empty liposomes or in untreated, infected wild-type mice (Figure 4E). We also examined whether the viability of E. histolytica trophozoites, which have strong phagocytic potential, was affected by clodronate treatment. Based on PAS staining, trophozoite membranes appeared to be intact, and phagocytosis of erythrocytes was unaffected by clodronate treatment (Figure 4F). Interestingly, the massive influx of immune cells into the abscess was abolished by clodronate treatment (Figure 4F). In addition, re-isolation experiments indicated viable E. histolytica trophozoites in all of the animals treated with clodronate liposomes irrespectively whether ameba were isolated on Day 1 or Day 3 post-infection (data not shown). Ly6Chi-expressing inflammatory monocytes are precursors of inflammatory tissue macrophages, the same cells that potentially mediate host tissue damage. Migration of Ly6Chi monocytes from the bone marrow into the circulation is controlled by the expression of CCR2 and its cognate ligand, CCL2 [30]. Using qPCR, we found a significant increase (P<0. 04) in CCL2 mRNA expression levels in the livers of infected mice compared with control mice as early as 6 h and up to 24 h post-infection (Figure 5A). Of note, in sham-operated animals (intrahepatic injection of culture medium), there was no increase in the expression of CCL2 mRNA. To investigate the importance of Ly6Chi monocyte recruitment in ALA, CCR2−/− mice were infected with E. histolytica trophozoites, and the abscess volumes were determined by MRI. There was a significant reduction in abscess size compared with that in wild-type mice as early as three days post-infection (Figure 5B), with a further reduction seen at five days post-infection (P<0. 0001). By contrast to wild-type mice, CCR2−/− mice had almost fully recovered from the abscess lesions at seven days post-infection (Figure 5B). Analysis of liver leukocytes from infected mice revealed a significant decrease in Ly6ChiLy6G−CD11b+ inflammatory monocytes in CCR2−/− mice five days post-infection compared with wild-type mice (Figure 5C, 1). By contrast, the proportion of Ly6CloLy6G−CD11b+ monocytes, which are thought to be involved in wound healing and tissue repair, did not appear to be affected (Figure 5C, 2). Likewise, there was no difference in the neutrophil population between infected CCR2−/− and wild-type mice (Figure 5C, 3). To confirm the role of Ly6Chi monocytes in promoting abscess development, adoptive transfer of purified, bone marrow-derived CD115+ monocytes was performed in CCR2−/− mice 6 h after intrahepatic amebic infection. As shown by MRI, abscess formation was more diffuse and multifocal in CCR2−/− mice following adoptive transfer of monocytes compared with the focal and dense character of the abscess formed in a wild-type mouse (Figure 5D). Importantly, there was an increase in abscess volume (P<0. 05) at Day 3 post-infection in animals that were nearly ALA-resistant prior to transfer (Figure 5E). These results indicated that Ly6Chi inflammatory monocytes contribute substantially to liver tissue destruction during ALA development. NO, produced by inducible nitric oxide synthase (iNOS), and TNFα are mediators of monocyte and macrophage cytotoxicity in host tissues [31]. Using qPCR, we investigated changes in iNOS and TNFα mRNA expression levels following intrahepatic amebic infection. Expression of iNOS and TNFα mRNA was upregulated following infection compared with that in naïve or sham-operated mice for up to 24 h, and declined thereafter (Figure 6A and B). To determine the relative contributions of iNOS and TNFα to tissue destruction following amebic infection, we induced ALA in iNOS−/− mice, in mice treated with the NO-inhibitor L-NMMA and in TNFα-neutralized mice, respectively (Figure 6C and D). In iNOS-deficient or L-NMMA treated mice, which lack NO, differences in abscess formation were evident only up to three days post-infection (Figure 6C), whereas in mice lacking TNFα, abscess sizes were reduced relative to wild-type mice on Day 5, and mice had largely recovered by seven days post-infection (P<0. 05) (Figure 6D). These results indicated that TNFα plays a critical role in promoting disease progression during ALA. To identify a potential source of TNFα during ALA, leukocytes isolated from the livers of infected, naïve and sham-operated mice were stimulated ex vivo with heat-killed listeria lysate and then analyzed by flow cytometry (Figure 6E). We identified a cell population that produced a low level of TNFα (TNFlo) and a population that produced a high level of TNFα (TNFαhi). At one day post-infection, there were significantly more TNFlo-expressing cells in infected mice compared with healthy, naïve mice (P<0. 05). The proportion of these cells gradually decreased until Day 5 post-infection. By contrast, the proportion of TNFαhi-expressing cells increased during the course of ALA development. Further characterization of TNFα-producing cells based on Ly6C expression revealed that TNFαlo-expressing cells comprised equal proportions of Ly6Clo- and Ly6Chi-expressing monocytes, while TNFαhi-expressing cells comprised mainly Ly6Chi inflammatory monocytes. In addition, we estimated the proportion of TNFα+F4/80+ Kupffer cells during ALA. Interestingly, the proportion of TNFα-producing Kupffer cells was low at the onset of intrahepatic amebic infection but raised until Day 5, correlating with the increasing numbers of Ly6Chi TNFαhi monocytes during ALA (Figure 6F). Thus, mainly Ly6Chi inflammatory monocytes promote disease progression during E. histolytica infection and mediate liver tissue damage, in part, through elevated expression of TNFα. E. histolytica is a protozoan parasite that normally persists as a harmless commensal organism in the intestine of humans. Parasite pathogenicity factors identified and characterized to date have been implicated in survival within the gut by mediating attachment to colonic mucins, as well as the uptake, killing and digestion of bacteria engulfed from the gut flora [32]. However, these effector molecules also enable penetration of the parasite into the submucosa, leading to chronic ulcerative gut inflammation. During this process, the parasite can spread via the blood stream to other organs of the body, in particular in the non-permissive microenvironment of the liver. Of long-standing debate is whether parasite effector molecules or host factors are responsible for the tissue destruction observed during ALA. To investigate whether host immune mechanisms contribute to ALA development, a recently established mouse model for ALA was used. In contrast to other rodent models for ALA, only the mouse model allows state of the art immunological investigations. Other immunocompetent animals used as models for ALA include the highly susceptible hamster and the gerbil (Meriones unguiculatus) [7], [33]–[35]. Like in the gerbil, but in contrast to the hamster model, the time course of abscess formation in the mouse model is self-limited and amebic lesions are cleared within 30 days post infection. However, similar to abscess formation in hamsters and gerbils, the mouse model shows a massive infiltration of immune cells at the site of infection, followed by necrosis in the center of the abscess at a later time point [9]. Epitheloid cells, indicative for granuloma formation that is usually detected in the infected liver of the hamster or the gerbil model for ALA [34], are not characteristic for ALA formation in the mouse. However, as is also shown in the hamster, amebic trophozoites are rarely detected in direct contact to hepatocytes leading to the assumption that tissue destruction is a result of accumulation and subsequent lysis of leukocytes and macrophages, as already suggested by others [7]. In agreement with histological studies in other animal models for ALA, neutrophils were the first immune cells to infiltrate the liver during the acute phase of invasive amebic infection. Neutrophils are thought to exert a protective role during ALA [36], and their presence at the site of infection was consistent with previous studies showing that E. histolytica-derived surface peptides act as neutrophil chemoattractants [37]. More recently, classical danger signals and chemokines released from injured hepatic cells were shown to be involved in the recruitment of neutrophils as well [10]. Using immunohistochemistry and quantitative flow cytometry, we showed that neutrophils comprised the majority of infiltrating immune cells in the abscess one day after intrahepatic amebic infection, and localized close to amebic trophozoites. By Day 3 post-infection, when the abscess reached its maximum size, neutrophil staining was more diffuse, suggesting that substantial cell death was occurring. By seven days post-infection, neutrophils represented a minor population of immune cells in the abscess, suggesting that most neutrophils had already undergone cell death. Neutrophils play a central role in host defense against invasive microorganisms, and in vitro stimulation with cytokines (i. e. IFNγ and TNFα) or LPS triggers amebicidal activity, presumably by inducing expression of reactive oxygen species (ROS) [36]. However, ROS, as well as the diverse array of proteases derived from neutrophils and expressed during the respiratory burst, can also mediate host tissue damage. This event is not necessarily detrimental to the host, since it can also lead to the initiation of wound healing [38]. To investigate the contribution of neutrophils to liver tissue destruction during ALA, we performed immune depletion experiments using anti-Ly6G and anti-GR1 monoclonal antibodies (mAbs). Anti-Ly6G recognizes the neutrophil-specific cell surface molecule Ly6G, and selectively depletes neutrophils. By contrast, anti-GR1, which is a classical neutrophil depletion agent, also recognizes Ly6C-expressing monocytes [39]. Immune depletion experiments in severe combined immune deficient (SCID) mice using anti-GR1 mAbs demonstrated a protective role for neutrophils during ALA. Abscesses in immune depleted mice were significantly larger, contained fewer immune cells, and had a greater number of amebic trophozoites compared to. However, SCID mice are not able to mount an appropriate immune response because they lack T and B lymphocytes; therefore, neutrophils play a more prominent role in the ALA SCID mouse model that may not reflect a normal physiological setting [40]. Interestingly, compared with the data obtained from SCID mice, the current results showed nearly the opposite phenomenon. Despite the fact that immune depletion of neutrophils with anti-Ly6G mAbs led to a significant decrease in the number of neutrophils, liver abscess size was slightly smaller compared with that in wild-type mice. Thus, our results indicated that neutrophils do not have a beneficial role in ALA and, in fact, contribute to liver damage during amebic infection. Of note, concomitant depletion of Ly6C-expressing monocytes using anti-GR1 mAb led to an even more pronounced reduction in abscess volume, which indicates that Ly6C+ inflammatory monocytes, as precursors of inflammatory F4/80-expressing macrophages [11], are also involved in liver tissue destruction during hepatic amebiasis. In contrast to neutrophils, on Day 1 post-infection, F4/80+ macrophages appeared to be less abundant and were not in direct contact with amebic trophozoites. At Day 5 post-infection, these cells formed a margin around the center of the abscess and eventually infiltrated the abscess completely. Using flow cytometry to further differentiate the F4/80+ macrophage subsets involved in ALA, we found no differences over the course of ALA in the number of resident Kupffer cells (CD11bloF4/80hi); rather, there was a strong increase in transient inflammatory liver macrophages (CD11bhiF4/80lo) in the abscessed liver area. On Day 1 post-infection, the majority of these cells also expressed the monocyte surface marker Ly6C; however, over time, Ly6C expression was lost, suggesting that these cells originated as infiltrating inflammatory Ly6Chi monocytes. Resident Kupffer cells are the first macrophage population in the liver to come into contact with invading E. histolytica trophozoites. In vitro and in vivo studies support a critical role for these cells in killing and eliminating parasites. Activated by pro-inflammatory cytokines or colony stimulating factor-1, resident Kupffer cells produce NO, which is amebicidal, as well as ROS, perhaps the most effective molecule for amebic killing [36], [41]. Activated Kupffer cells also contribute to liver tissue destruction in models of viral-induced or hepatotoxic liver diseases [20], [42]–[44]. In these models, activated Kupffer cells express CD68 and exert hepatotoxic effects by secreting inflammatory mediators such as TNFα, Fas ligand, or ROS [19], or by promoting the accumulation of cytotoxic T cells in the liver [45]. In the current study, the depletion of Kupffer cells by gadolinium chloride (GdCl3) or clodronate liposome treatment almost completely abolished ALA pathology. Surprisingly, Kupffer cells also contributed substantially to liver damage during ALA formation. The number of abscesses in clodronate-treated mice was significantly reduced compared with control mice. Using the gating strategies described by Karlmark et al. [28] and Kinoshita et al. [15], we demonstrated a substantial reduction in F4/80hiCD11b+ cells, as well as F4/80+CD68+ Kupffer cells, in the livers of clodronate-treated animals. Thus, activated CD68+ Kupffer cells play a major role in the immune pathology observed during ALA. Although abscess formation was significantly reduced, amebic trophozoites within the remaining lesions appeared healthy as determined by histology. Trophozoites were still engaged in phagocytosis of host cells and exhibited strong PAS staining, indicative of intact cell membranes. Interestingly, we found a near-complete absence of immune cells in the residual abscess lesions of clodronate-treated mice, which indicates that Kupffer cells may be involved in the initiation of inflammation during abscess formation. The high re-isolation rate of viable ameba trophozoites from the liver up to Day 5 post clodronate treatment further indicates a minor direct role of E. histolytica for liver damage. CD11b+Ly6C+ blood monocytes were recruited to the liver at an early time point after amebic infection. qPCR analysis indicated that the mRNA expression level of CCL2, one of the most potent chemoattractants of inflammatory Ly6Chi monocytes, was upregulated within hours after intrahepatic infection with E. histolytica. CCL-2 binds to CCR2-expressing Ly6C+ monocytes and initiates the migration of Ly6Chi monocytes from the bone marrow into the circulation. Knockout mice lacking CCR2 often show an increased susceptibility to microbial infections [21]. Abscess formation was almost abolished in CCR2−/− mice. This was accompanied by a significant reduction in the percentage of CD11b+Ly6Chi inflammatory monocytes, whereas the percentage of CD11b+Ly6Clo monocytes and neutrophils was unchanged or even elevated. Adoptive transfer of wild-type CD115+ monocytes into CCR2−/− mice restored abscess formation, and transferred monocytes were confirmed as mainly Ly6Chi-expressing cells. Thus, CCR2+Ly6Chi inflammatory monocytes appear to play a critical role in abscess formation. Interestingly, the abscesses in these mice appeared multifocal, in contrast to the dense appearance of the liver lesions in wild-type mice. These findings were similar to those seen with acetaminophen-, carbon tetrachloride-, or diet-induced models of liver injury [24]–[26], [46]. However, we do not believe that ALA is a “toxic-like” type of liver destruction in response to the complex culture medium co-injected with the amebic trophozoites. In contrast to other effector molecules, such as TNFα or iNOS, CCL2 mRNA expression was upregulated only in the presence of amebic trophozoites. Nitric oxide (NO) is reported to be a major cytotoxic molecule produced by macrophages that inhibits amebic pathogenicity factors like cysteine proteinases and alcohol dehydrogenase 2 [41]. In addition, in vitro data suggested that E. histolytica trophozoites or amebic components might modulate macrophages functions i. e. NO production [47] by competing for the substrate L-arginine [41]. Seydel et al. have shown that mice, lacking both the IFNγ receptor and iNOS (129/Sv/Ev 3 C57BL/6 (iNOS1/2) ) were unable to control ALA [48]. In contrast to the current opinion, our results indicate a minor contribution of NO for ALA control. Both iNOS−/− and L-NMMA treated mice indicated only moderate effect of NO on ALA development within the first three days after intrahepatic infection and argues that the effect seen in the double knock-out mice used by Seydel et al. might primarily be due to the lack of the ability to activate immune cells via IFNγ, a cytokine that is crucial in the control of ALA [9], [36], [49]. TNFα is a key cytokine that correlates with macrophage dependent tissue destruction [11]. TNFα mRNA expression was induced at the onset of ALA, but this was also observed, albeit to a lesser extent, in sham-operated mice. In contrast, intracellular production of TNFα protein in re-stimulated liver leukocytes was higher in ex vivo cultures from infected mice compared with sham mice, which suggests that TNFα was secreted specifically in response to E. histolytica infection. Ly6Chi inflammatory monocytes produced high levels of TNFα, whereas Ly6Clo monocytes expressed lower levels of TNFα. In addition, we found low numbers of TNFα-producing Kupffer cells that increased significantly during the disease progression. Neutralization of TNFN during amebic infection resulted in a decrease in the size of abscesses, supporting a critical role of this cytokine in liver tissue destruction. However, further experiments are required to investigate the crosstalk between Kupffer cells and monocytes leading to tissue destruction during ALA. In conclusion, data from the current study demonstrated that host immune responses play a major role in the liver pathology induced by E. histolytica infection. Challenging previous assumptions, we found that the contribution of neutrophils to ALA may be overestimated in certain models, since they neither contributed substantially to tissue destruction nor the progression of ALA. Rather, Kupffer cells and inflammatory monocytes are likely the main cell populations responsible for tissue destruction. TNFα was a critical cytokine mediator of tissue destruction during ALA. Additional studies are needed to unravel the complex interplay between activated Kupffer cells and inflammatory monocytes during ALA development. The study was carried out in accordance with the guidelines from the German National Board for Laboratory Animals and approved by the Authority for Consumer Protection and Health, Hamburg, Germany (ethical permits 23/09,41/11). Male C57BL/6 mice (aged 10 to 12 weeks) were obtained from Charles River Laboratories (Sulzfeld, Germany); CCR2−/− mice were kindly provided by Daniel Engels (University Clinic of Bonn, Germany); iNOS−/− mice (Max-Plank Institute for Infection Biology, Berlin, Germany) were housed and bred in the animal facility of the Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany. Mouse strains were backcrossed for more than 10 generations against a C57BL/6 background. ALA was induced using virulent cell line B derived from E. histolytica HM-1: IMSS through long-term culture [50]. Trophozoites of HM-1: IMSS were grown in axenic cultures in TYI-S-3 medium [51]. ALA was induced by intrahepatic injection [52] of 5×104 virulent E. histolytica trophozoites, cell line B [50], [52] as described previously [9]. Sham-operated mice received TYI-S-3 medium alone. MRI was performed at the indicated times post-infection using a small animal 7 tesla MRI scanner (ClinScan, Bruker Biospin GmbH, Ettlingen, Germany). MRI was performed using a T2-weighted turbo spin echo sequence (T2TSE). Total abscess volume was calculated by measuring the region of interest (ROI) in each slice showing the abscess on transversal sections of the abdomen using OsiriX Imaging Software DICOM Viewer (Open-source version 32-bit 4. 1. 1). Liver tissue from ALA mice was fixed in formalin (4%) and then embedded in paraffin. Sections (0. 2 µm) were stained with H&E, PAS, or prepared for immunohistochemistry. Neutrophils were visualized using rabbit anti-mouse 7/4 antibody (clone 7/4; Cedarlane; 1∶800 dilution) and macrophages were visualized using rat anti-mouse F4/80 antibody (clone Cl: A3-1; Serotec; 1∶3000 dilution) using standard methodology. Antibodies were detected using DCS SuperVision Single Species horse-radish peroxidase (HRP) -Polymere (Innovative Diagnostic-Systems) and the samples were counterstained with hemalaun. Immune depletion of neutrophils was performed by intraperitoneal (i. p.) administration of anti-Ly6G mAb (clone 1A8, BioXcell; 500 µg/animal) on Days -2, -1, and 0 (relative to the day of infection on Day 0), and on Days 1 and 2 post-infection. Similarly, immune depletion with GR-1 mAb (clone RB6-8C; 300 µg/animal) was performed by i. p. administration on Day -2 and Day 1 post-infection. To neutralize TNFα, rat anti-TNFα mAb (V1qH8, Abcam; 500 µg/animal) was administered i. p. 24 h prior to intrahepatic infection. Rat IgG (Jackson Laboratories Inc; 300 µg/animal) was used as a control mAb and administered i. p. as described for depletion mAbs. Cell suspensions were prepared from the bone marrow of C57BL/6 mice. Monocytes were labeled with biotinylated anti-CD115 mAb (clone AFS98; eBioscience) and then purified using streptavidin MicroBeads (Miltenyi) and magnetic-assisted cell sorting. Adoptive transfer was carried out using 1×106 monocytes via lateral tail vein injection 6 h post-infection. Mice were injected intravenously in the tail vein using 200 µl Clodronate liposome solution (ClodronateLiposomes. org, Amsterdam, Netherland) or empty liposomes as control two days prior to intrahepatic infection with E. histolytica trophozoites. L-NMMA (2 mg/in 100 µl phosphate buffered saline/animal) was applied i. p daily from Day 2 before until Day 7 after amebic challenge. Leukocytes were isolated from liver and whole blood. Livers were perfused with ice-cold PBS, minced, and then filtered through a 70 µm nylon mesh. After washing, the cell pellet was subjected to density gradient centrifugation using 30% Nycodenz (Nycoprep, Universal). Leukocytes were isolated from the interface and subjected to red blood lysis (RBL). Fc-γ receptors were blocked with rat anti-mouse CD16/CD32 antibody (Fc-γ III/II receptor) and then cells were stained with the indicated antibodies for FACS analysis. Whole blood was subjected to RBL, blocked as described above and then stained with the indicated combinations of the following mAbs: CD11b (cl: M1/70); CD115 (cl: AFS98); F4/80 (cl: BM8), GR1 (cl: RB6-8C5), CD68 (cl: FA-11), Ly6G (cl: 1A8), Ly6C (cl: HK1. 4), Isotype IgG1κ (BioLegend). Data were acquired with a BD Accuri C6 Flow Cytometer (Accuri Cytometers Inc.) and analyzed with FlowJo 7. 6. 3 (Treestar). For intracellular TNFα staining, purified spleen and liver lymphocytes (1×106 cells) were stimulated with 10 µl of heat killed Listeria monocytogenes (1. 6×109 HKL/ml) [31]. Un-stimulated control cells were incubated with 1 ml of complete RPMI 1640 medium. Liver and spleen lymphocytes were stimulated for 30 min at 37°C in a 5% CO2 atmosphere and then incubated for additional 4 hours with Brefeldin A. After blocking in Fc-γ receptor blocking solution, cells were washed and then subjected to surface antigen staining using the antibodies described for flow cytometry. Following fixation (Becton Dickinson), cells were permeabilized in Perm/wash solution (1∶10 dilution; Becton Dickinson). Intracellular cytokine staining was performed using an anti-TNFα mAb (cl: MP6-XT22) and followed by FACS analysis. For isolation of total RNA, abscessed liver material in an appropriate volume of Trizol (Ambion) was homogenized and subjected to isopropanol precipitation. Purification was performed using the RNeasy Mini Kit (Qiagen). cDNA was synthesized using the MaximaFirst Strand cDNA synthesis kit (Fermentas). qPCR was performed on a Rotor-Gene RG-3000 (Corbett Research) system using the Maxima SYBR Green qPCR Master Mix (Fermentas). Expression levels were calculated using the 2−ΔΔCt method [53], normalized to ribosomal protein S9 (RPS9) and calibrated against expression measured at 120 hours in sham-operated mice. Calculations were performed using rotor-gene 6 Version 6. 1 CR software (Corbett Research). The following mouse specific primer sequences were used for amplification: iNOS s: TGGTGGTGACAAGCACATTT; iNOS as: TGGTGGTGACAAGCACATTT; TNFα s: AGTTCCCAAATGGCCTCCCTCTCA; TNFα as: GTGGTTTGCTACGACGTGGGCT; CCL3 s: ATGAAGGTCTCCACCACTGC; CCL3 as: GATGAATTGGCGTGGAATCT; CCL2 s: TCTCTCTTCCTCCACCACCA; CCL2 as: CGTTAACTGCATCTGGCTGA RPS9 s: CCGCCTTGTCTCTCTTTGTC; RPS9 as: CCGCCTTGTCTCTCTTTGTC The non-parametric Mann-Whitney U test and unpaired Student' s t test were performed using Prism statistical software (GraphPad Prism 5).

Amebic liver abscess (ALA), an infectious disease caused by the protozoan parasite Entamoeba histolytica is characterized by severe focal liver damages. According to its name giving activity, destruction of liver tissue by E. histolytica has been attributed to parasite-specific effector molecules. However, abscess lesions contain considerable numbers of innate immune cells, such as neutrophils and macrophages, raising the question whether these host cells might contribute to the disease as well. We have investigated the role of the host immune response during ALA development using a mouse model for the disease. The results indicated that despite the presence of considerable numbers of neutrophils within the abscess lesions, these cells were dispensable for both the control of the disease and the tissue damage. On the other hand, two subsets of immune cells, the liver resident Kupffer cells and the inflammatory Ly6C-expressing monocytes were identified as the main effector cells responsible for liver tissue destruction. Furthermore, TNFα produced by the Ly6C-expressing monocytes, was found to be a cytokine that is critically involved in abscess development. Thus, our finding that host immune mechanisms are indeed responsible for liver tissue destruction during ALA development may change the view on the pathological mechanism of amebic disease.

Abstract Introduction Results Discussion Methods

medicine biology

2013

TNFα-Mediated Liver Destruction by Kupffer Cells and Ly6Chi Monocytes during Entamoeba histolytica Infection

As the central hub of the metabolism machinery, the mammalian target of rapamycin complex 2 (mTORC2) has been well studied in lymphocytes. As an obligatory component of mTORC2, the role of Rictor in T cells is well established. However, the role of Rictor in B cells still remains elusive. Rictor is involved in B cell development, especially the peripheral development. However, the role of Rictor on B cell receptor (BCR) signaling as well as the underlying cellular and molecular mechanism is still unknown. This study used B cell–specfic Rictor knockout (KO) mice to investigate how Rictor regulates BCR signaling. We found that the key positive and negative BCR signaling molecules, phosphorylated Brutons tyrosine kinase (pBtk) and phosphorylated SH2-containing inositol phosphatase (pSHIP), are reduced and enhanced, respectively, in Rictor KO B cells. This suggests that Rictor positively regulates the early events of BCR signaling. We found that the cellular filamentous actin (F-actin) is drastically increased in Rictor KO B cells after BCR stimulation through dysregulating the dephosphorylation of ezrin. The high actin-ezrin intensity area restricts the lateral movement of BCRs upon stimulation, consequently reducing BCR clustering and BCR signaling. The reduction in the initiation of BCR signaling caused by actin alteration is associated with a decreased humoral immune response in Rictor KO mice. The inhibition of actin polymerization with latrunculin in Rictor KO B cells rescues the defects of BCR signaling and B cell differentiation. Overall, our study provides a new pathway linking cell metablism to BCR activation, in which Rictor regulates BCR signaling via actin reorganization. B cell receptor (BCR) signaling is vital for B cell development and function. When BCRs are cross-linked by antigens, it induces the conformational changes of signaling subunits immunoglobulin α chain (Igα) and immunoglobulin β chain (Igβ). The conformational changes of Igα and Igβ lead to the phosphorylation of immunoreceptor tyrosine-based activation motif (ITAM) domains of Igα and Igβ. The phosphorylated ITAM domain recruits LYN proto-oncogene, Src family tyrosine kinase (Lyn) for phosphorylation, and the phosphorylation of Lyn activates spleen tyrosine kinase (Syk). This initiates the activation of downstream signaling cascades, such as the activation of Brutons tyrosine kinase (Btk) and phospholipase C gamma 2 (PLCγ2) [1–3]. At the end of activation of BCR signaling, the negative regulators of BCR signaling are also activated, such as phosphorylated SH2-containing inositol phosphatase (pSHIP), which is regulated by Lyn [4–7]. Recently, with the development of the high-resolution technique of total internal reflection fluorescent microscopy (TIRFm), the molecular details of the initiation events in BCR activation have been revealed [8–10]. The conformational changes of the BCR expose the Cμ4 domain of membrane immunoglobulin M (IgM) for BCR self-aggregation [11] and ITAMs for signaling molecules to bind [12]. The role of actin on BCR signaling has been characterized recently with TIRFm as well. Actin is known to be involved in BCR capping [13,14], and the disruption of actin delays the attenuation of BCR signaling in B cells induced by soluble antigen (sAg) [15] or even induces BCR signaling alone, without antigen stimulation [16]. TIRFm coupled with single-molecule tracking techniques has dissected the underlying mechanism that links the actin network and BCR movement. In resting B cells, actin and ezrin together form a network that both defines compartments containing mobile BCRs and establishes boundaries restricting BCR diffusion. The BCR diffusion coefficient is inversely related to the actin intensity on the plasma membrane. Breaking down of the actin fence by latruculin treatment increases the diffusion coefficient of BCRs and induces BCR signaling comparable to that triggered by BCR cross-linking [17,18]. Therefore, actin depolymerization is essential for the initiation of BCR signaling. As a core component of mTORC2, Rictor has been studied recently in all kinds of cells. Although Rictor deletion early in B cells using vav guanine nucleotide exchange factor (Vav) -Cre has a modest effect on the development of pro- and pre-B cells in the bone marrow by up-regulating forkhead box O1 (FoxO1) and recombination activating 1 (Rag-1) [19,20], it causes a severe impact on the peripheral development [19]. The reduction of marginal zone (MZ) B cells and B1a cells is more severe than folicular (FO) B cells. Furthermore, antibody production is greatly impaired when mature B cells lose Rictor expression after completing their development by using Cre-ERT2Rictorfl/fl mice[19]. Mechanistically, Rictor is vital for the induction of prosurvival genes, suppression of proapoptotic genes, nuclear factor κB (NF-κB) induction after BCR activation, and nuclear factor κB2/p52 generation [19]. Therefore, Rictor is critical for B cell survival signals initiated via Phosphotidylinositol 3 kinase (PI3K) [19]. However, it is unknown how Rictor affects BCR signaling or early B cell activation. The activation of both BCR and T cell receptor (TCR) induces the dephosphorylation of ezrin-radixin-moesin (ERM) proteins that are the linkers between the plasma membrane and the actin cytoskeleton and induces the detachment of ERM from the actin cytoskeleton [21–23]. Similar to the role of the actin cytoskeleton in the steady state, ezrin also forms a network that, together with actin, restricts the movement of BCRs and slows the diffusion rate [17]. The transient inactivation of ERM, such as dephosphorylation of ezrin, can increase the diffusion rate of unengaged BCRs. The dephosphorylation of ezrin can alter the interaction between the actin cystoskeleton and plasma membrane, which can in turn alter the B cell’s morphology by modulating the filopodia. Consequently, this impairs BCR clustering and B cell spreading during B cell activation [24]. The BCR-mediated phosphorylation of ezrin negatively regulates activation events such as the phosphorylation of tyrosine kinases [25]. In systemic lupus erythematosus (SLE) T cells, the binding of autoantibodies to the cluster of differentiation 3 (CD3) -TCR complex induces the phosphorylation of ezrin and actin polymerization [26]. The inhibition of ezrin with pharmacological inhibitors or small interfering RNA (siRNA) reduces the formation of actin stress fibers [27]. The phosphorylation of ezrin is regulated by serine/threonine kinases including rho-associated coiled-coil-containing protein kinase (ROCK) and protein kinase C (PKC) [28,29]. Considering that Rictor is involved in the reorganization of actin, it is not clear whether Rictor links ezrin to regulate BCR signaling as well as the underlying mechanism. In this study, we used cluster of differentiation 19 (cd19) -Cre to delete Rictor specifically in B cells and excluded the deletion outside of the B lineage by using Vav-Cre and mixed chimerism in non–B lineages, irradiation-induced load of apoptotic bodies when generating chimera mice. We found that Rictor positively regulates BCR signaling via up-regulating Btk and down-regulating SH2-containing inositol phosphatase (SHIP). Mechanistically, the reduction of BCR signaling is caused by the less mobile BCRs in the activation state, and Rictor deficiency disrupts the early actin depolymerization phase during BCR activation and enhances the actin polymerization and phosphorylation of ezrin. All of these account for the high intensity of ezrin-actin areas that restrict the diffusion of BCRs, which are essential for the triggering of BCR signaling. Furthermore, the reduction of FO B cells was more severe in immunized Rictor KO mice, but we did not observe changes for MZ B cells. Interestingly, the introduction of Latrunculin B, an actin inhibitor in vitro and in vivo, can rescue the defect of differentiation of FO B cells and BCR signaling. To determine whether Rictor is involved in BCR activation or not, we examined the spatiotemporal relationship between BCR and Rictor using phosphorylated antibody specific for activated Rictor by confocal microscopy (CFm). At 0 min, phosphorylated Rictor (pRictor) was distributed on the plasma membrane evenly (Fig 1A). At 5 min and 10 min, pRictor was redistributed and cocapped with the BCR cluster (Fig 1A). At 30 min, the degree of cocapping of BCR with pRictor was decreased as BCRs started to be internalized (Fig 1A). We used a correlation coefficient to determine the colocalization of BCR and pRictor quantitatively. The colocalization between BCR and pRictor was increased over 10 min and decreased by 30 min. It increased significantly at 5 min and 10 min compared to 0 min (Fig 1B). Additionally, the levels of pRictor measured with mean fluroscence intensity (MFI) by NIS-Elements AR 3. 2 software peaked at 10 min upon antigen stimulation (Fig 1C). These results suggest that Rictor is involved in BCR activation. In order to further determine whether Rictor signaling is also involved in BCR activation, we examined the location and expression levels of the downstream signaling molecule of Rictor, phosphorylated Akt (pAkt), in wild-type (WT) and Rictor KO B cells. First, in order to determine the deletion efficiency of cd19-Cre and line leakage, we examined the mRNA levels of rictor in B cells, CD4+, and CD8+ T cells using real time PCR (RT-PCR) and protein levels of Rictor in B cells using western blot. The mRNA levels of rictor and protein levels of Rictor were significantly lower in Rictor KO B cells but had no changes in CD4+ and CD8+ Rictor KO T cells (S1A and S1B Fig). This result suggests the cd19-Cre deletion efficiency is high in B cells without leakage in other types of immune cells. Similar to that of pRictor, the location of pAkt was distributed on the plasma membrane evenly at 0 min and cocapped with BCR at 5 min and 10 min and then began to have endocytosis at 30 min, together with BCR in WT B cells (Fig 1D). In contrast with that of WT B cells, the distribution of pAkt in Rictor KO B cells did not have obvious changes and BCR internalization was severely disrupted (Fig 1D and 1E). Additionally, the MFI of pAkt quantified with NIS-Elements AR 3. 2 software in WT B cells was increased over 10 min and decreased by 30 min, and it was significantly decreased in KO B cells (Fig 1F). We also used flow cytomery to quantifiy the pAkt levels in WT and KO B cells after sAg stimulation and observed similar results as with CFm (Fig 1G). The colocalization of BCR with pAkt was increased by 10 min and decreased at 30 min but did not show obvious changes in WT B cells and was significantly decreased in KO B cells (Fig 1H). Taken together, these results suggest that Rictor as well as Rictor signaling is involved in BCR activation. In order to determine the effect of Rictor deficiency on BCR signaling, we examined the levels of phosphorylated Brutons tyrosine kinase (pBtk) and pSHIP, the key postive and negative molecules of upstream BCR signaling, as well as total phosphotyrosine (pY) to indicate the total level of BCR signaling. The levels of pBtk and pY were increased over 10 min in WT B cells quantified by NIS-Elements AR 3. 2 software and decreased by 30 min (Fig 2A, 2C and 2D). pBtk and pY were colocalized with BCR at 5 min and 10 min after stimulation and the degree of colocalization was decreased at 30 min in WT B cells (Fig 2A and 2E). In contrast to that of WT B cells, the levels of pBtk and pY were significantly decreased in KO B cells and the signalosomes of pBtk or pY were always distributed on the plasma membrane (Fig 2A–2D). The colocalization of pY and pBtk with BCR was increased over 10 min and decreased at 30 min in WT B cells, but it was dramatically decreased in KO B cells (Fig 2A, 2B and 2E). In order to further confirm the reduction of pY and pBtk in KO B cells, we examined the levels of pBtk and pY in WT and KO B cells stimulated by sAgs with flow cytometry. Similarly, we found the levels of pY and pBtk were significantly decreased in KO B cells (Fig 2F and 2G). Since the mammalian target of rapamycin (mTOR) /Akt and phospholipase C gamma 2 (PLCγ) /Ca2+ mobilization are seen as separate pathways downstream of the BCR, we examined the Ca2+ mobilization with flow cytometry. We found the Ca2+ mobilization was reduced in Rictor KO B cells after stimulation with sAg (Fig 2H). Additionally, we tested the distal BCR signaling levels such as phosphorylated extracellular regulated protein kinases (pErk) and we found that it was decreased in Rictor KO B cells (Fig 2I). To further confirm the down-regulation of BCR signaling by Rictor deficiency, we tested the levels of pBtk, pAkt, pErk, and pY with western blot after sAg stimulation and found a reduction in early and distal BCR signaling (Fig 2J). Furthermore, we examined the effect of Rictor deficiency on the recruitment of the negative signaling molecule, pSHIP. The MFI of pSHIP was increased over time until 30 min in WT B cells quantified by NIS-Elements AR 3. 2 software, but it was significantly increased in KO B cells (Fig 3A–3C). Similar to the staining pattern of pY and pBkt (Fig 2A), pSHIP cocapped with BCR and went through internalization at 30 min in WT B cells (Fig 3A). In KO B cells, pSHIP was always colocalized with BCR (Fig 3B). To confirm the increase of pSHIP in KO B cells, we determined the levels of pSHIP in KO B cells by flow cytometry and found similar results (Fig 3D). We examined the colocalization of BCR and pSHIP using correlation coefficient in WT and KO B cells and found the colocalization of BCR and pSHIP was increased over time by 30 min in WT B cells and was significantly increased in Rictor KO B cells (Fig 3E). To exclude the effect of Rictor deficiency on BCR signaling that is due to B cell development, we examined the effect of Rictor deficiency on bone marrow and peripheral development in Rictor KO mice using flow cytometry. We found the frequency and number of pro-B cells were moderately increased and those of late pre-B cells and recirculating B cells were slightly decreased in Rictor KO mice (S2A–S2D Fig). We also examined the expression levels of interleukin 7 (IL-7) receptors and did not observe any changes (S2E Fig). Then, we examined the alteration of FO, MZ, and germinal center (GC) B cells in the spleen of Rictor KO mice without immunization. We found the frequency and number of FO and GC B cells was decreased and did not observe changes for MZ B cells (S3A–S3G Fig). Furthermore, we examined the expression levels of IgM and immunoglobulin D (IgD) and did not observe any differences of MFI of IgM and IgD between WT and KO B cells (S3H and S3I Fig). Overall, the deficiency of Rictor causes slight impact on the bone marrow and peripheral development. These results imply that Rictor regulates BCR signaling positively and the absence of Rictor leads to unbalanced positive and negative BCR signaling. Rictor has been reported in several types of cells to regulate the actin cytoskeleton, although its coordination with actin in lymphocytes still remains elusive [30,31]. Rictor-mTOR complex modulates the phosphorylation of protein kinase C α (PKCα) and the actin cytoskeleton [32]. Our previous studies have shown that actin can offer feedback to BCR signaling [16,33–35]. In order to investigate that the effect of Rictor on BCR signaling is coincident with actin alteration, we examined the Rictor deficiency and actin reorganization in B cells after stimulation with sAgs and membrane-associated antigens (mAgs). Filamentous actin (F-actin) was stained with phallodin, the spatiotemporal position was examined by CFm, and the levels of actin were quantified by NIS-Elements AR 3. 2 software and flow cytometry. Compared with the levels of F-actin in WT B cells, the levels of F-actin on the plasma membrane and in the cytoplasm of KO B cells were significantly enhanced at 10 min (Fig 4A–4C). However, the basal levels of F-actin examined by flow cytometry were not altered in KO B cells in the nonstimulated condition (Fig 4D). Interestingly, we found the total levels of F-actin were decreased by 5 min and then increased afterwards until 30 min by flow cytometry (Fig 4D), which indicates the depolymerization of actin in the early phase and polymerization of actin afterwards in WT B cells upon sAg stimulation (Fig 4D). However, we found a dramatic increase of the levels of F-actin by 5 min and a moderate decrease afterwards until 30 min in KO B cells (Fig 4D), and the levels of F-actin in KO B cells were always higher than that of WT B cells (Fig 4D). Because the levels of F-actin were highly condensed on the plasma membrane, we used TIRFm to determine the levels of F-actin in the contact zone between B cells and the antigen-tethered lipid bilayer. In WT B cells, the levels of F-actin on the contact zone were increased over 5 min and decreased at 7 min (Fig 4E and 4G), which is consistent with our previous study [16]. However, in KO B cells, the levels of F-actin were increased over time until 7 min and significantly higher than that of WT B cells (Fig 4E, 4F and 4G). We also determined the recruitment of BCR microclusters in the contact zone by measuring the MFI of the BCR cluster. In WT B cells, the MFI of the BCR cluster was increased over time and it was also increased over time in KO B cells, but it was significantly decreased in KO B cells compared to that of WT B cells (Fig 4E, 4F and 4H). The formation of the BCR microclusters triggers BCR signaling, and we examined the recruitment of activated Btk in the contact zone. The recruitment of pBtk was increased over 5 min and decreased by 7 min in WT B cells, and the recruitment of pBtk in KO B cells had a similar trend but was significantly decreased compared to that of WT B cells (Fig 4J). Our previous research has shown that upon stimulation with mAg, actin polymerizes first to facilitate spreading of B cells and depolymerizes later at the center to promote the formation of the central BCR cluster. During these events, F-actin colocalizes well with BCRs at first and then redistributes to the outer edge of the central BCR cluster [16,35]. As expected, in WT B cells, F-actin colocalized well with BCRs at early time points and redistributed to the outer edge of central BCR cluster (Fig 4E and 4K). The colocalization between BCRs and F-actin increased over 3 min and decreased thereafter (Fig 4E and 4K). In KO B cells, F-actin always colocalized well with BCRs for all the time points analyzed and the colocalization between BCRs and F-actin were increased until 7 min (Fig 4F and 4K). All these results suggest that actin reorganization has been altered in Rictor KO B cells and the absence of Rictor leads to enhanced actin polymerization both in the cytoplasm and on the plasma membrane. Batista et al. have shown that the actin network restricts the movement of BCRs in the steady state. In the region with higher intensity of actin, the diffusion coefficient was decreased for BCRs [17]. In order to analyze the behavior of a single BCR, we used single-particle tracking and analysis as previously reported [11]. Analyses of the single BCR trajectory footprints suggested that single BCR molecules were more mobile in WT B cells after stimulation than in KO B cells after stimulation with mAg from 5 min to 15 min but no differences in the steady state (Fig 5A–5D). Tracking thousands of single BCR molecules from WT and KO B cells showed that their short-range mean-square displacements (MSDs) did not have differences in the resting state but were significantly decreased in KO B cells after activation (Fig 5E and 5F). The mean diffusion coefficient of the BCRs in KO B cells also decreased significantly during the activation status (Fig 5G and 5H). Moreover, the short-range diffusion coefficients of each individual BCR molecule were calculated and their distribution was analyzed and displayed as a cumulative distribution probability (CDP) plot. The CDP of KO B cells was decreased compared to that of WT B cells upon antigenic mAg stimulation but without changes in the steady state (Fig 5I and 5J). The normal mobility of BCRs in KO B cells for the steady state was consistant with the unchanged basal levels of actin without stimulation (Figs 4D, 5A, 5C, 5E, 5G and 5I). These results imply that the BCRs from the WT and KO B cells almost had the same mobility in the steady state, but the BCRs from KO B cells became less mobile than those of WT B cells after activation. Furthermore, these results suggest that the disrupted actin depolymerization in the early phase and ehanced levels of actin in KO B cells after stimulation with sAg and mAg restrict the movement of BCRs after activation. In order to confirm the effect of actin on BCR signaling in Rictor KO B cells, we used Latrunculin B to reduce the levels of F-actin slightly [15] to see if the defect in BCR signaling and internalization can be rescued. Rictor KO B cells were pretreated with Latrunculin B for 30 min and stimulated with sAg in the presence of Latrunculin B. At 5 min, the levels of F-actin quantified by flow cytometry in KO B cells treated with Latrunculin B were decreased and then gradually increased, which had a similar trend to that of WT B cells, although the levels of F-actin were a little higher (Fig 6A–6D). The levels of F-actin in WT B cells treated with Latrunculin B were decreased compared to that of WT B cells without treatment (Fig 6D). To determine the coordination between actin and ezrin during BCR activation, we stained for activated ezrin by using phosphorylated antibodies. In WT B cells, the levels of phosphorylated Ezrin (pEzrin) decreased for the first 5 min and increased gradually to 30 min (Fig 6A and 6E), which is in line with the previous study [20]. However, in KO B cells, the basal level of pEzrin was significantly higher than that of WT B cells, decreased slowly to 30 min, but was still profoundly higher than that of WT B cells (Fig 6A–6C and 6E). The levels of pEzrin in WT B cells treated with Latrunculin B were decreased compared to those of WT B cells without treatment (Fig 6E). Latrunculin B treatment reduced the activation magnitude of ezrin significantly in KO B cells and induced the same trend as that of WT B cells (Fig 6A–6C and 6E). To further confirm the interplay between actin and ezrin, we used NSC668394 (an ezrin-specific inhibitor) and the inhibitors upstream of the ezrin signaling pathway, such as Y27632 (a ROCK-specific inhibitor) and bisindolylmaleimide I (Bis) (a PKC inhibitor). Not surprsingly, for all 3 inhibitors we found that the actin-polymerization phase starting at 5 min was completely disrupted and replaced with continuous actin depolymerization (S4 Fig). These results collectively suggest that actin and ezrin positively regulate with each other. For BCR internalization, KO B cells treated with Latrunculin B had some BCR caps at 10 min and further flow cytometry analysis found the BCRs remaining on the cell surface decreased significantly compared to that of untreated KO or treated WT B cells but were still higher than that of WT B cells (Fig 6A–6C and 6F). For BCR signaling, the levels of pY and pBtk in KO B cells treated with Latrunculin B increased profoundly compared to those of KO B cells after stimulation (Fig 6G–6K). The levels of pY and pBtk in WT B cells treated with Latrunculin B dropped down more slowly than those of WT B cells without treatment (Fig 6J and 6K). For pY, levels were comparable to those in WT B cells, although the levels of pBtk were still lower than those of WT B cells (Fig 6G–6K). We then looked at the colocalization between BCR, pY, and pBtk. The colocalization between BCR, pY, and pBtk was increased significantly in KO B cells treated with Latrunculin B compared to that of untreated KO B cells but still lower than that of WT B cells (Fig 6G–6I and 6L), and it was decreased in WT B cells treated with Latrunculin B compared to that of untreated WT B cells (Fig 6L). To further confirm that Latrunculin B can rescue the defect of differentiation of FO B cells and BCR signaling of Rictor KO mice in vivo, we fed the mice with 0. 5 μM Latrunculin B every week for 2 months and then euthanized the mice to analyze the subpopulations and BCR signaling in splenocytes. Latrunculin B treatment largely restored the frequency and number of FO B cells in Rictor KO mice compared to Rictor KO mice treated with vector only but had no effect on the formation of MZ B cells (S5A–S5C Fig). Moreover, the levels of pY or pBtk were also recovered in a large degree in Rictor KO mice treated with Latrunculin B (S5D and S5E Fig). Taken together, these results suggest that enhanced actin polymerization in KO B cells causes the reduction of BCR signaling and differentiation defect of FO B cells. In order to determine whether the distorted actin reorganization can affect the humoral immune response, we immunized the mice with T-cell dependent antigen–4-hydroxy-3-nitrophenylacetyl–keyhole limpet hemocyanin (NP-KLH). After 14 days, the mice were euthanized and analyzed for several key populations of B cells required for the humoral immune response. We found the percentage and number of FO B cells were profoundly reduced in KO mice after immunization but did not observe any changes for MZ B cells (Fig 7A–7C). Of note, the degree of the reduction of FO B cells in KO mice was greater in immunized mice than that of nonimmunized mice (S3 Fig, Fig 7A and 7C). Furthermore, we analyzed the frequency and number of GC B cells and they were decreased dramatically in KO mice compared to that of WT mice (Fig 7D and 7E). Additionally, we examined the effect of Rictor deficiency on the generation of antigen-specific memory B cells (MBCs); not surprisingly, we found a decrease of the percentage and number of MBCs in immunized KO mice (Fig 7F and 7G). Finally, we examined the plasma cells and plasmablasts in immunized WT and KO mice. We found a significant decrease of plasmablast (PBC) and plasma cell (PC) in immunized Rictor KO mice compared to that of WT mice (Fig 7H and 7J). To further confirm the effect of Rictor deficiency on humoral immune response, we examined the serum levels of NP-specific subclasses from the immunized mice and found the levels of both NP-specific IgM and IgG were decreased in Rictor KO mice (Fig 7K and 7L). Overall, all these results suggest that the distorted actin reorganization contributes to the noncompetent humoral immune response in Rictor KO mice. This study examined the effect of Rictor deficiency on BCR signaling. We found that the absence of Rictor leads to down-regulation of BCR signaling via decreasing pBtk and increasing pSHIP. Furthermore, the levels of actin are enhanced in both cytoplasm and plasma membrane in Rictor KO B cells stimulated with sAg. Interestingly, the early actin depolymerization phase in WT B cells after stimulation by sAg is replaced with drastically enhanced actin polymerization in Rictor KO B cells. By using the mAg system, we also found an increased level of actin in the contact zone of B cells with the lipid bilayer as well as decreased BCR clustering, B cell spreading, and recruitment of signalosomes in Rictor KO B cells. The increased levels of actin in Rictor KO B cells led to the reduced diffusion coefficient of BCRs in the activation state. Interestingly, we found the phosphorylation of ezrin is increased and the attenuation of phosphorylation is delayed in Rictor KO B cells and that Latrunculin B treatment can rescue the defect of BCR signaling and internalization as well as the FO differentiation. Finally, Rictor deficiency leads to the reduction of FO B cells more severely in immunized mice. Altogether, to our knowledge, this is the first report of how Rictor regulates BCR signaling by altering the actin reorganization via ezrin. To compare with what has been previously reported, Rictor deficiency causes an impact on the development of bone marrow B cells, although with varying degrees. These differences could be due to the different Cre systems used. Boothby’s group used VavCre and Yuan’s group used interferon-induced GTP-binding protein Mx1 (Mx1) Cre and in both, deletion is in the early stage of B cell development [19,20], and neither of them is B cell specific. In our cd19-Cre system, we found a slight impact on the progression of late pre-B cells and recirculating B cells and an increased accumulation of pro-B cells in Rictor KO mice. Boothby’s group also found a slight impact on the pro- and pre-B cells in Rictor KO mice and a profound reduction in MZ B cells that cannot be seen in the cd19-Cre KO mice [19]. Yuan’s group found pro-, pre-, and immature B cells are dramatically increased in Rictor KO mice [20]. To resolve the discrepancy between the different Cre systems that have different deletion stages, we are going to use cluster of differentiation 19 (cd19) -CreER mice to cross with Rictor flox/flox mice to observe any divergence caused by the deletion in different stages besides the deletion in different cells. Another remaining issue is the detailed link between Rictor and BCR signaling molecules or ezrin. First, it would be interesting to explore the direct interaction between Rictor and BCR signaling molecules such as Btk and SHIP. mTORC2 and the key component, Rictor, specifically, has been shown to phosphorylate Akt and protein kinase B (PKB) on Serine 473 (Ser473). This phosphorylation activates Akt/PKB, whereas dysregulation of Akt/PKB has been correlated with cancer and diabetes [36]. Tyrosine phosphorylation of ezrin regulates the activation of c-Jun N-terminal kinase (JNK) after BCR stimulation [37]. Therefore, Rictor possibly can regulate the phosphorylation of Btk and SHIP. The phosphorylation of ezrin can be regulated by rho-associated coiled-coil-containing protein kinase (ROCK) activation, and additionally mTORC2 has been shown to regulate the actin cytoskeleton through its stimulation of F-actin stress fibers via activation of paxillin, ras homolog family member A (RhoA), ras-related C3 botulinum toxin substrate 1 (Rac1), cell division control protein 42 homolog (Cdc42), and PKCα [32]. Therefore, it would be of interest to explore the possible links between Rictor and the upstream molecules of ezrin, such as rho-associated coiled-coil-containing protein kinase (ROCK), RhoA, or even Wiskott-Aldrich syndrome protein (WASP). Another possibility is the regulation of BCR signaling through transcriptional levels. As a kinase, mTORC2 cannot regulate the genes via transcriptional levels unless it goes through the furthest downstream transcriptional factors such as FoxO1. Therefore, we can examine the mRNA levels of Btk and SHIP as well as other signaling molecules or by microarray to search for other candidate genes, and then to determine whether FoxO1 can regulate these candidate genes using chromatin immunoprecipitation-sequencing (Chip-seq). In summary, this study has revealed not only a new pathway in BCR signaling but also the detailed molecular mechanism of how Rictor regulates BCR activation. Rictor deficiency leads to dysregulation of dephosphorylation of ezrin, which accounts for the enhanced actin polymerization. The high intensity ezrin-actin areas restrict the movement of BCRs after stimulation, which diminishes the triggering of BCR clustering and consequent BCR signaling. Overall, our study provides a new regulation pathway of Rictor to modulate BCR signaling by the actin-ezrin complex. All animal work was reviewed and approved by the Institutional Animal Care and Usage Committee of Children’s Hospital of Chongqing Medical University following institutional and NIH guidelines and regulations. Rictor conditional KO mice on a C57/BL6 background were obtained by crossing cd19-Cre mice with rictor flox/flox mice from Jackson lab. Splenic B cells were isolated as described previously [38]. Monobiotinylated Fab′ fragment of anti-mouse IgM+G Ab (mB-Fab′–anti-Ig) was made from the F (ab′) 2 (Jackson ImmunoResearch Laboratories) as described before [39]. The disulfide bond that connects the 2 Fab′ was reduced using 20 mM 2-mercaptoethylamine and then biotinylated by maleimide-activated biotin (Thermo Scientific). Fab′ was purified by using Amicon Ultracentrifugal filters (Millipore) and examined by a biotin quantification kit (Thermo Scientific) and then conjugated with AF546 (Invitrogen). To stimulate B cells with sAg, B cells were incubated with AF546–mB-Fab′–anti-Ig (2 μg/ml) together with mB-Fab′–anti-Ig (8 μg/ml) for 30 min and streptavidin (1 μg/ml) for 10 min at 4°C. Streptavidin was omitted as a negative control. The cells were washed and warmed up to 37°C for different time points. To stimulate B cells with mAg, cells were incubated with AF546–mB-Fab′–anti-Ig and mB-Fab′–anti-Ig tethered to lipid bilayers with streptavidin at 37°C for different time points. As a control, B cells were incubated with AF546–Fab–anti-mouse IgM+G (2 μg/ml) at 4°C and then incubated with transferrin (Tf) -tethered lipid bilayers, on which the density of Tf was equal to that of AF546–mB-Fab′–anti-Ig. The planar lipid bilayer was generated with previous protocol [40,41]. Liposomes were generated by sonicating 1,2-dioleoyl-sn-glycero-3-phosphocholine and 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine-cap-biotin (Avanti Polar Lipids) in a 100: 1 molar ratio in PBS to get 5 mM lipid. Aggregates in liposomes were discarded by ultra centrifugation and filtration. Coverslip chambers (Nalge Nunc International) were incubated with 0. 05 mM liposomes for 10 min and then incubated with 1 μg/ml streptavidin (Jackson ImmunoResearch Laboratories) after extensive washes, followed by 2 μg/ml AF546-mB-Fab′–anti-Ig mixed with 8 μg/ml mB-Fab′–anti-Ig Ab. Images were obtained using a Nikon A1R confocal and TIRF system on an inverted microscope (Nikon Eclipse Ti-PFS), installed with a 100×, NA 1. 49 Apochromat TIRF objective (Nikon Instruments), an iXon EM-CCD camera (Andor), and 3 solid-state lasers with wavelengths 405,488, and 546 nm. To image intracellular-signaling molecules, B cells were incubated with an Ag-tethered lipid bilayer at 37°C for different time points. Cells were fixed with 4% paraformaldehyde and permeabilized with 0. 05% saponin, followed by phallodin and Btk (pBtk, Y551; BD Biosciences) staining. The B cell contact area and MFI of each staining in the B cell contact zone were determined using IRM images and NIS-Elements AR 3. 2 software. Background fluorescence generated by Ag tethered to lipid bilayers in the absence of B cells or secondary Ab controls was subtracted. For each set of data, >20 individual cells from 2 or 3 independent experiments were analyzed. In order to reduce the variability, we consistently dropped cells right above the PBS medium surface using the same volume (10 μl) and cell number (2 x 105) and started timing the early BCR signaling events. We took images from 8 random fields at each time point. We carefully evaluated the morphology and contact area of the B cells landing on the lipid bilayer at different time points. After finishing the analysis of all the individual cells, we pooled all the values together and removed the values that are usually in a very low percentage (<5%) and are far away from the normal range of the majority of the B cells based on the observed morphology and contact area together. For confocal analyses, B cells were stimulated with AF546–mB-Fab′–anti-Ig without (−) or with streptavidin (sAg) at 4°C, washed, and warmed to 37°C for different time points. After fixation and permeabilization, the cells were stained for pRictor (T1135, Cell Signaling Technology), pY, pBtk, pSHIP, and pEzrin (T558, Cell Signaling Technology) and analyzed using CFm. For flow cytometric analyses, cell suspensions from BM and spleen were incubated with Fcγ receptor (FcγR) blocking Abs (anti-mouse CD16/CD32; BD Bioscience) on ice and stained at optimal dilutions of conjugated Abs in PBS supplemented with 1% FBS. Anti-mouse Abs and reagents used to stain BM cells included PB-anti-IgM (BioLegend), APC-anti-Ly-51 (BioLegend), PE-anti-CD43 (BioLegend), PerCP-Cy5. 5-anti-B220 (BD Bioscience), and PE-Cy7-CD24 (BioLegend) [42,43]. Gating strategy was as follows: A-pre-pro-B cells (BP1-CD24-), B-pro-B cells (BP1-CD24+), C-early pre-B cells (BP1+CD24+), D-late pre-B cells (B220+IgM-), E-immature B cells (B220IntIgM+), and F-recirculating B cells (B220highIgM+) included BV510-anti-IgD (Southern Biotech), FITC-anti-B220 (BioLegend), and PB-anti-IgM (BD Biosciences) [44]. Gating strategy was as follows: FO B cells (B220high IgMlow IgDhigh), MZ B cells (B220high CD21highCD23low). Anti-mouse Abs and reagents used to stain splenic MZ B cells included APC-anti-CD21 (BioLegend), FITC-anti-B220, and PE-anti-CD23 (BD Biosciences) [44]. Anti-mouse Abs and reagents to stain splenic GC B cells included FITC-anti-CD95 (BD Biosciences), APC-anti-GL7, and PerCP-Cy5. 5-anti-B220 (BD Biosciences) [45]. Anti-mouse Abs and reagents to stain splenic MBC, PBC, and PC B cells included FITC-anti-CD95 (BD Biosciences), Pac-Blue-anti-GL7 (Biolegend), BV510-anti-B220 (Biolegend), NP-PE (Biosearch Technologies), APC-anti-CD138 (Biolegend), PE-Cy7-anti-IgD (Biolegend), and PE-Cy7-anti-IgM (Biolegend). Anti-mouse Abs and reagents used to treat B cells for BCR signaling include: FITC-anti-B220. B cells were stimulated with F (ab′) -anti-Ig plus streptavidin (Jackson ImmunoResearch) at 37°C. The cells were fixed, permeabilized, and stained with pY (Millipore), phosphorylated Btk (pBtk, Y551; BD Biosciences), phosphorylated SHIP (pSHIP, Y1020; Cell Signaling Technology), phallodin, phosphorylated ezrin (pEzrin, T558; Cell Signaling Technology), phosphorylated Erk (pErk, T202/Y204; BD Biosciences), phosphorylated Akt (pAkt, S473; BD Biosciences). Stained cells were analyzed by a BD FACS Canto and analyzed using FlowJo software (Tree Star). Splenic B cells were incubated with mB-Fab′–anti-Ig without (−) or with streptavidin (sAg) at 4°C, washed, and warmed to 37°C for indicated times and lysed. Cell lysates were analyzed with SDS-PAGE and western blot and probed for pAkt (Ser473; Cell Signaling Technology), pERK1/2 (T202/Y204; Cell Signaling Technology), pBtk (pBtk, Y551; BD Biosciences), and pY (Millipore). Anti-mouse β-actin Ab (Sigma-Aldrich) was used for loading controls. For comparison of rictor gene expression in WT and Rictor KO B cells, RNA was isolated with RNAPURE kit (RP1202; BioTeke) and reverse transcribed with a PrimeScript RT reagent Kit (RR037A; Takara). The transcribed cDNA was used to analyze the expression of different genes with SsoAdvanced SYBR Green supermix (Bio-Rad) on a CFX96 Touch Real-Time System (Bio-Rad). rictor 5’primer: tgcgatattggccatagtga and 3’primer: acctcgttgctctgctgaat. WT and Rictor KO mice were bred and maintained in a specific-pathogen–free animal facility. All mice were male and aged 6–8 weeks. For NP-KLH immunization, 400 μg NP-KLH (Biosearch Technologies) in 400 μl Ribi Adjuvant (MPL+TDM Adjuvant System; Sigma) was injected in the flank subcutaneously at day (d) 1. At d 14 after immunization, the spleen was harvested and immune cells were isolated by sucrose density centrifugation using Lymphocyte Separation Media (LSM; MPbio). For detection of serum levels of NP-specific subclasses, mice were immunized and boosted with the same 2 week later. Serum collected 5 d after the boost (19 d after primary immunization) was analyzed by ELISA using NP-bovine serum albumin–coated plates and Ig isotype specific secondary Ab (Southern Biotech). B cells were pretreated with 0. 05 μM Latrunculin B, 1 μM Bis, 10 μM NSC668394, or 10 μM Y27632 (Calbiochem, Gibbstown, NJ) for 30 min at 37°C before stimulation with Ag in the presence of inhibitors. Mice were fed with 0. 5 μM Latrunculin B by IP injection every week for 2 months [46]. Splenic B cells were stimulated with biotinylated F (ab′) 2-goat anti-mouse IgG+M (10 μg/ml; Jackson ImmunoResearch) at 4°C and pulsed at 37°C. Biotin-F (ab′) 2–anti-IgG+M remaining on the cell surface after the stimulation was stained with PE-streptavidin and examined by flow cytometry. The data were shown as percentages of the cell-surface–associated biotin-F (ab′) 2–anti-IgG+M at time 0. Single BCR–molecule imaging was performed according to previous protocol [47]. In detail, prelabeled WT and Rictor KO B cells were imaged by TIRFm with a 640-nm laser in the epifluorescence mode at an output power of 10 mW at the objective lens. A region of 100 ×100 pixels of the area of the electron-multiplying CCD chip was used to obtain an exposure time of 30 ms/frame, the time resolution of which was enough to track the single-molecule BCRs as published [11,47]. Single-molecule tracking of BCR molecules was analyzed as described before [11,47]. Short-range diffusion coefficients and MSD for each BCR molecule trajectory were determined and plotted as CDPs from positional coordinates. The level of calcium flux was examined by flow cytometry using the calcium-sensitive dyes Fluo4 AM and Fura Red (Life) according to the established protocols. The relative levels of intracellular calcium flux were measured by a ratio of Fluo4 to Fura Red emission using FlowJo software (Tree Star, Inc. , Ashland, OR) [34]. Statistical significance was assessed using t-test or the Mann–Whitney U test. When multiple groups were compared, 1-way ANOVA with the Tukey test was performed (GraphPad Software, San Diego, CA). The p values were determined in comparison with WT or control B cells. * p < 0. 01, ** p < 0. 001.

As the central hub of cell metabolism, the mammalian target of rapamycin complex (mTORC) integrates immune signals and metabolic cues for the maintenance and activation of these systems. Rictor is the core component of the mammalian target of rapamycin complex 2 (mTORC2), and loss of this protein leads to an immunodeficiency that involves (among other things) impaired antibody production. B cell receptor (BCR) signaling is critical for antibody generation and although it has been shown that loss of Rictor in B cells negatively impacts this function, the underlying molecular mechanisms are unknown. Here, we show that both early and distal BCR signaling is reduced in Rictor knockout (KO) B cells. We find that the reduction in BCR signaling stems from defective clustering of BCRs during early B cell activation. This seems to be caused by the uncontrolled activation of the actin-connecting protein ezrin, which leads to a rigid actin fence that restricts the lateral movement of BCRs in the membrane. Interestingly, treatment of Rictor KO mice with an actin inhibitor rescues the BCR signaling. Our findings suggest that Rictor helps to allow effective BCR signaling in B cells by triggering reorganization of the actin network, thereby enabling an appropriate antibody response during infection.

Abstract Introduction Results Discussion Materials and methods

blood cells flow cytometry phosphorylation medicine and health sciences immune cells immunology cell processes immune receptor signaling membrane receptor signaling research and analysis methods contractile proteins specimen preparation and treatment actins white blood cells staining actin polymerization animal cells proteins spectrophotometry antibody-producing cells biochemistry cytoskeletal proteins cytophotometry cell staining signal transduction cell biology b cells post-translational modification biology and life sciences cellular types cell signaling spectrum analysis techniques

2017

Rictor positively regulates B cell receptor signaling by modulating actin reorganization via ezrin

Wolbachia is a bacteria endosymbiont that rapidly infects insect populations through a mechanism known as cytoplasmic incompatibility (CI). In CI, crosses between Wolbachia-infected males and uninfected females produce severe cell cycle defects in the male pronucleus resulting in early embryonic lethality. In contrast, viable progeny are produced when both parents are infected (the Rescue cross). An important consequence of CI–Rescue is that infected females have a selective advantage over uninfected females facilitating the rapid spread of Wolbachia through insect populations. CI disrupts a number of prophase and metaphase events in the male pronucleus, including Cdk1 activation, chromosome condensation, and segregation. Here, we demonstrate that CI disrupts earlier interphase cell cycle events. Specifically, CI delays the H3. 3 and H4 deposition that occurs immediately after protamine removal from the male pronucleus. In addition, we find prolonged retention of the replication factor PCNA in the male pronucleus into metaphase, indicating progression into mitosis with incompletely replicated DNA. We propose that these CI-induced interphase defects in de novo nucleosome assembly and replication are the cause of the observed mitotic condensation and segregation defects. In addition, these interphase chromosome defects likely activate S-phase checkpoints, accounting for the previously described delays in Cdk1 activation. These results have important implications for the mechanism of Rescue and other Wolbachia-induced phenotypes. Wolbachia are intracellular bacteria that infect some 65% of all insect species [1]. Their success is in large part due to their efficient maternal transmission and their ability to alter host reproduction such that infected females produce more offspring than uninfected females [2]. The most common form of altered reproduction is known as cytoplasmic incompatibility (CI), a form of conditional sterility resulting from crosses of Wolbachia-infected males to uninfected females [3]. These crosses produce defects in the first zygotic mitosis resulting in inviable embryos. Significantly, if both the female and the male are infected, no defects are observed and viable embryos are produced. This phenomenon is known as Rescue [4]. Consequently in Wolbachia-infected populations, infected females produce viable progeny whether they mate to infected or uninfected males. In contrast, uninfected females produce viable progeny only when mated to uninfected males. Thus infected females enjoy a tremendous selective advantage over uninfected females resulting in the rapid spread of Wolbachia via the maternal lineage [5]. The success of this strategy is underscored by the fact that CI has been documented in every insect order [3]. CI crosses produce embryos in which the paternal chromosomes are improperly condensed when aligned at the metaphase plate of the first mitotic division following fertilization [6]–[8]. It should be noted that the first mitotic division is unique in many insects, including Drosophila, because the paternal and maternal chromosomes reside on separate regions of the metaphase plate and are independently regulated with respect to entry into anaphase [7], [9]. As the embryo progresses into anaphase, paternal sister chromatids either fail to segregate, or exhibit extensive bridging and fragmentation during segregation, a hallmark of damaged or incompletely replicated chromosomes [9]. It is thought that strong CI elicits chromosome condensation defects severe enough to activate the spindle assembly checkpoint and prevent segregation while weak CI results in more mild defects in which the checkpoint fails to activate, allowing improper segregation [8]. Defects earlier in the cell cycle at the prophase/metaphase transition have also been reported. These include a delay in Cdk1 activation and nuclear envelope breakdown in the male pronucleus relative to the female pronucleus [10]. These observations leave unresolved the cause and effect relationship between the chromosome condensation and Cdk1 activation defects in CI embryos. It is well established that defects in DNA replication and chromosome condensation lead to cell cycle checkpoint induced delays in Cdk1 activation [11]. However Cdk1 activation is required to drive chromosome condensation and failed Cdk1 activation results in failed chromosome condensation [12]. To identify the proximal defects in CI embryos, we sought to determine whether CI-induced chromatin defects occur prior to Cdk1 activation during the interphase/prophase transition. Identification of earlier chromatin defects, during the sperm to male pronucleus transformation, would strongly argue that these are proximal to and the cause of the delayed Cdk1 activation and chromosome condensation/segregation defects observed during prophase and metaphase. Based on this reasoning, the work presented here focuses on sperm formation and sperm transformation into the male pronucleus in normal and CI crosses. To facilitate a compact configuration, the sperm chromatin is packaged with specialized small basic proteins known as protamines [13]. Another unique property of the Drosophila sperm is that the nuclear envelope lacks lamins and nuclear pores [14]. Immediately following fertilization, the nuclear envelope, the plasma membrane and the protamines are removed, and de novo nucleosome assembly is initiated using maternally supplied core histones [15]. This nucleosome assembly occurs prior to DNA replication, and is executed by a replication-independent pathway that uses histone variant H3. 3 and its specific chaperone HIRA [15]. In addition, the formation of the male pronucleus requires the ATP-dependent chromatin remodeling enzyme CHD1 [16]. After these remodeling events, the nucleus acquires a conventional nuclear envelope containing lamins and nuclear pores. As the egg completes meiosis, the newly formed male and female pronuclei initiate DNA replication while migrating towards one another. Once the replication is complete, Cdk1 activation triggers mitotic entry in the closely apposed pronuclei [17]. The studies presented here demonstrate CI- specific defects in H3. 3/H4 deposition and prolonged retention of PCNA in the male pronucleus. These results suggests that in CI crosses, the male pronucleus enters mitosis with improperly condensed chromatin and incompletely replicated DNA. Significantly remodeling of the sperm chromatin including protamine removal and H3. 3/H4 deposition occurs during interphase, well before Cdk1 activation and entry into mitosis. Thus our results suggest a model in which the initial defects in chromatin assembly in the male pronucleus activate cell cycle checkpoints delaying Cdk1 activation and mitotic entry. These chromatin remodeling defects also explain previous findings of defects during metaphase and anaphase in chromatin condensation and segregation. Because H3. 3 deposition plays a key role in the transcriptional regulation throughout development, our results may provide insight into other effects Wolbachia has on its host. To confirm that the CI-induced segregation and condensation defects are limited to the paternal chromosomes, we used an antibody directed against acetylated histone H4 that preferentially labels the de novo assembled paternal chromatin after protamine removal in Drosophila eggs (Figure 1, [15]). We used D. simulans rather than D. melanogaster, since CI is very robust in the former species only. In CI embryos, the maternal chromosomes segregate normally at anaphase while the paternal chromosomes lag on the metaphase plate. At late telophase, bridges are observed between separating paternal sister chromosome complements (Figure 1, [7]). This results in severe nuclear division failures and accounts for the pre-cellular embryonic lethality in CI crosses. In stronger CI cases, severe disruption of paternal chromosome segregation results in their exclusion from both daughter nuclei. In haplo-diplo species this pattern of segregation produces viable haploid males [8]. The detection of acetylated histone H4 also demonstrates that sperm chromatin remodeling is initiated in CI crosses and this led us to examine protamine removal and histone deposition during this period. During spermatogenesis in many higher eukaryotes, including Drosophila, core histones in the sperm nuclei are replaced by protamines, sperm-specific chromosomal proteins that allow a greater chromatin compaction [18]. To assay protamine deposition and removal in CI embryos, we created a transgenic D. simulans stock expressing D. simulans protamine fused to GFP under the control of its endogenous promoter. In non-infected and infected testis, the fusion protein was incorporated into spermatids and present in mature sperm in seminal vesicles. (Figure 2A, 2B, and 2C). In both, control and CI fertilized embryos, Protamine-GFP was removed immediately after sperm entry, before completion of the female meiotic division (Figure 2, n = 22 for CI (D–H), n>20 for control (J) ). To verify that Protamine-GFP can be visualized in early D. simulans embryos, we took advantage of rare double fertilization events (Figure 2I, asterisk). In this case Protamine-GFP was visible in the additional, non-activated sperm DNA while absent from the male chromosomes lagging on the metaphase plate (arrow). Thus, at the cytological level, no obvious differences in protamine removal and deposition are observed in CI embryos. Immediately following the removal of protamines from the male pronucleus, paternal nucleosomes are assembled using maternally supplied histones. This replication-independent nucleosome assembly specifically involves the H3. 3 histone variant, which is deposited along with H4, followed by H2A and H2B [19]. H3. 3 is thus specifically deposited in the male pronucleus before the completion of the female meiosis and remains enriched in paternal chromosomes throughout the first mitotic division. The paternal chromosomes lose H3. 3 by incorporation of canonical histone H3 with each new round of replication [20]. In order to take advantage of both the strong CI of D. simulans and of transgenic markers only available in D. melanogaster, we performed hybrid crosses between D. simulans males and D. melanogaster females. Previous studies demonstrated that this hybrid cross exhibits a robust CI and Rescue and is an appropriate system for studying CI [21]. Infected or non-infected D. simulans males were crossed with non-infected transgenic D. melanogaster females expressing a tagged H3. 3-FLAG histone (CI and control crosses, respectively). In all embryos examined from the above control hybrid cross (n = 51), a robust H3. 3 deposition was observed in the male pronucleus prior to completion of female meiosis, similar to the H3. 3 deposition observed in single species D. melanogaster control crosses (not shown). All exhibited normal H3. 3 deposition in the male pronucleus before the completion of female meiosis (n = 30, Figure 3A). However in hybrid CI crosses, 22% of the embryos exhibited an abnormal H3. 3 accumulation at the periphery of the male pronucleus before the completion of female meiosis (n = 63, Figure 3A). In all nuclei with an abnormal accumulation at the periphery, no H3. 3 staining was observed inside the nucleus suggesting a failure or an altered pattern of early H3. 3 deposition. No lamin is detected at this stage (Figure S1), which suggests that nucleosome assembly occurs prior to the formation of the pronuclear envelope, ruling out a general nuclear import defect. Double immunostaining experiments showed that histone H4 colocalized with H3. 3 in peripheral rings in CI embryos (Figure 3B). These abnormal rings of H3. 3 and H4 are never observed during pronuclei apposition (Figure 3A′, n>30 for control and CI crosses). This suggests that CI results in a delayed, but not complete inhibition of H3. 3/H4 nuclear deposition. Once the paternal chromatin is assembled with maternally supplied core histones including H3. 3 and H4, the DNA must replicate prior to mitotic entry in both pronuclei. We examined replication timing of pronuclei in control and CI embryos using an antibody directed against the Drosophila Proliferating Cell Nuclear Antigen (PCNA). PCNA is a conserved core component of the replication fork [22] and only present in S-phase nuclei [23]. To confirm this specificity in Drosophila, we examined PCNA localization in early embryos where the S-phase is well characterized with respect to chromosome and spindle morphology [24] (Figure S2). These studies demonstrate that PCNA is nuclear only during S-phase, confirming previous results. Early D. simulans embryos from uninfected and CI crosses were examined from the time of pronuclear migration to pronuclear apposition. In the uninfected crosses, both the male and female pronuclei exhibit robust PCNA staining during their migration, indicating that the S-phase is initiated during the early stages of pronuclei migration (Figure 4A, n>30). We always observed synchronous PCNA staining in both nuclei, indicating simultaneous S-phase initiation in the male and female pronuclei. During pronuclei apposition in the uninfected crosses, we either observe that both pronuclei possess (Figure 4A, “apposition I”) or lack PCNA staining (Figure 4A, “apposition II”). S phase was completed during pronuclear apposition and not earlier. S phase was completed synchronously between male and female pronuclei in 88% of embryos (n = 26, Figure 3A and 3B). We performed the same analysis in embryos derived from the Rescue cross. The results for both pronuclear migration and apposition were very similar to the control cross (n = 27, Figure 4A and 4B). Next, we analyzed PCNA staining in embryos derived from the CI cross. As with the control cross, both pronuclei stained positive for PCNA throughout migration (Figure 4A, n>30). Thus, like the control cross, S-phase is initiated simultaneously in the male and female pronuclei during the initial stages of pronuclear migration. Unlike the control crosses, however, we observed 43% of embryos (n = 36) with differential staining during apposition (Figure 4A and 4B). These results indicate that CI delays completion of replication in the male pronucleus. Because the timing of replication initiation does not appear to be altered in CI embryos, it is likely that the replication is slowed down or blocked in the male pronucleus of CI embryos relative to control embryos. Alternate interpretations include delayed release of PCNA or extra DNA replication in CI embryos. However delayed Cdk1 activation in the male pronucleus, presumably due to activation of cell cycle checkpoints, favors a model in which of disrupted replication in the male pronucleus of CI embryos. We also examined PCNA staining in control and CI D. simulans embryos that had progressed into prophase as evidenced by condensed DNA, spindle formation, and NEB. In control embryos, PCNA was never localized in the pronuclear DNA after NEB (n = 40, Figure 4C). In CI embryos however, 11% of pronuclei pairs observed after NEB showed a PCNA staining associated with the poorly and unevenly condensed male pronuclear DNA (n = 37, Figure 4C and 4D). Once the male pronuclei of CI embryos progress into metaphase, we no longer observe such PCNA staining. It has been reported that PCNA is associated with damaged as well as replicating DNA (for a review see [25]). We favor a replication defect to explain CI rather than DNA breaks, given that chromatin remodeling defects are strongly associated with replication defects [26]. In addition, chromosome bridging during the first telophase but not free chromosome fragments is well documented in CI embryos. This is more consistent with DNA replication rather than damage defects. Taken together, our data suggest that in CI embryos DNA replication is slowed down or blocked in the male pronucleus. Genetic and cellular analyses indicate that CI specifically disrupts paternal chromosome condensation, congression and segregation [9], [27]. Here we take advantage of anti-acetylated H4 histone antibodies that specifically stain the paternal chromosomes due to nucleosome assembly in the male pronucleus. This enabled us to directly demonstrate the effects of CI are limited to the paternal chromosomes. This implies that CI targets processes specific to the paternal chromosomes necessary for progression through mitosis. To identify these processes, we focused on the chromosome remodeling events that are specific to sperm formation and transform the sperm into a male pronucleus. Our cytological examination of protamine deposition and removal did not reveal obvious abnormalities in CI embryos. This of course does not rule out more subtle defects. Protamines are normally removed immediately following fertilization and replaced with the replication-independent variant histone H3. 3 and canonical H4, H2A/H2B histones. In CI embryos, a significant fraction of embryos exhibit delays in H3. 3 incorporation before completion of the female meiosis. This results in an abnormal ring of H3. 3 encompassing the male pronucleus. There is no nuclear envelope present at this early stage, indicating the H3. 3 ring phenotype is not due to defects in nuclear import. More likely it is due to a delay in loading H3. 3 onto the paternal chromosomes. These CI-induced defects in H3. 3 deposition are strikingly similar to those reported for mutants in the chromatin remodeling protein CHD1. Male pronuclei from chd1 mutants also exhibit an improper accumulation of H3. 3 around the male pronucleus. Like the CI-induced defects, chromosome condensation is severely disrupted presumably due to defects in H3. 3-based chromatin remodeling [16]. Mutations affecting HIRA, the H3. 3 chaperone, also prevent the formation of condensed paternal chromosomes [15]. These replication-independent histone deposition defects can explain the chromosome condensation and segregation defects observed in CI embryos since H3. 3 and H3 share a conserved N terminal tail, whose phosphorylation is crucial for chromosome condensation [28]. Defects in histone deposition can also explain the delayed progression through S phase, as proper nucleosome assembly is required for DNA replication [29]. Both replication dependent and independent nucleosome assembly machineries share common interactors, like the histone chaperone ASF1 [19]. ASF1 siRNA knock down experiments and mutants clearly show DNA replication defects [26]. Late DNA replication in ORC2 (Origin Recognition Complex 2) mutants also provoke chromosome condensation defects and reveals that proper replication timing is crucial for the chromatin to be fully competent to condense [30]. However it should be pointed out that chromosome condensation defects alone can produce segregation defects [31]. In addition to playing a role in paternal chromatin remodeling, H3. 3 plays a more general role in transcription regulation. The replication-independent deposition of H3. 3 is correlated with active chromatin states [32]. This raises the intriguing possibility that Wolbachia may influence the transcription state of its host nuclei by altering H3. 3 deposition. It has been shown that Wolbachia do not influence the in vivo expression level of antimicrobial peptides specifically [33], but microarray data from Drosophila cell culture suggest that Wolbachia has some influence on host transcript levels [34]. Another alteration of the host reproduction caused by Wolbachia is a phenomenon called male killing (MK) [35]. In male killing, Wolbachia infection results in death of the male but not the female progeny. The resulting increase in the proportion of female progeny is beneficial to the maternally transmitted Wolbachia. Moving a specific Wolbachia strain from one Drosophila species to another results in an instantaneous transition from CI to MK, indicating that these Wolbachia-induced phenotypes share a common molecular mechanism [36]. Studies in Drosophila demonstrate that disruptions in some chromatin remodelers have a much greater impact on organization of the X chromosomes in males than females [37]. This raises the possibility that CI and MK evolved from Wolbachia having a more general effect on the transcriptional state of its host cell by regulating H3. 3 deposition. To determine whether CI influences replication we monitored for the presence of PCNA, an indicator of replicating DNA, in the male and female pronuclei. This analysis demonstrates that in normal embryos, both initiation and completion of DNA replication occur simultaneously in the two pronuclei. In CI embryos while we find replication is initiated simultaneously, completion of replication is significantly delayed in the male pronucleus. In fact we observe instances of PCNA positive paternal chromosomes during metaphase of the first zygotic division. It is likely that the chromatin remodeling defects described above are responsible for the replication delays of the male pronucleus (see Figure 5). These delays readily account for the extensive chromosome bridging observed during anaphase: segregation of unreplicated chromosomes creates bridges [38], [39]. Delayed completion of replication of the paternal chromosomes provided an opportunity to more precisely determine the timing of CI rescue. Previous studies demonstrated that in the Rescue cross, the chromosome condensation defects at metaphase and segregation defects at anaphase are no longer observed [27]. Additional studies demonstrated that in CI crosses, activation of Cdk1, a highly conserved kinase that drives cells into mitosis [40] in the male pronucleus, is delayed relative to its activation in the female pronucleus [10]. These studies also demonstrated that in Rescue crosses, Cdk1 activation in the male and female pronuclei is synchronous. These studies raise the possibility that Rescue is achieved through correction of cell cycle defects in the male pronucleus. Alternatively, synchrony may be restored by a compensatory slowing of the female pronucleus cell cycle. Our data demonstrate that in Rescue crosses, we no longer observe a discordance in the state of PCNA staining in the male and female pronuclei, indicating the events mediating Rescue occur during interphase prior to Cdk1 activation during prophase. However, these studies do not resolve whether it is due to normalization of the interphase events in the male pronucleus or compensating delay in the female pronucleus. Evidence for the former alternative comes from our observation that unlike CI crosses, in Rescue crosses we never observe PCNA positive chromosomes after entry into metaphase in CI embryos. Embryos were collected every 15 minutes and immersed in a pure bleach solution for few seconds to remove the chorion. Next they were washed in distilled water and fixed by vigorous shaking in a 1∶1 heptane/methanol mix. RNAse A (Sigma) treatment was performed for 3 hours at 37°C (10 mg/mL). Primary and secondary antibodies were diluted in PBS+ 0. 2% Tween+ 2% BSA. Embryos were incubated overnight at 4°C with primary antibodies. For secondary antibodies, the embryos were incubated at 37°C for three hours. The following antibodies were used: Polyclonal anti-Drosophila PCNA (1∶300), polyclonal (1∶1000) and monoclonal (ADL84,1∶50) anti- Drosophila Lamin (all kindly provided by Paul Fisher), monoclonal anti-alpha tubulin (1∶500, Molecular Probes), polyclonal anti-GFP (1∶500, Chemicon), monoclonal anti-FLAG M2 antibody from Sigma was used to detect flagged H3. 3 at 1∶2000, polyclonal anti-acetylated H4 (1∶300, Upstate). Cy5 goat anti-rabbit IgG and Alexa Fluor 488 goat anti–mouse IgG antibodies were used at 1∶150 (Invitrogen). DNA was detected with propidium iodide (Molecular Probes, 1. 0 mg/mL solution) after a 20 minute incubation in PBS (1∶50) and a 5 minute wash. To better observe pronuclei deep within the cytoplasm, embryos were cleared and mounted in a (2∶1) benzyl benzoate and benzyl alcohol solution. Confocal microscope images were captured on an inverted photoscope (DMIRB; Leitz) equipped with a laser confocal imaging system (TCS SP2; Leica) using an HCX PL APO 1. 4 NA 63 oil objective (Leica) at room temperature. D. simulans stocks were used as Wolbachia riverside-infected or cured. D. melanogaster stocks were used as cured. The Wolbachia infection status of the stocks was established by both PCR [41] and Propidium iodide staining of fixed reproductive tissues. We used the previously described PW8-His3. 3-Flag [15]. To construct the PW8-ProtSim-GFP transgene, a D. simulans protamine gene was amplified from genomic DNA using the following pair of primers: This PCR fragment was cloned in the PW8 vector in frame with EGFP at the 3′ end of the protamine coding sequence. A homozygous viable and fertile transgenic PW8-ProtSim-GFP stock was obtained by P-mediated germline transformation of a D. simulans white stock (a gift from Elgion Loreto).

Wolbachia are among the most successful of all intracellular bacteria, infecting an estimated 65% of insect species. Wolbachia are also present in filarial nematodes and are the cause of African river blindness. Wolbachia' s success is due in part to its ability to induce a conditional form of sterility known as cytoplasmic incompatibility (CI), endowing infected females with a tremendous selective advantage. CI results in the severe reduction in progeny from crosses between uninfected females and Wolbachia-infected males. However, Wolbachia-infected females can mate with either infected or uninfected males with no reduction in progeny. CI may drive speciation and is intensively being pursued as a means to control insect-borne human disease. In spite of its biological and medical significance, the molecular basis of CI is not understood. We take advantage of newly generated chromatin reagents to demonstrate that prior to the well-documented defects in chromosome condensation and segregation, CI produces a delay in recruiting the replication-independent histone H3. 3/H4 complex to the male pronucleus. There is great interest in histone H3. 3 because of its general role in transcription and in remodeling of the sperm chromatin following fertilization. In addition, these findings may provide insight into other Wolbachia–host interactions such as CI–Rescue and male-killing.

Abstract Introduction Results Discussion Materials and Methods

infectious diseases/bacterial infections cell biology

2009

Wolbachia-Mediated Cytoplasmic Incompatibility Is Associated with Impaired Histone Deposition in the Male Pronucleus

The mechanisms underlying the selective death of substantia nigra (SN) neurons in Parkinson disease (PD) remain elusive. While inactivation of DJ-1, an oxidative stress suppressor, causes PD, animal models lacking DJ-1 show no overt dopaminergic (DA) neuron degeneration in the SN. Here, we show that aging mice lacking DJ-1 and the GDNF-receptor Ret in the DA system display an accelerated loss of SN cell bodies, but not axons, compared to mice that only lack Ret signaling. The survival requirement for DJ-1 is specific for the GIRK2-positive subpopulation in the SN which projects exclusively to the striatum and is more vulnerable in PD. Using Drosophila genetics, we show that constitutively active Ret and associated Ras/ERK, but not PI3K/Akt, signaling components interact genetically with DJ-1. Double loss-of-function experiments indicate that DJ-1 interacts with ERK signaling to control eye and wing development. Our study uncovers a conserved interaction between DJ-1 and Ret-mediated signaling and a novel cell survival role for DJ-1 in the mouse. A better understanding of the molecular connections between trophic signaling, cellular stress and aging could uncover new targets for drug development in PD. Specific and progressive loss of substantia nigra (SN) neurons is the central pathogenic event in Parkinson disease (PD), the most common movement neurodegenerative disorder, characterized by tremor, rigidity, and bradykinesia. A second pathological feature of PD is the presence of aggregated alpha-synuclein (Lewy Bodies) in the remaining SN neurons. In most PD patients the degree of dopaminergic axon degeneration in the SN target area, the striatum, exceeds that of SN cell body loss, suggesting a “dying back” model, whereby the axonal compartment is the first target of degenerative insults [1]. A major advance in PD research was the discovery of familial PD-associated genes and the characterization of their biochemical mechanisms [2], [3]. So far, transgenic mouse models that reproduce the genetic defects found in familial PD showed limited power in reproducing disease pathology, and most of them fail to exhibit degeneration of SN neurons [4] (see also [5]). This together with the fact that familial PD accounts for less than 10% of all PD cases (the rest being sporadic) suggests that multiple hits, including environmental factors, underlie selective neuronal death [6]. Access to neurotrophic factors is critical for maintenance of the nigrostriatal system in mice, and novel neurotrophic factors for SN neurons have recently been described [7]–[9]. We recently showed that genetic ablation of the receptor tyrosine kinase (RTK) Ret, the signaling receptor of glial cell line-derived neurotrophic factor (GDNF), led to adult-onset progressive and specific degeneration of the nigrostriatal system [10]. Consistent with a “dying back” model, Ret function was found to be important for striatal DA fiber maintenance, while its role in cell body survival was relatively moderate. Removal of GDNF in the adult brain led to more pronounced degeneration [8], suggesting that SN neurons in Ret mutant mice still have access to trophic support via Ret-independent pathways [11]. The PD-associated gene DJ-1 [12] encodes a small, dimeric, single domain protein that is thought to respond to oxidative stress and to protect neurons from environmental toxins [2], [13]–[16]. However, the molecular mechanisms underlying DJ-1 function are unclear. DJ-1 is localized to cytoplasm, nucleus, and mitochondria, and in each of these subcellular localizations DJ-1 may be neuroprotective [3], [17]. DJ-1 is an oncogene and was shown to synergize with the Ras/MAPK pathway in controlling cellular transformation [18]. It was suggested to negatively regulate the tumor suppressor PTEN, the major negative regulator of the phosphatidylinositol (PI) -3 kinase pathway [19]. DJ-1 ablation in mice alone did not affect the survival of SN neurons [20]–[25] but rendered SN neurons more sensitive towards the toxin MPTP [22]. A small (7%) population of ventral tegmental area (VTA) neurons requires DJ-1 during development for tyrosine hydroxylase (TH) expression [25]. Hence, it is currently not understood why loss of DJ-1 in humans causes specific loss of SN neurons. Based on the capacity of DJ-1 to interact with pathways implicated in RTK signaling (PI3K/Akt and Ras/MAPK) [18], [19], [26], we investigated a possible cooperation between Ret and DJ-1 in regulating SN neuron survival in vivo. To this end, we generated double mutant mice lacking expression of Ret in midbrain dopaminergic neurons and DJ-1 in all cells of the body (DAT-Cre; Retlx/lx; DJ-1−/− mice, in short DAT-Ret; DJ-1 mice). Here we show that DAT-Ret; DJ-1 mice have significantly fewer nigral DA neurons than either single mutant, indicating that under conditions of trophic impairment, DJ-1 promotes survival of aging DA neurons. Remarkably, the loss is specific to GIRK-2 positive SN neurons, which project exclusively to the striatum and are more vulnerable in PD. Moreover, DJ-1 does not appear to promote target innervation, suggesting that DJ-1 acts at the level of the DA cell body, not in the axon. To understand how Ret-mediated trophic support relates molecularly to DJ-1, we used Drosophila whose genome contains two genes, termed DJ-1A and DJ-1B, which share significant homology with human DJ-1. Drosophila DJ-1 mutants have been shown to be sensitive to environmental toxins associated with PD [15] and to genetically interact with the PI3K/PTEN/Akt signaling pathway [19], [26]. Our genetic interaction studies in the eye system revealed that DJ-1A/B interact genetically with constitutively active Ret and associated Ras/MAPK, but not PI3K/Akt signaling. Paralleling our mouse results, we found that combined deletion of ERK and DJ-1 in Drosophila enhanced the developmental defects during eye and wing development caused by ERK deletion, providing evidence for an important role for the interaction between DJ-1 and RTK-related signaling during evolution. We have previously shown that the Ret protein co-localizes with the dopaminergic marker TH [10]. Similarly, DJ-1 is expressed in SN and VTA neurons [27]. We determined by Western blotting that the expression of DJ-1 was not modified in the midbrain and striatum of aged DAT-Ret mice and vice versa (Figure S1). In cultured SH-SY5Y neuroblastoma cells, endogenous Ret expression was not modified when DJ-1 expression was downregulated by RNAi, nor were the levels of endogenous DJ-1 changed when these cells were stimulated with GDNF (Figure S1); thus, Ret and DJ-1 protein levels appear to be regulated by separate mechanisms. DAT-Ret; DJ-1 double mutant mice are viable and fertile. To detect morphological alterations in the nigrostriatal system, brain tissue sections of mutant and control mice were immunostained for TH and subjected to stereological quantification. In 3-mo-old DAT-Ret; DJ-1 double mutant mice, the numbers of TH-positive SN neurons were unchanged compared to age-matched controls (13,690±428 in control and 13,709±248 in DAT-Ret; DJ-1 mice, n = 3 mice/group, p = 0. 95, student' s t test) indicating that the nigrostriatal system developed normally in these mutants. When mutant mice were aged, however, the numbers of TH-positive SN neurons decreased significantly compared to age-matched controls (Figure 1A–C, G, H). In DAT-Ret; DJ-1 double mutant mice, the reduction was more pronounced (37% at 18 mo, 41% at 24 mo) than in DAT-Ret single mutant mice (24% at 18 mo, 25% at 24 mo). The difference between DAT-Ret; DJ-1 double and DAT-Ret single mutant mice was statistically significant (p<0. 01, t test) and was not additive, since DJ-1 single mutants had normal numbers of TH-positive neurons (Figure 1G, H). Anti-Pitx3 immunostaining was used as an independent marker and revealed a similar reduction in SN neurons in DAT-Ret; DJ-1 double and DAT-Ret single mutant mice (Figure 1D–F, I). Because approximately one third of the neurons in the SN are non-dopaminergic, we also used the pan-neuronal marker NeuN to label all neurons in the SN and found that aged DAT-Ret; DJ-1 double mutant mice had significantly fewer neurons in the SN relative to DAT-Ret or control mice (Figure 1J). Since the analysis of TH-, Pitx3-, or NeuN-immunolabeled neurons yielded similar numbers of missing neurons in DAT-Ret; DJ-1 mice, we conclude that combined deletion of Ret and DJ-1 causes enhanced degeneration of SN neurons, relative to deletion of Ret alone. As we had previously shown for DAT-Ret single mutants [10], the observed defects were region specific: The nearby VTA region was not affected in DAT-Ret; DJ-1 double mutants (Figure 1L). The previously observed small (7%) decrease in TH-positive VTA neurons in DJ-1−/− mice [25] was not seen in this analysis, possibly because of a small shift in genetic background due to the presence of the Retlx allele. Finally, we excluded that the DAT-Cre transgene and the mutant DJ-1 allele somehow genetically interacted by comparing the numbers of TH-positive neurons in DAT-Cre; DJ-1−/− compound mice to DAT-Cre transgenics and littermate controls (wild-type and DJ-1+/− mice; Figure 1K). Together these results indicate a requirement for endogenous DJ-1 in maintaining SN neurons, when they are impaired in receiving Ret-mediated trophic signals. Next we asked which SN subpopulation was affected by DJ-1: A9 neurons located in the ventral tier of the SN and projecting to the dorsal striatum are preferentially lost in PD [28]. They express the G-protein gated, inwardly rectifying potassium channel GIRK2 [29]. A9 neurons located in the dorsal tier of the SN and A10 neurons of the VTA project to different areas including limbic and neocortical regions. They express the calcium-binding protein Calbindin [30]. In 24-mo-old mice, removal of DJ-1 had no effect on the number of GIRK2-positive neurons as compared to littermate controls (Figure 2A, B, I). In contrast, removal of Ret alone caused a partial reduction of GIRK2-positive neurons (33% loss) and combined removal of Ret and DJ-1 had the strongest effect (51% loss; p<0. 001 DAT-Ret; DJ-1 double versus CTRL; p<0. 01 DAT-Ret; DJ-1 double versus DAT-Ret single mutants, t test; Figure 2C, D, I). Interestingly, the Calbindin-positive subpopulation in the SN was unaffected in all groups (Figure 2E–H, J). Our stereological quantifications revealed that approximately 9,600 SN neurons express GIRK2, while the remaining 3,700 neurons express Calbindin (Figure 2). If all 5,500 TH-positive neurons that were lost in DAT-Ret; DJ-1 mice were also GIRK2-positive, we would have expected a 57% loss of GIRK2 neurons (5,500 out of 9,600) and no loss of Calbindin-positive neurons. If, however, both populations had been equally vulnerable, we would have expected a 41% loss in both populations. The observed 51% loss in the GIRK2 subpopulation and no statistically significant loss of Calbindin-positive neurons suggest a much higher vulnerability of the GIRK2 subpopulation in DAT-Ret; DJ-1 and DAT-Ret mice. The quantification of soma sizes of surviving GIRK2-positive neurons revealed a small but significant reduction (9%) of the mean soma size in DAT-Ret single mutants compared to control littermates; this effect was not further enhanced in DAT-Ret; DJ-1 double mutants (Figure 2K–M). We next evaluated the possibility that Ret and DJ-1 cooperate in maintaining target innervation of nigral DA neurons. The quantification of TH-positive fiber density confirmed a marked decrease in the dorsal striatum of 18- and 24-mo-old DAT-Ret single mutants compared to age-matched controls (Figure 3A, C, H, I; see also [10]). In contrast, no significant reductions of TH-positive fibers were observed in DJ-1 single mutant mice (Figure 3B, H, I). Interestingly, DAT-Ret; DJ-1 double mutants displayed reductions of TH-positive fibers that were not significantly different from DAT-Ret single mutants (46% at 18 mo and 52% at 24 mo, Figure 3D, H, I). Similar results were obtained when the dopamine transporter (DAT) protein was used as an independent marker for DA terminals (54% reduction in both mutant lines; Figure 3E–G, J). In this case the DAT-Cre knock-in mice were used as controls, since they have reduced levels of DAT protein (unpublished data) due to the loss of one functional copy of the DAT gene. These results indicate that DJ-1 is not required for maintaining target innervation in DA neurons that are partially impaired in receiving trophic support. To evaluate the motor performance of aged mutant mice, we followed their horizontal activity in an open field arena. Consistent with previous observations [21], DJ-1 null mice were found to be hypoactive, despite having normal numbers of SN neurons and normal target innervation (Figure 3K). Mice carrying the DAT-Cre transgene inserted into the 5′ UTR of the DAT gene were slightly hyperactive (Figure 3K), in agreement with previous reports [31]. Removal of Ret or Ret and DJ-1 function did not further modify motor behavior compared to DAT-Cre control mice (Figure 3K). We then measured the levels of total striatal dopamine in these mutants and found a significant increase in dopamine levels in mice carrying the DAT-Cre transgene, while removal of Ret or Ret and DJ-1 did not further modify these levels compared to DAT-Cre control mice (Figure 3L). The TH enzyme is a critical regulator of dopamine production in DA neurons, and our analysis of TH levels in the different aging mutants revealed no significant differences in TH levels (Figure 3M, N). Taken together, these results suggest the existence of compensatory mechanisms that maintain dopaminergic homeostasis in DAT-Ret and DAT-Ret; DJ-1 mice, despite the occurrence of partial neurodegeneration in the SN and the striatum. Dopaminergic-specific deletion of Ret leads to enhanced astrogliosis, but not microglial recruitment in the striatum of 24-mo-old mice (Figure 4 and [10]). Using the microglial marker Ionized binding calcium adapter molecule (Iba-1) and the astrocytic marker glial fibrillary acidic protein (GFAP), we evaluated the occurrence of neuroinflammatory processes in the striatum of aged DAT-Ret; DJ-1 mice and corresponding controls. We found no enhanced recruitment of Iba-1-positive microglial cells in DAT-Ret; DJ-1 or DAT-Ret mice compared to controls (Figure 4A–D). The recruitment of reactive astrocytes in the DAT-Ret striatum was found to be significantly elevated after 24 mo, while additional removal of DJ-1 did not enhance this process (Figure 4E–N). These observations correlate with the above-mentioned histological, behavioral, and physiological measurements and suggest that removal of Ret and DJ-1 function does not exacerbate the structural and functional defects in SN axon terminals caused by Ret deprivation. To obtain independent evidence for genetic interaction between Ret and DJ-1 and to begin characterizing the underlying intracellular pathways, we used the developing Drosophila eye system, which is very sensitive to dosage changes in RTK signaling and downstream components of the PI3K/Akt and Ras/Mapk pathways. While Drosophila DJ-1B is ubiquitously expressed, DJ-1A appears to be enriched in certain tissues such as testes [14], [15]. We used a DJ-1 specific antibody [15] and confirmed that DJ-1B is expressed at high levels in the adult head; DJ-1A expression was not detected in WB, but the presence of the DJ-1A transcript was confirmed by RT-PCR (Figure 5B) [14]; moreover, overexpression of constitutively active versions of Ret, Raf, ERK/rolled, or wild-type Akt1 did not modify endogenous DJ-1 levels (Figure 5A). Flies homozygous for DJ-1A and/or DJ-1B null alleles and flies overexpressing DJ-1A or DJ-1B in the eye (using the photoreceptor neuron-specific driver GMR-Gal4) displayed normal eye development and ultrastructure (unpublished data). Drosophila Ret (dRet) is highly homologous to mammalian Ret [32] and exhibits activities associated with human Ret both in tissue culture cells and during Drosophila eye development [33], [34]. A function for dRet has so far not been described in Drosophila, and in addition, dRet does not bind mammalian GDNF; we therefore utilized previously generated constitutively active forms of dRet (dRetMEN2A/B) that interact with the same pathways as WT Ret and were used to screen for novel Ret interactors [34]. Flies carrying the GMR driver fused to dRetMEN2B (GMR-dRetMEN2B) [34] develop with adult eyes of reduced size and rough morphology. Ommatidia sizes were increased by 35% and individual ommatidia were often fused together, had abnormal polarity, and had poorly patterned interommatidial spaces (Figure 5G, H, J). Despite an increase in ommatidia size, the overall eye size in GMR-dRetMEN2B flies was decreased by 30% compared to controls (Figure 5C, D, F), as a result of a late (pupal) pro-apoptotic wave induced by excessive proliferation and differentiation defects [34]. To determine whether DJ-1 is a dRet interactor, we crossed GMR-dRetMEN2B flies with flies carrying DJ-1A and/or DJ-1B microdeletions [15]. Remarkably, the defects in eye and ommatidia sizes induced by overactive dRet were completely rescued in flies with reduced DJ-1A/B levels (Figure 5E, F, I, J). Similar results were obtained with independent DJ-1A/B loss-of-function alleles and the GMR-dRetMEN2A gain-of-function allele (Figure S2). To test whether overexpression of both Ret and DJ-1 led to a more severe phenotype than the ones induced by active dRet alone, we overexpressed DJ-1A in flies with a moderate Ret-overexpression phenotype (GMR-Gal4/UAS-dRetMEN2A). The resulting flies displayed an enhanced eye phenotype (Figure 5K–N). Thus, both DJ-1A/B interact genetically with overactive dRet in controlling cell size and differentiation in the developing fly retina. To gain insights into the mechanism (s) underlying the genetic interaction between Ret and DJ-1, we investigated the capacity of fly DJ-1A/B to genetically modify pathways that are known to mediate Ret function: PI3K/Akt and Ras/Mapk [33]. Strong overexpression of wild-type PI3K (GMR/PI3KWT at 30°C) led to a 25% increase in eye size and to a disorganized retina compared to controls (GMR-Gal4) (Figure 6A, B, D–F). These phenotypes were not rescued in a DJ-1B−/− background (Figure 6C, D, G). To test whether overexpression of DJ-1A/B could enhance the phenotype of increased PI3K/Akt signaling, we used the moderate eye phenotype induced by wild-type Akt1 overexpression (25% increase in eye and 20% increase in ommatidia sizes). The Akt1 overexpression phenotype was not further exacerbated by DJ-1A or DJ-1B overexpression, nor did the resulting eyes become disorganized (Figure 6H–Q). Similar results were obtained in flies expressing a constitutively active version of PI3K (PI3KCAAX, [35], Figure S3). Conversely, reduced PI3K activity (using the GMR-driven expression of a PI3K dominant negative version), leading to a moderate reduction in eye size and to loss of photoreceptors, was not further enhanced in a DJ-1B null background (unpublished data). Our findings are in apparent contrast to published reports indicating genetic interactions of Drosophila DJ-1 with PTEN, an inhibitor of Akt [19], and with mammalian DJ-1 being a modulator of PI3K/Akt signaling in cultured cells [19], [36]. We found that DJ-1A/B overexpression only mildly rescued the effects of PTEN overexpression (reduced eye and ommatidia size) and that a reduction in DJ-1A/B function did not visibly enhance the PTEN overexpression phenotype (Figure S3). Furthermore, our experiments using different cell lines failed to reproduce the previously reported modulation of Akt activation by DJ-1, in conditions of DJ-1 overexpression, RNAi knockdown, or in DJ-1−/− mouse embryonic fibroblasts (MEFs; Figure S4). Thus, our data suggest that DJ-1 does not synergize with PI3K/Akt signaling during eye development, nor does DJ-1 modulate the activation status of Akt under normal conditions. DJ-1 might interact with PTEN only in defined situations (e. g. , in oncogenic conditions) but in a PI3K-Akt independent manner (see Discussion). To investigate the interaction of DJ-1 with the Ras/ERK pathway, we used constitutively active versions of Ras and the Mapk ERK/rolled (rl), which impair eye development by promoting excessive proliferation and altered cell differentiation [37]. Overexpression of active Ras in R7 photoreceptor neurons with the sevenless promoter (Sev-RasV12) led to the induction of multiple R cells/ommatidium and to a rough eye phenotype (Figure 7A, B, F, G, U). This phenotype was rescued by reducing endogenous DJ-1A/B levels (Sev-RasV12/DJ-1A+/−/DJ-1B+/−) (Figure 7C, U). Sev-RasV12 flies displayed on average 8. 5 R cells/ommatidium while in Sev-RasV12/DJ-1A+/−/DJ-1B+/− flies only 7. 5 R cells/ommatidium were detected (Figure 7H, U). In addition, in Sev-RasV12 flies, 67% of ommatidia were abnormally fused with their neighbours, compared to only 5% in Sev-RasV12/DJ-1A+/−/DJ-1B+/− flies (Figure 7G, H and unpublished data). Conversely, overexpression of DJ-1A further enhanced the Ras-overexpression phenotype to 11 R cells/ommatidium in Sev-RasV12/GMR/DJ-1A retinas (Figure 7D, E, I–U). We next assessed the modulation of constitutively active rolled signalling (GMR/rlSEM) by DJ-1. Overexpression of rlSEM led to supernumerary photoreceptor neurons (9. 5 R cells/ommatidium). DJ-1A/B overexpression or partial reduction of DJ-1B levels did not modulate this phenotype (Figure 7K–T, V). Moreover, in cultured cells, increasing or decreasing DJ-1 levels did not modulate the phosphorylation status of ERK1/2 under basal conditions or following stimulation by growth factors (Figure S4; see also [38]). These results suggest that DJ-1A/B function either between Ras and ERK or in parallel to the Ras/ERK pathway to control cell differentiation and proliferation induced by overactive Ras/Mapk signaling. Our loss-of-function mouse experiments suggest that DJ-1 acts in parallel to Ret-mediated signalling to control dopaminergic neuron survival. The Drosophila interactions between DJ-1 and ectopic Ras signalling raise the possibility that DJ-1 acts in parallel to the Ret induced Ras/Erk pathway to control optimal activation of Mapk downstream targets. To test this possibility and to investigate whether DJ-1 interacts with endogenous Erk signalling in Drosophila, we performed double loss-of-function experiments. We chose to investigate this interaction in two places where Erk/rolled is known to play a crucial role during development: the development of photoreceptor neurons and wing venation. Flies carrying two hypomorphic rolled alleles (rl1) displayed a moderate eye phenotype caused by a mild reduction in the number of R cells/ommatidium (6. 64; Figure 8A, C, E–G, Q, R). While control and DJ-1B−/− flies had a normal appearance and a normal complement of 7 R cells/ommatidium, eyes of rl1/rl1; DJ-1B−/− flies were significantly smaller, rough, and displayed on average only 5. 34 R cells/ommatidium (Figure 8A–H, Q, R; p<0. 05 CTRL versus rl1/rl1; p<0. 001 rl1/rl1 versus rl1/rl1 DJ-1B−/−, t test). DJ-1B is thus required, as a rolled interactor to control photoreceptor neuron development. We then investigated the development of the wing and found that rl1/rl1 flies had a very mild defect in wing venation, the vein L4 being sometimes thinner (in about 20% of the animals; Figure 8I–P, S). While in control and DJ-1B−/− flies the L4 vein developed normally, in rl1/rl1; DJ-1B−/− mutants the thinning of the L4 vein was either short (in 33% of animals), long (37% of all cases), or the L4 vein was interrupted (in 25% of animals, Figure 8I–P, S). Such an enhanced phenotype was also seen in flies carrying a combination of rl1 and the stronger allele rl10 (a deficiency; [37]). DJ-1 is thus required, as a rolled interactor, to control the development of the wing. These results uncovered a novel DJ-1B−/− phenotype in the unchallenged fly and suggest that DJ-1B cooperates with Ras/Mapk signalling during photoreceptor neuron and wing imaginal disc development. Based on these results we propose a model in which DJ-1 primarily promotes the survival of DA neurons that have suffered from an independent hit (trophic insufficiency) and have greatly reduced target innervation. In aged Ret single mutant mice, we have previously shown that the loss of target innervation exceeded cell loss; hence these mice contained a fraction of cells that survived during aging but had strongly reduced target innervation [10]. The present study shows that in aged DAT-Ret/DJ-1 double mutants, additional cell loss occurs such that the degree of cell loss exactly matches the degree of target innervation loss. The simplest explanation is that additional DJ-1 removal primarily leads to loss of cells that have strongly reduced target innervation (due to Ret signaling loss), while the larger fraction of cells with functional connections to the striatum remains unaffected. Alternatively, DJ-1 removal may affect both cell populations; however, as the projections to the striatum do not decrease further in the double mutant, surviving neurons would have to resprout and innervate the vacated target area to compensate for the expected loss of innervation. Since our previous study [40] showed that Ret signaling controls DA resprouting after toxic lesions, we find the latter explanation less likely. The fact that removal of DJ-1 in Ret-deficient mice only accelerated the loss of DA cell bodies but not axons suggests that DJ-1 might exert its pro-survival activities in the SN dopaminergic cell body. Neurotrophic factor receptors, such as Ret, are transported from distal sites to the cell body, where they signal to promote survival. Components of the signaling machinery including activated Ras are also transported to the cell body in signaling vesicles [41]. Recent work succeeded in genetically uncoupling the survival requirements for the axon and cell body compartments. Specific molecules primarily regulate maintenance of cell bodies but not axons (including Bax, Bcl2, and JNK), further supporting the notion that different survival mechanisms operate in these two neuronal compartments [42]. Understanding the differential vulnerability of the axonal and cell-body compartments to aging and degenerative insults might improve our understanding of neurodegeneration and open new therapeutic avenues. Why is there a specific requirement for Ret and DJ-1 activity in the GIRK2-positive subpopulation of SN neurons, considering that Ret and DJ-1 are expressed by most if not all DA neurons in SN and VTA [10], [27], [43]? GIRK2-positive neurons appear to be more sensitive than calbindin-positive neurons to toxic insult [44], [45] and calbindin-positive SN neurons are specifically spared in PD [46]. The presence and activity of GIRK2 itself may be a cause of vulnerability, since elevating the levels of GIRK2 further sensitizes these neurons [44]. Remarkably, GDNF was found to acutely modulate the excitability of midbrain dopaminergic neurons by inhibiting A-type K+ channels, a function that specifically involves the Mapk pathway [47]. Although the effects of Ret signaling on GIRK2 have not been studied, it is tempting to speculate that the modulation of Ras/Mapk signaling by Ret and DJ-1 also affects GIRK2 function and vulnerability of dopaminergic neurons. Further studies focusing specifically on the GIRK2 subpopulation will better define the exact biochemical processes involved in their survival and their interplay with other age-related cellular changes. Our results show that DJ-1 promotes survival of dopaminergic neurons only in conditions of aging and trophic insufficiency, suggesting that the function (s) of DJ-1 might only be uncovered in specific circumstances. The lack of a strong phenotype in DJ-1 null mutants has prevented the analysis of DJ-1 function in vivo. In vitro studies have suggested several functions for DJ-1 [18], [19], [26], [48], [49]; however, these proposed functions remain to be validated in vivo. Because fly genetics has previously uncovered PD-associated mechanisms [50], we chose to investigate genetic interactions between Ret and DJ-1 in the Drosophila eye. Constitutively active versions of Ret mediate excessive cell proliferation, abnormal increases in cell size, differentiation, and polarity defects. These defects induce a late-onset pro-apoptotic wave (in late pupal stages), resulting in adult eyes of reduced size with fewer ommatidia. Thus, even though Ret is a pro-survival regulator in mammalian systems, its excessive activation in the fly developing retina induces developmental abnormalities that indirectly lead to partial eye degeneration. Loss of endogenous Drosophila DJ-1A/B reduces the signaling output of activated Ret and largely alleviates these developmental defects. Photoreceptor cell size and the balance between photoreceptor proliferation and differentiation are returned to normal in a DJ-1A/B loss-of-function background. When we instead activated the PI3K/Akt pathway in the eye, endogenous DJ-1A/B were not required, nor was DJ-1A/B overexpression sufficient, to modulate this phenotype, indicating that DJ-1A/B do not interact with the PI3K/Akt pathway. This is in contrast to previous work that proposed DJ-1 to be a potent modulator of the PI3K/Akt pathway [19]. However, recent work by the same group suggests that DJ-1 may do so only in oncogenic (hypoxia) situations [36], and our results suggest that the interaction between DJ-1 and PTEN is PI3K/Akt-independent. Indeed, emerging evidence suggests lipid phosphatase-independent roles of PTEN [51], the best studied being a protein-phosphatase-dependent inhibition of Ras/MAPK signaling [52], [53], modulation of JNK signaling [54], and several nuclear functions, including control of cell cycle progression and maintenance of genomic stability [55]. We found that DJ-1 is necessary and sufficient to mediate the effects of activated Ras during the development of the eye. Because DJ-1 failed to modulate constitutively active ERK/Mapk signalling during eye development, we propose that DJ-1 acts in parallel to Ras and either upstream or in parallel to ERK. We then investigated whether endogenous DJ-1B has any physiological function during the development of the fly. We found that DJ-1B is required, as an ERK/rolled interactor to control the development of photoreceptor neurons and wing venation. These observations establish a novel physiological role for DJ-1B in the intact fly. In vitro experiments previously suggested that DJ-1 might interact with Ras signaling. DJ-1 was first defined as a Ras-pathway interactor during oncogenic transformation [18] and a recent study reported that DJ-1 regulates the activation status of the ERK kinase in vitro [38]. Mechanistic studies in the Ret/DJ-1 mouse model are difficult to pursue, because of the region-specific and late-onset phenotype. Studies in which constitutively active or dominant negative Akt was virally delivered into the mouse brain suggested that Akt regulates the survival, cell size, and target innervation of SN neurons [56], [57]. Ret is a potent activator of both PI3K/Akt and Ras/ERK, and the loss of cell bodies, axons, and reduced cell size in DAT-Ret mice suggest possible defects in PI3K/Akt and/or Ras/ERK signaling. Our finding that DJ-1 does not interact with Akt signaling in Drosophila suggests that Akt signaling might not be the major pathway that cooperates with DJ-1 to regulate SN survival. Mice over-expressing activated Ras (RasV12) in the nervous system have larger neurons and embryonic mesencephalic neurons derived from these mice are resistant to toxin-induced degeneration, suggesting that Ras signaling promotes survival of SN neurons [58]. A recent analysis of Ret knockin mice revealed a critical role for Ras/B-Raf/IKK signaling, but not for PI3K and ERKs, in the survival of sympathetic neurons [59]. It is therefore possible that DJ-1 cooperates with Ras-associated signaling to promote survival of aging SN neurons deprived of trophic support. It is also possible that DJ-1 and associated Ras signaling cooperate with PI3K/Akt signaling to control common downstream effectors and further studies will address this possibility. Deletion of both Ret and DJ-1 leads to a presymptomatic parkinsonian state in aging mice characterized by the lack of alpha-synuclein deposits, behavioural alterations, and loss of total dopamine, raising the possibility that the mechanisms regulating cell survival and target innervations might differ from those regulating protein homeostasis and dopamine dynamics. Compensatory mechanisms are likely to exist in the nigrostriatal system that maintains dopaminergic homeostasis below a certain threshold of SN neurodegeneration [1]. Several potential compensatory mechanisms have been previously described [60] and Ret/DJ-1 mice could serve as basis for further investigations of these mechanisms. The low penetrance of PD and the variability of symptoms in family members who inherit PD-associated mutations have raised the possibility that several risk factors interact to promote SN neuronal demise (the multiple hit hypothesis of PD [6]). A combination of high cytoplasmic calcium, elevated levels of free cytoplasmic dopamine, and the presence of alpha-synuclein induces selective death of cultured DA neurons, and interference with any of these individual hits alleviated neuronal cell death [61]. We report here a chronic genetic mouse model in which the interplay between three factors (aging, trophic insufficiency, and increased cellular stress due to DJ-1 inactivation) synergize and cause the loss of approximately 50% of GIRK2 DA neurons in the SN. Although the relevance of this three-component network to PD remains to be demonstrated, our findings underscore the importance of higher-order interactions between “sub-lethal” dopaminergic insults in promoting cell death. In summary, we propose that the tight integration between aging, trophic signaling pathways, and the signaling network defined by PD-associated genes, including DJ-1, is critical for DA neuron survival. Further research on aging mechanisms coupled to studies on trophic factors and oxidative stress regulation may identify common denominators of these three processes and uncover new cellular targets for drug development in PD. The generation of Retlx [62] and Dat-Cre [63] alleles were described previously. The DJ-1−/− mice are described in a separate paper [25]. The GMR-dRetMEN2 and UAS-dRetMEN2 flies were generously provided by Ross Cagan (Mount Sinai). DJ-1A and DJ-1B knockout as well as UAS-DJ1A and UAS-DJ1B flies were a kind gift from Nancy Bonini (University of Pennsylvania). Wild-type, dominant negative (D954A), and constitutive active (CAAX) UAS-PI3K flies were from Sally Leevers (Cancer Research UK). Sev-RasG12V flies were from Marc Therrien (University of Montreal), and UAS-rlSEM flies were kindly provided by Jongkyeong Chung (KAIST, Korea). UAS-dPTEN flies were a kind gift from Tak Mak (University of Toronto). All other fly lines were from the Bloomington Stock Center. Immunohistochemistry, stereology, and fiber density measurements were essentially performed as previously described [10]. Thirty µm-thick free floating sections were used for immunostainings. Primary antibodies were directed against: Tyrosine hydroxylase-TH (mouse monoclonal, 1∶2000, DiaSorin, Stillwater Massachusetts, USA), Pitx3 (rabbit polyclonal, generously provided by M. P. Smidt (Utrecht University), 1∶1000, [64]), GIRK2 (rabbit polyclonal, 1∶80, Alomone labs), Calbindin (mouse monoclonal, 1∶500, Sigma), Iba1 (1∶1000, rabbit polyclonal, Wako, Neuss, Germany), and GFAP (1∶500 rabbit polyclonal, DakoCytomation, Glostrup, Denmark). For immunofluorescence, sections were first premounted, and then the following primary antibodies were used: anti-TH antibody (mouse monoclonal, 1∶2000, DiaSorin, Stillwater Massachusetts, USA) or with anti-DAT (rat polyclonal, 1∶500, Chemicon/Millipore). For stereology, every sixth section spanning the ventral midbrain was used for measurements. To quantify the density of astrocytes in the dorsal striatum, one picture was acquired from every sixth section of the dorsal striatum. Six to eight sections were analyzed/animal and at least 4 animals were analyzed per group. GIRK2 immunostained coronal sections were analyzed using a bright field microscope with a 40× objective. Random cells were selected with stereological methods using the StereoInvestigator software. Five to seven animals per group were analyzed by circling cell soma of 149–275 cells per animal. Eighteen-mo-old mice were sacrificed, brains were removed, snap-frozen, and the striata were dissected. The tissue was homogenized in 0. 1 M perchloric acid containing 0. 5 mM disodium EDTA and 50 ng/ml, 3,4-dihydroxybenzylamine as an internal standard, centrifuged at 50,000 g for 30 min, and filtered through a 0. 22 µM PVDF membrane. The samples were subjected to HPLC analysis as described previously [10]. To test general activity of aging control and mutant mice, mice were subjected to open field behavioral assessment. Eighteen-mo-old mice were housed individually in a room with 12 h/12 h reversed day-night cycle. All experiments were conducted during the night period in a quiet room by 12 lux light. Mice were placed into a 59 cm×59 cm large arena for 20 min, and their movement was followed using EthoVision Pro 2. 2. (Noldus, Sterling, USA). The experiment was repeated on the consecutive day and the average distance each mouse travelled during the two trials was determined. Experimental protocols were approved by the government of Oberbayern, Germany. Pictures of P1–P5 eyes and wings were acquired using a Leica MZ 9. 5 stereomicroscope equipped with a Leica DFC320 digital camera (LeicaMicrosystems, Wetzlar, Germany). For toluidine blue stainings, heads from P1–P5 animals were dissected and post-fixed in 2. 5% glutaraldehyde. After washing with PBS, heads were incubated in a 1% osmium tertaoxide solution (Science Services, Munich, Germany), then dehydrated in ethanol solutions of increasing concentrations, followed by a 10 min incubation in propylene oxide. Heads were then incubated overnight in a solution containing 50% propylene oxide and 50% durcupan epoxy resin, which contained 48% component A/M, 40% hardener B, 2. 25% accelerator C, and 9% plasticizer D (Sigma-Aldrich). Then, heads were incubated overnight in 100% durcupan epoxy resin. The next day, heads and fresh durcupan resin were transferred to molds, oriented tangentially, and then cooked overnight at 60°C. The heads were then removed from molds and cut using a 2088 ultrotome (LKB, Bromma, Sweden). Three µm-thick sections were collected, mounted, and then stained using a pre-warmed toluidine blue solution that contained 0. 1% toluidine blue (Serva Electrophoresis, Heidelberg, Germany) and 2. 5% sodium carbonate. After a quick wash in water, sections were allowed to dry and were then covered with paraffin oil. Pictures at different retinal depths were acquired for each head. To determine ommatidium size and the number of photoreceptor neurons/ommatidium, at least 150 ommatidia/animal from at least 4 animals were analyzed. Heads and bodies were separated from 10 WT and DJ-1−/− flies and snap frozen in liquid nitrogen. RNA preparation was performed using the RNAeasy kit and the QIAshredder spin column (Qiagen) according to the manufacturer' s instructions. The RNA concentration was determined using a NanoDrop ND1000 spectrometer, and 10 ng per sample of total RNA was subjected to RT-PCR amplification with 25,30, or 35 cycles using the Qiagen OneStep RT-PCR kit according to the manufacturer' s instructions. The following exon-spanning primer pairs were used: 5′-CAAGCAAGCCGATAGATAAACA-3′ (GAPDH forward) 5′-CAAGTGAGTGGATGCCTTGT-3′ (GAPDH reverse) 5′-GGAAAGATCCTTGTTACCGTG-3′ (DJ-1A forward) 5′-CCATCCTGGACCACAGTCTT-3′ (DJ-1A reverse). A pCMV-myc-DJ-1 construct and the empty pCMV-Myc vector were acquired as a kind gift from Phillip Kahle (Hertie Institute, Tübingen, Germany). SiRNA oligonucleotides (stealth-siRNA, Invitrogen) had the following sequences: (DJ-1) AGGAAAUGGAGACGGUCAU-CCCUGU; (CTRL) ACAGGGAUGACCGUCUCCAUUUCCU. The sequences have been described previously and validated for off target effects [13], [48]. MEFs were isolated from E13. 5 WT or DJ-1−/− embryos according to standard procedures. Experiments were performed at passage 4–6. MEFs, HeLa cells, and A549 cells were cultured in DMEM supplemented with 10% serum, 1% L-Glutamine, and 1% pen/strep. SH-SY5Y cells (ATCC #CRL-2266) were cultured in the DMEM/F12 (1∶1) supplemented with 10% serum, GlutaMAX, and 1% pen/strep. MEFs were transfected using the standard CaPO4-precipitate method. HeLa cells were transfected using Lipofectamine 2000 (Invitrogen) for plasmids or Lipofectamine RNAiMAX (Invitrogen) for siRNA by the forward transfection method, according to manufacturer' s instructions. SH-SY5Y and A549 cells were transfected using Lipofectamine 2000 (Invitrogen) for plasmids or Lipofectamine RNAiMAX (Invitrogen) by the reverse transfection method, according to the manufacturer' s instructions. Plasmid overexpression experiments were incubated for 24 h after transfections before analysis for all cell types. SiRNA knockdowns were incubated for 48 h after transfection for HeLa and A549 cells, while SH-SY5Y cells were incubated for 96 h after transfection. Fly P1–P5 heads from at least 50 animals were collected and snap frozen in liquid nitrogen, then stored at −80°C. The lysis and detection were performed as previously described [15] using an anti-DJ1A/B antibody (rabbit polyclonal, 1∶500, kind gift from Leo Pallanck). Cultured cell lines were harvested in a lysis buffer containing 1% Triton X-100,150 mM NaCl, 1 mM EDTA, 10 mM Tris-HCl (pH 7. 5), 100 mM NaF, 1 mM NaVO3,10 mM Na4P2O7, and Complete protease inhibitor mixture (Roche Diagnostics). Mouse brains were snap frozen, ventral midbrains and striata were dissected on ice, and homogenized in a buffer containing 150 mM NaCl, 50 mM Tris_HCl, pH 7. 4,2 mM EDTA, 1% Nonidet P-40,1% SDS, and Complete protease inhibitor mixture (Roche Diagnostics) by 10 strokes in a dounce homogenizer. Cell lysates and brain homogenates were centrifuged at 1,000 g for 10 min, supernatants were saved, and the protein concentration was determined using the DC protein assay (BioRad). Samples were subjected to SDS-PAGE and Immunoblotting according to standard techniques. The following antibodies were used: anti-AKT (9272, Cell Signaling Technology), anti-phospho-AKT (Ser473,9271, Cell Signaling Technology), DJ-1 (ab4150, Abcam), anti-phospho-p42/p44 MAPK (4376, Cell Signaling Technology), anti-p42/p44 MAPK (9102, Cell Signaling Technology), anti-Ret (70R-RG002, Fitzgerald), and anti-β-tubulin (T-8660, Sigma).

The major pathological event in Parkinson disease is the loss of dopaminergic neurons in a midbrain structure, the substantia nigra. The study of familial Parkinson disease has uncovered several disease-associated genes, including DJ-1. Subsequent studies have suggested that the DJ-1 protein is a suppressor of oxidative stress that might modify signaling pathways that regulate cell survival. However, because animal models lacking DJ-1 function do not show dopaminergic neurodegeneration, the function (s) of DJ-1 in vivo remain unclear. Using mouse genetics, we found that DJ-1 is required for survival of neurons of the substantia nigra only in aging conditions and only in neurons that are partially impaired in receiving trophic signals. Aging mice that lack DJ-1 and Ret, a receptor for a neuronal survival factor, lose more dopaminergic neurons in the substantia nigra as compared with aging mice that lack only Ret. Using the fruit fly Drosophila, we determined that DJ-1 interacts with constitutively active Ret and with its associated downstream signaling pathways. Therefore, understanding the molecular connections between trophic signaling, cellular stress and aging could facilitate the identification of new targets for drug development in Parkinson Disease.

Abstract Introduction Results Discussion Materials and Methods

neuroscience/neuronal signaling mechanisms neuroscience/neurobiology of disease and regeneration

2010

Pro-Survival Role for Parkinson's Associated Gene DJ-1 Revealed in Trophically Impaired Dopaminergic Neurons

The glutamic acid residues of the peptidoglycan of Staphylococcus aureus and many other bacteria become amidated by an as yet unknown mechanism. In this communication we describe the identification, in the genome of S. aureus strain COL, of two co-transcribed genes, murT and gatD, which are responsible for peptidoglycan amidation. MurT and GatD have sequence similarity to substrate-binding domains in Mur ligases (MurT) and to the catalytic domain in CobB/CobQ-like glutamine amidotransferases (GatD). The amidation of glutamate residues in the stem peptide of S. aureus peptidoglycan takes place in a later step than the cytoplasmic phase – presumably the lipid phase - of the biosynthesis of the S. aureus cell wall precursor. Inhibition of amidation caused reduced growth rate, reduced resistance to beta-lactam antibiotics and increased sensitivity to lysozyme which inhibited culture growth and caused degradation of the peptidoglycan. Peptidoglycan forms an essential stress-bearing and shape-maintaining layer in the bacterial cell envelope. Its biosynthetic pathway is the target of important classes of antimicrobials such as beta-lactams and glycopeptides, and the polymerized cell wall is targeted by antimicrobial enzymes like lysozyme. The biosynthesis of peptidoglycan is a complex process involving several consecutive enzymatic steps that take place in the cytoplasm and on the inner and outer surface of the cytoplasmic membrane. The cytoplasmic stage of biosynthesis culminates in the formation of the UDP-N-acetylmuramic acid (UDP-MurNAc) covalently linked to a pentapeptide which is composed of L-alanine, D-iso-glutamic acid, L-lysine (or meso-diaminopimelic acid, DAP) and D-alanyl D-alanine. The assembly of this stem peptide moiety involves a superfamily of enzymes, the Mur ligases [1]. In the next steps of biosynthesis, the UDP-MurNAc-pentapeptide is attached to a membrane acceptor undecaprenyl phosphate (C55-P) followed by the addition of GlcNAc to the MurNAc residues yielding the structure known as lipid II. Lipid II, i. e. , the bactoprenol linked disaccharide pentapeptide is then transported to the outer surface of the cytoplasmic membrane where it serves as a substrate for polymerization reactions catalyzed by transpeptidases and transglycosylases to form the polymeric cell wall peptidoglycan. Chemical analysis of the S. aureus peptidoglycan showed that the structure of these polymers differed from the structure of the cytoplasmic disaccharide pentapeptide cell wall precursor: some hydroxyl groups in the glycan chain were acetylated; and the second amino acid residue of the muropeptides was not iso-glutamic acid but its amidated version, iso-glutamine. The mechanisms of these secondary modifications of the cell wall are not well understood. Enzymes and genetic determinants involved with the acetylation of the glycan chain and the role of this structural modification in the resistance of S. aureus against host lysozyme - have only been described recently [2]. While amidation of the stem peptide residues at positions 2 or 3 or both is frequent among gram-positive bacteria, the physiological roles of this chemical modification have remained a matter of speculation [3] and the genetic determinants and enzymes responsible for the conversion of iso-glutamic acid to iso-glutamine residues have also remained unknown. In this communication we describe the identification of a small operon composed of two genes – murT and gatD – in the genome of the beta-lactam resistant S. aureus strain COL. Amino acid sequence of the protein products of these genes show similarity to murein ligases (murT) and to CobB/CobQlike glutamine amidotransferases (gatD). The properties of a conditional mutant of murT/gatD indicate that this operon is responsible for the conversion of isoglutamic acid to iso-glutamine residues in the peptidoglycan of S. aureus. Bacterial strains and plasmids used in this study are listed in Table 1. Staphylococcus aureus strains were grown at 37°C with aeration in tryptic soy broth (TSB) or tryptic soy agar (TSA) (Difco Laboratories, Detroit, Mich.). Transposition mutant RUSA208 [4] and the conditional mutant strains RN4220pCadmurT-gatD and COLpCadmurT-gatD, the double mutant RUSA208pCadmurT-gatD, the complemented strains COLpCadmurT-gatD+pSKmurT and COLpCadmurT-gatD+pSKgatD and the control strain COLpCadmurT-gatD+pSK were grown in the presence of the respective antibiotics (Table 1). The growth medium was supplemented with 0. 2 µM of cadmium chloride (CdCl2; Sigma, St. Louis, MO), unless otherwise described. Escherichia coli strains (Table 1) were grown in Luria-Bertani broth (LB; Difco Laboratories) with aeration at 37°C. Erythromycin (10 µg/ml), neomycin sulphate (50 µg/ml), kanamycin (50 µg/ml), chloramphenicol (10 µg/ml) and ampicillin (100 µg/ml) from Sigma were used for the selection and maintenance of S. aureus and E. coli mutants. The amino acid sequences of ORFs SACOL1951 (MurT) and SACOL1950 (GatD) were retrieved from the UniProtKB database [5], and their domain architecture was checked using the InterProScan tool [6]. The domains were aligned through TCoffee [7]. Given the limited similarity between sequences, secondary structure inference was used as an independent benchmark for the alignment. This inference was accomplished through Psipred [8]. Position specific annotation other than the one present in the InterPro documentation was collected from references [1], [9]. Restriction enzymes from New England Biolabs (Beverly, MA) were used as recommended by the manufacturer. Routine PCR amplification was performed with Tth DNA polymerase (HT Biotechnology, Cambridge, United Kingdom) and PCR amplification for cloning purposes was performed using Pfu DNA polymerase (Stratagene, Heidelberg, Germany). For plasmid DNA extraction High pure Plasmid Purification Kit (Roche, Basel, Switzerland) was used. PCR and digestion products were purified using High pure PCR Purification Kit (Roche). Ligation reactions were performed using Rapid DNA Ligation kit (Roche). Reverse transcription (RT) -PCR was performed as described [10] using total RNA from strain COL as the template. The primers used for the reverse transcription reactions are described in Table 2 and the amplification conditions were: 94°C for 2 min; 40 cycles of 94°C for 30 s, 53°C for 30 s, and 72°C for 2 min; and one final extension step of 72°C for 5 min. A 918- bp DNA fragment of murT gene was amplified by PCR using chromosomal DNA from strain COL as a template and the specific primers PmurT′-R and PmurT′-F (Table 2). The amplification conditions used were as follows: 94°C for 4 min; 30 cycles, each consisting of 94°C for 30 s, 50°C for 30 s, and 72°C for 1 min 30 s; and one final extension step of 72°C for 10 min. The amplified fragment and the integrative plasmid pBCB20, carrying a cadmium chloride inducible promoter (R. G. Sobral and M. G. Pinho, unpublished) were both digested with SmaI and ligated, generating plasmid pMurT′. Plasmid pMurT′ was electroporated into competent cells of RN4220 with a Gene Pulser apparatus (Bio-Rad, California) under conditions described previously [11]. Selection of the transformants was performed using kanamycin (50 µg/ml), neomycin sulphate (50 µg/ml) and 0. 2 µM of Cadmium chloride. The correct insertion of pMurT′ into RN4220 chromosome was confirmed by PCR, using an internal murT primer chosen outside the region cloned and an internal pBCB20 primer (Table 2). The murT-gatD conditional mutation was then transduced, by phage 80α to the background of COL as previously described [12] and mutant COLpCadmurT-gatD was obtained. A 1673 bp DNA fragment, including the complete murT coding sequence and 300 bp of the immediate upstream region was amplified from COL genome using the primers PmurTSalI and PmurTBamHI (Table 2). The amplified murT fragment and plasmid pSK5632 [13] were digested with SalI and BamHI and ligated, generating the replicative plasmid pSKmurT. The same strategy was used for the construction of the replicative plasmid pSKgatD, in which a 1088 bp DNA fragment including the complete gatD gene sequence and 300 bp of the immediately upstream region. Plasmids pSKmurT and pSKgatD were separately introduced into RN4220 by electroporation and subsequently transferred to COLpCadmurT-gatD by transduction, generating COLpCadmurT-gatD+pSKmurT and COLpCadmurT-gatD+pSKgatD, respectively. Plasmid pSK5632 was also introduced in the conditional mutant, providing the control strain COLpCadmurT-gatD+pSK. The murT-gatD conditional mutation was transduced, using phage 80α, to the background of RUSA208. The obtained double mutant RUSA208pCadmurT-gatD, has a transposon insertion in glnRA operon and the murT-gatD operon under the control of pCad promoter. Cells were grown in TSB at 37°C to mid-exponential phase (OD620 nm of 0. 7). Prior to harvesting the cells, the RNA protect reagent (QIAGEN, Hilden, Germany) was added to the cultures. Total RNA was isolated as previously described [14]. PCR amplified internal fragments of the murT, gatD, SACOL1949-SACOL1948, SACOL1952, glnA and pta genes were used as probes for hybridization (the primers used are listed in Table S1). The DNA probes were labeled with [α-32P]dCTP (Perkin Elmer, MA, USA). Isolation of cell wall was performed as described [2], [15]. Briefly, cells were harvested by centrifugation, washed twice with cold 0. 9% NaCl, resuspended in 0. 9% NaCl and boiled for 20 min. After chilling on ice, the suspension was centrifuged and washed twice with 0. 9% NaCl. The cells were disrupted using 106 µm glass beads (Sigma) and FastPrep FP120 apparatus (Bio 101, La Jolla, Calif.), purified, washed, and boiled for 30 min in 5% SDS, diluted in 50 mM Tris/HCl pH 7, to remove non-covalently bound proteins. After centrifugation, the cell wall fragments were diluted in 0. 1 M Tris-HCl (pH 6. 8) and incubated with 0. 5 mg/ml trypsin for 16 h at 37°C to degrade cell-bound proteins. Purified cell walls were washed with double-distilled water and lyophilized. Lyophylised cell wall was treated with 49% of hydrofluoric acid for 48 hours at 4°C in order to remove teichoic acids. The teichoic acid free peptidoglycan was washed with water several times to remove all traces of hydrofluoric acid and then lyophylised. Identical amounts of peptidoglycan were digested with mutanolysin (1 mg/ml; Sigma). The resulting muropeptides were reduced with sodium borohydride and separated by reverse-phase-high performance liquid chromatography (RP-HPLC) using a Hypersil ODS (Runcorn Cheshire, UK) column (3 µm particle size, 250×4. 6 mm, 120 Å pore size) and a linear gradient from 5% to 30% MeOH in 100 mM sodium phosphate buffer pH 2. 5 at a flow rate of 0. 5 ml/min as described [16]. Highly purified cell wall was prepared as previously described [2] and resuspended to a final concentration of 10 mg/ml. Cell wall material (500 µg) was digested with lysostaphin (300 µg) in 20 mM amonium acetate, pH 4. 8, for 24 h at 37°C with stirring. Subsequently, cellosyl (Höchst AG, Frankfurt, Germany) (15 µg) was added to the reaction mixture which was incubated for 12 h at 37°C. Finally, additional 15 µg of cellosyl was added and the incubation continued for an additional 12 h. The enzymatic reaction was stopped by boiling the samples for 5 min and insoluble contaminants were removed by centrifugation. The digested cell wall was reduced with sodium borohydride and the resulting monomeric muropeptides were separated by RP-HPLC using a Prontosil (Bischoff, Leonberg, Germany) column (3 µm, particle size, 250×4. 6 mm, 120 Å pore size), and a linear gradient from 0% to 30% MeOH in 10 mM sodium phosphate buffer pH 6. 0 at a flow rate of 0. 5 ml/min. The eluted fractions corresponding to the most predominant peaks of the chromatograms were collected after HPLC separation, concentrated to 10–20 µl, and acidified with 1% trifluoroacetic acid (TFA). The samples were then desalted and further concentrated using ZipTips (C- 18, Millipore, UK) according to the standard protocol recommended by the manufacturer. The material was eluted from the ZipTip with 3 µl of 50% acetronitrile, 0. 1% TFA and was sprayed directly into a Finnigan LTQ-FT mass spectrometer (Thermo, Bremen, Germany) operating in positive mode (Pinnacle Proteomics Facility, Newcastle University, UK) [17]. The UDP-linked peptidoglycan precursors from the cytoplasmic pool were isolated using a modified protocol [18]. Briefly, vancomycin (Sigma) was added (at five times the minimal inhibitory concentration) to mid-exponential grown cultures and incubation proceeded for additional 30 minutes. The cultures were then chilled below 10°C, cells were harvested, suspended in cold water and slowly stirred into the same volume of boiling water for 15 minutes. After centrifugation the supernatant was collected, lyophilized, dissolved in water and the pH was adjusted to 4. 0 using 20% phosphoric acid. The suspension was again centrifuged and the pH of the supernatant adjusted to 2. 0. The suspension was centrifuged at 4°C for 1 h at 200000 g. The UDP-linked peptidoglycan precursors were separated through the same column used to separate the muropeptides of peptidoglycan – using a linear gradient from 0 to 30% of MeOH in 100 mM sodium phosphate buffer (pH 2. 0), with a flow rate of 0. 5 ml/min. Compounds to be analyzed by MS were isolated and desalted using the same column as before with a linear gradient from 0 to 30% of MeOH in 10 mM of sodium phosphate (pH 4. 3) for 25 min with a flow rate of 0. 5 ml/min. Mass spectral data were obtained by MALDI-TOF analysis (Pinnacle Proteomics Facility, Newcastle University, UK). Overnight grown cultures of strains COL and COLpCadmurT-gatD, COLpCadmurT-gatD+pSKmurT, COLpCadmurT-gatD+pSKgatD and COLpCadmurT-gatD+pSK were diluted 1∶1,000 into fresh TSB supplemented with the respective antibiotics (Table 1). The conditional mutants were grown in media containing CdCl2 concentrations at 0,0. 01,0. 05 and 0. 2 µM. The cultures were incubated at 37°C with agitation and the OD620 nm was monitored over time. Overnight grown cultures of strains COL and COLpCadmurT-gatD, COLpCadmurT-gatD+pSKmurT and COLpCadmurT-gatD+pSKgatD and COLpCadmurT-gatD+pSK were plated on TSA supplemented with increasing concentrations of CdCl2 (0,0. 01,0. 05 and 0. 2 µM) and incubated at 37°C for 24 hours. Oxacillin (Sigma) diffusion disks (1 mg) were used to determine inhibition halos. To analyze the susceptibility of peptidoglycan to lysozyme hydrolysis, a turbidometric assay was used as described [2], [19]. Briefly, 0. 5 mg of purified peptidoglycan from the conditional mutant, grown with and without CdCl2, were sonicated in 1 ml of 100 mM Sodium-Potassium phosphate buffer pH 6. 6. Human lysozyme or hen egg white lysozyme (Sigma) was added to a final concentration of 300 µg/ml and the reaction was incubated at 37°C. The optical density was monitored at 660 nm. The impact of lysozyme on exponential growth was determined as described [19]. Overnight cultures of the conditional mutant grown with inducer were diluted to an OD620 nm of 0. 1 in fresh TSB (with and without inducer). The cultures were incubated at 37°C until an OD620 nm of 1. 0. Then, each culture was diluted 1∶10 into fresh TSB medium and lysozyme (300 µg/ml) was added at an OD620 nm of 1. 0. The growth was monitored for several hours. The same procedure was done using 20 µg/ml of Polymyxin B (Sigma), a cationic antimicrobial peptide. DNA sequence analyses of murT-gatD region suggested that murT and gatD are located in the same operon and might be co-transcribed from a common promoter: the murT stop codon and the gatD methionine codon are separated by 4 bp only; both genes are transcribed in the same direction and no promoter region sequence could be found upstream from gatD (Figure 1A). Reverse transcription-PCR (RT-PCR) was performed using total cDNA of strain COL with forward primers specifically binding to murT and a reverse primer specifically binding to gatD. The test yielded products of the expected size (Figure 1B, lanes C and D). No PCR product was obtained from the negative control using primers from the SACOL1949-1948 region, which was found by northern blotting not to be transcribed (Figure 1B, lane A). A PCR product of the expected size was obtained for the positive control, using primers internal to pta, a housekeeping gene. The results of the RT-PCR test indicated that both murT and gatD are co-transcribed from a common promoter. Analysis of genome sequences available showed that the murT and gatD genes occur, widespread among bacteria, as a syntenic block, although it is not a universal feature. This is in agreement with our RT-PCR results, which identified the two genes as a small operon. The distribution of this syntenic block among the prokaryotes, with emphasis on the Staphylococcaceae, is shown in Figure S1. In order to explore the functions of these uncharacterized genes we constructed a mutant strain containing a single chromosomal copy of murT-gatD under the control of an inducible promoter (pCad). A DNA fragment of murT gene which includes the first 298 codons and the ribosome binding site but not the promotor region, was cloned into the integrative plasmid pBCB20 (see Table 1). The recombinant plasmid was electroporated into RN4220 and the chromosomal construct was transduced into the background of the MRSA strain COL. The only complete functional copies of murT and gatD genes were located immediately downstream from the pCad, generating mutant COLpCadmurT-gatD (Figure S2). Hence, this strain expresses the murT-gatD genes when grown in the presence of Cd2+, and both genes are depleted when Cd2+ is absent from the growth medium (see below). Northern blotting assays were performed in order to confirm the specificity of transcription of the murT-gatD operon controlled by the CdCl2 concentration in the medium. The transcription of murT, gatD, SACOL1952 and SACOL1948-SACOL1949 genes was analyzed for COL and mutant COLpCadmurT-gatD grown with several concentrations of inducer. The level of murT and gatD transcription was found to increase with the inducer concentration in the medium (data not shown). No alterations were detected under the same conditions in the transcription level of the ORFs located in the immediate vicinity of the murT-gatD operon, SACOL1952 and SACOL1949-SACOL1948, which were found to be not transcribed even for strain COL (data not shown). The housekeeping gene pta was used as control. For strain COL, a single transcript was visualized for each gene: an mRNA structure of approximately 1780 nt long hybridized with murT probe and an mRNA structure of approximately 2300 nt long was obtained for hybridization with gatD probe. The size of this last transcript matches the size of both genes, consistent with their co-transcription. Cell walls of parental strain COL and of the conditional mutant, grown with and without Cd2+, were purified and digested with cellosyl and lysostaphin. The resulting monomeric muropeptides were reduced and analyzed by RP-HPLC. The muropeptide profiles revealed that, when the transcription of murT-gatD operon was inhibited, two new muropeptide structures appeared in the RP-HPLC profile (Figure 2A – peaks V and VI). These two muropeptide species showed shorter retention times than peak I, which is common to all the profiles. To identify the structural modifications, all peaks annotated in Figure 2A were isolated and analyzed by MS. The MS results (Table 2) indicate that the two new peaks (V and VI) observed in the profile of the murT/gatD depleted cells corresponded to muropeptide structures with D-iso-glutamate in the stem peptide replacing D-iso-glutamine. Peaks I, II, III and IV correspond to muropeptide structures with D-iso-glutamine (Figure 2B). Amidated muropeptides (Peak I) were still present when the transcription of murT-gatD operon was inhibited. This could be due to the activity of MurT and GatD expressed by residual transcription from the pCad promoter or to the presence of other enzymes with the same activity. These findings identify the protein products of murT-gatD as essential for the full amidation of the D-glutamic acid residues in the S. aureus peptidoglycan. The cell walls of the parental strain COL and the conditional mutant COLpCadmurT-gatD grown with different concentrations of inducer were extracted, the peptidoglycan purified, digested with muramidase and the muropeptides analyzed by RP-HPLC (Figure 3A). The elution profile of the conditional mutant grown in the absence of CdCl2 showed longer retention times for all peaks, when compared with COL. In addition, the peaks corresponding to muropeptide structures with higher oligomerization level (retention time over 60 min) were split into two or more smaller peaks eluting at very similar retention times. The elution profiles of the mutant grown with 0. 01 µM and 0. 05 µM of CdCl2 showed gradual re-establishment of the parental muropeptide pattern. For cells grown in 0. 2 µM CdCl2 supplemented medium, the optimal inducer concentration, the peptidoglycan HPLC profile was indistinguishable from that of strain COL. The muropeptide elution profile of COLpCadmurT-gatD, grown in the absence of inducer, showed similarities to the elution profile of the previously characterized glnRA transposition mutant RUSA208 [4] (Figure 3B). In RUSA208, the transposon inserted into the glnR gene which codes for the repressor of the glutamine synthetase operon glnRA, resulting in the abolishment of glnA transcription. The impact of the glnRA mutation on the peptidoglycan of RUSA208 has been described as the substitution of the normal D-iso-glutamine residues by D-iso-glutamic acid at position 2 of the stem peptide [4]. Substitution of iso-glutamine by iso-glutamic acid residues has been observed among muropeptide monomers (Peak 5A in Figure 3B), among dimeric muropeptides (Peaks 11A & 11B in Figure 3B), among the tripeptide structures (Peak 15B in Figure 3B) and among three of the stem peptides represented by peaks 15A, B & C in Figure 3B. All these structures are also present in the conditional mutant COLpCadmurT-gatD grown in the absence or at suboptimal concentrations of the inducer (see Figure 3A). The glnA gene sequence in COLpCadmurT-gatD was identical to that in strain COL, excluding the possibility that a mutation in glnA causes the deficiency in peptidoglycan amidation as it occurs in RUSA208 strain. Also, the transcription of the glnA gene did not vary with the Cd2+ concentration in COLpCadmurT-gatD (data not shown) discarding the hypothesis that murT and/or gatD may indirectly reduce glnA transcription. The peptidoglycan profiles of RUSA208 and COLpCadmurT-gatD grown with no CdCl2, showed that amidation of the muropeptides still occured partially. This may be due to a leaky expression of murT-gatD operon through pCad promoter in the absence of CdCl2. In the case of RUSA208, other sources of amino group, besides glutamine, may be used, although less efficiently. The peptidoglycan HPLC profile of the double mutant RUSA208pCadmurT-gatD showed a virtually complete lack of amidated muropeptides (Figure 3A), indicating that the gene products of these two operons are together needed for the amidation of the glutamic acid residue of the peptidoglycan. The transcriptional analysis showed that the expression of both murT and gatD genes is being controlled in COLpCadmurT-gatD mutant, through the concentration of inducer added to the medium. For this reason we constructed two independent complementation mutants, COLpCadmurT-gatD+pSKmurT and COLpCadmurT-gatD+pSKgatD, by separately introducing into the COLpCadmurT-gatD mutant, the replicative plasmid pSK5632 with either the murT or the gatD gene. Cloning of the murT-gatD operon into pSK5632 was also attempted, but this construct did not yield viable E. coli transformants. Strain COLpCadmurT-gatD+pSK harboring pSK5632 with no cloned gene was constructed and used as control. With the two complementation strains available, we obtained three distinct levels of re-establishment of the normal peptidoglycan: i) the in trans complementation with several copies of the murT gene showed a partially restored peptidoglycan with a small amount of muropeptides containing glutamic acid residues (COLpCadmurT-gatD+pSKmurT – 0 µM CdCl2, Figure S3A); ii) the in trans complementation with several copies of gatD gene showed no re-establishment of the normal peptidoglycan profile (COLpCadmurT-gatD+pSKgatD – 0 µM CdCl2, data not shown); iii) the in trans complementation with several copies of the murT gene and sub-optimal expression of the chromosomal copy of murT-gatD operon showed complete restoration of the peptidoglycan profile (COLpCadmurT-gatD+pSKmurT – 0. 01 µM CdCl2, Figure S3B). In the latter case (iii), the 0. 01 µM CdCl2 of added inducer is responsible for providing a sub-optimal number of copies of murT-gatD transcripts, adding to the already available copies of murT transcript provided in trans. The few copies of gatD provided in this condition are enough for a complete re-establishment of the normal peptidoglycan composition. Thus, complementation of the murT-gatD-depletion phenotype requires the expression of murT and at least a basal level of gatD. In order to identify the biosynthetic stage at which amidation occurred, the cell wall precursor pool was analyzed by RP-HPLC from strains COL and for the murT-gatD conditional mutant grown with and without the inducer. The HPLC profiles were identical for the three conditions analyzed (Figure S4). The major peak, eluting at 38 minutes, was isolated from the cytoplasmic fractions of COL and of the murT-gatD conditional mutant grown with and without the inducer. The corresponding structures were analyzed by MALDI-TOF MS. The results indicated an identical molecular mass of 1149. 35 (neutral mass) for each of the three samples, consistent with the structure of the UDP-MurNAc-L-Ala-D-iGlu-L-Lys-D-Ala-D-Ala, the last cytoplasmic precursor. The presence of D-iso-glutamate in these three structures indicated that the conversion of glutamic acid to iso-glutamine residues must occur at a later stage of cell wall precursor biosynthesis – most likely in the lipid phase – confirming an earlier finding [20]. MurT shares approximately 15% identity and 53% similarity with the sequence of the Mur ligases of S. aureus. Interestingly, while MurT shares the characteristic Mur ligase central domain [1], [21] as defined at InterPro (IPR013221), Pfam (PF08245) and Panther (PTHR23135) MurT lacks the flanking N- and C-terminal domains (Figure S5A). Among the conserved residues were some critical motifs required for ATP and Mg2+ binding and other conserved sites that may not be directly involved in catalysis (Figure S5A). In addition, the MurT protein has a C-terminal domain of unknown function (Pfam: DUF1727, InterPro: IPR013564), which is also found at the C-terminus of more than 900 sequences of prokaryotic proteins at UniProt, and in 5 different domain architectures, all of them sharing the same ORF, or in contiguous ORFs, with Mur central domain (PF08353). GatD shows similarity to one of the two domains of a cobyric acid synthetase protein: a glutamine-dependent amidotransferase (Gn-AT), with glutamine amide transfer (GAT) activity. Its architecture comprises the overlapping domain signatures of CobB/CobQ_GATase (InterPro: IPR017929), and GATase_3 (InterPro: IPR011698) domains. Through multiple sequence alignment of the N-terminal region of three known Gn-ATs, the absence of a large fragment was noted in GatD (Figure S5B). This missing fragment included important residues for the dethiobiotin synthase activity [9] and part of the ATP binding motif. By placing the representation of the secondary structures over the sequence alignment, we can observe considerable agreement between the shared regions, especially near the reactive center of GATase_3 (Figure S5B). This domain harbored the conserved residues directly involved in GAT activity, according to IPR011698. GatD was also found to contain the unusual Triad family glutamine amidotransferase domain with conserved Cys and His residues (Figure S5B), but lacking the Glu residue of the catalytic triad, as the CobB and CobQ proteins [9]. The murT-gatD operon emerged as a syntenic block that seems to be widespread among bacteria. Interestingly, for the distant taxa of Actinobacteria, in some rare cases, the two ORFs are merged into a single one (Figure S1). The genome co-localization of the two determinants, together with data from sequence analysis, led us to suggest a model for the coordinated function of MurT and GatD proteins in the peptidoglycan glutamate amidation (Figure 6). Both proteins together harbor all domain functions required for amidation of peptidoglycan precursor: MurT may be responsible for the recognition of the reaction substrates, the lipid-linked peptidoglycan precursor and ATP, while GatD could be the catalytic subunit involved in the transfer of the amino group from free glutamine to the peptidoglycan precursor. The GatD sequence lacks an ATP binding motif which is common to all members of the Gn-AT family suggesting an activity that depends on the MurT protein which exhibits a typical Mur ligase central domain including the ATP binding motif (Figure 6). Experiments are in progress to better define the roles of MurT and GatD proteins in the mechanism of amidation of S. aureus peptidoglycan precursor. Irrespective of mechanistic details, the results with the conditional mutant of murT/gatD clearly indicate that the amidation of glutamic acid residues in the S. aureus peptidoglycan is catalyzed by the concerted action of these two enzymes. The murT-gatD operon appears to be the last missing genetic determinant to account for the structural variation in the S. aureus peptidoglycan.

Genetic determinants and enzymes that catalyze the multiple steps in the assembly of bacterial cell wall peptidoglycan have been known for some time. On the other hand, the mechanism by which glutamic acid residues of this structure undergo modification to glutamine has remained unknown. In this communication, we describe the identification of two genetic determinants that appear to be responsible for the completion of the chemical structure of the cell wall of the important human pathogen S. aureus. The availability of a conditional mutant which allows modulation of this system has allowed us to recognize the importance of glutamine residues for optimal growth rate and drug resistance and sensitivity of the staphylococcal peptidoglycan to the host defense factor lysozyme.

Abstract Introduction Materials and Methods Results Discussion

biology

2012

Identification of Genetic Determinants and Enzymes Involved with the Amidation of Glutamic Acid Residues in the Peptidoglycan of Staphylococcus aureus

Endoplasmic reticulum (ER) stress is a feature of secretory cells and of many diseases including cancer, neurodegeneration, and diabetes. Adaptation to ER stress depends on the activation of a signal transduction pathway known as the unfolded protein response (UPR). Enhanced expression of Hsp72 has been shown to reduce tissue injury in response to stress stimuli and improve cell survival in experimental models of stroke, sepsis, renal failure, and myocardial ischemia. Hsp72 inhibits several features of the intrinsic apoptotic pathway. However, the molecular mechanisms by which Hsp72 expression inhibits ER stress-induced apoptosis are not clearly understood. Here we show that Hsp72 enhances cell survival under ER stress conditions. The UPR signals through the sensor IRE1α, which controls the splicing of the mRNA encoding the transcription factor XBP1. We show that Hsp72 enhances XBP1 mRNA splicing and expression of its target genes, associated with attenuated apoptosis under ER stress conditions. Inhibition of XBP1 mRNA splicing either by dominant negative IRE1α or by knocking down XBP1 specifically abrogated the inhibition of ER stress-induced apoptosis by Hsp72. Regulation of the UPR was associated with the formation of a stable protein complex between Hsp72 and the cytosolic domain of IRE1α. Finally, Hsp72 enhanced the RNase activity of recombinant IRE1α in vitro, suggesting a direct regulation. Our data show that binding of Hsp72 to IRE1α enhances IRE1α/XBP1 signaling at the ER and inhibits ER stress-induced apoptosis. These results provide a physical connection between cytosolic chaperones and the ER stress response. The human Hsp70 family consists of at least 12 members [1], [2]. Of these, the two best studied members are the constitutive or cognate Hsp70 (Hsc70) and a stress inducible form of cytosolic Hsp70 (Hsp72). Hsc70 is constitutively and ubiquitously expressed in tissues and has a basic and essential function as molecular chaperone in the folding of proteins [1], [2]. The second is an inducible form, called Hsp72, which is expressed at low levels under normal conditions and its expression is induced upon exposure to environmental stress that causes protein misfolding in the cytosol, such as heat shock, exposure to heavy metals, anoxia, and ischemia [1], [2]. Hsp72 has strong cytoprotective effects and functions as a molecular chaperone in protein folding, transport, and degradation. Moreover, the cytoprotective effect of Hsp72 is also related to its ability to inhibit apoptosis [3], [4]. Hsp72 has been shown to inhibit apoptosis by several distinct mechanisms [3], [5], [6]. It can prevent the formation of an active apoptosome by binding directly to Apaf-1, in in vitro conditions [7], [8]. Additionally, it has been shown that Hsp72 functions upstream of the caspase cascade by inhibiting the release of cytochrome c from the mitochondria [9], [10], [11]. Inhibition of cytochrome c release may be achieved by the ability of Hsp72 to prevent Bax translocation into the mitochondrial membrane in response to stress [9], [10], [11]. It has also been shown that Hsp72 inhibits apoptosis by suppressing JNK, a stress-activated protein kinase, thereby blocking an early component of a stress-induced apoptotic pathway [12]. Further, it has been shown that Hsp72 binds to apoptosis-inducing factor (AIF), another apoptogenic factor released from the mitochondria, thereby preventing the chromatin condensation and cell death that result from AIF [13], [14], [15]. Physiological or pathological processes that disrupt protein folding in the endoplasmic reticulum (ER) lead to ER stress and trigger a set of signaling pathways termed the unfolded protein response (UPR) [16]. This concerted and complex cellular response transmits information about the protein-folding status in the ER lumen to the cytosol and nucleus to increase protein-folding capacity [17], [18]. However, cells undergo apoptosis if these mechanisms of cellular adaptation are unable to alleviate the stress [19]. The three major transmembrane sensors of ER stress in metazoans are IRE1α (inositol requiring 1; ERN1, endoplasmic reticulum-to-nucleus signaling 1), PERK [double-stranded RNA-activated protein kinase (PKR) -like ER kinase; PEK, pancreatic eukaryotic initiation factor 2α kinase; EIF2AK3], and ATF6 (activating transcription factor 6) [17], [18]. IRE1α, the prototype ER stress sensor, is evolutionarily conserved from yeast to humans and the cytoprotective output of IRE1α is present across all eukaryotes. IRE1α is a Ser/Thr protein kinase and endoribonuclease that, upon activation, initiates the unconventional splicing of the X-box binding protein (XBP1) mRNA [20]. The spliced XBP1 form is a highly active transcription factor and one of the key regulators of ER folding capacity [21], [22]. In response to ER stress, IRE1α splices a 26 nucleotide long intron of unspliced XBP1 mRNA (XBP1u), generating an active and stable transcription factor XBP1s. XBP1s regulates several UPR target genes including ER chaperones (BIP/GRP78, ERdj4, ERdj5, HEDJ, GRP58, and PDIP5), ERAD components (EDEM, HERP, and p58IPK), transcription factors (CHOP and XBP1), and other proteins related to the secretory pathway [23]. The activation of IRE1α is regulated by a complex protein platform at the ER membrane, known as the UPRosome [24], [25]. BAX and BAK form a protein complex with the cytosolic domain of IRE1α, which requires their conserved BH1 and BH3 domains [26]. Similarly, ASK1-interacting protein 1 (AIP1) has been shown to associate with IRE1α and to enhance the dimerization of IRE1α, suggesting a direct role for AIP1 in regulating IRE1α activity [27]. We have shown that Bax inhibitor-1 (BI-1) also binds to IRE1α and has an inhibitory effect on IRE1α signaling [28], [29]. Furthermore, ER localized Protein Tyrosine Phosphatase 1B (PTP 1B) has been show to potentiate the IRE1α signaling pathway, however its interaction with IRE1α has not been determined [30]. It has been proposed that binding of anti- and pro-apoptotic proteins to IRE1α controls the amplitude of IRE1α signaling and determines cell fate during conditions of ER stress. The cytoprotective role of Hsp72 has been demonstrated in many tissues and its role as a neuroprotectant has been demonstrated in vitro and in animal models of neuronal degeneration in vivo [31], [32], [33]. Transgenic mice overexpressing the hsp72 gene show significant protection during focal cerebral ischemia [34], [35]. Further, injection of a vector carrying the hsp72 gene into the rat hippocampal CA1 region provides protection to cells in the vicinity of the injection site following 10 min of global ischemia [36]. Despite a large number of studies demonstrating neuroprotection by the chaperone Hsp72, in both animal [31], [34], [35] and cell culture models of ischemia [33], [37], the mechanisms of protection are poorly understood. The presence of chronic ER stress has been extensively described in neurodegenerative conditions linked to protein misfolding and aggregation, including Amyotrophic Lateral Sclerosis (ALS), prion-related disorders, and conditions such as Parkinson' s, Huntington' s, and Alzheimer' s disease. We reasoned that Hsp72 may provide cytoprotection by modulation of UPR signaling pathways emanating from the ER membrane. Here we have evaluated the effect of Hsp72 on UPR signaling. Overall our results identify Hsp72 as a new component of UPRosome where binding of Hsp72 to IRE1α enhances IRE1α-XBP1 signaling at the ER, promoting adaptation to ER stress and cell survival. Neuroprotective effects of Hsp72 overexpression have been reported in numerous studies during ischemia-like conditions in neuronal cells [15], [31], [32]. To assess the effect of Hsp72 expression on ER stress-induced apoptosis, we generated stable clones of PC12 cells expressing the inducible form of Hsp70 (Hsp72). The level of Hsp72 expression in PC12 cells used in this study was within the normal physiological range, because ectopic Hsp72 expression is comparable to the level of Hsp72 induced during thermotolerance in PC12 cells (Figure 1A). For induction of thermotolerance, cells were subjected to 1 h of heat shock at 42°C±0. 5°C and processed after a 6 h recovery at 37°C. To determine the effect of Hsp72 expression on ER stress-induced apoptosis, control (Neo) and Hsp72-expressing (Hsp72) PC12 cells were treated with either 0. 25 µM thapsigargin or 1 µg/ml tunicamycin for 48 h. We observed that Hsp72 expression partially protected PC12 cells from ER stress-induced cell death (Figure 1B, C). ER stress-induced caspase activity was found to be significantly reduced in Hsp72 cells as compared with Neo cells (Figure 1D). In agreement with reduced caspase activity, Hsp72 cells showed reduced processing of pro-caspase-3 to active caspase-3 (Figure 1E). These results suggest that caspase activity is required for ER stress-induced apoptosis and that Hsp72 can inhibit the ability of the cell to activate the caspase cascade. The loss of mitochondrial membrane potential (ΔΨm) and MOMP is a hallmark of apoptosis [38], [39]. Previous studies have shown that Hsp72 inhibits apoptosis by preventing mitochondrial outer membrane permeabilization and cytochrome c release [10], [11]. Next we evaluated the effect of Hsp72 on the dissipation of ΔΨm and release of cytochrome c to the cytosol upon exposure to ER stress stimuli. To quantify ΔΨm, TMRE, a potentiometric fluorescent dye that incorporates into mitochondria in a ΔΨm-dependent manner, was used. Cells were either left untreated or treated with 0. 25 µM thapsigargin. The cells were then incubated with TMRE for 30 min and analyzed by a flow cytometer. A drop in ΔΨm was observed in Neo cells following thapsigargin treatment (Figure 2A). The expression of Hsp72 inhibited the loss of ΔΨm (Figure 2A). At 48 h, loss of ΔΨm was detected in 80%–90% of Neo cells treated with thapsigargin or tunicamycin, respectively (Figure 2B). However, at the same time point, thapsigargin or tunicamycin only induced loss of ΔΨm in 50% of the Hsp72 cells (Figure 2B). To further study the involvement of mitochondria in ER stress-induced cell death, we assessed the release of cytochrome c into the cytosol. Western blot analysis of the cytosolic extracts of cells showed that exposure of Neo cells to thapsigargin for 24 h triggered release of cytochrome c from mitochondria (Figure 2C). However, at the same time point, the release of cytochrome c induced by thapsigargin was significantly reduced in Hsp72 cells (Figure 2C). These results suggest that Hsp72 may be acting upstream of MOMP to inhibit ER stress-induced apoptosis. Activation of the UPR and regulation of protein quality control is essential to restore cellular homeostasis and prevent ER stress-induced apoptosis [18], [19]. To investigate the possible regulation of the UPR by Hsp72, we compared the activation of IRE1α/XBP1 and PERK/CHOP axis in Neo and Hsp72 cells. First we determined the levels of XBP1 mRNA splicing by semi-quantitative RT-PCR and production of spliced XBP1 protein by Western blotting. Notably, upon treatment with thapsigargin Hsp72 cells displayed increased levels of the spliced XBP1 mRNA as compared to Neo cells, demonstrating a sustained signaling over time and late inactivation (Figure 3A, B). In agreement with the increased XBP1 mRNA splicing, enhanced expression of XBP1s protein was also observed in Hsp72 cells undergoing ER stress when compared with Neo cells (Figure 3D). Since JNK activation is also induced downstream of IRE1α activation, we next determined the effect of Hsp72 on JNK activation during ER stress signaling. Activation of JNK was detected by Western blotting with a phospho-specific antibody. ER stress-induced JNK phosphorylation was reduced in Hsp72 cells as compared to Neo cells (Figure 3C). Activation of the PERK/CHOP axis, a parallel pathway activated by ER stress, was also examined by measuring phosphorylation of eIF-2α, a direct target of PERK, and expression of CHOP. The level of ER stress-induced phosphorylation of eIF-2α and induction of CHOP was not significantly different in Hsp72 cells as compared to Neo cells, although Hsp72 cells showed slightly earlier kinetics in eIF-2α phosphorylation (Figure 3E). In conditions of ER stress, cellular adaptation is mediated by modulating the expression of a cohort of so-called UPR target genes. The IRE1α/XBP1 arm of the UPR specifically mediates the induction of specific target genes such as EDEM1, ERdj4, and P58IPK [22], [40]. Analysis of gene expression profiles by quantitative RT-PCR revealed that induction of EDEM1, ERdj4, HERP, P58IPK, and GRP78 was significantly enhanced in Hsp72 cells as compared to Neo cells (Figure 3F). Taken together, these observations suggest that Hsp72 specifically regulates ER stress signaling through the modulation of the IRE1α/XBP1 axis of the UPR. Recently it has been shown that experimental prolonging of IRE1α signaling independent of ER stress can promote cell adaptation to protein folding stress and survival [9], [41]. Our data show that the ability of Hsp72 to inhibit ER stress-induced apoptosis correlates with enhanced production of spliced XBP1. To determine the role of XBP1s in the cytoprotective effects of Hsp72, we used a dominant negative mutant of IRE1α to compromise the production of spliced XBP1 and evaluated its effect on the protection mediated by Hsp72. Expression vectors for various mutants of IRE1α (IRE1α KA, IRE1α ΔC, and IRE1α ΔRNase) (Figure 4A) were transfected into PC12 cells, and the levels of XBP1 mRNA splicing were examined upon ER stress. We observed that the three mutants of IRE1α reduced ER stress-induced splicing of XBP1 as compared to control pcDNA transfected cells (Figure 4B). Further experiments were performed with the IRE1α ΔRNase because the compromised kinase domain in IRE1α KA or the lack of kinase domain in IRE1α ΔC may alter the downstream events mediated by the kinase domain of IRE1α in addition to abrogating its endoribonuclease activity. The effect of IRE1α ΔRNase on cell viability was determined by measuring β-galactosidase activity after treatment with ER stress-inducing agents thapsigargin and tunicamycin, and two other apoptosis-inducing agents that do not act through ER stress, staurosporine and etoposide [42]. The IRE1α ΔRNase mutant was co-transfected with β-galactosidase plasmid into Neo and Hsp72 cells and the reduction in reporter enzyme activity was used to determine whether a gene has a detrimental effect on cell survival [42]. We observed that IRE1α ΔRNase mutant specifically reversed the protective effect of Hsp72 on ER stress-induced apoptosis, but it did not affect the protection against etoposide and staurosporine (Figure 4C). To further confirm the role of increased XBP1s protein in the cytoprotective effects of Hsp72, we knocked down XBP1s levels by introducing XBP1 targeted shRNAs into Hsp72 cells and then assessed their effects on cell survival. We found that all four shRNAs were able to silence XBP1s expression to varying degrees (Figure 4D). Notably, the protective effect of Hsp72 during ER stress-induced apoptosis was abrogated in four independent subclones of Hsp72 cells expressing XBP1 targeted shRNAs (Figure 4E). These results suggest that all four XBP1 targeting shRNAs are able to neutralize the effect of Hsp72 overexpression on ER stress-induced production of spliced XBP1 and apoptosis. The knockdown of XBP1 did not alter the cytoprotective effects of Hsp72 on staurosporine- or etoposide-induced apoptosis (Figure 4E). Collectively, these results suggest that Hsp72 enhances survival under ER stress conditions possibly by upregulation of the adaptive responses initiated by the IRE1α/XBP1 branch of the UPR. To determine the mechanism by which Hsp72 regulates IRE1α activity, we first explored the possibility of a physical interaction between Hsp72 and IRE1α. For this purpose, Hsp72 cells were transfected with IRE1α FL-HA or IRE1α ΔC-HA (Figure 5A) and interaction of Hsp72-IRE1α was determined by co-immunoprecipitation assays. The Hsp72-IRE1α complex was detected in the absence of ER stress and required the cytosolic C-terminal region of IRE1α, which encodes the kinase and endoribonuclease domains (Figure 5C). Further, the interaction of Hsp72 with IRE1α was not altered in cells undergoing ER stress triggered by thapsigargin treatment (Figure 5C). Under similar conditions, Hsc70, the constitutive form of Hsp72 did not interact with IRE1α (Figure 5C). Hsp72 consists of three structural motifs: an N-terminal ATPase domain, a C-terminal substrate binding domain, and a C-terminal sequence EEVD (Figure 5B). Hsp72 function requires coordinated action of all three domains. To map the critical domain of Hsp72 required for IRE1α binding, we transfected IRE1α FL-HA into PC12 cells expressing Hsp72, ΔATPase-Hsp72, or ΔEEVD-Hsp72 (Figure 5B) and association of IRE1α with wild-type and mutant Hsp72 was determined by co-immunoprecipitation assays. We observed that wild-type Hsp72 and ΔEEVD-Hsp72 associated with IRE1α (Figure 5D). However, ΔATPase- Hsp72 failed to interact with IRE1α, demonstrating that the ATPase domain of Hsp72 is necessary for interaction of Hsp72 with IRE1α (Figure 5D). We were able to detect a physical interaction of endogenous Hsp72 with ectopically expressed IRE1α FL-HA as well as endogenous IRE1α in HEK 293 cells by immunoprecipitations (Figure 5E–F). Based on the results of our immunoprecipitation experiments, we then monitored the possible effects of Hsp72 on the endoribonuclease activity of IRE1α in an in vitro assay. We have recently established an in vitro assay to monitor the endoribonuclease activity of purified IRE1α [28]. The cytosolic version of human IRE1α (recIRE1ΔN) was expressed and purified from insect cells, and then incubated with a mixture of total mRNA and ATP in the absence or presence of increasing concentrations of recombinant Hsp72. After 1 h of incubation, mRNA was re-extracted, and the cleavage of XBP1 mRNA in the splicing site was monitored by RT-PCR. As a control, actin levels were monitored. The activity of recIRE1ΔN was enhanced by the presence of recombinant Hsp72 in a dose dependent manner (Figure 5G). These results indicate that the effects of Hsp72 on IRE1α activity can be reconstituted in vitro, suggesting a direct regulation. The critical role of the Hsp72 ATPase domain in IRE1α binding prompted us to determine its role in ER stress-mediated IRE1α signaling. We evaluated the induction of EDEM1, ERdj4, HERP, P58IPK, and GRP78 in cells expressing Hsp72 or ΔATPase-Hsp72. Quantitative RT-PCR analysis revealed that induction of EDEM1, ERdj4, HERP, P58IPK, and GRP78 was significantly enhanced only in Hsp72 cells. Notably, induction of EDEM1, ERdj4, HERP, P58IPK, and GRP78 in ΔATPase-Hsp72 expressing cells was comparable to Neo cells (Figure 6A). The examination of ER stress-induced apoptosis and caspase activity in cells expressing Hsp72 or ΔATPase-Hsp72 revealed that wild-type Hsp72 expressing cells were more resistant to ER stress-induced apoptosis and caspase activation (Figure 6B–C). There was no significant difference in ER stress-induced apoptosis and caspase activation in ΔATPase-Hsp72 and Neo cells (Figure 6B–C). Collectively, these results show that the ability of Hsp72 to bind to IRE1α correlates with increased induction of UPR target genes downstream of IRE1α/XBP1 and protection against ER stress-induced apoptosis. Mammalian cells, when exposed to a non-lethal heat shock, have the ability to acquire a transient resistance to subsequent exposures at elevated temperatures, a phenomenon termed thermotolerance. We have previously shown that mild heat shock preconditioning can induce expression of Hsp72 and protect PC12 cells against a number of cytotoxic agents [43]. To evaluate the effect of Hsp72 on IRE1α-XBP1 axis in physiological conditions, we examined the acquisition of thermotolerance in control and XBP1 knockdown PC12 cells. For this purpose parental PC12 cells were transduced with control (PGIPZ) and XBP1 targeting shRNA (XBP1 shRNA) expressing lentiviral particles. Mild heat shock preconditioning induced the expression of Hsp72 in control and XBP1 knockdown PC12 cells to comparable levels (Figure 7A–B). However, the knockdown of XBP1 specifically abrogated heat-induced acquisition of resistance against ER stress-induced apoptosis in PC12 cells (Figure 7C), but not against etoposide or staurosporine (Figure 7C). These results suggest an important role for regulation of IRE1α/XBP1 axis by Hsp72 in attainment of ER stress tolerance induced upon heat preconditioning. More importantly, these data provide evidence of a molecular crosstalk between the cytosolic heat shock response and the UPR. The main physiological function of the XBP1 axis of the UPR is to modulate secretory pathway function, enhancing protein secretion [44], [45], [46]. PC12 is a cell line derived from a pheochromocytoma of the rat adrenal gland and secretes neurotrophins such as nerve growth factor (NGF) and brain-derived neurotrophic factor (BDNF). Therefore, we monitored the secretion of NGF and BDNF in Neo and Hsp72 cells after exposure to sublethal dose of either thapsigargin, tunicamycin, or 6-hydroxy dopamine (6-OHDA), a commonly used drug to mimic Parkinson' s disease-like features in animals that also triggers ER stress [47], [48], to modulate ER physiology. First we determined the effect of Hsp72 expression on 6-OHDA-induced death in PC12 cells. We found that Hsp72 cells were resistant to 6-OHDA induced death as compared to Neo cells (Figure S1). In addition secretion of NGF and BDNF into the cell-culture media of Hsp72 cells was more after treatment with thapsigargin, tunicamycin, and 6-OHDA, than in media of Neo cells (Figure 7D–E). These data indicate that Hsp72 regulates secretion of neurotrophins (NGF and BDNF) by PC12 cells most likely mediated by the modulation of IRE1α/XBP1 function. Previous studies have shown that Hsp72 overexpression protects cells from death induced by inhibiting multiple cell death pathways, including in models of hypoxia and ischemia/reperfusion [3]. Although the anti-apoptotic effects of Hsp72 have been noted in several systems, the molecular mechanisms that mediate this effect are largely unclear. In the present study, we revealed a new function of Hsp72 as a critical regulator of the UPR and adaptation to ER stress conditions. Chronic ER stress signals converge into the mitochondrial intrinsic death pathway that involves release of cytochrome c, Apaf-1, formation of apoptosome, and activation of caspase proteases [19], [39], [49], [50]. The interaction of Apaf-1 with cytochrome c and ATP, leading to activation of caspase-9, has been shown to be inhibited by Hsp72 in in vitro conditions [7], [8]. Our results suggest that Hsp72 can prevent cytochrome c release from the mitochondria and that the reported ability of Hsp72 to block caspase-9 activation in the cytosolic fraction is possibly due to the high salt concentration in the Hsp72 preparation [51]. In this study we describe a new function of Hsp72 where it acts upstream of MOMP by controlling adaptive responses against ER stress, enhancing cell survival. These effects were due to a direct interaction between Hsp72 and the UPR stress sensor IRE1α, possibly controlling IRE1α' s activity. Hsp72 has been reported to inhibit CHOP- and TNFα-induced apoptosis by binding to BAX and preventing its translocation to mitochondria [10]. Furthermore, the ATPase domain of Hsp72 is required for inhibition of CHOP- and TNFα-induced apoptosis [10]. Consistent with this, our results show that an ATPase domain deletion mutant of Hsp72 was unable to protect cells against ER stress-induced apoptosis. In contrast, the ATPase domain was dispensable for inhibition of apoptosis induced by oxygen-glucose deprivation [52], serum withdrawal, staurosporine [53], or heat [54], suggesting different mechanisms of action. There are a number of physiological and pathological conditions where concomitant induction of Hsp72 expression and ER stress has been reported. First, 6-OHDA induces ER stress and upregulation of Hsp72 in cellular models of Parkinson' s disease [47], [55], [56]. Second, proteasome inhibitors have been shown to increase the expression of Hsp72 [57] and to induce ER stress and UPR [58]. Third, Hsp72 expression is enhanced in cancer cells harboring mutant p53 due to derepression of Hsp72 promoter and Hsp72 has been reported to be overexpressed in many cancers [59]. Activation of the UPR is an adaptive response that allows cells to survive prolonged ER stress/hypoxia conditions in solid tumors [60], [61]. In light of our results we speculate that co-activation of Hsp72 and the UPR may represent a mechanism for the fine-tuning of IRE1α, providing a functional crosstalk between both stress pathways. What is the biological significance of the ability of Hsp72 to modulate the UPR by binding to IRE1α? Transcriptional activation of target genes that enhance ER protein-folding capacity and degradation of misfolded ER proteins plays an important role in cytoprotective function of XBP1[17]. Peter Walter' s group has shown that XBP1 mRNA splicing levels decline after prolonged ER stress, whereas PERK signaling is sustained over time [62]. Inactivation of XBP1 splicing was proposed to sensitize cells to cell death after chronic or irreversible ER stress. Similarly, knocking down XBP1 or IRE1α enhances cell death under conditions of chronic ER stress [28]. Further, experimental prolonging of IRE1α signaling independent of ER stress can promote cell survival [41], [62]. Here we demonstrate that IRE1α/XBP1 signaling is specifically enhanced by Hsp72 expression. Our data show that Hsp72 can enhance the amplitude of IRE1α signaling, delaying the inactivation phase of XBP1 mRNA splicing. These effects were functionally linked to the ability of Hsp72 to augment cell survival under conditions of ER stress. In addition to pathological conditions related to chronic or irreversible ER stress, the UPR plays a central role in physiological conditions associated with protein folding stress due to high demand for protein folding [25]. A key role of XBP1 has been proposed in vivo in different secretory cells including B cells, exocrine pancreas, and salivary glands [44], [45], [46], where activation of XBP1-transcriptional responses enhances secretion, improving cell survival. In agreement with this idea, we observed that Hsp72 expression enhances secretion of neurotrophins in PC12 dopaminergic cells (Figure 7). Our results confirm the notion that attenuation of IRE1α signaling during chronic ER stress is a key step in cell fate determination after induction of UPR. It has been shown that UPR induction can lead to proteolytic cleavage of IRE1α, releasing fragments containing the kinase and nuclease domains that accumulate in the nucleus [63]. However, we did not observe any change in subcellular localization of IRE1α during the UPR in Neo and Hsp72 cells (Figure S2). Furthermore Hsp90 has been shown to increase the half-life of IRE1α and PERK by binding to their cytoplasmic domains [64]. Hsp72 expression had no effect on the half-life of IRE1α (Figure S3). The ER-lumenal domain of PERK, IRE1α, and ATF6 interacts with the ER chaperone GRP78, however upon accumulation of unfolded proteins GRP78 dissociates from these molecules, leading to their activation [17]. Although ATF6, PERK, and IRE1α share functionally similar luminal sensing domains and are activated in cells treated with ER stress inducers in vitro, they are selectively activated in vivo by the physiological stress of unfolded proteins [17], [18]. These differences may explain the different kinetics in the activation of IRE1α, PERK, and ATF6 in response to various ER stress inducers. However, the differences in terms of tissue-specific regulation of the UPR in vivo may be explained by the formation of unique protein complexes through association of adaptor and modulator proteins. It has been proposed that a complex protein platform, known as the UPRosome [24], [25], operates at the ER membrane to control IRE1α activity. Our results identify Hsp72 as a new component of this UPRosome where binding of Hsp72 to IRE1α enhances IRE1α-XBP1 signaling at the ER and inhibits ER stress-induced apoptosis. Our results suggest that Hsp72 can bind to the monomeric and nonphosphorylated cytoplasmic tail of IRE1α. Furthermore the interaction of Hsp72 and IRE1α is not affected by ER stress mediated phosphorylation and oligomerization of IRE1α. What is the molecular mechanism by which Hsp72 stimulates the RNase activity of IRE1α? There are several possibilities by which Hsp72 might regulate RNase activity of IRE1α. Hsp72 may regulate IRE1α either by allosteric interactions or by altering the binding of other regulatory proteins (BAX, BAK, BI-1, AIP1, and RACK) to IRE1α. Our results showing that recombinant Hsp72 can enhance the RNase activity of purified IRE1α in in vitro conditions suggest an allosteric mechanism (Figure 5G). However it is possible that in a cellular context other mechanisms such as altering the binding of other regulatory proteins (BAX, BAK, BI-1, AIP1, and RACK) to IRE1α are also involved. Previous reports and our data collectively support a model in which a fine balance of anti- and pro-apoptotic proteins at the ER membrane modulates the amplitude of IRE1α signaling, thereby regulating the cellular sensitivity to ER stress conditions. Rat pheochromocytoma PC12 cells (obtained from ECACC) were cultured in Dulbecco' s modified Eagle' s medium (DMEM) from Sigma (D6429) supplemented with 10% heat inactivated horse serum, 5% foetal bovine serum, and 1% penicillin/streptomycin (Sigma) at 37°C, 5% CO2 in humidified incubator. Appropriate number of cells was seeded 24 h prior to treatment. To induce apoptosis, cells were treated with 0. 25 µM thapsigargin, 2 µg/ml tunicamycin, 150 nM staurosporine, or 25 µg/ml etoposide for the indicated time periods. Stock solutions of 6-Hydroxydopamine were made freshly in sodium metabisulfite (1 M) prior to experiment. PC12 cells were treated with 200 µM 6-OHDA for 24 h before analysis. All reagents were from Sigma-Aldrich unless otherwise stated. The plasmid expressing wild type Hsp72, ΔATPase-Hsp72, or ΔEEVD-Hsp72 under the CMV promoter were kind gifts from Dr. Tomomi Gotoh, Kumamoto University, Japan [10]. The expression vector for wild type IRE1α, IRE1α KA, IRE1α ΔC, and IRE1α ΔRNase under the CMV promoter were kind gifts from Dr. Kazunori Imaizumi, University of Miyazaki, Japan [65] and expression plasmids for wild type IRE1α-HA or IRE1α ΔC-HA are reported previously [28]. The plasmids containing shRNAs targeting rat XBP1 were obtained from GeneCopoeia, Rockville, USA (RSH045024-HIV U6). Transfections of cells were carried out using Lipofectamine 2000 (Invitrogen) according to the manufacturer' s protocol. Viability of cells after treatment was analyzed by MTT assay. After 48 h of treatment, 1 mg/ml concentration of MTT ( (3- (4,5-dimethylthiazol-2-yl) -2,5-diphenyl tetrazonium bromide) was added to the wells and incubated at 37°C for 3 h. The reaction was stopped with a stop mix containing 20% SDS in 40% dimethyl formamide. The color intensity is measured at 550 nm and percentage cell viability is calculated using the untreated samples as 100%. Externalization of phosphatidylserine (PS) to the outer leaflet of the plasma membrane of apoptotic cells was assessed with annexin V-FITC as described earlier [66]. Briefly, cells were collected by centrifugation at 350 g, washed once in ice-cold calcium buffer (10 mM HEPES/NaOH, pH 7. 4,140 mM NaCl, 2. 5 mM CaCl2), and incubated with annexin V-FITC or with annexin V-PE for 15 min on ice. Prior to analysis 300 µl of binding buffer containing 4 µl of PI (50 µg/ml) was added and analyzed on a FACSCalibur flow cytometer (Becton Dickinson). Cells were harvested and pelleted by centrifugation at 350 g. After washing in PBS, cell pellets were re-suspended in 50 µl of PBS and 25 µl was transferred to duplicate wells of a microtiter plate and snap-frozen in liquid nitrogen. To initiate the reaction, 50 µM of the caspase substrate carbobenzoxy-Asp-Glu-Val-Asp-7-amino-4-methyl-coumarin (DEVD-AMC, Peptide Institute Inc.) in assay buffer (100 mM HEPES, pH 7. 5,10% sucrose, 0. 1% CHAPS, 5 mM DTT and 0. 0001% Igepal-630, pH 7. 25) was added to cell lysates. Liberated free AMC was measured by a Wallac Victor 1420 Multilabel counter (Perkin Elmer Life Sciences) using 355 nm excitation and 460 nm emission wavelengths at 37°C at 60 s intervals for 25 cycles. The data were analyzed by linear regression and enzyme activity was expressed as nM of AMC released × min−1×mg−1 total cellular protein. Mitochondrial transmembrane potential was determined by using the fluorescent probe tetramethylrhodamine ethyl ester (TMRE, Molecular Probes) as previously described [67]. Briefly, cells were trypsinized and incubated with TMRE at RT for 30 min in the dark and analyzed by flow cytometry using a FACSCalibur instrument. Total RNA was isolated using RNeasy kit (Qiagen) according to the manufacturer' s instructions. Reverse transcription (RT) was carried out with 2 µg RNA and Oligo dT (Invitrogen) using 20 U Superscript II Reverse Transcriptase (Invitrogen). The cDNA product was subjected to 25–35 cycles of PCR using the forward primer 5-TTACGAGAGAAAACTCATGGGC-3 and reverse primer 5-GGGTCCAACTTGTCCAGAATGC-3 specific for Rat XBP1. GAPDH (forward: ACCACAGTCCATGCCATC; reverse: TCCACCACCTGTTGCTG) was used as an endogenous control. Real-time PCR method to determine the induction of UPR target genes has been described previously[68]. Briefly, cDNA products were mixed with 2× TaqMan master mixes and 20× TaqMan Gene Expression Assays (Applied Biosystems) and subjected to 40 cycles of PCR in StepOnePlus instrument (Applied Biosystems). Relative expression was evaluated with ΔΔCT method. The cells were washed in ice-cold PBS and lysed using cell lysis and mitochondrial intact buffer (CLAMI) containing 250 mM sucrose, 70 mM KCl dissolved in 1× PBS with 0. 5 mM DTT and 2. 5 µg/ml Pepstatin and 0. 2 g/ml digitonin. The cells were allowed to swell on ice for 5 min. The cell suspension was centrifuged at 400 g for 5 min and the pellet was removed. The supernatant was transferred to a clean eppendorf tube and the mitochondrial and microsomal fractions were separated by spinning at 20,000 g for 5 min. The cytosolic fraction was removed and prepared for Western blot by adding 5× sample buffer. Cells were washed once in ice-cold PBS and lysed in whole cell lysis buffer (20 mM HEPES pH 7. 5,350 mM NaCl, 0. 5 mM EDTA, 1 mM MgCl2,0. 1 mM EGTA, and 1% NP-40) after stipulated time of treatments and boiled at 95°C with Laemmli' s SDS-PAGE sample buffer for 5 min. Protein concentration was determined by Bradford method. Equal amounts (20 µg/lane) of protein samples were run on an SDS polyacrylamide gel. The proteins were transferred onto nitrocellulose membrane and blocked with 5% milk in PBS-0. 05% Tween. The membrane was incubated with the primary antibody Hsp72 (Stressgen SPA-810), Caspase-3 (Cell Signaling Technology, Cat# 9662), Cytochrome c (BD Pharmingen, Cat# 556433), XBP1 (Santa Cruz Biotechnology, Inc, Cat# sc-7160), CHOP (Santa Cruz Biotechnology, Inc, Cat# sc-793), phosphorylated eIF2α (Cell Signaling Technology, Cat# 3597), total eIF2α (Cell Signaling Technology, Cat# 2103), phosphorylated JNK (Cell Signaling, Cat# 9255S), IRE1α (Cell Signaling Technology, Cat# 3294S), or β-Actin (Sigma, Cat# A-5060) for 2 h at room temperature or overnight at 4°C. The membrane was washed 3 times with PBS-0. 05% Tween and further incubated in appropriate horseradish peroxidase-conjugated secondary antibody (Pierce) for 90 min. Signals were detected using West pico chemiluminescent substrate (Pierce). Immunoprecipitation of HA-tagged wild-type IRE1α or IRE1α ΔC was performed using Pierce Profound mammalian HA tagged IP/Co-IP kit (23615). Briefly, cell lysates were incubated with HA-agarose slurry in IP column overnight. Agarose beads were washed twice with TBS containing 0. 05% Tween. Protein complexes were extracted by boiling the beads with 2× lane marker buffer and analyzed by Western blotting as described above. For immunoprecipitation of Hsp72, cleared protein extracts were incubated with anti-Hsp72 polyclonal antibody (Stressgen SPA-811) overnight at 4°C, followed by 100 µl of a 12% suspension of protein A-Sepharose for 1 h at 4°C, and then washed three times with TBS-0. 05% Tween. Protein complexes were eluted by boiling in 2× lane marker buffer and analyzed by Western blotting as described above. The effect of Hsp72 on activity of IREα was monitored using recombinant human IRE1αΔN-HIS produced as GST fusion protein using the Prescission Protease cleaved system. IRE1α ΔN was incubated with recombinant Hsp70 (Stressgene) in a total volume of 50 µl for 1 h at 30°C with 10 µg of total mRNA as substrate (obtained from mouse brain cortex because of minimal basal levels of spliced XBP1 mRNA) in a buffer containing 20 mM HEPES (pH 7. 3), 1 mM DTT, 10 mM magnesium acetate, 50 mM potassium acetate, and 2 mM ATP. Then, mRNA was re-extracted with 500 µl of Trizol, and the endoribonuclease activity of IRE1α was monitored by RT-PCR using the XBP1 mRNA splicing assay that employs a set of primers that closely surround the processing site. Using this method, we observed a decrease in the amount of nonspliced XBP1 mRNA due to its cleavage by IRE1αN-HIS as we previously described [28]. We generated stable subclones of PC12-Hsp72 with reduced levels of XBP1 by targeting XBP1 mRNA with shRNA using the lentiviral expression vector psiHIV-U6 (GeneCopoeia). The targeting sequences identified for rat XBP1 were XBP1 shRNA1: 5-actgcgcgagatagaaaga-3; XBP1 shRNA2: 5- gttgcctcttcagattctg-3; XBP1 shRNA3: 5-gagagccaaactaatgtgg-3; and XBP1 shRNA4: 5-ctgaggtcttcaaaggtat-3. Cells were seeded in T25 flasks 24 h prior to heat preconditioning. The flasks were sealed with parafilm and immersed in water bath set at 42°C for 1 h. The cells were left to recover for 6 h at 37°C before treating with apoptosis inducing agents. Media was changed prior to treatment. Cells were treated with 0. 1 µM Tg or 0. 5 µg/ml Tm or 50 µM 6-OHDA for 24 h to induce UPR. Culture media was analyzed for NGF or BDNF release using β-NGF (DY 256) or BDNF (DY 248) DuoSet ELISA development kit according to manufacturer' s protocol (R&D Systems). The amount of NGF or BDNF released into the media was calculated using the standard curve generated in parallel with recombinant NGF and BDNF. All the experiments were repeated at least 2 times. Results are expressed as mean ± standard deviation. Statistical analyses of the results were done with Student' s t test using Graphpad (http: //www. graphpad. com).

The endoplasmic reticulum (ER) is responsible for production and folding of secreted proteins. When the protein folding machinery cannot keep up with demand, misfolded proteins accumulate, leading to a state of ER stress that contributes to diseases such as cancer, neurodegeneration, diabetes, and myocardial infarct. The unfolded protein response (UPR) is an intracellular signaling network activated in response to ER stress. It initially tries to restore normal ER homeostasis, but if the damage is too severe cell death pathways mediated by cytosolic and mitochondrial proteins are activated. The molecular mechanisms involved in the transition of the UPR from a protective to an apoptotic phase are unclear. IRE1α is an ER membrane protein that acts as a sensor of ER stress. A number of proteins can interact with IRE1α to regulate its function, which includes an RNase activity responsible for inducing the unconventional splicing of the transcript for a downstream signaling protein called XBP-1. Here, we report that Hsp72, a stress-inducible cytosolic molecular chaperone, can bind to and enhance the RNase activity of IRE1α, providing an important molecular link between the heat shock response and the ER stress response. Importantly, increased production of active XBP-1 was necessary for Hsp72 to exert its prosurvival effect under conditions of ER stress. Our results suggest a mechanism whereby Hsp72 overexpression helps cells adapt to long-term ER stress in vivo by enhancing the pro-survival effects of the IRE1α/XBP1 branch of the UPR.

Abstract Introduction Results Discussion Materials and Methods

biochemistry/transcription and translation cell biology/cellular death and stress responses biochemistry/cell signaling and trafficking structures cell biology/cell signaling

2010

HSP72 Protects Cells from ER Stress-induced Apoptosis via Enhancement of IRE1α-XBP1 Signaling through a Physical Interaction

Although epigenetic control of stem cell fate choice is well established, little is known about epigenetic regulation of terminal neuronal differentiation. We found that some differences among the subtypes of Caenorhabditis elegans VC neurons, particularly the expression of the transcription factor gene unc-4, require histone modification, most likely H3K9 methylation. An EGF signal from the vulva alleviated the epigenetic repression of unc-4 in vulval VC neurons but not the more distant nonvulval VC cells, which kept unc-4 silenced. Loss of the H3K9 methyltransferase MET-2 or H3K9me2/3 binding proteins HPL-2 and LIN-61 or a novel chromodomain protein CEC-3 caused ectopic unc-4 expression in all VC neurons. Downstream of the EGF signaling in vulval VC neurons, the transcription factor LIN-11 and histone demethylases removed the suppressive histone marks and derepressed unc-4. Behaviorally, expression of UNC-4 in all the VC neurons caused an imbalance in the egg-laying circuit. Thus, epigenetic mechanisms help establish subtype-specific gene expression, which are needed for optimal activity of a neural circuit. Epigenetic regulation of gene expression, e. g. , through histone modification, is essential to silence key developmental genes, prevent neural differentiation, and maintain the pluripotency of embryonic stem cells (ESCs) [1]. For example, methylation on the lysine 27 of histone 3 (H3K27) suppresses the expression of genes required for the neural lineage and prevents the differentiation of the mammalian ESCs into neural precursor cells (NPCs); ESCs deficient in the Polycomb-group (PcG) proteins, which promote H3K27 trimethylation, show an increased propensity to differentiate [2], [3]. H3K27 trimethylation and histone deacetylaiton are also responsible for silencing neuron-specific genes and inhibiting neurogenesis during the differentiation of NPCs into astroglial cells [4]. Despite the importance of epigenetic control in the cell fate choice of stem cells or neural precursor cells, the involvement of chromatin modification in the terminal differentiation of neurons has not been reported. Here we show that histone methylation restrains the expression of a functionally important transcription factor (TF) in a specific neuronal subtype in Caenorhabditis elegans. Within the ventral cord of C. elegans the six VC motor neurons help control egg laying [5]. VC neurons can be categorized into two subtypes according to their proximity to the vulva, their morphology, and their gene expression. The vulva VC neurons, VC4 and VC5, flank the vulva, have short processes in the ventral cord, and send branched processes dorsally along the vulval hypodermis on each side of the vulval slit. In contrast, the nonvulval VC neurons, VC1-3 and VC6, which are more distant from the vulva, send less-branched processes to the vulva and have longer processes in the ventral cord. All VC axons extend dorsal branches that innervate vm2 vulval muscles, but only VC1-3 and VC6 innervate ventral body muscles. All VC neurons generate acetylcholine (ACh), but its activity is only known for the vulval VC cells where it acts as a neuromodulator that inhibits the activity of egg-laying-inducing HSN motor neuron [5]. In addition, only the vulval VC cells release serotonin to activate vulval muscle and promote egg laying [5]. Since loss of VC4 and VC5 neurons increases egg laying [6], their overall activity is biased toward inhibition. We find that the vulval VC neurons, but not the nonvulval VC neurons, express the TF UNC-4 and that this expression is determined by both external signals from the vulva, which trigger unc-4 transcription in the adjacent vulval VC neurons through EGF signaling, and internal histone methylation, which silences unc-4 in the nonvulval VC neurons in the absence of EGF signals. Mutation of the H3K9 methyltransferase MET-2, the human HP1 homolog HPL-2 and the MBT repeats-containing protein LIN-61, which are recruited to H3K9me2/3, and a novel chromodomain protein CEC-3 leads to the loss of subtype-specificity of unc-4 expression; the gene is expressed in all six VC neurons. Epigenetic silencing of unc-4 occurs initially in all six neurons, but is relieved in the vulval VC cells due to the action of EGF signaling and the LIM domain TF LIN-11. Functionally, this release of epigenetic silencing of unc-4 expression in the vulval VC neurons helps balance the choice between egg retention and egg laying. The transiently expressed UNC-4 homeodomain protein plays an important role in the differentiation and synaptic formation of ventral nerve cord motor neurons in C. elegans [7]. To monitor the dynamics of unc-4 expression pattern, we used a 2. 5 kb promoter of the unc-4 gene (unc-4p) to drive the expression of a rapidly degraded form of GFP (uIs45; [8]). When compared to the transgene of unc-4 promoter-driven regular GFP, the expression from uIs45 labeled far fewer cells at nearly every developmental stage (Figure S1). uIs45 expression began in DA neurons in 3-fold embryos and lasted until the middle of first larval (L1) stage. The reporter was expressed next in the VA neurons beginning with the most anterior cells during the L2 stage. This expression was lost soon afterward; by the late L2 stage the most posterior VA neurons had expressed and then lost the reporter (Figure S2). Although head neurons SAB, AVF, and I5 constantly expressed the reporter throughout the larval and adult stages, virtually no ventral cord neurons expressed it from the L3 to early L4 stage. The reporter was expressed in VC4 and VC5 (the vulval VC neurons; Figure 1A) beginning at the same time as anchor cell invasion in early L4 animals. The expression stabilized in the mid-L4 when the hermaphrodite vulva formed (Figure 1A) and lasted throughout adulthood. The reporter was not observed in the other VC neurons at any time. In males, no ventral cord neuron expresses the reporter after L3 stage despite the expression in VA and DA neurons during earlier larval stages. We screened F2 animals representing 25,000 haploid genomes after EMS mutagenesis for mutants with increased expression of uIs45 in adult ventral cord neurons. Twenty-three mutants had more than the two neurons found in the parent strain (Table S1). Twelve mutants with strong phenotypes were identified by whole genome sequencing (see Methods) and had defects in three genes (pqe-1, cec-3, and ceh-20). The remaining eleven mutants have either weak phenotypes or low penetrance and were not studied further; all complemented null alleles of pqe-1, cec-3, and ceh-20. Of the twelve mutants we analyzed, the eight pqe-1 mutants and the two cec-3 mutants expressed uIs45 in all six VC neurons, whereas the two ceh-20 mutants prolonged uIs45 expression in adult VA neurons (Figure S3). In this paper we focus on the abnormal activation of unc-4 promoter in the VC neurons and the mechanisms inhibiting unc-4 expression in these neurons. pqe-1 was originally identified as a modifier of polyglutamine neurotoxicity; mutation of pqe-1 significantly enhanced polyglutamine-induced neurodegeneration of the ASH neurons [9]. All eight alleles in our screen harbored nonsense mutations and caused uIs45 expression in all six adult VC neurons with 100% penetrance (Table S1; Figure 1B). The allele u825 was used in subsequent studies. The extra GFP-expressing cells in these animals were identified as the VC1-3 and VC6 neurons because the labeled cells were in the correct anatomical positions and hermaphrodite-specific. Furthermore, the expression of a RFP version of uIs45 overlapped with the expression of the VC marker vsIs13[lin-11p: : pes-10p: : GFP] [6] in these extra cells, confirming their identity (Figure S4). The pqe-1 gene encodes two isoforms: the a isoform has a glutamine/proline-rich domain, whereas the b isoform has an additional C-terminal RNA exonuclease domain ([9]; Figure 1C). Because, ok1983, an allele that deletes a large portion of both isoforms and causes a subsequent frame shift, produced the same ectopic uIs45 expression as our eight pqe-1 alleles, all these alleles are likely to be null alleles. Additionally, since expression of the pqe-1a isoform from the VC-expressed promoter lin-11p: : pes-10p, which contains a 500 bp lin-11 enhancer and a pes-10 basal promoter [6], prevented the ectopic unc-4 expression, PQE-1 acts cell-autonomously and does not require the exonuclease domain (Figure 1B). Consistent with cell-autonomous activity, 4. 7 kb of DNA upstream of the start codon of pqe-1 drove RFP expression in ventral cord neurons including the VC neurons (Figure S5) and in head and tail neurons. Because lin-11p: : pes-10p is also expressed in 2° vulval cells, uterine pi cell progeny, and the spermatheca, we repeated the rescue experiments by expressing pqe-1a (+) from the ida-1 promoter [10], which is expressed in many neurons but only overlaps with the expression pattern of lin-11p: : pes-10p in the VC cells. We obtained similar results (data not shown). In this paper we show results of VC cell-specific rescue experiments with the lin-11p: : pes-10p promoter, but all of the results were confirmed with ida-1 promoter-driven transgenes. Moreover, to rule out the possible non-cell autonomous interactions among the VC neurons, which synapses on one another [11], we also performed mosaic analyses on pqe-1 (u825); uIs45 animals with an extragenic pqe-1 (+) array (see Text S1 for the method). In the 15 mosaic animals we examined, all VC1-3 and VC6 cells lacking the rescuing array expressed GFP strongly, suggesting that pqe-1 acts cell autonomously. The nonsense mutation cec-3 (u830) produced the same phenotype as the pqe-1 mutations. cec-3 encodes a chromodomain-containing protein. The ok3432 allele, which deletes the start codon of CEC-3 gave the same ectopic expression of uIs45 in all adult VC neurons (Figure 1D and 1E) and failed to complement u830, suggesting that u830 is also a null allele. Moreover, CEC-3 expression in VC neurons restored the normal unc-4 promoter expression pattern, indicating that it acts cell-autonomously in the VC neurons (Figure 1D). As with pqe-1, mosaic analysis confirmed the cec-3 cell autonomy in individual VC cells (data not shown). Because the CEC-3 protein has a chromodomain, a domain which binds to repressive histone modifications and generally mediates transcriptional suppression [12], we suspected that the abnormal expression of uIs45 in cec-3 mutants resulted from dysregulation of epigenetic silencing. Indeed, mutants defective in the histone H3K9 methyltransferase gene met-2 [13] did express uIs45 in all VC neurons (Figure 2A). Mutations in another histone methyltransferase MET-1, which mainly promotes H3K36 methylation [14] but affects the abundance of H3K9 methylation [13], also resulted in ectopic unc-4 expression. The met-2 animals showed higher penetrance and brighter GFP expression in VC1-3 and VC6 cells than the met-1 mutants (Figure 2A and 2C), consistent with previous reports that MET-2 plays a major role in promoting H3K9 methylation, whereas MET-1 is a minor contributor [13]. MET-2 is the C. elegans homolog of human SETDB1 and Drosophila Eggless [15], which specifically trimethylate H3K9 and contribute to HP1-mediated silencing of euchromatic genes [16]. Andersen et al. [13] found that met-2 mutant embryos had significantly less H3K9 trimethylation. Towbin et al. [17], however, subsequently showed that MET-2 was specific to mono- and di-methylation of H3K9, and another histone methyltransferase (HMT), SET-25, mediated H3K9 trimethylation in the germ line and embryos. We found that set-25 mutants did not show ectopic unc-4 expression (Figure 2C), suggesting H3K9 dimethylation may be mainly responsible for the repression of unc-4 in VC1-3 and VC6. Because we cannot rule out the possibility that MET-2 or some other HMTs promote H3K9 trimethylation in adult VC cells to silence unc-4, we have designated the modification caused in the VC cells as H3K9me2/3. Loss of MET-1 affects both H3K36 and H3K9 trimethylation in embryos [13] and either or both activities could be important for the effect on unc-4 expression. If the action of MET-1 is direct, the latter activity is likely to be important for the repression of unc-4, since 1) H3K36me3 is an epigenetic mark present in the coding sequence of actively transcribed genes [18]; and 2) mutation of mes-4, another HMT responsible for at least germline and embryo H3K36me3 [19], did not result in ectopic unc-4 expression (data not shown). A more indirect effect of MET-1, however, involving H3K36 trimethylation may also occur. We also examined mutants defective in other chromodomain-containing proteins, which are thought to be tissue or gene specific [20], and found that mutation of hpl-2 but not others genes in this family led to similar ectopic expression of uIs45 (Figure 2B and 2C). HPL-2 is the C. elegans homolog of human HP1 and is known to be recruited to H3K9me2/3 [21], [22]. Therefore, this result suggests that HPL-2 and perhaps the other chromodomain protein CEC-3 mediate the transcriptional repression of unc-4 through H3K9 methylation in VC1-3 and VC6 neurons. In addition, mutation of genes encoding the components of the Polycomb-like chromatin repressive complex (mes-2, mes-3, and mes-6), which promote H3K27 methylation, did not cause ectopic unc-4 expression (n>50 for each mutant). Similarly, treatment with histone deacetylase inhibitors (valproic acid or Trichostatin A) had no effect on the expression pattern of unc-4 (n>50 in both cases). Therefore, H3K9 methylation may contribute most to the silencing of unc-4. Yamada et al. reported that pqe-1 mutations increased transgene expression but not endogenous gene expression [23]. In contrast, using single molecule fluorescence in situ hybridization, which individually labels at least 80% of the cellular mRNA [24], we found that the level of endogenous unc-4 transcripts increased (Figure 3). VC1-3 and VC6 neurons in wild-type animals contained less than three fluorescently labeled unc-4 mRNA molecules (VC1: 2. 4±0. 3, VC2: 2. 8±0. 4, VC3: 2. 5±0. 4, and VC6: 2. 7±0. 4; mean ± SEM, N = 20), whereas VC4 and VC5 had about 11 labeled unc-4 transcripts (VC4: 10. 9±0. 5; VC5: 10. 6±0. 5). All VC neurons in pqe-1, cec-3 and met-2 mutants had >12 unc-4 transcripts. These results confirmed that unc-4 expression was significantly upregulated in VC1-3 and VC6 neurons in pqe-1, cec-3, and met-2 mutants and that uIs45 truly monitored endogenous unc-4 promoter activity. In addition to regulating unc-4 expression in the VC neurons, hpl-2, met-1, and met-2 also repress transcription of lin-3/EGF, which induces vulval development [13], [25]. Because all three genes are synMuv B genes (mutation of any of them, together with a mutation in a synMuv A gene, leads to a synthetic multivulva phenotype [26]), we tested whether other genes regulating vulval development also controlled unc-4 expression. Indeed, mutation of four other synMuv B genes, but no synMuv A genes, caused ectopic expression of unc-4p: : MDM2: : GFP in the VC neurons (Figure 2C). The four synMuv B genes were efl-1/E2F, which encodes a transcriptional repressor [27]; lin-13, which encodes a zinc-finger protein that forms a complex with HPL-2 and helps localize HPL-2 to certain genomic loci [28]; lin-61, which encodes a protein with four malignant brain tumor repeats that bind to di- and tri-methylated H3K9 [29], [30] and interacts genetically with hpl-2 and met-2 in vulva development [30]; and lin-65, which encodes a large acid-rich protein that lacks obvious similarity to non-nematode proteins [31]. pqe-1 and cec-3 were not, however, synMuv genes. Loss of either pqe-1 or cec-3 in the background of a class A or class B synMuv mutant, such as lin-15A or lin-15B, respectively, did not give a multivulva phenotype (data not shown). Thus, vulval and VC development utilize genetic pathways with overlapping yet divergent regulatory roles. PQE-1 and proteins involved in chromatin modification and remodeling acted similarly in several different situations. cec-3, met-2, and lin-13 mutations, like pqe-1 mutations [23], enhanced transgene expression in AIZ neurons (Figure S6A), suggesting these genes act together to inhibit transcription. Similarly, loss of cec-3, met-1, met-2 and lin-13, like loss of pqe-1 [9], enhanced polyglutamine (polyQ) repeat-induced neurodegeneration in ASH neurons (Figure S6B; see Text S1 for the method). Moreover, Bates et al. found that mutation of several histone deacetylases increased this polyQ-dependent neurodegeneration [32], indicating that histone modification-induced transcriptional suppression was generally protective for polyQ-mediated neuronal cell death. The fact that PQE-1 shares similar functions with HMT and chromodomain protein hints that PQE-1 may also regulate chromatin silencing. The morphological differentiation of VC neurons requires guidance cues from vulval cells [33]. Only the vulva-flanking VC4 and VC5 neurons branch into the vulval region and innervated vulval muscle in wild-type animals. However, when the vulva is displaced anteriorly to lie between VC3 and VC4 in dig-1 mutants, the axonal branching occurs in the now vulva-flanking VC3 and VC4 neurons, but not in VC5 [33]. unc-4 expression in VC neurons also depended on similar external cues. In dig-1 mutants, VC3 and VC4 neurons flanked the misplaced vulva and expressed unc-4, whereas VC5, which was no longer adjacent to the vulva, did not express unc-4 (Figure 4A). Moreover, the positions of VC neurons were not changed in dig-1 animals, and only VC3 and VC4 underwent morphological differentiation and migrated toward the vulva. These results indicate that the proximity to the vulva determines which VC neurons become the vulval subtype and activate unc-4 transcription. We also examined five Muv mutants, defective at various points in the pathway that controls vulval induction and development, and found they expressed uIs45 in VC neurons flanking both the vulva and pseudovulvae (Figure 4B and 4C). Thus, the unc-4 expression pattern in VC neurons is regulated by signals from vulval tissue. In addition, VC neurons flanking the pseudovulva, such as VC3 in lin-15AB animals, extended axons to ectopic vulval muscles, mimicking the normal differentiation of vulva-flanking VC4 and VC5 (Figure 4D). Since these morphological changes were not observed in ectopic VC neurons that expressed unc-4 in pqe-1 or cec-3 or other mutant animals (Figure 4D), unc-4 expression was not sufficient to induce these morphological changes. Moreover, both we and Bany et al. [6] found no defects in VC axonal processes in unc-4 mutants, suggesting that unc-4 was not needed for VC morphological differentiation. Other genetic pathways may control the axonal outgrowth of the vulval VC neurons. Therefore, the epigenetic regulation of unc-4 does not determine all the aspects of VC subtype identity. Since EGFR/RAS/MAPK signaling induces vulval development and differentiation [34], we tested whether mutation of genes in this pathway eliminated the expression of unc-4 in VC4 and VC5 neurons. Indeed, animals defective in lin-3/EGF and let-23/EGFR, which are vulvaless, failed to express uIs45 in VC4 and VC5 (Figure 4C and S7A). let-60/RAS and lin-45/RAF mutant, which lack downstream effectors of EGFR, also failed to express unc-4 in vulval VC neurons (Figure 4C and S7B). Importantly, unc-4 expression in VC4 and VC5 neurons was restored in animals with hypomorphic alleles of let-60 and lin-45 by VC-specific expression of the respective wild-type gene, indicating that the EGFR/RAS/RAF signaling cascade functions cell-autonomously in VC neurons (Figure 4C and S7C). unc-4 expression was also absent in VC neurons in mutants of sem-5/GRB2 (encoding an adaptor protein that links RAS to EGFR; [35]) and mek-2/MAPKK (encoding the downstream kinase of RAS; Figure 4C; [36]). Using the VC marker vsIs13, we have confirmed the presence of the six VC neurons in all the mutants that have diminished unc-4 expression in these cells (data not shown). These results further support the hypothesis that EGFR signaling is essential for inducing unc-4 transcription in the VC4 and VC5 neurons. The expression of unc-4 in the SAB, AVF, and I5 head neurons was not affected by mutations of the EGFR pathway, indicating that other mechanisms maintain the constant expression of unc-4 in these neurons. We next searched for the origin of the LIN-3/EGF signal that activates unc-4 expression in VC neurons during vulval development. The two known sources of LIN-3 in vulval development are the anchor cell, which secretes LIN-3 at the middle to late L3 stage to induce primary vulval cell fate and pattern the vulval precursor cells [37] and the vulF cells, which secretes LIN-3 to signal the presumptive uv1 cells [38]. The anchor cell is likely not the source of LIN-3 for unc-4 expression in the vulval VC neurons because unc-4 expression occurred much later, after vulval precursor patterning was completed and the anchor cell invasion had started. Laser ablation of vulF cells caused the loss of unc-4 expression in the VC4 and VC5 neurons (Figure 5A), suggesting that vulF cells are responsible for releasing EGF that activates unc-4. Moreover, the vulF cells, which are physically adjacent to the vulval VC neurons, are correctly positioned to activate unc-4 expression in the latter cells with high concentrations of LIN-3/EGF. To further confirm the importance of vulF cells in inducing unc-4, we examined lin-12d mutants, in which all the six vulval precursor cells adopt the 2° vulval cell fate and the 1° lineage progeny vulF cells are not generated [39]. The strong lin-12d allele n137 caused a multivulva phenotype but had no unc-4 expression in any VC cells (N = 35 animals); the weaker lin-12d allele n302 resulted in a vulvaless phenotype and the elimination of unc-4 expression in VC4 and VC5 neurons (95% of 78 animals lacked expression in these cells). These results support the hypothesis that vulF cells are the source of the developmental signal that activates unc-4. We next wanted to identify the transcription factor (TF) that regulates unc-4 expression in vulval VC cells. Although the ETS-domain-containing TF LIN-1 is a known nuclear target of the EGFR/RAS/MAPK signaling in vulval differentiation [40], loss of lin-1 caused ectopic unc-4 expression in nonvulval VC neurons and a multivulva phenotype instead of diminishing unc-4 transcription (Figure 4C), suggesting that another TF may be involved in activating unc-4 transcription. We screened six TFs (egl-18, lin-11, tag-97, zag-1, vab-15, and hlh-3) known to be expressed in VC neurons and found that mutation of lin-11, which encodes a LIM homeodomain protein [41], eliminated uIs45 expression in VC4 and VC5 (Figure 5B). Expression of lin-11 (+) using lin-11p: : pes-10p, which is active in VC neurons but transiently expressed in the developing vulval cells, mainly the vulC and vulD cells but not the vulF cells [42], produced a normal unc-4 expression pattern in lin-11 mutants (Figure 5C). We obtained a similar rescue using the ida-1 promoter, which is expressed in many neurons, including the VC neurons, but not vulval cells (data not shown). Thus, the action of lin-11 on unc-4 expression was cell autonomous. lin-11 expression in VC neurons started at the L2 stage (these cells are generated in the L1 stage) and all of the six VC neurons continued to express lin-11 in subsequent larval and adult stages (Figure 5D). In contrast, unc-4 expression in the vulval VC neurons began in the L4 stage and was absent in the nonvulval VC neurons, which also expressed LIN-11. Therefore, LIN-11 alone was not sufficient to activate unc-4 transcription. Either LIN-11 induction of unc-4 expression requires LIN-11 activation (e. g. , through post-translational modification, other coactivators) or changes to downstream genes that allow it to act. Apparently upstream EGFR signaling is needed for these changes. Because genes affecting EGFR signaling activate unc-4 expression and epigenetic factors maintain unc-4 silencing in non-vulval VC neurons, we examined the relationship between these two categories of genes. pqe-1 and cec-3 were epistatic to let-60/RAS and lin-45/RAF (Figures 6), since all six VC neurons expressed unc-4 in double mutants. These results suggest that the epigenetic factors that suppress unc-4 expression are independent of the EGFR signaling that induces unc-4 transcription. Importantly, the vulval neurons VC4 and VC5 lacking the epigenetic proteins still expressed unc-4 in the absence of the inductive EGFR signaling, indicating that all six VC neurons expressed unc-4 by default once the silencing mechanism was removed. These results also suggest that the EGFR signaling in vulval VC neurons overrides epigenetic silencing. Moreover, we noticed that the unc-4 reporter expression was clear and strong from early L4 stage in pqe-1, cec-3, or met-2 mutants, whereas expression in vulval VC cells of wild-type animals was not established until the late L4 stage (data not shown). This temporal difference in the onset of unc-4 expression is consistent with the idea that the histone methylation-associated transcriptional repression is established prior to the external EGF signal, which derepresses unc-4 gene by presumably removing the repressive histone modification. In fact, histone demethylases were also required for the derepression of unc-4 in vulval VC neurons. Among the 13 genes encoding predicted histone demethylases in C. elegans, we found that mutations in jmjd-2, jmjc-1, and spr-5 significantly reduced unc-4 expression in the VC4 and VC5 cells, but not in the unc-4-expressing head neurons (Figure 5E and Table S2). spr-5 encodes the C. elegans homolog of human LSD1. The human enzyme demethylates both H3K4me2 and H3K9me2 [43], [44], but only the H3K4me2 demethylase activity of SPR-5 has been studied in C. elegans [45]. If SPR-5 does demethylate H3K9me2, it may help remove the repressive histone modification on unc-4 gene. jmjd-2 and jmjc-1 encode homologs of human JMJD2a and MINA proteins respectively, both of which are involved in the demethylation of H3K9me3 [46]–[48], but the functions of these C. elegans proteins have not been studied. Thus, the removal of the repressive H3K9me2/3 mark could activate unc-4 expression in vulval VC neurons. Given the fact that the TF LIN-11 is required to induce unc-4 expression in VC4 and VC5 cells, we expected the doubles of lin-11 with the epigenetic factors should have no unc-4 expression at all. However, to our surprise, lin-11 double mutants with pqe-1, cec-3, met-2, hpl-2, and lin-13 all showed ectopic unc-4 expression in the six VC neurons (Figure 6C). This result suggests that LIN-11 does not directly activate unc-4 transcription. Instead, LIN-11 may be the downstream target of the EGFR signaling that helps remove the repressive chromatin modification of unc-4 gene. LIN-11 was also needed for the induction of ectopic unc-4 expression in the multivulva mutants, which have excessive EGF signals from the pseudovulvae, since the unc-4 expression in VC neurons near the pseudovulvae was prevented by mutation of lin-11 (Figure 6B). Thus, LIN-11 is required to alleviate the epigenetic silencing of unc-4 in VC neurons in response to the differentiation cue from vulval cells. VC neurons regulate egg laying in two ways: neuromuscular synapses from VC neurons activate the vulval muscle vm2 cell and allow eggs to be laid; and extrasynaptic release of ACh as a neuromodulator from VC neurons prevents egg laying by inhibiting the HSN neurons, which promote egg laying [5]. Since UNC-4 upregulates the expression of choline acetyltransferase (CHA-1) and the synaptic vesicle ACh transporter UNC-17 post-transcriptionally, mutation of unc-4 leads to reduced release of ACh, increased HSN activity, and thus hyperactivate egg laying [49]. Mutations in cha-1 and unc-17 also result in hyperactive egg laying [6], supporting the role of ACh in inhibiting HSN activity. Since all of the VC1-3 and VC6 neurons send out processes to the vulval region, ACh produced by these cells could reach the HSN neurons even if they don' t directly synapse onto HSNs. Therefore, we reasoned that mutants with ectopic unc-4 expression in the nonvulval VC neurons could produce extra amounts of ACh, causing hypersuppression of the HSNs and reduction in egg laying. Indeed, pqe-1, cec-3, and met-2 adults retained 7–8 more eggs than wild-type animals (pqe-1: 17. 8±1. 2; cec-3: 18. 7±0. 78; met-2: 18. 9±0. 75; wild type: 11. 1±0. 4; mean ± SEM; N = 30; Figure 7A). Consistent with the increased egg-retention, the age of the eggs that were laid was older in the pqe-1 and cec-3 animals (pqe-1: 48%; and cec-3: 62% at comma stage; N = 50) than wild type (62% at 21+ cell stage and nearly no eggs at comma stage; Figure S8). VC-specific rescue of either pqe-1 or cec-3 restored normal egg retention levels, suggesting that these genes act within VC neurons. Egg retention in these animals appeared to require UNC-4–mediated activation of cha-1 and unc-17, since unc-4, cha-1, and unc-17 were epistatic to pqe-1, cec-3, and met-2; double mutants all displayed hyperactive, instead of defective, egg laying (Figures 7A and 7B). Consistent with the model that unc-4 inhibits egg laying through its regulation of ACh, we found that pqe-1 and met-2 increased egg retention in tph-1 mutants and pqe-1, cec-3, and met-2 increased egg laying in heterozygotes containing one copy of an egl-6 gain-of-function mutation (Figure 7C). The tph-1 and egl-6 mutations reduce egg laying and thus provide a sensitized background in which to look for egg-laying defects. tph-1 encodes tryptophan hydroxylase, which synthesizes serotonin in HSN neurons [50]. Because serotonin is released by the HSN neurons to promote egg laying, mutation of tph-1 leads to reduced egg-laying activity. egl-6 encodes an FMRFamide neuropeptide receptor, the receptor of neuropeptide FLP-10 and FLP-17, which acts additively with ACh to inhibit the HSN neurons [51]. The constitutively active gain-of-function mutation of egl-6 resulted in hypersuppression of the HSN neurons and defects in egg laying. To further confirm that the unc-4-expressing non-vulval VC neurons caused the defects in egg laying, we ablated the aberrantly differentiated nonvulval VC neurons (VC1-3 and VC6) in cec-3 and met-2 animals to correct the phenotype. Ablated mutant animals retained the same number of eggs as ablated wild-type animals (Figure 7D). In addition, the ablated cec-3 and met-2 animals laid significantly fewer late-stage eggs than unablated controls (Figure S9). Ablating the vulva-flanking VC4 and VC5 neurons in the wild-type background led to hyperactive egg laying, whereas killing the other VC neurons had very little effect (Figure 7D). These data suggest that the differentiated vulval VC neurons, which normally express unc-4, were responsible for reducing egg laying and balancing the behavioral output of the egg-laying circuit, whereas epigenetic silencing of unc-4 expression in the non-vulval VC neurons prevented a further inhibition of egg laying. The six VC neurons are generated in L1 larvae but diversify into subtypes, adopting different morphologies and functions at the L4 stage during vulval development. At this later time the vulva-flanking VC4 and VC5 neurons differentiate further by migrating toward the vulva, extending branched processes dorsally to innervate vulval muscles, initiating unc-4 expression, and joining the egg-laying circuit. In contrast, the other VC neurons largely maintain their original cell shape and play only a minor role in egg laying. These later cells, however, have the potential to be more like the VC4 and VC5 cells as seen in the dig-1 mutants or mutants with multiple vulvae. The diversification of VC neurons into subtypes requires inhibition of vulval VC differentiation in the nonvulval VC neurons, which don' t receive the LIN-3/EGF developmental cue. As shown here, unc-4 expression is a convenient marker for this differentiation. We found that unc-4 is silenced epigenetically in nonvulval VC neurons. This epigenetic repression involves chromatin modifiers (including the histone H3K9 methyltransferase MET-2 [13] and H3K36 methyltransferase MET-1 that may indirectly promote H3K9 methylation [13], [14]), chromatin readers (including the MBT (malignant brain tumor) domain-containing protein LIN-61 [29], the HP1-like protein HPL-2 [20]), zinc finger protein LIN-13 that helps localize HPL-2 [28]), a novel chromodomain protein CEC-3, a transcriptional repressor EFL-1 [27], and a large, acid-rich protein LIN-65 with unknown function [31]. Mutation of any of these proteins led to ectopic unc-4 expression in all six VC neurons and resulted in the loss of subtype-specificity of the expression. Because UNC-4 regulates the level of proteins needed for the synthesis and release of ACh, the failure to restrain unc-4 expression in only VC4 and VC5 caused hyperinhibition of HSN activity and defects in egg laying. Thus, histone modification contributes to terminal neuronal differentiation by generating the correct gene expression pattern in VC cells. This control is essential for the regulation of a specific behavior, egg laying. In addition to these epigenetic proteins, we found that the Q/P-rich domain-containing protein PQE-1 also prevents unc-4 expression in non-vulval VC neurons. Although we do not know how this repression works, our results showed that PQE-1 acts in a similar way to the histone methyltransferases MET-1 and MET-2, the chromodomain protein CEC-3, and the HP1/HPL-2 binding partner LIN-13 in preventing transcription and protecting cells from polyQ neurodegeneration. Consistent with previous studies [9], we found that the C-terminal RNA exonuclease domain included in the b isoform of pqe-1 gene was dispensable for PQE-1 function, indicating the N-terminal Q/P-rich domain is mainly responsible for inhibiting unc-4 expression. Although the Q/P-rich domain is largely known to promote protein aggregation, the C-terminus of the TF TDP-43 has a Q/P-rich region that is required for its function in silencing the testis-specific gene SP-10 [52], [53]. Therefore, we speculate that the nuclearly localized PQE-1 protein may use its Q/P-rich domain to mediate transcriptional repression. Thus, Q/P-rich proteins may be a new class of epigenetic control factors. Previous studies showed that repressive epigenetic modifications are essential for silencing critical developmental genes and inhibiting differentiation in stem cells. ESCs deficient in PcG proteins, which promote H3K27 trimethylation and suppress transcription, derepressed neural genes, such as Ngns, Pax-6, and Sox-1, and were prone to differentiate [2]. Loss of the transcriptional repressor REST (RE1-silencing TF, which recruits histone modifiers and chromatin-binding proteins) or inhibition of DNA methyltransferase and histone deacetylases (both of which suppress transcription) induces aberrant differentiation and derepression of genes related to neurogenesis in ESCs and NPCs [54]–[56]. The H3K9 methyltransferase SetDB1 also contributes to the repression of genes encoding developmental regulators and to the maintenance of ESCs [57]. Consistent with these findings, we find that mutation of histone methyltransferase met-2 that is homologous to SetDB1 and promotes H3K9 methylation abrogated the repression of terminal differentiation marker unc-4 in undifferentiated VC neurons. Although our results suggest that H3K9 methylation is important for the regulation of unc-4 expression, we cannot definitively determine whether dimethylation or trimethylation is important. Unlike SetDB1, which specifically trimethylates H3K9, MET-2, the C. elegans homolog of SetDB1, mediates mono- and dimethylation of H3K9 [17]. Another SET domain protein, SET-25, which is homologous to the mammalian EHMT1/G9a and Suv39h1/2, is responsible for H3K9 trimethylation in early embryos [17], but its role in larvae and adults has not been examined. set-25 mutants did not show ectopic unc-4 expression, so either H3K9 dimethylation is responsible for unc-4 silencing or MET-2 or another HMT promotes H3K9 trimethylation of unc-4 DNA in adult VC cells. Our finding that the release of unc-4 epigenetic silencing needed jmjd-2 and jmjc-1, homologs of mammalian demethylases that have demethylation activities on H3K9me3 [46]–[48], argues for the second hypothesis. Among the chromatin readers that are important for unc-4 repression, LIN-61 and HPL-2 directly binds to H3K9me2/3 [22], [29], but the ability of CEC-3 to bind to H3K9me2/3 remains to be determined. Although HPL-2 can be indirectly recruited to H3K27me3 [21], our results suggest that H3K27 trimethylation is not the likely cause of unc-4 silencing because neither the polycomb complex components that catalyze H3K27 trimethylation nor the predicted H3K27me2/3 demethylases had any effect on unc-4 expression. Developmental signals can reverse H3K9 methylation-mediated gene silencing in undifferentiated neurons to allow the differentiation to proceed. In this study, we found that EGF/LIN-3 from the developing vulF cells acts through the EGFR/RAS/MAPK signaling pathway during vulval development to override the epigenetic silencing of unc-4 in VC neurons that are close to vulF cells. The EGF signal probably failed to activate unc-4 expression in the nonvulval VC neurons because the physical distance between these cells and the EGF source was too great. At least three events are needed for the correct timing of unc-4 expression in VC4-5: 1) epigenetic silencing prevents unc-4 expression in the early L4 stage (unc-4 is expressed in early L4 cells in cec-3 or met-2 mutants); 2) induction involving EGF, etc. leads to unc-4 expression in the late L4; and 3) additional factors (the presence of a negative factor or the absence of a positive factor) prevent unc-4 expression from the L1 to the late L3 stage. Therefore, unc-4 transcription would be on by default in all six VC cells if H3K9 methylation did not inactivate unc-4 prior to the differentiation cue. The epigenetic inhibition is normally relieved in VC4-5 neurons by the external signal from the developing vulva to allow the derepression of unc-4 gene and neuronal differentiation. Although the morphological differentiation of VC neurons is not controlled by the epigenetic mechanisms, our data demonstrate that H3K9 methylation helps create subtype-specific gene expression patterns during terminal neuronal diversification. We have also found that LIM-domain transcription factor LIN-11 is required to derepress unc-4 in the vulval VC neurons. Since lin-11 acts similarly as the EGFR signaling genes in genetic interaction studies, LIN-11 is likely to be part of the EGFR pathway, probably the downstream target of the signaling, and helps remove the repressive histone modification. Although there is no report showing the direct involvement of LIM domain proteins in histone demethylation, a close correlation between the expression patterns of lysine-specific histone demethylase 1 (LSD1) and four and a half LIM-domain protein 2 (FHL2) was found in prostate cancer [58]. Since both FHL2 and LSD1 serve as coactivators of the androgen receptor [59], the LIM domain protein may interact with the histone demethylase to activate gene expression. Since histone demethylase SPR-5/LSD1 was also required for the unc-4 VC expression, EGFR signaling is likely to activate LIN-11, resulting in the removal of H3K9 methylation and the derepression of unc-4. The fact that vulF cells from the developing vulva send out EGF signal to induce VC neuron differentiation suggested a coordination between the formation of the epithelial vulval structure and the differentiation of the egg-laying neurons. In fact, signals from the 1° vulval cells also control the axonal outgrowth, branching and fasciculation of the HSN neurons [60], which together with VC neurons form the egg-laying circuit. Moreover, only the vulva-flanking VC neurons undergo morphological changes and express unc-4, and similarly the proximity of the HSN cell body to vulval cells is important for HSN axonal guidance [60], supporting the hypothesis that communication between the vulval epithelial cells and neurons depends on secreted extracellular molecules. As VC and HSN neurons are generated much earlier than the vulva, signals from the developing vulva need to activate these neurons by regulating gene expression, inducing axonal outgrowth, and eventually joining them together to form the neural circuit. Therefore, the terminal differentiation of these neurons requires highly coordinated cell-cell interactions with the epithelial tissues. Finally, our findings are consistent with recent discoveries on the epigenetic regulation of cellular differentiation by H3K9 methylation in various tissues. Ling et al. found that mutation of the H3K9 methyltransferase G9a induced abnormal myogenesis during skeletal muscle differentiation by de-repressing the transcription of myogenic regulatory factor MyoD [61], and Herzog et al. found that the histone demethylase Kdm3a, which removes H3K9 methyl groups, is essential for the differentiation of mouse embryonic carcinoma cells into parietal endoderm-like cells in a mouse embryonal carcinoma model [62]. These studies demonstrate that H3K9 methylation silences developmental genes and prevents aberrant differentiation, and that the removal of this histone modification allows normal differentiation to proceed. Our studies extend these observations by showing that H3K9 methylation helps maintain the ground state among similar neurons by silencing key genes associated with terminal differentiation. C. elegans strains were maintained at 15°C or 20°C as described by Brenner [63]. Temperature-sensitive strains were maintained at 15°C and transferred to 25°C for one generation before testing. Most strains were provided by the Caenorhabditis Genetics Center, which is funded by NIH Office of Research Infrastructure Programs (P40 OD010440). cec-3 (ok3432) II and pqe-1 (ok1983) III were generated by the International C. elegans Gene Knockout Consortium (http: //www. celeganskoconsortium. omrf. org). jmjc-1 (tm3525) I, jmjd-2 (tm2966) II, hpl-2 (tm1489) III, and hpl-1 (tm1624) X were obtained from the National BioResource Project of Japan (http: //www. shigen. nig. ac. jp/c. elegans/index. jsp). cec-3 (u830) and pqe-1 alleles (u825, u829, u831, u832, u900, u901, u902, and u903) were isolated in this study. peIs304 and pqe-1 (ok1983); peIs304 strains were kindly provided by Dr. Yuichi Iino (University of Tokyo). The unc-4p: : MDM2: : GFP vector TU#703, which contains a 2. 5 kb unc-4 promoter and DNA encoding a truncated and mutated RING domain from human MDM2 attached to GFP [8], was injected together with pRF4 (containing a dominant roller marker) to generate uIs45, which was mapped onto the X chromosome. We also replaced GFP with RFP in TU#703 to create TU#1101 unc-4p: : MDM2: : RFP, which was used to generate transgene uIs147. To examine the unc-4 expression pattern in various genetic backgrounds, we crossed uIs45 into most of the mutants of interest except for these X-linked mutations, which were crossed with uIs147. A VC-specific promoter, which contains a ∼500 bp lin-11 promoter and a basal pes-10 promoter, was subcloned into the Gateway pDONR P4-P1R from pDM4 vector. The pDM4 vector was a gift from Michael Koelle (Yale University). The coding regions of pqe-1, cec-3, lin-45, let-60, and lin-11 genes were all cloned from wild type (N2) genomic DNA into Gateway pDONR 221. The resulted entry vectors, together with pENTR-VCprom, pENTR-unc-54-3′UTR, and destination vector pDEST-R4-R3 were used in the LR reaction to create the final VC promoter-driven expression vectors. The Gateway cloning method can be found at http: //www. invitrogen. com/site/us/en/home/Products-and-Services/Applications/Cloning/Gateway-Cloning. html by Life Technologies (Grand Island, NY). These constructs were injected into corresponding mutants to form extrachromosomal arrays to test the VC-specific rescue of the mutant phenotype. The transgene vsIs13[lin-11p: : pes-10p: : GFP] was used as a VC-specific marker [6]. After visually isolating the mutants, we outcrossed the mutant strains (u825, u830, and u834) with N2 at least 10 times and then subjected them to whole genome sequencing using an Illumina GAII genome analyzer [64]. We first identified the genetic variants by aligning the sequencing data to Wormbase reference sequences (version WS220) with MAQGene [65] and subtracting the background variants found in our wild-type strain. By visualizing the genomic positions of these variants, we discovered a variant-enriched region, which theoretically contained the phenotype-causing mutation because this region had the least chance to be recombined with wild-type chromosomes under constant selection. Within this ∼1 Mb region, we identified candidate mutations and performed complementation tests with known alleles to find the gene associated with the phenotype. We confirmed the results by PCR genotyping, testing knockout alleles of candidate genes, and injecting the cosmid or fosmid containing the wild-type copy of the gene into mutant animals and testing for rescue. Single-molecule fluorescence in situ hybridization was performed on young adult animals as described [24]. Forty-eight 20-nucleotide probes for unc-4 mRNA were designed using the program at www. biosearchtech. com/customoligos and synthesized and coupled to Cy5 by BioSearch Technologies (Novato, CA). We imaged the animals using a Zeiss Axio Observer Z1 inverted microscope with a CoolSNAP HQ2-FW camera (Photometrics, Tucson, AZ) and appropriate filters for Cy5. We collected stacks of 20–35 images spaced 0. 3 µm apart for each individual neuron and counted the number of fluorescent spots per neuron. Other imaging was conducted on either the same Zeiss Observer Z1 microscope with the CoolSnap camera or a Zeiss Axioskop II with a SPOT-2 slider camera (SPOT Imaging Solutions, Sterling Heights, MI). Ablations were performed as described previously [66]. Briefly, L4 larvae of strains carrying either uIs45 or vsIs13 as VC markers were placed in 1 µl of M9 buffer on a 2% agarose pad containing 1 mM sodium azide. GFP-positive cells were identified using a Zeiss Axioplan 2 equipped with a Micropoint Laser System (Photonic Instruments, Inc.), and their nucleoli were repeatedly targeted with the laser until they appeared ruptured. Mock-ablated animals were placed on the same pad and exposed to fluorescence excitation light for the same period of time, but not shot with the laser. The animals were recovered and examined 24–30 hours later with a fluorescence dissecting microscope to ensure absence of GFP-positive cells. Thirty hours after the ablation procedure, animals were assayed for egg-laying activity. To ablate vulF cells, syIs66[B0034. 1: : pes-10: : GFP] was crossed into uIs45 to help visualize the VulF cells, and unc-4 expression in VC neurons was examined 24 hours after the ablation. The average number of unlaid eggs in the uterus and the percentage of freshly laid eggs at various stages were quantified as described [51], [67]. We collected late L4 animals and cultured then at 20°C for 36 hr. In the unlaid egg assay, 30 synchronized adults were individually dissolved in 5% sodium hypochlorite, and their eggs were counted. In the egg-staging assay, 20 staged adults were placed on a thin lawn of OP50 bacteria and allowed to lay eggs for 1 hr. Each egg was examined under a dissecting microscope and categorized into six different stages according to previous studies (Ringstad and Horvitz, 2008). Eggs with eight cells or fewer were classified as “early stage”. Eggs at the comma stage or later stages were classified as “late stage”. Every experiment was repeated three times independently.

As neurons differentiate they express specific genes that give them their distinctive shapes, activities, and functions. Much of this differentiation is controlled by the expression of transcription factors, proteins that turn on the expression of other genes. We find, however, that another aspect of terminal neuronal differentiation is the removal of inhibitory constraints on gene expression. These constraints often involve the modification of DNA or of general DNA binding proteins such as histones. This modification, referred to as epigenetic regulation, can activate or inactive genes without changing the genetic material. We found that the differentiation of nematode motor neurons was affected by genes involved in histone modification. Specifically, a gene that is needed in a subset of the motor neurons is initially turned off in all cells by histone modification. Mutation of histone modification genes causes the gene to be on in all cells. Normally, however, this removal of the inhibition is triggered by an external signal that only affects the specific cells.

Abstract Introduction Results Discussion Materials and Methods

2013

Histone Methylation Restrains the Expression of Subtype-Specific Genes during Terminal Neuronal Differentiation in Caenorhabditis elegans