The effect of the chemical, biological, and physical environment on quorum sensing in structured microbial communities

As researchers attempt to study quorum sensing in relevant clinical or environmental settings, it is apparent that many factors have the potential to affect signaling. These factors span a range of physical, chemical, and biological variables that can impact signal production, stability and distribution. Optimizing experimental systems to natural or clinical environments may be crucial for defining when and where quorum sensing occurs. These points are illustrated in our case study of S. aureus signaling in biofilms, where signal stability may be affected by the host environment. The basic signaling schemes have been worked out at the molecular level for a few of the major quorum-sensing systems. As these studies continue to refine our understanding of these mechanisms, an emerging challenge is to identify if and when the local environment can affect signaling.
Introduction
As illustrated by many articles in this special issue, the diversity of mechanisms bacteria use to communicate with one another is remarkable. Quorum sensing is widespread among different bacterial species, emphasizing the importance of coordinating behavior as a group.
When considering the relevant context of quorum sensing, it is clear that the environment can be profoundly important. Many bacteria exist in spatially structured, multi-species communities, such as biofilms on submerged surfaces or flocs in the water column of aquatic environments [1, 2]. Within these communities bacteria may achieve the high cell numbers capable of generating the local signal concentrations required to produce a quorum sensing response. Quorum sensing undoubtedly occurs in situations where bacteria are growing planktonically, for example Vibrio harveyi quorum sensing-controlled bioluminescence in the ocean, thought to produce the “milky seas” phenomenon [3, 4]. An argument could, however, be made that quorum sensing is more likely to occur in structured communities. If the reader accepts this point, further considerations lead to some interesting questions for example:
Are certain signals better suited for signaling in different types of environments?What are the important parameters influencing signaling in different environments?
Take, as an example, the well-studied, environmentally ubiquitous Pseudomonas aeruginosa. This bacterium has been isolated from a range of soil and aquatic samples [5–7]. The nature of signaling in these environments could be quite different, because signal production and diffusion dynamics may change.
This review will discuss factors that may affect signaling in structured microbial communities. In addition, quorum sensing in Staphylococcus aureus, a peptide-based signaling system, will be briefly reviewed and its role in biofilm formation will be discussed.
Factors affecting signal diffusion, stability, and distribution
The traditional way of studying quorum sensing in the laboratory is in shaken liquid-batch cultures. The quorum sensing response occurs at a specific point in the growth curve, coinciding with a threshold concentration of signal. Volume is fixed in a culture flask and signal molecules produced by bacteria usually increase in concentration with time (unless they are degraded). Batch cultures represent a closed system in which only gaseous exchange occurs. Most structured communities are in open systems in which exchange occurs with the surrounding liquid [1]. In an open, flowing system, signals may be “washed” away by the overlying fluid. In open systems the concentration of signal molecules within structured communities is primarily a function of:
signal-production rate;the degradation rate or half-life of the signal;the diffusion properties of the signal; andthe external hydrodynamic or mass-transfer conditions.
The prevailing environmental conditions and resident biology are, to different degrees, important for each of these processes.
The effects of signal and environmental chemistry on quorum sensing
Some of the best-studied quorum-sensing signals include the acyl-homoserine lactones (AHL) used by many Gram-negative bacterial species, small peptides used by some Gram-positive species, and the furans of the phylogenetically widely spread AI-2 signaling (reviews are available elsewhere [8–10]). Signal chemistry varies widely in different quorum-sensing systems and is important for determining its stability and diffusion characteristics in different environments.
All AHL signals have a characteristic homoserine lactone moiety. The homoserine lactone is derived from S-adenosylmethionine, one of the substrates for AHL synthesis [11–13]. Although stable at neutral and slightly acidic pH, the lactone ring is subject to chemical hydrolysis under basic conditions [14, 15]. The resulting product (e.g. butyryl-homoserine lactone would become butyryl-homoserine) lacks biological signaling activity and is capable of spontaneously re-lactonizing when the pH is lowered. The formula describing the relationship between pH and the lactone ring stability is 1/(1×107×[OH−]) [14], which gives the half life in days. For example, at pH 7 the homoserine lactone ring would be stable for hours whereas at pH 8.5 the stability would be of the order of minutes (Fig. 1). In many environments this might be insignificant. In some alkaline environments, however, like the photosynthetic mats at Octopus Springs in Yellowstone National Park, the pH is high enough to potentially affect signal gradients. Kaufmann et al. recently demonstrated that in addition to lactone hydrolysis, the 3-oxo-C12 AHL of P. aeruginosa is capable of spontaneously undergoing a Claisen-like condensation reaction in aqueous environments, forming a tetramic acid product, 3-hydroxydecylidene 5-(2-hydroxyethyl) pyrrolidine-2,4-dione (Fig. 2) [16]. This compound was shown to have significant antimicrobial activity against other species and a high affinity for iron. It was proposed this iron-binding activity constituted an alternative means of acquiring iron by P. aeruginosa.
Fig. 1Predicted AHL half-lives in different alkaline environmentsFig. 2Structures of quorum-sensing signals and their derivatives. Letter designations for the Gram-positive peptide signals indicate amino acids. For the L. lactis signal nisin, the structural abbreviations were: Bu, dehydrobutirine with a lanthionine bridge; Ha, dehydroalanine; Hb, dehydrobutirine
Structured communities can create highly heterogeneous localized niches where chemistry can vary drastically over very small distances. For example, consumption of dissolved oxygen by laboratory biofilm cells can result in completely anaerobic regions within the biofilm only 100 μm from the fully oxygen saturated overlying liquid [17]. Similarly, pH can vary dramatically, as demonstrated by pH microelectrodes positioned in biofilms formed from the dental pathogen Streptococcus mutans. On addition of the growth substrate sucrose, the pH dropped from 7 outside the biofilm to 4.6 approximately 100 μm deep within the biofilm (Gieseke A, Nguyen D, von Ohle C, Stoodley P; unpublished results). Clearly, if the half-life of signaling molecules is a function of local chemistry it is reasonable to expect that degradation rates will vary in the community.
The acyl side-chains of AHLs range in length from 4 to 18 carbon atoms [18, 19]. The substitution chemistry of the acyl side-chain is also highly variable. Although most AHLs have a ketone group at the C1 position, some AHLs also have either a ketone or hydroxyl group at the C3 position. In addition, the acyl side-chain can vary in saturation. Although most AHLs have fully saturated acyl side-chains, some, for example the 3-hydroxy,7-cis-tetradecanoyl homoserine lactone of Rhizobium leguminosarum, are unsaturated [19, 20]. All these features of AHL acyl side-chain chemistry, in particular acyl chain length, affect the relative hydrophobicity of the signal. Pearson et al. observed this for P. aeruginosa, which produces two major AHL signals, a C4 (i.e. four-carbon side-chain) and 3-oxo-C12 AHL (Fig. 2) [21]. They found that the C4 signal diffused freely across the cell membrane whereas the 3-oxo-C12 signal partitioned to the cell membrane, where its extrusion was aided by the activity of the MexAB-OprM efflux pump.
The hydrophobicity of the local environment could have a large effect on AHL diffusion properties. For example, several AHL-utilizing bacterial species are also capable of producing highly hydrophobic, secreted polysaccharides (e.g. the pel/psl polysaccharides of P. aeruginosa) [22]. In a structured community, such as a biofilm, secreted polysaccharides can serve as the scaffolding holding the community together [23]. Under such conditions these polysaccharides might also act as an AHL-sequestering matrix. Charlton et al. provided evidence of this, indicating that the 3-oxo-C12 signal of P. aeruginosa partitioned into the extracellular biofilm matrix [24]. Perhaps the C4 signal has reduced reactivity with the biofilm matrix, making it a more effective signal in a biofilm and explaining why it is produced in greater amounts than 3-oxo-C12 by P. aeruginosa growing as a biofilm [25].
Another factor affecting AHL stability in the environment is biologically-driven signal degradation. Emphasizing this point, Wang et al. demonstrated that radiolabled AHLs fed to soil microbial communities were almost instantly mineralized [26]! Several bacterial species have the ability to use AHLs as carbon and nitrogen sources. These include species that produce their own AHLs and those known not to make them. Why some organisms both make and break down their own AHLs is a mystery. AHL degradation occurs as a result of two primary types of enzymatic activity, lactonases, which break the homoserine lactone ring, and acylases, which cleave the amide bond linking the acyl side-chain to the homoserine lactone ring [27–31]. Biological AHL degradation is a key consideration in multi-species environments. Leadbetter proposed the concept of “insulation” in which AHL-degrading organisms could prevent AHLs produced by bacteria spatially fixed in one location from reaching other bacteria of the same species [26].
Environmental chemistry also plays a key role in AI-2 signaling. The paradigm for this type of quorum sensing is the luxS system of Vibrio harveyi [8]. One of the key steps in AI-2 signal synthesis is the LuxS-catalyzed conversion of S-ribosyl homocysteine to homocysteine and 4,5-dihydroxy-2,3-pentanedione (DPD) [32]. DPD is believed to be the key precursor to all AI-2 signals. DPD undergoes non-enzymatic cyclization and a number of spontaneous, reversible rearrangements to form the active signal-a furanosyl borate diester and other furans [33, 34]. The AI-2 signal for Salmonella typhimurium was recently solved and found to be, (2R,4S)-2-methyl-2,3,3,4-tetrahydroxytetrahydrofuran, a molecule related to, but structurally distinct from, the V. harveyi AI-2 (Fig. 2) [35]. Spontaneous extracellular chemical rearrangements of S. typhimurium AI-2 can produce the V. harveyi AI-2 signal; the reverse is also true [35]. Current thinking is that the local chemical environment can affect the ratio of the different furanosyl esters. In particular, the presence of borate may shift the equilibrium toward formation of the V. harveyi AI-2 [35].
The chemical structures of the Gram-positive quorum-sensing peptides vary greatly in the number of residues and the types of modification (Fig. 2). Because of the post-translational modifications on these peptides and their inability to diffuse across membranes, their biosynthetic pathways are more complex than those of the AHLs and dedicated signal-export systems are usually required. Many also induce their own biosynthesis, and are thus frequently termed “autoinducing peptides”. Several signals are simple linear peptides, for example the 17-residue competence-stimulating peptide (CSP) which regulates competence in Streptococcus pneumoniae [36]. The corresponding linear competence pheromone in Bacillus subtilis, called ComX, is more unusual in that an internal tryptophan residue has been cyclized and isoprenylated with a geranyl moiety [37], a modification that is catalyzed by ComQ [38].
Perhaps the largest and most complex peptide signals are the lantibiotics, which also have antimicrobial activity [39]. These molecules, for example nisin produced by Lactococcus lactis, are known to positively regulate their own biosynthesis in a density-dependent manner. They also have extensive post-translation modifications, including dehydrated residues, such as dehydroalanine and dehydrobutyrine, and thioether bridges called lanthionines (Fig. 2 depicts the base structure of the lantibiotic, nisin, of L. lactis). An emerging class of peptide signals are the cyclic lactones and thiolactones [40]. The first of this class to be discovered was the type I autoinducing peptide (AIP) of Staphylococcus aureus, an eight-residue thiolactone-containing peptide with the C-terminus constrained through linkage with a cysteine side-chain [41]. Since the discovery of AIPs, related signals have been identified in Enterococcus faecalis [42], Listeria monocytogenes [43], and other Staphylococci [44].
Environmental conditions can have a significant effect on peptide signal stability. The linear peptides are likely to have short half-lives as they are rapidly metabolized by the action of secreted proteases. Indeed, this issue has led some to question their role as a means of communication, raising the possibility they serve as a general strategy for a single cell to time regulatory events [45]. In contrast, the lantibiotics have multiple lanthionine bridges that block the activity of many proteases [46], greatly improving the longevity of these molecules. Like AHLs, lantibiotics are pH-sensitive, with solubility and stability dropping rapidly at higher pH; those with dehydrated residues are also reactive with thiols [47]. Because of these chemical constraints, the lantibiotic nisin has a half-life of only 0.9 h in mouse serum [48], the pH of which is approximately 7.3. For L. lactis in the environment, however, growth and production of nisin are optimum at lower pH, at which stability is greater, facilitating autoinduction. In a similar fashion, the thiolactone-containing peptides are resistant to proteolysis and sensitive to high pH and reactive thiols (chemistry of the S. aureus AIP is discussed in more detail below), and some have increased sensitivity to oxidative damage [49]. Although it remains to be demonstrated, protease resistance may be one of the most significant environmental constraints faced by bacteria using peptide communication signals and extensive post-translational modifications could have evolved to overcome this problem.
The effect of the hydrodynamic environment on quorum sensing
Mass transfer is the physical process by which molecules are transported in a system, and it has the potential to affect cell-to-cell signaling in many ways. Delivery of nutrients to the active biomass in the structured community is one example. This could affect the relative metabolic activity of the community, which in turn has the potential to affect signal-production rates. Mass transfer is affected by the hydrodynamics of the bulk fluid and the geometry of the structured community. These two factors affect each other, because a microbial community both shapes, and is shaped by, its external environment.
If liquid flow is a feature of the environment, it will wash signal away, diluting its concentration within the community. In these circumstances the system can be divided into three zones (Fig. 3). First, the biomass of the community which is producing signaling molecules, second, the viscous sublayer (δ) located at the biomass-bulk fluid interface, and, third, the well mixed turbulent core of the overlying fluid. In laminar flow there is no turbulent core and δ essentially extends to either the center of the channel if flow is in a closed conduit (e.g. pipe or catheter) or to the fluid surface if flow is in an open channel (e.g. river). Using this simplified system we have constructed a one-dimensional model based on a flat biofilm to illustrate how different values of δ may result in different concentration profiles while the thickness of the biofilm, the cell density in the biofilm, and the rate of signal-production in the biofilm remain constant. The model is based on coupling the diffusion of signal through the viscous sublayer with the simultaneous diffusion and production of signal that occurs within the biofilm itself. In the fluid outside the biofilm, the concentration of signal changes linearly:
where Cz is the signal concentration (mg L−1) at depth z, C0 is the signal concentration outside the viscous sublayer (0 mg L−1), k0 is the signal production rate (2.96×10−17 mg cell−1 s−1), Xb is the active cell density in the biofilm (1×1010 CFU cm−3), Lf is the biofilm thickness (50 μm), δ is the thickness of the viscous sublayer (0, 25, or 50 μm), Daq is the diffusion coefficient of the signal in water (4.9×10−6 cm2 s−1), and δz is the proportional depth within δ (μm). Within the biofilm, the concentration of the signal changes quadratically with depth:
where De is the effective diffusion coefficient of signal in the biofilm (1.23×10−6 cm2 s−1) and Lz is the proportional depth within the biofilm (μm).
Fig. 3Schematic diagram illustrating how external flow may affect the concentration of a cell signal produced by biofilm cells. The biofilm is 50 μm thick (so there is no oxygen limitation and we can assume equal signal production throughout the depth of the biofilm) and the relative concentration profiles are for three flow conditions: (a) Very high shear, negligible diffusion boundary layer (δ). (b) Lower flow with a diffusion boundary layer of 25 μm (δB). (c) Lower flow, again, with a diffusion boundary layer of 50 μ (δC). The model is steady-state Fickian diffusion for flat-slab geometry with a uniform density of bacteria producing signals at a constant rate with no degradation. There is no convection within the biofilm and no flux at the substratum. The concentration of signal in the turbulent core of the flow is zero. The effective diffusion coefficient in the biofilm is assumed to be 0.25 times that of the bulk fluid based on the molecular weight of 3-oxo-C12 AHL [50]. For simplification the viscous sublayer is shown as the diffusion boundary layer. In reality the transition (the “buffer region” of the boundary layer) from the viscous sublayer to the turbulent core is not a sharp line, as depicted, but gradual. If we assume an inducing 3-oxo-C12 AHL concentration of 3 mg L−1 (10 μmol), depicted by the vertical dashed line, biofilm “A” is not induced at all, in biofilm “B” the bottom 50% is induced, and in biofilm “C” the bottom 75% is induced, as shown by the horizontal dashed lines where the inducing concentration intersects the concentration profile
Where known we used data as best we could, based on the signal 3-oxo-C12 AHL, unknown model data were hypothetical. All data were based on those used for illustrative purposes by Stewart [50].
These considerations led us to hypothesize that external flow conditions can affect quorum sensing in many ways. For example, in a somewhat closed, static system (e.g. a biofilm in a mud puddle or in the well of a microtitre plate), signals produced by a large, biofilm cell aggregate may induce a quorum-sensing response in neighboring bacteria that are not part of the aggregate. In contrast, in an open system subject to convective flow (e.g. a biofilm in a river, or the channel of a flow cell biofilm reactor), signals might be continuously removed from the system. Under these conditions mass transfer may prevent signals produced by a large biofilm cell aggregate from inducing cells in the vicinity of the aggregate. Experimental results on interspecies communications in dental plaque biofilms by Egland et al. [51] support this idea, and suggest that diffusible signals are adapted to function over short distances. At higher flows the higher flux of signal molecules from structured communities may reduce signal concentration in the interior or core of the community. A subsequent consequence is that more biomass would be necessary to produce an inducing concentration of signal.
Few studies have directly addressed the effect of hydrodynamics on quorum sensing in structured communities. Purevdorj et al. [52] found that at relatively high flow rates flow velocity was a stronger determinant of P. aeruginosa biofilm structure than quorum-sensing-required functions. Yarwood et al. [54] grew wild type (WT) and accessory gene regulator (agr) mutant biofilms of S. aureus by batch culture under static conditions, batch culture on spinning disks, and in flow cells. The QS mutation had the greatest effect under static conditions, resulting in an increase in biofilm formation, consistent with the study of Vuong et al. [55], who hypothesized that agr was a repressor of biofilm formation. Interestingly, there was no difference between WT and agr (quorum sensing) mutant biofilms when grown in flow cells. Care must, however, be taken in correlating growth system with phenotype, because media type and concentration were also varied in this study. Nevertheless, Fux et al. [56] obtained similar results for flow cell-grown biofilms. After 24 h the WT S. aureus biofilms had less biomass than a TRAP mutant but were significantly rougher. After 4 days, however, there was little difference between the biofilms as measured by COMSTAT confocal image-analysis software [57]. In the same study the opposite trend, as measured by viable plate counts, was observed for “filter colony biofilms” [58] grown under static conditions on nutrient agar. The WT biofilms contained an order of magnitude more cells than the TRAP mutant. Interestingly, a regulatory network mathematical model has predicted that at intermediate concentrations of 3-oxo-C12, bacteria can suddenly switch between induced and noninduced states [59]. This effect may help explain experimental observation of different biofilm phenotypes under what are assumed to be similar or even replicate growth conditions.
Quorum sensing in the gas phase with volatile signals
Quorum sensing is usually discussed and studied in the context of aqueous environments. Some environmental conditions, however, for example those found in soil, may not always be ideal for signaling in the liquid phase. Soil is subject to wetting and drying cycles, and even somewhat dry soils are teeming with metabolically active bacteria [60, 61]. Under these conditions it may be advantageous to some bacterial species to engage in quorum sensing. Local cell numbers may be quite high, but not linked to one another through the liquid phase. The production of signals able to act through the gas phase would circumvent these issues. There is precedence for volatile signaling in the microbial world. An interesting example is the signaling molecule, 3-OH palmitic acid methyl ester (3-OH PAME) of Ralstonia solanacearum (Fig. 2) [62]. The signal of this quorum-sensing system can act in the gas phase. This plant pathogen regulates the production of secreted polysaccharides (an important virulence factor) by use of 3-OH PAME [63, 64], the synthesis of which is catalyzed by the phcB gene product [63]. When 3-OH PAME concentrations reach ∼5 nmol L−1, 3-OH PAME-regulated genes are induced, presumably because of interaction of the signal with the membrane-associated PhcS sensor kinase. Although homologs of phcB are not found on many of the microbial genomes yet sequenced, signaling in the gas phase may be one of the next important frontiers in quorum sensing.
A case study of signaling in structured communities-Quorum-sensing in Staphylococcus aureus biofilms
Continuing with the themes of this review, a case study will be presented with a discussion on environmental, chemical, and biological factors discussed above that have the potential to affect quorum-sensing in S. aureus biofilms.
S. aureus is a human commensal that resides in a non-pathogenic state in the nasal airways. When there is a breach in the host defenses enabling access, S. aureus can convert to a pathogenic state and secrete an impressive array of toxins, hemolysins, and degratory enzymes [40], causing damage to host tissues. This lifestyle switch is mediated, in a cell-density-dependent manner, by the action of the AIP molecule. Early in the growth phase surface adhesins and antigens are produced, and when AIP reaches a critical concentration the quorum-sensing cascade is activated and these surface proteins are down-regulated and invasive factors are secreted. Microarray studies have revealed that 104 genes are up-regulated and 34 are down-regulated by the action of this quorum-sensing molecule [65]; this represents almost five percent of the genome. Intriguingly, there are four specific classes of AIP molecule that approximately correlate with the type of disease caused by the producing S. aureus strain. In a fascinating mechanism of bacterial cross-talk these different classes of AIP cross-inhibit quorum-sensing in other S. aureus groups and other staphylococcal species [41]; this may serve to isolate specific sub-populations for cooperative action.
The locus responsible for the quorum-sensing regulation in S. aureus is the accessory gene regulator or Agr locus and is known to contain two divergent transcripts, named RNAII and RNAIII [40]. The RNAII transcript is an operon of four genes, agrBDCA, that encode factors required to synthesize AIP and activate the regulatory cascade (Fig. 4). The biosynthetic pathway leading to functional AIP is not clear, but it is known that AgrD is the peptide precursor of AIP and AgrB is a membrane protease involved in its processing [66]. AgrC and AgrA form a typical two-component regulatory pair, and the binding of AIP to a surface receptor on AgrC activates this phosphoryl-transfer cascade [67]. When phosphorylated, AgrA is known to bind and induce expression of the RNAIII transcript [68], which encodes a regulatory RNA molecule that acts as the primary effector of S. aureus quorum-sensing [69]. AgrA also induces expression of the Agr proteins through the RNAII transcript, triggering the autoinduction phenomenon.
Fig. 4Schematic diagram of quorum-sensing systems of S. aureus. The gene locus for the agr system is shown in black and contains two divergent transcripts, RNAII and RNAIII, driven by the P2 and P3 promoters, respectively. The RNAII transcript encodes the agrBDCA operon, which encodes the signal, processing, and detection components for quorum-sensing in S. aureus (see text). The RNAIII transcript is a regulatory RNA that up-regulates and down-regulates all genes related to quorum-sensing. This transcript also encodes the amphipathic peptide δ-hemolysin. RAP and TRAP are part of a second quorum-sensing system thought to be a precursor of the agr system. RAP is secreted and induces TRAP phosphorylation, which in turn induces expression of the RNAII transcript. RIP is a heptapeptide known to inhibit the ability of RAP to induce TRAP phosphorylation
A second quorum-sensing cascade is thought to serve as a precursor to the Agr system [70], setting the stage for AIP regulation. As S. aureus cells multiply, the RNAIII-activating protein (RAP) is secreted and accumulates outside the cell, and at a threshold concentration, RAP triggers the phosphorylation of the cytoplasmic protein TRAP [71], which induces expression of the RNAII transcript (Fig. 3). Intriguingly, a linear heptapeptide called RIP is known to block the activity of RAP, enabling small-molecule control over S. aureus pathogenicity [72].
Chemistry of the S. aureus AIP signal
The reasons for evolving a cyclic thiolactone structure as a quorum-sensing signal are still not clear. The thioester bond and the presence of other labile amino acids reduce stability, because of the potential for oxidative damage, base-catalyzed hydrolysis, and thioester exchange. Indeed, the methionine residue in AIP is rapidly oxidized in vitro to a methionyl sulfoxide, converting the signal to an inactive byproduct [49]. Similarly, oxidants produced in vivo by the phagocyte NADPH oxidase are known to accelerate this inactivation [73]. Despite these issues, the AIP lifetime in host tissues is reported to be 3 h [74], which is more than adequate for regulation, bearing in mind that S. aureus doubles every 60 min in the host [75].
Structure-function studies have shown that the thiolactone cannot be replaced with a more stabile lactone, suggesting that the signal receptor might require acylation to activate the cascade [76], although a lactam substitution does activate at high concentrations [49]. As discussed above, constraining the peptide will improve metabolic stability to proteases [77], by protecting the C-terminus and impeding access to endoproteases, which probably increases the half-life compared with that of the linear AIP counterpart. Clearly, further study is necessary to determine the physiological benefit of this thiolactone structure.
The relationship between S. aureus quorum-sensing and biofilm communities
Biofilm formation is increasingly being recognized as an important virulence factor in S. aureus pathogenesis. Several biofilm-associated diseases, including osteomyelitis [78], endocarditis [79], medical device infections [80], and potentially even skin infections [81], have much clinical relevance. The formation of S. aureus biofilms progresses in a similar fashion to the Gram-negative biofilms, with attachment followed by development into a highly-structured cell community. Several attachment factors, for example the microbial surface components recognizing adhesive matrix molecules (MSCRAMMs) and the surface-attached Atl protein, have been implicated in this initial stage of biofilm formation (reviewed elsewhere [82, 83]). Secretion of a polysaccharide adhesin is thought to be critical for development of a structured biofilm [84], although some biofilm-forming S. aureus strains cannot produce this polymer [85]. Unlike some well-studied Gram-negative bacterial species, S. aureus are non-motile, which leads to significantly reduced biofilm architecture in flowing systems. The significance of this difference is not clear, because simple alterations of growth conditions for P. aeruginosa are known to eliminate higher-ordered biofilm structure [86].
Because the development of a robust P. aeruginosa biofilm under some conditions requires an active quorum-sensing system [53], one might assume the behavior of S. aureus is similar; the opposite seems to be true, however, because inactivation of the Agr system tends to enhance attachment [87]. Agr is known to down-regulate surface attachment factors, providing an explanation of this observation. The Agr cascade also up-regulates both the secretion of proteases that can degrade these attachment factors and the secretion of amphipathic peptides (phenol-soluble modulins) that facilitate detachment [83, 88], suggesting quorum-sensing may play a role in biofilm turnover. Despite these intriguing observations, deciphering the literature on this topic has been challenging, because of non-uniform biofilm culturing methods and strain-to-strain differences [89]. To emphasize this point, enhanced attachment by Agr mutants in static biofilm systems is not observed in flowing biofilm reactor systems [87, 90].
An elegant study by Yarwood et al. addressed some of the discrepancies in the literature and suggested an alterative role for S. aureus quorum-sensing in biofilms [87]. Under static conditions attachment of Agr mutants was markedly better than that of wild-type; as shear force increased with increased flow rate, however, this advantage was lost and under some experimental conditions became a disadvantage. As in P. aeruginosa biofilm studies, the method and type of experiment seemed to dictate the requirement for the Agr system. By following a quorum-sensing promoter in a flow cell biofilm, Yarwood et al. observed that only patches of surface cells activated the Agr cascade. Surprisingly, these cells detached from the biofilm in periodic waves, suggesting an alternative role for the Agr system in the detachment and recycling phase of biofilm development.
In a biofilm infection of the host it seems probable that S. aureus will be subjected to increased flow and/or shear force, in addition to many other factors, which will affect the quorum-sensing phenomenon. Increased flow will probably dictate the local AIP concentration, potentially deactivating quorum-sensing by washing away the signal, whereas increased shear force could also perform this function, while also accelerating cell detachment. We can only speculate on how this interplay will affect S. aureus in the context of an infection, because studies in this area are limited, but given the medical importance of these biofilms, it warrants further exploration.
Complicating these biofilm studies are the frequent occurrence of quorum-sensing negative isolates in clinical models. In one study, 36% of S. epidermidis isolates from joint prostheses infections were Agr mutants [80]; these mutants can also be isolated from chronic infections or through extended passage in vitro [91, 92]. It has been proposed that the metabolic burden of the Agr cascade leads to the mutations in this locus [40], with parallels to the in vitro conversion of mucoid P. aeruginosa strains to non-mucoid [93]. The frequent occurrence of Agr mutants suggests it may be advantageous for some portion of an S. aureus population to dispense with the quorum-sensing system. By inactivating Agr, a more heterogeneous, robust biofilm is likely to form, with attachment of the mutants improved by the higher level of surface factor expression. Although it remains to be investigated, this heterogeneity could mirror findings for P. aeruginosa biofilms, in which variants arise at a greater frequency than in planktonic cultures [94].
As studies on S. aureus progress, a plausible picture of the role of quorum-sensing in biofilm development is beginning to emerge. As S. aureus attaches and develops an initial biofilm, heterogeneity arising as a result of generation of Agr mutants may lead to a more robust structure with improved adherence properties. Communication between cells will be dictated by the secretion and sensing of AIP molecules, which will enable cross-activation or inhibition of quorum-sensing, depending on the staphylococcal subspecies present in close proximity. When a quorum is reached, a portion of the biofilm will slough off by down-regulation of adherence factors, which could potentially be dictated by the action of secreted proteases or amphipathic peptides. The detached S. aureus cells will have an activated quorum-sensing system that leads to the secretion of numerous invasive factors, enabling spread through the host tissues and the development of an infection. This model is in direct contrast to the well-studied P. aeruginosa paradigm, for which quorum sensing-regulated functions are important for maintaining the structural integrity of the biofilm. Clearly more studies are required to confirm or alter this hypothetical view of the S. aureus lifecycle, but as this model is refined, it may play a significant role in the development of treatment for staphylococcal diseases.

quorum sensing
biofilm
clinical/biomedical analysis
cell systems/single cell analysis
biosensors


Redundancy and evolution of GATA factor requirements in development of the myocardium

The transcription factors, GATA4, 5 and 6, recognize the same DNA sequence and are all expressed in the developing myocardium. However, knockout studies in the mouse have indicated that none of them are absolutely required for the specification of the myocardium. Here we present evidence for redundancy in this family for the first time. Using morpholinos in both Xenopus and zebrafish embryos, we show that GATA4 knockdown, for example, only affects cardiac marker expression in the absence of either GATA5 or GATA6. A similar situation pertains for GATA5 in Xenopus whereas, in zebrafish, GATA5 (faust) plays a major role in driving the myocardial programme. This requirement for GATA5 in zebrafish is for induction of the myocardium, in contrast to the GATA6 requirement in both species, which is for differentiation. This early role for GATA5 in zebrafish correlates with its earlier expression and with an earlier requirement for BMP signalling, suggesting that a mutual maintenance loop for GATA, BMP and Nkx expression is the evolutionarily conserved entity.
Introduction
The GATA factors are zinc finger transcriptional activators that bind to the consensus DNA sequence (A/T)GATA(A/G). They have been identified throughout eukaryotes and been shown to play critical roles in both haematopoiesis and cardiogenesis in vertebrates and Drosophila (Fossett and Schulz, 2001; Nemer and Nemer, 2001). Of the six evolutionarily conserved GATA genes in vertebrates, GATA4, 5 and 6 are expressed in the heart as it develops.
Loss and gain of function studies in P19 embryonal carcinoma cells indicated a requirement for GATA4 in the differentiation of cardiac restricted cells to beating cardiomyocytes (Grepin et al., 1997, 1995). In addition, overexpression of GATA4 in Xenopus embryos and explants resulted in expression of cardiac differentiation markers and in some cases spontaneously beating tissue (Jiang and Evans, 1996; Latinkic et al., 2003). However, in the GATA4 null mouse, normal amounts of myocardial tissue appeared to be formed (Holtzinger and Evans, 2005; Kuo et al., 1997; Molkentin et al., 1997; Narita et al., 1996). Thus, even though cardia bifida and defects in looping morphogenesis were observed in the null mouse embryos, specification of the myocardium appeared to take place normally. A suggested explanation for this was the elevated expression of GATA6 (Holtzinger and Evans, 2005; Kuo et al., 1997; Molkentin et al., 1997; Narita et al., 1996; Pu et al., 2004). Consistent with this proposed redundancy of function within the family, GATA5 and 6 are also active in the P19 cell line and Xenopus explant assays described above. Thus, it appears that each of these three GATA family members possesses the capability of inducing cardiac differentiation in gain-of-function assays, however demonstration that they exhibit such redundancy in vivo awaits combinatorial loss-of-function assays.
The GATA5 null mouse shows no cardiac phenotype, however it may not be a true knockout due to the potential formation of a truncated protein containing the DNA binding domain (Nemer and Nemer, 2002). In the zebrafish, a critical role for GATA5 in specification of the myocardium has been demonstrated by loss and gain of function assays (Reiter et al., 1999). The fausttm236 mutant shows a severe reduction in expression of cardiac markers and injection of GATA5 RNA induces ectopic expression of the same markers. However, GATA4 expression in the zebrafish fausttm23 mutant is significantly reduced and overexpression of GATA5 results in ectopic expression of GATA4. Thus, these studies raise the possibility that the GATA5 knockdown phenotype is due to the combined loss of GATA4 and 5.
The GATA6 null mouse is an embryonic lethal due to an extra-embryonic defect and chimeras have indicated that GATA6 is not required for specification of the myocardium (Kabrun et al., 1997; Koutsourakis et al., 1999; Kuo et al., 1997; Molkentin, 2000; Molkentin et al., 1997; Morrisey et al., 1997). However, we have presented evidence that GATA6 is required for the maintenance and differentiation of cardiac progenitors in zebrafish and Xenopus embryos (Peterkin et al., 2003). The likely resolution of these apparently contradictory data is that the major consequence of lost GATA6 function is non-cell autonomous and can therefore be rescued by surrounding wild type cells in mouse chimeras. The likely non-cell autonomous target for GATA6 is BMP (Peterkin et al., 2003). However, this requirement for GATA6 is for differentiation of cardiac progenitors and not for their initial specification.
In this study we use antisense morpholinos in Xenopus and zebrafish embryos to deplete combinations of GATA4, 5 and 6 for the first time. This has allowed us to provide the first experimental support for redundancy in vivo. In addition, we show that the strong dependence of zebrafish on GATA5 is not mirrored in Xenopus, where this GATA factor plays only a redundant role, like GATA4 in both species. The requirement for GATA5 in the zebrafish is for the induction of the myocardial programme, whereas in Xenopus, GATA activity is only required for differentiation. We propose that the primary function for GATA factors in development of the myocardium is in creating a sub-circuit of the regulatory network, involving another critical transcription factor, Nkx, and a crucial signalling pathway, BMP. This mutually supportive sub-network is evolutionarily stable, even though where the network is initiated appears to be more flexible.
Materials and methods
In situ hybridisation of whole-mounted and sectioned embryos
Xenopus and zebrafish were maintained and embryos were raised and staged using standard conditions (Nieuwkoop and Faber, 1967; Westerfield, 1993). In situ hybridisations on whole-mounted and sectioned embryos were carried out as previously described (Ciau-Uitz et al., 2000; Jowett, 2001; Walmsley et al., 1994). All RNA probes used were labelled with digoxigenin (DIG) except for MyoD and Krox20 which were used in double in situ hybridisations and labelled with fluorescein. Detection of the antibody–alkaline phosphatase was done using BM purple (Roche) or Fast red (Sigma). After in situ hybridisation, embryos were re-fixed in 4% paraformaldehyde, zebrafish embryos were transferred into 75% glycerol to be photographed. Cryostat sections were performed after in situ hybridisation, embryos were fixed as above and washed in 30% sucrose. Embryos were transferred into embedding chambers in O.C.T Compound (Tissue-Tek) and 30 μm sections were cut on a Leica CM3050S.
Morpholino (MO) injection
The GATA4/5/6 antisense morpholinos were designed and manufactured by Genetools. Morpholino sequences: Xenopus GATA4 MO 5′ctggcaactcaatccacaaaatcca3′ (data shown here), a second morpholino designed against the same pseudo-allele as described by Afouda et al. (2005) (data not shown, 5′agctatactctgatacatcctgatc3′), and a third GATA4 MO designed to block both pseudo-alleles (a kind gift from Todd Evans) (data not shown) gave the same results. Zebrafish GATA4 MO 5′gccatcgttacaccttgatacatat3′ or a second splice morpholino as described by Holtzinger and Evans (2005). For Xenopus GATA5 MO 5′gctacaaacctcacagctcc3′ see Afouda et al. (2005). Zebrafish splice GATA5 MO 5′tgttaagatttttacctatactgga3′. For Xenopus and zebrafish GATA6 MOs see Peterkin et al. (2003). MOs were diluted in deionised water and injected as described (Peterkin et al., 2003). For zebrafish, 25 ng GATA4 MO, 25 ng GATA5 MO and/or 5 ng GATA6 MO were injected into single-cell embryos individually and in combination. For Xenopus embryos, a total of 20 ng of GATA4 MO or GATA5 MO and 10 ng of GATA6 MO were injected individually or in combination.
Results
GATA6 is the only essential GATA activity in Xenopus myocardium
We have reported previously that GATA6 is required for the maintenance and maturation of cardiomyocytes in Xenopus (Peterkin et al., 2003). The phenotypes consequential upon depletion of GATA4 or 5 in Xenopus, however, have not been previously reported. In the case of GATA5, the need to know is made greater because of its major contribution in zebrafish, and the inability to determine if this is a general requirement in vertebrates by comparison with the mouse knockout, because the reported mutation in the mouse appears not to be a null (Nemer et al., 1999). Therefore, before examining depletion of combinations of GATA factors, we examined the individual loss of GATA4 and 5 in comparison to the already known GATA6 phenotype.
The design and quality control of MOs against Xenopus GATA4, 5 and 6 have been reported previously (Peterkin et al., 2003; Afouda et al., 2005). For GATA4, as well as the MO reported previously, two other MOs, one against both pseudo-alleles (Todd Evans, personal communication), were tested and gave the same results. For GATA5 and 6, MOs were designed to target both pseudo-alleles of the Xenopus laevis genes. The optimal amount of each MO injected was determined by titration to ensure that the maximum dose without non-specific effects was used. The extent of knockdown by these ATG MOs was determined by co-injection of tagged reporter RNAs followed by Western blotting (Afouda et al., 2005; Peterkin et al., 2003). Very little residual protein was detected in each case.
When MOs against individual GATA factors were injected separately into the presumptive heart field, the dorsolateral marginal zone, of 4-cell Xenopus embryos, cardia bifida was induced in each case (Fig. 1A, visualised by staining the cells for expression of Myosin Light Chain 2 (MLC)). Cardia bifida has been reported previously for the GATA4 knockout mouse (Kuo et al., 1997; Molkentin et al., 1997), the GATA5 mutant zebrafish, faust (Reiter et al., 1999), and GATA6 morphant Xenopus and zebrafish embryos (Peterkin et al., 2003), but this is the first direct comparison in a single species showing that all three GATA factors are required for the timely migration of cardiac precursors to the midline for fusion of the heart tube. This is in contrast to requirements in the differentiation of the myocardium (see below), where only one member of the family is essential. It seems likely that the requirement for GATA4, 5 and 6 in midline migration of myocardial precursors is actually in the underlying endoderm, where they are all expressed and which has been shown to be essential in mouse and zebrafish for heart tube fusion (Afouda et al., 2005; Alexander et al., 1999; Molkentin et al., 1997; Narita et al., 1997; Reiter et al., 1999; Weber et al., 2000).
To determine the effects of the GATA MOs on programming of the myocardial cells, as opposed to their morphological movements, the levels of expression of the transcription factors, Nkx2.5 and Tbx5, and of the contractile machinery genes, cardiac actin (CA), and MLC, were monitored by whole mount in situ hybridisation. In contrast to the GATA6 MO, which causes a profound reduction in the expression of these genes (Peterkin et al., 2003) (Fig. 1D), GATA4 and GATA5 MOs had minimal effects (Figs. 1B, C; for all three MOs and for each marker n was 30–50). Despite the cardia bifida at tailbud stages (stages 28–32) (Fig. 1A), the gross morphology of the hearts at later stages (stage 43) in GATA4 and GATA5 morphants looked similar to those in wild type embryos, i.e. the cardia bifida was only transient (data not shown). In contrast, as previously described (Peterkin et al., 2003), little or no cardiac tissue was observed in embryos depleted of GATA6 (data not shown). Thus, it would appear that, apart from the transient bifida, the loss of GATA4 or GATA5 has little effect on cardiogenesis in Xenopus. To ensure that the GATA4 and 5 morpholinos were properly functional, they were injected vegetally at the single-cell stage, and were shown to reduce the expression of Sox17α during gastrulation (data not shown) (Afouda et al., 2005). Furthermore, the gut of GATA5 morphants failed to coil properly, as previously reported (Afouda et al., 2005). In addition, for these and several of the combination experiments described below, all three GATA4 MOs gave the same results. We therefore conclude that for development of the myocardium in Xenopus embryos, GATA6 is the only essential GATA factor.
GATA factor redundancy in Xenopus myocardium
On the basis of slightly increased expression of GATA6 in GATA4 knockout mice, redundant roles for the GATA factors in the myocardium have been suggested (Kuo et al., 1997; Molkentin et al., 1997; Narita et al., 1996; Watt et al., 2004). In Xenopus, expression of neither GATA5 nor GATA6 was significantly increased in GATA4 MO injected embryos (data not shown). Similarly, in GATA5 and GATA6 MO injected embryos: in neither case was an increase in expression of the other two GATA factors observed (data not shown). However, redundancy does not necessarily depend on an increase in expression of the redundant family member: continued expression could suffice, and that is what we see in all three cases. Therefore to formally test redundancy within the GATA family, we injected combinations of MOs into the presumptive heart field of 4-cell Xenopus embryos, and monitored MLC and Nkx2.5 expression by whole mount in situ hybridisation (Fig. 2). Embryos were classified as unaffected (wild type, +), mildly (−) or strongly (− −) down regulated, or displaying no expression at all (− − −) (Fig. 2A). Numbers of embryos in each category were scored and the results displayed in histograms (n = 31–94) (Figs. 2B, C). The greater effects of the GATA6 MO are immediately apparent, with clear increases in the affected categories at the expense of the wild type category compared to both control embryos and also to GATA4 or GATA5 MO injected embryos.
When combinations of two MOs were injected, evidence for redundancy was revealed (Figs. 2B, C). Despite having little effect on their own, both GATA4 and GATA5 MOs made the phenotype of GATA6 MO injected embryos more severe when injected with it. Furthermore, the phenotype observed when GATA4 and 5 MOs were injected in combination was significantly worse than either alone, suggesting that the minimal phenotype for the single injections relied on the continued presence of the other GATA factor. When all three MOs were injected together, the phenotype was the most extreme of all with the vast majority of embryos having no expression of MLC at all. We therefore conclude that, while GATA6 is the only individually essential player in driving the myocardial programme in Xenopus, the other two GATA factors are responsible for the residual expression of cardiac genes. Furthermore, in the absence of GATA6, their roles are increased. This is evident from their significantly greater effects on embryo phenotypes when combined with GATA6 MO compared to on their own.
GATA activity is required for differentiation but not induction of the myocardium in Xenopus
We have shown previously that GATA6 is required for the maintenance/maturation of the myocardium rather than its induction in both Xenopus and zebrafish embryos (Peterkin et al., 2003). As expected, based on the absence of a late phenotype, embryos injected with GATA4 or GATA5 MOs had no effect on early Nkx2.5 expression, as seen for GATA6 MO (n = 60, 72, and 89, respectively) (Fig. 3A). In order to determine if the lack of an early effect, even for GATA6 which has a strong late phenotype, was the result of redundancy within the GATA family, we examined Nkx2.5 expression at neurula stages in embryos injected with all three MOs (n = 102) (Fig. 3A). Expression was unaffected, as seen with each of the MOs on their own. Although Nkx2.5 is also expressed in the underlying endoderm at this time, we showed by examining sections that the signal in the cardiac mesoderm is unaffected (Fig. 3B, territory delineated by dashed lines). Furthermore, a similar result was obtained for Nkx2.3 (n = 55) (Fig. 3C), which is not expressed or is very weak in the endoderm at this time (Fig. 3D). The expression of eHAND was also unaffected at this stage (Fig. 3E, territory delineated by dashed lines). We therefore conclude that, despite their earlier expression, GATA factors are not required for induction of the myocardial programme in Xenopus, as seen by the continued expression of the other early regulators, Nkx2.5, Nkx2.3 and eHAND, and their own continued expression, but rather for its maintenance/maturation.
GATA4 is not essential for induction or differentiation of zebrafish myocardium
GATA5 (faust) mutant zebrafish have profound defects in the myocardium, displaying reduced expression of several myocardial genes (Reiter et al., 1999). In addition, GATA6 has been shown to be required for maintenance/maturation of the myocardial programme in zebrafish as seen in Xenopus (Peterkin et al., 2003). In order to determine the relative effects of these two GATA factors, and to determine the contribution of GATA4, we separately injected into zebrafish embryos MOs against each of these GATA factors. The GATA4 MO was shown to specifically block translation of a co-injected GATA4 RNA and not GATA5 or GATA6 RNAs (Supplementary Figs. 1A, B, C). The GATA5 MO was designed to block splicing between exons 1 and 2 of the GATA5 gene, which was confirmed in injected embryos by RT–PCR (Supplementary Figs. 1D, E). This splice blocking morpholino was designed upstream of the exons encoding the zinc fingers to prevent any protein produced binding DNA. However, the creation of a dominant negative GATA5 via splicing from an upstream cryptic site is formally possible (see Supplementary Fig. 1D) but the ability of GATA4 and 6 morpholinos to enhance the cardiac phenotype in combinations (see below) makes this unlikely. Furthermore, the GATA5 morphant heart phenotype was indistinguishable from that seen in the faust mutant, both in single and combination experiments (Supplementary Fig. 1F and see below). The GATA6 MO has been reported previously (Peterkin et al., 2003).
The effects of the three MOs injected separately into zebrafish embryos were determined by monitoring expression of the transcription factor, nkx2.5, and the contractile machinery genes, ventricular myosin heavy chain (vmhc) and cardiac myosin light chain 2 (cmlc2) (Fig. 4). GATA5 and 6 MOs induced cardia bifida as described previously for the faust mutant and the GATA6 MO (Peterkin et al., 2003; Reiter et al., 1999). In contrast, in GATA4 MO injected embryos, the myocardial cells appeared to have migrated and fused normally at the midline. We therefore conclude that, in zebrafish, only GATA5 and 6 are required for the proper migration of cardiac precursors. In contrast to mice and Xenopus, GATA4 appears to be uninvolved in this process.
The previously reported effects on myocardial gene expression of GATA5 or GATA6 knockdown (Peterkin et al., 2003; Reiter et al., 1999) were immediately evident in these MO injected embryos (Fig. 4). GATA5 MO injection led to substantially reduced expression of nkx2.5 (19/19), vmhc (42/43) and cmlc2 (40/41), as seen for the faust mutant. GATA6 MO injection also resulted in reduced expression of these markers (6/6, 60/60 and 42/44) but to a lesser extent. In contrast, GATA4 MO injection had little or no effect on cardiac marker gene expression levels (n = 28, 69 and 53). Spatially the expression of the markers in the GATA4 morphants looks altered compared with the controls due to defects in late cardiac morphogenesis, consistent with those described by Holtzinger and Evans (2005). We therefore conclude that for laying down the myocardial programme in zebrafish, GATA5 has the greatest effect with a significant contribution from GATA6. In contrast, GATA4 makes little or no contribution, at least to the expression of the markers tested.
GATA factor redundancy in zebrafish myocardium
To determine if, as in Xenopus, there is redundancy within the GATA family in zebrafish, we injected the MOs in combinations (Fig. 4). Morphant embryos were classified into three types, unaffected (type +), down regulated (type −) or absent (type −−). Both the GATA5 and the GATA6 MO phenotypes were made worse by the co-injection of the GATA4 MO (Figs. 4B, D and F), as seen in Xenopus, and despite the fact that the GATA4 MO had little or no effect when injected on its own (n = 18–39). We therefore conclude that a significant proportion of the residual cardiac gene expression in GATA5 or GATA6 MO injected embryos is driven by GATA4, even though the consequences of its loss in the presence of GATA5 or GATA6 are minimal. Thus, redundancy within the GATA family is apparent in the zebrafish myocardium as in Xenopus.
The level of residual cardiac marker expression in the GATA4 and 5 MO combination or the GATA4 and 6 MO combination at 26 hpf was very low (Fig. 4). The level for the GATA5 and GATA6 MO combination was undetectable with 100% of the embryos losing expression, suggesting that, while GATA4 can cover for the absence of either GATA5 or GATA6, it cannot cover for the absence of both, which seems unlikely. We therefore monitored the expression of GATA4 in flat-mounted (Fig. 5A) MO injected 10-somite embryos to determine if it was still expressed (Fig. 5C). We found that GATA5 MO on its own caused a reduction in GATA4 expression (22/36 embryos), and residual expression was removed completely by the addition of the GATA6 MO (n = 35) (Fig. 5C). The GATA4 expression seen in GATA4 morphants reflects the use of a translation-blocking morpholino rather than a splice-blocker. In the same experiment the expression of nkx2.5 was affected in the same way as already described (Fig. 5B). We therefore conclude that the complete absence of cardiac marker expression in GATA5 plus GATA6 MO injected embryos results from the simultaneous absence of GATA4 expression. Thus, as seen for Xenopus embryos, the absence of all three GATA factors completely abolished cardiac marker expression.
GATA activity is required for induction of the myocardial programme in zebrafish
We have shown that GATA activity is only required for the maintenance/maturation of the myocardial programme in Xenopus. While we have shown that the GATA6 requirement in zebrafish is also late (Peterkin et al., 2003), nkx2.5 expression at 6 somites has been shown to be affected in zebrafish faust mutants (Reiter et al., 1999), suggesting that an additional difference between the species might be the timing of requirements for GATA activity. We therefore tested this earlier requirement with more markers and to determine if it is subject to redundancy. Firstly, we examined nkx2.5 expression in MO injected embryos at 5 somites when it is first expressed (Fig. 6A). For the GATA5 MO, we found a major reduction in expression totally consistent with the reductions seen later and with those reported for the faust mutant (Reiter et al., 1999). We also observed very little effect for the GATA4 or 6 MOs on their own, but both made the GATA5 MO phenotype more severe, consistent with their back-up roles being active at this early stage. Similar observations were made for GATA4 and hrt expression in 5-somite embryos and for tbx5 expression in 10-somite embryos (tbx5 expression is first detected at ∼ 7 somites) (Figs. 6B, C, D). In contrast, nkx2.7 expression was unaffected even by triple knockdown (Fig. 6E). We therefore conclude that, for the markers studied and in contrast to Xenopus, establishing the full early myocardial programme in zebrafish depends on GATA activity. The continued presence of cells expressing nkx2.7 suggested that apoptosis had not yet occurred, and this was confirmed by TUNEL and acridine orange assays (data not shown). Furthermore, re-specification to more anterior or more posterior mesodermal fates was not observed, as judged by the domains of expression of anterior lateral plate and pronephric markers (data not shown). We therefore conclude that in the absence of GATA activity, the cells remain undifferentiated at least up to the 10-somite stage.
Discussion
Redundancy
GATA4, 5 and 6 are an example of a gene family co-expressed in a specific tissue, in this case the myocardium. Although some differences in their binding site preferences have been detected (Sakai et al., 1998), all three bind to canonical GATA sites with high affinity. Because of this and the relatively mild phenotypes generated in loss of function experiments, they have been suggested to act redundantly (Jiang et al., 1998; Kuo et al., 1997; Molkentin et al., 1997; Narita et al., 1996; Watt et al., 2004). Here for the first time we present evidence in support of this with respect to laying down the genetic programme of the myocardium. The redundancy is particularly striking for GATA4, whose individual loss has essentially no effect on induction or maturation of the myocardium in either zebrafish or Xenopus, in contrast to assumptions of its importance in much of the literature. For this member of the family, its contribution is only revealed in the absence of GATA5 or 6, thereby constituting a formal demonstration of redundancy. Similar demonstrations are evident for both Xenopus GATA5 and in early heart induction for zebrafish GATA6, where they are not the essential players. These redundant GATA activities thus most likely account for the residual expression of cardiac markers in the absence of the essential GATA factor. Indeed little change was observed in expression of the remaining GATA factor in double morphant embryos compared to wild type siblings in either zebrafish or Xenopus (data not shown). The one exception was GATA5 and 6 double morphant zebrafish embryos where the complete loss of GATA4 was used to effect a triple knockout (Fig. 5C).
GATA factors are an ancient family and in vertebrates have existed with three family members in the heart at least since fish (Patient and McGhee, 2002). Thus, the redundancy reported here would appear to be evolutionarily very stable. Maynard Smith and colleagues have developed simple genetic models to analyse selection pressures on redundant genes and have concluded that evolutionary stability can be achieved if the two (or more) genes perform the same function, but with slightly different efficacies, as seen here (Nowak et al., 1997). The less efficient family member comes into its own when paired with a mutant form of the more efficient family member. Another evolutionarily stable model can be achieved where two (or more) genes perform more than one function: the redundancy occurring only with respect to one specific function. GATA4, 5 and 6 have an ever-growing list of functions in other tissues, so this scenario is more than adequately satisfied as well (Afouda et al., 2005; Capo-Chichi et al., 2005; Ketola et al., 2004; Molkentin, 2000; Yang et al., 2002). The evolutionary stability of this model depends on random mutations being more likely to render the genes inactive for all functions rather than just for one of their functions. Finally, yet another model suggests that redundancy should be more common in genes displaying specific spatio-temporal expression patterns during development, as is the case for GATA4, 5 and 6. For this model, the developmental error rates applicable to these genes need to be higher than their germ line mutation rates: a requirement that is currently unknown.
The primary GATA factor
An unexpected finding was that the member of the family whose loss has the biggest effect differs between Xenopus and zebrafish. For single knock downs, GATA6 has the strongest effect on myocardial gene expression in Xenopus whereas GATA5 does so in zebrafish. Although at first glance this might suggest a switch in roles for GATA5 and 6, a consideration of the timing of their actions suggests an alternative view. The action of GATA6 in Xenopus is after the initial expression of other early markers such as Nkx2.5, suggesting a role in differentiation of the myocardium (Peterkin et al., 2003). GATA6 knockdown in zebrafish has a very similar effect. Thus, in both organisms, knockdown of GATA6 leaves early marker expression initially intact but decaying with time, whereas when GATA5 was knocked down in zebrafish, expression of Nkx2.5 and other early markers was compromised from the outset (Reiter et al., 1999; this study). The difference between the two organisms therefore can be characterised as the gain or loss of an early function for GATA5. The early role for GATA activity in zebrafish appears not to be masked by redundancy in Xenopus because even triple knockdown of GATA4, 5 and 6 leaves early expression of myocardial markers intact.
The role of GATA5 in myocardial induction in mouse and chick embryos is currently unclear. Although in P19 embryonal carcinoma cells induced to differentiate into cardiomyocytes, GATA5 up-regulation occurs after Nkx2.5, precluding an early function during induction (Alexandrovich et al., 2006), the mouse knockout of GATA5 retained the capacity to synthesise a truncated form of the protein containing both zinc fingers, which would likely have significant activity, preventing a definitive conclusion (Nemer and Nemer, 2002). Likewise, the attempts to date to knock down GATA5 activity in the chick were only partial and, in addition, attempted after induction of the myocardium (Jiang et al., 1998). It is therefore not yet clear if the early role for GATA5 has been acquired by zebrafish or lost by Xenopus. In Drosophila, the GATA factor, pannier, is required both upstream and downstream of the Nkx2.5 homologue, tinman (Gajewski et al., 2001; Klinedinst and Bodmer, 2003). In the nematode, the GATA factors, Med1 and 2, are expressed in the mesendodermal precursor to the mesoderm giving rise to part of the pharynx, an organ that has homologies to the heart, and upstream of the Nkx2.5 homologue, ceh22, suggesting that an early role for GATA factors may be ancestral (Broitman-Maduro et al., 2006; Maduro et al., 2001; Rodaway and Patient, 2001). Mesendodermal expression is seen for both GATA5 and GATA6 in zebrafish, while in Xenopus, mesendodermal expression is seen for GATA4 and 6 (Fletcher et al., in press; Rodaway et al., 1999; J. Broadbent, A. Gibson and R. Patient, unpublished observations). Thus, the early role for GATA5 in zebrafish may reflect its early expression in the lineage of cells leading to the myocardium whereas, in Xenopus, early expression of GATA5 is restricted to the endoderm, appearing in the cardiac mesoderm at a later stage (Weber et al., 2000). That GATA6 is also expressed early in this lineage in both species, and GATA4 likewise in Xenopus, and yet neither plays a role in induction of the myocardium, suggests that GATA5, at least in zebrafish, is alone in containing the requisite amino acid sequence for this function. All three GATA factors recognise the same DNA sequence therefore, in the absence of known binding site preferences for GATA5, activities specific to GATA5 are likely to include protein–protein interactions. Thus, GATA5 may be more suited to the necessary interactions in the early mesendoderm than either GATA4 or 6.
Feedback loops and timing
Genetic regulatory networks (GRNs) consist of functionally linked regulatory genes encoding transcription factors and their controlling extra-cellular signals (Davidson et al., 2002). They contain motifs, or recurring wiring patterns, which occur with frequencies far greater than in randomised networks (Lee et al., 2002; Milo et al., 2002; Shen-Orr et al., 2002). One such motif is a feedback loop. Mathematical modelling of positive feedback loops indicate that they promote the persistence of signals and have the potential to store information, such that, for example, signalling can readily flip the system from one state to another (Bhalla and Iyengar, 1999). The observation in Drosophila that pannier is both upstream and downstream of tinman, raises the possibility that the establishment of a mutual regulatory loop for these two key regulators is critical evolutionarily (Gajewski et al., 2001; Klinedinst and Bodmer, 2003). Evidence for a similar feedback loop exists in mice, where a cardiac GATA gene has been shown to be Nkx dependent and vice versa (Brewer et al., 2005; Davis et al., 2000; Lien et al., 1999; Molkentin et al., 2000). Davidson and Erwin have recently proposed that regulatory motifs of this type are evolutionarily stable components of GRNs, which they called kernels (Davidson and Erwin, 2006). They highlight a heart-field specification kernel, which is conserved from Drosophila to vertebrates. Strikingly the Nkx2.5/tinman GATA/pannier feedback loop is central to this kernel. Our work supports this hypothesis and further suggests that the establishment of this kernel is more critical in evolution than where the loop is initiated. Thus, GATA activity is required to initiate Nkx2.5 expression in zebrafish but not in Xenopus, nevertheless both establish the loop (Fig. 7A).
Interestingly, as seen for GATA activity, the requirement for BMP signalling differs between zebrafish and Xenopus. Thus, in zebrafish, BMP signalling is required to initiate expression of cardiac markers including GATA5 (Reiter et al., 2001), whereas in Xenopus, it is only required for their maintenance (Walters et al., 2001). In view of the links between BMP and GATA factors in several different tissues, including the myocardium, it seems likely that the early requirement for GATA activity in zebrafish is linked to the early requirement for BMP, and likewise the later requirement in Xenopus (Fig. 7B) (Peterkin, 2003). The cascade of events in Drosophila predicts that the Drosophila BMP signal, Decapentaplegic (Dpp), activates tinman, and they then act in concert to initiate the expression of pannier. Subsequently tinman and pannier maintain each other's expression, whilst pannier (in the ectoderm) maintains Dpp expression. Dpp signalling feeds back to maintain expression of tinman and pannier thus completing the loop (Fig. 7C; for review see Sorrentino et al., 2005). Thus, in summary, the data imply that the initiating factor and the direction in which the loop flows are not important. Ultimately it is the establishment of the loop that is essential and failure to do so leads to the loss of differentiated myocardium.

redundancy
evolution
xenopus
zebrafish
bmp
nkx
heart
feedback loops
gene networks


Hypo, hype and ‘hyp’ human proteins

Genes with unknown function are called orphan genes while their transcripts and peptides are called hypothetical proteins. There are many genes and their associated proteins that remain uncharacterized in the human genome. A database of human hypothetical proteins with ascribed functions could be helpful for biologists to search for potential proteins of interest. In recent years, the rapid completion of genome sequences has created essential information to link genes to gene products. In order to better explain functions for un-annotated proteins we designed BioinformaTRICKS (an open source project) and used it to develop a database called HYPO.
Background
As the amount of genome sequence data now available is enormous with more than 750 genomes being either finished or in progress, a biologist is thrown into using
several databases with increasing attention to find any novel genes or proteins or function. However, various analyses based on sequence, structure, function and “Omic”
data have revealed different annotation criteria leading to different sets of predicted genes. However, more than 50% of proteins in the proteome zone remain un-annotated
and un-identified for function (Table 1 in supplementary material). The human genome contains many different regulatory sequences that have roles in controlling gene expression.
The protein-coding sequences is only less than 1.5 percent of the genome and then rest remains as non-coding inter and intra-genic regions with undetermined role.
[1]
The annotation of various human chromosomes is well supported by computational predictions where there is no similarity to known proteins or EST sequences. The genes that
have unknown function called as orphan genes code for proteins annotated as “hypothetical proteins”. Hence, there is a need to begin constructing and analyzing protein families
clustered as “hypothetical proteins” with an aim to elucidate function and protein subunit interactions.
After several drafts of Human Genome Project, there are many proteins that remain to be characterized. Tools have been developed to utilize evolutionary relationships
towards understanding uncharacterized proteins despite the need to generate functional interaction networks. [2,3]
In particular, these approaches are being used to annotate functions for hypothetical proteins. Although several databases explore protein functions through data-mining,
there is a requirement to list all hypothetical proteins. There are reports that address the problem of orphan genes. [4]
However, there is no adequate information to necessitate function of genes that cannot be based on homology alone, except connected to other known gene family.
There are several hypothetical proteins such as the KIAA that have remained hype for some time now. [5] Systems biology
integrated with protein-protein interaction (PPI) studies could identify the role of these unknown proteins (Figure 1a). Systems biology is a science of constructing networks of
genes and proteins thereby providing a framework for predicting models. [6] The proteins connected through the networks
could perhaps throw light on the plausible function of the hypothetical proteins through the bona fide interactions they are involved with.
In the context of PPI networks, we could consider if a model is to be developed from the network or a network is to be generated with an already established model. Precisely,
the putative function of a protein could be better known from a PPI network to develop a model from it. We show here an example in finding the putative function of a hypothetical
protein (figure 1b, NP_438169 - B3BP Homo sapiens Nedd4-binding protein 2) using a PPI network. The nearest interacting partners of the protein B3BP were mapped using the STRING
that could show the probable function of the query [7]. Information on “known” or “unknown” protein-protein interactions is
still mostly limited but integrating tools such as these could generalize a way to find the function of hypothetical proteins.
While we started mining the proteins, it seemed that there are a few hypothetical proteins that have amino acid residues HYP (histidine, tyrosine and proline) in succession.
These might have been long-established through the mutations that are introduced into the proteins at one or more predicted non-essential residues.
While a few KIAA proteins are conserved and have been known to be identified as novel [8], functional analysis of the
proteins encoded by these KIAA cDNAs could be established from our database of hypothetical proteins. [5] A “conservative
amino acid substitution” is the one in which the amino acid residue is replaced with an amino acid residue having a similar side chain. The families of amino acid residues having
similar side chains are distinct and include conserved amino acids such as histidine (aromatic/basic side chains), tyrosine (polar side chain) and proline (non polar side chains).
On the other hand, these might have appeared during annotation through the mutations introduced randomly along all or part of a coding sequence. Our database has over 6362 hypothetical
proteins that could be searched for different functions.
Observations and challenges
We observe in tandem that few hypothetical proteins present on different chromosomal loci are known to have the same putative function. Categorizing several approaches beyond
traditional sequence similarity that utilize tremendously large amounts of data that is available for computational prediction of functions is the need of the hour these days.
Having said this, one could use a subset of proteins that match from several of the experimental approaches and be used as a predictor to circumvent the use of wet laboratory
experiments in the near future.
Another concern now is specifically on hypothetical protein domains in intrinsically disordered proteins (IDPs). [7] With
Protein Data Bank (PDB), not accepting theoretical structures, there is an emphasis for experimental approach by researchers to determine the structure and functional relationship
of a protein. Nevertheless, carefully considering the functional annotation methods as discussed above could definitely devise selection for proteins that could be experimented.
This could be an interesting approach to pursue further.
Yet another issue to be noted is the appraisal to understand if any of the hypothetical proteins have proper functional annotations’ been attributed to sequence: structure:
function relationship in case of ordered proteins while sequence: un-structure: function in case of intrinsically disordered proteins. In conclusion, the current methods could
play an important role in establishing functions for proteins annotated as hypothetical in the genome.
Note
The title of the article contains hypo abbreviated for hypothetical proteins.
Supplementary material
Data 1

hypothetical proteins
database
networks
mining


No evidence for circulating HuD-specific CD8+ T cells in patients with paraneoplastic neurological syndromes and Hu antibodies

Aim In paraneoplastic neurological syndromes (PNS) associated with small cell lung cancer (SCLC) and Hu antibodies (Hu-PNS), Hu antigens expressed by the tumour hypothetically trigger an immune response that also reacts with Hu antigens in the nervous system, resulting in tumour suppression and neuronal damage. To gain more insight into the hypothesized CD8+ T cell-mediated immune pathogenesis of these syndromes, we searched for circulating HuD-specific CD8+ T cells in a large cohort of Hu-PNS patients and controls.
Introduction
Paraneoplastic neurological syndromes (PNS) are considered as naturally occurring, successful anti-tumour immune responses in humans [6]. However, this tumour immunity goes along with autoaggression against the nervous system, resulting in severe neurological dysfunction [9]. The mechanisms responsible for the anti-tumour response and neuronal damage are poorly understood. Antigens expressed by the tumour that are normally restricted to neurons (so-called onconeuronal antigens) hypothetically trigger an immune response that cross-reacts with the same antigens in the nervous system [7]. One of the most frequently involved tumours is small cell lung cancer (SCLC) and approximately 50% of patients with PNS and SCLC have high-titre Hu antibodies (Hu-PNS). Hu antibodies are directed against a family of neuron specific, mRNA binding proteins, of which HuD is the best-documented member. Consistent expression of HuD in all SCLC suggests that HuD plays a central role in triggering the immune response [15, 17].
High titres of Hu antibodies in serum and cerebrospinal fluid suggested a pathogenic role for these antibodies, that could, however, never be proven in animal models [3, 22]. Furthermore, expression of Hu antigens is exclusively intracellular, and it is therefore difficult to understand how such antibodies could target tumours or neurons [5]. In addition, pathological examination of PNS neuronal tissue demonstrated localized inflammatory cell infiltrates, containing B cells, CD4+ and CD8+ T cells, in the proximity of overt neuronal cell damage [2, 13, 18, 25]. The presence of an oligoclonal CD8+ T cell infiltrate in nervous tissues and tumours of Hu-PNS patients further suggests an immunopathogenic role for such cells [18, 25].
Some authors report the presence of HuD peptide-specific CD8+ T cells in the blood of these patients, but also in that of apparently healthy controls (HC) [19, 20, 24]. In addition, the presence of circulating HuD-specific CD4+ T cells has been suggested [1].
Here, we have investigated the presence of circulating HuD-specific T cells in a large cohort of Hu-PNS patients and controls. Despite a multifaceted approach mainly geared towards the detection of HuD-reactive CD8+ T cells and, to a somewhat lesser extent, CD4+ T cells, no such cells were detected in the blood of Hu-PNS patients and controls.
Materials and methods
Patients
Forty-three patients with high-titred Hu antibodies and a definite clinical diagnosis of PNS [12], 31 Hu antibody negative SCLC patients without neurological symptoms or signs (SCLC) and 54 apparently HC were tested. The Erasmus MC Institutional Review Board approved the study and all individuals provided written informed consent. The individuals’ class I HLA alleles were typed by standard diagnostic PCR at the two-digit resolution level. Patient characteristics are shown in the Table 1. Anti-Hu IgG titres were determined as described previously [21] and in Hu-PNS the dependence in activities of daily living was scored using the modified Rankin scale [21]. Twenty-five HC were male and 29 female, their median age was 46 years (range 17–89) and all were Hu antibody negative. No HC had received previous chemotherapy or immunosuppressive treatment and 30 HC (56%) were CMV-seropositive.
Table 1Patient characteristics at the time of study entryHu-PNS SCLC N4331Age (median, range)64 (4–81)a61 (40–83)Gender (M/F)15/2821/10Hu-Ab titre (median, range)12,800 (400–204,800)NegativeCMV serostatus (pos/neg)29/1417/14Paraneoplastic neurological syndromeNA PSN27 PEM5 PCD4 PLE/ BE3 Pseudo-obstruction2 Motor neuron disease2Tumour No tumour8b0 SCLC3131  Limited2817  Extended314 NSCLC20 Prostate10 Neuroblastoma10Prior treatment None2727 Chemotherapy ± immunosuppression164Neurological symptomsNA Interval onset symptoms (study entry)5 months (2–15)c Interval onset symptoms (diagnosis)4 months (1–12)c Progressive at study entryd34 (79%)Modified Rankin scoreNA MRS = 27 MRS = 322 MRS = 410 MRS = 54Hu-Ab Hu antibody, CMV cytomegalovirus, pos positive, neg negative, NA not applicable, PSN paraneoplastic sensory neuronopathy, PEM paraneoplastic encephalomyelitis, PCD paraneoplastic cerebellar degeneration, PLE paraneoplastic limbic encephalitis, BE brainstem encephalitis, SCLC small cell lung cancer, NSCLC non-small cell lung cancer, MRS modified Rankin scorea One Hu-PNS patient was a 4-year-old boy with an underlying neuroblastoma. The remaining Hu-PNS patients were aged between 49 and 81 yearsb No tumour mass visible on CT-scan or FDG-PET scanc Median (ranges) of intervals are shownd Progression of neurological symptoms was defined by the increase of at least one point on the modified Rankin scale during 2 months prior to study entry
Reagents
Ninety-three HuD protein-spanning synthetic peptides, 15-mers with 11 amino acids overlap, were pooled to constitute the HuD peptide mix (HuDmix) and smaller peptide pools [Jerini Peptide Technologies (JPT), Berlin, Germany]. For interferon-γ enzyme-linked immunosorbent spot-forming (IFN-γ ELISPOT) assays and the construction of HLA class-I multimers, HuD-derived 9- and 10-mers were selected based on previous studies [19, 20]. The phycoerythrin (PE)-labeled multimers and corresponding peptides used were: HLA-A*0101-147ELEQLFSQY155, HLA-A*0101-245RLDNLLNMAY254, HLA-A*0201-86SLGYGFVNYI95, HLA-A*0201-248NLLNMAYGV256, HLA-A*0201-315QLFGPFGAV323, HLA-A*0201-362RLGDRVLQV370, and HLA-A*2402-154QYGRIITSRI163 (ProImmune, Oxford, UK). As positive and negative controls, HLA-A*0201-495NLVPMVATV503 [CMV phosphoprotein-65 (CMV-pp65); Beckman Coulter, San Diego, CA] and HLA-A*0201 presenting an irrelevant peptide (ProImmune), respectively, were included.
To measure general T-cell responsiveness, we used phorbol myristate acetate (PMA) plus ionomycin, or phytohemagglutinin (PHA). A peptide pool containing 15-mers spanning CMV-pp65 (JPT) was used as positive- and negative antigen-specific control in CMV seropositive and seronegative individuals, respectively.
Cytokine flow cytometry
Peripheral blood mononuclear cells (PBMC) were isolated within 12 h after venipuncture and stimulated in duplicate as described elsewhere [14]. Briefly, 2 × 106 PBMC were incubated at 37°C in a CO2 incubator for 18 h with 1 μg/ml HuDmix, 1 μg/ml CMV-pp65, 1 μg/ml ionomycin plus 25 ng/ml PMA, or without antigen. After 2 h of stimulation, brefeldin A was added to one of the duplicate tubes allowing for intracellular accumulation of cytokines in activated T cells. Brefeldin A was not added to the second tube to allow detection of secreted cytokines in the supernatant. In some individuals, additional stimulation was performed in the presence of co-stimulatory monoclonal antibodies (mAb) directed against CD28 and CD49d (BD Biosciences, San Jose, CA). Stimulated PBMC were stained and analysed using anti-CD3 conjugated with peridinyl chlorophyllin (PerCP), anti-CD8 conjugated with allophycocyanin (APC), anti-interferon (IFN)-γ conjugated with fluorescein isothiocyanate (FITC), anti-tumour necrosis factor (TNF)-α conjugated with PE, or appropriate isotype control mAb (all from BD Biosciences) [14]. CD4+ T cells were defined as CD3+, 8−. Positive responses were defined by (1) percentage of cytokine-positive CD4+ or CD8+ T cells >2 times the negative control (i.e., no antigen) and (2) ≥0.1% of the total number of CD4+ or CD8+ T cells, each after subtraction of isotype control results.
Detection of secreted cytokines
The secretion levels of IFN-γ, TNF-α, interleukin (IL)-2, IL-4, IL-5 and IL-10 were measured in supernatants using a cytometric bead array (BD Biosciences). Based on CMV data (not shown), a positive result was defined as cytokine concentration >2 times the background (no antigen) and a minimum level of 50 pg/ml.
IFN-γ ELISPOT
PBMC (2 × 105/well) were pre-stimulated in duplicate in 96-well plates with culture medium containing 3 μg/ml HuD 9-mers, HuDmix, CMV-pp65, PHA, or no antigen for 1.5 h at 37°C and 5% CO2 [10]. The PBMC were subsequently transferred to anti-IFN-γ-coated ELISPOT plates (Nalge Nunc, Rochester, NY) for a further 18-h incubation. The ELISPOT assay was performed using standard protocols and automated reading (AELVIS GmbH, Hanover, Germany) [10]. The mean number of spot-forming cells (SFC) in duplicate wells was used as assay outcome. Positive results were defined by numbers of SFC/well >3 times background (no antigen) and a minimum of 15 SFC/well.
Detection of HuD-specific CD8+ T cells using Class-I HLA multimers
Thawed PBMC were stained as described previously [11]. Following acquisition of 1 × 105 viable (i.e., 7-amino-actinomycin-D [7AAD] negative) CD8+ T cells, a positive result required a percentage of ≥0.1% of viable CD8+ T cells binding the HuD multimer and a brightly staining HuD multimer-binding CD8+ T-cell population that did not overlap with the dimly staining irrelevant multimer-binding T-cell population.
Results
Cytokine flow cytometry of PBMC after stimulation with HuD-derived peptides
Stimulation with PMA and ionomycin induced IFN-γ production in both CD4+ and CD8+ T cells of all individuals (not shown). Whilst all CMV-seropositive individuals specifically responded to CMV-pp65, no HuD-specific T-cell reactivity was observed in any of the Hu-PNS patients, SCLC patients or HC (Fig. 1a, b). Similar results were obtained when intracellular TNF-α (not shown) or secreted cytokines (Fig. 1c) were measured. The use of co-stimulatory antibodies in combination with HuDmix did not result in the detection of HuD-specific T-cell reactivity (not shown).
Fig. 1Cytokine production in response to HuDmix and HLA-matched HuD 9-mers. Proportions of CD8+ (panel a) and CD4+ (panel b) T cells expressing intracellular IFN-γ after stimulation with HuDmix in Hu-PNS patients, SCLC and healthy controls. Each dot represents the result observed in a single individual (panel c). After stimulation of PBMC with HuDmix and control antigens, the indicated cytokines were measured in assay supernatants. The results are shown for Hu-PNS patients only. Horizontal lines indicate median values of each group (panel d). ELISPOT assay showing the number of IFN-γ SFC after stimulation of 2 × 105 PBMC with HuDmix or HuD 9-mers. Each dot represents the mean result of duplicates for each stimulus in each individual (panels a–d). Responses to CMV antigens are shown for CMV seropositive individuals only, as they were consistently negative in CMV seronegative individuals (not shown). The numbers of individuals tested are given in between brackets. NEG negative control (incubation without antigen), HuDmix HuD protein-spanning peptide pool, PP65 CMV pp-65 protein-spanning peptide pool, IL interleukin TNF tumour necrosis factor, IFN interferon, SFC spot-forming cell, PHA phytohemagglutinin. ELE, RLD, SLG, NLL, QLF, RLG and QYG designate individual HuD-based peptides
IFNγ-ELISPOT assay on PBMC stimulated with HuD-derived 9-mers
All individuals responded to PHA as determined by IFNγ-ELISPOT assay. In addition, all CMV-seropositive individuals responded to CMV-pp65. However, no T cell reactivity towards the previously described class-I HLA-binding HuD peptides [20] was detected in individuals with the appropriate HLA types in any of the study groups (Fig. 1d).
Analysis of HuD-specific CD8+ T cells using Class-I HLA multimers
Finally, we investigated the presence of HuD-specific CD8+ T cells in PBMC using Class-I HLA multimers containing HuD-derived peptides. Whilst CMV-seropositive individuals with the appropriate HLA types showed distinct populations of pp65 multimer-binding CD8+ T cells, no HuD-specific CD8+ T cells were observed in individuals with the appropriate HLA types in any of the study groups (Fig. 2).
Fig. 2Analysis of HuD peptide-loaded, Class-I HLA multimer-binding CD8+ T cells. PBMC from a CMV-seropositive Hu-PNS patient were stained with HLA-A*0201 multimers loaded with the HuD peptide SLGYGFVNYI (panel a), an irrelevant peptide (panel b) or with the CMV-pp65 peptide NLVPMVATV (panel c). All data shown are obtained after selection of T cells (i.e., CD3+, low sideward scatter signals) [12]. Binding of Class-I HLA multimers (panels a–c, horizontal axes) was analysed in relation to CD8 expression. In this example, the proportion of SLGYGFVNYI multimer-binding CD8+ T cells (0.36%) was similar to that of irrelevant multimer-binding CD8+ T cells (0.46%); binding resulted in low-intensity fluorescence signals only. In contrast, 2.46% of CD8+ T cells bound the NLVPMVATV multimer resulting in high-intensity fluorescence signals from most CD8+ T cells
Discussion
We set out to detect HuD-specific T cells in the blood of Hu-PNS patients as they are postulated to play a pivotal role in the immunopathology of this disease. Although we applied three different approaches we could not detect circulating HuD-specific T cells.
First, to induce cytokine responses in T cells, we used 15-mer protein-spanning peptide pools that have the advantage of covering the full protein sequence and of eliciting both CD8+ and CD4+ T-cell responses [14], as demonstrated by the CD8+ and CD4+ CMV-pp65-specific T-cell responses. However, no CD8+ or CD4+ HuD-specific T-cell responses were observed. These results are at variance with the detection of HuD-specific CD4+ T-cell proliferative responses in PBMC of Hu-PNS patients by Benyahia et al. [1]. This discrepancy may be explained by differences in read-out (i.e., 3-day lymphocyte proliferation in Benyahia’s study [1] vs. overnight cytokine production in ours) and the use of recombinant HuD protein [1] versus a protein-spanning 15-mer peptide pool (this manuscript).
We then studied responses to HLA class-I binding HuD peptides that were previously selected [20]. Using the same experimental setup and HuD 9-mer peptides, Rousseau et al. [20] reported HuD-specific T-cell reactivity in 7/10 Hu-PNS patients and in 3/10 HC [20]. In that small study a positive response was defined as an experimental value ≥2 times above background. With that criterion, 3 PNS, 2 SCLC and 4 HC would have been classified as HuD T-cell responders in our study. However, using that cut-off we would also have detected T-cell reactivity in individuals whose Class-I HLA molecules did not have the appropriate binding motifs (data not shown). Therefore, we used more stringent cut-off levels resulting in a negative outcome. As most patients in both studies had progressive neurological disease and were tested shortly after start of symptoms and prior to therapy, differences between the study populations do not explain this discrepancy.
Finally, we could not detect HuD-specific circulating CD8+ T cells using Class-I HLA multimers with the same fine specificities as defined by Rousseau et al. [20].
The absence of detectable circulating HuD-specific CD8+ T cells may not be surprising. In a PCR-based study, Plonquet et al. [18] detected the same T-cell clone in neoplastic and nervous tissues, but not in blood. This finding suggests that T cells involved in the pathogenesis of Hu-PNS circulate in concentrations below detection level. An immune response taking place in the central nervous system parenchyma may deplete the circulating pool of CD8+ T cells with that specificity [23]. Furthermore, vaccination studies in melanoma patients demonstrate that clinically effective anti-tumour immune responses may occur despite low levels of melanoma-specific cytotoxic T cells, i.e., below the detection limit of multimer-based assays [4, 16].
In conclusion, we were unable to detect HuD-specific T cells in a large cohort of Hu-PNS patients and controls. However, two of our three assays were designed for the detection of CD8+ T cells only. The IgG1 isotype predominance of serum Hu antibodies in Hu-PNS indicates a T-helper response to the Hu antigen [13]. Therefore, further studies are warranted that focus on the detection of circulating HuD-specific CD4+ T cells. In this context, regulatory CD4+ T cells—which down regulate immune responses towards auto-antigens and tumour-antigens—are of interest. Although the numbers of regulatory T cells are increased in cancer patients [26] and in PNS patients [8] (de Beukelaar et al. unpublished data) their (possibly impaired) function in PNS remains to be studied. Finally, examination of the antigen-specificity of T cells in affected tissues may shed further light on the role of HuD-specific T cells in the pathogenesis of Hu-PNS.

hud
paraneoplastic
sclc
anti-tumour immunity
t lymphocyte
hla class i multimer