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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081302ijms-17-01302ArticleAn Amphiprotic Novel Chitosanase from Bacillus mycoides and Its Application in the Production of Chitooligomers with Their Antioxidant and Anti-Inflammatory Evaluation Liang Tzu-Wen 12Chen Wei-Ting 2Lin Zhi-Hu 3Kuo Yao-Haur 3Nguyen Anh Dzung 4Pan Po-Shen 2Wang San-Lang 12*Sashiwa Hitoshi Academic Editor1 Life Science Development Center, Tamkang University, New Taipei City 25137, Taiwan; ltw27@ms55.hinet.net2 Department of Chemistry, Tamkang University, New Taipei City 25137, Taiwan; gash3520@gmail.com (W.-T.C.); popan@mail.tku.edu.tw (P.-S.P.)3 Division of Chinese Materia Medica Development, National Research Institute of Chinese Medicine, Taipei 11221, Taiwan; tiger77749@gmail.com (Z.-H.L.); kuoyh@nricm.edu.tw (Y.-H.K.)4 Institute of Biotechnology and Environment, Tay Nguyen University, Buon Ma Thuot 630000, Vietnam; nadzungtaynguyenuni@yahoo.com.vn* Correspondence: sabulo@mail.tku.edu.tw; Tel.: +886-2-2621-5656; Fax: +886-2-2620-992410 8 2016 8 2016 17 8 130228 6 2016 05 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).The objectives of this investigation were to produce a novel chitosanase for application in industries and waste treatment. The transformation of chitinous biowaste into valuable bioactive chitooligomers (COS) is one of the most exciting applications of chitosanase. An amphiprotic novel chitosanase from Bacillus mycoides TKU038 using squid pen powder (SPP)-containing medium was retrieved from a Taiwan soil sample, which was purified by column chromatography, and characterized by biochemical protocol. Extracellular chitosanase (CS038) was purified to 130-fold with a 35% yield, and its molecular mass was roughly 48 kDa. CS038 was stable over a wide range of pH values (4–10) at 50 °C and exhibited an optimal temperature of 50 °C. Interestingly, the optimum pH values were estimated as 6 and 10, whereas CS038 exhibited chitosan-degrading activity (100% and 94%, respectively). CS038 had Km and Vmax values of 0.098 mg/mL and 1.336 U/min, separately, using different concentrations of water-soluble chitosan. A combination of the high performance liquid chromatography (HPLC) and matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometer data revealed that the chitosan oligosaccharides obtained from the hydrolysis of chitosan by CS038 comprise oligomers with multiple degrees of polymerization (DP), varying from 3–9, as well as CS038 in an endolytic fashion. The TKU038 culture supernatant and COS mixture exhibited 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging activities. The COS activities were dose dependent and correlated to their DP. The COS with high DP exhibited enhanced DPPH radical scavenging capability compared with COS with low DP. Furthermore, the COS exhibited inhibitory behavior on nitric oxide (NO) production in murine RAW 264.7 macrophage cells, which was induced by Escherichia coli O111 lipopolysaccharide (LPS). The COS with low DP possesses a more potent anti-inflammatory capability to decrease NO production (IC50, 76.27 ± 1.49 µg/mL) than that of COS with high DP (IC50, 82.65 ± 1.18 µg/mL). Given its effectiveness in production and purification, acidophilic and alkalophilic properties, stability over ranges of pH values, ability to generate COS, antioxidant activity, and anti-inflammatory, CS038 has potential applications in SPP waste treatment and industries for COS production as a medical prebiotic. chitosanaseamphiproticsquid penBacillus mycoideschitooligomersantioxidant ==== Body 1. Introduction Chitin is one of the most abundant carbohydrate polymers in nature, and chitin can produce chitosan by full or partial deacetylation. Each year, nearly 80,000 metric tons of chitin are generated from marine wastes, including shrimps, crabs, and squids [1]. Chitooligomers (COS) are degraded compounds of chitosan. Chitosan with an average molecular weight of less than 3900 Da or degrees of polymerization of less than 20 are so-called COS [2]. Several studies have previously attracted interest in converting chitosan into COSs due to high water-solubility, low viscosity, and excellent biological properties of COSs [3,4,5,6,7]. In order to acquire these depolymerized molecules, two primary strategies have been developed: chemical and enzymatic. Enzymatic depolymerizing of chitosan is very useful and a more environmentally friendly process for producing COS with various degrees of polymerization (DP). Thus, acquiring an efficient protocol for chitosanase production and the transformation of chitosan into bioactive COS would be vastly desirable for efficiently generation of these oligomeric chitosans. Chitosanases have been discovered in richness in diversity of bacteria, including Bacillus sp. [8,9,10,11], Serratia sp. [12], Janthinobacterium sp. [13], Paenibacillus sp. [14], Pseudomonas sp. [15], Acinetobacter sp. [16] and Streptomyces sp. [17]. However, most chitosanases have optimum pH values of approximately 5–6 and weak acidic conditions. In addition, most chitosanases are unstable under acidic or alkaline condition, thus limiting their application, bioconversion, and utilization. Therefore, screening of new chitosanases that are stable under acidic or alkaline conditions similar to those of soil and marine environments is required for extending the application and utilization of chitosanase in industries and for waste treatment. In the effort to screen chitosanolytic enzymes that are suited for transforming chitosan into large size-oligomeric chitosans, a novel bacterial strain with chitosan degrading capability was obtained. A Bacillus mycoides strain, TKU038, which was able to utilize squid pen powder (SPP) to generate chitosanase with a satisfactory yield was identified from soil samples. The biochemical features of this chitosanase were fully illustrated after it was purified. The chitosanase was active over ranges of pH values and possessed increased catalytic activity under weak acidic and alkaline conditions compared with previously isolated chitosanases. Furthermore, the applications of the endo-type TKU038 chitosanase in functional chitooligomer generation were also studied. Subsequently, we investigated the antioxidant activity of COS against 2,2-diphenyl-1-picrylhydrazyl (DPPH). The effect of DP on DPPH radical scavenging activity was discussed to identify the optimal DP range with this method. The inhibitory profiles of all COSs on the generation of nitric oxide (NO) stimulated by lipopolysaccharide (LPS) in RAW 264.7 macrophage cells was also evaluated. 2. Results and Discussion 2.1. Screening and Identification of a Chitosanase-Producing Strain Over 200 bacterial strains gathered from a selection of cities in Taiwan were cultivated in SPP medium at 37 °C and 150 rpm for three days. Among them, strain TKU038 exhibited strong chitosan degrading capability and was chosen for more in-depth inspection. Based on morphological and biochemical studies, and 16S rDNA sequences [18], the strain was confirmed as Bacillus sp. Based on the Analytical Profile Index (API) identification [18], strain TKU038 was the closest to B. mycoides with 88.5% similarity. Hence, the isolate was identified as B. mycoides. 2.2. Production and Purification of Chitosanase Fifty milliliters of basal medium (0.1% K2HPO4 and 0.05% MgSO4·7H2O, pH 7) containing 0.5% SPP was the most suitable medium for the production of chitosanase by strain TKU038 at 25 °C. The highest chitosanase activity of B. mycoides TKU038 was detected in the culture on the fourth day of bacterial growth. The culture supernatant exerted strong chitosan degrading activities. The results suggested that the chitosanase from B. mycoides TKU038 may be secreted extracellularly. Extracellular chitosanase was purified from the cell free culture filtrate of B. mycoides TKU038 using a series of purification procedures. A summary of the CS038 purification is illustrated in Table 1. CS038 was purified to 130-fold with a recovery yield of 35% and a specific activity of 20.82 U/mg. The molecular mass of CS038 was approximately 48 kDa as confirmed by sodium dodesyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE) (Figure 1), which agreed with the gel-filtration chromatography results. Its molecular mass was similar to chitosanase from B. cereus [8,18,19,20,21], as shown in Table 2. Chitosanases from various microbes have been discovered, including bacteria, actinomyces, and fungi, especially Bacillus species [8,18,19,20,21,22,23,24,25,26,27,28]. Bacteria produce chitosanase more easily and rapid than fungi in large-scale fermentation systems. However, regarding chitosanase from Bacillus species, no study has reported on chitosanase produced by B. mycoides. This is the first report of the production of chitosanase from B. mycoides. 2.3. Identification of CS038 by LC-MS/MS Analysis To identify the CS038 that appeared as a prominent 48-kDa band via SDS-PAGE, the band was excised and analyzed after tryptic digestion. The SDS-PAGE gel band was subjected to electrospray tandem mass spectrometry analysis. The fragment spectra were subjected to a NCBI non-redundant protein database search. As shown in Table 3, the spectra of CS038 matched eight tryptic peptides that were identical to the chitosanase from B. cereus (GenBank accession number gi446936339) with 54% sequence coverage, and the other remaining peptides were unmatched. The peptide sequences indicate that CS038 belongs to the family 8 glycosyl hydrosylase based on the amino acid sequence similarity of the cited GH-8 enzymes from B. cereus. 2.4. Effect of pH and Temperature on the Activity and Stability of CS038 Enzyme activity and stability were markedly affected by pH and temperature. The effect of pH and temperature on CS038 was investigated and is presented in Figure 2. CS038 was active over a wide range of pH values. Comparing to previously isolated chitosanases (Table 2), it possesses higher catalytic activity either under weak acidic (pH 6) or alkaline (pH 10) conditions. Similar results of dual optimum pH were also found in those of Bacillus cereus TKU030 chitosanase (pH 4 and 7) [31] and Mycobacter AL-1 chitosanase (pH 5.0 and 6.8) [32]. CS038 was stable over a broad range of pH values from 4–10, as shown in Figure 2a. Further, the stability over a broad range of pH values may be due to the reversible denaturation of the protein such that there is no effect on the activity of the enzyme at different pH values. Furthermore, CS038 was found to be more stable in acidic and alkaline media than some of the chitosanases shown in Table 2. For the effect of temperature on activity, CS038 was active over the range of 37 to 60 °C and was the most active at 50 °C (Figure 2b). The effect of temperature on stability was investigated by measuring residual activity after pre-incubating the enzyme at different temperatures for 60 min. Greater than 65% of the initial activity was retained after incubation at 25, 30, 37, 40, and 50 °C (Figure 2b). Approximately 40% of the residual activity could be detected after incubation at 60 °C, but the enzyme was completely inactivated at 70 °C (Figure 2b). The optimal temperature and stability of CS038 was similar to those of the chitosanase from B. cereus TKU031 [19], B. cereus TKU033 [20], and B. cereus TKU034 [21], as shown in Table 2. Many industrial processes are performed at extreme pH values (either acidic or alkaline) and elevated temperatures; thus, the enzyme must suit the process requirements. In addition, higher temperatures (50–60 °C) increase the solubility of polymeric substrates, such as carbohydrates, thereby improving their mechanical handling characteristics and rendering them more amenable to enzymatic attack. Given its acidophilic and alkalophilic nature, tolerance to a broad range of pH values, high optimum temperature, and stability, CS038 is a novel chitosanase compared with those previously reported in Bacillus sp. 2.5. Substrate Specificity and Kinetic Parameters For the substrate specificity of purified CS038, chitin and chitosan with DD ranging from 60% to 98% were used as substrates (table not shown). The highest activity was observed in the presence of water-soluble chitosan; however, some detectable activity was observed against other substrates. However, these activities are not considered to be significant compared with chitosanase activity. CS038 showed no activity towards colloidal chitin, shrimp shells, shrimp heads and chitosan with 60% DD, but decomposed 73% DD chitosan at 34% of the activity of water-soluble chitosan. The kinetic constants (Km) and (Vmax) of CS038 were determined to be 0.098 mg/mL and 1.336 U/min mg, respectively, using a Lineweaver–Burk plot with different concentrations of water-soluble chitosan (0.005%–0.15% (w/v)). The Km value was lower than that of the other chitosanases, such as 0.63 mg/mL from B. criculans MH-K1 [28] and 2.1 mg/mL from Streptomyces griseus [29], suggesting that the affinity for the substrate of CS038 obtained in this study was better than that of chitosanases from other microorganisms. 2.6. Effects of Metal Ions The influence of metal ions on the activities of CS038 was studied, as shown in Table 4. The activity was inhibited by 5 mM of Cu2+, Ba2+, Zn2+, Fe2+, and Mn2+. As a chelator in the reaction mixture, ethylendiaminetetraacetic acid (EDTA) also decreased enzyme activity (Table 4) to levels similar to those of the chitosanases from B. cereus TKU031 [19] and B. cereus TKU034 [21]. Cu ions catalyse the auto-oxidation of cysteines to form intra molecular disulphide bridges or sulphenic acid [31]. Interestingly, the activity of CS038 was nearly unaffected by Na+, Mg2+, and Ca2+, which is similar to the chitosanase from B. cereus TKU030 [31]. However, unlike B. cereus TKU030, CS038 was inhibited by phenylmethanesulfonyl fluoride (PMSF). These results provide an insight of which metals or chemicals should be selected when specific industrial applications are needed. Purified CS038 was pre-incubated with the various reagents at 25 °C for 30 min, and residual chitosanase activity was determined as described in the text. One hundred percent was assigned to the activity in the absence of reagents. The relative activity of the chitosanase: 100% = 2.47 U/mL. 2.7. Chitosan Hydrolysis To evaluate the applicability of CS038 for the enzymatic digestibility of chitosan into oligosaccharides, the crude enzyme from B. mycoides TKU038 was used in the experiments. Selective precipitation in 90% methanol and acetone solutions was performed to obtain low DP oligomers, as described earlier [5]. The enzyme hydrolyzed products of colloidal chitosan were analyzed by both high performance liquid chromatography (HPLC) (not shown) and matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry [19], as shown in Figure 3. The hydrolysate ions present in the mass spectra were identified as sodium adducts [M + Na+]. The peaks corresponding to the COSs with DP 3–9 were monitored in the spectrum, whereas monomers and dimers were not detected due to the interference of the matrix (below 500 m/z). After hydrolysis from the enzymatic reaction over six days, (GlcN)2–GlcNAc (m/z 566), GlcN–(GlcNAc)2 (m/z 608), (GlcN)3–GlcNAc (m/z 727), (GlcN)2–(GlcNAc)2 (m/z 769), (GlcN)3–(GlcNAc)2 (m/z 930), (GlcN)2–(GlcNAc)3 (m/z 972), (GlcN)4–(GlcNAc)2 (m/z 1091), and (GlcN)3–(GlcNAc)3 (m/z 1133) were the major products. In addition, other clear signals (m/z 659, 811, 1175, 1294, 1455, and 1616) were also detected (Figure 3). The peaks were [M + Na+] ion-peaks with a 161 Da mass larger than the peak ahead, which was exactly the molecular mass of a GlcN residue. The differences in m/z values of these signals with those of the corresponding COSs were 42 atomic mass units, which corresponded to the mass weight of an acetyl group. Thus, the peaks were [M + Na+] ion-peaks with a 203 Da mass larger than the peak ahead, which was the exact molecular mass of a GlcNAc residue. The hydrolysates contained chitooligomers (GlcN-oligomers) and several partial N-acetylated forms. Further, high-performance liquid chromatography (HPLC) analysis using Bond pack (NH2 column) showed that the hydrolysis products contained COS ranging from 1- to 9-mers, which indicated similar results as those shown by the MALDI-TOF MS spectrum. The TKU038 chitosanase reaction product is a mixture of DP 1–9 hetero-chitooligomers. These results indicate that CS038 might hydrolyse chitosan in an endo-type fashion. Based on these results, chitosan hydrolysis by CS038 combined with a selective methanol precipitation is a quick and simple method to obtain good chitooligosaccharide yields with up to nine DPs and low molecular weight oligomers. CS038 may be a useful tool for the industrial production of COSs and for research on the structure and biological functions of CS038 in nature. 2.8. DPPH Radical Scavenging Activity of COS Previous studies reported that COS, chitin, chitosan, and peptide exhibited high antioxidant activity [32,33,34,35,36,37,38,39,40,41] and anticarcinogenic properties [5,6,7]. In the culture supernatant of TKU038 chitosanase production, the reducing sugar content increased dramatically on the second day. In order to reutilize the reducing sugars efficiently, we incubated B. mycoides TKU038 for six days under the optimal culture conditions described above (0.5% SPP, 25 °C) and analyzed the antioxidant activity of the culture supernatant. The antioxidant activity assayed was the DPPH scavenging ability. The antioxidant activity (1.10 U/mL) was found in the supernatant of unfermented medium (day 0) and increased to 1.82 U/mL after fermenting with TKU038 for two days (Figure 4). We hypothesized that the autoclave treatment (121 °C for 15 min) degraded SPP and produced some antioxidant materials; however, some of the antioxidant materials were produced from the metabolism of strain TKU038. The differences in optimal culture time for chitosanase production (four days) and antioxidant production (two days) has demonstrated that the production of antioxidant materials might not be related to TKU038 chitosanase. The antioxidant compound in the TKU038 culture supernatant is worthy of further investigation. On the other hand, the antioxidant activity of COS was also investigated. COSs were produced by the enzymatic hydrolysis of chitosan with 60% deacetylation from B. mycoides TKU038. After hydrolysis, the supernatant also showed antioxidant activity (Figure 4). Previous studies reported that COS exhibited high antioxidant activity, such as radical scavenging in vitro and inhibiting oxidative stress in cells. The antioxidant activity of COS was significantly related to the average molecular weight (MW) [42]. The effect of DP on DPPH radical scavenging activity was investigated further to identify the optimal DP range. The COS with different DP ranges were separated and lyophilized, and then, the antioxidant activity was investigated. The DPPH scavenging activities of COS with two types of DP ranges (S1, 8 < DP < 16 and S2, DP < 8) at different concentrations are presented in Figure 5. The two samples revealed apparent DPPH scavenging capabilities in a concentration-dependent fashion. The DPPH scavenging activity of S1 was higher than that of S2. The DPPH scavenging activity of S1 was 2 U/mL at 250 mg/mL. However, the DPPH scavenging activity of S2 was only 1.5 U/mL at 500 mg/mL. Similar results were reported by Li et al. [43] which showed the COS with DP 10–12 exhibited the strongest activity. Thus, we speculate that the COS with DP 11 played a major role in antioxidant activity. The TKU038 COS mixture with DP8-16 was more potent than the COS with DP < 8 in scavenging DPPH radical activity. These results confirmed that the COS with high DP (8 < DP < 16) would exhibit an enhanced ability to scavenge DPPH radicals when comparing to that with low DP (DP < 8). 2.9. Effect of COS on Cytotoxicity and Anti-Inflammation NO is an extremely reactive free radical species that involved in numbers of pathological and physical processes. It plays a significant part in the pathophysiology of numerous diseases and its role in macrophage toxicity is also well studied. NO is recognized as a key pro-inflammatory mediator, which is involved in certain inflammatory disorders including chronic hepatitis, pulmonary fibrosis, and rheumatoid arthritis [44,45]. Although suitable levels of NO production are crucial in many normal physiological functions, a significant quantity of NO production could be cytotoxic leading to chronic inflammation, sepsis, and carcinogenesis [45,46,47,48,49]. In this study, the anti-inflammatory activity of COSs (S1 and S2) was estimated in vitro model with LPS-stimulated RAW 264.7 cells. The inhibition of LPS-stimulated NO secretion was due to the anti-inflammation. First, to examine the potential cell cytotoxicity by S1 and S2, MTT assay was conducted. When RAW 264.7 macrophages were treated with S1 and S2 at a concentration of 0, 50, 100, and 200 µg/mL, along with 1 µg/mL LPS, the resulting viabilities of RAW 264.7 cells were summarized in Figure 6. The results of statistical analysis indicated that treatment with S1 (8 < DP < 16) (50, 100 µg/mL) and S2 (DP < 8) (50, 100, 200 µg/mL) had no noticeable toxic effect on cell growth when comparing to 0.05% DMSO group (100.00% ± 2.17%). Although the viability of cells exposed to 200 µg/mL S1 was 77.64% ± 2.90%, it was significantly different from 0.05% DMSO group (Figure 6). S1 at a high concentration had the effect of cytotoxicity. These results revealed that no observable cytotoxicity was observed at concentration levels between 50 and 100 µg/mL of S1 and concentration levels between 50 and 200 µg/mL of S2. S1 and S2 (0–200 µg/mL) inhibited LPS-induced NO production in a concentration-dependent fashion. Although both S1 and S2 (200 µg/mL) were capable of inhibiting NO production by 93.07% ± 2.02% and 91.51% ± 1.99%, respectively in LPS-stimulated cells, S1 showed significant cytotoxicity. The IC50 values of S1 and S2 representing anti-inflammatory effect were of 82.65 ± 1.18 and 76.27 ± 1.49 µg/mL, respectively (Figure 6). These results indicated that S1 and S2 exhibited different degrees of anti-inflammatory capabilities. Anti-NO (%) was higher for S2 than for S1 at a concentration of 100 mg/mL, whereas the opposite behavior was observed for the other concentrations of 200 and 50 mg/mL. Take the error margin into consideration; we assume that the S2 might have higher activity than that of S1. Similar results were reported, as the low DP COS possessed higher anti-inflammatory effects, while the one with high DP COS possessed lower effects [43]. The findings of anti-inflammatory capability along with antioxidant activity of S2 make it a promising candidate for further investigations and could be recognized as a promising anti-inflammation agent based on its inhibitory profile on NO production. Bioactive COS could have a substantial number of applications in biomedical and food industries. Chitosanase is the key enzyme that is required for the preparation of the bioactive COS from chitosan. Utilization of the squid by-products as the substrate for the production of chitosanase have commercial significance. Furthermore, our findings suggest that low DP COS could be the promising candidates for the development of potent anti-inflammatory agents. 2.10. Antitumoral Activities of COS As damaging events are generally associated with oxidative stress, the prevalence of antioxidant and antitumor features in a single material would be highly desirable in terms of preventive, as well as therapeutic purposes. Hence, the cytotoxic activities of S1 and S2 were assessed in four tumoral cell lines (Hep G2, HEp-2, WiDr, and A549). Mitomycin-C was utilized as a positive control. In our preliminary experiments, the effects on Hep G2, HEp-2, WiDr, and A549 cell proliferation were measured via the MTT assay upon treatment with 80 µg/mL S1 and S2. The inhibition percentage of Hep G2, HEp-2, WiDr, and A549 were 7.53% ± 2.18%, 5.84% ± 1.91%, 7.66% ± 0.32%, and 6.75% ± 1.98%, respectively, after S1 treatment and 8.77% ± 1.28%, 6.99% ± 1.02%, 9.84% ± 3.64%, and 7.41% ± 0.95%, respectively, after S2 treatment. Similar results were also obtained in previous studies [5], in which 100 µg/mL of COS had no significant growth inhibition effects on CT26 cells. Since the 1980s, there has been a large number of research programs focusing on developing the antitumor activity of COS. Among them, DP6 COS was found to be able to suppress the growth of sarcoma 180 and MM-46 solid tumors transplanted in mice [43]. Comparing to the other single COSs (DP2, DP3, DP4, or DP5), DP6 COS showed a more potent inhibitory capability [43]. Comparable to the other reports, our studies revealed that low DP COS (DP < 8) possess slightly higher antitumoral activities than those of high DP COS (8 < DP < 16). 3. Experimental 3.1. Materials Squid pens were acquired from Shin-Ma Frozen Food Co. (I-Lan, Taiwan). A water-soluble and low molecular weight chitosan (from 5 to 250 cps with minimal viscosity in water at 25 °C, 85% deacetylation degree, DD) from crab and shrimp shell waste was acquired from Charming and Beauty Co. (Taipei, Taiwan). Its average particle size was approximately 106 µm. Macro-prep DEAE was purchased from Bio-Rad. In addition, 2,2-diphenyl-1-picrylhydrazyl (DPPH) was bought from Sigma-Aldrich. Unless otherwise specified, all reagents used in this work were of the highest grade available. 3.2. Screening of Chitosanase-Producing Strains The microorganisms were retrieved from soil samples, which were obtained at different locations in Taiwan. They were cultivated in SPP medium (pH 7.2) supplemented with 0.05% MgSO4·7H2O and 1% SPP, 0.1% K2HPO4 to screen for chitosan degrading activity. The strains were cultivated in a 250-mL Erlenmeyer flask that contains 50 mL of medium at 37 °C and 150 rpm for three days. The supernatants obtained by centrifugation were gathered for the determination of chitosanase activity using the protocol described in our previous paper [19]. Strain TKU038, which exhibited the highest activity, was selected for the further investigation. 3.3. Chitosanase Activity Assay Chitosanase activity was assayed at 50 °C by the methods as described in our previous paper [9]. The reducing sugars released were determined with glucosamine as the reference compound to determine the enzyme activity [6]. 3.4. Purification of Chitosanase The chitosanolytic enzyme in the 768 mL cell free culture supernatant of B. mycoides TKU038 was concentrated with ammonium sulfate at 80% saturation, centrifuged at 12,000× g for 20 min to precipitate the enzyme, dissolved in a small amount of 50 mM sodium phosphate buffer (pH 7), and dialyzed using a 10-kDa molecular weight cut off membranes against 2 L of the same buffer for 24 h at 4 °C. The subsequent dialysate was charged onto a DEAE-Sepharose CL-6B column (5 cm × 30 cm) that had been pre-washed with the same phosphate buffer, and eluted with an isocratic gradient of 0.1 M NaCl-containing buffer. The fractions with high chitosanase activities were selected and concentrated by ammonium sulfate precipitation. After dialysis against the same phosphate buffer, the concentrates (5 mL) were charged on a Macro-prep DEAE column (12.6 mm × 40 mm). The chitosanase was eluted using an isocratic 0–1 M NaCl gradient in the same phosphate buffer. The chitosanase-active fractions were obtained and combined for subsequent characterization. Once the column chromatography operation was completed, the protein concentration was assessed by measuring the absorbance at 280 nm [6]. The concentration of the purified enzyme was determined based on the method reported by Bradford using bovine serum albumin as the standard. In addition, the molecular weight of the purified enzyme was determined by SDS-PAGE analysis. 3.5. Mass Spectrometry and Protein Identification The band of attention was excised from the SDS-PAGE gel and identified by the same method as described in our previous paper [19]. 3.6. Effects of pH and Temperature on Enzyme Activity and Stability The optimum pH required for the relative chitosanase activities was determined at 50 °C using various pH buffers (pH value 4–11) at a 50 mM concentration [9]. The pH stability and thermal stability of CS038 were determined at pH 6 under the method as described in our published paper [9]. 3.7. Kinetic Parameters Various concentrations of water-soluble chitosan (0.005%–0.15% (w/v)) with a pre-determined enzyme concentration were prepared. Measurements were executed following the standard assay conditions under optimal conditions. The maximum velocity (Vmax) and Michaels-Mention constant (Km) were obtained from a Lineweaver-Burk plot. 3.8. Effects of Various Metal Ions on Chitosanase Activities The influences of metal ions on the activities of CS038 were investigated as described in our previous paper [21]. The comparative activities were calculated under standard assay conditions, comparing to the one without metal ions and inhibitors (100%). 3.9. Enzymatic Production of the Chitosan Oligosaccharides Chitosan (0.5% (w/v)) with 60% deacetylation was utilized as the substrate. The mixture of a TKU038 crude enzyme solution (1 mL) with chitosanase activity (5 U/mL) and substrate (1 mL) was incubated at 50 °C. Samples were withdrawn at 0 to 6 days from reaction mixtures for further preparation of the COS as described earlier [5]. 3.10. Measurement of DPPH Radical Scavenging Activity The diluted sample solution (150 µL) was mixed with 37.5 µL of a methanol solution containing 0.75 mM DPPH radical. The scavenging capability was estimated as described in our previous paper [39]. One unit of scavenging ability was expressed as the amount of sample that releases 50% scavenging activity under standard assay conditions. 3.11. Assay for Anti-Proliferation The cytotoxicity of the tested sample was evaluated against HEp-2 (human laryngeal carcinoma), A549 (Human lung carcinoma), WiDr (human colon adenocarcinoma), and Hep G2 (human hepatocellular carcinoma) cell lines utilizing the MTT colorimetric protocol based on the well-established procedures [50]. The cells were cultured in MEM medium. After seeding cells in a 96-well microplate for 4 h, the 20 µL of sample was then placed in each well and incubated at 37 °C for additional 72 h. Then, 20 µL of MTT was added for 4 h. After washing off the medium and adding DMSO (200 µL/well) to the microplate with mechanical shaking for 30 min, the formazan crystals were re-dissolved and their absorbance was measured on a microtiter plate reader (Dynatech, MR 7000) at a wavelength of 550 nm. Mitomycin c (purity > 98%, Sigma-Aldrich) was utilized as a positive control. 4. Conclusions In summary, we succeeded in developing an efficient production and purification procedure for an amphiprotic novel chitosanase (CS038) produced by B. mycoides TKU038 using an inexpensive medium based on squid pen powder. To the best of our knowledge, this may be the first report on chitosanase produced by B. mycoides. CS038 exhibited optimal pH values of 6 and 10 and broad range pH stability (4–10). The enzyme properties are advantageous for high activation under alkaline conditions, and these properties indicate potential applications in food industries and for waste treatment. Enzymatic hydrolysis by CS038 could lead to a large chitosan oligosaccharide with antioxidant activity, which was identified as an endo-chitosanase. Thus, this enzyme is an efficient tool for treating medical components and functional foods. The antioxidant activity of COS was strongly related to its DP. The COSs with a high DP exhibited enhanced DPPH radical scavenging compared with those with a low DP. Besides, our results demonstrated that different DP of COS hydrolyzed from CS038, especially S2 (DP < 8), was capable of inhibiting NO production. Our findings suggested that S2 might have a potential effect on the treatment with anti-inflammatory and antioxidant activities. The possible anti-inflammatory mechanism of S2 will be reported in detail in due course. Acknowledgments This work was supported in part by a grant from the Ministry of Science and Technology, Taiwan (MOST 102-2313-B-032-001-MY3 and MOST 104-2811-B-032-001). Author Contributions San-Lang Wang conceived and designed the experiments; Wei-Ting Chen and Zhi-Hu Lin performed the experiments; San-Lang Wang, Yao-Haur Kuo, and Anh Dzung Nguyen analyzed the data; San-Lang Wang and Yao-Haur Kuo contributed reagents/materials/analysis tools; Tzu-Wen Liang and Po-Shen Pan wrote the paper. Conflicts of Interest The authors declare no conflict of interest. Figure 1 SDS-PAGE analysis of CS038. Lanes: M molecular markers (180, 130, 100, 75, 63, 48, 35, 28, 17 and 10 kDa); 1 culture supernatant; 2 crude enzyme; 3 adsorbed chitosanase fractions after DEAE-Sepharose CL-6B chromatography; 4 adsorbed chitosanase fractions after Macro-prep DEAE chromatography. Figure 2 Effects of pH (a) and temperature (b) on CS038 chitosanase activity (●) and stability (○). Figure 3 MALDI-TOF-MS spectrum of the chitooligomers (COS) obtained during chitosan hydrolysis with CS038. The proportion of low molecular weight oligomers was reduced by precipitation in the 90% methanol soluble/90% acetone insoluble fraction. The identified peaks are labelled with DP, in which DP indicates the degree of polymerization. The hydrolysis time is labelled in the spectrum. Figure 4 DPPH free radical scavenging activities of TKU038 culture supernatants (●) and CS038 hydrolysate (○) at various cultivation/reaction times. Figure 5 DPPH free radical scavenging activities of COSs hydrolyzed from CS038 with two types of degree of polymerization (DP) range (S1, 8 < DP < 16, (●); S2, DP < 8, (○)) at various concentrations. Figure 6 NO inhibitory activities of COSs hydrolyzed from CS038. Cell lines: The murine RAW 264.7 monocyte/macrophage cells. Cells were treated with LPS (1 µg/mL) or in combination with tested agents (200, 100, and 50 µg/mL) for 24 h. ijms-17-01302-t001_Table 1Table 1 Purification summary of CS038 a. Step Total Specific Activity (U/mg) Purification (Fold) Recovery (%) Volume (mL) Protein (mg) Activity (U) Culture supernatant 768 2764.8 443.9 0.16 1 100 (NH4)2SO4 precipitation 45 751.5 386.0 0.51 3.19 87.0 DEAE-sepharose 40 128.2 261.4 2.04 12.75 58.9 Macro-Prep DEAE 10 7.5 156.1 20.81 130.06 35.2 a B. mycoides TKU038 was grown in 50 mL of liquid medium in an Erlenmeyer flask (250 mL) containing 0.5% SPP, 0.1% K2HPO4, and 0.05% MgSO4·7H2O in a shaking incubator for four days at 25 °C. ijms-17-01302-t002_Table 2Table 2 Comparison of CS038 with chitosanase from other microbes. Strains MW (kDa) Optimal Stability Inhibitor References Temp. (°C) pH Temp. (°C) pH B. mycoides TKU038 48 50 6, 10 25–50 4–10 Cu2+, Ba2+, Zn2+, Fe2+, Mn2+, EDTA, PMSF This study B. cereus D-11 41 60 6 <50 5–10 Cu2+, Hg2+, Pb2+ [8] B. cereus TKU022 44 60 7 25–40 7–10 Mn2+ [18] B. cereus TKU031 43 50 5 20–50 5–9 Fe2+, Cu2+, Zn2+, Mn2+, EDTA [19] B. cereus TKU033 43 50 5 <40 5–7 Cu2+, Mn2+, EDTA [20] B. cereus TKU034 43 50 7 <50 4.5–7.5 Fe2+, Ca2+, Cu2+, Zn2+, Mn2+, EDTA [21] Bacillus sp. KCTC 0377BP 45 60 4–6 <55 4–8 Mn2+, Hg2+ [22] Bacillus sp. TKU004 29 37 7 <40 4–7 Cu2+, Fe2+ [23] B. subtilis TKU007 25 37 7 <37 4–9 Cu2+, Fe2+, EDTA [24] B. subtilis IMR-NK1 36 45 4 <40 5–9 Hg2+, PHMB [25] Bacillus sp. DAU101 27 50 7.5 - - Cu2+, Zn2+, Hg2+, Ni2+, Co2+ [26] Bacillus sp. MET 1299 52 60 5.5 - - Mn2+, Cu2+, Zn2+, Co2+, EDTA [27] B. criculans MH-K1 32 50 6.5 - - Hg2+, Cd2+, Ni2+, Zn2+, pCMB [28] Streptomyces griseus 35 37 8 - - Ag2+, Hg2+, Fe2+, Cu2+, pCMB [29] Streptomyces roseolus 41 50 5 30–60 5–7 Mn2+, Cu2+, Zn2+, Co2+, EDTA [17] Serratia sp. TKU016 65 50 7 <50 6–7 Mn2+ [30] S. marcescens TKU011 21 50 5 <50 4–8 Mn2+, Cu2+, PMSF [12] Acinetobacter calcoaceticus TKU024 66 60 7 <70 6–11 Mn2+, EDTA [16] 27 50 6 <90 4–10 -: Not detected. ijms-17-01302-t003_Table 3Table 3 Identification of CS038 by LC-MS/MS. Peptide Sequence Identified Protein and Coverage Rate Accession Number 81SYYDNWKK88 Chitosanase 54% Bacillus cereus: gi446936339 93NDLSSLPGGYYVKGEITGDADGFK PLGTSEGQGYGMIITVLMAGYDSNAQKIYDGLFK150 157SSQNPNLMGWVVADSKKAQGHFDSATDGD LDIAYSLLLAHKQWGSNGTVNYLKEAKDMITK217 221ASNVTNNNRLNLGDWDSKSSLD TRPSDWMMSHLRAFYEFTGDK263 283YSPNTGLISDFVVKNPPQPAPKDFLEE SEYTNAYYYNASR322 327IVMDYAMYGEK337 346VSSWIQNK353 397WVNSGWDWMK406 ijms-17-01302-t004_Table 4Table 4 Effects of various chemicals on the activities of CS038. Chemicals Relative Activity (%) None 100 Na+ 94 Mg2+ 93 Fe2+ 0 Ca2+ 88 Cu2+ 21 Ba2+ 57 Zn2+ 20 Mn2+ 0 EDTA 0 PMSF 0 ==== Refs References 1. Wang S.L. Liang T.W. Yen Y.H. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081303ijms-17-01303ArticleSecondary Metabolites in Ramalina terebrata Detected by UHPLC/ESI/MS/MS and Identification of Parietin as Tau Protein Inhibitor Cornejo Alberto 1*Salgado Francisco 2Caballero Julio 3Vargas Reinaldo 4Simirgiotis Mario 5Areche Carlos 2*Gomez Caravaca Ana Maria Academic EditorArraez-Roman David Academic Editor1 Facultad de Medicina, Escuela de Tecnología Médica, Universidad Andrés Bello, Sazié 2315, Primer Piso, Santiago 8370092, Chile2 Departamento de Química, Facultad de Ciencias, Universidad de Chile, Ñuñoa, Santiago 8320000, Chile; fsalgado@ug.uchile.cl3 Centro de Bioinformática y Simulación Molecular, Facultad de Ingeniería, Universidad de Talca, 2 Norte 685, Casilla 721, Talca 3460000, Chile; jcaballero@utalca.cl4 Departamento de Biología, Universidad Metropolitana de Ciencias de la Educación, Avda. Jose Pedro Alessandri 774, Ñuñoa, Santiago 8320000, Chile; reinaldo.vargas@umce.cl5 Laboratorio de Productos Naturales, Instituto de Farmacia, Facultad de Ciencias, Universidad Austral de Chile, Casilla 567, Valdivia 5090000, Chile; mario.simirgiotis@gmail.com* Correspondence: alberto.cornejo@unab.cl (A.C.); areche@uchile.cl (C.A.); Tel.: +56-2-2770-3610 (A.C.); +56-2-2978-7259 (C.A.)18 8 2016 8 2016 17 8 130324 6 2016 01 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Liquid chromatography coupled with mass spectrometry is an outstanding methodology for fast analysis of phenolic compounds in biological samples. Twenty two compounds were quickly and accurately identified in the methanolic extract of the Antarctic lichen Ramalina terebrata for the first time using ultra high pressure liquid chromatography coupled with photodiode array detector and high resolution mass spectrometry (UHPLC-PDA-Q/Orbitrap/MS/MS). In addition, the extract and the four compounds isolated from this species were tested for the inhibitory activity of tau protein aggregation, which is a protein involved in Alzheimer’s disease (AD). All compounds showed null activity with the exception of parietin, which it was able to inhibit aggregation process of tau in a concentration range between 3 µg/mL (10 µM) to 28 µg/mL (100 µM). In addition, we show how parietin interact with tau 306VQIVYK311 hexapeptide inside of the microtubule binding domain (4R) with the help of molecular docking experiments. Finally, the constituents present in the methanolic extract could possibly contribute to the established anti-aggregation activity for this extract and this in-depth analysis of the chemical composition of R. terebrata could guide further research into its medicinal properties and potential uses. Alzheimer’s diseasedockingRamalinatau proteinlichensparietinUHPLC/MS ==== Body 1. Introduction Lichens are symbiotic associations between heterotrophic fungi and algae and/or cyanobacteria. A peculiarity of lichen is its remarkable ability to tolerate extreme atmospheric conditions such as low temperatures in polar zones including the Arctic and Antarctic regions; these regions are very cold; the coldest temperature ever known on earth (−129 °F) was recorded in Antarctica. These environmental conditions are responsible for the diversity of secondary metabolites produced in lichens [1,2,3,4,5]. Lichen substances are mainly synthesized via poly-malonyl, shikimate, and mevalonic acid pathway, which have afforded several interesting and unique phenolic structures such as dibenzofurans, depsides, depsidones, depsones, quinones and pulvinic acid derivatives [1,2,3,4,5,6,7]. Alzheimer´s disease is the most common form of dementia. There are two main proteins involved, β-amyloid protein and microtubule-associated tau protein. Both proteins are characterized by the deposition of plaques and neurofibrillary tangles, respectively [8]. Physiologically, tau protein is involved in axonal transport and microtubule stability. However, once tau is hyper-phosphorylated, it detached from microtubules and starts to form aggregates in soma and dendrites of neuron cells [9]. Tau is an unfolded protein whose structure has two fibril-forming motif, 275VQIINK280 and 306VQIVYK311 [10]. Besides, in order to form the fibrillar structure of tau, is required the addition of polyanions such as heparin which suggest an important role of electrostatic interaction to form both fibrils and aggregates [11]. These two motifs are within the microtubule binding domain of tau and are prone to forming a cross β structure [12,13]. The determination of amyloid-like structure reveals the presence of moieties involved in β sheets pair formation [14], this “steric zipper” is formed from short self-complementary segments of the amyloid [15]. Thus, this formation is a central part of proto-filaments, whereas the rest of the protein remains unfolded outside of the main axis [16]. The Q-exactive focus is a newly released hybrid high resolution mass spectrometer used for metabolomics analysis including pesticides, herbicides, drugs, antibiotics, small peptides and several other organic molecules [17,18,19,20,21]. The hyphenated Q-exactive focus instrument combines ultra high pressure liquid chromatography coupled with photodiode array detector (UHPLC-PDA) with an orbital trap and a high-resolution collision cell, which allows high resolution MSn fragments [17,18,19,20,21]. On the other hand, molecular docking is a computational method used for the prediction of ligand-receptor interactions and is an important tool for rational drug design [22]. Today, molecular docking is the most important theoretical method to determine the orientation of the ligands inside a binding site. Particularly, the challenges of molecular docking are the following: the prediction of ligands proper orientation, the prediction of the binding energies and the prediction of novel, effective drugs by using the structural knowledge obtained from the models [22,23,24]. Several examples using these computational methods have been already reported [23,24,25,26,27,28]. The present work describes the UHPLC chromatographic fingerprints plus the isolation of the main secondary metabolites together with the tau aggregation inhibitory activity of methanolic extract of Ramalina terebrata. Hook. and Taylor. Based on the reported activity of fulvic acid [29] we decided to investigate parietin, in order to demonstrate its capacity to inhibit tau protein aggregation. Moreover, we modeled the structure of the complex between parietin and fibril-forming motif VQIVYK of tau using docking experiments. Hence, we are able to demonstrate that parietin is able to create hydrogen bonds (HB) with lysine residues. 2. Results and Discussion From the methanolic extract, the following compounds were isolated: parietin 1, usnic acid 2, atraric acid 3 and inositol 4 (Figure 1) using a combination of chromatographic techniques [30]. Initially, the methanolic extract was screened by Thioflavin T (ThT) at concentration ranging from 100 to 1000 µg/mL and the results reveled that at 1000 µg/mL the inhibitory activity against aggregation process of tau protein was almost complete (Figure 2). Therefore, we have performed the isolation of lichen substances from this extract for further testing of this activity. All isolated compounds were tested in ThT fluorescence assay, since it has been demonstrated that ThT is able to bind to fibrils from both synthetic and biological sources [31,32]. None of these compounds were active to prevent tau aggregation with the exception of parietin (Figure 2). Parietin, an orange anthraquinone pigment, is a metabolite very common in the family Teloschistaceae. Several biological activities for this compound have been summarized [1,4]. Besides, it is noteworthy to mention that parietin isolated from Xanthoria parietina (Linnaeus) Theodor Fries showed antibacterial activity against S. aureus (ATCC and clinical isolate strains), antifungal activity towards Rhizoctonia solani, Botrytis cinerea and Candida albicans. In contrast, parietin did not show any effect regarding anticancer and antiproliferative activity [2,3,4]. Gauslaa and Ustvedt reported that parietin may reduce the effect of UV radiations [2,3,4]. However, there is no information regarding its capacity to inhibit tau aggregation process. Furthermore, according to our knowledge, there is no published data on anti-aggregation properties of lichen compounds. The aggregation assay was performed using fragment 4R of the protein tau as positive control. Once we tested parietin by ThT, our results showed that parietin was able to inhibit aggregation process of tau in a concentration range between 3 µg/mL (10 µM) to 28 µg/mL (100 µM) (Figure 2) showing a dose-response effect. The inhibitory effect of parietin at 28 µg/mL was by 75%. Some compounds have been described for their anti-aggregating effect against over either amyloid-β or tau protein [33,34,35]. In addition, anthraquinones compounds such as daunorubicin, adriamycin and emodin inhibit tau aggregation and also diminish paired helical filaments in cells [36]. A previous report has shown that emodin, an anthraquinone related to parietin has profound effects on aggregation process of tau protein [36], this difference could be because parietin has a methoxy group at C-3 position instead of hydroxyl group of emodin. There are previous evidences that negatively charged molecules such as orange-G bind specifically to the lysine residues of tau fibril-forming motifs VQIVYK [37]. Considering that parietin has groups with a negative charge density, we proposed that its activity against aggregation process of tau is due to molecular interactions with fibril-forming motifs. In the complex between orange-G and VQIVYK, the fragment of tau has a β-sheet form with the dye binding between two sheets. Considering this information, we constructed the possible tridimensional structure of parietin in interaction with the tau VQIVYK motif using docking. Since we do not have information about the preferred protonation state of parietin forming the complex, we tested the protonation states P1 and P2 described in Materials and Methods section. We docked parietin structures inside a cavity formed between two steric zippers (model A) and on the surface of one steric zipper (model B); models A and B (Figure 3). Docking results for parietin in protonation states P1 and P2 inside the model A, shown in Figure 3A. The obtained models suggest that parietin could serve as inhibitor of aggregation by binding between steric zippers preventing higher-order β-sheet interactions as orange-G [37]. The phenolic groups and the oxygen of methoxy substituent form hydrogen bond (HB) interactions with different VQIVYK lysine side chains. At once, the methyl group of the methoxy substituent has hydrophobic interactions with the VYK valine. It is noteworthy that the results are comparable for both protonation states of parietin. The docking results for parietin in protonation states P1 and P2 inside the model B are shown in Figure 3B. The models obtained show that both protonation states have similar orientations forming HB interactions with lysine and glutamine side chains. Phenolic groups and carbonyl groups at position 9 of the anthracene-9,10-dione scaffold have HB interactions with glutamine side chains, while the oxygen of methoxy substituent and carbonyl at position 10 of the anthracene-9,10-dione scaffold have HB interactions with lysine side chains. At the same time, methyl groups orient near VYK valine forming hydrophobic interactions. Regarding the HPLC fingerprint of the methanolic extract, 22 compounds were identified for the first time in the methanolic extract of R. terebrata with the help of their characteristic UV-Vis spectra and high-resolution mass spectrometry [38,39]. All compounds were detected in negative mode using UHPLC-Q/Orbitrap/ESI/MS/MS (Table 1). Peak 22 was identified as parietin (molecular anion at m/z 283.0601). Peak 21 was identified as usnic acid, which showed a [M − H]− peak at m/z 343.0803. Major diagnostic daughter MS ions of usnic acid were [M − H − CH3]−, [M − H − C4H3O2]− and [M − H − C5H3O3]− (328.0583, 259.0612 and 231.0663 amu, respectively). Peak 20 was identified as lobaric acid (molecular anion at m/z 455.1712). The fragmentation of peak 20 also produced ions at 411.1808 [M − H − CO2]−, 367.1909 [M − H − 2CO2]−, 352.1675 [M − H − 2CO2 − CH3]−, and 296.1049 [M − H − 2CO2 − C5H11]− confirming this depsidone. Peak 19 and 17 had the same [M − H]− ion at m/z 375.1070 with different retention time based on UHPLC at 22.04 and 23.65 min, which were tentatively identified as placodiolic acid or pseudoplacodiolic acid, respectively. Peak 18 with a [M − H]− ion at m/z 527.2290 was identified as arthoniaic acid, and peak 16 as gyrophoric acid, which was identified by spiking experiments with an authentic standard. Peak 15 with a [M − H]− ion at m/z 497.1065 was identified as 3-hydroxyumbilicaric acid. Main daughter ion of peak 15 was at m/z 317.0652 [M − H − C9H8O4]−. Peak 8 could be tentatively identified as 4-O-dimethylbaemycesic acid (m/z 359.0756) which produced a MS2 ion at m/z 302.0417. Ten tetrahydroxy fatty acids (peak 1–3, 5, 7, 9–11 and 13–14) and three pentahydroxy fatty acids (peak 4, 6 and 12) were tentatively identified as the polihydroxy fatty acids reported by Huneck [30]. On the other hand, in recent years, some chemical studies belonging to Ramalina genus have been published and most of the works have been focused on secondary metabolites [40]. Ramalina terebrata Hook and Taylor from the Antarctic is the producer of usnic acid, ramalin, stereocalpin A and usimines A–C [40,41,42,43]. Besides, it has been reported from the Ramalina genus isousnic acid, usninic acid, the following depsides sekikaic acid and its 5-OH, 5-Cl derivatives, 4′-O-methylsekikaic acid, 4′-O-demethylsekikaic acid, 4′-O-methylnorsekikaic, 2′-O-methylsekikaic, homosekikaic acid, 4′-O-methylnorhomosekikaic acid, 4′-O-demethylhomosekikaic acid, atranorin, chloroatranorin, divaricatic acid, ramalinolic acid, obtusatic acid, chlorotumidulin, evernic acid, diffractaic acid, 4′-O-demethylbarbatic acid, ramalinaic acid, cryptochlorophaeic acid and its 4,4′-dimethyl derivative, gyrophoric acid, trivaric acid, perlatolic acid, 4′-O-methylpaludosic acid, boninic acid, stenosporic acid, olivetoric acid, paludosic acid, lecanoric acid, and bourgeanic acid. Also, the following depsidones salazinic acid, norstictic acid, hypoprotocetraric acid, conhypoprotocetraric acid, scopuloric acid, protocetraric acid, connorstictic acid, cryptostictic acid, peristictic acid, variolaric acid, gangaleoidin, physodic acid, and coquimboic acid have been isolated from Ramalina genus. Finally, the fatty acids reported from Ramalina were oleic, palmitic, stearic, linolenic, linoleic, and myristic acids, and the γ-lactone acids protolichesterinic, d-protolichesterinic, and nephrosterinic [40]. Some lichen extracts from the genus Ramalina have displayed a wide range of biological activities such as antimicrobial, antioxidant, antiviral, antitumoral, cytotoxicity, antiinflammatory, and antihelmintic [30,40,41,42,43,44,45,46]. An acetone extract of R. farinacea demostrated activity against Candida albicans and Candida glabrata at concentrations ranging between 3.3 to 6.6 µg/25 µL. Furthemore, a methanolic extract of R. pollinaria showed antibacterial activity and presented MIC values between 5.62–62.5 µg/µL, while the MIC values for R. polymorpha was 62.5 µg/µL. Cansaran [44] studied five Ramalina species, and among them the methanolic extract of R. fastigiata showed the best inhibition against Bacillus subtilis, Enterococcus faecalis, Escherichia coli and Proteus mirabilis. Methanolic extract from R. hossei showed better activity against Gram(+) than against Gram(−) bacteria [45]. The hexanic extract from R. roesleri showed a high activity against S. aureus and S. mutans. In other study, the antibacterial activity of methanolic extract of Antarctic lichen R. terebrata displayed considerable antimicrobial activity against Bacillus subtilis (MIC 33.8 ± 0.15 µg/µL) and S. aureus (MIC 85.7 ± 6.7 µg/mL) but no activity against C. Albicans, P. aeruginosa, and E. coli, while Paudel et al. [46] reported activity against S. aureus. Regarding to antioxidant activity, the methanolic extracts of R. pollinaria and R. polymorpha did no show antioxidant properties based on the DPPH method. However, a low inhibition was showed on the oxidation of linoleic acid/β-carotene method. The methanolic extract of R. hossei and R. conduplicans displayed antioxidant potential by the DPPH method and by the reduction of Fe+3 assay. On the other hand, an acetonic extract of R. peruviana presented antioxidant activity on the DPPH method (86%) and β-carotene assay (57.3%). A ethanol-water extract (1:1) of R. capitata displayed gastroprotective activity (66%) at a dose of 200 mg/kg on the gastric damage model induced by indomethacin, while a methanolic extract of R. cuspidata shows interesting cytotoxic activity particulary in the cell lineages K-562, U251, DU145 and MCF7 [40]. Regarding the lichen-derived substances, usimine C has showed anti-proliferative activity on human dermal fibroblasts [41]. Usnic acid, usimine A–C and ramalin isolated from Antarctic lichen R. terebrata, displayed activity against B. subtilis but no activity against S. aureus, where the values of MIC ranged from 1–26 μg/mL [40]. Ramalin showed to be a more powerful antioxidant than butylhydroxytoluene (BHT), vitamin E, Trolox and ascorbic acid as well as a very good antiinflammatory agent [43]. Sekikaic acid and homosekikaic acid showed IC50 values of 0.082 mg/mL and 0.276 mg/mL at the linoleic acid peroxidation assay, demonstrating that these compounds are promising antioxidants. The antioxidant activity of atranorin, protolichesterinic acid, usnic acid, 2-hydroxy-4-methoxy-6-propylbenzoic acid, homosekikaic acid, sekikaic acid, 2,4-dihydroxy-6-propylbenzoic acid and 2,4-dihydroxy-3,6-dimethylbenzoate isolated from the hexane extract from R. roesleri were assessed by the DPPH method. Among the compounds, the best antioxidant activity was exhibited by sekikaic acid, followed by homosekikaic acid. Usnic acid has demostrated to reduce the production of Junin virus in infected Vero cells in a dependent dose manner (EC50 9.9 μM), these results indicate that usnic acid present antiviral activity. Also, usnic acid is not genotoxic and cytotoxic towards human lymphocyte A549, promyelocytic leukemia HL-60, and ovarian carcinoma A2780. d-protolichesterinic acid and nephrosterinic acid showed activity against Ehrlich carcinoma. d-protolichesterinic acid and lobaric acid are considered 5- and 12-lipoxygenase inhibitors [40]. 3. Materials and Methods 3.1. Collection and Identification of Lichen Species Ramalina terebrata was collected in “Peninsula Fildes” Antarctic region, Chile during March, 2014. Lichens were carefully removed from the rocks by abrasion. A voucher specimens were deposited at the Extreme Natural Product laboratory, Universidad de Chile whose reference numbers is RT-010414. R. terebrata (50 g) were dried, powdered and extracted with methanol (3 × 0.5 L) to afford, after evaporation of the solvent under vacuum, 450 mg of a dark gum. 3.2. Extraction The methanolic extract was subjected to Sephadex LH-20 and eluted with MeOH. Fractions (10 mL) were monitored by TLC and combined to give two main fractions A–B. Further purification was done for the fractions A and B. Fraction A (200 mg) was chromatographed on silica gel (50 g, 63–200 µm) using n-hexane/EtOAc mixtures (0% to 100%) giving 30 mg of parietin 1 [30] and 54 mg of usnic acid 2 [30]. Fraction B (110 mg) was chromatographed on silica gel (30 g, 63–200 µm) using DCM/MeOH mixtures (0% to 100%) giving 10 mg of parietin 1, 15 mg of atraric acid 3 [30] and 10 mg of inositol 4 [30]. 3.3. Tau Protein Production Tau fragment 4R (htau244-372) was amplified by using the plasmid for htau40 as a template. The PCR sequence was subcloned into pET-28a vector (Novagen, Madinson, WI, USA) to produce a His-tagged protein. The recombinant fragment 4RMBD was expressed in Escherichia coli strain BL21 (DE3) as described [29]. LB medium containing kanamycin was inoculated with a stationary overnight culture. Bacterial culture was grown at 37 °C to OD600 of 0.5–0.6 and protein expression was induced by addition of 1 mM IPTG for 4 h; and cells were pelleted and sonicated. Recombinant tau was purified via a succession of Ni-Sepharose chromatography (equilibrated in 20 mM NaH2PO4, 500 mM NaCl, and 20 mM imidazole, pH 7.4, elution with buffer 200 mM imidazole) and side exclusion chromatography coupled to HPLC in a Proteema 100 column (PSS, Mainz, Germany) with buffer 50 mM NaH2PO4, 300 mM NaCl, pH 6.5. The purity of the protein was verified on a Coomassie Brilliant Blue-stained SDS-polyacrylamide gel. The protein was concentrated and stored at −80 °C until use. The concentration of purified 4RMBD was determined using the extinction coefficient at 280 nm (1520 M−1·cm−1). 3.4. Thioflavin T Assay The ThT fluorescence assay adopted here was modified from the reported by Pickhardt et al. [36]. Briefly, to examine the inhibition of tau aggregation, the total volume of the reaction mixture was 100 μL, which included 20 μM 4RMBD, 5 μM heparin in 100 mM sodium acetate, pH 7.0 with parietin at different concentrations. After 20 h of incubation at 37 °C, addition of 100 μL of a 25 μM solution of ThT was made and incubation continued for 1 h at room temperature prior to fluorescence reading. Then, the fluorescence was measured in a Biotek H1 multi-mode reader (Biotek Instruments, Winooski, VT, USA) with an excitation wavelength at 440 nm and emission wavelength of 485 nm in a 96-well plate. Each experiment was replicated at least three times and background fluorescence was subtracted. 3.5. UHPLC-Q/Orbitrap/MS/MS 3.5.1. Instrument A Thermo Scientific Dionex Ultimate 3000 UHPLC system equipped with a quaternary Series RS pump and a Thermo Scientific Dionex Ultimate 3000 Series TCC-3000RS column compartments with a Thermo Fisher Scientific Ultimate 3000 Series WPS-3000RS autosampler and a rapid separations PDA detector controlled by Chromeleon 7.2 Software (Thermo Fisher Scientific, Bremen, Germany) hyphenated with a Thermo high resolution Q Exactive focus mass spectrometer were used for analysis. The chromatographic system was coupled to the MS with a Heated Electrospray Ionization Source II (HESI II). Nitrogen (purity > 99.999%) obtained from a Genius NM32LA nitrogen generator (Peak Scientific, Billerica, MA, USA) was employed as both the collision and damping gas. Mass calibration for Orbitrap was performed once a week, in both negative and positive modes, to ensure a working mass accuracy lowers than or equal to 5 ppm. Cafeine and N-butylamine (Sigma Aldrich, Saint Louis, MO, USA) were the calibration standards for positive ions and buspirone hydrochloride, sodium dodecyl sulfate, and taurocholic acid sodium salt were used to calibrate the mass spectrometer. These compounds were dissolved in a mixture of acetic acid, acetonitrile, water and methanol (Merck Darmstadt, Hesse, Germany) and were infused using a Chemyx Fusion 100 syringe pump. XCalibur 2.3 software and Trace Finder 3.2 (Thermo Fisher Scientific, San Jose, CA, USA) were used for UHPLC control and data processing, respectively. Q Exactive 2.0 SP 2 from Thermo Fisher Scientific was used to control the mass spectrometer. 3.5.2. LC Parameters Liquid chromatography was performed using an UHPLC C18 column (Acclaim, 150 mm × 4.6 mm ID, 5 m) operated at 25 °C. The detection wavelengths were 254, 280, 320 and 440 nm, and PDA was recorded from 200 to 800 nm for peak characterization. Mobile phases were 1% formic aqueous solution (A) and acetonitrile (B). The gradient program (time (min), %B) was: (0.00, 5); (5.00, 5); (10.00, 30); (15.00, 30); (20.00, 70); (25.00, 70); (35.00, 5) and 12 min for column equilibration before each injection. The flow rate was 1.00 mL·min−1, and the injection volume was 10 µL. Standards and lichen extracts dissolved in methanol were kept at 10 °C during storage in the autosampler. 3.5.3. MS Parameters The HESI parameters were optimized as follows: sheath gas flow rate 75 units; aux. gas unit flow rate 20; capillary temperature 400 °C; aux gas heater temperature 500 °C; spray voltage 2500 V (for ESI−); and S lens RF level 30. Full scan data in both positive and negative was acquired at a resolving power of 70,000 FWHM (full width half maximum) at m/z 200. For the compounds of interest, a scan range of m/z 100–1000 was chosen; the automatic gain control (AGC) was set at 3 × 106 and the injection time was set to 200 ms. Scan-rate was set at 2 scans·s−1. External calibration was performed using a calibration solution in positive and negative modes before each sample series. In addition to the full scan acquisition method, for confirmations purposes, a targeted MS/MS analysis was performed using the mass inclusion list and expected retention times of the target analytes, with a 30 s time window, with the Orbitrap spectrometer operating both in positive and negative mode at 17,500 FWHM (m/z 200). The AGC target was set to 2 × 105, with the maximum injection time of 20 ms. The precursor ions are filtered by the quadrupole which operates at an isolation window of m/z 2. The fore vacuum, high vacuum and ultrahigh vacuum were maintained at approximately 2 mbar, from 105 and below 1010 mbar, respectively. Collision energy (HCD cell) was operated at 30 kV. Detection was based on calculated exact mass and on retention time of target compounds, as shown in Table 1. The mass tolerance window was set to 5 ppm for the two analysis modes. 3.6. Molecular Modeling The current structural information about tau protein is very limited; however, our results suggest that parietin could interact with tau fibril-forming motifs VQIINK (PHF6*), particularly with lysine residues. The coordinates of the hexapeptide VQIVYK were extracted from the X-ray crystal structure of tau VQIVYK segment complexed with orange-G (Protein Data Bank code 3OVL) [47,48]. The fiber structure was prepared using the VQIVYK coordinates. Zinc atoms were removed since zinc solution was not used in our study. We prepared two fiber structure models A and B: A contains 24 units of hexapeptide and B contains 12 units of the hexapeptide. In model A, each member of a pair of VQIVYK β-sheets is shifted relative to the other, without the dry interface in the typical steric zipper structure, forming a cylindrical cavity [47,48]; docking was executed inside the cavity. The model B just contains the half of peptides of model A, and B does not contain a cavity; therefore, the docking was executed at surface. Using models A and B, we evaluated the interactions between the parietin and the VQIVYK fiber structure using these models considering the presence and absence of a cavity. For docking, we considered two protonation states of parietin: P1 with neutral phenolic groups, and P2 with deprotonated phenolic groups. These two extreme cases allow studying the effect of the protonation state of molecular interactions with hexapeptide VQIVYK. Both models of parietin (P1 and P2) were sketched using Maestro’s molecular editor. Docking was performed using Glide method [47,48]. A grid box of 15 Å × 10 Å × 10 Å covered the whole cavity in model A, and whole surface in model B. Docking parameters were used as previously reported, a Glide extra-precision (XP) modes were explored during search. Docking hierarchy begins with systematic conformational expansion of ligand followed by placement on receptor site. Then, minimization of the ligand in the receptor field was carried out using the OPLS-AA [49] force field with a distance-dependent dielectric of 2.0. Afterwards, the lowest energy poses were subjected to a Monte Carlo procedure that samples the nearby torsional minima. The best pose for a given ligand was determined by the Emodel score, while different compounds were ranked using GlideScore [50]. Docking poses were analyzed by examining their relative total energy score. The most energetically favorable conformations were selected as best poses. 3.7. Statistical Analysis Results of statistical analysis were expressed as the mean ± SEM. In all experiments, statistical differences between treatments and their respective control were determined by Paired t-test. Significance level was set at p < 0.05. All statistical analyses were developed using GraphPad Prism 6 software (H. Motulsky, San Diego, CA, USA). 4. Conclusions Finally, our results demonstrate that parietin has a moderate activity against the aggregation process of tau protein, while the methanolic extract of Ramalina terebrata had also activity that could be attributed to sinergistic effects of the compounds detected in the extract. On the other hand, docking experiments suggest that parietin is bound to fiber-forming segment VQIVYK of tau mediated mainly by HBs interactions with the lysine residues. Based on UHPLC-Q/Orbitrap/ESI/MS/MS, 22 compounds were identified in the methanolic extract of the Antarctic lichen Ramalina terebrata. Finally, in-depth analysis of the chemical composition of R. terebrata could guide further research into its medicinal properties and potential uses. Acknowledgments This work was supported by grants from the INACH RT 13-13 to Alberto Cornejo and Carlos Areche, and INACH MT 03-14 to Francisco Salgado. Julio Caballero acknowledge funds of FONDECYT Regular # 1130141. Mario Simirgiotis acknowledge Fondequip (grant EQM140002) for the funding to purchase the UHPLC/Orbitrap/ESI/MS/MS equipment. Supplementary Materials Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1303/s1. Click here for additional data file. Author Contributions Carlos Areche and Alberto Cornejo conceived and designed the experiments; Francisco Salgado performed the experiments; Julio Caballero performed the molecular modeling; Reinaldo Vargas identified the specimens; Mario Simirgiotis analyzed the data of HPLC/MS; Carlos Areche and Alberto Cornejo wrote the paper. All authors read and approved the final manuscript. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Structures of compounds 1–4 isolated from Antartic lichen R. terebrata. Figure 2 Tau aggregation process inhibited by both Ramalina extract and parietin. Black and grey bars represent positive control (aggregation) and inhibition respectively. A paired samples-t-test was conducted in order to compare control (aggregation) and tau inhibitors (Ramalina extract and parietin). There was a significant difference for both Ramalina extract t (4) = 25, p < 0.05 and parietin t (4) = 3.223, p < 0.05 (data are represented as Mean ± SEM). Figure 3 (A) Docking of parietin in protonation state P1 in the interface of two poly-306VQIVYK311 hexapeptide zippers (a,c-left). Hydrogen bounds (HBs) between parietin and lysine residues are indicted as broken lines in the perpendicular view (b,d-right); (B) Docking of parietin in protonation state P2 in the interface of two poly-306VQIVYK311 hexapeptide zippers (a,c-left). Hydrogen bounds (HBs) between parietin and lysine residues are indicated as broken lines in the perpendicular view (b,d-right). ijms-17-01303-t001_Table 1Table 1 Identification of metabolites in Antarctic lichen R. terebrata by UHPLC-Q/Orbitrap/ESI/MS/MS. * Identified by spiking experiments with an authentic compound; retention time (min); theoretical and measured mass (m/z); accuracy (ppm). Peak Tentative Identification [M–H]− Retention Time Theoretical Mass Measured Mass Accuracy MSn Ions 1 9,10,12,13-tetrahydroxyheptadecanoic acid C17H33O6 14.53 333.2283 333.2267 4.8 – 2 9,10,12,13-tetrahydroxyoctadecanoic acid C18H35O6 15.54 347.2439 347.2423 4.6 – 3 9,10,12,13-tetrahydroxynonadecanoic acid C19H37O6 17.45 361.2596 361.2577 5.2 343.2472 4 9,10,11,12,13-pentahydroxydocosanoic acid C22H43O7 18.54 419.3014 419.2995 4.5 – 5 9,10,12,13-tetrahydroxyeicosanoic acid C20H39O6 18.61 375.2752 375.2736 4.2 357.2628; 187.0962 6 9,10,11,12,13-pentahydroxytricosanoic acid C23H45O7 19.21 433.3165 433.3150 3.5 – 7 9,10,12,13-tetrahydroxyheneicosanoic acid C21H41O6 19.29 389.2909 389.2890 4.8 371.2782 8 4-O-dimethylbaemycesic acid C18H15O8 19.58 359.0767 359.0756 3.0 – 9 9,10,12,13-tetrahydroxyeicosanoic acid C20H39O6 19.64 375.2747 375.2735 3.2 – 10 9,10,12,13-tetrahydroxydocosanoic acid C22H43O6 19.80 403.3065 403.3047 4.4 385.2940; 215.1274 11 9,10,12,13-tetrahydroxyheneicosanoic acid C21H41O6 19.95 389.2909 389.2892 4.3 371.2782 12 9,10,11,12,13-pentahydroxytetracosanoic acid C24H47O7 20.20 447.3327 447.3306 4.7 – 13 9,10,12,13-tetrahydroxydocosanoic acid C22H43O6 20.37 403.3065 403.3043 5.4 385.2938; 187.0961 14 9,10,12,13-tetrahidroxytricosanoic acid C23H45O6 20.79 417.3222 417.3198 5.7 399.3094 15 3-hydroxyumbilicaric acid C25H21O11 21.25 497.1089 497.1065 4.8 317.0652; 167.0336 16 Gyrophoric acid * C24H19O10 21.27 467.0978 467.0962 3.4 317.0647; 167.0336; 149.0230; 123.0438 17 Placodiolic acid or Pseudoplacodiolic acid C19H19O8 22.04 375.1079 375.1070 2.4 343.0807; 259.0598; 231.0648 18 Arthoniaic acid C29H36O9 22.78 527.2281 527.2290 −1.7 – 19 Pseudoplacodiolic acid or Placodiolic acid C19H19O8 23.65 375.1079 375.1068 2.9 343.0805; 259.0597; 231.0647 20 Lobaric acid * C25H27O8 24.82 455.1711 455.1712 −0.2 411.1808; 367.1909; 352.1675; 296.1049 21 Usnic acid * C18H15O7 26.17 343.0818 343.0803 4.3 328.0583; 259.0612; 231.0663 22 Parietin C16H11O5 27.21 283.0612 283.0601 3.9 – ==== Refs References 1. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081304ijms-17-01304ArticleGenome-Wide Analysis of the Synonymous Codon Usage Patterns in Riemerella anatipestifer Liu Jibin 12Zhu Dekang 2†Ma Guangpeng 3†Liu Mafeng 12Wang Mingshu 12*Jia Renyong 12Chen Shun 12Sun Kunfeng 12Yang Qiao 12Wu Ying 12Chen Xiaoyue 2Cheng Anchun 12*Woo Patrick C. Y. Academic Editor1 Institute of Preventive Veterinary Medicine, Sichuan Agricultural University, Wenjiang, Chengdu 611130, China; eaeas12@163.com (J.L.); liumafengra@163.com (M.L.); cqrc_jry@163.com (R.J.); sophia_cs@163.com (S.C.); 18981613263@163.com (K.S.); yangqiao721521@sina.com (Q.Y.); yingzi_no1@126.com (Y.W.)2 Key Laboratory of Animal Disease and Human Health of Sichuan Province, Sichuan Agricultural University, Wenjiang, Chengdu 611130, China; zdk24@sicau.edu.cn (D.Z.); chenxy_24@sina.cn (X.C.)3 China Rural Technology Development Center, Beijing 100045, China; maguangpeng1977@163.com* Correspondence: mshwang@163.com (M.W.); chenganchun@vip.163.com (A.C.); Tel.: +86-28-8629-1905 (M.W.)† These authors contributed equally to this work. 10 8 2016 8 2016 17 8 130425 6 2016 02 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Riemerella anatipestifer (RA) belongs to the Flavobacteriaceae family and can cause a septicemia disease in poultry. The synonymous codon usage patterns of bacteria reflect a series of evolutionary changes that enable bacteria to improve tolerance of the various environments. We detailed the codon usage patterns of RA isolates from the available 12 sequenced genomes by multiple codon and statistical analysis. Nucleotide compositions and relative synonymous codon usage (RSCU) analysis revealed that A or U ending codons are predominant in RA. Neutrality analysis found no significant correlation between GC12 and GC3 (p > 0.05). Correspondence analysis and ENc-plot results showed that natural selection dominated over mutation in the codon usage bias. The tree of cluster analysis based on RSCU was concordant with dendrogram based on genomic BLAST by neighbor-joining method. By comparative analysis, about 50 highly expressed genes that were orthologs across all 12 strains were found in the top 5% of high CAI value. Based on these CAI values, we infer that RA contains a number of predicted highly expressed coding sequences, involved in transcriptional regulation and metabolism, reflecting their requirement for dealing with diverse environmental conditions. These results provide some useful information on the mechanisms that contribute to codon usage bias and evolution of RA. Riemerella anatipestifercodon usage biasnatural selectionhighly expressed gene ==== Body 1. Introduction Riemerella anatipestifer (RA), belonging to the Flavobacteriaceae family, is a non-spore-forming, rod-shaped, and atrichous Gram-negative bacterium [1]. It can cause a contagious disease in domestic ducks, geese, turkeys, and various other wild birds. To date, more than 21 serovars have been identified [2]. In addition, no cross-protection has been observed with inactivated bacterins made from different serotypes of RA [3]. Thus, RA can easily cause large economic losses in the duck industry over the world. Codon usage bias (CUB) of genes generally exists in prokaryotes and eukaryotes. The genetic code in organisms is not strictly one-for-one code. Most amino acids, except Trp (UGG) and Met (ATG) can allow more than one codon (called synonymous codon). Synonymous codons usually differ by one base in the third codon position (or for some amino acids, in the second position) [4,5]. Among prokaryotes, it is well known that CUB is mainly influenced by mutational bias and natural selection [6,7]. Mutational bias can drive the change in the G + C content of the whole genome. Examples of mutational bias affecting codon usage can be illustrated in many prokaryotes with extremely AT or GC-rich genome [8]. Moreover, CUB may be associated with some other factors, including gene expression level [9,10], gene length [11], amino acid conservation, protein structure [12], gene function [13], and isoaccepting tRNA [14]. There are some variations in codon usage among the genomes of bacteria, which suggests that these genomes bear different pressure in evolution process. CUB analysis has important significance in many aspects. It was proved useful in studying molecular genetic engineering for codon optimization and heterologous protein expression in some species [15]. CUB analysis at genomic scale can also reveal the genetic information about the molecular evolution of individual genes and help to understand evolution of living organisms [16]. Furthermore, CUB can enrich our understanding about the relationship between pathogens and their hosts by analyzing their codon usage patterns [17]. At present, CUB in RA has not been investigated in any detail, and is not clear which factors shape the codon usage pattern. In this study, we analyzed the genome-wide codon usage patterns of 12 RA species. Our results show that natural selection is the main driving factor for codon usage patterns of RA. Additionally, the evolutionary relationship of the species shown in our study is different from that of the traditional classification. 2. Results 2.1. The Codon Usage Pattern between Riemerella anatipestifer (RA) To identify and understand codon usage patterns of RA, the values of relative synonymous codon usage (RSCU) were computed for every codon in each genome. A codon with an RSCU value of more than 1.0 has a positive codon usage bias, while a value of less than 1.0 has a negative codon usage bias. When the RSCU value is equal to 1.0, it means that this codon is chosen equally and randomly [18]. The results showed a general bias toward codons having nucleotides A or U in the third position while U was more frequently detected (Figure 1 and Table 1). There were 30 codons having the high RSCU values (RSCU > 1, Table 1; in bold), and optimal codons (shown in *, Table 1) identified by χ-squared test which were similarly biased. Among the RA strains, it can be clearly observed that the frequencies of UCU (Ser) and CCU (Pro) are considerably high. 2.2. The Codon Usage Bias of RA not Affected by Mutation Bias The preference of A or U in the third position of the codon in RA observed in the RSCU comparative analysis could be due to the overall GC bias within the genome. Differences in GC content were the greatest at the third codon position followed by the first and second positions [19]. The GC3s values of RA strains varied from 27.07% to 26.50% with a mean of 26.6% and standard deviation (SD) of 0.23. The effective number of codons (ENc) has been widely used to measure the codon bias level of individual genes. Among the 12 isolates, the values of ENc were higher than 40 (Table 2) ranging from 45.04 and 45.47. With the mean value of 45.20 and S.D. of 0.16 (p > 0.05), this indicates that CUB has no bias in RA genomes. Plotting ENc versus GC3s is an effective strategy to investigate patterns of synonymous codon usage [20]. The distribution plot of ENc and GC3s values for these genes have been presented in Figure 2. The solid line represents the curve if codon usage is only determined by GC3s. The actual ENc values for some genes lay near to the solid line on the left region of this distribution, and a majority of the points with low ENc values lay below the expected curve. This implies that not only mutation but also other factors are likely to be involved in determining the selective constraints on codon bias in RA genomes. 2.3. Correspondence Analysis (COA) To investigate the synonymous codon usage variation among RA strains, COA was performed on the variation of RSCU value for this study. The coordinate of each coding sequence (CDS) on the two principal axes (Axes 1 and 2) is shown in Figure 3. The relative inertia explained by the first axis in RA contributes approximately 10% of the total variation. It must be remembered that although the first principal axis explains a substantial amount of variation of codon usage among the genes in RA, its value is not remarkably high for relative inertia explained by the first axis in other organisms studied earlier [11,21,22]. The low value might be due to the AT-rich genomic composition of this genome. As it can be seen, these strains of RA isolated from different places, even the same serotype, have the same trend in codon usage variation. The previous studies have shown that the codon usage variation among the genes in the extremely AT or GC rich organisms is only shaped by compositional bias, The third codon position in the preferred codons should also have the base composition of A or T [23,24]. The mutation bias toward a high G + C content seems to have resulted in a preponderance of GC-rich optimal codons [25]. As shown in Table 1, the third positions of optimal codons in RA were preferred in A or T, which suggests that the strongest influence on the choice of codon usage might not be mutation bias, but translation optimality in RA. 2.4. Natural Selection Influences the Codon Bias of RA The GC content is calculated according to the first, second, and third codon positions (P1, P2 and P3 respectively). P12 is the average of P1 and P2, it is used for analysis of neutrality plot (P12 against P3). The neutrality plot is drawn to characterize the correlation among the three codon positions, and then used to estimate the extent of directional mutation pressure against selection on CUB. In the neutrality plot, each point represents one gene (Figure 4). If a gene is under neutral selection pressure, a point should be located on diagonal line with a significant correlation between its P12 and P3. If a gene is close to X-axis, below the diagonal line, meaning the gene is under mutational pressure. Thus, the slope less than 1 should indicate a whole genome trend of non-neutral mutational pressure [26,27]. In this study, all RA species had relative neutralities ranging from 9% to 15% (Figure 4). It means CUB was affected a little by neutral evolution since natural selection was more than 85%. The points in all RA species were located above the diagonal distribution and the regression curve (bold line) with a slope less than 1, indicated the whole genomes in RA species trend of non-neutral mutational pressure. The subsequent correlation analysis revealed little positive correlation between P12 and P3. These results showed that natural selective pressure dominated over mutation shaping the composition of coding sequences. 2.5. Cluster Analysis To gain more insight into evolution of the RA, the RSCU values between 12 species were used in hierarchical clustering (Figure 5B). Cluster analysis for RA family yielded five major clusters, similar to dendrogram based on genomic BLAST by neighbor-joining method (Figure 5A). Cluster I is composed of RA-CH-2, RA-GD, Yb2 and RCAD0122, meanwhile RA-CH-2 and Yb2 stay the closest and are isolated almost from the root. Cluster II contains ATCC11845, RA153, RA-SG and RA-YM. RA-SG and RA-YM appear closely related to RA153, but are on different branches compared to ATCC11845. RA17 has a close relationship with cluster II divided into cluster III. RA-CH-1 and CH3, belonging to the serotype 1, are clustered in cluster IV. The highly biased RA-JLLY is clustered alone as a minor cluster of cluster V and close to the branch of RA-CH-1 and CH3. 2.6. Understanding Pathway Level Functions in RA through CUB The codon adaptation index (CAI) for a gene is a measurement of its optimal codons usage, which is the codon commonly used by highly expressed proteins in a given genome [28]. CAI values of all CDS in RA genomes were calculated using the ribosomal protein codon usages as a reference set. As shown in Figure 6, the CAI values of all RA genes were distributed over a very wide range from 0.3 to 0.8 (the mean value of 0.6), but most of the genes had CAI values between 0.5 and 0.7. Only about 6% of the CAI values were greater than or equal to 0.7. No obvious correlation was observed between CAI values and the corresponding gene lengths (p > 0.05). This implies that codon bias is not the primary mechanism determining the translational efficiency of long genes in RA. Within each RA strain, the top 5% of genes with the highest CAI value were predicted to be highly expressed genes. This is corresponded to CAI cut-off of 0.701 in ATCC11845, 0.698 in RA-CH-1, 0.708 in CH3, 0.691 in RA-CH-2, 0.709 in RA-GD, 0.706 in RA-SG, 0.706 in RA-YM, 0.715 in Yb2, 0.708 in RA-JLLY, 0.703 in RA153, 0.700 in RA17, and 0.706 in RCAD0122 (included about 100 genes for each RA strains), respectively. To further analyze the highly expressed genes estimated by functional analysis, we used blastKOALA based on KEGG annotations [29]. As the limitation of gene annotation and functional studies, about forty-five orthologous high-level expression gene pairs from the all RA genomes were annotated as hypothetical proteins. In this way, their CAI values could rightfully indicate the gene expression level. These hypothetical proteins with the predicted high expression may become attractive candidates for experimental characterization, thus we assumed that they should have important functions in those organisms. Functional analysis showed that only half of genes in all 12 genomes were classified. The high-level expression genes were involved in genetic information processing, carbohydrate metabolism, energy metabolism, metabolism of cofactors and vitamins, nucleotide metabolism and cellular processes (Table 3). The high-level expression genes involved in genetic information processing were the largest functional group. An investigation of the functional categories to which the CAI reference genes (top 1% of genes) belong has revealed that RA contains a significant fraction of ribosomal proteins (large subunit ribosomal in 62.5% and small ribosomal subunits in 37.5%). This is in agreement with the ribosomal criterion defined by Carbone [30], which states that ribosomal proteins have significantly higher CAI value than other protein encoding genes in translationally biased organisms. The rplL encoding ribosomal protein L9 with the highest CAI value (0.834) was one of the most abundant proteins under the rapid growth conditions in RA while codon selection was expected to be effective. The second most high-level expression genes was for various enzymes including carbohydrate metabolism, metabolism of cofactors and vitamins, energy metabolism and nucleotide metabolism. As we know, acnA, mdh, sucC and sucD gene encoding aconitate hydratase, malate dehydrogenase and succinyl-CoA synthetase are participant in tricarboxylic acid cycle (TCA) pathway. Several genes encoding cytochrome, transferases, and ATP synthase were also found in the 12 RA strains. Enolase is involved in secondary metabolism. Apart from ribosomal proteins and enzymes, three genes encoding elongation factor Tu, G, Ts and two chaperone encoding GroEL and DnaK were observed as the high-level expression genes in RA genomes. In addition, the outer membrane protein was also found high in expression. This analysis has offered the prospective method to further carry out the characterization on those genes. 3. Discussion To confirm the observed dominance of mutational bias, the RSCU patterns are conducted in these strains. As a general rule, AT-rich genome of bacteria can result in the dominance of the A/U-ended codons. RA has extremely AT-rich genome, which is the main reason why there are 31 optimized codons ending with A/U among 32 optimized codons. This predominance of A and T at the synonymous sites is better displayed in Table 2, which reveals that amino acid usage is strongly associated with AT content in AT-rich genome [31]. In bacteria with extreme genomic GC compositions, synonymous codon usage could be dominated by strong compositional bias [32].What is more, the mutation is universally biased towards AT in bacteria [33,34]. Therefore, it likely can be concluded that the main force driving codon usage in RA is the strong compositional bias towards A and T. It is reasonable that compositional bias may be a potential bias in the evolution of the codons in RA. The codon usage bias was conserved in RA strains. The RSCUs of each codon were very similar in 12 RA strains. Meanwhile, the distributions of the plot of Axes 1 and 2 in each CDS were almost in the same region. The plot of Axes 1 and 2 of each open reading frame (ORF) shows that there is a quite small amount of the codon usage variation in RA strains. In addition, the COA also has highly negative correlation with the GC3s value, which suggests codon usage variation is directly related to mutational bias. The ENc values of RA genome are all more than 45, which demonstrates that codon usage bias is low in RA strains. The ENc-plot suggests that not only mutational pressure but also other factors affect the codon bias among the genes. This conclusion is also supported by the highly significant correlation analysis. Comparisons of 12 RA species show a significant positive correlation between ENc and GC3s (p < 0.01). Moreover, it is obviously that the codon usage bias has no significant difference by comparing the ENc-plot of 12 RA strains. In summary, the data presented herein reveals that the differences of codon usage are small among different RA strains. Most CAI values of RA genes are near to 0.6 that is lower than other bacteria, such as E. coli, Nocardia farcinica, and Streptomyces coelicolor [35,36,37]. The results provide evidence why RA strains need rich nutrition to grow but still slow and consequently have low environment adaptability. By correlation analysis between average RSCU values of RA ORFs and high/low ENc value groups, there are high correlations between RA ORFs and high/low ENc value groups. The codon usage patterns have no obvious difference between high and low ENc value genes. Hence, gene expression levels only have a weak influence on codon usage bias in RA. Finally, the CAI values were set as the expression level indicator of genes in RA. The notion of gene expression by CAI values was proposed for a long time ago, however, in recent years, the methods have been widely used to qualitatively assess high-level expression genes in prokaryote and eukaryote [38,39,40,41]. Fast development of the whole-genome analysis technologies, especially whole genome sequencing as well as proteomics has made it possible to compare computational data of codon usage and expression ability with experimental evidence. In our research, the highly expressed genes can be considered as the strength of relative codon bias, most of the highly expressed genes are identified by ribosomal proteins genes. Moreover, the genes encoding elongation factor, chaperone proteins, enzymes of essential carbon metabolism pathways of TCA cycle, genes of ATP synthesis, nucleotide biosynthesis, outer membrane protein, transport and binding protein are identified as highly expressed genes in our approach. The study also proves our prediction, based on their codon usage, that some of hypothetical proteins would be highly expressed. Further research of hypothetical proteins by integrated computational and experimental data will enhance our knowledge of the metabolism in RA. 4. Materials and Methods 4.1. Sequences Data A total of 12 RA genomes were used in this study. The coding sequences (CDS) datasets from the whole genome sequences were obtained from National Center of Biotechnology Information (NCBI). To minimize sampling bias in codon usage calculations only CDS of at least 100 codons in length with correct initiation were used in further analysis. Detailed information about these strains is listed in Table 4, and the distribution of these strains except ATCC11845 in the different provinces of China is shown in Figure 7. 4.2. Measurement Indices of Codon Usage Bias In order to normalize codon usage within datasets of different amino acid compositions, relative synonymous codon usage (RSCU) values were calculated by dividing the observed codon usage by the expected ones under the condition that all codons for the same amino acid are used equally. The RSCU was used to compute relative codon frequency. The codon adaptation index (CAI) has been proved to be the best gene expression value index and was extensively used as a measure of gene expression level. The CAI was generally calculated using the codon preference of genes for highly expressed proteins, such as ribosomal proteins and elongation factors. In this study, the values of CAI were calculated using a reference set of ribosomal proteins. Based on the calculated CAI value, 5% of the total genes with extremely high CAI values were regarded as the highly expressed datasets. Effective number of codons (ENc) was often used to quantify the codon usage bias of a gene. The ENc value of a gene could range from 20 (extreme bias where one codon for each codon family was used) to 61 (all synonymous codons were used randomly). As in the previous report, P1, P2, and P3 were calculated after excluding ATG, TGG, ATA, and the stop codons (TAA, TAG, or TGA) [52]. The value of GC3s was the frequency of G + C at the synonymous third position of sense codons and it was employed to better understand the codon usage variation and compositional constraints (i.e., excluding Met, Trp, and termination codons). The ENc value against GC3s was computed, which was assumed equal to the use of G and C (A and T) in degenerate codon groups. The expected ENc value under random codon usage was calculated for any value of GC3s as below: (1) ENc = 2 + s + 29[s2 + (1 − s)2]−1 where s represents the given GC3s value. If the G + C content at the third position is the only determinant factor that shapes the codon usage, the point of ENc should fall on the standard curve described by Formula (1). 4.3. Correspondence Analysis and Cluster Analysis The correspondence analysis (COA) was used to investigate the major trend in codon usage variation among genes of 12 RA strains. The CDS of each gene was represented as a 59 dimensional vector (excluding ATG, TGG, and the stop codons), and each dimension corresponds to the RSCU value of one sense codon. Since the first two axes, compared to the other axes, would be enough to explain the higher fraction of the variance of the data, genes and codons were plotted on these two axes only [53,54]. In the cluster analysis, RA species were clustered according to their RSCU values by hierarchical methods through measurement of the Squared Euclidean distance. 4.4. Software and Statistical Analysis RSCU, ENc, total G + C genomic content, as well as COA, were calculated by CodonW 1.4 version [55]. The heat map was drawn with HemI (Huazhong University of Science and Technology, Wuhan, Hubei, China) [56] and clustered the RSCU values using an average linkage cluster algorithm. Values of CAI, P1, P2 and P3 were calculated by CAIcal Server [57]. The highly-expressed-gene datasets were interpretation of high-level functions by BlastKOALA [58]. Correlation analysis was performed using the statistical software SPSS 19.0 (IBM, Chicago, IL, USA). Graphs were generated with GraphPad Prism 6.0 (GraphPad Software Inc., La Jolla, CA, USA). 5. Conclusions To summarize, our study reveals that codon usage bias in RA is slightly biased, and there is no significant difference between the strains in codon usage. Natural selection is the main factor that affects codon usage variation in RA. Other factors, such as GC content and gene expression also have an influence on codon usage pattern. In addition, all RA strains have the common highly abundant proteins. To our knowledge, this research is the first work of its kind to report of codon usage analysis in RA, and it gives us a basic understanding of the mechanisms for codon usage bias and gene expression during the evolution of RA. Moreover, this study has provided a basis for further studies on the mechanisms of codon usage that affects the RA strains through evolution. Acknowledgments The research was supported by National Natural Science Foundation of China (31572521), Integration and Demonstration of Key Technologies for Duck Industrial in Sichuan Province (2014NZ0030), China Agricultural Research System (CARS-43-8) and Special Fund for Key Laboratory of Animal Disease and Human Health of Sichuan Province (2016JPT0004). Author Contributions Jibin Liu, Dekang Zhu, and Guangpeng Ma designed/performed the experiments and wrote the paper. Mafeng Liu, Shun Chen, Renyong Jia, Xiaoyue Chen, Kunfeng Sun, Qiao Yang and Ying Wu contributed to data collection and helped in data analysis. Mingshu Wang and Anchun Cheng contributed to figure modification. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Comparison of RSCU between 12 different species of RA. The heat-map was drawn by HemI using hierarchical clustering method. The higher RSCU value, suggesting more frequent codon usage, was represented with darker shades of red. In Riemerella anatipestifer (RA) genomes, codons ending in A or U have higher RSCU value than codons ending in G or C. Figure 2 The ENc vs. GC3s plots of RA genomes. (A) ATTCC11845; (B) RA-CH-1; (C) CH3; (D) RA-CH-2; (E) RA-GD; (F) RA-SG; (G) RA-YM; (H) Yb2; (I) RA-JLLY; (J) RA153; (K) RA17; and (L) RCAD0122. The standard curve represents the expected ENc to GC3s. Most RA genes are far away from the standard curve, showing that their codon usage pattern might be affected by other factors besides nucleotide composition. Some genes with the ENc score of 61 display no bias and use all the 61 sense codons. Figure 3 The correspondence analysis (COA) of the genes in RA genomes. (A) ATTCC11845; (B) RA-CH-1; (C) CH3; (D) RA-CH-2; (E) RA-GD; (F) RA-SG; (G) RA-YM; (H) Yb2; (I) RA-JLLY; (J) RA153; (K) RA17; and (L) RCAD0122. Each point represents a gene corresponding to the coordinates of the first and second axes of variation generated from the correspondence analysis. Figure 4 Neutrality plots of RA genomes. (A) ATTCC11845; (B) RA-CH-1; (C) CH3; (D) RA-CH-2; (E) RA-GD; (F) RA-SG; (G) RA-YM; (H) Yb2; (I) RA-JLLY; (J) RA153; (K) RA17; and (L) RCAD0122. Individual genes are plotted based on the mean GC content in the first and second codon position (P12) versus the GC content of the third codon position (P3). Regression lines and Spearman’s rank correlation coefficients (ρ) are shown, with the asterisk (*) denoting p-values < 0.01. Figure 5 Comparison of phylogenetic tree with RSCU based clustering of RA strains. (A) The phylogenetic tree derived for the RA genome using genomic BLAST by neighbor-joining method; (B) Cluster analysis of the 12 species in RA based on RSCU value. The observed distances range from 1 to 25, the ratio of the rescaled distances within the dendrogram is the same as the ratio of the original Squared Euclidean distances. Figure 6 Frequency distribution of codon adaptation index (CAI) values for coding sequence (CDS) in the genomes of RA strains. Figure 7 Geographical location of the RA analyzed in this study. The main distribution of duck industries in China are shown in dark gray. The provinces (regions) of the RA strains in this study are indicated in red. ijms-17-01304-t001_Table 1Table 1 The RSCU analysis of the preferred codons, the optimal codons and the rare codons for RA. Amino Acids Codon RSCU 1 Amino Acids Codon RSCU 1 Ala GCG 0.49 Pro CCC 0.31 GCC 0.61 CCG 0.33 GCU * 1.66 CCA * 1.27 GCA * 1.24 CCU * 2.10 Cys UGC 0.57 Arg AGG 0.37 UGU * 1.43 CGC 0.71 Asp GAC 0.55 CGG 0.19 GAU * 1.45 AGA * 1.63 Glu GAG 0.60 CGA 1.14 GAA 1.40 CGU * 1.96 Phe UUC 0.47 Ser AGC 0.68 UUU * 1.53 UCC 0.46 Gly GGG 0.66 UCG 0.49 GGC 0.56 AGU * 1.32 GGU * 1.36 UCA 0.80 GGA * 1.42 UCU * 2.24 His CAC 0.80 Thr ACC 0.89 CAU * 1.20 ACG 0.50 Ile AUC 0.55 ACA * 1.20 AUU * 1.41 ACU * 1.41 AUA 1.04 Val GUG 0.82 Lys AAG 0.47 GUC 0.18 AAA * 1.53 GUU * 1.26 Leu CUC 0.51 GUA * 1.74 CUG 0.39 Try UAC 0.62 UUG 0.68 UAU * 1.38 CUA * 1.30 Gln CAG 0.57 CUU * 1.81 CAA * 1.43 UUA * 1.32 Stop UGA 0.20 Asn AAC 0.66 UAG 0.61 AAU * 1.34 UAA * 2.19 Met AUG 1.00 1 Average value of RSCU in 12 RA genomes; * represents the optimal codons (p-value < 0.01). The preferred codons (RSCU > 1) are in bold. ijms-17-01304-t002_Table 2Table 2 Characteristic in the indices of codon bias of RA genes. RA Strain GC% GC3s% ENc CAI ATCC11845 35.42 ± 3.63 26.50 ± 5.75 45.04 ± 5.13 0.616 ± 0.062 RA-CH-1 35.51 ± 3.85 27.05 ± 6.28 45.47 ± 5.15 0.604 ± 0.064 CH3 35.59 ± 3.89 27.07 ± 6.30 45.46 ± 5.17 0.582 ± 0.070 RA-CH-2 35.39 ± 3.62 26.64 ± 5.98 45.19 ± 5.19 0.616 ± 0.064 RA-GD 35.33 ± 3.64 26.67 ± 5.95 45.16 ± 5.20 0.602 ± 0.069 RA-SG 35.40 ± 3.60 26.52 ± 5.17 45.10 ± 5.07 0.609 ± 0.063 RA-YM 35.50 ± 3.58 26.69 ± 5.80 45.15 ± 5.09 0.610 ± 0.063 Yb2 35.34 ± 3.64 26.50 ± 5.74 45.12 ± 5.14 0.613 ± 0.062 RA-JLLY 35.45 ± 3.89 26.91 ± 6.25 45.34 ± 5.21 0.605 ± 0.067 RA153 35.44 ± 3.61 26.57 ± 5.86 45.18 ± 5.18 0.604 ± 0.064 RA17 35.52 ± 3.59 26.55 ± 5.77 45.14 ± 5.22 0.690 ± 0.064 RCAD0122 35.40 ± 3.65 26.69 ± 5.86 45.21 ± 5.14 0.607 ± 0.064 ijms-17-01304-t003_Table 3Table 3 Orthologous high-level expression genes found in 12 RA strains. Category Gene Proteins Strains Ribosome rplA Large subunit ribosomal protein L1 RA-CH-1, RA-GD, RA17 rplB Large subunit ribosomal protein L2 + rplD Large subunit ribosomal protein L4 + rplE Large subunit ribosomal protein L5 + rplF Large subunit ribosomal protein L6 + rplL Large subunit ribosomal protein L7/L12 + rplI Large subunit ribosomal protein L9 + rplJ Large subunit ribosomal protein L10 + rplK Large subunit ribosomal protein L11 RA-CH-1 rplN Large subunit ribosomal protein L14 + rplO Large subunit ribosomal protein L15 + rplP Large subunit ribosomal protein L16 RA-CH-2, CH3, ATCC11845 rplQ Large subunit ribosomal protein L17 + rplR Large subunit ribosomal protein L18 + rplS Large subunit ribosomal protein L19 + rplU Large subunit ribosomal protein L21 + rplV Large subunit ribosomal protein L22 + rplX Large subunit ribosomal protein L24 + rpsA Small subunit ribosomal protein S1 + rpsB Small subunit ribosomal protein S2 + Ribosome rpsC Small subunit ribosomal protein S3 + rpsD Small subunit ribosomal protein S4 + rpsE Small subunit ribosomal protein S5 RA-CH-1, CH3 rpsG Small subunit ribosomal protein S7 + rpsH Small subunit ribosomal protein S8 Except RA17, RA-GD rpsI Small subunit ribosomal protein S9 + rpsK Small subunit ribosomal protein S11 + rpsO Small subunit ribosomal protein S15 + rpsP Small subunit ribosomal protein S16 CH3 rpsR Small subunit ribosomal protein S18 + Elongation factor tuf Elongation factor Tu + fusA Elongation factor G + tsf Elongation factor Ts + Chaperone dnaK Molecular chaperone DnaK Except CH3 groEL Chaperonin GroEL + tig Trigger factor + Enzymes acnA Aconitate hydratase + sucC Succinyl-CoA synthetase β subunit + sucD Succinyl-CoA synthetase α subunit + mdh Malate dehydrogenase + gapA Glyceraldehyde 3-phosphate dehydrogenase + ccoP Cytochrome c oxidase cbb3-type subunit III + ccp Cytochrome c peroxidase + atpA F-type H+-transporting ATPase subunit α RA-CH-1, RA-CH-2, RA17, ATCC11845 atpF F-type H+-transporting ATPase subunit b Except CH3 pncA Nicotinamidase/pyrazinamidase + ndk Nucleoside-diphosphate kinase + tlpA Alkyl hydroperoxide reductase/thiol specific antioxidant/mal allergen + ppiA Peptidyl-prolyl isomerase + dsrO Molybdopterin-containing oxidoreductase RA-CH-1 katE Catalase RA153, ATCC11845, Yb2, RA-SG ahpC Peroxiredoxin Except RA153, RA17 sdhB Succinate dehydrogenase/fumarate reductase RA153, RA17, RA-SG, RA-YM eno Enolase + Enzymes nrfA Nitrite reductase RA-CH-2, RA153, RA17, RA-GD dam DNA adenine methylase RA17, ATCC11845, RA-GD tatD TatD DNase family protein RA-CH-1 pabC 4-Amino-4-deoxychorismate lyase RA-JLLY ald Alanine dehydrogenase RA-JLLY ribBA 3,4-Dihydroxy 2-butanone 4-phosphate synthase RA-JLLY - Peptidase s8 and s53 subtilisin kexin sedolisin + - Peptidase s46 RA17 - Putative FAD dependent oxidoreductase RA17 - Septum formation initiator RA17 - Serine protease ATCC11845 - Nodulation protein X acyltransferase 3 ATCC11845 Binding protein - Cyclic nucleotide-binding protein Except RA17 Transport protein arac Transcriptional regulator RA17 Apoptosis protein cys Cytochrome c Except RA17, RA-JLLY Structure protein gldl Gliding motility protein gldl RA17, ATCC11845 ompH Outer membrane protein + ompa/motb ompa/motb domain-containing protein + ftnA Ferritin RA153, ATCC11845, Yb2, RCAD0122 hinT Histidine triad (HIT) family protein CH3 - Phosphate-selective porin o and p protein RA-CH-1 + represents the gene found in all RA strains. ijms-17-01304-t004_Table 4Table 4 RA strains used in this study. Strain Serotype Geographic Location Accession No. CDS CDS (>300 bp) Reference ATCC11845 6 USA CP003388. 1941 1764 [42,43] RA-CH-1 1 Sichuan CP003787 2187 1953 CH3 1 Jiangsu CP006649 2181 1916 [44] RA-YM 1 Hubei AENH00000000 2010 1796 [45] RA-CH-2 2 Sichuan CP004020 2044 1844 [46] RA-GD 2 Guangdong CP002562 1985 1815 [47] Yb2 2 Jiangsu CP007204 2021 1877 [48] RA153 2 Fujian CP007504 1919 1730 RA17 ND 1 Fujian CP007503 1656 1613 RA-SG ND 1 Guangdong ANGF00000000 2066 1838 [49] RA-JLLY ND 1 Hubei LAVB01000000 2089 1858 [50] RCAD0122 ND 1 Guangdong LUDU00000000 2149 1892 [51] 1 ND: Not determined. ==== Refs References 1. Subramaniam S. Chua K.L. Tan H.M. Loh H. Kuhnert P. Frey J. Phylogenetic position of Riemerella anatipestifer based on 16S rRNA gene sequences Int. J. Syst. Bacteriol. 1997 47 562 565 10.1099/00207713-47-2-562 9103649 2. Swayne D.E. Glisson J.R. McDougald L.R. Diseases of Poultry Wiley-Blackwell Hoboken, NJ, USA 2013 811 813 3. Chang C.F. Lin W.H. Yeh T.M. Chiang T.S. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081305ijms-17-01305ArticleCharacterizing Aciniform Silk Repetitive Domain Backbone Dynamics and Hydrodynamic Modularity Tremblay Marie-Laurence 1Xu Lingling 1Sarker Muzaddid 1Liu Xiang-Qin 1Rainey Jan K. 12*Hardy John G. Academic Editor1 Department of Biochemistry & Molecular Biology, Dalhousie University, Halifax, NS B3H 4R2, Canada; mltremblay@dal.ca (M.-L.T.); ln217415@dal.ca (L.X.); muzaddid.sarker@dal.ca (M.S.); paul.liu@dal.ca (X.-Q.L.)2 Department of Chemistry, Dalhousie University, Halifax, NS B3H 4R2, Canada* Correspondence: jan.rainey@dal.ca; Tel.: +1-902-494-463210 8 2016 8 2016 17 8 130529 6 2016 04 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Spider aciniform (wrapping) silk is a remarkable fibrillar biomaterial with outstanding mechanical properties. It is a modular protein consisting, in Argiope trifasciata, of a core repetitive domain of 200 amino acid units (W units). In solution, the W units comprise a globular folded core, with five α-helices, and disordered tails that are linked to form a ~63-residue intrinsically disordered linker in concatemers. Herein, we present nuclear magnetic resonance (NMR) spectroscopy-based 15N spin relaxation analysis, allowing characterization of backbone dynamics as a function of residue on the ps–ns timescale in the context of the single W unit (W1) and the two unit concatemer (W2). Unambiguous mapping of backbone dynamics throughout W2 was made possible by segmental NMR active isotope-enrichment through split intein-mediated trans-splicing. Spectral density mapping for W1 and W2 reveals a striking disparity in dynamics between the folded core and the disordered linker and tail regions. These data are also consistent with rotational diffusion behaviour where each globular domain tumbles almost independently of its neighbour. At a localized level, helix 5 exhibits elevated high frequency dynamics relative to the proximal helix 4, supporting a model of fibrillogenesis where this helix unfolds as part of the transition to a mixed α-helix/β-sheet fibre. aciniform spidroin (AcSp1)wrapping silkrecombinant spider silkmodular proteinsreduced spectral density mappinghydrodynamics characterizationnuclear magnetic resonance spectroscopysplit inteinsegmental-labelling ==== Body 1. Introduction Spider aciniform (or wrapping) silk is the toughest type of silk and is a remarkable biomaterial with outstanding mechanical properties [1]. Spider silk proteins (spidroins) and silkworm silk proteins (fibroins) share a general architecture of a relatively long repetitive domain, comprising a concatenated series of repetitive units or sequence motifs, flanked by much shorter non-repetitive N- and C-terminal domains [2,3]. Aciniform spidroin (AcSp1) is the primary constituent of wrapping silk. In Argiope trifasciata, it is a modular protein containing at least 14 identical concatenated repeats of a 200 amino acid unit (termed here “W” units, from wrapping) [4]. Modular protein architecture, in which discrete structured modules are connected together by linkers that range from rigid to highly flexible, is common in nature [5,6,7]. The structure of individual domains are frequently studied in isolation by nuclear magnetic resonance (NMR) spectroscopy and/or X-ray diffraction, then placed in a multi-domain context through NMR spectroscopy [8,9], small angle X-ray scattering [10], or cryo-electron microscopy [11], allowing delineation of structure and dynamics in the context of the larger assembly. The orientation of domains relative to one another, their dynamics, and the relation between domains is crucial for expanding our understanding of their function [9,12,13]. Many multi-domain proteins comprise discrete, differing units (e.g., scaffolding units such as the SH2, SH3, PDZ, or PTB domains) connected by linkers with both widespread pathophysiological consequences [14] and potential for recombination for synthetic biology purposes [15]. From an NMR spectroscopy standpoint, di-ubiquitin [16,17] and GB1 [18] have been extensively studied as model multi-domain proteins. In contrast to these proteins, where discrete modules impart individual function, many fibrous proteins including spider silks employ repetitive modules [2]. This adds unique difficulties for structural biology, where the repetitive nature of these proteins leads to challenges in unambiguously tracking individual modules. We recently employed NMR spectroscopy to determine the solution-state structure of the recombinant W unit of A. trifasciata AcSp1 in the context of both the single unit (W1) and the two-unit concatemer (W2) [19]. W1 is composed of a predominantly helical globular domain composed of five defined α-helices with an unstructured ~12 residue N-terminal tail and ~50 residue C-terminal tail. In W2, the tails of neighbouring units become a linker that retains intrinsically disordered behaviour while the globular domains are identically structured giving rise to beads-on-a-string type conformation. Fibres cannot be formed from solutions of W1, but manual drawing of fibres is readily possible from solutions containing W2, W3, or W4 concatemers [20], including from solution-state NMR samples of W2 [19]. During fibre formation, AcSp1 undergoes a partial conversion from α-helical to β-sheet structuring [21], putatively seeded at helix 5 in the W unit [19,22]. This transition is recapitulated in recombinant W2 between the soluble and fibrous forms [19]. The 200 residue W unit from A. trifasciata differs significantly from other spider silks, such as the extensively studied major and minor ampullate silks, where short repetitive motifs such as An, (GA)n, GGX or GPGXX dominate the protein sequence [1,2]. Silkworm fibroin is also dominated by short motifs (e.g., GAGAGS and GAGAGY) in its repetitive domain [3,23]. Echoing these differences in primary structuring, the retention of α-helical character in aciniform silk fibre is distinct from both ampullate silks and silkworm silk, where fibres are completely depleted of α-helical character [1,2,21]. Hence, although recombinant W proteins are much shorter than native aciniform silk proteins and lack non-repetitive N- and C-terminal domains, with reduced strength and extensibility relative to native silk that scale approximately with the number of W units [24], the structural behaviour of these proteins is consistent with the native protein. Characterization of dynamics within proteins at the atomic-level is possible at a variety of time-scales using NMR spectroscopy [25,26]. Measurement of 15N nuclear spin relaxation properties, namely the longitudinal and transverse relaxation times (T1 and T2, respectively) and cross-relaxation through the heteronuclear 1H-15N nuclear Overhauser effect ([1H]-15N NOE) in particular, allow for characterization of small-amplitude, high-frequency motions as a function of position along the polypeptide backbone together with delineation of regions experiencing slower dynamic fluctuations [27]. Relation of these spin relaxation parameters to global and local motion is often carried out through the model-free [28] or extended model-free [29] approaches. In instances where a single global correlation time is not suitable, such as for proteins containing large unstructured regions, the reduced spectral density mapping approach provides residue-by-residue characterization of dynamics without a reliance on global rotational diffusion parameters [30]. These dynamics characterization methods rely upon unambiguous distinction of 1H-15N cross-peaks in 2D spectra, a situation impossible in concatemeric AcSp1 repeat units without some means to distinguish one W unit from the other. The technique of intein-mediated trans-splicing provides such a means, where individual W1 units can be selectively labelled with NMR active isotopes and investigated, in the present work, in the context of the larger fibre-forming W2 protein. Inteins are naturally-occurring protein segments that excise themselves from a polypeptide and ligate the flanking protein segments together with a native peptide bond. This reaction will occur provided that a nucleophilic Ser or Cys is present immediately C-terminal to the intein and that the intein-protein fragment pair are stable in solution and allow the ligation to occur [31,32]. There have now been many demonstrated applications of inteins in structural biology and biotechnology [32,33], including protein cyclization [34,35,36]; protein switches [37,38,39,40]; in vivo protein engineering and probe attachment for biophysical studies [41,42,43]; and, importantly for the present work, segmental isotope enrichment [19,31,44,45,46,47,48]. In our previous structural studies [19], we performed segmental-labelling using split intein trans-splicing [49,50], whereby either the first (W2-1) or second (W2-2) W unit in W2 was enriched with NMR-active 13C and/or 15N nuclei while the other W unit was at natural abundance. Although we demonstrated differential dynamics between the globular core and linker/tail regions through variation in the observed heteronuclear [1H]-15N NOE, a more in-depth analysis of backbone dynamics is necessary to compare and contrast the behaviour of isolated W units vs. concatemers and to provide insight into more subtle variations in dynamics within the W unit. Herein, new insight has been gained into the modularity, global conformation, and localized backbone dynamics behaviour of spider wrapping silk concatemers through characterization of ps–ns timescale NMR relaxation behaviour of each W unit in W2 relative to one another and to W1. In W1 and in each W unit of W2, reduced spectral density mapping is consistent with a structured five α-helix globular core having elevated dynamics in helix 5 with intrinsically disordered N- and C-terminal tails and, in W2, linker. Nuclear spin relaxation data are consistent with rotational diffusion by a compact globular core in W1 and with modular tumbling of each W unit in W2 almost independently of the remainder of the protein. Beyond ramifications for AcSp1 behaviour, the methods presented herein will serve as a useful atomic-level model for further characterization of modular proteins in solution. 2. Results 2.1. Nuclear Spin Relaxation Parameters Longitudinal (T1) and transverse (T2) relaxation time constants were measured at 16.4 T on a residue-by-residue basis [27] for both the monomeric (W1) and concatemeric (W2) states of recombinant AcSp1 (Figure 1). To facilitate direct comparison, and because these data are integral for the subsequent analysis, the [1H]-15N data that we previously reported [19] are also plotted in Figure 1. A segregation in spin relaxation behaviour is clear between (i) the folded domain (residues 12–149 for a given W unit, with secondary structure elements shown in linear form in Figure 1) and (ii) the N- and C-terminal tails of W1 and W2 and the linker spanning W2-1 to W2-2 in W2. Namely, T1 and the [1H]-15N NOE are elevated through the folded core of a given W unit and decrease in the linker and tail regions while T2 exhibits the opposite trend. For direct overall comparison, mean values of T1 and T2 were determined for four subdivided regions of the W unit chosen based upon our previous structural and 19F-NMR studies: the globular core (residues 12–149 and, in W2, 212–349), helix 5 within the core (residues 135–149 and, in W2, 335–349), tails (W1: residues 1–11 and 150–199; W2: residues 1–11 and 350–400), and the linker (residues 150–211 in W2 only) (Figure 2). Qualitatively, T1 is larger while T2 is smaller in the globular core for each W2 subunit relative to W1. The observed behaviour is consistent with the 15N relaxation behaviour to be expected on the basis of more rapidly (W1) vs. slowly (W2) tumbling molecules [27]. Notably, T1 values for W1, W2-1, and W2-2 are significantly different for the core (p-value < 0.0001) and helix 5 (p-value < 0.01), while the tails are relatively similar (albeit with large variance) regardless of protein size. In examining overall T2 behaviour (Figure 2), the most striking feature is the large difference in mean values between the globular core and the disordered tails/linker, with a significant elevation in T2 for the tails or linker in all W units. As with T1, W1 exhibits a significant difference in behaviour from both W2-1 and W2-2 (p-value < 0.0001), with an elevated T2 relative to either unit in W2. Although the significance is low (p-value < 0.1), helix 5 follows the same qualitative trend. Unlike with T1, however, W2-1 and W2-2 do not exhibit significant differences in T2 relative to each other. Although the tails would be expected to be less encumbered and more dynamic overall, there is no difference between the mean values observed for the tails and the linker. 2.2. Reduced Spectral Density Mapping The values of the reduced spectral density at J(0), J(ωN), and J(0.87ωH) were calculated independently for each W unit. All residues with T1 and T2 fits that met the goodness-of-fit criterion (χ2 < critical χ2 [51]) for a given dataset at 16.4 T were employed for spectral density determination, giving 145, 153, and 131 residues, respectively, for W1, W2-1, and W2-2. J(0.87ωH) and J(0) both strongly demonstrate disparate dynamics between the folded core and the tails/linker, mirroring the behaviour of the individual T1, T2, and [1H]-15N NOE parameters (Figure 3 and Figure 4). J(ωN), conversely, remains relatively uniform throughout all regions of a given unit of W1 or W2, without significant differences between globular and linker domains. Reflecting the relatively strong dependence of J(ωN) on T1, an expected decrease in J(ωN) is observed from W1, W2-1, to W2-2 for the globular core (p-value < 0.0001), with helix 5 following suit (p-value < 0.01), while those for the tails and linker are not significantly different between the W units. J(0.87ωH), which is very sensitive to differences in motion in the high frequency regime [52], exhibits localized increases in W1 and in each W unit of W2 around residues 36, 63, 80, 121, and 132 (Figure 3), correlating directly to the locations of loops or turns within the W unit [19]. It should be noted, though, that the increases in J(0.87ωH) observed at these locations are not of the same magnitude as those seen for the linker or tails (Figure 3). These variations are, therefore, likely reflective of regions of the protein experiencing increased dynamics but tumbling with the core of the folded domain rather than behaving as intrinsically-disordered segments. A statistically significant increase in J(0.87ωH) is also observed for helix 5 (residues 135–149) relative to helix 4 (residues 101–127) (p-values of 0.0060, 0.0001, and 0.0025 for W1, W2-1, and W2-2, respectively) or to the core (helices 1–4) (p-values of 0.019, 0.0011, and 0.0085 for W1, W2-1, and W2-2, respectively). Helix 1, like helix 5, is directly connected to a disordered tail or linker segment [19]; therefore, helix 1 would be expected to exhibit similarly elevated dynamics if proximity to a tail or linker were the only factor at play. Instead, helix 1 behaves more like a core helix, as demonstrated by a lack of significant differences in J(0.87ωH) for helix 1 vs. helix 4 (p-values of 0.11, 0.046, and 0.126 for W1, W2-1, and W2-2, respectively). There is also a qualitative difference between J(0.87ωH) of helix 5 in the W units, with W1 > W2-1 > W2-2 (Figure 4) following the same qualitative trend as the core as a whole. Like J(ωN), the behaviour at J(0) differs between W1, W2-1, and W2-2 in the globular core (p-value < 0.0001) and helix 5 (p-value < 0.001), increasing from W1, to W2-1 and W2-2 and opposite in trend to the observed decrease in J(ωN). W1 has the lowest mean frequency at J(0) over the folded core (4.26 ± 0.07), followed by W2-1 (5.02 ± 0.11), and then W2-2 (5.67 ± 0.37) (Figure 4). On average, the tail/linker regions do differ between W1, W2-1, and W2-2 but care must be taken during interpretation given that the tail group for W2-1 has seven values and W2-2 linker has six values. When the tails and linker are grouped together, there is no statistical difference between the W units. 2.3. Analysis of Rotational Diffusion Spin relaxation data were used to compare the suitability of isotropic, axially symmetric, or fully anisotropic rotational diffusion tensors for W1, W2-1, W2-2, and W2 based upon all residues with [1H]-15N NOE > 0.65 [53] using the software ROTDIF [54]. In each instance, an axially symmetric rotational diffusion tensor provided the best fit to the data (Table 1); notably, the degree of anisotropy observed was minimal in light of the fact that ~90% of an 878 monomeric protein dataset exhibited an anisotropy of 1.17 or higher [54]. W1 spin relaxation data were best fit with a prolate rotational diffusion tensor for all 20 members of the NMR structural ensemble (PDB entry 2MU3 [19]), with an anisotropy range of 1.09–1.18. Conversely, W2-2 was uniformly oblate (anisotropy 0.90–0.95 with W1 ensemble) while W2-1 varied depending upon the ensemble member employed, with anisotropy of 1.05–1.16 (16 members) or 0.88–0.95 (four members) observed. The fitting behaviour for W2-1 is consistent with the small degree of anisotropy observed, with minimal deviation from an isotropic fit. Additionally, diffusion tensors were modelled for a combined W2-1 and W2-2 spin relaxation data set using the ensemble of 20 inferred W2 structures [19]. In this instance, anisotropy remained minimal and most ensemble members led to oblate fits (anisotropy 0.8–0.92 for 18 members; 1.12–1.13 for two members). Goodness-of-fit, as judged by χ2 per degrees of freedom, was comparable in all instances (Table 1). Ensemble-averaged values of τc based upon anisotropic diffusion tensors demonstrated only modest increases from 7.9 ns for W1 to 9.0 ns for W2-1 to 9.6 ns for W2-2. To place this behaviour in context, a variety of rotational correlation time (τc) estimations were compared (Table 2). Using Stokes’ law (Equation (5)), crude values of τc were estimated both according to an assumption of spherical shape (Equation (6)) and according to our previously reported hydrodynamic radii determined by diffusion ordered NMR spectroscopy (DOSY) and verified by dynamic light scattering [19]. Our NMR-derived W1 structural ensemble and the inferred W2 structural ensemble were also used for detailed hydrodynamics calculations in HYDROPRO [55] to estimate τc. To test the effect of a more compact globular tumbling unit, τc values were also determined for the W1 globular core (i.e., excluding the N- and C-terminal tails) with and without the inclusion of the more dynamic (Figure 4) and less stable helix 5 [19,22]. It should also be noted that the viscosities employed for the Stokes’ law and HYDROPRO hydrodynamics calculations (Table 2) were experimentally determined, rather than estimated. This determination was carried out through DOSY experiments with use of an internal dioxane standard [56]. Estimated τc values for W1 were most consistent with the experimentally-observed behaviour for the most compact estimates of its conformation. W2, conversely, was estimated regardless of the hydrodynamic model employed to tumble much more slowly than was experimentally observed for each globular unit in the concatemer. The ratio of T1 to T2 can also be related to τc for the tumbling of a macromolecule in solution under the qualification that the 1H-15N spin pair in question does not experience significant rapid internal motion [27]. Extending this treatment, measured backbone relaxation time constants may be modified (giving T1’ and T2’, Equations (1) and (2), respectively) to remove high frequency spectral density contributions [17]. The ratio of these modified relaxation time constants, ρ (Equation (3)), is relatively insensitive to localized variations in dipolar coupling and 15N chemical shift anisotropy and, for a protein core, primarily dependent on overall tumbling. Direct comparison of the estimated τc obtained from average values of T1/T2 or T1′/T2′ on the basis of the 15N relaxation analysis of Kay et al. [27] neglecting high-frequency terms (Equation (7)) demonstrates excellent agreement with the far more rigorous ROTDIF calculation. At a more global level, discrete differences in 1/ρ are apparent between the globular core vs. tail and linker regions of W1, W2-1, and W2-2 (Figure 5), with the core of W1 being significantly decreased in 1/ρ (p-value < 0.0001) relative to W2-1 or W2-2 and mirroring of this behaviour by helix 5 (p-value < 0.01) (Figure 5B). Following the same trend as J(0) (Figure 4), W1 has a statistically significantly lower mean (p-value < 0.0001) 1/ρ in the core compared to W2-1; W2-1 is again significantly lower than W2-2 (p-value < 0.0001), reflecting differences in rotational diffusion between W1 and each of the W units in W2 (Figure 5; Table 1). Additionally, regardless of being in a tail or linker, the corresponding residues in a given W unit in W2 (i.e., residues 1–11 relative to 201–211 and 150–200 relative to 350–400) demonstrate very similar mean 1/ρ values. The variance accompanying 1/ρ is, however, too great to draw significance. 3. Discussion AcSp1 from A. trifasciata is a modular protein composed primarily of a repetitive domain of concatenated 200 amino acid W units [4]. We recently demonstrated that the W unit is composed of a well-folded globular domain of ~138 residues connected to adjacent globular domains by intrinsically-disordered linkers ~62 residues in length [19]. The functional necessity of this folded domain is further implied from the fact that it appears to be highly conserved (albeit considering a limited number of sequenced species), while the linker may vary both in length and sequence [57]. The modularity of AcSp1 was established through direct backbone chemical shift comparison between the monomeric (W1) and concatemeric (W2) states of AcSp1. Specifically, the chemical shifts of W2 are remarkably similar to W1 with exception of those in the linker immediately proximal to the covalent W unit linkage [19]. Beyond conservation of chemical shifts, heteronuclear [1H]-15N NOE data recorded at 16.4 T (Figure 1) also uphold the conformational independence of W units, given that W1 and each of the W units in W2 exhibit very similar NOE enhancement factor patterns as a function of position within the W unit [19]. In each case, higher NOE enhancement factors are exhibited in the folded domain (residues 12–149, numbering relative to each W unit) and lower or negative enhancements in the disordered terminal or linker regions (residues 1–11, 150–200) (Figure 1). The effect of concatemeric linking of W units is observed in the vicinity of the covalent linkage of the W units (residues ~190 to 210 of W2) through a less negative NOE enhancement relative to the free N- and C-terminal tails of W1 and of W2-1 and W2-2, respectively. Our previous studies showed clear modularity in the W unit both in terms of structuring of the globular domain and the intrinsic disorder of the linker. The 15N spin relaxation measurements and reduced spectral density mapping detailed herein demonstrate that this modularity clearly extends beyond structuring and into the dynamic behaviour along the polypeptide backbone. Segmental isotope-labelling mediated by split intein trans-splicing allowed us to track this behaviour unambiguously along the length of W2. It should be noted that the relaxation analysis methods employed herein are limited to probing motions in the ps-ns regime [26], but that the trans-splicing methodology, itself, can be directly applied to other NMR-based methods allowing characterization of longer time-scale motions. Direct comparison of the N- and C-terminal W units in the concatemer was, thus, possible alongside a comparison of W1 to W2. T1, T2, [1H]-15N NOE, and 1/ρ, in each case, explicitly delineate the globular vs. tail or linker domains (Figure 1, Figure 2 and Figure 5). For the most part, the globular domain exhibits uniform spin relaxation behaviour, with slight localized decreases in the [1H]-15N NOE delineating the secondary structure elements centred at residue 35–37 (between helix 1 and the converged, predominantly helical region over residues 40–60), residue 61 (between residues 40–60 and helix 2), residues 79–80 (between helices 2 and 3), residues 91–93 (near the helix 3 C-terminus), and residues 128–132 (between helices 4 and 5). Given the existence of discrete disordered and globular domains in the W unit, we employed the 15N reduced spectral density mapping approach [30] to analyze backbone dynamics, rather than the model-free [28] or extended model-free [29] formalism. This approach alleviates the requirement for defining specific motional modes and their independence or lack thereof, with demonstrated suitability for proteins of mixed ordered and disordered segments [52]. The resulting values of the spectral density function at three frequencies, J(0), J(ωN), and J(0.87ωH), derived from relaxation parameters T1, T2, and [1H]-15N NOE provide complimentary information along the peptide backbone (Figure 3 and Figure 4) and unequivocally support our original model of W2, based on W1 restraints [19], with the tail/linker regions and the globular domain being noticeably segregated. J(ωN) is much less sensitive to internal motions at the ps-ns time scale in comparison to J(0) and J(0.87ωH) and displays very little variability between the folded domain and the linker. The residues in the folded domain have large J(0) and small J(0.87ωH), while the linker and tail regions display the opposite trend (Figure 3 and Figure 4). This is consistent with a situation where the linker and tails experience motion over a wider range of frequencies relative to the globular domain, as would be expected for an intrinsically disordered domain. Noticeably, the mean J(0) and J(ωN) values over the globular domain differ significantly between W1, W2-1, and W2-2, increasing or decreasing, respectively, from W1 to W2-1 to W2-2. This behaviour is consistent with an increase in tumbling rates from W1 to W2-1 to W2-2. Based upon both heteronuclear NMR [19] and 19F-NMR [22], helix 5 (residues 135–149) and the portion of the globular domain in contact with it (falling in proximity to residue 36) are less stable than the remainder of the protein. Chaotropic denaturation or treatment with the detergent dodecylphopshocholine lead to helix 5 destabilization and a concomitant structural rearrangement in the globular core of the W unit [19,22]. Notably, therefore, the spectral density in helix 5 deviates from the remainder of the globular domain. Direct comparison to the proximal helix 4 shows elevated spectral density at J(0.87ωH). J(0) is also qualitatively lower for helix 5 than for the remainder of the globular core (helix 1–4) (Figure 3). This behaviour, as a whole, is consistent with a greater sampling of high-frequency motion in this region of the W unit regardless of whether it is in an isolated W unit or a concatemeric construct. Helix 5 falls immediately N-terminal to the intrinsically disordered linker. Our working hypothesis is that decompaction of the W unit occurs through loss of interaction of helix 5 with the core [22] followed by denaturation [19]. This would, in turn, greatly favour protein-protein entangling and interaction, inducing subsequent β-sheet formation during fibrillogenesis. Backbone-level dynamics are consistent with the distinct behaviour of helix 5 relative to the remainder of the globular domain and unambiguously demonstrate a propensity for increased high frequency motion. Before considering rotational tumbling behaviour in more detail, it should be noted that the viscosities measured for the W1 and W2 samples in NMR buffer (Table 2) are significantly higher than the ~0.82 cP that is derived on the basis of a linear combination of the expected [58] H2O and D2O viscosities at 30 °C. The source of this elevated viscosity is not fully clear. Given that W1 will, for example, spontaneously form nanoparticle (or micellar) structures in aqueous solution [59], supramolecular assembly was certainly a distinct possibility. Neither spin relaxation behaviour (Table 2) nor translational diffusion observed by DOSY [19] are consistent with long-lived entanglement of the proteins or of stable oligomer formation. Were entanglement, oligomerization, or nanoparticle/micelle formation happening in the bulk of the sample, substantially slower tumbling and diffusion than observed would be expected. The fact that the vast majority of protein in solution is still fully observable on the basis of both spin relaxation behaviour and signal intensity by heteronuclear ([19] and herein) and 19F [22] NMR implies that if intermolecular entangling and/or supramolecular assembly are occurring and increasing solution viscosity that this only involves a small fraction of the total protein. Tumbling of W1, reflected in the observed τc of 7.9 ns, is more rapid than would be anticipated strictly on the basis of the W1 hydrodynamic radius previously determined though DOSY [19] or through hydrodynamics calculations using the W1 structural ensemble (Table 2). W1, instead, exhibits tumbling consistent with a compact spherical particle of the same molecular weight with a half-shell of water. The overestimates in τc on the basis of overall W1 shape and dimensions are not surprising, given that the presence of intrinsically disordered domains in a protein leads to a general overestimation of τc by methods (such as HYDROPRO) that employ an assumption of rigid behaviour [60,61]. Truncation of the W1 structure either to the globular core or to the core without helix 5 lead to improved agreement between the inferred and observed τc values, with the helix 1–4 globular core leading to a predicted τc of ~8.4 ns. Rotational diffusion is, therefore, most consistent with a compact globular core where helix 5 is not always attached. Translational diffusion, conversely, agrees well with the overall shape of W1 [19]. Modest increases in τc, to 9.0 ns for W2-1 and 9.6 ns for W2-2, are observed relative to 7.9 ns for W1. These values are ~2/3 of those predicted for a compact sphere and ~1/2 those predicted on the basis of the DOSY-determined W2 hydrodynamic radius and <1/3 that predicted by HYDROPRO on the basis of the W2 structural ensemble. This behaviour is also directly reflected in the magnitude of the observed increases in 1/ρ from W1 to W2-1 to W2-2 (Figure 5). Namely, W2 does not exhibit anywhere near the expected [17] ~doubling of 1/ρ relative to W1 that would be observed if the two W units in W2 were rigidly tumbling together as a species of double the molecular weight. Following studies using ensemble methods to accurately predict rotational diffusion for molecules containing intrinsically-disordered linkers [61,62], this is instead consistent with mostly decoupled tumbling of each globular domain. The increased τc of W2-2 relative to W2-1 is consistent with greater hydrodynamic friction experienced from the asymmetric nature of the tails, with an ~11 residue disordered N-terminal tail for W2-1 vs. an ~50 residue disordered C-terminal tail for W2-2. 4. Materials and Methods 4.1. Sample Preparation Protein samples were prepared by recombinantly expressing W1 and W2 in Escherichia coli BL21(DE3), following previously-described protocols [19,63]. It should be noted that W1 consists of residues 1–199 of the AcSp1 repeat unit from A. trifasciata while W2-1 and W2-2 each comprise the full 200 amino acid repeat unit concatenated to form a 400 residue protein. An N-terminal Met is also present in W2 from the initiation codon; for simplicity of comparison between W1 and each unit in W2, the Met is not included in residue numbering. Uniformly 15N-enriched W1 (~0.2 mM), and selectively 15N-enriched W2-1 and W2-2 (~0.2 mM) NMR samples were prepared in sodium acetate buffer (20 mM d3-acetate (Sigma-Aldrich Canada, Oakville, ON, Canada) in H2O:D2O (Sigma-Aldrich Canada) at 90%:10% (v/v), 1 mM NaN3 (Sigma-Aldrich Canada), 1 mM 2,2-dimethyl-2-silapentane-5-sulfonic acid (DSS) (Wilmad, Buena, NJ, USA); pH 5). 4.2. Spin-Relaxation NMR Experiments NMR spin relaxation experiments were carried out at 30 °C on an Avance III NMR spectrometer operating at 16.4 T (Bruker Canada, Milton, ON, Canada) and equipped with a triple-resonance 5 mm indirect detect TCI cryoprobe. Two-dimensional phase-sensitive 1H-15N HSQC experiments were used to measure longitudinal relaxation times (T1; hsqct1etf3gpsi pulse program, Bruker library) and transverse relaxation times (T2; hsqct2etf3gpsi pulse program, Bruker library). All experiments were performed using 16 scans, 1.5 s recycle delay for W1 and 1.75 s for W2-1 and W2-2, spectral widths of 23 and 16 ppm with offsets of 115.5 ppm and at the water frequency (4.705 ppm), respectively, for 15N and 1H. W1 spectra contained 192 × 2048 complex points and W2-1 and W2-2 contained 128 × 2048 complex points for the 15N and 1H, respectively. The T1 data were collected using relaxation delays of 50, 100, 250, 500, 750, 1000, 1300, and 1700 ms and the T2 data were collected using 17, 34, 51, 85, 119, 152, 187, and 238 ms relaxation delays, with a Carr-Purcell-Meiboom-Gill pulse train applied as appropriate for a given relaxation delay during the recycle delay to compensate for heating effects. [1H]-15N steady-state heteronuclear nuclear Overhauser effects ([1H]-15N NOE; hsqcnoef3gpsi pulse program, Bruker library) for the 15N nuclei were measured in an interleaved manner as described previously [19]. Briefly, the [1H]-15N NOE measurements were performed using a total of 356 × 4096 complex points with 32 transients for W1 and 256 × 4096 complex points and 32 transients for both W2 domains. 4.3. Determination of Spin Relaxation Parameters and Reduced Spectral Density mapping Backbone 15N T1, T2, and [1H]-15N NOE as a function of 1H-15N cross-peak position were determined and correlated to our previously assigned chemical shifts (deposited in the Biological Magnetic Resonance Data Bank for W1 (BMRB entry 17899) and W2 (BMRB entry 25197) [19,63]). The 15N T1 and T2 values with associated errors were determined using the Mathematica version 8.0.4 (Wolfram, Champaign, IL, USA) notebook Relaxation Decay, freely available from Leo Spyracopoulos [51]. R1 (R1 (s−1) = 1/T1) and R2 (R2 (s−1) = 1/T2) relaxation rates were determined from nonlinear least-square fits to a two-parameter monoexponential decay. Errors were estimated based on the average spectral noise. The [1H]-15N heteronuclear NOE was measured as the ratio of the saturated spectrum to the reference spectrum as Isat/Iref where Isat and Iref are the intensities of the peaks in the 1H-15N HSQC spectra, with and without proton saturation during the recycle delay, respectively. Non-linear fits were used to minimize the statistical value of χ2. The χ2 goodness-of-fit test per residue was used and compared to the exact critical χ2 determined from 100 Monte Carlo simulations (9.146) for a single residue at a 95% confidence interval: 1/T1’ = R1’ = R1[1 − 1.249|γN/γH|(1 − NOE)](1) and: 1/T2’ = R2’ = R2 − 1.079|γN/γH|R1(1 − NOE)(2) where γN and γH are the gyromagnetic ratios of 15N and 1H, respectively. The ratio of these modified rates, was calculated as: ρ = [(2R2’/R1’) − 1]−1(3) Finally, per-residue values of J(0), J(ωN), and J(0.87ωH) were determined through 15N reduced spectral density mapping [30] using the Spectral Density Mathematica notebook [51]. 4.4. Viscosity Determination The viscosity (η) of each NMR sample was calculated using a dioxane internal standard [56]. DOSY experiments acquired and processed as detailed previously for W1 and W2 [19] were analyzed to directly determine the translational diffusion coefficient (DC) for dioxane in a given W sample. Coupling each measured DC with the known hydrodynamic diameter (dH) of dioxane (0.424 nm [56]), η may be determined through the Stokes-Einstein equation [64]: DC = (kBT)/(3πηdH)(4) where kB is the Boltzmann constant and T the absolute temperature (303 K). 4.5. Analysis of Rotational Diffusion To analyze rotational diffusion behaviour with respect the 15N spin relaxation data, isotropic, axially symmetric, and anisotropic diffusion tensors models were applied to W1, W2-1, and W2-2 using ROTDIF 3.1 [54]. Only residues with [1H]-15N NOE > 0.65 and not likely to be involved in conformational exchange were used for the analysis. For W1, W2-1, and W2-2, the 20-member W1 structural ensemble (PDB ID 2MU3) was iteratively analyzed through ROTDIF using robust least-square fitting to obtain global information with coordinates from the lowest energy member to model the diffusion tensor frame (D|| and D⊥ tensor axes for an axially symmetric system, or Dxx, Dyy, and Dzz tensor axes for a fully anisotropic system) and Euler angles (α, β, and/or γ). In addition, the W2 ensemble member with the calculated Rg closest to the experimental Rg was deemed the representative model for the reference frame of the diffusion tensor for W2 (merged W2-1 and W2-2 relaxation data). The robust least-squares optimization method was employed during fitting and full statistical analysis was employed to determine the most statistically upheld diffusion tensor model for a given ensemble member. 4.6. Estimations of Rotational Correlation Time The rotational correlation time (τc), assuming a hydrated sphere, may be estimated through Stokes’ law [64]: τc = (4πηrH3)/(3kBT) (5) where rH is the radius of hydration. For a hydrated protein, rH may be roughly estimated on the basis of the specific volume (υ = 0.73 cm3/g) as [65]: rH = [3υMr/(4πNA)]1/3 + rw(6) where Mr is the molecular weight, NA is Avogadro’s number, and rw is radius of the hydration layer surrounding the protein (1.6–3.2 Å for ½-1 hydration shell [66]). For direct comparison, the average ratios of T1/T2 (or T1′/T2′) for all residues with an NOE > 0.65 in a given protein were employed to estimate τc. Through neglecting of the high-frequency terms of the spectral density, the analysis of Kay et al. [27] may be simplified to: τc = [1/(4πνN)] (6T1/T2 – 7)1/2(7) where νN is the resonance frequency of 15N (in Hz). HYDROPRO [55] was also used to estimate ensemble-averaged τc values based upon the NMR-derived structural ensemble for W1 (PDB entry 2MU3) and the W2 ensemble inferred on the basis of concatenated NMR-derived restraints for W1 [19]. The resulting output was parsed for τc (harmonic mean (correlation) time) as calculated on the basis of the combined input of temperature, solvent viscosity, molecular weight, solute partial specific volume, solution density, and PDB structural coordinates. For comparison, calculations were carried out for W1 structural ensembles truncated using an in-house Tcl/Tk script to the globular core (residues 12–149) or the globular core excluding both the turn between helices 4 and 5 and helix 5 (residues 12–128). 4.7. Statistical Tests Statistical analyses were performed between units and protein regions as described above to evaluate significance between means through ordinary one-way ANOVA test when comparing 3 or more means or unpaired two tailed t-test with Welch’s correction for unequal variances when comparing two means within the Prism 6 or InStat software packages (both from GraphPad Software Inc., La Jolla, CA, USA). All distributions were assumed to be Gaussian. Unless otherwise noted, significance was determined at an α of 0.05. 5. Conclusions The core repetitive domain of AcSp1 is composed of concatenated 200 amino acid units, identical in sequence and very similar in tertiary structuring and internal motions. Through split intein-mediated trans-splicing, individual repeat units were selectively isotope-enriched and investigated in the context of the W2 protein capable of fibrillogenesis. Intein-mediated segmental-labelling is also highly promising for future studies of other modular proteins, whereby spectral complexity can be reduced without compromising the functional state of the protein. Backbone-level dynamics very clearly demonstrate the beads-on-a-string conformation of the AcSp1 repetitive domain, with structured globular domains linked by lengthy intrinsically-disordered segments forming a relatively viscous solubilized state. Although our previous translational diffusion studies imply that the linker is not highly extended, with W2 and W3 exhibiting relatively compact conformations, the 15N spin relaxation behaviour detailed herein demonstrate that each globular domain in W2 tumbles nearly independently of its neighbour. Regardless of the construct examined, helix 5 also exhibited elevated high-frequency dynamics relative to the remainder of the globular core. Rotational diffusion behaviour of W1 is also most consistent with a W unit globular core where helix 5 is not stably attached. Unambiguous measurement of backbone dynamics, therefore, improves our understanding of both AcSp1 repetitive domain modularity and allow direct demonstration of variations in localized stability that were implied by titration with chaotropes and detergent. Acknowledgments Thanks to Bruce Stewart for expert technical assistance; Leo Spyracopoulos for helpful discussions; and, Ian Burton, Nadine Merkley, and Ray Syvitski for 16.4 T NMR spectrometer support at the National Research Council Biological Magnetic Resonance Facility (NRC-BMRF, Halifax, NS, Canada) accessed through Dalhousie’s Nuclear Magnetic Resonance Resource (NMR3). This work was supported by Discovery Grants from the Natural Sciences and Engineering Research Council of Canada (NSERC; RGPIN/342034-2012 to Jan K. Rainey and RGPIN/41823-2015 to Xiang-Qin Liu); key infrastructure was provided through NSERC Research Tools and Instruments Grants and a Leaders Opportunity Fund award from the Canadian Foundation for Innovation (to Jan K. Rainey); and, a Dalhousie Medical Research Foundation Capital Equipment Grant (to Jan K. Rainey and Xiang-Qin Liu). The TCI probe for the 16.4 T NMR spectrometer at the NRC-BMRF were provided by Dalhousie University through an Atlantic Canada Opportunities Agency Grant. Jan K. Rainey is supported by a Canadian Institutes for Health Research New Investigator Award and Marie-Laurence Tremblay was supported by an NSERC Doctoral Postgraduate Scholarship. Author Contributions Xiang-Qin Liu and Jan K. Rainey conceived the research; Lingling Xu and Marie-Laurence Tremblay prepared protein samples; Marie-Laurence Tremblay, Muzaddid Sarker and Jan K. Rainey acquired experimental data; Marie-Laurence Tremblay, Lingling Xu, Muzaddid Sarker, Xiang-Qin Liu and Jan K. Rainey analyzed experimental data; Jan K. Rainey and Marie-Laurence Tremblay wrote the manuscript; all authors edited and commented on the manuscript. Conflicts of Interest The authors declare no conflict of interest. Figure 1 15N spin relaxation parameters as a function of residue at 16.4 T for W1, W2-1, and W2-2. Analysis and error propagation were carried out using Mathematica notebooks from Leo Spyracopoulos [51]. The secondary structuring of the W unit is depicted on the basis of PDB entry 2MU3 [19], with grey shading for each helical segment. Figure 2 Bar graphs representing the mean T1 and T2 (error bars: standard deviations) for key regions of the W unit. These were specifically defined as the core (residues 12–149 (W1, W2-1) and 212–349 (W2-2)), linker (residues 150–211 from W2-1 and W2-2), tails (W1: residues 1–11 and 150–199; W2: residues 1–11 and 350–400), and helix 5 (residues 135–149 (W1, W2-1) and 335–349 (W2-2)). Figure 3 15N reduced spectral density mapping [30] at 16.4 T as a function of residue for W1, W2-1, and W2-2. Analysis and error propagation were carried out using Mathematica notebooks from Leo Spyracopoulos [51]. Figure 4 Bar graphs representing the mean J(0), J(ωN), and J(0.87ωH) (error bars: standard deviation) over key W unit regions. These were specifically defined as the core (residues 12–149 (W1, W2-1) and 212–349 (W2-2)), linker (residues 150–211 from W2-1 and W2-2), tails (W1: residues 1–11 and 150–199; W2: residues 1–11 and 350–400), and helix 5 (residues 135–149 (W1, W2-1) and 335–349 (W2-2)). Figure 5 Inverse of ρ ratio of T1’ to T2’ (relaxation times modified to remove high-frequency components of spectral density) as defined by Fushman et al. [17]. (A) Plotted as a function of residue (green circles: W1; blue triangles: W2-1, red squares: W2-2) with secondary structuring of the W unit depicted on the basis of PDB entry 2MU3 [19] using grey shading to delineate each helical segment (B) bar graphs for mean (green: W1; blue: W2-1, red: W2-2; error bars: standard deviation) over key W unit regions. These were specifically defined as the core (residues 12–149 (W1, W2-1) and 212–349 (W2-2)), linker (residues 150–211 from W2-1 and W2-2), tails (W1: residues 1–11 and 150–199; W2: residues 1–11 and 350–400), and helix 5 (residues 135–149 (W1, W2-1) and 335–349 (W2-2)); and (C) plotted for W1 on the lowest-energy NMR ensemble member of PDB entry 2MU3 [19], with both backbone thickness and colour varied as a function of 1/ρ, as indicated. ijms-17-01305-t001_Table 1Table 1 Rotational diffusion tensor parameters that best fit indicated 15N spin relaxation data set. Protein 1 N–H Bonds 2 D⊥ (×10−7 s−1) D|| (×10−7 s−1) Anisotropy α (°) β (°) τc (ns) χ2/df 3 W1 102 2.01 ± 0.05 2.30 ± 0.08 1.14 ± 0.17 13 ± 52 43 ± 19 7.91 ± 0.03 1.409 W2-1 104 1.82 ± 0.07 1.93 ± 0.04 1.06 ± 0.13 164 ± 87 138 ± 20 8.98 ± 0.03 1.889 W2-2 93 1.78 ± 0.03 1.63 ± 0.03 0.92 ± 0.09 77 ± 37 155 ± 10 9.65 ± 0.04 1.906 W2 197 1.87 ± 0.04 1.68 ± 0.04 0.90 ± 0.11 26 ± 31 20 ± 22 9.24 ± 0.05 2.309 1 Diffusion tensor detailed for lowest-energy member of the W1 structural ensemble (PDB entry 2MU3 [19]) for W1, W2-1, and W2-2; and, for member of inferred [19] W2 structural ensemble with radius-of-gyration closest to that determined by diffusion ordered NMR spectroscopy for W2; 2 Residues having [1H]-15N NOE > 0.65 used in ROTDIF [54] fit; 3 df: degrees of freedom (equivalent to number of N–H bonds). ijms-17-01305-t002_Table 2Table 2 Rotational correlation time (τc) for indicated protein according to given method. Protein η (cP) 1 τc (ns) Stokes (Ideal) 2 Stokes (DOSY) 3 Structure 4 T1/T2 5 T1′/T2′ 5 ROTDIF 6 W1 1.040 7.8–9.9 10.6 14.1 ± 0.3 7 9.4 ± 0.2 8 8.4 ± 0.1 9 7.9 8.0 7.9 ± 0.01 W2-1 1.056 - - - 9.0 9.1 9.0 ± 0.01 W2-2 1.056 - - - 9.5 9.6 9.6 ± 0.01 W2 1.056 14.7–17.8 19.7 31.8 ± 0.7 9.3 9.4 9.3 ± 0.03 1 Viscosities determined using dioxane internal standard by DOSY (Equation (4)); 2 Calculated using Stokes’ law (Equation (5)) for a 100% 15N/13C-enriched protein mass using a hydration shell of either 1.6 Å (lower estimate) or 3.2 Å (upper estimate), assuming spherical shape (Equation (6)); 3 Calculated using Stokes’ law (Equation (5)) based upon hydrodynamic radii determined by DOSY [19]; 4 Average ± average deviation of HYDROPRO [55] predicted τc over 20-member ensembles of structures of W1 or W2 [19], or over globular core of W1; 5 Determined based upon indicated relaxation time constant ratio using Equation (7); 6 Average ± average deviation over all 20 structural ensemble members determined through axially-symmetric diffusion tensor ROTDIF in identical manner to Table 1; 7 Entire W1 structure; 8 Globular core (residues 12–149); and, 9 Globular core excluding helix 5 (residues 12–128). ==== Refs References 1. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081306ijms-17-01306ReviewNeuroimmunological Implications of AQP4 in Astrocytes Ikeshima-Kataoka Hiroko 12Ishibashi Kenichi Academic Editor1 Department of Pharmacology and Neuroscience, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan; ikeshima@1988.jukuin.keio.ac.jp; Tel.: +81-3-5363-3750; Fax: +81-3-3359-88892 Faculty of Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan10 8 2016 8 2016 17 8 130621 6 2016 04 8 2016 © 2016 by the author; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).The brain has high-order functions and is composed of several kinds of cells, such as neurons and glial cells. It is becoming clear that many kinds of neurodegenerative diseases are more-or-less influenced by astrocytes, which are a type of glial cell. Aquaporin-4 (AQP4), a membrane-bound protein that regulates water permeability is a member of the aquaporin family of water channel proteins that is expressed in the endfeet of astrocytes in the central nervous system (CNS). Recently, AQP4 has been shown to function, not only as a water channel protein, but also as an adhesion molecule that is involved in cell migration and neuroexcitation, synaptic plasticity, and learning/memory through mechanisms involved in long-term potentiation or long-term depression. The most extensively examined role of AQP4 is its ability to act as a neuroimmunological inducer. Previously, we showed that AQP4 plays an important role in neuroimmunological functions in injured mouse brain in concert with the proinflammatory inducer osteopontin (OPN). The aim of this review is to summarize the functional implication of AQP4, focusing especially on its neuroimmunological roles. This review is a good opportunity to compile recent knowledge and could contribute to the therapeutic treatment of autoimmune diseases through strategies targeting AQP4. Finally, the author would like to hypothesize on AQP4’s role in interaction between reactive astrocytes and reactive microglial cells, which might occur in neurodegenerative diseases. Furthermore, a therapeutic strategy for AQP4-related neurodegenerative diseases is proposed. astrocyteaquaporin 4 (AQP4)blood-brain barrier (BBB)central nervous system (CNS)endofootglial fibrillary acidic protein (GFAP)gliosisinterleukin (IL)-1βIL-6immunoglobulin G (IgG)microglianeuromyelitis optica (NMO)neuroimmunologyosteopontin (OPN)reactive astrocytetumor necrosis factor (TNF)-α ==== Body 1. Introduction 1.1. Aquaporin-4 (AQP4) Aquaporin 4 (AQP4) is the most abundantly expressed water channel in the brain, and is highly localized in the endfeet of astrocytes (a type of glial cell in the central nervous system (CNS)); these endfeet are in contact with blood vessels [1,2,3]. The distribution of AQP4 is diverse throughout the brain, and includes the cerebral cortex, corpus callosum, retina, cerebellum, magnocellular nuclei of the hypothalamus, and brain stem [4]. AQP4 has a tetrametric structure, enabling gases and ions to permeate through a central pore; however, the physiological role of this central pore remains unclear [1]. Use of stopped-flow analysis showed that mercury decreases AQP4 M23 water permeability in proteoliposomes via Cys178 residue located cytoplasmic loop D [5]. AQP4 transfected astrocyte cell line and primary culture of astrocytes revealed that lead (Pb2+) increased water permeability, mediated by Ser111, which is a phosphorylation site for calmodulin kinase II (CaMKII) [6]. Oocytes expressing rat AQP4 exhibit greater permeability for CO2 but lower permeability for NH3 [7]. AQP4 could protect the brain from rising NH3 levels in the blood, while allowing CO2 to pass. AQP4 is involved in astrocyte migration when water passes through the lamellipodium and into the cytoplasm by an osmotic gradient. Furthermore, AQP4 plays a role in neuroexcitation, in which isosmolar K+ is released by neurons, followed by the uptake of K+ and water by astrocytes on the other side of the synaptic cleft [3]. The inwardly rectifying K+ channel family member, Kir4.1, is co-localized with AQP4 at the endfeet of astrocytes, but not in neurons, to maintain water homeostasis in the CNS. These transmembrane channels seem to play important roles in neurological disorders [8]. The upregulation of metabotropic glutamate receptor (mGluR) 3 can be co-localized with AQP4, suggesting that astrocytic mGluR plays a role in the regulation of extracellular glutamate levels. In the hippocampal tissue of patients with temporal lobe epilepsy (TLE), the expressions of connexin 43 and AQP4 are increased, and the expressions of the key constituents of the AQP4 multi-molecular complex (Kir4.1, a-syntrophin, and dystrophin) are downregulated [9]. Hypotonicity induces a rapid and reversible relocalization of AQP4 in a calcium-, calmodulin-, and kinase-dependent manner in primary cortical rat astrocytes and transfected HEK293 cells [10]. AQP4 also reportedly plays a role in the development and maintenance of the blood-brain barrier (BBB) [11,12]; however, a detailed analysis, which included electron microscopy studies, revealed that the deletion of AQP4 does not alter BBB integrity or brain morphology [13]. We also examined the leakage of immunoglobulin G (IgG) to confirm BBB integrity in an independently established line of AQP4-deficient mice, compared with wild-type (WT) mice, and found that, not only was the integrity of the BBB maintained in a normal brain, but the recovery of the BBB after breakdown was also not altered, even in the absence of AQP4 [14]. AQP4 has also been implicated in learning and memory in the hippocampus and amygdala by influencing long-term potentiation (LTP) and long-term depression (LTD) [15]. Some evidence using AQP4-deficient mice has shown that LTP and LTD alterations are dependent on brain-derived neurotrophic factors [16], and a link between defective LTP and the downregulation of glutamate transporter-1 has been shown [17,18]. The deletion of AQP4 has been shown to result in the shrinkage of the extracellular space (ECS) volume in the mouse hippocampal CA1 region, which is associated with the activation of excitatory pathways [19]. ECS shrinkage was most pronounced in the pyramidal cell layer. These results imply that AQP4 regulates the dynamics of the extracellular volume. 1.2. Astrocytes 1.2.1. Glial Fibrillary Acidic Protein Astrocytes are a type of glial cell in the CNS that accounts for more than 50% of the total cells in the brain, and astrocytes are thought to be 10 times more abundant than neurons. Glial fibrillary acidic protein (GFAP) is an intermediate filament protein that is highly expressed in astrocytes and is considered to be an astrocyte marker [20]. The upregulation of GFAP can be observed in reactive astrocytes; such upregulation can be induced by traumatic injury, edema, inflammation, or infection in the brain. GFAP mutations cause Alexander disease, which is a fatal neurodegenerative disorder, characterized by astrocytic inclusions [21]. Several laboratories have independently developed GFAP-null mice, and such mice were found to develop normally, with no differences from WT mice in terms of brain architecture, numbers of neurons and astrocytes, BBB integrity, behavior, or motor activity (reviewed in [22]). At 14 months of age, however, GFAP-null mice develop hydrocephalus and reduced myelination in the corpus callosum, spinal cord, and optic nerve [23]. Thus, astrocytes might play a pivotal role in the long-term maintenance of myelination. Another astrocyte marker, tenascin-C (TN-C), is an extracellular matrix molecule (ECM) that is expressed in radial glia in the CNS during development [24,25], and in primary cultures of astrocytes [26], while its expression is attenuated in astrocytes in the normal brain. In brains that have experienced traumatic injury, inflammation, or infection, the expression of TN-C is enhanced simultaneously with the expression of GFAP in reactive astrocytes [27,28]. Because of the inhibition of the activation of astrocytes and microglial cells in AQP4-deficient mice, TN-C expression was reduced in mouse brains with stab wound injuries or primary cultures of astrocytes stimulated with lipopolysaccharide (LPS). Thus, the drastic expression of TN-C in reactive astrocytes might depend on AQP4 expression [14]. 1.2.2. Oxidative Stress in Astrocytes Oxidative stress is thought to be one of several causes of brain aging. Astrocytes contribute to neuronal homeostasis and couple with neurons in their response to oxidative stress so as to protect them [29]. Aging regulator insulin-like growth factor 1 (IGF-1) directly participates in astrocyte neuroprotection against oxidative stress [30]. Superfusion of retinal slices with a hypoosmolar solution induced a rapid swelling of Müller cell somata in tissues from AQP4-deficient mice, but not from wild-type mice [31]. AQP4 is involved in the rapid volume regulation of retinal glial cells in response to osmotic stress and that deletion of AQP4 results in an inflammatory response of the retinal tissue. 1.2.3. Calcium Signaling in Astrocytes Astrocytes rapidly swell during brain edema formation [32], and brain swelling triggers Ca2+ signaling in astrocytes; this signaling is reduced in AQP4-deficient mice. Thus, hypo-osmotic stress initiates astrocytic Ca2+ spikes in an AQP4-dependent manner [33]. The calcium dynamics in astrocytes have been thoroughly examined in both primary cultures and genetically modified awake mice, as previously reviewed [34,35,36]. 1.2.4. Astrocytes and Immune Cells Astrocytes interact with T-cells in inflammatory responses via T-cell receptor (TCR) and adhesion molecule lymphocyte function-associated antigen 1 (LFA-1), the so-called T-cell-astrocyte interface, to form immunological synapses (ISs). Bacterial LPS or tumor necrosis factor (TNF-α) strongly stimulates astrocytes to release chemokines [37]. In this manner, astrocytes act as chemokine producers or lymphocyte attractants for the recruitment of T-cell subsets into the brain parenchyma [38]. Immune modulator mesencephalic astrocyte-derived neurotrophic factor (MANF) has been identified in immune cells, and this biases immune cells toward an anti-inflammatory phenotype, enhanced neuroprotection and tissue repair, and improved the success of photoreceptor replacement therapies in both mouse and Drosophila [39]. Astrocytes, as well as microglia and macrophages, were important sources of IL-27 in the human disease, multiple sclerosis (MS). IL-27 triggered the phosphorylation of the transcription regulator STAT1, and can modulate immune properties of astrocytes and infiltrating immune cells in an MS patient’s brain [40]. 2. Neuroimmunological Role of AQP4 2.1. AQP4 in Neuromyelitis Optica (NMO) Neuromyelitis optica (NMO) is an autoimmune disease consisting of recurrent optic neuritis and transverse myelitis, and serologic testing for the AQP4-immunoglobulin G (IgG) autoantibody is useful for a differential diagnosis from multiple sclerosis (MS) [41,42,43]. Astrocytes in the optic nerve and spinal cord are the main targets. The loss of AQP4 and GFAP staining in NMO brain is distinct from the staining observed in MS patients [44]. An enzyme-linked immunosorbent assay (ELISA) to detect anti-AQP4 antibodies has been established, and can be used as a substitute for the conventional NMO-IgG assay [45]. Rat astrocytes and oligodendrocytes from primary cultures and rat optic nerves were exposed for 24 h to neuromyelitis optica (NMO)-IgG in the absence of complement, and it was found that there was a complement-independent effect of NMO-IgG/AQP4 antibody on astrocytes, with secondary damage to oligodendrocytes, possibly resulting from glutamate-mediated excitotoxicity [46]. Recently, our colleagues also established high-affinity monoclonal antibodies against the extracellular domains of AQP4, and these antibodies can block the binding of NMO-IgG, despite its heterogeneity. These antibodies could be applied in clinical treatments for NMO patients [47,48,49]. Highly pathogenic AQP4-peptide-specific T cells in Lewis rats have been reported. These cells recognize the AQP268–285 epitope and produce NMO-like lesions in the presence of NMO-IgG [50]. Comprehensive mutagenesis of the three extracellular loops of the M23 isoform of human AQP4 were analyzed, and the effects on binding of NMO AQP4-reactive recombinant IgG (rAbs) using quantitative immunofluorescence were evaluated. Amino acid substitutions at T137/P138 altered loop C conformation and abolished the binding of all NMO rAbs and NMO-IgG, and the authors concluded on the importance of loop C conformation to the recognition of AQP4 by pathogenic NMO Abs [51]. Another AQP4-autoimmunity disease, NMO-spectrum disorder (NMOSD), classifies, not only optic neuritis and myelitis as NMO, but also cerebral, diencephalic, brainstem, and area postrema syndromes [52,53]. Furthermore, non-neurologic features involving other AQP4-positive organs outside of the CNS have also been reported for NMOSD. Nevertheless, NMOSD can be treated by B-cell depletion through antibodies, or the infusion of “aquaporumab” to block AQP4 antibodies [54,55]. Myasthenia gravis (MG) is a disease affecting the neuromuscular junction, caused in approximately 85% of patients by IgG1- and IgG3-complement activating antibodies against the nicotinic acetylcholine receptor (AChR-Ab) [56]. Several cases or small series of MG patients show both NMO/NMOSD. A history of thymectomy for MG patients could be a possible risk factor for the later development of NMOSD [57]. Antibody titers for AQP4-Abs and AChR-Abs tend to change in opposite directions [58]. 2.2. AQP4 in Alzheimer’s Disease A defect in the clearance of β-amyloid (Aβ) in brain parenchyma is considered to be a cause of Alzheimer disease (AD) [59]. Astrocytes play a protective role in the clearance and degradation of Aβ through the recruitment of astrocytes toward monocyte chemoattractant protein-1 (MCP-1) in senile plaques [60]. However, the excessive uptake of Aβ causes astrocyte malformation and apoptosis [61,62]. AQP4 deficiency in cultured astrocytes resulted in reduced astrocyte activation induced by Aβ1–42 and its toxicity, the uptake of Aβ1–42, and the upregulation of LRP-1 induced by Aβ1–42, as well as altered levels of MAPK phosphorylation [63]. Thus, AQP4 in astrocytes is a molecular target for the treatment of AD. Moreover, direct evidence of interactions between AQP4 and glutamate transporter-1 (GLT-1) has been reported in astrocytes using AQP4-deficient mice, and these two proteins are part of the same supramolecular complex [64]. The collaboration of AQP4 and GLT-1 in astrocytes has a protective effect against glutamate-induced neuronal injury by Aβ, which might play a pivotal role in the regulation of distinct cellular responses that involve neuroprotection against AD [65]. 2.3. AQP4 in Parkinson’s Disease Parkinson disease (PD) is clinically characterized by the progressive, selective, and irreversible loss of dopaminergic (DA) neurons in the substantia nigra (SN) resulting in a poverty of voluntary movements (akinesia), slowness and impaired voluntary movement (bradykinesia), muscle rigidity, and tremors of the limbs at rest [66]. The administration of MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine)/probenecid in a PD mouse model with an AQP4 deficiency resulted in significantly enhanced gliosis, the aggravated loss of TH-immunoreactive neurons, an increase in the production of IL-1β and TNF-α, but a suppression of IL-6 in the midbrain, and the activation of the IKK/NF-κB pathway in vivo, compared with WT mice [67]. Neurotoxicity induced by AQP4 was observed, not only in the SN, but also in ventral tegmental area (VTA) neurons [68]. Another paper also reported that AQP-deficient mice showed hypersensitive to stimulation of MPTP or LPS compared to WT littermates [69]. MPTP-induced PD mouse model with AQP4-deficient mice showed more robust microglial inflammatory responses and more severe loss of DA neurons. Significantly lower numbers of CD4+ CD25+ regulatory T cells in AQP4-deficient mice compared to WT. The authors reported for the first time for AQP4 expression in mouse thymus, spleen, and lymph nodes. Thus, they concluded that AQP4 may have immunosuppressive regulator function. 2.4. AQP4 in Depression AQP4 may serve as a marker for an astrocytic pathology in major depressive disorder [70,71]. Mesenchymal stem cell therapy can repress inflammation during ischemic stroke in mice, thereby protecting the integrity of the blood-brain barrier, reducing brain edema and astrocyte apoptosis, and downregulating AQP4 expression via the p38 signaling pathway [72]. AQP4-deficient mice showed an improved neurological outcome after acute water intoxication and ischemic stroke [73]. In human glioma cells, AQP4 regulates migration and invasion and might be useful as a therapeutic target for cell infiltration [74]. 2.5. AQP4 in Blood-Retinal Barrier Breakdown Blood-retinal barrier (BRB) breakdown occurs in diabetic retinopathy, age-related macular degeneration, retinal vein occlusions, and uveitis, resulting in vasogenic edema and a loss of vision because of neural tissue damage [75]. AQP4 deletion is directly responsible for BRB dysfunction to the deep plexus capillaries, and strong GFAP upregulation was observed in astrocytes in the retina, while the expression of glutamate synthetase (GS), a Müller cell marker, was not observed [76], even though AQP4 was expressed in both types of cells. Since AQP4 expression is not homogeneous among all astrocytes, neither in mouse brains or primary cultures [14], Müller cell dysfunction might also be not homogeneous. An interaction between transient receptor potential isoform 4 (TRPV4) and AQP4 has been proposed as a key regulator in astroglial swelling, volume regulation, and the reorganization of downstream signaling pathways in retinal Müller cells [77], and the coordination of activity-dependent ionic/water fluxes at the BRB might be critically dependent on functional interactions among TRPV4, AQP4, and Kir4.1 channels. 2.6. AQP4 in Traumatic Brain Injury Traumatic brain injury causes brain edema resulting from an increased brain volume as a result of water uptake, with elevated intracranial pressures leading to brain herniation and neuronal death [78,79]. Vasopressin 1a receptor (V1aR) antagonists prevent brain edema, rescue astrocytic cell swelling, and attenuate GFAP and AQP4 expression after cortical contusion injury [80]. In short, V1aR inhibitors might be useful tools for reducing brain edema in future clinical studies. AQP4 is known to contribute to cytotoxic edema following traumatic brain injury (TBI). TBI leads to the transcriptional activation of Foxo3a, a mammalian forkhead transcriptional factor, and the upregulation of AQP4 in astrocytes at the injured site at 24 h after TBI [81]. Foxo3a directly binds to the AQP4 promoter (binding residue, ATAAACA), as verified using a gel shift assay and a chromatin immunoprecipitation assay. Furthermore, the depletion of Foxo3a reduces the induction of AQP4 and cerebral edema in TBI mice. A brain-wide network of paravascular channels, termed the “glymphatic” pathway, exists. In this pathway, subarachnoid cerebrospinal fluid (CSF) recirculates through the brain parenchyma along the paravascular spaces, exchanging with the surrounding interstitial fluid (ISF) to facilitate the clearance of interstitial solutes [82,83]. The paravascular CSF-ISF exchange and interstitial solute clearance depends on AQP4. TBI causes the loss of perivascular AQP4 polarization in the astrocytic endfeet in AQP4-deficient mice, impairing the paravascular clearance of interstitial solutes such as Aβ [84]. Furthermore, AQP4-deficiency promotes neurodegeneration and neuroinflammation, thereby exacerbating post-traumatic tau aggregation and cognitive impairment [82]. We have been analyzing a stab wound injury mouse model to clarify the functional role of astrocyte activation after brain injury. Using a microarray analysis, we found that AQP4 has neuroimmunological functions in injured mouse brain that are correlated with the pro-inflammatory cytokine inducer osteopontin (OPN) [85]. OPN plays numerous roles in immune-related diseases such as multiple sclerosis (MS), rheumatoid arthritis, lupus-related diseases, Sjögren syndrome, and colitis, and it also plays an important protective role in the immune response [86] and might contribute to MS lesions and NMO pathology because of the elevated production of OPN in cerebrospinal fluid [87,88]. 2.7. AQP4 in Ischemia In a normal CNS, AQP4 is only expressed in the endfeet of astrocytes, but its expression is dispersed throughout the cytoplasm of activated astrocytes. In bilateral carotid artery occlusion (BCAO), AQP4 deletion caused a reduction in astrocyte swelling and water accumulation in the brain, resulting in reduced BBB disruption, inflammation, and neuronal death [89]. Furthermore, AQP4 deficiency in mice improved the neurological outcome and exerted a neuroprotective effect against severe global cerebral ischemia [90]. Mitogen-activated protein kinase (MAPK) signal pathways are involved in changes in osmolality, and these pathways mediate AQP4 expression in an ischemia model, such as oxygen-glucose deprivation (OGD) in rat cortical astrocytes and middle cerebral artery occlusion (MCAO) in rats [91]. A p38 inhibitor protected against astrocyte cell death after OGD and restored AQP4 expression, attenuating edema and the infarct volumes after MCAO. 3. AQP4 in Reactive Astrocytes When the brain experiences a traumatic injury or inflammation, astrocytes become active and engage in proliferation, migration, and the upregulation of some marker proteins as well as the enhanced production of pro-inflammatory cytokines and the formation of a glial (consisting of reactive astrocytes and reactive microglia) scar [92,93]. AQP4 localizes, not only in endofeet, but is also dispersed in cytoplasm of reactive astrocytes [1]. Methylmercury (MeHg)-treated common marmosets showed lesions in the cerebrum, cerebellum, and peripheral nerves as a model of Minamata disease [94,95], and a slight increase in AQP4 was observed in the reactive astrocytes [96]. To understand the reactive astrocyte state, a microarray analysis was performed using two mouse injury models: ischemic stroke and LPS-induced neuroinflammation [97]. The analysis revealed that reactive astrocytes induced during ischemia might be neuroprotective, whereas reactive astrocytes induced by LPS may be detrimental. The authors concluded that reactive astrocytes are highly heterogeneous, although AQP4 expression is observed in both types of reactive astrocytes. Our previous observations of primary culture of astrocytes showed that AQP4 expression is not homogeneous in either the brain or primary cultures [14], suggesting that astrocytes could be classified into multiple groups according to their AQP4 expression levels. GFAP expression was intensively upregulated in reactive astrocytes at three days after a stab wound injury to the cerebral cortex, with the same kinetics observed for TN-C and AQP4 [14]. We found that the robust expressions of GFAP and TN-C were attenuated in stab wound injuries of the brain or in LPS-treated primary culture of astrocytes from AQP4-deficient mice, suggesting that the expressions of GFAP and TN-C in reactive astrocytes are dependent on AQP4 expression. 4. AQP4 in Neural Stem Cells Reactive astrocytes and neural stem cells share many characteristic hallmarks, and neural stem or progenitor cells can reportedly be instructed to exhibit multipotency and long-term self-renewal upon exposure to growth factors in vitro [98,99]. Interestingly, AQP4 is expressed in the adult forebrain subventricular zone (SVZ), where neural stem cells (NSCs) are known to reside [100]. The isolation and culture propagation of adult murine and human SVZ-derived NSCs (ANSCs) are differentially regulated at different stages of differentiation and maturation into neurons and glia, either astrocytes or oligodendrocytes. GFAP is also expressed in both NSCs and ANSCs with the potential to differentiate into neurons and glial cells [101]. The parallel expression of GFAP and AQP4 might be a good marker, not only for differentiated astrocytes or reactive astrocytes, but also for precursor cells. Unfortunately, AQP4 expression in induced pluripotent stem cells (iPS) has not yet been reported. 5. AQP4 in Microglia Microglia are the resident macrophages in the CNS, and they can dramatically transform from a resting state, such as a “surveying ramified state”, to an active state, such as an “amoeboid state” [102]. Microglial cells can hurriedly communicate, as if they are surveying, with astrocytes, oligodendrocytes, and neurons not only while they are in an active state, but also while they are at resting state [103,104]. Thus, microglia can promptly respond to damage signals in the brain within minutes, producing pro-inflammatory cytokines to protect neuronal cells from secondary damage [105]. Since astrocytes express receptors against interleukin (IL)-1β, IL-6, and tumor necrosis factor (TNF)-α the pro-inflammatory cytokines released from microglia may lead to the activation of astrocytes [106,107]. The intranigral injection of lipopolysaccharide (LPS) to induce neurotoxicity causes the activation of microglia, the loss of reactive astrocytes, the disruption of the BBB, and vasogenic edema in rats [108]. Activated microglial cells in the substantia nigra (SN) express AQP4 mRNA and protein in response to LPS injection. We also observed that AQP4 is expressed in activated microglial cells induced by a stab wound brain injury in mice [85]. However, the intracerebral injection of LPS did not induce AQP4 expression in reactive microglia [109]. These differences might depend on the procedure used to stimulate microglial activation or the microglial residence in the brain, but further studies are needed. 6. AQP4 Function in Astrocyte and Microglial Communication Astrocyte and microglia interactions are required for LPS to induce the expression of pro-inflammatory cytokines and glial cell line-derived neurotrophic factor (GDNF) in astrocytes. Furthermore, microglia-derived TNF-α plays a pivotal role as a paracrine signal for the neuroprotective functions of astrogliosis [110]. Astrocyte activation is promoted by reactive microglial cells in several neurodegenerative diseases, such as experimental autoimmune encephalomyelitis (EAE) and Alzheimer disease (AD), and microglial cells are activated earlier than astrocytes [111]. In human glioblastoma, orthogonal arrays of particles (OAPs) are redistributed to membrane domains because of the degradation of the proteoglycan agrin by the increased activity of matrix metalloprotease 3 (MMP3). Agrin binds with AQP4 and leads to a strong immunoreactivity distribution in tumor tissues such as astrocytomas or glioblastomas, so that it may facilitate infiltration into the brain parenchyma, whereas it is restricted to the perivascular endfeet in normal brain [112,113,114]. In a PD mouse model using AQP4-deficient mice, AQP4 deficiency promoted the activation of microglial cells when co-cultured with astrocytes and induced the release of ATP from astrocytes [67]. Thus, AQP4 might modulate astrocyte-to-microglia communication during neuroinflammation. AQP4 expression is upregulated by high-mobility group box 1 (HMBG1) via microglia-astrocyte interactions. The intracerebroventricular (i.c.v.) injection of HMGB1 significantly increased AQP4 protein and induced edema in the brain [115]. Furthermore, they used a primary culture of astrocytes and microglia and found that through diffusible factors, such as IL-1β from microglia, HMGB1 indirectly upregulated AQP4 and translocated NF-κB to the nucleus in astrocytes. Microglia and astrocytes are known to respond to cytokine challenges; however, a microarray analysis of human brain pericytes also revealed widespread changes in gene expression in response to interleukins and chemokines, and these changes might also be involved in BBB disruption [116]. The authors confirmed the translocation of nuclear factor NF-κB from the cytoplasm to the nuclei using primary human brain pericytes in the absence of microglia or astrocytes in responses to TNF-α, IL-1β, and LPS. The lack of astrocytic laminin induces the prevention of pericytes differentiation, inhibits AQP4 expression, and causes BBB breakdown in conditional knockout mice [117]. Brain pericytes surround endothelial cells and are in direct contact with astrocyte endfeet; thus, we must keep in mind that brain pericytes are responsible for neuroinflammation [118,119,120]. Pericytes could be another functional cell involved in neuroimmunological mechanisms. We recently reported that microglial cells attached to the primary culture of astrocytes from WT mice, as shown by the open arrowhead in Figure 1, but could not adhere to the astrocytes from AQP4-deficient mice. AQP4-mediated cell signals between astrocytes and microglia might be needed for the primary cultures. The atypical adhesion of microglia to astrocytes might be caused by a reduction in OPN or its receptors in primary culture of astrocytes and/or microglial cells because of the AQP4-deletion. The multiple functions of OPN are supported by its two isoforms, which are cleaved by thrombin or matrix metalloprotease: a secreted form of OPN (sOPN) and an intracellular form of OPN (iOPN) [121]. Receptors for sOPN include various integrin family members. We would like to hypothesise that AQP4 might provide a cooperative function in a neuroimmunological role with iOPN. Alternatively, one of the integrin receptors for sOPN might enable an indirect interaction with AQP4. A comprehensive analysis of AQP4 and OPN together might shed light on the treatment of autoimmune diseases in the CNS (Figure 2). 7. Conclusions Because of the various functions of AQP4 in the brain, it might be useful to focus on AQP4 as a therapeutic target for neurodegenerative diseases. Recently, we found that injured brain induces expression of many kinds of genes involved in inflammation or immunological function, and were significantly attenuated in AQP4-deficient mice (underlined in Table 1). The list must be a useful information to give some hints that several molecules in it could be a target for the neurodegenerative diseases, such as complement-dependent cytotoxicity therapy, chimeric antigen receptor-T-cell therapy, or developing neutralizing antibody. Acknowledgments I sincerely appreciate Professor Masato Yasui giving me the opportunity to write this review. This work was supported by Global Center of Excellence (GCOE) Program for Humanoid Metabolomic Systems Biology of the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan, Grant-in-Aid for Scientific Research of Japan Society for the Promotion of Science (JSPS; 15K15532), Waseda University Grants for Special Research Projects (2012, 2013, 2014, 2016a, 2016b), and The Kurata Memorial Hitachi Science and Technology Foundation. Conflicts of Interest The author declares no conflict of interest. Figure 1 Primary cultures of astrocytes from WT mice are composed of tile-shaped cells known as protoplasmic astrocytes (designated as P in (A)) and rocky-shaped cells known as fibrous astrocytes (designated as F in (A)). On the other hand, most of the cells from the AQP4-deficient mice were protoplasmic astrocytes (B). Microglial cells are indicated by open arrowhead. Bar scale = 100 μm. Reproduced and modified from Reference [14], with permission Copyright © 2014 Wiley Periodicals, Inc. Figure 2 Involvement of AQP4 in communication between reactive astrocytes and reactive microglial cells. Direct interaction between AQP4 and intracellular form of osteopontin (iOPN) could be hypothesized, although has yet to be demonstrated. Alternatively, one of the integrin receptors for secreted form of osteopontin (sOPN) might enable an indirect interaction with AQP4. ijms-17-01306-t001_Table 1Table 1 Top 20 upregulated genes analyzed in a microarray experiment comparison between without stab wound and 3 days after a stab wound to the cerebral cortex in WT and AQP4/KO mice categorized with gene function. Genes concerned in inflammation or immunological role are underlined. Values indicate fold change of expression level compared with without stab wound mice (D0). Reproduced and modified from Reference [85], with permission Copyright © 2013 Elsevier Limited, Inc. Function Gene Description WT AQP4/KO immune response secreted phosphoprotein 1 (Spp1) = osteopontin (OPN) 59.83 4.94 lipocalin 2 (Lcn2) 11.79 <1.5 macrophage expressed gene 1 (Mpeg1) 8.89 <1.5 chitinase 3-like 1 (Chi3l1) 4.67 <1.5 leukocyte immunoglobulin-like receptor, subfamily B, member 4 (Lilrb4) 4.36 <1.5 enzyme heme oxygenase (decycling) 1 (Hmox1) 23.19 11.49 transglutaminase 1, K polypeptide (Tgm1) 5.29 <1.5 lysosomal function lysozyme 2 (Lyz2) 18.53 1.9 compliment activation complement component 3a receptor 1 (C3ar1) 8.77 <1.5 complement component 1, q subcomponent, C chain (C1qc) 6.14 <1.5 complement component 1, q subcomponent, beta polypeptide (C1qb) 5.31 <1.5 cytoskeleton vimentin (Vim) 7.03 <1.5 glial fibrillary acidic protein (Gfap) 4.69 <1.5 lymphocyte cytosolic protein 1 (Lcp1) 4.52 <1.5 antigen expression CD180 antigen (Cd180) 6.38 <1.5 CD68 antigen (Cd68) 6.21 2.96 lymphocyte antigen 86 (Ly86) 5.19 <1.5 adipose function adipose differentiation related protein (Adfp) 6.85 2.49 chemokine chemokine (C-C motif) ligand 3 (Ccl3) 5.37 <1.5 signal transduction membrane-spanning 4-domains, subfamily A, member 6C (Ms4a6c) 5.32 <1.5 ==== Refs References 1. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081307ijms-17-01307ReviewSerum and Glucocorticoid Regulated Kinase 1 in Sodium Homeostasis Lou Yiyun 12Zhang Fan 1Luo Yuqin 1Wang Liya 1Huang Shisi 1Jin Fan 13*Matsuzawa Atsushi Academic Editor1 Department of Reproductive Endocrinology, Women’s Hospital, School of Medicine, Zhejiang University, Hangzhou 310006, Zhejiang, China; louyiyun@zju.edu.cn (Y.L.); zhangfanchina@zju.edu.cn (F.Z.); szyck@zju.edu.cn (Y.Lu.); wangliya@zju.edu.cn (L.W.); huangshisi@zju.edu.cn (S.H.)2 Department of Gynaecology, Hangzhou Hospital of Traditional Chinese Medicine, Hangzhou 310007, Zhejiang, China3 Key Laboratory of Reproductive Genetics, National Ministry of Education (Zhejiang University), Women’s Reproductive Healthy Laboratory of Zhejiang Province, Hangzhou 310058, Zhejiang, China* Correspondence: jinfan@zju.edu.cn; Tel.: +86-571-8701-3891; Fax: +86-571-8706-187810 8 2016 8 2016 17 8 130715 6 2016 03 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).The ubiquitously expressed serum and glucocorticoid regulated kinase 1 (SGK1) is tightly regulated by osmotic and hormonal signals, including glucocorticoids and mineralocorticoids. Recently, SGK1 has been implicated as a signal hub for the regulation of sodium transport. SGK1 modulates the activities of multiple ion channels and carriers, such as epithelial sodium channel (ENaC), voltage-gated sodium channel (Nav1.5), sodium hydrogen exchangers 1 and 3 (NHE1 and NHE3), sodium-chloride symporter (NCC), and sodium-potassium-chloride cotransporter 2 (NKCC2); as well as the sodium-potassium adenosine triphosphatase (Na+/K+-ATPase) and type A natriuretic peptide receptor (NPR-A). Accordingly, SGK1 is implicated in the physiology and pathophysiology of Na+ homeostasis. Here, we focus particularly on recent findings of SGK1’s involvement in Na+ transport in renal sodium reabsorption, hormone-stimulated salt appetite and fluid balance and discuss the abnormal SGK1-mediated Na+ reabsorption in hypertension, heart disease, edema with diabetes, and embryo implantation failure. serum and glucocorticoid regulated kinase 1 (SGK1)epithelial sodium channelsvoltage-gated sodium channelshypertensionedemaheart diseaseembryo implantation ==== Body 1. Introduction Serum and glucocorticoid regulated kinase 1 (SGK1) was originally isolated in a differential screen searching for glucocorticoid-inducible transcripts in Con8.hd6 rat mammary tumor cells [1,2]. Within 30 min, SGK1 transcript levels were altered strongly upon cell volume change, independent of de novo protein synthesis [3]. SGK1 is highly conserved throughout eukaryotic evolution [4], being identified in the genomes of various species [5,6,7,8,9,10,11,12,13,14,15]. Human SGK1 is ubiquitously expressed throughout the whole body (Table 1). As a serine-threonine protein kinase, SGK1 belongs to the protein kinase A/protein kinase G/protein kinase C (AGC) family, and is expressed at low levels under physiological conditions [47,48,49]. Both its expression levels and activities are regulated by hormonal and non-hormonal factors [50], including glucocorticoids [51,52], mineralocorticoids [16,42], androgen [53,54,55,56], gonadotropin-releasing hormone (GnRH) [57], excessive extracellular glucose concentrations [58,59], memory consolidation and reconsolidation [60], hypertonic and hypotonic stimuli [61], chronic stress [52], and peroxisome proliferator-activated receptor γ (PPARγ) [62]. SGK1 is also induced by lipopolysaccharides [63], tumor necrosis factor (TNF)-α [63,64], angiotensin [65], resistin [66], granulocyte-macrophage colony-stimulating factor (GM-CSF) [67], fibroblast growth factor-23 (FGF23) [68], as well as miR-27a [24], miR-424 [69], miR-155 [70] and miR-133b [71]. Under stimulation by transforming growth factor (TGF)-β [72,73] and insulin [74], SGK1 is phosphorylated via signaling pathways involving phosphatidylinositol 3-kinase (PI3K), 3-phosphoinositide-dependent kinases (PDK1) [74] and mammalian target of rapamycin complex 2 (mTORC2) [75,76]. By contrast, interleukin-6 (IL-6) induces SGK1 transcription mainly through the janus kinase/signal transducer and activator of transcription (JAK/STAT) cascade [77]. These genomic and non-genomic activations of SGK1 contribute to the regulation of multiple epithelial ion channels, several ion carriers, and many other molecules [78]. The first demonstrated physiologically relevant function of SGK1 was its regulation of ENaC-mediated Na+ transport [79]. The present review attempts to delineate the current knowledge on the physiological and pathophysiological significance regarding SGK1 in the regulation of Na+ homeostasis. 2. Serum and Glucocorticoid Regulated Kinase 1 (SGK1)-Dependent Regulation of Na+ Channels and Transporters 2.1. Epithelial Sodium Channel (ENaC) Over the past 20 years, SGK1 has emerged as a key modulator of ENaC in the aldosterone-sensitive distal nephron (ASDN) [80], hepatocytes [81], lung [82], corneal layers [22], and brain [83]. SGK1 increases the amiloride-sensitive Na+ current significantly in Xenopus laevis oocytes [81,84], mouse collecting duct cells (mpkCCDcl4) [85], mammalian M1-CCD cells [86], amphibian A6 cell line [87,88], COS7 cells [89], H441 human airway epithelial cells [90,91], and colonic HT-29/B6 cells [92]. Upon stimulations of hormonal and non-hormonal signals, SGK1 regulates Na+ transport in various cells by altering ENaC expression [93,94], enhancing this channel’s activity and open probability (Po) [95], facilitating ENaC channel trafficking, and attenuating its degradation and recycling [96]. Several mechanisms have been proposed for the SGK1-dependent regulation of ENaC [82,97]. The best understood explanation argues that aldosterone-induced SGK1 increases ENaC activity indirectly by reducing ubiquitination of ENaC via phosphorylation and inhibition of the E3 ubiquitin ligase neuronal precursor cell expressed developmentally down-regulated 4-2 (Nedd4-2) [93], which results in increased Na+ transport in Xenopus laevis oocyte. SGK1 phosphorylates specific residues of Nedd4-2, resulting in the recruitment of the 14-3-3 protein, which inhibits the interaction between Nedd4-2 and ENaC. This inhibition is dependent on SGK1-catalyzed phosphorylation of Nedd4-2 [98,99]. Consistent with this view, GSK650394, an SGK1 inhibitor, suppresses the dexamethasone-induced phosphorylation of Nedd4-2, and reduces the surface abundance of α subunit of ENaC in airway epithelial cells [91]. Therefore, SGK1 phosphorylates the negative regulator Nedd4-2 and recruits 14-3-3, thereby preventing the ubiquitination and subsequent internalization of ENaC, and inhibiting the removal of the channel. This results in accumulation of ENaC at the cell surface and increased Na+ reabsorption as reviewed in [100,101,102,103,104,105]. In the above model, ENaC, possibly with cholesterol, recruits proteins to form the ENaC-regulatory complex (ERC) for its own regulation [106,107]. In this respect, Soundararajan et al. [108] have identified an approximately 1.0–1.2 MDa ENaC-regulatory-complex (ERC) containing ENaC and certain key regulatory factors, including aldosterone-regulated SGK1, Nedd4-2, v-raf-1 murine leukemia viral oncogene homolog 1 (c-Raf), glucocorticoid-induced leucine zipper (GILZ1), and the connector enhancer of kinase suppressor of Ras isoform 3 (CNK3), at the plasma membrane in mpkCCDc14 cells [107,108]. GILZ1 physically interacts with SGK1 to alter its subcellular localization and selectively recruits it into the ERC [106]. Contrastingly, CNK3 reinforces the interactions within this complex, providing a platform to assemble the multiprotein ERC to trigger ENaC activation [108,109,110]. Moreover, IκB kinase-β (IKKβ) was shown recently to enhance ENaC surface expression by phosphorylating Nedd4-2 on the same site phosphorylated by SGK1 [111,112]. Stimulated by serum in MDA231 cells derived from human breast cancer [113] or using morpholino oligonuleotides against SGK1 in Xenopus laevis oocyte [114], SGK1 was demonstrated to function upstream of IKKβ; therefore, SGK1 could modulate the activities of Nedd4-2 in concert with IKKβ, contributing to the enhanced accumulation of ENaC channel at the apical membrane [98,111]. While the SGK1/Nedd4-2 pathway could lead to enhanced ENaC function [101,111], other studies point to alternative pathways for SGK1 to regulate ENaC activity, independently of Nedd4-2 [110,115]. In this regard, recombinant SGK1 has been shown to directly phosphorylate residue serine (Ser)-621 of the SGK1 consensus motif in the C terminus tail of α-ENaC in Xenopus laevis oocytes, contributing to the activation of ENaC channels that are already present in the plasma membrane [116]. Recent evidence has demonstrated that SGK1 also has a role in aldosterone-stimulated ENaC trafficking in mCCD cells. This mode of channel regulation involves the Rab GAP (GTPase activating protein) AS160, Akt/PKB substrate of 160 kDa, which stabilizes ENaC in a regulated intracellular compartment [117]. Upon SGK1 phosphorylation, AS160 promotes ENaC trafficking to the apical membrane by relieving stabilization of ENaC in the intracellular compartment, thus augmenting Na+ absorption [117]. In addition, FLAG-tagged SGK1 has been implicated in the regulation of ENaC in HEK293 cells by phosphorylating and thus inhibiting with no lysine kinase 4 (WNK4) [118,119], a serine/threonine kinase that inhibits ENaC activity [120]. SGK1 further regulates ENaC indirectly by phosphorylating inducible nitric oxide synthase (iNOS) [121]. Nitric oxide (NO) inhibits ENaC by reducing its Po without altering the apparent channel density or Na+ current [121,122]. Upon the stimulation of aldosterone, SGK1 phosphorylates mouse iNOS and consequently decreases NO produced by iNOS to increase Na+ transport in the mouse alveolar type II (ATII) epithelial cells [121]. SGK1 is proposed to up-regulate [123] or de-repress [124] the components of the Na+ transport machinery per se, primarily α-ENaC. Evidence from Sgk1 knockout mice and mouse inner medullary collecting duct cell (mIMCD3) indicated that aldosterone-induced SGK1 is involved in an epigenetic pathway regulating the transcription of SCCH1A (gene encoding α-ENaC) by diminishing hypermethylation of histone protein H3 at lysine 79 (H3K79) in the vicinity of the SCCH1A promoter [124]. SGK1 phosphorylates DNA-binding protein ALL1 fused gene from chromosome 9 (AF9), and thus promotes methyltransferase Disruptor of telomeric silencing 1 (Dot1a) to dissociate from the SCCH1A promoter, leading to inhibition of histone H3K79 methylation at the promoter and subsequently relief of repression [124,125,126]. Interestingly ALL-1 partner at 17q21 (AF17), a competitor of AF9 for binding Dot1a, relieves Dot1a-AF9 repression as well as increasing SGK1 expression to enhance SGK1-mediated AF9 phosphorylation, resulting in augmented ENaC-mediated Na+ transport [127,128,129,130,131]. Taken together, SGK1 regulates ENaC activity through Nedd4-2-dependent and Nedd4-2-independent mechanisms [101,111] (Figure 1). These mechanisms are not mutually exclusive. Upon the stimulation of hormonal (e.g., aldosterone, dexamethasone) or non-hormonal (e.g., serum) signals, the activation of SGK1 attenuates the degradation of ENaC to increase the surface abundance of this Na+ channel at the apical membrane [91,93,106,113,114,118,119], relieves the stabilization of ENaC in a regulated intracellular compartment [117], and facilitates ENaC activities by direct phosphorylation [116,117] in various cell lines. Moreover, aldosterone-induced SGK1 has long-term effect on the transcriptional expression of ENaC in an epigenetic pathway both in vivo and in vitro [124]. The discrepancies among the various mechanisms could be ascribed to the characteristics of different stimuli and the timing of SGK1’s action. 2.2. Voltage-Gated Na+ Channel Nav1.5 (SCN5A) The voltage-gated sodium channel Nav1.5 (encoded by the SCN5A gene), is the major Na+ influx channel for the cardiac action potential initiation of cardiac action [132]. As shown in Xenopus laevis oocytes, SGK1 up-regulates cardiac Nav1.5 [97,133]. Through phosphorylation and inactivation of Nedd4-2, SGK1 attenuates the inhibition on Nav1.5 by Nedd4-2, and alters channel trafficking, resulting in an increase in available Nav1.5 channels at the cell surface [97,133,134,135]. Conversely, inhibition of SGK1 in the dominant-negative Sgk1 mice blocked the biochemical changes in Nav1.5 [134]. In addition, peptide mapping identified three putative phosphorylation sites for SGK1 within the Nav1.5 sequence [132,134]. The mutation of serine to alanine in the SGK consensus sequences of Nav1.5 resulted in a reversal of the gating properties of the channel [97,133]. More recently, Bezzerizes et al. [132] observed that an alanine mutant abolished the increase in Na+ current from SGK1 activation. Thus SGK1 might modify the gating kinetics of Nav1.5 channels by direct phosphorylation of the channel protein [97]. 2.3. Sodium Hydrogen Exchanger (NHE1 and NHE3) NHE3 participates in Na+ reabsorption and H+ secretion in a variety of epithelia and is involved in the modulation of cytosolic pH in various epithelial and non-epithelial cells [48,136,137,138]. In cultured epithelial cells, SGK1 enhances NHE3 activity acutely [139,140]. SGK1 specifically phosphorylates NHE3 at Ser-663 in response to dexamethasone; therefore, mutation of Ser-663 abolished the stimulatory effect of dexamethasone on NHE3 transport activity [139]. This up-regulation requires the additional presence of the NHE regulatory protein 2 (NHERF2) [141], which tethers NHE3 and SGK1 to aid the phosphorylation of NHE3. Comparing short-term regulation of NHE3 by dexamethasone in Sgk1flox/flox:Villin-Cre mice and Nherf2−/− mice, He et al. [142] showed that SGK1 plays a major role in acute regulation of NHE3 in vivo in the intestine. Critically, SGK1 participates in the up-regulation of NHE1 by glucocorticoids in HL-1 cardiomyocytes in vivo [143,144]. Activation of NHE1 could induce cardiac hypertrophy and unbalanced cardiomyocyte pH, which may lead to myocardial remodeling and ischemic cardiac diseases [145,146,147]. SGK1 presumably phosphorylates NHE1 at Ser-703, promoting 14-3-3 binding and stimulating NHE1 activity by decreasing dephosphorylation and by stabilizing an active conformation of the exchanger [50,143]. Stimulated by dexamethasone, SGK1 would participate in the development of heart failure and other cardiac pathophysiology by activating cardiac NHE1 [143]. 2.4. Sodium-Chloride Symporter (NCC) The Na+-Cl− cotransporter, sodium-chloride symporter (NCC), is expressed in the apical plasma membrane of epithelial cells in the distal convoluted tubule (DCT) [148,149]. NCC reabsorption accounts for only 5%–10% of filtered Na+; however, is critical to the fine-tuning of renal sodium excretion in response to various hormonal and non-hormonal stimuli [149,150]. NCC can be regulated by changes in expression, trafficking and phosphorylation [151]. The total Sgk1 knockout mice generated by Fejes-Tóth et al. [80] exhibited a salt-wasting phenotype under a low salt diet, had reduced ENaC expression and decreased expression of NCC [84]. This phenotype was similar to that of kidney-specific Sgk1 knockout mice [80]. Furthermore, on a low-NaCl diet, NCC abundance in the DCT of normal mice increased as did phosphorylation of NCC at Thr-53, Thr-58, and Ser-71 [148]. This response, however, is attenuated in mice lacking Sgk1 (Sgk1−/−), suggesting that Sgk1 somehow affects NCC phosphorylation [148]. SGK1 is thought to modulate NCC activity by inhibiting WNK4 [120,149,152]. WNK4 negatively regulates the surface abundance of NCC by promoting lysosomal degradation [153]. Moreover, WNK4 has been demonstrated to reduce NCC abundance at the plasma membrane, resulting in the inactivation of NCC [154]. Constitutively active SGK1S422D phosphorylates WNK4 at Ser-1169 [118] and Ser-1196 [155], relieving the inhibitory effect of WNK4 on NCC’s activity [149,151]. In addition, aldosterone acutely stimulated Na+ reabsorption by NCC in the DCT, and this effect appeared to be dependent upon the presence of SGK1 and Nedd4-2 [156,157]. Accordingly, SGK1 has been proposed to be involved in the regulation of NCC by Nedd4-2 [158]. Similar to ENaC, Nedd4-2 is co-located with NCC and stimulates NCC ubiquitination at the apical plasma membrane. Phosphorylation of Nedd4-2 at Ser-328 and Ser-222 by SGK1 abrogates Nedd4-2-mediated inhibition of NCC [156]. Roy et al. proposed that SGK1 and Nedd4-2 cannot alter the phosphorylation status of NCC in WNK1 KO HEK-293T cells, representing another model of the effects of WNK1 deletion on Nedd4-2/SGK1 regulation of NCC [150]. 2.5. Na+-K+-2Cl− Cotransporter (NKCC2) SGK1 is not only involved in the regulation of ENaC, but also influences other renal tubular Na+ transport systems [159]. The Na+-K+-2Cl− cotransporter (NKCC2 or BSC-1) is one of the candidate downstream effectors. NKCC2 plays a critical role in Na+ reabsorption and urinary K+ excretion across the luminal membrane of the thick ascending limb (TAL) [160]. NKCC2-mediated Na+ flux was stimulated 6-fold by the co-expression of SGK1 in Xenopus laevis oocytes [161]. Stimulated by the increased extracellular glucose concentrations, the enhanced expression of SGK1 may contribute to the abnormal Na+ transport in diabetic nephropathy by regulating NKCC2 [160]. 2.6. Sodium/Potassium-Adenosine Triphosphatase (Na+/K+-ATPase) SGK1 has also been implicated in the regulation of Na+/K+-ATPase activity, the transporter responsible for basolateral Na+ efflux [162]. SGK1 co-localizes with the Na+/K+-ATPase in renal epithelial cells [162]. In Xenopus laevis oocytes, SGK1 increased the activity of both endogenous and exogenous Na+/K+-ATPase [48,97,163,164,165,166]. In A6 cells derived from the Xenopus laevis distal tubule, SGK1 expression increases Na+/K+-ATPase activity, independent of changes in abundance at the plasma membrane or protein expression [162]. Constitutively active mutant of SGK1 (SGK1S425D) stimulates existing Na+ pumps in the basolateral plasma membrane for the Na+ exiting [162], which would maintain the chemical driving force for Na+ entry through ENaC [162]. In addition, the stimulatory effect of SGK1 on Na+/K+-ATPase is mimicked by the isoforms SGK2 and SGK3 in Xenopus laevis oocytes [167]. 2.7. Type A Natriuretic Peptide Receptor (NPR-A) The human isoform of SGK1 has been identified as a cell volume-regulated gene that is modulated transcriptionally by cell swelling and shrinkage [3,168,169]. Accordingly, SGK1 has been shown to be involved in the extracellular tonicity-dependent stimulation of the NPR-A gene promoter in rat inner medullary collecting duct (IMCD) cells via the p38 mitogen-activated protein kinase (MAPK)-dependent pathway [170]. Beyond that, hypertonicity induces the expression of tonicity-responsive enhancer binding protein/nuclear factor of activated T cells 5 (TonEBP/NFAT5), which accounts for the osmosensitivity of the SGK1 gene promoter [61]. In turn, SGK1 does indeed serve as a key mediator in the osmotic induction of NPR-A gene expression [61]. Taken together, SGK1 acts as a key intracellular signal that regulates the activities of ENaC, Nav1.5, NHE1 and NHE3, NCC, NKCC2, Na+/K+-ATPase, and NPR-A, thus contributing to Na+ homeostasis (Table 2). 3. Physiological Role of SGK1 in Na+ Transport 3.1. SGK1-Dependent Renal Na+ Reabsorption The kidneys play a pivotal role in the maintenance of Na+ homeostasis [62,173]. Urinary Na+ reabsorption is regulated tightly to maintain a constant extracellular volume as limiting extrarenal Na+ loss during dietary Na+ restriction [47]. The final adjustment to renal Na+ balance is achieved in the ASDN: i.e., the distal convoluted tubule (DCT), the connecting tubule (CNT), the cortical collecting duct (CCD) and the medullary collecting duct (MCD) [120,163]. Aldosterone and vasopressin play major roles in regulating Na+ flux in epithelial tissues in these segments [85,174,175]. This effect is accomplished by the coordinated action of Na+ entry into the epithelial cells via ENaC channel on the apical membrane, as well as Na+ exit through the Na+/K+-ATPase pump on the basolateral plasma membrane [47,176]. As illustrated above, SGK1 regulates ENaC [62,85,173] in the apical membrane and the Na+/K+-ATPase in the basolateral membrane, thereby coordinating Na+ transport at both sides of epithelial cells [177]. In early distal tubules, the chlorothiazide-sensitive NCC mediates Na+ uptake [178,179]. SGK1 phosphorylates Nedd4-2 and WNK4, blocking their inhibitory effects on NCC [180]. In addition to stimulating Na+ uptake in the ASDN, SGK1 participates in Na+ transport in other renal segments. In rats and mice on a standard NaCl diet, expression of Sgk1 mRNA was detected in the glomeruli, proximal tubules [181], ASDN, and particularly strongly, in the IMCD [32,182]. SGK1 protein is localized to the TAL and ASDN [163], whereas very low protein expression was detected under basal conditions in the glomeruli, proximal tubule or MCD, including the papilla in rat kidneys [181,182]. Therefore, apart from ENaC, SGK1 increases Na+ reabsorption via various transporters: NHE3 in the proximal tubule (PT) [136,183,184]; NKCC2 in the loop of Henle of TAL; as well as the Na+ pump in different nephron segments [180]. The central role of SGK1 in the hormonal control of Na+ handling is further illustrated by the observations in mice lacking Sgk1 [80,185,186,187]. Under a normal-salt diet, the phenotype of the Sgk1−/− mouse was virtually identical to that of its wildtype littermates (Sgk1+/+) [80,178,186,188]. These Sgk1−/− mice showed no obvious defect in water and Na+ excretion, and maintained normal apical membrane staining for α-ENaC in the connecting tubule, except for higher circulating aldosterone levels, suggesting extracellular volume depletion [188,189]. However, when exposed to an NaCl-deficient diet, the Sgk1−/− mice presented a dramatic urinary salt wasting phenotype: weight loss caused by increased urine production, decreased systolic and diastolic blood pressure, increased urinary Na+ and K+ excretion with unchanging plasma Na+ and K+ levels, and higher plasma aldosterone [80,188]. Wulff et al. [188] reported a weaker amiloride-sensitive transepithelial transport potential difference in isolated collecting ducts (CD) of Sgk1−/− mice compared with Sgk1+/+ mice. In contrast, Fejes-Tóth et al. [80] reported increased amiloride-sensitive Na+ currents with decreased γ-ENaC cleavage, as well as diminished NCC protein expression, in isolated collecting ducts of Sgk1−/− mice compared with the wildtype mice. Recently, Faresse et al. [186] generated doxycycline-inducible nephron tubule-specific Sgk1 knockout mice (Sgk1Pax8/LC1), in which Sgk1 expression could be repressed within the kidney by treatment with doxycycline in the drinking water. The Sgk1Pax8/LC1 mice also exhibit a large defect in Na+ conservation when placed on a low-Na+ diet [186]. Sgk1Pax8/LC1 mice have a decreased expression of the βγ-ENaC protein, without any change in γ-ENaC cleavage and α-ENaC mRNA expression [186]. Moreover, a significant reduction of NCC protein and no difference in mRNA levels has been observed in Sgk1Pax8/LC1 mice, along with decreased phosphorylation of Nedd4-2 on Ser-222 and Ser-328 by Sgk1. This finding suggests a potential SGK1-dependent regulation of NCC in renal Na+ reabsorption [186]. 3.2. SGK1-Dependent Renal Na+ Excretion Since its discovery in 1993, SGK1 was first identified in the response to cell volume alterations in a human hepatoma cell line [168,169]. Cell shrinkage leads to a rapid induction of SGK1 transcription in different cell lines [61,92,168,190,191,192,193,194,195,196]. Hypertonicity in the early phase leads to an acute increase in urinary sodium excretion [61,170]. In rat IMCD cells, SGK1 transcription is modulated by tonicity-responsive enhancer (TonE) binding protein (TonEBP/NFAT5) [61], which in turn activates NPR-A, resulting in sodium excretion [61,170]. As demonstrated in rat and mouse assays, increased extracellular osmolality does indeed increase Sgk1 and Npr-A gene expressions concomitantly in the MCD. Furthermore, Chen et al. [61] reported that natriuretic peptide receptor 1 (Npr1) gene knockout mice (Npr1−/−) failed to elicit changes in urinary Na+ excretion when challenged with dehydration, despite elevated urinary osmolality and Sgk1 expression in the renal medulla. Collectively, these findings defined the contribution of the osmosensitive gene SGK1 to medullary sodium excretion (Figure 2), where it promotes the physiological response of the kidney to dehydration [61]. 3.3. Aldosterone-Induced Salt Appetite In addition to regulating renal Na+ transport, SGK1 is thought to be involved in the regulation of aldosterone-induced salt adaptation and salt appetite [47,50,197,198,199]. When treated with deoxycorticosterone-acetate (DOCA)/1% NaCl, Sgk1+/+ mice exhibited a pronounced increase in Na+ intake and proteinuria compared with Sgk1−/− mice [197]. The observation of pregnant mice further confirmed the role of SGK1 in the enhanced salt appetite as the preference for saline water was significantly stronger in Sgk1+/+ mice than in Sgk1−/− mice [200]. Therefore, SGK1 were expected to participate in the increased salt uptake during pregnancy, contributing to the increase extracellular fluid volume, which favors hypertension of pregnancy [200]. Although the underlying mechanism remains to be exploited, Vallon et al. [197] have proposed that SGK1 might contribute to the stimulation of salt appetite in response to mineralocorticoid excess by upregulating the activity of Na+/K+-ATPase in the amygdala, an area implicated in the modulation of salt appetite. Fu et al. [199] have assumed that aldosterone activates SGK1, Nedd4-2 and ENaC in both kidney and brain. They suggested that SGK1 and ENaC were involved in aldosterone-induced salt appetite, as ENaC also mediates the gustatory salt sensing. Thus, SGK1 appears to play a dual role in hormone-regulated Na+ homeostasis, attenuating urinary salt output by regulating ENaC-mediated renal Na+ reabsorption on the one side, and increasing salt intake through stimulating salt appetite on the other [197,200]. Notably, the dual effects converge to expand the extracellular volume, which is supposed to favor salt-sensitive hypertension [201,202,203,204,205,206]. 3.4. SGK1-Dependent Intestinal Sodium Absorption Under basal conditions, SGK1 is expressed robustly in the distal colon, ileum and jejunum, which are beyond the aldosterone-responsive segments, suggesting a constitutive role in absorptive epithelia [207]. Consistently, ENaC, which is phosphorylated regulated by SGK1, plays a pivotal role in minimizing intestinal water and sodium losses in the distal colon [208]. Therefore, Dames et al. [208] showed that decreased SGK1 expression due to the suppression of interleukin-13 (IL-13) impaired epithelial sodium absorption via ENaC. Aldosterone-induced intestinal Na+ absorption is also mediated by apical Na+-H+-exchangers (NHE2/3) and basolateral Na+/K+-ATPase [209]. SGK1 has been proposed to be part of this cascade [140]. Using human colonic Caco-2 and opossum kidney cells, Wang et al. [140] observed a biphasic activation of NHE3, which is responsible for the electrogenic Na+ absorption in the intestinal epithelium. Furthermore, Musch et al. [210] demonstrated a potential role for SGK1 in the two phases of aldosterone-induced intestinal Na+ absorption. The initial phase involves enhanced insertion of the α-subunit of Na+/K+-ATPase through a PI3K-SGK1-dependent pathway and subsequently increased levels of apical membrane NHE3.The later activation is mainly concerned with elevated expression and activities of total NHE3 and Na+/K+-ATPase (α-subunit), both of which are regulated by SGK1 [210]. 3.5. SGK1-Dependent Lung Fluid Absorption SGK1 is expressed strongly in the lower respiratory tract. The SGK1-dependent regulation of ENaC in pulmonary epithelial cells plays a critical role in sodium/fluid homeostasis and in lung fluid clearance [90]. In this regard, several studies have reported increased SGK1 expression in prenatal lung segments [7,211,212]. Thus, decreased SGK1 expression could contribute to the inability to clear excessive lung fluid immediately after preterm birth [213,214]. Using H441 human airway epithelial cells, Ismail et al. [91] showed that the activation of SGK1 by dexamethasone increases the surface expression of α-, β- and γ-ENaC, while the inhibition of SGK1 suppresses the phosphorylation of Nedd4-2 and reduces the surface abundance of α-ENaC, contributing to increased membrane Na+ transport [91]. Furthermore, in the lipopolysaccharide (LPS)-induced acute lung injury (ALI), activation of SGK1 promotes both the total gene expression and the surface abundance of ENaC, leading to a protective effect in the case of LPS-induced ALI [74,82,94]. Therefore, SGK1 is essential to the induction and maintenance of controlled Na+ absorption in the respiratory system, and is involved in the hormonal management of respiratory distress and pulmonary edema, which are clinical manifestations of abnormal pulmonary Na+ absorption [91]. 3.6. SGK1-Dependent Peripheral Na+ Transport SGK1 is co-expressed with ENaC in the human ocular ciliary epithelium and basal cells of corneal endothelium [74,82,94]. The activation of ENaC, NKCC2 and Na+/K+-ATPase induced by SGK1 could contribute to sodium transport in the human ocular ciliary epithelium and corneal endothelium, and further account for corneal transparency [22,23]. In the epithelium of the human middle ear, ENaC-mediated sodium transport is upregulated by dexamethasone via the glucocorticoid receptor (GR)-SGK1-Nedd4-2 pathway [25,215]. Zhong et al. [216] showed that SGK1 is expressed in various regions of guinea pig cochlea, being associated with the regulation of endolymph homeostasis by mediating passive entry of sodium into cells. Thus SGK1 could be involved in the therapeutic activity of glucocorticoids in the treatment of Meniere’s disease, a debilitating condition that manifests endolymphatic hydrops, which might be associated with Na+ hypoabsorption in the vestibular lumen [25,215]. 4. Pathological Role of SGK1 in Na+ Transport 4.1. Salt-Sensitive Hypertension Excessive renal Na+ retention can increase the circulating volume which may contribute to the development of high blood pressure [189]. SGK1 participates in facilitating hormonal actions involved in stimulating salt intake and inhibiting renal sodium loss; thereby influencing the long term control of arterial blood pressure, thus contributing to the development of hypertension. The daily salt intake seems to predispose certain individuals to develop salt-sensitive hypertension [201,217,218,219]. Sgk1 is believed to contribute to the preference for a high salt diet and be involved in hormone-induced salt adaptation [197,198]. In Dahl salt-sensitive (DS) rats, which show hypertension with a high salt diet, the renal expression of Sgk1 is elevated greatly [201]. Furthermore, in animals receiving a high-fat diet [202] or high fructose intake [203], in addition to high salt intake, increased blood pressure is only detected in Sgk1+/+ mice, but not in Sgk1−/− mice. Following high-salt intake, Sgk1-mediated up-regulation of ENaC, as well as Na+/K+-ATPase, stimulates Na+ transport in the cerebrospinal fluid and the brain, which would activate the renin–angiotensin system, leading to the release of ouabain-like compound (OLC) which in turn activates the renin–angiotensin system, thereby increasing blood pressure [220]. Renal salt retention is another culprit thought to be involved in the development of hypertension [96,171,200,221]. The renal re-absorption of Na+ is critical to whole body Na+ and water balance, and to the control of blood pressure [180,221]. This process, as discussed above, is accomplished partially via the mediation by SGK1. SGK1 enhances the activity of ENaC, NCC, NKCC2, and Na+/K+-ATPase, which in turn increase the Na+ re-absorption [189,222]. In particular, in primary aldosteronism or Liddle’s syndrome, SGK1 increases the activity of ENaC channels in response to aldosterone [223,224]. In addition, gene variants of these transporters and enzyme are also associated with increased blood pressure [189,225]. In fact, some distinct variants of the SGK1 gene are indeed indicated in increased blood pressure [189]. Polymorphisms in intron 6 [I6CC] and exon 8 [E8CC/CT] are associated with moderately enhanced blood pressure in individuals carrying these variants [226,227,228,229,230]. These gene variants affect about 3%–5% of the Caucasian population [226,227] and 11.6% of a healthy African population [231]. In a study of 421 hypertensive Caucasian participants, Rao et al. [217] determined that two single nucleotide polymorphisms (SNPs) of SGK1 (rs2758151 and rs9402571) were associated with effects upon blood pressure and plasma renin activity (PRA) as a result of dietary salt intake. The major allele homozygotes at either rs2758151 or rs9402571 were associated with high systolic blood pressure in response to a high salt diet and decreased PRA on a low salt diet [217]. Recently, Chu et al. [232] reported that a genetic polymorphism in SGK1 is significantly correlated with the blood pressure response to dietary sodium intervention: SNP rs9389154 was associated with systolic blood pressure (SBP), while SNPs (rs1763509 and rs9376026) were associated with diastolic blood pressure (DBP). SNP rs9376026 was significantly associated with both mean arterial pressure (MAP) and DBP, and SNP rs3813344 was significantly linked with SBP, DBP, and MAP. Accordingly, individuals with these genotypes would be prone to salt-sensitive hypertension [217,232]. Moreover, Sgk1 appears to be critical for the fetal programming of hypertension [172,233,234]. A protein-deficient diet during pregnancy leads to increased blood pressure in the offspring of Sgk1+/+ mothers mice [233,234]. Taken together, dysregulation of SGK1 activity or certain specific gene variants of SGK1 could be involved in salt-sensitive hypertension [233,234]. 4.2. Edema with Diabetes Mellitus Synthetic PPARγ agonists are used to improve insulin sensitivity in patients with diabetes mellitus; however, their use is limited by fluid retention [184,235,236,237,238]. This Na+ retention in nephrons may contribute to the development of edema and promote secondary hypertension in patients with type 2 diabetes mellitus, as a side effect of PPARγ treatment [62]. PPARγ agonists promote the activation of SGK1, the phosphorylation of Nedd4-2 and abolish ubiquitination and internalization of ENaC, leading to sodium and fluid retention [62]. Moreover, PPARγ stimulates Na+ transport in the distal tubular epithelia and proximal tubule cells via SGK1-dependent regulation of NHE3 [184,237,239]. Thus SGK1 contributes to the dysregulation of cellular Na+ and water transport in diabetes mellitus [184]. 4.3. Cardiac Dysfunction Dysregulation of Na+ homeostasis has been implicated in cardiac rhythm disorders as well as adverse ventricular remodeling [132,134]. SGK1 plays a pivotal role in early cardiac angiogenesis and vascular remodeling [135,240,241]. Chronic SGK1 activation in the heart increases mortality caused by cardiac arrhythmias [134,144]. This effect is paralleled by SGK1-dependent stimulation of the cardiac sodium channel Nav1.5 [134], the major influx channel responsible for the initiation of the cardiac action potential [132]. The SGK1-dependent upregulation of Nav1.5 alters sodium flux, leading to arrhythmia and cardiomyopathy [132,134,242]. Recent data suggested that the Na+/H+ exchanger NHE1, a target of SGK1, is involved in cardiac pathophysiology [143,144]. By increasing Na+ entry and subsequently decreasing the chemical Na+ gradient through a NHE1-mediated pathway, SGK1 contributes to myocardial remodeling, cardiac hypertrophy and progression to heart failure [78,134,143,144]. 4.4. Implantation Failure SGK1 has been detected in the human endometrium [243,244,245,246,247] and placenta [245,246,247,248]. Using a cDNA microarray, a previous study identified SGK1 as a gene aberrantly expressed specifically in luminal epithelia during the midsecretory receptive phase of the cycle in infertile women [243]. In line with this, Salker et al. [246] confirmed that transcription of SGK1 was higher in the uterine luminal epithelia of infertile women compared with fertile controls. They further demonstrated a transient down-regulation of Sgk1 transcription in the mouse luminal epithelium during the window of endometrial receptivity [245,246,248]. Moreover, the expression of ENaC was upregulated, accompanied by the downregulation of Nedd4-2 in the Sgk1−/− mice [245,246,248]. In this respect, SGK1 expression and functional activation account for a successful implantation, by modulating ENaC activities and consequent fluid absorption before engraftment [248]. 5. Conclusions and Perspectives SGK1 is a prominent regulator of multiple Na+ channels, pumps and carriers, and thus contributes to the regulation of epithelial Na+ transport, cell volume and sodium homeostasis. This kinase is not expected to possess housekeeping functions, judging by the mild phenotype shown in both ubiquitous gene knockout and inducible tissue-specific Sgk1 knockout mice [50,80,186,188,249]. By contrast, the gain of function of SGK1 is seemingly crucial for the pathophysiology of a wide variety of disorders [50,249]. Accordingly, SGK1 is thought to be involved in the formation of fibrosis which is characterized by dysregulated Na+ transport in several tissues [96,250]. Increased SGK1 expression has been implicated in various fibrotic diseases, such as cystic fibrosis [96,251], renal fibrosis and albuminuria [250], diabetic nephropathy [250], glomerulonephritis [250], Crohn’s disease, fibrosing pancreatitis, and liver cirrhosis [48,50,249]. Additionally, as an osmosis-sensitive gene, SGK1 might play a role in apoptosis, where cell shrinkage serves as a signal in programmed cell death or apoptosis [252]. In fact, downstream targets of SGK1, such as Na+/K+-ATPase and NHE1, are involved in cell apoptosis [252]. Recently, SGK1 has been proposed as a potential target of sodium intervention in immune cells [253,254]. NaCl affects the regulatory balance of type 1 helper T cell (TH1), TH2, TH17 and regulatory T cells (Treg cells) in an SGK1-dependent manner [253,254]. In this regard, more studies are needed to determine whether SGK1 is a major driver or just a passenger in the pathophysiology of various disorders characterized by dysregulated sodium transport. Acknowledgments This work was supported by grants from the National Basic Research Program of China (no. 2014CB943302; no. 2012CB944901); the National Natural Science Program of China (no. 81571500; no. 81370760); Natural Science Program of Zhejiang Province, China (no. Y2100822); Zhejiang Provincial Natural Science Foundation of China (no. LZ13H040001; no. LZ15H040001); Medical Scientific Research Program of Zhejiang Province (no. 2014KYA269; no. 2016KYA120); Health Science and Technology Program of Hangzhou (no. 2014A54); and Traditional Chinese medicine Program of Zhejiang Province (no. 2015ZA159). Author Contributions Yiyun Lou wrote and revised the manuscript; Liya Wang and Yiyun Lou were involved in drafting the figures; Fan Zhang, Yuqin Luo and Shisi Huang provided critical suggestions to the manuscript; Fan Jin critically reviewed and revised the manuscript. All authors read and approved the final manuscript. Conflicts of Interest The authors declare no conflict interest. Figure 1 Serum and Glucocorticoid Regulated Kinase1 (SGK1)-dependent regulation of ENaC channel. ① SGK1 phosphorylates the negative regulator Nedd4-2 and recruits 14-3-3 protein to reduce the ubiquitylation and degradation of ENaC; ② SGK1 interacts with GILZ1, CNK3, c-Raf, ENaC and Nedd4-2 to form the ENaC-regulatory complex (ERC) for stimulating ENaC function; ③ SGK1 phosphorylates IKKβ to reverse the Nedd4-2-mediated inhibition of ENaC; ④ SGK1 directly phosphorylates α subunit of ENaC; ⑤ SGK1 phosphorylates AS160 to promote ENaC trafficking to the apical cell membrane; ⑥ SGK1 activates ENaC via phosphorylating WNK4; ⑦ SGK1 enhances the open probability of ENaC channel by decreasing inhibitory NO through phosphorylating iNOs; and ⑧ SGK1 is involved in an epigenetic pathway regulating SCCH1A (gene encoded α-ENaC) transcription by phosphorylating AF9 and promoting Dot1a to dissociate from SCCH1A promoter, diminishing the hypermethylation of histone H3K79 methylation at the promoter of SCCH1A. The translocations of moleculars are marked in dashed arrows. The red arrows with flat head mean inhibitory modification. P, phosphate; PTEN, phosphatase and tensin homolog; IRS, insulin receptor substrate; MR, mineralocorticoid receptor; Aldo, aldosterone. Figure 2 SGK1-dependent Na+ reabsorption and excretion in the mammalian kidney tubule. SGK1 boosts Na+ reabsorption via multiple transporters in different renal segments: NHE3 in the PT, NKCC2 in the loop of Henle TAL, sodium-chloride symporter (NCC) in the early distal convoluted tubule (DCT), ENaC primarily in the connecting tubule (CNT) and cortical collecting duct (CCD), as well as Na+-K+-ATPase pump throughout different nephron segments. SGK1 regulates Na+ excretion in the medullary collecting duct (MCD) by activating NPR-A. SGK1, serum and glucocorticoid regulated kinase 1; NHE3, sodium hydrogen exchanger 3; NHERF2, NHE regulatory protein 2; WNK4, with no lysine kinase 4; Nedd4-2, neuronal precursor cell expressed developmentally down-regulated 4-2; ENaC, epithelial sodium channel; NPR-A, type A natriuretic peptide receptor. ijms-17-01307-t001_Table 1Table 1 Serum and glucocorticoid regulated kinase 1 (SGK1) expressions throughout the body. Organ Distribution of SGK1 Reference Brian Hypothalamic nuclei [16] Ventral striatum [17] Dorsal horn [18] Dopamine neurons [19] Cortical pyramidal neurones [20] Blood-brain barrier (BBB) [21] Eye Ocular ciliary epithelium [22] Corneal endothelium [23] Ear Cochlear sensory epithelium [24] Apical membrane of middle ear epithelium [25] Thymus Epithelial cell [2] Heart Heart chamber [26] Lung Epithelial cell [27] Breast Mammary gland [28] Liver Epithelial cell [29] Pancreas Pancreatic tissue [30] Intestine Epithelium of the duodenum, jejunum, ileum, and colon [31] Kidney Epithelium lining the nephrons (distal tubules, glomeruli, and inner medulla) [32] Bladder Detrusor tissue [19] Muscle Skeletal muscle [33] Adipose tissue Adipocyte [34,35] Blood Platelets [36,37] Immune system T-lymphocytes [38] Dendritic cell [39] Macrophage [40] Mast cell [41] Reproductive system Several ovarian cell types including the oocytes of primordial follicles [42] Sperm [43] Primordial germ cell [44] Decidua [45] Placental trophoblast [46] ijms-17-01307-t002_Table 2Table 2 SGK1-dependent mediators of Na+ channels and transporters. * Na+ Channels and Transporters Mediators SGK1 Regulation Possible Mechanism Reference ENaC Nedd4-2/14-3-3 protein SGK1 phosphorylates and sequesters the negative regulator Nedd4-2. Meanwhile, SGK1 recruits 14-3-3 to stabilize Nedd4-2 interacting with 14-3-3 Nedd4-2 interacts with ENaC to induce ubiquitination and retrieval of ENaC channel; whereas 14-3-3 binds to Nedd4-2 and inhibits the interaction between Nedd4-2 and ENaC [102,171] iNOS SGK1 phosphorylates iNOS NO reduces the open probability of ENaC [172] AF9-Dot1a complex SGK1 phosphorylates AF9 and promotes Dot1a to dissociate from the α-ENaC promoter AF9-Dot1a complex facilitates Dot1a to hypermethylate Lys79 of histone H3 and suppress α-ENaC transcription [126] WNK4 SGK1 phosphorylates WNK4 WNK4 inhibits ENaC activity [118] NDRG2 SGK1 phosphorylates NDRG2 protein NDRG2 stimulates ENaC activity and increase its surface expression [33] Nav 1.5 Nedd4-2 SGK1 phosphorylates and inactivates Nedd4-2 Nedd4-2 inhibits Nav1.5 activity [135] NHE1 14-3-3 protein SGK1 recruits 14-3-3 binding 14-3-3 facilitates SGK1 to phosphorylate and stimulate NHE1 [143] NHE3 NHERF2 SGK1 interacts with NHERF2 NHERF2 tethers SGK1 and NHE3 to facilitate the phosphorylation of NHE3 [141] NCC Nedd4-2 SGK1 Phosphorylates Nedd4-2 and abrogates Nedd4-2-mediated inhibition Nedd4-2 is co-located with NCC and involved in the ubiquitylation of NCC transporter [156] * See text for abbreviations. ==== Refs References 1. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081308ijms-17-01308ReviewThe Significance of Epithelial-to-Mesenchymal Transition for Circulating Tumor Cells Kölbl Alexandra C. Jeschke Udo *Andergassen Ulrich Marchetti Dario Academic EditorDepartment of Gynecology and Obstetrics, LMU Munich, Maistrasse 11, 80337 Munich, Germany; alexandra.koelbl@med.uni-muenchen.de (A.C.K.); ulrich.andergassen@med.uni-muenchen.de (U.A.)* Correspondence: udo.jeschke@med.uni-muenchen.de; Tel.: +49-89-4400-5424011 8 2016 8 2016 17 8 130830 6 2016 04 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Epithelial to mesenchymal transition (EMT) is a process involved in embryonic development, but it also plays a role in remote metastasis formation in tumor diseases. During this process cells lose their epithelial features and adopt characteristics of mesenchymal cells. Thereby single tumor cells, which dissolve from the primary tumor, are enabled to invade the blood vessels and travel throughout the body as so called “circulating tumor cells” (CTCs). After leaving the blood stream the reverse process of EMT, the mesenchymal to epithelial transition (MET) helps the cells to seed in different tissues, thereby generating the bud of metastasis formation. As metastasis is the main reason for tumor-associated death, CTCs and the EMT process are in the focus of research in recent years. This review summarizes what was already found out about the molecular mechanisms driving EMT, the consequences of EMT for tumor cell detection, and suitable markers for the detection of CTCs which underwent EMT. The research work done in this field could open new roads towards combating cancer. circulating tumor cellsepithelial to mesenchymal transitionmarkerprognosiscancermetastasis ==== Body 1. Introduction Frequently, during the process of tumor outgrowth, single cells dissolve from the primary tumor and enter circulation. These cells are called “circulating tumor cells” (CTCs) [1]. If they extravasate again they can settle down in different sites of the body and are considered to be the main reason for remote metastasis formation. They can also enter bone marrow and stay there for long time periods in a certain state of dormancy [2,3,4,5,6,7]. These cells are called “disseminated tumor cells” (DTCs). Both, CTCs and DTCs have a prognostic relevance for affected patients [1,8,9] and their presence/absence is already included in the international tumor staging systems [10,11,12]. As DTCs could only be obtained by bone marrow aspirations, CTCs are in the focus of cancer research. But also the detection of CTCs bears some obstacles: first, their number, in comparison to the surrounding blood cells is rather small (1/106 blood cells [13]), so that most of the available detection methods require an enrichment step [14]. Second, during the process of detachment from the primary tumor and invasion of the blood stream these cells undergo a number of phenotypical changes. CTCs which are in fact of epithelial origin change their properties, like cell adhesion, cell mobility, and invasiveness, and loss of epithelial markers, so they become mesenchymal-like cells in a process called epithelial-to-mesenchymal transition (EMT) [15,16]. A graphical summary of the EMT process is presented in Figure 1. Thereof result difficulties for detection, because it gets more difficult to distinguish these cells from mesenchymal blood cells [17,18], and tumor cells can no longer be related to their primary tumor. This process is reversed, when these cells extravasate again and settle down in distant tissues of the body to form a metastatic bud (mesenchymal-to-epithelial transition (MET) [19,20,21]). For the reason of this heterogeneity of CTCs lots of research work on CTCs undergoing EMT had been done in the last five years and the results are summarized in the following. 2. Historical Background One of the rather early findings in CTC-EMT research was, that CTCs show EMT and stem cell characteristics, and it was assumed, that this might be an indicator for therapy resistance and an inferior prognosis [22], as CTCs displaying the mesenchymal phenotype are believed to have an increased metastatic potential [23]. Additionally, this fact creates the need to adjust detection methods [18,24]. Another suggestion was that tumor cells do not migrate throughout the body as single cells but as clusters, so called tumor cell microemboli (TCM), creating a protective surrounding for the dissolved tumor cells, especially as apoptotic marker are absent from these cell clusters [21,25,26]. The first report on EMT markers in CTCs was published by Kallergi et al. [27] in 2011. They analyzed the expression of two EMT markers, Twist and vimentin, in CTCs of breast cancer patients by immunofluorescence staining. CTCs expressing these two markers were found with higher frequency in metastatic breast cancer patients than in patients with early stages of the disease, pointing towards the malignant potential of EMT [27]. Furthermore, CTCs coexpressing epithelial, EMT and cancer stem cell (CSC) markers were found in patients with metastatic diseases [28]. Bonnomet et al created a dynamic model in 2012, examining the EMT process and CTC formation in a mouse xenograft model. They found that EMT already occurs within the primary tumor, conceding to cells the possibility to intravasate and form CTCs. The inverse process, mesenchymal-to-epithelial transition (MET) is proposed to occur in secondary organs, giving rise to metastatic outgrowth [29,30]. The analysis of CTCs for EMT and stem cell markers in primary breast cancer patients could in fact not be correlated with the prognostic clinical markers but again the need for an adjustment of detection systems and as well as for the examination of their prognostic relevance is highlighted [31]. To overcome the obstacle of CTC isolation, a negative depletion enrichment methodology, removing CD45+ cells first, was proposed [32]. In another study the presence of EMT-CTCs in Her2+ metastatic breast cancer patients was shown by an assessment of EMT-inducing transcription factors. In almost 90% of the CTCs at least one EMT-associated transcription factor was upregulated, pointing towards the presence of a high number of EMT-CTCs [33]. Subsequently, it was demonstrated that EMT-CTCs in metastatic breast cancer patients under high dose chemotherapy and autologous hematopoietic stem cell transplantation, are a prognostic factor for shorter progression free survival (PFS) and relapse [34]. In patients with hepatocellular carcinoma especially vimentin and Twist expression in CTCs could serve as a biomarker for the evaluation of metastasis formation and prognosis of the disease [35]. Charpentier et al. proposed a model of metastasis formation in breast cancer, claiming that CTCs with both, EMT and CSC characteristics were necessary for metastatic outgrowth. CSCs and EMT-CTCs have a flexible cytoskeleton and anoikis resistance, so they can survive in the blood stream, and additionally the expression of vimentin leads to a formation of microtentacles, which help the cells to reattach to new tissue sites. The authors therefore conclude that the combination of these characteristics increases metastatic efficiency [36]. These findings were confirmed in a later study, finding that CTCs expressing ALDH1A1 together with nuclear expression of twist were more frequently detected in patients with metastatic breast cancer [37]. As normal CTCs and EMT-CTCs vary in size so that in an enrichment process, only by size many cells would escape isolation, an interesting approach was published by Ito et al.: they used a telomerase–specific, replication selective oncolytic adenoviral agent tagged to a GFP (green fluorescent protein) protein. Blood from patients with gastritic cancer was infected with this agent and green fluorescent cells could be detected easily and were recognized as a prognostic marker for overall survival (OAS) [38]. EMT-characteristic markers like E-cadherin, vimentin, N-cadherin, Twist, and SNAI1/2 were also examined in tissue of lymph node metastases. Interestingly, and also in contrast to the primary tumor, E-cadherin expression is high, EMT-transcription factors are upregulated, while Ki67-rates are decreased in lymph nodes, which might be kind of a survival strategy [39]. A recent approach describes the classification of CTCs by the measurement of expression of EMT markers by RNA-ISH (RNA in situ hybridization). Three types of CTCs could be discerned: epithelial, epithelial/mesenchymal, and mesenchymal CTCs. This classification is rather important as mesenchymal CTCs are frequently found in patients with metastatic stages of the disease, giving hints for prognosis and treatment [40,41,42]. EMT and stem cell specific transcripts were also shown to correlate with clinical stage and can be detected in patients negative for epithelial marker expression [43]. Thereof arises the necessity to identify different tumor cell subpopulations, which might be able by different methods, but also a standardization of these variegated techniques is important [44]. Furthermore, it has to be taken into account, that certain treatment strategies might alter phenotypes of CTCs, so that they might be able to escape detection routines, as was shown for colorectal cancer patients, who were treated with bevacizumab for longer time periods [45]. 3. Molecular Mechanisms Leading to EMT in Circulating Tumor Cells (CTCs) To clarify the mechanisms leading to EMT in CTCs and the analysis of underlying signal transduction cascades would make up a step stone for combating metastasis formation. One of the earliest findings on this topic was that inflammation seems to increase the EMT-rate in pancreatic cancer [26,46]. Acidosis seems to be another factor leading to EMT [47,48]. Also hypoxia was found to play a role in early events of EMT, as described in a study on multiple myeloma. Hypoxia decreased E-cadherin expression, resulting in less cell adhesion and entry of the dissolved cells into the circulation [49], but also the whole tumor microenvironment is able to stimulate cell migration and invasion. The tumor stroma can for example, initiate EMT on the invasive front of the tumor, which also gives signals back to the primary lesion [50]. Signal-peptide-CUB-EGF-like domain containing protein3 (SCUB3) is an example of an important signaling molecule, as it promotes tumor angiogenesis and EMT. A knockdown of this gene results in lower vascular permeability and decreased metastatic potential in non-small cell lung cancer NSCLC, and could thereby be a potential therapeutic target [51]. The role of fibroblast growth factor receptors 1 and 3 (FGFR1/3) was described in bladder cancer. FGFR1 is a transcription factor for the expression of mesenchymal genes like ZEB1 and vimentin, FGFR3 in contrast influences the expression of epithelial markers like E-cadherin and p63. The interplay between these two receptors seems to play a role in the outgrowth of bladder cancer [52]. In prostate cancer Hsp27, a molecular chaperone, drives EMT via IL-6-mediated modulation of STAT/Twist. Hsp27 inhibition leads to a decreased number of CTCs, so it might become rather interesting for therapeutic strategies in prostate cancer [53]. Hepatocyte growth factor and its receptor c-Met are associated with tumor progression and metastasis in hepatocellular carcinoma. In the CTCs of this cancer entity, a high expression of these molecules comes along with an EMT phenotype, due to a lack of CpG-methylation in the c-Met region [54]. Forkhead box protein M1 (FOXM1) in contrast was a key regulator of EMT in breast cancer, as it binds and stimulates the promotor of Slug, which is responsible for EMT-promotion. Via this signaling pathway FOXM1 leads to metastasis formation [55]. In colorectal cancer EMT is induced by PLS3 via TGFβ-signaling cascades, resulting in invasive properties of cancer cells [56], but also special tumor treatments were shown to promote metastasis: in hepatocellular cancer, transcatheter arterial embolization is a common palliative treatment, but it was shown that it simultaneously upregulates hypoxia-inducible factor 1a (HIF1a) and epithelial to mesenchymal marker proteins like N-cadherin and vimentin thereby stimulating the metastatic potential of tumor cells [57]. Also, core needle biopsy in breast cancer seems to increase EMT and facilitates additionally the release of CTCs, which might contribute to remote metastasis formation [58]. Some tumor treatments are also known to increase the number of TCMs, and these clusters are more resistant to apoptosis as single tumor cells, giving rise to metastasis with a higher probability [26]. However, there are already also treatments which abolish EMT, like gemcitabine treatment of NSCLC-patients. It not only decreases the number of EpCAM (epithelial cell adhesion molecule) positive CTCs, but also inhibits EMT via the HGF/c-Met pathway [59]. Another signaling cascade regulating EMT was presented by Yuan et al. They could show, that an inhibition of p-Akt led to an upregulation of miR-200s, which in turn leads to a downregulation of EMT markers [60]. In bladder cancer, miR-34a has a suppressive role for angiogenesis and metastasis by regulating EMT-related proteins [61]. Another study demonstrates, that the knockdown of multiple kinases, like MAPK7 (mitogen-activated protein kinase 7), induces the expression of epithelial markers, inhibits cell migration, and maintains epithelial phenotypes, thereby reducing tumor invasiveness [62]. Also Leucine Zipper Transcription Factor-like1 (LZTFL1) seems to convey protective effects to lung epithelial cells by regulation of EMT-associated genes. LZTFL1 maintains an epithelial phenotype and inhibits mechanisms leading to EMT [63]. EMT furthermore induces tissue factor (TF), which in turn stimulates coagulation leading to EMT-positive CTCs or CTC-clusters, which have a great metastasizing potential. Silencing of ZEB1 inhibits TF-expression while Snail stimulates its expression. These EMT-TF-axis creates a new target for therapeutic interventions in the process of metastasis formation [64]. 4. New CTC-Detection Strategies By the fact, that even CTC-negative patients could develop remote metastasis due to an escape of EMT-CTCs to CTC detection, it became clear, that new techniques for the detection of such CTCs had to be developed [17,65]. CTCs, which undergo EMT sometimes present with an “intermediate” phenotype. Such cells were detected five years ago in patients with metastatic non-small cell lung cancer (NSCLC) by a fluorescent co-staining of vimentin and cytokeratins [66]. At the same time, it was discovered that epithelial tumors are characterized by a complex aneuploidy, which is inherited by the dissolved, circulating tumor cells, which therefore do not express CD45 or cytokeratins, but can be detected based on this chromosome rearrangement [67]. Another approach is to use CD146 to detect EpCAM-negative tumor cells and CD49f for the detection of CK-negative cells, improving detection rates [68]. The CTCscope method, which was published in 2012, is based on an RNA-ISH detecting epithelial as well as EMT-markers from blood samples. The advantage of the method is a simultaneous enumeration and characterization only of viable cells [69]. Another interesting approach is to sort cells by size is the DC impedance-based microcytometer. Thereby it could be discriminated between blood and tumor cells without the need of cell labelling [70]. In 2013 an approach for the detection of CTCs in NSCLC by the use of CK-coated beads, and they found a good correlation of tumor cell counts with the clinical history of malignancy of the respective tumors [71]. A multicolor detection system, which was based on flow cytometry was presented by Watanabe in 2013. After a CD45-depletion cells were fixed and labelled with fluorescently labelled antibodies against CD45, EpCAM, and CK, helping to distinguish different levels of EpCAM expression. Additional antibodies against EMT-markers could also be used in this system, allowing a characterization of the tumor cells [72]. A high throughput system for EMT-CTC detection could also be a microchip filter device, which sorts cells for Caveolin-1, a marker, which was shown to be upregulated during EMT-process [73]. Another interesting marker gene for CTC-isolation and enumeration purposes could be cell-surface vimentin (CSV), which is only expressed on cancer cells and never found on the surface of healthy blood cells. With the help of monoclonal antibody 84-1 CSV-positive cells can be filtered with high specificity, and CTC-counts could be related to therapeutic response [74]. A recently published technique used miRNA in situ hybridization for tumor cell detection. miRNA-21, a known onco-miRNA, has been shown to be a good target for this method, as it is only expressed in tumor cells, in which EMT takes place [75]. Furthermore, a rather complete depletion of white blood cells can be achieved by the introduction of a single-step treatment of the cells with sulfuric acid, creating a certain nanoscale roughness, which results in an increased binding of CD45-positive white blood cells to CD45-conjugated surfaces. Thereby isolation and characterization of tumor cells is increased [76]. A novel EpCAM independent enrichment strategy was proposed by Schneck et al. in 2015: they combined different antibodies specific for cell surface proteins and components of the extracellular matrix (ECM) components. The capture molecules (Trop2, CD49f, c-Met, CK8, CK44, ADAM8, CD146, TEM8, CD47) were first tested in single- and multi-spot arrays with breast cancer cell line with known EpCAM-expression patterns. EpCAM-low/negative cells could be captured easily by this method, so that patient samples were used for further experiments. EpCAM-negative CTCs could be isolated thereby and the malignant nature of those cells could be shown by a comparative genomic hybridization, demonstrating again the importance for detection of EpCAM-negative tumor cells [77]. The CanPatrol CTC enrichment technique first isolates CTCs via filter-based method, then CTCs were classified according to their EMT-markers by RNA in situ hybridization. It was recognized, that the number of epithelial CTCs was related to tumor size, the cells with mixed EMT/epithelial properties correlated to tumor number and mesenchymal CTCs could be related to metastasis formation, highlighting the usefulness of the presented methodology [78]. The most recent approach for EMT-CTC detection comes from Pramanik et al. who describe the use of multifunctional multicolor nanoprobe assay, which is used for the capturing and mapping of heterogenous CTCs [79]. 5. Markers for Epithelial to Mesenchymal Transition-Mesenchymal-to-Epithelial Transition (EMT-CTC) Detection A rather important topic in the detection of CTCs which underwent EMT is of course, to find appropriate detection markers or marker panels. In a microfluidics-based PCR system expression profiles from 84 EMT-related genes were analyzed in blood samples of prostate cancer patients. Although gene expressions were quite heterogenous, some marker genes common for mesenchymal cancer cells could be identified: IGF1, IGF2, EGFR, FOXP3, and TGFB3. Furthermore, some EMT-related genes were found to be expressed commonly: PTPRN2, ALDH1, ESR, and WNT5A. Therefore it could be concluded that the analysis of the expression of EMT-markers provides opportunities for a personalized treatment of some cancer entities [80]. In colorectal cancer, Plastin3 (PLS3) was identified as a marker for EMT-CTCs, helping to detect CTCs as this marker is not expressed in healthy blood cells. It was furthermore recognized to be a marker for metastatic CTCs, conveying a prognostic relevance [81]. In early breast cancer, a high expression of MMP1 could be detected in EMT-CTCs, which could additionally be correlated to tumor grade [82]. Furthermore, EGFR seems to play an important role in EMT process. An activation of EGFR signaling in MCF7 cells led to an increase of EMT-phenotypes, inhibited apoptotic events, and induced the loss of cytokeratin expression so that the analysis of EGFR could be an important prognostic and predictive marker in breast cancer [83]. An analysis of CTCs from blood samples of high risk endometrial adenocarcinoma patients (grade 3, stage IB–IV) showed a high plasticity in the expression of EMT markers like ETV5, NOTCH1, SNAI1, TGFB1, ZEB1, and ZEB2. Furthermore, markers of stemness and potential therapeutic targets could be found within this analysis, demonstrating the heterogeneity of CTCs [84,85,86]. The distinct expression of cytokeratin, N-cadherin, and CD133 was examined in metastatic breast cancer samples. Also, for this small marker panel, a strong heterogeneity could be demonstrated, outlining the importance of tumor cell characterization [87]. Doublecortin-like kinase1 (DCLK1) is regarded to be a stem cell marker in pancreas carcinoma and is additionally upregulated in other tumor entities. It also is known to be a regulator of EMT and is therefore involved in metastasis formation. The measurement of DCLK1 levels in serum samples and of its expression in CTCs could hence be an important marker for tumor malignancy [88] and EMT. It was shown that it might prevent the hepatocellular carcinoma growth by an miRNA dependent mechanism [89]. The correlation of two famous markers, CK and vimentin, in breast cancer and their significance for patient outcome was described by Polioudaki et al. They used blood samples from metastatic breast cancer patients and breast cancer cell lines to calculate the ratio of CK/vimentin, but especially in the patient samples CK/vimentin ratios varied a lot, displaying again the heterogeneity of CTCs undergoing EMT [90]. For squamous cell lung cancer (SQCLC) fibroblast growth factor 1 was used for FISH and immunocytochemical detection of CTCs which had undergone EMT [91]. After the isolation of CTCs from blood of patients with pancreatic ductal adenocarcinoma (PDAC) via size-based filtration device and an immunofluorescence staining of the isolated cells for the EMT-marker ZEB1 and the epithelial marker CK, the cells were analyzed for KRAS (proto-onkogene) mutations. It was shown, that patients bearing a KRAS mutation had a significantly better survival rate, attributing KRAS marker properties [92]. Another study demonstrated the presence of EMT markers like BMI1 and TWIST1 and stem cell markers like CD133 and ALDH1A1 in CTCs, concluding that cells carrying stem cell features are present as well in the primary tumor as in CTCs [93]. Vimentin and Ki67 were furthermore tested for their prognostical value in CTCs of patients with advanced prostate cancer. Both are well-characterized proliferation and EMT markers. If one or both markers were found to be overexpressed in patient samples, the respective patients had poorer survival outcomes [94]. Another approach to characterize CTCs from breast cancer patients was done by Hensler et al. They performed a gene expression profiling with a panel of 55 breast cancer associated genes on enriched CTCs and peripheral blood mononuclear cells from breast cancer patients. The genes, which were found to be overexpressed in the CTC samples were associated with functions involved in the proteolytic degradation of the ECM and in the EMT-process [95]. A characterization of single CTCs from ovarian cancer was done via multiplex PCR, in order to identify therapy resistant tumor cells. The multimarker gene panel consisted of genes for epithelial (EpCAM, Muc-1, CK5/7), EMT (N-cadherin, vimentin, Snai1/2, CD117, CD146, CD49f) and stem cell (CD44, ALDH1A1, Nanog, Sox2, Notch1/4, Oct4, Lin28) features. Single cells were isolated by micromanipulation and most of them were found positive for stem cell and EMT markers, but expression was quite heterogenous, making further analysis indispensable [96]. A similar analysis using multimarker qPCR was also done for metastatic breast cancer. From this study it was also concluded that CTCs are quite heterogenous and analysis has to be done for each single patient to be able to treat the patients accordingly [97]. 6. Conclusions Epithelial to mesenchymal transition plays a major role in tumor formation and metastatic outgrowth and has been in the focus of research in the last few years. It has extensive consequences for the detection of circulating tumor cells, which was already introduced as a prognostic parameter in the international tumor staging systems. However, as most of the systems for the detection of circulating tumor cells were based on the epithelial cell surface marker EpCAM, which is downregulated within the EMT process. Therefore new ways for the detection and also for the enrichment of CTCs have to be explored, otherwise a part of the circulating tumor cell population escapes detection. EMT-associated genes and proteins have to be included in the tumor cell detection systems to ensure diagnosis and to reinforce the predictive value of CTCs. As CTCs contribute to a large extent to metastasis formation, it is rather important to characterize and treat them as accurately as possible, to increase the therapeutic efficiency and to reduce side effects. Exploring EMT-processes also conveys chances for tumor therapy. EMT and the signal transduction pathways within this process might serve as potent targets for therapeutic approaches. Inhibiting these signal transduction cascades by specifically tailored drugs could help to diminish or even abolish metastasis formation, maybe even without doing harm to healthy cells. Analyzing and understanding the mechanisms, which lead to EMT, especially in CTCs, which are the main root of remote metastasis formation, could therefore give rise to new treatment strategies. However, we are still at the beginning of exploring the complex underlying mechanisms which lead from tumor cell dissociation from the primary tumor through EMT towards the formation of metastasis, including MET. Lots of work still has to be done in this field, but it might offer promising perspectives for the future. Acknowledgments The authors thank Stephan Beißner for help with the graphical implementation. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Remote metastasis formation by circulating tumor cells (CTCs) undergoing epithelial to mesenchymal transition-mesenchymal-to-epithelial transition (EMT-MET) changes of their cellular characteristics. Single cells dissolve from the primary tumour, adopt mesenchymal properties, enabling them to invade into the blood stream, after extravasation CTCs regain epithelial characteristics, thus they can seed in secondary tissues, building bud for remote metastasis formation. ==== Refs References 1. Franken B. de Groot M.R. Mastboom W.J. Vermes I. van der Palen J. Tibbe A.G. Terstappen L.W. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081309ijms-17-01309ArticleST6GALNAC5 Expression Decreases the Interactions between Breast Cancer Cells and the Human Blood-Brain Barrier Drolez Aurore 1Vandenhaute Elodie 1Delannoy Clément Philippe 2Dewald Justine Hélène 2Gosselet Fabien 1Cecchelli Romeo 1Julien Sylvain 3Dehouck Marie-Pierre 1Delannoy Philippe 2Mysiorek Caroline 1*Kim Cheorl-Ho Academic Editor1 Université d’Artois (UArtois), EA2465, Laboratoire de la Barrière Hémato-Encéphalique (LBHE), Lens F-62300, France; aurore.drolez@univ-artois.fr (A.D.); elodie.vandenhaute@univ-artois.fr (E.V.); fabien.gosselet@univ-artois.fr (F.G.); romeo.cecchelli@univ-artois.fr (R.C.); mpierre.dehouck@univ-artois.fr (M.-P.D.)2 Structural and Functional Glycobiology Unit, Unité Mixte de Recherche (UMR) du Centre National de la Recherche Scientifique (CNRS) 8576, University of Lille, Villeneuve d’Ascq F-59655, France; cl-ment.delannoy@etudiant.univ-lille1.fr (C.P.D.); justine.dewald@etudiant.univ-lille1.fr (J.H.D.); philippe.delannoy@univ-lille1.fr (P.D.)3 Cell Plasticity and Cancer, U908 INSERM, University of Lille, Villeneuve d’Ascq F-59655, France; sylvain.julien@univ-lille1.fr* Correspondence: caroline.mysiorek@univ-artois.fr; Tel.: +33-321-791-74611 8 2016 8 2016 17 8 130925 6 2016 03 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).The ST6GALNAC5 gene that encodes an α2,6-sialyltransferase involved in the biosynthesis of α-series gangliosides, was previously identified as one of the genes that mediate breast cancer metastasis to the brain. We have shown that the expression of ST6GALNAC5 in MDA-MB-231 breast cancer cells resulted in the expression of GD1α ganglioside at the cell surface. By using a human blood-brain barrier in vitro model recently developed, consisting in CD34+ derived endothelial cells co-cultivated with pericytes, we show that ST6GALNAC5 expression decreased the interactions between the breast cancer cells and the human blood-brain barrier. breast cancerblood-brain barriergangliosidesGD1αST6GALNAC5sialyltransferasebrain metastasis ==== Body 1. Introduction The modification of cell surface glycosylation is one of the most important phenotypic rearrangements that occur during carcinogenesis. It mainly affects the terminal part of the carbohydrate moiety of glycoproteins and glycolipids, leading to the expression of tumor-associated carbohydrate antigens (TACA). Most TACAs are sialylated and changes in sialylation were clearly demonstrated to affect cellular recognition, cell adhesion, and signaling and, consequently, the cell’s behavior. Gangliosides are glycosphingolipids (GSLs) carrying one or several sialic acid residues. They are essentially located on the outer leaflet of the plasma membrane where they can interact with transmembrane receptors or signal transducers involved in cell proliferation, adhesion, and motility. In adult, complex gangliosides from b- and c-series are normally restricted to the nervous system but a re-expression of complex gangliosides is observed in a variety of cancers including neuro-ectoderm-derived cancers, non-small cell lung carcinoma, and breast cancer [1]. In particular, GD3 and GD2 are considered as melanoma- and neuroblastoma-associated antigens playing a key role in tumor development, and are used as targets for cancer immunotherapy [2]. However, the mechanisms by which tumor-associated gangliosides induce invasive and metastatic phenotypes of tumor cells remain to be clarified. α-Series gangliosides define a particular sub-class of GSLs containing Neu5Ac α2,6 linked to the GalNAc residue of the gangliopentaosyl backbone Neu5Acα2-3Galβ1-3GalNAcβ1-4Galβ1-4Glc (IV3Neu5Ac1Gg4). The typical α-series ganglioside GD1α (IV3Neu5Ac1,III6Neu5Ac1Gg4-Cer) was first isolated as a minor compound from rat hepatoma cells [3] and from bovine brains [4], with an expression restricted to particular cell populations of the brain and cerebellum [5]. Three members of the CMP-Neu5Ac: β-N-acetylgalactosaminide α2,6-sialyltransferase family (ST6GalNAc III, V, and VI) were shown to catalyze in vitro the transfer of a sialic acid residue onto GM1b (IV3Neu5Ac1Gg4-Cer) to form GD1α [6]. However, according to its substrate specificity and expression pattern, ST6GalNAc V is considered as the main GD1α synthase. ST6GALNAC5 cDNA was cloned from mouse brains [7,8] and the st6galnac5 gene is specifically expressed in mouse brain tissues, mostly in the forebrain and cerebellum [8]. When expressed as a soluble recombinant protein, the mouse ST6GalNAc V showed α2,6-sialyltransferase activity almost exclusively for GM1b, while being inactive toward glycoproteins [7]. The enzymatic activity of human ST6GalNAc V was never investigated in detail, but we have recently shown that transfection of human ST6GalNAc V cDNA into MDA-MB-231 breast cancer cells resulted in the expression of GD1α at the cell surface [9]. To date, the specific function of α-series gangliosides is poorly understood. It has been proposed that GD1α could play a role in Purkinje cell functions in the cerebellum [5] and that GD1α could serve as an adhesion molecule for high-metastatic murine lymphosarcoma cells in the adhesion to hepatic endothelial cells [10]. Recently, ST6GALNAC5 was identified as one of the genes over-expressed in breast cancer cell populations selected for their ability to produce brain metastases [11]. ShRNA inhibition of ST6GALNAC5 expression reduced the capacity of breast cancer cells to produce brain metastases, whereas the expression of ST6GALNAC5 in parental cell lines promoted brain metastases formation [11]. Moreover, ST6GALNAC5 was shown to improve the capacity of breast cancer cells to transmigrate across a human umbilical vein endothelial cells (HUVECs) in vitro model of the blood-brain barrier [11]. The blood-brain barrier (BBB), localized at the level of brain capillary endothelial cells (ECs), controls and restricts the exchanges between the blood and the brain tissues. The BBB presents a specific architecture where the capillary ECs share a split basement membrane with pericytes and are surrounded together by astrocyte end-feet. The BBB forms with pericytes, neurons, glial cells, and the extracellular matrix, the neurovascular unit (NVU). The interplays and communications between the different components of NVU allow the BBB-specific differentiation of ECs, which exhibit a network of tight junctions, express efflux pumps and specific receptors and transporters. These specific and restrictive properties control and limit the access to the brain parenchyma of many cells and substances. During the last decades, most in vitro BBB models were developed using animal cells (mouse, rat, bovine, pig) isolated from brain microvessels as the primary culture or immortalized [12], whereas human culture models commonly use HUVECs, which display only a limited tightness and not a BBB phenotype. In vitro approaches are required to identify cellular and molecular interactions between cancer cells and BBB endothelium. However, while numerous studies were performed with in vitro models, the heterogeneity and the quality of BBB models used is a limitation to the extrapolation of results to in vivo context, showing that the choice of a model that fulfills the properties of human BBB is essential. In that context, we recently developed a human BBB in vitro model consisting in CD34+ hematopoietic stem cells derived endothelial cells co-cultivated with brain pericytes [13,14] and displaying improved BBB properties closed to those observed in vivo. The model proved valuable in the study of cancer cells tropism as the adhesion and transmigration capacities of breast cancer cells were found to be in accordance with the cancer cell molecular subtypes, fitting well with their propensity to form brain metastases [15,16]. We have used this CD34+ derived human BBB model to investigate the role of GD1α in adhesion and transmigration of breast cancer cells and contrary to what was observed in a HUVECs in vitro model, ST6GALNAC5 cDNA expression resulted in a decrease of the interactions between MDA-MB-231 breast cancer cells and the CD34+ derived human BBB model. 2. Results 2.1. Brain Targeting Cells Interaction Analysis on the Human in Vitro Blood-Brain Barrier (BBB) Model In order to investigate the mechanisms of brain tropism during the initial steps of breast cancer brain metastases formation, the interactions of breast cancer cells with the BBB were analyzed using an in vitro approach. For this purpose, adhesion and transmigration assays of brain-targeting breast cancer cells were performed on a human BBB in vitro model named Brain-Like endothelial Cells (BLECs) that we recently developed [13,14]. The BLECs model consists of endothelial cells derived from CD34+ hematopoietic stem cells co-cultivated with brain pericytes. The BLECs model displays improved BBB properties close to those observed in vivo, such as low permeability to the BBB integrity marker, continuous localization at the cell border of tight junction proteins (Claudin-5, occludin, ZO-1), and expression of functional efflux pumps (P-gP, BCRP) [13,14]. The adhesion and transmigration of MDA-MB 231 BrM2 cell line (BrM2) was compared to the parental cell line MDA-MB-231 wild type (wt). The BrM2 cell line was previously generated by two rounds of in vivo selection in mice, and showed a significant increase in brain metastases formation [11]. As shown in Figure 1a, after two hours of incubation, the adhesion rate of BrM2 on the BBB ECs was 63.4% lower than the parental cells and no increase in BBB permeability to lucifer yellow (LY) was observed for MDA and BrM2 (0.57 ± 0.08 × 10−3 cm·min−1 and 0.66 ± 0.1 × 10−3 cm·min−1, respectively) compared to the control condition before adhesion (permeability coefficient (Pe) = 0.58 ± 0.07 × 10−3 cm·min−1). As the adhesion of breast cancer cells on the BBB ECs is required, but not enough to reach the brain parenchyma, the transmigration was quantified and it was revealed that the BrM2 transmigrate at the same rate compared to the parental cell line MDA-MB-231 wt (Figure 1b). No increase in BBB permeability to LY was measured following transmigration of MDA and BrM2 (0.73 ± 0.03 × 10−3 cm·min−1 and 0.78 ± 0.01 × 10−3 cm·min−1, respectively) compared to the control condition without transmigration. 2.2. Molecular Characterization of MDA-MB-231 BrM2 Cells The BrM2 cell line was previously described to over-express a set of genes potentially involved in brain metastasis, including COX2, HBEGF and ST6GALNAC5 [11]. The expression of these genes was quantified by qPCR. As shown in Figure 2, a 23-fold increased expression of ST6GALNAC5 and 10-fold increase of COX2 were measured in BrM2 compared to parental MDA-MB-231 wt. However, no difference of expression was measured for HBEGF in BrM2 compared to parental MDA-MB-231 wt. ST6GALNAC5 gene encodes a GalNAc α2,6-sialyltransferase involved in the biosynthesis of α-series gangliosides, mainly GD1α. According to the fact that GD1α could serve as an adhesion molecule for breast cancer cells in the adhesion to BBB endothelial cells and promote brain metastasis, the GSL composition was analyzed to determine the impact of the increased ST6GALNAC5 expression on the glycosylation of MDA-MB-231 BrM2 cells. Total GSLs were extracted from MDA-MB-231 wt and BrM2, purified by reverse phase chromatography and permethylated prior to Matrix assisted laser desorption-ionization-mass spectrometry (MALDI-MS) analysis. As previously shown [18] MDA-MB-231 wt expresses neutral globosides Gb3 and Gb4 and monosialylated gangliosides, mainly GM3 (Figure 3a). The precursor lactosylceramide (LacCer) was also detected, as well as a monosialoganglioside at m/z 1933, which was confirmed to correspond to GM1b by matrix assisted laser desorption-ionization time-of-flight (MALDI-TOF)/TOF fragmentation analysis (data not shown). Two ceramide isoforms are commonly expressed in human tissues due to the substitution of the sphingosine moiety by palmitic acid C16:0 (Cer*) or lignoceric acid C24:0 (Cer**). As shown in Figure 3b, the composition in GSLs of BrM2 cells was similar to wt cells and no expression of GD1α was detected as indicated by the absence of signal at m/z 2293.9, which was identified in MDA-MB-231 green fluorescent protein positive (GFP+) cell population (Figure 3c) by MALDI-TOF/TOF fragmentation analysis (Figure S1). 2.3. Involvement of GD1α Over-Expression in Interaction Processes of Breast Cancer Cell Lines with the BBB In order to specifically identify the effect of ST6GALNAC5 over-expression on the adhesion and transmigration of breast cancer cells, two cell populations were generated, Clone #13 and a polyclonal GFP-positive cell population, in which ST6GALNAC5 cDNA was 10-fold and 60-fold over-expressed compared to control MDA-MB 231 wt, respectively [9]. These two cell populations were previously demonstrated to express GD1α ganglioside by MALDI-TOF/TOF fragmentation analysis (Figure S1). The adhesion and transmigration capacities of these two cell populations were determined using the human BBB in vitro model. As shown in Figure 4a, the results obtained were similar to those obtained with BrM2 cells as adhesion of the Clone #13 and GFP+ cell population were 40% and 50% decreased compared to MDA-MB-231 wt, respectively. A 55% and 50% decrease of transmigration rate was also observed for Clone #13 and the GFP+ cell population, respectively (Figure 4b). Following adhesion and transmigration assays, no increase in BBB permeability was measured for Clone #13 and GFP+ cell populations (0.87 ± 0.03 × 10−3 cm·min−1 and 0.92 ± 0.07 × 10−3 cm·min−1 respectively) compared to the control condition before transmigration (Pe = 0.90 ± 0.04 × 10−3 cm·min−1). 2.4. Does the Specificity of Interactions Depend on Cells Species? As the BrM2 cells were generated and selected according to their increased brain metastatic activity in immunodeficient mice, a mouse-specific molecular mechanism of interaction could take place between human breast cancer cells and murine brain endothelial cells. Hence, in order to determine if the capacity of interaction could be modified according to the species, we measured adhesion of breast cancer cells on a mouse BBB in vitro model. As shown in Figure 5, after two hours of incubation, a significant 25% increase in adhesion was observed for BrM2 cells compared to control MDA-MB-231 wt. However, no increase of adhesion was measured for ST6GalNAc V over-expressing cells, Clone #13, and GFP+ cell population. Following adhesion assay of cancer cell populations, no increase in BBB permeability was measured (0.47 ± 0.1 × 10−3 cm·min−1) compared to the control condition before adhesion (Pe = 0.36 ± 0.12 × 10−3 cm·min−1). 3. Discussion With the improvement of cancer therapeutic strategies to treat systemic disease, the incidence of brain metastases has increased. Brain metastases (BM) represent the most frequent intracranial tumors in adults. Breast cancer is after lung cancer, the second type of cancer which has the highest incidence to develop metastasis in the brain; about 30% of women with breast cancer developing metastases to the brain. To form metastases in distant organs, cancer cells from the primary tumor have to successfully achieve the multistep process of metastatic cascade that includes the escape from the primary tumor, the survival in the circulation, the interactions with the vascular wall of the targeted organ, the extravasation through the endothelial cell layer and finally the adaptation to the host environment. In the case of brain metastases, the cancer cells also have to interact and cross the highly restrictive and specific BBB, localized at the level of brain capillary endothelial cells. This barrier maintains the brain homeostasis thanks to specific properties that limit the access to the brain parenchyma. In the therapeutic strategy against BM, besides the surgical resection and radiotherapy, the chemotherapy plays an underlying role with a limited efficacy mainly attributed to the BBB. In this context, the understanding the biology is prime of important for both the prediction of patients with high risk to develop BM and the discovery of new drug targets [19]. For breast cancer patients, the risk to develop BM is associated with the breast cancer molecular subtypes, which are described to have different clinical behaviors. In this context, analyses of cellular and molecular events are performed during the metastatic cascade in order to better understand the behavior of breast cancer cells and to identify a molecular signature that could predict the risk of brain metastases. The widely used experimental strategy consists of the transcriptional profiling of cancer cells following multiple cycles of injection in rodents and a comparison with the molecular profile obtained from brain metastases clinical samples. This experimental procedure was used by Bos et al. [11] in order to generate a brain targeting breast cancer cell line named BrM2, deriving from the triple negative breast cancer cell line MDA-MB-231. The gene encoding the ganglioside specific sialyltransferase ST6GalNAc V was one of the genes whose expression was upregulated in BrM2 and was identified as a potential mediator for cancer cells transmigration through an endothelial in vitro barrier model using HUVECs. Here, using our well-characterized human BBB in vitro model, we clearly demonstrate a decreased adhesion and no change in transmigration of BrM2 cells compared to the control. These results are not line with the data from Bos et al. who previously showed that the expression of ST6GALNAC5 cDNA was sufficient to increase the transmigration activity of MDA-MB-231 BrM2 cells through the HUVECs BBB in vitro model [11]. As a matter of fact, the phenotype of endothelial cells used in the in vitro approach is of prime importance to study cellular and molecular interactions occurring at the level of the BBB, in order to specifically identify the mechanisms occurring at this particular interface as previously described [15]. Hence, the use of endothelial cells without a validated BBB phenotype, can generate data that cannot be correlated with the in vivo situation. In vitro, ST6GalNAc V is known to catalyze the transfer of a sialic acid residue onto GM1b to form GD1α [6] and is considered as the main enzyme for the biosynthesis of α-series gangliosides, the expression of which being normally restricted to the brain [5]. Interestingly, it has been recently shown that GD1α could serve as an adhesion molecule for highly-metastatic murine lymphosarcoma cells in the adhesion to hepatic endothelial cells [10]. Similarly, the expression of GD1α at the cell surface of breast cancer cells could improve their capacity to interact with brain capillary endothelial cells. We also used a cell line deriving from parental MDA-MB-231 by transfection of human ST6GALNAC5 cDNA and over-expressing ST6GalNAc V. Our experiments have revealed that the over-expression of the active form of ST6GalNAc V, associated with the expression of GD1α at the cell surface, displays a reduced capacity of interaction and transmigration in the human BBB in vitro model. These results were similar to those we obtained with BrM2 cells indicating that the over-expression of ST6GalNAc V decreased the amount of breast cancer cells able to interact with the BBB ECs. Nevertheless, as ST6GALNAC5 gene encodes a sialyltransferase involved in the biosynthesis of gangliosides and should, therefore, modify the cell surface glycosylation, mass spectrometry analysis was performed on BrM2 cells in order to identify a change in GSLs composition potentially involved in the interaction of breast cancer cells with the BBB. Surprisingly, no difference in GSLs content was observed compared to the parental MDA-MB-231 cells whereas BrM2 cells express the substrate of ST6GalNAc V. As the analysis of BrM2 glycosylation was only performed on the glycosphingolipid fraction, we cannot exclude an unusual activity of ST6GalNAc V in BrM2 cells that could sialylate O-glycans. However, these results allowed us to assume that the expression of ST6GALNAC5 in BrM2 does not lead to a functional enzyme that modulates the GSLs content. At least, as the BrM2 cells were generated in mice, and considering that cancer cells can develop a host adaptation to maximize their colonizing properties, we analyzed the adhesion of BrM2 and ST6GalNAc V over-expressing cells on a mouse BBB in vitro model. Our results revealed that the adhesion of BrM2 is increased on mouse BBB endothelial cells compared to the ST6 GFP+ and Clone #13, indicating that the cells generated in mouse display an increase of interaction contrary to the cells in which ST6GALNAC5 cDNA was transfected. Environment adaptation is a well-known mechanism and the adaptation of breast cancer cells to the murine environment could explain at least in part the increased adhesion of BrM2 cells on a mouse BBB in vitro model. However, this result is difficult to correlate with the fact that the expression of ST6GALNAC5 cDNA in BrM2 MDA-MB-231 increased the transmigration of cells through HUVECs [11]. Hence, our experimental approach highlighted that ST6GalNAc V does not seem to be a mediator that increases breast cancer cell interaction with the human BBB. In addition, the expression of ST6GalNAc V in cancer cells is not directly correlated with the expression of its product GD1α that is depending on the presence of the precursor GM1b. As the expression of ST6GalNAc V is normally restricted to the brain, the expression of the enzyme in cancer cells seems particularly suitable for the development of tumors in the brain parenchyma. However, it was recently shown that, using specific monoclonal antibodies reactive with GD1α or GM1b, only a few human cancer cell lines show significant expression of these gangliosides [20]. These antibodies could be used in the near future to determine the expression of these gangliosides in breast cancer tumors, as well as the implication of GD1α in the formation of breast cancer cell metastases. Moreover, molecular analysis of the transmigrated breast cancer cells, and also studies of their capacities of colony-forming, should be done in order to better characterize brain-metastatic breast cancer cells. 4. Materials and Methods 4.1. Human Brain-Like Endothelial Cells (BLECs) BBB in Vitro Model The human BBB in vitro model consists of endothelial cells derived from CD34+ cord blood hematopoietic stem cells in co-culture with bovine brain pericytes, as described by Cecchelli et al. 2014 [13]. The collection of human umbilical cord blood requires infants’ parents signed consent form in compliance with French legislation. The protocol was approved by the French Ministry of Higher Education and Research (CODECOH Number DC2011-1321). All experiments were carried out in accordance with the approved protocol. According to the method described by Pedroso et al. [21], CD34+-cells are isolated from human umbilical cord blood and then differentiated into endothelial cells following exposure to Vascular Endothelial Growth Factor (VEGF from PrepoTech Inc., Rocky Hill, NJ, USA) at 50 ng/mL. To perform the co-culture, CD34+ derived endothelial cells (CD34+-ECs) were seeded on Matrigel™ (BD Biosciences, San Jose, CA, USA)-coated filters (Costar Transwell inserts, pore size 0.4 or 3 µm, 12-well format, Corning Inc., Corning, NY, USA) (8 × 104 cells/cm2). After six days of culture alone without medium in the lower compartment, CD34+-ECs were placed above a well containing a bovine brain pericyte culture. The phenotype of the pericytes was characterized according to Vandenhaute et al. [22]. The co-culture medium, endothelial cell medium (ECM) supplemented with 5% heat inactivated fetal calf serum (FCS), and 50 µg/mL gentamicin, was changed every two days. After six days of co-culture, the model was stable and ready for experiment [13]. The co-culture system allows the delimitation of two compartments, the luminal compartment with endothelial cells (blood side) and the abluminal compartment with the pericytes (brain side). The co-culture system was described in details in Cecchelli et al. 2014 [13]. 4.2. Murine BBB in Vitro Model In accordance with the French legislation the animal house of the Université d’Artois obtained approval from the protecting population departmental directorate under number B62-498-5. In compliance with the new European directive (Directive 2010/63/EU), all of the procedures were submitted to the ethics committee (comité d'éthique en experimentation animale Nord—Pas-de-Calais; C2EA 75) and the French Ministry (ministère de l’enseignement supérieur et de la recherche: direction générale pour la recherche et l'innovation) for authorization, were approved and referenced under the number 2015090115412152. Mice (C57Bl6/J) were supplied by Laboratoire Janvier (Le Genest-Saint-Isle, France) and housed in a temperature-controlled pathogen-free room with light from 07:00 to 19:00 (daytime) and had free access to food and water and live in an enriched environment. Endothelial cells were extracted from mice brain microvessels using the method described by Coisne et al. [23] and seeded on Matrigel™-coated filters (Costar Transwell 0.4 µm, 12-well format). All experiments were performed within the framework of the French legislation that controls animal experimentation. Cells were cultivated until confluence in Dulbecco’s modified Eagle medium (DMEM) medium, 5% heat-inactivated FCS, 2 mM l-glutamine, 50 µg/mL gentamicin, and 1 ng/mL basic fibroblast growth factor (bFGF). This medium was changed every day. Endothelial cells formed a confluent monolayer and were used for experiment after five days. 4.3. Human Breast Cancer Cell Lines Culture Breast cancer cell lines MDA-MB-231 (HTB-26, ATCC®, Manassas, VA, USA), MDA-MB-231-Clone #13-, and GFP+ ST6GALNAC5-transfected cells [9], and MDA-MB-231 BrM2 [11] cells were cultivated in DMEM medium with 4.5 g/L d-glucose, 10% heat-inactivated FCS, 2 mM l-glutamine, and 5 µg/mL penicillin-streptomycin. Cells were cultivated for three weeks before being used in adhesion or transmigration experiment. 4.4. Adhesion and Transmigration Assays Adhesion and transmigration assays were carried out as previously described [15]. Breast cancer cells were loaded with a fluorescent CellTracker before adhesion and transmigration kinetics (Invitrogen, Carlsbad, CA, USA). After treatment with ethylenediaminetetraacetic acid (EDTA) and mechanical dissociation, cancer cells were seeded at 2 × 104 or 8 × 104 per filter containing EC monolayers for adhesion or transmigration assays, respectively. After 120 min (adhesion) or 16 h (transmigration) filters were fixed with 4% paraformaldhehyde solution for 10 min. After the staining of nuclei with Hoechst 33358 (BisBenzimide, MP Biochemicals, Irvine, CA, USA), the filters were mounted using Mowiol solution containing DABCO (1,4-Diazobicyclo-(2.2.2-octane)) as an anti-fading agent. The quantification of adherent and transmigrated cancer cells was done manually on the total surface of each filter under a Leica DMR fluorescence microscope (Leica Microsystem, Wetzlar, Germany). The number of adherent or transmigrated MDA-MB-231was set to 100%. All results were expressed as the mean ± standard error of the mean (SEM) from two or more independent experiments. Statistical significance was assessed by t-test. All statistical analyses were performed using GraphPad Prism version 5.0 for Windows (GraphPad Software, San Diego, CA, USA). 4.5. BBB Permeability Measurement The integrity of BBB ECs was evaluated by the permeability measurement according to the method described by Dehouck et al. [24] using the diffusion of hydrophilic molecules, Lucifer Yellow (LY, lucifer yellow CH dilithium salt, Sigma-Aldrich, St-Louis, MO, USA) or [14C]-saccharose (1 μCi/mL, molecular mass (MM) = 342) (Amersham Biosciences, Piscataway, NJ, USA), that cross the BBB poorly and are used as integrity markers. The protocol was performed as previously described [15]. The endothelial permeability coefficients (Pe) of the molecules are expressed in cm/min. 4.6. mRNA Extraction and PCR Analysis Cancer cells were lysed with 500 μL of RLT lysis buffer (Qiagen, Les Ullis, France). mRNA was purified using the RNeasy total RNA extraction kit (Qiagen), following the manufacturer’s instructions. After this step, the mRNA’s purity and concentration were assessed by measuring the absorbance at 260, 280, and 320  nm using the Take 3 microplate reader protocol (Synergy™ H1, BioTek Instruments, Colmar, France). For each condition, cDNAs were obtained from 0.5 μg of mRNA using iScript™ Reverse Transcription Supermix (BioRad, Marnes-la-Coquette, France), according to the manufacturer’s instructions. Real-time PCR experiments were performed using the Sso Fast EvaGreen Master Mix kit (BioRad). Primers for ACTB (sense: 5′-ggagcacagagcctcgcctt-3′, antisense: 5′-acatgccggagccgttgtcg-3′), ST6GALNAC5 (sense: 5′-ggatcccaatcacccttcag-3′, antisense: 5′-tagcaagtgattctggtttcca-3′), COX2 (sense: 5′-tccaccaacttacaatgctgac-3′, antisense: 5′-cacaggaggaagggctctagta-3′) and HBEGF (sense: 5′-ggttaccatggagagaggtgtc-3′, antisense: 5′-gaccagcagacagacagatgac-3′) were designed using Primer 3 software. For each primer, amplification was carried out for 40 cycles with an annealing temperature of 60 °C in a CFX96 thermocycler (BioRad). The efficiency was calculated for each primer pair (CFX Manager, BioRad). Melting curve analysis was performed after the amplification cycles, in order to check the specificity/purity of each amplification. Gene expression levels were evaluated according to the ΔΔCt method and normalized against Actin expression. 4.7. Extraction and Preparation of Glycolipids Twenty dishes (10 cm diameter) of cultured cells were washed twice with ice-cold PBS and cells were sonicated on ice in 200 µL of water. The resulting material was dried under vacuum and sequentially extracted by CHCl3/CH3OH (2:1, v/v), CHCl3/CH3OH (1:1, v/v) and CHCl3/CH3OH/H2O (1:2:0.8, v/v/v) using intermediary centrifugations at 2500× g for 20 min. Supernatants were pooled, dried and subjected to a mild saponification in 0.1 M NaOH in CHCl3/CH3OH (1:1) at 37 °C for 2 h and then evaporated to dryness [25]. Samples were reconstituted in CH3OH/0.1% TFA in H2O (1:1, v/v) and applied to a reverse phase C18 cartridge (Waters, Milford, MA, USA) equilibrated in the same solvent. After washing with CH3OH/0.1% TFA in H2O (1:1, v/v), GSL were eluted by CH3OH, CHCl3/CH3OH (1:1, v/v), and CHCl3/CH3OH (2:1, v/v). The elution fraction was dried under a nitrogen stream. 4.8. Mass Spectrometry Analysis of Glycosphingolipids (GSLs) Prior to mass spectrometry analysis, GSL were permethylated according to Ciucanu and Kerek [26]. Briefly, compounds were incubated 2 h in a suspension of NaOH in dry dimethylsulfoxyde (DMSO) (400 µL) and CH3I (200 µL). The methylated derivatives were extracted in CHCl3 and washed several times with water. The reagents were evaporated and the sample was dissolved in CHCl3 in the appropriate dilution. MALDI-MS and MS/MS analyses of permethylated GSL were performed on a 4800 Proteomics Analyzer (Applied Biosystems, Framingham, MA, USA) mass spectrometer, operated in the positive reflectron mode. For MS acquisition, 5 µL of diluted permethylated samples in CHCl3 were mixed with 5 µL of 2,5-dihydroxybenzoic acid matrix solution (10 mg/mL dissolved in CHCl3/CH3OH (1:1, v/v)). The mixtures (2 µL) were then spotted on the target plate and air dried. MS survey data comprises a total of 50 sub-spectra of 3000 laser shots. Peaks observed in the MS spectra were selected for further MS/MS. Collision-induced dissociation (CID) MS/MS data comprises a total of 100 sub-spectra of 3000 laser shots. Two or more spectra can be combined post-acquisition with mass tolerance set at 0.1 Da to improve the signal-to-noise (S/N) ratio. The potential difference between the source acceleration voltage and the collision cell was set to 1 kV and argon was used as the collision gas. Acknowledgments This work was supported by the University of Lille, the Artois University, the Centre National de la Recherche Scientifique (CNRS), the comité du Pas-de-Calais de La Ligue contre le Cancer and La Région Nord-Pas-de-Calais (fellowship Drolez A and Vandenhaute E). We thank the Joan Massagué from the Memorial Sloan Kettering Cancer Center for providing us the MDA-MB-231BrM2 cell line. Supplementary Materials Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1309/s1. Click here for additional data file. Author Contributions Aurore Drolez performed the experiments, analyzed the data and participated to the redaction; Elodie Vandenhaute, Clément Philippe Delannoy performed the experiments and analyzed the data; Justine Hélène Dewald performed the experiments; Fabien Gosselet, Romeo Cecchelli, Marie-Pierre Dehouck contributed reagents/materials/analysis tools; Sylvain Julien participated to design the experiments; Philippe Delannoy, Caroline Mysiorek conceived and designed the experiments, analyzed the data and wrote the manuscript. Conflicts of Interest The authors declare no conflict of interest. Abbreviations BBB Blood-brain barrier BLECs Brain-like endothelial cells BM Brain metastases Cer Ceramide ECs Endothelial cells GFP Green fluorescent protein GSLs Glycosphingolipids HUVECs Human umbilical vein endothelial cells LY Lucifer Yellow MALDI-TOF Matrix assisted laser desorption-ionization time-of-flight MS Mass Spectrometry NVU Neurovascular unit TACA Tumor-associated carbohydrate antigen Figure 1 Adhesion (a); and transmigration (b) of MDA-MB-231 wild type (wt) and BrM2 breast cancer cell lines on the human BLECs model. The number of adherent or transmigrated MDA-MB-231 wt cells was set up to 100% and equal to, respectively, 579 and 98 cells. Results are the mean in triplicate, and representative of two independent experiments N.S: not significant; *** p < 0.001. Figure 2 qPCR analysis of COX2, HB-EGF, and ST6GALNAC5 in MDA-MB-231 wt and BrM2. Quantification was performed by the method described by Pfaffl [17] and normalized to Actin. N.S: not significant; *** p < 0.001. Figure 3 Comparison of mass spectrometry (MS) profiles of permethylated glycosphingolipids purified from MDA-MB-231 wt (a); BrM2 (b); and green fluorescent protein positive (GFP+) (c) cell population. Glycosphingolipids (GSL) are present as d18:1/C16:0 (Cer*) and d18:1/C24:0 (Cer**) isomers. , Gal; , Glc; , GalNAc; , Neu5Ac. Figure 4 Adhesion (a); and transmigration (b) of MDA-MB-231wt, Clone #13 (ST6 cl. 13), and GFP+ breast cancer cell population (ST6 GFP+) on the human Brain-Like endothelial Cells (BLECs) model. The number of adherent or transmigrated MDA-MB-231 wt cells was set up to 100% and equal to, respectively, 533 and 117 cells. Results are the mean in triplicate, and representative of two or three independent experiments. N.S: not significant; * p < 0.01; ** p < 0.005; *** p < 0.001. Figure 5 Adhesion of MDA-MB-231 wt, BrM2, Clone #13, and GFP+ breast cancer cell populations on a mouse blood-brain barrier (BBB) model. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081310ijms-17-01310ArticleUrinary Metabolic Phenotyping Reveals Differences in the Metabolic Status of Healthy and Inflammatory Bowel Disease (IBD) Children in Relation to Growth and Disease Activity Martin Francois-Pierre 1*Ezri Jessica 2Cominetti Ornella 1Da Silva Laeticia 1Kussmann Martin 1Godin Jean-Philippe 3Nydegger Andreas 2*Han Ting-Li (Morgan) Academic Editor1 Nestlé Institute of Health Sciences, 1015 Lausanne, Switzerland; ornella.cominetti@rd.nestle.com (O.C.); Laeticia.DaSilva@rd.nestle.com (L.D.S.); Martin.Kussmann@rd.nestle.com (M.K.)2 Division of Pediatric Gastroenterology, University of Lausanne, 1011 Lausanne, Switzerland; jessica.ezri@chuv.ch3 Nestlé Research Center, 1000 Lausanne, Switzerland; jean-philippe.godin@rdls.nestle.com* Correspondence: francois-pierre.martin@rd.nestle.com (F.-P.M.); andreas.nydegger@chuv.ch (A.N.); Tel.: +41-21-632-6161 (F.-P.M.); +41-79-556-6074 (A.N.)11 8 2016 8 2016 17 8 131022 6 2016 04 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Background: Growth failure and delayed puberty are well known features of children and adolescents with inflammatory bowel disease (IBD), in addition to the chronic course of the disease. Urinary metabonomics was applied in order to better understand metabolic changes between healthy and IBD children. Methods: 21 Pediatric patients with IBD (mean age 14.8 years, 8 males) were enrolled from the Pediatric Gastroenterology Outpatient Clinic over two years. Clinical and biological data were collected at baseline, 6, and 12 months. 27 healthy children (mean age 12.9 years, 16 males) were assessed at baseline. Urine samples were collected at each visit and subjected to 1H Nuclear Magnetic Resonance (NMR) spectroscopy. Results: Using 1H NMR metabonomics, we determined that urine metabolic profiles of IBD children differ significantly from healthy controls. Metabolic differences include central energy metabolism, amino acid, and gut microbial metabolic pathways. The analysis described that combined urinary urea and phenylacetylglutamine—two readouts of nitrogen metabolism—may be relevant to monitor metabolic status in the course of disease. Conclusion: Non-invasive sampling of urine followed by metabonomic profiling can elucidate and monitor the metabolic status of children in relation to disease status. Further developments of omic-approaches in pediatric research might deliver novel nutritional and metabolic hypotheses. pediatricmetabolismphenotypegrowthinflammatory bowel diseaseCrohn’s diseaseulcerative colitis ==== Body 1. Introduction Whilst the prevalence of inflammatory bowel disease (IBD) has increased considerably over recent decades, its clinical features do not allow accurate prediction of disease progression or response to therapy [1]. Approximately a quarter of patients will develop IBD during childhood and adolescence, the majority of them around their pubertal growth spurt [2]. Growth failure and delayed puberty are major complications in pediatric patients with IBD, especially in those with Crohn’s disease (CD) [2,3]. These features are already present before the onset of clinical symptoms, with a frequency ranging from 14% to 88% of patients [4]. Therefore, optimization of growth is one of the critical aims in the management of pediatric IBD. However, growth delay might persist despite reduced disease activity [5,6], with diminished final adult height in almost one in five IBD children [7]. The origin of growth retardation is multifactorial, including malnutrition, active inflammation, and steroid therapy among the principal determinants [2,8,9]. Malnutrition is mainly due to anorexia induced by inflammation [10,11], reduced energy intake due to digestive symptoms, and malabsorption of nutrients. Furthermore, CD children with growth failure have normal growth hormone (GH) secretion but diminished plasma concentration of insulin-like growth factor-1 (IGF-1), suggesting a certain degree of GH resistance that may be related to malnutrition and inflammation [12]. Metabonomics is nowadays commonly used as a systems biology approach to explore physiological regulatory processes in human clinical research with regard to disease etiology, diagnostic stratification and potentially to mechanisms of action of therapeutic solutions. Metabonomics has been defined as the quantitative measurement of dynamic metabolic changes of living systems in response to genetic modifications or physiological stimuli, including nutrients and drugs [13,14]. Metabonomics is achieved through global or targeted profiling of low molecular weight molecules in biofluids, as diverse as blood, urine, saliva, cerebrospinal fluid, as well as in stools and tissues [15]. Since the measured biochemical species are the products and by-products of the various biochemical pathways existing in all living systems, metabonomics is a well-established approach to capture and monitor intra- and extra-cellular regulatory processes [16]. From an analytical approach, metabonomics is based on either proton nuclear magnetic resonance (1H NMR) spectroscopy or mass spectrometry (MS). MS methods can be combined with a separation of metabolites using either gas chromatography or liquid chromatography. Both NMR and MS methods generate high density data, from which meaningful biological information are recovered using multivariate data analytical approaches [17,18]. Metabonomics has already begun to contribute to the field by generating key metabolic insights [1,19,20,21]. In the context of the study of pediatric subjects, metabonomics offers a unique opportunity to capture metabolic fingerprints of an individual using minimally invasive samples, such as blood spots or urine. Application of metabonomics to urine is a robust approach to generate metabolic phenotypes that associate with time-averaged representations of recent biochemical events within an organism, including gut microbial metabolic interactions with host metabolic pathways [22,23]. In this study, we applied 1H NMR-based metabonomics to characterize the biochemical fingerprints of urine samples from IBD and healthy patients. Advanced clinical and anthropometric phenotyping were also conducted on IBD children, where association with their metabolic status was explored to identify biochemical processes varying according to growth and disease activity (over one year with three visits at six-month intervals). 2. Results 2.1. Clinical Parameters of IBD and Healthy Subjects A total of 21 (15 Crohn’s disease, CD, 6 ulcerative colitis, UC) pediatric patients and 27 healthy children were enrolled during the study period, with urine samples available for metabonomics analysis and extensive clinical phenotyping. The IBD population was chronologically slightly older than the healthy group (Table 1). Moreover, CD patients showed lower z-scores for body weight (p < 0.01), height (p < 0.01) and body mass index (BMI) (p < 0.05) at baseline and throughout the follow-up of the IBD patients (Table 1). At baseline, CD patients had lower resting energy expenditure (p < 0.05). No significant differences were noted overtime in the biological parameters in IBD patients (Table 1). 2.2. Urine Metabonomics Describes Differences between IBD and Healthy Subjects For metabonomics urinary analysis, two metabolic profiles were discarded due to extreme dilution (one from a healthy subject at baseline, and one from a CD subject at the six-month visit). Multivariate data analysis was performed on the metabolic profiles using principal component analysis (PCA) and a modification of Projection to Latent Structures Discriminant Analysis (OPLS-DA). These multivariate data analyses explore the variance in the metabonomics that may explain statistical differences between groups of samples. Here, significant metabolite concentration differences were observed in the urine composition between IBD patients and healthy subjects at baseline, as noted by the OPLS-DA model generated with one predictive and two orthogonal components (R2X = 0.17, R2Y = 0.96, Q2Y = 0.18, where R2X corresponds to the explained variance in the metabonomics data (urine metabolites), R2Y to the explained group variance (healthy and IBD groups) and Q2Y to the robustness of the model). Additional analyses highlighted that the metabolic differences between IBD and healthy children were also present after 6 months (R2X = 0.20, R2Y = 0.94, Q2Y = 0.13) and 12 months of monitoring (R2X = 0.20, R2Y = 0.86, Q2Y = 0.24). Inspection of the model loadings allowed the identification of influential metabolites contributing to discriminate the groups of subjects by multivariate analysis. Representative signals of metabolites were integrated and reported in Table 2. Metabolite set enrichment analysis (MSEA) [24] was applied to identify meaningful patterns that are significantly enriched on the quantitative metabonomics data. Briefly, MSEA enables the identification of small but consistent changes among a group of related compounds. Here, major metabolic changes related to IBD conditions pointed towards the Krebs cycle and amino acid metabolic pathways (Figure 1). Variables identified by multivariate analysis were further probed by univariate testing as indicated in Table 2, with the aim to describe variations in urinary excretion of these metabolites. When compared to healthy subjects, pediatric IBD patients show higher urinary excretion of phenylacetylglutamine (PAG, p < 0.05), and lower urinary excretion of cis-aconitate (p < 0.05), hippurate (p < 0.05), and urea (p < 0.05). Additional inspection of data showed sub-group specificities (see Supplementary Materials). In particular, CD subjects were characterized with a lower excretion of carnitine when compared to healthy subjects. 2.3. Integration of Clinical and Urine Metabonomics Data in IBD Patients In this exploratory study, the analysis aims for the identification of shared metabolic features linked to changes in growth and disease activity, irrespectively of the therapeutic treatment. Analysis of variance using principal component analysis (PCA) was applied only on clinical and anthropometric data of IBD patients (%FFM, Tanner score, age, height z-score, weight z-score, BMI z-score, growth velocity z-score, fecal calprotectin, erythrocyte sedimentation rate (ESR), CRP, urea, IGF-1, insulin-like growth factor-binding protein 3 (IGFBP-3), caloric intake, and resting energy expenditure (REE)). An overall PCA model was generated with six principal components (PCs) explaining 81% of the total variance of the data, the first two components explaining 44% of the variance. Data were visualized by means of principal component scores (Figure 2A), where each point represents an individual at a given time point based on its clinical and anthropometric data. The clinical variables responsible for any detected differences between samples in the scores plot can be extracted from the corresponding loadings plot, where each coordinate represents a single clinical parameter. The distribution of the patients along the first component was driven by the values for %FFM and REE for which variations were negatively associated with fecal calprotectin values (i.e., a subject with low %FFM, REE values has high calprotectin). Along the second component, the distribution of the subjects was determined by variations associated to caloric intake, BMI-z score, weight-z score, ESR, and fecal calprotectin. The plotting of individual trajectories in this PCA space (Figure 2A) showed that some subjects have clinical sub-phenotypes evolving over the duration of the study. Based on the PCA scores, sub-groups of IBD patients were objectively defined in order to subsequently assess their distinctive clinical and metabolic status. To achieve this, hierarchical clustering analysis (HCA) was performed on the PCA scores generated previously, as illustrated in Figure 2B. The dendrogram obtained by performing HCA (average linkage) showed three clusters of patients of similar size. These groups were defined by visual inspection of the tree, the top nodes of which relate to different clinical statuses. For each defined group, clinical and biological parameters are reported in Table 3. In particular, group 1 was characterized by lower weight z-score, height z-score, BMI z-score, growth velocity z-score, %FFM, REE. Group 2 was characterized by a higher weight z-score and BMI z-score, lower urea, and lower caloric intake, whereas group 3 showed higher growth velocity z-score, and lower ESR and CRP. Using these three groups defined according to clinical and anthropometric data, urine metabolic profiles were analyzed using supervised multivariate data analysis. A first OPLS-DA model showed the occurrence of statistically significant differences in the biochemical composition of urine between the three groups, as noted by the model parameters (R2X = 0.19, R2Y = 0.68, Q2Y = 0.25, 2 predictive and 1 orthogonal components). Additional pairwise comparisons highlighted group-specific signatures using one predictive and one orthogonal component; group 1 vs. group 2 (R2X = 0.15, R2Y = 0.91, Q2Y = 0.27), group 1 vs. 3 (R2X = 0.12, R2Y = 0.94, Q2Y = 0.44), and group 2 vs. group 3 (R2X = 0.13, R2Y = 0.90, Q2Y = 0.24). Inspection of the model loadings enabled the identification of variables contributing to the distinction of the groups of samples. Representative signals of the previously identified metabolites were integrated and tested by univariate analysis (Table 4). Group 1 showed a distinctive urine profile, marked by a higher urine concentration of acylcarnitine compared to other IBD groups and healthy subjects. Group 2 showed higher levels of mannitol and an unassigned metabolite Uk3 when compared to the other IBD groups and healthy subjects, and the greatest increase in 4-hydroxyphenylacetate when compared to healthy subjects. Group 3 showed a low level of methanol compared to other IBD groups and healthy subjects, and had the greatest decrease in urinary urea when compared to healthy subjects. Furthermore, groups 2 and 3 showed high levels of 4-hydroxyphenylpyruvate, PAG, and tryptophan when compared to healthy subjects. In addition, to unravel more specific associations between urinary metabolites and clinical endpoints in CD patients, Spearman’s rank correlation analysis was performed and reported in Supplementary Figure S1. In particular, blood CRP and fecal calprotectin both showed negative correlations with urinary levels of PAG, 4-hydroxyphenylacetate, tryptophan, creatinine, as well as %FFM, height and growth velocity z-scores, and REE—but a positive correlation with urinary urea and formate. ESR showed positive correlations with the urinary level of formate and fecal calprotecin, but negative correlations with urinary levels of 4-hydroxyphenylpyruvate, cis-aconitate, 3-hydroxy-isobutyrate, and 3-methyl-2-oxovalerate, as well as blood IGFBP-3. 3. Discussion To the best of our knowledge, this is the first study showing a relation between clinical characteristics of pediatric patients with IBD and their urinary metabolic profiles over time in relation to disease state. Despite the limited number of subjects, the longitudinal experimental design with a healthy reference group offers key opportunities to explore metabolic status in childhood in relation to growth and disease state. Urinary metabolic profiles of IBD children differ significantly from healthy controls. Such metabolic differences include central energy metabolism (Krebs cycle), amino acid and gut microbial metabolic pathways, which are discussed here below. 3.1. Urine Metabonomics Reflects Different Metabolic Requirements in Pediatric IBD Patients Compared to Healthy Subjects Generally, the pediatric IBD condition shows growth failure and weight loss as hallmarks of subjects with CD, but less with UC [8,25]. In our study, CD pediatric patients showed lower z-scores for body weight, body height, and BMI compared to healthy controls. Healthy controls generally had higher IGF-1 and IGFBP-3 levels—more likely through adequate secretion—with inferred effects on growth, muscle, and fat mass development. The concomitant decrease in resting energy expenditure for CD patients reveals further differences in whole-body energy metabolism and related metabolic processes [26,27]. Such hypotheses are supported by the urine analysis that shows variations in key metabolic pathways indicating changes in protein and energy metabolism. Such changes are described through differences in the urea and Krebs cycle, namely with a decreased urinary excretion of urea and cis-aconitate—a precursor of alpha-ketoglutarate. The urinary excretion pattern of phenylacetylglutamine (PAG), which is a major nitrogenous metabolite, was also found to be higher in IBD pediatric subjects. PAG synthesis depends on the availability of phenylacetate—from either host or gut-microbial metabolism—and glutamine, mainly generated in the Krebs cycle from alpha-ketoglutarate. PAG is a key means to shuttle excess nitrogen out of the body. Its increased urinary excretion closely mirrors the decreased levels of urinary urea and cis-aconitate. Interestingly, previous reports documented how PAG may also replace urea as a waste of nitrogen product in specific disease conditions, such as in uremic patients [28,29,30]. In contrast to studies with adult IBD patients [31,32,33], fasting blood urea, urine citrate, and succinate remained unchanged in this pediatric cohort. This peculiar excretion of end products of protein metabolism indicates a different handling of nitrogen in pediatric IBD patients. 3.2. IBD Clinical Sub-Phenotypes Link to Different Metabolic Status Based on the IBD population characteristics, three clinical sub-phenotypes could be ascribed. Group 1 was characterized by lower weight z-score, height z-score, BMI z-score, growth velocity z-score, %FFM, and REE. This cluster corresponds to the pediatric patients with chronic mild disease state. Group 2 was characterized by a higher weight z-score and BMI z-score, lower urea, lower caloric intake, whereas group 3 showed higher growth velocity z-score, lower ESR and CRP. This set of clinical endpoints might be seen as two different stages of patients with growth improvement with group 2 reflecting longstanding remission or diseases interfering less with growth (i.e., UC), and group 3 showing patients with catch-up growth. Metabonomics analysis has been able to ascribe specific metabolites to disease sub-phenotypes. For instance, groups 2 and 3 correspond to patients having stable growth or growth improvement, as well as reduced inflammatory status. It is therefore worth noting that groups 2 and 3 have higher urinary levels of PAG and tryptophan, compared to healthy subjects. Supported by correlation analysis, this pattern suggests a metabolic relationship linking PAG and tryptophan to changes in %FFM, growth, and inflammatory conditions in pediatric patients in remission. Similarly, the reduced urinary excretion of methanol and urea in group 3, two metabolites significantly correlated to CRP and fecal calprotectin, may serve as readout for monitoring patients that recover towards remission and show growth improvement. Such observation also further supports the relevance of urinary urea and PAG for monitoring protein and muscle metabolism in pediatric patients with IBD. Last but not least, the group 1 shows a distinctive urine profile marked by higher urinary excretion of acylcarnitine compared to other IBD groups and healthy subjects. Despite the fact that the urinary pattern did not correlate with any studied clinical endpoint, such metabolic readouts illustrate the need to further study the different metabolic requirements in fatty acid use and oxidation under inflamed conditions. 3.3. Host-Gut Microbial Urinary Co-Metabolites Describe Relationships between Dietary Sources of Nitrogen, Carbamyl Phosphate Synthetase, and Host Metabolism As already reported in adults with IBD [31,32,33], the urinary excretion of hippurate was decreased in children with IBD compared to healthy controls. Williams et al. previously reported that dietary factors and deficit in the conjugation of benzoate to glycine did not explain the differences in the metabolism of hippurate [33], thus providing strong evidence for dysbiosis. Indeed, IBD is associated with reduced microbiota diversity, lower microbial capacity for butyrate production and increased pro-inflammatory bacteria [1,34], some features being further discussed in Supplementary Materials. Furthermore, despite urinary hippurate being significantly different from healthy subjects, our analysis did not show any differences amongst the IBD sub-phenotypes. In addition, groups 2 and 3 tended to have an increased urinary excretion of other gut microbial metabolites; 4-hydroxyphenylacetate and 4-hydroxyphenylpyruvate, as compared to healthy subjects. These differences also indicate persistent changes in microbial metabolism and processing of dietary components. Since these human-microbial metabolites correlate with inflammatory markers (CRP, ESR), their relevance to monitoring the normalization of gut microbial metabolic processes in pediatric IBD should be further investigated. Previous relationships between urinary urea nitrogen excretion and appearance of urine hippurate and/or PAG nitrogen were reported in normal subjects given sodium benzoate or sodium phenylacetate, respectively [29]. It is also important to note that the use of amino acid acylation pathways has been successfully exploited in empiric studies of patients with inborn errors of urea synthesis (e.g., carbamyl phosphate synthetase (CAD) deficiency) [29]. In the management of such clinical conditions, treatment with sodium benzoate and sodium phenylacetate activates the synthesis and excretion of hippurate and PAG, both of which may serve as waste nitrogen products [29]. Moreover, we found that PAG is positively correlated to %FFM and urine creatinine, but negatively correlated to urinary urea and inflammatory markers CRP and calprotectin. This may be of particular importance, as monitoring nitrogen excretion gives insights into the state of growth of a subject in childhood and net degradation of protein. Our current study suggests that the increased excretion of nitrogen products is related to an increase in fat free mass in CD pediatric patients, and one of its other markers—creatinine [35]—in parallel to decreased inflammatory conditions. In the context of IBD, CAD, an enzyme required for de novo pyrimidine nucleotide synthesis; was identified as a NOD2-interacting protein expressed at increased levels in the colon epithelium of patients with CD compared with controls [36]. The bacterial sensor NOD2 has been associated with CD, and the authors speculate that CAD is a negative regulator of NOD2 and might be a pharmacologic target for CD therapies [36]. Therefore, the relationships between urinary nitrogen excretion through urea, hippurate, and PAG may be a potential readout for CAD-NOD2 activity in pediatric IBD. Moreover, as already reported in adults with IBD [31,32,33], the urinary excretion of hippurate was decreased in children with IBD as compared to healthy controls. The main source of variations in hippurate metabolism comes from dietary factors (e.g., dietary sources of polyphenolic compounds such as fruits and vegetables), and hepatic and gut microbial metabolism of its precursors (mainly benzoic acid) [37,38]. Williams et al. previously provided a strong evidence for dysbiosis [33]. In particular, IBD was associated with reduced microbiota diversity, lower microbial capacity for butyrate production and increased pro-inflammatory bacteria [1,34]. Furthermore, UC patients show a consistent trend towards higher levels of other gut microbial metabolites, 4-hydroxyphenylacetate and 4-hydroxyphenypyruvate, that are mainly formed in the colon by bacterial fermentation [38,39], which may support region-specificity of gut metabolic dysbiosis. Patients with UC have a consistent trend in higher urinary excretion of two products of branched chain amino acid (BCAA) metabolism, 3-methyl-2-oxovalerate and 2-oxoisocaproate, and lactate—end product of anaerobic carbohydrate metabolism, suggesting an upregulation of BCAA and carbohydrate catabolism. Concomitantly, urinary excretion of fatty acid β-oxidation intermediates, carnitine and acylcarnitine, tends to decrease, thus indicating a downregulation of fatty acid breakdown through β-oxidation. Taken together with changes in PAG and the Krebs cycle, this urinary pattern describes a further remodeling of energy, amino acid and fatty acid metabolism in relation to the altered metabolic requirements of UC pediatric patients. 4. Materials and Methods 4.1. Subjects Eligible patients were aged between 10 and 18 years old, with a diagnosis of CD or UC, confirmed according to international criteria [40]. IBD subjects were assessed at baseline (T0), after 6 (T6) and 12 months (T12), respectively. All patients were in remission and underwent therapeutical management of the disease according to recommended drugs (see supplementary Table S1 for information). To be noted that none was treated with enteral nutrition and no endoscopy was performed to assess mucosal inflammation. Control healthy subjects were recruited among the general pediatric population. They were matched for age, pubertal stage, and gender to the IBD subjects. They had neither chronic inflammatory disease nor family history of inflammatory bowel. Anthropometric data and urine samples for metabolic analyses were collected at one time point. An informed written consent was obtained from the parents and an oral assent from each child. 4.2. Anthropometric and Clinical Measures 4.2.1. Anthropometric Assessment Body weight was measured using a calibrated digital scale (Seca, Hamburg, Germany) to the nearest 0.1 kg. Height was measured using a wall-mounted stadiometer (Holtain, Crosswell, UK) to the nearest 0.1 cm. Body mass index (BMI, kg/m2) was determined by dividing the weight in kilograms by the square of the height in meters. Height velocity was calculated as the amount of growth in centimeters divided by the time interval between measurements in years. All values were expressed in z-scores [41,42]. Pubertal stage was assessed according to Tanner score [43]. 4.2.2. Body Composition Bioimpedance analysis (BIA) was performed using Body Impedance Analyzer Akern (Florence, Italy). While the subject was lying comfortably without his limbs touching the body, electrodes were placed just below the phalangeal-metacarpal arch in the middle of the dorsal side of the dominant hand and just below the metatarsal arch on the superior side of the foot of the same side. Fat free mass in kg (FFM) was then calculated using the software BodyGram Pro® supplied by the manufacturer (which uses weight, age, and an impedance index (height2/resistance)) [44,45]. Percentage of FFM (%FFM) was calculated by dividing FFM with the body weight of the subject expressed in kg. 4.2.3. Disease Activity in Patients with IBD Disease activity was scored using the pediatric Crohn’s disease activity index (PCDAI) [46] for CD, a 100 point scale where a score >30 indicates severe disease, and the pediatric ulcerative colitis activity index (PUCAI) [47] for UC, an 85-point scale where a score >35 indicates severe disease. Remission was defined as PCDAI or PUCAI score lower than 10. No endoscopic control was performed since all patients were in remission. 4.2.4. Blood and Stool Markers Inflammatory markers (erythrocyte sedimentation rate (ESR), C-reactive protein (CRP)), urea, and growth factors (insulin-like growth factor 1 (IGF-1) and insulin-like growth factor-binding protein 3 (IGFBP-3), expressed in z-scores) were obtained after a fasting period of at least 6 h. Fecal calprotectin was measured and a cut-off value of 275 μg/g was set to determine possible relapse of disease [48]. 4.2.5. Dietary Intake All subjects underwent a 24-h food recall with the help of a questionnaire showing pictures of different sizes of plates for the different foods with the same examiner (dietician). Qualitative and quantitative analyses were made using the software Prodi 5.8 Expert (Nutri-Science GmbH, Hausach, Deutschland, Germany). Daily intake was expressed as kcal per day. 4.2.6. Resting Energy Expenditure Resting energy expenditure (REE, kcal) was measured using Quark RMR (Cosmed, pulmonary function equipment, Delta Medical, Rome, Italy). Prior to each measurement, the indirect calorimeter was calibrated with a standard gas of a known composition (95% O2, 5% CO2). Measurements were performed in a quiet thermoneutral room (20 °C) after a fasting period of at least 6 h, to minimize any effect attributable to the thermic effect of food. Oxygen consumption and carbon dioxide production were measured every 5 s for at least 20 min and REE was defined as the mean energy expenditure over the measured period. 4.3. Metabonomics Analysis Morning spot urine samples were collected at baseline for all subjects, and at the 6-month and 12-month visit for the IBD patients. Urine samples (1 mL) were collected by means of sterile plastic tubes, and were stored at −80 °C, prior to analysis. 40 µL of urine were mixed with 20 µL deuterated phosphate buffer solution 0.6 M KH2PO4, containing 1 mM of sodium 3-(trimethylsilyl)-[2,2,3,3-2H4]-1-propionate (TSP, chemical shift reference δH = 0.0 ppm). The homogenates were centrifuged at 17,000× g for 10 min and 60 µL of the supernatant were transferred into 1.7 mm NMR tubes. 1H NMR metabolic profiles were acquired with a BrukerAvance II 600 MHz spectrometer equipped with a 1.7 mm probehead 300 K (BrukerBiospin, Rheinstetten, Germany), using a standard pulse sequence with water suppression, and processed using TOPSPIN (version 2.1, Bruker, Germany) software package. 4.4. Statistical Analysis Chemometric analysis was performed on clinical and metabonomics data using the software package SIMCA-P+ (version 12.0, Umetrics AB, Umeå, Sweden). Principal component analysis (PCA) and a modification of partial least squares regression (PLSR) that removes all information orthogonal to the response variable during the fitting process were employed. This variant, orthogonal projection to latent structures (O-PLS) [49] provides sparser models (improving their interpretability) with the same degree of fit as PLSR. To highlight the weight of individual variables in the model, variable importance in projection (VIP) was used, with a value above 1 used as a threshold by convention. Influential metabolites were relatively quantified by signal integration and analyzed using t-tests. Metabolic pathway analysis was conducted by performing a metabolite set enrichment analysis, using the web-based MetaboAnalyst 3.0 tool [24], to the list of influential metabolites obtained through multivariate data analysis. Visualization of the trajectories in the principal components (PC) space was performed using Plotly (Plotfly Technologies Inc., Montréal, QC, Canada). 4.5. Ethics This clinical study was approved by the Ethical Committee of the University of Lausanne, Switzerland (protocol 69/10) on 22 March 2010, and conducted in the Pediatric Gastroenterology outpatient clinic of the University Hospital of Lausanne, Switzerland. Informed written consent was obtained from the patients and their parents. 5. Conclusions The present study shows how non-invasive sampling of urine followed by metabonomic analysis might elucidate and monitor the metabolic status of children in relation to disease state. Such metabolic profiles provide biological insights into host and bacterial metabolism by means of which we might assess metabolic fingerprints at different stages of disease. Despite the limited number of subjects, the longitudinal experimental design enabled the identification of a peculiar metabolite pattern to monitor metabolic requirements. Urinary urea and phenylacetylglutamine—two readouts of nitrogen metabolism—appeared particularly relevant and should be further investigated in follow-up studies. In particular, the levels of these particular metabolites correlate with the level of FFM in pediatric subjects, and could offer cost-effective alternative to DXA or bioelectrical impedance analysis, and enable regular assessment of lean mass for optimal growth catch-up under standard care practice. Therefore, further developments of such omic-approaches in pediatric research are needed and will deliver novel nutritional and metabolic hypotheses. Acknowledgments This work was supported by grants from the Swiss National Science Foundation (Grant # 32003B_135466). We would like to thank Tania Chatton, dietician, and Sylvie Poget, study nurse, for their substantial support during the study period, and Ivan Montoliu and Sebastiano Collino at NIHS for their input during scientific discussions. Supplementary Materials Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1310/s1. Click here for additional data file. Author Contributions Andreas Nydegger, Jessica Ezri, Jean-Philippe Godin, and Francois-Pierre Martin conceived and designed the experiments; Laeticia Da Silva and Francois-Pierre Martin performed the experiments; Ornella Cominetti, Laeticia Da Silva, and Francois-Pierre Martin analyzed the data; all the authors wrote the paper. Conflicts of Interest Francois-Pierre Martin, Ornella Cominetti, Laeticia Da Silva, Martin Kussmann, Jean-Philippe Godin are employees of Nestle SA, a food and beverage company. Figure 1 Summary plot of over representation analysis of urinary metabolites, using metabolite set enrichment analysis (MSEA). Figure 2 (A) Principal component analyses of clinical and biological parameters in inflammatory bowel disease (IBD) over time. Data points in orange represent samples (at either of the three time points) of ulcerative colitis (UC) patients and in blue, samples of Crohn’s disease (CD) patients. Trajectories of individual subjects are depicted in unique colors, with the arrows indicating the directions of the time points; (B) Dendrogram obtained by performing hierarchical clustering analysis (HCA) (average linkage) showing three groups of patients of similar size, which may be further subdivided into smaller clusters. By visual inspection of the tree, three groups were obtained by cutting the tree at the top nodes which relates to different clinical status. ijms-17-01310-t001_Table 1Table 1 Population characteristics. Group Healthy Controls Crohn’s Disease (CD) Ulcerative Colitis (UC) Visit T0 T0 T6 T12 T0 T6 T12 N total (males) 27 (16) 15 (8) 14 (8) 12 (7) 5 * (2) 6 (2) 6 (2) Age (Years) 12.9 ± 1.9 (10.1–16.7) 14.9 ± 1.3 b (12.4–16.7) 15.2 ± 1.2 (12.9–17.2) 15.7 ± 1.3 (13.4–17.7) 15 ± 1.2 a (12.7–16.1) 15.2 ± 1.3 (13.1–16.7) 15.8 ± 1.2 (13.8–17.2) Tanner Score 3 ± 1 (1–5) 3 ± 1 (2–5) 4 ± 1 (2–5) 4 ± 1(2–5) 4 ± 1 (3–5) 4 ± 1 (2–5) 4 ± 1 (3–5) Weight z-score 0.5 ± 1 (−2.1–1.8) −0.7 ± 0.9 b (−2.1–1.2) −0.7 ± 0.8 (−1.8–0.9) −0.7 ± 0.9 (−2.2–0.6) −0.1 ± 0.9 (−1.3–1.2) −0.3 ± 1 (−1.5–0.9) −0.1 ± 1 (−1.6–1) Height z-score 0.5 ± 0.9 (−1.7–2.2) −0.8 ± 1 b (−3.2–0.5) −0.7 ± 1 (−3.1–1.1) −0.5 ± 1.1 (−2.5–1.7) −0.1 ± 1.5 (−2.4–1.8) −0.3 ± 1.2 (−2.1–1.6) −0.2 ± 1.1 (−1.8–1.5) BMI z-score 0.4 ± 0.9 (−1.7–1.6) −0.5 ± 1.2 a (−2.8–1.7) −0.5 ± 1 (−2.3–1.4) −0.7 ± 1.1 (−2.7–1.1) 0 ± 0.9 (−1.1–1.1) −0.2 ± 1 (−1.3–1.2) −0.1 ± 1 (−1.3–1.6) GV z-score NA 0.8 ± 1.7 (−1.4–3.8) 0.7 ± 1.6 (−1.1–4.3) 0.9 ± 1.7 (−1–4.6) 0.1 ± 0.6 (−0.4–1.4) −0.3 ± 1.4 (−1.8–2.1) 0.4 ± 1.2 (−0.8–2.7) %FFM 39.6 ± 10.9 (23.5–60.6) 35.0 ± 5.1 (26.7–43.7) 37.3 ± 5.4 (27.7–48.1) 38.9 ± 7.3 (29.7–57.7) 37.8 ± 4.1 (33.4–43.8) 36.7 ± 4.2 (27.8–40.2) 39.4 ± 5.1 (29.6–44.8) REE (Kcal) 1531.2 ± 275.6 (958–2036) 1338.1 ± 147 a (1065–1702) 1381.3 ± 188.6 (1050–1824) 1374 ± 213.2 (1177–1942) 1472.6 ± 86.6 (1362–1561) 1355.3 ± 201.8 (1118–1599) 1467.7 ± 174.6 (1249–1761) Blood Urea (mmol/L) 438.8 ± 109.9 (116.8–647.9) 379.7 ± 111.2 (208.5–548.4) 395.9 ± 101.2 (224.4–541.5) 401.3 ± 123.4 (189.8–615.6) 394.4 ± 86 (310.7–545.7) 382.1 ± 145.1 (156.1–586.4) 389.5 ± 182.1 (90.7–598.6) PCDAI in CD/PUCAI in UC NA 9.8 ± 9 (0-30) 7.7 ± 7 (0-22.5) 6.3 ± 8.9 (0-25) 5 ± 4.5 (0-10) 5.8 ± 5.3 (0-15) 3 ± 4 (0-10) ESR (mm/h) NA 15.1 ± 8.5 (2–32) 16 ± 17.8 (3–70) 18 ± 20.2 (1–70) 26.6 ± 25.9 (9–78) 33.2 ± 31 (10–94) 23.8 ± 12.2 (13–47) CRP ** (mg/L) NA 3.5 ± 1.9 (2–8) 7.1 ± 10.1 (2–35) 11.1 ± 17.3 (2–60) 5 ± 3.3 (2–11) 6.5 ± 6.5 (2–18) 7.7 ± 11.4 (1–33) Fecal calprotectin (µg/g) NA 660.8 ± 673.9 (10–1500) 372.3 ± 464.4 (20–1500) 714.8 ± 643 (20–1500) 1046.7 ± 501.5 (367–1500) 966.7 ± 644.2 (20–1500) 1500 ± 0 (1500–1500) IGF-1 z-score NA −0.6 ± 0.4 (−1.1–0.3) −0.7 ± 0.3 (−1.1–0.0) −0.7 ± 0.3 (−1.2–0.1) −0.5 ± 0.5 (−1.0–0.1) −0.4 ± 0.5 (−0.9–0.4) −0.4 ± 0.4 (−0.9–0.4) IGFBP-3 z-score NA −0.5 ± 0.2 (−0.9–−0.2) −0.5 ± 0.1 (−0.8–−0.3) −0.5 ± 0.2 (−0.7–0.1) −0.4 ± 0.5 (−1.0–0.0) −0.4 ± 0.4 (−0.8–0.1) −0.4 ± 0.3 (−0.8–0.1) BMI, body mass index; GV, growth velocity; %FFM, percentage of fat free mass measured by bioimpedance; REE, resting energy expenditure; PCDAI, pediatric Crohn’s disease activity index; PUCAI, pediatric ulcerative colitis activity index; ESR, erythrocyte sedimentation rate; CRP, C-reactive protein; IGF-1, insulin-like growth factor-1; IGFBP-3, insulin-like growth factor-binding protein 3. Data are reported as mean ± standard deviation (SD) (min–max values). a and b designate statistically significant differences (Student t-tests) between IBD groups and Healthy controls at baseline, as 95% and 99% confidence interval, respectively. ** CRP median values were 3.0, 2.0, 2.0 for CD at T0, T6 and T12, and 5.0, 2.0, 3.0 for UC at T0, T6 and T12, respectively. No statistically significant changes were observed in CD and UC groups over time. * One UC subject did not provide urine at baseline. NA: Not available. ijms-17-01310-t002_Table 2Table 2 Urine metabolite patterns in the IBD subjects and healthy controls. Group Healthy Controls CD UC Metabolites (a.u.)/Visit T0 T0 T6 T12 T0 T6 T12 Uk1 4.6 ± 2.5 4.1 ± 2.3 4.3 ± 2.0 3.2 ± 1.2 b 3.7 ± 1.5 6.2 ± 3.2 7.9 ± 7.5 b Uk2 13.1 ± 1.2 13.5 ± 1.6 13.4 ± 1.9 14.2 ± 3.5 15.4 ± 3.2 a 13.8 ± 1.5 16.4 ± 5.1 a Uk3 40.9 ± 4.6 59.7 ± 53.0 b 50 ± 22.0 a 96.7 ± 150.9 b 184.4 ± 166.3 a 90 ± 83.8 a 263.9 ± 259.6 a Methanol 37.4 ± 8.7 36.0 ± 12.3 30.2 ± 6.6 a 33.9 ± 9.3 35.0 ± 4.0 35.5 ± 14.3 39.7 ± 17.7 Acyl-carnitine 95.7 ± 15.6 99.3 ± 22.1 108.6 ± 30.6 b 99 ± 27.4 93.6 ± 18.3 81.9 ± 16.6 b 82.9 ± 12.9 b cis-Aconitate 36.4 ± 4.6 31.1 ± 7.7 a 30.2 ± 6.9 a 30.1 ± 5.6 a 30.2 ± 1.7 a 29.2 ± 3.0 a 27.9 ± 4.7 a Betaine 179.9 ± 62.3 215.2 ± 228.6 176.8 ± 86.9 163.1 ± 55.6 202.7 ± 53.3 144.5 ± 68.5 165.5 ± 78.9 Urea 380.0 ± 124.8 281.3 ± 153 a 227.4 ± 139.9 a 258.5 ± 147.7 a 357.9 ± 89.6 289.1 ± 139.1 273.1 ± 142.5 b 4-Hydroxyphenylacetate 3.4 ± 0.6 3.4 ± 0.7 3.1 ± 0.9 10.5 ± 20.1 b 11.8 ± 14.2 a 7.3 ± 6.6 a 24.9 ± 28.7 a 4-Hydroxyphenylpyruvate 13.3 ± 6.0 12.8 ± 4.8 9.6 ± 3.9 b 20.8 ± 22.5 31.2 ± 26.5 a 14.1 ± 10.3 35.2 ± 28.6 a Phenylacetylglutamine 6.9 ± 1.1 9.0 ± 4.5 a 7.5 ± 1.2 b 9.6 ± 3.8 a 8.9 ± 1.6 a 13.1 ± 12.4 a 9.6 ± 2.5 a Tryptophan 6.6 ± 2.6 8.2 ± 7.0 7.4 ± 10.1 17.9 ± 43.9 49.2 ± 51.7 a 20.3 ± 29.0 a 68.4 ± 74.9 a Hippurate 140.9 ± 92.3 53.6 ± 32.8 a 57.3 ± 45.6 a 81.5 ± 69.6 b 67.1 ± 32.1 b 62.8 ± 59.6 b 64.4 ± 50.2 b Glycine 91.7 ± 32.2 102 ± 39.4 103.5 ± 35.4 119.7 ± 68 b 106.1 ± 38.9 128.6 ± 59 a 122.9 ± 71.3 Taurine 117.2 ± 28.7 99.9 ± 37.0 118.1 ± 33.4 94.8 ± 37.1 a 101.8 ± 35.9 81.8 ± 30 a 112.7 ± 68 Mannitol 364.7 ± 67.9 404.5 ± 77.9 b 379.7 ± 44.5 369 ± 30.4 379.4 ± 28.6 391.1 ± 95.2 351.7 ± 79 Carnitine 57.1 ± 31.3 35.4 ± 15.7 a 51.4 ± 47.0 27.6 ± 7.9 a 44.2 ± 27.6 41.8 ± 13.8 30.4 ± 12.6 b Succinate 27.2 ± 4.9 24.6 ± 5.9 24.6 ± 4.8 26.3 ± 6.4 22.1 ± 3.8 a 28.8 ± 9.5 23.9 ± 3.7 3-Methyl-2-oxovalerate 16.1 ± 1.6 16.9 ± 2.6 17.4 ± 3.4 15.9 ± 2.1 15.5 ± 1.5 35.6 ± 46.9 a 20.3 ± 11 a 3-Hydroxyisobutyrate 15.0 ± 2.5 13.2 ± 3.2 a 13.9 ± 3.9 13.4 ± 3.4 12.6 ± 1.8 a 35.9 ± 55.4 a 17.4 ± 9.4 2-Oxoisocaproate 21.7 ± 3.3 22.3 ± 3.9 22.5 ± 2.4 21.0 ± 3.4 20.1 ± 3.5 31.0 ± 20.0 a 26.5 ± 9.6 a Citrate 180.9 ± 54.3 168 ± 79.2 178.1 ± 65.4 180.9 ± 85.0 152.3 ± 31.1 197.4 ± 156 196.5 ± 58 Creatinine 862.7 ± 152.6 866.3 ± 152.1 856.9 ± 152.6 844.7 ± 107.4 832 ± 101.7 841.0 ± 263.3 846.6 ± 140.5 3-aminoisobutyrate 17.9 ± 2.6 17.3 ± 3.2 18.9 ± 4.9 17.9 ± 4.3 18.2 ± 2.5 20.9 ± 9.4 18.4 ± 2.6 Lactate 49.9 ± 12.5 52.3 ± 8.9 156.4 ± 372.2 51.2 ± 12.1 47.9 ± 6.1 69.8 ± 50.5 b 76.6 ± 67.6 b Formate 5.7 ± 1.8 4.6 ± 2.4 4.5 ± 2.7 4 ± 1.7 a 4.6 ± 1.4 17.7 ± 30.4 a 4.6 ± 1.7 Data are reported as mean ± SD. Relative quantitation is obtained through calculating the area under the curve for a representative signal in the nuclear magnetic resonance spectra, and are reported using an arbitrary unit (a.u.). a and b, difference between IBD groups and healthy subjects are significant at 95% and 99% confidence interval, respectively. Uk 1, 2, 3: unassigned metabolite 1, 2 or 3. ijms-17-01310-t003_Table 3Table 3 Clinical phenotypes. Clinical Parameters Group 1 (6% UC) Group 2 (53% UC) Group 3 (30% UC) Healthy p-Values Weight z-score −1.5 ± 0.5 0.4 ± 0.6 −0.7 ± 0.7 0.5 ± 1 x,y,z,a,c Height z-score −0.8 ± 0.4 -0.2 ± 1.2 −0.7 ± 1.3 0.5 ± 0.9 x,a,b,c BMI z-score −1.6 ± 0.7 0.5 ± 0.8 −0.4 ± 0.7 0.4 ± 0.9 x,y,z,a,c GV z-score −0.2 ± 0.9 0.4 ± 1.5 1.2 ± 1.8 NA z %FFM (kg on the weight) 32.3 ± 3.7 38.1 ± 4 39.7 ± 6.3 39.8 ± 11.3 x,z,a REE (kcal) 1284.9 ± 128.6 1452.9 ± 152.9 1401.6 ± 216.4 1537.1 ± 284.8 x,a Blood urea (mmol/L) 381.7 ± 128.9 321.1 ± 133.6 442 ± 90.4 437.9 ± 114.1 y,b Caloric intake (Kcal/day) 2094.1 ± 422.5 1504.3 ± 256.5 1834 ± 581.6 1935 ± 461.1 x,y,b ESR (mm/h) 21.6 ± 19.2 32.2 ± 26.3 9.8 ± 5.5 NA y,z CRP (mg/L) 11.6 ± 16.9 8.4 ± 9.3 2.7 ± 1.4 NA y,z Fecal calprotectin (µg/g) 689.2 ± 614.7 1086.4 ± 598.1 516.7 ± 635.2 NA y IGF-1 z-score −0.7 ± 0.2 −0.4 ± 0.5 −0.6 ± 0.3 NA x IGFBP-3 z-score −0.5 ± 0.2 −0.4 ± 0.3 −0.5 ± 0.2 NA x BMI, body mass index; GV, growth velocity; %FFM, percentage of fat free mass measured by bioimpedance; REE, resting energy expenditure. Data are reported as mean ± SD. x,y,z, difference between groups 1∔2, 2∔3, and 1∔3 is statistically significant at 95% confidence interval, respectively. a,b,c difference between Healthy-group 1, Healthy-group 2 and Healthy-group 3 is statistically significant at 95% confidence interval, respectively. ijms-17-01310-t004_Table 4Table 4 Urinary metabolites and clinical phenotypes. Metabolites (a.u.) Group 1 Group 2 Group 3 Healthy Controls p-Values Uk3 44.7 ± 5.6 142.7 ± 186.2 108.8 ± 131.2 40.9 ± 4.6 x,a,b,c Methanol 36.5 ± 11.9 39.9 ± 12.8 29.4 ± 5.6 37.4 ± 8.7 y,z,c Acyl-carnitine 115 ± 26.7 90.6 ± 16 90.2 ± 23.5 95.7 ± 15.6 x,z,a cis-Aconitate 31.5 ± 5.2 30.3 ± 7.5 28.9 ± 5.1 36.4 ± 4.6 a,b,c Urea 295.2 ± 157.1 308.1 ± 111.7 228.4 ± 140.6 380 ± 124.8 c 4-Hydroxyphenylacetate 4.3 ± 3.6 11.7 ± 19.6 8.5 ± 15.2 3.4 ± 0.6 b 4-Hydroxyphenylpyruvate 10 ± 5.8 24 ± 23.9 18.8 ± 16.6 13.3 ± 6 x,z,b Phenylacetylglutamine 7.8 ± 2.2 11.5 ± 8 8.7 ± 2.7 6.9 ± 1.1 b,c Tryptophane 5.1 ± 2.2 32.5 ± 54.2 24.1 ± 39 6.6 ± 2.6 b,c Hippurate 57.9 ± 37.2 55.2 ± 60.1 73.4 ± 47.2 140.9 ± 92.3 a,b,c Glycine 97.7 ± 31.5 108 ± 43.2 123 ± 62.9 91.7 ± 32.2 c Taurine 106.8 ± 33.7 95.3 ± 38.8 104.9 ± 44.3 117.2 ± 28.7 b Mannitol 381.7 ± 53.7 411.5 ± 67.7 361.8 ± 56.5 364.7 ± 67.9 y,b Carnitine 38.1 ± 21.2 36.2 ± 11.9 39.9 ± 36.5 57.1 ± 31.3 a,b Succinate 28.2 ± 5.9 23.6 ± 7 24.2 ± 4.4 27.2 ± 4.9 z,c Creatinine 785.6 ± 107.6 826.1 ± 179.3 914.3 ± 125.3 862.7 ± 152.6 z Data are reported as mean ± SD. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081311ijms-17-01311ReviewApoptotic Pathways Linked to Endocrine System as Potential Therapeutic Targets for Benign Prostatic Hyperplasia Minutoli Letteria 1*Rinaldi Mariagrazia 1Marini Herbert 1Irrera Natasha 1Crea Giovanni 2Lorenzini Cesare 2Puzzolo Domenico 3Valenti Andrea 1Pisani Antonina 3Adamo Elena B. 3Altavilla Domenica 3Squadrito Francesco 1Micali Antonio 3Lemarié Anthony Academic Editor1 Department of Clinical and Experimental Medicine, University of Messina, Azienda Ospedaliera Universitaria Policlinico “G. Martino”, 98125 Messina, Italy; mrinaldi@unime.it (M.R.); hrmarini@unime.it (H.M.); nirrera@unime.it (N.I.); andrea.valenti@unime.it (A.V.); fsquadrito@unime.it (F.S.)2 Department of Human Pathology, University of Messina, Azienda Ospedaliera Universitaria Policlinico “G. Martino”, 98125 Messina, Italy; giovanni.crea@unime.it (G.C.); cesare.lorenzini@unime.it (C.L.)3 Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, 98125 Messina, Italy; puzzolo@unime.it (D.P.); apisani@unime.it (A.P.); elenabianca.adamo@unime.it (E.B.A.); daltavilla@unime.it (D.A.); amicali@unime.it (A.M.)* Correspondence: lminutoli@unime.it; Tel.: +39-090-2213652; Fax: +39-090-221330011 8 2016 8 2016 17 8 131108 7 2016 04 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Benign prostatic hyperplasia (BPH) is a chronic condition common in older men that can result in bothersome lower urinary tract symptoms. The molecular mechanisms and networks underlying the development and the progression of the disease are still far from being fully understood. BPH results from smooth muscle cell and epithelial cell proliferation, primarily within the transition zone of the prostate. Apoptosis and inflammation play important roles in the control of cell growth and in the maintenance of tissue homeostasis. Disturbances in molecular mechanisms of apoptosis machinery have been linked to BPH. Increased levels of the glycoprotein Dickkopf-related protein 3 in BPH cause an inhibition of the apoptosis machinery through a reduction in B cell lymphoma (Bcl)-2 associated X protein (Bax) expression. Inhibitors of apoptosis proteins influence cell death by direct inhibition of caspases and modulation of the transcription factor nuclear factor-κB. Current pharmacotherapy targets either the static component of BPH, including finasteride and dutasteride, or the dynamic component of BPH, including α-adrenoceptor antagonists such as tamsulosin and alfuzosin. Both these classes of drugs significantly interfere with the apoptosis machinery. Furthermore, phytotherapic supplements and new drugs may also modulate several molecular steps of apoptosis. benign prostatic hyperplasiaapoptosistreatment ==== Body 1. Introduction The most common non-malignant urological disease among aging men is the benign prostatic hyperplasia (BPH), which affects more than 40% of individuals over the age of 60 [1]. It is a progressive disease, which causes an increase in prostate volume, a decrease in maximum urinary flow rate, as well as the development of acute urinary retention (AUR) [2]; thus, it represents a risk in terms of health [3]. In the next few years, the rate of male population treated for BPH will increase as an outcome of aging [4,5]. In adult men, BPH is commonly characterized by lower urinary tract symptoms (LUTS) in association with sexual dysfunctions (SD), such as erectile and ejaculatory dysfunction, decreased libido, and overall dissatisfaction [3]. Furthermore, LUTS is often associated with diabetes mellitus, urinary tract infections, and neurological disorders [3]. Preclinical and clinical studies showed that LUTS and SD share similar pathogenic mechanisms [6]. The specific processes leading to the phenotype observed in BPH have not yet been completely clarified. A lot of evidence indicates a molecular link between androgens, estrogens, growth factors, and/or neurotransmitters in the evolution of BPH. The increase of prostate volume is caused by a complex and gradual growth involving both prostate glandular epithelium and fibromuscular stroma [7], primarily in the transitional zone [8]. Cell growth in BPH may also cause nodules in the periurethral region of the prostate, able to partially or completely obstruct the urethra [9]. Recent evidences showed a series of pathological conditions in BPH, such as chronic inflammation [10], deregulation of circulating hormonal levels, and abnormal tissue remodeling [11,12]. These processes also include an altered expression of cytokines and chemokines [13], a disturbance of immune surveillance and recognition, as well as a pathologic modification of stroma, observed in several fibroplasias and in different malignant tissues [7,12,14]. The development of prostate is linked to the endocrine system; in particular, epithelium and mesenchyme are controlled by androgens. For these reasons, many of the diseases affecting the prostate are correlated to the endocrine system and their treatments are directed at the manipulation of this very complex system [15]. While it is known that in the genesis of prostate cancer an unbalance between cellular proliferation and cell death plays a prominent role, no concordant data are currently available in the genesis of BPH [16,17]. Apoptosis, or programmed cell death, is a complex process implicated in development and cellular stability [18]. Apoptosis is primarily triggered by specific stimuli, such as an activation or inactivation of several molecules through a multifaceted regulation [19]. Apoptosis is also involved in embryonic development and homeostatic maintenance of tissues and organs [20,21], being regulated by different death- or survival-related genes [22]. Programmed cell death includes many molecular steps that culminate in the clearance of impaired and altered cells, while at the same time avoiding the leaking of deleterious substances into the surrounding tissues [23,24]. Overall, the identification of the apoptotic pathways involving the prostatic cells undergoing hyperplasia may offer new data on the carcinogenesis, thus providing novel therapeutic targets [17]. 2. Endocrine Control of Prostatic Growth In BPH, an important role is played by hormonal imbalance; in fact, the development and the growth of the prostate is controlled by a synergy of normal levels of androgens and estrogens [25] and, consequently, by a controlled balance between cell growth and apoptosis [16]. In fact, dihydrotestosterone (DHT), which binds the androgen receptor (AR), strategically modulates the proliferation and increase of the prostate volume [26]. An adequate level of androgens is necessary to regulate the growth of prostate [27]: in fact, androgen deficiency leads to a reduction of the prostatic glandular epithelium [28,29]. Interestingly, stromal cells can use the signaling pathways of growth factors—such as epidermal growth factor (EGF), basic fibroblast growth factor (bFGF), insulin-like growth factor (IGF), and transforming growth factor β1 (TGFβ1) [30] (Figure 1)—to activate genes sensitive to androgens, although in an androgen-independent way [31]. These paracrine pathways are important in the regulation of proliferation and apoptosis of prostate epithelial cells [32]. Androgens are also implicated in the development of BPH and prostate cancer. In a model of castrated rats, testosterone inhibits cell death in the ventral prostate by controlling procaspase and caspase-3 and -6 mRNA levels, as well as active proteins levels [33]. In BPH, the abnormal growth is related to the activation of proliferative processes, and vice versa for the inhibition of apoptotic pathways, which is induced by androgen stimulation, thus demonstrating a key role of DHT in the development of the disease [34]. Another important role in BPH development is played by estrogens (Figure 1), even if concordant data are not present. In fact, serum estrogens were reported to be correlated with prostate volume and other aspects of BPH [35]. On the contrary, this relationship was not found by Miwa et al. [36]. The types of estrogens and of estrogen receptors (ER) may influence the estrogen action in the prostate; similarly, stromal cells from BPH may respond differently from normal stromal cells to estrogenic ligands. Indeed, the true roles of ER-α and ER-β in BPH have not yet been elucidated. In particular, ER-α activation causes hyperplasia, inflammation, and dysplasia of the prostatic tissue [37]. On the contrary, ER-β reduces cellular proliferation in the prostate and activates apoptosis in BPH in an androgen-independent manner [38]. In fact, in ER-β knockout (ER-β KO) mice, prostatic hyperplasia progresses with age, which differs from wild-type or ER-α KO mice [39]. In this way, the ratio ER-α/ER-β may play an important role in estrogen-induced proliferation. 3. Molecular Pathways of the Apoptosis Machinery and Benign Prostatic Hyperplasia (BPH) It is well known that normal cells grow through a delicate equilibrium between the stimulation and the inhibition of specific pathways of cellular death. Among them, an important role is played by the genes encoding B cell lymphoma (Bcl)-2 and Bcl-2 associated X protein (Bax) [40]. In particular, cells showing a higher expression of Bax undergo apoptosis, while those overexpressing Bcl-2 often undergo carcinogenesis, a condition characterized by a suppression of apoptosis [41]. In the prostate, the expression of anti-apoptotic protein Bcl-2 is limited to basal epithelial cells, which are able to resist androgen deprivation [42]. In BPH, the expression of Bcl-2 in epithelial cells is low, while the activity of Bax is high; in particular, Bax activity is positively linked with age [43]. The upregulation of Bcl-2 was shown to be related to the prognosis of prostate cancer advance in hormone-deprivation, augmented tumor stage, and poor outcome [42,44]. It has been suggested that the Bax/Bcl-2 ratio is very important for the androgen regulation of apoptosis, determining cell fate; indeed, an increase in this ratio during apoptosis, induced by androgen withdrawal, has been observed [12,45] (Figure 1). The apoptotic machinery of prostate cells is modulated by TGFβ, which stimulates cell death and involves several transcription factors [46,47,48,49]. In particular, TGFβ1 regulates extracellular matrix production and degradation, cell differentiation, and proliferation [50,51]. This cytokine plays a role in regulating prostate growth [11] and induces apoptosis in prostate epithelial cells [52]; in response to terazosin or castration, an upregulation of TGFβ1 expression determines prostatic epithelial apoptosis [53]. In BPH, TGFβ shows an inhibitory role, as it controls proliferation and stimulates apoptosis in epithelial cells [54]. In prostatic epithelial cells, proliferative activity is under control of different signaling pathways, among which the glycoprotein Dickkopf-related protein 3 (Dkk-3) is included [55]. This glycoprotein is encoded by a gene at the 11p15 locus, usually eliminated in tumors [56], and includes four proteins named Dkk-1, Dkk-2, Dkk-3 and Dkk-4 [57]. Dkk-3 does not modulate Wnt signaling proteins, a family of paracrine signaling growth hormones that plays a crucial role especially in development and carcinogenesis [56,58,59,60]. An in vitro study on the effects of Dkk-3 on prostatic cell growth revealed that cellular action of Dkk-3 is mediated by receptors on the cell surface [59]. However, other studies reported actions against proliferation, or vice versa, upon Dkk-3 overexpression, likely induced by the endoplasmic reticulum stress [59]. Dkk-3 is mainly expressed in the epithelial tissue in physiological conditions, while in the prostatic disease it is elevated in the stroma, predominantly in endothelial cells [59]. Moreover, an overexpression of this glycoprotein in BPH causes an inhibition of the apoptosis machinery through a reduction in Bax expression [59]. Again, a high rate of Dkk-3 in vessels downregulates local expression of angiopoietin-1, that, in turn, leads to vessel destabilization and sprouting of microvessels into the stroma [56]. These data further confirm the pathophysiological implications of stromal remodeling in BPH [47,49]. Indeed, the typical molecular and biological functions of Dkk-3 are still not clear, and also the role of high levels of Dkk-3 in the prostatic stroma disease is not clearly identified. Apoptosis in BPH is also regulated by the inhibitors of apoptosis proteins (IAPs), able to interfere with caspases [24]. To date, eight mammalian IAPs are known: X-chromosome-linked IAP (XIAP) [61], cellular IAP-1 and -2 (cIAP-1 and cIAP-2), neuronal apoptosis inhibitory protein (NAIP), survivin [62], BRUCE, livin- and testis-specific IAP (Ts-IAP). In testis, IAP-like protein 2 (ILP-2), a tissue-specific homologue of XIAP, directly inhibits caspase-9. Increased IAPs expression has been shown in pathological human prostate including BPH, prostatic intraepithelial neoplasia, and cancer [63]. cIAP-1 and cIAP-2 were demonstrated on the basis of their capability to bind tumor necrosis factor-associated factor 2 (TRAF2) and they are mainly involved in the regulation of the extrinsic pathway of the apoptosis through the modulation of caspases activity [64]. Another member of IAPs family, survivin, is highly expressed in embryonic tissues and is also present in prostate cancer [62,65]. In experimental BPH, cIAP-1, cIAP-2, NAIP, and survivin have been detected by molecular analysis [24], so that IAPs might be confirmed as diagnostic markers in different pathologies [66] (Figure 1). 4. Treatments of BPH The clinical approach to the treatment of BPH has changed considerably over the past 20 years, and medical treatment is nowadays preferred [67,68,69,70]; in fact, the number of prostatectomies for BPH-related diseases in the United States has progressively lowered [71]. In a recent study [5], it was shown that the majority of BPH patients (54.8%) are managed with drugs, while only 1.1% undergoes surgical procedures. The reduction of surgical treatment can be also related to better-tolerated and effective medical treatments [72], such as the αl-adrenergic-receptor antagonists (α1-ARAs) and/or 5α-reductase inhibitors (5-ARIs) [70]. Recently, particular attention was focused on the healthcare and management of BPH and relative complications, such as AUR and others [5]. Classical drug targets control the increase in the prostatic size (static component) or in the tone of smooth muscle cells (dynamic component) in BPH [70]. The better treatments in terms of efficacy for BPH are considered those reducing the tone of smooth muscle cells [69]. Finasteride and dutasteride act by inhibiting the proliferative action of androgens; conversely, α1-ARAs, such as tamsulosin and alfuzosin, target the dynamic component of BPH [70]. Both these drug classes significantly interfere with the apoptosis machinery. The main targets of current medical therapies for LUTS/BPH are to (i) ameliorate bothering symptoms; (ii) improve life quality; and (iii) prevent disease progression. Conventional medical treatments of symptomatic BPH include: (i) α1-ARAs; (ii) 5-ARIs; and (iii) the combination of α1-ARAs and 5-ARIs. α-Blockers (αB) or α1-ARAs, 5-ARI, anticholinergics, and their associations are usually employed in the treatment of male LUTS [73,74,75,76]. However, some drugs for the treatment of LUTS/BPH may cause sexual dysfunction, with interclass and intraclass drug effects differences [77,78]. The growing interest in phytotherapy made available new therapeutic alternatives for various medical conditions, including BPH. Along with αB and 5-ARIs, Serenoa repens (SeR) is without doubt the most widely used phytotherapic. Together with Pygeum africanum, SeR is available in many European countries for symptomatic BPH [77]. Phytotherapy for the treatment of LUTS in association with BPH is common also in most of western countries. In Germany and Austria, phytotherapy represents more than 90% of all treatments prescribed for BPH, and its use has increased considerably in the USA [77,79]. Epidemiological studies showed that several patients have chosen a nonsurgical therapy for BPH, such as a phytotherapic approach alone or in association with other drugs [79,80]. Consequently, in the last years, many efforts to assess the clinical evidence on these alternative treatments for BPH have been conducted [81]. Finally, recent evidences pointed out the positive role of NX-1207, a therapeutic protein with selective pro-apoptotic properties, in BPH therapeutic management [82]. 5. α1-Blockers The α1-ARAs, including alfuzosin, doxazosin, tamsulosin, and terazosin, are considered (from the American Urological Association Guidelines in 2010) the most common therapy for BPH-related LUTS [72]; all of these drugs are equally efficacious, even if they present adverse effects [72]. The α1-ARAs’ mechanism of action in BPH is the blockade of α1-adrenergic-receptors (α1-ARs), which are particularly present in the smooth muscle cells of the prostate and of the bladder neck [83]. To date, three α1-AR subtypes, α1A, α1B and α1D, have been identified. The α1A subtype is usually implicated in the regulation of the tone of smooth muscle cells in the prostate and in the bladder neck, while the α1B subtype modulates blood pressure by contracting the smooth muscle cells in the blood vessels [83]. The α1D subtype is probably involved in the contraction of the bladder muscle and in innervations of sacral spinal cord [83]. Acting on these receptors, α1-ARAs relax prostatic smooth muscle cells and improve urinary flow, as well as LUTS and BPH-related bladder outlet obstruction [84]. Furthermore, it was shown that α1-blocker doxazosin triggers prostate cell apoptosis in BPH patients [85]. Doxazosin and terazosin block α1-adrenergic innervations and relax smooth muscle cells in the prostate; however, this action only partially accounts for the long-term clinical effects in the treatment of BPH [86,87]. Experimental and clinical studies were performed to elucidate whether the activation of apoptosis in prostate cells by α1-adrenoceptor antagonists could represent a key molecular mechanism justifying their long-term efficacy in the management of BPH-associated LUTS and in the potential reduction of prostate cancer growth [88]. In this context, it has been suggested that apoptosis represents a good target for the long-term therapeutic impact of doxazosin and terazosin in BPH [89]. Different studies demonstrated that doxazosin could induce apoptosis in benign and malignant cells of prostate through a mechanism mediated by tumor necrosis factor receptors (TNFRs) [12,89]. Interestingly, TNFRs’ self-assembly process should be recognized as one of the potential mechanisms of triggering apoptosis [90]. Moreover, the apoptotic effect of doxazosin and terazosin, elicited without involving cell proliferation in prostate cancer, may have high clinical significance in the management of the disease [86]. This effect is confirmed by the presence of different mechanisms, independent from α1-adrenoceptor; in fact, tamsulosin, a sulfonamide-based α1-antagonist, was not able to induce an apoptotic response [91]. Many randomized clinical trials indicated the efficacy of various α1-ARAs in the treatment of BPH. Furthermore, α1-ARAs are characterized by a rapid onset to action, a high urine flow rate, and a significant improvement in patients’ symptom scores. In addition, α1-ARAs show a good profile of safety, thus representing a valuable choice of first-line treatment in patients with moderate to severe LUTS [92,93,94,95]. Overall, the significant relationship between apoptosis activation and symptom scores of BPH amelioration in patients with prostate cancer suggests that enhanced apoptosis is a possible therapeutic goal, also considering the long-term efficacy of doxazosin in the LUTS treatment [86]. It must be kept in mind that the abovementioned effect is not only typical of doxazosin: in fact, terazosin treatment induced apoptosis in prostate cells of BPH patients, with no effect on the cellular proliferation [85]. In addition, an experimental model of BPH documented the doxazosin capability to cause prostate cell death without affecting their proliferative capacity [96]. In vitro studies demonstrated that the apoptotic action is exclusively achieved by the quinazoline-based α-adrenoceptor antagonists. Again, malignant prostatic epithelial cells, in association with prostatic benign epithelial and smooth muscle cells, activate apoptosis after treatment with quinazolines in a dose-dependent manner [97] (Figure 1). 6. Finasteride Finasteride is a selective 5-ARIs type II isoenzyme that prevents the conversion of testosterone to DHT in the prostate, causing a reduction of the gland size via induction of apoptosis [31]. Inhibitors of 5α-reductase reduce the size of BPH tissues through the activation of apoptosis, but their mode of action is still not clear. These drugs relieve symptoms of bladder outlet obstruction and reduce the risk of AUR [98]. Both α1-blockers and 5-ARIs cause apoptosis in the prostate gland, without affecting cellular proliferation [86,99]. These data correlate well with the concept that inhibition of DHT production in the prostate triggers apoptosis without affecting DHT-stimulated cellular proliferation [100] (Figure 1), and in agreement with the evidence that patients with 5α-reductase deficiencies never develop prostate cancer [53,101]. Five milligrams daily of finasteride for four years produced a significant reduction in serum levels of DHT [102,103]. Interestingly, finasteride therapy prevents AUR and delays the need for invasive therapy in responders. Finasteride shows good efficacy, particularly in men with larger prostates [104], also reducing typical symptoms associated with BPH, like hematuria [105,106,107,108]. An increase in cell death by apoptosis and a reduction of microvessel density in BPH human samples were demonstrated after finasteride treatment [31]. Finally, finasteride administration induced the apoptosis cascade in BPH tissues by activating effector caspases-3 and -6; this effect was transient, because the apoptotic process was no longer observed after about one month of treatment [98] (Figure 1). 7. Phytotherapic Supplements Phytotherapic compounds such as β-sitosterol, Pygeum africanum, Cernilton, and SeR showed good efficacy on the symptoms and the urinary flow measures related to BPH, with mild and infrequent adverse events [79,81,109,110,111,112,113,114]. β-Sitosterol, a phytosterol mainly originating from South African star grass, acts by inhibiting 5α-reductase, the predominant enzyme in the prostatic metabolism of testosterone. It improves urological symptom scores and urodynamic measurements, including maximum flow rate and post-void residual urine volume (PVR); moreover, this phytotherapic compound shows mild adverse effects, without a significant difference in adverse event rates compared with placebo [110,111,114]. An extract of the African evergreen tree Pygeum africanum may disable androgen receptors by blocking their nuclear translocation and inhibiting cellular growth factors, such as fibroblast and epidermal growth factors [80] and TGFβ1 [115]; in addition, it has also an anti-estrogen and anti-inflammatory effect [26]. Pygeum significantly ameliorates BPH symptoms, such as nocturia and PVR, and shows minor adverse events, such as rare gastrointestinal problems [112]. Cernilton, an extract from ryegrass pollen, protects acinar epithelial cells, inhibits stromal proliferation, and acts on smooth muscle tone; furthermore, it enhances apoptosis and shows antiandrogenic effects [116]. It improves BPH subjective symptoms, including nocturia, without significant difference in urodynamic measures when compared with placebo [109,116,117]. SeR, an extract from the berry of the American saw palmetto or dwarf palm plant [118,119,120,121], induces evident LUTS relief. Its proposed mechanisms of action are the following: (i) antiestrogenic and antiandrogenic effects [122] by weakly inhibiting the conversion of T to DHT [123]; (ii) modulation of apoptosis [78,124]; (iii) inhibition of 5-ARIs in the stroma and epithelium of the prostate [125]; and (iv) relaxation of smooth muscle cells of the detrusor through interaction with α1-adrenergic receptors [126]. SeR is prescribed alone or in association with other natural substances, such as selenium (Se) and lycopene (Ly). These compounds were recently investigated in an experimental model of testosterone-dependent BPH [24] and in clinical trials [127]. It was demonstrated that BPH animals had enhanced expression of NAIP and survivin, whereas the association of SeR, Se, and Ly exerted the highest activity in inhibiting IAPs, stimulating apoptosis, and reducing prostate enlargement [24]. Also growth factors, such as vascular endothelial growth factor (VEGF) and epidermal growth factor (EGF), were inhibited by treatment with Se-Ly-SeR combination [113,120] (Figure 1). 8. NX-1207 NX-1207 is a novel cysteine-containing linear protein [128] and is also the first drug in phase III trials suitable for BPH patients [129]. It is administered to BPH patients through a transrectal intraprostatic inoculation under ultrasound guidance [130]. Different studies have shown that NX-1207 activates apoptosis [82,130,131,132,133,134] in BPH tissue, causing a reduction in the prostate volume with symptomatic improvement [130]. In in vitro studies, NX-1207 positively controlled apoptotic markers such as caspases and annexin V [82]. Clinical evidence revealed that NX-1207 caused a substantial symptomatic improvement and, if compared with AB and 5-ARIs, did not show any kind of compliance issues even in the elderly people undergoing multiple therapies. Finally, NX-1207 has not revealed any kind of sexual side effects (impotence, loss of the libido, etc.) [132] (Figure 1). 9. PRX-302 PRX-302, known also as topsalysin, is a modified recombinant protein able to be selectively activated by prostate specific antigen (PSA), causing localized cell death and tissue disruption without any damage of the neighboring tissues [128]. PRX-302 binds to glycosylphosphatidyl inositol (GPI) receptors placed on the cell surface of prostate cells. Once activated by PSA, PRX-302 combines with other similar molecules, forming a stable transmembrane pore that activates cell death [135]. PRX-302 is currently being tested in the treatment of LUTS in BPH. Clinical evidence revealed that the intraprostatic injection of PRX-302 significantly reduced the International Prostate Symptom Score (IPSS), with mild to moderate transient adverse events [136]. The treatment did not show any negative effects on the erectile function [129] (Figure 1). 10. Conclusions LUTS are commonly observed in patients with BPH, which is considered an increasing problem for public health. Even if some successes in treating BPH patients with α-adrenoblockers and 5-ARIs have been achieved, the combined use of the drugs is appropriate because the differences in mechanisms of action permit both to act on the smooth muscle tissue, producing its relaxation, and to reduce the size of prostate by the induction of apoptosis, which ultimately induces the maximum therapeutic effect [137]. An uncontrolled growth of both the glandular and the connective tissue cells in the prostatic transitional zone is involved in the development of BPH. While in the pathogenesis of prostatic tumor an imbalance between cellular proliferation and cell death plays a prominent role, no concordant data are currently available about its role in the genesis of BPH. However, hormonal imbalance plays an important role in BPH; in fact, the normal development of the prostate is obtained through a balance between cell growth and apoptosis, which are regulated by normal levels of androgens and estrogens. Therefore, the development of new therapeutic approaches for BPH requires the knowledge of the molecular pathways involved both in the proliferation and in the programmed death of prostate cells. Author Contributions Letteria Minutoli, Domenico Puzzolo wrote the manuscript and critically revised the manuscript. Mariagrazia Rinaldi, Antonina Pisani wrote the manuscript and collaborated in figure management. Giovanni Crea, Cesare Lorenzini, Andrea Valenti confirmed the content. Antonio Micali, Herbert Marini, Elena B. Adamo collaborated in technical editing of the manuscript. Letteria Minutoli, Natasha Irrera, Domenico Puzzolo, Francesco Squadrito, Domenica Altavilla performed manuscript editing. Conflict of Interest The authors declare no conflict of interest. Abbreviations BPH, Benign prostatic hyperplasia; AUR, Acute urinary retention; LUTS, Lower urinary tract symptoms; SD, Sexual dysfunctions; DHT, Dihydrotestosterone; AR, Androgen receptor; EGF, Epidermal growth factor; bFGF, Basic fibroblast growth factor; IGF, Insulin-like growth factor; TGFβ1, Transforming growth factor β1; Bcl-2, B cell lymphoma gene-2; Dkk-3, Dickkopf-related protein 3; PI3K, Phosphoinositide 3-kinase; IAPs, Inhibitors of apoptosis proteins; XIAP, X-chromosome-linked IAP; cIAP-1 and cIAP-2, Cellular IAP-1 and -2; NAIP, Neuronal apoptosis inhibitory protein; Ts-IAP, Testis-specific IAP; ILP-2, IAP-like protein 2; TRAF2, Tumor necrosis factor-associated factor 2; α1-ARAs, αl-adrenergic-receptor antagonists; 5-ARIs, 5α-reductase inhibitors; TNFRs, Tumor necrosis factor receptors; Se, Selenium; Ly, Lycopene; SeR, Serenoa repens; COX, Cyclooxygenase; LOX, 5-lipoxygenase. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081312ijms-17-01312ReviewThe Potential Role of Kallistatin in the Development of Abdominal Aortic Aneurysm Li Jiaze 1Krishna Smriti Murali 1Golledge Jonathan 12*Mousa Shaker A. Academic Editor1 Queensland Research Centre for Peripheral Vascular Disease, College of Medicine and Dentistry, James Cook University, 4811 Townsville, Australia; jiaze.li@jcu.edu.au (J.L.); smriti.krishna@jcu.edu.au (S.M.K.)2 Department of Vascular and Endovascular Surgery, The Townsville Hospital, 4811 Townsville, Australia* Correspondence: jonathan.golledge@jcu.edu.au; Tel.: +61-7-4433-141711 8 2016 8 2016 17 8 131218 7 2016 05 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Abdominal aortic aneurysm (AAA) is a vascular condition that causes permanent dilation of the abdominal aorta, which can lead to death due to aortic rupture. The only treatment for AAA is surgical repair, and there is no current drug treatment for AAA. Aortic inflammation, vascular smooth muscle cell apoptosis, angiogenesis, oxidative stress and vascular remodeling are implicated in AAA pathogenesis. Kallistatin is a serine proteinase inhibitor, which has been shown to have a variety of functions, potentially relevant in AAA pathogenesis. Kallistatin has been reported to have inhibitory effects on tumor necrosis factor alpha (TNF-α) signaling induced oxidative stress and apoptosis. Kallistatin also inhibits vascular endothelial growth factor (VEGF) and Wnt canonical signaling, which promote inflammation, angiogenesis, and vascular remodeling in various pre-clinical experimental models. This review explores the potential protective role of kallistatin in AAA pathogenesis. kallistatinserine proteinase inhibitorsabdominal aortic aneurysmvascular remodellingoxidative stress ==== Body 1. Introduction Abdominal aortic aneurysm (AAA) is usually defined as a permanent dilation of the abdominal aortic wall beyond a maximum diameter of ≥30 mm [1,2]. Progressive AAA dilatation can lead to rupture of the aorta, which causes bleeding and commonly death. AAAs most commonly affect men aged over 65 years [3], and clinical practice lacks effective treatment other than surgical approaches to repair AAAs [4]. Patients who have small AAAs (<55 mm), which are at low risk of rupture, are generally monitored through imaging surveillance. Patients with large (≥55 mm), rapidly growing (>10 mm/year) or symptomatic AAAs usually undergo repair by open surgical techniques or endovascular stents. However, postoperative morbidity and mortality are still common [2,5]. Studies of pre-clinical AAA animal models and biopsies of large human AAAs have implicated a range of mechanisms to be involved in the pathogenesis of AAA including degradation of the aortic extracellular matrix by a range of proteolytic enzymes, such as matrix metalloproteinases (MMPs); dysfunction of aortic vascular smooth muscle cells (VSMC) associated with their loss from the aortic media through apoptosis [6,7,8,9]; and inflammatory cells infiltration into the aortic wall which once activated produce pro-inflammatory cytokines, chemokines and proteolytic enzymes, which promote cell migration and vessel remodeling [2,10,11,12,13,14]. Other mechanisms implicated in AAA pathogenesis include angiogenesis [15] and oxidative stress [16]. AAA is often accompanied by atherosclerosis. This is in contrast to the aneurysms observed in genetic disorders, such as Marfan and Loeys-Dietz Syndromes. Kallistatin is a member of the serine proteinase inhibitors (SERPIN) family. In human, it is encoded by the SERPINA4 gene. It was first identified as a kallikrein binding protein that regulates the kinin-kallikrein pathway [17,18]. Kallikrein produces kinin from kininogens by proteolysis. Kallistatin binds to kallikrein to inhibit this process. Kallistatin has also been shown to have direct vascular effects, such as promoting vasodilation within rat models when human kallistatin is administered through gene overexpression [19]. Kallistatin is expressed in both endothelial cells (ECs) and VSMCs [20]. Kallistatin is also found in plasma, which is believed to reflect its production in the liver [17]. Decreased kallistatin levels have been previously associated with various disease conditions [21,22]. For example, Ma et al. reported decreased kallistatin level in the vitreous fluids in patients with diabetic retinopathy [21]. Zhu et al. reported decreased plasma kallistatin levels in apparently healthy African American adolescents with increased adiposity and cardio-metabolic risk [22]. Recent work has revealed potential protective functions of kallistatin in many pathophysiological processes implicated in AAA, such as inflammation [23,24,25,26], oxidative stress [25,27], angiogenesis [26,28,29], and hypertension [19,30,31]. The heparin binding domain of kallistatin is considered important for these functions [32,33,34]. Evidence from pre-clinical studies suggests that reducing inflammation [35], decreasing oxidative stress [36,37] and inhibiting angiogenesis [38] may limit AAA progression. Hence, in clinical management of AAAs, treatments targeting these mechanisms are considered to have potential benefits in managing AAAs [39]. In this review, we sought to highlight the potential regulatory roles of kallistatin in mechanisms relevant in AAA pathogenesis and also the downstream signaling pathways through which kallistatin exerts its actions. 2. Potential Roles of Kallistatin in AAA Pathogenesis 2.1. Kallistatin Attenuates Oxidative Stress Tumor necrosis factor alpha (TNF-α) is a pro-inflammatory cytokine that has been consistently reported to be upregulated in AAAs [40]. TNF-α signaling initiates through binding of its membrane bound receptors TNFR-1 and 2. TNFR-2 is mainly expressed in immune cells and its functions remain unclear, while TNFR-1 initiates three major signaling pathways in cells, such as EC, as shown in Figure 1 [41,42]. Kallistatin has been shown to inhibit TNF-α induced oxidative stress and subsequent inflammation and apoptosis in experimental studies (Table 1) [25,27,43,44,45]. The inhibitory effects of kallistatin on TNF-α was discovered to be through competitive binding of TNF-α to the TNFRs through its heparin binding domain, thus inhibiting its signaling, which resulted in attenuated inflammation, oxidative stress and apoptosis of ECs [24,26,27]. Oxidative stress is caused by excessive production of reactive oxygen species (ROS). The ROS signaling pathway is also known as redox signaling [46]. High level of ROS have been shown to promote apoptosis of ECs, while continuous low level of ROS promote EC proliferation and migration that promote angiogenesis [47,48]. Nicotinamide adenine dinucleotide phosphate (NADPH) oxidase is the main source of ROS in ECs [46]. Interestingly, redox signaling and vascular endothelial growth factor (VEGF) signaling appear to be in feedback interaction in ECs [46,49]. Numerous stimuli are able to activate NADPH oxidase in ECs including VEGF, angiopoietin-1, angiotensin II, cytokines, shear stress and hypoxia [47,50,51]. There is a close relationship between oxidative stress and kallistatin activity. Oxidative stress has been shown to suppress circulating levels of kallistatin and EC specific expression of kallistatin [52,53], while kallistatin has been shown to suppress ROS production in cardiac and renal cells [45,54]. Many studies have suggested that kallistatin has anti-oxidative stress functions through inhibiting NADPH oxidase activities in various cell types, such as cardiac, epithelial progenitor cells (epi-PCs) and endothelial progenitor cells (endo-PCs), as well as in experimental models of myocardial infarction, hypertension and diabetes in rodents [44,54,55,56]. Furthermore, administration of anti-kallistatin antibody to rats has been reported to increase superoxide formation within the aorta and increased NADPH activity in the kidney and heart which eventually led to organ hypertrophy, inflammation and fibrosis. This was evidenced by a concomitant increased expression of pro-inflammatory genes such as TNF-α [25,54]. A study by Shen et al. reported that kallistatin attenuated aortic superoxide formation in salt induced hypertension in rats as well as inhibited TNF-α induced NADPH activity, oxidative stress and apoptosis through the PI3K-Akt-eNOS pathway in ECs [27]. ECs produce nitric oxide (NO) through endothelial nitric oxide synthase (eNOS), which neutralizes ROS. However, in oxidative stress conditions, the formation of peroxynitrite from superoxide and NO causes eNOS uncoupling and production of ROS [57,58]. ROS is known to induce apoptosis through inducing c-Jun NH2-terminal kinase (JNK) mediated Bim (a Bcl2 binding protein) nuclear translocation [53]. In an alternative pathway, kallistatin induces NO production through kruppel like factor–4 (KLF4) mediated eNOS activation and expression [43]. Thus, the switch of eNOS to produce NO by kallistatin stimulation inhibits ROS induced JNK-Bim mediated apoptosis [27]. Since ROS and cell apoptosis are implicated in AAA, stimulating kallistatin to upregulate NO production and limit cell apoptosis could be a potential target for therapy for AAA (Figure 1) [19,24,26,27,42,43,44,54,56,59,60,61,62,63,64]. 2.2. Kallistatin Attenuates Angiogenesis and Inflammation A previous study has shown that AAAs is associated with a marked angiogenic response directly related to the extent of inflammation within the aortic wall [65]. In this process, ECs proliferate and produce inflammatory cytokines, chemokines and MMPs. This initiates an influx of inflammatory cells which produce more cytokines, chemokines and MMPs that foster further endothelial activation, proliferation and inflammatory cell recruitment [66,67,68,69,70,71]. Upregulation of pro-angiogenic cytokines and medial neovascularization have been reported at the site of AAA rupture in human samples suggesting that angiogenesis plays an important role in AAA rupture [72]. VEGF is the most well-known and potent pro-angiogenic factor, especially the VEGF-A isoform [73,74,75,76,77]. There are three VEGF receptors—1, 2 and 3, identified so far. Among them, the type 2 receptor, VEGFR-2, which is also known as kinase insert domain receptor (KDR), a type III receptor tyrosine kinase, is the one that mediates downstream signaling of VEGF-A to induce EC activation and proliferation and promote angiogenesis (Figure 2) [29,32,44,46,48,54,56,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99]. Kallistatin had been shown to inhibit VEGF signaling within in vitro studies. Huang and colleagues showed that recombinant human kallistatin inhibited VEGF165 mediated tyrosine phosphorylation of VEGFR-2 in human umbilical vein endothelial cells (HUVECs). Furthermore, it was also shown that the kallistatin mediated inhibition of VEGFR-2 was also accompanied by reduced downstream Akt and ERK phosphorylation [29]. The study reported by Miao and colleagues provided direct evidence of the ability of kallistatin to inhibit VEGF signaling by competitive binding to a VEGF receptor in human dermal microvascular endothelial cells (HDMECs). Using a site directed mutant of human kallistatin (K312A/K313A), they also confirmed that the heparin binding domain of kallistatin is important to this function [34]. The Wingless (Wnt) signaling pathway is a tightly regulated, highly complex system which mediates a diverse range of cellular activities including proliferation, apoptosis, migration and differentiation, all of which are relevant in AAA pathogenesis. There are 19 potential Wnt ligands that are able to bind to 10 transmembrane G-protein-coupled receptors of the Frizzled (Fzd) family [100]. The signaling pathways activated as a consequence of Wnt/Fzd binding are categorized into canonical and non-canonical pathways (Figure 3) [101,102,103,104,105,106,107,108]. In vitro experiments suggest that kallistatin inhibits the Wnt pathway at the extracellular or the receptor level. Kallistatin binds to the extracellular domain of low density lipoprotein receptor-related protein 6 (LRP6) which blocks Wnt canonical pathway signaling through β-catenin [105,109,110]. This has potential anti-angiogenic and anti-inflammatory effects as shown by Liu et al. in a diabetic mouse model of retinopathy [105]. It was shown that overexpression of human kallistatin in the retina of Akita mice, significantly decreased the expression of pro-angiogenic factors such as VEGF, intercellular adhesion molecule (ICAM)-1 as well as the number of CD11+ b leukocytes suggesting that overexpression of kallistatin suppressed Wnt signaling induced by ischemia or diabetes [105]. They also demonstrated that human kallistatin reduced VEGF and TNF-α levels which were increased in retinal cells treated with high glucose in culture [105]. Similar phenomenon of attenuated VEGF and TNF-α production by kallistatin were also observed in breast cancer and wound healing models [110,111]. The Wnt non-canonical pathway is mostly mediated by Wnt4, 5a and 11 resulting in increased Ca2+ which activates PKC and CAMKII which often activate nuclear factor of activated T-cells that promotes VEGF induced angiogenesis. Another signaling pathway activated by the Wnt non-canonical pathway is JNK which leads to gene transcription by activating AP-1. The Wnt non-canonical pathway is also able to activate the PCP pathway which leads to cell polarization and cytoskeletal rearrangement in ECs. A number of preclinical studies have suggested that kallistatin had anti-angiogenic functions (Table 2) [26,28,105,112,113]. In animal models of diabetes or oxygen induced retinopathy and neovascularization, administering human kallistatin to retinal cells or overexpressing human kallistatin in transgenic mice ameliorated neovascularization through inhibiting VEGF activity, endo-PC release from bone marrow and reducing activation of the Wnt canonical pathway [105,112,113]. The Wnt canonical pathway has been shown to stimulate EC proliferation and survival through VEGF-A upregulation [104,114]. In addition, Wang and colleagues reported that kallistatin inhibited proliferation of HDMECs and reduced vessel density in the ankles of arthritic rats through reducing TNF-α [26]. TNF-α was previously shown to induce the gene expressions of VEGF-A, VEGFR-2 and its co-receptor neuropilin-1 [60]. Further to this, kallistatin was shown to inhibit the Wnt canonical pathway through binding to LRP6 and inhibiting TNF-α in cancer cells, both of which also resulted in reduced VEGF expression [28,110]. Thus, it is evident that kallistatin has anti-angiogenic effects. 2.3. Kallistatin Attenuates Defective Vascular Remodeling Vascular remodeling is a dynamic process that changes the structure of blood vessels to maintain a healthy state however in excess it contributes to AAA formation. Over activation of several cellular activities including apoptosis, proliferation, migration and degradation of the extracellular matrix contribute to excess vascular remodeling [115]. TNF-α is involved in all these four processes through inducing production of VEGF, interleukins, cellular adhesion molecules and MMPs. The ability of kallistatin to inhibit TNF-α and thereby limit angiogenesis, apoptosis, oxidative stress, inflammation, and cell proliferation and migration may ameliorate defective vascular remodeling and may play a protective role in vascular disorders, such as AAA. Kallistatin was originally shown to inhibit kallikrein and thereby limit kinin formation [17,18]. Kinins have been implicated in AAA formation within rodent models. Kinin B2 receptor blockade has been reported to protect against AAA development, growth and rupture in a mouse model, as well as reducing MMP secretion from human AAA explant in vitro [116]. Kinin B2 receptor blockade also limited neutrophil activation and development of an inflammatory phenotype in VSMCs in vitro. The inhibitory effect of kallistatin on tissue kallikrein would be expected to limit kinin generation and thereby antagonize the pro-aneurysmal effects of the kallikrein-kinin pathway. However, there is evidence suggesting that kallistatin also increases MMP-2 activity in human endo-PCs through enhancing NO and VEGF levels by activating PI3K-Akt signaling [44]. Although this may facilitate vascular repair and regeneration through promoting migration of endo-PCs. MMP-2 activity also contributes to medial matrix degradation in AAAs and possibly even rupture [9]. Thus, the protective role of kallistatin in maintaining positive vascular remodeling remains to be investigated further in pre-clinical AAA models. 3. Conclusions In summary, AAA is a vascular disorder that is characterized by inflammation, apoptosis and extracellular matrix degradation. This review illustrates the potential of kallistatin in suppressing AAA development through attenuating a wide range of pathological mechanisms (Figure 4) including VEGF induced angiogenesis and inflammation, oxidative stress induced angiogenesis and apoptosis, TNF-α induced inflammation, apoptosis and MMPs production, as well as Wnt canonical signaling induced angiogenesis and inflammation. Direct studies examining the role of kallistatin in AAA are warranted and also should assess the potential beneficial effect of kallistatin. Since kallistatin has a range of actions, some of which may be detrimental, as well as beneficial, future studies should consider both systemic and local upregulation of kallistatin. Achieving elevated kallistatin levels through recombinant protein delivery and transgenic overexpressing methods has been reported to reduce blood pressure in animal models [19,31,117], which could be beneficial in treating AAA patients. However, this warrants further preclinical studies using established AAA models. Acknowledgments This work is funded in part by grants from the National Health and Medical Research Council (1079369, 1098717), the Queensland Government, the Townsville Hospital Private Practice Trust, the Research Infrastructure Block Grant, and the Medicine Incentive Grant, College of Medicine, James Cook University (JCU-QLD-547271). Jonathan Golledge holds a Practitioner Fellowship from the National Health and Medical Research Council, Australia (1019921) and a Senior Clinical Research Fellowship from the Queensland Government. The funding bodies played no role in generation of the data presented in this publication. Author Contributions Jiaze Li designed the review and did the literature search, was responsible for figures and tables preparation and writing the manuscript; Smriti Murali Krishna contributed to manuscript preparation and revision; Jonathan Golledge contributed to manuscript preparation and critically revised the manuscript. Conflicts of Interest The authors confirm that there are no known conflicts of interest associated with this publication and there has been financial support for this work as noted above from external grants. Abbreviation AAA abdominal aortic aneurysm eNOS endothelial nitric oxide synthase MMPs matrix metalloproteinases NO nitric oxide ROS reactive oxygen species TNF-α tumor necrosis factor alpha VEGF vascular endothelial growth factor Figure 1 Kallistatin inhibits oxidative stress, inflammation and apoptosis through inhibiting TNF-α signaling and promotes NO production through eNOS stimulation. Kallistatin blocks TNF-α signaling through competitive binding to TNFR. This inhibits downstream signaling pathways that are activated by TNF-α, such as IκB/NF-κB and p38 MAPK pathway, which activate many pro-inflammatory and pro-angiogenic markers, such as TNF-α, VEGF, interleukins, MCP-1, MMPs and adhesion molecules. Kallistatin also inhibits TNF-α induces oxidative stress and the caspase cascade to induce apoptosis through TNFR-1. Alternatively, kallistatin is able to directly inhibit NADPH oxidase activity to attenuate ROS production, as well as activating eNOS through KLF4 to produce NO, which neutralizes ROS [43]. Abbreviations: endo-PC-endothelial progenitor cell, HUVEC-human umbilical vein endothelial cell, TNF-α-tumor necrosis factor alpha. Abbreviations: Akt/PKB—protein kinase B; ATF1—activating transcription factor 1; Bim—Bcl2 binding protein; eNOS—endothelial nitric oxide synthase; IκB—inhibitor of nuclear factor κ B; IKK—IκB kinase; JNK—c-Jun N-terminal kinase; KLF4—kruppel like factor 4; MAPK—mitogen activated protein kinase; MKK—MAPK kinase; NADPH—nicotinamide adenine dinucleotide phosphate; NF-κB—nuclear factor κB; NO—nitric oxide; P—phosphorylation; PI3K—phosphoinositide 3 kinase; ROS—reactive oxygen species; TNF-α—tumor necrosis factor alpha; TNFR—TNF-α receptor. The blue arrow lines indicate promotional activity; the red stop lines indicate inhibiting activity; the red dashed cross indicates degradation. Figure 2 Kallistatin inhibits angiogenesis and inflammation through blocking VEGF signaling and NADPH oxidase activity. Kallistatin inhibits VEGF signaling through VEGFR-2 by its heparin-binding domain. VEGF-VEGFR signaling through PI3K-Akt pathway; the p38 MAPK pathway; and the PLC pathway leads to ROS/NO production, apoptosis, gene expression, cell migration, cell proliferation and inflammation. All of which are involved in angiogenesis. Kallistatin also directly inhibits NADPH oxidase activity and attenuates ROS production. NADPH oxidase is a complex consisting of several components. NADPH activity and VEGF-A/VEGFR-2 signaling have close interaction that is able to induce or activate many proangiogenic factors, such as MCP-1, VEGF, NF-κB, IL-8, VCAM-1, VE-cadherin and HIF1α in endothelial cells. Abbreviations: Akt—also known as protein kinase B—PKB; DAG—diacylglycerol; ERK—extracellular signal-regulated kinase; eNOS—endothelial nitric oxide synthase; HIF1α—hypoxia induced factor 1 alpha; IκB—inhibitor of nuclear factor κB; IL-8—interleukin-8; MAPK/MEK—mitogen activated protein kinase; MCP-1—monocyte chemoattractant protein-1; NADPH—nicotinamide adenine dinucleotide phosphate; NF-κB—nuclear factor κ B; NO—nitric oxide; PDK1/2—3-phosphoinositide dependent protein kinase 1 and 2; PI3K—phosphatidylinositol-3 kinase; PIP2—phosphatidylinositol 4,5-bisphosphate; PKC—protein kinase C; PLC—phospholipase C; PTP—protein tyrosine phosphatase; Rac1—small GTPase; ROS—reactive oxygen species; SOD—manganese superoxide dismutase; Src—non-receptor tyrosine kinase; VCAM-1—vascular cell adhesion molecule-1; VE—vascular endothelial; VEGF-A—vascular endothelial growth factor-A; VEGFR-2—VEGF receptor-2. The blue arrow lines indicate promotional activity; the red stop lines indicate inhibiting activity. Figure 3 Kallistatin inhibits Wnt canonical pathway induced angiogenesis and inflammation. In the Wnt canonical pathways, mostly mediated by Wnt1, 3, 3a, 7a and 7b, Wnt/Fzd binding phosphorylates the associated co-receptor LRP5/6. This recruits Dsh which leads to binding of Axin at the membrane. Axin forms a degradation complex with APC, CK1α and GSK3β for β-catenin degradation. The recruitment binding of Axin to the membrane caused by Wnt/Fzd leads to an inactive degradation complex and the accumulation of β-catenin. The accumulated β-catenin mediates Wnt signaling by activating transcription factors, such as TCF, which induces transcription of genes, such as VEGF, ICAM-1 and TNF-α. Kallistatin binds to LRP6 and prevents LRP6 from phosphorylation which results in β-catenin degradation. Without β-catenin, Wnt canonical signaling is blocked. Abbreviation: AP-1—activator protein-1; APC—adenomatous polyposis coli; CAMKII—calmodulin dependent protein kinase; CK1α—casein kinase 1α; Dsh—the protein disheveled; GSK3β—glycogen synthase kinase-3β; ICAM-1—intracellulcar adhesion molecule-1; LRP5/6—low density lipoprotein receptor-related protein 5 or 6; PCP—planar cell polarity; OPG—osteoprotegerin; OPN—osteopontin; TCF—T-cell factor; TNF-α—tumor necrosis factor alpha; VEGF—vascular endothelial growth factor; the red arrow indicates increase in level; the tubular structure on the left represents cytoskeleton. The blue arrow lines indicate promotional activity; the red stop lines indicate inhibiting activity; the red dashed cross indicates degradation. Figure 4 Illustration of postulated mechanisms of kallistatin attenuating AAA. Kallistatin has the potential of inhibiting various mechanisms that contribute to AAA formation. The pathological processes that are attenuated by kallistatin include oxidative stress, ROS signaling, apoptosis, angiogenesis, inflammation and MMP activity. The proposed AAA protective role of kallistatin are through its ability to inhibit various pathways, such as TNF-α, VEGF and Wnt canonical signaling pathways, as well as kallistatin’s ability to increase NO production through eNOS. The black arrow lines indicate promotional activity; the red stop lines indicate inhibiting activity. ijms-17-01312-t001_Table 1Table 1 Studies showing the inhibitory effects of kallistatin mediated through blocking TNF-α signaling on pathologies relevant to abdominal aortic aneurysm such as oxidative stress, inflammation and apoptosis. Inhibited Pathology In Vitro Model In Vivo Model References Oxidative stress/inflammation Proximal tubular cells, mesangial cells Dahl-salt sensitive rats [45] HUVEC – [43] – Hypertensive rats [25] Oxidative stress/apoptosis Rat and human endo-PC Deoxycorticosterone acetate salt-hypertensive rats [44] HUVEC Rats [27] Abbreviations: endo-PC—endothelial progenitor cells; HUVEC—human umbilical vein endothelial cells; TNF-α—tumor necrosis factor alpha. ijms-17-01312-t002_Table 2Table 2 Studies assessing the inhibitory effects of Kallistatin mediated through blocking VEGF, TNF-α and Wnt canonical signaling pathways on pathologies relevant to abdominal aortic aneurysm such as angiogenesis and inflammation. Inhibited Pathology Pathways In Vitro Model In Vivo Model References Retinal neovascularisation/angiogenesis VEGF Retinal capillary ECs Brown Norway rats [112] Angiogenesis in cancer TNF-α/VEGF MCF-7 cells, HUVEC Fertilized chicken egg [28] Angiogenesis/inflammation arthritis TNF-α HDMEC Rats [26] Angiogenesis/Inflammation Diabetic or OIR Wnt canonical pathway Retinal cells Kallistatin transgenic mice with OIR or type I diabetes [105] Oxygen induced retinopathy/angiogenesis Wnt canonical pathway – Kallistatin transgenic mice, bet-gal mice [113] Abbreviations: EC—endothelial cells; HDMEC—human dermal microvascular endothelial cells; HUVEC—human umbilical vein endothelial cells; MCF—Michigan Cancer Foundation (MCF-7 is a breast cancer cell line); OIR—oxygen induced retinopathy; TNF-α—tumor necrosis factor alpha; VEGF—vascular endothelial growth factor. ==== Refs References 1. Sakalihasan N. Limet R. Defawe O.D. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081313ijms-17-01313ReviewDeep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications Pastur-Romay Lucas Antón 1Cedrón Francisco 1Pazos Alejandro 12Porto-Pazos Ana Belén 12*González-Díaz Humberto Academic EditorTodeschini Roberto Academic EditorPazos Sierra Alejandro Academic EditorArrasate Gil Sonia Academic Editor1 Department of Information and Communications Technologies, University of A Coruña, A Coruña 15071, Spain; pastur90@gmail.com (L.A.P-R.); flanciskinho@gmail.com (F.C.); apazos@udc.es (A.P.)2 Instituto de Investigación Biomédica de A Coruña (INIBIC), Complexo Hospitalario Universitario de A Coruña (CHUAC), A Coruña 15006, Spain* Correspondence: ana.portop@udc.es; Tel.: +34-881-011-38011 8 2016 8 2016 17 8 131316 5 2016 25 7 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure–Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods. artificial neural networksartificial neuron–astrocyte networkstripartite synapsesdeep learningneuromorphic chipsbig datadrug designQuantitative Structure–Activity Relationshipgenomic medicineprotein structure prediction ==== Body 1. Introduction Machine Learning (ML) is a subfield of Artificial Intelligence which attempts to endow computers with the capacity of learning from data, so that explicit programming is not necessary to perform a task. ML algorithms allow computers to extract information and infer patterns from the record data so computers can learn from previous examples to make good predictions about new ones. ML algorithms have been successfully applied to a variety of computational tasks in many fields. Pharmacology and bioinformatics are “hot topics” for these techniques because of the complexity of the tasks. For example, in bioinformatics, ML methods are applied to predict protein structure and genomics (and other omics) data mining. In the case of pharmacology, these methods are used to discover, design and prioritize bioactive compounds, which can be candidates for new drugs [1]. Moreover, ML can be helpful to analyze clinical studies of these compounds, optimize drug forms, and evaluate drug quality [2,3]. The development of a drug has different phases; in the first step a set of molecular representation, or descriptors, are selected. These descriptors represent the relevant properties of the molecules of interest. The encoded molecules are compared to one another using a metric or scoring scheme. Next, the data set is usually divided into three parts: training set, validation set and test set. The final step involves the use of ML methods to extract features of interest that can help to differentiate active compounds from inactive ones. Quantitative Structure-Activity Relationship (QSAR) is used to find relationships between the structure of a compound and its activity, both biological and physicochemical [4]. There are similar mathematical models that look for other relationships, such as Quantitative Structure-Property Relationship (QSPR), Quantitative Structure–Toxicity Relationship (QSTR) or Quantitative Structure–Pharmacokinetic Relationship (QSPkR) [5]. It is of major importance to select the right descriptors to extract valuable features from the input data. The accuracy of these data, and the statistical tools used, are also relevant in the development process [4]. Over the past decades, the ML techniques used in pharmaceutical and bioinformatics applications were “shallow”, with only a few layers of feature transformations. Some of the most used algorithms are: principle component analysis, k-means clustering, decision trees, Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) [1,4]. The ANNs have been applied to pharmacology and bioinformatics for more than two decades. Historically, the first report on the application of ANNs in these fields was published by Qian and Sejnowski in 1988 [6]. They used ANNs for the prediction of the protein secondary structure. In 1990, Aoyama et al. presented the first report on the application of ANNs to QSAR [7], whereas in 1993, Wikel and Dow disclosed an application of the ANNs in the description of the pruning step of QSAR [8]. An example of an effective application of ANNs was with a data set of HIV-1 reverse transcriptase inhibitors, in the descriptor selection process [9]. Kovalishyn et al. developed a pruning method based on an ANN trained with the Cascade-Correlation learning method in 1998 [10]. These are some examples of early applications of ANNs, but a huge advance had been made in these ML techniques. To get a historical perspective, and to understand in detail the applications of ANNs, and other ML algorithms, to pharmacology and bioinformatics, the reader is referred to these reviews [1,2,3,4,5,11,12,13,14,15,16,17,18]. Although ANNs were soon identified as useful tools for pharmacology and bioinformatics, SVMs and random forest made great progress, dominating the field until recently. The reasons of the limited application of ANNs were: “scarcity” of data, difficulty to understand the features extracted, and the computational cost entailed by the network training. Over the past decade, DNNs have become the state-of-the-art algorithms of ML in speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning and the exponential increase of the chip processing capabilities, especially GPGPUs. The Big Data term can be understood by the exponential grow of data, since 90% of the data in the world today has been created in the last two years alone. This data explosion is transforming the way research is conducted, making it necessary to acquire skills in the use of Big Data to solve complex problems related to scientific discovery, biomedical research, education, health, national security, among others. In genomic medicine, this can be illustrated by the fact that the first sequenced human genome cost nearly $3 billion, today it can be done for less than $1000. In cancer research, data produced by researchers can be analyzed to support this research. Multiple protein sequences can be analyzed to determine the evolutionary links and predict molecular structures. In Medicine and Bioinformatics, there are numerous opportunities to make the most of the huge amount of data available. Some of the challenges include developing safer drugs, reducing the costs of clinical trials, as well as exploring new alternatives, such novel antibiotics, to fight against resistant microorganisms; and finally, extracting value information from the vast amount of data generated by the public health. In order to make the most of the huge amount of information available, different data analysis software frameworks, such as Hadoop, have been created [19]. These frameworks allow the use of simple programming models to process large data sets from thousands of computers. Figure 1 shows a general workflow for Big Data. DL is a new area of ML research, which is inspired by the brain and data abstraction created by multiple stages of processing. The DL algorithms allow high-level abstraction from the data, and this is helpful for automatic features extraction and for pattern analysis/classification. A key aspect of DL was the development of unsupervised training methods to make the best use of the huge amount of unlabeled data available [11]. Deep Feedforward Neural Networks (DFNN), Deep Belief Networks (DBN), Deep AutoEncoder Networks, Deep Boltzmann Machines (DBM), Deep Convolutional Neural Networks (DCNN) and Deep Recurrent Neural Networks (DRNN) are examples of artificial neural networks with deep learning. They have been applied to fields such as computer vision, automatic speech recognition or natural language processing, where they have been shown to produce state-of-the-art results on multiples tasks, (see Table 1). The idea of building DNNs is not new but there was a historical problem, called “vanishing gradient problem” [20]. It is difficult to train these types of large networks with several layers when the backpropagation algorithm is used to optimize the weights, because the gradients which propagate backwards rapidly diminish in magnitude as the depth of the network increases, thus the weights in the early layers changes very slowly [21]. DNNs have become the leading ML technology for a range of applications since Geoffrey Hinton examined the issues around training large networks [22], and came up with a new approach that had consequences for the cost of training of these networks [23,24]. Over the past decade, a variety of algorithms and techniques have been developed to design and train different architectures of DNN [25,26,27,28,29,30,31]. Finally, GPUs were created to process graphics, especially for gaming and design. Some researchers programmed GPUs using API, but this was a difficult task [33]. In 2007, NVIDIA published “Compute Unified Device Architecture” (CUDA) [34], a programming language based on C to optimize GPGPU application. CUDA allows researchers to make the most of the computing capabilities of GPUs for parallel programming. Nowadays, almost every supercomputer in the TOP500 combines CPUs and GPUs [35]. GPUs are beneficial for DL because the training of DNN is very intensive, so this training can be parallelize with GPUs and a performance improvement greater than 10× can be obtained. However, the ongoing work on design and construction of neuromorphic chips should be pointed out, as they represent a more efficient way to implement DNNs [36]. The neuromorphic chips attempt to mimic the neuronal architectures present in the brain in order to reduce several orders of magnitude in terms of energy consumption and to improve the performance of the information processing. However, to run DNNs in a neuromorphic chip, they should be mapped in a spiking artificial neural network (SNN) [37]. In this review, the main architectures of DNNs and their applications in pharmacology and Bioinformatics are presented. The future need for neuromorphic hardware for DNNs is also discussed, and the two most advanced chips that have already implemented DL are reviewed: IBM TrueNorth and SpiNNaker. In addition, this work points out the importance of considering astrocytes in DNNs and neuromorphic chips, given the proven importance of this type of glial cells in the brain. 2. Deep Artificial Neural Networks in Pharmacology and Bioinformatics DL is a branch of ML that attempts to mimic the information processing in layers of neurons in the neocortex. DNNs are trained to learn to recognize patterns in digital representations of sounds, images, and other data. Usually, there is an unsupervised pre-training process, which helps to initialize the weights. There are different DNN architectures, but in this review, only the most representative types are briefly explained, we divided them in: Deep Auto-Encoder Networks (DAENs), Deep Convolutional Neural Networks (DCNNs) and Deep Recurrent Neural Networks (DRNNs). DAENs encompass Deep Feedforward Neural Networks (DFNNs), Deep Belief Networks (DBNs), Deep Restricted Boltzmann Machines (DRBMs) and Deep Auto-Encoder Networks. There are differences between these architectures, but they have in common big differences with respect to DCNNs and DRNNs. These differences are highlighted, and some featured applications in Pharmacology and Bioinformatics of each architecture are presented in Table 2. For a more detailed analysis of the DL architecture, the differences, the training and the historical perspective, the reader should refer to these reviews [25,26,27,28,29,30,31]. 2.1. Deep Auto-Encoder Networks As previously mentioned, the breakthrough of how to train DAENs was made by Hinton and his team [23,24]. DAENs are models composed of multiple layers of neurons, trained one by one, and could be stacked to as many as possible layers. Different DL architectures, such as DFNN, DBN, DRBM and Deep Auto-Encoder Networks, were grouped together by us. There are some differences between these architectures, but in general the idea of DAENs is stacking various layers of neurons, to be pre-trained one by one, using each layer to train the next one. In the first layer, neurons learn to recognize low level features. In an image, they could recognize basic forms such as lines, edges, etc. The intermediate layers detect more abstract features, the ones detected depending on the data set used to train the networks. For example, if a data set of faces is used, the intermediate layers can recognize parts of the faces like eyes, mouth or ears. Finally, the last layer is trained to detect the most abstract features, for example to recognize a person, a car or an animal in an image. Usually, the training falls into two steps: the first step is layer-wise pre-training and the second step is fine-tuning. Compared to how a neural network is traditionally trained, the first step can also be seen as a clever way of initialization, whereas the second step can be as simple as backpropagation, depending on the model to be trained. 2.1.1. Pharmacology A team led by George Dahl, from Hinton’s group, won the Merck Molecular Activity Challenge organized by Kaggle in 2012, indicating the high potential of DL in drug design, and drawing the attention of the pharmacology community. Merck’s data sets include on-target and ADME (absorption, distribution, metabolism, and excretion) activities. Each molecule is represented by a list of features, i.e., descriptors in QSAR nomenclature The DAEN have three hidden layers, each layer having 2000 neurons, so the network has over 24 million tunable values. Generative unsupervised pretraining and the procedure of dropout are used to avoid overfitting [38,39]. However, the small scale of Merck’s data set, 11,000 descriptors, 164,000 compounds, and 15 drug targets, does not allow assessing the value of DL in drug target prediction. In 2014, Unterthiner et al. analyzed the performance in a bigger data set, similar to the in-house data of pharmaceutical companies [40]. The ChEMBL database has 13 million compound descriptors, 1.3 million compounds, and 5000 drug targets. DAEN was compared to seven target prediction methods, including two commercial predictors, three predictors deployed by pharmaceutical companies, and ML methods that could scale to this data set. DAEN outperformed all the other methods and surpassed the threshold to make VS possible. This showed the potential of DL to become a standard tool in industrial drug design [40]. Unterthiner’s team won Tox21 Data Challenge within the “Toxicology in the 21st Century” (Tox21) initiative launched by the United States agencies (NIH, EPA and FDA). The goal of this challenge was to assess the performance of computational methods in predicting the toxicity of chemical compounds. The DAEN used by Unterthiner’s team clearly outperformed all the other participating methods [41]. In the first column of Table 3 this method is shown, and the area under the Receiver Operating Characteristic curve (AUC) value is presented in the second column. The last column shows the p-value of a paired Wilcoxon with the alternative hypothesis that the DAEN has on average a larger AUC [40]. Dahl et al. also performed an experiment on assay results deposited in PubChem (see Table 4); they used a DAEN to learn a function that predicts activities of compounds for multiple assays at the same time, which is called multi-task. Cellular and biochemical assays were included in the dataset. Multiple related assays, for example assays for different families of cytochrome P450, were used [42,43]. In a series of empirical studies performed by Google and Stanford, several aspects of the use of massively multi-task framework for VS were analyzed. To characterize each molecule, Extended Connectivity Fingerprints (ECFP4) was used. This method decomposes each molecule in fragments that are centered on a non-hydrogen atom. The fragments are labeled with an identifier, and all the identifiers from a molecule are stored into vector of fixed length which represents the molecular fingerprint. The results showed that both additional data and additional tasks improve accuracy. Overall, 259 data sets, containing 37,800,000 experimental data points for 1,600,000 compounds, were used [44]. 2.1.2. Bioinformatics Yanjun Qi et al. [45] created a DAEN to predict local properties of a protein based on its sequence. Some of the properties predicted were the solvent accessible surface area, transmembrane topology, DNA-binding residues, signal peptides and the secondary structure (see Figure 2). The DAEN used the amino acid sequence as an input to predict the class labels. The method has three levels: first is a layer for the feature extraction from the amino acid; the second is a layer for sequential feature extraction; and, third, different layers of ANN. This method obtained state-of-the-art results [45]. DL architectures could be applied to predict the protein contact map. A group from the University of California used a method with three levels of resolution steps. In the first step, coarse contacts and orientations between elements of the secondary structure were predicted using 2D RNN. Subsequently, to align these elements, a method based on energy was used, and the contact probabilities between residues in strands or α-helices were predicted. In the third step, the information over space and time was integrated to refine the predictions. The DL methods only achieve a 30% of accuracy, but this represents an important improvement over other methods [46]. Eickholt and Cheng predicted contacts between protein residues using a DAEN. The method was evaluated with the official Critical Assessment of protein Structure Prediction (CASP) assessors, and with the cluster accuracy and cluster count metrics. The predictor achieved better results predicting long-range contacts than residue-residue contacts. For the top L/10 long-range contacts, the DAEN obtained a 66% of accuracy, using a neighborhood of size 2 [47,48]. In 2014, Lyons et al. published a paper about the use of a DAEN to predict the backbone Cα angles and dihedrals based on the sequences of proteins. The mean absolute error for the predicted angles was between 34 degrees for τ and 9 degrees for θ. The structures constructed of 10 residue fragments based on the prediction, only differ 1.9 Å in average, measured with the root-mean-square distance [49]. A more complete study, published in Nature, showed the potential of DL for the prediction of the protein secondary structure, solvent accessibility and local backbone angles. To evaluate the DL method, a test data set with 1199 proteins was used. The DAEN predicted the secondary structure of the proteins with 82% accuracy, while the predicted and the real solvent surface area had a 76% correlation. The backbone angles had mean absolute errors between 8 and 32 degrees [50]. DAENs can also be applied to assess the quality of the protein models, and obtain better results than the methods based in energy or scoring functions. A DL method was proposed by Nguyen et al., and it was called DL-Pro. The distance between two residues C-α atoms was used to create a representation that is independent of the orientation. A dataset from the CASP competition was used, and the DL-Pro achieve better results than the state-of-the-art methods [51]. Tan et al. applied DAENs to unsupervised feature construction and knowledge extraction to analyze the gene expression data from a breast cancer database. The constructed features extracted valuable information, from both a clinical and molecular perspective. This DAEN learnt to differentiate samples with a tumor, the state of estrogen receptor, and molecular subtypes [52]. DAENs were trained by a group from the University of California, Irvine, to annotate the pathogenicity of genetic variants using training data consisting of 16M observed variants and 49M simulated variants. This model improved considerably the performance of other methods, around 15% [53]. The genes are very important in all biological processes, and nowadays their study has been facilitated due to the DNA microarray technology. The expression of thousands of genes is measured in one go, and this produces a huge amount of data. Gupta et al. proposed a DL architecture to learn the structure in gene expression, with an application to gene clustering [54]. 2.2. Deep Convolutional Neural Networks The CNN are inspired by the structure of the visual cortex, discovered by Hubel and Wiessel [55], which is formed by a complex pattern of neurons that are sensitive to small sub-regions, creating receptive fields which act as local filters. The natural images, and other types of data, present a strong correlation between nearby pixels, or input data points, and this relationship can be exploited by these receptive fields to extract valuable patterns of features. The CNNs mimic this architecture and have convolutional layers in which each neuron is connected with a subset of neurons of the previous layer [56]. For example, in Figure 3, the neurons of the layer m are connected to 3 neurons from the layer m-1, therefore each neuron only receives information from the sub-region of the input space. The CNNs trained with natural images learnt to recognize different patterns in the pixels. Each neuron acts like a filter, but only on a subset of the input space. The neurons from the top layers integrated information from more pixels, thus they can detect more abstract patterns. CNNs [25,26,27,28] were designed to recognize visual patterns from insufficiently preprocessed pixels and can recognize patterns with extreme variability, exhibiting robustness to distortions and transformations. There are three types of layers: convolutional, Max-Pooling and fully-connected (see Figure 4). CNNs are not limited to two-dimension input data, like images, and can be applied to 1, 3 or even more dimensions of data, for example one dimension audio for speech recognition and 3 or 4 dimension for functional magnetic resonance imaging. 2.2.1. Pharmacology DCNNs have been used to predict drug toxicity both at the atomic and molecular level. Hughes et al. published a study that described a new system, used to predict the formation of reactive epoxide metabolites. This method needs to be combined with additional tools in order to predict the toxicity of drugs. For example, while this model predicts the formation of epoxides, it does not score the reactivity of these epoxides (see Figure 5) [57]. Figure 6 shows how information flowed through the model, which was composed of one input layer, two hidden layers, and two output layers. This model computed a molecule-level prediction for each test molecule as well as predictions for each bond within that test molecule [57]. 2.2.2. Bioinformatics DCNNs were used to predict the target of microRNA, which regulates genes associated with various diseases. Cheng et al. presented a DCNN that outperforms the existing target prediction algorithms and achieves significantly higher sensitivity, specificity and accuracy with values of 88.43%, 96.44% and 89.98%, respectively [58]. DCNNs can also be applied to predict the sequence specificities of DNA and RNA binding proteins. Alipanahi et al. developed a DL approach called DeepBind that outperforms other state-of-the-art methods, even when training on in vitro data and testing on in vivo data (see Figure 6) [59,60]. 2.3. Deep Recurrent Neural Networks RNNs are a type of ANN that has recurrent connections, thus the network represents a directed cycle [61]. The RNNs can exhibit dynamic temporal behavior so they can process sequence of inputs due to their internal memory containing the recurrent connections. This makes them well suited to be applied to tasks like handwriting recognition with unsegmented characters [62] or speech recognition [63]. In a feedforward neural network, the depth is measured as the number of layers between the input and output. Unfortunately, this definition does not apply trivially to a recurrent neural network (RNN) because of its temporal structure. A DRNN is a DNN with recurrent connections in each layer [64,65]. When the network is updated, the information flows in both directions, up and down, thus the sequential information can be learned (see Figure 7). The sequence of updates allows the networks to integrate the information in different time scales, creating a temporal hierarchy. 2.3.1. Pharmacology Lusci et al. presented a brief overview of some applications of DRNNs aimed at the prediction of molecular properties, such as aqueous solubility. Undirected cyclic graphs are usually used to describe the molecules; however, the RNN typically uses directed acyclic graphs. Therefore, there was a need to develop methods that would address the discrepancy by considering a set of DRNN associated with all possible vertex-centered acyclic orientations of the molecular graph. The results obtained proved that the DRNN performance is equal to or better than the other methods [66]. Over the past 50 years, drug-induced liver injury has cost a huge amount of money to the pharmaceutical companies due to the drug withdrawal caused by this problem. DL methods has been successfully applied to predict drug-induced liver injury Xu et al. compared different DL architectures to predict drug-induced liver injury using four large data sets, and the best results were obtained by a novel type of DRNN (see Figure 8). The structure of glycine is converted into a primary canonical SMILES structure. Subsequently, each of the atoms in the SMILES structure is sequentially defined as a root node. Finally, the information for all the other atoms is transferred along the shortest possible paths. The best model achieved an accuracy of 86.9%, sensitivity of 82.5%, specificity of 92.9%, and area under the curve (AUC) of 0.955 [67]. 2.3.2. Bioinformatics DRNNs can be used to analyze biological sequence data, like predicting the subcellular location of proteins. Sønderby et al. created a DRNN using only the protein sequence, and achieved 92% of accuracy in the prediction of the location of proteins, outperforming the current state-of-the-art algorithms. The performance was improved by the introduction of convolutional filters and the authors experimented with an attention mechanism that let the network focus on specific parts of the protein [68]. 3. Neuromorphic Chips Since Alan Turing created the first computer, the progress in computer science has been remarkable. This progress was predicted by Gordon Moore in 1965, who foretold that the number of transistors that could be manufactured on a single silicon chip would double every 18 months to two years. It is known as Moore’s Law, and over the past century it has been accomplished by making transistors increasingly smaller. As CMOS transistors get smaller they become cheaper to make, faster, and more energy-efficient. This win-win scenario has driven the society to a digital era in which computers play a key role in almost every walk and aspect of our lives [22]. However, Moore’s Law has limitations when it comes to shrinking transistors; there is a physical limit in the size of the atom. At this scale, around 1 nm, the properties of the semi-conductor material in the active region of a transistor are compromised by quantum effects like quantum tunneling. In addition, there are also other limitations, such as the energy wall [69,70] and memory wall [71], which denote the high power density and low memory bandwidth [72,73]. There are also economic limitations, since the cost of designing a chip and the cost of building a fabrication facility are growing alarmingly [74]. Trying to avoid some of these limitations, in the early years of this century, all of the major microprocessor manufacturers moved from ever-faster clock speeds to multicore processors. Over the past decade, instead of creating faster single-processor machines, new systems include more processors per chip. Now we have CPUs with multicores, and GPUs with thousands of cores [22]. As already stated, DNNs have become the state-of-the-art algorithms of ML in many tasks. However, both training and execution of large-scale DNNs require vast computing resources, leading to high power requirements and communication overheads. The ongoing work on design and construction of neuromorphic chips, the spike-based hardware platforms resulting from the book about VLSI (Very Large Scale Integration) written by Lynn Conway and Carver Mead, and published in the 1980s [75], offered an alternative by running DNNs with significantly lower power consumption. However, the neuromorphic chips have to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal [36]. Moreover, it is necessary to design the structure, neurons, network input, and weights of DNN during training, to efficiently map those networks to SNN in the neuromorphic chips (see Figure 9) [76]. Focusing on projects involving neuromorphic hardware, the IBM TrueNorth chip [77] is one of the most impressive silicon implementation of DNNs. SpiNNaker, a project developed by the University of Manchester, also achieved excellent results implementing DNNs. Both [78] chips are digital, they compute the information using the binary system. However, some neuromorphic chips are analog, they consist of neuromorphic hardware elements where information is processed with analog signals; that is, they do not operate with binary values, as information is processed with continuous values [22]. In analog chips, there is no separation between hardware and software, because the hardware configuration is in charge of performing all the computation and can modify itself [79]. A good example is the HiCANN chip, developed at the University of Heidelberg, which uses wafer-scale above-threshold analog circuits [80]. There are also hybrid neuromorphic chips, like the Neurogrid from Stanford [81], which seek to make the most of each type of computing. It usually processes in analog and communicates in digital. This review will focus only on digital neuromorphic chips, the IBM TrueNorth and the SpiNNaker chip, because are the most advanced projects, obtained the best results implementing DNNs and published the highest number of technical papers. For further details about other projects and the differences between digital, analog and hybrid neuromorphic chips, the reader should refer to other reviews [82,83]. 3.1. TrueNorth International Business Machines (IBM) The DARPA SyNAPSE (System of Neuromorphic Adaptive Plastic Scalable Electronics) initiative selected and funded the proposal “Cognitive Computing via Synaptronics and Supercomputing (C2S2)” of the Cognitive Computing Group at IBM Research-Almaden directed by Dharmendra Modha [77]. The project is based on the design and creation of a neuromorphic chip called TrueNorth, which has a non-von Neumann architecture. It is characterized by modularity, parallelism and scalability. It is inspired by the brain and its function, low power, and compact volume (see Figure 10). This chip can be used to integrate spatio-temporal and real-time cognitive algorithms for different applications [84]. Currently in the final phase of the project, the researchers created a board with 16 TrueNorth neuromorphic chips, capable of simulating 16 million neurons and four billion synapses. In 2015, they assembled a system consisting of 128 chips and 128 million neurons [85]. The next goal is to integrate 4096 chips into a single rack, which would represent four billion neurons and one trillion synapses, consuming around 4 kW of power [86]. The TrueNorth prototype was created in 2011 [87], and it was a neurosynaptic core with 256 digital leaky integrate-and-fire neurons [37] and up to 256,000 synapses. The core is composed of memory and processor, and the communication takes places through all-or-none spike events. This allows an efficient implementation of a parallel asynchronous communication and Address Event Representation (AER) [88,89]. In this communication system, the neurons have a unique identifier, called address, and when a neuron spikes, the address is sent to other neurons. In 2012, Compass [90] was developed, a simulator to design neural networks to be implemented in the neuromorphic chip. Compass is a multi-threaded, massively parallel functional simulator and a parallel compiler. It uses the C++ language, sends spike events via MPI communication and uses OpenMP for thread-level parallelism. A simulator for GPGPU [91] was also developed. Modha’s team simulated in 2007 the brain of a rat in an IBM BlueGene/L supercomputer [92]. In 2010, they simulated a monkey’s brain [93] in IBM BlueGene/P supercomputers from a network map of long-distance neural connections in the brain obtained with 410 anatomical studies (Collation of Connectivity data on the Macaque brain). Later that same year, they published the results of a simulation with Compass of 2048 billion neurosynaptic cores and 5.4 × 1011 neurons and 1.37 × 1014 synapses [94]. The execution was 1542× times slower than real time, and 1.5 million Blue Gene/Q supercomputers were needed. A program in the TrueNorth chips consists of a definition of the inputs and outputs to the network and the topology of the network of neurosynaptic cores. The parameters of the neurons and the synaptic weights should be specified, as well as the inter- and intra-core connectivity [84,95]. The programming paradigm has four levels: The lowest level is the corelet, which represents an abstraction of a TrueNorth program like a blackbox, only showing the inputs and outputs, and hiding the other details. The next level is the Corelet Language which allows the creation and combination of corelets. The validated corelets are included in the Corelet Library and can be reused to create new corelets. This is like a repository and makes up the third level. The last level is the Corelet Laboratory, a programming environment to develop new applications. It is integrated with Compass, the TrueNorth simulator [84]. The corelet library has a collection of several functions that were implemented in the TrueNorth chip verified and parameterized. Some examples are algebraic, logical and temporal functions, convolutions, discrete Fourier transformations and many others. Using these functions different algorithms were implemented in the TrueNorth chip, like CNN (see Figure 11) and Restricted Bolztmann Machines for feature extraction, hidden Markov models, spectral content estimators, liquid state machines, looming detectors, logistic regression, backpropagation and some others. The corelet algorithm can be re-used in different applications, and there are different corelet implementations for the same algorithm, showing the flexibility of the corelet construction [76,96]. TrueNorth was used in different applications, such as recognition of voices, composers, digits, sequences, emotions or eyes. It was also used in collision avoidance and optical flow [96,97]. TrueNorth was also applied to bioinformatics by a group from the University of Pittsburgh, who used the RS130 protein secondary structure data set to predict the local conformation of the polypeptide chain and classified it into three classes: α helices, β-sheets, and coil [74]. 3.2. SpiNNaker. University of Manchester SpiNNaker is a project developed at the University of Manchester, whose principal investigator is Steve B. Furber [78]. Within this project, chips, which contain many small CPUs, were produced. Each CPU is designed to simulate about 1000 neurons, such as neural models of leaky integrate and fire or Izhikevich’s model [37], which communicate spike events to other CPUs through a network package. Each chip consists of 18 ARM968 processors, one of them acting as a processor monitor. In 2015, a cabinet with 5760 chips was created, which can simulate 100 million point neurons with approximately 1000 synapses per neuron [98]. The chips are connected with adjacent chips by a two-dimensional toroidal mesh network and each chip has six network ports [99,100,101]. This system is expected to mimic the features of biological neural networks in various ways: (1) native parallelism—each neuron is a primitive computational element within a massively parallel system [102]; (2) spiking communications—the system uses AER, thus the information flow in a network is represented as a time series of neural identifiers [103]; (3) event-driven behavior—to reduce power consumption, the hardware was put into “sleep” mode, waiting for an event; (4) distributed memory—this system uses memory local to each of the cores and an SDRAM local to each chip; and (5) reconfigurability—the SpiNNaker architecture allows on-the-fly reconfiguration [104]. In order to configure a large number of cores, with millions of neurons and synapses, PACMAN [105] was developed. It is a software tool that helps the user to create models, translate and run in SpiNNaker. This allows the user to work with neural languages like PyNN [106] or Nengo [107,108]. The SpiNNaker was created simulate real-time models, but the algorithms had to be defined in the design process, therefore the models were static. In 2013, a paper [109] was published, in which a novel learning rule was presented, describing its implementation into the SpiNNaker system, which allows the use of the Neural Engineering Framework to establish a supervised framework to learn both linear and non-linear functions. The learning rule belongs to the Prescribed Error Sensitivity class. SpiNNaker supports two types of Deep Neural Networks: Deep Belief Networks: These networks of deep learning may be implemented, obtaining an accuracy rate of 95% in the classification of the MNIST database of handwritten digits. Results of 0.06% less accuracy than with the software implementation are obtained, whereas the consumption is only 0.3 W [36,110]. Convolutional Neural Networks: This type of networks has the characteristic of sharing the same value of weights for many neuron-to-neuron connections, which reduces the amount of memory required to store the synaptic weights. A five-layer deep learning network is implemented to recognize symbols which are obtained through a Dynamic Vision Sensor. Each ARM core can accommodate 2048 neurons. The full chip could contain up to 32,000 neurons. A particular ConvNet architecture was implemented in SpiNNaker for visual object recognition, like poker card symbol classification [111]. Currently, there are no applications in pharmacology or bioinformatics, but SpiNNaker showed its potential by implementing DNNs and DCNNs to visual recognition and robotics. In the future, it could be trained in drug design, protein structure prediction or genomic, and other omics, data mining. 4. Discussion As was pointed out, DNNs have become the state-of-the-art algorithms of ML in speech recognition, computer vision, natural language processing and many other tasks (see Table 1) [26,27]. According to the results obtained, DNNs match the human capabilities, and even surpass them on some tasks. Besides, the inner work of DNNs has similarities with the processing of information in the brain. The pattern of activation of the artificial neurons is very similar to that observed in the brain due to the sparse coding used, which may, for example, be applied to audio to obtain almost exactly the same functions (see Figure 12). In the case of images, it was also shown that the functions learned in each layers were similar to the patterns recognized by each layer of the human visual system (V1 and V2). This review analyzed applications in pharmacology and bioinformatics (see Table 2). DNNs can be used in the drug discovery, design and validation processes, ADME properties prediction and QSAR models. They also can be applied to the prediction of the structure of proteins and genomic, and other omics, data mining. All these applications are very intensive from a computational perspective, thus DNNs are very helpful because of their ability to deal with Big Data. Besides, DL complement the use of other techniques, for example the quality and success of a QSAR model depend strictly on the accuracy of input data, selection of appropriate descriptors and statistical tools, and most importantly validation of the developed model. Feature extraction from the descriptor patterns is the decisive step in the model development process [4]. Regarding architectures, nowadays, the largest DNN has millions of artificial neurons and around 160 billion parameters [112]. Building large networks will improve the results of DL, but the development of new DL architectures is a very interesting way to enhance the capabilities of the networks. For example, the latest DRNN architectures with “memory” show excellent results in natural language processing, one of the hardest task for ML [26,27,28,29,31]. Some authors, such as Ray Kurzweil [114], claim that the exponential growth based on Moore’s Law and The Law of Accelerating Returns [115] will be maintained, therefore, in the next decades, building a machine with a similar number of neurons as the human brain, of around 86 billion neurons, should be possible. As previously mentioned, there are some physical limitations to the current architecture of computers, such as the memory wall [69,70] and energy wall [71], which denote the high power density and low memory bandwidth [72,73]. There are also economic limitations; the cost of designing a chip and the cost of building a fabrication facility are growing alarmingly [74]. However, these limitations will probably be surpassed using other technologies and architectures, like GPU clusters or networks of Neuromorphic chips. It was historically calculated that the human brain computes approximately 20 billion operations per second [116,117,118,119]. Some authors think that these values underestimate the brain capacity, and calculated around 1021 operations per second [120]. However, reaching the human brain capacity is not enough, because one of the main features of the brain is its connectivity of the billions of cells that forms trillions of synapses. Natural evolution has molded the brain for millions of years, creating a highly complex process of development. This was remarkably pointed out by Andrew Ng, neurons in the brain are very complex structures, and after a century of study the researchers still are not able to fully understand how they work. The neurons in the ANN are simple mathematical functions that attempt to mimic the biological neurons. However, the artificial neurons only reach the level of loose inspiration. Consequently, reaching the level of human brain computation will not necessarily mean that the future computers will surpass human intelligence. In our opinion, the advances in understanding the human brain will be more important in order to make a breakthrough that will lead us to new types of DNNs. In this regard, it should be pointed out that the human brain is composed of neurons, but also glial cells, and there is almost the same number of both [121]. More importantly, over the past decade, it has been proven that astrocytes, a type of glial cells of the central nervous system, actively participate in the information processing in the brain. There are many works published over the past two decades on multiple modes of interaction between neurons and glial cells [122,123,124,125]. Many studies suggest the existence of bidirectional communication between neurons and astrocytes, a type of glial cells of the central nervous system [126,127]. This evidence has led to the proposal of the concept of tripartite synapse [128], formed by three functional elements: presynaptic neuron, postsynaptic neuron and perisynaptic astrocyte (see Figure 13). The relation between these three elements is very complex and there are different pathways of communication: astrocytes can respond to different neurotransmitters (glutamate, GABA, acetylcholine, ATP or noradrenaline) [130] liberating an intracellular Ca2+ signal, known as calcium wave that could be transmitted to other astrocytes through GAP junctions. In addition, astrocytes may release gliotransmitters that activate presynaptic and postsynaptic neuronal receptors, leading to a regulation of the neural excitability, synaptic transmission, plasticity and memory [131,132]. The possibility of a quad-partite synapse, in which microglia are engaged [133], has recently been proposed. In addition, there is interesting scientific evidence that suggests the important role of glial cells in the intelligence of the species. Although there are no major differences between neurons of different species of mammals, the glial cells have evolved in the evolutionary chain. For example, a rodent’s astrocytes may include between 20,000 and 120,000 synapses, while a human’s may include up to two million synapses [134,135]. Not only should the complexity of the astrocytes be pointed out, but also their size. Human astrocytes have a volume 27 times greater than the same cells in the mouse’s brain [134,135]. Besides, the ratio of glial cells to neurons increased along the evolutionary chain. One of the most striking research events has been the discovery of a single glial cell for every 30 neurons in the leech. This single glial cell receives neuronal sensory input and controls neuronal firing to the body. As we move up the evolutionary ladder, in a widely researched worm, Caenorhabditis elegans, glia cells are 16% of the nervous system. The fruit fly’s brain has about 20% glia. In rodents such as mice and rats, glia cells make up 60% of the nervous system. The nervous system of the chimpanzee has 80% glia, while the human 90%. The ratio of glia to neurons increases with our definition of intelligence [123]. The number of astrocytes per neuron also increases as we move up the evolutionary ladder, humans having around 1.5 astrocytes per neuron [136]. Furthermore, the ratio of glial cells to neurons varies in different brain regions. In the cerebellum, for instance, there are almost five times more neurons than astrocytes. However, in the cortex, there are four times more glial cells than neurons [121,137]. All these data suggest that the more complex the task, performed, by either an animal or a brain region, the greater the number of glial cells involved. Currently, there are two projects aimed at implementing astrocytes in neuromorphic chips, one is BioRC developed by the University of Southern California [138,139,140,141] and the other project is carried out by the University of Tehran and University of Kermanshah, Iran [142,143,144]. Moreover, the RNASA-IMEDIR group from the University of A Coruña developed an Artificial Neuron-Glia Network (ANGN) incorporating two different types of processing elements: artificial neurons and artificial astrocytes. This extends classical ANN by incorporating recent findings and suppositions regarding the way information is processed via neural and astrocytic networks in the most evolved living organisms [145,146,147,148,149]. In our opinion, neurons are specialized in transmission and information processing, whereas glial cells in processing and modulation. Besides, glial cells play a key role in the establishment of synapses and neural architecture. That is why it would be interesting to combine these two types of elements in order to create a Deep Artificial Neuron–Astrocyte Network (DANAN). 5. Conclusions DNNs represent a turning point in the history of Artificial Intelligence, achieving results that match, or even surpass the human capabilities in some tasks. These results motivated major companies like Google, Facebook, Microsoft, Apple and IBM to focus their research on this field. Nowadays, DNNs are used every day unknowingly, since in our smartphones there are numerous applications based on Deep Learning. For example, some cameras use a DNN to perform face recognition, while others employ a voice recognition piece of software, which is also based on DL. There are many other applications with DNNs that perform state-of-the-art results. Pharmacology and bioinformatics are very interesting fields for DL application, because there is an exponential growth of the data. There is a huge potential in applying DNNs in the process of drug discovery, design and validation that could improve performance and greatly reduce the costs. However, the most promising area is genomics, and other omics, like proteomics, transcriptomics or metabolomics. These types of data are so complex that it is almost impossible for humans to extract valuable insights. Thus, the use of DNNs would be necessary to extract the information useful to understand the relationships between the DNA, epigenetics variations, and different diseases. Consequently, scientific and economic interests have led to the creation of numerous R&D projects to keep improving DNNs. Developing new hardware architectures is also important in order to improve the current CPUs and GPUs. The neuromorphic chips represent a great opportunity to reduce the energy consumption and enhance the capabilities of DNNs, being very helpful to process a vast volume of information generated by the Internet of Things. Besides, using neuromorphic chips may lead to the creation of a large-scale system that would attempt to represent an Artificial General Intelligence, moving from the current Artificial Narrow Intelligence. Finally, it would be of great interest to create networks with two types of processing elements, to create DANANs that will work more similarly to the human brain. This should be considered a very resourceful way of improving the current systems, and our group’s objective is to implement this first type of DANAN. This type of networks will consider the proven capabilities of the glial cells in the processing of information, regulation of the neural excitability, synaptic transmission, plasticity and memory, to create more complex systems that could bring us closer to an Artificial General Intelligence. Acknowledgments This work is supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (Reference GRC2014/049) and the European Fund for Regional Development (FEDER) in the European Union, the Galician Network for Colorectal Cancer Research (REGICC) funded by the Xunta de Galicia (Reference R2014/039) and by the “Collaborative Project on Medical Informatics (CIMED)” PI13/00280 funded by the Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation 2013–2016 and the European Regional Development Funds (FEDER). We also want to acknowledge its resources to Supercomputation Center of Galicia (CESGA), Spain. Author Contributions Lucas Antón Pastur-Romay has conceived the design, ideas and researched the materials for this review; Lucas Antón Pastur-Romay and Ana Belén Porto-Pazos have written this paper; Francisco Cedrón and Alejandro Pazos have contributed to write and review the paper. Conflicts of Interest The authors declare no conflict of interest. Abbreviations ADME Absorption, Distribution, Metabolism, and Excretion AER Address Event Representation ANGN Artificial Neuron-Glia Networks ANN Artificial Neural Networks AUC Area Under the Receiver Operating Characteristic Curve CASP Critical Assessment of protein Structure CNN Convolutional Neural Networks CPU Central Processing Unit CUDA Compute Unified Device Architecture DAEN Deep Auto-Encoder Networks DANAN Deep Artificial Neuron–Astrocyte Networks DBN Deep Belief Networks DCNN Deep Convolution Neural Networks DFNN Deep Feedforward Neural Networks DL Deep Learning DNN Deep Artificial Neural Networks DBM Deep Boltzmann Machines DRNN Deep Recurrent Neural Networks ECFP4 Extended Connectivity Fingerprints GPGPUs General-Purpose Graphical Processing Units GPU Graphical Processing Unit ML Machine Learning QSAR Quantitative Structure–Activity Relationship QSPkR Quantitative Structure–Pharmacokinetic Relationship QSPR Quantitative Structure–Property Relationships QSTR Quantitative Structure–Toxicity Relationship SANN Spiking Artificial Neural Network SVM Support Vector Machines VLSI Very Large Scale Integration VS Virtual Screening Figure 1 Big Data Workflow. Figure 2 Deep neural network architecture from Yanjun Qi et al. [45]. The input to the first layer is the protein sequence represented by the single-letter amino acid code, for example the letter “A” (in green) represents “Alanine”. This method uses a sliding window input {S1, S2… Sk}, in this case k = 7. The first layer consists a PSI-Blast feature module and an amino acid embedding module, the green boxes represent the feature vector derived from the Alanine in both modules. In the second layer, the feature vectors are concatenated to facilitate identification of local sequence structure. Finally the derived vector is fed into the Deep Artificial Neural Network. Figure 3 Convolutional layers that extract features of the input to create a feature map. The artificial neurons are represented by the circles, and the weights by the narrows. Weights of the same color are shared, constrained to be identical [56]. Figure 4 Architecture of a Deep Convolutional Neural Network (DCNN), alternating the convolutional layer and the max-pooling layer (or sub-sampling layer), and finally the fully-connected layer [56]. Figure 5 This diagram represents a simplification of the structure of the epoxidation model, which was made up of one input layer, two hidden layers, and two output layers. The actual model had several additional nodes in the input and hidden layers. In the input layer, M represents the molecule input node, B is the bond input node, and two atom input nodes (for each atom associated with the bond). The bond epoxidation score (BES) quantifies the probability that the bond is a site of epoxidation based in the input from the nodes of the first hidden layer (H1 and H2). The molecule epoxidation score (MES) reflects the probability that the molecule will be epoxidized. This score is calculated with the information from the all molecule-level descriptors and the BES. The bond network and the molecule network are represented in orange and purple respectively [57]. Figure 6 Details of inner workings of DeepBind developed by Alipanahi et al. and its training procedure. In “a”, five independent sequences of DNA are being processed in parallel, each composed by a string of letters (C, G, A and T) which represent the nucleotides. The scores are represented in white and red tones, and the outputs are compared to the targets to improve the model using backpropagation; In “b”, The Calibration, training, and tasting procedure is represented in more detail [59]. Figure 7 Different Recurrent Neural Networks architectures, the white circles represent the input layers, the black circles the hidden layers, and the grey circles the output layers [65]. Figure 8 Schematic diagram of Youjun Xu et al. network encoding glycine, first using primary canonical SMILES strucuture. Then, each of the atoms in the SMILES structure is sequentially defined as a root node. Finally, information for all other atoms is transferred along the shortest possible paths, in which case is obtained following the narrows [67]. Figure 9 Mapping a Deep Artificial Neural Network (DANN) (a) to a neuromorphic chip like the TrueNorth (b). The input neurons are represented with the red and white shapes (x and x’), and the output neurons with the grey shapes (z and z’). The weights (w) to the neuron z are approximated using a Pseudo Random Number Generator (PRNG), resulting in the weights (w’) to the neuron z’ in the neuromorphic chip [74]. Figure 10 (A) The neurosynaptic core is loosely inspired by the idea of a canonical cortical microcircuit; (B) A network of neurosynaptic cores is inspired by the cortex’s two-dimensional sheet, the brain regions are represented in different colors; (C) The multichip network is inspired by the long-range connections between cortical regions shown from the macaque brain; (D–F) Structural scheme of the core, chip and multi-chip level. The white shapes represent axons (inputs) and the grey shapes the neurons (outputs); (G–I) Functional view at different level; (J–L) Image of the physical layout [77]. Figure 11 Mapping of a CNN to TrueNorth. (A) Convolutional network features for one group at one topographic location are implemented using neurons on the same TrueNorth core, with their corresponding filter support region implemented using the core’s input lines, and filter weights implemented using the core’s synaptic array. The inputs are represented with white shapes, and the grey triangles represent the neurons. The filter used in each case is implemented mapping the matrix of weights (the numbers in the green boxes) into the synaptic array (grey circles); (B) For a neuron (blue points) to target multiple core inputs, its output (orange points) must be replicated by neuron copies, recruited from other neurons on the same core, or on extra cores if needed [76]. Figure 12 Sparse coding applied to audio. In red 20 basis functions learned from unlabeled audio, in blue the functions from cat auditory nerve fibers [113]. Figure 13 Tripartite synapse represented by a presynaptic neuron, postsynaptic neuron and perisynaptic astrocyte (astrocyte process). The presynaptic neuron release neurotransmitters that are received by the postsynaptic neuron or the perisynaptic astrocyte [129]. ijms-17-01313-t001_Table 1Table 1 Deep Artificial Neural Networks Achievements. Adapted from a slide developed by Yann Lecun, Facebook and NYU [32]. Task (Year) Competition Handwriting recognition (2009) MNIST (many), Arabic HWX (IDSIA) Volumetric brain image segmentation (2009) Connectomics (IDSIA, MIT) OCR in the Wild (2011) StreetView House Numbers (NYU and others) Traffic sign recognition (2011) GTSRB competition (IDSIA, NYU) Human Action Recognition (2011) Hollywood II dataset (Stanford) Breast cancer cell mitosis detection (2011) MITOS (IDSIA) Object Recognition (2012) ImageNet competition (Toronto) Scene Parsing (2012) Stanford bgd, SiftFlow, Barcelona datasets (NYU) Speech Recognition (2012) Acoustic modeling (IBM and Google) Asian handwriting recognition (2013) ICDAR competition (IDSIA) Pedestrian Detection (2013) INRIA datasets and others (NYU) Scene parsing from depth images (2013) NYU RGB-D dataset (NYU) Playing Atari games (2013) 2600 Atari games (Google DeepMind Technologies) Game of Go (2016) AlphaGo vs. Human World Champion (Google DeepMind Technologies) ijms-17-01313-t002_Table 2Table 2 Applications of different Deep Neural Networks (DNNs) architectures. Network Architecture Pharmacology Bioinformatics DAEN [1,2,3,4,5,6,7,23] [8,9,10,11,12,13,14,15,16,17] DCNN [18] [19,20,21] DRNN [22,23] [24] ijms-17-01313-t003_Table 3Table 3 Performance of target prediction methods analyzed by Unterthiner et al., in terms of mean AUC (Area Under the Receiver Operating Characteristic curve) across targets [40]. Method AUC p-Value Deep Auto-Encoder Network 0.830 – Support Vector Machine 0.816 1.0 × 10−7 Binary Kernel Discrimination 0.803 1.9 × 10−67 Logistic Regression 0.796 6.0 × 10−53 k-Nearest neighbor 0.775 2.5 × 10−142 Pipeline Pilot Bayesian Classifier 0.755 5.4 × 10−116 Parzen-Rosenblatt 0.730 1.8 × 10−153 Similarity Ensemble Approach 0.699 1.8 × 10−173 ijms-17-01313-t004_Table 4Table 4 List of assays from Pubchem that were used for the study of Dahl et al. [42,43]. Article Identifier Assay Target/Goal Assay Type #Active #Inactive 1851(2c19) Cytochrome P450, family 2, subfamily C, polypeptide 19 Biochemical 5913 7532 1851(2d6) Cytochrome P450, family 2, subfamily D, polypeptide 6, isoform 2 Biochemical 2771 11,139 1851(3a4) Cytochrome P450, family 3, subfamily A, polypeptide 14 Biochemical 5266 7751 1851(1a2) Cytochrome P450, family 1, subfamily A, polypeptide 2 Biochemical 6000 7256 1851(2c9) Cytochrome P450, family 2, subfamily C, polypeptide 9 Biochemical 4119 8782 1915 Group A Streptokinase Expression Inhibition Cell 2219 1017 2358 Protein phosphatase 1, catalytic subunit, α isoform 3 Biochemical 1006 934 463213 Identify small molecule inhibitors of tim10-1 yeast Cell 4141 3235 463215 Identify small molecule inhibitors of tim10 yeast Cell 2941 1695 488912 Identify inhibitors of Sentrin-specific protease 8 (SENP8) Biochemical 2491 3705 488915 Identify inhibitors of Sentrin-specific protease 6 (SENP6) Biochemical 3568 2628 488917 Identify inhibitors of Sentrin-specific protease 7 (SENP7) Biochemical 4283 1913 488918 Identify inhibitors of Sentrin-specific proteases (SENPs) using a Caspase-3 Selectivity assay Biochemical 3691 2505 492992 Identify inhibitors of the two-pore domain potassium channel (KCNK9) Cell 2094 2820 504607 Identify inhibitors of Mdm2/MdmX interaction Cell 4830 1412 624504 Inhibitor hits of the mitochondrial permeability transition pore Cell 3944 1090 651739 Inhibition of Trypanosoma cruzi Cell 4051 1324 615744 NIH/3T3 (mouse embryonic fibroblast) toxicity Cell 3102 2306 652065 Identify molecules that bind r (CAG) RNA repeats Cell 2966 1287 ==== Refs References 1. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081314ijms-17-01314EditorialAdvances in Chronic Kidney Disease Parrish Alan R. Tikkanen Ritva Academic EditorDepartment of Medical Pharmacology and Physiology, School of Medicine, University of Missouri, Columbia, MO 65212, USA; parrishar@health.missouri.edu; Tel.: +1-573-884-439111 8 2016 8 2016 17 8 131422 7 2016 08 8 2016 © 2016 by the author; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). ==== Body Chronic kidney disease (CKD) is characterized by renal dysfunction that is present for more than 3 months; it is also associated with a number of comorbidities [1,2]. There are a number of causes of CKD, most notably type 2 diabetes and hypertension [3,4,5,6,7]. CKD is staged using estimated glomerular filtration rate (eGFR) and albuminuria [1,2]. These stages range from an asymptomatic condition (eGFR > 60 mL/min/1.73 m2 and urinary albumin < 30 mg/g) to end-stage renal disease (ESRD) (eGFR < 15 mL/min/1.73 m2 and urinary albumin > 300 mg/g), which requires dialysis or transplantation. The prevalence of CKD is significant, ranging from 2.5% to 11.2% of adults in Europe, Asia, North America and Australia [8]. In the United States the prevalence was reported to be 13.7% between 2007 and 2012 [9] and it is predicted that the prevalence will increase to 14.4% in 2020 and 16.7% in 2030 [10]. The progression of CKD is variable, necessitating further research into factors that accelerate or attenuate the disease, as well as the pathogenic mechanisms that underlie the heterogeneity of the CKD progression. CKD patients also experience poorer quality of life and loss of function compared to healthy individuals [11,12,13]. While directly assessing the economic burden of CKD and ESRD is difficult, the medical cost of patients with stage 4 and 5 CKD not requiring dialysis ranges from $7,000 to $65,000 annually, with the yearly medical care cost of ESRD patients estimated at $65,000 (Medicare) to $96,000–$180,000 (private insurance) per patient [14]. Worldwide, it is estimated that 1 trillion dollars is spent annually on ESRD patients [7], indicating the tremendous impact of this disease on health care costs. This Special Issue contains papers addressing a number of important issues in CKD with a major focus on: (1) mechanisms of pathogenesis; and (2) therapeutic interventions. In terms of mechanisms, a review focuses on the role of receptor tyrosine kinases (RTKs), most notably growth factor RTKs, in the progression of CKD [15]. In this article the potential of RTK inhibitors as therapeutic agents is also addressed. The role of autophagy and the innate immune response in the pathology of acute kidney injury (AKI) is also reviewed [16]. Given that AKI often occurs with a background of CKD [17], and that AKI can lead to CKD [18], this review may stimulate research into the role of these mechanisms in the relationship between acute and chronic renal dysfunction. The AKI–CKD connection is further investigated in a study showing that toll-like receptor 4 (TLR4) knockout mice are protected against AKI, but not fibrosis post ischemic injury [19]. Microvascular rarefaction after injury, however, was attenuated. Angiotensin II induces dipeptidyl protease 4 (DPP4) concurrent with suppression of megalin expression; this may have an important role in progression of obesity related renal dysfunction. The molecular pathway linking these pathways is elucidated in an elegant study [20]. The expression of kidney injury molecule-1 in proximal cysts in a rat polycystic kidney disease (PKD) model (PKD/Mhm) suggests that it may play a role in disease progression [21]. An interesting study demonstrates an inverse relationship between endometriosis and CKD, an effect that is abrogated by menopause [22]. These results should stimulate further research on the role of hormones in CKD. Proteomic analysis revealed that proteins involved in inflammation, coagulation, vascular damage and calcification are altered in atherosclerosis-related CKD and provide important data to examine the pathogenesis, as well as therapeutic targets, for this CKD subtype [23]. Given the important role of hemodialysis in treated ESRD patients, maintaining vascular access via arterio-venous fistula (AVF) is critical. Interestingly, Chen et al. identify two single nucleotide polymorphisms in the angiotensin II receptor 1 that are associated with AVF malfunction [24]. In diabetic patients on hemodialysis, glycated albumin was shown to be a more accurate measure of glycemic control than HbA1c [25]. Emerging therapeutic strategies to attenuate CKD are also addressed. Anemia is a profound complication associated with CKD and two papers in this Issue address therapies for this comorbidity. CKD-associated anemia is treated with recombinant human erythropoietin (rHuEPO); however resistance often develops limiting therapeutic effectiveness. In the rat 5/6 nephrectomy of CKD, resistance was shown to correlate with renal hypoxia, inflammation and fibrosis [26]; this could stimulate research into adjuvant therapies to treat anemia. In clinical studies, darbepoetin α (DA) and continuous erythropoietin receptor activator (CERA) have similar effects on hemoglobin levels in pre-dialysis CKD patients [27]. Imig and coworkers present compelling data that an omega-3 fatty acid metabolite—19,20-epoxydocosapentaenoic acid—prevents fibrosis in the mouse unilateral ureteral obstruction model, presumably by reducing epithelial-to-mesenchymal transition (EMT) [28]. Using a combination of in vitro and in vivo approaches, it is shown that metformin may be protective against renal fibrosis via inhibition of ERK signaling [29]. The use of angiotensin-converting enzyme inhibitors (ACEI) with atorvastatin may be renoprotective in male patients with coronary artery disease, assessed by GFR [30]. Finally, the role of mTOR inhibitors as therapeutic agents for the treatment of non-clear cell renal cell carcinoma, diabetic nephropathy and renal transplantation, with emphasis on the mechanistic findings underlying the renoprotective effects, is reviewed [31]. The 15 publications in this Special Issue summarize the significant amount of progress that has been made in our understanding of issues surrounding CKD. Importantly, these papers also provide direction for future studies to combat the disease. I wish to thank all the authors for their contributions and the staff at the International Journal of Molecular Sciences for their work on this Special Issue. Conflicts of Interest The author declares no conflict of interest. ==== Refs References 1. National Kidney Foundation KIDOQI clinical practice guidelines for chronic kidney disease: Evaluation, classification and stratification Am. J. Kidney Dis. 2002 39 S1 S266 11904577 2. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease Kidney Int. Suppl. 2013 3 1 150 3. Eboh C. Chowdhury T.A. Management of diabetic renal disease Ann. Transl. Med. 2015 3 10.3978/j.issn.2305-5839.2015.06.25 4. Jha V. Garcia-Garcia G. Iseki K. Li Z. Naicker S. Plattner B. Saran R. Wang A.Y. Yang C.W. Chronic kidney disease: Global dimension and perspectives Lancet 2013 382 260 272 10.1016/S0140-6736(13)60687-X 23727169 5. Mora-Fernandez C. Dominguez-Pimentel V. de Fuentes M.M. Gorriz J.L. Martinez-Castelao A. Navarro-Gonzalez J.F. Diabetic kidney disease: From physiology to therapeutics J. Physiol. 2014 592 3997 4012 10.1113/jphysiol.2014.272328 24907306 6. Su S.L. Lin C. Kao S. Wu C.C. Lu K.C. Lai C.H. Yang H.Y. Chiu Y.L. Chen J.S. Sung F.C. Risk factors and their interaction on chronic kidney disease: A multi-centre case control study in Taiwan BMC Nephrol. 2015 16 10.1186/s12882-015-0065-x 26077152 7. Wouters O.J. O’Donoghue D.J. Ritchie J. Kanavos P.G. Narva A.S. Early chronic kidney disease: Diagnosis, management and models of care Nat. Rev. Nephrol. 2015 11 491 502 10.1038/nrneph.2015.85 26055354 8. James M.T. Hemmelgarn B.R. Tonelli M. Early recognition and prevention of chronic kidney disease Lancet 2010 375 1296 1309 10.1016/S0140-6736(09)62004-3 20382326 9. United States Renal Data System (USRDS) USRDS 2014 Annual Data Report 2015 Available online: http://www.usds.orf/adr/aspx (accessed on 1 September 2015) 10. Hoerger T.J. Simpson S.A. Yarnoff B.O. Pavkov M.E. Rios Burrows N. Saydah S.H. Williams D.E. Zhuo X. The future burden of CKD in the United States: A simulation model for the CDC CKD Initiative Am. J. Kidney Dis. 2015 65 403 411 10.1053/j.ajkd.2014.09.023 25468386 11. Gorodetskaya I. Zenios S. McCulloch C.E. Bostrom A. Hsu C.Y. Bindman A.B. Go A.S. Chertow G.M. Health-related quality of life and estimates of utility in chronic kidney disease Kidney Int. 2005 68 2801 2808 10.1111/j.1523-1755.2005.00752.x 16316356 12. Pagels A.A. Soderkvist B.K. Medin C. Hylander B. Heiwe S. Health-related quality of life in different stages of chronic kidney disease and at initiation of dialysis treatment Health Qual. Life Outcomes 2012 10 10.1186/1477-7525-10-71 22710013 13. Palmer S. Vecchio M. Craig J.C. Tonelli M. Johnson D.W. Nicolucci A. Pellegrini F. Saglimbene V. Logroscino G. Fishbane S. Prevalence of depression in chronic kidney disease: Systematic review and meta-analysis of observational studies Kidney Int. 2013 84 179 191 10.1038/ki.2013.77 23486521 14. Wang V. Vilme H. Maciejewski M.L. Boulware L.E. The economic burden of chronic kidney disease and end-stage renal disease Semin. Nephrol. 2016 36 319 330 10.1016/j.semnephrol.2016.05.008 27475662 15. Liu F. Zhuang S. Role of receptor tyrosine kinase signaling in renal fibrosis Int. J. Mol. Sci. 2016 17 10.3390/ijms17060972 27331812 16. Duann P. Lianos E.A. Ma J. Lin P.-H. Autophagy, innate immunity and tissue repair in acute kidney injury Int. J. Mol. Sci. 2016 17 10.3390/ijms17050662 27153058 17. Waikar S.S. Curhan G.C. Wald R. McCarthy E.P. Chertow G.M. Declining mortality in patients with acute renal failure, 1988 to 2002 J. Am. Soc. Nephrol. 2006 17 1143 1150 10.1681/ASN.2005091017 16495376 18. Takaori K. Yanagita M. Insights into the mechanisms of the acute kidney injury-to-chronic kidney disease continuum Nephron 2016 in press 10.1159/000448081 27398799 19. Dagher P.C. Hato T. Mang H.E. Plotkin Z. Richardson Q.V. Massad M. Mai E. Kuehl S.E. Graham P. Kumar R. Inhibition of toll-like receptor 4 signaling mitigates microvascular loss but not fibrosis in a model of ischemic acute kidney injury Int. J. Mol. Sci. 2016 17 10.3390/ijms17050647 27136544 20. Aroor A. Zuberek M. Duta C. Meuth A. Sowers J.R. Whaley-Connell A. Nistala R. Angiotensin II stimulation of DPP4 activity regulates megalin in the proximal tubules Int. J. Mol. Sci. 2016 17 780 10.3390/ijms17050780 27213360 21. Gauer S. Urbschat A. Gretz N. Hoffmann S.C. Kranzlin B. Geiger H. Obermuller N. Kidney injury molecule-1 is specifically expressed in cystically-transformed proximal tubules of the PKD/Mhm (cy/+) rat model of polycystic kidney disease Int. J. Mol. Sci. 2016 17 10.3390/ijms17060802 27231899 22. Huang B.-S. Chang W.-H. Wang K.-C. Huang N. Guo C.-Y. Chou Y.-J. Huang H.-Y. Chen T.-J. Lee W.-Y. Wang P.-H. Endometriosis might be inversely associated with developing chronic kidney disease: A population-based cohort study in Taiwan Int. J. Mol. Sci. 2016 17 10.3390/ijms17071079 27399682 23. Luczak M. Suszynska-Zajczyk J. Marczak L. Formanowicz D. Pawliczak E. Wanic-Kossowska M. Stobiecki M. Label-free quantitative proteomics reveals differences in molecular mechanisms of atherosclerosis related and non-related to chronic kidney disease Int. J. Mol. Sci. 2016 17 10.3390/ijms17050631 27144566 24. Chen Y.-W. Wu Y.-T. Lin J.-S. Yang W.-C. Hsu Y.-H. Lee K.-H. Ou S.-M. Chen Y.-T. Shig C.-J. Lee P.-C. Association of genetic polymorphisms of renin-angiotensin-aldosterone system-related genes with arterio-venous fistula malfunction in hemodialysis patients Int. J. Mol. Sci. 2016 17 10.3390/ijms17060833 27240348 25. Kobayashi H. Abe M. Yoshida Y. Suzuki H. Maruyama N. Okada K. Glycated albumin versus glycated hemoglobin as a glycemic indicator in diabetic patients on peritoneal dialysis Int. J. Mol. Sci. 2016 17 10.3390/ijms17050619 27120597 26. Garrido P. Ribeiro S. Fernandes J. Vala H. Rocha-Pereira P. Bronze-da-Rocha E. Belo L. Costa E. Santos-Silva A. Reis F. Resistance to recombinant human erythropoietin therapy in a rat model of chronic kidney disease associated anemia Int. J. Mol. Sci. 2016 17 10.3390/ijms17010028 26712750 27. Furukawa T. Okada K. Abe M. Tei R. Oikawa O. Maruyama N. Maruyama T. Randomized controlled trial of darbepoetin a versus continuous erythropoietin receptor activator injected subcutaneously once every four weeks in patients with chronic kidney disease at the pre-dialysis stage Int. J. Mol. Sci. 2015 16 30181 30189 10.3390/ijms161226229 26694377 28. Sharma A. Khan A.H. Levick S.P. Sing S. Lee S. Hammock B.D. Imig J.D. Novel omega-3 fatty acid epoxygenase metabolite reduces kidney fibrosis Int. J. Mol. Sci. 2016 17 10.3390/ijms17050751 27213332 29. Shen Y. Miao N. Xu J. Gan X. Xu D. Zhou L. Xue H. Zhang W. Lu L. Metformin prevents renal fibrosis in mice with unilateral ureteral obstruction and inhibits Ang II-induced ECM production in renal fibroblasts Int. J. Mol. Sci. 2016 17 10.3390/ijms17020146 26805826 30. Wieczorek-Surdacka E. Swierszcz J. Surdacki A. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081315ijms-17-01315ArticleDe Novo Transcriptome Analysis of Differential Functional Gene Expression in Largemouth Bass (Micropterus salmoides) after Challenge with Nocardia seriolae Byadgi Omkar Chen Chi-Wen Wang Pei-Chyi *Tsai Ming-An Chen Shih-Chu *Woo Patrick C. Y. Academic EditorDepartment of Veterinary Medicine, College of Veterinary Medicine, National Pingtung University of Science and Technology, Pingtung 91201, Taiwan; omkarcof1@gmail.com (O.B.); e6261816@yahoo.com.tw (C.-W.C.); p9416004@mail.npust.edu.tw (M.-A.T.)* Correspondence: pc921003@gmail.com (P.-C.W.); scchen@mail.npust.edu.tw (S.-C.C.); Tel.: +886-8-7740569 (P.-C.W. & S.-C.C.)11 8 2016 8 2016 17 8 131525 5 2016 02 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Largemouth bass (Micropterus salmoides) are common hosts of an epizootic bacterial infection by Nocardia seriolae. We conducted transcriptome profiling of M. salmoides to understand the host immune response to N. seriolae infection, using the Illumina sequencing platform. De novo assembly of paired-end reads yielded 47,881 unigenes, the total length, average length, N50, and GC content of which were 49,734,288, 1038, 1983 bp, and 45.94%, respectively. Annotation was performed by comparison against non-redundant protein sequence (NR), non-redundant nucleotide (NT), Swiss-Prot, Clusters of Orthologous Groups (COG), Kyoto Encyclopaedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Interpro databases, yielding 28,964 (NR: 60.49%), 36,686 (NT: 76.62%), 24,830 (Swissprot: 51.86%), 8913 (COG: 18.61%), 20,329 (KEGG: 42.46%), 835 (GO: 1.74%), and 22,194 (Interpro: 46.35%) unigenes. Additionally, 8913 unigenes were classified into 25 Clusters of Orthologous Groups (KOGs) categories, and 20,329 unigenes were assigned to 244 specific signalling pathways. RNA-Seq by Expectation Maximization (RSEM) and PossionDis were used to determine significantly differentially expressed genes (False Discovery Rate (FDR) < 0.05) and we found that 1384 were upregulated genes and 1542 were downregulated genes, and further confirmed their regulations using reverse transcription quantitative PCR (RT-qPCR). Altogether, these results provide information on immune mechanisms induced during bacterial infection in largemouth bass, which may facilitate the prevention of nocardiosis. Illumina paired-end sequencingimmune responselargemouth bass (Micropterus salmoides)Nocardia seriolaetranscriptome ==== Body 1. Introduction During intensive aquaculture, fish are always exposed to stressors which may facilitate host infection by opportunistic pathogens existing in the water [1]. Indeed, detecting the invading pathogens depends on the host’s ability to recognize the pathogens [2,3]. Therefore, for rapid elimination of pathogens, fish rely on innate or nonspecific immune responses [1]. Against this background, transcriptome profiling analysis during infection in the host can facilitate genome studies and functional gene identification. However, in fish the broad identification of immune-related genes at the genome or transcriptome levels are limited to a few species [4,5]. Since the genome sequence for many non-model fish species is unknown, the study on immune genes is difficult. Moreover, the introduction of RNA deep sequencing technologies (i.e., Solexa/Illumina RNA-Seq and digital gene expression) have contributed much to the identification of important immune-related genes in fish [6,7,8,9]. In this study, we concentrated on Nocardia seriolae, a Gram-positive, acid-fast bacterium with branched hyphae which causes nocardiosis in cultured marine and freshwater fish in Taiwan, Japan, and China [10,11,12,13,14,15,16]. Nocardia seriolae infections frequently result in considerable economic loss to fish farmers in Taiwan. Recently, after infection with pathogenic microorganisms Aeromonas hydrophila in zebrafish (Danio rerio) [17] and Vibrio anguillarum infection in sole (Cynoglossus semilaevis) [18], the transcriptome profile has been reported. Additional examples of transcriptome profiling analyses include the orange-spotted grouper (Epinephelus coioides) [19], blunt snout bream (Megalobrama amblycephala) [20], Chilean abalone Concholepas (Gastropoda, Muricidae) [21], grass carp (Ctenopharyngodon idella) [22], blowfish or fugu (Takifugu rubripes) [23], large yellow croaker (Larimichthys crocea) [24], and Nile tilapia (Oreochromis niloticus) [25,26]. Several studies have also reported transcriptome profiles for L. crocea in response to immune stimuli, pathogenic infection, or environmental stress [27,28,29]. However, to our knowledge there are no studies related to fish transcriptomes for identification of gene expression profiles in response to Nocardia seriolae infection. In this study, we assembled the transcriptome of largemouth bass (M. salmoides) spleen and compared the gene expression profiles among Nocardia seriolae-infected and control groups to exhibit the molecular fitness mechanisms against bacterial infection and frame a possible strategy to prevent the outbreak of nocardiosis. 2. Results 2.1. Transcriptome Sequence Assembly Of 47,881 unigenes, 37,712 (78.76%) were annotated using at least one database, including 36,686 (97.27%) in NT, 28,964 (76.80%) in NR, 24,830 (65.81%) in Swiss-Prot, 8913 (23.63%) in KOG, 20,329 (53.90%) in KEGG, 22,194 in Interpro (58.85%), and 835 (2.21%) in GO (Tables S1 and S2). 2.2. Functional Classification Overall, 8913 (23.63%) annotated putative proteins from COG were grouped into 25 different categories (Figure 1). After filtering the poorly characterised proteins (“general function prediction only” and “function unknown”) based on the number of unigenes, the top three functional clusters were determined to be “replication recombination and modification” (1491, 16.72%), which is followed by “transcription” (1354, 15.19%) and “translation, ribosomal structure, and biogenesis” (1263, 14.17%) (Figure 1). Furthermore, 37,712 (78.76%) unigenes were assigned to 835 GO terms based on sequence homology and a total of 52 functional groups were clustered into biological process, cellular component, and molecular function (Figure 2). The unigene sequences from molecular function were clustered into 13 different classifications. Further, the largest subcategory within molecular function was “binding”, followed by “catalytic activity” In the biological process, sequences were distributed into 24 classifications. The most represented subcategories were “cellular processes” and “metabolic processes”. “Cell part” and “cell” were the most represented among 13 subcategories within the cellular component category. Overall, 20,329 (53.90%) sequences had significant matches were allocated to 244 KEGG pathways. Moreover, the highest number of genes categorised from KEGG analysis related to human disease accounted for 9567 (28.10%) genes, with sub-groups from bacterial infectious diseases (1850 genes), viral infectious diseases (1862 genes), and cancer-related genes (1349 genes). Further, 6919 (20.32%) genes were related to organismal systems where the majority of the genes were categorised as immune system-related (2076 genes), followed by endocrine system-related (988 genes), nervous system-related (977 genes), digestive system-related (894 genes), and development-related (778 genes) (Figure 3). Subsequently, metabolism, cellular processes, environmental information processing, and genetic information processing accounted for 5959 (17.50%), 4823 (14.17%), 3514 (10.32%), and 3256 (9.56%) genes, respectively. 2.3. Differentially Expressed Genes after Nocardia seriolae Challenge A total of 1384 transcript-derived unigenes were upregulated, whereas 1542 genes were downregulated in phosphate buffered saline (PBS) control and bacterial infection groups, respectively (Figure S1). The top 20 enriched pathways are shown in Figure 4, with genes involved in immune-related “Cell adhesion molecule”, “Cytokine receptor interaction”, “Hematopoietic cell lineage”, and “Phagosome” categories being the most significantly enriched. Natural killer cell-mediated cytotoxicity, hematopoietic cell lineage, toll-like receptor signalling, Fc γ R-mediated phagocytosis, antigen processing and presentation, NOD-like receptor signalling, and chemokine signalling (Table S3) were differentially expressed3among immune-related categories. These results suggest an important role for these unigenes during N. seriolae infection in largemouth bass. The differential expression in immune-related genes were identified from 13 pathways (Table S3) and were mapped to the KEGG database and observed their association among cytokines and their receptors (e.g., IL6, IL8, IL8R, IL4R, IL13RA1, IL12RB2, CXCL12, CXCR4, CCR5), toll-like receptor signalling (TLR) pathways (Figure S2) (e.g., LBP, CASP8, IKK γ, IKK α, IKK β, TRAF6, RIP1, CTSK, TLR3, IFN-αβR, IKKε, STAT1, IRF3, IRF7, p38, TNF α, IL1β, IL12, IL8, RANTES, CD40, CD86, IP10), and T cell receptor signalling (e.g., TCR, CD3, CD4/8, CD28). N. seriolae infection also influenced genes significantly related to transcriptional regulation, including NF-κB signalling (Figure S3) (NEMO, TRIM25, IKBKG, and RIP1), and JAK-STAT signalling (Figure S4) (STAM, STAT1, SOCS, and SHP2). Unigenes representative of genes differentially expressed during bacterial infection are listed in Table 1. 2.4. Differentially Expressed Gene Validation Using Real-Time PCR We identified immune-related gene sequences that were upregulated from DEG in largemouth bass (Table S4), and evaluated their homology with those from other fish species using the NCBI database. These sequences will be used for our future studies in immune response of largemouth bass to Nocardia seriolae. The expression levels of seven differentially expressed genes related to pathways including TLR, RIG I-like receptors, cytokine-cytokine receptor interaction, natural killer cell mediated cytotoxicity, and antigen processing and presentation (T-cell receptor (TCR)) were evaluated from spleen tissue. The expression levels were largely consistent with the transcriptome profile analyses suggesting that the transcriptome data were reliable (Figure 5). 3. Discussion In the present study, Illumina sequencing of control and infection treatment groups yielded 47,881 merged unigenes from spleen tissue of largemouth bass (M. salmoides). This study selected the spleen of largemouth bass 24 h after challenge as experimental samples. After challenge with N. seriolae we observed upregulations of many immune-related genes in the largemouth bass. Noticeably, immune-related pro-inflammatory cytokines and signal transduction related genes, including IL-1β, TNF receptor, CXC chemokine, TGF-β, and NF-κB, were the most significantly upregulated transcripts. After assembly, 47,881 unigenes were generated with an average length of 1038 bp and an N50 of 1983 bp, longer than the sequences achieved in previous studies using a Roche GS FLX 454 system (Basel, Switzerland) with a MIRA assembler [30] or an Illumina/Hiseq-2000 with assembling program SOAP [31]. This difference in sequence quality may be explained by differences in the sampling tissue and de novo assemblers. Since largemouth bass has an absence of a reference genome in the database, the Trinity program used in this study showed better performance compared to other tools in transcriptome assembly [32,33]. In contrast to Trinity, SOAP or MIRA assemblies adopted in previous studies [30,31] have been shown to be more fragmented with high levels of errors in sequencing and polymorphism [33,34]. In this study, the largemouth bass transcriptome yielded 47,881 merged unigenes from the Illumina/Hiseq-2000 RNA-Seq platform compared to 29,682 unigenes from the Roche 454 system and 2139 unigenes from a SMART cDNA library [35]. It is noteworthy that only 37,712 unigenes were annotated from the databases in this study based on sequence similarity; this annotation limitation also exists in other marine organism transcriptomes [36]. This could be explained due to the absence of a genomic database and genomic studies on commercially important aquaculture species [32,37,38,39]. The GO, COG, and KEGG databases used in this study for functional annotation provide valuable information about biological features of largemouth bass challenged by N. seriolae. For example, in the KEGG analysis of 20,329 sequences assigned to 244 KEGG pathways, genetic information processing accounted for 9567 pathways related to pathogen infection (Figure 3). Together, these findings indicate that primary host immune pathways are conserved in largemouth bass which are activated to protect against pathogen infections. Cytokines are proteins which transfer information among cells to initiate complex intracellular biological processes upon binding to corresponding cell-surface receptors. Moreover, cytokine levels initiate an inflammatory response to bacterial exposure which guides towards leukocyte attraction and activation of antimicrobial pathways [40,41]. Against this background, tumour necrosis factor alpha (TNF-α), which is a first cytokine released during infection activates the downstream expression of other cytokines such as IL-1β and chemokines [42,43]. In the present study, after N. seriolae infection it was observed that different cytokines and cytokine receptor families are upregulated in cytokine–cytokine receptor interaction signalling pathways (Table 1), including chemokine receptors (CXCL10, CXCR3, XCR1, CCL 20, 25, 19, 21, 5, CCR3), hematopoietin receptors (IL11RA IL6R), TNF receptors (SF11B, TNFSF12, SF14, and SF6B), TGF-β receptors (TGFBR2), and IL-1 receptors (IL-1β, IL-18, and IL-1R1). These data indicate that, in the case of largemouth bass in early stages of N. seriolae infection, cytokine–cytokine receptor interaction may represent an important anti-bacterial mechanism. In the host, pattern-recognition receptors (PRRs) recognise pathogen-associated molecular patterns (PAMPs) to defend against pathogen invasion and activate immune responses through signalling pathways, such as TLRs, RIG-I-like receptors (RLRs), NOD-like receptors (NLRs) [44], and C-type lectin receptors (CLRs) [45,46]. In this study, a total of 29 gene transcripts, which are involved in the TLR signalling pathway, are found to be upregulated, including the fish-specific TLRs (TLR22), and downstream effector molecules, such as LBP, CASP8, IKK α, IKK β, TRAF6, TAK, TBK, IKK, and RANTES. Additionally, we observed downstream effector molecules of cytokines and transcription factors including p38, IRF3, IRF7, STAT1, IL-12, IL-8, CD40, CD86, and IP10. These suggest that TLR mechanisms are conserved from fish to mammals. We observed upregulations in the expression of pro-inflammatory cytokines in our study after N. seriolae infection including IL-1β, IL-8, and TNF-α (Figures S2 and S3). Our results on TNF-α and IL-1β were in agreement with the study on Japanese flounder (Paralichthys olivaceus) in spleen after immersion challenge with N. seriolae, wherein TNF-α and IL-β were upregulated at 24 h post challenge, while CC chemokine downregulated [47]. Moreover, in the case of human monocytes, cytokines induced within 24 h following Gram-positive and Gram-negative bacterial infections [48]. The Janus kinase/signal transducers and activators of transcription (JAK-STAT) pathway initiated due to interleukins, IFNs, and growth factors present in the surrounding microenvironment [49]. Different cytokine receptors are associated with JAK for proliferation, survival, and differentiation in lymphoid cell precursor [50,51], while STAT1 activated upon IFN-γ signalling, resulting in enhanced bacteria killing and protection [48]. In this study, the members of the JAK-STAT, including STAM and Stat1, were upregulated (Figure S4). This can suggest that, the JAK-STAT pathway activated upon N. seriolae infection in largemouth bass, which can further induce other pathways, namely NF-κB signalling, the TGF-β activated SMAD pathway, and apoptosis [52]. 4. Materials and Methods 4.1. Animal Maintenance Healthy largemouth bass (Micropterus salmoides) without pathogen infection weighing 125 ± 10 g were used in this study. The fish were kept in an indoor facility at a constant temperature of 26 °C and fed daily with commercial feed. The experiment was performed two weeks after acclimatisation. Fish were anaesthetised for handling with 2-phenoxyethanol. Approval for the following animal studies was obtained from the Centre for Research Animal Care and Use Committee of the National Pingtung University of Science and Technology under protocol number 101-027, dated 19 March 2012. 4.2. Isolation, Cultivation, and Challenge with Nocardia seriolae The bacterium N. seriolae was isolated from striped bass and found to be highly virulent in farmed fish [53]. The species was identified by API ZYM and 16S rDNA sequencing, grown in Brain Heart Infusion (BHI) broth for five days at 25 °C, and enumerated prior to the challenge test. Fifteen fish were anaesthetised and injected intraperitoneally with 1.0 × 106 cfu N. seriolae that were suspended in 100 μL phosphate-buffered saline (PBS, pH 7.2). The remaining 15 fish per group received only PBS (pH 7.2) as a control. After the fish were returned to the observation tanks, samples were taken at 24 h post infection (hpi). Three fish each from the challenge (treatment) and control groups (n = 3) were examined. Spleen tissue was dissected and total RNA was isolated. 4.3. Total RNA Extraction, Preparation of cDNA Library, and Sequencing Total RNA was extracted using TRIzol® reagent (Invitrogen Corp., Carlsbad, CA, USA). RNA integrity was assessed using Agilent Bioanalyzer 2100 system (Agilent Technologies, Palo Alto, CA, USA). A TruSeq™ RNA Sample Preparation Kit (Illumina, Inc., San Diego, CA, USA) was used for cDNA library construction. Further, 40 μg total RNA was used for mRNA isolation using poly-T oligo-attached magnetic beads. First-strand cDNA was synthesized using random hexamer primers and Superscript III (Invitrogen, Carlsbad, CA, USA); this was followed by second-strand cDNA synthesis, end repair, and adaptor ligation. The RNA-Seq library was sequenced on the Illumina HiSeq™ 2000 (Illumina, Inc., San Diego, CA, USA) platform as paired-end reads to 100 bp at Genomics Bioscience Technology Co., Ltd. (Taipei, Taiwan). The transcriptome raw sequencing datasets are available from Sequence Read Archive (SRA) database in NCBI and the accession numbers are SRX1739692 and SRX1738842. All of the information on the assembled unigene sequences and annotations are available from the corresponding authors upon request. 4.4. Filtering of Sequencing Reads Raw reads were defined as adaptor-polluted reads containing low-quality or unknown base (N) reads; these reads were removed before downstream analyses. Internal software was used to filter reads, removing (1) reads with adaptors; (2) reads in which unknown bases comprised greater than 5% of the read; and (3) low quality reads (defined as the percentage of bases for which quality is less than 10 and greater than 20% in a read). After filtering, the remaining reads were called “Clean Reads” and stored in FASTQ [54] format. 4.5. De Novo Transcriptome Assembly Trinity [55] was used to perform de novo assembly with clean reads. Next, TIGR Gene Indices clustering tools, or Tgicl, was used to cluster transcripts to unigenes. In the case of two or more samples, Tgicl would be re-executed with each sample’s unigene to obtain the final unigene for downstream analysis. Unigenes were divided into two classes: clusters (CL), comprised of several unigenes with shared similarity greater than 70%, and singletons (Unigenes). 4.6. Functional Unigene Annotation and Classification For gene annotation, following database were used; NCBI non-redundant protein database [56], gene ontology (GO) [57], Clusters of Orthologous Groups [58], and the Kyoto Encyclopaedia of Genes and Genomes [59] with E-values less than 10−5 using BlastP (Version 2.2.25) [60]. With functional annotation, we selected the region of the unigene that best mapped to functional databases in a priority order of NR, SwissProt, KEGG, and COG as its coding sequence (CDS), and displayed this sequence region from 5’ to 3’ in FASTA format. Unigenes that could not be aligned to any database mentioned above were predicted by ESTScan [61] using Blast-predicted CDS as the model. 4.7. Differentially Expressed Genes Expression data from two libraries (treatment and control) were determined by mapping to the transcriptome assembly using Bowtie2 software [62,63]. The fragments per kilobase of transcripts per million fragments mapped (FPKM) values were analysed further using RESM [64] to get differentially expressed genes (DEGs) in the spleen between the control and infected groups. Further, to determine the threshold p-value in multiple tests, a false discovery rate (FDR) was used. Furthermore, significant enrichment was calculated when FDR was <0.05 and FPKM values showed at least a two-fold difference between the two samples reads. 4.8. Real-Time Polymerase Chain Reaction PCR primers were designed based on transcriptome sequences using Primer 2 Plus software (Table 2). cDNA was synthesised from 2 µg of total RNA using 200 U of M-MLV reverse transcriptase (Promega). β-Actin served as internal control and RT-qPCR was performed using iQSYBR Green Supermix (Bio-Rad Laboratories, Hercules, CA, USA), and each sample was run in triplicate. The thermal gradient feature (CFX96, Bio-Rad Laboratories) was used to determine the optimal annealing temperature for all primers. The real-time PCR program used was 95 °C for 3 min, followed by 40 cycles of 95 °C for 15 s, 58 °C for 15 s, and 72 °C for 35 s. Dissociation and melting curves of amplification products were performed and results were analysed using the CFX Manager Software package (Bio-Rad Laboratories). The 2−ΔΔCt method was chosen as the calculation method [65]. The difference in the cycle threshold (Ct) value of the target gene and its housekeeping gene (β-actin), called ΔCt, was calculated using the following equation: ΔΔCt = (ΔCt of bacterial challenge or PBS-injected group for the target gene at each time point) − (ΔCt of the initial control). 4.9. Statistical Analyses Statistical analyses were performed using SPSS 16.0 software. All data are given as mean ± SD. Significant differences between samples were analysed by one-way analysis of variance (ANOVA), and Duncan’s tests at a significance level of 0.05. 5. Conclusions This study provides necessary information on differential immune gene transcriptome profiling in largemouth bass (M. salmoides) infected with N. seriolae. Moreover, this transcriptome assembly could be used as a reference for studies related to comparative biology within the genus or family. Of course, we acknowledge that this transcriptome-level response to N. seriolae infections is a preliminary study and larger scale studies are required to further understand the defence mechanisms in largemouth bass. Acknowledgments This work was financially supported by National Science Council under grant no. MOST 104-2313-B-020-009-MY3 and NSC 101-2313-B-020-015-MY3 from the Ministry of Science and Technology, Taiwan. The authors thank Genomics Bioscience Technology Co. Ltd. (Taipei, Taiwan) for assistance with transcriptome sequencing. We are grateful to all those who contributed to the development of this research and provided input during the study. Supplementary Materials Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1315/s1. Click here for additional data file. Author Contributions Omkar Byadgi designed the experiment, conducted, analysed the results and drafted the manuscript, Chi-Wen Chen helped during the experimental design and conduct. Ming-An Tsai reviewed the experimental design and suggested during manuscript draft. Shih-Chu Chen and Pei-Chyi Wang monitored throughout the experimental process and provided substantial contribution towards data analysis and manuscript revision. All authors have read and approved the final version of the manuscript. Conflicts of Interest The authors declare no conflict of interest. Abbreviations DGEdigital gene expression Tgiclis a pipeline for analysis of large Expressed Sequence Tags (EST) NRNCBI non-redundant protein NTNCBI nucleotide COGClusters of Orthologous Groups KEGGKyoto Encyclopedia of Genes and Genomes ORFopen reading frame KOKEGG Orthology CDScoding region of protein INDELinsertion and deletion HISAT is a fast and sensitive spliced alignment program for mapping RNA-Seq reads GATKThe Genome Analysis Toolkit FPKMfragments per kilobase of transcripts per million fragments mapped RESMis a software package for estimating gene and isoform expression levels from RNA-Seq data FDRFalse discovery rate NLRNOD-like receptors TLRToll-like receptors PAMPPathogen-associated molecular patterns PRRPattern-recognition receptors CLRC-type lectin receptors SRANCBI Sequence Read Archive SSRSimple Sequence Repeat SNPsingle nucleotide polymorphism Figure 1 The cluster of orthologous groups (COG) classification. 8913 (23.63% of the total annotated putative proteins) were grouped into 25 different categories. Figure 2 Functional distribution of GO annotation. Figure 3 KEGG classification of assembled unigenes from control and treated groups. (A) Cellular processes; (B) Environmental information processing; (C) Genetic information processing; (D) Human diseases; (E) Metabolism; and (F) Organismal systems. Figure 4 Scatterplot of the top 20 enriched KEGG pathways. Rich Factor is the ratio of differentially expressed gene numbers annotated in this pathway terms to all gene numbers annotated in this pathway term. q ≤ 0.05 as significantly enriched. Figure 5 Comparative gene expression analysis from qPCR and RNA-Seq in spleen from the infected largemouth bass with N. seriolae and compared with those in the control at the 24 h time point. Expression of target genes was normalized to β-actin as a reference gene. Statistically significant differences from control are presented, with * p < 0.05. ijms-17-01315-t001_Table 1Table 1 Immune-related differentially expressed genes (DEGs) regulated after infection. Name Description Fold Change Change RIG I like receptor trim25 Tripartite motif-containing protein 25 1.32 Up dhx58 ATP-dependent RNA helicase dhx58 3.41 Up ddx3x ATP-dependent RNA helicase ddx3x 6.28 Up ikbke Inhibitor of nuclear factor κ-B kinase subunit epsilon 2.54 Up ikbkg Inhibitor of nuclear factor κ-B kinase subunit γ 2.87 Up irf3 Interferon regulatory factor 3 2.50 Up irf7 Interferon regulatory factor 7 1.10 Up casp8 Caspase 8 1.35 Up casp10 Caspase 10 1.08 Up ikkβ Inhibitor of nuclear factor κ-b kinase subunit β −1.62 Down traf6 TNF receptor-associated factor 6 3.11 Up p38 p38 MAP kinase −8.64 Down il-8 Interleukin-8 2.27 Up ip-10 Chemokine (c-x-c motif) 10 2.32 Up tnf-α Tumor necrosis factor superfamily, member 2 2.32 Up il-12 Interleukin-12a 3.41 Up lbp Lipopolysaccharide-binding protein 2.94 Up casp8 Caspase 8 1.35 Up rip1 Receptor-interacting serine/threonine-protein kinase 1 1.31 Up ctsk Cathepsin K −11.52 Down tlr-3 Toll-like receptor 3 1.16 Up ifnar1 Interferon receptor 1 1.67 Up stat1 Signal transducer and activator of transcription 1 2.38 Up il1b Interleukin 1, β 2.44 Up rantes Chemokine (c-c motif) 5 2.03 Up cd40 Tumor necrosis factor receptor superfamily, member 5 1.15 Up cd86 cd86 antigen −1.22 Down Cytokine-cytokine receptor interaction cxcl7 Platelet basic protein −1.51 Down cxcl10 Chemokine (c-x-c motif) 10 2.27 Up cxcl13 Chemokine (c-x-c motif) 13 2.27 Up cxcl14 Chemokine (c-x-c motif) 14 −2.59 Down il8rb Interleukin 8 receptor, β −1.16 Down il8ra interleukin 8 receptor, α −1.16 Down cxcr3 Chemokine (c-x-c receptor) type 3 1.13 Up xcr1 Chemokine xc receptor 1 3.82 Up ccl20 Chemokine (c-c motif) 20 5.16 Up ccl25 Chemokine (c-c motif) 25 −1.17 Down ccl19 Chemokine (c-c motif) 19 5.16 Up ccl21 Chemokine (c-c motif) 21 −1.17 Down ccl5 Chemokine (c-c motif) 5 2.03 Up ccr3 Chemokine (c-c receptor) type 3 3.82 Up il6r Interleukin 6 receptor −2.11 Down il11ra Interleukin 11 receptor α 4.20 Up csfr Colony-stimulating factor receptor (granulocyte) 1.76 Up il13ra1 Interleukin 13 receptor, α-1 2.91 Up il12rb2 Interleukin 12 receptor, β-2 1.76 Up il23r Interleukin 23, receptor 1.76 Up csf2ra Granulocyte-macrophage colony-stimulating factor receptor α 2.91 Up il1ra Interleukin 1 receptor, α 1.15 Up il21r Interleukin 21, receptor −2.67 Down epor Erythropoietin receptor −1.85 Down ghr Growth hormone receptor −9.49 Down mpl Thrombopoietin receptor −1.26 Down flt1 FMS-like tyrosine kinase 1 1.18 Up met Proto-oncogene tyrosine-protein kinase met −2.17 Down egf Epidermal growth factor −1.07 Down egfr Epidermal growth factor receptor −1.64 Down csf1r Macrophage colony-stimulating factor 1 receptor 1.80 Up ifnar1 Interferon receptor, 1 1.67 Up ifnar2 Interferon receptor, 2 1.52 Up il10ra Interleukin 10 receptor, α 4.20 Up il10rb Interleukin 10 receptor, β −1.50 Down tnfsf11b Tumor necrosis factor receptor superfamily, member 11B 1.80 Up tnfsf12 Tumor necrosis factor ligand superfamily, member 12 −1.10 Down Tnfb Tumor necrosis factor b (TNF superfamily, member 2) 2.33 Up tnfsf14 Tumor necrosis factor (receptor) superfamily, member 14 1.20 Up tnfsf6b Tumor necrosis factor (receptor) superfamily, member 6b 1.80 Up faslg Tumor necrosis factor (ligand) superfamily, member 6 1.13 Up cd40 Tumor necrosis factor (receptor) superfamily, member 5 1.15 Up tnfsf13b Tumor necrosis factor (ligand) superfamily, member 13B −1.19 Down tgfbr2 TGF-β receptor type-2 −2.10 Down Antigen processing and presentation psme1 Proteasome activator subunit 1 1.57 Up hsp70 Heat shock 70 kDa protein 4.01 Up hsp90 Molecular chaperone HtpG 2.00 Up tap1/2 ATP-binding cassette, subfamily b (MDR/TAP), member 2 2.72 Up tapbp Tap binding protein (tapasin) 2.46 Up pdia3 Protein disulfide isomerase family a, member 3 10.67 Up mhci Major histocompatibility complex, class I 5.19 Up b2m β-2-microglobulin 1.25 Up mhcii Major histocompatibility complex, class II 1.99 Up ciita Class II, major histocompatibility complex, transactivator 1.74 Up tcr-α T cell receptor α chain v region −9.97 Down Natural Killer Cell Mediated Cytotoxity cd48 cd48 antigen 2.29 Up trailr Tumor necrosis factor (receptor) superfamily, member 10 −1.02 Down prf1 Perforin 1 9.91 Up grb Granzyme B −1.74 Down igg Immunoglobulin heavy chain g 5.30 Up fcγr3 Low affinity immunoglobulin γ Fc region receptor III 1.59 Up fasl Tumor necrosis factor (ligand) superfamily, member 6 1.13 Up shp-2 Tyrosine-protein phosphatase non-receptor type 11 9.78 Up dap-12 Tyro protein tyrosine kinase binding protein −2.26 Down vav1 Guanine nucleotide exchange factor vav 1.24 Up 3bp2 sh3-domain binding protein 2 9.78 Up slp-76 Lymphocyte cytosolic protein 2 −3.12 Down shc1 SHC-transforming protein 1 9.78 Up can Serine/threonine-protein phosphatase 2B catalytic subunit 1.58 Up pkc Classical protein kinase c α type 1.0 Up ijms-17-01315-t002_Table 2Table 2 Primer name, sequence, target gene, and their application used in the present study. Name Sequence Target Gene Application LMBIL-12 F1Q TCTTCCATCCTTGTGGTCTTCC IL-12p40 qPCR LMBIL-12 R1Q CAGTTCCAGGTCAAAGTGGTC LMBIL-8 F1Q GAGCCATTTTTCCTGGTGACT IL-8 LMBIL-8 R1Q TCCTCATTGGTGCTGAAAGATC LMBIL-1 F1Q CAAGATGCCTAAGGGACTGGA IL-1 LMBIL-1 R1Q AGGTGAACTTTGCGGTTCTC LMBTCR F1Q ATCATCTTTGGAAGTGGAACC TCR LMBTCR R1Q GATGTTGAAGACGACGGTCTT LMBCD40 F1Q TACAAGTGAAACATGGGGCAAC CD40 LMBCD40 R1Q TGATGAAGAGTCCACCTTACCG LMBβ-Actin375F CCACCACAGCCGAGAGGGAA β-actin LMBβ-Actin375R TCATGGTGGATGGGGCCAGG LMBIL-1βF TTGCCATAGAGAGGTTTA IL-1β LMBIL-1βR ACACTATATGCTCTTCCA LMBTNFα-F CTAGTGAAGAACCAGATTGT TNF-α LMBTNFα-R AGGAGACTCTGAACGATG ==== Refs References 1. Camp K.L. Wolters W.R. Rice C.D. Survivability and immune responses after challenge with Edwardsiella ictaluri in susceptible and resistant families of channel catfish, Ictalurus punctatus Fish Shellfish Immunol. 2000 10 475 487 10.1006/fsim.2000.0261 11016583 2. Ellis A.E. Innate host defense mechanisms of fish against viruses and bacteria Dev. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081316ijms-17-01316ArticleDevelopment of a Modular Assay for Detailed Immunophenotyping of Peripheral Human Whole Blood Samples by Multicolor Flow Cytometry Rühle Paul F. Fietkau Rainer Gaipl Udo S. *Frey Benjamin Piva Terrence Academic EditorDepartment of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen 91054, Germany; paul.ruehle@uk-erlangen.de (P.F.R.); rainer.fietkau@uk-erlangen.de (R.F.); benjamin.frey@uk-erlangen.de (B.F.)* Correspondence: udo.gaipl@uk-erlangen.de; Tel.: +49-9131-85-44258; Fax: +49-9131-85-3933511 8 2016 8 2016 17 8 131622 6 2016 28 7 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).The monitoring of immune cells gained great significance in prognosis and prediction of therapy responses. For analyzing blood samples, the multicolor flow cytometry has become the method of choice as it combines high specificity on single cell level with multiple parameters and high throughput. Here, we present a modular assay for the detailed immunophenotyping of blood (DIoB) that was optimized for an easy and direct application in whole blood samples. The DIoB assay characterizes 34 immune cell subsets that circulate the peripheral blood including all major immune cells such as T cells, B cells, natural killer (NK) cells, monocytes, dendritic cells (DCs), neutrophils, eosinophils, and basophils. In addition, it evaluates their functional state and a few non-leukocytes that also have been associated with the outcome of cancer therapy. This DIoB assay allows a longitudinal and close-meshed monitoring of a detailed immune status in patients requiring only 2.0 mL of peripheral blood and it is not restricted to peripheral blood mononuclear cells. It is currently applied for the immune monitoring of patients with glioblastoma multiforme (IMMO-GLIO-01 trial, NCT02022384), pancreatic cancer (CONKO-007 trial, NCT01827553), and head and neck cancer (DIREKHT trial, NCT02528955) and might pave the way for immune biomarker identification for prediction and prognosis of therapy outcome. immune monitoringmulticolor flow cytometryimmunophenotypingliquid biopsywhole bloodinnate immune systemadaptive immune system ==== Body 1. Introduction In the last decades, immunotherapy (IT) has become a prominent part in multimodal cancer therapy complementing the classical treatments of surgery, chemotherapy (CT) and radiotherapy (RT). It has successfully been established for certain cancers, but unfortunately not all cancer therapies benefit from its promising potential. Furthermore, challenges exist in finding optimal combinations and suited time points for its inclusion. Here, the knowledge of the immune status during therapy is becoming increasingly important particularly in the prediction and prognosis of therapy responses in multimodal cancer treatments [1]. It has become clear that classical tumor therapies such as RT and CT do not only destroy tumor cells, but also modulate their phenotype and, especially in the combination with further IT, can initiate systemic immune-mediated anti-tumor responses [2]. Once the relationships between tumor stage, therapy and immune status have been identified, prognostic and predictive markers might be derived [3,4,5]. Thereby, one big challenge is to monitor the immune status in a close-meshed manner to identify optimal time points for integration of IT into existing RT/CT protocols [6]. Apparently, the immune monitoring would ideally be performed in the affected tissues. However, these are not always accessible or a repetitive extraction is prohibited. Thus, liquid biopsies such as whole blood are mandatory in addition to solid biopsies that only give hints on the immune status at restricted time points of the disease due to limited availability. Indeed, the peripheral blood is of great significance for a close-meshed immune monitoring because it is relatively easy to obtain and still carries a high informative value as the immune cells pass it to reach their target tissues. Thus, immune modulations in the distant tumor microenvironment might also affect the immune status in the peripheral blood allowing the recognition of therapy responses [7]. Consequently, the immune monitoring of blood is ideal for the analysis of cancer progression and therapeutic outcomes [8] complementing standard analyses performed with solid biopsies [9]. Here, the multicolor flow cytometry can easily make its way into clinical routine, especially, when blood is the biomaterial. The possibility of measuring multiple parameters at once on a single-cell level combined with a high throughput makes flow cytometry to one of the most powerful technologies for determining cell subsets in a mixed suspension [10]. Over the last years, several groups have developed multicolor flow cytometry-based assays that are suitable for an immune monitoring of patients. These assays widely differ in their level of detail ranging from one cell type [11,12,13] over lymphocytes [14] or myeloid cells [15] to a comprehensive immune status [16,17,18] from which, however, often the granulocytes (neutrophils, eosinophils and basophils) were omitted [17,18]. Recently, the focus was furthermore set on the establishment of harmonized assays that are suited for an application in multi-centric analyses [18,19,20]. These assays often include the pre-analytic isolation of peripheral blood mononuclear cells (PBMC) to enhance the sample durability which allows sample storage and long-term shipments. However, as this procedure is time consuming and omits certain cell types, it also carries some disadvantages. We present here a multicolor flow cytometry-based assay that examines the detailed immune status covering 34 different immune cell subsets and three non-immune cell subsets in only 2 mL of human peripheral blood. It was optimized for a direct staining of whole blood samples which on the one hand allows the detection of all circulating immune cells and on the other hand reduces the required preparation steps. Thus, in addition to minimizing effort and variations in sample preparation, the direct staining procedure also is time-saving, a further prerequisite for an easy clinical application, involving less than 20 min hands-on time. The assay was designed to allow a detailed immunophenotyping of blood (DIoB) identifying almost all circulating immune cells. These cover all major immune cell types such as T cells, B cells, natural killer (NK) cells, dendritic cells (DCs), monocytes, neutrophils, eosinophils, and basophils, as well as circulating stem cells, progenitor cell and endothelial cells which already have been connected to certain cancer therapy responses. Moreover, the functional state is analyzed by the additional staining for activation markers. The DIoB assay was designed in a modular principle comprising 12 panels which each is dedicated to determine one specific type of cell and its subsets. In total, it is suited for the monitoring of the detailed immune status of patients in short intervals paving the way for optimization or even individualization of multimodal cancer therapies. 2. Results 2.1. Examining 37 Cell Subsets The DIoB multicolor flow cytometry assay allows the identification of 34 well defined immune cell subsets in human peripheral whole blood samples (Figure 1). These encompass all major immune cell types which are differentiated into 34 different subsets. Additionally, three non-immune cells which have been associated with disease progression are evaluated. The DIoB assay was designed in a modular system comprising 12 different panels (Table 1). Thereof, 11 panels (P01–P11) are each dedicated to one cell type. In contrast, the 12th panel determines the absolute cell count of the identified subsets. Additionally, 27 activation markers were included for determination of the activation state of these cells. The gating strategy for identification of these cells and its activation states is outlined in the following results sections including their phenotypical descriptions in the literature. An overview of definitions for each cell is provided in Table 2. 2.2. Morphology of All Leukocytes The identification of all subsets was performed by the analysis of surface antigens. However, the first step in all panels was the definition of a few identical gates, which were based on the morphologic properties of the cells, to discriminate the unwanted events from leukocytes and thus creating a consistent basis for the subsequent surface marker investigations. First, the Flow-gates were defined analyzing the event count against the time parameter (Figure 2A) to check for irregularities during acquisition and to discriminate these. Then, cell doublets were excluded by the integral of the forward scatter (FSC) signal (area of signal) versus the FSC time of flight (width of signal; Figure 2B) followed by FSC integral vs. FSC peak (height of signal; Figure 2C). Subsequently, the All Cells-gate was defined, representing the circulating leukocytes and non-leukocytes, based on its FSC (size) and side scatter (SSC: complexity) parameters (Figure 2D). We only considered cells that were in good shape and excluded all events that had a lowered FSC signal (Figure 2D). This All Cells gate might further be distinguished into lymphoid cells (PBL, small and less complex), monocytes (Mo, more complex) and granulocytes (Gr, biggest and most complex) as shown in Figure 2E. However, the subsequent identification of cell subsets should be performed on basis of all leukocytes (All Cells-gate). These few morphology gates were defined in the same manner for all panels, except P12, to ensure the examination of the same set of events in all panels. Eventually, similar gates were defined for P12 by adjusting for its scatter characteristics, meaning higher FSC properties because the sample had not been centrifuged (described below in Section 2.11). Intentionally, we did not include a dead cell marker as dead cells were not a big issue in fresh whole blood samples and were always below 1% as confirmed by preceding analyses (not shown). Besides, we observed that almost all propidium iodide (PI) positive cells were located within the cells that shifted to a lower FSC, which were excluded anyway. Nevertheless, if some users wish to include a dead cell marker, PI would be ideal since the corresponding fluorescence channel was left blank in all panels. 2.3. T Cell Subsets The T cell subsets are the most intensively studied ones and with about 20%–30% also the second most common in the peripheral blood. Thus, until today a plethora of different subsets were identified and characterized. Likewise, our assay identified the most subsets within the T cells which were determined in P01, P02 and P03 (Table 1: red rows). Additionally, their activation state was determined in P05 together with B cells (Table 1: red/green row). First, in all four panels the same CD3+ gate was defined (Figure 3A) followed by individual sub-gating. In P01 the CD4+ T helper cells (TH) and CD8+ cytotoxic T cells (TC) were distinguished whereby T cells expressing both antigens were excluded from these definitions (Figure 3B). For TH definition, only CD4hi cells were considered. In contrast, we included both the CD8hi and CD8lo cells into the definition of TC as CD8 might be down-regulated in certain subsets [21]. These cells were analyzed individually (T8lo and T8hi) and subsequently merged into all TC by the definition of a Boolean gate (see Table 3). The remaining T cells (5%–10%) differed from the classical view of circulating T cells and thus were excluded from continuing examinations, but were still recorded as double negative (DNT: CD4−/CD8−), double positive (DPT: CD4hi/CD8hi), CD4lo/CD8−, CD4hi/CD8lo or CD8hi/CD4lo T cells (Figure 3B: for the sake of clarity the last three gates are not shown). We never observed a subset with jointly low expression of both markers (CD4lo/CD8lo). This analysis was continued by individual subdivisions of TH and TC by their CD45RA and CD197 (CCR7) expression into naïve (CD45RA+/CD197+), effector (TEFF, CD45RA+/CD197−), effector memory (TEM, CD45RA−/CD197−) and central memory (TCM, CD45RA−/CD197+) T cells (Figure 3C–E), as previously described by [22,23]. In P02 the TH were further distinguished into the TH1 (CD183+/CD196−), TH2 (CD183−/CD196−) and TH17 (CD183−/CD196+) subsets (Figure 3H,I), as described by [24,25,26]. A fourth heterogeneous TH population (Figure 3I: CD183+/CD196+) was observed which was described to comprise TH1 and cells producing both IFNγ and IL-17 [24]. Furthermore, the regulatory T cells (TREG) were identified by their CD25hi/CD127−/lo phenotype [27,28] as shown in Figure 3J. The panel P03 was included to explore the TCR expression and to thereby distinguish the T cells into TCRα/β+ and TCRγ/δ+ T cells [29] (Figure 3K). In addition to the subset identification, the T cells were investigated for their activation state. This, on the one hand, was explored for the different TH and TC subsets in P01 by analyzing the expression of the typical T cell activation marker CD38 [30] (Figure 3E–G). On the other hand, common lymphocyte activation markers such as CD25 (IL2 receptor), CD69 (very early activation antigen), CD80 (B7-1) and CD86 (B7-2) were determined in P05 for the general T cell population without distinguishing any subsets (Figure 3M–P). In addition, the expression of HLA-DR was monitored (Figure 3Q) which might be expressed upon activation [31]. Such HLA-DR+ T cells are able to present auto antigens to other T cells and thus directly suppress them [32]. Moreover, the immune checkpoint protein CD279 (PD1: Programmed Cell Death Protein 1) was monitored in P05 (Figure 3R). It plays an important role in T cell balance and immune tolerance and has already been described to be regulated in certain cancers (reviewed in [33]) and is also a target for IT [34]. In parallel, its ligands CD274 (PD-L1), CD80, and CD86 that are expressed by other immune cells such as DCs and monocytes were monitored in the respective panels (described below). In parallel, also the expression of the immunosuppressive CD152 (CTLA-4: cytotoxic T-lymphocyte-associated protein 4) was analyzed on T cells in P03 (Figure 3L). The CD152 is of clinical interest as it is, like CD279 (PD1), a prominent suppressor of T cell activation and upregulated following T cell activation to prevent an excessive immune reaction. It moreover also represents a target for IT (reviewed in [2]). 2.4. B Cell Subsets The B cells belong with about 5% of all leukocytes to the less common peripheral cells and unfortunately also their subset distinction is not always uniform. Even though their role in the circulation is not completely understood, their number was described to be altered in numerous diseases and cancers and therefore should be monitored. We distinguished six well-described subsets in P04 (Table 1: green row) and examined the activation state of B cells in P05 (Table 1: green/red row). To identify B cells the pan marker CD19 is widely used, but has the disadvantage of low expression and down-regulation in certain diseases and cancer [35]. Consequently, we additionally included CD20 as another common B cell marker which is expressed at high levels on most B cells. Both markers were separately analyzed in P04 (Figure 4A,B), but combined prior to the subset examinations by defining a Boolean gate (CD19 or 20 B cells; see Table 3). Consequently, the potential regulation of one of these markers could be estimated in the course of different blood withdrawals. Then, in a two-step gating process the B cells were further divided into six subsets by their differential expression of CD27, CD38, CD5 and CD24 (summarized in [36,37]) (Figure 4C–G). Thus, we identified pre-naïve [38] (Figure 4D: CD27−/CD38−/lo/CD5+), naïve [38,39,40] (Figure 4D: CD27−/CD38−/lo/CD5−), memory [39,40,41] (Figure 4E: CD27+/CD38−/lo/CD5−/CD24+) and transitional B cells [42,43] (Figure 4F: CD27−/CD38hi/CD5+/CD24+), as well as plasmablasts [44] (Figure 4G: CD27+/CD38hi/CD5−/CD24−). In addition, we identified the rare immunosuppressive B10 regulatory B cells (BREG) which do not express a unique set of surface markers but are CD27+/CD24hi [45]. Thus, we gated on all CD27+/CD24hi B cells after the exclusion of the other five subsets. Therefore, a Boolean gate was defined and labeled as Rest of B cells which covered all B cells that were not located in the pre-naïve, naïve, memory, transitional or plasmablast gates (see Table 3). These cells were then investigated for their CD27 and CD24 expression (Figure 4H). The activation state of B cell subsets was examined in P05. For this, both pan markers (CD19 and CD20) were combined into the same fluorescence channel (Figure 4J) resulting in the saving of one channel. Subsequently, the expression of CD25, CD69, CD80, CD86 and HLA-DR was examined on B cell level (Figure 4K–O), as described above for the T cells. 2.5. Natural Killer Cells The distribution of the innate NK cells is with 3%–5% similar to that of B cells. In general, NK cells are identified by their CD56 expression while lacking the common T cell marker CD3 [46] and are then distinguished into different subsets by their CD16 (FCγRIIIA) co-expression [47]. Despite, also NK cells lacking of CD56 expression exist, in particular in the periphery of patients with chronic viral infections [48]. For their evaluation as well as further distinction of NK cells in up to five subsets a comparison of the co-expression of CD94 [49] and/or CD57 [50] could be taken into account (discussed in [51]). Investigating the CD16 molecule on NK cells one should pay attention in choosing an antibody clone which specifically detects its isoform A. Although the antibody clone 3G8 is widely used, we included the B73.1 clone as we obtained better results in whole blood staining. In addition to NK cells also monocytes express the isoform A of the FcγRIII, but neutrophils express FcγRIIIB (summarized in [52]). Both isoforms are up to 95% identical; however, the B73.1 clone specifically binds to the FcγRIIIA and the 3G8 clone binds both isotypes [53]. Consequently, using the B73.1 clone results in a stable staining of CD16+ NK cells and monocytes even in neutrophil-rich patient blood samples. However, this advantage might be tarnished as there is a rare mutation in the CD16a molecule leading to loss of the binding epitope for the B73.1 antibody. This rare mutation is linked to primary NK cell immunodeficiency and repeated infections by herpes viruses [54]. Though, it is advised to apply both clones in persons with suspicion of such a CD16a mutation or when analyzing study populations of primary NK cell immunodeficiencies. In the DIoB assay, we focused on the CD56+ NK cells (Figure 5A,B) and distinguished them into three different subtypes (Figure 5C) in both NK cell panels P06 and P07 (Table 1: blue rows) which only differ in the examination of functional markers. The CD56lo/CD16+ cells (here termed NK1) accounted with nearly 90% by far for the main subset. These NK1 cells were described as predominantly cytotoxic [47,55,56]. In contrast, the CD56hi/CD16− cells (here termed NK2) were described as having a rather supporting role by predominantly secreting cytokines [47,55,57]. In addition, we summarized the CD56lo/CD16− NK cells as NK3 subset. This subset has functionally not been described yet, but was proposed to be in-between developmental states [58]. All these gates were linked between P06 and P07. Most NK cells express CD314 (NKG2D: natural-killer group 2 member D) [59,60,61] (Figure 5D). Thus, this marker can also be useful for the cross-checking of the NK cell identification (Figure 5I,J) as the low expression of CD56 sometimes complicates the NK cell identification. However, it should be noted that the CD314 expression might be regulated under certain conditions [62,63,64] making it inappropriate for a direct NK cell identification. In order to determine the activation state, the common lymphocyte activation markers CD25 (Figure 5G) and CD69 were analyzed (Figure 5H) in P06 as suggested by [65,66,67]. Furthermore, we characterized the NK cells by their expression of CD159a (NKG2A), CD159c (NKG2C) and CD94. The CD159a and CD159c form heterodimers with CD94 and function as inhibitory (CD94/NKG2A) or activating (CD94/NKG2C) receptor complex (summarized in [68]) and consequently were analyzed in co-expression (Figure 5E,F). For the determination of those molecules on the different NK cell subsets, Boolean gates were defined accordingly (not shown). 2.6. NKT Cells The NKT cells are a not-uniformly defined type of cells expressing markers from both T and NK cells. Consequently, they are defined as CD3 positive cells that simultaneously express CD56, CD314 or CD16 and have been described in several studies such as [69,70,71,72,73,74]. Thus, we investigated the expression of CD56, CD16, CD314, CD159a, CD159c and CD94 on all CD3+ cells in parallel to the expression on NK cells in the panels P06 and P07 (Figure 5K–O). Then, all cells belonging to a least one of these gates were termed NKT cells. For determination of NKT cell prevalence, Boolean gates were defined to prevent the repeated counting of cells which belonged to more than one of the NKT gates (see Table 3). 2.7. Monocytes The monocytes constitute for 7%–10% of all leukocytes and have typical SSC properties in-between that of the SSChi granulocytes and that of the SSClo lymphoid cells. However, with CD14 they also express a very characteristic pan marker. Their subset definition is not always uniform, but it is generally accepted to divide them by their CD16 (FCγRIIIA) co-expression into two, three or even four subsets. These subsets could then further be subdivided by their co-expression of other markers such as HLA-DR. The nomenclature of monocyte subsets is well-summarized in [75]. As already mentioned for the NK cell subsets, one should concern choosing an antibody-clone which binds the isoform A of the CD16-molecule. Moreover, monocytes generally have a high unspecific binding capacity and therefore all antibodies used for their characterization have to be checked very carefully. Furthermore, it already has been described that monocytes also bind certain fluorochrome dimers such as PE-Cy5 [76], PE-Cy7 and APC-Cy7 [77] and we observed the same to be true for the PE-Vio770. Consequently, these fluorochromes should not be used for monocyte evaluation. The DIoB assay identifies the monocytes in P08 (Table 1: brown row) by their CD14 expression (Figure 6A) and subdivides them by their CD16 co-expression into four subsets (Figure 6B). The CD14hi/CD16− monocytes obviously accounted with 80%–90% for the main subset. This subset is often referred to as classical monocytes, but we termed them Mo1 in our assay to avoid any confusion; the other three subsets were labelled in a similar way. Roughly 10% of all monocytes express CD16 and were subdivided into the CD14lo/CD16+ Mo2 subset (also referred to as non-classical monocytes) and the CD14hi/CD16+ Mo3 subset (often referred to as intermediate monocytes). Following the identification of these three monocyte subsets, a fourth subset remained which expresses CD14 at low levels and no CD16. These cells were labeled as Mo4 despite that these cells may not be monocytes per se, but rather DC precursors [78]. As this subset may have a very low or even no HLA-DR expression, it can also be found as CD14+/HLA-DR−/lo monocytes in the literature and has already been associated with cancer prognosis [79]. In order to determine the activation state of the monocytes, the expressions of CD80, CD86, CD64 (FcγRI) and HLA-DR were investigated (Figure 6C–F). There is no or only very weak expression of CD80 on resting monocytes (Figure 6C), which has been described to be upregulated upon stimulation only [80]. In contrast, almost all monocytes constantly expressed CD86 and CD64 which, however, both might be further upregulated upon activation [80,81]. Thus, both markers were analyzed in two patterns (Figure 6D,E). Likewise, the HLA-DR is constantly expressed on most monocytes, but may be upregulated [82] or lost [83] under certain diseased conditions. This regulation or loss of HLA-DR has already been described in several studies as a prognostic marker for severe inflammation with connection to therapy outcome [84,85,86,87,88,89,90,91,92,93,94], but the continuous expression of HLA-DR may complicate the evaluation of its up or downregulation in patient samples. However, as performed for CD64 and CD86, the expression patterns could roughly be divided into two non-separated patterns which were defined and analyzed as HLA-DR+ (normal state) and HLA-DRhi (upregulated state) as shown in Figure 6F. In defining gate boarders, the comparison of the four subsets might be useful. With the help of Boolean gates these activation states might easily be further evaluated on the subset level of monocytes (not shown). 2.8. Granulocytes The granulocytes comprise the neutrophils, eosinophils and basophils. The first two have very characteristic high SSC properties and are therefore easy to identify; this is further improved by the expression of CD66 as a typical pan marker [95]. In contrast, the basophils show no typical scatter characteristics and are located in-between the lymphoid (SSClo) and monocyte (SSCint) populations. They also lack the expression of a characteristic pan marker and therefore are often not examined in patient blood. However, these cells seem to have a regulatory potential [96] and therefore their characterization could be worthwhile. In the DIoB assay the neutrophils and eosinophils were identified in panel P09 (Table 1: pink row) by their high SSC attributes and joint expression of CD66 (Figure 6G). Then, they were distinguished from each other by their differential CD16 expression [97] (Figure 6H). The neutrophils express, in contrast to NK cells and monocytes, the isoform B of the CD16 molecule and we included the 3G8 clone for its examination as already described above in the NK cell section. Subsequently, the neutrophils and eosinophils were investigated for their expression of CD64, which is, in contrast to the constitutive expression on monocytes, expressed on neutrophils and eosinophils upon stimulation only [98,99] (Figure 6I,J). The basophils were identified together with DCs in P10 (Table 1: violet row). As both cell types lack characteristic identification markers, the first step in their identification is the exclusion of all other immune cells. Therefore, a lineage cocktail (LIN) was introduced comprising pan markers (CD3, CD14, CD16, CD19, CD20 and CD56) for identification and exclusion of those cells (Figure 6K). Then, within the remaining cells the basophils were identified by their HLA-DR negative and CD123 positive phenotype [100,101] (Figure 6M,L). 2.9. Dendritic Cells The DCs are spread over the whole body predominantly residing the tissues, but also circulating the periphery in an immature form. Even though they circulate in very small numbers counting for less than 1% of all leukocytes, they are potent regulators of the immune system and thus even small modulations could cause extensive effects. It is well described that blood DCs do not express any specific lineage markers, but are positive for HLA-DR and can then further be subdivided by their expression of CD11c, CD123 and CD1c (overview of DC subsets provided in [102,103]). The DCs were determined together with basophils in P10 (Table 1: violet row) as for both immune cell types the discrimination of all other immune cells is crucial. Therefore, a lineage cocktail (LIN: CD3, CD14, CD16, CD19, CD20 and CD56) was included eliminating the unwanted immune cells (Figure 6K). In the next step, DCs were identified by their expression of HLA-DR (Figure 6L) and then divided into the two major subsets of plasmacytoid (pDC: CD123hi/CD11c−) and myeloid DCs (mDC: CD11chi/CD123−) as shown in Figure 6N. The mDC were further classified by their CD1c expression into type 1 (mDC-1, CD1c+) and type 2 (mDC-2, CD1c−) [104,105,106] (Figure 6O). The mDC-2 are characterized as CD141+, but are negative for CD1c [106,107]. For simplification we gated on the CD1c− fraction. However, one might also add the CD141 antibody as an additional marker. Due to the very low frequency of the mDC-2 subset, we stained 300 µL of whole blood to obtain sufficient cell numbers. Eventually, the DC subsets were investigated for their expression of the immune tolerance inducing CD274 (PD-L1: Programmed Cell Death Ligand 1) [108,109,110] as shown in Figure 6P. Therefore, the Boolean gate all DCs was defined (see Table 3). This expression was then also compared to the CD279 (PD-1) expression on T cells as these interactions are key immune checkpoint regulators (summarized in [33]). Although it has been described that most circulating DCs in the peripheral blood are immature [111,112], the expression of the common maturation marker CD83 was analyzed (Figure 6P). 2.10. Non-Immune Cells In addition to the manifold immune cell subsets also some non-immune cells were identified which circulate the blood in a very low frequency. These include the hematopoietic stem cells (HSC), endothelial progenitor cells (EPC) and circulating endothelial cells (CEC). In healthy persons these cell types are very rare counting for less than 0.01% of all white blood cells, but were described to become more frequent in various diseases and cancer. In the steady state the HSC predominantly replenish myeloid immune cell subsets in the peripheral tissues, but an increased number was connected to immunosuppression and disease progression in various solid tumors [113]. The EPC and CEC play both important roles in neovascularization and angiogenesis and were also associated with poor therapy outcome in cancer [114,115]. These rare and heterogeneous cell populations are characterized in P11 (Table 1: yellow row) evaluating the expression of CD34, CD146 and CD133 in addition to the common leukocyte marker CD45 [116] (Figure 7). Here, one has to stress that no precise phenotypic definitions exist for identification of these rare cells. However, consensus was found in defining CECs as CD146+ and CD133− [115,117,118,119]. In contrast, the EPCs lack CD146, but express CD133 [116,117,120,121]. The HSCs are generally characterized as CD45−/lo and CD34+. Furthermore, they normally lack CD146 and potentially express the hematopoietic marker CD133 [117,122]. Based on these definitions, we gated on all CD146− cells which also express CD133 to identify EPCs (Figure 7A,B). As various groups described that there are CD45− and CD45+ EPC, we also took this into consideration (Figure 7B). Then, we identified the circulating HSC which express no or only little CD45 and lack CD146, but are positive for CD34 (Figure 7C,E,F). As there are reports about CD133+ and CD133− HSC, one might include this distinction additionally (Figure 7F). In parallel, the CEC were identified as cells also expressing no or only little CD45, but in contrast to HSC are CD146+/CD133− (Figure 7C,D). Due to the low event number the CD34 and CD133 gates were adjusted on the All cells-level (Figure 7G). We prepared 300 µL of whole blood for the analyses as these cells are very rare. This volume might be further elevated to also identify those cells in healthy persons. Here, one should consider that due to the low number of cells and lack of consensus in phenotypic characterization, a collective identification might be appropriate. Therefore, one could define these cells as hematopoietic stem and progenitor cells (HSPC) and sum the individually identified cells using Boolean gates. Alternatively, a simplified gating strategy could be applied. 2.11. Determination of Absolute Cell Numbers Since leukocyte numbers vary during therapies and the DIoB assay will especially be used for longitudinal monitoring of patients during therapy, we additionally determined the absolute leukocyte count in panel P12 (Table 1: uncolored row). Therefore, the blood was transferred into a TruCount tube from BD Bioscience containing a definite number of beads and stained with antibodies against various pan markers (CD3, CD16, CD19, CD20, and CD56). Then, cells and beads were simultaneously acquired allowing the determination of the leukocyte count in the blood sample. In the first step, the morphologic properties were, similar to the gating of the other 11 panels, examined to discriminate any unwanted events. However, here, the gate settings were slightly adapted to the elevated FSC properties as this sample has not been centrifuged. Thus, a Flow-gate, two singlet-gates and the All Cells-gate were defined (Figure 8A–D). As no washing steps were performed, the leukocytes were discriminated from debris defined by their CD45-expression (Figure 8E) which was not necessary in the other panels. In order to identify all major immune cells the gating was performed in the following sequence: T cells (CD3+), B cells (CD19+ or CD20+), monocytes (FSC vs. SSC), granulocytes (FSC vs. SSC), NK cells (CD56 vs. CD16) and Rest of PBL (Figure 8F–J). Thereby, the already identified cells were excluded from the next analysis step resulting in enrichment of the down-stream identified smaller immune cell populations. Additionally, the CD16 expression of granulocytes and monocytes (Figure 8K,L) was examined. In parallel, the number of acquired beads was determined. Therefore, a Beads-gate (FSClo/SSChi) was created (Figure 8D) and the fluorescence emission of the beads was checked in all fluorescence channels. Using our cytometer settings (see Table S2), the fluorescence channels FL1, FL2, FL3, FL4 (blue laser; Figure 8M–P), and FL8 (red laser; Figure 8Q) were most suitable for a distinct visualization of the beads. The identification of beads was very clear and without any variations between the fluorescence channels. Nevertheless, we observed two populations with the same SCC but different FSC characteristics concluding a singlet (>97%) and a doublet (<3%) population. Thus, the doublets were added twice to the singlets and the mean of all five channels was calculated. Finally, we obtained the absolute cell number for all determined cell types per µl of blood using the formula stated at the bottom of Figure 8: (“cells acquired”/“beads acquired”) × (“absolute beads per tube”/“absolute blood sample volume”). These absolute cell counts were then transferred onto the major cells determined in the other eleven panels allowing the calculation of an absolute number for all 37 cell subsets determined in the DIoB assay. 2.12. Determination of a General Assay Robustness In order to determine the robustness of the DIoB assay, we processed and analyzed the blood samples of two normal healthy donors (NHD) three times separately and calculated the CV between all populations (Figure 9). In total, the proportional distributions of 208 populations were calculated. Hereby, we observed that high CV values only occurred within small populations and within the activation state determinations, as also described by others. Thus, all populations that consisted of less than 100 events were excluded from further interpretation as an informative value cannot be granted. The evaluation of the remaining 176 populations showed that most populations (67%) had a very small CV below 5% (Figure 9), and nearly all populations (93%) had a small CV below 10%. Only 11 populations (6%) had a CV between 10 and 15% which were all rather small subsets (<0.1% of leukocytes) or low-frequently expressed activation markers. In the end, only one population (CD80+ monocytes) had with 19.8% a CV above 15% which is an activation marker almost not expressed on resting cells in blood samples of NHD. 3. Discussion Today, the immune monitoring not only focuses on the distinction of particular cell subsets, but also on the interactions between these cells. Furthermore, as so far no single biomarkers have been identified that are connected to cancer progression or therapy outcome, one searches for groups of markers that jointly reveal their predictive or prognostic potential [123]. Consequently, the here presented DIoB assay was designed for recognizing a multitude of immune cells and its subtypes that circulate the blood including granulocytes. In the past, neutrophils and eosinophils were often overlooked, but recently both cell types became increasingly important as they might carry regulatory potential in various diseases [124,125,126], which also was described for basophils [96]. Their identification has been feasible, as the DIoB assay was established for a direct staining of whole blood samples. Many immune monitoring assays require the isolation of PBMC prior to the staining procedure [18,19,127]. In contrast to whole blood samples, this isolation of cells allows long-term shipments and even sample storage by cryopreservation. However, one has to keep in mind when designing translational monitoring assays to adapt them to the respective needs of study. PBMC isolation procedures exclude neutrophils and eosinophils from investigation and cryopreservation might strongly impact on DC [18]. Moreover, the sample preparation is more complex compared to a whole blood staining, resulting in increased expenditure of time, working capacities and potential sources of errors. Furthermore, direct staining of whole blood samples reduces the loss of cells and minimizes the alteration of the cell phenotype. It already has been reported that the isolation [128] or the freezing/thawing-cycles of isolated PBMC [129] can alter the expression of certain surface markers. And most important, the direct staining procedure allows the determination of an absolute cell count as one should always consider that leukocyte numbers might vary during therapy. Thus, in- or decreasing leukocyte numbers can be followed that would remain hidden in proportional analysis. Likewise, the in- or decrease of one subset might alter the percentages of other subsets in proportional analysis even though these remained unchanged in total numbers. The DIoB assay is a robust multicolor flow cytometry-based assay in a modular design allowing the identification of up to 37 different cells circulating in the peripheral blood. These include all major immune cell types and three additional non-immune cell subsets which have already been associated with therapy outcome. In addition, several activation markers are determined to evaluate the phenotype of the identified cells. The assay robustness is high as increased CV values only occurred in the very small populations. Consequently, all gates containing less than 100 events were excluded from interpretation as previously described and suggested [14,130]. Even though studies on minimal residual diseases have shown that cells can also be identified by less than 100 events [131], we excluded such populations from interpretation as the focus was not set on such very rare cells. Nevertheless, we elevated the amount of blood in the panels P10 and P11 to allow the detection of some low-frequency cells. One has to stress that also some disadvantages arise with the DIoB assay: the antibody costs per measurement are not low. Additionally, the costs for cytometer and staff have to be taken into account. Furthermore, the first establishment of the assay requires experiences and consumes time. Moreover, the analysis procedures need to be coordinated, as huge amounts of data have to be analyzed. Recently, Finak et al. [19] reported that in multi-centric studies the intra-site variability is generally low, but the inter-site variability is a major problem, which also was confirmed by others [18,20,127]. This predominantly was due to unintended deviations in preparation and analyses between the different sites even though detailed SOPs (standard operating procedures) were provided [19]. Such variations due to preparation purposes can be reduced by a centralized training of the technical staff as described by Streitz et al. [20]. However, the most prominent variations were due to subjectivity during analyses and thus it is general consensus to perform centralized [18,19,20,127] or automated analyses [19,132]. The flow cytometry community and future clinical trials greatly benefit from such studies for harmonization of multi-centric trials. Likewise, single-center trials profit from it, but here the establishment of flow cytometry assays has wider margins. The DIoB assay was developed for trials which go along with a centralized preparation of fresh blood samples. Consequently, the inter-site variability does not apply and also the on-site variability has been no problem as shown by the low CV values. Thus, the DIoB assay is well-suited for clinical trials or routine examinations which allow a centralized sample preparation and analysis. Nevertheless, as it was optimized for an easy, fast and consistent sample preparation with a limited number of steps, an application of the DIoB assay in multi-centric trials is also feasible. The requirement of a maximum of eight different fluorescence channels makes this assay suitable for all modern cytometers. And the repeated use of antibodies in different panels reduces the effort in establishing this assay to suit the local requirements. We measured all panels using the same cytometer settings (see Table S2) which strongly simplifies the transfer of this assay. Nevertheless, attention should be paid during analysis as the different antibody combinations required individual compensation values leading to varying signals in both, the negative and the positive populations. This, however, was balanced by adjusting the logicle scales independently for every parameter of each panel. In conclusion, the DIoB assay is a robust multicolor flow cytometry assay for identification of circulating immune cell subsets, as well as non-immune cells and additional activation markers in whole blood samples. The modular design makes the DIoB assay suitable for a wide range of applications as one may select, depending on the study objectives, the desired panels which each are dedicated to characterize a certain cell type. This might be important in explorative studies as one can assess a comprehensive immune status at the beginning and reduce it later on by still allowing direct comparisons. Moreover, it is relatively simple to adapt existing panels by adding or replacing certain activation or subset markers. Likewise, the blood withdrawal of only 2.0 mL is a small burden for most patients. Further, the direct staining of cell surface proteins in whole blood samples also decreases preparation effort, time, and variations. Worthy of note is that the assay is suited for the measurement on every cytometer that is capable of determining at least 8 different colors. Merely, the fluorochromes or filter settings need to be adapted to suit the local requirements. Everything else, like sample preparation, antigens, antibody clones or gating strategy could be applied directly. As a consequence, one could assess therapy effects of single patients and thus estimate individual responses. This contributes to the further development of personalized therapies and to the identification of immune biomarkers. The DIoB assay has already been applied for clinical immune monitoring of cancer patients. Analyses of patients with glioblastoma multiforme (IMMO-GLIO 01 trial, NCT02022384), pancreatic cancer (CONKO-007 trial, NCT01827553), and head and neck cancer (DIREKHT trial, NCT02528955) are currently ongoing. 4. Materials and Methods 4.1. Blood Withdrawals from Healthy Donors For the establishment of the DIoB assay, the peripheral blood was drawn from 15 healthy volunteers using EDTA-Monovette tubes (Sarstedt, Nümbrecht, Germany) and processed within 4 h. This was approved by the ethics committee of the Bayerische Landesärztekammer (#12131) in accordance with the principles described in the current version of the Declaration of Helsinki. All donors accepted and provided written informed consent. 4.2. Choice of the Antibodies and Preliminaries Based on literature review and previous experiences, we focused on 37 different immune and non-immune cell subsets for investigation. For their distinct identification, the required surface antigens and additional activation markers were assigned. Subsequently, antibodies were selected according to vendor availability and with regard to common practice for choosing fluorochromes, such as using bright fluorochromes for low expressed antigens and the other way around. We also took into consideration that antibody clones recognizing different epitopes could lead to varying signals between different cell types. The panel overview is provided in Table 1 and further details on applied antibodies are available in Table S1. All antibodies were titrated in whole blood samples and cross-checked for specificity against their unstained and their respective isotype controls. The titrations were estimated for signal to noise ratio and only distinct positive signals were accepted. Then, best antibody dilutions were combined to the panels and cytometer settings were revised. In order to determine the compensation values, we stained VersaComp Antibody Capture Beads (Beckman Coulter, Krefeld, Germany) according to the manufacturer’s instructions and calculated them using the Kaluza analysis software (version 1.3, Beckman Coulter). These compensation values were then cross-checked in single-staining of whole blood samples in comparison to their according fluorescence minus one (FMO) controls which make false positive events clearly visible. The FMO controls were moreover beneficial for the exact discrimination between positive and negative populations allowing the definition of gate settings for later analyses. The antibody concentrations (Table S1), cytometer settings (Table S2) and compensation values were then transferred to the final panels. 4.3. Sample Preparation The samples were always handled at room temperature until fixation and kept on ice afterwards. Cooling of the blood prior to fixation can lead to degranulation of the neutrophils which might cause further damage to other cells, thereby altering the sample condition. However, following fixation they should be stored refrigerated if an immediate measurement is not possible. The measurement should be performed within 4 h. In order to standardize sample preparation, we defined SOPs allowing an easy procedure of the DIoB assay with low variations. It can be handled by any person who works according to these SOPs. Prior to the staining procedure an antibody master mix was prepared for every panel based on the previous titrations (Table S3). Then, for all panels, except P12, 100 µL of whole blood were distributed into 5 mL polypropylene tubes (Sarstedt) and stained with the respective antibody mix for 25 min at room temperature in the dark. To obtain sufficient cell numbers within the panels P10 and P11, three times 100 µL of blood were stained in parallel and pooled directly before measurement. Simultaneously, the TruCount tubes (BD Biosciences, Heidelberg, Germany) containing counting-beads for the determination of absolute leukocyte numbers in P12 were prepared according to the manufacturer’s instructions. In short, dry beads were resolved in 20 μL of antibody mix and 50 μL of whole blood were added. Then, together with the other tubes they were incubated for 25 min at room temperature in the dark. In the next step, the stained blood samples were prepared in a standardized manner with an automated 3-step process using the TQ-Prep Workstation (Beckman Coulter) according to the manufacturer’s instructions. In short, first erythrocytes were lysed with formic acid, then leukocytes were re-buffered with carbonate and finally, a fixation with paraformaldehyde (PFA, Sigma-Aldrich, Taufkirchen, Germany) was performed. This TQ-prep-Workstation from Beckman coulter provides an automated sample preparation under constant conditions. Obviously one could also manually lyse and fixate the blood. Therefore, first 600 μL of formic acid (0.12% in ddH2O) are added to the stained blood sample and gently vortexed for 10 s. Then, 265 μL of carbonate buffer (56.6 mM Na2CO3 (Merck-Millipore, Darmstadt, Germany), 248.1 mM NaCl (Carl Roth, Karlsruhe, Germany) and 219.0 mM Na2SO4 (Sigma-Aldrich) in ddH2O) are added and immediately vortexed for 10 s. Finally, 100 µL of phosphate-buffered saline (PBS, Sigma-Aldrich) containing 1% of PFA are added and also vortexed for 10 s (for preparation of solutions see Table S4). Finally, all samples, except P12, were washed twice with PBS (300× g, 5 min) and pellets were dissolved in 200 µL of PBS containing 1% of PFA. As far as possible, the samples were measured immediately. Otherwise, they were kept on 4 °C in the dark for a maximum of 4 h until acquisition. 4.4. Data Acquisition The samples were measured on the Gallios flow cytometer (3 Laser, 10 colors, standard filter configuration) from Beckman Coulter. The cytometer settings (photomultiplier tubes, compensation) were based on the optimizations described above (see Table S2). For all acquisition protocols, the same scatter settings were defined and fluorescence channels not in use were deactivated. The number of total events required for a clear discrimination of the subsets was defined for each panel and adjusted through measurement duration or indirectly through the acquired sample volume. Thus, for panels which were focused on rare subsets or deep sub gating the complete sample amount of 200 µL (equivalent to 100 µL of initial blood sample) was acquired, but for panels focusing on more frequent subsets only 50%–70% of the sample volume was needed. For the panels P10 and P11 the complete sample volume of 600 µL (equivalent to 300 µL of initial blood) was used. 4.5. Data Analysis The obtained data were analyzed using the Kaluza analysis software (version 1.3). Therefore, a composite protocol with the ability to directly link multiple acquisition files and thereby connecting the different data files of the same sample was created. This allowed an easy definition of the same gates for multiple files which reduced variations. If the local analysis software does not support such a linking feature, one of course could also transfer the gate settings from one file to the other still guaranteeing the analysis of the same populations. To define gate boarders, positive and negative populations were compared to FMOs, single and isotype stainings. Based on these definitions, the gates were adjusted for varying signals due to different cell numbers during the analyses of patient samples. All parameters were analyzed using density dot plots. The FSC signals were examined in linear scales and the SSC signals were recorded logarithmically. In contrast, all fluorescence parameters were analyzed using logicle (bi-exponential) scales (as proposed by [133,134]) and their scale settings (decades, negative percentages) were adjusted separately for each parameter, but kept constant between all analyses. This resulted in a better visualization and distinction of positive, dim, and negative populations as lower signals no longer stuck to one axis. In addition, over or undercompensated data can be displayed very clearly [133]. Thus, it was possible to detect collapsing fluorochrome dimers by cross-checking compensations in every analysis preventing false results. Finally, the raw data were exported into MS Excel (Microsoft, Redmond, USA) to calculate the proportional distribution of all determined subsets. First, we calculated the percentages of every major cell type (e.g., all CD3+ T cells) out of all cells. Then, every subset was calculated out of its major cell type (e.g., CD4+/CD8− TH of all T cells). The activation states were determined by calculating all positive events in relation to their input gate. Finally, we transferred the absolute cell numbers determined for all leukocytes and the main cell types in P12 onto the other panels to determine the absolute cell number per milliliter of blood for each investigated subset. 4.6. Assay Robustness The assay robustness was determined by the individual processing and analyzing of blood samples from two volunteers for three times. We then calculated the mean and the coefficient of variation (CV in %: standard deviation/mean × 100) for all 208 obtained populations. Acknowledgments This work was supported by the German Federal Ministry of Education and Research (GREWIS, 02NUK017G) and in part by the European Commission (European Network of Excellence, DoReMi, contract FP7-249689). We further acknowledge the support by the German Research Foundation and the Friedrich-Alexander-Universität Erlangen-Nürnberg within the funding program Open Access Publishing. Supplementary Materials Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1316/s1. Click here for additional data file. Author Contributions Paul F. Rühle carried out most of the practical work, and drafted and wrote the manuscript together with Benjamin Frey and Udo S. Gaipl. Rainer Fietkau contributed to the design of the work. Udo S. Gaipl drafted and designed the study, drafted the manuscript and wrote it together with Paul F. Rühle and Benjamin Frey. Benjamin Frey carried out part of the practical work, drafted and designed the study, drafted the manuscript and wrote it together with Paul F. Rühle and Udo S. Gaipl. All authors read and approved the manuscript. Conflicts of Interest The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results. Abbreviations The following abbreviations are used in this manuscript: BREG B10 regulatory B cell CD Cluster of differentiation CEC Circulating endothelial cell CTLA4 Cytotoxic T lymphocyte-associated protein 4 (CD152) CV Coefficient of variation DC Dendritic cell DIoB Detailed Immunophenotyping of Blood DNT Double negative T cell DPT Double positive T cell EPC Endothelial progenitor cells FMO Fluorescence minus one FSC Forward scatter HPC Hematopoietic stem cell NHD Normal healthy donor P Panel PD-1 Programmed cell death protein-1 (CD274) PD-L1 Programmed death-ligand 1 (CD279) PFA Paraformaldehyde SSC Side scatter TC Cytotoxic T cell TCM Central memory T cell TEff Effector T cell TEM Effector memory T cell TH T helper cell TREG Regulatory T cell The following abbreviations for fluorochromes are used in this manuscript: A488 Alexa-488 APC Allophycocyanin APCH7 APC-H7 APCR700 APC-R700 APCV770 APC-Vio770 BV421 Brilliant Violet 421 FITC Fluorescein isothiocyanate KO Krome Orange PCC5.5 PerCp-Cy5.5 PE Phycoerythrin PECy7 PE-Cy7 PEV770 PE-Vio770 V450 Horizon V450 Figure 1 Schematic overview of the 34 immune cell and 3 non-immune cell subsets that can be identified by the here presented detailed immunophenotyping of blood (DIoB) assay. Whole blood samples were stained with specific antibody mixes in 11 different panels. Thus, all major circulating leukocytes such as T cells (red), B cells (green), dendritic cells (DC; violet: myeloid DC, plasmacytoid DC), monocytes (brown), granulocytes (pink: eosinophils, neutrophils, basophils), and natural killer (NK) cells (blue) were detected. Additionally, circulating non-immune cells (yellow/orange) such as hematopoietic stem cells (HSC), circulating endothelial cells (CEC) and endothelial progenitor cells (EPC) were monitored. These main cell types are depicted as bigger cells and all subsets, which were differentiated out of them, are depicted as smaller cells. For all of them, the markers that are necessary for their identification are indicated. This overview is completed by the enumeration of 27 activation markers which were assigned to corresponding cell types (colored boxes). The arrows represent the gating strategy in a simplified manner. Figure 2 Definition of the All Cells-gate as crucial basis for the subset identifications by surface markers. (A) Acquisition characteristics were evaluated and irregularities were excluded by definition of Flow-gates for each panel; (B,C) Then, doublets were excluded by cross-checking the forward scatter (FSC) signal for its integral (INT) versus time of flight (TOF) and peak (PEAK) characteristics; (D) Finally, the All Cells-gate was defined based on its scatter characteristics. Hereby, the events that shifted to a lower FSC signal were considered as dead or dying cells and removed together with the debris from that definition; (E) The All Cells-gate could be subdivided on its scatter characteristics into granulocytes (GR), monocytes (MO) and lymphoid cells (PBL); and (A–E) Black arrows represent the gating strategy and the depicted percentages depend on the respective input gate. Figure 3 Gating strategy for the identification of fourteen T cell subsets and the determination of their activation state. (A) The T cells were identified by their CD3 expression defining the same gates for the panels P01, P02, P03 and P05; (B–G) The P01 was used for the identification of TC and TH subsets; (B) Thus, first the TH and TC were identified by their differential CD4 and CD8 expression, whereby the TC were differentiated into CD8hi and CD8lo populations. Additionally, the double negative (DNT) and double positive (DPT) T cells were recorded; (C–E) The TH, TC8hi and TC8lo were further distinguished into naïve, effector (Eff), effector memory (EM), and central memory (CM) subsets by their CD197 and CD45RA expression; (F,G) In order to determine the activation state of these subsets, the CD38 expression was examined; (H,I) In P02, the TH were differentiated into TH1, TH2, and TH17 by their CD186 and CD196 co-expressions; (J) In addition, the TREG were identified by their CD25hi/CD127-/lo phenotype; (K,L) The P03 was introduced for identification of the general TCR expression as well as the examination of the regulation of the immunosuppressive CTLA-4; (M–R) Finally, the P05 investigated the activation state of T cells in general, examining the expression of CD25, CD69, CD80, CD86, and HLA-DR, as well as that of the CD279; and (A–R) Black arrows represent the gating strategy and the depicted percentages depend on the respective input gate. Figure 4 Gating strategy for the identification of six B cell subsets (P05: A–I) and determination of their activation state (P06: K–P). (A,B) For the definition of B cells, the expression of CD19 (A) and CD20 (B) was initially individually investigated, and then merged by the definition of a Boolean gate for subsequent analyses; (C–H) Following, the subsets were characterized in a two-step process by their expression of CD27, CD38, CD5 and CD24; (C) First, a CD27 vs. CD38 quadrant was defined; (D–G) Then, these four gates were investigated for their CD5 vs. CD24 co-expression allowing the definition of pre-naïve (D), naïve (D), memory (E) and transitional B cells (F) as well as plasmablasts (G); (H) All B cells not belonging to one of these subsets were defined as Rest of B cells by a Boolean gate and analyzed for their CD27+/CD24hi phenotype to identify the BREGs; (I) As B cells are sometimes sparsely distributed especially in cancer patients, the gate settings might be aligned comparing the expression patterns on PBL level; (J–O) In addition, common activation markers were examined together with T cells in P05. Therefore, CD19 and CD20 were combined into one fluorescence channel (J). Then, the expression of CD25 (K), CD69 (L), CD80 (M), CD86 (N), and HLA-DR (O) was analyzed; and (A–O) Black arrows represent the gating strategy and the depicted percentages depend on the respective input gate. Figure 5 Gating strategy for the identification of three NK cell subsets (A–C) and their functional states (D–H), as well as several NKT cell subsets (K–O). (A–C) The NK cells were determined in P06 and P07 as CD3−/CD56+ cells and subsequently distinguished into three subsets by their CD56 and CD16 co-expression into NK1, NK2 and NK3; (D–F) Their cytotoxic activity was determined examining the expression of CD314, the suppressing CD159a and the activating CD159c, whereby the latter two were analyzed in co-expression to CD94; (G,H) In addition, their activation state was determined by examining the expression of CD25 and CD69; (I,J) As CD56 is generally low expressed, the NK cell identification was cross-checked by the CD314 expression on CD3− cells and also the three subsets were validated; (J,K) In parallel, NKT cells were determined as all CD3+ cells which simultaneously expressed one of the typical NK cell markers CD16, CD56, CD94, CD159a, CD159c or CD314; and (A–O) Black arrows represent the gating strategy and the depicted percentages depend on the respective input gate. Figure 6 Gating strategy for identification of myeloid cells such as monocytes (P08: A–F), neutrophils and eosinophils (P09: G–J), as well as basophils and dendritic cells (P10: K–O). (A,B) The monocytes were identified in P08 by their CD14 expression and subsequently distinguished into four subsets by their CD16 co-expression; (C–F) For determination of their activation state, the expression of CD80, CD86, CD64 and HLA-DR was analyzed; (G,H) In P09 the neutrophils (Neu) and eosinophils (Eos) were identified by their shared expression of CD66 and high SSC characteristics, but differential CD16 expression; (I,J) Then, both cell populations were examined for their expression of CD64 as an activation marker. Here, attention should be paid to the high auto fluorescence characteristics of eosinophils; (K) In P10, the DCs and basophils were examined following the exclusion of most other cells by their lineage markers (LIN: CD3, CD14, CD16, CD19, CD20, CD56); (L,M) Then, the basophils were identified by their lack of HLA-DR while expressing CD123; (L,N) In contrast, the DCs express HLA-DR and can be distinguished by their differential expression of CD11c and CD123 into pDC and mDC; (O) The mDC were further divided into mDC-1 and mDC-2 by their differential expression of CD1c; (P) Finally, the maturation marker CD83 and the PD1 ligand CD274 (PD-L1) were determined on all DCs; and (A–O) Black arrows represent the gating strategy and the depicted percentages depend on the respective input gate. Figure 7 Gating strategy for the identification circulating non-leukocytes which have been associated with cancer therapy outcome. (A,B) The EPCs were identified by their lack of CD146 while expressing CD133 and directly divided into CD45+ and CD45− EPC; (C,D) The CECs express no or just little CD45 and lack CD133, but are positive for CD146; (C,E,F) Likewise, the HSC express no ore just little CD45, but lack CD146 and are positive for CD34. In the gating process they were directly distinguished into CD133− and 133+ HSC; (G) As all these cell types are very rare in most persons, the CD34 and CD133 gates were aligned according to its expression on the all cells level; and (A–G) Black arrows represent the gating strategy and the depicted percentages depend on the respective input gate. Figure 8 Determination of absolute cell counts by a simultaneous acquisition of cells and beads. (A–D) Similar to all other panels, first irregularities were excluded from analysis, followed by doublet discrimination and the definition of the All Cells-gate; (E–J) Then, various major cells were determined keeping a strict gating order always excluding the already identified cell types from the next gating step by the use of Boolean gates (termed as Rest); (E) First, leukocytes were discriminated from debris by their CD45 expression; (F) Then, the CD3+ T cells were identified; (G) followed by B cell identification by their expression of CD19 or CD20 within the CD3−cells; (H) Within the remaining cells the granulocytes and monocytes were detected by their particular scatter characteristics; (I) followed by detection of NK cells expressing CD56 and/or CD16; (Q) Then, all left-over cells were defined as Rest of cells containing non-determined cells such as DCs, basophils or HSCs; (K,L) Besides, the granulocytes and monocytes were further subdivided by their CD16 expression into eosinophils and neutrophils or CD16+ and CD16− monocytes respectively; (D) In parallel, the bead count was determined. Therefore, a Beads-gate was defined based on its dense scatter characteristics whereby it was important not to exclude the small beads together with debris; and (M–Q). Then, these beads were verified by their auto fluorescence properties. We found two populations in the FL1, FL2, FL3, FL4 (blue laser) and FL8 (red laser) representing a major singlet and a minor doublet population. Consequently, for each channel we added the doublet population twice to the singlet population and calculated the mean value out of all five channels (see table at the bottom). Then, using the indicated formula, the absolute cell count per µL of initial blood was calculated for all acquired cells as shown for the representative example. Figure 9 General robustness of the DIoB assay. Three whole blood samples of two different healthy donors were independently processed, measured and analyzed. These were analyzed by calculating the percentage distribution for 208 populations. Thereof, all populations counting for less than 100 events were excluded. In the analysis of these NHD blood samples 176 valid populations remained for determination of variations. Therefore, the coefficients of variation (CVs) were separately calculated for both samples. Then, the mean values were calculated representing the general robustness of the DIoB assay. The graph shows that the majority of populations (67%) had a CV below 5% and nearly all populations (93%) had a CV below 10%. Only 11 populations had a CV between 10% and 15% and one above 15%. ijms-17-01316-t001_Table 1Table 1 Overview of the 12 staining panels each dedicated to a specific cell type which is indicated by individual colors. Laser and filter settings as well as the applied fluorochromes which are coupled to the monoclonal antibodies that are directed against the antigens listed beneath are depicted. The panels examine T cells (red: P01–P03, P05h), B cells (green: P04, P05), NK cells (blue: P06, P07), monocytes (brown: P08), neutrophils and eosinophils (pink: P09), basophils and dendritic cells (violet, P10) as well as circulating non-leukocytes (yellow: P11). The last panel (P12) contains counting beads and antibodies directed against several major cells to determine the absolute cell count for all identified subsets. Panel Blue: 488 nm Red: 638 nm Violet: 405 nm 525/40 575/30 695/30 755/LP 660/20 755/LP 450/50 550/40 FITC|A488 PE PCC5.5 PECy7|PEV770 APC APCH7 V450|BV421 KO P01 CD8 CD197 CD4 CD45RA CD38 CD3 P02 CD127 CD196 CD4 CD25 CD183 CD3 P03 TCRγ/δ TCRα/β CD152 CD3 P04 CD20 CD24 CD5 CD38 CD27 CD19 P05 CD19/20 CD25 CD86 CD69 CD279 CD80 CD3 HLA-DR P06 CD69 CD16 CD56 CD314 CD25 CD3 P07 CD159c CD16 CD56 CD94 CD159a CD3 P08 CD14 CD16 CD86 CD80 CD64 HLA-DR P09 CD66 CD64 CD16 P10 LIN 1 CD274 CD123 CD83 CD1c CD11c HLA-DR P11 CD146 CD133.1 CD45 CD34 P12 CD19/20 CD16 CD56 CD45 CD3 1 LIN includes CD3, CD14, CD16, CD19, CD20 and CD56. ijms-17-01316-t002_Table 2Table 2 Definition of monitored cell subsets. Cell Subset Definition Leukocytes Forward scatter (FSC) vs. Side scatter (SSC) T cells CD3+ T helper cells (TH) CD3+/CD4+/CD8− TH1 CD3+/CD4+/CD8−/CD183+/CD196− TH2 CD3+/CD4+/CD8−/CD183−/CD196− TH17 CD3+/CD4+/CD8−/CD183−/CD196+ TREG CD3+/CD4+/CD8−/CD25hi/CD127−/lo Naïve TH CD3+/CD4+/CD8−/CD197+/CD45RA+ Effector TH CD3+/CD4+/CD8−/CD197−/CD45RA+ EM TH CD3+/CD4+/CD8−/CD197−/CD45RA− CM TH CD3+/CD4+/CD8−/CD197+/CD45RA− Cytotoxic T cells (TC) CD3+/CD8+/CD4− Naïve TC CD3+/CD8+/CD4−/CD197+/CD45RA+ Effector TC CD3+/CD8+/CD4−/CD197−/CD45RA+ EM TC CD3+/CD8+/CD4−/CD197−/CD45RA− CM TC CD3+/CD8+/CD4−/CD197+/CD45RA− TCRα/β T cells CD3+/TCRαβ+/TCRγδ− TCRγ/δ T cells CD3+/TCRγδ+/TCRαβ− B cells CD19+ or CD20+ Pre−naïve B CD19+ or CD20+/CD27−/CD38−/lo/CD5+ Naïve B CD19+ or CD20+/CD27−/CD38−/lo/CD5− Memory B CD19+ or CD20+/CD27+/CD38−/lo/CD5−/CD24+ Transitional B CD19+ or CD20+/CD27−/CD38hi/CD5+/CD24hi Plasma blasts CD19+ or CD20+/CD27+/CD38hi/CD5−/CD24− BREG CD19+ or CD20+/CD27+/CD24hi following the exclusion of the other five B cell subsets (see Table 3: Rest of B cells) NK cells CD3−/CD56+ NK1 CD3−/CD56+/CD16+ NK2 CD3−/CD56hi/CD16− NK3 CD3−/CD56lo/CD16− NKT CD3+/CD56+ or CD3+/CD16+ or CD3+/NKG2D+ Neutrophils CD66+/CD16+ Eosinophils CD66+/CD16− Basophils CD3−/CD14−/CD16−/CD19−/CD20−/CD56−/HLADR−/CD123+ Dendritic cells (mDC or pDC) mDC CD3−/CD14−/CD16−/CD19−/CD20−/CD56−/HLADR+/CD11chi/CD123−/lo mDC−1 CD3−/CD14−/CD16−/CD19−/CD20−/CD56−/HLADR+/CD11chi/CD123−/lo/CD1c+ mDC−2 CD3−/CD14−/CD16−/CD19−/CD20−/CD56−/HLADR+/CD11chi/CD123−/lo/CD1c− pDC CD3−/CD14−/CD16−/CD19−/CD20−/CD56−/HLADR−/CD123hi/CD11c− Monocytes CD14+ Mo1 CD14hi/CD16− Mo2 CD14lo/CD16+ Mo3 CD14hi/CD16+ Mo4 CD14lo/CD16− HSC CD45−/CD146−/CD34+ CEC CD45−/CD146+ EPC CD146−/CD133+ ijms-17-01316-t003_Table 3Table 3 Definition of Boolean gates required for the subset identification. Panel Boolean Gate Definition P01 TC “T8hi” or “T8lo” P04 CD19 or CD20 B cells “CD19+ B cells” or “CD20+ B cells” P05 Rest of B cells “CD19 or CD20 B cells” and (not (pre−naïve or (naïve or (“memory” or (“transitional” or “plasma blasts”))))) P06 NKT “CD3+” and (“CD94+ NKT” or (“CD56+ NKT” or (“NKG2C+ NKT” or (“NKG2A+ NKT” or “CD16+ NKT”)))) P17 NKT “CD3+” and (“CD56+ NKT” or (“CD16+ NKT” or “NKG2D+ NKT”)) P10 All DCs “MDC” or “PDC” P01 Rest 1 “CD45+ Leu” and (not “CD3+ T cells”) P01 Rest 2 “Rest 1” and (not (“CD19/20+ B cells”) P01 Rest 3 “Rest 2” and (not (“Monocytes” or “Granulocytes”) P01 Rest 4 “Rest 3” and (not (“CD56+/CD16+ NK” or “CD56+/CD16− NK”) ==== Refs References 1. Galon J. Angell H.K. Bedognetti D. Marincola F.M. The continuum of cancer immunosurveillance: Prognostic, predictive, and mechanistic signatures Immunity 2013 39 11 26 23890060 2. Derer A. Frey B. Fietkau R. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081317ijms-17-01317ReviewMicroRNAs: Potential Biomarkers and Therapeutic Targets for Alveolar Bone Loss in Periodontal Disease Kagiya Tadayoshi Cho William Chi-shing Academic EditorDivision of Functional Morphology, Department of Anatomy, Iwate Medical University, 2-1-1 Nishitokuta, Yahaba-cho, Iwate 028-3694, Japan; tkagiya@iwate-med.ac.jp; Tel.: +81-19-651-5111; Fax: +81-19-908-801011 8 2016 8 2016 17 8 131728 6 2016 03 8 2016 © 2016 by the author; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Periodontal disease is an inflammatory disease caused by bacterial infection of tooth-supporting structures, which results in the destruction of alveolar bone. Osteoclasts play a central role in bone destruction. Osteoclasts are tartrate-resistant acid phosphatase (TRAP)-positive multinucleated giant cells derived from hematopoietic stem cells. Recently, we and other researchers revealed that microRNAs are involved in osteoclast differentiation. MicroRNAs are novel, single-stranded, non-coding, small (20–22 nucleotides) RNAs that act in a sequence-specific manner to regulate gene expression at the post-transcriptional level through cleavage or translational repression of their target mRNAs. They regulate various biological activities such as cellular differentiation, apoptosis, cancer development, and inflammatory responses. In this review, the roles of microRNAs in osteoclast differentiation and function during alveolar bone destruction in periodontal disease are described. alveolar bone lossexosomesextracellular vesiclesmicroRNAsosteoclastsperiodontal disease ==== Body 1. Introduction Periodontal disease is one of the most common oral diseases worldwide. It is an inflammatory disease caused by bacterial infection of tooth-supporting structures that causes the destruction of alveolar bone [1]. Bone mass is determined by the balance between osteoblastic bone formation and osteoclastic bone resorption. Although bone formation and bone resorption are balanced under normal physiological conditions, excessive bone resorption occurs in periodontal disease, which results in the destruction of alveolar bone. The only cells that resorb bone are osteoclasts. Osteoclasts are large, tartrate-resistant acid phosphatase (TRAP)-positive multinucleated cells derived from hematopoietic stem cells (Figure 1) [1,2]. Osteoclast differentiation is controlled by a variety of hormones, growth factors, and cytokines. Among them, receptor activator of nuclear factor κB ligand (RANKL), which is expressed in stromal cells, osteoblasts, and T cells, is essential for osteoclast differentiation [1]. The binding of RANKL to its receptor, receptor activator of nuclear factor κB (RANK), finally activates nuclear factor of activated T cells, cytoplasmic 1 (NFATc1), which is a key regulator of osteoclast differentiation. NFATc1 works with other transcription factors such as activator protein-1 (AP-1), PU.1, and microphthalmia-associated transcription factor (MITF) to induce various osteoclast-specific genes (Figure 2) [2,3]. Since the discovery of the first microRNA (miRNA) in Caenorhabditis elegans in 1993 [4,5], RNA biology has advanced greatly. miRNAs are small, endogenous, non-coding RNAs approximately 20–22 nucleotides in length. They act in a sequence-specific manner to regulate gene expression at the post-transcriptional level through cleavage or translational repression of their target mRNAs [1,2,6]. To date, 2588 miRNAs have been identified in humans (miRBase database, http://www.mirbase.org/). miRNAs participate in the regulation of several biological activities such as cellular differentiation, apoptosis, cancer development, and inflammatory responses. Recently, the involvement of miRNAs in periodontal disease has been reported [1,7,8,9,10,11]. Focusing on alveolar bone loss in periodontal disease, this paper describes the roles of miRNAs in osteoclast differentiation and function. 2. Biogenesis of MicroRNAs (miRNAs) miRNA is either transcribed from its own promoter in an intergenic region or is processed from the intronic region of a coding gene as a long primary transcript, known as pri-miRNA. This pri-miRNA is processed into a 70–100 nucleotide precursor miRNA (pre-miRNA) by the RNase III enzyme Drosha and its co-factor DGCR8 in the nucleus. The RNA is then exported to the cytoplasm by a transport protein, Exportin-5. In the cytoplasm, it is further processed by another RNase III enzyme, Dicer. Thus, pre-miRNA is cleaved into a mature miRNA duplex. The resulting single-stranded mature miRNAs are ultimately incorporated into an RNA-induced silencing complex (RISC) that contains argonaute (Ago) family proteins [2,6,12,13]. miRNAs regulate gene expression by binding to mRNA. The selectivity of miRNA action is conferred mainly via nucleotides 2–7 located at the 5’ end, termed the “seed region”, which pairs to its complementary site in the 3’-untranslated region (UTR) of the target mRNA [14]. Although a perfect match is not required for base-pairing of the miRNA to its target mRNA, the seed region must be perfectly complementary (Figure 3). Thus, the RISC inhibits the translation of or degrades the target mRNAs. 3. Osteoclasts and miRNAs Recent studies have revealed that miRNAs play important roles in osteoclast differentiation and function [6]. We reported that the expression of 52 mature miRNAs differed more than two-fold between untreated cells and cells treated with RANKL during osteoclastogenesis [1]. Table 1 lists the miRNAs that have been implicated in periodontal disease-related osteoclastogenesis. This section discusses selected important miRNAs. miR-21 is highly expressed not only in the gingiva during periodontitis (Table 1) but also in cells during osteoclastogenesis [1]. Some critical pathogenic factors in periodontal disease induce miR-21 expression. Lipopolysaccharide (LPS) is a major pathogenic component of the cell wall of Gram-negative bacteria and an important factor contributing to periodontal disease. LPS signaling is mediated by Toll-like receptors leading to nuclear factor κB (NF-κB) activation [15]. In macrophages, LPS promotes NF-κB activation and decreases programmed cell death 4 (PDCD4) protein levels via miR-21 induction [16]. RANKL-induced c-Fos also upregulates miR-21 gene expression, which downregulates the expression of PDCD4, a negative regulator of osteoclastogenesis [17]. Tumor necrosis factor-α (TNF-α), which is present at high levels in both gingival crevicular fluid and periodontal tissues of diseased sites, is involved in the pathogenesis of periodontitis [1]. TNF-α acts through several pathways including NF-κB, which is involved in inflammation and apoptosis [18]. miR-21 is an NF-κB transactivational gene, and the combination of TNF-α and RANKL treatment increases miR-21 expression compared with RANKL treatment alone during osteoclast differentiation [1]. The miR-29 family includes miR-29a, miR-29b, and miR-29c, which are overexpressed in gingiva during periodontitis (Table 1). miR-29 plays critical roles in bone tissues as well as in the gingiva [6,8,10]. miR-29a and miR-29c positively regulate osteoblast differentiation by controlling the expression of osteonectin [19]. Canonical Wnt signaling, which is activated during osteoblast differentiation, induces miR-29 expression [19]. miR-29b also promotes osteogenesis by directly downregulating inhibitors of osteoblast differentiation [20]. miR-29b is induced by NF-κB and is upregulated in cells treated with TNF-α/RANKL relative to RANKL-treated cells during osteoclastogenesis [1,21]. Rossi et al. [22] reported that miR-29b expression decreases progressively during human osteoclast differentiation. Ectopic miR-29b expression suppresses c-Fos and matrix metalloproteinase 2 (MMP-2) expression and inhibits osteoclast formation. By contrast, Franceshetti et al. [23] reported that miR-29 family members are positive regulators of osteoclast differentiation. The expression of all miR-29 family members increases during osteoclast differentiation. Knockdown of miR-29 causes impaired osteoclastic commitment and migration of pre-osteoclasts without affecting cell viability, actin ring formation, or apoptosis in mature osteoclasts [6,23]. Furthermore, miR-29 directly targets cell division control protein 42 (Cdc42), SLIT-ROBO Rho GTPase-activating protein 2 (Srgap2), G protein-coupled receptor 85 (Gpr85), nuclear factor I/A (NFI-A), CD93, and calcitonin receptor (Calcr), which are important for osteoclast differentiation and function [23]. In summary, miR-29 is considered to play a critical role in osteoclast differentiation and function despite conflicting reports. miR-31 is expressed in a wide variety of tissues and cells [6,24]. The expression of miR-31 is decreased in gingiva with periodontitis compared to healthy gingiva (Table 1). miR-31 plays a critical role in osteoclastic bone resorption. The activated osteoclast has a ring-shaped osteoclast-specific podosome belt called the “actin ring” (Figure 4). The actin ring forms a sealing zone where the osteoclast adheres tightly to the bone surface [6,25]. miR-31 is one of the highly upregulated miRNAs during osteoclast development. Inhibition of miR-31 suppresses osteoclast formation and bone resorption. miR-31 controls the cytoskeleton in osteoclasts by regulating the expression of RhoA, which regulates the formation of the actin ring [26]. miR-34a was recently shown to be involved in osteoclast and osteoblast differentiation. It is reported that miR-34a blocks osteoporosis and bone metastasis by inhibiting osteoclast differentiation [27]. The expression level of miR-34a decreases during osteoclastogenesis and knockdown of miR-34a promotes osteoclast differentiation, while overexpression of miR-34a impairs this differentiation [6,27]. In contrast, osteoblast differentiation is inhibited in miR-34a knockout mice but is promoted in osteoblastic miR-34a conditional transgenic mice. Krzezinski et al. [27] identified transforming growth factor-β-induced factor 2 (Tgif2) as a direct target of miR-34a. Tgif2 is an essential factor for osteoclast differentiation, and NFATc1 and AP-1 induce Tgif2 expression during osteoclastogenesis [27]. Although miR-124 is highly abundant in the brain and contributes to the differentiation of neural progenitors into mature neurons [6,28], it also plays an important role in osteoclast formation. miR-124 expression decreases in a time-dependent manner during murine osteoclast differentiation [29]. Inhibition of miR-124 enhances osteoclastogenesis and the expression of NFATc1. Ectopic miR-124 expression inhibits osteoclast differentiation and NFATc1 expression without affecting the expression of the NF-κB p65 subunit or c-Fos [29]. These results indicate that miR-124 may directly regulate NFATc1 expression. Indeed, Nakamachi et al. [28] demonstrated that NFATc1 is a direct target of miR-124 in human osteoclasts. miR-124 inhibits the progression of adjuvant-induced arthritis in a rat model of rheumatoid arthritis (RA) by reducing osteoclast formation. RA and periodontitis share a similar pathophysiology, characterized by destructive inflammation that culminates in localized bone loss. Considering this similarity, miR-124 may play a critical role in alveolar bone loss in periodontal disease. miR-125a expression is increased in gingiva with periodontitis compared to healthy gingiva (Table 1), and it is also involved in osteoclastogenesis. We reported that treatment of RAW264.7 cells with TNF-α/RANKL and RANKL triggers time-dependent upregulation of miR-125a expression during murine osteoclast differentiation [1]. De la Rica et al. [30] reported that two miRNA clusters, miR-212/132 and miR-99b/let-7e/125a, display rapid upregulation during human osteoclast differentiation. These miRNAs are activated directly by NF-κB, and their inhibition impairs osteoclast formation; however, Guo et al. [31] reported that miR-125a expression is dramatically downregulated during human osteoclast formation caused by M-CSF/RANKL treatment. Ectopic miR-125a expression impairs osteoclastogenesis, while its inhibition has the opposite effect. TNF-receptor-associated factor 6 (TRAF6), the main adapter molecule of RANK, was reported to be a direct target of miR-125a. In addition, Guo et al. [31] reported that NFATc1 binds to the promoter of miR-125a and inhibits transcription of miR-125a. Taken together, miR-125a plays a critical role in osteoclast differentiation, although reports differ on the exact mechanism of action. miR-146a is an NF-κB-dependent gene that plays an important role in innate immunity [6,21,32]. For example, LPS rapidly induces miR-146a through the Toll-like receptor/NF-κB pathway in human macrophage-like cells [21]. Mature miR-146a is highly expressed in gingiva with periodontitis (Table 1). We reported that miR-146a expression increases during TNF-α-regulated osteoclast differentiation [1]; however, miR-146a overexpression inhibits osteoclast formation [33]. TRAF6 is a direct target gene of miR-146a [32]. Without TNF-α, the time-dependent miR-146a expression decreases in RAW264.7 cells during osteoclastogenesis [1]. Furthermore, overexpression of miR-146a does not affect proinflammatory cytokine production in human macrophage-like cells [34]. These reports suggest that miR-146a alone does not induce proinflammatory cytokine production in macrophages, while proinflammatory cytokines, such as TNF-α and LPS, promote miR-146a expression. Collectively, although miR-146a plays important roles in inflammatory responses, excessive miR-146a expression may serve as a negative feedback regulator of osteoclastogenesis. miR-155 is an inflammation-associated miRNA that regulates inflammation and immune cell function at multiple levels [6,35]. For example, leukotriene B4 (LTB4) is a lipid mediator formed from arachidonic acid and is one of the most potent stimulants of macrophages. LTB4 levels in gingival crevicular fluid correlate with periodontitis severity [36]. LTB4 enhances the generation of miR-155 to promote MyD88-dependent macrophage activation [37]. We reported that the expression level of miR-155 in murine bone marrow macrophages (BMMs) is upregulated by TNF-α/RANKL/M-CSF treatment, but is modestly downregulated by RANKL/M-CSF treatment during osteoclast formation [1]. Mann et al. [38] reported that miR-155 expression decreases during osteoclast differentiation. Our findings in BMMs are compatible with these reports; however, miR-155 expression was not significantly different between M-CSF-treated and RANKL/M-CSF-treated human peripheral blood CD14+ cells during osteoclastogenesis (Figure 5A). Mizoguchi et al. [39] reported that miR-155 levels in BMMs from wild-type mice are not significantly altered by RANKL treatment. Fewer osteoclasts were generated in vitro from BMMs of miR-155-deficient mice than from those of wild-type mice [40]. By contrast, Mann et al. [38] reported that ectopic miR-155 expression inhibits osteoclast formation by repressing MITF and PU.1, which are transcription factors important for osteoclast differentiation. Taken together, these reports suggest that miR-155 plays a critical role in osteoclast differentiation and that its downregulation may not be necessary for osteoclastogenesis [1,6]. Although miR-223 expression is increased in gingiva with periodontitis compared with healthy gingiva (Table 1), miR-223 is expressed in human monocytes, granulocytes, and platelets [1,6], and it is a central modulator of myeloid differentiation. Osteoclasts are hematopoietic stem cell-derived cells of the monocyte/macrophage lineage, and miR-223 plays a crucial role in osteoclast differentiation [6]. We and other researchers have observed that miR-223 expression decreases during murine osteoclastogenesis [1,41]. miR-223 regulates NFI-A and the expression of the c-Fms, M-CSF receptor, which is critical for osteoclast differentiation and function [42]. Ectopic miR-223 expression inhibits murine osteoclastogenesis, while its inhibition has the opposite effect [41]. However, miR-223 expression was not significantly different during human osteoclast differentiation (Figure 5B). Moutoula et al. [43] reported that overexpression of miR-223 promotes human osteoclastogenesis, whereas its inhibition has the opposite effect. In summary, although the mechanism of miR-223 action may differ between human and murine osteoclastogenesis, miR-223 plays a critical role in osteoclast differentiation. 4. Extracellular miRNAs Recently, miRNAs were reported to be present in body fluids such as saliva, serum, urine, and cerebrospinal fluid [49]. These extracellular miRNAs are considered potential biomarkers and therapeutic targets. They are divided into two populations: vesicle-associated and non-vesicle-associated forms [2,6,49]. In the vesicle-associated form, miRNAs are present in exosomes and microvesicles. In the non-vesicle-associated form, miRNAs are detected in complexes with Ago proteins, high-density lipoproteins, or other proteins [49,50]. Of these, the study of exosomes is an area of intense interest. Exosomes are a type of extracellular vesicle (EV). EVs are membranous vesicles naturally released by most cells and are divided into three main types: apoptotic bodies, microvesicles, and exosomes. Apoptotic bodies are released by apoptotic cells and are 800–5000 nm in diameter. Microvesicles are produced by budding directly from the plasma membrane and are 50–1000 nm in diameter. Exosomes are originated from endosomes and are 40–100 nm in diameter [2,6]. The current techniques are inadequate for collecting each type of EV [2,51,52], and a consensus has yet to be reached with regard to the nomenclature of exosomes and microvesicles [35,53]. Thus, this section does not use the term “exosomes” but rather “EVs”. Recent studies have begun to uncover that EVs play a role in cell-to-cell communication by the transfer of miRNAs, mRNAs, proteins, and lipids to recipient cells [49,54,55,56]. The release of EVs depends on the cell type and biological condition [2,49,57]. Although studies have revealed that miRNAs play important roles in bone metabolism, including alveolar bone, whether osteoclasts secrete EVs containing miRNAs was unknown until recently. We examined eight miRNAs in EVs that seemed to be critical for osteoclast differentiation including let-7e, miR-21, miR-33, miR-155, miR-210, miR-223, miR-378, and miR-1224. Of these, the expression levels of miR-378, miR-21, and miR-210 were very high, whereas no significant expression of miR-33 or miR-1224 was detected [2,58]. These results indicate that osteoclasts release EVs containing specific miRNAs, but not the entire set of intracellular miRNAs. Among the miRNAs detected in EVs of osteoclasts, miR-378 was highly expressed in the supernatant of LPS-treated macrophage-like cells compared with that from non-LPS-treated macrophage-like cells [59]. miR-378 is also highly expressed in the serum of breast cancer patients with bone metastasis compared with that of healthy people [45]. Osteolytic bone metastasis, caused by excessive osteoclast activity, frequently occurs during the later stages of breast cancer [2]. Collectively, these reports support miR-378 as a candidate biomarker for alveolar bone loss in periodontal disease. Alexander et al. [60] reported that two critical miRNAs that regulate inflammation, miR-146a and miR-155, are released from dendritic cells within EVs and are taken up by recipient dendritic cells. They also reported that miR-146a within EVs inhibits LPS-induced inflammation in mice, while miR-155 within EVs promotes this. This report suggests that miRNAs within EVs can be transferred between immune cells in periodontal tissues. Periodontal disease is an infectious disease; serum miR-146a and miR-223 have been reported to be reduced significantly in septic patients compared with healthy controls [61]. As discussed above, these miRNAs are associated with alveolar bone loss in periodontal disease. Therefore, they may represent candidates for periodontal disease markers in serum and saliva. 5. Materials and Methods 5.1. Bone Marrow Macrophage Culture and Fluorescence Staining of Actin and Nuclei All animal experiments were evaluated and approved by the Animal Use and Care Committee of Iwate Medical University (registration number: 17-0068, Morioka, Japan). Five-week-old male ddY mice were purchased from Japan SLC Inc. (Hamamatsu, Japan). The mice were sacrificed, and their femurs and tibias were removed and dissected free of adherent soft tissue. The ends of the bones were cut, and the marrow cells were collected as described previously [1]. Red blood cells were removed by treatment with phosphate-buffered saline (PBS) containing 10 mM Tris and 0.83% NH4Cl. After washing with α-minimum essential medium (α-MEM; Invitrogen, Fredrick, MD, USA), the cells were seeded at a density of 2 × 105 cells/cm2 and cultured in α-MEM containing 10% fetal bovine serum (FBS; Moregate Biotech, Bulimba, Australia) and 10 ng/mL recombinant mouse macrophage colony-stimulating factor (M-CSF) (R&D Systems Inc., Minneapolis, MN, USA). After two days, the medium was changed, and the cells were cultured in the presence of M-CSF (10 ng/mL) and recombinant human soluble RANKL (PeproTech EC, London, UK) (100 ng/mL) for an additional three days. After culture, the cells were fixed with 4% paraformaldehyde in 0.1 M phosphate buffer (pH 7.4) for 10 min at room temperature. The samples were washed three times with PBS. The cells were incubated for 90 min at 37 °C with 0.5 mg/mL phalloidine-tetramethylrhodamine B isothiocyanate (Sigma-Aldrich, St. Louis, MO, USA). After washing three times with PBS, the cells were incubated for 10 min at room temperature with DAPI solution (diluted 1:1000; Dojindo, Kumamoto, Japan). The cells were then washed three times with PBS and observed using a confocal laser scanning microscope (LSM-510; Zeiss, Oberkochen, Germany). 5.2. Human Peripheral Blood CD14+ Cell Culture and Quantitative RT-PCR Analysis Human peripheral blood CD14+ cells were purchased from Lonza (Basel, Switzerland). The cells were seeded at a density of 2 × 104 cells/cm2 and cultured in α-MEM containing 10% FBS and 50 ng/mL recombinant human M-CSF (R&D Systems). After three days, the medium was changed, and the cells were cultured in the presence of M-CSF (25 ng/mL) and recombinant human soluble RANKL (50 ng/mL) for an additional nine days. The medium was exchanged every three days. To evaluate miRNA expression, total RNAs were harvested on days 3 or 12 using a mirVana™ miRNA Isolation Kit (Ambion, Austin, TX, USA). The RNA was reverse-transcribed using a TaqMan MicroRNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA). Expression of mature miRNAs was analyzed using appropriate TaqMan® miRNA assays (Applied Biosystems) and Premix Ex Taq™ probe qPCR (Takara Bio, Otsu, Japan) according to the manufacturers’ protocols. Quantification was performed using RNU6B as an endogenous control. The 2−ΔΔCt method was used to calculate relative miRNA expression levels. 6. Conclusions Recent studies have demonstrated that miRNAs are involved in periodontal tissue homeostasis and pathology. Note that miRNAs highly expressed in periodontal disease gingiva are prone to being miRNAs that are important for osteoclast differentiation. Although whether gingival crevicular fluid contains miRNAs is unknown, miRNAs probably can be used as periodontal disease-specific biomarkers in saliva, serum, and gingival crevicular fluid. If EVs are transferred between cells in periodontal tissues, EVs may serve as therapeutic targets and can be used as drug delivery systems by packaging them with specific miRNAs, mRNAs, and proteins. Additional research is required to determine whether miRNAs can be used for periodontal disease treatment. We anticipate that this new therapeutic target for periodontal disease will open a new door in alveolar bone loss treatment. Acknowledgments This work was supported by the grant from the Scientific Research (C) (No. 26462853) from the Ministry of Education, Culture, Sports, Science and Technology, Japan. Conflicts of Interest The author declares no conflict of interest. Figure 1 Schematic view of osteoclast differentiation. Osteoclasts are derived from hematopoietic stem cells. Cytokines macrophage colony-stimulating factor (M-CSF) and receptor activator of nuclear factor κB ligand (RANKL) are essential for osteoclastogenesis. Binding of RANKL to its receptor, receptor activator of nuclear factor κB (RANK), activates nuclear factor of activated T cells, cytoplasmic 1 (NFATc1), which is a master regulator of osteoclastogenesis. NFATc1 works with other transcription factors, such as activator protein-1 (AP-1), PU.1, and microphthalmia-associated transcription factor (MITF) to induce various osteoclast-specific genes, such as TRAP, Calcr, and Cathepsin K. RANK-RANKL interaction is inhibited by the decoy receptor osteoprotegerin (OPG) expressed by stromal cells and osteoblasts. Blue, cytokines essential for osteoclastogenesis; red, transcription factors. Figure 2 A key osteoclastogenesis signaling cascade. Cited from [2]. The binding of M-CSF to its receptor, c-Fms, induces the transcription factor c-Fos, whereas the binding of RANKL to its receptor, RANK, leads to the recruitment of TNF-receptor-associated factor 6 (TRAF6), the main adapter molecule of RANK. TRAF6 activates nuclear factor κB (NF-κB) and mitogen-activated kinases including c-Jun N-terminal kinase (JNK). JNK activates the transcription factor c-Jun. RANKL/RANK also induces c-Fos to form AP-1, a heterodimeric transcription factor, with c-Jun. AP-1 and NF-κB then induce NFATc1, a master transcription factor that regulates osteoclast differentiation. NFATc1 works with other transcription factors, such as AP-1, PU.1, and microphthalmia-associated transcription factor (MITF) to induce various osteoclast-specific genes. Figure 3 Binding of the microRNA (miRNA) seed region to its complementary site within the target mRNA. The miRNA sequence typically located from nucleotides 2 to 7 at the 5’ end is termed the seed region. This region binds to its complementary site within the 3’-untranslated region (UTR) of the target mRNA. Although a perfect match is not required for base pairing between the miRNA and its target mRNA, binding at the seed region must be perfectly complementary. Figure 4 Microscopic image of a cultured osteoclast. Murine bone marrow macrophages were incubated for 82 h with RANKL (100 ng/mL) and M-CSF (10 ng/mL), resulting in osteoclast formation. After culturing, cells were fixed with 4% paraformaldehyde in phosphate buffer and stained with DAPI and Rhodamine B-conjugated phalloidin. Osteoclasts are multinucleated giant cells with a ring-shaped osteoclast-specific podosome belt termed the actin ring. Blue, nuclei; red, actin; scale bar = 50 µm. Figure 5 Expression levels of miR-155 and miR-223 during human osteoclast differentiation. Human peripheral blood CD14+ cells were treated with M-CSF (50 ng/mL) for three days, followed by RANKL (50 ng/mL) and M-CSF (25 ng/mL) for an additional nine days. Total RNAs were harvested at day 3 (macrophages) or day 12 (osteoclasts). The expression levels of miR-155 (A) and miR-223 (B) were then analyzed by quantitative RT-PCR. Values for each miRNA are expressed relative to those on day 3, which were set to 1. Quantification was performed using RNU6B as an endogenous control. The experiments were performed in triplicate. Data are presented as means ± SD. Student’s t-tests were performed to assess significant differences. ijms-17-01317-t001_Table 1Table 1 Important miRNAs in periodontal disease-related osteoclastogenesis. miRNA Function(s) Target(s) Reference(s) Gingiva with Periodontitis Reference(s) miR-21 P Pdcd4, FasL [17,44] UP [8] miR-29b N C-FOS, MMP-2 [22] UP [8] miR-29a/b/c P Calcr, Cd93, Cdc42, Gpr85, Nfia, Srgap2 [23] UP [8] miR-31 P RhoA [26] DOWN [10] miR-34a N Tgif2 [27] UP/DOWN [8,11] miR-124 N NFATc1 [28,29] Not reported miR-125a P/N TRAF6, TNFIP3 [30,31] UP [8] miR-141 N Mitf, Calcr [45] DOWN [10] miR-146a N TRAF6 [32] UP [9] miR-148a P MAFB [46] UP [10] miR-150 N Opg [47] UP [11] miR-155 N Mitf, Socs1, Pu.1 [1,38,48] UP/DOWN [9,10] miR-223 P/N NFI-A [1,41,42,43] UP [10,11] P, Positive regulator of osteoclastogenesis; N, Negative regulator of osteoclastogenesis. ==== Refs References 1. Kagiya T. Nakamura S. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081318ijms-17-01318ArticleAnti-Atherosclerotic Effects of a Phytoestrogen-Rich Herbal Preparation in Postmenopausal Women Myasoedova Veronika A. 12Kirichenko Tatyana V. 3Melnichenko Alexandra A. 2Orekhova Varvara A. 34Ravani Alessio 1Poggio Paolo 1Sobenin Igor A. 24Bobryshev Yuri V. 256*Orekhov Alexander N. 237Mousa Shaker A. Academic Editor1 Centro Cardiologico Monzino, IRCCS, Milan I-20138, Italy; veronika.myasoedova@gmail.com (V.A.M.); alessio.ravani@ccfm.it (A.R.); paolo.poggio@ccfm.it (P.P.)2 Institute of General Pathology and Pathophysiology, Moscow 125315, Russia; zavod@ifarm.ru (A.A.M.); sobenin@cardio.ru (I.A.S.); a.h.opexob@gmail.com (A.N.O.)3 Institute for Atherosclerosis Research, Skolkovo Innovative Center, Moscow 143025, Russia; t-gorchakova@mail.ru (T.V.G.); varvaraao@gmail.com (V.A.O.)4 Russian Cardiology Research and Production Complex, Moscow 121552, Russia5 Faculty of Medicine, School of Medical Sciences, University of New South Wales, Sydney, NSW 2052, Australia6 School of Medicine, University of Western Sydney, Campbelltown, NSW 2560, Australia7 Department of Biophysics, Biological Faculty, Moscow State University, Moscow 119991, Russia* Correspondence: y.bobryshev@unsw.edu.au; Tel./Fax: +61-2-9385-121711 8 2016 8 2016 17 8 131831 3 2016 03 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).The risk of cardiovascular disease and atherosclerosis progression is significantly increased after menopause, probably due to the decrease of estrogen levels. The use of hormone replacement therapy (HRT) for prevention of cardiovascular disease in older postmenopausal failed to meet expectations. Phytoestrogens may induce some improvements in climacteric symptoms, but their effect on the progression of atherosclerosis remains unclear. The reduction of cholesterol accumulation at the cellular level should lead to inhibition of the atherosclerotic process in the arterial wall. The inhibition of intracellular lipid deposition with isoflavonoids was suggested as the effective way for the prevention of plaque formation in the arterial wall. The aim of this double-blind, placebo-controlled clinical study was to investigate the effect of an isoflavonoid-rich herbal preparation on atherosclerosis progression in postmenopausal women free of overt cardiovascular disease. One hundred fifty-seven healthy postmenopausal women (age 65 ± 6) were randomized to a 500 mg isoflavonoid-rich herbal preparation containing tannins from grape seeds, green tea leaves, hop cone powder, and garlic powder, or placebo. Conventional cardiovascular risk factors and intima-media thickness of common carotid arteries (cIMT) were evaluated at the baseline and after 12 months of treatment. After 12-months follow-up, total cholesterol decreased by 6.3% in isoflavonoid-rich herbal preparation recipients (p = 0.011) and by 5.2% in placebo recipients (p = 0.020); low density lipoprotein (LDL) cholesterol decreased by 7.6% in isoflavonoid-rich herbal preparation recipients (p = 0.040) and by 5.2% in placebo recipients (non-significant, NS); high density lipoprotein (HDL) cholesterol decreased by 3.4% in isoflavonoid-rich herbal preparation recipients (NS) and by 4.5% in placebo recipients (p = 0.038); triglycerides decreased by 6.0% in isoflavonoid-rich herbal preparation recipients (NS) and by 7.1% in placebo recipients (NS). The differences between lipid changes in the isoflavonoid-rich herbal preparation and placebo recipients did not reach statistical significance (p > 0.05). Nevertheless, the mean cIMT progression was significantly lower in isoflavonoid-rich herbal preparation recipients as compared to the placebo group (6 μm, or <1%, versus 100 μm, or 13%; p < 0.001 for the difference). The growth of existing atherosclerotic plaques in isoflavonoid-rich herbal preparation recipients was inhibited by 1.5-fold (27% versus 41% in the placebo group). The obtained results demonstrate that the use of isoflavonoid-rich herbal preparation in postmenopausal women may suppress the formation of new atherosclerotic lesions and reduce the progression of existing ones, thus promising new drug for anti-atherosclerotic therapy. Nevertheless, further studies are required to confirm these findings. atherosclerosismenopauseherbal preparationpreventionintimal medial thickensisoflavonoidsphytoestrogens ==== Body 1. Introduction Postmenopausal status increases cardiovascular risk due to accelerated atherosclerosis progression. Cardiovascular diseases remain the leading cause of mortality and morbidity among postmenopausal women. The cardiovascular risk related to postmenopausal status is predominately due to the rapid decrease of estrogen levels, which are attributed to the indirect protective effect on lipid and glycemic control, and to the direct effect on endothelial function [1,2]. The use of hormone replacement therapy (HRT) in cardiovascular prevention failed to meet expectations and it has been recognized that long-term use of hormone therapy may actually increase the risk of cardiovascular disease (CVD) in postmenopausal women, as shown in the Heart and Estrogen/progestin Replacement Study (HERS) trial, which was conducted in older postmenopausal women with established coronary heart disease (CHD) [3,4]. The known side effect of HRT—that is, an increased risk of malignant hormone-dependent tumors—also produced a negative impact on the perspectives of such therapy [5,6]. The general outcome of several large-scale trials was that neither estrogen nor estrogen/progestin decreased cardiovascular disease [7]. However, later analysis has shown that HRT started in early postmenopause provides cardiovascular benefit and no harm [8,9]. In spite of these findings, the expert opinion says that HRT should not be used for the primary or secondary prevention of CHD; it should be limited to the treatment of menopausal symptoms at the lowest effective dosage over the shortest duration possible, and continued use should be re-evaluated on a periodic basis [10,11]. Phytoestrogens, mainly isoflavonoids, are believed to be an alternative to HRT in postmenopausal women. Phytoestrogens comprise a rather heterogeneous group of natural compounds of plant origin with structures similar to estrogen E2. Three of the most active compounds are coumestans, prenylflavonoids, and isoflavones. The hypothetical effects of phytoestrogens are mediated via estrogen receptors (ERα and ERβ), and the G protein-coupled estrogen receptor (GPER). It is also known that phytoestrogens have high affinity for ERβ, which explains their different action from endogenous estrogens [12]. Similar to endogenous estrogens, phytoestrogens may provide beneficial effects on cardiovascular system through the effects on the vascular endothelium [13], vascular smooth muscle cells [14,15], intracellular cholesterol metabolism [16,17,18], extracellular matrix synthesis [19], and vascular inflammation [20]. Dietary supplementation with phytoestrogens may inhibit the development of atherosclerotic lesions. It has been demonstrated that phytoestrogens from grapes prevent cholesterol accumulation in blood-derived cultured monocytes from postmenopausal women [17]. Animal studies support the anti-atherogenic properties of phytoestrogens; for example, genistein inhibited atherogenesis in hypercholesterolemic rabbits mostly via its beneficial effects on endothelial dysfunction [21]. Resveratrol (stilbene with known estrogen-like activity) exhibited multiple anti-atherogenic effects [22], including inhibition of intimal hyperplasia [23] and inhibition of low density lipoprotein (LDL) oxidation [24]. The results of experimental studies demonstrate that phytoestrogens have a potential in anti-atherosclerotic therapy, because they are able to modulate several mechanisms of atherosclerosis progression. Previously, in an ex vivo model, we evaluated the anti-atherosclerotic effect of phytoestrogen-rich plants and their combinations [17]. Based on the results of dose titration studies, qualitative compositions of isoflavonoid-rich anti-atherosclerotic herbal preparation—active ingredients: tannins from grape seeds, green tea leaves, hop cone powder, and garlic powder—was developed. The aim of the present study was to investigate the effect of this isoflavonoid-rich herbal preparation on the progression of subclinical carotid atherosclerosis in healthy postmenopausal women. The intima-media thickness of common carotid arteries (cIMT) measured by B-mode ultrasound is a widely accepted marker of subclinical atherosclerosis; it is well correlated with the degree of coronary atherosclerosis and is a significant predictor of clinical manifestations of atherosclerosis. This instrumental marker is used in clinical and epidemiological studies to assess the impact of conventional and novel cardiovascular risk factors and treatment regimens on atherosclerosis progression [25,26,27]. Several clinical trials were aimed at the assessment of the effects of HRT or phytoestrogens on cIMT progression in postmenopausal women [28,29,30,31]. Thus, in this study we have used ultrasound examination of common carotid arteries and cIMT measurement as a tool for quantitative assessment of atherosclerosis, with annual cIMT progression as the endpoint. 2. Results 2.1. Baseline Data In total, 157 asymptomatic postmenopausal women were included in the study, 77 in the isoflavonoid-rich herbal preparation group, and 80 in the placebo group. The groups did not differ in age, body mass index, smoking status, family history of coronary artery disease, blood level of triglycerides, and high density lipoprotein (HDL) cholesterol (HDL-C), the prognostic risk of myocardial infarction, and sudden death. No difference was found between groups in mean and maximum cIMT, and in the size of asymptomatic carotid atherosclerotic plaques. However, systolic and diastolic blood pressure levels were significantly higher in placebo group, whereas total cholesterol and low density lipoprotein (LDL) cholesterol (LDL-C) levels were significantly higher in isoflavonoid-rich herbal preparation recipients at the baseline. The proportion of patients with diabetes was also higher in isoflavonoid-rich herbal preparation recipients. Baseline characteristics of study participants are given in Table 1. 2.2. Follow-up Of the 157 enrolled study participants, 131 completed study protocol (57 isoflavonoid-rich herbal preparation recipients and 74 placebo recipients). Among dropouts, 16 were lost for follow-up examination (12 in the isoflavonoid-rich herbal preparation recipients group, 4 in the placebo group) and 10 refused to visit for personal reasons and withdrew their informed consent (8 in the isoflavonoid-rich herbal preparation recipients group, 2 in the placebo group). In participants available for follow-up examination no adverse or side effects were registered in both groups. Thus, the higher dropout rate observed in isoflavonoid-rich herbal preparation recipients can hardly be explained by some adverse or side effects of the study medication. The comparison of odds ratios for dropout have shown that the observed dropout values are better explained by chance, taking into account rather small sample size. After 12-month follow-up, blood pressure, lipid profile, as well as ultrasound characteristics of carotid atherosclerosis were determined in both groups. Blood lipid levels decreased in both groups, and in the placebo group these changes were statistically significant for total cholesterol (from 252 to 239 mg/dL, or by 5.2% reduction, p = 0.020) and HDL-C (from 74 to 71 mg/dL, or by 4.5% reduction), in isoflavonoid-rich herbal preparation recipients for total cholesterol (from 271 to 254 mg/dL, or by 6.3% reduction) and LDL-C (from 170 to 157 mg/dL, or by 7.6% reduction; p = 0.040). The decrease in serum triglyceride levels was statistically insignificant in both groups. The difference between lipid changes in isoflavonoid-rich herbal preparation and placebo recipients did not reach statistical significance neither for total cholesterol, nor for LDL-C, HDL-C, and triglycerides. Blood pressure levels and body mass index did not change in either group. The changes of clinical and biochemical characteristics from the baseline after 12-month follow-up are given in Table 2. In isoflavonoid-rich herbal preparation recipients, no significant increase of mean cIMT was observed; the increment accounted for 6 μm (less than 1%), and the growth of atherosclerotic plaque growing accounted for 27% of the baseline value. C, in the placebo group the rate of atherosclerosis progression was higher (i.e., the increment of mean cIMT accounted for more than 100 μm (13%) and the growth of atherosclerotic plaques accounted for 40% of the baseline value) (Table 3). There was a significant difference between the isoflavonoid-rich herbal preparation and placebo recipients in mean cIMT increase over 12-month follow-up (p < 0.001), but not in maximum cIMT increase (p = 0.89) or in the growth of existing atherosclerotic plaques (p = 0.30). The samples of actual individual ultrasound images and the mean values of cIMT at the baseline and after follow-up are shown in Figure 1. 3. Discussion The results of this study have demonstrated that mean cIMT progression was slower in asymptomatic postmenopausal women who received isoflavonoid-rich herbal preparation, as compared to women who received the placebo. In addition, the herbal preparation decreased the total cholesterol, LDL-C levels, and suppressed cIMT progression after 12 months of herbal preparation administration. It should be noted that in our study, the baseline LDL-C and total cholesterol level, as well as the prevalence of diabetes in isoflavonoid-rich herbal preparation recipients were higher than in the placebo group; these risk factors of atherosclerosis could suggest more pronounced cIMT and plaque progression. However, we have seen the reverse effect; therefore, it may be expected that the anti-atherosclerotic potency of isoflavonoid-rich herbal preparation may even be underestimated in this study. On the other hand, the reduction in total cholesterol and LDL-C after 12-month follow-up in the isoflavonoid group could be due to regression towards the mean, since they were higher at the baseline. CVD related to atherosclerotic process is responsible for the majority of deaths in postmenopausal women. The prevention of the lipid accumulation in cells is the key mechanistic factor for inhibition of atherosclerotic plaque formation at the early stages of atherosclerosis progression. Phytoestrogens have the capacity to affect plasma lipid profile, but little is known regarding their effects on atherosclerosis progression. In women undergoing coronary angiography for suspected myocardial ischemia, beneficial association between blood levels of the phytoestrogen daidzein and lipoproteins, particularly lower triglycerides and higher HDL-C levels were previously reported [32]. The main association of phytoestrogens with lipoprotein levels was incrementally related to diadzein, but not with other lipoprotein modulators. In another clinical study it has been shown that isoflavones induce the reduction of total cholesterol and LDL-C plasma levels without affecting triglycerides or HDL-C levels [33]. Our results are in line with previous findings; however, in our study blood lipid levels were decreased in both groups: in the placebo group, these changes were significant for total cholesterol and HDL-C; and in the isoflavonoid-rich herbal preparation recipients, for total cholesterol and LDL-C. The ability of phytoestrogens to reduce the accumulation of cholesterol in cells is a possible mechanism to explain the effects on mean cIMT. Previously, we have evaluated the anti-atherogenic effect of phytoestrogen-rich plants using an ex vivo model based on primary cultures of monocytes isolated from the blood of healthy donors. In this model, the ability of human serum to induce accumulation of cholesterol in cultured cells (serum atherogenicity) was measured, as well as the effect of single dose oral administration of plant extract on serum atherogenicity [17,34]. Grape seeds extract (100 mg) lowered serum atherogenicity by 71%, 78%, and 81% at 2, 4, and 6 h after oral intake of a single dose. Similar effects were observed for hop cones (250 mg), garlic powder (150 mg), sage leaves (100 mg), green tea leaves (250 mg), sea kelp (500 mg), fucus (250 mg), and carrot (1000 mg). The ability of soya beans extract (35 mg) to lower serum atherogenicity by 28%, 38%, and 30% at 2, 4, and 6 h after a single dose administration, respectively, was demonstrated [17,35]. The main endpoint of the current study was to identify the annual changes in cIMT progression. Several studies demonstrated that cIMT is a significant and independent predictor of cardiovascular events, and allows for non-invasive evaluation of early atherosclerosis progression in asymptomatic patients [36]. Only a few clinical trials investigated the effect of phytoestrogens on atherosclerosis progression in postmenopausal women. In the recent long-term intervention trial (2.7 years) with soy isoflavones, the inhibition of subclinical atherosclerosis progression evaluated by cIMT was demonstrated. Healthy postmenopausal women were randomized in two groups; the first group who received daily supplement with 25 g soy protein containing 91 mg of isoflavones, and the second group who received a placebo. In both groups, the increment of cIMT was observed. However, in the soy group the cIMT progression was not statistically significant (p = 0.35), and was 16% lower than in the placebo group. That study has enrolled 350 participants, and the duration was more than two years. The authors suggested that further use of isoflavone-rich dietary supplements would allow achieving the significant difference in the rate of atherosclerosis progression between the two groups [28]. In our study, mean cIMT changes in both groups were observed. However, in herbal preparation recipients this increase was negligible, but in the placebo group the increment was significantly higher than in herbal preparation recipients, and accounted for 111 μm, or 13% increase. This fact indicates that in postmenopausal women the rate of carotid atherosclerosis progression is notably high. In the study by Rossi et al. [37], the mean cIMT progression accounted for 103 μm (range from −250 to 567 μm; IQR from 0 to 200 μm) per year in hypertensive postmenopausal women, and this progression rate is very close to our data. It should be noted that in our study the difference between the two groups in systolic and diastolic blood pressure at the baseline was statistically significant (135/83 versus 127/79 in placebo group and herbal preparation group, respectively), and this fact may partly explain the rather high progression of cIMT in the placebo group, which has not been replicated in any other studies [29,30]. On the other hand, Colacurci et al. have demonstrated rather similar cIMT progression rates in non-hypertensive women [31]. The limitations of our study, such as the duration of the follow-up and rather limited sample size, did not allow defining the proportion of cIMT progression rates in placebo recipients explained by the higher blood pressure. It should be noted that the time since menopause is of the essence when studying atherosclerosis progression and medical intervention. There exists an opinion that timely HRT may offer protection against CVD, whereas in older women there may be cardiovascular harm associated with HRT use [8,9]. Hodis et al. have recently demonstrated that anti-atherosclerotic effects of HRT on cIMT progression differed between early and late postmenopause. Oral estradiol therapy was associated with less progression of subclinical atherosclerosis measured as cIMT dynamics than was placebo when therapy was initiated within six years after menopause, but not when it was initiated ten or more years after menopause [29]. Thus, estradiol was shown to be effective in reducing cIMT progression. On the other hand, in the Kronos Early Estrogen Prevention Study (KEEPS) performed in more than 700 healthy women aged 42 to 59 within three years after menopause, the carotid ultrasound studies showed similar rates of progression of cIMT in all three treatment groups (0.45 mg a day of Premarin—an oral conjugated equine estrogen (o-CEE)—or 50 µg a day of transdermal estradiol via a Climara patch, or placebo) over the four years of study. However, these changes were reported to be generally small; therefore, slow cIMT progression limited the statistical power to detect any differences among the groups [30]. According to the results of our study, isoflavonoid-rich herbal preparations may provide a direct anti-atherosclerotic effects, but no direct comparisons with estrogens were performed. In general, it may be proposed that anti-atherosclerotic action of drugs should be realized via prevention of intracellular cholesterol accumulation in vascular wall cells, but it is unclear if estrogens may possess the same mechanistic effect at the cellular level. In our previous studies on prevention of intracellular cholesterol accumulation, modified LDL were used to induce intracellular lipid deposition, and the effects of drugs or chemical compounds mainly related to LDL binding, uptake, internalization, and metabolism in cells were in focus. In contrast, Wang et al. explored the alternative way of preventing foam cell formation via cholesterol efflux modulation. They have demonstrated that 17β-estradiol promotes cholesterol efflux from vascular smooth muscle cells and reduces foam cell formation via ERβ- and liver X receptor (LXR) α-dependent upregulation of ABCA1 and ABCG1 [38]. Another mechanism of atherosclerosis prevention may be related to anti-inflammatory effects, and it was shown that estradiol can regulate monocyte chemotactic protein-1 (MCP-1) in human coronary artery smooth muscle cells [39], increase prostacyclin synthesis in cells from atherosclerotic lesions [40], impair endothelial function in postmenopausal women [41], transform growth factor activity [42], and attenuate atherogenesis via selective estrogen receptor beta modulator 8β-VE2 [43]. On the other hand, anti-inflammatory effects of phytoestrogens are also known [44,45,46,47]. Therefore, anti-atherogenic effects of both estrogens and isoflavonoids are not limited to the inhibition of direct accumulation of cholesterol in cells only. Finally, the findings of our study are in line with the results obtained from the study aimed to evaluate the effect of selective estrogen receptor modulator Raloxifene on atherosclerosis progression in postmenopausal clinically healthy women. In a prospective study enrolling 155 postmenopausal women without clinical manifestations of CVD, study participants were randomized in two groups, receiving Raloxifene 60 mg daily or placebo for 18 months. The cIMT progression for 18 months was 11.2 μm in Raloxifene group versus 85.7 μm in the placebo group (p < 0.004). Thus, the lower risk of cIMT progression was demonstrated in Raloxifene recipients (odds ratio = 0.41; 0.32–0.70 at a 95% confidence interval) [31]. Nevertheless, our study has certain limitations. The main one is the duration of the follow-up, only 12 months. Long-term effects of isoflavonoid-rich dietary supplement Karinat need to be further studied in order to evaluate its effects on main cardiovascular risk factors and long-term outcomes, such as myocardial infarction and stroke [48]. Indeed, longer observation may help to better understand the effects of isoflavonoid-rich herbal preparations on main outcomes of CVD, but in this study that was not the primary endpoint. The second notable limitation is a rather small sample size. To interpret the results from our study, the limited number of enrolled subjects needs to be taken into consideration, as it may lead to confounding results, despite randomization. Finally, it should be noted that the effect of isoflavonoids or other estrogen-like molecules on cardiovascular health may be realized more through endothelial function/dysfunction. In our study we have evaluated only the effects on lipids, and the effect of treatment on the arterial wall that reflects atherosclerotic profile. It should be expedient to study the effects of isoflavonoid-rich herbal medications also on endothelial function using, for example, flow-mediated dilatation. 4. Materials and Methods 4.1. Study Medication Isoflavonoid-rich herbal preparation contained tannins from grape seeds (Vitis vinifera L.), green tea leaves (Camellia sinensis L.), hop cone powder (Hunulus lupulus), and garlic powder (Allium sativum L.). Commercially available purified compounds were used. This preparation was officially registered as a dietary supplement “Karinat” and was manufactured by INAT-Farma (Moscow, Russia). The quantified chemical constituents are provided in Table 4. The content of toxic elements, pesticides, dichlorodiphenyltrichloroethane (DDT), and its metabolites and microbiological purity have been controlled. The measurement of cathechines and allicin contents was performed by high performance liquid chromatography (HPLC). Based on previous dose titration studies, the dosage regimen for isoflavonoid-rich herbal preparation was determined [17]. The quantity of herbal constituents was 500 mg per capsule; a total of three capsules were given daily, independently of meals, for 12 months. The dosage regimen of Karinat (three capsules daily) provides for estimated daily intake of 27.3 mg procyanidin, 2.5 mg genistein, 11.8 mg daidzein, 4.6 mg flavones, 3.5 mg resveratrol, and 44.6 mg of other polyphenolic compounds [49]. 4.2. Study Design The study was performed in the Outpatient Clinic Nº 202 at Moscow State University. In total, 157 asymptomatic postmenopausal women were included in double-blind, placebo-controlled clinical study (ClinicalTrials.gov Identifier, NCT01742000). The inclusion criteria were as follows: the menopausal state (physiological or surgical) at least for the last five years; maximum cIMT more than 0.80 mm as determined by ultrasound B-mode examination of carotid arteries; the absence of climacteric syndrome (no more than two points by the Blatt-Kupperman score [50]). Exclusion criteria were as follows: the use of HRT during the peri- and postmenopausal period; the use of the lipid-lowering drugs for at least six months prior to inclusion; the absence of signed informed consent; the permanent use of sugar-lowering drugs (more than two months per year); the history of myocardial infarction, stroke, heart failure, uncontrolled hypertension (blood pressure above 145/95 mm·Hg in patients receiving antihypertensive treatment); cancer; chronic kidney disease; chronic liver disease; intolerability of the components of isoflavonoid-rich herbal preparation; and/or adverse reactions and/or side effects revealed during the follow-up. Some inclusion and exclusion criteria were intentionally defined to be compatible in general with those used in the KEEPS [51], in order to allow the possibility of tentative comparison of the rate of cIMT progression in early menopausal women. The use of lipid- and sugar-lowering medications was considered as a limitation for the inclusion in the study, since they may provide their own effects on cIMT progression [52,53,54,55]. The study participants were randomized into two groups: the first group who received isoflavonoid-rich herbal preparation (Karinat, INAT-Farma), three capsules daily for 12 months, and the second group who received a placebo. Karinat and placebo capsules looked identical. 4.3. Baseline Examination Clinical and laboratory examinations were performed at the inclusion to the study and included anthropometric parameters (i.e., age, body mass index, blood pressure); personal and family history of arterial hypertension, diabetes mellitus, and coronary heart disease; lipid profile (i.e., cholesterol, triglycerides, LDL-C, HDL-C), B-mode ultrasound examination of common carotid arteries, as well as evaluation of the severity of menopausal symptoms by the Blatt-Kupperman score [50]. 4.4. Follow-up Examination Follow-up examination was performed after 12 months of treatment and included the same clinical and laboratory examinations, as at the baseline. The rate of cIMT progression was the primary endpoint of the study, since it is conventionally used as an intermediate outcome for vascular risk estimation. It was demonstrated that cIMT progression may be rather slow [56]. We have investigated the cIMT progression in healthy postmenopausal women after five years of menopause, and in this age the cIMT progression was expected to be accelerated. Therefore, 12-month follow-up was considered to be sufficient to detect significant changes in cIMT in this cohort. On the other hand, the studies aimed to investigate the atherosclerosis progression and/or the role of anti-atherosclerotic therapy in postmenopausal women employed a one year (12-month) follow-up [37,57]. These considerations prevented us from evaluating lipid results and carotid arteries earlier than 12-month intervals. 4.5. Blood Sampling and Lipid Measurements Venous blood was taken after overnight fasting. Commercially available enzymatic kits (Fluitest CHOL, Fluitest TG, Fluitest HDL-CHOL, Analyticon, Potsdam, Germany) were used for total cholesterol, triglycerides, and HDL-C measurements in blood serum. LDL-C was calculated with the Friedewald formula. 4.6. Calculation of Prognostic Cardiovascular Risk The calculation of ten-year prognostic risk of fatal and non-fatal myocardial infarction and sudden death was performed in accordance with PROCAM Study-derived Cox proportional hazards model [58]. Such variables as female gender, age, blood pressure, smoking, diabetes mellitus, total cholesterol, triglycerides, and family history of acute myocardial infarction (first-grade relatives with the events occurred before the age of 60 years), were used for risk calculation, and the regional adjustment coefficient was applied [59]. 4.7. Carotid Artery Ultrasound Examination To examine the carotid arterial wall, B-mode high-resolution ultrasonography with a linear vascular 7.5 MHz probe (SSI-1000 scanner, SonoScape, Shenzhen, China) was performed by three operators. The examination included a scanning of the left and right common carotid arteries, the carotid sinus area, as well as external and internal carotid arteries, with a focus on the far wall of the artery in three fixed projections (anterior, lateral, and posterior [60]). The measurements were performed on distal 10 mm of common carotid artery (the opposite site from carotid sinus of the common carotid artery). Reproducibility of cIMT measurements was assessed according to the protocol of the IMPROVE Study [61]. Within-operator coefficient of variation (CV) was 2.6%; reproducibility coefficient accounted for 0.040. The frozen scans were digitized for subsequent cIMT quantitative measurement using specialized software package (M’Ath ver. 3.1, IMT, Paris, France). The cIMT far wall was measured as the distance from the leading edge of the first echogenic zone to the leading edge of the second echogenic zone. The measurements were performed by an independent certified reader in a blinded manner. The mean of all measurements in the anterior, posterior, and lateral projections were considered as integral measurements of cIMT. 4.8. Statistical Analysis The significance of differences was analyzed with the IBM SPSS 21.0 program package (IBM, Armonk, NY, USA). The Mann–Whitney statistics or t-test were applied for between-group valuations, Wilcoxon statistics were performed for within-group effect comparisons, and Pearson's chi-squared was used for the assessment of nominal variables distributions. Pearson's correlation analysis and regression analysis were applied for the evaluation of the relationship between the values of risk changes and clinical and biochemical variables. The data are reported as the mean and standard deviation (SD). The differences were considered statistically significant at the 0.95 level of confidence (p < 0.05). 5. Conclusions Our data suggest that the use of the isoflavonoid-rich herbal preparation Karinat may play an important role in the prevention of atherosclerosis progression in postmenopausal women, since it essentially suppressed the formation of new atherosclerotic lesions approximately by 1.5-fold and slowed the progression of existing ones. Further evaluation of the study results should be based on the precise knowledge of cardioprotective, metabolic, and anti-atherosclerotic effects of isoflavonoids, other phytoestrogens and their combinations. The isoflavonoid-rich herbal preparation used in our study provides intake of a mix of polyphenolic compounds, including procyanidin, genistein, daidzein, flavones, and resveratrol [49], but the role of each compound in the inhibition of cIMT and plaque progression remains to be unraveled. Our study unambiguously suggests that there is the potential for this herbal supplement for the prevention of atherosclerosis in postmenopausal women. However, it is worth noting that the present study is preliminary in nature, and the herbal preparations are still limited to prevention, but not treatment. Thus, the use of isoflavonoid-rich herbal preparations may be considered nowadays as a promising approach for the development of anti-atherosclerotic therapy. Nevertheless, further studies are required to confirm this possibility. Acknowledgments This study was supported in part by the Ministry of Education and Sciences, Russian Federation (Project # RFMEFI61614X0010). Author Contributions All authors contributed to the design and implementation of this study. Veronika A. Myasoedova, Tatyana V. Kirichenko, Alexandra A. Melnichenko, Varvara A. Orekhova and Alessio Ravani performed the examination of study participants and analyzed the data obtained. Igor A. Sobenin and Alexander N. Orekhov have elaborated the concept of the study and supervised the project. All authors contributed to the writing of this manuscript. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Actual individual ultrasound images and cIMT values at the baseline and after 12-month follow-up. (a) Normal cIMT in apparently healthy postmenopausal women; (b) Abnormally increased cIMT in apparently healthy postmenopausal women; (c) Dynamics of cIMT in isoflavonoid-rich herbal preparation and placebo recipients at the baseline (open bars) and after 12-months follow-up (filled bars). The data are presented as mean and S.E.M. *, represents a significant difference between baseline and follow-up cIMT values; p < 0.05. ijms-17-01318-t001_Table 1Table 1 Baseline characteristics of study participants. Variable Isoflavonoid-Rich Herbal Preparation Recipients, n = 77 Placebo Recipients, n = 80 p-Value Age, years 65 (7) 65 (6) 0.804 Body mass index, kg/m2 27.1 (4.0) 26.9 (3.8) 0.782 Systolic BP, mm·Hg 127 (13) 135 (18) 0.006 Diastolic BP, mm·Hg 79 (8) 83 (9) 0.006 Smoking, n (%) 3 (5) 7 (10) 0.362 Diabetes, n (%) 6 (11) 1 (1) 0.022 Hypertension, n (%) 29 (51) 41 (56) 0.532 Family history of CAD, n (%) 16 (30) 19 (26) 0.634 Family history of hypertension, n (%) 30 (53) 37 (51) 0.827 Family history of diabetes, n (%) 5 (9) 9 (14) 0.387 Total cholesterol, mg/dL 271(55) 252 (42) 0.024 Triglycerides, mg/dL 134 (78) 126 (51) 0.456 HDL-C, mg/dL 74 (15) 74 (18) 0.745 LDL-C, mg/dL 170 (47) 153 (42) 0.034 Risk of MI, PROCAM score, % 1.64 (3.34) 1.24 (1.40) 0.363 cIMT mean, mm 0.829 (0.138) 0.849 (0.133) 0.415 cIMT max, mm 0.950 (0.172) 0.981 (0.161) 0.287 Carotid plaque, relative size, score 0.77 (0.78) 0.76 (0.72) 0.908 The data are presented as mean and standard deviation (in parentheses), if not otherwise indicated. BP: blood pressure; cIMT: intima-media thickness of common carotid arteries; HDL-C: high density lipoprotein cholesterol; LDL-C: low density lipoprotein cholesterol; MI: myocardial infarction; n: number of cases. ijms-17-01318-t002_Table 2Table 2 The changes of characteristics of study participants after 12-month follow-up. Variable Isoflavonoid-Rich Herbal Preparation Recipients, n = 56 Placebo Recipients, n = 71 Change p-Value Change p-Value Body mass index, kg/m2 −0.01 (0.8) 0.978 −0.07 (1.6) 0.708 Systolic BP, mm·Hg 5 (19) 0.051 −1 (18) 0.666 Diastolic BP, mm·Hg −1 (8) 0.806 −1 (9) 0.150 Total cholesterol, mg/dL −17 (46) 0.011 −13 (41) 0.020 Triglycerides, mg/dL −9 (53) 0.232 −9 (40) 0.106 HDL-C, mg/dL −3 (11) 0.114 −3 (12) 0.038 LDL-C, mg/dL −13 (45) 0.040 −8 (39) 0.126 The data are presented as mean and standard deviation (in parentheses). ijms-17-01318-t003_Table 3Table 3 Carotid atherosclerosis progression. Variable Isoflavonoid-Rich Herbal Preparation Recipients, n = 56 Placebo Recipients, n = 71 Change p-Value Change p-Value cIMT mean, μm +6 (85) 0.6 +111 (91) <0.001 cIMT max, μm +8 (101) 0.6 +4 (220) 0.9 Carotid plaque, score +0.21 (0.59) 0.009 +0.31 (0.55) <0.001 The data are presented as mean and standard deviation (in parentheses). ijms-17-01318-t004_Table 4Table 4 Herbal and technological composition of isoflavonoid-rich herbal preparation “Karinat”. Constituent Mg Per Capsule % Humulus lupulus L. 160 34.04 Camellia sinensis L. 115 24.46 Allium sativum L. 100 21.27 Vitis vinifera L. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081319ijms-17-01319ArticleImmunosuppressive Effect of Litsea cubeba L. Essential Oil on Dendritic Cell and Contact Hypersensitivity Responses Chen Hsin-Chun 1Chang Wen-Te 2Hseu You-Cheng 1Chen Hsing-Yu 2Chuang Cheng Hsuan 2Lin Chi-Chen 3Lee Meng-Shiou 2*Lin Ming-Kuem 2*Andrade Paula Academic Editor1 Department of Cosmeceutics, College of Biopharmaceutical and Food Sciences, China Medical University, No. 91, Hsueh-Shih Road, Taichung 40402, Taiwan; c0706@mail.cmu.edu.tw (H.-C.C.); ychseu@mail.cmu.edu.tw (Y.-C.H.)2 Department of Chinese Pharmaceutical Sciences and Chinese Medicine Resources, College of Biopharmaceutical and Food Sciences, China Medical University, No. 91, Hsueh-Shih Road, Taichung 40402, Taiwan; wtchang@mail.cmu.edu.tw (W.-T.C.); cappuccino2027@hotmail.com (H.-Y.C.); zhushaun920@gmail.com (C.H.C.)3 Institute of Medical Technology, College of Life Science, National Chung Hsing University, Taichung 402, Taiwan; lincc@dragon.nchu.edu.tw* Correspondence: leemengshiou@mail.cmu.edu.tw (M.-S.L.); linmk@mail.cmu.edu.tw (M.-K.L.); Tel.: +886-4-2205-3366 (ext. 5208) (M.-S.L.); +886-4-2205-3366 (ext. 5212) (M.-K.L.)12 8 2016 8 2016 17 8 131927 5 2016 08 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Litsea cubeba L., also named as Makauy, is a traditional herb and has been used as cooking condiment or tea brewing to treat diseases for aborigines. The present study was undertaken to explore the chemical compositions of the fruit essential oil of L. cubeba (LCEO) and the immunomodulatory effect of LCEO on dendritic cells and mice. The LCEO was analyzed using gas chromatography (GC) and gas chromatography/mass spectrometry (GC/MS) with direct injection (DI/GC) or headspace-solid phase microextraction (HS-SPME/GC). In total, 56 components were identified, of which 48 were detected by DI/GC and 49 were detected by HS-SPME/GC. The principal compounds were citral (neral and geranial). An immunosuppressive activity of LCEO was investigated with bone marrow-derived dendritic cells (DCs) which have a critical role to trigger the adaptive immunity. Additionally, the inhibitory effect of LCEO on immune response was elucidated by performing the contact hypersensitivity (CHS) responses in mice. Our results clearly showed that LCEO decreases the production of TNF-α and cytokine IL-12 in a dose-dependent manner in lipopolysaccharide (LPS)-stimulated DCs. CHS response and the infiltrative T cells were inhibited in the tested ears of the mice co-treated with LCEO. We demonstrate, for the first time, that the LCEO mainly containing citral exhibits an immunosuppressive effect on DCs and mice, indicating that LCEO can potentially be applied in the treatment of CHS, inflammatory diseases, and autoimmune diseases. Litsea cubebaessential oildendritic cellimmunosuppressivecitral ==== Body 1. Introduction In the immune system, various immune cells are highly communicative with each other by various cytokines and are in charge of the defense against foreign pathogen infection and maintaining health. Inflammation is a major component of our immune response. Although inflammation is a natural defense, the persistence of the process for abnormally long periods can be harmful and has been recognized as a major risk factor for various human diseases, including cardiovascular disease, metabolic disorder, neurological disease, and cancer [1,2]. Thus, reduction of chronic inflammation would be beneficial to prevent the pathological progression of these human diseases. In such cases, the inflammatory response should be suppressed. Among the immune cells, dendritic cells (DCs) are the best antigen-presenting cells and are in charge of the induction of adaptive immunity [3,4,5]. To initiate adaptive immunity, DCs present a specified antigen on the surface to be recognized by naïve T cells. This recognition triggers the differentiation of the specified antigen-specific T cells. Next, a strong and specific T cell-based immune response is built up to attack the “pathogens” which present the specified antigen. With such a critical role, DCs are thought to be an ideal target when attempting to evaluate potential immune modulators [6,7,8]. Litsea cubeba L. belongs to the family Lauraceae, which is widely distributed in Japan, Taiwan, Southern China, and Southeastern Asia [9]. All parts of this plant emanate a pungent gingery odor [10]. Fruits of L. cubeba are spicy condiments, frequently used in the aboriginal cuisine of Taiwan [11]. The essential oil of its fruit has been used as a flavor enhancer in cigarettes, cosmetics, and foods, and as raw material to produce citral (neral and geranial) [12]. Its pharmacological effects have been reported to have an antimicrobial [9,13], antioxidative [14], anticancer [10,15], anti-inflammatory [11], and insecticidal activities [12,16]. However, there is no report on immunosuppressive effects of L. cubeba on DCs and its relative immune response in vivo. In this study, L. cubeba essential oil (LCEO) was extracted from the fresh fruits. The inhibitory effect of the LCEO on dendritic cell activation was examined. In addition, the contact hypersensitive response was conducted to examine the in vivo immunosuppressive effects in mice. 2. Results and Discussion 2.1. Constituents of the Essential Oils Punyarajun and Nandhasri (1981) extracted essential oils from unripe L. cubeba berries of Thai origin and the yield was ca. 3.0% [17]. Ho et al. (2010) used hydrodistillation to extract the leaf and fruit essential oils of L. cubeba from Taiwan, and the yields were 13.9% ± 0.09% and 4.0% ± 0.03%, v/w, respectively [10]. Liu and Yang (2012) extracted the fruit essential oils of L. cubeba from Taiwan, and the yields were 4.5% ± 0.2% [9]. Jiang et al. (2009) reported that the fruit of L. cubeba contains 3%–5% of essential oils which are rich in citral [12]. In the present study, the yield of the essential oils obtained from the fresh fruit of L. cubeba by steam distillation was 3.7% ± 0.4%. The yield is similar to that reported in these published studies. As shown in Table 1, a total of 48 components were identified by gas chromatography using direct injection (DI/GC). These components include 12 monoterpenes, five sesquiterpenes, seven terpene alcohols, three terpene aldehydes, two terpene ketones, six terpene esters, five terpene oxides, four aliphatic aldehydes, one aliphatic ketone, and three aliphatic ester. The principal compounds were citral (neral and geranial) accounting for 88.02%. Other constituents identified in significant proportions were 6-methyl-5-hepten-2-one, β-myrcene, limonene, linalool, citronellal, and verbenol. Terpene aldehydes (89.25%) were the most abundant compounds in the essential oil (Table 1). In line with the studies from Seo et al. [16], Ho et al. [10], Kejlová et al. [18], Liu and Yang [9], this study showed that terpene aldehydes were the most abundant volatile compounds and neral and geranial were the most major components in the oil. The headspace-solid phase microextraction (HS-SPME) method has been reported to be an excellent tool for the analysis of herbs because it is simple, fast, and does not leave any residues [19]. In this study, a total of 49 components were identified by GC and GC/MS with HS-SPME method. These components include 15 monoterpenes, five sesquiterpenes, seven terpene alcohols, three terpene aldehydes, three terpene ketones, seven terpene esters, six terpene oxides, four aliphatic aldehydes, one aliphatic ketone, one aliphatic alcohol, and three aliphatic esters. Terpene aldehydes (75.09%) were the most abundant compounds in the oils (Table 1). Comparative analysis of these compounds identified by these two methods, showed that 56 compounds were detected in total, of which, 48 were identified by DI/GC and 49 by HS-SPME/GC. As shown in Table 1, HS-SPME/GC analysis revealed higher percentages of monoterpenes, terpene alcohols, and aliphatic ketone, but lower percentages of terpene aldehydes and terpene esters than DI/GC analysis. Some monoterpenes such as α-thujene, α-phellandrene, and trans-β-ocimene could be identified only by HS-SPME/GC. This indicates that the more complete constituents of essential oils can be identified with the combination of DI/GC and HS-SPME/GC. 2.2. L. cubeba Essential Oils (LCEO) Inhibited the Activation of Dendritic Cells (DCs) To test that the cytotoxic effect of L. cubeba essential oils (LCEO) on dendritic cells (DCs), the viability of mouse bone marrow-derived DCs treated with the different concentrations of LCEO was examined. The result showed no significant effect of 5 × 104-, 1 × 105-, 2 × 105- and 4 × 105-fold diluted LCEO on DCs, although 5 × 104-fold diluted LCEO exhibited a little cytotoxic effect (Figure 1). Thus, the concentrations of 1 × 105-, 2 × 105-, and 4 × 105-fold diluted LCEO was used for the following inhibition experiment. TNF-α and IL-12 are hallmarks of DC activation [3,6,7,8]. To elucidate the immunomodulatory activity of the LCEO, the effect of LCEO on TNF-α and IL-12 production by DCs stimulated by lipopolysaccharides (LPS) was examined. The results showed that the amounts of TNF-α and IL-12 produced by LPS-induced DCs were inhibited by the presence of LCEO in a dose-dependent manner (Figure 2). This indicated that LCEO possess an inhibitory activity to DC activation. The IC50 of LCEO for TNF-α and IL-12 was approximately 1 × 105- and 2 × 105-fold dilution, respectively. 2.3. The Contact Hypersensitivity (CHS) Response Is Attenuated in Mice Co-Treated with LCEO The above findings indicate that LCEO is able to inhibit the activation of DCs and, thus, we are able to postulate logically that LCEO is able to prevent DC-mediated immune response. Therefore, DNFB-induced CHS was performed to examine the inhibition as the immune response stimulated by DNFB is a type of cell-mediated response. Mice were sensitized by painting their abdomens with DNFB in the absence or presence of LCEO. The hypersensitivity response to DNFB at the ears was then examined. The results of the histological analyses (Figure 3A) and the increase of thickness of the tested ears (Figure 3B), showed that the tested ears were significantly swollen in DNFB-sensitized mice but not in DNFB plus LCEO-treated mice (by painting), indicating that LCEO significantly inhibits the CHS in the DNFB-sensitized mice. Moreover, CD3+ T cells, which are activated by DC cells, were examined by immunostaining analysis in the tissue of the tested ears. The results showed that infiltrative T cells are significantly reduced (Figure 4A). To quantify the inhibitory effect, the number of the infiltrative T (CD3+) cells in the tested ears was counted. The results showed that infiltrative CD3+ cells were significantly reduced by the presence of LCEO (Figure 4B). Collectively, these results provided evidence that LCEO have the potential to prevent or treat delayed-type hypersensitivity/type-4 hypersensitivity; for example, allergic contact dermatitis. By chemical analysis, we found that neral and geranial were the most common components. Liao et al. separated citral into neral and geranial in pure forms and demonstrated their anti-inflammatory activity, and neral showed a greater anti-inflammatory activity, including significant inhibition of cytokine secretion and inflammatory molecule expression of LPS-stimulated macrophages [11]. Therefore, it is likely that neral and geranial can be the major active constituents in LCEO which contribute to the immunosuppressive effects exhibited in the present study. Increasingly, recent research has focused on identifying immune modulators in native resources, particularly in edible material. The reason is that the compounds in such materials are relatively safe to humans and, thus, may be regarded as safe immune modulators. L. cubeba has long been used to treat various diseases and as a functional food for aborigines, thus can be taken as a good native resource candidate. In this study, LCEO extracted from L. cubeba fruits was shown to possess immunomodulatory activity, as seen by the immunosuppressive activity to DCs in DNFB-sensitized mice. Therefore, the in vitro and in vivo results revealed that LCEO has the ability to inhibit hypersensitivity responses by affecting DC functioning. Moreover, DCs play a role to develop chronic inflammation and autoimmunity [20,21]. Thus, we have provided, for the first time, evidence that LCEO may be a promising agent for the treatment of inflammation and autoimmune diseases. 3. Materials and Methods 3.1. Plant Material Fresh Litsea cubeba fruits were collected from a spicebush farm Wanrong Township, Hualien, Taiwan. These fruits were washed using running water and then air-dried at room temperature overnight. 3.2. Methods 3.2.1. Preparation of L. cubeba Essential Oil Fresh fruits of L. cubeba (400 g) were homogenized for 2 min with 1200 mL of deionized water. The homogenate was put into a 5 L round-bottom flask and steam-distilled for 4 h to extract the essential oils. The oil was dried over anhydrous sodium sulfate. The prepared samples were immediately stored in brown flasks at −20 °C (freezer) prior to analyses by gas chromatography (GC) and bioassays. 3.2.2. Analysis of the Volatile Constituents (1) Direct injection analytic method (DI): 1 μL of essential oil was injected into the gas chromatograph injection unit. All experiments in the present study were performed in triplicate. (2) Headspace-solid phase microextraction (HS-SPME) analysis: A 50/30 μm divinylbenzene/carboxen/polydimethylsiloxane fiber (Supelco, Inc., Bellefonte, PA, USA) was exposed to each sample (1 mL) as placed in a 22 mL vial (precleaned # 27343 clear screw cap vials; Supelco, Bellefonte, PA, USA) for 20 min at 25 °C; the fiber was then injected into the gas chromatograph injection unit. (3) Analysis of GC: quantitative analyses of the volatile compounds were performed using an Agilent 7890A GC (Santa Clara, CA, USA) equipped with a DB-1 (60 m × 0.25 mm i.d., 0.25 μm film thickness) fused-silica capillary column with a flame ionization detector. The oven temperature was held at 40 °C for 1 min and then raised to 150 °C at 2 °C/min and held for 1 min, and then increased from 150 to 200 °C at 10 °C/min and held for 3 min. Injector and detector temperatures were maintained at 250 °C and 300 °C, respectively. The nitrogen gas flow rate was 1 mL/min. Kovats indices were calculated for the separated components relative to a C5-C25 n-alkanes mixture [22]. The method used was modified as previously described [23]. (4) Analysis of GC-MS: qualitative analyses of volatile compounds were identified using an Agilent 7890B GC (Santa Clara, CA, USA) equipped with a DB-1 (60 m × 0.25 mm i.d., 0.25 μm film thickness) fused-silica capillary column coupled to an Agilent model 5977 N MSD mass spectrometer (MS) (Agilent model 5977 N MSD mass spectrometer). The GC conditions in the GC-MS analysis were the same as in the GC analysis. The injector temperature was maintained at 250 °C. The helium gas flow rate was 1 mL/min. The electron energy was 70 eV at 230 °C. The constituents were identified by matching their spectra with those recorded in an MS library (Wiley 7n). The constituents were confirmed by comparing the Kovats indices or GC retention time data with data published in the literature or those of authentic standards. The method used was modified as previously described [23]. 3.3. Preparation of Mouse Bone Marrow-Derived Dendritic Cells C57BL/6 mice, which were purchased from Taiwan, were used in this study. All animals were housed in a specific pathogen-free facility in the Division of Laboratory Animals, China Medical University. All mice were maintained and handled according to standard protocols and the protocols was approved (103-156-N, 27 December 2012) by the Institutional Animal Care and Use Committee, China Medical University. The bones of mice were collected and bone marrow-derived dendritic cells (DCs) were prepared as previously described [6,7,8]. 3.4. Cytotoxicity Assay of LCEO The cytotoxicity of LCEO was examined by Cell Counting Kit-8 (CCK-8) assay (Sigma-Aldrich, St. Louis, MO, USA). The LCEO was diluted into 50-fold diluted stock with dimethyl sulfoxide. The DCs were treated with LCEO at different concentrations (5 × 104-, 105-, 2 × 105-, and 4 × 105-fold dilution in final) at 37 °C in 5% CO2/air for 24 h. The cells were then harvested and the viability measured according to manufacturer’s instruction. 3.5. Measurement of Cytokines Production by DCs Cytokine production was measured by enzyme-linked immuno sorbent assay (ELISA) as described previously [6,7,8]. The DCs were treated with lipopolysaccharide (LPS, 100 ng/mL) from Escherichia coli 055:B5 (Sigma) or LPS + LCEO (5 × 104-, 1 × 105-, and 2 × 105-fold dilution in final) for 6 h for TNF-α and 24 h for IL-12. The production of TNF-α and IL-12p70 was measured using the ELISA kit (eBioscience, San Diego, CA, USA). 3.6. The Assay of Contact Hypersensitivity (CHS) Response 2,4-Dinitro-1-fluorobenzene (DNFB; Sigma-Aldrich, St. Louis, MO, USA)-stimulated hypersensitivity was conducted as previously described [8,24]. Briefly, 12 mice were used and grouped into four groups. To bring about sensitization, their abdomens were painted with vehicle, DNFB, 50-fold diluted LCEO, DNFB + 50-fold diluted LCEO, or DNFB + 100-fold diluted LCEO every day for 5 days. Then, both ears of all mice were painted with DNFB on the sixth day. The phenotype of the CHS were determined histologically in 24 h using hematoxylin and eosin staining. The thickness of the tested ear were measured. The increase of the thickness was calculated by the thickness of the challenged ear minus the thickness of the unchallenged ear. By immunostaining analysis using anti-CD3 antibody, the number of infiltrating T cells in the tested ear was detected and calculated as previously described [8]. 3.7. Data Analysis In order to assess the significance of the differences in the levels of the cytokines and the increase of thickness of ear, the Mann–Whitney U-test was used. In order to assess the significance of the differences in the numbers of CD3+ T cells, the Student’s t-test with a two-tailed distribution and two-sample equal variance was used. Values of ** p < 0.01 and *** p < 0.001 were considered highly significant. A value of * p < 0.05 was considered significant. 4. Conclusions A total of 56 components were identified in LCEO. Forty-eight were detected by DI/GC, and 49 were detected by HS-SPME/GC. The principal compounds were neral and geranial (citral). LCEO inhibits DC functioning. Thus, LCEO may be useful in the treatment of inflammatory diseases. Acknowledgments This work was supported by research grants from the Council of Agriculture, Executive Yuan (Taiwan) (104AS-3.2.2-FD-Z1), Ministry of Education (Taiwan) (1038142*), and China Medical University (CMU104-TC-02). Author Contributions Conceived and designed the experiments: Hsin-Chun Chen, Meng-Shiou Lee, Ming-Kuem Lin. Performed the experiments: Hsin-Chun Chen, Hsing-Yu Chen, Cheng Hsuan Chuang. Analyzed the data: Meng-Shiou Lee, Wen-Te Chang, Ming-Kuem Lin. Contributed reagents/materials/analysis tools: Wen-Te Chang, You-Cheng Hseu, Chi-Chen Lin. Wrote the paper: Hsin-Chun Chen, Meng-Shiou Lee, Ming-Kuem Lin. Conflicts of Interest The authors declare no conflict of interest. Figure 1 L. cubeba essential oils (LCEO) did not impair cell viability of dendritic cells (DCs). DCs were treated with LCEO at different concentrations (5 × 104-, 1 × 105-, 2 × 105- and 4 × 105-fold dilutions) at 37 °C in 5% CO2/air for 24 h. The cytotoxicity of LCEO was examined by Cell Counting Kit-8 (CCK-8) assay (Sigma-Aldrich, St. Louis, MO, USA). NS p > 0.05 (Mann–Whitney U-test) for the comparison between LCEO-treated and untreated DCs. Figure 2 The release of TNF-α (A) and IL-12 (B) by lipopolysaccharide (LPS)-induced DCs were inhibited by LCEO. The DCs were treated with LPS or LPS + LCEO at different concentrations (1 × 105-, 2 × 105- and 4 × 105-fold dilutions). Supernatants were collected after 6 h to detect TNF-α and 24 h to detect IL-12. The amounts of TNF-α and IL-12 were determined by enzyme-linked immuno sorbent assay (ELISA). Each value represents the mean ± SD (standard deviation) of the data obtained from three wells for each treatment. * p < 0.05, ** p < 0.01 and *** p < 0.001 (Mann–Whitney U-test) for the comparison between the LCEO treated LPS-activated DC groups and the untreated LPS-activated DC group. All data are representative of three independent experiments. Figure 3 The contact hypersensitivity (CHS) response was attenuated in mice that had been treated with LCEO. 2,4-Dinitro-1-fluorobenzene (DNFB)-induced hypersensitivity response was carried out as described in the “Materials and Methods”. Mice were sensitized with vehicle (blue), 0.5% DNFB (purple), 100-fold diluted LCEO (red), 50-fold diluted LCEO (green), 0.5% DNFB + 100-fold diluted LCEO (light blue), or 0.5% DNFB + 50-fold diluted LCEO (orange) by painting their abdomens. The hypersensitivity response was examined by histological analysis using hematoxylin and eosin staining (A), and by measuring the thickness of the tested ear at 4, 8, 12, and 24 h (B). The scale bar represents 0.2 mm. Each value represents as mean ± SD from data of each group. ** p < 0.01 (Mann–Whitney U-test) for the comparison between the LCEO-treated DNFB-sensitized mouse group and the untreated DNFB-sensitized mouse group. Figure 4 Infiltrative CD3+ cells were significantly reduced in the tissue of the tested ears treated with LCEO. (A) CD3+ cells were detected by CD3 immunohistochemistry in the tissue of the tested ears. The scale bar represents 0.2 mm; (B) The number of CD3+ cells in ten immunostained tissue samples from the tested ears of each group was determined by manually counting the number of red cells (CD3+) under a light microscope. The counts were summarized and then used to make the plot. Each value represent as the mean ± SD. *** p < 0.001 (Student’s t-test) for comparison with the untreated DNFB-sensitized mouse group. ijms-17-01319-t001_Table 1Table 1 Means of volatile compounds in Litsea cubeba essential oils (LCEO) analyzed by gas chromatography with direct injection (DI/GC) and headspace-solid phase microextraction (HS-SPME/GC). Compound RI z Content (%) y DI/GC HS-SPME/GC Monoterpenes 4.01 11.91 α-thujene 921 – w 0.02 α-pinene 931 0.22 0.72 camphene 945 0.03 0.10 sabinene 972 0.07 0.06 β-pinene 976 0.09 0.20 β-myrcene 980 0.77 2.11 α-phellandrene 998 – 0.01 α-terpinene 1007 <0.01 – ρ-cymene 1014 0.01 0.01 limonene 1026 2.73 8.50 cis-β-ocimene 1026 0.01 0.02 trans-β-ocimene 1032 – 0.02 γ-terpinene 1050 0.01 0.01 α-terpinolene 1078 0.04 0.11 1,3,8-ρ-menthatriene 1094 0.03 0.02 Sesquiterpenes 0.10 0.06 α-copaene 1366 0.01 0.01 β-elemene 1382 0.04 <0.01 β-caryophyllene 1429 0.03 0.04 α-humulene 1441 0.02 0.01 δ-cadinene 1525 <0.01 <0.01 Terpene alcohols 2.75 5.22 linalool 1079 1.23 1.11 isopulegol 1128 0.03 – verbenol 1130 1.31 3.81 α-terpineol 1183 0.07 0.06 cis-carveol 1189 0.07 0.19 cis-geraniol 1237 0.03 0.05 nerolidol 1558 0.01 – Terpene aldehydes 89.25 75.09 citronellal 1127 1.23 1.63 neral 1226 38.02 34.17 geranial 1256 50.00 39.29 (2854.05 mmol/L) Terpene ketone 0.14 0.10 camphor 1113 0.14 0.04 piperitone 1230 <0.01 <0.01 piperitenone 1308 – 0.06 Terpene ester 0.32 0.14 methyl salicylate 1163 0.05 0.01 bornyl acetate 1286 0.01 0.02 terpinenyl acetate 1335 0.07 0.02 citronellyl acetate 1357 0.02 0.02 geranyl acetate 1362 0.16 0.06 neryl acetate 1366 0.01 0.01 methyl cinnamate 1384 – <0.01 Terpene oxide 0.16 0.17 1,8-cineole 1019 0.12 0.14 trans-linalool oxide 1055 <0.01 0.02 cis-rose oxide 1086 <0.01 <0.01 trans-rose oxide 1089 – <0.01 limonene oxide 1128 0.01 – caryophyllene oxide 1571 0.03 0.01 Aliphatic aldehydes 0.01 0.03 3-methyl butanal 631 <0.01 – 2-methyl butanal 636 <0.01 – pentanal 697 – <0.01 hexanal 776 <0.01 0.01 2,6-dimethyl hept-5-enal 1047 0.01 0.02 Aliphatic ketone 1.19 2.23 6-methyl-5-hepten-2-one 962 1.19 2.23 Aliphatic alcohol – <0.01 2-methyl-3-buten-2-ol 600 – <0.01 Aliphatic esters 0.01 0.01 ethyl isovalerate 825 <0.01 – isoamyl acetate 864 0.01 0.01 ethyl tiglate 915 <0.01 <0.01 Z Retention indices, using paraffin (C5-C25) as references; y Values are means of triplicates; w undetectable. ==== Refs References 1. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081320ijms-17-01320ReviewG Protein-Coupled Receptors in Cancer Bar-Shavit Rachel 1*Maoz Myriam 1Kancharla Arun 1Nag Jeetendra Kumar 1Agranovich Daniel 1Grisaru-Granovsky Sorina 2Uziely Beatrice 1Van Craenenbroeck Kathleen Academic EditorDrummen Gregor Academic Editor1 Sharett Institute of Oncology, Hadassah-Hebrew University Medical Center, Jerusalem 91120, Israel; Myriamm@hadassah.org.il (M.M.); kancharlarn@gmail.com (A.K.); jeetendr.nag@mail.huji.ac.il (J.K.N.); Danyaag@gmail.com (D.A.); Beatrice@hadassah.org.il (B.U.)2 Department of Obstetrics and Gynecology, Shaare-Zedek Medical Center, Jerusalem 91031, Israel; sorina@szmc.org.il* Correspondence: Rachelbar@ekmd.huji.ac.il; Tel.: +972-2-677-7563; Fax: +972-2-642-748512 8 2016 8 2016 17 8 132006 6 2016 08 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Despite the fact that G protein-coupled receptors (GPCRs) are the largest signal-conveying receptor family and mediate many physiological processes, their role in tumor biology is underappreciated. Numerous lines of evidence now associate GPCRs and their downstream signaling targets in cancer growth and development. Indeed, GPCRs control many features of tumorigenesis, including immune cell-mediated functions, proliferation, invasion and survival at the secondary site. Technological advances have further substantiated GPCR modifications in human tumors. Among these are point mutations, gene overexpression, GPCR silencing by promoter methylation and the number of gene copies. At this point, it is imperative to elucidate specific signaling pathways of “cancer driver” GPCRs. Emerging data on GPCR biology point to functional selectivity and “biased agonism”; hence, there is a diminishing enthusiasm for the concept of “one drug per GPCR target” and increasing interest in the identification of several drug options. Therefore, determining the appropriate context-dependent conformation of a functional GPCR as well as the contribution of GPCR alterations to cancer development remain significant challenges for the discovery of dominant cancer genes and the development of targeted therapeutics. G protein-coupled receptors (GPCRs)proteaseprotease-activated receptorprotease-activated receptors (PARs)PH-domainoncogenescancerLPA(1-6)CXCR4Wnt/β-cateninHippo/YAP ==== Body 1. Introduction G protein-coupled receptors (GPCRs) comprise the largest family of cell surface receptors in the human genome, regulating a plethora of physiological responses and serving as frequent drug targets. Despite their broad physiological functions and associated disease processes, which have resulted in their designation as favorable sites for pharmacological drug development, their role in tumor biology is underappreciated. Conformational changes take place after ligand binding, inducing the activation of complex signaling schemes that in turn lead to a cell response. Canonical GPCR agonist activation involves the recruitment of G proteins followed by the phosphorylation of the receptor by G protein-coupled receptor kinase (GRK), “allowing” the binding of β-arrestin 1 and 2 (e.g., referring to arrestin 2, the first non-visual arrestin, and 3 (the second cloned non-visual arrestin), respectively) and subsequent internalization into endosomes. Internalized receptors can either recycle back to the cell surface or undergo degradation. In the past, the “two-state” receptor model (inactive and active states) was widely accepted to explain GPCR function; however, a more intricate and complex “multi-state” model entailing high GPCR conformational dynamics is now favored. GPCRs are pleiotropic with respect to the cell signal proteins they activate within a cell, and therefore more than one conformation of a receptor exists. Hence, different ligands can induce distinct receptor conformational states following activation, initiating several specific downstream signaling profiles. Several conformational changes in a single GPCR, eliciting discrete signaling pathways, is termed “biased agonism” [1,2,3,4]. As an example, it has been demonstrated that, in addition to regulating the GPCR signaling that induces internalization and desensitization, β-arrestin 1 and 2 are also capable of initiating distinct signals on their own [5]. For example, in the activation of the endothelin receptor (ETA) receptor via endothelin (ET-1/ETAR) in epithelial ovarian cancer, β-arrestin 1 is required to maintain NFκB transcriptional activity in response to endothelin receptor A (ETAR) activation. In addition, in response to ETAR activation (via ET-1), β-arrestin 1 increases its nuclear localization and binds to nuclear β-catenin, thereby enhancing β-catenin transcriptional activity, a central path in ovarian cancer [6,7]. Four main groups of GPCRs are recognized and classified according to their pharmacological properties by the guidelines of the International Union of Basic and Clinical Pharmacology: Class A is rhodopsin-like; Class B is secretin-like; Class C is comprised of metabotropic glutamate/pheromone; and Class D is comprised of frizzled receptors. Class A is the largest and best-studied family, and includes several members that play a major part in tumor biology, for example protease-activated receptors, or protease-activated receptors (PARs). Class A has been further subdivided into four groups: α, β, γ, and δ. The δ group contains, among others, the leucine-rich repeat-containing receptors (LGRs) including LGR5, a bona fide stem cell marker for colon and breast tissues. While GPCRs regulate many aspects of tumorigenesis as well as many cancer-associated signaling pathways [8,9], only a few drugs aiming to inhibit GPCRs are currently used in cancer. Genome-wide major analyses of multiple human tumors have exposed novel GPCRs that are modified in cancer and might be potential candidates for cancer drug development. Importantly, it is imperative to differentiate between cancer driver genes and bystanders to identify valid targets for personalized medicine in the future. Indeed, pharmacological treatments targeting GPCRs will become increasingly attractive as more data associating GPCRs with cancer emerges. Understanding the molecular machinery of GPCRs in tumor development may contribute to tumor-related GPCR drug development. In this review we discuss recent advances in cancer-associated GPCRs and signal proteins such PARs, chemokine receptors, Gα12/13 proteins, lysophosphatidic acid (LPA), and GPCR-mediated pathways such as the WNT and Hippo signaling pathways. We also describe potential drug design targets such as the pleckstrin-homology (PH) binding motifs that were found and characterized in PAR-implicated tumor biology. 2. Biasing towards Specific G-Proteins in Cancer The structural signature of seven transmembrane domains that couple to G proteins for signaling are among the common themes in GPCRs. G proteins are divided into four main sub-groups: Gαs, Gαq/11, Gαi/o and Gα12/13 which are associated selectively, upon ligand activation, to initiate a potential downstream signaling pathway. G proteins are composed of three subunits, Gα, Gβ and Gγ which are located in the inner part of the plasma membrane. Upon ligand binding the signal is transmitted through conformational changes, which consequently result in the initiation of the G protein cycle of association. In fact, GPCRs function as guanine nucleotide exchange factors for α subunit of the G protein, promoting the exchange of bound GDP for GTP-α. Bound GTP-α allows the switch from an inactive state (of the bound trimeric G proteins) to an active status of the GTP-α subunit and the release of βγ subunits. These βγ subunits consequently activate downstream signaling partners such as Src, phospholipase C, adenylyl cyclase, phosphodiesterases and ion channels. The cycle is terminated by the hydrolysis of α subunit-bound GTP to GDP, and its re-association with βγ G proteins for turning off the signal. A significant feature of a biased GPCR ligand is the ability to activate either of the G protein subfamilies, Gαs, Gαq/11, Gαi/o or Gα12/13, for selectively harnessing and recruiting a specifically selected downstream signal pathway. While most of the G proteins are not associated with cancer, the Gα12/13 family is connected with cell transformation (e.g., fibroblasts) [10,11], thus directing toward tumor-related processes. Gα12/13 family members may be also involved in the control of the Rho-dependent formation of stress fibers, the Jun kinase/stress-activated protein kinase pathway, and the Na+/H+ exchanger [12,13,14]. Genetic ablation of Gα13 in mice results in embryonic lethality at a stage when gastrulation is already completed (about embryonic day 9.5). On the other hand, the ablation of Gα12, the other family member, results in viable mice exhibiting a normal phenotype. This genetic outcome points to distinct roles of the Gα12/13 family members. In addition, a defective assembly of the vascular system, which is prominent mostly in the yolk sac and in the head mesenchyme, was also demonstrated in Gα13-deficient mouse embryos [15]. LPA receptors are coupled to Gαq/11, Gαi and Gα12/13 [16,17,18]. In NIH 3T3 and neuroblastoma B103 cells, the LPA3 receptor is coupled to Gαi, leading to Ras-GTP accumulation of mitogen-activated protein kinase (MAPK) activation and enhanced cell proliferation [19,20]. LPA1, LPA2 and LPA3 receptors in PC12 cells are coupled to Gαq/11 following neurokinin A or endothelin binding to these receptors, thereby inducing signaling via tyrosine kinase c-Src but not Ras and β-catenin via β-arrestin 1 [7,21]. Other examples in PC12 cells demonstrate that LPA1-3 binds to Gα12/13 [22] G proteins may direct biased agonism also in various metabolic disease systems (other than cancer) as for example, coupling of GPR109A to Gαi/o, following biased ligand activation, leading to induced levels of high-density lipoprotein and consequently to a decrease in triacylglycerol levels. As a result it leads to a significant decrease in cardiovascular morbidity and mortality [23]. Protease-activated receptor 1 (PAR1) as a member of the GPCR family, binds also to various heterotrimeric G protein subtypes within a cell, promoting different cellular functions. For example, as recently elegantly demonstrated, N-linked glycosylation of PAR1 at extracellular loop 2 (ECL2) favors coupling to Gα12/13-dependent Rho activation. In contrast, a mutant form lacking in glycosylation at the ECL2 region couples more efficiently to Gαq, mediating phosphoinositide (PI) signaling [24]. Traditionally, PAR1 stimulates phospholipase C (PLC)-induced PI signaling via Gαq, while inhibiting Gαi-mediated adenylate cyclase signaling. Biased agonism could activate not only different G protein subtypes, but also stimulate an alternate system to G proteins as signaling transducers, such as β-arrestins [25]. The β-arrestin bias may, in some cases, confer positive effects and rather than mediating internalization and degradation, ir directs the recruitment, activation, and scaffolding of cytoplasmic signaling complexes via β-arrestins 1 and 2. For example, a β-arrestin–biased ligand, PTH (parathyroid hormone), promotes bone formation and homeostasis, thus reducing hypercalcemia of malignancy and osteoporosis [26]. Nevertheless, the molecular mechanism of biased ligands between β-arrestin and the G protein pathways are not yet known. β-arrestin has also been shown as critical for Wnt/β-catenin signal transduction. The axis of Dishevelled (DVL)-β-arrestin interaction is important for the Wnt/β-catenin signaling. It has been demonstrated that in mouse embryonic fibroblasts (MEFs), genetic ablation of β-arrestin 1 and/or 2 impairs wnt-3A-induced activation of DVL and β-catenin signaling [27]. In ovarian cancer the activation of the endothelin-A receptor (ETAR) by endothelin-1 (ET-1) plays a central role in ovarian cancer progression. Silencing of both β-arrestin 1 and β-arrestin 2 inhibits these receptors’ (e.g., ETAR) signaling, reducing, among others, Src and serine/threonine kinase Akt (AKT) activation finally affecting the β-catenin pathway [28]. 3. G Protein-Coupled Receptor (GPCR) and Oncogenicity The first link between cellular transformation and GPCRs was discovered in 1986 with the identification of the MAS oncogene [29]. The mas gene product exhibits properties characteristic of GPCRs, including seven pass of the membrane, and is made up of 325 amino acids. MAS is the receptor for the metabolite angiotensin-(1–7) (Ang-(1–7)), which is formed by angiotensin-converting enzyme 2 on angiotensin II and functions as a vasodilator and antiproliferative agent [30]. Using a cDNA expression library screen for oncogenes revealed two other GPCRs with oncogenic properties, namely G2A and PAR1. The role of the PAR family (whereby PAR1 is the first and prototype member) in cancer biology will be discussed below. Another oncogene, G2A, was independently identified by Kay and colleagues [31] and Witte et al., [32]. They showed [31] that forced expression of G2A in NIH3T3 cells led to an increased number of cells in the G2/M cell-cycle phase; therefore, they identified this gene as G2A (G2 accumulation). In addition, overexpression of G2A antagonized the activity of Bcr-Abl in Rat-1 fibroblasts and in mouse bone marrow cells, inducing increased cell proliferation. Therefore, G2A may either promote or antagonize growth, depending on the cellular context. G2A mRNA is expressed most often in hematopoietic tissue and cell lines. No mutations were found in G2A and no known ligand for G2A has been identified. It is not yet known whether G2A transformation is ligand-independent or takes place as a consequence of an unknown serum ligand or a NIH3T3 cell-residing ligand. Whitehead and co-workers identified PAR1, which encodes the prototype member of the PAR family. Using the anchorage-independent foci formation assay in NIH3T3 cells, they demonstrated in two independent screens [33,34] that overexpression of naïve human PAR1 caused a similar transforming activity; both loss of anchorage- and serum-dependent growth were observed in NIH 3T3 cells with PAR1 overexpression. This activity was described in addition to its potent foci-forming activities. We have demonstrated that PAR1 overexpression induces invasion in pathological cancer cells as well as in the physiological invasion process of placenta implantation into the uterus deciduas [35]. Significantly, no mutations have been found in any of the PAR family members. Its transforming activity is attributed to receptor overexpression in malignant epithelial cells as compared to no expression in normal epithelium. It is postulated that PAR-transforming activities are ligand-dependent, and that they are seen in correlation with the plethora of serine-protease present in the dynamic milieu of a tumor microenvironment. 4. Gep Oncogenes Members of the Gα12 family, Gα12 and Gα13, were isolated at first as the gep oncogenes with transforming capabilities [10,36]. These G proteins regulate various cellular processes, among which are migration, proliferation, transformation, platelet aggregation, neurite retraction, and actin-stress fiber formation [37,38,39]. Gα12 and Gα13 belong to the large family of G proteins consisting of α, β, and γ subunits. They induce signals from GPCRs to intracellular effectors [40,41,42]. The α-subunit is a protein of 37–42 kDa including the guanine nucleotide-binding site and the intrinsic GTPase activity. Ligand-activated GPCRs catalyze the exchange of bound GDP to GTP in the α subunit. GTP-bound subunits stimulate distinct downstream effectors. A constitutively activated mutant of Gα12 (Gα12QL), as well as Gα13QL, effectively transform NIH3T3 fibroblasts, as determined via foci-forming activities [43,44,45]. They also control small GTP-binding proteins (i.e., the Ras and Rho family) and have an impact on the activity of several transcription factors such as serum response factor (SRF), activating protein 1 (AP-1), the nuclear factor of activated T cells (NFAT), a signal transducer and activator of transcription 3 (STAT3), and nuclear factor-kB [46,47,48,49]. The small GTPases Rho and Rac play critical roles in communicating Gα12/Gα13 signaling through the Rho family of GTPases [47,48]. Indeed, GPCR ligands such as thrombin, LPA, and S1P are involved in stimulating tumor growth and invasion via coupling of their cognate receptors to Gα12/13 proteins. Hence, a considerable challenge is the identification of anticancer drugs targeting Gα12/13, PARs, LPA and SIP receptors. 5. Lysophosphatidic Acid (LPA) Receptors in Tumor Biology The phospholipid LPA signals via no fewer than six receptors (LPA1–LPA6) belonging to the GPCR family. LPA1–LPA3 (also known as EDG2, EDG4, and EDG7, respectively) are expressed by endothelial differentiation genes [17]. LPA4 (also known as GPR23/P2y9), LPA5 (GPR92), and LPA6 (GPR87) are included within the purinergic family of GPCRs [50,51,52]. Inducing LPA and/or an aberrant expression of its receptors may lead to cancer initiation and progression [53,54]. This has been demonstrated in breast cancer [55] and ovarian cancer [56], where LPA acts via activation of the Rho-dependent transduction pathway to elicit migration and tumor formation. In addition to the manipulation of LPA activity via cognate receptors, novel and selective agonists and antagonists might be employed therapeutically through understanding the differences between LPA1-3 receptors, since LPA biosynthesis is considered a feasible target for therapeutics [57]. 6. Chemokine Receptors Cancer cells that metastasize preferentially to specific organs via the blood and lymphatic vessels present a great challenge in cancer eradication. One family of GPCRs that is closely linked to tumor metastasis is the chemokine receptors. Chemokines enhance the motility and survival of cancer cells in the vicinity and milieu of a tumor following their local release in either an autocrine or paracrine fashion into the microenvironment of tumor-surrounding regions [58]. Among these are chemokines that are involved in metastatic cancer cell homing [59] as well as cancer cell growth and survival [60], such as chemokine receptors CCR7 and CCR10. Local chemokine generation in the tumor milieu may recruit macrophages and leukocytes, which can then induce the release of matrix metalloproteases (MMPs) promoting tumor cell survival, growth, and invasion as well as improving the cytokine-rich microenvironment. CXCR4 is a well-documented chemokine receptor driving cancer metastasis. Moreover, cells in the most frequent sites of metastasis, including the lungs, bone marrow, lymph nodes, and liver, express the chemokine ligand CXCL12/SDF-1 [61]. Tumor cells frequently express high levels of CXCR4, facilitating cell growth, survival, and migratory capability. For example, while CXCR4 is not found in normal breast tissues, it is rather overexpressed in breast cancer cells [62] and a marked inhibition of breast cancer metastatic spread is achieved by inhibiting CXCR4 [58,62,63]. However, treatment with CXCR4 inhibitors requires caution, since CXCR4 inhibition induces progenitor/stem cell mobilization from the bone marrow. Hypoxia-inducible factor-1 (HIF-1α), which is activated by hypoxia, increases CXCR4 transcription [64]. In highly aggressive basal-like breast cancer cells, CXCR4 may also couple to Gα12/13 when Gα13 protein is highly upregulated, and consequently drives spread via lymphatic vessels and site-specific metastasis in a Gα12/13-RhoA-dependent manner [65]. This molecular machinery is mediated similarly via PARs and LPA, all of which may serve as possible targets for metastasis prevention and treatment. 7. Wnt Signaling Wnt proteins were first identified in Drosophila, from which their name was coined. They are critically involved in controlling both normal development and tissue homeostasis, as well as pathological processes such as cancer. Intensive efforts have been made to unravel the Wnt (Wingless ad INT-1) signaling pathway. Frizzled (Fz) receptors are a subgroup of GPCRs that play a pivotal role in development, tissue homeostasis, and cancer, serving as receptors for Wnts. Wnt signaling stabilizes β-catenin through Fz and the low-density lipoprotein-related protein 6 (LRP6) receptor complex that antagonizes the β-catenin “destruction complex”. The canonical Wnt pathway refers to the activation of the highly conserved Wnt/β-catenin signaling pathway, involving the stabilization of β-catenin via Wnt binding to Fz cell surface receptors and LRP5/6 co-receptors. In the absence of Wnt, the key effector of this pathway, β-catenin, is continuously degraded by the “degradation complex”. This complex is comprised of Axin, adenomatis polyposis coli (APC), glycogen synthase kinase3β (GSK3β), casein kinase1α (CK1α), and the E3 ubiquitin ligase subunit β-TrCP1. Axin provides a scaffolding site for GSK3β to phosphorylate the N-terminal portion of β-catenin (after priming by CK1α), thereby generating a phosphorylated form of β-catenin that is recognized by the ubiquitin ligase adaptor β-TrCP [66,67]. Wnt stimulation dismantles the degradation complex, leading to the accumulation of unphosphorylated β-catenin. Once β-catenin is stabilized, it is translocated to the cell nucleus. There it alters the activity of the lymphoid enhancer factor (Lef)/T cell factor (Tcf) family members. The Lef/Tcf family belongs to HMG-box transcription factors and acts as a transcriptional switch, recruiting various chromatin modifiers and remodelers to Lef/Tcf target genes, inducing expression of an array of genes downstream (Scheme 1; [67,68]). A wide range of cancers exhibit hyperactive stabilized β-catenin, either because of oncogenic mutations in its N-terminal phosphorylation site or through mutational inactivation of APC or Axin, its negative regulators [68,69]. Activated β-catenin can be oncogenic, driving the onset of a wide spectrum of carcinomas. Noncanonical Wnt signaling does not involve β-catenin/Tcf activity and does not utilize the LRP5/6 co-receptor. For example, Wnt5a/b are prototypes of this Wnt pathway [70]. In vertebrates, noncanonical Wnt signaling is involved in planar cell polarity (PCP), dorsoventral patterning, tissue regeneration, convergent extension movements, and tumorigenesis. Throughout these processes, alternative Wnt signaling induces the small G protein Rho. Rho activates Rho-associated kinase (ROCK), which is one of the major regulators of the cytoskeleton and, in-general, this noncanonical signaling antagonizes canonical Wnt/β-catenin signaling. Fz receptors transduce both Wnt/β-catenin and noncanonical Wnt signaling. An interesting yet unresolved aspect of Fz is the involvement of G proteins. While Gα proteins have been shown to alter Wnt signaling in some studies [71,72,73], other research has failed to identify Gα proteins as essential components of Wnt/β-catenin signaling [74,75]. Thus, the involvement of G proteins in Wnt signaling pathways is yet an open, controversial issue. 8. GPCR Regulation of Hippo Signaling Pathway The Hippo-YAP/TAZ pathway has emerged as a major conserved path that integrates diverse stimuli in a broad range of functions, including control of cell growth and organ size as well as mechanical and cytoskeletal proteins, apico-basolateral polarity, and cell adhesion [76,77]. Dysregulation of the Hippo signaling pathway leads to cancer development. This dysregulation enables two central downstream effectors of Hippo signaling, Yes-associated protein (YAP) and its homolog protein TAZ, to translocate to the cell nuclei and serve as transcription factors that are considered major components involved in cancer (Scheme 2). As a result, research has been aimed at the development of pharmacological inhibitors to both YAP and TAZ, which serve as potent tumor drug targets. The search for physiological activators of YAP/TAZ led to the finding that GPCRs are actually powerful inducers of the YAP oncogenic pathway [78,79,80,81]. The tumor-suppressing Hippo pathway plays a major role in inhibiting YAP/TAZ nuclear localization and transcriptional activity, and the oncogenic YAP path is initiated upon abrogation of the Hippo path. Once the Hippo enzymatic cascade of events is inhibited, YAP/TAZ are dislodged from their cytoplasmic anchorage site and translocate to cell nuclei. In the nuclei, they serve as transcription co-activators and stimulate downstream target genes, consequently inducing oncogenicity through binding to TEAD family transcription factors. The Mst1/2-Lats1/2 kinase cascade of the Hippo pathway inhibits YAP/TAZ through direct phosphorylation, leading to cytoplasmic retention via the binding of 14-3-3, which further promotes β-TrCP-mediated YAP/TAZ ubiquitination and degradation. GPCRs that are involved in cell proliferation are in fact capable of stimulating transcriptional activity of the co-activator YAP [76,80,82,83,84]. It has been shown that GPCRs inhibit the activity of LATS via Gα12/13, thus releasing YAP from LATS-dependent inhibition [80]. Studies from the Gutkind lab [85] have demonstrated that oncogenic mutations in Gαq lead to the activation of YAP by a mechano-sensing pathway as well as actin polymerization and not by intervention in the Hippo-suppressing pathway. In addition, recent studies from the Guan lab [86] have described YAP/TAZ as bona fide downstream effectors of the noncanonical Wnt signaling pathway. It was proposed that Wnt5a/b and Wnt3a induce YAP/TAZ activation independent of canonical Wnt/β-catenin signaling, and instead via the noncanonical Wnt-YAP/TAZ signaling axis, consisting of Wnt-FZD/ROR Gα12/13-Rho GTPases-Lats1/2, to promote stimulation of oncogenic YAP/TAZ- and TEAD-mediated gene transcription. The regulatory mechanisms controlling YAP and TAZ activity appear to vary between tissues. Based on the fact that induced transcriptional activities of YAP/TAZ are centrally involved in cancer, attenuation of YAP and/or TAZ is a rather logical approach for the treatment and prevention of a wide array of malignancies. One approach could be reducing YAP dosage by shRNA depletion. Screens for shRNA-induced lethality in a large panel of human cancer cell lines showed that tumor cell lines activated for WNT signaling are specifically sensitive to the knock-down of YAP [87]. Thus, Rosenbluh et al. [87], underscore the concept that YAP inhibition does not necessarily correlate with levels of YAP activity, and may involve important TEAD-independent interactions mediated via YAP that may be critical for some types of cancer cells. 9. Protease-Activated Receptors, PARs, and Cancer Proteinases and their inhibitors [88] account for over 2% of human genes. While proteases act by both mechanisms of nonreceptor- and receptor-mediated functions, the fact that they represent a significant percentage of the genome indicates their importance in regulating diverse tissue functions. Cell signaling may be controlled also via digestion of a wide spectrum of zymogens such as kininogens, chemokines, prohormones, and growth factor receptors, for example insulin receptors as well as cytokine precursors. It has also been known for over 40 years that the proteolytic enzymes thrombin and trypsin can induce cell proliferation through cell surface receptor activation, much like traditional growth factors such as epidermal growth factor and insulin [89,90,91,92], although the mechanistic details were not understood until sometime later. After an exhaustive search for a receptor that mediates functional responses of the main protease in the coagulation cascade, thrombin, yielded a “thrombin receptor”, which acts to induce platelet aggregation as well as cultured cell proliferation. This receptor was called protease-activated receptor, or PAR [93,94]. PARs form a subfamily within the larger GPCRs of rhodopsin-like class A GPCRs, and include four members: PAR1, PAR2, PAR3, and PAR4 [95]. PAR1, the first and prototype member of the family, mediates the response to thrombin signaling in most cell types, and was thus designated as a “thrombin receptor” [94]. While PAR3 and PAR4 provide a “back-up” system to PAR1 [96,97,98], PAR2 is not considered as a thrombin receptor” but is rather activated via a trypsin serine-protease and also by proteases that present upstream to thrombin [99]. PARs are activated via enzymatic digestion of the N-terminal extracellular portion, which gives rise to newly exposed ligands that act through intramolecular binding to extracellular loop number two for signal transmission [100]. Once activated, conformational changes transmitted via the transmembrane ™ domains to the cytoplasmic tails enable the binding association with α subunits of G proteins localized within the membrane, inside the cell compartment [101]. 10. Novel Signaling of PARs Endowing Critical PH-Domain Binding Motifs Although a growing number of roles for PAR1&2 in oncogenesis have been identified, the basic signaling machinery has not yet been elucidated. Signal-associating motifs in PAR1&2 C-tails have been shown to be essential for breast cancer development [102] through binding of signal proteins that possess a pleckstrin-homology (PH)-domain. These include Akt/PKB-PH domain as well as Etk/Bmx and Vav3, all of which can potently associate with both PAR1 and PAR2. The association takes place in a hierarchical manner, whereby priority is attributed to Etk/Bmx. A point mutation in H349APAR2, but not in R352A, potently inhibits PH-protein binding and is sufficient to markedly eliminate PAR2-induced breast tumor growth in vivo and placental extravillous trophoblast (EVT) invasion in vitro. Along this line, the PAR1 mutant hPar1-7A, which is incapable of associating with the PH domain, markedly inhibits mammary tumor development and EVT invasion, demonstrating the physiological significance and importance of these novel PAR1 and PAR2 PH domain binding motifs in both pathological and normal invasion processes. PH domains that are present in diverse signal transducing proteins are highly preserved motifs. They function as versatile modules in protein-protein interactions, inducing a multitude of physiological events [103,104]. These associating motifs are required for both tumor development and physiological placental EVT-uterus interactions. In spite of the fact that primary sequence identity between PH domains is limited, striking similarity is found in their tertiary structures. Interestingly, the binding motifs in PARs [102] are either lipid-independent and mediated via protein-protein association, as demonstrated for PH-Etk/Bmx, or lipid-dependent, as is the case of Akt-PH association. One possible explanation is that membrane targeting is mediated via palmitoylation of a cysteine residue in the PAR1&2 C-tail. The previously identified, conserved eighth helix (H8) found in rhodopsin is located within the C-tail of PAR1 and PAR2, as in other receptors that belong to Class A. Specifically, the PAR1 PH domain binding site is found within the H8 loop. Likewise, palmitoylation of PAR2 is required for post-translational modification and is necessary for potent cell surface expression and desensitization of PAR2. In general, regardless of whether binding occurs via lipids or directly through protein-protein interactions, PH motifs that serve as pivotal binding modules demonstrate that interactions between separate motifs in a signal protein support transmission of a biochemical signal and also ensure a robust response to developmental cues, with sufficient specificity at precisely the right time to protect against premature and disastrous induction of a cell fate alteration. Cell surface receptors that play a central role in cancer biology, for example PARs, act to effectively relay cell signaling by recruiting PH domain signal proteins. PARs may also activate integrins, and vice versa [105]. Formation of the FAK-PH-Etk/Bmx complex, as well as FAK-PH-Rgnef (e.g., binding via PH domain), may initiate signaling in an “inside-out” manner that consequently will affect the extracellular portion of integrins and “activate” them. Once activated, integrins can associate with PARs ([104], see Scheme 3), which then will transmit signaling in an “outside-in” fashion and associate with PH-signal proteins. 11. Concluding Remarks and Future Directions GPCRs are known to activate different signaling pathways initiated by ligand binding (see Table 1 summarizing driver GPCR signaling in cancer). The prospect of biased agonism conferring selectivity of signaling via the binding of several ligands to the same receptor offers advantages in clinical settings. It is desired to help design drugs with fewer unfavorable side effects, specifically in designing a biased ligand that will have a weak activity under one pathway condition but a much stronger activity in another. While hypothetically all GPCRs should demonstrate biased signaling, until now several were found to have this property. It is still not understood how a variety of stabilized conformations of a given receptor give rise to different signaling pathways. This challenging mechanism is in its early phase and needs to be further explored. It remains to be conclusively determined whether changes in the receptor conformation are indeed the ground basis for biased agonism signaling. Among other approaches, for example, is the identification of new binding motifs within GPCR C-tails that may allow for the design of selective potent drugs. Such therapeutic medicaments are expected to inhibit diverse signaling pathways initiated by a signaling partner that harbors a specific “signal-motif” for association with GPCRs and initiation of a signaling cascade. For example, the “PH domain binding motifs” within PARs are effectively capable of associating with several PH signal-possessing proteins (e.g., Etk/Bmx, Akt and Vav). There is a plan to identify such PH-binding motifs within the spectrum of GPCRs for future effective drug design. Acknowledgments The studies are supported by grants from the Israel Science Foundation (ISF) and Monsa foundation (RB-S). The authors thank Shifra Fraifeld, a medical writer at the Hadassah-Hebrew University Medical Center, for her editorial assistance during manuscript preparation. Conflicts of Interest The authors declare no conflict of interest. Schemes and Table ijms-17-01320-sch001_Scheme 1Scheme 1 Illustration of Wnt/β-catenin canonical and noncanonical pathways. In the presence of a Wnt ligand (e.g., Wnt 3A), Frizzled receptor (Fz) co-associates with LRP5/6, leading to stabilization of β-catenin. In contrast, in the absence of a Wnt ligand, β-catenin is rapidly degraded via the proteasomal compartment. Stabilized β-catenin enters the nuclei and functions as a co-transcription factor, inducing a spectrum of gene signature downstream. Noncanonical Wnt signaling (e.g., Wnt 5a) is mediated via Fz affecting, among others, activation of JNK and the cytoskeleton. Rred cross: Inhibition of signal cascade. ijms-17-01320-sch002_Scheme 2Scheme 2 The Hippo/YAP pathway is physiologically initiated via GPCRs. The Hippo pathway takes place following the phosphorylation of Ltats1/2 by Mst1/2 which leads to the phosphorylation of YAP and its anchoring localization in the cytoplasmic compartment. YAP is activated by inhibition of the Hippo pathway via the de-phosphorylation of YAP, resulting in YAP nuclear localization and its function as a co-transcription factor. ijms-17-01320-sch003_Scheme 3Scheme 3 PH domains power tumor growth. The activation of PAR1&2 results in binding of PH signal proteins. These association motifs are essential for tumor development. The “inside-out” mode of integrin activation via either PH-Etk/Bmx-FERM/FAK or PH-Rgnef-FAK can induce interactions with PARs through all stages of cancer development. ijms-17-01320-t001_Table 1Table 1 Cancer GPCRs and signaling pathways. Summary of cancer driver GPCRs and their signaling. As such, examples of GPCRs which are implicated in human cancer are listed. Lysophosphatidic acid receptors 1-6 (LPA1-6), protease-activated receptors (PAR1&2), Yes-associated protein (YAP), Frizzled receptors (Fz), parathyroid receptor1 (PTHR1), endothelin receptors A and B (ETAR and ETBR), endothelin1-3 (ET1-3), prostaglandin receptors (PE2, PE4), prostaglandin (PGE2), bradykinin receptor type 1 and 2 (B1R, B2R), sphingosine-phosphate receptor 1 (S1PR1). Receptor Ligand Pathways Lysophosphatidic acid Receptors (LPA1-6) Lysophosphatidic acid Rho-dependent pathway [37,106] β-Catenin stabilization [107,108] Kruppel-like factor 5 [109] Protease activated receptors (PAR1&2) LPA Thrombin, Trypsin, respectively, or TFLLRN (G12/13, PAR1) or SLIGKV (G12/13, PAR2) [79] Lysophosphatidic acid (Gαq) [80] Hippo/YAP pathways via activation of Gα12/13-coupled receptors or Gαq. Inhibition of Hippo pathway (via the inhibition of Lats1/2 kinases ) results in activation of YAP co-transcriptional activity [110] Frizzled (Fz) PAR1 Parathyroid receptor1 (PTHR1) Wnt 3A (canonical pathway) Canonical Wnt signaling stabilization of β-catenin [66,114] and its transcription activity Thrombin or TFLLRN [111,112] PTH [113] Chemokine receptor (CXCR4) CXCL12, SDF-1 PI3K, Akt, Src PIP2, IP3, Ras, Raf, ERK1/2, PLC, JNK [115] PAR1 and PAR2 Thrombin or TFLLRN (PAR1) Trypsin or SLIGKV (PAR2) PH domain signal partners such as Etk/Bmx or Akt [102] Gα12/13, Rho [24] Endothelin receptors (ETAR and ETBR) endothelin-1-3 (ET-1, ET-2, ET-3) C-Src/cross talk with EGFR β-arrestin -1or-2 PDZRhoGEF and Rho A, C β-catenin stabilization [7,21,116] Prostaglandin receptors (PE2, PE4) PGE2 Cyclooxygenase (COX-2)pathway P13K (coupling to Gs) [117,118,119] Bradykinin Receptor Type 1 and 2 (B1R, B2R) Kinins Gαq and Cross talk with EGFR Ras, Raf, ERK Sphingosine1-phosphate receptor1 (S1PR1) S1P Ras-ERK, PI3K-Akt-Rac, Rho, STAT3 (coupling to Gαi) [120,121] ==== Refs References 1. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081321ijms-17-01321ArticleExogenous Nitric Oxide Suppresses in Vivo X-ray-Induced Targeted and Non-Targeted Effects in Zebrafish Embryos Kong E.Y. 1Yeung W.K. 1Chan T.K.Y. 1Cheng S.H. 23*Yu K.N. 13*Lin Li Academic Editor1 Department of Physics and Materials Science, City University of Hong Kong, Hong Kong, China; yikong3-c@my.cityu.edu.hk (E.Y.K.); wkyeung46-c@my.cityu.edu.hk (W.K.Y.); kayauchan4-c@my.cityu.edu.hk (T.K.Y.C.)2 Department of Biomedical Sciences, City University of Hong Kong, Hong Kong, China3 State Key Laboratory in Marine Pollution, City University of Hong Kong, Hong Kong, China* Correspondence: bhcheng@cityu.edu.hk (S.H.C.); peter.yu@cityu.edu.hk (K.N.Y.); Tel.: +852-3442-9027 (S.H.C.); +852-3442-7812 (K.N.Y.); Fax: +852-3442-0549 (S.H.C.); +852-3442-0538 (K.N.Y.)12 8 2016 8 2016 17 8 132105 6 2016 04 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).The present paper studied the X-ray-induced targeted effect in irradiated zebrafish embryos (Danio rerio), as well as a non-targeted effect in bystander naïve embryos partnered with irradiated embryos, and examined the influence of exogenous nitric oxide (NO) on these targeted and non-targeted effects. The exogenous NO was generated using an NO donor, S-nitroso-N-acetylpenicillamine (SNAP). The targeted and non-targeted effects, as well as the toxicity of the SNAP, were assessed using the number of apoptotic events in the zebrafish embryos at 24 h post fertilization (hpf) revealed through acridine orange (AO) staining. SNAP with concentrations of 20 and 100 µM were first confirmed to have no significant toxicity on zebrafish embryos. The targeted effect was mitigated in zebrafish embryos if they were pretreated with 100 µM SNAP prior to irradiation with an X-ray dose of 75 mGy but was not alleviated in zebrafish embryos if they were pretreated with 20 µM SNAP. On the other hand, the non-targeted effect was eliminated in the bystander naïve zebrafish embryos if they were pretreated with 20 or 100 µM SNAP prior to partnering with zebrafish embryos having been subjected to irradiation with an X-ray dose of 75 mGy. These findings revealed the importance of NO in the protection against damages induced by ionizing radiations or by radiation-induced bystander signals, and could have important impacts on development of advanced cancer treatment strategies. zebrafish embryosnitric oxideionizing radiationbystander effect ==== Body 1. Introduction Nitric oxide (NO) is an important biological mediator in biological systems, and is involved in many different pathways such as immune response, neurotransmission and vasodilatation [1]. NO is endogenously generated from l-arginine by NO synthase (NOS) isoenzymes. NOS can be categorized into two functional classes, namely, the calcium-dependent constitutive NOS (cNOS) and the calcium-independent inducible NOS (iNOS) [2]. NO can diffuse through the cytoplasm and plasma membranes over distances of a few cell diameters [3]. NO is involved in both targeted effects as well as non-targeted bystander effects induced by ionizing radiations. Radiation-induced bystander effect (RIBE) in cells/organisms refer to the phenomenon that unirradiated cells/organisms respond as if they have been irradiated after having been partnered with the irradiated cells/organisms or after having been introduced into the medium previously conditioning the irradiated cells/organisms. Ionizing radiations can activate the ataxia telangiectasia mutated (ATM)-NF-κB signaling pathway [4]. NF-κB enters the nucleus and acts as a transcription factor for cyclooxygense-2 (COX-2) and iNOS genes. After its induction, iNOS can produce sustained high concentrations of NO. On the other hand, ionizing radiations can transiently stimulate the activity of cNOS [5]. Secreted or membrane-associated forms of cytokines, such as TNFα, can also activate IκB kinase (IKK)-mediated phosphorylation of IκB, which releases NF-κB that enters the nucleus. TNFα also activates MAPK pathways that, via AP-1 transcription factor, additionally upregulate expression of COX-2 [6] and iNOS. There was also evidence that NO could induce expression of COX-2 in cells [7,8]. Activation of COX-2 provided a continuous supply of reactive radicals and cytokines for the propagation of bystander signals [9]. NO has some intriguing properties in that it can lead to opposite biological functions, e.g., NO can be both pro-apoptotic and anti-apoptotic [10,11]. In a previous in vivo study [12], we examined the influence of NO on the bystander effect between embryos of the zebrafish, Danio rerio, irradiated with high-dose X-rays and naive unirradiated embryos. We demonstrated that the RIBE between partnered irradiated and naïve unirradiated zebrafish embryos was suppressed through the addition of the NO scavenger 2-(4-carboxyphenyl)-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide (cPTIO) into the medium [12]. As RIBE was also induced in zebrafish naïve embryos introduced into the irradiated embryo conditioned medium (IECM) alone, i.e., without directly partnering with the irradiated embryos, and in view of the short life of NO in the IECM, NO should have been involved in the generation of bystander signals in irradiated embryos, or in generation of bystander response (in terms of apoptosis) in naïve unirradiated embryos upon receiving bystander signals, or both [12]. In relation to this, previous in vitro studies by other groups also demonstrated that pretreatment with c-PTIO could eliminate the RIBE [13,14,15]. In a subsequent in vivo study, Choi et al. [16] further investigated the radioadaptive response (RAR) induced in zebrafish embryos by 3.4 MeV protons at 5 h post fertilization (hpf) against a challenging exposure of 2 Gy of X-ray irradiation at 10 hpf. The RAR (in terms of mitigation of apoptosis) was suppressed by adding cPTIO to the medium at 5 h after the priming exposure, when de novo synthesis of factors required by RAR should have been completed, which suggested that NO was required for the repair machineries against apoptosis in the bystander cells. The results from these two in vivo studies showed that NO could be both pro-apoptotic and anti-apoptotic in bystander zebrafish embryos. As such, it would be pertinent to examine the influence of exogenous NO on RIBE in zebrafish embryos. In the present work, the influence of exogenous NO on X-ray-induced targeted effects and non-targeted bystander effects in zebrafish embryos were studied. The exogenous NO was generated using a NO donor, S-nitroso-N-acetylpenicillamine (SNAP). SNAP could spontaneously release NO within 15 min upon light stimulation, and 100 μM of SNAP could generate ~27 μM of NO [17]. Zebrafish has become a popular vertebrate model in genetic, pharmacological and behavioral studies. In relation, zebrafish embryos have also been widely employed for examining biological effects of ionizing radiations [18,19,20,21,22,23]. The most important advantage of using zebrafish as an animal model in studying the biological effects is that zebrafish share considerable genetic sequence similarity with humans [24,25]. Other advantages include high fecundity, low maintenance cost, transparent embryos and rapid development. 2. Results Figure 1 shows the comparison between the fluorescence intensities from diaminofluorophore 4-amino-5-methylamino-2′-7′-difluorofluorescein diacetate (DAF-FM DA), which surrogated the NO levels, in zebrafish embryos treated with 20 or 100 μM SNAP and their corresponding experimental controls treated with 0.02% or 0.1% dimethyl sulfoxide (DMSO), respectively. The average DAF-FM DA fluorescence intensities from zebrafish embryos treated with 20 and 100 μM SNAP were significantly larger than their corresponding experimental controls (treated only with 0.02% and 0.1% DMSO, respectively), with p-values smaller than 0.039 and 0.0061, respectively. In other words, the NO levels in SNAP-treated zebrafish embryos were indeed higher than those in the experimental controls. Figure 2 shows representative images of acridine orange (AO)-stained embryos in different groups: (a) S20 group treated with 20 μM SNAP, and its experimental control D2 group (treated with 0.02% DMSO); (b) S100 group treated with 100 μM SNAP, and its experimental control D10 group (treated with 0.1% DMSO); (c) IS20 irradiated group treated with 20 μM SNAP, and its experimental control ID2 group (irradiated and treated with 0.02% DMSO); (d) IS100 irradiated group treated with 100 μM SNAP, and its experimental control ID10 group (irradiated and treated with 0.1% DMSO); (e) BS20 bystander group treated with 20 μM SNAP, and its experimental control BD2 group (treated with 0.02% DMSO); and (f) BS100 bystander group treated with 100 μM SNAP, and its experimental control BD10 group (treated with 0.1% DMSO); and all the corresponding control groups. 2.1. Cytotoxicity of 20 and 100 μM SNAP on Zebrafish Embryos The cytotoxic effects of SNAP with concentrations of 20 and 100 μM were examined. For each concentration of 20 and 100 μM of SNAP, three sets of experiments were performed separately. The differences among all groups of embryos were assessed using analysis of variance (ANOVA) and the results obtained for 20 and 100 μM of SNAP showed that the p-values were >0.05 (Table 1 and Table 2, Figure 2a,b), which indicated that SNAP with concentrations of 20 and 100 μM did not have significant effects on zebrafish embryos. 2.2. Effects of SNAP on Zebrafish Embryos Irradiated with 75 mGy X-ray To examine whether NO could alleviate the X-ray induced targeted effect, we first treated the zebrafish embryos with SNAP at 3 hpf for 2 h. The embryos were then transferred into fresh E3 medium and irradiated with X-ray. Table 3 and Figure 2c showed that when the embryos were treated with 20 μM of SNAP before being irradiated with X-rays, the number of apoptotic events was smaller than that obtained in the experimental control but there was no significant difference between these two groups. As such, 20 μM of SNAP could not mitigate the damage in terms of apoptosis when the embryos were exposed to 75 mGy X-ray irradiation. In contrast, Table 4 and Figure 2d showed that when the embryos were treated with 100 μM of SNAP before being irradiated with X-rays, the number of apoptotic events was significantly smaller than that obtained in the experimental control. This indicated that 100 μM of SNAP could alleviate the X-ray induced damages in the zebrafish embryos. 2.3. Effects of SNAP on Radiation-Induced Bystander Effect in Zebrafish Embryos To examine whether NO could protect the unirradiated naïve zebrafish embryos having partnered with irradiated embryos, we also pretreated the naïve zebrafish embryos with SNAP with concentrations of 20 and 100 μM at 3 hpf for 2 h. At 5 hpf, the pretreated naïve embryos were transferred into fresh E3 medium, which were then partnered with the irradiated embryos for 19 h. Table 5 and Table 6, and Figure 2e,f showed that the numbers of apoptotic events on the embryos treated with 20 and 100 μM of SNAP were significantly smaller than those for the experimental control groups for all sets of experiments. In other words, SNAP with a concentration of 20 or 100 μM effectively suppressed the bystander effects in the naïve embryos partnered with the irradiated embryos. 3. Discussion The present work studied the influence of NO on X-ray-induced targeted effects in irradiated zebrafish embryos and non-targeted effect in bystander naïve embryos partnered with irradiated embryos. These effects were assessed using the number of apoptotic events in the zebrafish embryos at 24 hpf revealed through AO staining, which was a common adopted biological endpoint for studying radiation-induced effects in zebrafish embryos [20]. The present work showed that zebrafish embryos did not exhibit deleterious effects in terms of apoptotic events when treated by 20 and 100 μM of SNAP. The targeted effect was alleviated if the zebrafish embryos were pretreated with 100 µM (but not with 20 µM) SNAP prior to irradiation with an X-ray dose of 75 mGy. The results agreed with previous results from other groups [26,27,28]. Liebmann et al. [26] demonstrated that pretreatment with the NO-releasing agent, (C2H5)2N[N(O)NO–]Na+ (DEA/NO), enhanced the survival of mice having been subjected to whole body irradiation. Tokumizu et al. reported that treatment of RAW264.7 cells with NO radical donors reduced the micronuclei frequency induced by γ-irradiation [27]. Suschek et al. showed that NO could protect endothelial cells against UVA-induced apoptosis [28]. These findings confirmed that exogenous NO could act as a protector against radiation-induced targeted effects. The present work also showed that the non-targeted effect in terms of apoptotic events was eradicated if the bystander naïve zebrafish embryos were pretreated with 20 or 100 µM SNAP for 2 h prior to their partnering with zebrafish embryos having already been irradiated with an X-ray dose of 75 mGy. Interestingly, previous research suggested that scavenging NO with cPTIO could suppress RIBE in the bystander zebrafish embryos [12]. Taken together, both scavenging NO from bystander embryos and adding exogenous NO to bystander embryos were anti-apoptotic. These findings were intriguing in that the biological effect (in terms of apoptosis) changed in the same direction (suppression of apoptosis) regardless of removal of NO from or addition of NO to the zebrafish embryos. The toxicity of NO could be attributed to peroxynitrite (ONOO−) formed by the combination of NO and O2−, which was an oxidizing free radical that could cause DNA fragmentation and lipid peroxidation, protein nitration and cell death [29,30]. As such, suppression of apoptosis would be expected through scavenging NO from the targeted embryos or non-targeted bystander embryos. On the other hand, mechanisms were proposed for suppression of apoptosis through adding exogenous NO. For example, NO could be involved in the activation of Hdm2, inhibition of p53 activation and/or inactivation of the p53 protein [31,32]. Furthermore, mechanisms were also proposed where NO was involved in repairing DNA damage. Xu et al. showed that exposure of cells to NO resulted in a 4–5-fold increase in the expression of the DNA-dependent protein-kinase catalytic subunit (DNA-PKcs), which was one of the important enzymes involved in repairing DNA double strand breaks [33]. Matsumoto et al. reported that accumulation of the heat-shock protein HSP72 and wild-type TP53 in NO recipient cells co-cultivated with heat-shocked NO donor cells were induced through an intercellular signal transduction pathway initiated by NO [34,35]. Gansauge et al. [36] reported that endogenous production of NO caused G1-phase arrest in human carcinoma cell lines. During G1-phase arrest induced by NO, the accumulated TP53 and HSP72 might induce DNA repair machinery that repaired DNA damages and protein repair machinery that repaired denatured proteins, respectively [34,35]. The toxicity of NO due to the formation of ONOO−, its capability to suppress apoptosis through activation of Hdm2, inhibition of p53 activation and/or inactivation of the p53 protein, as well as its involvement in repairing of DNA damage through expression of DNA-PKcs, accumulation HSP72 and wild-type TP53 and/or G1-phase arrest lead to opposite biological effects (in terms of apoptosis). This might explain why the biological effects (in terms of apoptosis) changed in the same direction regardless of removal of NO from or addition of NO to the zebrafish embryos. For example, although it would be expected that increasing the NO level in embryos would lead to an increase in the ONOO− toxicity and thus an increased number of apoptotic events, the increased NO level would also suppress apoptosis and facilitate DNA repair through the mechanisms described above, so the resultant final outcome could still be a decreased number of apoptotic events. These results could have important impacts on development of advanced cancer treatment strategies in which NO could play various critical roles. Some studies showed that NO could be a potent tumor radiosensitizer [37,38], but reduction of NO levels could also lead to radiosensitization [39]. More studies would be needed before final conclusions could be made. 4. Materials and Methods 4.1. Zebrafish Maintenance The animal studies in Hong Kong were approved by the Department of Health, Government of the Hong Kong Special Administrative Region, with the ref. No. Ref: (13-7) in DH/HA&P/8/2/5 Pt.1, and were performed in accordance with the guidelines. To provide the embryos required for the experiments in the present work, about 30 adult zebrafish were kept in a 45 L fish tank under a 14/10-light/dark cycle. The water temperature was maintained at 28.5 °C using a thermostat. The zebrafish were fed with dry fish food four times and brine shrimp once daily. To collect the embryos, a specially designed plastic collector was used [19]. To ensure synchronization of the stages of the collected embryos, the collector was left in the fish tank for only 15 min from the start of light-induced spawning. The collected embryos were transferred to a Petri dish with E3 medium (5 mM NaCl, 0.33 mM MgSO4, 0.33 mM CaCl2, 0.17 mM KCl, and 0.1% methylene blue) and then manually dechorionated using a pair of forceps (Dumont, Hatfield, PA, USA) under a stereomicroscope. The dechorionated embryos were then returned into the incubator maintained at 28.5 °C for further development until 3 hpf. 4.2. Treatment with SNAP A 0.1 M SNAP stock solution was prepared by dissolving SNAP (Thermo Fisher Scientific, cat No.: N7892, Eugene, OR, USA) in DMSO and was stored at −20 °C. In the present experiments, 20 and 100 μM SNAP solutions were employed, which were freshly prepared by dissolving the SNAP stock solution in E3 solution each time before the experiments. For each studied group of zebrafish embryos to be treated by SNAP, a total of 15 dechorionated embryos at 3 hpf were accommodated in a 35 mm Petri dish with 2 mL solution (20 or 100 μM SNAP) at 3 hpf for 2 h. For the corresponding experimental control embryos, the 20 or 100 μM SNAP were replaced with 0.02% or 0.1% DMSO, respectively. For studies on the cytotoxicity or the targeted effects, each studied group consisted of no more than 12 embryos transferred from the Petri dish into fresh E3 medium. For the studies on the non-targeted effects (RIBE), each studied group consisted of 10 embryos transferred from the Petri dish into fresh E3 medium to match the same number (10) of partnered irradiated embryos. In order to confirm that the NO levels in SNAP-treated zebrafish embryos were indeed higher than those in the experimental controls, we performed an extra experiment to label the zebrafish embryos with DAF-FM DA (Thermo Fisher Scientific, lot No.: 1351904) to reveal their NO levels. When the zebrafish embryos developed to 3 hpf, SNAP or DMSO stock solutions loaded with 5 μM DAF-FM DA were added into the E3 medium. After incubation for 2 h in dark at 28.5 °C, the embryos were rinsed with fresh E3 medium before in vivo visualization. The average DAF-FM DA fluorescence intensities from zebrafish embryos were analyzed using the NIS-Elements Basic Research software (version 4.40.00, Nikon Instruments Inc., Melville, NY, USA). 4.3. X-ray Irradiation After SNAP treatment for 2 h, the zebrafish embryos were transferred into a fresh E3 medium and were then X-ray irradiated using an X-ray generator, namely, X-RAD 320 irradiator (Precision X-ray Inc., North Branford, CT, USA), with voltage and current set at 150 kV and 2 mA, respectively, while the source–surface distance was set at 70 cm. In the present experiments, an X-ray filter made of 2 mm thick aluminum (Al) was also employed to harden the X-ray [40]. With these operation parameters, the dose rate was ~46 mGy/min and the dose received by the embryos was 75 mGy. For a reference, under the same irradiation parameters, Kong et al. [40] revealed that X-ray doses larger than 50 mGy induced detrimental effects in zebrafish embryos. The dose rates were monitored using the PTW UNIDOSE Universal Dosemeter (SN006861, PTW, Freiburg, Germany). Throughout the experiments, the embryos were kept at the room temperature. After irradiation, the embryos were incubated at 28.5 °C until 24 hpf for further analyses. 4.4. Cytotoxicity of SNAP To examine whether SNAP with concentrations of 20 and 100 μM were harmful to the zebrafish embryos, embryos at 3 hpf were incubated with SNAP with concentrations of 20 or 100 μM in 35 mm Petri dish for 2 h. In the experimental control experiments, the SNAP treatments were replaced with 0.02% and 0.1% DMSO, respectively. Furthermore, another control group without any treatment was also set up to compare with the group of SNAP-treated embryos. The experimental protocol is illustrated in Figure 3. 4.5. X-ray-Induced Targeted Effects and Non-Targeted Bystander Effects For studies on X-ray-induced targeted effects, the experimental protocol is illustrated in Figure 4. Embryos were pretreated with 20 or 100 μM SNAP to form the irradiated groups IS20 or IS100, respectively, or pretreated with 0.02% or 0.1% DMSO for 2 h at 3 hpf to form the experimental control groups ID2 or ID10, respectively. After pretreatment, the irradiated groups and the experimental control groups were transferred to new E3 media and were then irradiated with 75 mGy of X-ray. The embryos were then incubated at 28.5 °C until 24 hpf for vital dye AO staining and analyses. For studies on X-ray-induced non-targeted bystander effects, the experimental protocol is illustrated in Figure 5. Naïve embryos were pretreated with 20 or 100 μM SNAP to form the bystander groups BS20 or BS100, respectively, or pretreated with 0.02% or 0.1% DMSO for 2 h at 3 hpf to form the experimental control groups BD2 or BD10, respectively. After pretreatment, the bystander groups and the experimental control groups were transferred to new E3 media and were then partnered for 19 h with embryos already irradiated with 75 mGy of X-ray. The embryos were then incubated at 28.5 °C until 24 hpf for vital dye AO staining and analyses. 4.6. Vital Dye Acridine Orange (AO) Staining Apoptosis in the 24 hpf embryos was employed as the biological endpoint in the present work. The AO dye (Sigma, St. Louis, MO, USA) with a concentration of 5 μg/mL was used to quantify apoptotic cells, which had been commonly employed to quantify the level of apoptosis in zebrafish embryos [41,42,43]. Zebrafish embryos at 24 hpf were transferred into each of the 24 wells containing the AO dye and were kept at 28.5 °C for 45 min. During staining, the embryos were kept in the dark in order to minimize fading of the AO color. The embryos were then washed twice using deionized water thoroughly to remove excessive AO. After anaesthetizing the embryos by 0.0016 M tricaine (Sigma, St. Louis, MO, USA), three images with focuses on different sections of each anaesthetized embryo were captured using SpotBasic (SPOT 4.7, Diagnostic Instruments Inc., Sterling Heights, MI, USA) with a magnification of 40× under a fluorescent microscope. The apoptotic events in each embryo were counted with the help of a computer program. 4.7. Data Analysis Under each experimental condition, all experiments were carried out in triplicate on different days. The data were shown as the average numbers of apoptotic counts ± standard error of the mean (SEM). One-way analysis of variance (ANOVA) was used to check the cytotoxicity of different concentrations of SNAP. As regards the X-ray-induced targeted effects and non-targeted bystander effects, t-tests were used to check the differences between groups, where p-values < 0.05 were considered to correspond to significant differences between the compared groups. 5. Conclusions The present paper studied X-ray-induced targeted and non-targeted effects in zebrafish embryos and examined the influence of exogenous NO generated using SNAP on these effects, with the number of apoptotic events in the embryos at 24 hpf revealed through AO staining as the biological endpoint. The targeted effect was mitigated if the embryos were pretreated with 100 µM SNAP prior to irradiation with an X-ray dose of 75 mGy but was not alleviated if the embryos were pretreated with 20 µM SNAP. On the other hand, the non-targeted effect was eliminated in the bystander naïve embryos if they were pretreated with 20 or 100 µM SNAP prior to partnering with embryos having been subjected to irradiation with an X-ray dose of 75 mGy. These findings revealed the importance of NO in the protection against damages induced by ionizing radiations or by radiation-induced bystander signals. Acknowledgments The work described in this paper was supported by the University Grant Committee’s Teaching Development Grant. Funding for covering the cost to publish this article in open access was provided by the State Key Laboratory in Marine Pollution, City University of Hong Kong. Author Contributions E.Y. Kong, W.K. Yeung, T.K.Y. Chan. and K.N. Yu conceived and designed the experiments; E.Y. Kong, W.K. Yeung and T.K.Y. Chan performed the experiments; E.Y. Kong, W.K. Yeung and T.K.Y. Chan analyzed the data; S.H. Cheng and K.N. Yu contributed reagents/materials/analysis tools; E.Y. Kong and K.N. Yu wrote the paper. Conflicts of Interest The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results. Figure 1 Comparison between the diaminofluorophore 4-amino-5-methylamino-2′-7′-difluorofluorescein diacetate (DAF-FM DA) fluorescence intensities (in arbitrary units; larger values corresponding to brighter fluorescence) in zebrafish embryos treated with 20 or 100 μM S-nitroso-N-acetylpenicillamine (SNAP) and their corresponding experimental controls treated with 0.02% or 0.1% dimethyl sulfoxide (DMSO), respectively. All experiments were carried out in triplicate on different days, and each set consisted of 15 examined zebrafish embryos. The data were shown as mean DAF-FM DA fluorescence intensities ± standard error of the mean (SEM). Cases with p < 0.05 are asterisked. Figure 2 Representative images of acridine orange (AO)-stained embryos in different groups. (a) S20 group treated with 20 μM SNAP, and its experimental control D2 group; (b) S100 group treated with 100 μM SNAP, and its experimental control D10 group; (c) IS20 irradiated group treated with 20 μM SNAP, and its experimental control ID2 group; (d) IS100 irradiated group treated with 100 μM SNAP, and its experimental control ID10 group; (e) BS20 bystander group treated with 20 μM SNAP, and its experimental control BD2 group; (f) BS100 bystander group treated with 100 μM SNAP, and its experimental control BD10 group. (a–f) all corresponding control groups also shown. Images were captured using a florescent microscope with 40× magnification. Scale bar: 100 µm. Figure 3 Comparison among different groups of embryos to test the cytotoxicity of SNAP. Figure 4 Schematic diagram showing the protocols for studying the influence of SNAP on X-ray-induced targeted effects. Embryos were pretreated with 20 or 100 μM SNAP to form the irradiated groups IS20 or IS100, respectively, or pretreated with 0.02% or 0.1% DMSO for 2 h at 3 hpf to form the experimental control groups ID2 or ID10, respectively. After pretreatment, the irradiated groups and the experimental control groups were transferred to new E3 media and were then irradiated with 75 mGy of X-ray. The embryos were then incubated at 28.5 °C until 24 hpf for AO staining and analyses. Figure 5 Schematic diagram showing the protocols for studying the influence of SNAP on X-ray-induced non-targeted bystander effects. Naïve embryos were pretreated with 20 or 100 μM SNAP to form the bystander groups BS20 or BS100, respectively, or pretreated with 0.02% or 0.1% DMSO for 2 h at 3 hpf to form the experimental control groups BD2 or BD10, respectively. After pretreatment, the bystander groups and the experimental control groups were transferred to new E3 media and were then partnered for 19 h with embryos already irradiated with 75 mGy of X-ray. The embryos were then incubated at 28.5 °C until 24 hpf for AO staining and analyses. ijms-17-01321-t001_Table 1Table 1 Mean number of apoptotic events (±SEM) obtained in the control, S20 and D2 groups from 3 sets of experiments. n: numbers of embryos involved in the analyses. # p values obtained using ANOVA. Set Control S20 D2 p # 1 92.5 ± 13.6 (n = 10) 86.1 ± 11.8 (n = 8) 126 ± 19 (n = 8) 0.164 2 83.4 ± 5.4 (n = 7) 95.2 ± 10.4 (n = 6) 89.4 ± 7.2 (n = 7) 0.566 3 173 ± 11 (n = 11) 156 ± 11 (n = 10) 173 ± 18 (n = 10) 0.619 ijms-17-01321-t002_Table 2Table 2 Mean number of apoptotic events (±SEM) obtained in the control, S100 and D10 groups from 3 sets of experiments. n: numbers of embryos involved in the analyses. # p values obtained using ANOVA. Set Control S100 D10 p # 1 96.0 ± 7.4 (n = 7) 108 ± 10 (n = 9) 110 ± 15 (n = 9) 0.689 2 65.5 ± 5.1 (n = 12) 64.5 ± 6.0 (n = 12) 62.7 ± 6.2 (n = 12) 0.941 3 87.5 ± 5.4 (n = 12) 84.5 ± 6.1 (n = 11) 86.6 ± 5.0 (n = 10) 0.922 ijms-17-01321-t003_Table 3Table 3 Mean number of apoptotic events (±SEM) obtained in the control, IS20 and ID2 groups from 3 sets of experiments. n: numbers of embryos involved in the analyses. # p values obtained by comparing the IS20 and ID2 groups using two-tailed t-test. Set Control IS20 ID2 p # 1 87.9 ± 6.4 (n = 7) 186 ± 13 (n = 9) 202 ± 19 (n = 9) 0.517 2 108 ± 5 (n = 10) 196 ± 12 (n = 11) 198 ± 14 (n = 11) 0.898 3 83.4 ± 5.4 (n = 7) 136 ± 13 (n = 8) 157 ± 11 (n = 7) 0.236 ijms-17-01321-t004_Table 4Table 4 Mean number of apoptotic events (±SEM) obtained in the control, IS100 and ID10 groups from 3 sets of experiments. n: numbers of embryos involved in the analyses. # p values obtained by comparing the IS100 and ID10 groups using one-tailed t-test (cases with p < 0.05 are asterisked). Set Control IS100 ID10 p # 1 74.3 ± 4.3 (n = 10) 157 ± 28 (n = 10) 250 ± 37 (n = 9) 4.65 × 10−3 * 2 108 ± 5 (n = 10) 177 ± 8 (n = 10) 210 ± 10 (n = 11) 9.69 × 10−3 * 3 110 ± 9 (n = 7) 131 ± 9 (n = 10) 204 ± 13 (n = 10) 1.99 × 10−4 * ijms-17-01321-t005_Table 5Table 5 Mean number of apoptotic events (±SEM) obtained in the control, BS20 and BD2 groups from 3 sets of experiments. n: numbers of embryos involved in the analyses. # p values obtained by comparing the BS20 and BD2 groups using the one-tailed t-test (cases with p < 0.05 are asterisked). Set Control BS20 BD2 p # 1 82.7 ± 10.4 (n = 10) 72.4 ± 7.2 (n = 9) 154 ± 15 (n = 9) 1.65 × 10−4 * 2 102 ± 11 (n = 10) 91.4 ± 7.1 (n = 8) 137 ± 12 (n = 9) 2.78 × 10−3 * 3 63.0 ± 2.9 (n = 9) 67.3 ± 4.3 (n = 9) 95.3 ± 7.6 (n = 8) 3.46 × 10−3 * ijms-17-01321-t006_Table 6Table 6 Mean number of apoptotic events (±SEM) obtained in the control, BS100 and BD10 groups from 3 sets of experiments. n: numbers of embryos involved in the analyses. # p values obtained by comparing the BS100 and BD10 groups using one-tailed t-test (cases with p < 0.05 are asterisked). Set Control BS100 BD10 p # 1 129 ± 15 (n = 10) 113 ± 12 (n = 9) 239 ± 21 (n = 9) 7.14 × 10−5 * 2 103 ± 14 (n = 8) 104 ± 14 (n = 9) 156 ± 15 (n = 9) 9.77 × 10−3 * 3 96.0 ± 7.4 (n = 7) 87.6 ± 6.8 (n = 8) 129 ± 15 (n = 8) 1.67 × 10−2 * ==== Refs References 1. Moncada S. Palmer R.M. Higgs E.A. Nitric oxide: Physiology, pathophysiology, and pharmacology Pharmacol. Rev. 1991 43 109 142 1852778 2. Brennan P.A. Conroy B. Spedding A.V. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081322ijms-17-01322ReviewTelocytes and Their Extracellular Vesicles—Evidence and Hypotheses Cretoiu Dragos 12†Xu Jiahong 3†Xiao Junjie 4*Cretoiu Sanda M. 12*Drummen Gregor Academic Editor1 Division of Cellular and Molecular Biology and Histology, Department of Morphological Sciences, Carol Davila University of Medicine and Pharmacy, Bucharest 050474, Romania; dragos@cretoiu.ro2 Victor Babeş National Institute of Pathology, Bucharest 050096, Romania3 Department of Cardiology, Tongji Hospital, Tongji University School of Medicine, Shanghai 200065, China; jhx_tj@hotmail.com4 Cardiac Regeneration and Ageing Lab, Experimental Center of Life Sciences, School of Life Science, Shanghai University, Shanghai 200444, China* Correspondence: junjiexiao@live.cn (J.X.); sanda@cretoiu.ro (S.M.C.); Tel.: +86-21-6613-8131 (J.X.); +40-724-319277 (S.M.C.)† These authors contributed equally to this work. 12 8 2016 8 2016 17 8 132229 6 2016 26 7 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Entering the new millennium, nobody believed that there was the possibility of discovering a new cellular type. Nevertheless, telocytes (TCs) were described as a novel kind of interstitial cell. Ubiquitously distributed in the extracellular matrix of any tissue, TCs are regarded as cells with telopodes involved in intercellular communication by direct homo- and heterocellular junctions or by extracellular vesicle (EVs) release. Their discovery has aroused the interest of many research groups worldwide, and many researchers regard them as potentially regenerative cells. Given the experience of our laboratory, where these cells were first described, we review the evidence supporting the fact that TCs release EVs, and discuss alternative hypotheses about their future implications. telocytestelopodesextracellular vesiclesexosomesectosomes ==== Body 1. Introduction Living cells communicate between themselves by different modalities, which are represented by cell junctions and the cell secretion of different soluble factors. The latter can act in an autocrine, paracrine, or endocrine manner. The last decade brought in a new evolutionary concept—that cellular communication can also be mediated by the transfer of genetic information [1]. This genetic transfer (e.g., mRNA, microRNA, long non-coding RNA, and occasionally genomic DNA) is intermediated by extracellular vesicles generated and released by prokaryotic and eukaryotic cells [2]. Extracellular vesicles (EVs) are nano-sized membrane-surrounded structures originating in the endosomal compartment or shed from the plasma membrane. Classified by their size and mechanisms of biogenesis, EVs can, in general, be categorized into three classes: (a) exosomes; (b) ectosomes or shedding microvesicles; and (c) apoptotic bodies. Exosomes were discovered almost three decades ago as “cell debris”, and have an endocytic origin and variable diameters between 30 and 100 nm [3,4]. Ectosomes (also known as microvesicles) have diameters between 100 and 1000 nm and form by direct budding from the plasma membrane [5]. Apoptotic bodies (50 nm–2 μm) are released by cells undergoing programmed cell death via outward blebbing of the apoptotic cell membrane. EVs carry receptors, bioactive lipids, proteins, and, most importantly, nucleic acids, such as mRNA, microRNA (miRNA), and non-coding RNAs. Their membrane composition (marker proteins) is particular according to the vesicle type, and their content is also variable [4,6] Telocytes are no exception to this mode of communication, being able to release and receive different types of vesicles (Figure 1). The presence of EVs has been reported in interstitial spaces and in all biological fluids, including plasma, saliva, urine, cerebrospinal fluid, sputum, bronchial lavage fluid, malignant ascites, amniotic fluid, breast milk, and seminal fluid [7,8,9,10]. EVs were also detected as a heterogeneous population in the secretome from cells cultured in vitro, in conditioned media [11,12]. Protected by their external lipid bilayer, the content of EVs targets the recipient cells by three different mechanisms: direct fusion with their plasma membranes, receptor-mediated uptake, and endocytosis (phagocytosis) [13,14,15]. EVs have been found to have important roles in many important physiological processes, such as stem cell upkeep [16,17], tissue repair [18], immune surveillance [19] and vascular hemostasis [20]. Moreover, EVs seem to play an important role in several diseases, such as cancer, neurodegenerative, cardiovascular, and metabolic diseases [21,22,23,24,25,26]. Nowadays, the importance EVs in research is highlighted by the immense interest of the extracellular vesicle community, since EVs are considered as biomarkers, and also as drug, vaccine, and gene vector delivery tools in human diseases [27,28,29]. In this review, we summarize the recent research on the characterization of a new cell population within the stromal compartment, namely the telocytes (TCs). We also highlight the fact that TCs are able to release EVs, and we assess the research being carried out and the current progress examining the roles of these cells as communicating devices. 2. Telocytes as a Particular Type of Interstitial Cells Telocytes (TCs) represent a recently-discovered cell population of the connective tissue (the stromal compartment forming the supportive framework of any organ) [30,31]. According to Popescu, their discoverer, the simplest description of TCs is cells with telopodes [32]. Telopodes are extremely long extensions (tens to hundreds of micrometers) which arise from the small cell body of TCs (Figure 2) [33]. Telopodes are characterized by a moniliform appearance in the bidimensional plane of ultrathin sections, with dilated portions called podoms and very thin regions named podomers (Figure 3). A three-dimensional perspective changes the first impression of telopodes, which appear to be long, flattened irregular veils and tubular structures with uneven caliber, because of irregular dilations corresponding to the podoms (Figure 4) [34,35]. Telocytes are nowadays seen as connecting devices, since numerous papers describe their ability to interact with themselves by homocellular junctions and with other cell types by heterocellular junctions (for details see review [36]). In addition, TCs also contact—directly or at a certain distance—important surrounding structures, such as blood vessels, nerve endings, smooth muscles, glandular elements, and covering epithelia [37,38]. Telocytes are functionally distinct from mesenchymal stem cells and fibroblasts with regard to their gene expression profile, and might have specific roles in cell signaling, tissue homeostasis, remodeling, and angiogenesis [39]. Chromosomal analysis also revealed that specific genes in lung TCs are different from those of pneumocytes, airway cells, mesenchymal stem cells, and lymphocytes [40,41,42,43,44]. Recently, TCs were characterized with the aid of various omics technologies such as mass spectrometry and multiplexed assays [45,46,47]. Several proteins were found to be up-regulated in TCs’ proteome—e.g., mitochondrial thioredoxin-dependent peroxide reductase, protein disulphide-isomerase A3, myosin-14, myosin-10, filamin-B, sodium/potassium-transporting ATPase subunit α-1 and keratin, type II cytoskeletal 1. These proteins are also regularly found in the proteome of mammalian extracellular vesicles, and therefore it has been proposed that TCs are involved in extracellular environment homeostasis, possibly influencing stem cell niches and leading to cell differentiation [47]. 3. Telocytes and the Horizontal Transfer of Information Telocytes—formerly known as interstitial Cajal-like cells (ICLC)—were shown to release EVs. Mandache et al. showed the presence of such vesicles soon after the first detailed ultrastructural characterization of ICLC [48]. Since that early study, which suggested the existence of a paracrine and/or juxtacrine intercellular mutual modulation between these special cells and the surrounding cells, much interest was dedicated to this type of intercellular communication. In the stromal space of different organs, other studies revealed the existence of EVs derived from the cellular body of TCs and also from their telopodes [38,49,50,51]. In addition, a morphometric comparison was performed between extracellular membranous vesicles (exosomes and shedding microvesicles) found in human non-pregnant and pregnant uterus [52]. In these two physiological conditions, exosome release seemed to be more pronounced in pregnancy, suggesting a horizontal transfer of important macromolecules among neighboring cells [52]. Telocytes have been shown to be implicated in a variety of human pathologies (as reviewed in [53]), where they are significantly reduced and altered. Therefore, we can consider that the release of EVs might also be affected in this context through altered intercellular signaling. Additionally, as based on their different immunohistochemical subtypes (suggesting organ-specific phenotypes of TCs [54,55]), their local and distal microcommunication mechanisms might also be diverse, including the content of the EVs. The release of EVs by TCs has also been demonstrated in vitro with the aid of electron microscopy and electron tomography. Fertig et al. described that cardiac TCs in culture release exosomes (45 ± 8 nm), ectosomes (128 ± 28 nm), and multivesicular cargos (MVC; 1 ± 0.4 μm) [56]. To gain insight into the third dimension of the arborescent conformation of TCs, focused ion beam scanning electron microscope (FIB-SEM) tomography was recently used to highlight human skin TCs. The 3D analysis of the reconstructed ultrastructural volume depicted the biological fine structure of some EVs (diameter 438.6 ± 149.1 nm, n = 30) at high resolution (Figure 5) [35]. The budding phenomenon was caught in progress, and represents valuable data about the three-dimensional morphology of telopodes and their capability to furnish extracellular vesicles at nanoscale dimensions (Figure 6). It is known that the transfer of microRNA is mediated by EVs, which function as effective delivery vehicles. In fact, it has been shown that EVs are enriched in miRNAs and that secreted miRNAs are protected by the membrane structures of EVs [57,58]. Several miRNAs were reported as associated or not with TCs. In an effort to identify a biomarker for the identification of TCs, it has been demonstrated that the lack of miR-193 expression differentiates microdissected TCs from other stromal cells (3T3 fibroblasts) in cell culture. Moreover, they do not express any of the cardiomyocyte-specific miRNAs (miRs) (miR-1, 133a, or 208a). Instead, various levels of miR-21, 22, 29, and 199a-5p were detected, in accordance with TCs’ mesenchymal origin [59]. Recently, additional evidence has accumulated (in vivo and in vitro) about the importance of EVs in accomplishing the role of TCs in intercellular communication. Cismasiu and Popescu demonstrated the microRNA exchange between TCs and cardiac stem cells in cell cultures, when the EVs released from TCs are taken up by cardiac stem cells via endocytosis [60]. Furthermore, they demonstrated that cardiac stem cells deliver microRNA-loaded EVs to TCs, and suggested that “there is a continuous, post-transcriptional regulatory signal back and forth between TCs and stem cells” [60]. Several miRNAs with pro-angiopoietic potential (miR-126, miR-130, let-7e, and miR-100) were found to be expressed by TCs, and, moreover, the level of expression is increased in the myocardium soon after acute myocardial infarction [60]. Several experiments also reported an association between TCs and cardiac stem cells, stressing the contribution of TCs to neo-angiogenesis, especially in the infarcted myocardium [45,61,62]. This might also explain why cardiac TCs were found to be significantly increased in exercised heart, where they might contribute to cardiac renewal and regeneration [63]. The participation of TCs in angiogenesis is likely possible, since human lung telocytes could produce soluble factors such as VEGF and EGF, and were shown to induce the proliferation of pulmonary endothelial cells in cell cultures [64]. In a recent study, Li et al. [65] showed that both vascular TCs and vascular smooth muscle cells express miR-24, but the expression level of miR-24 is higher in TCs. Whether or not this miRNA is a cargo of the EVs and directly responsible for the effect remains to be established, but what is certain is that the supernatant of TCs in culture promoted the proliferation of vascular smooth muscle cells. Some other soluble factors in the supernatant—e.g., cytokines, including VEGF (vascular endothelial growth factor), IL-6 (interleukin-6), MIP-1α (macrophage inflammatory protein 1-α) might contribute to the repair process, too [45]. 4. Future Directions On one hand, there is a current growing interest in EVs is based on their physiological role in intercellular communication (especially in stem cell biology, where they can maintain the stemness capacity intervening in tissue repair) [66,67], and in pathological conditions (particularly in the pre-metastatic niche formation, cancer progression, and in the spread of numerous pathogens) [68,69,70]. On the other hand, the discovery of a new cell type known as telocytes, which are able to release EVs and interfere upon stem cells in their niches and upon other different somatic cells, allows us to speculate that they need special attention in the future. We need to learn more about the cargo in the EVs released by TCs, and if these vesicles are different in physiological and pathological conditions. Besides releasing vesicles, TCs were shown to have endocytic properties in the enteric wall (colon) and to participate in the uptake and storage of endogenous or exogenous particles in the skin and periodontal tissues (e.g., hemosiderin, melanin, and some components of dental amalgam). Therefore, Diaz-Flores et al. suggested that TCs are the principal non-macrophage cells with phagocytic-like properties [71]. As a consequence, one can consider that TCs represent important players in intercellular communication in between cells, locally or at a distance. A lot of information must be gathered before deciphering their precise role in physiological processes. In addition, TCs seem to change phenotype according to organ location [72,73,74,75], a phenomenon possibly explained by the different cargo in EVs and their shuttle trafficking between different cell types in response to diverse stimuli. Deciphering whether the content of EVs released/received by TCs is different according to location will open promising perspectives for controlling tissue homeostasis. It is possible that in the future we will be able to control the formation of telopodes [76], as well as the use of EVs as therapeutic potential agents. As mentioned above, several hypotheses were raised about the functional roles of TCs; however, hardly any have been addressed to date. Although there are several papers discussing the interrelation between TCs and stem cells, the existing information about the involvement of TCs in cancer is scarce. Only two papers address this topic. Mirancea et al. [77] showed that TCs in normal dermis of the skin establish more heterocellular junctions in comparison with TCs of tumor dermis of basal cell carcinoma and squamous cell carcinoma, concluding that by decreasing their number of junctions, TCs might induce changes in intercellular communication into the peritumoral stroma and, consequently, into the whole tumor mass. Mou et al. stated that in situ “TCs communicate with breast cancer cells as well as other stromal cells, and might serve as a bridge that directly links the adjacent cells through membrane-to-membrane contact”, while in experimental conditions of a reconstituted breast cancer “TCs and other breast stromal cells facilitated the formation of typical nest structure, promoted the proliferation of breast cancer cells, and inhibited their apoptosis” [78]. In conclusion, TCs are cells capable of acting as integrators of many intercellular functions; however, there is a long way ahead until their functional capabilities are elucidated. Moreover, the specific cargo for their EVs must be characterized, and the biodistribution of these vesicles also remains to be established. TCs are seen by different groups as future targets with implications for regenerative medicine [79,80,81]. Acknowledgments This work was supported by grants from the Romanian National Authority for Scientific Research, CNCS—UEFISCDI, PNII projects number 82/2012 (for Sanda M. Cretoiu) and 194/2014 (for Dragos Cretoiu). This work was supported by Grants from the National Natural Science Foundation of China 81570362 (for Junjie Xiao) and 81270314 (for Jiahong Xu) and Shanghai Medical Guide Project from Shanghai Science and Technology Committee 134119a3000 (for Jiahong Xu). Author Contributions All authors contributed to the design, concepts and writing of this review. All authors read and approved the final manuscript. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Schematic diagram of EVs transfer between cells, particularized for telocytes (TCs). Cells produce three types of extracellular vesicles (EVs): exosomes, ectosomes, and apoptotic bodies. The vesicles may be endocytosed, might fuse directly with the plasma membrane, or determine biological processes by ligand–receptor interactions on the cell surface. Arrows are indicative of the fact that the transfer is bidirectional and that EVs can shuttle between cells to communicate and exchange genetic material. Depending on the site of biogenesis, EVs’ heterogeneity, size, and composition are slightly different. ncRNA: non-coding RNA; miRNA: microRNA; MVB: multivesicular body. Figure 2 Transmission electron microscopy (TEM) of a telocyte in human non-pregnant myometrium. (A) Two cellular bodies (TC1, TC2) can be easily seen in the interstitial space between smooth myocytes. One telocyte has long, convoluting telopodes (TC2). Scale bar = 5 μm; (B) Higher magnification detail of the area marked with a dotted square in (A). Note that the heterochromatin is mostly confined to the periphery of the nucleus, but is also dispersed throughout. Scale bar = 1.5 μm. TC: telocyte; Tp: telopode; SMC: smooth muscle cell; m: mitochondrion; rER: rough endoplasmic reticulum; N: nucleus; arrowhead: exosome; Ect: ectosome; arrow: cellular junction. Figure 3 Transmission electron microscopy (TEM) of a telocyte in human non-pregnant myometrium. Image obtained by concatenation of seven microscopic fields. The telocyte exhibits a spindle-shape cell body, from where two extremely long telopodes are emerging. In the close proximity, other telopodes with tortuous trajectories contact the central telocyte by homo-cellular junctions, creating an intricate network. One can also observe numerous extracellular vesicles (arrowheads: exosomes; Ect: ectosomes) either shedding from or surrounding the telopodes. Arrows: cellular junctions; Tp(s) = telopode(s). Scale bar = 5 μm. Figure 4 Focused ion beam scanning electron microscope (FIB-SEM) tomography. Three-dimensional reconstruction details of telopodes (Tps), from different viewing angles. (A) From this angle, four telopodes can be seen; (B) Tp2 has enlarged segments (podoms) alternating with slender segments; (C) Telopode with anfractuous contour. Extracellular vesicles appear in purple. Reproduced with permission from [35]. Figure 5 FIB-SEM of extracellular vesicle dynamics around a telocyte. (A–F) Six non-consecutive serial images depicting the biological fine structure of some EVs. Scale bar is 0.5 μm. Reproduced with permission from [35]. Figure 6 (A–D) FIB-SEM serial images of a human dermal telocyte presenting an extracellular vesicle (purple) budding from a podom. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081323ijms-17-01323ReviewThe Role of Matrix Metalloproteinase Polymorphisms in Ischemic Stroke Chang Jason J. 1*Stanfill Ansley 2Pourmotabbed Tayebeh 3Maki Masatoshi Academic Editor1 Department of Neurology, University of Tennessee Health Science Center, Memphis, TN 38104, USA2 Department of Nursing and Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN 38104, USA; astanfi4@uthsc.edu3 Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, TN 38104, USA; tpourmot@uthsc.edu* Correspondence: jjwchang@hotmail.com; Tel.: +1-901-448-2319; Fax: +1-901-448-594012 8 2016 8 2016 17 8 132323 6 2016 26 7 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Stroke remains the fifth leading cause of mortality in the United States with an annual rate of over 128,000 deaths per year. Differences in incidence, pathogenesis, and clinical outcome have long been noted when comparing ischemic stroke among different ethnicities. The observation that racial disparities exist in clinical outcomes after stroke has resulted in genetic studies focusing on specific polymorphisms. Some studies have focused on matrix metalloproteinases (MMPs). MMPs are a ubiquitous group of proteins with extensive roles that include extracellular matrix remodeling and blood-brain barrier disruption. MMPs play an important role in ischemic stroke pathophysiology and clinical outcome. This review will evaluate the evidence for associations between polymorphisms in MMP-1, 2, 3, 9, and 12 with ischemic stroke incidence, pathophysiology, and clinical outcome. The role of polymorphisms in MMP genes may influence the presentation of ischemic stroke and be influenced by racial and ethnic background. However, contradictory evidence for the role of MMP polymorphisms does exist in the literature, and further studies will be necessary to consolidate our understanding of these multi-faceted proteins. ischemic strokematrix metalloproteinasepolymorphismincidenceepidemiology ==== Body 1. Introduction Cerebrovascular disease (CVD) is characterized by brain ischemia that results in focal, acute neurological deficits and remains the fifth leading cause of death in the United States [1]. Over eighty percent of CVD consist of ischemic stroke (IS), defined by focal blood vessel occlusion. The main mechanisms of IS are thrombosis, embolization, and lacunar infarction that result in oxygen loss and ATPase dysfunction. The resulting cerebral ischemia initiates several pathological events including blood-brain barrier (BBB) disruption, vasogenic edema, secondary hemorrhagic transformation, and neuronal death. The principal risk factors for IS incidence remains the metabolic syndrome, characterized by the co-incidence of hypertension, diabetes, obesity, high triglycerides, and low high-density lipoprotein [2,3]. More recently, IS incidence has also been found to be associated with environmental exposures such as alcohol, smoking, diet, and exercise [4,5]. However, not all individuals exposed to similar environmental factors suffer equally from IS. Differences in incidence, pathogenesis, and clinical outcome have long been noted when comparing IS among patients with different racial and ethnic backgrounds. Epidemiological studies highlight significant racial disparities with blacks having significantly higher stroke incidence compared to whites [6], younger age of incidence [7], and poorer functional outcomes [8]. Still, clinical, environmental, and demographic risk factors do not fully explain disparities in IS disease progression [9]. As with many other diseases, IS results from the interaction between an individual’s genetic makeup and environmental exposures [10]. For instance, high-density lipoprotein cholesterol and smoking in blacks, systolic blood pressure in Asians, and age in both Hispanics and blacks resulted in different atherosclerotic disease progression compared to whites [11]. Asians and Hispanics were found to have slower progression of carotid intima-media thickness, and blacks had less carotid plaque formation [12]. Finally, one study showed that blood pressure significantly correlated with stroke incidence in South Asian males, but not in European males [13]. Improved understanding has suggested a role for genetics in IS susceptibility [14]. Significant research has been carried out to establish the relationship between functional variants of different genes and IS risk [15,16,17,18,19,20,21]. One such potential genetic risk factor may be matrix metalloproteinase (MMP) polymorphisms [22]. The role of MMP polymorphisms in gene transcription and their associations with various diseases has been evaluated. Roles for specific MMP polymorphisms have been found in cancer incidence [23], coronary artery disease [24], and glaucoma [25]. Not surprisingly, burgeoning research in MMP polymorphisms for various types of populations have been conducted in IS as well. This review will evaluate MMP polymorphisms and provide some insight into their roles in IS incidence and clinical outcome. 2. Background on Matrix Metalloproteinases MMPs are members of a unique family of zinc-binding endopeptidases that are secreted as catalytically latent species and processed to their activated forms in vivo by other proteases, the tissue plasminogen activator-plasmin system, or post-translational modifications [26,27,28]. At least twenty-five different MMPs have been identified [29]. Members of this protease family have been divided into five subclasses based on structural similarity and substrate specificity that include the following: collagenases (MMP-1, MMP-8, and MMP-13) [30,31,32], gelatinases (MMP-2 and MMP-9) [33,34,35], stromelysins (MMP-3 and MMP-10) [36,37], metalloelastases [38,39], membrane-type MMPs (MT-MMP, MMP-14, MMP 15, MMP-16, MMP-17, MMP-24, and MMP-25) [40,41,42,43,44,45], and others (MMP-7, MMP-11, MMP-12, MMP-19, MMP-20 and MMP-23) [46,47]. As ubiquitous proteins, MMPs have extensive roles that include epithelial repair against pathogens in innate immunity [48,49], control of chemokine activity (including CCL-2, 7, 8, and 13) [50], and the activation of inflammatory cytokines (interferon-β, vascular endothelial growth factor, epidermal growth factors, fibroblast growth factors, and transforming growth factor-β1) [51]. However, MMPs also play a significant role in normal and pathological conditions that involve ECM degradation and remodeling [52]. MMPs have also been shown to be involved in the complex pathophysiology of IS. Mechanisms of MMP in IS include atherosclerotic plaque maturation, plaque degradation and rupture [53], development of subclinical periventricular white matter disease (i.e., leukoaraiosis) [54], and hemorrhagic transformation (HT) particularly after thrombolysis [55]. While MMP-8 [56] and MMP-10 [57] have been related to atherosclerosis and brain ischemic, respectively, MMP-1, 2, 3, 9, and 12 have been most identified in the pathophysiology of IS. We will discuss each of these in turn. 3. Matrix Metalloproteinase-1 MMP-1 is a 53 kDa protein that plays an active role in the degradation of interstitial collagen types I, II, and III, and major structural components of the fibrous plaque, which forms the mechanism behind atherosclerotic stroke. The active-site zinc works with a hinge region and carboxyl terminal domain to unwind the triple-helical collagen structure [58]. Although implicated in neuronal cell death [59], MMP-1 has also been closely linked to advanced atherosclerotic plaques and has been postulated to exacerbate atherosclerosis by degrading plaques, which leads to a cycle of plaque expansion and rupture [53]. Clinical Role of MMP-1 Polymorphisms A single-guanine (1G to 2G) polymorphism located at the MMP-1 promoter region -1607 1G/2G has been identified. The 2G-allele of this promoter has been noted to increase transcriptional activity by creating an E26 transcription factor binding site [60]. But Chehaibi et al. evaluated the role of this 1G vs. 2G allele in IS incidence in Tunisian patients and did not find any association between the -1607 promoter gene polymorphism and IS incidence [61]. But this failed association might be due to the similarities in genotype frequency between patients with carotid artery atherosclerosis and controls (p = 0.085). However, MMP-1 2G/2G homozygotes and MMP-3 polymorphisms were shown to synergistically lead to an increase in carotid atherosclerosis (odds ratio (OR) 3.31, p = 0.004) [62]. To date, no clinical study has evaluated the role of MMP-1 polymorphisms in clinical outcome of IS, and their role in IS incidence remains undetermined. 4. Matrix Metalloproteinase-2 MMP-2 (gelatinase-A) is a 72 kDa protein that degrades collagen IV, fibronectin, laminin, aggrecan, gelatin, elastin and non-matrix substrates, latent TGFβ9, monocyte chemoattractant protein-3, fibroblast growth factor receptor 1, big endothelin-1, and plasminogen. MMP-2 has several roles in IS. It disrupts the BBB and leads to HT [63]. It is also extensively associated with leukoaraiosis. This has been shown in animal models utilizing MMP-2 knockout mice and the corresponding inhibitors that significantly decreased chronic white matter disease [64]. Pathological studies demonstrated that MMP-2 levels are higher in patients with vascular dementia [65] and lacunar strokes [66]. Yet, MMP-2 serum levels immediately after acute IS are not markedly elevated [66,67]. However, the activity of MMP-2 increases in the late phase of IS [66,67,68], suggesting that it plays a neuroprotective role within the ischemic core. Clinical Role of MMP-2 Polymorphisms Five MMP-2 polymorphisms have been found to be associated with the development of lacunar strokes [69]. However, only a few MMP-2 genetic studies demonstrate its role in atherosclerotic disease. Price et al. identified a -1306 (C vs. T allele) polymorphism in the promoter region of MMP-2. This C to T transition disrupted the promoter, leading to lower MMP-2 expression and enzymatic activity [70]. One mechanism potentially linking MMP-2 with increased IS incidence is its association with leukoaraiosis. Zhang et al. evaluated the -1306 C/T promoter of MMP-2 in a Chinese population and found the CC genotype resulted in higher transcriptional activity and independently predicted leukoaraiosis (p = 0.027) [71]. However, Nie et al. evaluated this same promoter in a Chinese population and did not find an association between the CC genotype and IS incidence [72]. Instead, they found the -735 C-allele (C vs. T allele) in the MMP-2 promoter region was associated with a greater incidence of IS (OR 1.516, 95% confidence interval (CI) 1.185–1.940, p = 0.001) in their Chinese population. The C-allele of the -735 promoter is associated with significantly higher MMP-2 transcriptional activity [72]. High MMP-2 production and activity may be one risk factor for IS incidence. Unlike other MMPs, the presence of elevated MMP-2 after IS in the acute phase has been associated with improved clinical outcome [66]. Manso et al. found significant associations between two single-nucleotide polymorphisms (SNP) rs2241145 and rs1992116 and clinical outcome after Bonferroni correction. For rs2241145 (G vs. C allele), the G-allele was significantly associated with good outcome after stroke (OR 1.66, 95% CI 1.20–2.30, p = 0.0439). In rs1992116 (G vs. A allele), the G-allele was significantly associated with improved outcome after stroke (OR 1.67, 95% CI 1.20–2.31, p = 0.0385) [73]. However, the relationship between these polymorphisms and transcriptional activity is unclear and is an area in need of further research. 5. Matrix Metalloproteinase-3 MMP-3 (stromelysin-1), is a 51 kDa protein produced by various cells types, including fibroblasts, smooth muscle cells, and macrophages. MMP-3 degrades major ECM components—collagens I, III, IV, V, IX, and X [74], fibronectin, denatured collagens, laminin, and cartilage proteoglycans [75]. Studies have consistently noted a role for MMP-3 in progression of carotid atherosclerosis [76]. MMP-3 is upregulated in infarcted human brain after IS [57], and high MMP-3 expression has been found to be associated with increased ischemic brain injury [77]. Clinical Role of MMP-3 Polymorphisms MMP-3 polymorphisms in both promoter and coding regions have been linked to IS incidence. However, the role of MMP-3 polymorphisms in clinical outcomes has remained controversial with conflicting associations between polymorphisms and IS incidence and clinical outcome when studied in different racial and ethnic populations [78]. A variant in the promoter region of the MMP-3 gene -1612 (5A vs. 6A) was found to regulate the transcription of MMP-3 [79,80]. This variant has been studied extensively with the 6A-allele found to be associated with carotid artery atherosclerosis (OR 1.58, 95% CI 1.08–2.33, p = 0.017) [62]. In a Finnish population, homozygotes for the 6A-allele appeared to be predisposed to arterial wall thickening [79]. Patients with the 6A/6A genotype in MMP-3 also had significantly greater intima-media thickness when compared to 5A/5A and 5A/6A genotypes (p < 0.03) [81]. Finally, Rundek et al. found patients with the 6A/6A genotype in MMP-3 to have significantly greater intima-media thickness in comparison to 5A/5A and 5A/6A genotypes (p = 0.044) [82] and therefore a potential predictor for atherosclerotic IS. The 6A-allele is associated with decreased MMP-3 expression [83]. Flex et al. found the 5A-allele of the -1171 (5A vs. 6A allele) promoter region polymorphism in MMP-3 was significantly associated with IS incidence in an Italian population (OR 2.2, 95% CI 1.1–4.2, p = 0.01) [84]. The 5A-allele has higher promoter activity and higher MMP-3 transcriptional activity [85]. However, this may not be true in a heterozygous state as no association between the -1171 5A/6A polymorphism and IS incidence was found in an Indian population [86]. Adding to the controversy, Sherva et al. studied MMP-3 polymorphisms in 39,114 patients from the United States, Canada, Puerto Rico, and US Virgin Islands in the Genetics of Hypertension Associated Treatment study and conversely found the 6A/6A genotype to be associated with higher IS incidence in patients randomized to take lisinopril for hypertension [87]. Association of IS incidence with polymorphisms in the coding regions of MMP-3 have been shown to be dependent on ethnicity. Three SNPs [rs520540 (Ala362Ala), rs602128 (Asp96Asp), and rs679620 (Lys45Glu)] in the coding region of MMP-3 were significantly associated with IS (p < 0.05) in a Korean population. However, haplotype analysis revealed that no combination of these SNPs was associated with IS [78]. Conversely, Matarin et al. found no association between IS incidence and rs520540 and/or rs679620 gene polymorphisms in a North American population of European descent [88]. To date, no MMP-3 polymorphisms have been associated with IS clinical outcome. 6. Matrix Metalloproteinase-9 MMP-9 (gelatinase B), is a 92 kDa protein that cleaves most ECM proteins, particularly collagen types IV [35,89] and V [90]. It also activates numerous pro-inflammatory cytokines and chemokines such as CXCL-8, interleukin 1β, and tumor necrosis factor-α. MMP-9 is involved in inflammatory responses trigged by ischemia. In addition, due to digestion of type IV collagen, it facilitates leukocytes transport across the endothelium. Moreover, by digesting occludins and claudins, MMP-9 plays an important role in BBB destruction. Finally, elevated intranuclear MMP-9 degrades poly-ADP-ribose polymerase-1 and X-ray cross-complementary factor 1 [91] and promotes accumulation of damaged DNA in neurons [92]. Thus, MMP-9 is involved in BBB destruction [93] and subsequent HT [94] that occurs particularly after thrombolysis [55,95]. MMP-9 is also involved in generation of free radicals that result in atherosclerotic lesions [96], plaque rupture in carotid atherosclerotic plaques [97], coronary artery plaque destabilization and rupture with subsequent myocardial infarctions [98,99], and embolic IS [100]. The high concentration of MMP-9 in plasma within the acute phase of IS increases the risk of HT within the ischemic core [101,102]. MMP-9 mRNA concentration was a predictor of poor outcome and mortality in IS [103]. Clinical Role of MMP-9 Polymorphisms Polymorphisms in the promoter and coding regions of MMP-9 have been associated with carotid atherosclerosis. Lin et al. found that post-menopausal women carrying T-alleles (C/T and T/T) at position -1562 in the promoter region of MMP-9 had stiffer arteries than patients with C/C genotypes even after adjusting for age and metabolic covariates [104]. Armstrong et al. evaluated the R279Q polymorphism (R vs. Q allele) and found that the R/R genotype resulted in significantly more carotid intima-media thickness when compared to the R/Q or Q/Q genotype (p = 0.006) [105]. Unsurprisingly, with evidence linking MMP-9 and its polymorphisms to carotid artery atherosclerosis, studies show that MMP-9 polymorphisms are also associated with increased IS incidence. Yuan et al. studied rs1056628 (A vs. C allele) in a Chinese population and noted dose-dependent increased expression of the C-allele (p < 0.01) and CC genotype (p < 0.05) in IS patients when compared to controls [106]. The promoter region -1562 (C vs. T allele) polymorphism was also shown to influence IS incidence with the T-allele associated with increased IS incidence (OR 1.543, 95% CI 1.144–2.080, p = 0.004) [72]. Although extensive evidence shows an association between MMP-9 expression and HT [94,102] particularly after administration of intravenous thrombolysis [55,95,107], the effect of MMP-9 polymorphisms on HT susceptibility is unclear. Zhang et al. evaluated the -1562 (C vs. T allele) promoter region polymorphism in a Chinese population and found that the C/C genotype was associated with significantly more HT compared to the C/T and T/T genotype (p = 0.037), and the frequency of the C-allele was significantly higher in patients with HT compared to the T-allele (p = 0.035) [108]. However, the -1562 C/T polymorphism did not play a role in HT after systemic thrombolysis in a Mediterranean population [109]. Despite extensive evidence linking MMP-9 expression with worse clinical outcome in IS, the role of MMP-9 polymorphisms remains unclear [110] and to date, no MMP-9 polymorphisms have been associated with clinical outcome in IS [73]. 7. Matrix Metalloproteinase-12 Macrophage metalloelastase (MMP-12) is secreted as a 54 kDa pro-form protein that undergoes self-activation through autolytic processing to produce 45 kDa and 22 kDa active forms. MMP-12 plays a significant role in the pathophysiology of IS. It displays a broad substrate specificity, including degradation of the extracellular proteins—fibronectin, laminin, elastin, vitronectin, type IV collagen, and heparan sulfate [111,112,113]. Thus, MMP-12 enables macrophages to penetrate injured tissues during inflammation and facilitates monocytes transmigration across the endothelium [111,112]. In addition to digesting basement membrane components, MMP-12 also activates MMP-2 and MMP-3 [114], thus, synergistically activating a cascade of proteolytic processes, which ultimately lead to BBB disruption. Clinical Role of MMP-12 Polymorphisms Similar to other MMPs, MMP-12 polymorphisms have been found to play a role in the development of carotid atherosclerosis, which may be a potential mechanism in the association between certain alleles and IS incidence. The MMP-12 polymorphism rs660599 was found to be significantly overexpressed in patients with carotid plaques compared to atherosclerosis-free controls (p = 1.2 × 10−15) [115]. Chehaibi et al. suggested that the MMP-12 promoter polymorphism rs2276109 (A vs. G allele) may be a risk factor for IS in diabetic patients. They reported that the MMP-12 rs2276109 AA genotype was significantly associated with IS incidence in a Tunisian population (OR 1.79, 95% CI 1.29–2.69, p = 0.001). This relationship, however, was not found to be replicated in a non-diabetic population. The A-allele of the MMP-12 rs2276109 polymorphism (A vs. G allele) has been shown to have a higher affinity for the transcription factor activator protein-1 [116], resulting in increased expression of MMP-12 [61]. Similarly, the MMP-12 rs652438 (357 Asn/Ser) polymorphism, located in the coding region of the hemopexin domain that is responsible for MMP-12 activity [117], was found to be associated with IS incidence in diabetic individuals (adjusted OR 1.68, 95% CI 1.22–2.56, p = 0.001) [61]. However, to date, no direct association between MMP-12 polymorphisms and clinical outcome in IS has been reported. 8. Conclusions The clinical roles for MMP polymorphisms are complex due to multifactorial mechanisms. Progression of carotid artery atherosclerosis (Table 1) represents a major pathophysiological mechanism responsible for IS incidence. However, as shown when comparing MMP polymorphisms responsible for IS incidence (Table 2) with polymorphisms responsible for carotid artery atherosclerosis, these polymorphisms do not necessarily lead to straightforward associations. Similarly, the multifactorial mechanisms and past medical history associated with clinical outcome after IS makes targeting the association between specific polymorphisms and clinical outcome extremely difficult (Table 3). Although several MMPs have a well-established relationship with HT, there is a less clear relationship between certain MMP polymorphisms and HT (Table 4). 9. Future Studies of Investigation The classification of MMPs into their respective roles represents a challenging question for future study. First, individual MMPs must be studied further to evaluate for their respective roles in CVD pathology. The most important and widely-studied MMPs appear to be MMP-2, 3, 9, and 12. Second, once the important MMPs are targeted, the functional genetic variations of each of these MMPs must be addressed. Third, the role of these MMP polymorphisms in protein activity and transcriptional activity must be better characterized. Fourth, the association between these polymorphisms and ethnic disparities in clinical outcome after stroke must be related [8]. And finally, as ubiquitous proteins that appear to have both pathological and neuroprotective roles [118], the mechanism of action of these MMPs must be better characterized. Author Contributions Jason J. Chang study concept and design, writing of manuscript and revision, critical revision of manuscript for important intellectual content. Ansley Stanfill critical revision of manuscript for important intellectual content. Tayebeh Pourmotabbed study concept and design, critical revision of manuscript for important intellectual content. Conflicts of Interest Jason J. Chang reports no disclosures. Ansley Stanfill reports no disclosures. Tayebeh Pourmotabbed reports no disclosures. ijms-17-01323-t001_Table 1Table 1 Matrix metalloproteinase (MMP) polymorphism relation to carotid artery atherosclerosis and intima-media thickness. MMP Studied Polymorphism Studied Gene Region Population and/or Location Findings Transcriptional Activity Reference MMP-1 -1607 (1G/2G) Promoter Tunisian No difference Increased with 2G-allele [62] MMP-3 5A/6A Promoter Tunisian 6A-allele associated with ICA atherosclerosis * Decreased with 6A-allele [62] MMP-3 5A/6A Promoter Unknown 6A-allele associated with ICA atherosclerosis ± Decreased with 6A-allele [81] MMP-3 -1612 (5A/ 6A) Promoter Hispanic, Black, and White 6A/6A genotype associated with greater carotid atherosclerosis ± Decreased with 6A-allele [82] MMP-9 -1562 (T/C) Promoter Chinese, post-menopausal women T-allele associated with stiffer arteries ± Increased with C-allele [104] MMP-9 R279Q (R/Q) Coding German R/R genotype associated with greater mean carotid intima-media thickness * n/a [105] MMP-12 -rs660599 Coding Unknown SNP overexpressed in patients with carotid plaques * n/a [115] * Association found via logistic regression analysis; ± Association found via multivariable linear regression analysis; n/a = not applicable. ijms-17-01323-t002_Table 2Table 2 MMP polymorphism relation to ischemic stroke incidence. MMP Studied Polymorphism Studied Gene Region Population and/or Location Finding Transcriptional Activity Reference MMP-1 -1607 (1G/2G) Promoter Tunisian No difference Increased with 2G-allele [61] MMP-2 -1306 (C/T) Promoter Chinese C-allele associated with leukoaraiosis * Increased with C-allele [71] MMP-2 -1306 (C/T) Promoter Chinese No difference Increased with C-allele [72] MMP-2 -735 (C/T) Promoter Chinese C-allele associated with greater incidence of IS * Increased with C-allele [72] MMP-3 -1171 (5A/6A) Promoter Italian 5A-allele associated with greater incidence of IS * Increased with 5A-allele [84] MMP-3 -5A/6A Promoter Multi-racial 6A/6A genotype associated with greater incidence of IS in patients taking lisinopril Increased with 5A-allele [87] MMP-3 -rs520540 (G/A) Coding Korean G-allele associated with greater incidence of IS n/a [78] MMP-3 -rs520540 (G/A) Coding Unknown No difference n/a [88] MMP-3 -rs3025058 (5A/6A) Coding Korean G/G and G/A genotype associated with greater incidence of IS n/a [78] MMP-3 -rs3025058 (5A/6A) Coding Unknown No difference n/a [88] MMP-3 -rs679620 (G/A) Coding Korean G-allele associated with greater incidence of IS in women only n/a [78] MMP-3 -rs679620 (G/A) Coding Unknown No difference n/a [88] MMP-3 -rs602128 (C/T) Coding Korean C-allele associated with greater incidence of IS n/a [78] MMP-9 -rs1056628 (A/C) Coding Chinese C/C genotype associated with greater incidence of IS n/a [106] MMP-9 -1562 (C/T) Promoter Chinese T/T genotype associated with greater incidence of IS * Increased with T-allele [72] MMP-12 -rs2276109 [82] (A/G) Promoter Tunisian A/A genotype associated with greater incidence of IS * Increased with A-allele [61] MMP-12 -rs652438 [1082] (A/G) Promoter Tunisian A/A genotype associated with greater incidence of IS * Increased with A-allele [61] MMP-12 -rs660599 Coding Unknown Replication of SNP associated with greater incidence of IS with large-artery mechanism * n/a [115] IS = ischemic stroke. SNP = single nucleotide polymorphism; * Association found via logistic regression analysis; n/a = not applicable. ijms-17-01323-t003_Table 3Table 3 MMP polymorphism relation to clinical outcome. MMP Studied Polymorphism Studied Gene Region Population and/or Location Finding Transcriptional Activity Reference MMP-2 -rs2241145 (G/C) Intronic Portuguese G-allele associated with improved clinical outcome * n/a [73] MMP-2 -rs1992116 (G/A) Intronic Portuguese G-allele associated with improved clinical outcome * n/a [73] * Association found via logistic regression analysis; n/a = not applicable. ijms-17-01323-t004_Table 4Table 4 MMP Polymorphism relation to hemorrhagic transformation. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081324ijms-17-01324ArticleGenes Expressed Differentially in Hessian Fly Larvae Feeding in Resistant and Susceptible Plants Chen Ming-Shun 12*Liu Sanzhen 3Wang Haiyan 4Cheng Xiaoyan 2El Bouhssini Mustapha 5Whitworth R. Jeff 2Maffei Massimo Academic EditorBarbero Francesca Academic Editor1 Hard Winter Wheat Genetics Research Unit, USDA-ARS, 4008 Throckmorton, Kansas State University, Manhattan, KS 66506, USA2 Department of Entomology, Kansas State University, Manhattan, KS 66506, USA; xycheng@ksu.edu (X.C.); jwhitwor@k-state.edu (R.J.W.)3 Department of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA; liu3zhen@ksu.edu4 Department of Statistics, Kansas State University, Manhattan, KS 66506, USA; hwang@ksu.edu5 International Center for Agricultural Research in the Dry Area, Rabat 10106, Morocco; M.BOHSSINI@cgiar.org* Correspondence: mchen@ksu.edu or ming-shun.chen@ars.usda.gov; Tel.: +1-785-532-471912 8 2016 8 2016 17 8 132417 6 2016 05 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).The Hessian fly, Mayetiola destructor, is a destructive pest of wheat worldwide and mainly controlled by deploying resistant cultivars. In this study, we investigated the genes that were expressed differentially between larvae in resistant plants and those in susceptible plants through RNA sequencing on the Illumina platform. Informative genes were 11,832, 14,861, 15,708, and 15,071 for the comparisons between larvae in resistant versus susceptible plants for 0.5, 1, 3, and 5 days, respectively, after larvae had reached the feeding site. The transcript abundance corresponding to 5401, 6902, 8457, and 5202 of the informative genes exhibited significant differences (p ≤ 0.05) in the respective paired comparisons. Overall, genes involved in nutrient metabolism, RNA and protein synthesis exhibited lower transcript abundance in larvae from resistant plants, indicating that resistant plants inhibited nutrient metabolism and protein production in larvae. Interestingly, the numbers of cytochrome P450 genes with higher transcript abundance in larvae from resistant plants were comparable to, or higher than those with lower transcript abundance, indicating that toxic chemicals from resistant plants may have played important roles in Hessian fly larval death. Our study also identified several families of genes encoding secreted salivary gland proteins (SSGPs) that were expressed at early stage of 1st instar larvae and with more genes with higher transcript abundance in larvae from resistant plants. Those SSGPs are candidate effectors with important roles in plant manipulation. plant resistanceeffectorscompatible interactionincompatible interactionsalivary glandscytochrome P450RNA-sequencinggall midge ==== Body 1. Introduction The Hessian fly, Mayetiola destructor, is a parasitic pest of wheat plants and causes serious yield loss in nearly all wheat regions in USA, North Africa, and Europe [1,2,3]. Like biotrophic plant pathogens, the Hessian fly interacts with wheat in a typical gene-for-gene relationship, namely, for every resistance (R) gene in the host plant, there is a corresponding avirulence (Avr) gene in the pathogen or insect parasite [4,5]. So far, 34 R genes have been identified and are named H1 to H32, Hdic, and H34 [6]. All 34 R genes are dominant except h4, which is recessive, and all are single effective genes except for H7H8, which need to be together to be effective. No Hessian fly R gene in wheat has been cloned and characterized. However, several lines of evidence suggest that Hessian fly R genes share structural similarity with plant disease R genes and belong to the super-group of receptor-like kinases which possess a nucleotide binding site and leucine zip repeats. Two Hessian fly Avr genes have been cloned and characterized [7,8]. These Avr genes encode secreted proteins that are likely injected into plant tissue through saliva during feeding. Except for the presence of a typical secretion signal peptide, the structures of the two identified Avr proteins are completely different, and therefore, are likely to perform different functions. In order to live and develop in a host plant, Hessian fly larvae need to manipulate the host plant extensively, including inhibiting plant growth, inducing the formation of nutritive cells, and preventing secondary infestation by microbes that may kill the plant [9,10]. The exact molecular mechanisms for Hessian fly larvae to manipulate host plants is not known. However, several lines of evidence suggest that Hessian fly larvae manipulate plants through the secretion of saliva that contains effector proteins. Through analyses of dissected salivary glands and genome sequencing, nearly two thousand genes were identified encoding secreted salivary gland proteins (SSGPs). The large number of SSGP-encoding genes can be classified into super-families and families based on their evolutionary relationship. A few families of SSGP genes are very big with more than 500 members. Evidence also suggests that SSGP genes are frequently duplicating and diversifying. Members from the same super-family or family share a so called unconventional conservation pattern, in which the 5′- and 3′-untranslated regions, the region encoding the secretion signal peptide, and introns are highly conserved whereas the regions encoding mature proteins are highly diversified [11,12]. This type of unconventional conservation is likely formed under high selection pressure that selects mutated SSGPs, which may allow Hessian fly to overcome plant resistance. Indeed, plant resistance to Hessian fly conferred by individual R genes is short-lived, lasting for only 6–8 years after an R gene is deployed to the field [13]. Even though numerous SSGP genes have been identified, the specific function and importance of individual SSGP genes that allows Hessian fly to parasitize wheat plants are not yet known. To identify potential key SSGP genes for further research, we hypothesize that the expression of those putative effector genes critical for Hessian fly to manipulate host plants should be elevated when Hessian fly larvae feed in resistant plants. Therefore, the first objective of this study is to examine SSGP genes expressed differentially between Hessian fly larvae feeding in resistant plants and those feeding in susceptible plants via RNAseq, and to identify potentially critical effector genes for further genetic research. Hessian fly is mainly controlled by developing and deploying resistant wheat cultivars. Resistance in wheat to Hessian fly is by antibiosis, namely, Hessian fly larvae become physically inactive in resistant wheat after 4–5 days and eventually die before developing into second instars [3,5]. The exact mechanisms that cause Hessian fly larvae to die in resistant plants is not yet known. Different types of defensive, toxic chemicals such as protease inhibitors, reactive oxygen species, toxic lectins, and secondary metabolites were induced specifically in resistant plants upon Hessian fly larval attack [14,15,16,17,18,19,20]. Therefore, Hessian fly larvae may die due to the toxicity of the defense chemicals from host plants. On the other hand, larvae from resistant plants can survive once they are transferred onto susceptible plants [19]. The cell wall is promptly strengthened in resistant plants in response to Hessian fly larval attack, and no nutritive cells are formed at the feeding site in these plants [10,19]. These observations indicate that the death of Hessian fly larvae in resistant plants may be due to the lack of nutrients. We hypothesize that changes in gene expression in larval feeding in resistant plants reflect differences in insect physiology, and identification of those genes will reveal the mechanism for Hessian fly larval death. Therefore, the second objective of this study is to identify changes in gene expression and pathways that could help to infer molecular mechanisms for Hessian fly larval death in resistant plants. 2. Results Samples from 0.5-, 1-, 3-, and 5-day larvae feeding in either resistant (wheat cultivar “Molly”) or susceptible (wheat cultivar “Newton”) plants were subjected to mRNA sequencing (RNA-Seq) using the Illumina HiSeq2000 sequencing platform (Illumina, San Diego, CA, USA). Three biological replicates for each time point were conducted. On average 33.5 million of 2 × 101 bp paired-end raw reads per sample were obtained. The raw reads were processed as described previously [21], and were mapped to the Hessian fly draft genome sequence (Mdes20100623, https://i5k.nal.usda.gov/Mayetiola_destructor) [22] via GSNAP, an intron-aware aligner [23]. A total of 18,593 informative gene models (defined in the Methods and referred to as genes hereafter) were identified. 2.1. Changes in Overall Gene Expression Transcript abundance of informative genes was compared pair-wise between samples from larvae in resistant plants and those in susceptible plants at the same time. At 0.5, 1, 3, and 5 days, 11,832, 14,861, 15,708, and 15,071 genes were informative for the comparisons between larvae feeding in resistant versus susceptible plants, respectively. The transcript abundance corresponding to 5401, 6902, 8457, and 5202 of the informative genes exhibited significant differences between these paired samples at 5% false discovery rate (FDR). Further analysis revealed that the percentages of genes with higher transcript abundance in larvae feeding in resistant plants were slightly lower than the percentages of genes with lower transcript abundance, except for the sample from 5-day larvae, in which a much greater percentage of genes exhibited higher transcript in the larval sample from resistant plants (Figure 1). In addition, the transcript abundance of the informative genes was also compared pair-wise between two successive insect stages fed on the same host, namely, samples from 1- versus 0.5-day larvae, 3- versus 1-day larvae, and 5- versus 3-day larvae fed in either resistant or susceptible plants, respectively (Figure 1B). The numbers of informative genes were 11,138, 15,530, and 16,532 for the comparisons between 1- versus 0.5-day, 3- versus 1-day, and 5- versus 3-day larvae feeding in resistant plants; and 10,957, 15,635, and 14,886 for the comparisons between 1- versus 0.5-day, 3- versus 1-day, and 5- versus 3-day larvae feeding in susceptible plants. Transcript abundance corresponding to 1143, 3640, and 4038 of the informative genes exhibited significant differences (5% FDR) between samples of 1- versus 0.5-day, 3- versus 1-day, and 5- versus 3-day of larvae feeding in resistant plants. Transcript abundance corresponding to 821, 7254, and 6080 of the informative genes exhibited significant differences (5% FDR) between samples of 1- versus 0.5-day, 3- versus 1-day, and 5- versus 3-day larvae feeding in susceptible plants, respectively. For the larvae feeding in resistant plants, the percentages of genes with significant changes in expression increased gradually as larvae advanced into later stages. Larvae feeding in susceptible plants, however, had percentages of genes with significant changes in gene expression increased dramatically when larvae advanced from 1- to 3-days. 2.2. Changes in Gene Expression in Specific Functional Categories To determine what types of genes were up- or down-regulated between samples, the 18,593 informative genes were annotated based on BLASTX search results as described previously [21], and were updated when this paper was prepared. Based on updated gene annotation, 10,477 (56.3%) of the informative genes had matches with Genbank sequences with E-values ≤ 1 × 10−30. Among the matched genes, 9760 (53.5% of the total informative genes) had known functions, whereas the remaining (3.9%) had unknown functions (Table S1). Genes with known functions were divided into eight functional categories based on their Gene Ontology (GO) terms (Table S1): “nutrient metabolism” (1109, 11.4%), “reduction/oxidation (redox) and detoxification” (129, 1.3%), “structure and adhesion” (579, 5.9%), “RNA metabolism” (551, 5.6%), “protein metabolism” (1247, 12.8%), “transport” (1140, 11.7%), “regulatory proteins” (3320, 34.0%), and “SSGPs” (1685, 17.3%). Each category was further divided into subcategories (see below). Changes in gene expression in specific functional categories are shown in Figure 2, Tables S2, and S3. Genes in each functional category exhibit a different pattern (percentages) with greater or lesser transcript abundance between samples of larvae from resistant versus susceptible plants (Figure 2, left panels). The category with the most differences between larvae feeding in resistant versus susceptible plants is “RNA metabolism”. This category had over 30% of the genes exhibiting lower transcript abundance in the sample from 0.5-, 1-, and 3-day larvae from resistant plants, compared to the corresponding samples from susceptible plants, whereas less than 5% of the genes exhibited higher transcript abundance in the corresponding samples from resistant plants. Interestingly, the percentages of genes with either greater or lesser transcript abundance in 5-day larvae from resistant versus susceptible plants were similar. Three other categories including “protein metabolism”, “nutrient metabolism”, and “SSGPs” had much greater percentages of genes that exhibited lesser transcript abundance in 0.5-, 1-, and 3-day larval samples from resistant plants. An opposite pattern was observed in the category “regulatory proteins”, in which greater percentages of genes exhibited higher transcript abundance in all samples from resistant plants compared to corresponding samples from susceptible plants. The category ‘transport’ also exhibited greater percentages of genes with greater transcript abundance in all samples except for the 5-day samples from resistant versus susceptible plants, even though the differences between greater and lesser percentages were smaller. In the category “structure and adhesion”, there were also greater percentages of genes which exhibited greater transcript abundance in the 0.5- and 1-day larval samples from resistant plants than those from susceptible plants. However, the situation was reversed and the opposite was observed in the samples from 3- and 5-day larvae. The gene patterns with differential expression seemed to be the results of varied expression dynamics between larvae feeding in resistant versus susceptible plants. As shown in the right panels in Figure 2, the dynamics of gene expression were distinct from category to category between samples from two successive larval stages feeding in either resistant or susceptible plants. 2.3. Changes in Gene Expression in Specific Functional Subcategories and Pathways Genes in each functional category were further divided into subcategories (Figure S1). The five subcategories with the most differences between the percentages of genes with greater and lesser transcript abundance are shown in Figure 3. These subcategories of genes are involved in “citric acid cycle & energy metabolism”, “tRNA synthesis”, “RNA transport”, “protein synthesis”, and “protein folding”. All five subcategories exhibited greater percentages of genes with lesser transcript abundance in larval samples from resistant plants than larval samples from susceptible plants. 2.4. Changes in Transcript Abundance of Cytochrome P450 Genes Cytochrome P450s (P450s) are one of the largest superfamilies of genes involved in a wide range of functions [24,25]. Many insect P450s are involved in detoxification of toxic chemicals ingested from host plants or produced endogenously [26,27,28]. Sixty-one P450 genes have been identified from the Hessian fly genome. Differences in transcript abundance of these genes between larvae feeding in resistant plants and those feeding in susceptible plants are shown in Figure 4A. The pattern of transcript abundance was strikingly different from that observed previously in Figure 3. For P450 genes, there were 8%–10% more genes with greater transcript abundance in 0.5- and 5-day larvae from resistant plants compared with the corresponding samples from susceptible plants. Figure 4B shows 8%–10% of P450 genes with greater transcript abundance between 3- versus 1-day and 5- versus 3-day larvae from resistant plants, indicating that the expression of P450 genes increased as larvae survived in resistant plants. 2.5. Changes in Transcript Abundance of Secreted Salivary Gland Proteins (SSGPs) Genes The left panel of Figure 5 shows the percentages of selected SSGP gene families with greater or lesser transcript abundance between larvae from resistant plants versus those from susceptible plants. Each SSGP gene family exhibited a different pattern. For family 9, significantly more genes showed higher transcript abundance in 1-, 3-, and 5-day larvae from resistant plants versus larvae from susceptible plants. For families 8, 16, and 5, more genes exhibited greater transcript abundance in 3- and 5-day larvae from resistant plants. For families 14 and 4, more genes exhibited lower transcript abundance in 1- and 3-day larvae from resistant plants. For family 3, more genes exhibited lower transcript abundance in 0.5-, 1-, and 3-day larvae from resistant plants. For family 71, much higher percentages of genes exhibited lower transcript abundance in all stage larvae from resistant plants. The right panel of Figure 5 shows the percentages of SSGP genes with greater or lesser transcript abundance when two successive larval stages from the same type of host plants were compared. Again the pattern was distinct from gene family to gene family. 3. Discussion During the long course of co-evolution, insects and host plants have formed intimate relationships, particularly for parasitic insect species. Insect attacks on plants cause extensive changes in gene expression in host plants. Identification of the changes in gene expression in host plants after an insect attack can reveal plant defense mechanisms in response to insect infestation [19]. Extensive studies have been carried out to examine differential gene expression between resistant and susceptible plants upon insect attacks through various high throughput technologies such as microarrays and RNAseq [16,29,30]. In turn, plant defensive reactions to insects may cause significant changes in gene expression in insects, and therefore, identification of the insect changes in gene expression may reveal their adaptation strategies to plant defense and death mechanisms on resistant plants. So far, very little is known about genome-wide changes in gene expression between insects feeding on resistant and susceptible plants [31]. The wheat—Hessian fly interaction results in extreme outcomes for either the infested plant or the attacking insect. During a compatible interaction, plant physiology is manipulated extensively by a Hessian fly larva, including complete inhibition of wheat growth, induction of nutritive cell formation at the feeding site, and eventual death of the plant [9]. Biochemically, growth-oriented metabolism in a susceptible plant is engineered to nutrient-accumulation-oriented metabolism by the Hessian fly, resulting in extensive pathway changes in the plant [32]. An insect feeding in a susceptible plant, however, grows normally and changes in gene expression proceed according to a development regulatory regime. During an incompatible interaction, plants resume normal growth after some initial growth deficit following the Hessian fly attack [33]. Biochemically, nutrient and energy metabolism is temporarily suppressed, whereas various plant defense pathways are activated, including the strengthening of cell walls and elevation of toxic chemicals such as reactive oxygen species, protease inhibitors, lectins, and secondary metabolites [14,15,16,17,18,19,20]. On the other hand, insects feeding in a resistant plant become inactive in 4–5 days and eventually die without developing into the second instar. Significant changes should be expected in metabolism and gene expression to adapt and fight against plant defense in larvae feeding in a resistant plant. Unfortunately, genome-wide data is not yet available on larvae feeding in resistant plants. In this study, we have systematically analyzed changes in gene expression genome-wide through three-way comparisons: differences in gene expression between larvae feeding in a resistant plant versus those feeding in a susceptible plant at the same time; differences in gene expression between larvae feeding on resistant plants but at a different time; and differences in gene expression between larvae feeding in susceptible plants but at a different time. Our data revealed that overall 10%–18% of genes exhibited either greater or lesser transcript abundance in larvae feeding in resistant plants versus those feeding in susceptible plants. The proportions of genes with greater and lesser transcript abundance is relatively balanced except in 5-day larvae, which had more genes with greater transcript abundance in larvae from resistant plants. These differences were apparently caused by dynamic changes in gene expression in larvae due to feeding in either resistant or susceptible plants. When the genes were classified into different functional categories and subcategories, more genes exhibited lesser transcript abundance in larvae from resistant plants, in the categories “nutrient metabolism”, “RNA metabolism”, and “protein metabolism” (Figure 2), particularly those genes involved in “tRNA synthesis”, “RNA transport”, “protein synthesis”, “protein folding”, and “citric acid cycle and energy metabolism” (Figure 3). Genes in the subcategories “tRNA synthesis”, “RNA transport”, “protein synthesis” and “protein folding” participate in protein synthesis either directly or indirectly. Genes in the subcategory “citric acid cycle and energy metabolism” participate in the production of energy and intermediates. Lower level expression of genes involved in citric acid cycle and protein synthesis would result in lower levels of energy, metabolic intermediates, and proteins available for larval growth and development. The reduction in the available supply of energy, intermediates, and proteins probably explains why Hessian fly larvae fail to develop into second instars before dying in resistant plants. In contrast to genes involved in nutrient metabolism and protein synthesis, the number of P450 genes with greater transcript abundance was less different from the number of P450 genes with lower transcript abundance between larvae from resistant versus susceptible plants (Figure 4A). In fact, more P450 genes had greater transcript abundance than those with reduced transcript abundance in 0.5- and 5-day larvae from resistant plants, and more P450 genes were induced when larvae developed from 1- to 3-days and from 3- to 5-day again in resistant wheat (Figure 4B). Phylogenetic analysis revealed that the P450 genes with reduced transcript abundance in larvae from resistant plants are mainly distributed in two clusters, whereas the P450 genes with greater abundance are scattered in different clusters (Figure S2). We speculate that the P450s with less transcript abundance in larvae from resistant plants participate in internal physiological development processes, whereas the P450s with greater transcript abundance are likely to play important roles in counteracting host defense. P450s neutralize toxic chemicals which are either produced endogenously or ingested orally and perform a wide range of other physiological functions [24,25,26,27,28]. Our postulation is consistent with the fact that many P450 genes are expressed either exclusively or predominantly in Hessian fly larval midgut [34]. Greater expression levels of P450 genes were also found in soybean aphids feeding on resistant soybean plants [31]. In addition to P450s, a gene encoding a functionally-related enzyme, flavin-containing monooxygenase (dimethylaniline monooxygenase, Mdes007374), showed consistently high transcript abundance from larvae feeding in resistant plants (Table S2). Flavin-dependent monooxygenases are found as a detoxification mechanism in other insect species [35]. Similarly, several genes including Mdes009489 and Mdes007220, which encode protease inhibitors, exhibited transcript abundance in insects from resistant plants (Table S2). These inhibitors could suppress wheat proteases involved in immunity to avoid host defense reactions in response to herbivore attack [36]. Overall our data suggest that toxic defense play an important role in wheat resistance to Hessian fly larvae. We speculate that toxic chemicals slow down insect feeding, which gives resistant plants more time to strengthen cell walls and other host defenses, resulting in the eventual death of Hessian fly larvae due to lack of nutrients. Thus, the death of Hessian fly in resistant plants could be a combination of toxic defense and the strengthening of cell walls [19,20]. Young Hessian fly larvae produce and likely inject a large number of SSGPs into host plants during feeding [11,12,22,37]. One of the objectives of this study was to identify critical candidate effector proteins that are crucial for host manipulation. Based on our data, members in families 9, 8, and 16 are likely important for Hessian fly larvae to manipulate host plants. First, many members in these families exhibited greater transcript abundance in larvae from resistant plants (Figure 5). Since Hessian fly larvae feed inside wheat plants and cannot migrate, the only choice the larvae have in response to plant defense is to secrete more critical effectors into plants in order to survive. Therefore, those putative effector genes with greater levels of expression are likely critical for plant manipulation. Second, most members in these families of SSGPs are so-called early genes, namely they were expressed most abundantly in 0.5-day larvae, and expression levels then decreased as the larvae advanced into later stages (the right panel of Figure 5). Host plant manipulation is irreversible in the Hessian fly—wheat interaction [9]. We hypothesize that Hessian fly larvae inject early effectors into plants for host manipulation, and then stop producing these effectors when the larvae sense that plant manipulation has been accomplished. When larvae feed in resistant plants, they fail to manipulate plants, and therefore, continue to produce these effectors over a long time. In contrast to families 9, 8, and 16, genes in families 3 and 71 exhibited different expression patterns. Most members in these two families exhibited lower transcript abundance in 1- and 3-day larvae from resistant plants, and these genes belong to so called late genes, which were expressed most abundantly in 3-day larvae from susceptible plants. We speculate that these late SSGPs are injected into host plants for other functions, and one of the possible functions for the late effectors is to protect the attacked host plants from secondary infestation or infection. Susceptible plants become physically weak after Hessian fly attack, and therefore are vulnerable to microbes in the surrounding environment. Hessian fly larvae are parasites and when plants die, insects in the plants also die. SSGP gene families are large in Hessian fly, with >500 members in a family. Different family members are highly diversified and therefore could perform entirely different functions among family members. Members within the same family have exhibited different expression patterns. Therefore, further research with individual genes in different families is still needed to confirm their roles in host plant manipulation. 4. Materials and Methods 4.1. Insect Hessian fly biotype GP was used in this study. The population was derived from a colony collected in Scott County, Kansas, in 2005 [38]. The colony has been maintained on the wheat cultivar “Karl 92” in the greenhouse since then. 4.1.1. Wheat Cultivars, Infestation, and Sample Collection Two isogenic wheat lines, “Newton” and “Molly”, were used as the host plants for Hessian fly infestation and sample collection. Hessian fly biotype GP was used in this study. Newton is a winter wheat line susceptible to Hessian fly, whereas Molly is a Hessian fly-resistant line (containing the resistance gene H13) derived from Newton through seven cycles of backcrossing [39]. For infestation and sample collection, 20 germinated wheat seeds were planted in 10-cm-diameter pots filled with PRO-MIX “BX” potting mix (Hummert Inc., Earth City, MO, USA) in a growth chamber programmed at 20:18 °C (Light:Dark) with a photoperiod of 14:10 (L:D) h. When wheat seedlings reached the 1.5 leaf stage (stage 11 on Zadoks scales), the plants were infested with an average 0.5 Hessian fly females per plant by confining the adult flies in a screened cage. After five days, eggs hatched into neonates that migrated into wheat plants. When the first larva was found at the feeding site, the time was set at zero and larval age started from that time. Larvae were collected at day 0.5, 1, 3, and 5, by dissecting plants to expose the insects. Since larvae become inactive in resistant plants, no samples were compared beyond this stage. The dissected plants were soaked in water in a micro-centrifuge. After enough insects were collected in a tube, the water was removed and larvae frozen in liquid nitrogen for RNA extraction. Three independent replicates were carried out for each time point. 4.1.2. RNA Extraction and Quantification Total RNA was extracted using TRI reagent (Molecular Research Center Inc., Cincinnati, OH, USA), according to the manufacturer procedures. RNA concentrations were determined using a Nanodrop ND-2000 spectrophotometer (NanoDrop Technologies Inc., Wilmington, DE, USA). Quality of the RNA samples was further confirmed by analyzing the integrity of the samples on an Agilent TapeStation Bioanalzer (Agilent Technologies, Palo Alto, CA, USA). 4.1.3. RNA Library Construction and Sequencing mRNA was selected through a oligo-dT column. Libraries were constructed from purified mRNA samples following the Illumina’s sample preparation instructions (Illumina, San Diego, CA, USA). Briefly, ~20 µg of total RNA from each sample was digested with DNase I (Sigma, St. Louis, MO, USA) to remove potential DNA contamination. mRNA was then purified by oligo(dT) magnetic beads and fragmented into 100–400 bp fragments. cDNA was produced from the RNA fragments using reverse transcriptase (Invitrogen, Carlsbad, CA, USA) with random hexamers as primers. An Agilent TapeStation Bioanalzer (Agilent Technologies, Palo Alto, CA, USA) was used to qualify and quantify the libraries. Libraries were sequenced using an Illumina HiSeq2000 system (Illumina Inc.). 4.1.4. Analysis of RNA-Seq Data Illumina sequence reads were processed to remove adaptors using Trimmomatic (version 0.32) [23] and the resulting reads were aligned to the Hessian fly draft genome sequence (https://i5k.nal.usda.gov/) [22] using Genomic Short-read Nucleotide Alignment Program (GSNAP) [40]. Uniquely aligned reads were used to determine the read depth per annotated gene in each sample by an in-house Perl script. To test the null hypothesis that no difference in gene expression existed of each gene between two groups, the generalized linear model method, assuming negative binomial distribution of read counts implemented in the DESeq2 package (version 1.4.5), was used to compute a p-value for each gene [41]. The parameters of “Independentfiltering = yes” in DESeq2 were used to filter genes that were unlikely to be differentially expressed. The genes left from the filtering were kept as informative genes. An FDR (false discovery rate) approach was adapted to convert p-values to q-values to account for multiple tests [42]. Genes with q-values no larger than 5% were declared to be differentially expressed. 4.2. BLASTX Search to Annotate Transcripts Sequences of a set of Hessian fly transcripts (n = 18,832) were searched against the GenBank non-redundant protein squence database (nr) using BLASTX to identify homologous hits. For each transcript, only the best hit with the E-value no larger than 1 × 10−30 was reported. 4.2.1. Classification of Genes According to Their Functions Based on the Genbank search results, the genes with known functions were divided into eight functional categories based on their GO terms (http://www.uniprot.org/uniprot/) [43]. The eight categories are “nutrient metabolism”, “reduction/oxidation (redox) and detoxification”, “structure and adhesion”, “RNA metabolism”, “protein metabolism”, “transport”, “regulatory proteins” and “SSGPs”. Each category was further divided into sub-categories, again based on their GO terms. The subcategories were described in the results section and in reference [21]. 4.2.2. qRT-PCR Validation of Transcript Abundance To validate changes observed in RNA-seq analyses, primers covering 12 representative transcripts selected from different categories were designed using the tool available on the website http://www.ncbi.nlm.nih.gov/tools/primer-blast/ (Figure S3). The primer sets were used for qRT-PCR analysis. DNase-treated RNA was used as template for cDNA synthesis using random hexamers with an iScript cDNA synthesis kit (BioRad, Hercules, CA, USA), according to the manufacturer’s guidelines. Samples were then treated with RNase H (Invitrogen). cDNA was quantified on a Nanodrop ND-2000 spectrophotometer (NanoDrop Technologies Inc.) and samples were diluted to 15 ng/µL to ensure equal amounts of cDNA template for quantification of mRNA abundance. qRT-PCR was conducted on an Applied Biosystems StepOne plus machine with SYBR Green I (Applied Biosystems, Foster City, CA, USA). The following parameters were used: 95 °C for 10 min, 40 cycles of 95 °C for 3 s and 60 °C for 30 s. Transcript abundance of the gene encoding a hexokinase (Mdes009091) was used as an endogenous control in qRT-PCR. This gene is expressed relatively equally in all stages according to RNA-seq data. Quantification of transcript levels detected by qRT-PCR was based on the Relative Standard Curve Method). Statistical significance for the log-transformed arbitrary expression values was analyzed by ANOVA using the PROCMIXED procedure of SAS (SAS institute Inc., SAS/STAT User’s Guide, Version 9.13). Data from three biological replicates (each replicate assayed two times in independent qRT-PCR experiments) were combined and included as a random effect in the analysis. Log fold-change calculations were performed by comparing transcript abundance of the selected genes in larvae feeding in resistant plants with transcript abundance for the same genes in larvae feeding in susceptible plants. Fold-change was considered statistically significant if the p-value was <0.05. Acknowledgments This paper is a joint contribution from the United States Department of Agriculture-Agriculture Research Service at Manhattan, Kansas, and the Kansas Agricultural Experiment Station. Mention of a commercial or proprietary product does not constitute an endorsement or recommendation for its use by the USDA. USDA is an equal opportunity provider and employer. The research was partially supported by a grant from the U.S. Department of Agriculture (USDA NIFA 2010-03741). Supplementary Materials Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1324/s1. Click here for additional data file. Author Contributions Ming-Shun Chen designed the experiments, analyzed the data, and wrote the paper. Sanzhen Liu and Haiyan Wang performed bioinformatic analysis and participated in preparation of the manuscript. Xiaoyan Cheng collected samples, analyzed data, and participate in writing. Mustapha El Bouhssini and R. Jeff Whitworth provided suggestions, contributed reagents and other financial support, and edited the manuscript. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Percentages of total genes with higher (blue bars, H) and lower (red bars, L) transcript abundance. (A) Percentages of genes with higher and lower transcript abundance between larvae feeding in resistant (Molly) and susceptible (Newton) plants at 0.5, 1, 3, and 5 days; (B) Percentages of genes with higher and lower transcript abundance between two successive stages of Hessian fly larvae feeding in either resistant (Molly) or susceptible (Newton) plants. Figure 2 Percentages of genes in different functional categories with higher (blue bars. H) and lower (red bars, L) transcript abundance between larvae feeding in resistant (Molly) and susceptible (Newton) plants at 0.5, 1, 3, and 5 days (left panels), and between two successive stages (1 versus 0.5 day, 3 versus 1 day, and 5 versus 3 days) of Hessian fly larvae feeding in either resistant (Molly) or susceptible (Newton) plants (right panels). Gene categories are marked on each pair of graphs. The numbers of genes in each category were given in parenthesis. Figure 3 Percentages of genes in selected subcategories with higher (blue bars, H) and lower (red bars, L) transcript abundance between larvae feeding in resistant (Molly) and susceptible (Newton) plants at 0.5, 1, 3, and 5 days (left panels), and between two successive stages (1 versus 0.5 day, 3 versus 1 day, and 5 versus 3 days) of Hessian fly larvae feeding in either resistant (Molly) or susceptible (Newton) plants (right panels). Gene subcategories are marked on each pair of graphs. The numbers of genes in each subcategory were given in parenthesis. Figure 4 Percentages of 61 cytochrome P450 genes with higher (blue bars, H) and lower (red bars, L) transcript abundance between larvae feeding in resistant (Molly) and susceptible (Newton) plants at 0.5, 1, 3, and 5 days (left panels), and between two successive stages (1 versus 0.5 day, 3 versus 1 day, and 5 versus 3 days) of Hessian fly larvae feeding in either resistant (Molly) or susceptible (Newton) plants (right panels). Figure 5 Percentages of secreted salivary gland protein (SSGP) genes in selected families with higher (blue bars, H) and lower (red bars, L) transcript abundance between larvae feeding on resistant (Molly) and two susceptible (Newton) plants at 0.5, 1, 3, and 5 days (left panels), and between two successive stages (1 versus 0.5 day, 3 versus 1 day, and 5 versus 3 days) of Hessian fly larvae feeding in either resistant (Molly) or susceptible (Newton) plants (right panels). 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081325ijms-17-01325ArticleSpectrophotometric Determination of Phenolic Antioxidants in the Presence of Thiols and Proteins Avan Aslı Neslihan 1Demirci Çekiç Sema 1Uzunboy Seda 1Apak Reşat 12*Battino Maurizio Academic Editor1 Department of Chemistry, Faculty of Engineering, Istanbul University, 34320 Istanbul, Turkey; neslihanavan@hotmail.com (A.N.A.); sema@istanbul.edu.tr (S.D.Ç.); sedauzunboy@hotmail.com (S.U.)2 Turkish Academy of Sciences (TUBA) Piyade St. No. 27, 06690 Çankaya Ankara, Turkey* Correspondence: rapak@istanbul.edu.tr; Tel.: +90-212-473-702812 8 2016 8 2016 17 8 132529 6 2016 05 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Development of easy, practical, and low-cost spectrophotometric methods is required for the selective determination of phenolic antioxidants in the presence of other similar substances. As electron transfer (ET)-based total antioxidant capacity (TAC) assays generally measure the reducing ability of antioxidant compounds, thiols and phenols cannot be differentiated since they are both responsive to the probe reagent. In this study, three of the most common TAC determination methods, namely cupric ion reducing antioxidant capacity (CUPRAC), 2,2′-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt/trolox equivalent antioxidant capacity (ABTS/TEAC), and ferric reducing antioxidant power (FRAP), were tested for the assay of phenolics in the presence of selected thiol and protein compounds. Although the FRAP method is almost non-responsive to thiol compounds individually, surprising overoxidations with large positive deviations from additivity were observed when using this method for (phenols + thiols) mixtures. Among the tested TAC methods, CUPRAC gave the most additive results for all studied (phenol + thiol) and (phenol + protein) mixtures with minimal relative error. As ABTS/TEAC and FRAP methods gave small and large deviations, respectively, from additivity of absorbances arising from these components in mixtures, mercury(II) compounds were added to stabilize the thiol components in the form of Hg(II)-thiol complexes so as to enable selective spectrophotometric determination of phenolic components. This error compensation was most efficient for the FRAP method in testing (thiols + phenols) mixtures. antioxidant capacity assaysCUPRACABTSFRAPthiol stabilizationHg(II)-thiol reaction ==== Body 1. Introduction There is a critical balance between reactive oxygen/nitrogen species (ROS/RNS) and antioxidants (AOx) in the human body. Although there are some important endogenous AOx sources, such as small molecule antioxidants (bilirubin, uric acid, glutathione, etc.) and enzymes (e.g., catalase, superoxide dismutase and glutathione reductase), substantial amounts of AOx are taken by foods. Polyphenols have a special importance among dietary antioxidants as being the most consumed phytochemicals. Plenty of foodstuffs and beverages can be mentioned as dietary polyphenol sources such as vegetables, fruits, juices, tea and coffee. Polyphenol intake is generally accepted to be effective on the prevention of oxidative stress-originated diseases, such as cardiovascular and neurodegenerative diseases and cancer. Polyphenols are usually accepted as antioxidants serving cell survival and contributing to the regulation of cellular redox status; they may interfere at the initiation, development, and progression of cancer through the modulation of certain cellular processes and signaling pathways [1]. However, they may also act as pro-oxidants under certain circumstances and help the prevention of tumor growth [2]. An antioxidant can be defined as a substance (small molecule or complex system) that, when added to an oxidizable molecule in small amounts, is able to protect such molecules by delaying, retarding, or inhibiting their autoxidation (where the protected substrate is usually a biomacromolecule, like a lipid, protein, or DNA) [3]. Though used interchangeably, “antioxidant activity” and “antioxidant capacity” terms are not identical, as the former deals with the rate, while the latter is concerned with the thermodynamic conversion of antioxidant action. Foti argues that in order to establish whether a test compound (AH) is a potent antioxidant or not, it is necessary to compare the rate at which AH quenches peroxyl radicals to the rate at which peroxyl radicals attack the substrate [4]. Valgimigli and Amorati are of the opinion that certain antioxidant activity measurement methods have different soundness and are often used with little consideration of the chemistry behind them, and that some yield numerical values or rankings of antioxidant performance not corresponding to physical/biochemical reality [5]. Total antioxidant capacity (TAC) is a useful parameter reflecting the cumulative effect of several antioxidants in complex matrices rather than individual antioxidative properties of relevant components. By definition, TAC should be equal to the sum of the antioxidant capacities of components constituting a mixture. TAC is usually measured by several spectrophotometric tests, but absorbances of mixture components is not always additive, and synergistic or antagonistic effects may frequently emerge as a result of interactions among several components. These different interactions can be explained by AOx structure and reactivity. Knowing the initial antioxidant capacity of a single component can give useful information to solve this interaction problem [6]. Wang et al. concluded that antioxidant interactions can not only result in positive effects but also could produce negative effects on the total antioxidant capacities of foods or diets [7]. It is natural that in vitro investigation of the antioxidant activity of a given antioxidant compound cannot provide enough knowledge about its bioavailability. Due to the complexity of foodstuffs, combinations of antioxidants may cause a great variety of interactions among them (such as “synergistic”, where the whole exceeds the sum, or “antagonistic”, whereas the whole stays behind the sum of individual antioxidative powers) and in vivo activities of bioavailability and metabolism may add more complex interactions to the existing ones. In a specific study on berry fruits, researchers reported that the TAC of suitable combinations of the tested samples were higher than the sum of the corresponding individual antioxidant capacities [8]. In another study, Wang et al. prepared different combinations of three fruits, four vegetables, and four legumes and they investigated the TAC values of these mixtures using ferric reducing antioxidant power (FRAP), 2,2-diphenyl-1-picryl-hydrazyl (DPPH•), and oxygen radical absorbance capacity (ORAC) methods. The authors reported some synergistic and antagonistic interactions, and more than half of these combinations gave additive results. Among the tested samples, mixtures of raspberry and adzuki bean extracts showed synergistic interactions in all three TAC determination methods [7]. In addition, Iacopini et al. investigated catechin, epicatechin, quercetin, rutin, and resveratrol in red grape using high performance liquid chromatography–ultra violet detector (HPLC–UV). They also investigated the reactive species scavenging ability of these compounds using DPPH and peroxynitrite (ONOO−). The researchers reported a potential for synergistic interaction towards ONOO− consumption for quercetin, rutin, and resveratrol combinations. On the other hand, they reported an additive effect between catechin and epicatechin [9]. In addition to in vitro TAC, some researchers were interested in in vivo effects of polyphenolic antioxidants. In a critical review, Fraga investigated the antioxidant action mechanism of these compounds under in vivo conditions with a thorough evaluation of free radical scavenging and metal chelating effects added to some protein and lipid interactions [10]. Although the exact mechanism of synergistic action of antioxidants is unknown, it may be speculated for lipid peroxidation and membrane protection that synergism arises when one antioxidant may spare or regenerate another during the course of oxidation (e.g., as seen in ascorbic acid and α-tocopherol pair, via ascorbate reduction of oxidized vitamin E back to the original α-tocopherol in vivo) [11]. Interactions between polyphenols and proteins have been highly investigated. Gilani et al. reported a non-covalent binding between polyphenols and proteins [12]. It is known that astringency in the polyphenol-rich foods, such as tea, is the consequence of interactions of certain polyphenols and salivary proteins [13]. Plant-derived food phenolic compounds interact with saliva proteins, presenting a great variety; especially, proline-rich proteins can bind phenolic compounds [14]. It is also known that resveratrol has weak water solubility, and to obtain a certain concentration, it should be bound to plasma proteins, such as human serum albumin (HSA) and hemoglobin (Hb) [15]. Dufor and Dangles reported flavonoid-HSA complexation in their study [16]. There are also different studies on interactions of flavonoids and food proteins such as ovalbumin, gelatin, α-lactalbumin, and milk proteins (β-casein, β-lactoglobulin); amino acid composition and protein conformation are the two important factors affecting these interactions [17]. Polyphenol-protein interactions can be reversible or irreversible. Irreversible interactions are usually a consequence of covalent bonding, whereas in reversible interactions, polyphenols and proteins are held together with non-covalent forces (hydrogen bonding, van der Waals forces, etc.) [18]. These interactions may affect the measured TAC. Arts et al. noted that the trolox equivalent antioxidant capacity (TEAC) antioxidant capacity of several components of green and black tea with α-, β-, and κ-casein or albumin was not additive, and that a part of the total antioxidant capacity was masked by protein-flavonoid interactions. They also observed a maximum masking effect in the presence of β-casein, epigallocatechin gallate, and gallate combinations, so they reported that the efficiency of antioxidants depends on sample matrix [19]. In another study, Lorenz et al. expressed an antagonistic interaction between tea polyphenols and milk proteins. As a result of these interactions, the authors concluded that consumption of tea with milk may reduce the beneficial effect of tea on vascular diseases, and that increasing milk content of epicatechin-containing chocolate may decrease its health assistance [20]. Likewise, Gallo et al. concluded with the help of mass spectrometry and antioxidant activity measurements that various milk protein fractions caused a decrease in the antioxidant activity of cocoa polyphenols [21]. Interactions of small molecule thiols with polyphenols were also investigated. Fujimoto and Masuda examined the interactions between polyphenols and thiols under radical oxidation conditions and studied resulting cross-coupling products using liquid chromatography–mass spectrometry (LC–MS) [22]. Boots et al. identified adducts between oxidized quercetin and glutathione (GSH), and reported that, in the absence of GSH, oxidized quercetin could give harm to some vital enzymes [23]. Awad et al. also investigated the formation of reversible glutathionyl flavonoid adducts [24]. Some antioxidant researchers argue that in physiologically-relevant antioxidant activity testing, there should be an oxidizable substrate (i.e., lipid, protein, DNA) whose oxidation inhibition by the antioxidant is to be measured. Direct (competitive) antioxidant assays involve a fluorogenic or chromogenic probe and biologically-relevant reactive species (i.e., ROS/RNS), whereas in indirect (non-competitive) antioxidant assays, physiological redox reactions are simulated on an artificial probe without a biologically-relevant reactive species [25]. In this regard, electron transfer-based antioxidant capacity assays like trolox equivalent antioxidant capacity (ABTS/TEAC), FRAP, and cupric ion reducing antioxidant capacity (CUPRAC) are non-competitive, using a single probe which changes or loses color upon reduction by antioxidants. Naturally, non-competitive TAC assays do not necessarily yield the same antioxidant ranking as physiologically-relevant antioxidant activity tests (e.g., inhibition of lipid peroxidation). For example, the CUPRAC test gives an antioxidant capacity order for hydroxy-cinnamic acids as caffeic > ferulic > p-coumaric acids in accordance with the inhibitive order of low density lipoprotein (LDL) oxidation, whereas Rice-Evans et al. gives the reverse order for the ABTS/TEAC assay (i.e., p-coumaric > ferulic > caffeic acids) [26,27,28]. It would be interesting to see whether covalent or non-covalent bindings of thiols to polyphenols would indeed give rise to significant deviations from additivity of absorbances in corresponding mixtures. In the presented study, three of the most common TAC determination methods, namely FRAP [29], ABTS/TEAC [30], and CUPRAC [31], were applied to different phenolic AOx in the presence of selected thiols and proteins. Since precise additivity was observed in the mentioned mixtures only by using the CUPRAC method, mercury(II) salts were added to stabilize thiols in such mixtures in order to compensate for deviations noted with FRAP and ABTS/TEAC methods. In accordance with the Hard and Soft Acids and Bases (HSAB) Theory [32], Hg(II) is characterized as Class B (soft Lewis acid) metal ion with highly-polarizable outer shell electrons, preferring soft Lewis base sulfur-donor ligands to form stable complexes. It is a well-known phenomenon that mercury tends to bind sulfur-containing proteins and GSH [33]. 2. Results 2.1. Optimization of the Amount of Hg(Ac)2 for the Ferric Reducing Antioxidant Power (FRAP) Method The FRAP reagent, i.e., Fe(III)-2,4,6-tris(2-pyridyl)-S-triazine (TPTZ) complex, accepts an electron from antioxidants to form the chromophore (Fe(II)-TPTZ chelate), and the absorbance increase at 595 nm is related to AOx concentration. The FRAP method was applied as stated in Section 4.6. for gallic acid (GA) (alone), gallic acid + cysteine (GA + CYS), and GA + CYS + Hg2+ mixture solutions. For a series of CYS:Hg2+ solutions prepared at 1:0.5, 1:1, 1:2.5, 1:5, and 1:10 mol/mol ratios, the measured absorbances (with roughly ±5% deviation) are shown in Table 1. As can be seen from Table 1, although CYS alone was nearly non-responsive to FRAP, the FRAP absorbance (AFRAP) of (CYS + GA) greatly exceeded the sum of the absorbances of CYS and GA. This obvious synergetic effect could be overcome by the addition of Hg(II) acting as a selective complexing agent for thiol, which resulted in the restoration of the individual absorbance of GA in the mixture (Table 1). For Hg(II):CYS mole ratios ≥1:1, Hg(II) was effective in thiol stabilization of mixtures and one could obtain only GA absorbance. However, when Hg(II):CYS ratio exceeded 2.5, a slight turbidity was observed which may apparently increase the absorbance. Therefore, this ratio was set at an optimal value of 2.5 for further experiments. 2.2. FRAP Method Experiments For selected thiol compounds (homocysteine (HCYS), N-acetyl-l-cysteine (NAC)), phenolic AOxs (gallic acid (GA), caffeic acid (CFA), catechin (CAT), epicatechin (EC)), and different (thiol + polyphenol) binary mixtures, the FRAP method was applied (as stated in Section 4.6.) in the presence and absence of Hg(Ac)2. To calculate percentage relative error, RE% = |[(Aexp − Atheo)/Atheo]| × 100 formula was used (Aexp: experimentally found absorbance, Atheo: theoretically calculated absorbance, as the mathematical sum of individual absorbance values for the binary mixtures). The results are shown in Table 2. As can be seen from Table 2, the presence of thiol compounds caused a huge difference in the absorbance of (thiol + phenolic) mixtures and, consequently, very high RE% values. In the presence of Hg2+ salts, the experimental absorbance values for binary mixtures were very close to the theoretical ones, and the resulting RE values were roughly between 1% and 9%. Similar binary mixtures were tested for all mentioned polyphenols. The biggest RE (378%) was calculated for the (0.1 mL CFA + 0.1 mL CYS) mixture. On the other hand, when the experiments were repeated in the presence of Hg2+, all RE values were reduced to <5%. The absorbance differences in the absence and presence of Hg2+ salts are shown in Figure 1. 2.3. Optimization of the Amount of Hg(Ac)2 for the ABTS/TEAC Method Hg(II) optimization experiments were performed as stated in Section 4.7., and the obtained results are depicted in Table 3. As can be seen from Table 3, the presence of Hg(II) as a thiol stabilization agent did not interfere with the ABTS•+ reference reading, and for the Hg(II):CYS mol ratio ≥5, the obtained absorbances were nearly the same—within experimental error—as the reference absorbance. We can conclude that Hg(II) could complex all CYS in solution and prevent the oxidation of thiols with ABTS•+ (which would normally cause an absorbance drop with respect to that of the ref.). For further experiments, 1:10 mol ratio was selected as optimal. 2.4. Measurements with the ABTS/TEAC Method The ABTS•+ chromogenic radical accepts an electron from antioxidants to convert into the colorless ABTS form, where the absorbance decrease at 734 nm is related to AOx concentration. ABTS/TEAC method studies were realized as described in Section 4.7. Absorbance values for all tested samples and reference (ref.) were read against phosphate-buffered saline (PBS) buffer at 734 nm, and ∆A values were calculated as the difference between Aref. and Asample (∆A = Aref. − Asample). The relative errors were calculated by using the equation: RE% = |[(∆Aexp. − ∆Atheo)/∆Atheo]| × 100. For GSH and CAT mixtures, the RE values were calculated between 0.1% and 5.7% even in the absence of Hg2+, so these experiments were not repeated in the presence of Hg2+. For binary mixtures of 0.1 mL of 1.0 × 10−4 M HCYS (or NAC) and 0.05–0.25 mL volumes of 5.0 × 10−5 CAT, the results are depicted in Table 4. For 0.1 mL of 1.0 × 10−4 thiol compounds and 0.05–0.25 mL EC mixtures, the lowest RE was calculated as 0.5% for HCYS + 0.1 mL EC and the highest RE was 23% for NAC + 0.1 mL EC mixtures; however, the RE was brought down to 2.4% for the latter mixture in the presence of Hg2+. For mixtures consisting of 0.1 mL of 1.0 × 10−4 M thiol compound and 2.0 × 10−4 M CFA, the lowest RE was calculated as 0.8% for CYS + 0.3 mL CFA and the highest RE was calculated to be 33.6% for HCYS + 0.2 mL of the CFA mixture; however, the RE was brought down to 5.4% in the presence of Hg2+. All results are summarized in Figure 2. As can be seen from Table 4, the presence of Hg2+ provided better RE results, i.e., Hg(II) addition brought relative errors up to 22% down to <10%. For ternary mixtures, the RE values ranged between 1.6%–14.3% for 0.1 mL of 1.0 × 10−4 M GSH + 0.1 mL of 4.0 × 10−5 M GA + 5.0 × 10−5 M CAT and 0.1 mL of 0.1 × 10−4 M CYS + 0.1 mL of 2.0 × 10−4 M CFA + 0.1 mL of 5.0 × 10−5 M EC, respectively. When the same experiments were repeated in the presence of Hg2+, the RE for CYS + CFA + EC was calculated as 1.6%, again confirming the thiol-stabilizing effect of added Hg(II). 2.5. ABTS/TEAC Method Experiments for Polyphenol–Protein Mixtures ABTS/TEAC method experiments for polyphenol–protein mixtures were applied as stated in Section 4.9. For all tested combinations of CAT and CFA + casein mixtures, ABTS/TEAC results were additive. When different amounts of CFA and CAT were added to 0.1 mL of 0.125% bovine serum albumin (BSA), the RE values for CFA-BSA mixtures were between 9.2% and 16.0%; and for CAT-BSA mixtures, the RE values were between 8.0% and 19.7%. These experiments were repeated in the presence of 0.1 mL 1.0 × 10−3 M Hg(II) but, unfortunately, the results were not better than those in the absence of Hg(II). For CAT and CFA mixtures with 0.3 mL of 1:20 diluted egg white protein solutions, the RE% values were between 3.5 and 12.6 for CAT + egg white protein solution and 0.6–19.3 for CFA + egg white protein solution. The experiments were repeated in the presence of 0.1 mL of 1.0 × 10−3 M Hg2+, with no significant improvement. 2.6. Cupric Ion Reducing Antioxidant Capacity (CUPRAC) Method Experiments for Thiol-Polyphenol Mixtures The CUPRAC reagent, i.e., Cu(II)-neocuproine complex, accepts an electron from antioxidants to form the orange-yellow colored chromophore (Cu(I)-neocuproine chelate), and the absorbance increase at 450 nm is related to AOx concentration. To investigate the effects of thiols on determination of polyphenols using the CUPRAC method, different binary and ternary thiol-polyphenol mixtures were prepared as stated in Section 4.11. The CUPRAC method was applied to all mentioned compounds (both polyphenols and thiols) individually, and then was applied to binary and ternary mixtures. Theoretical absorbance values were calculated as the mathematical sum of the absorbances due to individual AOx compounds. These values were compared with experimentally-obtained absorbance values of binary and ternary mixtures. Percentage RE values were calculated using the formula |[(Aexp. − Atheo)/Atheo]| × 100. Among all tested TAC methods, CUPRAC gave the most additive results with RE < 5%, without requiring Hg(II) correction. The obtained results for ternary mixtures are shown in Table 5. 2.7. CUPRAC Determination of Polyphenols in Mixtures with Hg(Ac)2 Correction for Thiols For CUPRAC measurements of polyphenols in the presence of thiols, different volumes between 0.1 and 0.5 mL of 1.0 × 10−4 M CAT were determined in the presence of 0.2 mL of 1.0 × 10−3 M GSH or NAC. These mixtures gave perfectly additive results with CUPRAC, and the observed absorbances at 450 nm arose from the Cu(II)-neocuproine oxidation of thiol and polyphenol components in an additive manner. The same experiments were repeated in the presence of 0.1 mL of 1.0 × 10−2 M Hg(Ac)2 added as a stabilization agent for the thiol component. During the experiments, the thiol compound, phenolic AOx, and Hg2+ were mixed together, and this mixture was added as sample into the CUPRAC reagent mixture. After incorporating Hg(II), only the polyphenol component (CAT) responded to the CUPRAC assay, confirming that the polyphenol component of a mixture could be selectively determined with CUPRAC in the presence of a thiol. 2.8. CUPRAC Method Experiments for Protein–Polyphenol Mixtures Related experiments were conducted as mentioned in Section 4.12. For all tested protein–polyphenol mixtures, percentage RE values were lower than 10%, except for CFA-BSA mixtures, where the relative error remained in the range of 10%–17%. 3. Discussion Since polyphenols may exist together with thiol- or protein-type antioxidants in natural and functional food formulations to extend the shelf-life, accurate and precise measurement of the antioxidant capacity of such food mixtures is of vital importance. The definition of TAC usually necessitates the additivity of individual antioxidant capacities of mixture components, but synergistic or antagonistic interactions may occur among various antioxidants giving rise to large deviations from additivity. In this study, three of the most common spectrophotometric TAC determination methods (FRAP, ABTS/TEAC, and CUPRAC) were applied to different thiol-polyphenol and protein-polyphenol mixtures. The most additive results were obtained for the CUPRAC method, and the largest deviations were seen for the FRAP method, whereas ABTS/TEAC exhibited medium level deviations. The additivity of CUPRAC antioxidant capacities of thiol-containing proteins in admixture with polyphenols was previously shown by the authors’ laboratory [34]. The CUPRAC method has a rather straightforward chemistry toward polyphenols and thiols, oxidizing them to the corresponding quinones and disulfides, respectively, while converting its reagent (cupric-neocuproine) to the CUPRAC chromophore, cuprous-neocuproine complex. Since a single chromophore emerges in the system as a result of the concerned redox reaction, Beer’s law is perfectly obeyed and absorbances are additive for mixtures. However, the ABTS/TEAC method may produce several products from thiols, comprising not only disulfides but sulfinic and sulfonic acids, as well [35]. Thus, perfect additivity may not be expected for thiol-phenol mixtures with the use of the ABTS/TEAC method, and the observed results showed medium level deviations from additivity. On the other hand, even though the FRAP reagent is thermodynamically capable of oxidizing thiols, it cannot do so at an appreciable extent during the protocol time of the FRAP assay, due to kinetic reasons ascribed to the electronic configuration of high-spin Fe(III). Moreover, thiols are usually oxidized through thiyl radicals (RS•) [36] which are not formed at an appreciable extent at the acidic pH (i.e., pH 3.6) of the FRAP reaction protocol [25]. As another example of thiyl radical involvement, ferricyanide oxidation of thiols (e.g., 3-mercaptopropionic acid) was demonstrated to generate thiyl radicals, and the pH-dependence of the rate of oxidation suggested that only ionized sulfhydryl groups (RS−) were directly involved in the oxidation reaction [37]. Thus, in the presence of polyphenols, thiols may presumably have some H-bonding interaction with phenols, thereby facilitating thiol ionization at acidic pH. Stahl and Jencks showed that, compared with oxygen anions of similar pKa, thiol anions cause larger shifts in the infrared stretching frequency of hydrogen-bonded acids in non-aqueous solvents, a behavior consistent with a larger contribution of covalent interaction to hydrogen bonds of sulfur compared with oxygen anions [38]. Thapa and Schlegel included up to three explicit molecules of water hydrogen-bonded to the sulfur of thiols/thiolates in density functional theory calculations, which was found to lower the deviation between theoretical and experimental pKa values of thiols [39]. It should be remembered that small pH changes may lead to large increases in the oxidation rate of thiols; for example, Pryor et al. showed that cysteine may be oxidized to cysteine by NO in aqueous solution at pH ≥ 5, but not so at pH < 4 [40]. Even a small enhancement in thiol ionization can expedite the generation of thiyl radicals during oxidation, which is expected to strengthen the antioxidative ability of thiols [25,36]. In cases when phenols are oxidized faster than thiols (which is the case with the FRAP reagent, known to exhibit slow kinetics toward thiols), the Michael addition of nucleophilic thiols to quinones (i.e., emerging as the oxidation products of phenols) may rapidly produce new products (thiol-quinone adducts), depending on the nature, number, and position of substituents on the quinone, and this addition may also give rise to overoxidation of phenols and thiols via redox cycling involving molecular oxygen and ROS [41,42]. Overall, such interactions may play important parts to explain the seemingly high synergy experimentally observed for thiol-phenol mixtures using the FRAP method. Cysteine can be oxidized to form several different products. These include the thiyl radical (–S•) by a one-electron transition, sulfenic acid (–SOH), and disulfide (–S–S–) by a two-electron transition, sulfinic acid (–SO2H) by a four-electron transition, and eventually sulfonic acid (–SO3H) by a six-electron transition [43]. Reversible enzymatic oxidation of GSH produces oxidized glutathione (GSSG), meaning that 2 GSH molecules lose two electrons to oxidize to GSSG [44]. The CUPRAC method assigns a TEAC coefficient of about 0.5 to either cysteine or GSH, which is in accordance with the reversible physiological oxidation of these biologically important thiols. On the other hand, overoxidation of thiols (such as irreversible 4-e and 6-e oxidations to sulfinic and sulfonic acids, respectively) represents physiologically irrelevant oxidations, and such reactions, if experimentally observed, do not correspond to a true “synergy” between antioxidants but instead—in the view of the authors—reflects an inherent error of the used TAC methods because they cannot effectively simulate physiological redox reactions in vitro. It is known that the TEAC values of either FRAP or ABTS methods assigned to cysteine and GSH are quite different from 0.5, which may change further in the analysis of mixtures [35]. The formation of stable Hg(II)-thiol complexes proved to be successful for independent determination of the polyphenolic content of thiol-phenol mixtures using all three TAC assay methods, because the strongly-complexed thiol components of the mentioned mixtures did not respond to these spectrophometric tests. Addition of Hg(II) to protein-phenol mixtures was basically unsuccessful in achieving additivity of TAC values, probably because of the fact that most protein thiols are not freely available for metal complexation and are buried within the macromolecular backbone, and that phenolic oxidation products (quinones) may bind site-specifically to proteins through thiol addition and addition-elimination reactions [45]. Thus, the absence or scarcity of free RSH groups in proteins rule out the possibility of additivity correction via RS-Hg+ complexation. To conclude with future recommendations, it can be stated that the CUPRAC method is capable of TAC determination of thiol-phenol mixtures in a perfectly additive manner without Hg(II) correction for thiol components. 4. Materials and Methods 4.1. Instrumentation and Chemicals Chemicals and samples were weighed using a Radwag AS 220/C/2 analytical balance (Radwag, Radom, Poland); pH measurements were performed using a HANNA HI 221 pH-meter (Hanna Instruments, Woonsocket, RI, USA), and an Elmasonic ultrasonic water bath was used to dissolve chemicals. A Varian Cary 100 Bio UV-VIS spectrophotometer (Varian, Sydney, Australia) with matched HELLMA quartz cuvettes (light path = 10 mm) was used for optical absorption measurements. All reagents were of analytical grade and purchased from the corresponding sources: iron(III) chloride hexahydrate, copper(II) chloride dihydrate, potassium dihydrogen phosphate, potassium monohydrogen phosphate, mercury(II) acetate, dodecyl sulfate sodium salt, tri-sodium citrate-5,5-hydrate, and bovine serum albumin were purchased from E. Merck (Darmstadt, Germany); N-acetyl-l-cysteine, 2,4,6-Tris(2-pyridyl)-S-triazine (TPTZ) were from Fluka (St. Louis, MO, USA); caffeic acid, (±)-catechinhydrate, epicatechin, dl-homocysteine, 2,2′-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS), neocuproine (2,9-dimethyl-1,10-phenantroline) hydrochloride, casein from bovine milk, trizma, and glycine were obtained from Sigma (Sigma Chemical Co., St. Louis, MO, USA); gallic acid, l-glutathione (reduced), acetic acid, ethanol, potassium persulfate, urea, and trichloroacetic acid (TCA) were purchased from Sigma-Aldrich (St. Louis, MO, USA); l-cysteine from Aldrich and sodium acetate trihydrate, hydrochloric acid, sodium hydroxide, sodium chloride, and ammonium acetate were from Riedel de Haen (Seelze, Germany). 4.2. Stock and Working Solutions 4.2.1. Antioxidant (AOx) Solutions Stock solutions of phenolic (GA, CFA, CAT, EC) and thiol-type antioxidants were prepared at 10 mM concentration. GSH, NAC were dissolved and diluted to volume with distilled water. HCYS CYS were dissolved with 0.5 mL of 1.0 M HCl and diluted to volume with distilled water. Solutions of phenolic AOxs were prepared in ethyl alcohol. For preparing working solutions of FRAP method, stock solutions of phenolic AOxs were diluted 100 times with ethanol and stock solutions of thiol-type AOxs were diluted 50 times with distilled water. For use in the ABTS method, AOx working solutions were diluted to the following concentrations: GA 4.0 × 10−5 M, CFA 2.0 × 10−4 M, CAT and EC 5.0 × 10−5 M, thiol-type antioxidants were at 1.0 × 10−4 M. For the CUPRAC method, working solutions of CAT, EC, CFA were at 1.0 × 10−4 M, GA was at 2.0 × 10−4 M and thiol-type AOxs at 1.0 × 10−3 M. 4.2.2. Bovine Serum Albumin (BSA) Solution To prepare a solution at 0.125% (w/v) for ABTS method and at 2.0% (w/v) for the CUPRAC method, BSA was dissolved with distilled water. 4.2.3. Casein Solution To obtain a solution at 0.5% (w/v) for ABTS/TEAC method and 1.0% (w/v) for the CUPRAC method, suitable amounts of casein were weighed, 0.4 mL of 1.0 M NaOH, distilled water was added, and the mixture was slightly heated. The pH of the solution was adjusted to 8.0 with 1.0 M HCl, and diluted to volume with distilled water. 4.2.4. Protein Dissolution Buffer (pH 6.8) For preparation of protein dissolution buffer, 50 mM tris (hydroxymethyl)aminomethane, 2% (w/v) sodium dodecyl sulfate and 8.0 M urea solutions were mixed, and the pH was adjusted to 6.8 with 6.0 M HCl. 4.2.5. Egg White Protein Solution Egg white and yolk were separated; egg white was suspended in distilled water using an ultrasonic water bath, and then filtered. Five milliliters of an aliquot was withdrawn, 5.0% TCA (w/v) was added to precipitate the proteins, and the mixture was centrifuged at 5000 rpm for 5 min. The separated precipitate was washed with distilled water 3–4 times and redissolved with protein dissolution buffer. Egg white protein solution was diluted 20 times with distilled water prior to use for the ABTS method, and it was used directly for the CUPRAC method. 4.2.6. Mercury(II) Acetate Solution A stock solution at 1.0 × 10−2 M concentration was prepared by dissolving a suitable amount of Hg(Ac)2 with 0.5 mL of 1.0 M HCl and distilled water. The working solutions at 5.0 × 10−4 M for use in the FRAP method and at 1.0 × 10−3 M for use in the ABTS method were prepared from the stock solution by suitable dilution with distilled water. 4.3. FRAP Method Reagents Buffer solution at pH 3.6 (0.3 M): A weight of 3.1 g of sodium acetate was dissolved in distilled water, 16.0 mL acetic acid was added to the NaAc solution, and diluted to 1.0 L. FeCl3·6H2O solution (2.0 × 10−2 M): A suitable amount of FeCl3·6H2O was dissolved using 0.5 mL of 1.0 M HCl and diluted to 25 mL with distilled water. 2,4,6-Tris(2-pyridyl)-S-triazine (TPTZ) solution: TPTZ solution at 1.0 × 10−2 M concentration was prepared by dissolving the suitable weight of TPTZ in ethyl alcohol. FRAP reagent: pH 3.6 buffer solution:FeCl3·6H2O:TPTZ solutions were mixed at 10:1:1 volume ratio. 4.4. ABTS Method Reagents ABTS/persulfate solution: 7.0 mM ABTS solution in distilled water was mixed with 2.45 mM potassium persulfate (K2S2O8) and let to stand for 12–16 h in the dark; then it was diluted 40 times with 5 mM phosphate buffered saline (PBS) solution at pH 7.4. Phosphate-buffered saline (PBS) solution (pH 7.4): 100 mM KH2PO4 solution was added drop by drop onto 50 mL of 100 mM K2HPO4 to obtain a buffer solution at pH 7.4; 0.5 g NaCl was added and diluted to 5.0 mM with distilled water. 4.5. CUPRAC Method Reagents CuCl2·2H2O solution: Prepared from CuCl2·2H2O in distilled water at 1.0 × 10−2 M concentration. Neocuproine (Nc) solution: Prepared from neocuproine hydrochloride in ethyl alcohol at 7.5 × 10−3 M concentration. Ammonium acetate (NH4Ac) solution: Prepared from NH4Ac in distilled water at 1.0 M concentration. Urea buffer at pH 7: Tris (hydroxymethyl)aminomethane, glycine, sodium citrate, and urea at 0.086 M, 0.09 M, 4 mM, and 8.0 M final concentrations, respectively, were mixed and pH was adjusted to 7.0 with 6.0 M HCl. Standard tris buffer at pH 8: Tris (hydroxymethyl)aminomethane, glycine, and sodium citrate at 0.086 M, 0.09 M, and 4 mM final concentrations, respectively, were mixed and pH was adjusted to 8.0 with 6.0 M HCl solution. 4.6. FRAP Method Application of FRAP to a phenolic AOx and/or thiol compound: 0.1 mL thiol solution + x mL of phenolic antioxidant solution + (0.4 − x) mL ethyl alcohol + 0.1 mL distilled water (or 0.1 mL of 5.0 × 10−4 M Hg(Ac)2) and 3.0 mL of FRAP reagent solution were mixed respectively. The reaction mixture was let to stand at room temperature for six minutes and absorbance was read at 595 nm against reagent blank. Optimization of the Hg(Ac)2 amount for FRAP Method: To obtain stable Hg-thiol complexes, Hg(II) salt was added to thiol + polyphenol mixtures. GA was used as phenolic and CYS as thiol type AOx, and a series of thiol:Hg(Ac)2 mol ratios were investigated. For this purpose, three types of solution were prepared, namely GA alone, GA + CYS and GA + CYS + Hg2+ mixtures. The amount of (GA + CYS) was kept constant while Hg2+ amount was varied. A series of CYS:Hg2+ solutions were prepared at 1:0.5, 1:1, 1:2.5, 1:5, and 1:10 mol ratios, and the FRAP method was applied. FRAP method experiments: Different binary mixtures of thiols and phenolic AOx were tested with the FRAP method. To obtain binary mixtures, 0.1 mL of 2.0 × 10−4 M CYS, GSH, NAC, and HCYS were taken and different volumes of 1.0 × 10−4 M polyphenols were added to each thiol solution individually. For this purpose, different volumes ranging between 0.05–0.3 mL of GA, 0.1–0.4 mL of CFA, CAT, and EC were mixed and the FRAP method was applied. Each mentioned AOx compound (thiol or phenol) was tested individually and absorbance values were recorded. For binary mixtures to control additivity, the mathematical sum of these values were calculated and compared with experimental findings. Then all experiments were repeated in the presence of 0.1 mL of 5.0 × 10−4 M Hg(Ac)2. Since proteins precipitated in the presence of ethanol, protein mixtures could not be tested. 4.7. ABTS Method Application of ABTS/TEAC to a phenolic AOx and/or thiol compound: 0.1 mL thiol solution + 0.4 mL distilled water, x mL phenolic antioxidant + (0.5 − x) mL ethanol + 2.0 mL ABTS+ were mixed in this order. After the reaction mixture was let to stand for six minutes at room temperature, absorbance was read against PBS at 734 nm. Application of ABTS/TEAC to a protein-phenol antioxidant mixture: 1.0 mL PBS + 0.3 mL protein sample solution + 0.1 mL distilled water + x mL phenolic antioxidant + (0.5 − x) mL ethanol + (0.1 mL Hg(Ac)2) + 1.0 mL ABTS/persulfate (1:20 diluted with PBS) were mixed, and absorbance at 734 nm was read after 6 min standing. Optimization of the amount of Hg(Ac)2 for the ABTS/TEAC Method: For 0.1 mL of 1.0 × 10−4 M CYS, Hg(II) salt was added at different mol ratios between 1:1–1:10 and ABTS/TEAC method was applied. The same amount of Hg(II) was also added to the reference (blank) solution. Both reference and CYS-Hg(II) samples were read against PBS, and the difference calculated (∆A = Aref. − Asample). 4.8. ABTS Tests for Mixtures of Polyphenolic and Thiolic AOx Compounds To investigate the effect of thiols on the determination of phenolic AOx compounds, different binary and ternary mixtures were prepared. To prepare binary mixtures, 0.1 mL volumes were taken from 1.0 × 10−4 M GSH, CYS, NAC, HCYS, and each aliquot was mixed with 0.1–0.4 mL of 2.0 × 10−4 M CFA, 0.1–0.4 mL of 4.0 × 10−5 M GA and 0.05–0.25 mL of 5.0 × 10−5 M CAT and EC separately in the presence or absence of 0.1 mL of 1.0 × 10−3 M Hg2+. Using 0.15 mL CAT and 0.1 mL EC, CFA, CYS, and GSH; CYS + EC + GA; CYS + CFA + CAT; CYS + GA + CAT; GSH + EC + GA; GSH + CFA + CAT; GSH + GA + CAT; GSH + CFA + EC ternary mixtures were tested in the presence or absence of Hg(II) as stated earlier. 4.9. ABTS/TEAC Tests for Polyphenol–Protein Mixtures To investigate polyphenol-protein interactions in the ABTS/TEAC method, three types of proteins were used: 0.125% BSA, 0.5% casein, and 1:20 diluted egg white protein solution. Different volumes between 0.1–0.4 mL of 2.0 × 10−4 M CFA were added to 0.2 mL of casein solution, and the ABTS/TEAC method was applied. Then, different volumes between 0.1–0.5 mL of 2.5 × 10−5 M CAT were added to 0.2 mL of 0.5% casein. These phenolic AOx compounds were also added to 0.1 mL of 0.125% BSA. The same antioxidant mixture was also prepared with 0.3 mL of 1:20 diluted egg white protein solution. Experiments were repeated in the presence and absence of 0.1 mL of 1.0 × 10−3 M Hg2+. 4.10. CUPRAC Method Application of CUPRAC to a phenolic AOx and/or thiol compound: 1.0 mL CuCl2·2H2O + 1.0 mL Nc + 1.0 mL NH4Ac, x mL phenolic antioxidant + (1 − x) mL ethanol + 0.2 mL thiol solution, and 0.8 mL distilled water were mixed in this order. The mixture was let to stand for 30 min at room temperature. Absorbance was read at 450 nm against a reagent blank. Application of CUPRAC to a protein-phenolic antioxidant mixture: 1.0 mL CuCl2·2H2O + 1.0 mL Nc, 2 mL pH 7 urea buffer + 1.0 mL standard tris pH 8.0 buffer + x mL phenolic antioxidant + (0.5 − x) mL ethanol + 0.4 mL protein and 0.1 mL distilled water were mixed, and the measurement was also made after 30 min standing. Here, NH4Ac buffer was replaced with urea buffer to prevent protein precipitation [34]. 4.11. CUPRAC Measurements of Binary and Ternary Thiol-Phenol Mixtures To prepare binary and ternary mixtures, 1.0 × 10−3 M thiol compounds (CYS, HCYS, NAC, GSH), 2.0 × 10−4 M GA, 1.0 × 10−4 M CAT, EC, and CFA were used. To prepare binary mixtures (i) 0.1–0.4 mL of GA; (ii) 0.2–0.6 mL of CFA; and (iii) 0.1–0.5 mL CAT (or EC) were added to 0.2 mL of thiol solution (CYS, HCYS, NAC, GSH) individually. For ternary mixtures, 0.2 mL aliquots of the corresponding components of mixtures CYS + EC + GA; CYS + CFA + CAT; CYS + GA + CAT; CYS + CFA + EC were mixed; then, the same phenolic AOx compounds were mixed with 0.2 mL of GSH instead of CYS. 4.12. CUPRAC Measurements for Protein–Phenol Mixtures Aliquots of 0.4 mL of 1.0% casein, 2.0% BSA, and egg white protein solution (without any dilution) were mixed with 0.1–0.5 mL of 1.0 × 10−4 M CFA, 0.1–0.5 mL of 1.0 × 10−4 M CAT, EC, and 0.1–0.4 mL of 2.0 × 10−4 M GA individually. The effect of Hg+ was also investigated for the CUPRAC method, and in the presence of Hg(II), the results were not significantly different. 5. Conclusions Accurate and precise measurement of the antioxidant capacity of thiol-phenol mixtures is a priority challenge in food analysis since any synergistic or antagonistic interactions between the mixture components may affect the shelf-life of food. Synergism between antioxidants is defined as the more effective behavior of mixtures compared to single compounds, and usually used in the sense of protection of one antioxidant by the other against lipid peroxidation. However, in non-competitive spectrophotometric TAC assays, a chromogenic redox probe oxidizes the tested antioxidants and changes color, where the additivity of TAC values is a consequence of Beer’s law applied to mixtures. Three of the most common spectrophotometric TAC determination methods (FRAP, ABTS/TEAC, and CUPRAC) were applied to different thiol-polyphenol and protein-polyphenol mixtures, where the most additive results were obtained for the CUPRAC method; the largest deviations were seen for the FRAP method, whereas ABTS/TEAC exhibited medium level deviations. Thus, overoxidation of thiol-phenol mixtures primarily observed in the FRAP method (and to a minor extent in the ABTS method) seems to arise from systematic methodological errors. The best way to correct this error (i.e., deviations from additivity) was to add Hg(II) salts to strongly complex the thiol components so that the true TAC values manifested by phenols in such mixtures could be observed. Applying the same measure (i.e., Hg(II) complexation) to protein-phenol mixtures was not as effective, possibly due to the fact that most protein thiols are buried within the macromolecular backbone. The CUPRAC method worths to be recommended for the TAC determination of thiol-phenol mixtures to avoid systematic errors and get additive results. Acknowledgments The authors wish to express their gratitude to Istanbul University—Application and Research Center for the Measurement of Food Antioxidants (Istanbul Universitesi Gida Antioksidanlari Olcumu Uygulama ve Arastirma Merkezi). Additionally, thanks are extended to the Scientific Research Projects Coordination Unit of Istanbul University (BAP), who supported this work with the project number: 40700. Author Contributions Reşat Apak and Sema Demirci Çekiç conceived and designed the experiments; Aslı Neslihan Avan and Seda Uzunboy performed the experiments; Aslı Neslihan Avan contributed the reagent/metarials/analysis tools; Reşat Apak and Sema Demirci Çekiç recognized the analytical problem subject to this study, analyzed the data and wrote the paper. Conflicts of Interest The authors declare no conflict of interest. Abbreviations ET Electron transfer TAC Total antioxidant capacity ROS/RNS Reactive oxygen/nitrogen species AOx Antioxidant HSA Human serum albumin Hb Hemoglobin TEAC Trolox equivalent antioxidant capacity GSH Glutathione FRAP Ferric reducing antioxidant power ORAC Oxygen Radical Absorbance Capacity CUPRAC Cupric ion reducing antioxidant capacity ABTS 2,2′-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt DPPH 2,2-diphenyl-1-picryl-hydrazyl HSAB Hard and Soft Acids and Bases GA Gallic acid CYS Cysteine HCYS Homocysteine NAC N-acetyl-l-cysteine CFA Caffeic acid CAT Catechin EC Epicatechin BSA Bovine serum albumin RS Thiyl radical TPTZ 2,4,6-Tris(2-pyridyl)-S-triazine PBS Phosphate buffered saline Nc Neocuproine Figure 1 Binary mixtures of 0.1 mL of 2.0 × 10−4 M cysteine (CYS) and 0.1–0.4 mL volumes of 1.0 × 10−4 M caffeic acid (CFA) (a) in the absence, and (b) presence, of 0.1 mL of 5.0 × 10−4 M Hg(Ac)2. FRAP: ferric reducing antioxidant power. Figure 2 Binary mixtures of 0.1 mL of 1.0 × 10−4 M homocysteine (HCYS) and 0.1–0.4 mL volumes of 2.0 × 10−4 M CFA (a) in the absence, and (b) in the presence, of 0.1 mL of 1.0 × 10−3 M Hg(Ac)2. ABTS: 2,2′-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt. ijms-17-01325-t001_Table 1Table 1 Thiol:Hg2+ mol ratio optimization for ferric reducing antioxidant power (FRAP) method. Sample A(FRAP) 1 Only GA 0.3130 ± 0.016 Only CYS 0.0187 ± 0.019 GA + CYS 0.6455 ± 0.023 GA + (1:0.5) CYS:Hg 0.3865 ± 0.029 GA + (1:1.0) CYS:Hg 0.3149 ± 0.011 GA + (1:2.5) CYS:Hg 0.3226 ± 0.006 GA + (1:5.0) CYS:Hg 0.3656 ± 0.016 1 Absorbance = x¯ ± (t0.95s/N½); N = 5 (x¯ = mean, s = standard deviation). GA, gallic acid; CYS, cysteine. ijms-17-01325-t002_Table 2Table 2 FRAP method results for individual thiols, phenolic antioxidant (AOx) solutions, and binary mixtures. In the experiments, 0.1 mL of 2.0 × 10−4 M thiols and different volumes of 1.0 × 10−4 M phenolic AOx compounds were used in the presence and absence of 5.0 × 10−4 M Hg2+. Volume (V) (mL) of Mixture Components (AOx and/or Thiol) Aexp 1 (in the Absence of Hg2+) Aexp 1 (in the Presence of Hg2+) Relative Error % (in the Absence of Hg2+) Relative Error % (in the Presence of Hg2+) 0.10 mL HCYS 0.0016 ± 0.002 - - - 0.10 mL NAC 0.1430 ± 0.010 0.05 mL GA 0.1557 ± 0.001 - - - 0.10 mL GA 0.2867 ± 0.016 0.20 mL GA 0.6060 ±0.008 0.30 mL GA 0.9551 ± 0.005 0.05 mL GA + 0.1 mL HCYS 0.3865 ± 0.024 0.1695 ± 0.003 148.2 8.9 0.10 mL GA + 0.1 mL HCYS 0.5307 ± 0.034 0.3039 ± 0.013 85.1 6.0 0.20 mL GA + 0.1 mL HCYS 1.0427 ± 0.025 0.6314 ± 0.009 72.1 4.2 0.30 mL GA + 0.1 mL HCYS 1.3319 ± 0.012 0.9636 ± 0.007 39.4 0.9 0.05 mL GA + 0.1 mL NAC 0.5217 ± 0.012 0.1580 ± 0.007 74.7 1.5 0.10 mL GA + 0.1 mL NAC 0.7309 ± 0.032 0.3092 ± 0.021 70.1 7.8 0.20 mL GA + 0.1 mL NAC 1.0345 ± 0.014 0.6636 ± 0.009 38.1 9.5 0.30 mL GA + 0.1 mL NAC 1.3758 ± 0.039 0.9709 ± 0.021 25.3 1.6 1 Absorbance = x¯ ± (t0.95s/N½); N = 5 (x¯ = mean, s = standard deviation). HCYS: homocysteine; NAC: N-acetyl-l-cysteine. ijms-17-01325-t003_Table 3Table 3 Optimization of thiol:Hg2+ mol ratio for the ABTS/TEAC method. Sample A(ABTS/TEAC) Ref. (without Hg) 0.9306 Ref. (1:1 Hg) 0.9382 Ref. (1:5 Hg) 0.9277 Ref. (1:10 Hg) 0.9316 Only CYS 0.6967 1:1 CYS:Hg(II) 0.8396 1:5 CYS:Hg(II) 0.9146 1:10 CYS:Hg(II) 0.9162 ABTS/TEAC: 2,2′-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt/trolox equivalent antioxidant capacity; [30]. ijms-17-01325-t004_Table 4Table 4 The absorbance drops (∆A) using the ABTS/TEAC method for individual components or binary mixtures consisting of 0.1 mL of 1.0 × 10−4 M HCYS (or NAC) and 0.05–0.25 mL of 5.0 × 10−5 M catechin (CAT). V (mL) of AOx (Thiol, Catechin, or Mixture) ∆Aexp. 1 (without Hg2+) ∆Aexp. 1 (with Hg2+) RE% (without Hg2+) RE% (with Hg2+) 0.10 mL HCYS 0.2011 ± 0.026 - - - 0.10 mL NAC 0.2825 ± 0.034 - - - 0.05 mL CAT 0.0738 ± 0.013 0.0482 ± 0.026 - - 0.10 mL CAT 0.1243 ±0.019 0.1134 ± 0.018 - - 0.15 mL CAT 0.2099 ± 0.024 0.1791 ± 0.008 - - 0.20 mL CAT 0.2658 ± 0.015 0.2912 ± 0.013 - - 0.25 mL CAT 0.3856 ± 0.008 0.3970 ± 0.028 - - HCYS + 0.05 mL CAT 0.2958 ± 0.015 0.0530 ± 0.019 7.6 5.4 HCYS + 0.10 mL CAT 0.3864 ± 0.015 0.1216 ± 0.027 18.8 5.2 HCYS + 0.15 mL CAT 0.4528 ± 0.013 0.1939 ± 0.021 10.2 7.0 HCYS + 0.20 mL CAT 0.5434 ± 0.018 0.3004 ± 0.037 16.4 2.4 HCYS + 0.25 mL CAT 0.6475 ± 0.023 0.4027 ± 0.021 10.4 1.1 NAC + 0.05 mL CAT 0.3110 ± 0.015 0.0754 ± 0.034 12.7 6.6 NAC + 0.10 mL CAT 0.3528 ± 0.010 0.1430 ± 0.010 13.3 8.5 NAC + 0.15 mL CAT 0.3889 ± 0.027 0.1898 ± 0.021 21.0 0.2 NAC + 0.20 mL CAT 0.4693 ± 0.009 0.2553 ± 0.022 14.4 10.0 NAC + 0.25 mL CAT 0.5227 ± 0.047 0.3363 ± 0.023 21.8 6.6 1 ∆A = x¯ ± (t0.95s/N½); N = 5 (x¯ = mean, s = standard deviation). ijms-17-01325-t005_Table 5Table 5 Cupric Ion Reducing Antioxidant Capacity (CUPRAC) absorbances of ternary mixtures of 0.2 mL volumes of 1.0 × 10−3 M thiol compounds (CYS, HCYS, NAC, GSH) with polyphenols, namely 2.0 × 10−4 M GA and 1.0 × 10−4 M CAT, EC, and CFA. Sample Aexp. 1 Atheo RE% CYS 0.3061 ± 0.011 - - GSH 0.3222 ± 0.008 - - CAT 0.2274 ± 0.018 - - EC 0.2826 ± 0.016 - - CFA 0.1924 ± 0.024 - - GA 0.3988 ± 0.030 - - CYS + EC + GA 0.9930 ± 0.037 0.9875 0.6 CYS + CFA + CAT 0.7468 ± 0.028 0.7259 2.9 CYS + GA + CAT 0.9548 ± 0.038 0.9323 2.4 CYS + CFA + EC 0.7848 ± 0.028 0.7811 0.5 GSH + EC + GA 1.0131 ± 0.021 1.0036 1.0 GSH + CFA + CAT 0.7690 ± 0.021 0.7420 3.5 GSH + GA + CAT 0.9872 ± 0.027 0.9484 4.1 GSH + CFA + EC 0.8324 ± 0.019 0.7972 4.4 1 Absorbance= x¯ ± (t0.95s/N½); N = 5 (x¯ = mean, s = standard deviation). GSH: glutathione; EC: epicatechin; CFA: caffeic acid. ==== Refs References 1. Finley J.W. Kong A.-N. Hintze K.J. Jeffery E.H. Ji L.L. Lei X.G. Antioxidants in foods: State of the science important to the food industry J. Agric. Food Chem. 2011 59 6837 6846 10.1021/jf2013875 21627162 2. Scalbert A. Johnson I.T. Salmarsh M. Polyphenols: Antioxidants and beyond Am. J. Clin. Nutr. 2005 81 215S 217S 15640483 3. Ingold K.U. Pratt D.A. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081326ijms-17-01326Article25(OH)D Is Effective to Repress Human Cholangiocarcinoma Cell Growth through the Conversion of 25(OH)D to 1α,25(OH)2D3 Chiang Kun-Chun 1*†Yeh Chun-Nan 2†Huang Cheng-Cheng 3Yeh Ta-Sen 2S. Pang Jong-Hwei 4Hsu Jun-Te 2Chen Li-Wei 5Kuo Sheng-Fong 6Kittaka Atsushi 7Chen Tai C. 8Juang Horng-Heng 9*Drummen Gregor Academic Editor1 General Surgery Department and Zebrafish Center, Chang Gung Memorial Hospital, Chang Gung University, Keelung 204, Taiwan2 General Surgery Department, Chang Gung Memorial Hospital, Chang Gung University, Kwei-Shan, Taoyuan 244, Taiwan; yehchunnan@gmail.com (C.-N.Y.); tsy471027@adm.cgmh.org.tw (T.-S.Y.); hsujt2813@adm.cgmh.org.tw (J.-T.H.)3 Pathology Department, Chang Gung Memorial Hospital, Chang Gung University, Keelung 204, Taiwan; rrooyyaall@yahoo.com4 Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Kwei-Shan, Taoyuan 244, Taiwan; jonghwei@mail.cgu.edu.tw5 Department of Gastroenterology, Chang Gung Memorial Hospital, Chang Gung University, Keelung 204, Taiwan; leiwei@adm.cgmh.org.tw6 Department of Endocrinology and Metabolism, Chang Gung Memorial Hospital, Chang Gung University, Keelung 204, Taiwan; shengfoung@gmail.com7 Faculty of Pharmaceutical Sciences, Teikyo University, 2-11-1 Kaga, Itabashi, Tokyo 173-8605, Japan; akittaka@pharm.teikyo-u.ac.jp8 Endocrine core lab, boston University School of Medicine, Boston, MA 02118, USA; taichen@bu.edu9 Department of Anatomy, College of Medicine, Chang Gung University, Kwei-Shan, Taoyuan 244, Taiwan* Correspondence: robertviolet6292@yahoo.com.tw (K.-C.C.); hhj143@mail.cgu.edu.tw (H.-H.J.); Tel.: +886-2-2431-3131 (ext. 2625) (K.-C.C.); +886-3-211-8800 (H.-H.J.); Fax: +886-2-2433-2655 (K.-C.C.); +886-3-211-8112 (H.-H.J.)† These authors contributed equally to this work. 12 8 2016 8 2016 17 8 132616 7 2016 09 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Cholangiocarcinoma (CCA) is a devastating disease without effective treatments. 1α,25(OH)2D3, the active form of Vitamin D, has emerged as a new anti-cancer regimen. However, the side effect of hypercalcemia impedes its systemic administration. 25(OH)D is biologically inert and needs hydroxylation by CYP27B1 to form 1α,25(OH)2D3, which is originally believed to only take place in kidneys. Recently, the extra-renal expression of CYP27B1 has been identified and in vitro conversion of 25(OH)D to 1α,25(OH)2D3 has been found in some cancer cells with CYP27B1 expression. In this study, CYP27B1 expression was demonstrated in CCA cells and human CCA specimens. 25(OH)D effectively represses SNU308 cells growth, which was strengthened or attenuated as CYP27B1 overexpression or knockdown. Lipocalcin-2 (LCN2) was also found to be repressed by 25(OH)D. After treatment with 800 ng/mL 25(OH)D, the intracellular 1α,25(OH)2D3 concentration was higher in SNU308 cells with CYP27B1 overexpression than wild type SNU308 cells. In a xenograft animal experiment, 25(OH)D, at a dose of 6 μg/kg or 20 μg/kg, significantly inhibited SNU308 cells’ growth without inducing obvious side effects. Collectively, our results indicated that SNU308 cells were able to convert 25(OH)D to 1α,25(OH)2D3 and 25(OH)D CYP27B1 gene therapy could be deemed as a promising therapeutic direction for CCA. cholangiocarcinoma25(OH)DCYP27B11α-OHasevitamin D ==== Body 1. Introduction Cholangiocarcinoma (CCA), the second most primary liver malignancy, accounts for 10%–15% of primary liver cancers with an increase of incidence and mortality recently [1,2]. Radical surgery is the most effective treatment for CCA, however, the late diagnosis and high recurrent rate usually render CCA patients unfit to receive operation [3]. Adding that traditional chemotherapy and radiotherapy fail to improve CCA patients’ survival, finding new therapeutic directions and regimens for CCA should be prioritized. Vitamin D is originally deemed to only have mineral functions, but has become more and more popular in recent decades due to the unveiling of its non-mineral functions, such as pro-differentiation, pro-apoptosis, anti-angiogenesis, and anti-growth [4]. Thus, 1α,25(OH)2D3, the active form of Vitamin D, has emerged as a new regimen for cancer treatment [5]. To overcome the drawback of hypercalcemia induced by systemic administration of 1α,25(OH)2D3, thousands of 1α,25(OH)2D3 analogs have been synthesized aiming to lessen the hypercalcemia-inducing effect and to strengthen anti-tumor effect [6]. Besides Vitamin D analogs, another way to apply Vitamin D in cancer treatment is the usage of 25(OH)D. After obtaining Vitamin D through intake or photo-conversion in the skin, Vitamin D is bound with Vitamin D binding protein (VDP) and then carried to the liver to be hydroxylated to form 25-hydroxyvitamin D [25(OH)D] [7], which is also the best index of human Vitamin D status and biological inert. 25(OH)D would be further hydroxylated to form 1α,25(OH)2D3 in the kidney, which is catalyzed by 25(OH)D-1α-hydroxylase (1α-OHase or CYP27B1). The finding that CYP27B1 exists in a variety of human tissues in addition to the kidney only [8,9,10] implies the extra-renal conversion of 25(OH)D to 1α,25(OH)2D3 seems reasonable. In fact, the extra-renal conversion had been proven, and the converted 1α,25(OH)2D works in an intra-, auto-, or para- crine manner [11,12], which indicates the application of 25(OH)D as a systemic treatment is a safe and feasible way to provide tissues with active Vitamin D. Previously, the conversion of 25(OH)D to 1α,25(OH)2D3 had been demonstrated in prostate and pancreas cancer cells with 25(OH)D exhibiting significant growth inhibition against cancer cells [13,14]. Our group also showed 25(OH)D could be converted to 1α,25(OH)2D3 by hepatocellular carcinoma cells and could repress hepatocellular carcinoma cell growth [15]. In this work, we aimed to show the conversion of 25(OH)D to 1α,25(OH)2D3 by CCA cells and application of 25(OH)D to treat CCA cells in vitro and in vivo. Through our work, we hope we can provide a new therapeutic regimen for CCA treatment. 2. Result 2.1. SNU308 Cells Expressed CYP27B1 Expressions and 25(OH)D Inhibited SNU308 Cells Growth The effect of 25(OH)D on SNU308 cell growth was evaluated by CyQUANT proliferation assay kit. As shown in Figure 1A, 1 × 10−7, 5 × 10−7, 1 × 10−6, and 5 × 10−6 M 25(OH)D treatments (7 days) suppressed SNU308 cell growth to 94% ± 4%, 72% ± 3%, 43% ± 3%, and 31% ± 3% of the control, respectively. We then transfected CYP27B1 into SNU308 cells (SNU308-CYP27B1) and knocked down CYP27B1in SNU308 cells (SNU308-CYP27B1si). Figure 1B shows that ∆CT of SNU308, SNU308-CYP27B1si, and SNU308-CYP27B1 cells was 8.24, 10.21, and 3.32, respectively. As treated by 1 × 10−6 M 25(OH)D for 7 days, 55% ± 3%, 40% ± 2.5%, and 73% ± 2.9% growth inhibition was observed in SNU308, SNU308-CYP27B1, and SNU308-CYP27B1si cells, respectively (Figure 1C). Our result indicates that SNU308 cells were growth-inhibited by 25(OH)D and overexpression or knockdown of CYP27B1 could strengthen or attenuate the 25(OH)D-induced growth inhibition. A similar inhibitory effect of 25(OH)D on SNU1079 cells was also observed (Supplementary Material Figure S1). 2.2. Evaluation of 25(OH)D Effect on LCN2 Expression in SNU308 Cells LCN2 had been shown to be one of 1α,25(OH)2D3 downstream genes in SNU308 cells [16]. In this current study, we thus investigated LCN2 expression in SNU308 cells after 25(OH)D treatment. As shown in Figure 2, LCN2 expression was repressed by 10−6 M 25(OH)D in SNU308, SNU308-CYP27B1si, and SNU308-CYP27B1 cells to 0.48 ± 0.09, 0.69 ± 0.1, and 0.19 ± 0.06-fold, respectively. 2.3. Evaluation of Conversion of 25(OH)D to 1α,25(OH)2D3 in SNU308 Cells Since 25(OH)D is biologically inactive, the growth inhibition effect of 25(OH)D on SNU308 cells needs conversion of 25(OH)D to 1α,25(OH)2D3. To investigate whether the conversion exists or not, SNU308 and SNU308-CYP27B1 cells were treated with 800 ng/mL 25(OH)D and intracellular 1α,25(OH)2D3 concentration was determined by ELISA. As shown in Figure 3, 2.8 and 4.9 ng/mL of 1α,25(OH)2D3 were found in SNU308 cells at 30 and 60 min after treatment; while 4.6 and 15 ng/mL of 1α,25(OH)2D3 were noted in SNU308-CYP27B1 cells. Our result indicates that SNU308 cells were capable of converting 25(OH)D to 1α,25(OH)2D3, leading to the growth inhibition induced by 25(OH)D. 2.4. Evaluation the Anti-Growth Effect of 25(OH)D on SNU308 Cells in Vivo Since SNU308 cells possessed the ability to convert 25(OH)D to 1α,25(OH)2D3, we thus applied 25(OH)D to treat xenografted SNU308 cells in nude mice. Figure 4A shows that 1α,25(OH)2D3 treatment (0.3 μg/kg, twice per week) reduced tumor weight to 64% ± 7% of the control; while 25(OH)D treatment reduced tumor weight to 84% ± 5% or 66% ± 6% of the control, at the dose of 6 μg/kg or 20 μg/kg, twice per week. The mice body weight and serum calcium did not change as obviously in the treated groups compared to the control group (Figure 4B,C). Our result indicates that both 25(OH)D and 1α,25(OH)2D3 could repress SNU308 cell growth in vivo, suggesting the in vivo conversion of 25(OH)D to 1α,25(OH)2D3 by SNU308 cells. 2.5. Evaluation of CYP27B1 Expression in Human CCA Specimens We next analyzed CYP 27B1 expression in 83 CCA patients undergoing hepatectomy by immunohistochemical staining. The result of IHC showed that 33 (39.8%), 36 (43.4%), and 14 (16.9%) specimens were categorized as having weak, moderate, and strong staining intensity for CYP27B1, respectively (Figure 5). 3. Discussion In this study, we demonstrated that SNU308 cells presented with CYP27B1 expression and were growth inhibited by 25(OH)D in a dose dependent manner. The inhibitor effect of 25(OH)D on SNU308 cells growth was strengthened or weakened as CYP27B1 overexpression or knockdown. A similar effect of 25(OH)D on SNU308 cells’ LCN2 expression was observed. The above findings all suggested that SNU308 cells were able to convert 25(OH)D to 1α,25(OH)2D3, leading to the inhibition of SNU308 cells’ growth and LCN2 expression. As we measured intracellular 1α,25(OH)2D3 concentration of SNU308 cells after 25(OH)D treatment, SNU308-CYP27B1 cells had higher 1α,25(OH)2D3 concentration than SNU308 cells, confirming the conversion of 25(OH)D to 1α,25(OH)2D3 by SNU308 cells. 1α,25(OH)2D3 (0.3 μg/kg) and 25(OH)D (6 and 20 μg/kg) were then applied to treat SNU308 cells in a xenograft model and a significant anti-tumor growth effect was induced by both 25(OH)D and 1α,25(OH)2D3 with no obvious side effects noted in both groups. We further showed that CYP27B1 expression in human CCA specimens. Collectively, our result indicated that 25(OH)D was a promising and safe regimen for CCA treatment. CYP27B1—an enzyme that functions to hydroxylate 25(OH)D to form 1α,25(OH)2D3 and the active form of Vitamin D—was generally considered to only be expressed in the kidney under normal physical conditions until the mid-1980s. Extra-renal CYP27B1 expression was found during the ensuing years, with Bike at al. reporting the first extra-renal synthesis of 1α,25(OH)2D3 in cultured human keratinocytes [17]. So far, a variety of cells have been reported to have CYP27B1 expression and could convert 25(OH)D to 1α,25(OH)2D3, such as prostate, breast, pancreas, and liver [14,15,18,19]. Adding that the extra-renal converted 1α,25(OH)2D3 works in a para- or endo-crine manner which suggests the safety of systemic application of 25(OH)D. 25(OH)D has therefore emerged as a promising regimen to treat cancers with CYP27B1 expression. This concept is supported by the epidemiological studies showing Vitamin D deficiency is associated with higher incidence of cancers [20,21,22,23], for which the explanation was Vitamin D deficiency represented lower circulated 25(OH)D concentration, leading to the fewer converted 1α,25(OH)2D3 in the extra-renal tissues due to the fewer substrates for CYP27B1 to convert. Our data clearly showed that 25(OH)D could effectively repress growth of SNU308 cells in vitro (Figure 1A). As we xenografted SNU308 cells into nude mice and treated them with 25(OH)D and 1α,25(OH)2D3 through intraperitoneal injection, both drugs exhibited potent anti-growth effect on xenografted tumors (Figure 4A). These results suggested that SNU308 cells could convert 25(OH)D to 1α,25(OH)2D3 both in vitro and in vivo, resulting in the growth inhibition of 25(OH)D against SNU308 cells. The steadily increased body weight and constant serum calcium concentration of nude mice during the experimental period indicated the safety of systemic application of 25(OH)D (Figure 4B,C). Collectively, our results indicated that 25(OH)D constituted an effective and safe regimen to treat CCA. To further confirm the conversion of 25(OH)D to 1α,25(OH)2D3 by SNU308 cells, CYP27B1 was then overexpressed or knocked down in SNU308 cells. Figure 1C shows that the growth inhibition effect of 25(OH)D on SNU308 cells was increased or decreased as CYP27B1 forced expression or knock down, respectively, which suggested that SNU308 cells could convert 25(OH)D to 1α,25(OH)2D3. The results shown in Figure 3 further confirmed the conversion since SNU308-CYP27B1 cells could yield higher concentration of 1α,25(OH)2D3 than SNU308 cells as treated by 25(OH)D, which was measured by ELISA. The finding that the transfection of CYP27B1 into SNU308 cells could strengthen the anti-growth effect of 25(OH)D also implicated the possibility of CYP27B1 gene transfection therapy for CCA (Figure 1C). In fact, CYP27B1 gene expression had been negatively correlated with invasiveness of ovarian cancer [24]. In this current study, we demonstrated that human CCA specimen presented with CYP27B1 expression (Figure 5), which encouraged the application of 25(OH)D in clinical trials for CCA treatment. 1α,25(OH)2D3 modulates the downstream gene expressions through binding to Vitamin D receptor (VDR), which would further conjugate with retinoid X receptor (RXR) to form a heterodimer. The heterodimer then binds to Vitamin D response element (VDRE) to modulate gene expression [25,26]. Lipocalin-2 (LCN2), belonging to the lipocalin superfamily, was originally deemed as a stress protein [27], with the oncogene role of LCN2 later being explored in a variety of cancers [16,28,29,30], including CCA. Previously, we had demonstrated that 1α,25(OH)2D3 could inhibit LCN2 expression in CCA cells and knockdown of VDR could abolish this effect, indicating LCN2 was susceptible to 1α,25(OH)2D3 in a VDR dependent manner [16]. Since 25(OH)D is inert biologically, the downregulation of LCN2 in SNU308 cells as treated by 25(OH)D suggested the added 25(OH)D was converted to 1α,25(OH)2D3, leading to the inhibition of LCN2 (Figure 2). The increase or decrease of LCN2 inhibition noted in SNU308-CYP27B1 or SNU308-CYP27B1si cells further confirmed the extra-renal synthesis of 1α,25(OH)2D3 by SNU308 cells. 4. Materials and Methods 4.1. Cell Culture and Chemicals 1α,25-dihydroxyvitamin D and 25-dihydroxyvitamin D were obtained from Sigma-Aldrich Co. (St. Louis, MO, USA). SNU 308 cells, human CCA cell lines, was purchased from Korean Cell Line Bank (KCLB: 28 Yongon-dong, Chongno-gu, Seoul, Korea). Cells were grown in RPMI 1640 medium supplemented with 10% FBS and 1% antibiotic-antimycotic agents. Culture medium was changed 3 times per week. 4.2. Western Blot Assay The detailed procedures for Western blot were described previously [31]. The primary antibodies used in this study was monoclonal antibodies against LCN2 (#PAB9543, Abnova Corporation, Taipei, Taiwan). The secondary antibodies (1:5000) were anti-rabbit (111-035-003, Jackson Immunoresearch, West Grove, PA, USA) or anti-mouse secondary antibodies (Zymed 81-6520, San Francisco, CA, USA). The blots were detected using ECL reagents (WBKLS0500, Millipore, Billerica, MA, USA). Membranes were detected by VersaDocTM Imaging System (Bio-Rad, Hercules, CA, USA) for analysis. 4.3. Cell Proliferation Assay The cell proliferation of SNU308 cells with or without treatment was determined using CyQUANT cell proliferation assay kit (Invitrogen, Carlsbad, CA, USA). 4.4. Knockdown CYP27B1 SNU308 cells were transduced with CYP27B1 small hairpin RNA lentiviral particles (sc-60479-V; Santa Cruz Biotechnology) as described by the manufacturer. Two days after transduction, the cells (SNU308-CYP27B1si) were selected with puromycin dihydrochloride. 4.5. Real-Time Reverse Transcription-Polymerase Chain Reaction (RT-qPCR) Total RNA from cells was isolated using Trizol reagent, cDNA was synthesized, and real-time polymerase chain reaction (qPCR) was performed according to the manufacturer’s protocol. FAM dye-labeled TaqMan MGB probes and PCR primers for human CYP27B1 (Hs00168017_m1) was purchased from Applied Biosystems (Foster City, CA, USA). β-actin (Hs01060665_g1) was used with a FAM reporter dye-labeled TaqMan MGB probe as an internal control. 4.6. Expression Vector Constructs and Stable Transfection The human CYP27B1 expression vectors were constructed by ligation the CYP27B1 cDNA (cat # SC123873, Origene, Rockville, MD, USA) into the pcDNA3.1/Zeo expression vector (Invitrogen) with Eco R1 and Xba 1 cutting site. Proper ligation was confirmed by extensive restriction mapping and sequencing. Electroporation was performed using the ECM 830 (BTX, San Diego, CA, USA) with a single 70 ms pulse of 180V, and transfected SNU308 (SNU308-CYP27B1) cells were selected in a RPMI medium with 10% FCS and 100 μg/mL Zeocin (Invitrogen) as described before [15]. 4.7. Measurement of 1α,25(OH)2D3 The detailed procedures were accorded to the manufacturer’s protocol (#E01D0002, BLUE GENE). Each well was treated by 800 ng/mL 25(OH)D and the cells were collected at indicated time points. 1α,25(OH)2D3 concentration was measured by ELISA (#E01D0002, BLUE GENE). 4.8. Xenograft Animal Study The study was approved by the Chang Gung University Animal Research Committee (Permit Number: 2014022601). Young male BALB/C Nu/Nu mice, age 6 weeks old and average weight 20–30 g, were purchased from National Taiwan Animal Center. A total of 24 adult male Young male BALB/C Nu/Nu mice were used in this experiment. 5 × 106 of SNU-308 cells were resuspended in 100 μL of PBS and injected into the subcutaneous. One week after tumor injection, different treatments were started. Four groups were included in this study, i.e., ethanol treatment group as the shame group (twice per week, intraperitoneal injection, n = 6), 1α,25(OH)2D3 treatment group (0.3 μg/kg, twice per week, intraperitoneal injection, n = 6), 25(OH)D low-dose group (6 μg/kg, twice per week, intraperitoneal injection, n = 6), and 25(OH)D high-dose group (20 μg/kg, twice per week, intraperitoneal injection, n = 6). The body weight and blood calcium were measured weekly. Tumor masses were harvested after 5 weeks. Blood calcium was measured using quantitative colorimetric calcium assay kits (BioChain, Newark, CA, USA) according to the manufacturer’s protocol. 4.9. CYP 27B1 Immunohistochemistry The study was approved by the local institutional review board of Chang Gung Memorial Hospital (clinical study numbers 99-2886B, 99-3810B and 102-5813B), and written informed consent for immunohistochemical tumor analysis was obtained from each patient. CYP27B1 expression levels in the 83 CCA patients were examined by immunohistochemical staining (IHC). Tissue sections (4-μm) prepared from the formalin-fixed, paraffin-embedded hepatectomy specimens were incubated with the primary antibody against CYP27B1 (AP9056b 1:100 dilution; Abgent, San Diego, CA, USA) at 4 °C overnight. After 3-time washes with TBST (5 min each), the signals were visualized with the Dako Labelled Streptavidin-Biotin2 (LSAB2) System-HRP (Dako A/S, No. K0675; Dako, Glostrup, Denmark). Control slides were incubated with the secondary antibody only. For the assessment of immunohistochemical staining, the percentage of stained target cells was evaluated in 10 random microscopic fields per tissue section (×400 magnification), and their averages were subsequently calculated. Staining intensities were scored as 0, (negative), 1 (negative to weak), 2 (weak), 3 (weak to moderate), 4 (moderate), 5 (moderate to strong), or 6 (strong). Specimens with staining intensity scores of ≤2, 3–4, or 5–6 were classified as having weak, moderate, or strong expression, respectively. 4.10. Statistical Analysis The data from each group were compared by two sample, unpaired, and two tails t-test. For animal studies, Mann-Whitney U test was applied. p-Value < 0.05 was considered as a significant difference. 5. Conclusions Our results demonstrated that CCA cells demonstrated CYP27B1 expression and were able to convert 25(OH)D to 1α,25(OH)2D3. 25(OH)D was an effective and safe agent to repress growth of SNU308 cells in vitro and in vivo. CYP27B1 gene transfection would strengthen 25(OH)D-induced growth inhibition in SNU308 cells. Adding the finding that human CCA specimens exhibited CYP27B1 expression, application of 25(OH)D and CYP27B1 gene transfection to treat CCA seem to be promising approaches. Further clinical trials are justified. Acknowledgments This work is supported by 103-2314-B-182A-085- and 104-2314-B-182A-017- (belong to ministry of science and technology) CMRPG2D0191, 0192, and 0193 (belong to Chang Gung memorial hospital) to Kun-Chun Chiang. This work is also supported by 105-2320-B-182-020-MY3- (belong to ministry of science and technology) and CMRPD1F0041 (belong to Chang Gung memorial hospital) to Horng-Heng Juang. Supplementary Materials Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1326/s1. Click here for additional data file. Author Contributions For research articles with several authors, a short paragraph specifying their individual contributions must be provided. Kun-Chun Chiang and Chun-Nan Yeh wrote the manuscript and designed this experiment; Ta-Sen Yeh, Jun-Te Hsu, Li-Wei Chen, Sheng-Fong Kuo, Jong-Hwei S. Pang helped conduct the experiment; Atsushi Kittaka and Tai C. Chen helped review the article and design experiments; Cheng-Cheng Huang helped conduct IHC staining and reading; Horng-Heng Juang was in charge of the whole experiment conduction and paper writing. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Anti-proliferative effects of 25(OH)D on SNU308, SNU308-CYP27B1, and SNU308-CYP27B1si cells (A) Two, four, and six days after plating, cells were treated by indicated concentrations of 25(OH)D and cell proliferation was measured by WST-1 method; (B) CYP27B1 mRNA expression of SNU308, SNU308-CYP27B1si, and SNU308-CYP27B1 cells; (C) The cell proliferation of SNU308, SNU308-CYP27B1si, and SNU308-CYP27B1 cells after 10−6 M 25(OH)D treatment was measured by WST-1 method. Each value is a mean ± SD of three to five determinations. * p < 0.05, ** p < 0.001 versus control. Figure 2 Evaluation of 25(OH)D effect on LCN2 expression in SNU308 cells with or without modulation of CYP27B1 gene expression. (A) A Western blot depicting LCN2 expression in SNU308, SNU308-CYP27B1si, and SNU308-CYP27B1 cells after two days of 10−6 M 25(OH)D treatment; (B) Quantitative analysis of LCN2 expression. Each value is a mean ± SD of three to five determinations. * p < 0.05, ** p < 0.001 versus control. Figure 3 Evaluation of intracellular 1α,25(OH)2D3 concentration after 25(OH)D treatment in SNU308 and SNU308-CYP27B1 cells. The intracellular 1α,25(OH)2D3 concentrations of SNU308 and SNU308-CYP27B1 cells after 800 ng/mL 25(OH)D treatment were measured by ELISA at indicated time points. Each value is a mean ± SD of three to five determinations. Figure 4 Evaluation of 25(OH)D and 1α,25(OH)2D3 effect on SNU308 cells growth in vivo through a xenograft animal model. (A) Four weeks after treatment, the xenografted tumors from each group were weighed; (B) The body weight of nude mice in each group; (C) The serum calcium concentration of nude mice in each group. Each value is a mean ± SD of six determinations. * p < 0.05, ** p < 0.001 versus control. Figure 5 CYP27B1 expression in human CCA. Immunohistochemical staining of CYP27B1 in human CCA specimens with different intensity scores. (A) High expression; (B) Moderate expression; (C) Low expression; (×400, Scale bar = 50 µm). ( Arrows indicate positive expression of CYP27B1). ==== Refs References 1. Gores G.J. Cholangiocarcinoma: Current concepts and insights Hepatology 2003 37 961 969 10.1053/jhep.2003.50200 12717374 2. Shaib Y. El-Serag H.B. The epidemiology of cholangiocarcinoma Semin. Liver Dis. 2004 24 115 125 10.1055/s-2004-828889 15192785 3. Wang Y. Li J. Xia Y. Gong R. Wang K. Yan Z. Wan X. Liu G. Wu D. Shi L. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081327ijms-17-01327ArticleProtective Effects of Berberine on Renal Injury in Streptozotocin (STZ)-Induced Diabetic Mice Zhang Xiuli 123*He Hui 2Liang Dan 4Jiang Yan 2*Liang Wei 2Chi Zhi-Hong 5Ma Jianfei 6Cai Lu Academic EditorWang Yuehui Academic Editor1 Department of Nephrology, Benxi Center Hospital, 29 Victory Road, Benxi 117000, Liaoning, China2 Science Experiment Center, Benxi Center Hospital, Benxi 117000, Liaoning, China; lnbxwxf@yeah.net (H.H.); liangbixiang@sohu.com (W.L.)3 Key Laboratory of Medical Cell Biology of Ministry of Education, China Medical University, Shenyang 110001, Liaoning, China4 Troops of 95935 Unit, Haerbin 150111, Heilongjiang, China; dl7711@163.com5 Department of pathophysiology, China Medical University, Shenyang 110001, Liaoning, China; chizhihong2015@126.com6 Department of Nephrology, the First Affiliated Hospital, China Medical University, Shenyang 110001, Liaoning, China; jfeima@sohu.com* Correspondence: zhang2013_11@163.com (X.Z.); zhang2013_12@163.com (Y.J.); Tel./Fax: +86-244-289-0511 (X.Z.)12 8 2016 8 2016 17 8 132719 4 2016 01 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Diabetic nephropathy (DN) is a serious diabetic complication with renal hypertrophy and expansion of extracellular matrices in renal fibrosis. Epithelial-to-mesenchymal transition (EMT) of renal tubular epithelial cells may be involved in the main mechanism. Berberine (BBR) has been shown to have antifibrotic effects in liver, kidney and lung. However, the mechanism of cytoprotective effects of BBR in DN is still unclear. In this study, we investigated the curative effects of BBR on tubulointerstitial fibrosis in streptozotocin (STZ)-induced diabetic mice and the high glucose (HG)-induced EMT in NRK 52E cells. We found that BBR treatment attenuated renal fibrosis by activating the nuclear factor-erythroid 2-related factor 2 (Nrf2) signaling pathway in the diabetic kidneys. Further revealed that BBR abrogated HG-induced EMT and oxidative stress in relation not only with the activation of Nrf2 and two Nrf2-targeted antioxidative genes (NQO-1 and HO-1), but also with the suppressing the activation of TGF-β/Smad signaling pathway. Importantly, knockdown Nrf2 with siRNA not only abolished the BBR-induced expression of HO-1 and NQO-1 but also removed the inhibitory effect of BBR on HG-induced activation of TGF-β/Smad signaling as well as the anti-fibrosis effects. The data from present study suggest that BBR can ameliorate tubulointerstitial fibrosis in DN by activating Nrf2 pathway and inhibiting TGF-β/Smad/EMT signaling activity. renal tubular epithelial cellsberberbineEMTdiabetic nephropathyNrf2 pathwayTGF-β/Smad signaling pathway ==== Body 1. Introduction Diabetic nephropathy (DN) is the leading cause of chronic kidney failure and end-stage renal disease [1,2]. In the early stage of DN, the patients exhibit with the increased kidney size, glomerular volume and kidney function, along with the accumulation of glomerular extracellular matrix and the increase of urinary albumin excretion, glomerular sclerosis and tubular fibrosis [2]. Moreover, once the patients with DN develop fibrosis in glomerulus and tubulointerstitium, their kidney structure and filtration are susceptible to be damaged [3,4]. Accumulating evidence has demonstrated that tubulointerstitial fibrosis usually develops at the early stage of diabetic renal injury and is also related to the decline of renal function, which happened to some diabetes patients [5,6]. In addition, hyperglycemia, advanced products of nonenzymatic protein glycation and an excess accumulation of extracellular matrix (ECM) are connected with prolonged injury, these factors also enhance the kidney fibrosis process [7,8]. To date, some studies has verified that EMT of mature tubular epithelial cells plays a crucial role in accelerating the progression of DN relative matrix proteins as well [9]. Thus, investigation of antifibrotic mechanisms remains a promising therapeutic target in the diabetic renal diseases [10]. Several studies have revealed that oxidative stress may be involved to the EMT of tubular epithelia cells in diabetes [11,12]. NF-E2-related factor 2 (Nrf2) is one of important cellular defense factors to counteract oxidative stress and regulates intracellular antioxidants and phase II detoxifying enzymes, which can detoxify xenobiotics and neutralize ROS so that promoting cell survival and maintaining cellular redox homeostasis [13,14]. In normal condition, Nrf2 is held as an inactive complex in the cytoplasm by the repressor molecule, Keap1 (Kelch-like ECH-associated protein 1), which can facilitate its ubiquitination once exposing to oxidative stress and/or electrophiles. When exposing to oxidative stress or electrophiles, Nrf2 occurs ubiquitination and translocates into nuclei and then activates the transcription of antioxidant genes such as NADPH quinone oxidoreductase-1 [NQO-1] and heme oxygenase-1 [HO-1]. Both genes can induce the production of anti-inflammatory, anti-fibrosis and anti-apoptotic metabolites [15,16,17]. In vivo studies also identificated that renal ischemia-reperfusion can elevate Nrf2 levels and activate their downstream targets in kidneys [18]. Meanwhile, targeting Nrf2 can not only mitigate cisplatin-induced nephrotoxicity but also attenuate cyclosporin A-induced EMT in renal fibrosis [19,20,21]. Additionally, there was a report showed that improving Nrf2 expression by sulforaphane can suppress ROS triggered by hyperglycemia and restored metabolic dysfunction in human microvascular endothelial cells [22]. In contrast, transcription factor Nrf2 deficiency promotes the deteripration of ischemic and nephrotoxic acute kidney injury [23]. Nrf2 knockout mice with STZ-induced diabetes increased their urinary nitric oxide metabolites levels (an indirect evidence of oxidative stress) and induced renal injury [14]. Thus, Nrf2 has been considered as a potential therapeutic target for renal diseases. Berberine (BBR, C20H18NO4) is an isoquinoline alkaloid isolated from Coptidis Rhizoma and Cortex Phellodendri with multiple pharmacological activities [24,25]. BBR has a variety of biological effects, for instance, antioxidant, anti-inflammatory, anti-tumor and anti-fibrosis effects [25,26,27]. In addition, recent studies proofed that BBR ameliorated renal dysfunction in diabetic rodents and suppressed high glucose-induced glomerular mesangial cell proliferation by supperessing TGF-β1 expression and ECM accumulation, this function suggested BBR can as a potential drug for DN [28,29,30,31]. Furthermore, the antioxidant effect of BBR have been demonstrated it not only can improve renal function in certain diabetic animal models [32], but also ameliorate in endothelial function by reducing endothelial microparticles-mediated oxidative stress [33]. Most importantly, Yu et al. have recently proofed that BBR protects human renal proximal tubular cells from hypoxia/reoxygenation injury via inhibiting endoplasmic reticulum and mitochondrial stress [26]. However, the underlying molecular mechanisms of protection of BBR on tubulointerstitial fibrosis in DN remain unclear. Therefore, in the study, we investigate protective effects of BBR against renal injury in streptozotocin (STZ)-induced diabetic mice and the underlying mechanisms. 2. Results 2.1. The Effects of BBR on Metabolic and Biochemical Parameters in STZ-Induced Diabetic Mice As showed in Table 1, STZ-induced diabetic mice exhibited typical diabetic symptoms compared with those in normal mice, for instance, increase in diet, drink and urine while weight loss. Fasting blood glucose (FBG) and kidney weight/body weight ratio (KW/BW) in diabetic mice were significantly higher but their body weight was loss. At the end of the 12th week, all blood serum creatinine (Cr), urea nitrogen (BUN) and 24-albuminuria were increased in diabetic mice. These results suggested that diabetic renal dysfunction was emerged (Table 1). The level of FBG, KW/BW, Cr, BUN and diabetic symptom were dramatically ameliorated in BBR-treatment mice compared with those in diabetic mice (Table 1). All these findings indicated that BBR could effectively cure the renal function in diabetic mice. 2.2. BBR Can Supress the Fibrosis in Diabetic Kidneys by Inhibiting EMT In order to investigate the anti-fibrotic effects of BBR in connection with the inhibition of EMT in diabetic kidneys, double-immunofluorescence staining for Lectin (a tubular marker) and α-SMA (a mesenchymal marker) or collagen-I were used to analyze their expression and localization in the renal interstitium. The data showed that α-SMA protein (red) was specifically localized in the interstitial compartment and the tubular epithelial cells of diabetic kidneys, whereas α-SMA was virtually absent in sham control kidneys (Figure 1a1–3), these findings suggesting de α-SMA, as a special diagnostic markers, can be detected in in tubular epithelia expression under diabetic conditions (Figure 1b1–3). However, the staining of α-SMA of interstitial compartment and the tubular epithelial cells in the BBR-treated group was weaker than that in untreated diabetic mice (Figure 1c1–3). Additionally, double-immunofluorescence staining for Lectin and collagen-I was performed to detect the distribution of collagen-I protein in the renal interstitium. As shown in Figure 2, the expression of Collagen-I increased in tubularinterstitial apparatus in DN and BBR attenuated this response. Furthermore, Western blot also showed the BBR-treated groups significantly suppress the expression of α-SMA and collagen-I compared with those in diabetic kidneys (Figure 3). These observations were consistent with the results of immunofluorescence staining. It is clear that BBR decreased tubulointerstitial fibrosis in STZ-induced diabetic mice with nephropathy. 2.3. BBR Enhanced Activation of Nrf2/HO-1 Signaling in Diabetic Mice Kidneys Oxidative stress plays important roles in initiating diabetic fibrotic effects. To further explore if the anti-fibrosis role of BBR is relate to Nrf2 signaling pathway, the expressions of Nrf2 relative genes (NQO1 and HO-1) in diabetic mice kidneys were detected. As shown in Figure 4, the renal Nrf2 expression slightly increased in diabetic group compared to those in control (p > 0.05) while BBR treatment can induce renal Nrf2 expression in diabetes group (Figure 4). Similarly, the expression of the Nrf2 target gene products including NQO1 and HO-1 were both found (Figure 4). These results indicated that BBR activated Nrf2-mediated anti-oxidative cascades in this model. 2.4. The Influence of BBR on HG-Induced EMT Markers in NRK-52E Cells To explore whether BBR decreases diabetes-induced tubulointerstitial fibrosis and the potential mechanisms, NRK-52E cells were used to as model in vitro to evaluate the effect of BBR on renal fibrotic [12]. First, we used MTT assay to select optimal concentrations in vitro and 30 μM BBR sulfate was chosen (Figure 5a) as reported previously [3_ENREF_300]. Next, to verify if HG can successfully induce EMT development, the levels of E-ca and α-SMA of NRK-52E cells were detected by western blot and immunofluorescence staining after cultured in HG conditions with or without BBR pretreatment. E-ca is an epithelial marker and plays an essential role in the maintenance of epithelial integrity and even its loss is the earliest key cellular event after tubular EMT induced by HG and a-SMA. Hence it often acts as a mesenchymal marker in renal fibrosis [34]. As shown in Figure 5b,c, the reduction of E-ca protein expression in HG conditions was accompanied by an increase of α-SMA protein expression, which was close associated with our preliminary experiments, and confirmed that HG promotes EMT development in NRK-52E cells [35]. However, HG-induced EMT was decreased after pre-treating the NRK-52E cells with BBR, these demonstrated that BBR could reduce α-SMA and E-ca expression together. Additionally, the inhibitory effect of BBR on HG-stimulated EMT was further confirmed by immunofluorescence staining that HG treatment in the cells for 48 h leading to NRK-52E cell morphology changed into a fibroblast-like shape along with increased and decreased expression of α-SMA and E-ca respectively (Figure 5d–j), which could be attenuated by treating with 30 μM BBR that was consistent with the results of Western blot. These data demonstrated that BBR reversed HG-stimulated EMT in NRK-52E cells. 2.5. BBR can Affect HG-Induced Nrf2/HO-1 Signaling in NRK-52E Cells HG slightly increased HO-1 and NQO-1expression. BBR treatment with HG enhanced up-regulated HO-1 and NQO-1 expression. In parallel to the effects on the expression of target genes, Nrf2 nuclear levels were increased by Berberine stimulation, while Nrf2 cytoplasmic levels remained relatively even (Figure 6). 2.6. Knockdown Nrf2 Abrogates BBR-Induced NQO1 and HO-1 Expression Next, we further examined the impact of Nrf2 on NQO1 and HO-1 expressions. We used Nrf2-siRNA to identify the Nrf2 real function. Relative expression of Nrf2 were detected by Western blot at various time-points after siRNA-Nrf2 transfection though transient methods (Figure 7a). Similar to the effects of BBR, the results confirmed that siRNA-Nrf2 significantly inhibited the NQO1 and HO-1 expression (Figure 7b,c). 2.7. The Antifibrosis Effect of BBR on DN Is Depended on the Inhibition of Nrf2 Mediated the TGF-β/Smad Signaling Pathway It has been proofed that the TGF-β/Smad pathway plays an important role in the pathogenesis of ECM accumulation in diabetic nephropathy. Therefore, we used the Nrf2 siRNA technology to determine if BBR-mediated protection against the HG-induced EMT by activating of antioxidant factor-Nrf2 and inhibition of Smad2/3. As shown in Figure 8, the level of phospho-Smad2/3 of in HG-treated NRK-52E cells was significantly increased comparison with those in control group. In contrast, the level of phospho-Smad2/3 in HG-induced NRK-52E pretreated with 30 um BBR was dramatically decreased. Similarly, the inhibitory effect of BBR on the activity of TGF-/Smad signaling pathway and the increases of phospho-Smad2/3 level was canceled by siRNA-Nrf2. Furthermore, the level of EMT marker proteins in HG-treated cells was significantly increased and the Nrf2-siRNA reversed the inhibitory the protective effects of BBR on HG-stimulated EMT maker protein expression. These results were consistent with the notion that BBR-induced anti-fibrosis effects depend on activating Nrf2-mediated TGF-β/Smad inhibition to protect the NRK-52E cells from HG-induced EMT processes. 3. Discussion In the present study, we demonstrated that BBR treatment not only markedly ameliorated renal dysfunction, but also significantly increased the Nrf2 expression and attenuated the progression of early stage tubulointerstitial fibrosis in STZ-induced diabetic mice. Moreover, the study of mechanism in BBR prevented HG-induced EMT events is because BBR can abrogate HG-induced oxidative stress and induce the expression of Nrf2 and two Nrf2-targeted anti-oxidative genes NQO-1 and HO-1 in NRK-52E cells. More importantly, knockdown of Nrf2 with small interfering RNA abolished the BBR-induced NQO-1 and HO-1 expression. Knockdown of Nrf2 canceled the inhibitory effect of BBR on HG-induced TGF-β/Smad signaling activation. These findings demonstrated that BBR ameliorate the renal injury in diabetic mice by activating Nrf2 and inhibiting TGF-β/Smad pathway. In DN, EMT of mature tubular epithelial cells of the kidney has been considered involving in the progression of tubulointerstitial fibrosis by accumulation of renal accumulation of matrix proteins [36,37]. Hyperglycemia induced EMT of tubular cells was usually views as the initial factor, which can result to matrix accumulation and deposition, so EMT is also a key mechanism of renal tubulointerstitial fibrosis in DN. Hence, targeting EMT has been as a potential therapeutic method to attenuate the progression of renal fibrogenesis in the diabetic kidney. Tubular EMT is also defined as epithelial cell loss epithelial cell-cell adhesion owing to the down-regulation of E-ca, which can induce TGF β1 activation under the HG conditions and lead to the accumulation of interstitial fibroblasts and decline in renal excretory function [38]. In addition, tubular EMT can produce many ECM components and further to assemble in the extracellular compartment, these can cause massive tissue fibrosis development as seen in diseased kidney. Previous studies indicated that BBR could ameliorate liver fibrosis by suppressing hepatic oxidative stress, fibrogenic potential, and lipid peroxidation [39,40]. Another study also demonstrated the beneficial effects of BBR against bleomycin mediated fibrotic challenge through activating Nrf2 and suppressing NF-κB dependent inflammatory and TGF-β1 mediated fibrotic events in pulmonary [41]. Although glomerulosclerosis is a defined feature of DN and tubulointerstitial injury, which can that determine the rate of decline in renal function [31,42,43], little is known about the role and underlying mechanism of BBR in tubulointerstitial, especially in DN. In the present study, it has been confirmed that HG can induce the changes of EMT markers through decreasing the epithelial marker E-ca and increasing α-SMA than that in the control. Moreover, BBR pretreatment may protect against HG-induced EMT and decrease the α-SMA and E-ca expression, which were associated with the EMT in NRK-52E cells. Patients with DN usually are caused by EMT in the tubular epithelial cells and these things are usually regarded to be the result of hyperglycemia-induced oxidative stress, but this symptoms of EMT events in tubular epithelial cells can be reverse by anti-oxidants effect [11,12,37]. Nrf2, as a transcription factor, is one critical regulator of anti-oxidant response and an essential signaling factor, has been found that it can protect in many animal disease models, including oxidative stress caused lung injury, fibrosis, asthma and brain ischemia-reperfusion [44,45]. Recent studies have supported the potential therapy of Nrf2 in diabetes, which controlled oxidative stress and regulated inflammatory cytokines [46,47]. Moreover, the protective role of Nrf2 against renal damage, mediating free radicals, was demonstrated by STZ-induced diabetic models [14,47]. In contrast, the levels of nitric oxide metabolites in urine were found increased in Nrf2 knockout mice with STZ-induced diabetes so aggravating renal injury [48]. These data demonstrated that up-regulation of Nrf2 is a potential therapeutic target by mitigating oxidative stress-induced tissue injury. On mechanism, a study also reveals that BBR activates Nrf2 nuclear translocation and protects against oxidative damage via a PI3K/Akt-dependent mechanism in NSC34 motor neuron-like cells, which leads to be increased Nrf2 binding to the antioxidant response element in the promoters of target genes [49]. Most importantly, BBR has been demonstrated that it can attenuate hyperglycemia-induced apoptotic death and promote Nrf2-dependent NGF protein expression and neurite outgrowth, providing a potential therapeutic use of BBR on the treatment of diabetic complication [27]. In the present study, an animal model was used to demonstrate that BBR significantly decreased diabetes-induced renal oxidative damage and tubulointerstitial fibrosis in vivo, which connects with the upregulation of Nrf2 expression. Renal Nrf2 expression remained slight increase in diabetic mice at 12 weeks after diabetes onset, these results may be related to the time of treatment was relatively short. For mechanism, Nrf2 is verified that it can quickly be up-regulated in cells and tissues after exposed to various oxidative stresses while down-regulated in cells or tissue after exposed to chronic oxidative stress [50]. Interestingly, we also found when cells were exposured to high glucose, BBR can enhance the translocation of Nrf2 into the nucleus and upregulated Nrf2-driven antioxidant systems effectively so that attenuates cellular oxidative stress under diabetic conditions. Previous studies have indicated that upregulation of the NQO1 and HO-1 genes can be widely used for assessment of Nrf2 signaling activation [51]. The production of NQO1 and HO-1 expression by BBR has been verified in several type cell lines including human renal tubular cells and renal fibroblasts. A previous study demonstrates that BBR treatment may be related with AMPK pathway together with Nrf2 pathway may lead to the increase of NQO1 and HO-1 in both LPS-shocked macrophages and mice, and that means AMPK is a upstream of Nrf2 [52]. In addition, BBR can induce the activation of PI3K/Akt and p38 pathway, which both can upregulate the expression and activity of HO-1 as well as NQO1 [35]. Moreover, other studies have revealed that BBR as an Nrf2 activator against glucose neurotoxicity so that attenuating high glucose-induced neurotoxicity, these findings provided another potential therapeutic use of BBR on the treatment of diabetic complications [27]. A previous study has suggested that the NRF2-HO-1system plays a protective role against CsA-induced renal fibrosis by changing EMT gene [15]. In addition, HO-1 deficiency proofed that HO-1 was associated with the increase of fibrosis, tubular TGF-β1 expression, inflammation, and EMT process in renal fibrosis [53]. However, a few known natural substances stimulate NQO1 and HO-1 expression in tubulointerstitial fibrosis in DN. In the study, we further discover the possible downstream mechanisms how Nrf2 affect the protection of BBR from tubulointerstitial fibrosis in DN, the expression of NQO1 and HO-1 was investigated in NRK-52E cells after treated with BBR and HG following Nrf2 siRNA. Consistently, our data presently demonstrated that Nrf2 activation is essential for BBR-stimulated NQO1 and HO-1 expression based on the results of Nrf2-siRNA which inhibited BBR-induced NQO1 and HO-1 protein expression. Simultaneously, the role of HG suppressed E-ca reduction and upregulated α-SMA was reversed by BBR, whereas knockdown of Nrf2 with siRNA down-regulated the anti-fibrosis effect of BBR. These findings suggest that the protection of BBR is depended on the Nrf2 related pathway so that they can active subsequently the key target genes to protect NRK-52E cells from HG-induced EMT processes. As known that TGF-βis usually high expressed in variety of renal disease, including obstructive nephropathy, and TGF-β, as a major mediator of ECM, can induce the fibrosis development in diabetic nephropathy and tubulointerstitial fibrosis by phoshorylating downstream fators Smad2/3 which can induce the expression of ECM proteins such as fibronectin and collagen-I [54,55,56]. This phenomenon has been confirmed in diabetic animal models and diabetic patients [57,58,59]. In contrast, chronic treatment of db/db mice with TGF-β-neutralizing antibodies markedly diminished the expression of collagen and fibronectin and reduced mesangial matrix expansion [2,60]. Therefore, suppressing the activation of TGF-β signaling has been supposed a good therapeutic approach for preventing renal fibrosis [38,55]. Additionally, a previous study demonstrated that sulforaphane can effectively induce Nrf2 activation and inhibited hepatic fibrosis via suppressing TGF-β/Smad signaling [21]. Recent study suggested that DMF can attenuated renal fibrosis by activating Nrf2 and inhibiting ARE-independent TGF-β/Smad signaling pathway. These studies have confirmed that BBR inhibited HG-induced EMT through the Nrf2 and TGF/Smad signaling pathways and it also suggest that increase of oxidative stress and TGF/Smad activation have been linked to the development and progression of diabetic complications including diabetic nephropathy. 4. Materials and Methods 4.1. Animal Treatment Six-eight weeks aged C57BL/6J mice (20~25 g of bodyweight) were purchased from Experimental Animal Center of China Medical University (Shenyang, China). All animal experiments were approved by the Experimental Animal Ethical Committee of China Medical University and performed according with the NIH Guide for the Care and Use of Laboratory Animals. Firstly, these mice were randomly divided into three groups, (1) Normal group; (2) diabetic group; (3) BBR-treated group. For streptozotocin (STZ)-induced diabetic mice model, the mice were injected with STZ 150 mg/kg (Sigma, St. Louis, MO, USA) diluted in 0.1 M citrate buffer (pH 4.5) by intraperitoneal injection as described previously [34], when the blood glucose levels of STZ-induce diabetic mice were >16 mmol/L, these mice were diagnosed as diabetic. For BBR-treated diabetic group, these mice were treated with oral BBR (200 mg/kg) in distilled water every day. Mice in the Normal and the diabetic groups were respectively administered orally with equal volume of distilled water. For 12 weeks after diabetic model establishment (at the age of 21–23 weeks), the mice were moved to metabolic cages. At the last day, their urine albumin was collected and detected with Murine ELISA kit (Exocell, Inc., Philadelphia, PA, USA). Finally, the mice were sacrificed, and their blood samples and right kidneys were collected, and tissues samples were fixed in 10% buffered formalin for immunofluorescence staining. 4.2. Cell Culture The NRK 52E normal rat kidney tubular epithelial cells were purchased from the American Type Culture Collection (Manassas, VA, USA). The cells were maintained in DMEM (low glucose; Gibco Life Technologies, Grand Island, NY, USA) with 10% FBS (Gibco), 4 mM L glutamine (Boster Biological Technology Ltd., Wuhan, China) and 1% penicillin/streptomycin (Sigma Aldrich, St. Louis, MO, USA) in a humidified atmoshoere containing 5% CO2. Before drugs treatment, NRK 52E cells were cultured in serum free DMEM for 24 h at 37 °C to arrest and synchronize cell growth. In vitro, the cells were divided into six groups, (1) Control group, in which cells were treated with fresh serum free DMEM with 5 mM glucose; (2) siRNA group, cells were transfected with Nrf2 siRNA; (3) BBR group, cells were given with 30 μM BBR for 24 h; (4) HG group, cells were treated with 30 mM HG for 48 h; (5) HG/BBR group, in which cells were treated with with 30 mM HG at 37 °C (Boster Biological Technology Ltd.) for 48 h after pretreatment with 30 μM BBR 24 h; (6) HG/BBR/Nrf2 siRNA group, in which cells, after transfected with Nrf2 siRNA 24 h, were treated with 30 μM BBR for 24 h and next following with 30 mM HG for 48 h. Finally, the concentration of glucose in these cells were detected as previously described 36. 4.3. Cell Viability The cellular viability was detected with MTT assay. Briefly, 10 μL MTT (500 μg/mL; Sigma, St. Louis, MO, USA) was added to 100 µL culture medium in 96-well plates. After incubated for 3 h at 37 °C, MTT solution was removed and 100 μL DMSO (Sigma, St. Louis, MO, USA) was added to dissolve the formazan crystals. Finally, the absorbance was measured at 540 nm with Sunrise microplate reader (Tecan Group Ltd., Männedorf, Switzerland). 4.4. Transient Transfection with Nrf2-Small Interfering RNA (siRNA) The cells were plated in 6-well plates at a density of 2 × 105 cells/well in 2 mL DMEM. Next day, cells were transfected with Nrf2 specific siRNA (sense, 5′ GCACGGU GGAGUUCAUGATT 3′ and antisense, 5′ UCAUUGAACUCCACCGUGCCT 3′) (Santa Cruz Biotechnology, Inc., San Francisco, CA, USA). According to Lipofectamine® 3000 manufacturer’s instructions (Invitrogen Life Technologies, Carlsbad, CA, USA), the cells were transfected with siRNA when they reached 50%–60% confluence in the plate. The cells were re-incubated in medium with 10% FBS after transfected 24 h. The knockdown effect of Nrf2 in these cells was verified by Western blot after transfected 24 h. 4.5. Immunofluorescence Staining The expression of lectin, collagen-I and α-SMA (α-smooth muscle actin) in mice kidneys were detected in the cryostat sections. Firstly, these cryostat sections of mice kidneys were incubated with normal donkey serum (1:20) or anti-lectin (1:100) (polyclonal antibody, Sigma) or anti-collagen I (1:100) or anti-α-SMA (1:100) for 1 h or overnight at RT. Then rinsing with PBS, these sections were incubated with DAR-FITC (1:50) and Texas Red-DAM (1:50) for 2 h at RT. Finally, a confocal laser scanning microscope was used to analyze the expression and location of this target protein (CLSM, SP2, Leica, Germany). All antibodies were purchased from Jackson ImmunoResearch (West Grove, PA, USA). For immunofluorescence staining of E cadherin (E ca) and α SMA in NRK-52E cells, cells were fixed in 4% paraformaldehyde and permeabilized in 0.1% Triton X-100 at first, and then incubated with mouse monoclonal anti-E-ca (1:100) and mouse monoclonal anti-α-SMA (1:100) as described above. Finally, the cells were incubated with DAR-FITC (1:50) and Texas Red-DAM (1:50) for 2 h at RT. The fluorescent images were visualized with a Fluoview 300 fluorescence microscope (Olympus, Tokyo, Japan). 4.6. Western Blot Analysis Goat polyclonal anti-α SMA, mouse monoclonal anti-E-ca, mouse monoclonal anti Nrf2, mouse monoclonal anti HO-1, mouse anti-NQO1, mouse anti-Keap1, and mouse anti β-actin were purchased from Santa Cruz Biotechnology (San Francisco, CA, USA), while Smad2, Smad3, p-Smad2, p-Smad3 were bought from Cell Signaling Technology (3 Trask Lane Danvers, Danvers, MA, USA). Western blot analysis was performed as previously described [61]. Briefly, equal amounts of protein extracts were loaded to SDS-PAGE 10% gels, and the proteins were transferred to PVDF membranes (Millipore, Temecula, CA, USA). Then the blots were incubated with primary antibodies after blocking with 5% fat-free milk and washed with TBST. Next, the membranes were then incubated with HRP-conjugated secondary antibodies and washed repeated with TBST. After then, the membranes were added with enhanced chemiluminescence kit (Walterson Biotechnology Inc., Beijing, China) and imaged with G BOX EF Chemi HR16 gel imaging system (Syngene, Frederick, MD, USA), Finally, the bands of blots were analyzed and quantified with ChemiDoc™ XRS system (Bio-Rad, Hercules, CA, USA) with Quantity One software version 4.6 (Bio-Rad Laboratories, Inc., Bio-Rad, Hercules, CA, USA). The blots were repeated at least three times for each condition. 4.7. Statistical Analysis Data were displayed with the means ± standard error. All data were analyzed with One-way ANVA and Tukey’s multiple comparison tests. The differences were considered to be significant at p < 0.05. 5. Conclusions In summary, the present study provides evidence that BBR inhibits tubulointerstitial fibrosis in DN in vivo and HG induces EMT in NRK-52E cells in vitro by including Nrf2-mediated anti-oxidative effects and suppression of TGF-β/Smad signaling pathway. Therefore, these findings mean BBR can act as a beneficial and potential drug for the prevention or treatment of tubulointerstitial fibrosis in DN. Acknowledgments This work was supported by the National Grand Fundamental Research 973 Program of China (2012CB722405), the Natural Science Foundation of China (81170561, 81370856), and postdoctoral Science Foundation of China (2014MM551144). Author Contributions Xiuli Zhang, Zhi-Hong Chi, Jianfei Ma, Hui He, Yan Jiang and Wei Liang designed the study; Xiuli Zhang, Dan Liang and Zhi-Hong Chi. contributed to data collection and analyses; Xiuli Zhang, Zhi-Hong Chi and Dan Liang drafted the manuscript; Hui He, Yan Jiang and Wei Liang involved in data interpretation and scientific discussion; Xiuli Zhang reviewed and edited the manuscript. All authors read and approved the final manuscript. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Double immunofluorescence staining shows localization of α-SMA (red) and the tubular cell marker lectin (green) in control (a1–3) and diabetic kidneys (b1–3); BBR-treated diabetic kidneys (c1–3) (respectively. Scale bar, 20 μm). Figure 2 Double immunofluorescence staining shows localization of collagen-I (red) and the tubular cell marker lectin (green) in control (a1–3) and diabetic kidneys (b1–3); BBR-treated diabetic kidneys (c1–3) (respectively. Scale bar, 20 μm). Figure 3 Antifibrotic effects of BBR were associated with the inhibition of EMT in diabetic kidneys. Representative western blots of α-SMA and collagen-I in the kidneys of control mice, diabetic mice or BBR-treated diabetic mice. All experiments were performed three times with virtually identical results. β-actin was used as loading control. (** p < 0.01 vs. control group; ## p < 0.01 vs. control diabetic group. n = 8). Figure 4 BBR enhanced activation of Nrf2/HO-1 signaling in diabetic kidneys. Representative western blots demonstrated that BBR treatment increased protein expression of Nrf2, NQO1 and HO-1 in the kidneys of diabetic mice (n = 8). Each value represents mean ± SEM. All experiments were performed three times with virtually identical results. β-actin was used as loading control. (** p < 0.01 vs. control group; ## p < 0.01 vs. control diabetic group. n = 8). Figure 5 Effects of BBR on HG-induced EMT in the NRK-52E cells. (a) The NRK-52E cells were treated with various doses of BBR (3, 30 or 300 μM) for 6, 12, 24, 48 or 72 h, and the cell viability was analyzed by MTT assay. The data were mean ± SEM (n = 5); (b,c) The NRK-52E cells were treated with 30 mM HG for 48 h, in the presence or absence of 30 μM BBR. Representative western blot analysis shows that BBR treatment prevents high glucose (HG)-induced (b) E-ca downregulation, as well as (c) á-smooth muscle actin (á-SMA) upregulation at 48 h (n = 6). Values represent mean ± SEM of three independent experiments performed (** p < 0.01 vs. control; * p < 0.05 vs. control; ## p < 0.01 vs. HG; # p < 0.05 vs. HG); (d–j) Phase contrast microscopy shows that BBR treatment prevents non-epitheliod phenotype acquisition of the NRK-52E cells. Scale bar, 20 μm. For confocal microscopy of immunofluorescence-stained samples, cells were fixed and stained with a primary antibody against E-ca (d through g), α-SMA (h through j).Cultures were untreated (d,h) or exposed to HG (e,i) or with the addition of BBR (g,j). Results are representative of two experiments. Figure 6 Influence of BBR on HG-induced Nrf2/HO-1 signaling in NRK-52E cells. The NRK-52E cells were treated with 30 mM HG for 48 h, in the presence or absence of BBR. The expression NQO1, HO-1 and Nrf2 was analyzed using western blot (n = 6). (a–e) The results were representatives of three independent experiments. β-actin was used as loading control. (** p < 0.01 vs. control; ## p < 0.01 vs. HG). Figure 7 siRNA knockdown of Nrf2 abrogates BBR-induced NQO1 and HO-1 expression. (a) NRK-52E cells were transfected with Nrf2-siRNA and western blot analysis was performed with an antibody against Nrf2 was performed at various time-points following transfection (24, 48 and 72 h). Relative Nrf2 expression levels were calculated and normalized to the loading control. Corresponding protein levels were assessed using densitometry and expressed in relative intensities. All results were obtained from three independent experiments. Values are expressed as the mean ± SEM (n = 6; ** p < 0.01, vs. control); (b,c) The cells were divided into six groups as description in this paper. Western blotting was performed with an antibody against NQO1 and HO-1. Relative NQO1 and HO-1 expression levels were calculated and normalized to the loading control. Corresponding protein levels were assessed using densitometry and were expressed in relative intensities. All results were obtained from three independent experiments. Values are expressed as the mean ± SEM (n = 6) (** p < 0.01 vs. control; ## p < 0.01 vs. HG; # p < 0.05 vs. HG; ΔΔ p < 0.01 vs. HG + BBR). Figure 8 Nrf2 is involved in the inhibitory effect of BBR on the TGF-β/Smad signaling pathway. NRK-52E cells were divided into six groups as mentioned above, corresponding protein levels of Smad2/3, phospho-Smad2/3 and EMT markers were assessed using densitometry and expressed in relative intensities. All results were obtained from three independent experiments. All results were obtained from three independent experiments. Values are expressed as the mean ± SEM (n = 6) (* p < 0.05 vs. control; ** p < 0.01 vs. control; ## p< 0.01 vs. HG; # p < 0.05 vs. HG; ΔΔ p< 0.01 vs. HG + BBR). ijms-17-01327-t001_Table 1Table 1 Effects of BBR on metabolic and biochemical parameters in STZ-induced diabetic mice. Items Control DM DM + BBR Blood glucose (mM) 5.15 ± 0.12 26.32 ± 4.43 ** 15.48 ± 4.97 **,## Body weight (g) 29.3 ± 1.15 20.35 ± 2.58 * 24.92 ± 2.16 Kidney weight (g) 0.33 ± 0.03 0.31 ± 0.02 0.32 ± 0.04 KW/BW (%) 1.13 ± 0.02 1.52 ± 0.08 * 1.28 ± 0.05 # BUN (mM) 6.12 ± 0.53 41.39 ± 5.33 ** 19.63 ± 2.87 **,## Cr (µM) 28.54 ± 1.68 87.57 ± 11.61 ** 45.82 ± 6.17 **,## Albuminuria (µg/24 h) 9.7 ± 0.58 42.3 ± 3.86 ** 25.9 ± 2.17 **,## Results are presented as means ± SEM. (* p < 0.05 vs. control group; ** p < 0.01 vs. control group; ## p < 0.01 vs. control diabetic group; # p < 0.05 vs. control diabetic group. n = 8). ==== Refs References 1. Williamson J.R. Tilton R.G. Chang K. Kilo C. Basement membrane abnormalities in diabetes mellitus: Relationship to clinical microangiopathy Diabetes Metab. Rev. 1988 4 339 370 10.1002/dmr.5610040404 3292174 2. Gilbert R.E. Cox A. Wu L.L. Allen T.J. Hulthen U.L. Jerums G. Cooper M.E. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081328ijms-17-01328ReviewVitamin C and Heart Health: A Review Based on Findings from Epidemiologic Studies Moser Melissa A. Chun Ock K. *Lamuela-Raventós Rosa Academic EditorDepartment of Nutritional Sciences, University of Connecticut, Storrs, CT 06269, USA; melissa.a.moser@uconn.edu* Correspondence: ock.chun@uconn.edu; Tel.: +1-860-486-6275; Fax: +1-860-486-367412 8 2016 8 2016 17 8 132801 6 2016 08 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Vitamin C is a powerful dietary antioxidant that has received considerable attention in the literature related to its possible role in heart health. Although classical vitamin C deficiency, marked by scurvy, is rare in most parts of the world, some research has shown variable heart disease risks depending on plasma vitamin C concentration, even within the normal range. Furthermore, other studies have suggested possible heart-related benefits to vitamin C taken in doses beyond the minimal amounts required to prevent classically defined deficiency. The objective of this review is to systematically review the findings of existing epidemiologic research on vitamin C and its potential role in cardiovascular disease (CVD). It is well established that vitamin C inhibits oxidation of LDL-protein, thereby reducing atherosclerosis, but the cardiovascular outcomes related to this action and other actions of vitamin C are not fully understood. Randomized controlled trials as well as observational cohort studies have investigated this topic with varying results. Vitamin C has been linked in some work to improvements in lipid profiles, arterial stiffness, and endothelial function. However, other studies have failed to confirm these results, and observational cohort studies are varied in their findings on the vitamin’s effect on CVD risk and mortality. Overall, current research suggests that vitamin C deficiency is associated with a higher risk of mortality from CVD and that vitamin C may slightly improve endothelial function and lipid profiles in some groups, especially those with low plasma vitamin C levels. However, the current literature provides little support for the widespread use of vitamin C supplementation to reduce CVD risk or mortality. vitamin Ccardiovascular diseaseobservational cohort studiesclinical trialsmeta-analyses ==== Body 1. Introduction In their 2004 Science Advisory Report, the American Heart Association stated that the current literature does not support the use of antioxidant vitamin supplements for the prevention or treatment of cardiovascular disease (CVD) [1]. Although the research conducted in this area is not uniformly positive, a substantial body of work has hinted at a possible reduction in CVD risk associated with antioxidant intake. Of particular interest to this review is the potential role of vitamin C in heart health. Vitamin C’s functions as an antioxidant and as an enzyme cofactor are well established, but the ways in which these functions may modify chronic disease risk are less well established. The belief that vitamin C may benefit heart health has stemmed from multiple pieces of evidence and lines of reasoning. First, much work has documented the beneficial effects of fruit and vegetable consumption on heart health, which has led to the hypothesis that, among other nutrients, vitamin C may be partially responsible for this relationship. Importantly, the link between fruit and vegetable consumption and improved cardiovascular outcomes has primarily been established through cohort studies, as randomized controlled trials in this area are scarce. Nevertheless, the wealth of evidence accumulated from these epidemiological and cohort studies has consistently demonstrated the cardiovascular benefits of fruits and vegetables. For example, in the Nurse’s Health Study (NHS) and the Health Professionals Follow-Up Study (HPFS), which together looked at over 126,000 healthy adults, it was shown that individuals in the highest quintile of fruit and vegetable intake had a relative risk for coronary heart disease (CHD) of 0.8 (95% confidence interval [CI]: 0.69–0.93) compared to those in the lowest quintile of intake [2]. Of note, green leafy vegetables and vitamin C-rich fruits contributed most to the apparent protective effect of total fruit and vegetable intake. Another cohort of 1725 men in Sweden showed similar benefits of fruits and vegetables [3]. Further, a meta-analysis of 13 independent cohorts including 278,459 total participants showed that compared with individuals who had less than three servings per day of fruits and vegetables, the pooled relative risk of CHD was 0.93 (95% CI: 0.86–1.00) for those consuming three to five servings per day and 0.83 (95% CI: 0.77–0.89) for those consuming more than five servings per day. In this study, increased consumption of fruits and vegetables from less than three to more than five servings per day was related to a 17% reduction in CHD risk [4]. The hypothesis that vitamin C may play a role in CVD prevention also draws support from the vitamin’s antioxidant capabilities. The epidemiological evidence relating fruit and vegetable intake to reduced risk of CVD may be explained, at least in part, by antioxidant content, and especially the role of these antioxidants in preventing oxidative changes to LDL [5]. Oxidized LDL is a target for scavenger receptors, which incorporate it into plaque [6]. Therefore, the prevention of LDL oxidation by vitamin C may prevent atherosclerosis, thereby mediating a potential role in CVD risk reduction. Various other functions of vitamin C may also bolster the hypothesis that vitamin C can reduce cardiovascular risk. For example, vitamin C has been shown to reduce monocyte adhesion to the endothelium [7]. Adhesion of circulating monocytes to endothelial cells is one key in the formation of atheromas, and is considered one of the early signs of the development of atherosclerosis [7]. Additionally, vitamin C has been shown to improve nitric oxide production of the endothelium, which, in turn, increases vasodilation, reducing blood pressure [8]. Furthermore, vitamin C may prevent apoptosis of vascular smooth muscle cells, which helps keep plaques more stable if atherosclerosis has developed [9]. The requirement for dietary vitamin C is based on its role as an antioxidant, and was determined by estimating the quantity of dietary vitamin C needed to maximize its concentration in neutrophils, where it reduces reactive oxygen species produced during phagocytosis [10]. Given that vitamin C may relate to heart disease risk through more than one mechanism, it is possible that the recommended or typical levels of vitamin C intake are incongruous with the intake levels needed to provide protection against CVD. Therefore, even the Recommended Dietary Allowances (RDA) for vitamin C of 75 mg for women and 90 mg for men may be inadequate to obtain potential benefits. Based on a recent analysis of US adults in the National Health and Nutrition Examination Survey (NHANES) data, the mean intake of vitamin C from food alone is above the RDA for both men and women (104.6 ± 3.4 mg and 86.6 ± 2.7 mg, respectively) [11]. Many of the clinical trials testing hypotheses related to vitamin C and CVD have used supplemental vitamin C in doses of 500–1000 mg per day. Therefore, if vitamin C does alter cardiovascular risk, questions still remain regarding the relevant mechanisms, the differential effects of supplemental and naturally occurring vitamin C, and the recommended intakes of vitamin C to minimize heart disease risk. This review examines key pieces of literature seeking to answer these questions. 2. Methods This review covers the cardiovascular effects of vitamin C in human studies and attempts to explain the underlying mechanisms involved. A literature search was conducted using three databases: PubMed, Web of Science, and Scopus. Articles were identified in these databases using the search “vitamin C cardiovascular disease”, and additionally using a MeSH search in PubMed: (“Ascorbic Acid” (Mesh)) AND “Cardiovascular Diseases” (Mesh). Other key words added to this search were myocardial infarction, low-density lipoprotein, hypertension, and endothelial function. The reference lists of relevant articles identified in this systematic review were searched manually for additional inclusions. Articles chosen for this review were published by May 2016. Publications identified by these methods were then limited to only those meeting all of the following criteria: (1) randomized controlled trials, observational studies, or meta-analyses examining vitamin C intake (dietary or supplemental) or plasma vitamin C concentrations; (2) studies involving adults aged ≥18 years; (3) studies reporting changes in cardiovascular outcomes or risk factors as an endpoint (mortality, CVD or CHD incidence, lipid profile, endothelial function, blood pressure). 3. Observational Cohort Studies While they are not positioned to establish cause and effect relationships, observational cohort studies have investigated this topic with large groups of individuals and lengthy follow-up periods. The results of these studies, though, have been highly variable (Table 1). Two of the most well -known cohort studies that have investigated the link between vitamin C and CVD are the HPFS and the NHS. Over 121,000 women were followed for 16 years in the NHS. Dietary data were collected from food frequency questionnaires (FFQ) administered at baseline and intervals of every two to four years afterwards [12]. This study found a modest inverse association between vitamin C intake and CHD risk. However, this association was only apparent in women taking supplements containing vitamin C. Importantly, vitamin C intake was not associated with risk of death from CHD. The HPFS, an all-male study designed to complement the NHS, showed even less promise for vitamin C. In their analysis of 39,910 men, researchers found an inverse association between intake of the antioxidant vitamin E and the risk of CHD, but could not demonstrate the same for vitamin C intake. Additional analysis for supplemental vitamin C also showed no significant association with total CVD mortality after adjustment for other CVD risk factors [13]. While these two cohorts examined vitamin C intake in healthy adults, others have examined the relationship between diet and heart disease risk in other populations, including diabetics. In an analysis that combined 520 diabetic participants from the Insulin Resistance Atherosclerosis Study and 422 diabetics from the San Luis Valley Diabetes Study, vitamin C was not associated with CVD risk factor status [18]. The Iowa Women’s Health Study Cohort, which included 1923 diabetic postmenopausal women, found that higher intakes of total vitamin C from supplements and food were associated with an increased relative risk of coronary artery disease and with death from CVD. When dietary and supplemental vitamin C were analyzed separately, only supplemental vitamin C showed a positive association with mortality from CVD [19]. Although these major cohorts largely failed to show a protective effect of vitamin C, or demonstrated possible harm in certain populations, multiple cohort studies have shown that higher plasma vitamin C concentrations may be related to reduced heart disease risk. The European Prospective Investigation into Cancer and Nutrition (EPIC) Study prospectively examined a cohort of 19,496 men and women who were separated into gender-specific quintiles of plasma vitamin C. After a four-year follow-up it was determined that plasma ascorbic acid was inversely associated with the risk of heart failure, as well as all-cause mortality and mortality from CVD and ischemic heart disease [14,15]. Interestingly, the majority of participants in the lowest quintile of vitamin C intake were above the threshold for vitamin C deficiency, demonstrating that this inverse relationship may hold true even within the range of clinically normal vitamin C concentrations. An analysis of 1605 men from the Kuopio Ischaemic Heart Disease Risk Factor Study reached similar conclusions. Of these participants, 91 (5.7%) were found to be deficient in vitamin C (plasma concentrations of less than 11.4 μmol/L). After adjusting for age, year examined, and season of the year examined, men who were deficient in vitamin C had a relative risk of acute myocardial infarction of 3.5 (95% CI: 1.8–6.7) compared to those who were not deficient [17]. It should also be considered, though, that the men deficient in vitamin C were more likely to smoke, consume alcohol, be of a lower socioeconomic group, have lower dietary iron, and have higher systolic blood pressure than those without the vitamin deficiency. The cardiovascular benefit of adequate intakes and plasma concentrations of vitamin C was also demonstrated in an analysis of 2884 participants in the Coronary Artery Risk Development in Young Adults Study (CARDIA) [16]. The hazard ratio for the development of hypertension per 19.6 μmol/L (1 standard deviation) higher plasma vitamin C was 0.85 (95% CI: 0.79–0.92). Additionally, it was found that dietary vitamin C intake, but not intake from supplements, was inversely related to the development of hypertension. Overall, these major cohort studies have produced variable results. While several have shown no relationship between vitamin C and CVD risk, one supports the use of vitamin C supplements in healthy adults, and another suggests increased risk to diabetic patients when using vitamin C supplements. It is fairly well established, though, that low plasma concentrations of vitamin C are predictive of heightened CVD risk. 4. Clinical Trials Major clinical trials have largely been unable to demonstrate a benefit of vitamin C in heart health, either in healthy populations or in those with existing heart disease or related risk factors (Table 2). The Physicians Health Study II was one key study that showed no cardiovascular benefit of vitamin C to healthy men. In this randomized, double-blind, placebo-controlled, 2 × 2 × 2 × 2 factorial trial, the effects of vitamin E, vitamin C, and a multivitamin on the prevention of CVD were evaluated in 14,641 males. During a mean follow-up of eight years, it was determined that long-term daily supplementation of 500 mg of vitamin C did not reduce the primary endpoint of incidence of major cardiovascular events. Vitamin C did not reduce total myocardial infarction, total stroke, cardiovascular death, congestive heart failure, total mortality, angina, or coronary revascularization [20]. Another primary prevention trial conducted in healthy adults was the Supplementation en Vitamines et Minéraux Antioxydants (SU.VI.MAX) study. In this trial, 8112 healthy men and women received either a placebo or an antioxidant vitamin blend containing 120 mg vitamin C, 30 mg vitamin E, 6 mg beta-carotene, 100 μg selenium, and 20 mg zinc. As was shown with vitamin C alone in the Physicians Health Study, participants randomized to the antioxidant blend used in this study experienced no change in ischemic CVD incidence compared to the placebo [21]. To investigate the effects of vitamin C in females with a history of CVD and in those with multiple cardiovascular risk factors, the Women’s Antioxidant Cardiovascular Study supplemented 8171 women with vitamin C, vitamin E, beta-carotene, or a placebo. With a mean follow-up of 9.4 years, it was shown that there was no effect of 500 mg daily ascorbic acid supplementation on cardiovascular events [22]. Similarly, the Women’s Angiographic Vitamin and Estrogen (WAVE) trial was a study of 423 postmenopausal women with at least one 15%–75% coronary stenosis at baseline. Participants receiving 400 IU of vitamin E twice daily plus 500 mg of vitamin C twice daily had a 0.044 mm/year worsening in coronary progression. Those taking a placebo experienced milder coronary progression of 0.028 mm/year. Death, nonfatal myocardial infarction, or stroke occurred in 26 participants taking the antioxidant vitamins compared to 18 taking the placebo [23]. A small study conducted last year on 64 Palestinian men and women who were obese and hypertensive and/or diabetic yielded similar results [24]. Participants were randomized into an experimental group, which received 500 mg vitamin C twice daily, or a control group that received no supplement. After eight weeks, those taking daily vitamin C supplements experienced no significant improvements in total cholesterol or triglyceride levels compared to the control. Another trial investigating the effects of antioxidants on CVD risk was the HDL-Atherosclerosis Treatment Study (HATS). In this three-year double-blind trial, 160 participants with CHD, low HDL cholesterol, and normal LDL cholesterol were assigned to one of four groups: Simvastatin plus niacin, antioxidants (800 IU vitamin E, 1000 mg vitamin C, 25 mg beta carotene, and 100 μg selenium), Simvastatin plus niacin and antioxidants, or placebos. The group taking Simvastatin plus niacin experienced a protective increase in HDL, but this was attenuated by concurrent therapy with antioxidants. The antioxidant group experienced no change in mean LDL or HDL, signifying no cardiovascular benefits of the antioxidant blend [25]. Overall, each of these clinical trials showed either no cardiovascular benefit with vitamin C supplementation, or possible harm related to vitamin C supplement use. 5. Meta-Analyses Reviewed above are several major clinical trials that have investigated the effects of vitamin C on cardiovascular health. Other research groups have focused more narrowly on the effects of vitamin C or antioxidant blends on particular markers of cardiovascular health including arterial stiffness, endothelial function, lipid profile, and blood pressure (Table 3). A 2015 meta-analysis of randomized controlled trials analyzing the effect of antioxidant supplements on arterial stiffness, which included 20 studies and a total of 1909 participants, showed that antioxidant supplements significantly reduced arterial stiffness. However, this benefit was observed only in studies using vitamin E or combined antioxidants, but not with vitamin C alone. Additionally, antioxidant vitamins were shown to be most effective in participants with low baseline plasma concentrations of vitamins C and E [26]. The same research group published a meta-analysis in 2014 of randomized controlled trials analyzing the effect of vitamin C and E supplements on endothelial function. They looked at 46 studies, 17 of which included participants receiving supplements of vitamin C alone. These studies showed a significant improvement in endothelial function, but they had significant heterogeneity, making this result less convincing. Again, the effects were most pronounced in participants with low baseline plasma concentrations of vitamins C and E [27]. A third meta-analysis by this group analyzing the effect of vitamin C on lipid profiles included 40 studies with a total of 1981 participants. Overall, vitamin C was ineffective in improving total cholesterol, LDL, HDL, or triglycerides. Some improvements were seen, however, in certain subgroups. A small reduction in total cholesterol (−0.26 mmol/L) was observed in participants younger than 52 years old and a small increase in HDL cholesterol (0.06 mmol/L) was observed in diabetics [28]. One meta-analysis that showed more promising results with vitamin C supplementation looked at its effects on blood pressure. This analysis looked at 29 trials with a median dose of 500 mg daily vitamin C. The median duration of supplementation in these trials was only eight weeks, but in these short-term studies, it was shown that vitamin C supplementation reduced both systolic and diastolic blood pressure [29]. In addition to the limitation of the short duration of the intervention, this meta-analysis and others are limited by only looking only at supplemental vitamin C. Overall, these meta-analyses demonstrate that supplementation with vitamin C may improve endothelial function and reduce blood pressure. In concert with the antioxidant vitamin E, but not alone, vitamin C may also reduce arterial stiffness. Although cardiovascular benefits are not consistently seen the in the experimental or observational data, these meta-analyses looking at individual components of heart disease risk lend support to possible mechanisms by which vitamin C may be related to heart disease. 6. Conclusions The current literature provides little support for the use of vitamin C supplementation to reduce heart disease risk. Many cohort studies and randomized trials have shown no relationship between vitamin C intake and heart disease risk, while few have suggested moderate benefits, and some have even suggested slight increases in risk. Importantly, multiple studies have documented increases in cardiovascular risk associated with the use of supplemental vitamin C, even when taken in doses of about 1000 mg per day, which is half of the established Tolerable Upper Intake Level (UL) of 2000 mg [19,23]. Although several studies have shown similar absorption of vitamin C from supplements and from food sources [30,31], the mechanisms behind the apparent differential effects of supplemental and dietary vitamin C require further examination. Interestingly, meta-analyses examining particular risk factors of heart disease show that vitamin C may favorably affect blood pressure and endothelial function. While these are only single elements of CVD, they do lend support to the hypothesis that vitamin C may play a role in heart disease. Additionally, multiple observational studies have confirmed that CVD risk and mortality are increased in those with the lowest plasma concentrations of vitamin C, even if they are not classified as deficient in this vitamin. All of these studies, however, are limited in several ways, including the use of only supplemental vitamin C, limited follow-up or intervention time, and reliance on self-reported diet or self-reported compliance with interventions. The lack of consistency within the body of research on this topic has called into question the roles of antioxidants in the human body, and has even cast doubt on the LDL oxidative hypothesis [32,33,34]. A key limitation in understanding the relationship between vitamin C and CVD is the lack of mechanistic studies in humans. Most evidence supporting this link is based on animal studies and observational studies in humans, which both may fail to capture other factors that could play an important mediating role in this relationship. Despite these issues, there is still compelling evidence that warrants continued investigation of the role of vitamin C and other antioxidants in CVD. Conflicts of Interest The authors declare no conflict of interest. ijms-17-01328-t001_Table 1Table 1 Cohort studies investigating vitamin C and cardiovascular disease (CVD). Cohort Population Mean Follow-up Size (n) Age (years) Outcomes EPIC [14,15] Healthy men and women 4 years 19,496 45 to 79 Plasma vitamin C inversely related to risk of heart failure and mortality from CVD and ischemic heart disease CARDIA [16] Healthy men and women 15 years 2884 18 to 30 Dietary vitamin C inversely related to hypertension Kuopio Ischaemic Heart Disease Risk Factor Study [17] Healthy men 5 years 1605 42, 48, 54, or 60 Vitamin C deficiency associated with increased CHD risk NHS [12] Healthy women 16 years 85,118 30 to 55 Vitamin C from supplements (but not from foods) associated with lower risk of CHD HPFS [13] Healthy males 4 years 39,910 40 to 75 Vitamin C intake not associated with CHD risk IRAS and SLVDS [18] Diabetic men and women 4 years IRAS n = 520, SLVDS n = 422 IRAS: 40 to 69; SLVDS: 20 to 74 Vitamin C not associated with CVD risk factor status Iowa Women’s Health Study [19] Postmenopausal diabetic women 15 years 1923 55 to 69 Supplemental vitamin C intake associated with an increased risk of CVD mortality EPIC: European Prospective Investigation into Cancer and Nutrition; CARDIA: Coronary Artery Development in Young Adults Study; NHS: Nurses’ Health Study; HPFS: Health Professionals Follow-Up Study; IRAS: Insulin Resistance Atherosclerosis Study; SLVDS: San Luis Valley Diabetes Study. ijms-17-01328-t002_Table 2Table 2 Clinical trials investigating vitamin C and CVD. Clinical Trial Population Size (n) Age (years) Intervention Trial Duration Outcome(s) Women’s Antioxidant Cardiovascular Study [22] Females with history of CVD or 3 or more CVD risk factors 8171 40 and older 500 mg/day ascorbic acid 9.4 years No effect on myocardial infarction, stroke, coronary revascularization, or CVD death Vitamin C Trial in Obese Adults [24] Obese men and women with hypertension and/or diabetes 64 20–60 500 mg/day ascorbic acid twice daily 8 weeks No effect on total cholesterol of triglycerides Physicians Health Study II [20] Healthy males 14,641 50 and older 500 mg/day ascorbic acid 8 years No effect on major cardiovascular events, myocardial infarction, stroke, cardiovascular mortality, or total mortality SU.VI.MAX [21] Healthy men and women 13,017 (7876 women, 5141 men) Women 35–60; men 45–60 Daily blend of 120 mg vitamin C, 30 mg vitamin E, 6 mg beta-carotene, 100 μg selenium, and 20 mg zinc 7.5 years No effect on incidence of ischemic CVD HATS [25] Men and women with coronary disease 160 Men under 63; women under 70 800 IU vitamin E, 1000 mg vitamin C, 25 mg natural beta-carotene, 100 μg selenium; and simvastatin and niacin 3 years No effect on LDL or HDL with antioxidant supplement WAVE [23] Postmenopausal women with CVD 423 Postmenopausal (mean age of 65) 800 IU vitamin E and 1000 mg of vitamin C 2.8 years Higher all-cause mortality in the antioxidant group versus the placebo SU.VI.MAX: Supplementation en Vitamines et Minéraux Antioxydants; HATS: HDL-Atherosclerosis Treatment Study; WAVE: Women’s Angiographic Vitamin and Estrogen trial. ijms-17-01328-t003_Table 3Table 3 Meta-analyses investigating vitamin C and cardiovascular markers. Meta-Analysis Topic Number of Studies Total Participants (n) Trial Sizes (n) Trial Durations Vitamin C Supplement Dosages Outcome Arterial Stiffness [26] 20 1909 8 to 1162 8 h to 2635 days 120 to 4000 mg daily Arterial stiffness was reduced with vitamin E and combined antioxidant supplement, but not with vitamin C alone Endothelial Function [27] 46 (17 supplemented with vitamin C alone) 1817 (478 in trials that supplemented with vitamin C alone) 7 to 197 4 to 240 weeks 500 to 2000 mg daily Significant improvements in endothelial function Lipid Profile [28] 40 1981 8 to 305 2 to 240 weeks 125 to 4500 mg daily No significant change in blood lipids Blood Pressure [29] 29 1407 10 to 120 2 to 26 weeks 60 to 4000 mg daily Vitamin C supplementation reduced systolic and diastolic blood pressure ==== Refs References 1. Kris-Etherton P.M. Lichtenstein A.H. Howard B.V. Steinberg D. Witztum J.L. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081329ijms-17-01329ReviewEpigenetic Mechanisms in Bone Biology and Osteoporosis: Can They Drive Therapeutic Choices? Marini Francesca Cianferotti Luisella Brandi Maria Luisa *Cho William Chi-shing Academic EditorDepartment of Surgery and Translational Medicine, University of Florence and Metabolic Bone Diseases Unit, University Hospital of Florence, Largo Palagi 1, 50139 Florence, Italy; f.marini@dmi.unifi.it (F.M.); luisella.cianferotti@unifi.it (L.C.)* Correspondence: marialuisa.brandi@unifi.it; Tel.: +39-055-794-6304; Fax: +39-055-794-630312 8 2016 8 2016 17 8 132901 7 2016 05 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Osteoporosis is a complex multifactorial disorder of the skeleton. Genetic factors are important in determining peak bone mass and structure, as well as the predisposition to bone deterioration and fragility fractures. Nonetheless, genetic factors alone are not sufficient to explain osteoporosis development and fragility fracture occurrence. Indeed, epigenetic factors, representing a link between individual genetic aspects and environmental influences, are also strongly suspected to be involved in bone biology and osteoporosis. Recently, alterations in epigenetic mechanisms and their activity have been associated with aging. Also, bone metabolism has been demonstrated to be under the control of epigenetic mechanisms. Runt-related transcription factor 2 (RUNX2), the master transcription factor of osteoblast differentiation, has been shown to be regulated by histone deacetylases and microRNAs (miRNAs). Some miRNAs were also proven to have key roles in the regulation of Wnt signalling in osteoblastogenesis, and to be important for the positive or negative regulation of both osteoblast and osteoclast differentiation. Exogenous and environmental stimuli, influencing the functionality of epigenetic mechanisms involved in the regulation of bone metabolism, may contribute to the development of osteoporosis and other bone disorders, in synergy with genetic determinants. The progressive understanding of roles of epigenetic mechanisms in normal bone metabolism and in multifactorial bone disorders will be very helpful for a better comprehension of disease pathogenesis and translation of this information into clinical practice. A deep understanding of these mechanisms could help in the future tailoring of proper individual treatments, according to precision medicine’s principles. gene expressionhistone modificationsDNA methylationmicroRNAsprecision medicinefragility fracture ==== Body 1. Introduction Osteoporosis, a metabolic skeletal disorder, is a consequence of disrupted normal bone turnover, resulting in reduction of bone mass and mineralization and severe alteration of bone micro-architecture. As a consequence of these structural modifications, osteoporotic bone tissue presents reduced strength that is associated with a high risk of fragility fractures. It is a complex multifactorial disorder resulting from an interaction between genetic and environmental factors, dietary habits, and lifestyle. The focal point of osteoporosis is the deregulation of bone turnover. Normally, in adult skeleton bone remodelling is maintained in a tight balance by highly regulated differentiation, activity, and apoptosis of bone-forming osteoblasts and bone-resorbing osteoclasts. Differentiation of osteoblasts and osteoclasts is finely tuned by profound changes in the expression of numerous regulatory genes. Changes in the expression of these genes could result in altered bone homeostasis and the manifestation of osteoporosis. Family and twin studies confirmed the importance of heritability and genetic profiles in the determination of bone phenotypes, bone mineral density (BMD), and osteoporosis risk. Numerous genes have been related to the regulation of bone metabolism, and some of their polymorphisms have been associated with variations in bone mass, osteoporosis predisposition, and fracture risk. However, together these genetic variants account for less than 10% of the phenotypic variance in BMD [1]. Given the dynamic nature of bone functions, such as to be a major reservoir of essential ions such as calcium and phosphate, this tissue has to provide very rapid responses to organic changes and requirements, which are constantly granted by numerous and complex epigenetic regulatory mechanisms of gene expression. Epigenetics consists of modular changes in gene and protein expression that are independent of inherited DNA nucleotide sequences, and include histone modifications, DNA methylation, and post-transcriptional microRNA (miRNA)-mediated negative regulation of target mRNAs (Table 1 and Figure 1). Epigenetic modifications are all reversible, highly dynamic, age-, cell-, and tissue-specific, and sensitive to endogenous signals and/or environmental stimulation. Every individual may have multiple epigenomes that can rapidly change during development, lifetime, and aging, in response to internal and external signals, both in physiology and physiopathology. Epigenetic processes are currently considered key mechanisms by which environmental factors and stochastic endogenous and exogenous stresses may promote the development of numerous complex multifactorial pathologies, such as osteoporosis. Moreover, recently, modification of epigenetic mechanisms and their activity has also been associated with ageing. In this light, epigenetics could represent a link between the genome and the environment, strongly influencing bone phenotypes and osteoporosis risk, together (and also synergically) with inherited genetic variants, which could explain why genetic factors alone are not sufficient to explain the predisposition to osteoporosis development and fracture occurrence (Figure 2). Alterations in one or more epigenetic mechanisms can be associated with deregulation of bone homeostasis, as described in detail in the next sections, and subsequent changes of bone-related gene expression may help to account for the “missing heritability” of osteoporosis risk. Moreover, the study and knowledge of these mechanisms, in normal or pathological bone tissues, may help both in assessing osteoporosis and fracture risk and in driving therapeutic options, as well as in identifying novel therapeutic anti-fracture molecules. Establishing the epigenetic signature in an individual could help in tailoring appropriate treatments, according to the science of precision medicine. Herein, the main epigenetic mechanisms involved in skeletal physiology and pathophysiology will be described. 2. Post-Translational Histone Modifications in Bone Biology Post-translational histone modifications include a large number of histone changes, such as methylation, acetylation, phosphorylation, ubiquitination, ADP ribosylation, sumoylation, deamination, and non-covalent proline isomerisation [2]. These modifications, induced or removed by specific enzymes, alter the structure of chromatin, exposing or hiding gene promoters and, thus, respectively, promote or repress gene transcription, through a dynamic phenomenon called the “histone code”. One of the principal histone modifications is the acetylation/deacetylation process that adds or removes acetyl groups to histone lysine side chains. Histone acetylation usually relaxes the chromatin, allows transcription factors and RNA polymerase II to access the DNA, and, thus, is associated with the promotion of gene transcription, while histone deacetylation induces compaction of the chromatin and is responsible for blocking gene expression. Histone acetyltransferases (HATs) and histone deacetylases (HDACs) act in a counteractive manner to regulate histone acetylation status in response to incoming signals, and are involved in the control of numerous important biological processes (Figure 3). Bone remodelling has also been demonstrated to be sensitive, at various cellular levels, to histone modifications. It is conceivable that these enzymes could rapidly regulate bone turnover in response to endogenous or external changes. Eighteen HDAC enzymes have been identified in humans, and several of them have been demonstrated to strongly contribute to regulate the correct skeletal development and accrual, bone mass peak acquisition, and optimal bone mass maintenance during lifetime and aging. These enzymes regulate histone acetylation status and directly deacetylate (i.e., inactivate) key proteins that drive bone homeostasis, such as the early osteoblast differentiation regulator Runt-related transcription factor 2 (RUNX2) (Table 2). HDACs are divided into four classes (I, II, III, and IV) based on their structure, function, cellular localization, and expression. Classes I, II, and IV (commonly referred as HDACs) present a zinc-dependent catalytic sites, while class III (commonly named as Sirtuins; Sirts) needs NAD+ for the catalytic action. HDAC1 and HDAC2 are highly homologous and can form hetero- or homo-dimers with each other. They both play a role in suppressing osteoblastogenesis, and levels of their mRNAs are usually decreased during osteoblast differentiation to grant the correct development of this process [3]. Recently, it has been shown that HDAC2 expression is increased during receptor activator of nuclear factor-κB ligand (RANKL)-induced osteoclast differentiation of bone marrow-derived precursors [4]. It was revealed to be a key regulator of osteoclastogenesis by activating Akt kinase that directly phosphorylates and inhibits Forkhead box protein O1 (FoxO1), a negative regulator of osteoclast differentiation [4]. HDAC3 is highly expressed in osteoblasts at all stages of differentiation, and it directly interacts with the transcription factor RUNX2 [5]. This enzyme was demonstrated to be important for bone mass acquisition and maintenance during aging, and for correct craniofacial development. Mouse models deficient in HDAC3, in osteochondral precursor cells, showed reduced bone length and decreased cortical and trabecular bone mass and mineralization, severe osteoporosis, and suffered frequent fragility fractures [6]. The complete germline knockout of HDAC3 is lethal at the embryonic level [7]. Conversely, the RNA interference-driven in vitro suppression of HDAC3 in pre-osteoblasts increases matrix mineralization [5]. HDAC4 has been shown to directly deacetylase RUNX2, repressing its transcriptional activity and promoting its degradation in mature osteoblasts [8]. Loss of HDAC4 in mouse models accelerates endochondral bone formation, inducing premature ossification of multiple cartilaginous sites that lead to severe skeletal defects, including shortened long bones, vertebral body fusion, and premature endochondral ossification of the skull. Conversely, the over-expression of Hdca4 results in a severe deficit of endochondral ossification and slows the ossification of cartilage in vivo [9]. Intramembranous bone formation is normal in both cases, proving that HDAC4 has a role specifically in regulating the process of endochondral ossification from cartilage. HDAC4 also directly controls bone morphogenetic protein (BMPs) and TGFβ signalling during bone development [10]. In humans, loss-of-function mutations of the HDAC4 gene were found to be the cause of Brachydactyly-Mental Retardation syndrome, characterised by distinctive craniofacial features and shortened metacarpal and metatarsal bones. Conversely, HDAC8 has a crucial role in intramembranous ossification. Indeed, germline deletion of this gene in mice is detrimental to skull bone formation and leads to perinatal death [11]. In humans, inactivating mutations of the HDAC8 gene has been identified in patients with Cornelia de Lange syndrome and with a clinical condition similar to Wilson-Turner linked mental retardation syndrome, both characterized by shortened long bones and distinctive craniofacial dimorphisms [12,13], the former also by delayed fontanel closure and small hands and feet. HDAC5 is expressed in mature osteoblasts where, in association with HDAC4 and TGFβ1, it represses the transcriptional activity of RUNX2 [8], and also directly deacetylates RUNX2, promoting its Smurf-mediated degradation. Two related young patients with primary juvenile osteoporosis showed elevated HDAC5 protein level and reduced RUNX2 expression in bone [14]. HDAC7 is highly expressed both in mature osteoblasts and in osteoblast precursors, and it has been demonstrated to repress RUNX2 by deacetylation-independent mechanisms [15] and to be necessary for endochondral ossification. In vitro suppression of HDAC7 by RNA interference favours osteoblast differentiation in a bone morphogenetic protein 2 (BMP2)-dependent manner [15]. Inactivation or suppression of HDAC7 expression results in proliferation of osteoclast precursors and promotion of RANKL-induced osteoclastogenesis [16]. Also, HDAC9 knock-out mice showed an increased number of active osteoclasts, a higher bone resorption, reduced bone formation indices, and an osteopenic phenotype. Conversely HDAC9 expression suppresses osteoclastogenesis, presumably by negatively regulating Rankl expression. These data suggest a possible involvement of HDAC7 and HDAC9 in osteoporosis [17]. Sirt1 and Sirt6 are the only class III HDACs that have shown, to date, a proven role in bone tissue development and metabolism [17]. Activation of Sirt1 in mesenchymal stem cells (MSCs) promotes osteogenic differentiation, blocking adipocyte differentiation. Conversely, inhibition of Sirt1 expression results in a promotion of adipogenic differentiation [18]. Ovariectomy and subsequent estrogen depletion result in Sirt1 protein absence in animal models; this could explain the increased adiposity in bone marrow and the rapid bone loss after menopause. The restoration of Sirt1 expression could reveal a possible approach to preventing post-menopausal osteoporosis. Both Sirt1 and Sirt6 facilitate endochondral ossification—Sirt6, particularly, by controlling chondrocyte proliferation and differentiation. Histone deacetylase inhibitors (HDIs, i.e., valproic acid, trichostatin A) are small molecules able to inactivate HDACs (Figure 3) by binding to their zinc-containing catalytic sites. HDIs have been demonstrated to induce a transient increase in osteoblast proliferation and viability, to enhance osteoblast differentiation in vitro, to augment alkaline phosphatase (ALP) production and expression of type I collagen, osteopontin (OPN), bone sialoprotein, osteocalcin (OCN), and RUNX2, and to accelerate the extracellular mineralization in osteogenic cell lines and mesenchymal progenitor cells [19]. Moreover, these molecules induce MSCs to differentiate into mature osteoblasts. HDIs have also been demonstrated to block the formation of pre-osteoclast-like cells and their fusion into multinucleated osteoclast-like cells in rat bone marrow cell culture, and to reduce osteoclast-specific synthesis of cathepsin K [20], and to act on mature osteoclasts, causing their apoptosis [21]. The increased acetylation of histones H3 and H4, at the RANKL promoter, favours RANKL transcription, indicating that epigenetic chromatin remodelling is involved in RANKL expression [22] and, thus, in the regulation of osteoclast activation. These in vitro results suggest a possible application of HDIs as anti-fracture therapy in osteoporosis, able both to reduce osteoclast bone resorption and promote osteoblast bone formation. However, both in vivo studies in animal models [23,24] and human epidemiological studies on HDI treatment for epilepsy and mental disorders [25,26] evidenced a negative effect of prolonged HDI therapy on BMD value, associated, in some cases, with a higher risk of fracture [27]. The molecular causes of controversial results between HDI effects on bone in vitro or in vivo, as well as the exact mechanisms by which HDIs reduce BMD in vivo, remain to be fully elucidated. Current HDIs act on various different HDACs, which play crucial roles at various points of osteoblastogenesis, so that long-term HDI therapy can affect all of them at the same time. Ideal HDIs should target only one specific HDAC. With this in mind, a new generation of drugs of this kind is currently under development. Recently, a study by Feng et al. tested the ability of resveratrol, a plant polyphenol acting as an agonist of Sirt1, to promote osteogenesis in vitro and prevent bone loss in animal models and, thus, its possible protective effect on osteoporosis [28]. In rats with bilateral ovariectomy, resveratrol moderately restored the serum level of ALP and OCN and improved bone structure. Moreover, resveratrol promoted the in vitro osteoblast differentiation of bone marrow mesenchymal stromal cells via the Sirt1-NF-κB signalling pathway. The authors demonstrated that the beneficial bone effects of resveratrol were exerted by its direct action on Sirt1, since the RNA silencing of Sirt1 resulted in a loss of any positive effect of resveratrol administration. These results suggested a therapeutic potential of resveratrol against post-menopausal osteoporosis. Histone methylation is another common histone modification, finely tuned by the opposing enzymatic actions of histone lysine methyltransferases (KMTs) and histone lysine demethylases (KDMs). The histone methylation state has been revealed to be an important modulator of MSC differentiation into the osteogenic lineage or the adipogenic lineage. SETDB1 and EZH2 are two KMTs that inhibit osteogenic differentiation of MSCs via blocking RUNX2, transcription factor 7 (TCF7), and Osterix (OSX) transcription, and promote adipogenesis of MSCs by inducing the expression of peroxisome proliferator-activated receptors gamma (PPARγ), myocyte enhancer factor 2 interacting transcriptional repressor (MITR), and Wnt-related genes [29]. Conversely, KDM4B, KDM6A, and KDM6B are KMTs that promote osteogenesis and repress adipogenesis by inducing the transcription of RUNX2, BMP, OCN, distal-less homeobox 5 (DLX5), and HOX-related genes [29]. Other numerous KMTs are involved in the alternative regulation of these two differentiation processes and, thus, in normal bone turn-over or pathologies, and they have been extensively reviewed elsewhere recently [29]. 3. DNA Methylation in Bone Biology DNA methylation consists of the reversible covalent addition of a methyl group to the fifth carbon of a cytosine residue (5-mC) located in the CpG islands of gene promoters, and it is commonly associated with the repression of gene expression. The enzymes responsible for DNA methylation are de novo DNA methyltransferases (DNMT3A and DNMT3B) that act preferentially on unmethylated and hemimethylated DNA and are important during embryo development, and maintenance methyltransferase (DNMT1) with a preference for hemimethylated DNA and able to maintain the methylation patterns during DNA replication. Demethylation is both a passive reduction of methylation during DNA duplication and an active mechanism not yet fully elucidated. DNA methylation state has an important role in both bone metabolism and age-related disorders, such as osteoporosis. Methylation of several genes is involved in the regulation of bone cell differentiation and in the normal bone remodelling process. In osteoblasts, DNA methylation state co-regulates the expression of various important genes involved in bone cell functions, such as alkaline phosphatase (ALPL), sclerostin (SOST), OSX, DLX5, oestrogen receptor alpha (ESR1), OPN, RANKL, osteoprotegerin (OPG), secreted frizzled-related protein 1 (SFRP1), and leptin (LEP) [30]. ALPL promoter methylation is inversely associated with gene transcription and expression and is differently regulated at different stages of osteoblast differentiation. Indeed, osteoblasts show poor ALPL methylation, bone lining cells have an intermediate methylation status, and an ALPL hypermethylation is characteristic of osteocytes, associated with no expression of ALPL gene. In parallel, transition from osteoblasts to inactive osteocytes is associated with a progressive reduction in levels of SOST promoter methylation. Little is known about DNA methylation and the regulation of osteoclast differentiation and activity. It can be also inferred that DNA methylation has a direct role in the pathogenesis of osteoporosis, but, to date, few studies directly support this hypothesis, and results are still inconclusive. Recently, a study by Nishikawa et al. [31] demonstrated that Dnmt3a positively regulates osteoclast differentiation by methylation, and expression induction, of the S-adenosylmethionine (SAM) gene. SAM protein expression results in the induction of osteoclastogenesis by repression of anti-osteoclastic genes, such as RANKL. The authors also showed that mice osteoclast precursors deficient in Dnmt3a were unable to efficiently differentiate into mature osteoclasts in vitro, and that Dmnt3a knock-out mice model showed a reduced number of active osteoclasts and a higher bone mass with respect to the normal control. Furthermore, they investigated the possibility that targeted Dnmt3a inhibition may have a positive effect in preventing bone loss, by administration of the Dnmt3a inhibitor theaflavin-3,3′-digallate (TF-3) to ovariectomized female mice, as a model of postmenopausal osteoporosis. Treatment with TF-3 resulted in a reduction of number of activated osteoclasts, a higher bone mass, and protection against bone loss, suggesting that the inhibition of Dnmt3a could be a beneficial strategy for preventing bone loss and controlling postmenopausal osteoporosis. Human clinical trials on osteoporosis and other bone disorders characterized by excessive osteoclast activity are surely needed to confirm this preliminary result and to design a possible novel therapeutic approach for these diseases. Analysis of DNA methylation in bone tissue samples from osteoporotic subjects, with respect to control individuals, revealed a hypermethylation of OPG and RANKL CpG islands, which influences the transcription level of these genes and, subsequently, the osteoclastogenesis [32]. However, the concomitant increase of the RANKL: OPG ratio in patients with osteoporotic fractures seemed to be derived from other methylation-independent mechanisms [32]. A case-control study analysed DNA methylation at four regions upstream of the SOST transcription start site in bone biopsies from post-menopausal osteoporotic women and normal BMD controls, indicating a statistical difference in methylation state between affected patients and the healthy group [33]. The authors suggested that the increased methylation of SOST promoter in osteoporosis could be a mechanism that, through the reduction of serum SOST protein level and, subsequently, the enhancement of canonical Wnt signalling, aims to induce compensatory bone formation, counteracting osteoporosis-associated bone loss. A genome-wide study evaluated the DNA methylation profile, at the genome level (methylation of 23,367 GpG sites in 13,463 genes was analysed), in femoral head trabecular bone specimens from osteoporotic hip fractures with respect to hip samples of patients with osteoarthritis [34]. The results showed an inverse correlation between methylation and whole gene expression in both cases. A significant difference in methylation level was found in 241 CpG sites located in 228 genes, the great majority of them involved in glycoprotein, neuronal differentiation, adherence, homeobox, and cell proliferation pathways. Among these, the homeobox superfamily genes, differentially methylated between osteoporosis and osteoarthritis, include genes that are directly involved in skeletal embryogenesis. It could be hypothesised that the existence of one or more developmental regulatory components, probably including DNA methylation, acting during development and growth, is responsible for correct skeletal formation and homeostasis and also for the further development of osteoporosis or osteoarthritis during aging. Environmental factors have been associated with DNA methylation and skeletal development during the early phase of embryogenesis in animal studies and in humans. Indeed, maternal dietary habits influence the bone mass of the progeny [35,36], presumably also through the induction of methylation variations and/or changes in histone modifications in genes important for bone cell differentiation and function, such as PPARγ and glucocorticoid genes [37,38]. DNA methylation patterns are highly cell-specific, and can differ between different stages during a lifetime or in correlation with diseases. Therefore, they could be used as viable diagnostic, prognostic, and/or therapy-guiding biomarkers in numerous biological samples, thanks also to the great stability of methylation in spite of external perturbations and sample manipulations. In this light, large-scale studies comparing methylation states between cases and relative controls can identify this epigenetic trait associated with specific disorders. Nevertheless, unlike in tumours, where DNA methylation screenings have validated clinical applications, no methylation tests are currently available or under investigation for common bone disorders. 4. miRNAs in Bone Biology miRNAs are endogenous single-stranded small non-coding RNAs that negatively regulate gene expression, at the post-transcriptional level, by selectively binding to the 3′ non-coding region (3′UTR) of specific target mRNAs through base pairing. By this mechanism, miRNAs can suppress the expression of their target proteins by directly blocking the translation process or enhancing the degradation of mRNAs. Over 2000 human miRNAs have been identified, to date, all of them involved in the regulation of important biological processes such as development, cell growth, and cell differentiation. The fundamental role of miRNAs in the regulation of growth and differentiation of both osteoclasts and osteoblasts has been clearly assessed. Indeed, the genetic deletion of Dicer, the enzyme responsible for maturation and activation of all miRNAs, in osteoblast progenitors [39] and pre-osteoclast lineage cells [40] resulted, respectively, in severe impairment of osteoblast differentiation and maturation with subsequent alterations in bone tissue structure and matrix mineralization, or in a reduced number of active osteoclasts with subsequent decreased bone resorption and osteopetrosis. Dicer excision in differentiated osteoblasts results in an increased bone mass in adult mice, with double cortical bone width and trabecular thickness with respect to normal controls [39]. In recent years, numerous miRNAs have been demonstrated to directly regulate, either positively or negatively, osteoblastogenesis and osteoclastogenesis, by interacting with specific factors involved in the control of these two processes. Herein, a brief overview of the principal miRNAs involved in the regulation of osteoblast or osteoclast differentiation is given, with the description of their specific action in these processes. Fourteen miRNAs (miR-23a, miR-30a-d, miR-34c, miR-133a, miR-135a, miR-137, miR-204, miR-205, miR-211, miR-217, miR-218, miR-335, miR-338, miR-433, and miR-3077-5p) target the 3′UTR of RUNX2, the master transcription factor in osteoblast differentiation [41,42,43,44]. All these miRNAs control osteogenic differentiation at different levels, blocking the osteoblast lineage progression of committed pre-osteoblasts or directing multi-potent mesenchymal stem cells toward adipogenic differentiation. Conversely, two miRNAs, miR-2861 and miR-3960, have been shown to indirectly induce RUNX2 expression by targeting its inhibitors HDAC5 and HOXA2, and, thus, promoting osteoblast differentiation [45]. miR-2861 and miR-3960 are transcribed together from the same miRNA polycistron during BMP2-induced osteogenesis. Over-xpression of miR-2861 and miR-3960 promotes BMP2-induced osteoblastogenesis, while their inhibition reduces osteoblastogenesis. RUNX2 itself was demonstrated to directly bind to the promoter of miR-3960 and miR-2861, positively regulating their expression. Moreover, it has been shown that RUNX2 negatively regulates the expression of the miR cluster 23a-27a-24-2, by DNA direct binding to specific elements in the promoter of these genes [46]. The reduced expression of this miR cluster is responsible for the promotion of osteoblast differentiation. Taken together, all these data suggest that the RUNX2 factor establishes complex regulatory networks with miRNAs that play, in this way, a central role in the control of correct progression and maintenance of the osteoblast phenotype. A homozygote mutation of the gene encoding miR-2861 has been associated with a rare form of primary juvenile osteoporosis in two adolescents [14]. Consistent with data obtained from mouse models, bone samples from these two patients showed increased levels of HDAC5 and decreased levels of RUNX2, confirming that miR-2861 is an important physiological regulator of osteoblast differentiation. Thus, deregulation of miR-2861 can contribute to the establishment of osteoporosis via its effect on osteoblasts and could be an optimal target for future anti-osteoporotic therapies. Also, BMPs and the Wnt signalling pathway, fundamental and key mechanisms driving skeletal-related gene expression involved in the formation of cartilage and bone, have been demonstrated to interact with various miRNAs to control osteoblast differentiation. BMP2 controls the switch between muscle and bone differentiation by regulating miRNA expression. BMP2 has been shown to downregulate 22 miRNAs that inhibit the translation of crucial factors of osteoblastogenesis, by directly targeting their 3′UTR, such as SMAD5 (the intracellular receptor of BMP2 within pre-osteoblasts) and RUNX2 [42]. Indeed, two miRNAs downregulated by BMP2, miR-135 and miR-133, are, respectively, negative regulators of SMAD5 and RUNX2. miR-135 inhibits directly SMAD5 expression, and, thus, indirectly also RUNX2 expression. miR-133 showed the dual function of promoting myogenesis and, at the same time, inhibiting differentiation of MSCs into the osteoblast lineage. Specific miRNAs were shown to regulate the Wnt/β-catenin signalling pathway, interfering with osteoblast differentiation. miR-29a gene expression is enhanced by canonical Wnt signalling, and its expression is increased during osteogenic differentiation of mesenchymal osteoblast precursors [47]. miR-29a targets and inhibits expression of Dikkopf-1 (DKK1), Kremen2, and secreted frizzled related protein 2 (SFRP2), three key negative regulators of Wnt signalling, thus potentiating the effect of Wnt signalling, promoting the expression of Wnt-regulated genes, and favouring osteoblast differentiation and bone formation. Wnt signalling also induces the expression of miR-128 during osteoblast differentiation, while miR-128 promotes osteogenic commitment and differentiation of bone marrow MSCs by directly inhibiting the translation of three repressors of Wnt signalling, SOST, Dickkopf-2 (DKK2), and SFRP2, through a positive feedback loop [48]. Additional miRNAs enhance osteoblast differentiation through the modulation of the canonical Wnt pathway. miR-335-5p activates Wnt signalling and promotes osteogenic differentiation by downregulating DKK1, and is highly expressed in both osteoblasts and hypertrophic chondrocytes during embryogenesis [49]. Treatment of pre-osteoblasts with anti-miR-335-5p reverses the effect of miR-335-5p and reduces osteoblastogenesis. miR-27 is overexpressed during osteoblast differentiation, and its inhibition is associated with blocking of cell differentiation [50]. miR-27 directly targets and inhibits adenomatous polyposis coli (APC) gene expression, a fundamental component of the axin complex that positively regulates phosphorylation and subsequent degradation of β-catenin, by blocking the Wnt signalling. Inactivation of APC by miR-27 induces the accumulation of β-catenin, its translocation into the nucleus, and the transcription of Wnt-regulated genes, resulting in a promotion of osteogenesis. Increased expression of miR‑142‑3p has been seen during osteoblast differentiation of mesenchymal precursors [51]. This miRNA acts in the same way as miR-27, by targeting and repressing APC. All miRNAs described above are important positive or negative mediators of osteoblast differentiation (Figure 4), and could be novel promising targets for the development of preventive or therapeutic agents against osteogenic disorders. The involvement of miRNAs in the regulation of osteoclast differentiation has been markedly less investigated (Figure 5). Three principal miRNAs have been associated with the regulation of osteoclastogenesis: miR-21, miR-155, and miR-223. A significant stimulation of miR-21 during RANKL-induced osteoclastogenesis has been found [52]. miR-21 targets programmed cell death 4 (PDCD4). The PDCD4 protein is known to directly negatively affect the activity of the transcription factor AP1. This results in decreased expression of AP1-regulated genes such as c-FOS, an important transcription factor for inducing osteoclast differentiation and osteoclast-specific downstream target genes. Diminished expression of PDCD4, by miR-21, removes the repression from c-FOS expression and promotes osteoclast differentiation. Moreover, it has been shown that RANKL-induces c-FOS expression that upregulates miR-21 expression in a positive feedback loop responsible for promotion of RANKL-mediated osteoclastogenesis. The high expression of miR-21 promoting osteoclast differentiation is inhibited by oestrogens [53]. In particular, oestrogens downregulate miR-21 biogenesis, resulting in an increase of Fas Ligand (FasL, a target of miR-21), which induces the apoptosis of osteoclasts [53]. These data could suggest miR-21 as a target for novel antagomir-based antiresorptive therapy as an alternative to oestrogen replacement therapy in post-menopausal women. Conversely, miR-155 has a suppressive effect on osteoclast differentiation in vitro, mediated by its inhibition of expression of SOCS1 and MITF, two essential positive regulators of osteoclastogenesis [54]. Expression of miR-155 is induced by interferon-β, which, in this way, exerts negative action on osteoclast differentiation. miR-155 could represent a novel viable agent for the treatment of osteoclast-mediated diseases, such as osteoporosis, but its systemic role in osteoclast suppression and bone remodelling remains to be clearly elucidated. Another miRNA, miR-223, plays a critical role in osteoclast differentiation. An increased expression of miR-223 blocks the maturation of pre-osteoclastic cells into tartrate-resistant acid phosphatase (TRAP)-positive multinucleated mature osteoclasts, while normal expression of miR-223 favours this cell differentiation [55]. A recent study associated low expression of miR-223 with the promotion of osteoclastogenesis [56]. High concentrations of inorganic phosphate (Pi) decreased miR-223 expression in osteoclast precursors in vitro, resulting in a marked decrease in osteoclastogenesis. miR-223 targets the Nuclear Factor IA (NFIA), a negative regulator of macrophage colony stimulating factor (M-CSF), and Ras homolog gene family ()member B RhoB, a transcription factor involved in cell proliferation. Data from these two studies suggested that both too high or too low a level of miR-223 precludes efficient osteoclast differentiation, and indicated that this miRNA is a promising therapeutic target for bone metabolic disorders characterised by excessive osteoclast activity. Given their important roles in the normal regulation of osteoblast and osteoclast development and function, deregulation of miRNA activity is surely an important factor in bone disorders. Three mechanisms could be responsible for altered functions of specific miRNAs in bone tissue, leading to the development of osteoporosis or other metabolic bone disorders: (1) mutations in genes encoding miRNAs involved in the regulation of bone metabolism, which deregulate their function and/or their expression; (2) mutations affecting the 3’UTR binding sites of target mRNAs, fundamental for the regulation of transcription, and modulated by miRNAs; (3) abnormalities in miRNA expression altering regulatory networks involved in the control of osteoblast and/or osteoclast differentiation and activity. Recently, it has been demonstrated that miRNAs are not only active locally within the cells that express them, but can also be secreted via extracellular microvesicles or exosomes. Indeed, circulating miRNAs have been identified in 12 different body fluids [57]. Circulating miRNA signatures have been identified as diagnostic and/or prognostic biomarkers of various disorders, including age-associated diseases. The role of circulating miRNAs as biomarkers for osteoporosis and other metabolic bone disorders has not received much attention until recent years. Indeed, the finding that osteoblasts seem to communicate via exosome shuttling and the discovery that exosome miRNA content changes during osteoblast differentiation both support the possible use of extracellular miRNAs as biomarkers of bone metabolism. Circulating miRNAs have the potential to be surrogate biomarkers (bioproducts) of bone metabolism as well as provide information on the cellular and molecular processes involved in bone turnover and be minimally invasive markers with specific functional relevance to osteoblast, osteoclast, and osteocyte differentiation and activity. Measurement of specific serum miRNAs could help in the near future to identify patients at high risk of osteoporosis and fragility fractures and/or to monitor the efficacy of antiresorptive therapies and bone-forming agents, either used alone or in association with existing biological markers of bone metabolism. In this setting, few studies have tried to identify circulating miRNAs associated with BMD, osteoporosis, and fracture risk. Li et al. evaluated, by quantitative RT-PCR (RT-qPCR), the expression of three specific miRNAs (miR-21, miR-133a, and miR-146) in the plasma of 120 post-menopausal Chinese women with normal, osteopenic, or osteoporotic range of BMD, evidencing a higher level of miR-133a and a lower level of miR-21 in osteoporotic and osteopenic women with respect to the normal group [58]. Seelinger et al. measured an 83-miRNA RT-qPCR panel in a pool of 10 serum samples from osteoporotic Caucasian men and women vs. a pool from 10 non-osteoporotic Caucasian women, both with hip fractures [59]. Results from PCR-array have been validated on further serum samples from 30 osteoporotic patients vs. 30 normal controls and on miRNA samples isolated from the bone tissue of 20 osteoporotic and 20 non-osteoporotic patients. A signature of nine circulating miRNAs (miR-21, miR-23a, miR-24, miR-93, miR-100, miR-122a, miR-124a, miR-125b, and miR-148a) has been found to be significantly upregulated in patients with osteoporosis, with miR-21, miR-23a, miR-24, miR-25, miR-100, and miR-125b displaying significantly higher expression in bone tissue from osteoporotic patients. Weilner et al. tested a panel of 175 miRNAs on serum samples from seven female patients with recent osteoporotic hip fracture and seven age-matched female controls [60]. Six miRNAs (miR-10a-5p, miR-10b-5p, miR-22-3p, miR-133b, miR-328-3p, and let-7g-5p) showed significantly different serum levels in the presence of a fracture. These data were validated on a further 12 fractured women vs. 11 controls, confirming miR-22-3p, miR-328-3p, and let-7g-5p as differentially expressed in the presence of a fracture. Panach et al. measured a panel of 179 miRNAs in the serum samples of eight osteoporotic women with fractures vs. five controls, and validated data on 15 patients with fractures vs. 12 controls [61]. They determined that miR-122-5p, miR-125b-5p, and miR-21-5p were upregulated in the presence of a fracture. In particular, for miR-21-5p, the difference detected between fractured women and controls was independent of age, and its circulating levels were correlated to those of CTx, a marker of bone resorption. These studies support a future application of circulating miRNA dosage as a biomarker for assessing osteoporosis status and fracture risk. However, all these studies analysed a small number of samples, strongly reducing the efficacy of association, and they evaluated only restricted panels of miRNAs. Further studies are surely needed, including larger populations and the application of high-throughput technologies, such as next-generation sequencing (NGS), to evaluate, at the same time, all the known human miRNAs, in order to identify specific signatures, possibly driving diagnosis and therapeutic choices. 5. Discussion Epigenetics mechanisms play a fundamental role in regulating biological processes, also in response to exogenous environmental influences. In this light, there is increasing epidemiological and biological evidence that some environmental stimuli, such as viral and bacterial infections, prolonged exposition to chemical agents and pollutants, an imbalance of nutrients, long-term pharmacological treatments, and physical and mental stresses, can induce important epigenetic changes, significantly influencing gene expression and biological processes. They can result in favouring and/or promoting the development of complex multifactorial disease during aging. Since it is now well known that epigenetic factors play a major role in skeletal development and bone maintenance, a deregulation of these regulatory mechanisms, induced by environmental factors (i.e., dietary habits and lifestyle aspects), could be an important determinant of the development of osteoarticular diseases, including osteoporosis, acting in a synergic manner with predisposing genetic determinants. Indeed, bone development at the embryo stage and skeletal growth and bone mass peak acquisition during infancy, adolescence, and early adulthood, as well as bone mass maintenance during aging and bone progressive loss in the elderly, can all be modified by environmental influences such as maternal and perinatal nutrition, dietary habits, physical activity, smoke, alcohol intake, hormonal therapies, etc. The effect of these exogenous factors on bone metabolism is mediated by the epigenetic mechanisms. In particular, nutri-epigenetic studies demonstrated the influence of some nutrients on foetal and/or placental epigenetic mechanisms. Food is more than simple energy for our body and/or the main source of key metabolites for synthesis of biological macromolecules and cell and tissue activity. Nutrients are important determinants of epigenetic functions and can exert variable effects on these endogenous regulatory mechanisms; derived early epigenetic modifications can alter gene expression in a way that may influence long-term health and diseases. Indeed, maternal nutritional imbalance and deficiency not only have a severe effect on correct foetal development but may also have a persistent effect on the health of the offspring and even be transmitted to the next generation [62]. Maternal diet influences post-natal bone mass and size, and birth weight has been positively associated with young adult bone mineral content and bone size [63,64] and with adult hip and spine bone mineral content [65]. Low maternal calcium and vitamin D intake during late pregnancy has been associated with reduced bone mineral content. Maternal under-nutrition or maternal high fat diet were both demonstrated to affect DNA methylation of the foetus during intrauterine development, altering the pre- and post-natal expression of multiple genes that may regulate skeletal growth and bone mass acquisition. Prenatal and perinatal diet-induced DNA methylation changes can persist during life and also for multiple generations. A review by Bocheva et al. [66] gives an interesting overview of the epigenetic modulation of intrauterine and post-natal skeletal development and of placental transfer of nutrients and their future impact on osteoporosis development. In this light, a better comprehension of the effect of maternal diet on skeletal development and bone mass determination and of the exact underlying epigenetic mechanisms is needed. This will be very helpful to design and activate large-scale public health nutrition programs for pregnant women, infants, and children, in order to reduce the risk of osteoporosis and fragility fracture in the elderly. Regarding molecules influencing epigenetic mechanisms that could find a future application in the area of treatment of metabolic bone disorders, currently few preliminary data are available on resveratrol, HDIs, and TF-3. The effects of HDIs on bone tissue appear to be controversial and should be better investigated in prospective clinical trials, focusing on the design of novel HDIs not targeting a wide range of HDACs but specific for only one enzyme. Also, the beneficial effect of resveratrol on bone mass and the protective effect of TF-3 on bone loss both need to be confirmed and validated in clinical trials. Calcium is commonly administered as a supplement for the prevention and treatment of osteoporosis. A recent study [67] showed that in the presence of a high concentration of calcium ions and in the absence of reducing agents, vertebrate DNMTs (principally DNMT1, DNMT3B, and, to a lesser extent, DNMT3A) can act, in vitro, as demethylases and remove the methyl group from the 5-mC, altering the normal DNA methylation status and presumably having an effect on gene expression and the risk of associated disease development. No data are available on the effect of this activity on bone cells, and since the calcium ion concentration (≥10 μm) needed for the in vitro DNA demethylation reaction is higher with respect to the normal intracellular level of calcium, the intervention of other cofactors and/or specific signal transduction pathways could be required for the in vivo demethylation reaction to act. 6. Conclusions and Future Perspectives In conclusion, better comprehension of both normal and deregulated epigenetic processes in bone metabolism will provide insights into normal skeletal development and maintenance, as well as into disease pathogenesis. Increasing advances in high-throughput technologies for epigenetic research, as well as the progressive reduction of the cost-efficiency ratio will enable, in the near future, a better understanding of the epigenetic basis of both normal and pathological bone metabolism, helping us to develop better diagnostic tools, foresee disease development, grant a more favourable prognosis, and identify novel drug target for the design of innovative therapies. Moreover, since the epigenome—conversely to the genome—is reversible, we could manipulate identified altered epigenetic processes responsible for the development of diseases. Indeed, epigenetic mechanisms can be specifically targeted by pharmacological agents, an approach that was demonstrated to be effective in some tumours and neurological disorders and that also holds great promise for future treatment of bone diseases. In the setting of precision medicine, all these features can be exploited to grant tailored treatment for the individual. Conflicts of Interest All the authors declare no conflict of interest. Figure 1 Schematic representation of the mechanism of action of epigenetic mechanisms. Histone acetylation positively regulates gene expression by inducing the opening of chromatin conformation and, thus, favouring the binding of transcription machinery. Histone methylation promotes the opening (panel A) or closing (panel B) of the chromatin conformation depending not only on the specific lysine residue modified, but also on its degree of methylation (Table 1). In this way, histone methylation can specifically induce or repress gene expression. MicroRNAs (miRNAs) suppress gene expression by selectively binding to the 3′ non coding region (3′UTR) of their mRNA targets through base-pairing. miRNAs can negatively regulate gene expression by two different post-transcriptional mechanisms: the cleavage of the mRNA target or the physical blocking of translation machinery. The choice of mechanism of action is determined only by the nucleotide complementarity between the miRNA and its mRNA target: the miRNA will cleave the target when it has sufficient complementarity to the miRNA itself, or it will repress translation, by physically blocking ribosome activity, if the mRNA does not have sufficient complementarity. In the first case, after the cleavage the miRNA remains intact and active and can proceed to the cleavage of other mRNA targets. Figure 2 Schematic representation of the role of genetics and epigenetics in bone development and maintenance. Static genetic traits and dynamic epigenetic marks interact with inner and outer environmental stimuli to determine bone features at all ages. While genetics may modulate the expression of epigenetics marks, epigenetic markers can regulate the expression of many genes coding for key molecules driving skeletal modelling in growing bone and remodelling in adult bone. Thus, all processes from bone development to peak bone mass attainment and maintenance can be influenced by epigenetic signatures, implying the possibility of modulating epigenetics in order to prevent/treat bone deterioration. Figure 3 Schematic representation of the role of histone acetyltransferases (HATs) and histone deacetylases (HDACs). HATs induce histone acetylation by transferring an acetyl group from the acetyl coenzyme A to histone lysine side chains (mostly at histone 3 lysine 4 (H3K4Ac) or histone 3 lysine 9 (H3K9Ac)), inducing the opening of chromatin status and promoting gene transcription. Conversely, HDACs remove the acetyl groups from histones, inducing the closing of chromatin status and blocking gene transcription. Histone deacetylase inhibitors (HDIs) inhibit the catalytic activity of HDACs by directly binding to their catalytic sites. Figure 4 Schematic representation of positive (↑) and negative (↓) miRNA regulators of osteoblast differentiation. Figure 5 Schematic representation of positive (↑) and negative (↓) miRNA regulators of osteoclast differentiation. ijms-17-01329-t001_Table 1Table 1 Main epigenetic mechanisms and their principal effects on gene expression. Epigenetic Process (Post-Translational Histone Modifications) Molecular Mechanism Involved Enzymes Mechanism of Action Effects on Gene Expression Histone acetylation/deacetylation The lysine residues at the N-terminal of histone tails are subjected to either addition (acetylation) or removal (deacetylation) of acetyl groups. (1) Histone acetyltransferases (HATs); Acetylation removes positive charges from lysine residues and reduces the affinity between histones and DNA, thereby opening the condensed chromatin structure, favouring the access to gene promoters. Histone acetylation promotes gene expression. Conversely, histone deacetylation prevents gene expression. (2) Histone deacetylases (HDACs) Histone methylation/demethylation Histone methylation occurs on different lysine residues, with the potential addition of one, two, or three methyl groups. (1) Histone lysine methyltransferases (KMTs); The effect of histone methylation on chromatin state is dependent not only on the specific lysine residue modified, but also on its degree of methylation. Histone methylation at H3K4, H3K36, or H3K79 has been associated with gene transcription activation. (2) Histone lysine demethylases (KDMs) Histone methylation at H3K9, H3K20, or H4K27 is implicated in gene expression inactivation or silencing. DNA methylation Addition of a methyl group at the 5′ position of the cytosine ring within CpG islands of gene promoters. (1) DNA methyltransferases (DNMT3A and DNMT3B); Methylated gene promoters are not accessible to transcription factors. DNA methylation is strongly associated with gene transcription silencing. (2) DNA maintenance methyltransferase (DNMT1) MicroRNAs (miRNAs) miRNAs selectively bind to the 3’ non coding region (3’UTR) of specific target mRNAs, through base-pairing. None Binding of a miRNA on the 3’UTR of the target mRNA blocks protein synthesis by two distinct post-transcriptional mechanisms: mRNA cleavage or translational repression. miRNAs negatively regulate the expression of target genes, at post-transcriptional level, by blocking the translation of their proteins. ijms-17-01329-t002_Table 2Table 2 Role of histone deacetylases (HDACs) in bone biology. HDAC Class Affected Protein Expression Effects on Bone Biology Reference HDAC1 I RUNX2 (down-regulation) Suppression of osteoblast differentiation [6] HDAC2 I FoxO1 (down-regulation) Promotion of RANKL-induced osteoclastogenesis [13] HDAC3 I RUNX2 (down-regulation) Maintenance of bone mass during development and aging [3] HDAC4 II RUNX2 (down-regulation) Suppression of endochondral ossification [8,9] HDAC5 II RUNX2 (down-regulation) Suppression of osteoblast differentiation [8] HDAC7 II RUNX2 (down-regulation) Regulation of endochondral ossification [15] HDAC8 I Homeobox transcription factors Otx2 (up-regulation) and Lhx1 (up-regulation) Regulation of intramembranous ossification [11] HDAC9 II RANKL (down-regulation) Suppression of osteoclastogenesis [17] Sirt1 III NA Promotion of endochondral ossification, and of osteoblast differentiation of mesenchymal stem cells [17,18] Sirt6 III NA Promotion of endochondral ossification [17] NA = non available. ==== Refs References 1. Hsu Y.H. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081330ijms-17-01330ArticleMolecular Insights into the Potential Toxicological Interaction of 2-Mercaptothiazoline with the Antioxidant Enzyme—Catalase Huang Zhenxing 123Huang Ming 1Mi Chenyu 1Wang Tao 1Chen Dong 1Teng Yue 12*Bondy Stephen C. Academic Editor1 School of Environment and Civil Engineering, Jiangnan University, Wuxi 214122, China; biogashuang@jiangnan.edu.cn (Z.H.); huangmingjn@hotmail.com (M.H.); michenyujn@hotmail.com (C.M.); wangtaojn@hotmail.com (T.W.); chendongjnedu@hotmail.com (D.C.)2 Jiangsu Key Laboratory of Anaerobic Biotechnology, Jiangnan University, Wuxi 214122, China3 Jiangsu Collaborative Innovation Center of Technology and Material of Water Treatment, Suzhou 215009, China* Correspondence: tengyue@jiangnan.edu.cn; Tel./Fax: +86-510-8519-709116 8 2016 8 2016 17 8 133006 7 2016 05 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).2-mercaptothiazoline (2-MT) is widely used in many industrial fields, but its residue is potentially harmful to the environment. In this study, to evaluate the biological toxicity of 2-MT at protein level, the interaction between 2-MT and the pivotal antioxidant enzyme—catalase (CAT) was investigated using multiple spectroscopic techniques and molecular modeling. The results indicated that the CAT fluorescence quenching caused by 2-MT should be dominated by a static quenching mechanism through formation of a 2-MT/CAT complex. Furthermore, the identifications of the binding constant, binding forces, and the number of binding sites demonstrated that 2-MT could spontaneously interact with CAT at one binding site mainly via Van der Waals’ forces and hydrogen bonding. Based on the molecular docking simulation and conformation dynamic characterization, it was found that 2-MT could bind into the junctional region of CAT subdomains and that the binding site was close to enzyme active sites, which induced secondary structural and micro-environmental changes in CAT. The experiments on 2-MT toxicity verified that 2-MT significantly inhibited CAT activity via its molecular interaction, where 2-MT concentration and exposure time both affected the inhibitory action. Therefore, the present investigation provides useful information for understanding the toxicological mechanism of 2-MT at the molecular level. 2-mercaptothiazolinecatalasespectroscopic techniquemolecular dockingtoxicityprotein conformation ==== Body 1. Introduction Catalase (CAT, hydrogen peroxide oxidoreductase; EC.1.11.1.6) is a common enzyme found in nearly all living organisms exposed to oxygen, which plays a pivotal role in protecting cells from oxidative injuries by catalyzing the detoxification of hydrogen peroxide to oxygen and water (2H2O2 → O2↑ + 2H2O) [1,2]. Recently, there has been growing evidence that CAT is a major factor against various pathological conditions such as diabetes, inflammation, sickle cell disease, and cancer [3,4,5,6]. However, the intake of exogenous environmental chemicals (e.g., organic pollutants, heavy metals, nanomaterials) is likely to trigger the destruction of protein conformation and to further suppress the catalytic activity of the enzyme [7,8,9]. Hence, as an important antioxidant enzyme, the toxic effects of contaminants on CAT as well as their action mechanisms should be intensively investigated in vivo and in vitro. 2-Mercaptothiazoline (2-MT) is a heterocyclic organic compound with two tautomeric forms including a thione form (1,3-thiazolidine-2-thione) and a thiol form (2-thiazoline-2-thiol) [10] (Scheme 1), and 1,3-thiazolidine-2-thione is the predominant form in aqueous solution [11]. Due to its electron donor properties derived from –(NH)–(C=S)– group, 2-MT has been widely used in many fields [12]. For instance, 2-MT has been applied to synthesize brightening and stabilization agents for the printed wiring board industry as well as the photography industry [13]. In addition, based on its adsorption/complexation behaviors on the surface of metal products, 2-MT is commonly used as an organic inhibitor against corrosion and diffusion by forming metal–chelate Schiff base complexes [14]. In the environmental protection field, chemically modified 2-MT can be used to remove heavy metal ions, such as Hg(II) ions, from water solutions [15]. 2-MT derivatives are also used in the medical industry as potent anti-thyroid drugs [16]. However, 2-MT is resistant to natural and biological degradation, and thus its extensive use can result in significant residue accumulation in the environment [17]. Many studies have reported that 2-MT residue can be detected in wastewater treatment plants, surface water, as well as soils, and it is considered to be a typical organic contaminant [18,19]. 2-Mercaptothiazoline is classified as a harmful organic compound, according to its Material Safety Data Sheet. Hence, the potential risk of 2-MT exposure to organisms should be investigated. Teng et al. demonstrated that 2-MT could bind into site II (subdomain IIIA) of bovine serum albumin, and provided valuable information for understanding the effects of 2-MT on the transportation of substances in the blood [20]. In addition, Thomes et al. reported that 2-MT could inhibit the activities of horseradish peroxidase, lactoperoxidase, and thyroid peroxidase [21]. Nevertheless, the current knowledge about the toxicological mechanisms of 2-MT is still severely lacking. In addition, to the best of our knowledge, there are no studies that focus on elucidating the biological toxicity of 2-MT on antioxidant enzymes at the molecular level. Given the considerations above, in the present study, the potential toxicological interaction of 2-MT with the pivotal antioxidant enzyme—catalase (CAT) was investigated by multiple spectroscopic techniques and molecular modeling. The quenching mechanism as well as the relevant binding parameters, including the number of binding sites, binding constant, and binding forces were identified. Moreover, the interaction behaviors were further elucidated by molecular docking simulation and conformation dynamic characterization. Finally, the potential inhibitory effects of 2-MT on CAT activity caused by their interaction were verified by confirmatory experiments. 2. Results and Discussion 2.1. Characterization of the Binding Interaction between 2-MT and CAT by Fluorescence Analysis 2.1.1. Fluorescence Quenching Mechanism Fluorescence spectroscopy is a favorable technique for exploring molecular interactions since it’s has high sensitivity [22]. Fluorescence methods have been widely used to investigate the interaction between ligands and proteins, and can provide information about the quenching mechanism, binding constants and binding sites [23,24]. The fluorescence emission spectra of CAT with various doses of 2-MT additions are shown in Figure 1. The fluorescence intensity of CAT was remarkably reduced as the 2-MT concentration increased. It is known that tryptophan and tyrosine residues are mainly responsible for protein fluorescence, and the fluorescence intensity is associated with the protein structure. Thus, the CAT fluorescence quenching suggests that 2-MT could bind into CAT and potentially alter its molecular structure. Quenching mechanisms are usually classified into either dynamic or static quenching. Since a higher temperature can result in a larger diffusion coefficient, the dynamic quenching constant should increase with increasing temperature. On the other hand, raising the temperature tends to weaken the complex stability, which can reduce the static quenching constant [25]. Hence, to better understand the mechanism of CAT fluorescence quenching caused by 2-MT, the fluorescence spectra at different temperatures were analyzed according to the Stern-Volmer equation [26]. (1) F0F=1+Ksv[Q]=1+kqτ0[Q] where F and F0 respectively represent the fluorescence intensities with and without the quencher, [Q] is the quencher concentration, τ0 is the average fluorescence lifetime in the absence of quencher (τ0 = 10−8 s [27]), kq is the quenching rate constant of the biological macromolecule, and KSV is the Stern-Volmer quenching constant. Figure 2 showed the fluorescence intensity data that were analyzed based on F0/F versus [Q] at different temperatures (291 K, 300 K, 309 K, and 318 K). Subsequently, KSV was determined using Equation (1) by a linear regression of F0/F against [Q]. As shown in Table 1, the KSV values reduced from 1.59 × 104 to 1.23 × 104 L·mol−1·s−1 with the increasing temperature. For dynamic quenching, the maximum quenching rate constant (kq) is 2.0 × 1010 L∙mol−1·s−1 [28]. However, the kq values under various temperatures were much greater than 2.0 × 1010 L∙mol−1·s−1 in this study (Table 1). Therefore, according to the results above, it could indicate that the CAT fluorescence quenching caused by 2-MT is dominated by the static quenching mechanism, through formation of the 2-MT/CAT complex, rather than dynamic quenching. 2.1.2. Binding Parameters Determination When small molecules bind independently to equivalent sites on a macromolecule via a static fluorescence quenching mechanism, the relevant parameters including the number of binding sites (n) and the binding constant can be determined based on the following equation [29,30]. (2) lg(F0-F)F=lgKa+nlg[Q] where F0, F, and [Q] are the same as in Equation (1), Ka is the binding constant and n is the number of binding sites. Using a linear fit of lg[(F0 – F)/F] versus lg[2-MT] under different temperatures (291 K, 300 K, 309 K and 318 K), the corresponding values of Ka and n can be determined from the slope and intercept. As shown in Table 2, the values of n were approximately 1, which implied that there should be only one site for 2-MT to bind to CAT. Moreover, the values of Ka reached an order of magnitude of 103 to 104, thereby indicating a strong interaction between 2-MT and CAT. Namely, even at a low concentration in cells, 2-MT can still easily bind with CAT. 2.1.3. Identification of Binding Forces Non-covalent intermolecular forces primarily cover Van der Waals’ forces, hydrogen bonding, electrostatic forces, and hydrophobic interactions, which can be identified by the calculation of thermodynamic parameters [31]. For moderate temperature change under a constant pressure, the enthalpy change (ΔH°) of a specific reaction can be regarded as a constant [32]. Thus, the values of ΔH° and ΔS° can be approximately calculated by the linear fit of lnK versus 1/T based on the Van’t Hoff equation (Equation (3)) [33]. Meanwhile, the free-energy change (ΔG°) under different temperatures can be acquired according to the classic thermodynamic equation (Equation (4)). (3) lnK=-ΔH°RT+ΔS°R (4) ΔG°=ΔH°-TΔS° where K is analogous to the binding constant (Ka) in Equation (2) at a specific temperature (291 K, 300 K, 309 K, and 318 K in this study). R is the universal gas constant (8.314 J·mol−1·K−1). The linear regression of ln K versus 1/T for the interaction between 2-MT and CAT is shown in Figure 3. The ΔH° and ΔS° were obtained from the slope and intercept, listed in Table 2. The thermodynamic laws were used to evaluate the types of non-covalent forces: If ΔH° < 0, ΔS° < 0, the intermolecular interactions are dominated by Van der Waals’ force and hydrogen bonding; If ΔH° > 0, ΔS° > 0, hydrophobic interactions play a pivotal role in the binding reaction; If ΔH° < 0, ΔS° > 0, electrostatic interaction is the dominant force [33]. In this study, the values of ΔH° (−80.79 kJ·mol−1) and ΔS° (−192.26 J·mol−1·K−1) were both negative, implying that Van der Waals’ forces and hydrogen bonding are primarily responsible for the interaction between 2-MT and CAT. In addition, the negative ΔG° calculated from Equation (4) indicated that the interaction process should be spontaneous. Taken together, the results demonstrate that 2-MT could spontaneously bind to CAT at one binding site to form the 2-MT/CAT complex, mainly through the use of Van der Waals’ forces and hydrogen bonding. 2.2. Molecular Docking Simulation In this study, the molecular docking simulation was performed using AutoDock 4.2 to further characterize the interaction behaviors between 2-MT and CAT. The CAT model was obtained from the Protein Data Bank (PDB Code: 1TGU). As the predominant tautomeric form in aqueous solution, the thione form (1,3-thiazolidine-2-thione) was selected as the ligand for molecular docking, and its 3D structure was downloaded from the ZINC Database (http://zinc.docking.org/). The binding stereostructure with the lowest energy state is shown in Figure 4, which represents the optimum binding mode and the most likely binding sites. It was found that 2-MT could bind to the junctional region of the CAT subdomains (Figure 4a), and the binding site was close to the CAT active sites on chain A and chain D. The detailed docking results (Figure 4b) further revealed that the amino acid residues used to link the binding site were composed of ILE 68 (chain A), GLU 70 (chain A), SER 119 (chain A), ILE 68 (chain D), and GLU 70 (chain D). The molecular docking also indicated that the interaction between 2-MT and CAT was dominated by Van der Waals’ forces and hydrogen bonding, which was in accordance with the fluorescence analysis results above (Section 2.1.3). Figure 4b illustrates that the hydrogen bond exists between the hydrogen atoms (HN) of 2-MT and the oxygen atom (OE1) of GLU 70 (chain A). 2.3. CAT Conformation Dynamic Characterization 2.3.1. UV–Vis Absorption Spectroscopy UV–Vis absorption spectroscopy is a widely used technique to explore the structural changes of proteins. The UV–Vis absorption spectra of CAT in the absence and presence of 2-MT is shown in Figure 5. Depending on its backbone conformation, the maximum absorption of CAT approximately appeared at 205 nm. However, with increasing 2-MT addition, the UV–Vis absorbance was gradually reduced, and the position of the absorption peak was red-shifted to roughly 208 nm. The dynamic changes implied that the interaction with 2-MT could lead to the loosening and unfolding of the CAT peptide chains [34]. Thus, the hydrophobicity of the CAT skeleton decreased. 2.3.2. Synchronous Fluorescence Spectroscopy Synchronous fluorescence spectroscopy can provide important information on the polarity changes of the microenvironments around molecular fluorophores [35]. The technology is based on simultaneous scanning of the excitation and emission monochromators while keeping a constant wavelength interval. When the wavelength interval (Δλ) is fixed at 15 or 60 nm, the synchronous fluorescence is characterized by tyrosine residues or tryptophan residues, respectively [36]. The CAT synchronous fluorescence spectra with and without 2-MT addition is shown in Figure 6. When the Δλ was kept at 15 nm (Figure 6A), the variation of the 2-MT concentration did not obviously change the position of the emission peaks, which suggested that 2-MT had fewer effects on the microenvironment around the tyrosine residues of CAT. However, the position of the emission peak at Δλ = 60 nm was red-shifted from 338 nm to 345 nm as the 2-MT concentration increased from 0 to 6 × 10−5 mol·L−1 (Figure 6B). The results indicated that the CAT interaction with 2-MT could increase the exposure of its tryptophan residues to a more polar microenvironment [37], thereby further confirming the 2-MT impacts on CAT conformation. 2.3.3. Circular Dichroism The results above preliminarily indicated the CAT conformational changes are induced by 2-MT. In order to further investigate the 2-MT effects on the secondary structure of CAT, circular dichroism (CD) measurements were performed in the absence and presence of 2-MT. Shown in Figure 7, there were two negative peaks (at 208 and 221 nm) in the CD spectra of CAT, which is characteristic of the protein α-helix [38]. Subsequently, the CD spectra was analyzed using the CDPro software package, and the results are summarized in Table 3. It was shown that CAT contained the secondary structures of α-helix (19.7%), β-sheet (31.1%), turns (23.3%), and unordered (25.8%). However, the addition of 2-MT into the CAT solution (molar ratio: 10:1) reduced α-helix proportion by 0.7%. Meanwhile, the proportions of β-sheet and turns were respectively increased by 0.4% and 0.3%. The comparison demonstrated that 2-MT could cause some secondary structural changes in CAT. The decrease of α-helix proportion suggests a certain unfolding and denaturation of CAT induced by 2-MT [9], which is in accordance with the results from the UV–Vis absorption and synchronous fluorescence analysis. 2.4. Evaluation of 2-MT Inhibitory Effect on CAT Activity According to the experimental results above, it was found that 2-MT could induce CAT conformational changes by binding to the junctional region of the CAT subdomains, and the binding site was close to the enzyme active sites on chain A and chain D (Section 2.2 and Section 2.3). In order to verify the potential toxic effects of 2-MT caused by the interaction, the CAT activities under different 2-MT concentrations and exposure times were determined. As shown in Figure 8, since there was a strong interaction between 2-MT and CAT with a high binding constant (Section 2.1), the enzyme activity was immediately inhibited when exposed to 2-MT for just 10 min. The relative activities were significantly decreased to 83.3% and 66.7% when 2-MT concentrations were 3 × 10−5 mol·L−1 and 1 × 10−4 mol·L−1, respectively. In addition, the CAT activity gradually reduced as the exposure time increased, and the residual activity was only 56.1% with 2-MT inhibition (1 × 10−4 mol·L−1) for 120 min. Therefore, these experiments demonstrated that both 2-MT concentration and exposure time could influence the inhibition of CAT activity by 2-MT. 3. Materials and Methods 3.1. Reagents Catalase (from bovine liver) was purchased from Sigma and was dissolved in ultrapure water to prepare stock solution (1.0 × 10−5 mol·L−1), which was then preserved at 0–4 °C. All the other chemicals were of analytical grade, and were purchased from Sinopharm Chemical Reagent Co., Ltd. The stock solution (1.0 × 10−3 mol·L−1) of 2-mercaptothiazoline (2-MT) was prepared. The reaction pH was controlled using 0.2 mol·L−1 phosphate buffer (mixture of NaH2PO4·2H2O and Na2HPO4·12H2O, pH 7.4). The NaCl stock solution (0.5 mol·L−1) was used to simulate the osmotic pressure in organisms. All solutions were prepared with ultrapure water (18.25 MΩ). 3.2. Apparatus and Measurements 3.2.1. Fluorescence Measurements The fluorescence measurements were conducted on a fluorescence spectrophotometer (RF-5301PC, Shimadzu, Kyoto, Japan) equipped with a 1 cm cell. The excitation wavelength was set at 280 nm, and the emission spectra were recorded in the range of 290–500 nm with a scan speed of 1200 nm/min and a photomultiplier tube (PMT) voltage of 700 V. The excitation and emission slit widths were both 5 nm. 3.3.2. Molecular Docking Investigation Docking calculations were performed on a CAT protein model (PDB code 1TGU) using AutoDock 4.2. As the predominant tautomer of 2-MT in solution [11], the thione form was used for the molecular docking. The 3D structure of 2-MT was downloaded from the ZINC Database (http://zink.docking.org/). With the aid of AutoDock, the ligand root of 2-MT was detected and the rotatable bonds were defined. In addition, H2O molecules were removed from the CAT model, while polar hydrogen atoms and Compute Gasteiger charges were added. Along the X-, Y-, and Z-axes with 0.375 Å grid spacing, the molecular docking was first performed with the grid size set to 126, 126 and 126 Å. On this basis, the grid size was subsequently shrunk to 86, 86 and 86 Å in order to improve the docking accuracy. Docking simulations were carried out by the Lamarckian genetic algorithm (LGA) search method. Each run of the docking experiment was set to terminate after a maximum of 250,000 energy evaluations, and the population size was set to 150. By the molecular docking simulation, the conformation with the lowest binding free energy was used for further analysis. 3.3.3. UV–Visible Absorption Measurements The UV–Visible absorption was measured using a double beam spectrophotometer (UV-6100, Mapada, Shanghai, China), with 1 cm quartz cells and a slit width of 2 nm. The wavelength range was set at 200–240 nm. The reference was the corresponding 2-MT solution without CAT addition. 3.3.4. Synchronous Fluorescence Measurements The synchronous fluorescence spectra of CAT with different 2-MT concentrations were measured (Δλ = 15 nm, λex = 260–340 nm and Δλ = 60 nm, λex = 300–400 nm, respectively) using a fluorescence spectrophotometer (RF-5301PC, Hitachi, Japan). The excitation and emission slit widths were set at 5 nm. The scan speed and PMT voltage were 1200 nm/min and 700 V, respectively. 3.3.5. Circular Dichroism (CD) Measurements The CD measurements were performed on a spectrophotometer (MOS-450/AF-CD, Bio-Logic, Claix, France). The CAT spectra in the absence and presence of 2-MT were recorded in the wavelength range of 200–260 nm, with a bandwidth of 4 nm and a scanning speed of 1 nm/2 s. 3.3.6. CAT Activity Determination The CAT catalyzes the decomposition of hydrogen peroxide into water and oxygen, and its activity can be assayed by monitoring the absorbency decrease of hydrogen peroxide at 240 nm [39]. The determination was carried out in a pH 7.4 phosphate buffer (20 mM) containing 9 mM hydrogen peroxide. The reaction was initiated by adding the CAT that was exposed to various doses of 2-MT for different times. The relative activity of CAT was calculated by the following equation: (5) Relative activity=ΔA1ΔA0×100% where ΔA0 represents the absorbency reduction (240 nm) in 2 min after adding CAT without any exposure to 2-MT; ΔA1 represents the absorbency reduction in 2 min after adding the CAT that was exposed to various doses of 2-MT for different time. 4. Conclusions In this study, the toxicological interaction of 2-mercaptothiazoline (2-MT) with the pivotal antioxidant enzyme—catalase (CAT) was investigated at molecular level by multiple spectroscopic techniques and molecular modeling. 2-MT could quench the CAT fluorescence via a static quenching mechanism through formation of the 2-MT/CAT complex. Van der Waals’ forces and hydrogen bonding were primarily responsible for the interaction of 2-MT with CAT at one binding site. The molecular docking simulation indicated that 2-MT could bind to the junctional region of the CAT subdomains, and the binding site was close to the CAT active sites on chain A and chain D. The interaction had few effects on the microenvironment around the tyrosine residues of CAT, but increased the exposure of tryptophan residues to a more polar microenvironment. The presence of 2-MT also induced secondary structural changes, and caused a certain unfolding and denaturation in CAT. Due to the molecular binding, the activity of CAT was significantly inhibited by 2-MT, where 2-MT concentration and exposure time both affected the inhibitory action. Taken together, this work provides valuable insight into the interaction between 2-MT and CAT at the molecular level, and helps us to understand the mechanism of 2-MT toxicity on the environment and human health. Acknowledgments The work was financially supported by the National Nature Science Foundation of China (NSFC 21307043, 21506076, 21276114), the China Postdoctoral Science Foundation (2016M590411), and the Scientific Practice Project for University Graduate of Jiangsu Province (1125210232141500). Author Contributions Zhenxing Huang and Yue Teng conceived and designed the experiments. Zhenxing Huang and Ming Huang carried out the experiments, analyzed the data, and drafted the paper. Chenyu Mi participated in fluorescence measurements and CAT activity determination. Tao Wang and Dong Chen assisted in molecular docking investigation and circular dichroism measurements. Zhenxing Huang and Yue Teng revised the manuscript. All authors have read and approved the manuscript. Conflicts of Interest The authors declare no conflict of interest. Figures, Scheme and Tables ijms-17-01330-sch001_Scheme 1Scheme 1 2-Mercaptothiazoline (2-MT) tautomers: thione form (left) and thiol form (right). The balls of white, gray, yellow and blue respectively represent the atoms of hydrogen (H), carbon (C), sulfur (S) and nitrogen (N), respectively. Figure 1 Fluorescence emission spectra of catalase (CAT) with different doses of 2-MT. Conditions: CAT: 1.0 × 10−7 mol·L−1, 2-MT: 0 mol·L−1 (a); 1 × 10−5 mol·L−1 (b); 2 × 10−5 mol·L−1 (c); 3 × 10−5 mol·L−1 (d); 4 × 10−5 mol·L−1 (e); 5 × 10−5 mol·L−1 (f); 6 × 10−5 mol·L−1 (g); pH 7.4; T = 291 K. Figure 2 Stern–Volmer linear regression analysis for CAT fluorescence quenching caused by 2-MT under different temperatures (291 K, 300 K, 309 K, and 318 K). Figure 3 Van’t Hoff linear regression analysis for the interaction of 2-MT with CAT. Figure 4 Molecular docking for the interaction between CAT and 2-MT. (a) Binding site of 2-MT to CAT; (b) Detailed illustration of the binding between CAT and 2-MT. Figure 5 UV-Vis spectra of CAT with different concentrations of 2-MT (versus the same concentration of 2-MT solution). Conditions: CAT: 1.0 × 10−7 mol·L−1; 2-MT: 0 mol·L−1 (a); 1 × 10−5 mol·L−1 (b); 2 × 10−5 mol·L−1 (c); 3 × 10−5 mol·L−1 (d); pH 7.4; T = 291 K. Figure 6 Synchronous fluorescence spectra of CAT with different concentrations of 2-MT. Conditions: (A) Δλ = 15 nm and (B) Δλ = 60 nm; CAT: 1.0 × 10−7 mol·L−1; 2-MT: 0 mol·L−1 (a); 1 × 10−5 mol·L−1 (b); 2 × 10−5 mol·L−1 (c); 3 × 10−5 mol·L−1 (d); 4 × 10−5 mol·L−1 (e); 5 × 10−5 mol·L−1 (f); 6 × 10−5 mol·L−1 (g); pH 7.4; T = 291 K. Figure 7 CD spectra of CAT in the absence and presence of 2-MT. Conditions: CAT: 1 × 10−7 mol·L−1; 2-MT: 0 mol·L−1 (a); 1 × 10−6 mol·L−1 (b); pH 7.4; T = 291 K. Figure 8 Inhibitory effect of 2-MT on CAT activity. Conditions: CAT: 1.0 × 10−6 mol·L−1; 2-MT: 0 mol·L−1 (A); 3 × 10−5 mol·L−1 (B); 1 × 10−4 mol·L−1 (C); pH 7.4, T = 291 K. ijms-17-01330-t001_Table 1Table 1 Stern–Volmer quenching constants for the interaction of 2-MT with CAT under different temperatures (291 K, 300 K, 309 K, and 318 K). pH T (K) KSV (×104 L·mol−1·s−1) kq (×1012 L·mol−1·s−1) R 1 S.D. 2 7.4 291 1.59 1.59 0.9935 0.0429 7.4 300 1.46 1.46 0.9939 0.0384 7.4 309 1.35 1.35 0.9922 0.0398 7.4 318 1.23 1.23 0.9910 0.0391 1 R is the correlation coefficient; 2 S.D. is the standard deviation for the KSV values. ijms-17-01330-t002_Table 2Table 2 Binding constants and thermodynamic parameters from 2-MT/CAT interaction (pH 7.4). T (K) Ka (× 104 mol·L−1) n R 1 ΔH° (kJ·mol−1) ΔS° (J·mol−1·K−1) ΔG° (kJ·mol−1) 291 2.97 1.14 0.9987 −80.79 −192.26 −24.84 300 1.07 1.07 0.9973 −23.11 309 0.41 0.96 0.9955 −21.38 318 0.17 0.83 0.9956 −19.65 1 R is the correlation coefficient. ijms-17-01330-t003_Table 3Table 3 Effects of 2-MT on the proportion of secondary structural elements in CAT at 291 K. Molar Ratio of CAT to 2-MT Secondary Structural Elements in CAT α-Helix (%) β-Sheet (%) Turns (%) Unordered (%) 1:0 19.7 31.1 23.3 25.8 1:10 19.0 31.5 23.6 25.9 ==== Refs References 1. Gaetani G.F. Ferraris A.M. Rolfo M. Mangerini R. Arena S. Kirkman H.N. Predominant role of catalase in the disposal of hydrogen peroxide within human erythrocytes Blood 1996 87 1595 1599 8608252 2. Sofo A. Scopa A. Nuzzaci M. Vitti A. Ascorbate peroxidase and catalase activities and their genetic regulation in plants subjected to drought and salinity stresses Int. J. Mol. Sci. 2015 16 13561 13578 26075872 3. Giustarini D. Dalledonne I. Tsikas D. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081331ijms-17-01331ArticleResveratrol Attenuates Acute Inflammatory Injury in Experimental Subarachnoid Hemorrhage in Rats via Inhibition of TLR4 Pathway Zhang Xiang-Sheng 1†Li Wei 1†Wu Qi 1Wu Ling-Yun 1Ye Zhen-Nan 2Liu Jing-Peng 2Zhuang Zong 1Zhou Meng-Liang 1Zhang Xin 12*Hang Chun-Hua 12*Arráez-Román David Academic Editor1 Department of Neurosurgery, School of Medicine, Nanjing University, Jinling Hospital, 305 East Zhongshan Road, Nanjing 210002, China; zhangxssp@163.com (X.-S.Z.); lwxzlw@gmail.com (W.L.); njwuqi@gmail.com (Q.W.); dr.wulingyun@gmail.com (L.-Y.W.); zongzhuang@126.com (Z.Z.); mlzhou1979@hotmail.com (M.-L.Z.)2 Department of Neurosurgery, School of Medicine, Southern Medical University (Guangzhou), Jinling Hospital, 305 East Zhongshan Road, Nanjing 210002, China; ringaly@126.com (Z.-N.Y.); 13677670746@163.com (J.-P.L.)* Correspondence: zhangxssp@gmail.com (X.Z.); hang_neurosurgery@163.com (C.-H.H.); Tel.: +86-25-8086-0071 (X.Z. & C.-H.H.)† These authors contributed equally to this work. 12 8 2016 8 2016 17 8 133105 7 2016 08 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Toll-like receptor 4 (TLR4) has been proven to play a critical role in neuroinflammation and to represent an important therapeutic target following subarachnoid hemorrhage (SAH). Resveratrol (RSV), a natural occurring polyphenolic compound, has a powerful anti-inflammatory property. However, the underlying molecular mechanisms of RSV in protecting against early brain injury (EBI) after SAH remain obscure. The purpose of this study was to investigate the effects of RSV on the TLR4-related inflammatory signaling pathway and EBI in rats after SAH. A prechiasmatic cistern SAH model was used in our experiment. The expressions of TLR4, high-mobility group box 1 (HMGB1), myeloid differentiation factor 88 (MyD88), and nuclear factor-κB (NF-κB) were evaluated by Western blot and immunohistochemistry. The expressions of Iba-1 and pro-inflammatory cytokines in brain cortex were determined by Western blot, immunofluorescence staining, or enzyme-linked immunosorbent assay. Neural apoptosis, brain edema, and neurological function were further evaluated to investigate the development of EBI. We found that post-SAH treatment with RSV could markedly inhibit the expressions of TLR4, HMGB1, MyD88, and NF-κB. Meanwhile, RSV significantly reduced microglia activation, as well as inflammatory cytokines leading to the amelioration of neural apoptosis, brain edema, and neurological behavior impairment at 24 h after SAH. However, RSV treatment failed to alleviate brain edema and neurological deficits at 72 h after SAH. These results indicated that RSV treatment could alleviate EBI after SAH, at least in part, via inhibition of TLR4-mediated inflammatory signaling pathway. toll-like receptor 4subarachnoid hemorrhageearly brain injuryresveratrolinflammation ==== Body 1. Introduction Subarachnoid hemorrhage (SAH) is a fatal subtype of stroke with high mortality and morbidity rates. Early brain injury (EBI), which starts from the onset of SAH and lasts up to 72 h, is a major complication of SAH [1]. Accumulating evidence indicates that EBI is the main course of the poor outcome after SAH [2,3,4]. Therefore, novel treatment against EBI is believed to be a principal goal for patients with SAH [5,6]. It has been proven that a complex series of pathophysiological processes are involved in the pathogenesis of EBI. Among them, acute inflammatory injury plays an important role in EBI [4,7,8]. Therefore, prevention and reduction of inflammation may be a promising target for the treatment of SAH. There are multiple signaling pathways that are activated early after the initial bleeding, which contribute to inflammatory response after SAH, are identified [7,8,9]. Among them, toll-like receptor 4 (TLR4), one of the most studied toll-like receptors (TLRs), plays an important role in initiating the inflammatory response following SAH [10,11]. Several stimuli can activate TLR4, including heat shock proteins, extracellular matrix degradation products, high-mobility group box 1 (HMGB1) [12]. Once activated, myeloid differentiation factor 88 (MyD88), a key adapter protein for TLR4, leads to direct activation of nuclear factor-κB (NF-κB) and the subsequent induction of prion-inflammatory cytokines implicated in the SAH-induced inflammatory responses [13]. In addition, activation of TLR4 could lead to cell death via NF-κB-dependent apoptosis [13]. Importantly, a growing number of experimental and clinical studies have demonstrated that TLR4 expression is upregulated in the brain. In addition, inhibiting TLR4 signaling pathway by pharmacological treatment can be against brain injury after SAH [14,15,16]. These indicate that suppressing TLR4 is a valid target for therapeutic intervention following SAH. Resveratrol (RSV), a natural occurring polyphenolic compound, is widely distributed in grapevines, pines and legumes [17]. In recent years, its multiple functions including neuroprotective, anti-inflammatory, and anti-oxidant properties were extensively studied in different fields [18,19,20]. In vivo, RSV has been proven to cross the blood–brain barrier (BBB), and it has been proposed for the treatment of various neuroinflammatory and neurodegenerative diseases in the central nervous system (CNS) [21,22,23]. Thus far, although previous study reported that RSV could reduce neuroinflammation after SAH [22], the molecular mechanisms underlying RSV-dependent anti-inflammatory effects remain obscure. Accumulating studies indicated that RSV could inhibit the activation of TLR4 and the subsequent downstream signaling pathways in different fields [24,25,26]. Thus, we designed this study to confirm the hypothesis that RSV could attenuate SAH-induced EBI by modulating the TLR4 signaling pathway. 2. Results 2.1. Mortality Rates A total of 156 rats were used in our experiment. Twelve rats died after the operation and were excluded from further analyses. No animals died in the sham-injured group and sham + RSV group. In the SAH + vehicle group, seven rats died and the mortality was 16.3%. Five rats died in the SAH + RSV group, and the mortality rate was 12.2%. 2.2. Effects of RSV on Neuroinflammation at 24 h Post SAH Activation of microglia is a critical source of pro-inflammatory cytokines in the brain. As shown in Figure 1A,B, there was a low level of Iba-1 expression in the sham group. SAH insults significantly increased the expression of Iba-1 when compared with sham group, but with RSV treatment, Iba-1 expression was markedly decreased. In addition, RSV markedly reduced the number of microglia in the rat cortex after SAH (Figure 1C,G). When the effects of RSV on the release of downstream inflammatory cytokines were evaluated, RSV administration after SAH was shown to significantly reduce IL-1β, IL-6, and TNF-α concentrations compared with SAH + vehicle group (Figure 1D–F). 2.3. Effects of RSV on the TLR4 Signaling Pathway The expression and distribution of TLR4, MyD88, NF-κB, and HMGB1 were identified by Western blot analysis and immunohistochemistry. Results showed that SAH induced a marked increase in TLR4, MyD88, NF-κB, and HMGB1 expression in the brain samples, as compared with that in the sham group. Treatment with RSV significantly reduced the levels of TLR4, MyD88, NF-κB, and HMGB1 as compared with SAH + vehicle group (Figure 2A–E). To further confirm these results, brain sections were immunohistochemically stained with all the markers mentioned above. More TLR4, MyD88, NF-κB, and HMGB1 positive immunostained neural cells appeared in the SAH + vehicle group. After ATX administration, the immunoreactivity of TLR4-, MyD88-, NF-κB-, and HMGB1-positive cells was significantly decreased as compared with the SAH + vehicle group (Figure 3A–E). 2.4. Effects of RSV on Neural Apoptosis, Brain Edema, and Neurological Function at 24 h Post SAH Neural apoptosis and brain edema are two main components of EBI, which are responsible for poor outcomes after SAH [27]. As shown, at 24 h post-SAH, RSV evidently decreased the elevated levels of cleaved caspase-3 and Bax, and enhanced the diminished level of Bcl2 (Figure 4A–D). In addition, RSV administration after SAH significantly reduced the number of TUNEL-positive neural cells (Figure 4E,F). At the same time, RSV significantly reduced brain water content in the cerebrum as compared with the SAH + vehicle group (Figure 4G). For a better understanding of the efficacy of RSV for SAH-induced EBI, the neurological function was recorded. As expected, at 24 h post-SAH, the impairment of neurological function was significantly decreased when RSV was administered as treatment (Figure 4H). 2.5. Effects of RSV on Neural Survival, Brain Edema, and Neurological Function at 72 h Post SAH To further determine the possible long-term neuroprotective effects of RSV, neural survival, brain edema, and neurological function at 72 h after SAH were evaluated. As shown, considerable neuronal loss was observed in the SAH + vehicle group, which was significantly decreased when RSV was administered as treatment. Curiously, we found that there were no evident differences in brain edema and neurological function between SAH + vehicle group and SAH + RSV group (Figure 5C,D). 3. Discussion In the present study, we studied the effects of RSV treatment on SAH-induced acute neuroinflammation and the possible role of the TLR4/MyD88/NF-κB pathway in the neuroprotective effects of RSV in SAH. The main findings can be summarized as follows: (1) RSV treatment ameliorated SAH-induced neuroinflammation, including microglia activation and pro-inflammatory cytokine release; (2) the TLR4/MyD88/NF-κB pathway was activated early after SAH, which could be inhibited by RSV treatment; and (3) after administration of RSV, SAH-induced neural apoptosis, brain edema, and neurological impairment were attenuated. These findings suggested for the first time that RSV could provide neuroprotection against SAH-induced neuroinflammation through inhibiting the TLR4/MyD88/NF-κB pathway. Accumulating evidence indicates that neuroinflammation plays a key role in the pathogenesis of EBI, and that anti-inflammatory treatment may be beneficial in experimental or clinical SAH [4,7,8]. One key factor in the inflammatory response is microglia activation [14]. Microglia, the principle resident macrophages of the central nervous system (CNS), are involved in the innate immune response, producing and releasing a number of pro-inflammatory cytokines when activated [28]. These pro-inflammatory cytokines may not only cause direct damage to surrounding neural cells but also disrupt BBB permeability, exacerbate cerebral edema, and induce neural cell death to further exacerbate brain damage after SAH [6,28,29]. Consistent with the theory, we found marked increase of microglia activation and pro-inflammatory cytokine release leading to aggravated brain injury in the early period after SAH. RSV is a natural polyphenol widely distributed in grapes and wine [30]. Recently, mounting evidence has demonstrated that RSV may serve as a promising therapeutic reagent in a variety of acute and chronic neurodegenerative diseases [17,20,31,32,33]. It has been proven that RSV has various pharmacological functions such as anti-inflammatory, anti-oxidant, anti-apoptotic, anti-bacterial, and anti-cancer properties [20,32,34,35,36]. Among these beneficial roles, the anti-inflammatory property is significant in the neuroprotective effect of RSV. In the brain injury models, including ischemic stroke, traumatic brain injury (TBI), and spinal cord injury (SCI), administration of RSV could remarkably reduce the levels of pro-inflammatory cytokines and microglia activation [32,33,37,38]. However, there is a paucity of studies exploring the anti-inflammatory effects of RSV in SAH. In the current study, we first evaluated the potential effects of RSV on neuroinflammation after SAH. We observed that RSV treatment could significantly inhibit the microglia activation and pro-inflammatory cytokine release in the brain cortex after SAH. Meanwhile, our data revealed that administration of RSV ameliorated neural apoptosis, brain edema, and neurological impairment at 24 h after SAH. These results strongly supported a neuroprotection role of RSV in SAH. However, the underlying mechanisms of RSV-dependent anti-inflammatory effects on EBI remain obscure. TLRs, a family of pattern recognition receptors, play a pivotal role in the inflammatory response [13]. TLR4, a key member of the TLRs, is highly expressed on microglia [39]. It can be activated by endogenous ligands released from various cells, such as HMGB1 [12]. When activated, TLR4 initiates the MyD88-dependent pathway leading to direct NF-κB activation and induction of a number of pro-inflammatory genes and chemokines [13]. Most importantly, mountain evidence have demonstrated the critical role of the TLR4 signaling pathways on initiating an inflammatory response after SAH, and that inhibition of the TLR4 signaling pathways could attenuate microglia-induced neuroinflammation [14,15,30]. Regarding the relationship between RSV and TLR4, accumulating studies indicated that RSV could regulate the TLR4 pathway both in vivo and in vitro [24,26,38,39]. For example, Byun et al. (2015) reported that RSV could negatively regulate lipopolysaccharides (LPS)-induced NF-κB signaling through TLR4 in macrophages [26]. Li et al. (2015) demonstrated that RSV could attenuate inflammation in the rat heart subjected to ischemia-reperfusion by inhibition of the TLR4/NF-κB pathway [24]. Therefore, we hypothesized that RSV could protect against neuroinflammation induced by SAH through the inhibition of TLR4/MyD88/NF-κB signaling pathway. In agreement with previous study [16], our data showed that the TLR4/MyD88/NF-κB pathway was activated and involved in the neuroinflammation in the early period after SAH. When treated with RSV, we found that the expression of TLR4-mediated agents, including HMGB1, MyD88, and NF-κB, was significantly inhibited. Given that these proteins play critical roles in SAH physiology [24,26,40,41], our experiment suggested that RSV could reduce neuroinflammation after SAH by inhibiting TLR4/MyD88/NF-κB signaling pathway. For a better understanding the neuroprotective effects of RSV after SAH, we further evaluated the neuroprotective effects of RSV at 72 h after SAH. Curiously, our data showed that RSV treatment could improve neural survival at 72 h after SAH. However, RSV treatment failed to ameliorate brain edema and neurological impairment at 72 h after SAH. We noted that there was no statistically significant difference in brain edema between sham and SAH groups. Thus, we speculated that the prechiasmatic SAH model used in our study might be not severity enough to discriminate statistical difference in different groups in the late period after SAH, although evident neural cell death could be seen at 72 h post SAH. The results suggested that more sever prechiasmatic SAH model or other SAH models might be needed to further evaluate the neuroprotective effects of RSV in the late phase of SAH. Combining the research listed above, we speculated that RSV could regulate a complex series of inflammatory responses contributing to EBI after SAH through inhibiting the TLR4/MyD88/NF-κB signaling pathway. However, we cannot exclude other molecular mechanisms also involved in the neuroprotective effects of RSV in EBI. For instance, the nuclear factor erythroid-related factor 2 pathway, the sirtuin 1 pathway, and the mitogen-activated protein kinase pathway are all involved in the anti-inflammatory effect of RSV. Additionally, the therapeutic time windows of RSV after SAH remain obscure. Hence, future studies are warranted to address these issues. 4. Materials and Methods 4.1. Animal Preparation Adult male Sprague-Dawley rats (250–300 g) were purchased from the Animal Center of Jinling Hospital (Nanjing, China). All procedures were approved by the Animal Care and Use Committee of Nanjing University (approval no. 2015013, June 2015) and were conformed to Guide for the Care and Use of Laboratory Animals by National Institutes of Health. 4.2. Prechiasmatic Cistern SAH Model The prechiasmatic cistern SAH model was performed according to a previous study [42]. Briefly, the animal’s head was fixed in a stereotactic frame after intraperitoneal anesthetization with 10% chloral hydrate (0.35 mL/100 g). The hair on the head and near the inguinal region was shaved. After careful disinfection, a midline scalp incision was made and a 1 mm hole was drilled 8.0 mm anterior to the bregma in the midline. Approximately 0.35 mL non-heparinized fresh autologous arterial blood from the femoral artery was slowly injected into the prechiasmatic cistern in 20 s with a syringe pump under aseptic technique. Then, the burr hole was sealed with bone wax, and the incision was surgically sutured. The animals were then kept in a 30 °C heads-down position for 20 min, after which the rats were returned to their cages and housed at 25 °C for recovery from anesthesia. 4.3. Experimental Groups As shown in Figure 6, rats were randomly divided into four groups: a sham group (n = 36); a sham + RSV group (n = 36); a SAH + vehicle group (n = 36); and a SAH + RSV group (n = 36). In the animals of sham + RSV and SAH + RSV groups, RSV (Sigma-Aldrich, St. Louis, MO, USA) was formulated with 1% dimethylsulfoxide (DMSO) and physiological saline, and was given by intraperitoneal administration at a dose of 60 mg/kg at 2 and 12 h post-injury. The dose was selected according to our previous study [40]. Rats in SAH + vehicle group received an equal volume of vehicle, also by means of intraperitoneal injection at the same time points as mentioned above. Sham operation animals were injected with 0.35 mL saline instead of blood into prechiasmatic cistern. In the first experiment setting, the animals were killed at 24 h after surgery. Post-assessments included neurological function, brain edema, enzyme-linked immunosorbent assays (ELISA), molecular tests, and histopathology. In the second experiment, a separate cohort of rats was used to further evaluate the possible long-term benefits of RSV. Neuronal survival, brain edema, and neurological function at 72 h after SAH were investigated. 4.4. Perfusion Fixation and Tissue Preparation All rats were anesthetized and perfused through the left cardiac ventricle with 0.9% normal saline solutions (4 °C) until effluent from the right atrium was clear. The brain tissue was harvested on ice, and the temporal lobe tissue adjacent to the clotted blood was used for analysis. The brain samples were stored at −80 °C for Western blot and ELISA. For immunohistochemistry and immunofluorescence, rats were perfused with 0.9% normal saline (4 °C) followed by 4% buffered paraformaldehyde (4 °C), after which brains were immersed in 4% buffered paraformaldehyde (4 °C). 4.5. Enzyme-Linked Immunosorbent Assay The frozen brain samples were mechanically homogenized in 1 mL lysate buffer and centrifuged at 12,000× g for 20 min at 4 °C. The supernatant was then collected and total protein determined using a bicinchoninic acid assay kit (Bio-Rad Laboratories, Hercules, CA, USA). The inflammatory mediator’s protein levels were quantified using ELISA kits according to the manufacturer’s instructions (R&D Systems, Minneapolis, MN, USA) and cytokines concentrations within the brain tissue were expressed as picogram per milligram protein. 4.6. Total/Nuclear Protein Extraction and Western Blot Analysis Rat brain tissue total/nuclear protein was extracted from rat brains using an established protocol from our laboratory [41]. The protein concentration was estimated by the method of Bradford with a standard commercial kit (Bio-Rad Laboratories, Hercules, CA, USA). For Western blot analysis, equal protein concentrations per lane were separated by 10% SDS-PAGE and transferred to polyvinylidene-difluoride (PVDF) membrane. After blocking, the membrane was incubated with primary antibodies against TLR4 (1:200, Santa Cruz Bio-Technology, Santa Cruz, CA, USA), MyD88 (1:200, Santa Cruz Bio-Technology, Santa Cruz, CA, USA), NF-κB P65 (1:200, Santa Cruz Bio-Technology, Santa Cruz, CA, USA), HMGB1 (1:1000, Cell signaling Technology, Beverly, MA, USA), Bcl-2 (1:200, Santa Cruz Bio-Technology, Santa Cruz, CA, USA), Bax (1:200, Santa Cruz Bio-Technology, Santa Cruz, CA, USA), caspase-3 (1:500, Cell Signaling Technology, Beverly, MA, USA), Histone-3 (1:3000, Bioworld Technology, Minneapolis, MN, USA), and β-actin (1:4000; Bioworld Technology, Minneapolis, MN, USA). The membranes were then incubated with goat anti-rabbit horseradish peroxidase (HRP)-conjugated IgG (1:5000). The blotted protein bands were visualized by using the enhanced chemiluminescence (ECL) Western blot detection reagents (Amersham, Arlington Heights, IL, USA). Quantification of band density was performed using the UN-Scan-It 6.1 software (Silk Scientific Inc., Orem, UT, USA). 4.7. Immunohistochemistry For immunohistochemistry, brain sections (4 μm thickness) were incubated overnight at 4 °C with primary antibody against TLR4 (1:200), MyD88 (1:200), and NF-κB P65 (1:200) followed by a 15 min wash in phosphate buffered saline (PBS). After that the sections were incubated with HRP conjugated IgG (1:500) for 60 min at room temperature. Slides were visualized by incubated with 3,3′-diaminobenzidine (DAB) and hydrogen peroxide, followed by the assessment of staining intensity (five grades). “0” indicates that there were no detectable positive cells; “1” indicates very low density of positive cells; “2” indicates a moderate density of positive cells; “3” indicates a higher, but not maximal density of positive cells; and “4” indicates the highest density of positive cells. 4.8. Immunofluorescence Staining Immunofluorescence was performed according to one of our previous studies [41]. Frozen tissue sections (6 μm thickness) were sliced and blocked with 5% normal fetal bovine serum in PBS containing 0.1% Triton X-100 for 2 h at room temperature, thereafter, sections were incubated with rabbit anti-Iba1 (1:100) overnight at 4 °C. Alexa Fluor 594 goat anti-rabbit IgG (1:200, Invitrogen, Shanghai, China) was used to detect the immunoreactivity of Iba1. 4-diamidino-2-phenylindole (DAPI) was used as a nuclear stain. Negative controls were prepared by omitting the primary antibodies. Fluorescence microscopy imaging was performed using ZEISS HB050 inverted microscope system and handled by Image-Pro Plus 6.0 software (Media Cybernetics, Rockville, MD, USA) and Adobe Photoshop CS5 (Adobe Systems, San Jose, CA, USA). 4.9. TUNEL Staining Terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling (TUNEL) staining was performed according to the manufacturer’s instructions using an in situ cell death detection kit (Roche, South San Francisco, CA, USA). The number of apoptosis cells to DAPI was regarded as an apoptosis index (apoptosis cells/DAPI). 4.10. Nissl Staining Nissl staining was performed to evaluate neuronal survival at 72 h after SAH. Brain sections (4 μm thickness) were hydrated in 1% toluidine blue for 10 min, washed with double distilled water, dehydrated and mounted with permount. Normal neurons have relatively big cell body, rich in cytoplasm, with one or two big round nuclei, whereas damaged cells have shrunken cell bodies, condensed nuclei, dark cytoplasm, and numerous empty vesicles. 4.11. Cell Counting For each segment, six random high power fields (400×) in each coronary section were selected, and the mean percentage of positive cells in the six fields was used for final analysis. A total of four sections from each sample were used for quantification. All these processes were conducted by two investigators blinded to the experimental condition. 4.12. Neurological Scores The clinical scores were recorded 24 and 72 h before being euthanized based on the independent observations by a veterinarian who was blinded to the experimental groups. Three behavioral activity examinations (Table 1) including appetite, activity, and neurological deficits were used in the scoring methodology. 4.13. Brain Water Content Brain water content was measured at 24 and 72 h after surgery. Briefly, rats were anesthetized and decapitated, and the brains were quickly removed and separated into cerebrum, cerebellum and brain stem. Each part was immediately weighed as wet weight. Samples were then placed in an oven for 72 h at 100 °C before determining the dry weight. The percentage of brain water content (%) was calculated as follows: ((wet weight − dry weight)/wet weight) × 100%. 4.14. Statistical Analysis All data were presented as mean ± SD. SPSS Statistics, version 19.0.0 (SPSS, Inc., Chicago, IL, USA) was used for statistical analysis of the data. All data were subjected to one-way analysis of variance (ANOVA) combined with Tukey post-hoc test. Statistical significance was inferred at p < 0.05. 5. Conclusions In summary, we prove that RSV treatment exerts neuroprotection against SAH by combating with neuroinflammation, at least in part via inhibition of TLR4/MyD88/NF-κB-mediated signaling pathway. Our study suggests that RSV is an effective candidate for the treatment of SAH in a rat model. Acknowledgments This work was supported by grants from The National Natural Science Foundation of China (NSFC): No. 81371294 (Chun-Hua Hang), 81471183 (Xin Zhang), 81401029 (Wei Li), 81400980 (Zong Zhuang), 81571162 (Meng-Liang Zhou), and 81501022 (Xiang-Sheng Zhang); the National Science Foundation of Jiangsu province: No. BK20141375 (Chun-Hua Hang) and BK20141377 (Xin Zhang); and a project funded by Jinling Hospital: No. 2015013 (Xiang-Sheng Zhang). Author Contributions Xiang-Sheng Zhang and Wei Li performed the studies and wrote the manuscript. Qi Wu participated in making experimental animal model. Ling-Yun Wu performed the Western blotting. Jing-Peng Liu and Zhen-Nan Ye contributed to the immunohistochemical and immunofluorescence staining. Zong Zhuang and Meng-Liang Zhou analyzed the samples and data. Chun-Hua Hang and Xin Zhang contributed to the design and analysis of the study and wrote the manuscript. All authors analyzed the results and approved the final version of the manuscript. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Effects of resveratrol (RSV) treatment on pro-inflammatory cytokine release and microglia activation at 24 h post subarachnoid hemorrhage (SAH). (A,B) Western blot analysis showing that RSV administration significantly suppressed Iba-1 (microglia marker) expression after SAH; (C,G) Immunofluorescence staining indicted that RSV could evidently reduce the number of activated microglia; (D–F) RSV markedly alleviated the expression of IL-1β, IL-6, and TNF-α in the brain cortex after SAH. Bars represent the mean ± SD. ** p < 0.01, * p < 0.05, and ns means non-significant. Scale Bars = 50 μm. Figure 2 Effects of resveratrol (RSV) on the expression and activation of toll-like receptor 4 (TLR4)-related pathway 24 h post subarachnoid hemorrhage (SAH). (A) Representative Western blot images to detect the effects of RSV on the expressions of high-mobility group box 1 (HMGB1), TLR4, myeloid differentiation factor 88 (MyD88), and nuclear factor-κB (NF-κB); (B–E) Quantitative analyses of HMGB1, TLR4, MyD88, and NF-κB among all experimental groups. RSV treatment significantly reduced HMGB1, TLR4, MyD88, and NF-κB protein levels as compared with SAH + vehicle group. Bars represent the mean ± SD. *** p < 0.001, ** p < 0.01, * p < 0.05, and ns means non-significant. Figure 3 Effects of resveratrol (RSV) on high-mobility group box 1 (HMGB1), toll-like receptor 4 (TLR4), myeloid differentiation factor 88 (MyD88), and nuclear factor-κB (NF-κB) distribution at 24 h after subarachnoid hemorrhage (SAH). (A–E) RSV treatment could significantly reduce the immunoreactivity of HMGB1, TLR4, MyD88, and NF-κB in the cerebral cortex when compared with SAH + vehicle group. Bars represent the mean ± SD. *** p < 0.001, ** p < 0.01, * p < 0.05, and ns means non-significant. Scale Bars = 50 μm. Figure 4 Effects of resveratrol (RSV) on neural apoptosis, brain edema, and neurological function at 24 h post subarachnoid hemorrhage (SAH). (A–D) RSV significantly reduced the elevated levels of cleaved caspase-3 and pro-apoptotic protein Bax, and enhanced the diminished level of Bcl2; (E,F) RSV administration markedly decreased the number of TUNEL-positive neural cells compared with the SAH + vehicle group; (G,H) RSV ameliorated brain edema and neurological behavior impairment at 24 h post SAH. Bars represent the mean ± SD. *** p < 0.001, ** p < 0.01, * p < 0.05, and ns means non-significant. Scale Bars = 50 μm. Figure 5 Effects of resveratrol (RSV) on neuronal survival, brain edema, and neurological function at 72 h post subarachnoid hemorrhage (SAH). (A,B) RSV treatment significantly increased the proportion of survived neurons compared with the SAH + vehicle group; Higher magnification of Nissl staining was shown in the red box for all groups; (C,D) RSV failed to alleviate brain edema and the impairment of neurological behavior compared with SAH + vehicle group at 72 h post SAH. Bars represent the mean ± SD. *** p < 0.001, * p < 0.05, and ns means non-significant. Scale Bars = 50 μm. Figure 6 Schematic illustration of the experiment design. Resveratrol (RSV) was administered intraperitoneally (ip) at 2 h and 12 h after initial bleeding. In the first set of experiments, neurological scores, brain edema, Western blot analysis, immunohistochemistry, and TUNEL apoptosis were evaluated at 24 h after subarachnoid hemorrhage (SAH). In another set of experiments, neuronal survival, brain edema, and neurological function were evaluated at 72 h after SAH to determine the possible long-term benefits of RSV. ijms-17-01331-t001_Table 1Table 1 Behavior scores. Category Behavior Score Appetite Finished meal 0 Left meal unfinished 1 Scarcely ate 2 Activity Walk and reach at least three corners of the cage 0 Walk with some stimulation 1 Almost always lying down 2 Deficits No deficits 0 Unstable walk 1 Impossible to walk 2 ==== Refs References 1. Macdonald R.L. Delayed neurological deterioration after subarachnoid haemorrhage Nat. Rev. Neurol. 2014 10 44 58 10.1038/nrneurol.2013.246 24323051 2. Li B. He Y. Xu L. Hu Q. Tang J. Chen Y. Tang J. Feng H. Zhang J.H. Progranulin reduced neuronal cell death by activation of sortilin 1 signaling pathways after subarachnoid hemorrhage in rats Crit. Care Med. 2015 43 304 311 10.1097/CCM.0000000000001096 26010686 3. Wang Y. Gao A. Xu X. Dang B. You W. Li H. Yu Z. Chen G. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081332ijms-17-01332ReviewThe Role of Cyclo(His-Pro) in Neurodegeneration Grottelli Silvia 1*Ferrari Ilaria 2Pietrini Grazia 2Peirce Matthew J. 1Minelli Alba 1Bellezza Ilaria 1Prokai-Tatrai Katalin Academic Editor1 Dipartimento di Medicina Sperimentale, Università di Perugia, Polo Unico Sant’Andrea delle Fratte, Piazzale Gambuli, 06132 Perugia, Italy; mattpeirce69@gmail.com (M.J.P.); alba.minelli@unipg.it (A.M.); ilaria.bellezza@unipg.it (I.B.)2 Dipartimento di Biotecnologie Mediche e Medicina Traslazionale, Università degli Studi di Milano ed Istituto di Neuroscienze, Consiglio Nazionale delle Ricerche, Via Vanvitelli 32, 20129 Milano, Italy; Ilaria.ferrari87@hotmail.it (I.F.); grazia.pietrini@unimi.it (G.P.)* Correspondence: silvia.grottelli@unipg.it; Tel.: +39-075-5858-22712 8 2016 8 2016 17 8 133204 7 2016 08 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Neurodegenerative diseases may have distinct genetic etiologies and pathological manifestations, yet share common cellular mechanisms underpinning neuronal damage and dysfunction. These cellular mechanisms include excitotoxicity, calcium dysregulation, oxidative damage, ER stress and neuroinflammation. Recent data have identified a dual role in these events for glial cells, such as microglia and astrocytes, which are able both to induce and to protect against damage induced by diverse stresses. Cyclo(His-Pro), a cyclic dipeptide derived from the hydrolytic removal of the amino-terminal pyroglutamic acid residue of the hypothalamic thyrotropin-releasing hormone, may be important in regulating the nature of the glial cell contribution. Cyclo(His-Pro) is ubiquitous in the central nervous system and is a key substrate of organic cation transporters, which are strongly linked to neuroprotection. The cyclic dipeptide can also cross the brain-blood-barrier and, once in the brain, can affect diverse inflammatory and stress responses by modifying the Nrf2-NF-κB signaling axis. For these reasons, cyclo(His-Pro) has striking potential for therapeutic application by both parenteral and oral administration routes and may represent an important new tool in counteracting neuroinflammation-based degenerative pathologies. In this review, we discuss the chemistry and biology of cyclo(His-Pro), how it may interact with the biological mechanisms driving neurodegenerative disease, such as amyotrophic lateral sclerosis, and thereby act to preserve or restore neuronal function. oxidative stressendoplasmic reticulum stressneuroinflammation ==== Body 1. Introduction Neurodegenerative diseases such as Alzheimer’s disease, amyotrophic lateral sclerosis (ALS), Huntington’s disease (HD) and Parkinson’s disease (PD), are late-onset multifactorial disorders with a progressive loss of function of neurons, which leads to a progressive functional decline. More than 30 million people world-wide are affected, most commonly in their seventh decade, while in the EU, the proportion of the population aged 65 or over is predicted to increase from 15.4% to 22.4% by 2025. Currently available therapies provide only symptomatic relief and completely fail to address the likely inflammatory basis of these diseases. The academic and pharmaceutical research is currently focused on the discovery of novel drugs for the treatment of neurodegenerative disorders. The past decades have seen a growing awareness of the role of peptide transmitters and regulators in biological systems [1,2,3,4,5,6,7,8,9,10,11,12]. More recently, one particular class, cyclic dipeptides (CDPs), or 2,5-diketopiperazines, have been the subject of intense research interest. CDPs are simple compounds derived from the non-enzymatic cyclisation of dipeptides and their amides. Thyrotropin-releasing hormone (TRH), a tripeptide synthesized by the hypothalamus, acts as a neuroendocrine signal that elicits many behavioral responses [13]. In addition, accumulating data now point to a significant neuroprotective role of TRH. For example, substantial literature [1,14,15,16] reports that traumatic brain or spinal cord injuries can be significantly improved by TRH or TRH analogues. The catabolic product of TRH is the (His-Pro) dipeptide, which, by spontaneous cyclization, produces cyclo(His-Pro), and is the topic of this review. Here, we focus on the potential protective role of cyclic dipeptide (His-Pro) in neurodegeneration, beginning with an overview of the biochemistry and biology of cyclo(His-Pro), followed by an overview of the diverse biological mechanisms of neurodegeneration and, finally, describing how cyclo(His-Pro) may modulate these mechanisms to protect, or even restore, neuronal function. 2. Cyclo(His-Pro): Chemistry and Biology The major mechanism responsible for the extracellular inactivation of TRH within the CNS is the hydrolytic removal by pyroglutamyl aminopeptidases (PPs) of the amino-terminal pyroglutamic acid residue [17]. Following cleavage, the His-Pro-NH2 dipeptide undergoes cyclization at 37 °C by a non-enzymatic pH-dependent process (optimal at pH 6.0 to 7.0) [18], producing the cyclic dipeptide histidyl-proline (cyclo(His-Pro)). This cyclization reaction confers resistance to cleavage by peptidases and is also required for its active transport in the intestine [19] and the passage of the blood brain barrier (BBB) [20,21,22], a key characteristic for delivery and specific targeting of cyclo(His-Pro) therapy in the CNS. Adding to their suitability as therapeutic agents, cyclic dipeptides often exhibit significantly greater stability than their linear counterparts in vivo. While cyclo(His-Pro) can be generated from TRH as described above, the large majority is in fact endogenously synthesized de novo. In addition, cyclo(His-Pro) also possesses its own unique receptors, metabolic pathways and biological effects [23]. Cyclo(His-Pro) (Figure 1) is ubiquitous in the CNS and has been found in blood, in the gastrointestinal (GI) tract, as well as in several body fluids [24]. As first reported by Perry and colleagues [18], cyclo(His-Pro)-like immunoreactivity (CHP-LI) has been identified in foods and in several common nutritional supplements [25], while dietary intake of CHP-LI-rich supplements in healthy volunteers were reported to increase the levels of cyclo(His-Pro) in plasma significantly above the baseline values (1848 ± 117 pg/mL vs. 2148 ± 112 pg/mL) [26]. Importantly for the potential therapeutic application of these agents, acute consumption of 24 mg cyclo(His-Pro)/die can be absorbed from the GI tract without any toxicity in humans weighing an average of 70 kg [27]. Cyclo(His-Pro) as a Key Substrate of Organic Cation Transporter The mechanisms of the reabsorption and excretion of drugs were studied after discovering mammalian drug efflux transporters of the ATP binding cassette (ABC) family [28,29], such as the H+/oligopeptide cotransporter family SLC15 [30], the organic anion transporting family SLC01 [31,32], the organic cation/anion/zwitterion transporter family SLC22 [33] and the multidrug and toxin extrusion (MATE) H+/drug antiporters [34]. Polyspecific organic cation transporters belong to the SLC22 family and the MATE family [35]. The SLC22 family comprises three subtypes of passive diffusion organic cation transporters, called OCT1 (SLC22A1), OCT2 (SLC22A2) and OCT3 (SLC22A3), characterized by 12 α-helical transmembrane domains, an intracellular N-terminus, two extracellular loops and an intracellular C-terminus [36]. OCTs translocate a variety of organic cations in either direction [37]. Cyclo(His-Pro) has structural features essential for OCT transport. Indeed, cyclo(His-Pro) is selectively transported by OCT2 in the brain [36]. OCT2 is mostly expressed in the dopaminergic brain regions, particularly in substantia nigra pars compacta (SNc). It is perhaps worth noting that this is also the brain area associated with one of the commonest neurodegenerative diseases, Parkinson’s disease. With the exception of kidney, where OCT2 may be involved in the clearance of cyclo(His-Pro), OCT2 levels in peripheral tissues are all considerably lower than in SNc [36,38,39]. The distribution of cyclo(His-Pro) itself in rat brain reveals striking coincidence with both dopaminergic areas and OCT2 consistent with the potential functional link previously reported in the literature [23,40]. Structure/function studies have identified the proline residue and the presence of unsaturated systems as structural elements contributing to the nootropic and cognitive-enhancing properties, as well as the overall neuroprotective action of the natural/synthetic cyclic dipeptides [2,3,4,5,6,11,41,42]. Pretreatment of OCT2-transfected HEK-293 cells, SH-SY5Y and HTZ-146 cells with cyclo(His-Pro) prior to neuronal insult substantially diminished cell degeneration, by inhibiting excitotoxic calcium influx and its damaging sequelae: mitochondrial impairment and subsequently apoptosis [37]. Therefore, high expression of OCT2, as well as that of cyclo(His-Pro) are crucial for the maintenance of dopaminergic cell integrity. A decline in protective cyclo(His-Pro) may underpin calcium-triggered apoptotic cell death, possibly contributing to the selective chronic nigral degeneration observed in Parkinson’s disease [37]. 3. Role of Cyclo(His-Pro) in Common Mechanisms of Neurodegeneration 3.1. Oxidative and Nitrosative Stress Neurons are extremely susceptible to oxidative stress because of their terminally-differentiated state and complex morphology. They depend largely on surrounding glial cells for metabolic substrates and glutathione [43,44]. Thus, the brain is highly sensitive to changes in redox status, and maintaining redox homeostasis is critical for preventing oxidative damage. Because of the limited capacity of the neurons to protect themselves, the bulk of this critical task falls to the glial cells. In the absence of the redox homeostasis provided by glial cells, oxidative stress (OS) and nitrosative stress (NS) thus result in the accumulation of oxidized molecules and the disruption of normal neuronal processes. Energy generation via the process of oxidative phosphorylation in the mitochondrial electron transport chain is the main endogenous source of reactive oxygen species (ROS). Indeed, mitochondrial dysfunction, strongly associated with neurodegenerative diseases, leads to increased ROS generation while decreasing ATP production. Moreover, in the context of failing mitochondria, NADPH oxidase yields superoxide anions, which combined with nitric oxide in this setting produced largely by inducible nitric oxide synthase (iNOS), generates the highly reactive peroxynitrite (RNS) [45,46,47,48]. The activity of iNOS is largely controlled by its transcription [49], which requires the activation of the nuclear factor-κB (NF-κB) that, in turn, may be influenced by ROS [50,51,52]. NF-κB is composed of p65 and p50 heterodimers, which are maintained in an inactive form in the cytosol by association with the IκB family. The stimulation of cells with pro-inflammatory agents causes the phosphorylation of IκBα, resulting in its polyubiquitination and proteasomal degradation [53]. Thus released from IκBα, p65/p50 heterodimers are able to enter the nucleus, driving the expression of cell adhesion molecules and pro-inflammatory factors [54]. Moreover, NF-κB is a redox-sensitive transcription factor that drives the expression of genes governing inflammation, growth and apoptosis [50,51,55]. Oxidative/nitrosative damage can affect nucleic acids, proteins and lipids. Markers of OS and NS are a defining feature of all neurodegenerative diseases, strongly suggesting a causal link between ROS/RNS and neurodegeneration [47,56,57,58,59]. To counteract these stresses, the cells must maintain their cellular redox homeostasis via a complex interplay of many redox-sensitive transcription factors, which together orchestrate the expression of an array of protective genes [50,51,60]. The pathway of the Nrf2 (nuclear factor erythroid 2-related factor 2) antioxidant response element (ARE) is critical for this protective response. Nrf2 is sequestered in the cytosol by Keap1 (Kelch ECH-associating protein), an actin-bound protein [61,62]. Keap1, a Cul3-based E3 ligase, polyubiquitinates Nrf2, triggering its proteasomal degradation [63,64]. Upon oxidative stress, Keap1 cysteine residues are modified, thus releasing Nrf2 that translocates to the nucleus, binds ARE sequences and upregulates several antioxidant genes [65,66]. NF-κB and Nrf2-signalling pathways are activated by several physiological and/or pathological stimuli. On the other hand, anti-inflammatory and/or anti-carcinogenetic compounds suppress NF-κB and activate the Nrf2 signaling pathways [67,68,69,70,71,72]. ROS levels are critical determinants of cell fate. Indeed, chronically-elevated ROS levels induce cell death, activate NF-κB and lead to inflammation [73,74], whereas moderate ROS levels activate Nrf2 and lead to the upregulation of stress-inducible genes, such as heme oxygenase-1 (HO-1) [10]. HO activity exerts anti-inflammatory and adaptive survival responses upon oxidative insults [10,71,75,76,77,78] suggesting that anti-inflammatory and anti-oxidant pathways are coordinated through a complex mechanism. We showed that cyclo(His-Pro) protected dopaminergic PC12 cells from oxidative stress by activating the Nrf2-ARE pathway. Indeed, cyclo(His-Pro) augmented the expression of several ARE-containing genes. Moreover, cyclo(His-Pro) reduced ROS production and prevented glutathione depletion induced by rotenone, paraquat and β-amyloid treatment, suggesting that the dipeptide may act as an antioxidant compound [9,10]. Consistent with this possibility, we also showed that cyclo(His-Pro) abolished hydrogen peroxide-mediated ROS and NO generation and glutathione depletion, which lead to apoptotic cell death [10]. The mechanism of protection against cellular redox stress is due to both the thioredoxin system, which regulates the redox status of protein thiols involved in signal transduction and gene regulation, and the glutathione system, which maintains a low redox potential and high free thiol levels [79]. Cyclo(His-Pro) upregulates genes related to both redox systems (glutathione-synthesizing/regenerating enzymes and thioredoxin-1 isoform), thus indicating that cyclo(His-Pro) acts as an Nrf2-inducing agent. It is to note that the increase in mitochondrial ROS generation due to the disturbance of glutathione metabolism is implicated in both ageing and neurodegenerative disorders [44,80]. Cyclo(His-Pro) counteracted the hydrogen peroxide-mediated increase in NO production (along with the expression of NOS isoforms), while at the same time preventing glutathione depletion, suggesting that cyclo(His-Pro) may be a potential therapeutic agent in oxidative stress-based diseases [9]. Recently, the antioxidant properties of cyclo(His-Pro) were studied in microglial cells overexpressing the mutated human gene SOD1G93A, which are used as a glial model of ALS [81]. By exposing microglial SOD1G93A cells to an oxidative stressor such as paraquat, we found that the exogenous oxidative stress worsens the neurotoxic effect of the mutated microglia cells, confirming the contribution of ROS to disease progression. More importantly, we observed that the use of cyclo(His-Pro), by reducing the oxidative burden and triggering the protective response, was able to partially attenuate ROS toxicity. 3.2. Endoplasmic Reticulum Stress In the endoplasmic reticulum (ER), proteins are folded multi-subunit protein complexes, lipids are assembled, sterols are synthesized and calcium is stored. Various stressful environmental stimuli including, calcium dysregulation and OS, can alter ER function leading to the accumulation of unfolded/misfolded proteins within the lumen of the ER. These events trigger the unfolded protein response (UPR) [82]. The UPR is regulated by ER-resident proteins, i.e., inositol-requiring enzyme 1 (IRE1), PKR-like endoplasmic reticulum kinase (PERK) and activating transcription factor (ATF) 6. The downstream activation of all three pathways is important both in protective or adaptive responses to protein accumulation, but also in the promotion of apoptosis through the expression of various apoptotic activators, such as C/EBP-homologous protein (CHOP). The decision to induce an adaptive or pro-apoptotic response depends on the accumulation of misfolded proteins and the duration of the stress exposure [83]. Short-term stress and moderate misfolded protein accumulation induce the UPR, whereby accumulated misfolded proteins are cleared either through the ER-associated degradation (ERAD) machinery linked to the ubiquitin proteasome system (UPS) or through autophagy, restoring cellular homeostasis [83]. Longer term stress and/or severe protein accumulation might result in cell death rather than adaptive cell maintenance programs. For example, neurodegenerative diseases have been linked to the constitutive activity of the ER stress response [83,84,85,86,87,88,89,90,91,92]. Our results showed that cyclo(His-Pro) attenuates ER stress in BV-2 microglial cells [69]. The inhibitor of protein glycosylation, tunicamycin, failed to induce detectable NO, but it led to a concentration-dependent decrease in cell viability, which was reduced by treatment with cyclo(His-Pro). This effect was linked to the cyclo(His-Pro)-mediated early activation of three UPR transducers, thus increasing the phosphorylation of the α subunit of eIF2α, responsible for initiating the UPR. Furthermore, it increased the protein levels of Bip (GRP78), an ER chaperone, while decreasing the levels of the apoptosis-inducer CHOP. Whereas tunicamycin caused a biphasic response in NF-κB nuclear translocation, cyclo(His-Pro) treatment induced a stable NF-κB nuclear translocation. On the other hand, nuclear translocation of Nrf2, a known PERK substrate [93], was enhanced by cyclo(His-Pro). These results show that cyclo(His-Pro) can modulate the Nrf2–NF-κB axis when present at the same time as an ER stressor. LPS is known as an inducer of ER stress [69,94,95]. Treatment with cyclo(His-Pro) hastened the LPS-induced activation of the three UPR transducers, while at the same time increasing Bip levels and increasing the phosphorylation of eIF2α. Notably while CHOP protein levels were strongly upregulated by the ER stress-inducer tunicamycin, they were undetectable following treatment with LPS with or without cyclo(His-Pro), demonstrating qualitative differences in the cellular responses to these two different ER stressors [95]. These results indicate that cyclo(His-Pro) increases the sensor of ER stress and launches the UPR designed to alleviate the ER stress by upregulating Bip. 3.3. Excitotoxicity and Calcium Overload Neuronal excitotoxicity like other cellular responses to major stresses can ultimately lead to neuronal death, which is a conserved pathological feature of neurodegenerative diseases, including Huntington’s disease (HD), Alzheimer’s disease (AD), Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS) [96,97,98,99,100,101]. Excitotoxicity stems from the excessive release of the neurotransmitter glutamate, which in turn causes post synaptic over excitation of neurons. One of the mechanisms involved is calcium overload, a result of excessive glutamate signaling, which, by activating calcium-dependent enzymes and increasing ROS and RNS, results in cell death [102,103]. In our Lab, we observed that dopaminergic PC12 cells, exposed to a high concentration of glutamate/hydrogen peroxide, showed robust increases in intracellular calcium levels, which, along with increases in ROS and NO generation and decreases in glutathione levels, eventually resulted in cell death. These changes were significantly reversed by pre-treatment with cyclo(His-Pro), thus increasing cell survival [9], and demonstrate the protective effect of cyclic dipeptide against glutamate toxicity. 4. Neuroinflammation The term neuroinflammation defines a situation characterized by a broad range of immune responses within the CNS, driven primarily by cross-talk between microglia, astrocytes and the BBB. The BBB is an active player in neuroinflammation since it responds to peripheral inflammatory stimuli, generates inflammation mediators and allows leukocyte migration. Thus, peripheral inflammation might be one of the causes of damaging neuroinflammation. The neuroinflammatory response impairs synaptic transmission and causes neuronal death. Microglial cells play a crucial role in the process of neuroinflammation. Indeed, in response to mediators of acute inflammation, microglia, the resident macrophages of the CNS, assume an amoeboid phagocytic state, with abundant filopodia, and release additional pro-inflammatory mediators. Long-lasting microglial activation is toxic to neighboring neurons, and the resulting neuronal damage can itself further amplify microglial activation and initiate a self-propelling cycle of inflammation and progressive neuronal damage [104]. Astrocytes, the other class of glial cell, respond to CNS insult through reactive astrogliosis, characterized by progressive changes in gene expression and other cellular changes [105,106,107,108,109,110]. Excessive and prolonged neuroinflammation abolishes the capacity of astrocytes to maintain brain function and, thus, is relevant to CNS disease progression. In conclusion, neuroinflammation can initiate, amplify and prevent the normal resolution of acute stress responses, promoting the chronic conditions that result in neurodegeneration. 4.1. Cyclo(His-Pro) Acts as an Anti-Inflammatory Agent Dopaminergic PC12 cells, exposed to strong oxidative stressor, responded to the insult by increasing the nuclear translocation of both Nrf2 and NF-κB transcription factors. Pre-treatment with cyclo(His-Pro) increased the nuclear level of Nrf2 and, by blocking IκB-α degradation, inhibited NF-κB nuclear translocation, promoting adaptive responses while reducing potentially damaging apoptotic and inflammatory responses and confirming the interplay between the suppression of NF-κB signaling and the activation of the Nrf2 pathway [111]. Notably, HO-1 induction has been shown to be protective in various experimental models of vascular, cardiac and pulmonary injury, as well as against damage from certain inflammatory conditions [75,76,77,78]. Cyclo(His-Pro), via Nrf2 activation, increased HO-1 expression, elevated HO activity and protected PC12 cells from ROS toxicity [10], thus suggesting that HO activity resulting in increased production of antioxidant end-products suppresses ROS-mediated NF-κB activation. Indeed, conclusive evidence of the correlation between the anti-oxidant and anti-inflammatory effects of cyclo(His-Pro) was obtained with in vivo experiments by subjecting mice to a model of acute skin edema induced by a single topical application of TPA (12-O-tetradecanoylphorbol 13-acetate), to the ear. Cyclo(His-Pro) pre-treatment significantly inhibited the TPA-induced edema response confirming its anti-inflammatory effects. 4.2. Cyclo(His-Pro) Reduces Microgliosis/Neuroinflammation In LPS-treated microglial BV-2 cells [69], pre-treatment with cyclo(His-Pro) was able to reduce LPS-induced NO and ROS generation while increasing cell viability and largely maintaining resting morphology (Figure 2). Moreover, the increased nuclear localization of NF-κB coupled with the reduced nuclear localization of Nrf2, which normally follow LPS treatment of these cells, were at least partially reversed by cyclo(His-Pro); levels of nuclear Nrf2 were enhanced while those of NF-κB were reduced. These data confirm the ability of cyclo(His-Pro) simultaneously to activate endogenous antioxidant defenses and inhibit pro-inflammatory pathways. At the transcriptional level, these changes in transcription factor nuclear localization were reflected in strongly downregulated inflammatory gene expression (iNOS and COX-2 (cyclooxygenase 2)) and augmented protective responses (HO-1 expression). It is to note that cyclo(His-Pro) also reduced the expression of the key enzymes in superoxide generation, the NADPH oxidase membrane-bound subunit (gp91phox) and the NADPH oxidase organizer subunit (47phox) (Figure 3). These results are in agreement with the dual-key mechanism of inflammatory neurodegeneration [112]. In addition to these effects reducing the capacity to induce oxidative cell damage, we also found [69] that cyclo(His-Pro) reduced the inflammatory capacity of these cells; the production of several pro-inflammatory cytokines, such as IL-6, TGF-β and INF-γ, was reduced and, moreover, protected primary neuronal cultures against microglial neurotoxicity, i.e., pro-inflammatory/neurotoxic factors contained in LPS-activated BV-2 culture media. These results were largely recapitulated in vivo by showing that cyclo(His-Pro) also reduced glial inflammation caused by systemic LPS administration. Analysis of the animals by Fourier transform infrared (FTIR) spectroscopy showed that those treated with cyclo(His-Pro) before LPS exposure were strikingly similar to the control animals and were markedly different from LPS-treated animals. Moreover, in vivo cyclo(His-Pro) treatment strongly reduced mRNA levels of TNFα in liver and brain, as well as mRNA levels of Il-1β in hippocampus. Collectively, these results provided strong evidence for the in vivo glial anti-inflammatory properties of the cyclic dipeptide [69]. 5. Role of Cyclo(His-Pro) in in Vitro Models of Familial Amyotrophic Lateral Sclerosis Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disorder that results in progressive loss of motor function and ultimately death. Ten percent of ALS cases are inherited, while the rest are considered sporadic. Twenty percent of inherited ALS is caused by mutations in the gene encoding for superoxide dismutase 1 (SOD1) providing further evidence of the connection between neurodegeneration and oxidative stress. While it is now clear that all of the different mutations of SOD1 associated with pathology augment rather than inhibit its function, the molecular mechanisms leading to motor neuron damage remain to be defined [113]. A role for non-neuronal cells, such as microglia and astrocytes, has been suggested for ALS pathogenesis by experimental and clinical observations, thus opening a new area for identifying potential therapeutic targets [114,115]. Given our findings on the anti-inflammatory and anti-oxidative effects of cyclo(His-Pro), we tested whether cyclo(His-Pro) might reduce the capacity of microglial cells to induce inflammatory and/or oxidative neuronal damage and thereby protect against neurodegeneration in ALS [116]. First, by using microglial cell lines from cortical cultures derived from human SOD1G93A transgenic mice, immortalized in our laboratory, we observed increased levels of nuclear NF-κB in cells expressing the G93A mutant of human SOD1 compared to wild-type control lines. In line with microglial activation induced by the mutant SOD1, we measured a high nuclear/cytoplasmic ratio of NF-κB in the immortalized microglia from SOD1G93A cultures, even under basal conditions (Figure 4A). Upon LPS challenge, the nuclear/cytoplasmic ratio doubled, whereas in the presence of 50 μM cyclo(His-Pro), this ratio was reduced, and 200 μM cyclo(His-Pro) completely prevented the LPS-induced NF-κB nuclear translocation (Figure 4A). These data further demonstrate the in vitro anti-inflammatory properties of cyclo(His-Pro) and suggest that these properties may be effective against inflammatory responses resulting from SOD1 mutants associated with ALS. To investigate the potential neuroprotective mechanism of cyclo(His-Pro), primary cultures of rat cortical neurons (kindly provided by Drs. Verpelli C. and Sala C., Consiglio Nazionale delle Ricerche (CNR)-Institute of Neuroscience of Milano) were transfected with cDNA encoding human SOD1G93A. In these cells, we observed that striking defects in neurite outgrowth, when compared to mock transfected GFP cells, were largely inhibited in the same cells cultured in medium supplemented with 50 μM cyclo(His-Pro) (Figure 4B). Collectively, these data indicate that cyclo(His-Pro) may inhibit the neuronal damage associated with SOD1 mutations both indirectly, at the level of microglial inflammatory responses, and by direct effects on neurons themselves, suggesting its possible utility as a therapeutic agent to prevent or delay disease progression in ALS. Pre-clinical trials are needed to confirm the promising in vitro data. 6. Conclusions Cyclo(His-Pro) is a dipeptide with much greater stability in vivo than its linear counterpart and thus shows much greater promise as a therapeutic agent. Conventional anti-inflammatory therapeutics are mostly unsuitable for treating neuroinflammation since they cannot cross the BBB. Being a BBB-permeable drug, cyclo(His-Pro) can be administered by both parenteral and oral routes, thus increasing patients’ compliance. The dipeptide shows a remarkable bioactivity in reducing inflammatory responses in glial cells and in inducing a protective state in neurons. As discussed here, published data provide a dual-pronged justification for the therapeutic use of cyclo(His-Pro) against neuroinflammation-related diseases. Author Contributions All the authors contributed to the preparation of the manuscript. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Cyclo(His-Pro) chemical structure. Figure 2 Effects of cyclo(His-Pro) (CHP) on glia morphology. BV2 microglial cells were pre-treated with 50 μM cyclo(His-Pro) (24 h) prior to 10 µg/mL lipopolysaccarde (LPS) (24 h). Morphology was assessed by TRIC-labelled phalloidin staining (red) and nuclei were counterstained with DAPI (blue). Magnification: 20×. Figure 3 Proposed glia nuclear events in the presence of cyclo(His-Pro). NF-κB (nuclear factor kappa B), NOX (NADPH oxidase), Nrf2 (nuclear factor erythroid 2–related factor 2), iNOS (inducible nitric oxide synthase), IL-6 (interleukin 6), ONOO (peroxynitrite), TGF-β (transforming growth factor beta), INF-γ (interferon gamma). Red X indicates inhibition of peroxynitrite production by cyclo(His-Pro). ↑ indicate an increase; ↓ indicate a decrease. Figure 4 Role of cyclo(His-Pro) in ALS. (A) Cyclo(His-Pro) prevents the toxic effects of mutant SOD1 in microglia. Microglial cells immortalized from neuronal primary cultures derived from SOD1G93A transgenic mice were pre-treated with 50 or 200 μM cyclo(His-Pro) (40 h) prior to 1 µg/mL LPS exposure in serum-free medium. Representative immunofluorescence images were quantitatively analyzed to classify the cells into three different categories on the basis of the NF-κB distribution between the nucleus and cytoplasm (magnification: 65×). The scatter plot of the effects of increasing the concentration of cyclo(His-Pro) on NF-κB distribution; (B) Cyclo(His-Pro) prevents the toxic effects of mutant SOD1 in neurons. Primary cortical neurons were transfected after one day in vitro with GFP or GFP-SOD1G93A and fixed with paraformaldehyde at Day 4 in vitro. Representative immunofluorescence images of neurons expressing GFP-SOD1G93A untreated or treated for 65 h with 50 μm cyclo(His-Pro) are shown. Bar: 50 μm. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081333ijms-17-01333ReviewThe Risk of Congenital Heart Anomalies Following Prenatal Exposure to Serotonin Reuptake Inhibitors—Is Pharmacogenetics the Key? Daud Aizati N. A. 12*Bergman Jorieke E. H. 3Kerstjens-Frederikse Wilhelmina S. 3Groen Henk 4Wilffert Bob 15Angelini Sabrina Academic EditorRavegnini Gloria Academic Editor1 Department of Pharmacy, Unit of PharmacoTherapy, -Epidemiology and -Economics, University of Groningen, 9713AV Groningen, The Netherlands; b.wilffert@rug.nl2 School of Pharmaceutical Sciences, Discipline of Clinical Pharmacy, Universiti Sains Malaysia, 11800 Penang, Malaysia3 Department of Genetics, University Medical Center Groningen, University of Groningen, 9713AV Groningen, The Netherlands; j.e.h.van.kammen@umcg.nl (J.E.H.B.); w.s.kerstjens@umcg.nl (W.S.K.-F.)4 Department of Epidemiology, University Medical Centre Groningen, University of Groningen, 9713AV Groningen, The Netherlands; h.groen01@umcg.nl5 Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, 9713AV Groningen, The Netherlands* Correspondence: n.a.a.daud@rug.nl; Tel.: +31-50-363-295413 8 2016 8 2016 17 8 133301 6 2016 27 7 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Serotonin reuptake inhibitors (SRIs) are often prescribed during pregnancy. Previous studies that found an increased risk of congenital anomalies, particularly congenital heart anomalies (CHA), with SRI use during pregnancy have created concern among pregnant women and healthcare professionals about the safety of these drugs. However, subsequent studies have reported conflicting results on the association between CHA and SRI use during pregnancy. These discrepancies in the risk estimates can potentially be explained by genetic differences among exposed individuals. In this review, we explore the potential pharmacogenetic predictors involved in the pharmacokinetics and mechanism of action of SRIs, and their relation to the risk of CHA. In general, the risk is dependent on the maternal concentration of SRIs and the foetal serotonin level/effect, which can be modulated by the alteration in the expression and/or function of the metabolic enzymes, transporter proteins and serotonin receptors involved in the serotonin signalling of the foetal heart development. Pharmacogenetics might be the key to understanding why some children exposed to SRIs develop a congenital heart anomaly and others do not. congenital heart defectsheart abnormalitiesantidepressive agentsteratogenesisserotonin reuptake inhibitorsdrug-induced birth defects ==== Body 1. Introduction The use of antidepressants during pregnancy, particularly the use of selective serotonin reuptake inhibitors (SSRIs), has increased globally over the last few decades with the percentage of pregnant women users ranging between 1.2% and 6.2% up to 2005 [1,2,3,4]. SSRIs were considered to cause fewer side effects compared to the first generation of antidepressants until 2005, when a warning about the increased risk of foetal congenital heart anomalies (CHA) with SSRI use in pregnancy was released by the US Food and Drug Administration. This warning was shown to cause a decline, by 1.48 prescriptions per 1000 women per month, in the prescribing of SSRIs among pregnant women in the US and Canada between 2005 and 2007 [5]. Following this warning, many studies were carried out to evaluate the risk of congenital anomalies in children exposed to SSRIs during the first trimester of pregnancy. Most of these studies used data from healthcare monitoring systems and, while a large number of studies were done, the results have been inconsistent. Some studies reported an association, while other studies did not. With the emergence of genomic testing and personalized therapy, we now have the opportunity to explore the pharmacogenetic parameters that may explain why some children exposed to SSRIs develop a congenital heart anomaly and others do not. This review presents our current knowledge about the associations between serotonin reuptake inhibitors (SRIs) and CHA, about the pharmacogenetic predictors that are potentially involved in the pharmacokinetics of SRIs during pregnancy and about the genetic predictors involved in the plausible biological mechanisms linking CHA to SRIs exposure, taking into consideration maternal and foetal factors. We use the classification of serotonin reuptake inhibitors (SRIs) because it includes SSRIs and serotonin/noradrenaline reuptake inhibitors (i.e., venlafaxine and duloxetine), both of which are based on the same mechanism of serotonin inhibition. 2. The Risk of Congenital Heart Anomalies (CHA) Associated with Maternal Use of Serotonin Reuptake Inhibitors (SRIs) during the First Trimester of Pregnancy To have an insight into the current knowledge about the association between maternal use of SRIs during pregnancy and the risk of CHA, we performed a literature search for cohort and case-control studies published between January 2005 and May 2015 using the PubMed database (Figure S1). Among 27 articles that were selected for review, no consistent pattern has been observed in the reported risk of CHA. A slight increase in risk was found, particularly for paroxetine, in a number of studies in various countries (see Table S1) [6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22], but a number of other studies reported no increased risk (see Table S2) [23,24,25,26,27,28,29,30,31,32]. The dose of SRIs may also be an important determinant of the risk. A dose–effect relationship was observed for paroxetine in one study [21], but it was not replicated in a subsequent study [33]. The results of a meta-analysis by Wurst and colleagues in 2010 indicate an increased prevalence of cardiac malformations (odds ratio (OR) 1.46, 95% confidence interval (CI) 1.17–1.82) after paroxetine use during the first trimester of pregnancy [34]. Another meta-analysis by Grigoriadis and colleagues in 2013, using adjusted data and excluding studies below a specified quality threshold, has also reported a significantly higher risk of cardiovascular malformations after maternal paroxetine use (risk ratio (RR) = 1.43, 95% CI 1.08–1.88) [35]. Similar findings were also reported in a meta-analysis performed by Myles and colleagues in 2013 (OR 1.44, 95% CI 1.12–1.86) [36]. The most recent meta-analysis, performed in 2015 including only prospective cohort studies, however, found no association of first trimester exposure to overall SRIs with an increased risk of CHA [37]. Most studies are population-based, linking drug exposure data from prescription databases with foetal outcome data from hospitals or birth defect registries. This approach has many limitations because these cohorts were not designed to investigate the foetal outcome following exposure to specific drugs [8,17,18,21,23,24,25,29,31]. Consequently, many confounding factors cannot be addressed, and biases in exposure and outcome definitions have always been major considerations [38]. While there are no perfect studies, each represents a different population and different risk factor assessments, and the study designs have improved over the years. A recent Bayesian analysis by the National Birth Defects Prevention Study (NBDPS), based on the results of previous population-based studies and new NBDPS data, has reported that paroxetine and fluoxetine use during pregnancy were associated with a higher risk of several subtypes of CHA [39]. Paroxetine was associated with atrial septal defects (ASDs) with posterior OR 1.8, 95% credible interval (CrI) 1.1–3.0 and right ventricular outflow tract obstruction defects (RVOTO) (posterior OR 2.4, 95% CrI 1.4–3.9). Fluoxetine was also associated with RVOTO (posterior OR 2.0, 95% CrI 1.4–3.1) and ventricular septal defects (VSDs) (posterior OR 1.4, 95% CrI 1.0–1.9). Although VSDs and ASDs are the most common subtypes of CHA (34% and 13%, respectively, of total CHA cases worldwide) [40], the absolute risk among children who were exposed to both SSRIs may still be considered low. There are concerns among the patients who were taking these medications when they became pregnant, but there is still no definite answer if SRIs increase the risk of CHA in offspring. Because congenital heart anomalies are not common diseases (8/1000 live-borns babies), and the number of cases exposed to SRIs is low, this inevitably leads to difficulties in obtaining a large enough sample to prove an association. Patients’ worry about the risk may lead to noncompliance of SRIs among pregnant women, which may potentially cause serious consequences for their therapeutic management. The best practice at present is to assess the individual risk factors before prescribing SRIs to pregnant women. Studies on the pharmacogenetics of SRIs can contribute to the understanding of the variability in risk estimates of SRI-induced CHA, and may assist in identifying mothers who are at a higher risk of having a child with CHA. 3. Pharmacogenetic Predictors of SRI Pharmacokinetics During pregnancy, the pharmacokinetics (absorption, distribution, metabolism and excretion) of SRIs are known to be altered because of the physiological changes associated with pregnancy. These changes include increased total body water (including blood volume), reduced albumin concentration (by up to 10 g/L and crucial for SRIs with high protein binding, e.g., fluoxetine, sertraline, paroxetine, duloxetine), modulation of metabolic enzymes by pregnancy hormones and increased renal function and drug clearance [41,42]. These physiological adaptations influence the level of SRIs in the maternal circulation, and subsequently affect the amount transferred to the foetus (Figure 1). The passage and metabolism of SRIs supposedly occur through the yolk sac in the early stage of the first trimester up until the placenta forms in the late stage of the first trimester. Unlike other species, little is known about the transporters and binding proteins in the human yolk sac relevant for the availability and toxicity of chemicals to the embryo [43,44]. Nevertheless, drug transport in early pregnancy is postulated to be affected by pH gradients and protein binding between maternal and foetal compartments [44]. SRIs, with molecular weights around 300 g/mol, are able to cross the placenta, although the amount transferred in the first trimester is difficult to measure. In term placenta, the mean ratio of umbilical cord concentration to maternal serum concentration varies among SRIs depending on their molecular weight and polarity. The highest ratio was found for venlafaxine (range 0.72–1.1) and citalopram (0.71–0.83), followed by fluoxetine (0.64–0.73). The transfer of paroxetine and sertraline across the placenta seemed to be much lower (0.15–0.54 and 0.29–0.33, respectively) [45,46,47]. However, term data may not be representative of the first trimester of pregnancy. 3.1. Maternal Metabolic Enzymes The most important enzymes in SRI metabolism are the cytochrome P450 (CYP) enzymes, including CYP1A2, CYP2B6, CYP2C9, CYP2C19, CYP2D6, and CYP3A4 isoenzymes. These enzymes are responsible for the inactivation of SRIs, and are mainly expressed in the maternal liver, with the exception of CYP3A4, which is also expressed in the small intestine [48]. In the placenta, mRNAs were found for CYP1A2, CYP2D6, CYP3A4, CYP3A5 and CYP3A7 in the first trimester, but their protein expression and functionality was not widely characterized. Meanwhile, for CYP1A1, the mRNA, protein and functional activity were detected during the first trimester, but not in subsequent trimesters [49,50]. In term placenta samples, high expression and functional activity were detected for CYP19A1, which is responsible for the conversion of androgens to oestrogens [51]. The metabolism of each SRI agent varies depending on its affinity towards the isoenzymes. Fluoxetine, paroxetine, venlafaxine and duloxetine are metabolized to a major extent by CYP2D6, and to a lesser extent by CYP1A2 (for duloxetine), CYP2C9 and CYP3A4 (for fluoxetine), and CYP2C19 and CYP3A4 (for venlafaxine) [52,53,54,55]. CYP2C19 is the major metabolic enzyme for citalopram and escitalopram (CYP3A4 and CYP2D6 to a lesser extent); CYP3A4 for sertraline (CYP2B6, CYP2C9, CYP2C19, CYP2D6 to a lesser extent); and CYP2D6, CYP1A2 and CYP3A4 for fluvoxamine [52,53,56]. Unlike other SRIs, fluoxetine is a prodrug that will be metabolized to an active enantiomer, norfluoxetine, to promote pharmacological action. As for CYP19A1, there is no data found for the metabolism of SRIs with this enzyme. The knowledge of genetic variation of CYP enzymes has been used in practice for dose modification of certain drugs [57,58,59]. The polymorphisms of CYP2C9, CYP2C19 and CYP2D6 are well documented and cause changes in protein expression and function, leading to alterations in the plasma level of substrate drugs that consequently affect the clinical efficacy and toxicity (Table 1). A dosing guideline for SSRIs (paroxetine, fluvoxamine, citalopram, escitalopram and sertraline) for CYP2D6 and CYP2C19 genotypes was recently introduced [59] based on the results of numerous clinical and association studies [48,60]. Our great concern is for mothers with single nucleotide polymorphisms (SNP) leading to a poor metabolizer phenotype (i.e., CYP2D6*3/*4, *4/*4, *5/*5, *5/*6 or CYP2C19*2/*2, *2/*3, *3/*3), who are at a greater risk of SRI overdosing and side effects. The slower metabolism of SRIs leads to a greater concentration of these drugs in the mother’s bloodstream, which could lead to a higher concentration crossing the placental barrier. However, only a few studies have focused on the effect of CYP enzyme genotypes on the SRI pharmacokinetics during pregnancy. The maternal CYP2D6 genotype of intermediate and poor metabolizers showed an increase in plasma concentration of paroxetine of 0.82 mg/L (95% CI 0.42–1.22) for each week over the course of pregnancy, which is in contrast to the decline observed among extensive and ultra-rapid metabolizers [61]. CYP2C9*2 and CYP2C9*3 were associated with a lower activity of CYP2C9 enzymes, which are thought to be responsible for the metabolism of fluoxetine, sertraline and venlafaxine, but these studies used minimal data and found a low strength of association [62,63]. Furthermore, the effect of genetic polymorphisms of CYP1A2 has been studied less and is thought to contribute little to the pharmacokinetics of SRIs [60]. Apart from genetic polymorphisms, the inhibition or induction of CYP enzymes by certain drugs taken together with SRIs will also affect the metabolism of SRIs. For example co-medication with a CYP2D6 inhibitor was shown to be associated with increased plasma concentrations of citalopram, sertraline and venlafaxine, similar to the effect of the poor metabolizer phenotype [64]. 3.2. Foetal Metabolic Enzymes Little is known about the expression or activity of metabolic enzymes in the foetus. In the foetal liver, CYP3A7 has previously been reported as the dominant isoenzyme, and its expression decreases postnatally when it is substituted by CYP3A4 [65]. Genetic polymorphisms of CYP3A4 contribute to a minor extent to drug pharmacokinetics and clinical therapy, including that of SRIs [66]. However, more recent evidence suggests high phenotypic inter-individual variability in foetal expression of CYP3A4 and CYP3A7, and that gestational age is not the most important covariate [67]. Foetal SNP CYP3A7*1E has been clinically demonstrated to reduce the efficacy of betamethasone in stimulating foetal lung maturity following maternal antenatal administration, although the exact mechanism remains unknown [68]. Meanwhile, in adult liver and intestinal cells, the interindividual variability in CYP3A7 expression was very pronounced, while the variant alleles of CYP3A7*1B and CYP3A7*1C were found to be associated with an increase in enzyme expression [65]. However, with regard to the metabolism of SRIs, there is no data so far indicating the role of CYP3A7 in the metabolism of these drugs. Although CYP2C9 and CYP2C19 were also shown to have functional activity in some foetal liver samples, there is a high variability in the expression profile between samples [69,70]. Among 60 foetuses aged less than 30 weeks of gestational age, CYP2D6 protein expression (5% as of adult) and functional activity (1% as of adult) was detected in only 30 of all liver samples [71]. Overall, the expression and activity of CYP2D6 in the first and second trimester foetal samples were either undetectable or very low, and the expression and activity increased in the third trimester [72]. In general, our knowledge of foetal metabolic enzymes is limited, and a high interindividual variability in the expression profile was observed. As the activity of these enzymes in the foetal liver may need further investigations, the contribution of these enzymes to the foetal metabolism of SRIs, particularly in the first trimester, is probably minor. 3.3. Placental Transporter Proteins The placenta expresses several transporter proteins that are involved in the regulation of the chemical environment of the foetus by transporting and removing toxic substrates [73,74,75]. Meanwhile, transporter proteins expressed in other organ cells, e.g., the intestine, kidney and liver, are important for the absorption, distribution and excretion of SRIs and their metabolites. One of the most-studied placental transporters is P-glycoprotein (P-gp), which is expressed in the maternal-facing membrane of the placental syncytiotrophoblast [76,77]. P-gp facilitates the efflux transport of a wide range of substrate drugs, including SRIs [78,79,80,81]. The expression of P-gp is highest in the early stages of pregnancy [82,83] denoting the role of P-gp in limiting the foetal exposure to xenobiotics or other harmful substances. Our previous study has shown that the inhibition of P-gp efflux activity of drug substrates was associated with an increased risk of congenital anomalies for drugs that were associated with certain types of congenital anomalies [84]. The polymorphisms of the ABCB1 gene encoding for P-gp have been studied extensively with a focus on its effect on the pharmacokinetics, clinical efficacy and toxicity of antidepressants [85,86,87,88]. These studies focused on P-gp expression in the blood–brain barrier, which plays an important role in the bioavailability of these antidepressants in the brain. Under normal conditions, P-gp effluxes the substrates out of the brain cells, which can either lead to lower efficacy or reduced side effects of the substrates. Several ABCB1 SNPs (3435C>T, 1236C>T, 2677G>T) previously associated with reduced P-gp expression, have also been associated with increased efficacy or increased side effects that lead to switching and discontinuation of therapy [89,90,91]. In the placenta, 3435C>T, 1236C>T and 2677G>T SNPs were associated with a reduced mRNA and/or protein expression of P-gp in human placental samples, suggesting a weaker foetal protection against potential teratogens [92,93,94]. This finding was supported by two clinical studies that found an increased risk of cleft lip [95] and CHA [96] associated with a maternal 3435T variant allele in mothers taking any medication during the first trimester of pregnancy. The risk was even higher in mothers who did not take folic acid supplements [95,96]. Several other ABCB1 SNPs relevant to the pharmacogenomics of SRIs were found to be associated with SRI response and adverse events. In Table 1, the predicted effect on protein expression/activity in the placenta and the predicted effect on foetal exposure to SRIs are shown. Maternal metabolic CYP enzymes and placental transporters both play an important role in determining the foetal SRI exposure. Metabolic enzymes affect the concentration of SRIs in the maternal circulation, while placental P-gp determines the amount transported into the foetal circulation. Any changes in the expression and function of these enzymes and transporters may lead to variation in foetal SRI exposure. Despite the need to evaluate the extent of foetal SRI exposure, there are a limited number of ways to measure it directly, e.g., using animal studies and in vivo, in vitro or ex vivo placental transfer models [74,75,97]. When examining the genetic factors, one should take both the mother and the foetus into consideration as both provide several mechanisms to limit foetal exposure to SRIs. 4. Pharmacogenetic Predictors of CHA Associated with Exposure to SRIs Serotonin (5-HT) is a neurotransmitter that also acts as a growth factor and is an important regulatory factor during a critical period of embryo development. The period of about 20–70 days following fertilization involves the formation of the brain [43,116]. The foetal heart also undergoes gross morphological changes within the first 112 days of development, including septation (between 35 and 53 days), formation of the valve components (between 49 and 56 days) and delamination of the leaflets into the tricuspid valve (between 56 and 112 days) [117]. The cardiac morphogenesis is dependent on the migration, survival and proliferation of neural crest cells, which are regulated by 5-HT, mainly via the 5-HT2B receptor [118,119]. 5-HT is also one of the factors in the signalling cascade driving the establishment of laterality in heart cells. Disruptions in the laterality cascade result in laterality defects of the heart such as atrial isomerism, transposition of the great arteries, double outlet right ventricle and common truncus arteriosus [120]. The pathology of heart defects has also been postulated to be associated with the pattern of intracardiac blood flow [121], which is another link between 5-HT and heart development because 5-HT acts as a potent vasoconstrictor and is important in maintaining an optimal uteroplacental blood flow [122]. During embryogenesis, the embryo is supplied with 5-HT from the maternal blood. 5-HT in the maternal circulation can be transported to the foetal circulation by the serotonin transporter (SERT) expressed in the placenta, and signals through serotonin receptors in the foetus [123]. However, in depressed mothers, there is an abnormally reduced function of the serotonergic system in the brain. It is commonly agreed that for women who took antidepressants during pregnancy, the effect on foetal outcome is difficult to measure and disentangle from the effect of depression itself, since there is a lack of evidence to conclude whether depression itself poses an increased risk for CHA [8,124,125]. Therefore, we are looking for other possible factors, for instance the polymorphisms of SERT and foetal serotonin receptors that might possibly be among the predictors of the risk of CHA. 4.1. Serotonin Transporter in Foetal Cardiac Cells and in the Placenta Based on animal and in vitro studies, the effect of SRIs on embryonic heart development can occur via modulation of serotonin transporter levels and prenatal 5-HT levels [126,127]. In humans, this effect occurs via direct exposure to SRIs, which are readily passed through the placenta, to the foetal serotonergic system. In the foetus, SRIs inhibit SERT expressed in the foetal cardiac cells, which subsequently reduce the transport of 5-HT into the cells and could, in theory, disturb the normal development of the heart. In addition, SRIs can also inhibit SERT expressed in the placenta, which will limit the transport of 5-HT and/or other important growth factors through the placenta for foetal use [116]. Polymorphisms of the SLC6A4 gene encoding for SERT may also play a role in the serotonin signalling in foetal heart development. The genetic variation in the SERT promoter gene region, SERTPR (formerly 5-HTTLPR), was previously associated with SRI response and adverse risk events (Table 2). This insertion/deletion polymorphism includes a short (S) and a long (L) allele, and the S allele is associated with reduced activity in placental tissue and increased risk of adverse neonatal outcome events associated with SRI use [128,129]. Another polymorphism, rs25531 is putatively located in the sixth repeat of the SERTPR, with LA or LG alleles. The expression of SERT is known to be higher in the LA allele, while it is reduced in the LG allele to a level similar to the SERTPR S allele [130]. Since the SRIs inhibit SERT, less expression of this transporter may increase the inhibition rate. That is, foetuses with S or LG genotype are likely to receive a higher “effective” dose, considering there is less SERT to be blocked. As a consequence, a lower amount of 5-HT is permitted into the foetal circulation [128] to regulate normal cardiac morphogenesis. 4.2. Foetal Serotonin Receptors 5-HT activates seven distinct families of 5-HT receptors with 16 subtypes, and most of the receptors are G-protein coupled [131]. Several SNPs of genes encoding for 5-HT1A, 1B, 2A and 3B were reported to be correlated with SRI response and side effects, which might be related to the alteration in receptor expression or activity in the nervous system. Some polymorphisms were associated with a better response to SRIs, for example, of the HTR2A rs6314, rs1928040, rs7997012, rs6311 [132,133,134,135,136,137], and of the HTR1A rs1364043 and of the HTR1B rs6296 in the treatment of citalopram [138]. Other polymorphisms, on the other hand, were shown to reduce the response of several SRIs, e.g., HTR1A rs6295 in the treatment of fluoxetine, fluvoxamine, and citalopram [138,139,140,141] Furthermore, an increase in side effects of paroxetine was reported among patients with HTR2A rs6313, HTR3B rs1176744 and HTR3B rs3831455 [132,142,143]. Unlike other genes, there are limited data on the polymorphisms of the gene encoding for the 5-HT2B receptor, which is more important, in this regard, in the developmental stage of the foetal heart [118,119,144]. When a woman in the first trimester of pregnancy is required to take SRIs, we can assume a reduced amount of 5-HT may be transferred into the foetal circulation following the inhibition of placental SERT. The reduced concentration of 5-HT in the foetal circulation, together with the changes in the expression and/or activity of the 5-HT receptors, may subsequently affect the normal development of the foetal heart. 4.3. Other Genes Most CHA have a complex aetiology, with some caused by a Mendelian trait or a chromosomal aberration. The genetic aetiology of CHA is not yet well understood, and the known genetic causes of CHA account for less than 20% of CHA cases [121,145] The genetic variations of other genes involved in the pathway of foetal heart development are not emphasized in this review, but should also be taken into consideration in determining the true causal relationship between SRI exposure and CHA. A recent study found that the placenta of SSRI-treated mothers had a lower expression of the ROCK2 gene, which is thought to play a role in the development of the cardiovascular system of the foetus, as compared to untreated depressed and healthy mothers [125]. In contrast, a study with a similar setting, but focused on the neurotrophic growth factor signalling pathway, found an increased level of the ROCK2 gene and of phosphorylated ROCK2 in SSRI-treated women in comparison to depressed and healthy women [146]. Despite the contrary findings, both studies speculated that ROCK2 expression could be altered in the placenta of SSRI-treated women, and might disturb the normal development of the foetal cardiovascular system. Another aspect to be considered is the effect of foetal epigenetic programming, which is currently being investigated as the candidate molecular mechanism underlying physiological alterations in exposed foetuses [125]. Our understanding of the biological plausibility, corroborated by the evidence, may indicate that prenatal use of SRIs causes an alteration in the signalling pathway important for the development of the foetal heart. This alteration is also dependent on the pharmacokinetics of SRIs in maternal and foetal circulation. Moreover, any alteration in the expression and/or function of the enzymes, proteins, transporters and receptors involved in the signalling, modulated by the genetic polymorphisms, may theoretically alter the risk of CHA. 5. Conclusions The scope of research on the risk of CHA associated with prenatal exposure to SRIs should be extended to include the role of pharmacogenetics in pregnancy. While implementing the results in clinical practice may still seem a distant prospect, we need to begin developing theories and doing model simulations that will help us understand the complex interactions between maternal and foetal genetics and their effect on foetal SRI exposure and the risk of CHA. A better understanding of these interactions is a crucial step toward considering personalized drug treatment models for pregnant women with depression. Acknowledgments We thank Kate Mc Intyre for English editorial help. Aizati N. A. Daud also thanks the Ministry of Education in Malaysia and Universiti Sains Malaysia for the scholarship. Supplementary Materials Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1333/s1. Click here for additional data file. Author Contributions Aizati N. A. Daud drafted the manuscript. Jorieke E. H. Bergman, Wilhelmina S. Kerstjens-Frederikse, Henk Groen and Bob Wilffert contributed to the concept and provided critical reviews on the manuscript. Conflicts of Interest The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results. Figure 1 Factors influencing foetal drug exposure divided into factors in the maternal circulation, placenta layer and foetal circulation: *, substances that induce or inhibit the activity of enzymes/transporters, which may include pregnancy hormones and other drugs taken by the mother; ¥, important for serotonin reuptake inhibitors (SRIs) with high protein binding (e.g., fluoxetine, paroxetine, sertraline); † molecular size, polarity, charge, and lipophilicity of the drug; GFR, glomerular filtration rate; the double headed arrows indicate passive diffusion of drugs. ijms-17-01333-t001_Table 1Table 1 Overview of polymorphisms significantly associated with serotonin reuptake inhibitors (SRIs) pharmacokinetics and their predicted effect on foetal SRI exposure. Gene SNPs rs Numbers MAF (%) a Pharmacokinetics and/or Clinical Effects Phenotype (Predicted Expression/Activity of CYP Enzymes/Transporter Proteins) Predicted Effect on Foetal SRI Exposure b SRIs Likely to Be Affected Caucasians Asians Africans CYP1A2 −3113G>A rs2069521 3 8 11 Increased severity of side effects of escitalopram [98] Increased c Reduced Fluvoxamine, duloxetine −10 + 103 T>G rs2069526 3 8 12 Increased severity of side effects of escitalopram [98] Increased c 832 − 249 C>T rs4646425 3 8 0 Increased severity of side effects of escitalopram [98], reduced efficacy of paroxetine [99] Increasedc 1253 + 81 T>C rs4646427 3 8 11 Increased severity of side effects of escitalopram [98] Increased c 1042 + 43 G>A rs2472304 59 16 4 Increased efficacy of paroxetine [99] Reduced c Increased 1548C>T rs2470890 59 16 3 Increased efficacy of paroxetine [99] Reducedc CYP2C9 *2 rs1799853 11 0 4 Reduced metabolism of fluoxetine [62,63] Reduced d Increased Fluoxetine, sertraline, venlafaxine *3 rs1057910 7 3 2 Reduced metabolism of fluoxetine [62,63] Reduced d Increased CYP2C19 *2 rs4244285 15 33 17 Reduced tolerance to citalopram [100] and reduced metabolism of escitalopram [101] Reduced c,d Increased Citalopram *, escitalopram *, sertraline, venlafaxine *3 rs4986893 0 5 0 Reduced metabolism of escitalopram [102] Reduced d Increased *17 rs12248560 23 2 22 Increased metabolism of citalopram [103], escitalopram [102,104] Increased d Reduced CYP2D6 *3 rs35742686 2 0 0 Reduced metabolism of escitalopram [104], venlafaxine [105] No activity d Increased Paroxetine *, fluoxetine *, venlafaxine *, fluvoxamine, sertraline *4 rs3892097 19 0 6 Reduced metabolism of escitalopram [104], venlafaxine [105,106] No activity d Increased *5 whole gene deletion 4 7.2 ND Reduced metabolism of paroxetine [107] No activity d Increased *10 rs1065852 20 52 9 Reduced metabolism of paroxetine [107] Reduced d Increased ABCB1 (P-gp) 3435C>T rs1045642 53 40 15 Increased efficacy of escitalopram [108,109], venlafaxine [109], increased concentration of fluvoxamine [110], a group of antidepressants [89] Reduced c,d Increased Paroxetine, fluoxetine, venlafaxine, fluvoxamine, sertraline, venlafaxine, citalopram, escitalopram 1236C>T rs1128503 43 66 14 Increased concentration and side effects of antidepressants [89] Reduced c,d Increased 3489 + 1573G>A rs1882478 26 57 63 Increased efficacy of escitalopram [108] Reduced c Increased 2677G>T rs2032582 43 45 3 Reduced concentration and efficacy of citalopram [111], increased efficacy of paroxetine [90] Increase or reduced c,d Increased or reduced 2493 + 49T>C rs2035283 13 6 22 Increased efficacy of paroxetine [112] and side effects of SSRIs [113] Reduced c Increased 2481 + 24G>A rs2235040 13 6 20 Increased efficacy of paroxetine [112] and side effects of SSRIs [113] Reduced c Increased 2482 − 236A>G rs4148739 13 6 22 Increased efficacy of SSRIs [114] Reduced c Increased 61A>G rs9282564 9 0 0 Increased efficacy of paroxetine [115] Reduced c Increased 287 − 1234G>C rs10256836 29 15 8 Reduced efficacy of escitalopram [108] Increased c Reduced 2927 + 314G>A rs28401781 13 6 20 Increased efficacy of SSRIs [114] Reduced c Increased Abbreviations: MAF, minor allele frequency; ND, no data; CYP, cytochrome P450; P-gp, P-glycoprotein; SSRIs, selective serotonin reuptake inhibitors. * Causes dose modification in patients with polymorphic variants [57,58,59]; a MAFs from SNPedia, www.cypalleles.ki.se, PharmGkb, 1000 Genomes, HapMap; b predicted effect on foetal SRI exposure: the exposure is predicted to be increased if the expression/activity of CYP enzymes is reduced, leading to an increase in SRI concentration in the maternal circulation and more SRI transported through the placenta (and vice versa); c based on clinical data; d based on pharmacokinetic data. ijms-17-01333-t002_Table 2Table 2 Polymorphisms of the serotonin transporter (SERT) and their predicted effect on congenital heart anomalies (CHA) risk in offspring exposed in utero. Gene SNPs rs Numbers MAF (%) a Clinical Effects Phenotype (Predicted Enzyme/Protein Expression or Activity) Predicted Effect on CHA Risk b SRIs Likely to Be Affected Caucasians Asians Africans SLC6A4 (SERT) SERTPR or 5-HTTLPR (S and L alleles) rs4795541 40 (S) 80 (S) 17 (S) S-allele: poor response to venlafaxine [147], fluoxetine [139,148], increase side effects of fluvoxamine [137], citalopram [149], escitalopram [150], paroxetine [151] and overall SSRIs [152,153] Reduced with S allele Increased Fluoxetine, citalopram, sertraline, paroxetine, escitalopram, fluvoxamine −1936A>G (SERTPR LA/LG allele) rs25531 9 8 21 LG allele: increased risk of side effects and poor response citalopram [149] and overall SSRIs [153] Reduced with LG allele Increased 5HTT VNTR (9,10 or 12 repeat) rs57098334 47 (10) 10 (10) 26 (10) 12 allele was associated with higher rates of side effects of SSRIs [153] Increased transcription with 12 repeats Reduced Abbreviations: MAF, minor allele frequency; S, short allele; L, long allele. a MAFs from SNPedia, www.cypalleles.ki.se, PharmGkb, 1000 Genomes, HapMap; b predicted effect on CHA risk: based on hypothetical conditions (see text). ==== Refs References 1. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081334ijms-17-01334ArticleSerum Levels of MicroRNA-206 and Novel Mini-STR Assays for Carrier Detection in Duchenne Muscular Dystrophy Anaya-Segura Mónica Alejandra 12Rangel-Villalobos Héctor 3Martínez-Cortés Gabriela 3Gómez-Díaz Benjamín 4Coral-Vázquez Ramón Mauricio 5Zamora-González Edgar Oswaldo 6García Silvia 7López-Hernández Luz Berenice 8*Cho William Chi-shing Academic Editor1 Center for Research and Assistance in Technology and Design of the State of Jalisco (CIATEJ, A.C.), Guadalajara 44270, Mexico; monica207383614@gmail.com2 Asociación de Distrofia Muscular de Occidente A.C., Guadalajara 44380, Mexico3 Centro Universitario de la Ciénega, Universidad de Guadalajara, Ocotlán 47820, Mexico; hrangel@cuci.udg.mx (H.R.-V.); gaby_ed15@yahoo.com.mx (G.M.-C.)4 National Center for Research and Care in Sports Medicine, National Institute of Rehabilitation, México City 14389, Mexico; bngomez@inr.gob.mx5 Section of Postgraduate Studies and Research, Superior School of Medicine, National Polytechnic Institute, México City 11340, Mexico; rcoral@ipn.mx6 Centro Universitario del Norte, Universidad de Guadalajara, Colotlán 46200, Mexico; edgar.zamora@cunorte.udg.mx7 Servicio de Investigación Clínica, Centro Médico Nacional “20 de Noviembre”, Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado, México City 03100, Mexico; rolasil@yahoo.com.mx8 División de Investigación Biomédica, Centro Médico Nacional “20 de Noviembre”, Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado, México City 03100, Mexico* Correspondence: lblhmedgen@gmail.com or luz.lopezhe@issste.gob.mx; Tel.: +52-55-5200-5003; Fax: +52-33-3632-620013 8 2016 8 2016 17 8 133403 5 2016 01 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Duchenne Muscular Dystrophy (DMD) is an X-linked neuromuscular disorder in which the detection of female carriers is of the utmost importance for genetic counseling. Haplotyping with polymorphic markers and quantitation of creatine kinase levels (CK) allow tracking of the at-risk haplotype and evidence muscle damage, respectively. Such approaches are useful for carrier detection in cases of unknown mutations. The lack of informative markers and the inaccuracy of CK affect carrier detection. Therefore, herein we designed novel mini-STR (Short Tandem Repeats) assays to amplify 10 loci within the DMD gene and estimated allele frequencies and the polymorphism information content among other parameters in 337 unrelated individuals from three Mexican populations. In addition, we tested the utility of the assays for carrier detection in three families. Moreover, given that serum levels of miR-206 discern between DMD patients and controls with a high area under the curve (AUC), the potential applicability for carrier detection was assessed. The serum levels of miR-206 of non-carriers (n = 24) and carriers (n = 23) were compared by relative quantitation using real-time PCR (p < 0.05), which resulted in an AUC = 0.80 in the Receiver Operating Characteristic curve analysis. In conclusion, miR-206 has potential as a “liquid biopsy” for carrier detection and genetic counseling in DMD. microRNA-206mini-STRcarrier detectionDMDliquid biopsy ==== Body 1. Introduction Pathogenic variants within the DMD cause a spectrum of recognizable phenotypes known as dystrophinopathies. Duchenne Muscular Dystrophy (DMD) is the most severe and affects male newborns in almost all cases [1]; since it is an X-linked recessive disorder, carrier detection is of the utmost importance for genetic counseling. Female relatives require genetic testing and genetic counseling to avoid the birth of affected children without enough knowledge of the disease to make informed decisions [2]. In addition, manifestations of dystrophinopathies are present in a range of 8%–40% of heterozygous females, and this affects their life quality [3,4]. About 8% of them manifest clinically relevant features of the disease such as abnormal gait, calf hypertrophy, cardiomyopathy, cramps, exercise intolerance, and intellectual and learning disabilities, among other signs and symptoms [5]. Therefore, the Muscular Dystrophy Surveillance Tracking and Research Network (MD STARnet) has established clinical guidelines to support definite diagnosis. A definite case of dystrophinopathy should be pursued prior to carrier detection. Precise diagnosis requires a combination of clinical data, family history (FH), creatine kinase (CK) levels, genetic analysis (direct/indirect) [2], and in some cases, immunostaining of muscle biopsies [6]. Consequently, genetic counseling for at-risk females and prenatal diagnosis remain challenging for cases in which deep intronic mutations in the DMD are difficult to detect even with next-generation sequencing which often covers only exonic regions [7,8]. Indirect tracking of the haplotype that co-segregates with the disease within a family is required in those cases [2,6]. For that reason, improvements in the integration of data derived from segregation analyses with informative DNA markers such as short tandem repeats (STRs) or single nucleotide polymorphisms (SNPs), the measurement of serum biomarkers such as CK and the consideration of familiar structure allow reliable calculation of recurrence risks with specialized software such as RiscalW [8]. However, limitations of such approaches are: (1) Low heterozygosity affects segregation analysis, since the detection of different alleles between affected and unaffected males allows the indirect detection of the at-risk haplotype. Thus, the number of alleles and their frequency within a population are relevant parameters that affect carrier detection using indirect analysis and haplotyping; (2) Degraded DNA samples are challenging to amplify with conventional amplicons [9]; (3) CK levels are not optimal biomarkers for carrier detection (diagnostic efficiency = 50%) [10]. Therefore, exploration of alternative non-invasive biomarkers for carrier detection and the use of optimized multiplex amplicons represent an attractive option that may in turn outperform the above mentioned approaches. In this regard, it was recently shown that a small group of microRNAs called “dystromirs” miR-1, miR-133, and miR-206 are valuable diagnostic biomarkers for DMD because they are able to distinguish among DMD and non-affected males; in particular, the skeletal muscle-specific miR-206 is upregulated in satellite cells following muscle injury [11,12]. Interestingly, miR-206 showed higher AUCs (Area Under the Curve) to differentiate not only DMD vs. normal individuals, but also Becker Muscular Dystrophy (BMD) (a less severe type of dystrophinopathy) [11] and DMD phenotypes compared to the other microRNAs. Hence, since female DMD carriers may have subclinical muscle involvement [13], quantitation of serum levels of miR-206 may harbor potential for carrier detection. For that reason, the aims of this work were the following: (1) designing novel mini-STR assays and estimating the polymorphic information content (PIC) values in three Mexican populations; (2) performing haplotype analysis in DMD families; and (3) testing differences in serum levels of miR-206 between healthy females and DMD carriers to test its diagnostic potential for carrier detection. Cell-free nucleic acids, which include cell-free DNA, cell-free mRNA, and circulating miRNAs, are being explored for various clinical applications, such as “liquid biopsy” and “non-invasive prenatal testing” (NIPT) [14]. The approaches presented herein harbor high potential to extend the above-mentioned clinical applications for carried detection in DMD. 2. Results 2.1. Population Analysis Most present-day Mexicans denominated “mestizos” as a population group, are the result of the ancestral admixture of different populations (Europeans, Amerindians and Africans). This process did not occur equally through all the geographical regions of Mexico; such heterogeneity in allele frequencies prevails nowadays [15]. This fact should be considered for genetic studies aimed at detecting candidate genes for medical traits as well as for applications of genetic variations such as the use of STRs for the detection of the at-risk haplotypes in DMD [16]. Therefore, genetic diversity was analyzed based on allele frequencies of 10 STRs within the DMD in the three Mexican population samples (Table 1); for instance, 15 alleles were found for the 5′-5n4 marker (Figure 1C). The markers presented a wide diversity of alleles, where at least four alleles were found for DXS1234 and up to 15 were found for DI623 (Table 1). The north region was in Hardy-Weinberg equilibrium (HWE) for all the analyzed loci, whereas the west and southeast regions presented HWE deviations (p < 0.05) for DXS1237, 5′-7n4 and DXS1234, DXS1237, and 5′-5n4 markers, respectively (Table 1). However, after applying the Bonferroni correction these deviations were not significant (p > 0.005). On the other hand, an indicator of the utility of the STRs for segregation analysis is the PIC value; which defines the expected fraction of informative offspring from one particular type of human pedigree (Table 2). Interestingly, although in most cases PIC values were similar between the studied populations, the north region showed elevated values in all STR loci (PIC > 0.60). PIC values observed for the 10 STRs in the three Mexican populations ranged from 0.52 to 0.88, whereas DXS1236 and DI623 were the most informative markers with more alleles (Table 1). Although in other populations similar PIC values were observed with respect to the Mexican regions studied herein, some differences were noticed as indicated in Table 2. 2.2. STRs for Carrier Detection Seven females belonging to three unrelated families requested the result of the genetic testing derived from our research study (Table 3), and their samples were used to perform an indirect haplotype analysis. While half of the analyzed markers were conclusive for two families, seven STRs were conclusive for the remaining. The DXS1236, DI623 and DXSDMD-In30 markers were determinant in all three families, even when DXSDMD-In30 showed a PIC smaller than 0.5. The markers 5’-5n4, 5′-7n4 and DXS1237 were determinant in the first two cases, and DXSDMD-In60 was determinant exclusively in case number 3. One female carrier was detected (Figure 1D and Figure 2), while the remaining six were not carriers (Table 3). The DI623 marker was used in a DMD case (index case II-4) with a positive family history (Figure 1D). Three sisters of the index case (II-1, II-2 and II-3) were tested and compared to the grandmother (I-2) who was an obligate carrier; she had a family history of DMD and an affected son (II-4). The older sister gave birth to non-identical triplets and the boys were tested at six years old. One of them was affected and one was healthy; the girl was considered too young to be tested for carrier detection due to ethical issues. 2.3. Quantitation of Serum Levels of MicroRNA-206 for Carrier Detection After the comparative analysis between carriers and non-carriers, miR-206 was enriched in the carriers group (Figure 3A). Serum levels of miR-206 are able to classify true DMD carriers and healthy females (Figure 3B), with an association criteria >0.45; with this number, the AUC in our study (Figure 3C) reached a value of 0.803, a standard error (SE) of 0.065, sensitivity = 78.26%, and specificity = 70.83%, with a p-value < 0.0001 *, whereas the positive predictive value (PPV) was 78.26 and the negative predictive value (NPV) was 99.69. 3. Discussion A general definition of a biomarker is: “A characteristic that is objectively measured and evaluated as an indicator of normal, pathogenic and biological processes or pharmacological responses to a therapeutic intervention” [25]. Therefore, research to identify non-invasive biomarkers for conditions such as dystrophinopathies is a growing field nowadays. In the specific case of DMD it has been shown that levels of miR-206 positively correlate with scores of the North Star Ambulatory Assessment (NSAA), which is an endpoint to evaluate the ability of DMD patients to perform common activities [26]. Therefore, biomarkers able to detect subtle changes among related phenotypes are of particular interest, as is the case of female carriers that present almost exclusive sub-clinical muscle involvement. The field of cell-free RNAs and specifically miRNAs has generated a growing interest, because some of these miRNAs reflect the physiological status of a disease from a non-invasive method of sampling [27]. Therefore, at least for carrier detection, miR-206 and other biomarkers present in serum are of utmost importance, since dystrophin abnormalities in muscle tissue of DMD carriers are not associated with clinical variables; only 26% of non-manifesting carriers have dystrophin-negative fibers in muscle biopsies [13] which are considered invasive. Therefore, the use of the “liquid biopsy” approach may potentially outperform the use of biopsies obtained directly from muscle tissue in DMD patients, but could probably gain more relevance for carrier detection. According to Rao et al., tests for detecting true DMD carriers must satisfy the following conditions: (a) to be positive in affected boys; (b) to not produce false negative results; (c) testing carried out using a significant number of carriers and an equal number of age- and sex-matched controls; and (d) results should be compared with an established test [28]. In this regard, a recent study of proteomics explored proteins in 16 serum samples of asymptomatic DMD carriers showing that CA3 (Carbonic anhydrase III), MDH2 (Malate Dehydrogenase 2), MYL3 (Myosin Light Chain 3), ETFA (Electron Transfer Flavoprotein Alpha Subunit) and TNNT3 (Troponin T3, Fast Skeletal Type) proteins were decreased in females compared to DMD patients treated with steroids, [29]. In addition, m-calpain from platelets was recently proven useful for identifying true DMD carriers with 90% of informative tests [28], as well as other proteins such as GDF-8 (myostatin), AUC = 0.706 (p = 0.028), and FSTN (follistatin), AUC = 0.877 (p < 0.0001) proposed by our group as biomarkers for carrier detection in DMD [30]. Nevertheless, the isolation of platelets for m-calpain detection is laborious and the ELISA technique for measuring protein concentrations may imply cross-reactivity antibody concerns for particular proteins. Therefore, herein we investigated, as an alternative, the possible use of miR-206 as a biomarker for carrier detection in DMD. Previous studies showed that miR-206 expression is muscle-specific, it is produced by myoblasts and correlates with disease progression in DMD [31], it distinguishes between DMD and BMD [12], and it increases when muscular damage occurs; therefore, miR-206 is thought to be linked to muscle repair [12,32]. Hence, we hypothesized that it may be increased in female DMD carriers. We explored the potential applicability of the isolation of miR-206 from human serum samples and its amplification by real-time PCR for carrier detection in DMD because, to our knowledge, this application was not previously explored. We did not find any correlation of miR-206 with age or body mass index (BMI) in any group. In addition, GDF-8 and FSTN did not correlate with miR-206 in this cohort. Interestingly, there were five cases with high levels of the microRNA within the carrier group, and these were all obligate carriers, but manifestations such as weakness were not reported by them, with the exception of the samples with the highest and the third-highest values of all. Those samples correspond to the mother of family 3 (Table 3) (with severe cardiac involvement) and her older daughter, the mother of the index case, a male children with a severe DMD phenotype who lost independent walking at five years old and needed assistance for walking at the age of four (the patient underwent steroid treatment for a short period of time). Cardiac involvement in the mother of the above-mentioned patient is still unknown. Our data suggest that miR-206 allows the detection of DMD female carriers (who are, in most cases, asymptomatic), which demonstrates advantages over to CK as biomarker. For instance, CK decreases when muscle mass is highly diminished, even in male patients (advanced disease state) whose phenotype is more evident compared to females (usually asymptomatic), and it is altered by several conditions such as physical activity, race, age and gender [33]. In fact, CK provides a diagnostic efficiency = 50% [10], PPV = 100%, NPV = 33.3%, specificity = 100%, and sensitivity = 33.3% [10]; the latter is lower than the sensitivity displayed by miR-206 (78.2%) reported herein. Although further studies are required to confirm our findings (e.g., including larger cohorts with manifesting carriers), we demonstrated the utility of combining mini-STR assays and serum microRNAs as biomarkers to improve carrier detection and genetic counseling (Figure 4), especially in complex cases with unknown mutations and a negative family history. In addition, our mini-STR assay allowed the amplification of multiple targets, and the study of alleles present in our population revealed the best markers for segregation analyses in Mexican families. It should be noted that best practices for genetic testing in DMD suggest that at least three DNA markers should be used to appropriately flank the gene [34], in order to avoid overlooking the recombination events that may affect genetic counseling when the location of the mutation is unknown (Figure 1). Agreeing with these recommendations, our assays included 10 loci evenly distributed along the gene, and smaller amplicons (mini-STRs) instead of the longer ones to improve the probabilities of success in analyzing highly degraded DNA samples. Most STR markers were highly polymorphic in the populations studied herein, suggesting a high level of informativeness of the markers, since the linkage between two variable loci is based on its heterozygosity. With this argument, it is widely known that there is high degree of substructure between Mexican populations that may underlie important biomedical phenotypes [16]. Although recent publications regarding the “liquid biopsy” approach are mainly focused on cancer, the use of miR-206 obtained from the serum for carrier detection in DMD as the approach reported herein could be considered a clinical application of liquid biopsies to support the diagnosis of neuromuscular disorders such as DMD. The applicability of the mini-STR assays described in this work to amplify cell-free DNA requires further exploration, although other authors have previously shown the successful amplification of 19 STR markers from maternal plasma for NIPT using mini-STR assays [35]. Integration of the analysis of microRNAs and DNA markers may improve carrier detection and genetic counseling in DMD. Therefore, in the present work, new tools to improve carrier screening in DMD through “liquid biopsy” approach are proposed. 4. Materials and Methods 4.1. Sampling, DNA Isolation and Quantification Potential carriers belonging to DMD families were referred to our laboratory between 2010 and 2015. All cases correspond to female relatives of a DMD patient diagnosed according to MD STARNET criteria [6]. Diagnosis was established considering patients clinical evaluation, which included proximal and/or distal weakness, positive Gowers maneuver, age at onset, CK levels and family history [30]. Written informed consent was obtained from all participants; this study was approved by the Institutional ethics and research committees. Control group had a mean age of 34.65 ± 12.47 (18 min–56 max) whereas the DMD carrier group showed a mean age of 40.77 ± 13.79 (17 min–86 max). There were no differences in age and BMI, between groups. Medical records of participants and clinical inspection included the following data: presence of endometriosis, cancer (any type), arthritis, active infections at the time of sample collection, osteoporosis/fractures, hepatitis and/or cirrhosis, positive cases were excluded of the analysis. Nucleic acid isolation was performed as follows: Genomic DNA was extracted from 5 mL peripheral lymphocytes using the CTAB-DTAB method [36]. In order to gain insights regarding the allele diversity in our country, we analyzed Mexican population samples from different geographic regions, as follows: (1) North region (n = 105), including individuals from Chihuahua, Sinaloa, and Sonora states; (2) West region (n = 101), represented by the Jalisco state; and (3) Southeast region (n = 131), including volunteers of the Yucatán state. A Nanodrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA) was used to measure DNA sample concentration. One hundred and five ng of DNA were used to perform MLPA and STR assays, respectively. On the other hand, human serum samples from female relatives of patients underwent purification of total RNA including miRNA with the miRNeasy Mini Kit (©QIAGEN GmbH, Hilden, Germany). 4.2. Mutation Detection by MLPA Genetic screening for copy number variations within the DMD gene was done in order to confirm carrier status using MLPA technique, according to manufacturer’s instructions (P034/P035, MRC-Holland, Amsterdam, Netherlands). Data were analyzed using GeneMarker v1.91 software (© 2016 SoftGenetics, LLC., State College, PA, USA) as previously described [37]. 4.3. Mini-STR Assay Design and Genotyping Primers were designed according to standard conditions for multiplex mini-STR assays, which are “reduced-size STR amplicons” that outperform conventional amplicons when amplifying degraded DNA [38]. Reverse primers were labeled with different fluorescent dyes. Conditions for each assay are shown in Table 4. DNA samples were genotyped for the markers: DXS1236, DI623, 5′-5n4, 5′-7n4, DXS1237, DXS997, DXSDMD-In60, DXSDMD-In30, DXS1242 y DXS1234 at the DMD, using fluorescent polymerase chain reaction (PCR) (Figure 1A). Standard cycling was performed in a thermal cycler (Bio-Rad Laboratories, Inc, Berkeley, CA, USA). STR genotypes were obtained by fluorescent capillary electrophoresis with the ABI 310 Genetic Analyzer (Applied Biosystems, Carlsbad, CA, USA). About 1.2 μL of PCR product, 9 μL of Hi-Di formamide (Applied Biosystems), and 0.3 μL of Genescan-500 LIZ size standard (Applied Biosystems) were mixed and loaded into capillaries with POP-4 (Applied Biosystems) as separation matrix. Finally, alleles were assigned by GeneMarker HID Human STR Identity software (© 2016 SoftGenetics, LLC., State College, PA, USA) (Figure 1B). 4.4. Quantitation of MicroRNAs by Real Time PCR MicroRNAs were isolated from 200 µL of human serum samples of participants using ©QIAGEN® miRNeasy kit following manufacturer’s instructions. The amount of total RNA was measured with a Nanodrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE USA). Using microRNAs as templates, cDNA was synthesized and amplified using TaqMan MicroRNA assay for miR-206 (dystromir) and miR-223* (granulocyte-specific) (Applied Biosystems®); the last one was used as endogenous control, as previously reported [11]. Relative quantitation of both probes (miR-206 and miR-223*) was performed in a LightCycler® 480 II (Roche, GmbH, Mannheim, Germany). Normalization was performed using Pfaffl method [39] considering true efficiency of each reaction, which is derived from serial dilutions of the target (miR-206, Efficiency, (E) = 1.845, detection limits 0.18–1.8 × 10−3 ng/μL) and reference gene (miR-223* E = 2.136, detection limits 0.18–1.8 × 10−4 ng/μL). Results were automatically calculated from the Cp values (detection threshold point) of the target and the reference amplicons. 4.5. Statistical Analysis Allele frequencies and genetic diversity parameters represented by heterozygosity and PIC were estimated for each locus with the Excel spreadsheet Powerstats [40]. Exact tests to check HWE agreement for each locus were carried out using the Arlequin software [41]. This assessment was adjusted for multiple comparisons using Bonferroni correction. Values of miR-206 were tested for normality by the Shapiro-Wilk test. Statistically significant differences among experimental groups (between median values of each group) were analyzed by the Mann-Whitney U test since were non-normally distributed. Acknowledgments We thank all participants and financing from Fondos Sectoriales CONACYT 2015 project 262150 (young researcher grant) and we also thank the E015 program ISSSTE for supporting research. We acknowledge the funding of Administración del Patrimonio de la Beneficencia Pública/Ministry of Health provided to ADMO A.C. project 2016. We also thank MSc. Victor Martínez-Sevilla for technical assistance, Ikuri Alvarez Maya for suggestions and Dulce Jimenez for infographic design. Author Contributions Mónica Alejandra Anaya-Segura performed MicroRNA experiments, mini-STR amplification, initial statistical analysis and helped write the article, Héctor Rangel-Villalobos performed statistical analysis, sample collection and critically reviewed the manuscript, Gabriela Martínez-Cortés designed the mini-STR assay and performed statistical analysis, Benjamín Gómez-Díaz performed the MLPA test and analysis, Ramón Mauricio Coral-Vázquez planned the microRNA experiments, analyzed data and critically reviewed the manuscript, Edgar Oswaldo Zamora-González performed sample isolation and critically reviewed the manuscript, Silvia García reviewed and supervised clinical data and Luz Berenice López-Hernández planned the experimental work, supervised and coordinated the group and wrote the paper. All authors read and approved the final version of the work. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Mini-STR assays. (A) Simplified map of the DMD gene showing the location of the 10 intragenic mini-STRs, which are evenly distributed to cover the DMD gene from the 5′ to 3′ regions, markers are depicted with their corresponding labeled primers: NED (yellow), PET (red), VIC (green), or FAM (blue); (B) Amplification of one of our novel mini-STR assays. Differentially labeled primers allow the analysis of different targets in the same channel; (C) Genetic diversity was analyzed based on allele frequencies of 10 STRs in the three Mexican population samples (Table 1); for instance, a total of 15 different alleles were detected for the 5′-5n4 marker in these population samples; (D) Example of the genotype of a female for the marker DI623. Figure 2 Segregation analysis in a pedigree with family history of the disease. Figure 3 Serum levels of MicroRNA-206 for carrier detection. (A) Serum levels of miR-206 between DMD carriers and healthy females. The median levels are depicted with (●) and the horizontal lines bisecting the purple box plot show the interquartile range, individual values of samples are shown in (●). The most extreme values are indicated as (■); (B) ROC (Receiver Operating Characteristic) curve for miR-206 to classify true DMD carriers, the optimal cut-off is indicated by (●); (C) Heat map of AUC (Area Under the Curve) values (best values are shown on top and purple) for miR-206 to classify different phenotypes reported by others (black text and lower-case letters) and the AUC of miR-206 obtained in our study (orange text, (d)) (a) [11]; (b)[23]; (c) [12]; (d) Present study and (e) [24]. Figure 4 Algorithm for carrier detection using mini-STR assays and serum biomarkers. ijms-17-01334-t001_Table 1Table 1 Estimation of genetic diversity and Hardy-Weinberg equilibrium in the three populations studied. STR loci North West Southeast HE HO NA HWE p-Value HE HO NA HWE p-Value HE HO NA HWE p-Value DXS1242 0.765 0.722 8 0.5349 0.742 0.576 8 0.0581 0.713 0.848 8 0.1857 5′-5n4 0.751 0.786 12 0.9521 0.763 0.720 13 0.1182 0.578 0.442 9 0.0419 5′-7n4 0.508 0.529 5 1.0000 0.665 0.833 7 0.0127 0.566 0.769 6 0.3488 DXSDMD-In30 0.500 0.500 5 1.0000 0.503 0.423 5 0.4792 0.505 0.396 7 0.1583 DXS1237 0.815 0.615 9 0.2737 0.754 0.500 10 0.0141 0.723 0.578 11 0.0229 DXS997 0.575 0.750 5 0.2965 0.475 0.389 6 0.3778 0.373 0.333 6 0.1780 DXS1236 0.862 0.889 14 0.9764 0.800 0.969 13 0.9764 0.809 0.909 14 0.9764 DXSDMD-In60 0.652 0.667 7 0.4278 0.613 0.500 7 0.4278 0.647 0.627 8 0.4278 DI623 0.766 0.647 14 0.6746 0.776 0.613 13 0.2555 0.746 0.791 15 0.6673 DXS1234 0.675 0.389 6 0.0931 0.467 0.406 4 0.1117 0.539 0.290 5 0.0109 Expected heterozygosity (HE), Observed heterozygosity (HO), Number of alleles per locus (NA), Hardy–Weinberg equilibrium test (p > 0.005) (HWE). ijms-17-01334-t002_Table 2Table 2 Comparison of polymorphism information content (PIC) of the 10 STRs in different populations. STR loci Mexico Other Populations Reference North West Southeast DXS1242 0.77 0.80 0.70 0.77 USA [17] 5′-5n4 0.73 0.78 0.58 0.64 New Zealand [18] 5′-7n4 0.62 0.69 0.62 0.52 New Zealand [18] DXSDMD-In30 0.61 0.56 0.63 0.65 China [19] DXS1237 0.82 0.78 0.73 0.89 USA [20] DXS997 0.67 0.58 0.53 0.70 France [21] DXS1236 0.88 0.87 0.88 0.93 USA [20] DXSDMD-In60 0.76 0.77 0.77 0.69 China [19] DI623 0.88 0.87 0.87 0.91 Japan [22] DXS1234 0.74 0.52 0.55 0.34 Japan [22] ijms-17-01334-t003_Table 3Table 3 Application of the STR assays in Mexican families. Family History (FH), Multiplex Ligation Dependent Probe Amplification (MLPA). Family Case Description Applicability of STRs Indirect Haplotyping Analysis Concluding Remarks 1 Three sisters of an index case with positive FH and two male children (triplets) were tested by MLPA and the assay proposed herein to detect the at-risk haplotype (see Figure 2). Informative markers: 5′5n4, DXSDMD-In30, DXS1237, DXS1236, DI623. MLPA confirmed a deletion of exons 6–7 that segregated with the at-risk haplotype traced with our assay. Two out of three sisters of the index case were carriers of the at-risk haplotype. 2 Two aunts of an index case with positive FH in which a mutation was not found after MLPA analysis solicited genetic testing. Informative markers: 5′-5n4, 5′-7n4, DXSDMD-In30, DXS1236, DI623. Both analyzed females were non-carriers of the at-risk haplotype; all the obligate carriers shared the at-risk haplotype found in the index case. 3 Three aunts of an index case with positive FH in which a mutation was not found after MLPA analysis solicited genetic testing. Informative markers: DXS1242, 5′-7n4, DXS1237, DXS1236, DXSDMD-In60, DXSDMD-In30, DI623. None of the analyzed females were carriers of the at-risk haplotype. Next-generation sequencing confirmed that a point mutation (stop codon) in exon 30 co-segregated with the at-risk haplotype. ijms-17-01334-t004_Table 4Table 4 STR assay conditions for the 10 polymorphic microsatellite sites. Marker Locus Primer Sequences (5′→3′) Fluorophore PCR Product (bp) Conditions for Multiplex STRs Assays PCR Denaturation Annealing Extension DX997 Intron 48 AGCTGGCTTTATTTTAAGAGGACA FAM 88 95 °C, 25 s 64 °C, 30 s 72 °C, 30 s GGGTAGCCTTCCAAGAATAGG DXS1237 Intron 45 GGCTATAATTCTTTAACTTTGGCAAG FAM 169 CCACCTCTTTCCCTCTT DXS1236 Intron 49 CGTTTACCAGCTCAAAATCTCAAC PET 111 95 °C, 18 s 62.5 °C, 20 s 72 °C, 15 s GGCTTTGGCCATACAGAAAA DI623 Intron 62 CGAGACACCCCACCTCTG FAM 140 GCCATGGTGAATGATCAGAAA 5′-5n4 Intron 4 GAGAGAAGGGAAAATGATGAATAAAA VIC 148 TGTCAGAACTTTGTCACCTGTCTT 5′-7n4 Intron 25 CTTTTAAGGCAGTTGGTGAAGC PET 181 TCCAGGATCCAACAATATCTCA DXSDMD-In30 Intron 30 GTTAGTCCCTATTCTATTCCTTTC PET 162 95 °C, 30 s 57 °C, 40 s 72 °C, 30 s AAGAATGCCACCAAAATGAC DXSDMD-In60 Intron 60 CGAGGGGATCAGGGTAATA NED 136 CTGTTCTCTTCTCTGGTCATCA DXS1234 Region 3′ CTGTTTGCGACATTGGCTAT VIC 151 95 °C, 20 s 57 °C, 40 s 72 °C, 30 s GCAAACATCATGGTGATAACTGA DXS1242 Region 5′ CAAAAATCAAATGGAAGTAGAATAGC NED 112 95 °C, 30 s 59 °C, 50 s 72 °C, 40 s TCGCTATTCTGAAATAGTGTTTTCC ==== Refs References 1. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081335ijms-17-01335ArticleChemokine-Like Receptor 1 mRNA Weakly Correlates with Non-Alcoholic Steatohepatitis Score in Male but Not Female Individuals Neumann Maximilian 1Meier Elisabeth M. 1Rein-Fischboeck Lisa 1Krautbauer Sabrina 1Eisinger Kristina 1Aslanidis Charalampos 2Pohl Rebekka 1Weiss Thomas S. 3Buechler Christa 1*Haybaeck Johannes Academic Editor1 Department of Internal Medicine I, Regensburg University Hospital, 93053 Regensburg, Germany; maximilian.neumann@stud.uni-regensburg.de (M.N.); elisabeth.meier@klinik.uni-regensburg.de (E.M.M.); lisa.rein-fischboeck@klinik.uni-regensburg.de (L.R.-F.); sabrina.krautbauer@klinik.uni-regensburg.de (S.K.); kristina.eisinger@gmx.de (K.E.); rebekka.pohl@klinik.uni-regensburg.de (R.P.)2 Institute of Clinical Chemistry and Laboratory Medicine, Regensburg University Hospital, 93053 Regensburg, Germany; charalampos.aslanidis@klinik.uni-regensburg.de3 Children’s University Hospital (KUNO), Regensburg University Hospital, 93053 Regensburg, Germany; thomas.weiss@klinik.uni-regensburg.de* Correspondence: christa.buechler@klinik.uni-regensburg.de; Tel.: +49-941-944-700918 8 2016 8 2016 17 8 133531 5 2016 09 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).The chemokine-like receptor 1 (CMKLR1) ligands resolvin E1 and chemerin are known to modulate inflammatory response. The progression of non-alcoholic fatty liver disease (NAFLD) to non-alcoholic steatohepatitis (NASH) is associated with inflammation. Here it was analyzed whether hepatic CMKLR1 expression is related to histological features of NASH. Therefore, CMKLR1 mRNA was quantified in liver tissue of 33 patients without NAFLD, 47 patients with borderline NASH and 38 patients with NASH. Hepatic CMKLR1 mRNA was not associated with gender and body mass index (BMI) in the controls and the whole study group. CMKLR1 expression was similar in controls and in patients with borderline NASH and NASH. In male patients weak positive correlations with inflammation, fibrosis and NASH score were identified. In females CMKLR1 was not associated with features of NAFLD. Liver CMKLR1 mRNA tended to be higher in type 2 diabetes patients of both genders and in hypercholesterolemic women. In summary, this study shows that hepatic CMKLR1 mRNA is weakly associated with features of NASH in male patients only. liver steatosisfibrosisgendertype 2 diabetes ==== Body 1. Introduction Non-alcoholic fatty liver disease (NAFLD) is a widespread cause of chronic liver injury and its progressive form non-alcoholic steatohepatitis (NASH) is characterized by hepatic inflammation and fibrosis [1,2]. NAFLD is related to the metabolic syndrome, and importantly, these patients have a high prevalence of developing hypertension, type 2 diabetes and dyslipidemia [3]. Chemokine-like receptor 1 (CMKLR1) is expressed by immune cells including subsets of dendritic cells, macrophages and natural killer cells [4,5]. The adipokine chemerin attracts CMKLR1-expressing cells to sites of inflammation [4,5]. Hepatic chemerin levels are changed in NAFLD, suggesting a function of this chemoattractant factor herein [6,7,8]. Importantly, higher chemerin expression has been described in human NASH while levels are unchanged in patients with borderline NASH and even reduced in human fatty liver [6,7,8]. Serum chemerin is increased in obesity and positive associations with serum lipids, blood glucose and blood pressure suggest a function of this chemokine in metabolic diseases [9]. Data on serum chemerin in NAFLD are not concordant and higher as well as normal levels have been described [10]. CMKLR1 also binds the potent anti-inflammatory mediator resolvin E1 which is generated from ω-3 eicosapentaenoic acid [11]. CMKLR1 deficiency in mice is not related to changes in body weight, inflammation, glucose tolerance and dyslipidemia. Importantly, comparable results have been obtained in animals fed a standard chow and mice given a high-fat diet [12]. In a second study, a high-fat, high-cholesterol diet did not differentially affect body weight and insulin resistance in CMKLR1-null mice and the respective control animals. Of note, hepatic inflammation and expression of fibrotic genes is unchanged in the liver of CMKLR1-deficient mice [13]. Nevertheless, reduced body weight and body fat irrespective of low- or high-fat diet has also been reported in CMKLR1 knock-out mice. Despite decreased hepatic and adipose tissue inflammation, these animals have impaired glucose disposal in muscle and fat [14]. CMKLR1 is highly abundant in the liver and is expressed by primary human hepatocytes, hepatic stellate cells, Kupffer cells and bile duct cells [15]. In patients with chronic hepatitis C, liver CMKLR1 mRNA is, however, not related to the inflammatory activity grade. CMKLR1 mRNA levels are comparable in males and females and expression is significantly reduced in women with advanced liver fibrosis [16]. In human NASH liver, CMKLR1 mRNA is even induced and IL-6 is suggested to contribute to CMKLR1 upregulation [7]. Associations of hepatic CMKLR1 expression with hepatocyte ballooning, lobular inflammation and fibrosis have not been identified in human NAFLD [7]. The limitation of this study is that only three patients with NASH were enrolled [7]. To our knowledge, gender-specific expression of CMKLR1 in human NAFLD has not been analyzed so far. Here, hepatic CMKLR1 was quantified in a relatively large cohort of patients with histologically proven NAFLD. Analysis was performed for both genders separately to identify possible sex-related differences. 2. Results 2.1. Hepatic Chemokine-Like Receptor 1 (CMKLR1) mRNA in the Human Liver Recently, Döcke et al. analyzed CMKLR1 mRNA in 34 controls, 10 patients with a NASH score of 3–4 (undefined or borderline NASH) and three patients with a score equal or above 5 [7]. Because in that cohort the number of patients with definite NASH was quite small, we decided to determine CMKLR1 mRNA in a larger study group. CMKLR1 mRNA was measured in a cohort of 118 patients including 33 controls with normal liver, 47 patients with a NASH score ranging from 1.0 to 4.5 (borderline NASH) and 38 patients with a NASH score equal or above 5 (Table 1). Indications for surgery (hepatocellular carcinoma, adenoma, hepatic metastases of extrahepatic tumours, focal nodular hyperplasia of the liver and cholangiocarcinoma) were not associated with altered CMKLR1 mRNA levels in the liver tissues used herein (Figure 1A). In the patients with normal liver and in the whole study group, CMKLR1 mRNA did not correlate with age (r = −0.249, p = 0.162 in the control group and r = 0.050, p = 0.596 in the whole cohort) or Body mass index (BMI) (r = −0.189, p = 0.292 in the control group and r = 0.057, p = 0.546 in the whole cohort; data not shown). CMKLR1 mRNA was similarly expressed in the liver of normal-weight (BMI ≤ 25 kg/m2), overweight (BMI > 25 and < 30 kg/m2) and obese patients (BMI ≥ 30 kg/m2) (Figure 1B). CMKLR1 mRNA was not related to gender (Figure 1C). 2.2. Hepatic CMKLR1 mRNA in Human Non-Alcoholic Fatty Liver Disease (NAFLD) In patients with definite NASH (NASH score ≥ 5), CMKLR1 mRNA was not significantly increased compared to controls and compared to patients with borderline NASH (NASH score < 5) (Figure 2A). When the first two groups with similar median values of CMKLR1 mRNA were combined, levels were significantly lower compared to NASH patients (Figure 2B). Receiver operating characteristic (ROC) curve analysis (Figure 2C) revealed an area under the curve (AUC) of 0.648 excluding analysis of CMKLR1 mRNA as a tool for NASH diagnosis. Alanine aminotransferase (r = 0.025, p = 0.802), aspartate aminotransferase (r = −0.79, p = 0.447), alkaline phosphatase (r = 0.139, p = 0.167) and bilirubin (r = −0.104, p = 0.295) in serum were not associated with hepatic CMKLR1 mRNA (data not shown). CMKLR1 did not correlate with steatosis grade (r = 0.146, p = 0.114) but positively correlated with inflammation (r = 0.248, p = 0.007), fibrosis (r = 0.425, p < 0.001) and NASH score (r = 0.272, p = 0.003) (Figure 2D–F and data not shown). Type 2 diabetes, hypertension and dyslipidemia are commonly diagnosed in NASH patients [3]. Systemic chemerin positively correlates with low-density lipoprotein cholesterol, insulin resistance and systolic as well as diastolic blood pressure [9]. Whether hepatic CMKLR1 is associated with metabolic diseases such as hypercholesterolemia or type 2 diabetes has not been evaluated to our knowledge so far. CMKLR1 mRNA was similar in the 14 patients with and those without hypercholesterolemia (data not shown). In the 45 hypertensive patients, hepatic CMKLR1 mRNA was not changed (data not shown). CMKLR1 mRNA was elevated in the 15 patients with type 2 diabetes (Figure 2G). Type 2 diabetes is a risk factor for NAFLD [2] and the NASH score was significantly higher (p = 0.001) in this group. In the NASH group 11 patients had diabetes, and here, CMKLR1 mRNA was comparable to that of non-diabetic NASH patients (p = 0.201, Figure 2H). Similarly, CMKLR1 mRNA was unchanged in the 10 patients with NASH and hypercholesterolemia (p = 0.935) and the 17 hypertensive NASH patients (p = 0.367) compared to NASH patients not suffering from these co-morbidities (data not shown). 2.3. Hepatic CMKLR1 mRNA in Females Recently, gender-specific associations of hepatic CMKLR1 expression with liver histology have been identified in chronic hepatitis [16]. Therefore, CMKLR1 mRNA was analyzed in both genders separately. In the female patients 22 had a normal weight, 15 were overweight and 19 were obese. CMKLR1 mRNA expression was, however, not associated with BMI (r = 0.031, p = 0.823; Figure 3A). Of the 56 female patients, 17 had normal liver, 25 borderline NASH and 14 NASH. CMKLR1 mRNA was similarly expressed in the three groups (Figure 3B). There was no difference in the hepatic levels of CMKLR1 mRNA compared to the combined values of the controls and those with borderline NASH (p = 0.609). CMKLR1 mRNA did not correlate with steatosis grade (r = 0.075, p = 0.582), inflammation (r = 0.054, p = 0.693), fibrosis (r = 0.248, 0.068) and NASH score (r = 0.137, p = 0.314) (Figure 3C and data not shown). Alanine aminotransferase (r = −0.040, p = 0.787), aspartate aminotransferase (r = −0.234, p = 0.113), alkaline phosphatase (r = 0.170, p = 0.249), and bilirubin (r = −0.066, p = 0.647) in serum were not associated with hepatic CMKLR1 mRNA (data not shown). CMKLR1 levels tended to be increased in the six females with hypercholesterinemia (Figure 3D). There was a modest trend to a higher expression in the liver of the five females with type 2 diabetes (p = 0.103, Figure 3E). CMKLR1 expression was not related to hypertension diagnosed in 16 females (data not shown). 2.4. Hepatic CMKLR1 mRNA in Males In the male cohort 17 patients had a normal weight, 28 were overweight and 17 were obese. CMKLR1 mRNA was not associated with BMI (r = 0.068, p = 0.607; Figure 4A). Of the 62 male patients, 16 had normal liver, 22 borderline NASH and 24 NASH. CMKLR1 mRNA was similar in the liver of male NASH patients compared to those with borderline NASH and controls. When the last two cohorts were combined, CMKLR1 mRNA was lower compared to that of NASH patients (Figure 4C). ROC analysis (Figure 4D) revealed an AUC of 0.723. The optimal cut-off point was 1.7 with a sensitivity of 88% and a specificity of 48% to detect NASH. CMKLR1 mRNA positively correlated with inflammation score (r = 0.404, p = 0.001), fibrosis score (r = 0.555, p < 0.001; Figure 4E) and NASH score (r = 0.392, p = 0.002). Alanine aminotransferase (r = 0.028, p = 0.839), aspartate aminotransferase (r = 0.055, p = 0.705), alkaline phosphatase (r = 0.225, p = 0.108) and bilirubin (r = −0.132, p = 0.346) in serum were not associated with hepatic CMKLR1 mRNA (data not shown). In males, CMKLR1 expression tended to be higher in the 10 patients with type 2 diabetes (p = 0.058, Figure 4F). Expression was not related to hypertension (29 patients) and hypercholesterinemia (8 patients) (Figure 4G and data not shown). To exclude that the higher number of type 2 diabetic patients accounts for associations of CMKLR1 mRNA with NASH in male patients; correlation analysis was performed using data of the 52 males without this co-morbidity. CMKLR1 mRNA still correlated with inflammation (r = 0.378, p = 0.006), fibrosis (r = 0.547, p < 0.001) and NASH score (r = 0.312, p = 0.024). 3. Discussion We present evidence that hepatic CMKLR1 mRNA expression is associated with NASH in male patients. Here, positive correlations of CMKLR1 mRNA with inflammation score, fibrosis score and consequently NASH score have been identified. The correlation coefficients are rather low but associations are highly significant. In females, hepatic CMKLR1 expression is not related to features of NASH. Although the current study could not identify increased CMKLR1 expression in human NASH, a recent study reported elevated CMKLR1 in those patients. Gender-related analysis has not been performed in this cohort [7]. CMKLR1 mRNA is comparable in both genders in the cohort analyzed herein. Levels of mRNA are also similar in female and male patients with chronic hepatitis C [16]. In hepatitis C patients, CMKLR1 mRNA is not related to inflammation and a negative association with fibrosis has been identified in females only [16]. CMKLR1 mRNA regulation in chronic liver disease is therefore influenced by gender and etiology of hepatic injury. The prevalence of NASH is higher in males, and this may be due to the fact that sex hormones affect NASH severity [17,18]. Modest upregulation of CMKLR1 in male NASH patients obviously contributes an additional factor responsible for gender-related differences in NASH pathology. Obesity is a risk factor for NAFLD [1]. The mature-onset obesity phenotype has been observed in male but not female CMKLR1-deficient mice [12]. Our results do not show any relation between hepatic CMKLR1 mRNA and BMI, arguing against an association of liver CMKLR1 levels and body weight. Interestingly, hepatic CMKLR1 tends to be increased in type 2 diabetes patients in both genders and upregulation is significant in the whole cohort. This suggests that elevated CMKLR1 mRNA in the liver of these patients is not necessarily related to NASH which has a higher prevalence in type 2 diabetes [2]. There is, however, no difference in CMKLR1 mRNA in male NASH patients with and without type 2 diabetes. Hypercholesterinemia in females is also linked to an increase in hepatic CMKLR1. Blockage of cholesterol synthesis in adipocytes does not affect CMKLR1 protein levels, while chemerin is strongly reduced [19]. In hepatocytes, elevation of cellular cholesterol does not change CMKLR1 protein [15]. Therefore, CMKLR1 levels seem not to be related to cellular cholesterol concentrations. Dyslipidemia may nevertheless affect hepatic CMKLR1 activity in females independent of NAFLD. A limitation of the current study is the relatively low number of patients with type 2 diabetes and hypercholesterolemia. There are no patients suffering from these co-morbidities in the control group and only few patients in the borderline NASH group. Therefore, the association of CMKLR1 with type 2 diabetes and/or hypercholesterolemia has to be evaluated in different cohorts using patients who ideally do not suffer from NASH. The main intention of the present study was, however, to identify NAFLD-related changes of this hepatic chemokine receptor. CMKLR1 mRNA was still associated with inflammation, fibrosis and NASH score when those suffering from type 2 diabetes were excluded. The association of CMKLR1 with NASH score in male NASH patients may suggest a higher activity of CMKLR1-related signaling pathways. Chemerin is abundantly expressed in the liver, and in patients with chronic hepatitis C mRNA levels are similar in males and females [8,16]. In human NASH, hepatic chemerin expression is induced [7] while Deng et al. described lower levels in human fatty liver [6]. Gender-related regulation of liver chemerin has not been evaluated so far. Serum chemerin is found increased in females in some but not all studies [20]. The CMKLR1 receptor is only activated by proteolytic cleaved chemerin [9] and we are unaware of data on gender-related activation of this adipokine. Additional investigations are needed to evaluate whether chemerin signaling is indeed enhanced upon higher expression of hepatic CMKLR1. Further, the physiological and pathophysiological roles of CMKLR1/chemerin signaling in the liver have to still be clarified. Resolvin E1 is an additional ligand of CMKLR1 [11]. In ob/ob mice, this lipid ameliorates insulin sensitivity and hepatic steatosis [21]. In a murine model of liver fibrosis induced by Schistosoma japonicum infection, resolvin E1 treatment reduces the growth of granulomas and thereby delays hepatic fibrogenesis [22]. However, resolvin E1 fails to improve liver injury in mice fed an atherogenic diet to induce NASH [23]. Resolvins are derived from ω-3 polyunsaturated fatty acids [24] and ethyl-eicosapentanoic acid could not ameliorate NASH in a clinical trial [25]. Therefore, the potential beneficial effects of resolvins in NASH have to be proven in future studies. The non-availability of protein from the respective liver tissues may be considered as a limitation of our study. Recently, our group has shown that CMKLR1 protein is reduced in human steatotic liver [15]. However, the liver tissue of only 14 patients was analyzed and gender-related regulation was not determined. In summary, the present study demonstrates a weak association of hepatic CMKLR1 expression with features of NASH in male patients. 4. Materials and Methods 4.1. Study Group Liver tissues of controls and patients with NAFLD were received and details of the patients are summarized in Table 1. These samples have been introduced in a recent study [26]. Details of the histological scoring which was done as described [27] are summarized in Table 2. The scores were summed up and ranged from 0 to 9. Patients with a score of ≥5 were defined as NASH patients. Alcohol abuse, viral infections and drugs are known to cause liver injury, and therefore, these patients were excluded. Indications for surgery was hepatic metastases of extrahepatic tumours for 70 patients, focal nodular hyperplasia of the liver for nine patients, adenoma for six patients, cholangiocarcinoma for 15 patients, hepatocellular carcinoma for 12 patients and other diseases in 6 patients. Only healthy tissue was used for isolation of RNA. Hypertension, hypercholesterolemia and type 2 diabetes diagnosis had been documented. Serum lipids and glucose were not recorded. Experimental procedures accord to the guidelines of the charitable state controlled foundation Human Tissue and Cell Research and the study was authorized by the local ethical committee of the University of Regensburg (Identification code: 12-101-0048; date: 29 March 2012). The written informed consent was obtained from each patient. 4.2. Monitoring of Gene Expression by Real-Time RT-PCR The LightCycler FastStart DNA Master SYBR Green I kit from Roche (Mannheim, Germany) was used for analyzing the expression of mRNA semi-quantitatively by real-time RT-PCR. Total cellular RNA was reverse transcribed using the Promega Reverse Transcription System (Promega, Madison, WI, USA). The cDNA was used for amplification in glass capillaries (LightCycler, Roche). Oligonucleotides were synthesized by Metabion (Planegg-Martinsried, Germany). Real-time RT-PCR was performed as described and sequencing of the amplified DNA fragments (Geneart, Regensburg, Germany) confirmed the specificities of the PCRs [19]. Serially diluted cDNA was used to create a standard curve for each gene analyzed. The second derivative maximum method was used for quantification with the LightCycler software. Primers to amplify human CMKLR1 were 5′-ACC TGC ATG GGA AAA TAT CCT-3′ and 5′-GAG GTT GAG TGT GTG GTA GGG-3′. The 18S rRNA was used for normalization and amplified with 5′-GAT TGA TAG CTC TTT CTC GAT TCC-3′ and 5′-CAT CTA AGG GCA TCA CAG ACC-3′. 4.3. Statistical Analysis Data are displayed as box plots and median, lower and upper quartiles and range of the values are shown. The Mann-Whitney U Test (SPSS Statistics 21.0 program, IBM, Leibniz Rechenzentrum, München, Germany) was used for comparison of two data sets and Anova followed by a Dunnett post-hoc test was used for comparison of three data sets. ROC analysis and Spearman correlation were done using SPSS Statistics 21.0 program. Youden index was calculated to identify the best cut-off point. A value of p < 0.05 was regarded as significant. Distribution of gender and co-morbidities listed in Table 1 was analyzed with the Chi-square test. Acknowledgments The study was partly supported by the German Research Foundation (BU 1141/7-1 and BU 1141/13-1) for Christa Buechler and the German Federal Ministry of Education and Research (Virtual Liver Network Grants 0315753) for Thomas S. Weiss. Author Contributions Kristina Eisinger, Sabrina Krautbauer and Christa Buechler conceived and designed the experiments; Maximilian Neumann, Elisabeth M. Meier, Lisa Rein-Fischboeck and Rebekka Pohl performed the experiments; Christa Buechler and Maximilian Neumann analyzed the data; Thomas S. Weiss and Charalampos Aslanidis contributed materials; Christa Buechler wrote the paper which was read and corrected by all authors. Conflicts of Interest The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results. The authors declare no conflict of interest. Figure 1 CMKLR1 mRNA (normalized for 18S rRNA) in the human liver. (A) CMKLR1 mRNA in liver tissues of patients stratified for surgery indications (M, liver metastases of extrahepatic tumours; CCC, cholangiocarcinoma; HCC, hepatocellular carcinoma; FNH, focal nodular hyperplasia of the liver; Aden., adenoma); (B) CMKLR1 mRNA in the liver of normal weight (Body mass index (BMI) ≤ 25 kg/m2), overweight (BMI > 25 and < 30 kg/m2) and corpulent (BMI ≥ 30 kg/m2) patients; (C) CMKLR1 mRNA in female (F) and male (M) liver. Figure 2 CMKLR1 mRNA (normalized for 18S rRNA) in non-alcoholic fatty liver disease (NAFLD). (A) CMKLR1 mRNA in liver tissues of patients with healthy liver (Cont.), a non-alcoholic steatohepatitis (NASH) score (N) <5 and ≥5; (B) CMKLR1 in controls and patients with a NASH score <5 compared to those with NASH; (C) Receiver operating characteristic (ROC) curve analysis; (D) Correlation of hepatic CMKLR1 mRNA with inflammation; (E) Correlation of hepatic CMKLR1 mRNA with fibrosis; (F) Correlation of hepatic CMKLR1 mRNA with the NASH score; (G) CMKLR1 mRNA in patients with and without type 2 diabetes; (H) CMKLR1 mRNA in NASH patients with and without type 2 diabetes. The p-values for significant differences/correlations are shown in the figure. Figure 3 CMKLR1 mRNA (normalized for 18S rRNA) in female NAFLD patients. (A) CMKLR1 mRNA in the liver of normal-weight (BMI ≤ 25 kg/m2), overweight (BMI > 25 and < 30 kg/m2) and obese (BMI ≥ 30 kg/m2) female patients; (B) CMKLR1 mRNA in liver tissues of female patients with healthy liver (Cont.), a NASH score (N) <5 and ≥5; (C) Correlation of hepatic CMKLR1 mRNA in females with fibrosis; (D) Hepatic CMKLR1 in females with and without hypercholesterolemia (HC); (E) CMKLR1 mRNA in liver tissues of female patients with and without type 2 diabetes (T2D). Number in brackets indicates a trend. Figure 4 CMKLR1 mRNA (normalized for 18S rRNA) in male NAFLD patients. (A) CMKLR1 mRNA in the liver of normal-weight (BMI ≤ 25 kg/m2), overweight (BMI > 25 and < 30 kg/m2) and obese (BMI ≥ 30 kg/m2) male patients; (B) CMKLR1 mRNA in liver tissues of male patients with healthy liver (Cont.), a NASH score (N) <5 and ≥5; (C) CMKLR1 in male controls and borderline NASH compared to male NASH patients; (D) ROC analysis; (E) Correlation of hepatic CMKLR1 mRNA in males with fibrosis; (F) Hepatic CMKLR1 mRNA in males with and without type 2 diabetes (T2D); (G) Hepatic CMKLR1 in males with and without hypercholesterolemia. The p-values for significant differences are shown in the figure. Numbers in brackets indicate a trend. ijms-17-01335-t001_Table 1Table 1 Characteristics of the cohort enrolled in the present study. Data are given as median values and range of the values. Uppercase numbers are shown where data were not available for all of the patients. Significant differences between controls and patients with borderline non-alcoholic steatohepatitis (NASH) are identified by *, between controls and patients with NASH with # and between patients with borderline NASH and NASH with &. Control Borderline NASH NASH p-Values Males/Females 16/17 22/25 24/14 Age 58 (20–82) 60 (24–84) 66 (33–82) 0.015 # Body mass index (BMI) kg/m2 24.7 (18.3–30.5) 28.0 (22.0–46.0) 28.4 (21.0–57.7) <0.001 *,# Type 2 Diabetes 0 4 11 0.01 # Hypertension 7 21 17 Hypercholesterolemia 0 4 10 Alanine aminotransferase U/L 21 (8–50) 32 35 (17–623) 36 32 (10–984) 35 <0.001 *,# Aspartate aminotransferase U/L 23 (8–42) 27 31 (11–688) 35 30 (9–389) 34 0.014 * 0.012 # Alkaline phosphatase U/L 102 (46–203) 29 97 (37–444) 36 91 (45–826) 35 Bilirubin mg/dL 0.6 (0.19–1.95) 30 0.56 (0.19–1.99) 37 0.53 (0.20–0.53) 36 Steatosis 0 (0–0) 2 (1–2) 2.5 (1–3) <0.001 *,#,& Inflammation 0 (0–0) 0 (0–2) 2 (0–3) 0.005 * <0.001 #,& Fibrosis 0 (0–0) 0 (0–2) 2 (0–4) 0.047 * <0.001 #,& NASH Score 0 (0–0) 2 (1–4.5) 6 (5–9) <0.001 *,#,& ijms-17-01335-t002_Table 2Table 2 Scoring of steatosis, inflammation and fibrosis. Scores Description Steatosis 0 <5% steatosis Steatosis 1 5%–33% steatosis Steatosis 2 >33%–66% steatosis Steatosis 3 >66% Inflammation 0 No foci/20 × field Inflammation 1 <2 foci/20 × field Inflammation 2 2–4 foci/20 × field Inflammation 3 >4 foci/20 × field Fibrosis 0 No fibrosis Fibrosis 1 Zone 3 perisinusoidal/pericellular fibrosis; focally or extensively present Fibrosis 2 Zone 3 perisinusoidal/pericellular fibrosis with focal or extensive periportal fibrosis Fibrosis 3 Zone 3 perisinusoidal/pericellular fibrosis and portal fibrosis with focal or extensive bridging fibrosis Fibrosis 4 Liver cirrhosis ==== Refs References 1. Buechler C. Wanninger J. Neumeier M. Adiponectin, a key adipokine in obesity related liver diseases World J. Gastroenterol. 2011 17 2801 2811 21734787 2. Yeh M.M. Brunt E.M. Pathological features of fatty liver disease Gastroenterology 2014 147 754 764 10.1053/j.gastro.2014.07.056 25109884 3. Friis-Liby I. Aldenborg F. Jerlstad P. Rundstrom K. Bjornsson E. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081336ijms-17-01336ArticleMouse Maternal High-Fat Intake Dynamically Programmed mRNA m6A Modifications in Adipose and Skeletal Muscle Tissues in Offspring Li Xiao 1†Yang Jing 1†Zhu Youbo 1Liu Yuan 2Shi Xin’e 1Yang Gongshe 1*Borejdo Julian Academic Editor1 Creative Group of Muscle Biology & Genetic Improvement of Pigs, College of Animal Science and Technology, Northwest A&F University, Yangling 712100, China; nice.lixiao@gmail.com (X.L.); yangjing@nwsuaf.edu.cn (J.Y.); zhuyoubo_2015@nwsuaf.edu.cn (Y.Z.); xineshi@nwsuaf.edu.cn (X.S.)2 College of Life Sciences, Northwest A&F University, Yangling 712100, China; lolily89@outlook.com* Correspondence: gsyang999@hotmail.com; Tel./Fax: +86-29-8709-2430† These authors contributed equally to this work. 19 8 2016 8 2016 17 8 133607 7 2016 09 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Epigenetic mechanisms have an important role in the pre- and peri-conceptional programming by maternal nutrition. Yet, whether or not RNA m6A methylation—an old epigenetic marker receiving increased attention recently—is involved remains an unknown question. In this study, mouse high-fat feeding prior to conception was shown to induce overweight and glucose intolerant dams, which then continued to be exposed to a high-fat diet during gestation and lactation. The dams on a standard diet throughout the whole experiment were used as a control. Results showed that maternal high-fat intake impaired postnatal growth in male offspring, indicated by decreased body weight and Lee’s index at 3, 8 and 15 weeks old, but the percentages of visceral fat and tibialis anterior relative to the whole body weights were significantly increased at eight weeks of age. The maternal high-fat exposure significantly increased mRNA N6-methyladenosine (m6A) levels in visceral fat at three weeks old, combined with downregulated Fat mass and obesity-associated gene (FTO) and upregulated Methyltransferase like 3 (METTL3) transcription, and these changes were reversed at eight weeks of age. In the tibialis anterior muscle, the maternal high-fat diet significantly enhanced m6A modifications at three weeks, and lowered m6A levels at 15 weeks of age. Accordingly, FTO transcription was significantly inhibited at three weeks and stimulated at 15 weeks of age, and METTL3 transcripts were significantly improved at three weeks. Interestingly, both FTO and METTL3 transcription was significantly elevated at eight weeks of age, and yet the m6A modifications remained unchanged. Our study showed that maternal high-fat intake could affect mRNA m6A modifications and its related genes in offspring in a tissue-specific and development-dependent way, and provided an interesting indication of the working of the m6A system during the transmission from maternal nutrition to subsequent generations. maternal high-fat dietm6A methylationFTOMETTL3 ==== Body 1. Introduction It is well documented that maternal high-fat diet programs tissue development and energy metabolism in offspring [1]. In addition, epigenetic changes such as DNA methylation, resulting from maternal malnutrition, have been demonstrated to play important roles in transgenerational links with metabolic disease [2,3]. N6-methyladenosine (m6A) is currently the most prevalent internal modification of mRNA in eukaryotes, and has been intensively studied recently. An increasing number of studies have shown the fundamental role of m6A modification in RNA stability, alternative splicing and translation efficiency, and dysregulation of m6A modification may underline a wide range of pathological progressions related to obesity, neurological disorders and male infertility [4]. M6A mRNA modification is a highly dynamic process. M6A on RNA is believed to be installed by the METTL3 (Methyltransferase like 3)-METTL14-WTAP (Wilms’ tumor 1-associated protein) complex, which is denoted as an m6A “writer” [5]. M6A modifications can be directly removed by demethylase FTO (Fat mass and obesity-associated protein) [6] and AlkBH5 (AlkB homologue 5) [7], both of which belong to the iron- and 2-oxoglutarate (2OG)-dependent family of AlkB oxygenases [8,9], and are called m6A “erasers”. Distinct from AlkBH5, highly expressed in testis and essential for spermatogenesis [7], FTO is most abundant in the hypothalamus and in fat and skeletal muscle, and is strongly related to obesity [10]. The roles of m6A modification in the transmission from maternal nutrition to subsequent generations is of great interest, for the m6A modification-related genes, especially FTO, are reported to be tightly related with the developmental and metabolic status of adipose tissue and skeletal muscle [10], the two tissues most highly sensitive to maternal nutrition [11,12]. This study is designed to test the effects of maternal high-fat intake on m6A modification in adipose and skeletal muscle tissues in the offspring in mice. 2. Results 2.1. High-Fat Diet Caused Obesity and Glucose Intolerance in Dams As shown in Figure 1, dams were fed with either a high-fat diet (45% fat included) or a standard chow diet (10% fat) throughout the whole experiment. Six weeks later, the body weights of female mice fed on a high-fat diet (HFD) became significantly higher than that of their chow-fed counterparts (Figure 2A). In addition, dams in the HFD group showed a slower glucose clearance rate (Figure 2B). At the end of lactation, the body weight of dams in the HFD group were also greatly higher than that of those in the control group (27.95 ± 1.56 vs. 33.18 ± 2.01, p < 0.05). 2.2. Maternal High-Fat Intake Impaired the Growth of Male Litters The litter sizes (9.6 ± 0.3 vs. 9.1 ± 0.2, p = 0.210) and litter weights (16.80 ± 0.43 vs. 15.85 ± 0.35, p = 0.104) were comparable between Standard and HFD dams, and the average birth weights (litter weight/litter size) were also similar (1.75 ± 0.01 vs. 1.74 ± 0.01, p = 0.517). The postnatal weights and Lee’s index of male progeny were significantly reduced after exposure to a maternal high-fat diet (Figure 3A,B). Although the weights of visceral fat tissue were not significantly affected by the maternal diet, the percentage of tissue relative to body weight was significantly decreased at three weeks and increased at five weeks in HFD mice (Figure 3C,D). The weight of tibialis anterior muscle significantly declined at 15 weeks (Figure 3E). The reduced weight of tibialis anterior muscle at 15 weeks of age might be a result of the retarded body growth, for the percentage of tibialis anterior muscle relative to body weight was unchanged (Figure 3F). However, the percentage of tibialis anterior muscle relative to body weight increased greatly at eight weeks in the HFD group (Figure 3F). 2.3. Maternal High-Fat Diet Altered the m6A Pattern in Fat and Skeletal Muscle in a Development-Dependent Way The effects of a maternal high-fat diet on mRNA m6A varied depending on the developmental stage. In visceral fat, mRNA m6A levels were significantly increased in three-week-age males, and reduced at eight weeks in the HFD group (Figure 4A). Accordingly, FTO, the “m6A eraser”, significantly decreased at three weeks and increased at eight weeks (Figure 4B). The changes of METTL3, the “m6A writer”, were opposite to that of FTO (Figure 4C). As in visceral fat, mRNA m6A modifications were significantly higher in the tibialis anterior muscle of three-week-age litters in the HFD group (Figure 4D), along with decreased FTO (Figure 4E) and increased METTL3 transcription (Figure 4F). A maternal high-fat diet exerted no significant effects on m6A levels in eight-week-age males (Figure 4D), while the expressions of FTO (Figure 4E) and METTL3 (Figure 4F) were simultaneously upregulated. Moreover, exposure to a maternal high-fat diet sharply decreased m6A modifications at 15 weeks of age (Figure 4D), accompanied by enhanced FTO expression (Figure 4E), and yet left unchanged the METTL3 expression (Figure 4F). In 15 week old males, insulin sensitivity was assessed using the homoeostasis model assessment of insulin resistance (HOMA-IR), and no significant differences were observed (p > 0.05, Table 1). 3. Discussion It is accepted that maternal nutrition has long-lasting effects on offspring [12,13,14]. In our study, high-fat intake resulted in dams becoming overweight and glucose intolerant (Figure 2), and also impaired the postnatal growth even in adulthood (Figure 3A,B); a result similar to that in a new report on C57BL/6 mice [13]. Interestingly, the weights of visceral fat and tibialis anterior muscle relative to total body weight in offspring from HFD dams were significantly higher than that from the control group (Figure 3D,F) at eight weeks old. This increase in adipose and skeletal muscle growth at eight weeks might be a transient compensation for the impaired fetal growth resulting from pre-gestational and gestational exposure to maternal HFD [13]. In our study, the HOMA-IR values between HFD and control groups were comparable, which was inconsistent with previous reports. The difference might be due to different sampling times. In our study, young adults (at 15 weeks old) were used, while much older adults (24 weeks old, or even 12 months old) were studied in other reports [15,16]. Therefore, more time points after 15 weeks would be helpful for evaluating metabolic status in offspring. Epigenetic mechanisms, such as DNA m5C methylation, have been documented to mediate the programming effects of maternal nutrition [3,17]. M6A is a predominant mRNA modifier and has gained increasing attention recently [18,19]. M6A mRNA modification operates through two main approaches: coordinating protein-RNA interactions, or interacting with m6A binding proteins to directly induce RNA splicing, degradation, and translation [4]. M6A modifications are developed or “written” by the METTL3-METTL4-WTAP complex [5] and “erased” by FTO [6] or AlkBH5 [7]. In visceral fat, m6A methylation in progeny from HFD dams was significantly increased at three weeks and decreased at eight weeks (Figure 4A), which was in striking contrast to the changes of relative visceral fat weight (Figure 3D). There was a similar result indicating that mRNA m6A levels downregulate adipogenesis in porcine adipocytes [20]. Other reports show that demethylation of mRNA m6A is required for the adipogenesis of the 3T3-L1 preadipocyte cell line [21,22]. Moreover, during the adipogenic process, the demethylation of mRNA m6A is exerted by FTO [21,22]. The dynamic regulation of m6A by FTO in adipocytes is important in the determination of splicing and transcription of genes contributing to the regulation of adipogenesis (such as RUNX1 translocation partner 1 (RUNX1T1)) [21]. That may be one of the reasons why FTO overexpression promotes obesity [23], and inactive FTO competes with and suppresses obesity [24]. In contrast to FTO, overexpression of METTL3, a critical component of the multiprotein methyltransferase complex for m6A methylation [4], could inhibit the expression of pro-adipogenic genes such as PPARγ, and thus suppress adipogenesis and reduce cellular triglyceride content [20]. Taken together, our data show that the m6A system may be involved in the adipose tissue development programmed by maternal nutrition. In tibialis anterior muscle, maternal high-fat intake significantly enhanced m6A levels at three weeks old and repressed m6A modifications at 15 weeks of age. Given the limited reports within our knowledge about mRNA m6A in skeletal muscle growth or metabolism, we could not arrive at a clear deduction about the biological significance of the fluctuation of m6A modifications in muscle. As for m6A related genes, both FTO and METTL3 were simultaneously elevated in the HFD group at eight weeks of age (Figure 4), which was puzzling as their physiological functions are assumed to be contradictory [5,6]. We suppose that this may be due to a compensatory mechanism, for there are indeed several documents describing the mismatch of METTL3 [20] and FTO [25,26] expression with m6A levels, especially under physiological conditions where many complicated compensatory pathways exist, and small differences in conditions could affect m6A levels. It is worth mentioning that the expression pattern of FTO is closely matched to the compensatory growth of fat and muscle growth at eight weeks of age in our study, which reminds us of other studies where anabolic pathways were significantly enhanced, catabolism was reduced in abdominal white fat and skeletal muscle of genetically FTO overexpressed (FTO-4) mice [26], and FTO deficiency lead to postnatal growth retardation accompanied by a significant reduction in adipose tissue and lean body mass [24]. In addition to its well-documented pro-adipogenic roles [21,22], FTO also seems to promote muscle growth, for FTO mRNA in muscle was found to increase in the fast growing stages of chickens [27], and FTO transcription is much higher in the breast muscle of fast-growing recessive White Plymouth Rock chickens than that of indigenous Qingyuan partridge chickens at one and eight weeks of age [27]. Of course, the decreased muscle weight with increased FTO expression in 15-week-age progeny argues against the somatotrophic role of FTO discussed above. Mice generally reach body maturation around 10–12 weeks of age, thus we suppose that the subsequent role of FTO in skeletal muscle may be related to energy homeostasis. A previous study has shown that FTO expression is significantly increased in muscles from type 2 diabetic patients, and FTO overexpression could enhance lipogenesis and oxidative stress, reduce the mitochondrial oxidative function, and induce a cluster of metabolic defects associated with type 2 diabetes [28]. The relationship of the elevated FTO expression in muscle with the susceptibility to insulin resistance or type 2 diabetes programmed by maternal high-fat diet [16] should be delicately evaluated in a further study, and more time points after 15 weeks are recommended to better trace the metabolic changes in adult offspring. Taken together, the current study provides an attractive scenario about the m6A landscape during the programming procedure provided by maternal nutrition to the next generation, and the m6A system might be a novel and potent mediator from maternal environments to offspring. However, the current study was only focused on the male progeny, and sex-specific responses to maternal over-nutrition have been reported [1,29,30], so that both male and female offspring should be included in the future to better explore the roles of m6A system in maternal programming. 4. Materials and Methods 4.1. Ethics Statement The study protocol was approved by the Animal Ethics Committee of Northwest A&F University (2014-12-10). Animal handling and sample collection were conducted in accordance with the guidelines of the Management Measures of experimental animals of Shaanxi Province (2011-06-01). 4.2. Animals and Diet Twenty female Kunming mice (purchased from the experimental animal center in The Fourth Military Medical University) around four weeks old (body weight: 20.32 ± 1.78 g) were randomly and equally allocated to one of the two dietary groups: control (Standard) group, receiving the standard laboratory chow diet ad libitum (10% fat, TROPHIC Animal Feed #LAD-0011); and high-fat diet (HFD) group, fed on a high-fat diet ad libitum (45% fat, #TP-0861, TROPHIC Animal Feed High-tech Co., Ltd., Nantong, China). All mice received water freely. Body weight gain was recorded weekly. Eleven weeks later, females were bred with age-matched males and continued on the same diet throughout gestation and lactation. The male mice were fed a standard diet. After weaning, all of the offspring were fed on a standard diet. At 3, 8 and 15 weeks of age, 10 male litters from each group were anesthetized via ether inhalation and killed by bloodletting from the heart. Visceral fat and the tibialis anterior muscle were quickly removed and stored in liquid nitrogen. 4.3. Glucose Tolerance Test The glucose tolerance test was carried out in dams at 15 weeks of age (after 11 weeks of experimental diet feeding) after the animals had been denied access to food overnight. Glucose (1 mg/g body weight) was administered to the mice by intraperitoneal injection. Tail blood samples were taken before (0 min) and at 30, 60, 90 and 120 min after the glucose administration. The blood glucose levels were determined using an automated blood glucose meter (Sannuo, Changsha, China). 4.4. RNA Isolation Total RNA was extracted by TRIzol Reagent (Invitrogen, Carlsbad, CA, USA), and then treated with DNase I (TaKaRa Bio, Inc., Dalian, China) to remove genomic DNA contamination. Dynabeads® mRNA Purification Kit (Invitrogen, Carlsbad, CA, USA) was used to purify mRNA. Concentrations of RNA were determined with NanoDrop2000 (Thermo Fisher Scientific, Waltham, MA, USA). 4.5. RNA Dot-Blot RNA dot-blot was conducted strictly as previously reported [20]. Firstly, an aliquot of 200 ng mRNA was denatured by heating at 95 °C for 3 min, and immediately cooled down on ice. Aliquot were spotted on nitrocellulose membrane, and subjected to UV cross-linking (1500 × 100 J/cm2). The membrane was blocked with 5% of non-fat milk in TBST, and incubated with anti-m6A antibody (1:2000, Synaptic Systems, Goettingen, Germany) overnight at 4 °C. After washing 3 times in 1× TBST, the membrane was incubated with anti-rabbit IgG secondary antibody (1:10,000, Boster, Wuhan, China), and visualized by ECL Western Blotting Detection Kit (Thermo, Waltham, MA, USA). 4.6. qRT-PCR Total RNA was subjected to electrophoresis using a 2% agarose gel to verify their integrity. Samples with a 28S/18S rRNA ratio between 1.5 and 2.0 without smears were used for the subsequent RT reaction using a PrimeScriptTM RT reagent Kit (TaKaRa Bio, Inc., Dalian, China). qRT-PCR was conducted in technical triplicates with the Multicolor Real-Time PCR detection system (iQ5, Bio-Rad Laboratories, Inc., Hercules, CA, USA). Melting curve analysis was performed at the end of each PCR program to monitor nonspecific product formation. β-actin was used as the internal control. Primers were obtained from PrimerBank (Avaliable online: https://pga.mgh.harvard.edu/primerbank/, PrimerBank ID: 6671509a1), and sequences are given in Table 2. 4.7. Insulin Sensitity Assay Male offspring at 15 weeks old were denied food for 2 h, and blood glucose concentrations (FBG) were determined as described above. Then more blood samples were collected from the heart. The blood samples were left to stand at room temperature for 4 h and then centrifuged at 2000× g for 15 min to get serum. Serum insulin concentrations were measured using the Mouse Ultrasensitive ELISA kit (ALPCO, Windham, NH, USA). Insulin sensitivity was assessed using the homoeostasis model assessment of insulin resistance (HOMA-IR). HOMA-IR = FBG (mmol/L) × FIns (mIU/L)/22.5. 4.8. Statistical Analysis All data sets were analyzed with Independent t tests by IBM SPSS 20 (Chicago, IL, USA). Results are presented as means ± SEM. Statistical significance was set at p < 0.05. Acknowledgments This work is supported by the National Natural Science Foundation (31501925), the China Postdoctoral Science Foundation (2014M562465), the Natural Science Foundation of Shaanxi Province (2014JQ3103) and Major Projects for Genetically Modified Organisms Breeding (2014ZX0800947B). Author Contributions Conception and design of experiments: Xiao Li, Jing Yang; RNA dot-blot: Jing Yang, Youbo Zhu; Animal feeding and qPCR assay: Yuan Liu; Analysis of data and writing the paper: Xiao Li. Our studies were also introduced by Xin’e Shi, Gongshe Yang. Conflicts of Interest The authors declare no conflicts of interest. Figure 1 Experimental design. Kunming females were fed a standard or a high-fat diet throughout the whole experiment. Glucose tolerance tests were performed before mating. Figure 2 High-fat diet induced overweight (A) and glucose intolerance (B) in the dams. n = 10, * indicates p < 0.05. Figure 3 Exposure to maternal high-fat diet resulted in retarded growth in male offspring. Body weights (A), Lee’ index (B), visceral fat weights (C), visceral fat relative weights (D), tibialis anterior weights (E) and tibialis anterior relative weights (F) have been shown. Lee’s index = [(Body weight (g) × 1000)1/3]/Body length (cm); it is an indicator of mouse growth and adiposity. n = 10, * indicates p < 0.05. Figure 4 Maternal high-fat intake altered m6A methylations and its related genes’ expression in visceral fat and tibialis anterior muscle. (A–F) represent the fold changes of the HFD group relative to the control group of the same age (the means of the control groups were set as one at each timepoint). n = 10, * indicates p < 0.05. ijms-17-01336-t001_Table 1Table 1 Homoeostasis model assessment of insulin resistance (HOMA-IR) assay at 15 weeks of age (mean ± SEM). Groups FBG (mmol/L) Fins (mIU/L) HOMA-IR Standard group 5.18 ± 0.25 9.76 ± 0.87 2.20 ± 0.15 HFD group 5.67 ± 0.26 10.12 ± 0.68 2.51 ± 0.16 ijms-17-01336-t002_Table 2Table 2 Primers for real-time polymerase chain reaction (PCR). Genes Access No. Sequences (5′→3′) Amplicon Size FTO NM_011936 F: CCGTCCTGCGATGATGAAGT 119 bp R: CCCATGCCGAAATAGGGCTC METTLS NM_019721 F: GAGTTGATTGAGGTAAAGCGAGG 75 bp R: GGAGTGGTCAGCGTAAGTTACA β-actin NM_007393 F: GGCTGTATTCCCCTCCATCG 154 bp R: CCAGTTGGTAACAATGCCATGT ==== Refs References 1. Yokomizo H. Inoguchi T. Sonoda N. Sakaki Y. Maeda Y. Inoue T. Hirata E. Takei R. Ikeda N. Fujii M. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081337ijms-17-01337ArticleT315 Decreases Acute Myeloid Leukemia Cell Viability through a Combination of Apoptosis Induction and Autophagic Cell Death Chiu Chang-Fang 123Weng Jing-Ru 45Jadhav Appaso 6Wu Chia-Yung 5Sargeant Aaron M. 7Bai Li-Yuan 13*Brown Geoffrey Academic Editor1 Division of Hematology and Oncology, Department of Internal Medicine, China Medical University Hospital, Taichung 40447, Taiwan; d5686@mail.cmuh.org.tw2 Cancer Center, China Medical University Hospital, Taichung 40447, Taiwan3 College of Medicine, School of Medicine, China Medical University, Taichung 40402, Taiwan4 Department of Marine Biotechnology and Resources, National Sun Yat-sen University, Kaohsiung 80424, Taiwan; columnster@gmail.com5 Department of Biological Science and Technology, China Medical University, Taichung 40402, Taiwan; kelen0523@yahoo.com.tw6 Division of Medicinal Chemistry, College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA; appaso06@gmail.com7 Charles River Laboratories, Preclinical Services, Spencerville, OH 45887, USA; aaron.Sargeant@crl.com* Correspondence: lybai6@gmail.com; Tel.: +886-4-2205-2121 (ext. 5051); Fax: +886-4-2233-767515 8 2016 8 2016 17 8 133723 6 2016 10 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).T315, an integrin-linked kinase (ILK) inhibitor, has been shown to suppress the proliferation of breast cancer, stomach cancer and chronic lymphocytic leukemia cells. Here we demonstrate that T315 decreases cell viability of acute myeloid leukemia (AML) cell lines (HL-60 and THP-1) and primary leukemia cells from AML patients in a dose-responsive manner. Normal human bone marrow cells are less sensitive than leukemia cells to T315. T315 down regulates protein kinase B (Akt) and p-Akt and induces caspase activation, poly-ADP-ribose polymerase (PARP) cleavage, apoptosis and autophagy through an ILK-independent manner. Interestingly, pretreatment with autophagy inhibitors rescues cells from apoptosis and concomitant PARP cleavage, which implicates a key role of autophagic cell death in T315-mediated cytotoxicity. T315 also demonstrates efficacy in vivo, suppressing the growth of THP-1 xenograft tumors in athymic nude mice when administered intraperitoneally. This study shows that autophagic cell death and apoptosis cooperatively contribute to the anticancer activity of T315 in AML cells. In conclusion, the complementary roles of apoptotic and autophagic cell death should be considered in the future assessment of the translational value of T315 in AML therapy. T315acute myeloid leukemiaapoptosisautophagyautophagic cell death ==== Body 1. Introduction Acute myeloid leukemia (AML) is a hematological malignancy characterized by the proliferation of clonal neoplastic hematopoietic cells and diverse clinical presentations. Chemotherapy with or without hematopoietic stem cell transplantation remains the mainstay of AML treatment. While advances in medicine and supportive care have led to complete remission for 70%–80% of adult AML patients, only 20%–30% of these patients have long term disease-free survival [1]. The major cause of this discrepancy is the acquisition of chemoresistance in refractory or relapsed AML. Alternative compounds or strategies are therefore needed to more effectively manage patients with AML. T315, N-methyl-3-(1-(4-(piperazin-1-yl)phenyl)-5-(4′-(trifluoromethyl)-[1,1′-biphenyl]-4-yl)-1H-pyrazol-3-yl)propanamide, was originally identified as an integrin-linked kinase (ILK) inhibitor and characterized by Lee et al. [2]. Subsequent studies demonstrate the efficacy of T315 against several types of cancers. In breast cancer, T315 suppressed γ-secretase-mediated Notch1 activation in caveolae of IL-6-abundant cells through inhibition of ILK [3]. Amelioration of NF-κB (nuclear factor κ-light-chain-enhancer of activated B cells) was thought to be responsible for the anticancer activity mediated by T315 in human gastric cancer cells [4]. T315 also has antitumor activity independent of the canonical ILK inhibition. In chronic lymphocytic leukemia, Liu et al. demonstrated that T315 directly abrogated protein kinase B (Akt) activation by preventing translocation of Akt into lipid rafts, and induced caspase-dependent apoptosis by suppressing B-cell receptor, CD49d, CD40, and Toll-like receptor 9-mediated Akt activation in an ILK-independent manner [5]. In the present study, we examine the anticancer activity and possible underlying mechanisms of T315 against two AML cell lines and primary leukemia cells from patients with AML. In addition, the ability of T315 to inhibit leukemia growth is demonstrated in athymic nude mice bearing THP-1 xenografts. 2. Results 2.1. T315 Increases Apoptotic Cells and Reduces Viability of Acute Myeloid Leukemia (AML) Cell Lines and Primary Leukemia Cells from AML Patients The annexin-V/PI staining and the MTS assay were used to determine the effect of T315 on the viability of HL-60 and THP-1 cells which were treated with 0, 1, 2, 3 or 4 µmol/L T315 for 24 or 48 h. There was a dose-dependent increase of apoptotic cells in both HL-60 and THP-1 cells treated with T315 (Figure 1A). Figure 1B demonstrates the dose and time-dependent decrease in cell viability induced by T315. The IC50 values were 2.53 and 2.72 µmol/L at 24 h, and 2.01 and 2.90 µmol/L at 48 h for HL-60 and THP-1, respectively. In order to determine the efficacy of T315 on primary AML cell viability, freshly isolated AML cells were treated with T315 (ranging from 0, 1, 2, 4 and 8 µmol/L) and the cell viability was evaluated by annexin-V/PI staining analysis. The mean IC50 at 24 h for 26 patients was 4.2 ± 1.6 µmol/L (Figure 1C). Importantly, the normal bone marrow nucleated cells were less sensitive to T315 with an IC50 of 6 ± 1.9 µmol/L at 24 h (n = 16, Figure 1D). The IC50 of T315 for AML cells was significantly lower than the IC50 for normal marrow cells (p = 0.003). 2.2. T315 Induces Down-Regulation of Protein Kinase B (Akt) and Phosphorylated Akt in AML Cell Lines T315 has been reported as an ILK inhibitor [2]. We evaluated the influence of T315 on the expression of pThr173-ILK and total ILK, as well as proteins regulating cell proliferation and survival in AML cells (Figure 2). T315 treatment did not change the protein expression of pThr173-ILK and total ILK in either HL-60 or THP-1 cells (Figure 2A). This suggested that T315 induced cytotoxicity of AML cells through an ILK-independent manner. Although the expression of Akt did not change (Figure 2B), cells treated with T315 exhibited down regulation of both pThr308-Akt and pSer473-Akt which was in contrast with the effect of T315 on prostate and breast cancer cells [2]. There was no change in protein expression of extracellular signal–regulated kinase 1 and 2 (ERK1/2) and phosphorylated ERK1/2 after T315 treatment. 2.3. T315 Induces Apoptosis, Caspase Activation and Poly-ADP-Ribose Polymerase (PARP) Cleavage in AML Cell Lines In order to determine if PARP cleavage and caspase activation occur in T315-mediated cytotoxicity, HL-60 and THP-1 cells were incubated with T315 at 0, 1, 2 or 3 µmol/L for 24 h. Western blotting showed that T315 induced PARP cleavage and caspase-3 and caspase-7 activation in HL-60 and THP-1 cell lines in a dose-dependent manner (Figure 3A). The histogram of cleaved PARP versus β-actin, cleaved caspase-3 versus β-actin, and cleaved caspase-7 versus β-actin change folds are shown in Figure 3B (n = 3). The time course of PARP cleavage and caspase-3 activation induced by T315 is shown in Figure 3C. In order to further validate the caspase-3 activation induced by T315, HL-60 cells were incubated with T315 for 24 h with or without pretreatment of 50 µmol/L Z-Val-Ala-Asp(OMe)-fluoromethyl ketone (Z-VAD(OMe)-FMK), a pan-caspase inhibitor (Figure 3D). The increased caspase-3 activity was completely prevented by Z-VAD(OMe)-FMK treatment. 2.4. T315 Induces Autophagic Cell Death in AML Cell Lines Autophagy is a physiological process in which cellular components are degraded by lysosomal activity. Either autophagic cytoprotection or autophagic cell death has been shown to be important for the antileukemic effect of different chemotherapeutic agents [6]. Therefore, in addition to apoptosis, we investigated if autophagy was involved in T315-mediated cytotoxicity. Treatment with T315 for 24 h induced dose-dependent increases in microtubule-associated protein 1A/1B light chains 3B (LC3B)-II expression in HL-60 and THP-1 cells (Figure 4A). For comparison, histograms of fold changes of LC3B-II/glyceraldehyde 3-phosphate dehydrogenase (GAPDH) protein expression are shown in Figure 4B. Next, to see if autophagic cell death contributed to T315-mediated cytotoxicity, HL-60 and THP-1 cells were treated with dimethyl sulfoxide (DMSO) vehicle control or T315 for 24 h with or without pretreatment of 3 kinds of autophagy inhibitors, chloroquine (CQ), 3-methyladenosine (3-MA), and bafilomycin-A1, and then analyzed for apoptosis (Figure 4C–E). Although the degree of apoptosis rescue varied, all 3 autophagy inhibitors lessened the cell apoptosis induced by T315. These findings implied that autophagic cell death contributed to T315-mediated cell apoptosis. Compatible with the autophagy inhibitor-mediated rescue of cell apoptosis in flow cytometric analysis, pretreatment with bafilomycin-A1 for 1 h also lessened the PARP cleavage in AML cell lines (Figure 4F,G) and, more important, in primary AML cells (Figure 4H). In summary, T315 induced autophagic cell death, not protective autophagy, in AML cells. 2.5. T315-Mediated Cytotoxicity Is Rescued by Combination of an Apoptosis Inhibitor and an Autophagy Inhibitor In light of the generation of both apoptosis and autophagic cell death, we further examined the combinatorial effect of an apoptosis inhibitor and an autophagy inhibitor on cell death induced by T315 (Figure 5A,B). For HL-60 cells, the combination of Z-VAD(OMe)-FMK and bafilomycin-A1 rescued more cells than Z-VAD(OMe)-FMK alone (p = 0.001). However, the difference of apoptosis rescued between Z-VAD(OMe)-FMK plus bafilomycin-A1 treatment and bafilomycin-A1 alone were less significant (p = 0.414). This suggests that autophagic cell death plays a more important role than apoptosis in T315-mediated death of HL-60 cells. 2.6. T315 Slows the Growth of THP-1 Xenografts and Prolongs the Survival of Tumor-Bearing Athymic Nude Mice To investigate the anti-leukemia effect of T315 in vivo, thirteen male athymic nude mice were xenografted with THP-1 cells. Six mice in the treatment group received T315 intraperitoneally at a dose of 37.5 mg/kg per day, and seven mice in the placebo-control group received the DMSO vehicle daily. T315 had a trend to delay the growth of xenograft tumors (Figure 6A). Although mice in the T315 group had less body weight compared with those in the placebo-controlled group in the first days after initiation of treatment, the loss of body weight did not exceed the 20% endpoint criterion (Figure 6B). In terms of survival time, five mice in the placebo-treated and 4 mice in the T315-treated group reached the humane sacrifice criterion of tumor size (≥2000 mm3). Although most mice were sacrificed early due to tumor size, a T315-mediated delay in tumor growth was still evident. T315 prolonged the tumor-defined survival time by approximately eight days compared with controls, with median survival times of 20.0 ± 5.2 days and 28.0 ± 6.5 days in placebo control mice and T315 treated mice, respectively (Figure 6C, p = 0.373). 3. Discussion We have described here the anticancer activity of an ILK inhibitor, T315, in both AML cell lines and primary AML cells. The T315-mediated decrease in cell viability is through both apoptosis and autophagic cell death. Akt and p308-Akt are also down-regulated. In addition, the tumor inhibitory effect of T315 is demonstrated in a THP-1 xenograft mouse model. Autophagy is a cellular process in which intracellular components are engulfed, digested and recycled via the formation of autophagosomes and autolysosomes, important for cell survival under stress and harmful conditions [6]. This anti-apoptosis function of autophagy has important biological and pathological implications including ischemic injury, cancer therapy and chemoresistance [7]. In the context of cancer, this protective role of autophagy may actually promote tumor survival in a cellular environment of inadequate nutrition or during therapy. Over periods of prolonged stress or poor nutrition, however, autophagy may signal cell death by apoptosis when a cell can no longer survive by recycling organelles. Therefore, the process of autophagy is a double-edged sword in cancer and can either facilitate cancer cell survival or promote cell death depending on other internal and exernal stimuli [8,9]. Cell death mediated by autophagy, referred to as autophagic cell death or type II cell death, has been induced by cancer therapies and was thought to contribute to the death of leukemia [10,11], malignant glioma [12] and lung cancer cells [13]. Indeed, methods used to interfere with the autophagic cell death in these studies rescued the treated cells. Our study shows that T315 induces autophagic cell death but not protective autophagy in AML cells. Regarding the induction of both apoptosis and autophagic cell death in the present study, it is interesting to note that pretreatment with different autophagy inhibitor rescues both HL-60 and THP-1 cells from apoptosis. This observation implies crosstalk between these modes of cell death rather than 2 independent pathways in AML cells (Figure 4). However, the interplay of apoptosis and autophagic cell death remains undefined. Various studies have demonstrated an overlap in the regulatory machinery for apoptosis and autophagic cell death. Beclin 1, for example is a protein required for autophagy and also belongs to an apoptosis-requlating domain of proteins. Stressed cells can undergo autophagy induced by Beclin 1 or can undergo apoptosis [8]. Caspase-mediated Beclin 1 cleavage and Beclin 1-Bcl-2 interaction are 2 examples of nodes of crosstalk between autophagy and apoptosis reviewed by Su et al. [9]. One of the most studied and characterized molecular regulator of autophagy and apoptosis is p53 localization [7]. It has been reported that cytoplasmic p53 inhibits autophagy and induces apoptosis while nuclear localization of p53 stimulates both apoptosis and autophagy via the transactivation of target genes [14,15]. In our experiment, the rescue of cell apoptosis by autophagy inhibitors also suggests a crosstalk between autophagy and apoptosis (Figure 4). Our data provides evidence that autophagic cell death and apoptosis can act cooperatively to achieve a cell killing effect. Further mechanistic studies are needed to better characterize the crosstalk between autophagy and apoptosis in AML. Although our results show a convincing antileukemia effect of T315 in vitro and in vivo, some limitations of our study are noteworthy. First, while the combination of Z-VAD(OMe)-FMK and an autophagy inhibitor resulted in greater rescue from apoptosis induced by T315, the rescue was not complete. This partial rescue of apoptosis in AML cells by both Z-VAD(OMe)-FMK and autophagy inhibitors suggests the existence of other mechanisms of T315-mediated cell death in AML cells. Second, even though T315 delayed the growth of THP-1 xenografts compared to the vehicle control group, most animals in both groups were sacrificed early due to the tumor size reaching the pre-set size criterion. Further modification of T315 to improve the efficacy and to reduce the toxicity is necessary for clinical application. In conclusion, T315 exhibits a potent antileukemia effect in both AML cell lines and primary AML cells with cell death mediated, at least in part, through generation of apoptosis and autophagic cell death. In vivo, T315 inhibits AML xenograft tumor growth. Collectively, this study provides additional clarity to the anticancer activity of T315 that will be useful in furthering its development for the treatment of AML and possibly other hematological malignancies. 4. Materials and Methods 4.1. Cells and Culture Conditions Primary AML cells were isolated from freshly collected bone marrow using Ficoll-PaqueTM PLUS (GE Healthcare Bio-Sciences AB, Uppsala, Sweden) according to the manufacturer’s instructions if the leukemia cells accounted for more than 90% of non-erythroid mononucleated cells of bone marrow. Normal bone marrow nucleated cells were harvested using Ficoll-PaqueTM PLUS from patients with treatment-naive non-Hodgkin’s lymphoma for whom bone marrow examination for lymphoma staging was performed but determined to be normal. All bone marrow samples were obtained under a protocol approved by the China Medical University Hospital internal review board (CMUH102-REC1-124 issued on 26 May 2014). Written informed consent was obtained from all patients in accordance with the Declaration of Helsinki. Human AML cell lines HL-60 (ATCC CCL-240) and THP-1 (ATCC TIB-202) were from American Type Culture Collection (ATCC, Manassas, VA, USA). All cells were incubated in RPMI-1640 media (Invitrogen, Carlsbad, CA, USA) supplemented with 10% heat-inactivated fetal bovine serum (FBS; Invitrogen) and penicillin (100 U/mL)/streptomycin (100 µg/mL) (Invitrogen) at 37 °C in the presence of 5% CO2. 4.2. Reagents T315 {N-methyl-3-(1-(4-(piperazin-1-yl)phenyl)-5-(4′-(trifluoromethyl)-[1,1′-biphenyl]-4-yl)-1H-pyrazol-3-yl)propanamide} was synthesized as previously described [2], with identity and purity (≥99%) verified by proton nuclear magnetic resonance, high-resolution mass spectrometry, and elemental analysis. For in vitro experiments, T315 was dissolved in dimethyl sulfoxide (DMSO), and added to the culture medium with a final DMSO concentration less than 0.1%. The pharmacological agents were purchased from the respective vendors: bafilomycin-A1 (Cayman Chemical, Ann Arbor, MI, USA); chloroquine (Sigma-Aldrich, St. Louis, MO, USA); 3-methyladenine (3-MA; Sigma-Aldrich); Z-VAD(OMe)-FMK (Santa Cruz Biotechnology, Santa Cruz, CA, USA). 4.3. MTS Assay Measurement of cell growth was performed using CellTiter 96 Aqueous Non-radioactive Cell Proliferation Assay kit purchased from Promega (Madison, WI, USA). Cells (0.25 × 106/mL) were placed in 200 µL volume in 96-well microtiter plates with the indicated reagent and incubated at 37 °C [16]. MTS solution [3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium] and PMS (phenazine methosulfate) solution were mixed 20:1 by volume. The colorimetric measurements were performed 4 h later at 490-nm wavelength by a VersaMax tunable microplate reader (Molecular Devices, Sunnyvale, CA, USA). The cell viability was expressed as a percentage of absorbance value in treated samples compared to that observed in control vehicle-treated samples (subtract the blank in both conditions). 4.4. Cell Viability and Apoptosis Assay by Flow Cytometry Cell viability was assessed by dual staining with annexin V conjugated to fluorescein isothiocyanate (FITC) and propidium iodide (PI) [17]. Cells (0.5 × 106) were stained by annexin V-FITC (BD Pharmingen, San Diego, CA, USA) and PI (BD Pharmingen) according to the manufacturer’s instructions. Cells were analyzed by a flow cytometer BD FACSCanto II (BD, Franklin Lakes, NJ, USA). Viable cells were those with both annexin V-FITC negative and PI negative staining. The viable cells in each sample were expressed as % by normalizing annexin V-/PI- cells to control. Annexin V-FITC positive cells were identified as apoptotic cells [18]. 4.5. Western Blotting Cell lysates were prepared using RIPA buffer (150 mmol/L NaCl, 50 mmol/L Tris pH 8.0, 1% NP40, 0.5% sodium deoxycholate and 0.1% sodium dodecyl sulfate) supplemented with protease inhibitor (Sigma-Aldrich) and phosphatase inhibitor cocktail (Calbiochem, Darmstadt, Germany) [19]. Antibodies against various proteins were obtained from the following sources: poly-ADP-ribose polymerase (PARP), pThr308-Akt, pSer473-Akt, cleaved caspase-3, LC3B, cleaved caspase-7 (Cell Signaling, Danvers, MA, USA); Akt, ERK1/2, pThr202Tyr204-ERK1/2, GAPDH, ILK, pThr173-ILK (Santa Cruz Biotechnology); β-actin (Sigma-Aldrich). The goat anti-rabbit IgG-horseradish peroxidase (HRP) conjugates and goat anti-mouse IgG-HRP conjugates were purchased from Jackson ImmunoResearch Laboratories, Inc. (West Grove, PA, USA). 4.6. Analysis of Caspase-3 Activity Caspase-3 activity was assessed using a FITC rabbit anti-active caspase-3 kit (BD Pharmingen) according the manufacturer’s protocol. 4.7. In Vivo Therapeutic Efficacy Evaluation of T315 in the THP-1 Xenograft Model The in vivo efficacy evaluation of T315 was carried out using a xenograft model in athymic nude mice [16]. Thirteen male nude mice of 5 to 7 weeks of age were obtained from the National Laboratory Animal Center (Taipei, Taiwan). The mice were housed under conditions of constant photoperiod (12 h light and 12 h dark) with ad libitum access to sterilized food and water. THP-1 cells were cultured in RPMI-1640 supplemented with 10% heat-inactivated FBS. Before inoculation, THP-1 cells were washed with PBS twice and resuspended in a mixture of RPMI-1640 and Matrigel (BD MatrigelTM Basement Membrane Matrix; BD) with a 1:1 volume ratio. Each mouse was inoculated over the flank subcutaneously with 1 × 107 THP-1 cells in a total volume of 0.2 mL. Tumor diameter was measured every three days using calipers and the tumor volume was calculated using a standard formula: width2 × length × 0.52. Body weights of the mice were measured every three days. When the mean tumor volume had reached 50 mm3, mice were randomized to two groups (seven mice and six mice in placebo-control group and treatment group, respectively). The mice in the treatment group received T315 (concentration 18.75 mg/mL = 35.14 mmol/L) intraperitoneally once daily at a dose of 37.5 mg/kg per day (for example, volume injected is 50 µL for a mouse weighting 25 g), and the mice in the placebo-control group received the DMSO vehicle. All mice received treatments daily until reaching the endpoint. Humane endpoint criteria included body weight loss more than 20% or tumor size more than 2000 mm3. Scheduled terminal sacrifice for surviving mice occurred on day 35 after initiation of T315 or placebo. The in vivo experiment protocol was approved by the Institutional Animal Care and Use Committee of China Medical University (Taichung, Taiwan, IACUC Approval no.: 104-87-N, period of protocol valid from 1 August 2015 to 31 July 2017). 4.8. Statistical Analysis Nonlinear mixed models were used to obtain IC50. Two-tailed unpaired t-test was used for comparisons of two sets of data. Kaplan-Meier overall survival curve of mice was analyzed using log rank test. All statistical analysis was performed with SPSS for Windows (SPSS, Inc., Chicago, IL, USA). Acknowledgments We thank Tse-Yen Yang of China Medical University Hospital for the statistical assistance. This work was supported in part by grants from the Ministry of Health and Welfare, China Medical University Hospital Cancer Research Center of Excellence (MOHW105-TDU-B-212-134003), Ministry of Science and Technology, R.O.C. (MOST 103-2314-B-039-022, MOST 104-2314-B-039-049), China Medical University (CMU104-S-33) and China Medical University Hospital (DMR-105-018, DMR-105-141). Author Contributions Chang-Fang Chiu, Jing-Ru Weng and Li-Yuan Bai designed the research study; Chang-Fang Chiu, Appaso Jadhav and Chia-Yung Wu performed the study; Aaron M. Sargeant and Li-Yuan Bai performed data analysis; Jing-Ru Weng, Aaron M. Sargeant and Li-Yuan Bai prepared the manuscript; all authors approved the final version of the manuscript. Conflicts of Interest The authors declare no conflict of interest. Abbreviations AML acute myeloid leukemia DMSO dimethyl sulfoxide FITC fluorescein isothiocyanate HRP horseradish peroxidase ILK integrin-linked kinase PARP poly-ADP-ribose polymerase PI propidium iodide Figure 1 Cell viability inhibition study of T315 in acute myeloid leukemia (AML) cell lines, primary AML cells and normal marrow cells. (A) HL-60 and THP-1 cells (0.25 × 106 cells/mL) were incubated with T315 or dimethyl sulfoxide (DMSO) vehicle for 24 h. The apoptotic cells were analyzed by annexin V-FITC and propidium iodide (PI) staining, as described in Materials and Methods. Upper panel: one example; Lower panel: apoptotic cell percentage (n = 3); (B) HL-60 and THP-1 cells (0.25 × 106 cells/mL) were incubated with T315 or DMSO vehicle for 24 h () or 48 h (□). The cells were analyzed by MTS assay, as described in Materials and Methods; (C) Primary AML cells (0.25 × 106 cells/mL) were incubated with T315 or DMSO for 24 h. The cells were stained with annexin V-FITC and PI to assess apoptotic cells percentage (n = 26); (D) Normal bone marrow nucleated cells (0.25 × 106 cells/mL) were incubated with T315 or DMSO for 24 h. The cells were stained with annexin V-FITC and PI to assess apoptotic cells percentage (n = 16). * denotes p < 0.05; ** denotes p < 0.01 compared to the control group (in panel A) or compared to the primary AML cells at the same concentration of T315 (in panel D). Figure 2 T315 induces dephosphorylation of protein kinase B (Akt) without change of integrin-linked kinase (ILK) in AML cell lines. Cells (0.25 × 106 cells/mL) were treated with T315 at the indicated concentration or DMSO for 24 h, and 20 µg protein extract from cell lysates in each condition were used for Western blot analysis. (A) T315 did not change the pThr173-ILK and total ILK expression. Histogram of fold change of pThr173-ILK/ILK and ILK/glyceraldehyde 3-phosphate dehydrogenase (GAPDH) were shown in lower panels (n = 3); (B) T315 down regulated both pThr308-Akt and pSer473-Akt, but not Akt, p-ERK and ERK expression. Histogram of fold change of pThr308-Akt/Akt, pSer473-Akt/Akt, Akt/GAPDH, p-ERK/ERK, and ERK/GAPDH were shown in lower panels (n = 3). * denotes p < 0.05; ** denotes p < 0.01 compared to the control group. Figure 3 T315-mediated cytotoxicity is dependent on caspase activation and apoptosis. (A) T315 induced poly-ADP-ribose polymerase (PARP) cleavage and activation of caspase-3 and caspase-7 in HL-60 and THP-1 cells at 24 h. Protein extract of 20 µg from cell lysates were used for Western blot analysis; (B) Fold change of cleaved PARP/β-actin, cleaved caspase-3/β-actin, and cleaved caspase-7/β-actin in treatment with T315 of 1, 2 or 3 µM compared with DMSO control (n = 3); (C) Time course change of PARP cleavage and caspase-3 activation induced by T315 of 2 µM or DMSO control; (D) The increased caspase-3 activity in HL-60 cells treated with T315 for 24 h was rescued by pretreatment of 50 µmol/L Z-Val-Ala-Asp(OMe)-fluoromethyl ketone (Z-VAD(OMe)-FMK). Figure 4 T315 induces autophagic cell death but not protective autophagy in AML cells. (A) T315 induced upregulation of LC3B-II in HL-60 and THP-1 cells. Cells (0.25 × 106 cells/mL) were treated with indicated concentrations of T315 for 24 h. 20 µg protein from cell lysates were used for Western blot analysis; (B) Histogram of fold change of LC3B-II/GAPDH protein expression in cells treated with T315 for 24 h (n = 3); (C) T315-induced apoptosis was partially rescued by chloroquine (CQ), an autophagy inhibitor. Cells were treated with DMSO vehicle or T315 for 24 h with or without pretreatment of CQ for 1 h, and then analyzed by a flow cytometer. (n = 3 for HL-60 and n = 4 for THP-1 cells); (D) T315-induced apoptosis was partially rescued by 3-methyladenosine (3-MA), an autophagy inhibitor. Cells were treated with DMSO vehicle or T315 for 24 h with or without pretreatment of 3-MA for 1 h, and then analyzed by a flow cytometer. (n = 4 for HL-60 and n = 5 for THP-1 cells); (E) T315-induced apoptosis was partially rescued by bafilomycin-A1 (Baf), an autophagy inhibitor. Cells were treated with DMSO vehicle or T315 for 24 h with or without pretreatment of Baf for 1 h, and then analyzed by a flow cytometer. (n = 5 for HL-60 and n = 5 for THP-1 cells); (F) T315-induced PARP cleavage was partially rescued by Baf. Cells were treated with DMSO vehicle or T315 for 24 h with or without pretreatment of Baf for 1 h, and then analyzed by Western blotting; (G) Histogram of fold change of cleaved PARP/β-actin protein expression in cells treated with T315 with or without pretreatment of Baf for 1 h (n = 3); (H) T315-induced PARP cleavage in primary AML cells was partially rescued by Baf. Primary AML cells were treated with DMSO vehicle or T315 for 24 h with or without pretreatment of Baf for 1 h, and then analyzed by Western blotting (two patients’ data shown here). Figure 5 T315-mediated cytotoxicity is rescued by combination of an apoptosis inhibitor and an autophagy inhibitor. Cells were treated with DMSO vehicle or T315 for 24 h with or without pretreatment of Z-VAD(OMe)-FMK and/or bafilomycin-A1 (Baf) for 1 h, and then analyzed by a flow cytometer. (A) For HL-60 cells (n = 5); (B) For THP-1 cells (n = 5). Figure 6 T315 mitigates the growth of THP-1 xenografts and prolongs the survival of tumor-bearing athymic nude mice. (A) Mice bearing THP-1 xenografts were treated with DMSO vehicle (······, n = 7) or T315 (――, n = 6) at 37.5 mg/kg/day intraperitoneally. The data represent group means and were plotted until day 10 when one mouse in the control group reached the endpoint tumor size (≥2000 mm3) and was sacrificed; (B) Body weight change of mice. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081338ijms-17-01338ArticleMicroRNAs and Drinking: Association between the Pre-miR-27a rs895819 Polymorphism and Alcohol Consumption in a Mediterranean Population Barragán Rocío 12Coltell Oscar 23Asensio Eva M. 12Francés Francesc 12Sorlí José V. 12Estruch Ramon 24Salas-Huetos Albert 25Ordovas Jose M. 678†Corella Dolores 12*†Taguchi Y-h. Academic Editor1 Department of Preventive Medicine and Public Health, School of Medicine, University of Valencia, Valencia 46010, Spain; rocio.barragan@uv.es (R.B.); eva.m.asensio@uv.es (E.M.A.); francesc.frances@uv.es (F.F.); jose.sorli@uv.es (J.V.S.)2 CIBER Fisiopatología de la Obesidad y Nutrición, Instituto de Salud Carlos III, Madrid 28029, Spain; oscar.coltell@uji.es (O.C.); RESTRUCH@clinic.cat (R.E.); albert.salas@uab.cat (A.S.-H.)3 Department of Computer Languages and Systems, School of Technology and Experimental Sciences, Universitat Jaume I, Castellón 12071, Spain4 Department of Internal Medicine, Hospital Clinic, IDIBAPS, Barcelona 08036, Spain5 Human Nutrition Unit, Biochemistry and Biotechnology Department, IISPV, University Rovira i Virgili, Reus 43003, Spain6 Department of Cardiovascular Epidemiology and Population Genetics, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid 28029, Spain; jose.ordovas@tufts.edu7 IMDEA Alimentación, Madrid 28049, Spain8 Nutrition and Genomics Laboratory, JM-USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA 02111, USA* Correspondence: dolores.corella@uv.es; Tel.: +34-963-864-800† These authors contributed equally to the work. 16 8 2016 8 2016 17 8 133816 6 2016 10 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Recently, microRNAs (miRNA) have been proposed as regulators in the different processes involved in alcohol intake, and differences have been found in the miRNA expression profile in alcoholics. However, no study has focused on analyzing polymorphisms in genes encoding miRNAs and daily alcohol consumption at the population level. Our aim was to investigate the association between a functional polymorphism in the pre-miR-27a (rs895819 A>G) gene and alcohol consumption in an elderly population. We undertook a cross-sectional study of PREvención con DIeta MEDiterránea (PREDIMED)-Valencia participants (n = 1007, including men and women aged 67 ± 7 years) and measured their alcohol consumption (total and alcoholic beverages) through a validated questionnaire. We found a strong association between the pre-miR-27a polymorphism and total alcohol intake, this being higher in GG subjects (5.2 ± 0.4 in AA, 5.9 ± 0.5 in AG and 9.1 ± 1.8 g/day in GG; padjusted = 0.019). We also found a statistically-significant association of the pre-miR-27a polymorphism with the risk of having a high alcohol intake (>2 drinks/day in men and >1 in women): 5.9% in AA versus 17.5% in GG; padjusted < 0.001. In the sensitivity analysis, this association was homogeneous for sex, obesity and Mediterranean diet adherence. In conclusion, we report for the first time a significant association between a miRNA polymorphism (rs895819) and daily alcohol consumption. microRNAsalcoholmiR27aMediterranean ==== Body 1. Introduction In recent years, multiple and important regulatory functions have been attributed to microRNAs (mirRNA) [1]. It is known that miRNAs, small noncoding RNAs, whose final product is a ~22-nucleotide functional RNA molecule that regulates gene expression, play an important role in processes, such as controlling oxidative stress, the development, progression and metastasis of cancer, influence the processes of atherosclerosis, cardiovascular diseases, obesity and diabetes and control senescence and many other key processes [1,2,3,4,5]. Although it has been suggested that the miRNA may also play an important role in influencing food and beverage intake [6,7,8], there have been far fewer studies on this, and further studies that delve deeper into this issue are required. Among the different foods and beverages, where there is indeed more data suggesting an important regulation by microRNAs, is alcohol intake [8]. The association between moderate alcohol consumption and health is a matter of on-going debate in the scientific community [1,3,9,10]. Likewise, there is a debate over the factors that influence alcohol intake, these being attributed both to environmental factors (socioeconomic level, one’s relatives’ alcohol consumption behavior, one’s social network, social myths and violence, among others) and genetic (variations in candidate genes), without their contribution having been clearly quantified [7,8,11]. Among the genetic factors, as is the case with environmental factors, there appears to be a huge complexity of influences that are still not well understood [7,8,11,12,13,14,15]. Although the first studies on the influence of alcohol intake specifically analyzed genetic variants in candidate genes related to the different pathways on which alcohol acts or is metabolized (mainly in polymorphisms in genes related to alcohol-metabolizing enzymes, including: alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH), where important associations have been found) [11,12], it is known that there are many other factors to be investigated. Indeed, a new line of research into the genetic-epigenetic factors that may have an influence on alcohol intake involving regulation by miRNAs is emerging [8,16]. miRNAs are highly abundant in the brain and play significant roles in several biological processes [17]. Accordingly, it has been reported that miRNAs also seem to mediate the cellular adaptations induced by exposure to some drugs of abuse [18], including cocaine [19], opioids [20] and alcohol [21,22,23]. Moreover, Gedik et al. [24] hypothesized that single nucleotide polymorphisms (SNPs) in the miRNA biogenesis pathway may result in dysregulation of miRNA levels and association with alcohol dependence. Consequently, they found statistically-significant associations between several SNPs in the miRNA biogenesis and alcohol dependence when alcohol-dependent patients were compared to healthy controls. The SNPs analyzed were: rs595961, rs4961280, rs910924 and rs1640299 in the genes AGO1 (Argonaute 1), AGO2 (Argonaute 2), GEMIN4 (gem nuclear organelle-associated protein 4), DGCR8 (DiGeorge syndrome critical region 8 complex subunit), respectively; adding more evidence to the role of microRNAs in alcohol intake. However, although there have been various studies that have analyzed the differences of miRNA expression associated with differing conditions of alcohol consumption, statistically-significant differences being found between the micro-RNA profile of alcoholics compared to non-alcoholics [19,21,22,23,25,26,27,28], there are hardly any that have investigated the association between SNPs in genes that encode miRNAs and alcohol intake [29]. miRNA genes are transcribed and processed initially into precursor miRNAs (pre-miRNAs). The pre-miRNAs are further processed into mature miRNAs. SNPs in the pre-miRNA genes could affect the processing and subsequent maturing of miRNAs. Interestingly, it has been reported that the occurrence of SNPs in miRNA sequences is relatively rare [30], suggesting that variation in the miRNA sequence might be functionally important. There are various miRNA candidates for which the study of the effects of their sequence SNPs on alcohol intake would be interesting. We first focused our attention on the pre-miR-27a rs895819 A>G polymorphism, located on the terminal loop of the miR-27a, because some recent studies have related this miRNA with alcohol modulation in different processes [31,32], as well as with the behavioral response to chronic opioid administration [33]. Moreover, there are previous studies that have shown that that polymorphism is functional, an increased miR-27a expression being detected in G-allele carriers compared to AA [26,27,34,35]. In parallel, this polymorphism has been associated with increased risk of some cancers related to alcohol consumption (gastric, colorectal, etc.) G-allele carriers [26,27,28,34,35,36]. Although a recent study has found that the plasma of alcoholic patients has an increased number of extracellular vesicles that contained high levels of miR-27a compared to healthy controls [31], no study has investigated the association between the pre-miR-27a rs895819 SNP and alcohol consumption. What is more, as far as we know, there has been only one study published to date that has examined the association between a miRNA SNP and alcohol [29], but this analyzed the prevalence of alcohol-related disorders without investigating habitual alcohol intake on the population level. Thus, given this lack of studies, our aim was to analyze the association between the pre-miR-27a rs895819 polymorphism and total alcohol intake, as well as the different alcoholic drinks consumed (wine, beer, spirits) in a well-characterized elderly Mediterranean population, recruited in one of the centers participating in the PREDIMED (PREvención con DIeta MEDiterránea) study [37]. 2. Results The participants analyzed in this study were all recruited at the PREDIMED-Valencia field center [38], one of the centers participating in the PREDIMED multicenter study [37]. The PREDIMED-Valencia study is where most patients have been recruited and randomized (n = 1094) for the PREDIMED study. Figure S1 presents a flow chart of the study participants. We analyzed 1007 men and women who had valid genotypes for the pre-miR-27a rs895819 A>G polymorphism and their alcohol consumption determined. Table 1 provides an overview of the population distribution of the demographic, clinical, biochemical and lifestyle characteristics of the 1007 participants according to the pre-miR-27a rs895819 A>G polymorphism. Prevalence of the genotypes was 53.6% AA (n = 540); 37.8% AG (n = 381) and 8.5% GG (n = 86), similar to the prevalence expected for European populations. The mean age of the study participants (mean ± SE) was 66.8 ± 0.2 years and did not differ among the pre-miR-27a rs895819 genotypes. There were also no statistically-significant differences in sex, type 2 diabetes prevalence, weight-related variables, blood pressure, lipid levels, fasting glucose, smoking, physical activity, total energy intake, macronutrients (fat, carbohydrates and proteins) or adherence to the Mediterranean diet (MedDiet) between the genotypes, so minimizing the bias that these factors may have on the association between the SNP and alcohol consumption. 2.1. Association between the Pre-miR-27a rs895819 Polymorphism and Total Alcohol Consumption and Types of Alcoholic Beverages We analyzed the association between the pre-miR-27a rs895819 polymorphism and total alcohol intake, as well as with alcoholic beverages in the population as a whole. We measured the intake of alcoholic beverages (different types of wine, beer and spirits) by a validated [39] food frequency questionnaire (FFQ) as detailed in the Methods. In this FFQ, there were questions about the average intake for each beverage over the previous year, including the baseline visit, which is when the questionnaire was administered. Alcohol intake (g/day) was calculated by multiplying the amount of the corresponding beverage in the FFQ (mL/day) by the respective alcohol content (see the Methods in Section 4.2). The sum of all of that is the total amount of alcohol in g/day consumed for each person. Abstainers were those individuals for whom the sum of alcohol consumption was zero grams per day. For the analysis of specific beverages, total wine, total beer and total spirits were first considered (see the Methods in Section 4.2). That does not imply that a person who drinks beer does not also drink wine or spirits. When the association between the pre-miR-27a rs895819 polymorphism with total alcohol intake and alcoholic beverages was analyzed (Table 2), we obtained statistically-significant results. Total alcohol consumption was considered as a continuous variable, and men and women were analyzed together. The average alcohol consumption (and standard error) for the whole population was 5.8 (0.3) g/day. Taking into account that the analyzed population was an elderly population not including alcoholics (see the Methods in Section 4.1), the mean intake of alcoholic beverages was relatively low. We observed a statistically-significant association between the pre-miR-27a rs895819 polymorphism and total alcohol intake (g/day). Total alcohol consumption in carriers of the variant G-allele was higher than in the other genotypes: 5.2 (0.3) g/day in AA; 5.9 (0.5) g/day in AG and 9.1 (1.8) g/day in GG, p = 0.020 in the unadjusted general linear model (GLM) for a linear trend. This association remained statistically significant even after multivariable adjustment for sex, age, type 2 diabetes, hypertension, dyslipidemia, obesity, smoking, physical activity and total energy intake (p = 0.016) in the adjusted model (GLM). Alcohol intake had a skewed distribution and required normalization for statistical testing. Then, although means of alcohol intake were shown as untransformed variables, p-values were computed using the square root-transformed variables for total alcohol intake, as well as for alcoholic beverages, to improve normality. The association of the pre-miR-27a rs895819 polymorphism with alcohol intake was especially relevant in the subgroup of drinking males (Figure 1). Supplementary Figure S2 (Figure S2) shows the means (and SE) of alcohol consumption in males (A) and in females (B) (both including drinkers and non-drinkers), depending on the pre-miR-27a rs895819 polymorphism. Similar associations were found. Although the association between the pre-miR-27a rs895819 polymorphism and alcohol intake was tested in the codominant model (including the three genotypes), homozygous subjects for the variant allele (GG) presented higher means of alcohol consumption, supporting a recessive effect. This recessive effect was also confirmed later in the categorical analysis of drinking categories. When alcoholic beverages were analyzed, we detected higher consumptions for wine (including red and white wine), beer and spirits (whisky, vodka, gin, rum, liquors, etc.) in GG subjects from the whole population, suggesting non-specificity for the pre-miR-27a rs895819 association with alcoholic beverages. However, differences among genotypes in the multivariable adjusted models only remained statistically significant for wine (p = 0.043). For beer, although in the unadjusted model, statistically-significant differences were detected (p = 0.041), this association did not reach the significance level after the multivariable adjustment. The association with spirits had a similar trend, but did not reach statistical significance. Therefore, factors related to statistical power may explain the difference in the statistical significance of the association between the pre-miR-27a rs895819 polymorphism and wine or beer, rather than arriving at the conclusion of a specific association of the SNP with wine intake. Thus, when we separately analyzed red wine and white wine (Supplementary Figure S3), we observed the same trend for the association (higher intake of red wine or of white wine in GG subjects), but the p-values were borderline significant for those wines. 2.2. Association between the Pre-miR-27a rs895819 Polymorphism and Drinking Categories To minimize the influence of dealing with the potential limitations of the continuous variables of alcohol consumption, we subsequently considered categorical variables for alcohol intake. Thus, three groups of alcohol consumption were defined according to the reported daily intake of alcohol and sex-specific cut-off points defined at the international level [40,41,42]. This classification has been previously used by us in PREDIMED [42,43]. The categories were as follows (see the Methods for details): (1) no intake (0 g/day); (2) moderate alcohol intake (≤26.4 g/day for men and ≤13.2 g/day for women); and (3) high intake (>26.4 g/day for men and >13.2 g/day for women), corresponding to one “typical drink” (12 g of ethanol)/day for women and two drinks/day for men (i.e., exceeding recommended daily moderate drinking limits) [40,41,42,43,44,45]. In this population, 55.2% of women and 23.1% of men were non-drinkers. Conversely, 3.6% of women and 14.8% of men (7.5% of the population) consumed more than the sex-specific moderate recommendation (classified as having a high intake). We found a strong association between the pre-miR-27a rs895819 polymorphism and these categories of drinking (Table 3). For the whole population, the p-value for the association between the polymorphism and drinking categories was statistically significant (p = 0.005). Considering the population as a whole, the prevalence of a high alcohol intake was 17.5% in subjects with the GG genotype, whereas it was only 5.9% in AA subjects (p < 0.05). This association was seen both in men (30.0% of high drinkers among GG subjects versus 11.4% among AA subjects; p = 0.024) and in women (10.7% of high drinkers among GG subjects versus 2.7% among AA subjects; p = 0.010). Non-drinkers and moderate-drinkers were further grouped, and we estimated the association between the polymorphism and high alcohol consumption. In Table 4 we estimated the risk (by calculating the odds ratio (OR) and the 95% CI) of being a high alcohol drinker depending on the pre-miR-27a rs895819 polymorphism in the whole population and in men and women separately, after adjustment for potential confounders (see the Methods in Section 4.4). The AA genotype was considered the reference category, and we estimated the OR of being a high alcohol drinker (versus moderate and non-drinkers grouped) for the AG genotype and for the GG genotype. We found statistically-significant and highly consistent results in both men and women. For the population as a whole, subjects with the GG genotype were more likely than subjects with the AA genotype (OR: 3.84; 95% CI: 1.83–8.04, p < 0.001) to be high alcohol drinkers even after multivariate adjustment for potential confounders. This is a strong association considering the magnitude of the OR. Moreover, in women, we see approximately the same pattern of results as in men. For both, the associations seem to follow a recessive model as no statistically-significant differences were detected in AG individuals. Therefore, we merged AA + AG subjects in the same category for further sensitivity analyses. 2.3. Sensitivity Analysis of the Association between the Pre-miR-27a rs895819 Polymorphism and Drinking Finally, we also performed a sensitivity analysis in order to estimate the magnitude of the association between the pre-miR-27a rs895819 polymorphism and drinking in relevant subgroups (sex, obesity, adherence to the Mediterranean diet (MedDiet), type 2 diabetes and hypertension) to analyze the homogeneity or heterogeneity of the associations. For this analysis, we used a dichotomous variable both for drinking (high drinker versus moderate + non-drinker) and for the polymorphism (AA + AG versus GG). We also estimated the interaction terms between the corresponding subgroup analyzed (sex, obesity, etc.) and the pre-miR-27a rs895819 polymorphism in determining the risk of being a high drinker to test the statistical significance of the heterogeneity of the associations in the corresponding strata. Table 5 shows sensitivity analysis estimations by sex, obesity, adherence to the MedDiet, type 2 diabetes and hypertension. We have detected a highly homogeneous effect (p for interactions >0.05 for all of the variables considered) in the association between the pre-miR-27a rs895819 polymorphism and alcohol drinking. The highest homogeneity was detected for obesity status in such a way that subjects with the GG genotype were more likely than subjects with the (AA + AG) genotype of being a high drinker, nearly with the same magnitude in both non-obese (OR: 3.31; 95% CI: 1.34–8.18, p = 0.01) and in obese subjects (OR: 3.87; 95% CI: 1.21–12.35, p = 0.022). Conversely, higher heterogeneity per type 2 diabetes status was observed (although without the interaction term being statistically significant), in such a way that the association between the pre-miR-27a polymorphism and high alcohol intake was attenuated in type 2 diabetic patients. 3. Discussion This study has found a strong association between the functional polymorphism (rs895819) in the pre-miR-27a gene, consisting of a change from A>G, and total alcohol intake in an elderly Mediterranean population. Although several previous studies on humans have reported an influence of the miRNAs on alcohol consumption [16,21,24,27,28,29,46,47], those studies have mainly focused on measuring the expression of certain miRNAs in different tissues rather than on analyzing the influence of genetic polymorphisms in the genes that encode the miRNAs [29]. Pioneering studies measured the differential expression of the miRNA profile in the brain of alcoholics and compared this with the miRNA profiles in non-alcoholic controls, finding significant differences in the expression of various miRNAs [27,28], so suggesting an important regulatory role of the miRNAs in alcohol consumption. Specifically, Lewohl et al. [27], in 2011, undertook a study to analyze the differences in the profile of miRNA expression in the frontal cortex of 14 alcoholics and 13 age- and sex-matched controls. The alcoholics consumed more than 80 g of ethanol per day for most of their adult lives. Controls were defined as low alcohol consumption individuals (less than 20 g per day on average). They found significant differences in approximately 48 miRNAs. All were upregulated in the frontal cortex of alcoholics with a fold change of between 16% and 72%. The five miRNAs that showed the greatest differences of expression in the frontal cortexes between alcoholics and non-alcoholics were: miR-553, miR-369-3p, miR-18a, miR-339-5p and miR-1. The miR27a, which we have focused on in this study, did not appear in the list of the 48 differentially-expressed miRNAs. However, in our study, we are comparing a moderate alcohol intake, and Lewohl et al. [27] analyzed the effect of large amounts of ethanol. The authors concluded that the miRNAs could play an important role in the development of alcohol-related changes in the human brain, suggesting that the upregulation of miRNAs in the frontal cortex of human alcoholics may contribute to the deterioration and concomitant adaptation of neuronal functioning observed in individuals who abuse alcohol. Later, Manzardo et al. [28] analyzed the differences of expression of the miRNAs isolated from the frontal cortex of nine alcoholics and nine matched controls. The authors also found statistical differences in the profile of miRNA expressed in cases and controls. However, although they found several upregulated miRNAs in alcoholics, outstanding among which were the miR-375, miR-29b, miR-377 and miR-379, the top-ranked miRNA did not overlap between both studies [27]. Despite the very important preliminary information that these studies provide us with, a comparison between these and later studies is not always easy, as different controls and different arrays are used. Additionally, in both studies, the controls were not abstemious individuals, but with moderate alcohol consumption that perhaps does not separate the differences well enough. Later studies have focused on the profiles of miRNAs circulating in plasma/serum as biomarkers of alcohol intake [28,47], also using different inclusion criteria for the analyzed individuals. Here again, the results are not very consistent, so emphasizing the need for greater standardization in defining cases, controls, the array used and a more direct measurement of the amount of alcohol consumed. Faced with the current difficulties due to the lack of standardization when measuring miRNA expression, the results of which can also be different depending on the method and the tissue used for the measurement (brain, blood, etc.), the analysis of polymorphisms in genes encoding miRNAs is another interesting approach to investigating the role of miRNAs on alcohol consumption. This approach could be much more reproducible, given that the presence or absence of an SNP in the genes encoding an miRNA does not change whether the DNA comes from leukocytes or any other type of cell. Moreover, it is known that a single miRNA can target hundreds of mRNA transcripts for either translation repression or degradation, and the detection of a polymorphism in a gene encoding a particular miRNA can affect many mRNAs and have a great influence [48,49]; such as an intermediate hairpin precursor miRNA (pre-miRNA), which is transported to the cytoplasm by exportin-5 and further processed by another RNase III–like enzyme, Dicer, to the mature miRNA (for a review, see Kim, 2005). miRNAs are initially transcribed as primary miRNAs (pri-miRNA). This long pri-miRNA (having several hundred nucleotides) is further processed into an intermediate hairpin precursor miRNA (pre-miRNA). The pre-miRNAs is further processed by Dicer, to the mature miRNAs [30,48]. Pre-miRNA polymorphisms may have an important functional role for miRNA binding and posttranscriptional regulation [49]. Moreover, genetic variation has been reported in pre-miRNAs to be relatively rare (only ten percent of human pre-miRNAs have identified SNPs [30]), so highlighting their functional relevance. Despite this functional importance, there have been very few studies that have focused on studying SNPs in genes encoding microRNAs so as to analyze their relationship with alcohol intake. As far as we know, only one previously-published study has examined the association between a polymorphism in a miRNA gene and alcohol, in this case for alcohol-related disorders [29]. In that work, Novo-Veleiro et al. [29], using a case-control study, analyzed differences in prevalence for the miR-146a G>C (rs2910164) polymorphism in 301 male patients with alcohol-related disorders and 156 sex-matched healthy volunteers and reported a significantly higher prevalence of C-allele carriers (47.8%) among patients when compared to controls (35.9%). In that study, the total consumption of alcohol intake was not measured, nor were the different types of alcoholic drinks. Therefore, although Novo-Veleiro et al. [29] have indeed been the first to report a significant association between the rs2910164 polymorphism in a gene encoding an miRNA (the miR-146a G>C) and alcohol related disorders, we can claim that our study is the first that has shown an association between a polymorphism in a gene encoding an miRNA (in this case the miRNA27a) and daily alcohol consumption (in g/day), as well as the with alcoholic beverages, measured by a validated questionnaire at the population level including both men and women. Unfortunately, we do not have the miR-146a G>C (rs2910164) genotyped in our population to compare the results. Both investigations contribute to providing new knowledge regarding the potential role of miRNA polymorphisms in alcohol consumption. Moreover, in our study, on classifying individuals as non-consumers, moderate consumers and high consumers of alcohol, the pre-miR-27a polymorphism (rs895819) was strongly associated with high alcohol consumption, fundamentally in a recessive way in individuals who present the two variant alleles (GG), even after adjustment for potential confounders. Additionally, we carried out a sensitivity analysis to test the homogeneity of this association depending on relevant variables, and we found a strong homogeneity across the different strata, so supporting the consistency of our findings. Thus, subjects with the GG genotype were more likely (approximately three times) than subjects with the AA genotype to report drinking more than the moderate daily limits for both men and women, in obese and in non-obese and in having or not a high adherence to the MedDiet pattern. For type 2 diabetic subjects, although we did not detect a statistically-significant interaction term with type 2 diabetes in the sensitivity analysis, the association was attenuated. This could reflect an environmental modification of the genetic influence. Thus, one of the recommendations that is usually made to type 2 diabetic subjects by health staff is that they reduce their alcoholic beverage intake. Hence, in the PREDIMED study, we have observed that alcohol intake in type 2 diabetic subjects is lower than in non-diabetics at baseline (mean ± SE: 4.6 ± 0.49 g/day versus 6.8 ± 0.42 g/day, respectively, p < 0.001; as well as the % of subjects having a high alcohol consumption: 4.3% versus 10.4%; p < 0.001). We may consider that the recommendation to reduce alcohol consumption could be an environmental factor that can modulate genetic susceptibility (in this case, for subjects having the GG genotype in the pre-miR27a polymorphism). As this is the first time that an association between the pre-miR-27a A>G polymorphism (rs895819) and alcohol consumption has been found and also being an epidemiological study on humans, we do not know the mechanisms through which this association can take place. There are previous studies that have consistently shown that this polymorphism is functional, and the variant G allele is associated with higher levels of miR27a [34,35]. Likewise, there are also several previous studies that have associated this polymorphism with a greater risk of cancer [34,35,36,50,51]. Interestingly, many of those cancers are related to alcohol consumption (gastric, colorectal, lung, etc.) and perhaps this pre-miR-27a polymorphism could be acting as an indirect indicator of the amount of alcohol consumed, being greater in carriers of the variant G-allele, associated with a higher cancer risk, that being an example of a Mendelian randomization approach [52]. Mendelian randomization use genetic variants (mainly SNPs) as instrumental variables for exposures in association studies between the exposure (using the SNP as proxy for the exposure) and the outcome. [52]. Therefore, if the association between the pre-miR-27a-rs895819 polymorphism and alcohol intake is confirmed in further studies, this SNP could be used as an instrumental variable (proxy or indicator) acting as a genetic biomarker of alcohol intake in Mendelian randomization studies, in the same way as the SNPs in the alcohol-metabolizing enzymes, ADH1B (alcohol dehydrogenase 1B, Class I) or ALDH2 (aldehyde dehydrogenase 2 family), are used as instrumental variables for Mendelian randomization studies on cancer [52,53]. The strength of our study is that we have measured the habitual alcohol intake and the different types of alcoholic beverages consumed with a previously-validated questionnaire that ensures good validity and reliability, in an elderly population sample with moderate alcohol intake throughout the week. This population forms part of a study in which other variables are measured, and we, therefore, have a well-characterized population as far as the presence of other diseases, biochemical data, dietary data, exercise, etc., are concerned, all of which allows us to control the possible confounding factors. As a limitation, we should point out that, despite data on multiple diseases having been obtained, we did not specifically ask about alcoholic liver disease, which could reflect an indirect association with alcohol intake. However, bearing in mind that the alcohol intake of this population is moderate to low and that alcoholics were excluded, the prevalence of that disease in the population would be very low. This assumption can be supported by Table S1, showing liver enzyme activities and mean corpuscular volume by drinking categories in a random sample of participants in this study. Means of these biomarkers of potential alcohol damage are in general low. Another possible limitation is the generalization of these results to other populations with different characteristics of age and alcohol intake patterns, but the publication of these results will contribute to the undertaking of new studies and meta-analyses so as to compare that generalization. The mechanisms through which the rs895819 in the pre-miR27a may influence alcohol consumption would also have to be investigated in greater depth, as well as how it affects the target mRNAs. In terms of target mRNAs for the miR-27a, there are several candidate genes that require investigation in greater depth. However, the fact that there have been previous studies [34,35] that have shown that the polymorphism is functional and can change the structure of the microRNA and its binding capability, with an increased miR-27a expression being detected in G-allele carriers compared to AA, allows us to argue that this polymorphism can alter the normal interaction between the miR27a and certain targets involved in regulating alcohol intake. Specifically, it is known that one of the targets of the miR27 is the serpin peptidase inhibitor clade I (Serpini1) [33], a protein primarily secreted by axons in the brain and known to be involved in the development of analgesic tolerance and, specifically, with morphine tolerance. Serpini1 knockout mice developed less analgesic tolerance than wild-type mice, supporting a role for miR27a and Serpini1 in the response to chronic opioid consumption [33]. Similarly, it is known that the effects of alcohol are mediated through intricate interactions between multiple neurochemical systems, including the opioid system [7]. Based on that, a closer binding between the miR27a and its target would make it less tolerant to alcohol (when the SNP is not present), whereas a looser binding between miR27a and its target (when the variant allele is present) may make it more tolerant to alcohol and necessary to drink more, as observed in homozygous subjects for the variant allele for this polymorphism. This potential mechanism is totally speculative, and more additional work is needed to support it. The results of our work have been obtained from an elderly Mediterranean population with moderate, habitual alcohol consumption throughout the week. The generalization of the association of the pre-miR-27a rs895819 polymorphism with alcohol intake in different populations from the one studied (young people, greater alcohol consumers, high weekend alcohol consumers, etc.) needs to be established in future studies. However, the fact that this association is detected both in men and in women and that it has no statistically-significant heterogeneity by sex, obesity or degree of adherence to the Mediterranean diet allows us to hypothesize that its generalization for other populations may be high, although that would have to be checked in further studies. 4. Materials and Methods 4.1. Subjects We analyzed 1007 participants (368 men and 639 women) in the PREDIMED (PREvención with DIeta MEDiterránea) trial recruited in the Valencia field center for whom DNA was available, the pre-miR-27a determined and valid data on alcohol intake obtained (See Flowchart in Figure S1). The PREDIMED is a multi-center, randomized, controlled clinical trial (controlled-trials.com number, ISRCTN35739639; ethics approval by the Institutional Review Board of the Hospital Clinic at Barcelona, Spain, 16/07/2002, under the protocol number “G03/140”) aimed at assessing the effects of the Mediterranean diet on the primary prevention of cardiovascular diseases [37]. The PREDIMED-Valencia field center, located on the East Mediterranean coast of Spain, was the field center that recruited the highest number of PREDIMED participants (n = 1094). Genotyping of the pre-miR-27a rs895819 polymorphism was carried out on 1042 participants with high quality DNA available. The 1007 participants with successful genotyping and alcohol data included in this analysis did not differ in the main characteristics from those of the total PREDIMED-Valencia cohort. PREDIMED eligible subjects were community-dwelling people (55–80 years of age for men; 60–80 years of age for women) who fulfilled at least one of two criteria: type 2 diabetes; 3 or more cardiovascular risk factors: current smoking, hypertension (blood pressure ≥140/90 mmHg or treatment with antihypertensive drugs), low-density lipoprotein cholesterol (LDL-C) ≥160 mg/dL (or treatment with hypolipidemic drugs), high-density lipoprotein cholesterol (HDL-C) ≤40 mg/dL, body mass index (BMI) ≥25 kg/m2 or a family history of premature cardiovascular diseases. Exclusion criteria included a personal history of cardiovascular disease, any severe chronic illness and drug or alcohol addiction [54]. For exclusion, in addition to the medical records known by the doctors, all of the participants were given the CAGE questionnaire (which is an acronym of its four questions: Cutting down, Annoyance by criticism, Guilty feeling, and Eye-openers) [37] on possible alcohol addiction. Out of the 4 questions in the questionnaire, a positive response to 2 was the reason for exclusion in accordance with the protocol for that questionnaire. None of the patients included in the PREDIMED-Valencia study had a positive response to 2 or more items in the CAGE questionnaire. The Institutional Review Board of the Valencia University (protocol number “G03/140”, 22 May 2003) approved the study protocol, and all participants provided written informed consent. In this report, we present data of the cross-sectional analysis at baseline. 4.2. Demographic, Clinical, Anthropometric, Dietary and Other Lifestyles Measurements The baseline examination included the assessment of standard cardiovascular risk factors, medication use, socio-demographic factors and lifestyle variables, by validated questionnaires previously detailed [37]. These variables, as well as anthropometric variables, were used as covariates to adjust for potential confounding in the multivariate regression models. Weight and height were measured with light clothing and no shoes with calibrated scales and a wall-mounted stadiometer, respectively. BMI was calculated as the weight (in kg) divided by the height (in m2). Obesity was defined as a BMI ≥ 30 kg/m2 and in accordance with World Health Organization (WHO, Geneva, Switzerland) criteria. Blood pressure was measured by trained personnel using a validated semi-automatic oscillometer (Omron HEM-70CP; Hoofddrop, The Netherlands) with the subject seated as previously reported [37]. The level of adherence to the Mediterranean diet was measured by a validated 14-item questionnaire, and subjects were classified as having low or high adherence, based on the population mean (9 points) [55]. Food consumption was determined by a validated 137-item semi-quantitative FFQ [39]. This FFQ, which also included nine questions on consumption of different alcoholic beverages (different types of wine, beer and spirits), was used to measure the intake of beverages with alcohol. In this FFQ, both for foods and drinks, there were questions about the average intake for each item over the previous year, including the baseline visit, which is when that questionnaire was administered. For each alcoholic beverage included in the questionnaire (vintage red wine, young red wine, young rosé, white wine, cava, beers and spirits, including whisky, gin, rum, vodka and liqueurs), questions were asked for a typical Spanish measure of the same (as detailed in our previous publication [39]). Alcohol intake (g/day) was calculated by multiplying the amount of the beverage (mL) by the respective degree (% alcohol) and the constant 0.80 to transform alcohol volumes into weight. The sum of all of that is the total amount of alcohol in g/day consumed for each person. Abstainers were those individuals for whom the sum was zero grams per day. For the analysis of specific beverages, total wine, total beer and total spirits were considered as grouped variables. Further, red wine (including vintage red wine, young red wine and young rosé) and white wine were also analyzed. This FFQ was previously validated by us [39] in a similar population through a standard validation procedure in the following way: The FFQ was administered twice (FFQ1 and FFQ2) to explore reproducibility at 1 year. Four 3-d dietary records (DR) were used as a reference to explore validity; participants therefore recorded their food intake over 12 days in the course of 1 year. The intra-class correlation coefficient between alcohol intake from the FFQ and repeated DR was 0.82 [39]. This high correlation coefficient value allows us to conclude that the FFQ measurements have good reproducibility and a relative validity similar to those of FFQs used in other prospective studies and that we, therefore, have a good instrument for measuring habitual alcohol and alcoholic beverage intake. Once the amount of alcohol consumed was calculated (in g/day as the annual average) for each individual on the basis of the type and amount of alcoholic beverages consumed, we undertook an additional classification into alcohol intake categories. Although there are several cut-off points to classify alcohol intake, we used international criteria of alcohol intake [40,41,42], as we did in previous studies [42,43], with the aim of ensuring the best comparability between publications. Thus, three groups of alcohol consumption were defined according to the reported daily intake of alcohol: no intake (0 g/day), moderate intake (≤26.4 g/day for men and ≤13.2 g/day for women) and high intake (>26.4 g/day for men and >13.2 g/day for women). These gram amounts correspond to 1 drink/day for women and 2 drinks/day for men [41,42,43]. The cut-off points were chosen to facilitate the interpretation of the gram-based alcohol categories and according to recommended upper limits of daily alcohol consumption of one drink/day for women and two drinks/day for men stated by the several organizations and frequently used in epidemiological studies on alcohol intake [41,42,43,44,45]. Physical activity was estimated by the validated Minnesota Leisure-Time Physical Activity questionnaire, as previously reported [37]. 4.3. Biochemical Determinations, DNA Extraction and Genotyping Fasting blood samples were obtained for each participant and stored at −80 °C until biochemical analyses. Fasting glucose, total cholesterol, triglycerides, HDL-C and LDL-C were determined as previously reported [56]. In a random sample of participants (detailed in Table S1), liver enzymes and mean corpuscular volume were also determined at baseline by standard procedures. LDL-C concentrations were estimated with the equation of Friedewald et al. whenever triglycerides were <400 mg/dL. Genomic DNA was extracted from buffy coat with the MagNaPure LC DNA Isolation kit (ROCHE Diagnostics, Indianapolis, IN, USA). The pre-miR-27a rs895819 A>G polymorphism was genotyped using the HumanOmniExpress Illumina BeadChip (Illumina, San Diego, CA, USA) by standard techniques. Genotype frequencies were consistent with Hardy–Weinberg equilibrium (p = 0.111). 4.4. Statistical Analyses Chi-square tests were used to compare proportions. Triglyceride concentrations were log-transformed for the statistical analyses. Alcohol intake was square root transformed for the statistical analyses. In both cases, untransformed values were presented for means and standard error, but the p-values were obtained with the square root-transformed variables. ANOVA tests were applied to compare crude means according to the pre-miR-27a rs895819 genotypes. Co-dominant and recessive models of inheritance were first tested to know the effects of the variant allele. In the analysis for the additive model, the pre-miR-27a rs895819 A>G polymorphism was considered as a linear term coded as 0, 1 or 2 depending on the number of G-alleles, with the homozygote wild-type coded as 0. Taking into account that similar effects were detected in AA and AG subjects, a recessive model GG versus (AA + AG) was also considered. Results were presented both for the co-dominant/additive and for the recessive models for the whole population. In addition, a stratified analysis of total alcohol intake in male drinkers, men (drinkers and non-drinkers) and women (drinkers and non-drinkers) according to the pre-miR-27a rs895819 polymorphism was undertaken. Unadjusted (crude) and multivariable adjusted general linear models (GLM) were used for continuous variables, and logistic regression models were used for dichotomous variables (categories of alcohol consumption). Models were adjusted for potential confounders including age, sex, type 2 diabetes, obesity, hypertension, dyslipidemia, physical activity, smoking and total energy intake. Odds ratios (OR) and 95% confidence intervals (CI) for the risk of being in the category of high alcohol consumption were estimated, and a multivariable logistic regression was performed. Analyses were done for the whole population and stratified by sex when indicated. A sensitivity analysis to test the homogeneity of the pre-miR-27a rs895819 polymorphism (as recessive) with the risk of having a high drinking pattern was carried out taking into account the following categories: sex, obesity, type 2 diabetes, hypertension and adherence to Mediterranean Diet. Interaction terms between the pre-miR-27a rs895819 polymorphism (recessive) and the corresponding variables for stratification were calculated in the corresponding hierarchical regression model (logistic). The stratified estimations of the associations were also carried out with the multivariate adjusted regression models. Statistical analyses were performed with the IBM SPSS Statistics Version 21.0 (IBM, Armonk, NY, USA). All tests were two-tailed, and p-values <0.05 were considered statistically significant. 5. Conclusions In conclusion, in this study, we have reported, for the very first time, a significant association between an SNP in a miRNA (rs895819 A>G in the pre-miR27a) and total alcohol consumption (higher in homozygous subjects for the variant G-allele) and specific beverages, so providing more information on the importance of miRNAs in regulating alcohol intake, not only through epigenetic mechanisms, but also through the genetic variants in their DNA sequence, which has to be taken into account in later omic integration studies analyzing the miRNAs’ regulome. The generalization of the association of the pre-miR-27a rs895819 polymorphism with alcohol intake in different populations from the one studied needs to be established in future studies. Acknowledgments This study was funded, by the Spanish Ministry of Health (Instituto de Salud Carlos III) and the Ministerio de Economía y Competitividad-Fondo Europeo de Desarrollo Regional (Projects CNIC-06/2007, RTIC G03/140, CIBER 06/03, PI06-1326, PI07-0954, PI11/02505, SAF2009-12304, AGL2010-22319-C03-03 and PRX14/00527), by the University Jaume I (Project P1-1B2013-54), by Contracts 53-K06-5-10 and 58-1950-9-001 from the U.S. Department of Agriculture Research Service, USA, by the Generalitat Valenciana (ACOMP2010-181, AP111/10, AP-042/11, ACOM2011/145, ACOMP/2012/190, ACOMP/2013/159 and ACOMP/213/165), and with the collaboration of the Real Colegio Complutense at Harvard University, Cambridge. MA, USA. Rocío Barragán’s contract is funded by the Ayudas para la contratación de personal investigador en formación de caracter predoctoral, Programa “VALencia Investigación más Desarrollo” (VALi+d). Conselleria d’Educació, Investigació, Cultura i Esport. Generalitat Valenciana, Spain (ACIF/2013/168). Supplementary Materials Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1338/s1. Click here for additional data file. Author Contributions Oscar Coltell and Dolores Corella contributed to the data analysis; Oscar Coltell designed and developed the data management system and elaborated the tables and figures; Rocío Barragán, Eva M. Asensio, Francesc Francés, José V. Sorlí, Ramon Estruch and Albert Salas-Huetos contributed to the collection of phenotype and clinical data; Ramon Estruch, Albert Salas-Huetos and Jose M. Ordovas critically evaluated the manuscript; Dolores Corella, Jose M. Ordovas and Rocío Barragán conceptualized the study; Dolores Corella and Oscar Coltell wrote and edited the manuscript. All authors read and approved the final manuscript. Conflicts of Interest The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results. Figure 1 Adjusted means of total alcohol intake (g/day) in drinker males depending on pre-miR-27a rs895819 polymorphism. Means and standard errors (SE) were estimated by the multivariable general linear model adjusted for age, type 2 diabetes, obesity, hypertension, dyslipidemia, physical activity, smoking and total energy intake. For statistical significance, the transformed (square root) alcohol consumption variable was used. AA (n = 148); AG (n = 112); GG (n = 23). * p-value obtained from the multivariable GLM including the pre-miR-27a rs895819 polymorphism as additive. ijms-17-01338-t001_Table 1Table 1 Demographic, clinical and lifestyle characteristics of the study participants at baseline according to the pre-miR-27a-rs895819 A>G polymorphism 1. pre-miR-27a-rs895819 A>G genotypes Variable Total (n = 1007) AA (n = 540) AG (n = 381) GG (n = 86) p 2 Male sex: n, % 368 (36.5%) 201 (37.2%) 137 (36.0%) 30 (34.9%) 0.876 Type 2 diabetes: n, % 3 468 (46.5%) 243 (45.0%) 188 (49.3%) 37 (43.0%) 0.342 Hypertension: n, % 4 844 (83.8%) 451 (83.5%) 320 (84.0%) 73 (84.9%) 0.944 Dyslipidemia: n, % 769 (76.4%) 408 (75.6%) 290 (76.1%) 71 (82.6%) 0.361 Obesity: n, % 5 512 (50.8%) 269 (49.8%) 199 (52.2%) 44 (51.2%) 0.769 Smokers: n, % – – – – – – – – 0.504 Current 127 (12.6%) 67 (12.4%) 44 (11.5%) 16 (18.6%) – Former 234 (23.2%) 128 (23.7%) 88 (23.1%) 18 (20.9%) – Never 646 (64.2%) 345 (63.9%) 249 (65.4%) 52 (60.5%) – Age (years) 66.8 (0.2) 67.0 (0.3) 66.9 (0.3) 66.1 (0.7) 0.467 Weight (kg) 77.2 (0.4) 77.2 (0.5) 77.2 (0.6) 77.1 (1.4) 0.997 BMI (kg/m2) 30.6 (0.1) 30.7 (0.2) 30.7 (0.2) 30.4 (0.5) 0.842 Waist circumference (cm) 103.0 (0.4) 103.0 (0.5) 103.6 (0.6) 102.2 (1.3) 0.553 SBP (mm Hg) 147.1 (0.7) 147.0 (0.9) 147.8 (1.1) 144.5 (2.2) 0.444 DBP (mm Hg) 82.0 (0.3) 82.1 (0.5) 82.3 (0.6) 79.5 (1.0) 0.092 Heart rate (bpm) 72.4 (0.3) 72.1 (0.4) 72.8 (0.6) 72.3 (1.1) 0.678 Total cholesterol (mg/dL) 208.1 (1.3) 208.1 (1.7) 207.6 (2.1) 210.7 (4.6) 0.806 LDL-C (mg/dL) 129.4 (1.1) 129.2 (1.5) 129.3 (1.9) 130.6 (4.0) 0.946 HDL-C (mg/dL) 52.6 (0.4) 52.7 (0.6) 52.3 (0.7) 54.0 (1.8) 0.568 Triglycerides (mg/dL) 131.5 (2.2) 133.3 (3.1) 129.6 (3.2) 129.4 (9.7) 0.674 Fasting glucose (mg/dL) 120.4 (1.3) 120.3 (1.8) 120.9 (2.0) 118.1 (3.6) 0.843 Energy intake (kcal/day) 2210 (20) 2198 (28) 2221 (31) 2238 (74) 0.780 Total fat (g/day) 95.1 (1.0) 95.5 (1.4) 94.8 (1.5) 93.7 (2.8) 0.852 Saturated fat (g/day) 25.1 (0.3) 25.1 (0.4) 25.4 (0.5) 23.7 (0.8) 0.337 MUFA (g/day) 46.4 (0.5) 46.7 (0.7) 46.1 (0.8) 46.1 (1.5) 0.812 PUFA (g/day) 15.6 (0.2) 15.7 (0.3) 15.6 (0.3) 15.2 (0.7) 0.856 Proteins (g/day) 92.8 (0.8) 92.7 (1.2) 92.6 (1.3) 93.5 (3.0) 0.962 Carbohydrates (g/day) 235.6 (2.6) 232.6 (3.5) 239.1 (4.2) 239.3 (10.9) 0.463 Adherence to the MedDiet (points) 6 8.4 (0.1) 8.5 (0.1) 8.4 (0.1) 8.8 (0.2) 0.246 Physical activity (METs-min/day) 169.8 (5.5) 169.6 (7.8) 173.1 (8.7) 156.3 (15.8) 0.721 1 Values are expressed as the mean (standard error) for continuous variables or as (n, %) for categorical variables. MUFA, monounsaturated fatty acids; PUFA, polyunsaturated fatty acids; MedDiet, Mediterranean diet; MET, Metabolic Equivalent of Task; 2 unadjusted p-value obtained in the ANOVA test; 3 Type 2 diabetes was defined as a fasting blood glucose level of 126 mg/dL or higher on two occasions, a 2-h plasma glucose level of 200 mg/dL or higher during a 75-g oral glucose-tolerance test or the use of antidiabetic medication; 4 Hypertension was defined as a systolic blood pressure (SPB) of 140 mm Hg or higher, a diastolic blood pressure (DBP) of 90 mm Hg or higher or the use of antihypertensive therapy; 5 Obesity was defined as Body Mass Index (BMI) greater or equal to 30 kg/m2; 6 Based on a 14-point screener of adherence. ijms-17-01338-t002_Table 2Table 2 Association of the pre-miR-27a-rs895819 A>G polymorphism with total alcohol consumption and alcoholic beverages in the whole population 1. Genotypes Alcoholic Beverage 2,3 Total (n = 1007) AA (n = 540) AG (n = 381) GG (n = 86) p 4,5 Total alcohol (g/day) 2 5.8 (0.3) 5.2 (4.4–6.0) 5.9 (4.8–6.9) 9.1 (5.6–12.6) 0.020 Total alcohol (g/day) 3 – – 7.4 (6.3–8.6) 8.2 (6.9–9.5) 11.0 (8.2–13.1) 0.016 Total wine (mL/day) 2 36.8 (2.3) 34.8 (28.7–40.7) 35.7 (28.8–42.5) 54.9 (32.0–77.7) 0.036 Total wine (mL/day) 3 – – 47.8 (38.8–56.1) 49.4 (39.9–58.8) 66.4 (50.9–82.0) 0.043 Total beer (mL/day) 2 39.9 (3.4) 35.1 (27.6–42.6) 38.8 (28.3–49.3) 75.5 (33.4–117.8) 0.041 Total beer (mL/day) 3 – – 56.7 (39.5–65.8) 56.0 (41.6–70.4) 88.2 (64.6–111.7) 0.142 Total spirits (mL/day) 2 2.0 (0.3) 1.4 (0.8–1.9) 2.6 (0.9–3.8) 2.9 (0.9–5.0) 0.172 Total spirits (mL/day) 3 – – 3.6 (1.4–3.6) 3.7 (2.5–4.9) 4.0 (2.0–6.0) 0.073 1 Values are expressed as the mean (and standard error) for the whole population and as the mean (and 95% confidence intervals: CI for genotype groups); 2 The first row presents the p-value of the association between the SNP and total alcohol/alcoholic beverage in the crude (unadjusted model). The square root transformed variables were used to test the statistical significance of the crude association. The p-value vas obtained as a linear trend for the genotype; 3 The second row presents the adjusted p-value of the association between the SNP and total alcohol/alcoholic beverage in the multivariable model adjusted for sex, age, type 2 diabetes, hypertension, dyslipidemia, obesity, smoking, physical activity and total energy intake in the general linear model (GML) for the corresponding square root transformed variables; 4 unadjusted means, SE and 95% CI for the corresponding untransformed variables for total alcohol and alcoholic beverages in the whole population and by genotypes; beverages; 5 Adjusted (for sex, age, type 2 diabetes, hypertension, dyslipidemia, obesity, smoking, physical activity and total energy intake) mean and 95% CI for total alcohol intake and alcoholic beverages by genotype. The polymorphism was tested for a linear trend. ijms-17-01338-t003_Table 3Table 3 Association between the pre-miR-27a-rs895819 A>G polymorphism and drinker category in the whole population and stratified by sex 1,2. Whole Population Men Women Alcohol Consumption Non-Drinkers (0 g/Day) Moderate (<26.4 g/Day for Men) (<13.2 g/Day for Women) High (>26.4 g/Day for Men) (>13.2 g/Day for Women) Non-Drinkers + Moderate High Non-Drinkers + Moderate High Genotypes (n = 540) (n = 381) (n = 86) (n = 315) (n = 53) (n = 616) (n = 23) p3 polymorphism 0.005 – – – – 0.024 – – 0.010 – – AA: n (%) 244 (45.2%) 264 (48.9%) 32 (5.9%) 178 (88.6%) 23 (11.4%) 330 (97.3%) 9 (2.7%) AG: n (%) 160 (42.0%) 192 (50.4%) 29 (7.6%) 116 (84.7%) 21 (15.3%) 236 (96.7%) 8 (3.3%) GG: n (%) 34 (39.5%) 37 (43.0%) 15 (17.4%) 21 (70.0%) 9 (30.0%) 50 (89.3%) 6 (10.7%) 1 Values are expressed as n and %; 2 the three groups were defined according to the reported daily intake of alcohol: no intake (0 g/day), moderate intake (<26.4 g/day for men and <13.2 g/day for women) and high intake (>26.4 g/day for men and >13.2 g/day for women); 3 unadjusted p-values obtained in the chi square tests for the association between genotypes and drinking categories in the whole population or in men and women separately. ijms-17-01338-t004_Table 4Table 4 Association between the pre-miR-27a-rs895819 A>G polymorphism and the risk of having a high alcohol intake in the whole population and stratified by sex 1. Whole Population Polymorphism Model 1 Model 2 Genotypes OR 95% CI p OR 95% CI p AA (n = 540) 1.00 (reference) – 1.00 (reference) – AG (n = 381) 1.34 (0.79–2.29) 0.276 1.45 (0.81–2.53) 0.190 GG (n = 86) 3.57 (1.79–7.16) <0.001 3.84 (1.83–8.04) <0.001 Men Polymorphism Model 1 Model 2 Genotypes OR 95% CI p OR 95% CI p AA (n = 201) 1.00 (reference) – 1.00 (reference) – AG (n = 137) 1.40 (0.73–2.64) 0.311 1.52 (0.78–2.99) 0.220 GG (n = 30) 3.01 (1.22–7.45) 0.017 3.42 (1.28–9.11) 0.014 Women Polymorphism Model 1 Model 2 Genotypes OR 95% CI p OR 95% CI p AA (n = 339) 1.00 (reference) – 1.00 (reference) – AG (n = 244) 1.24 (0.47–3.27) 0.660 1.44 (0.52–3.96) 0.486 GG (n = 56) 4.39 (1.50–12.87) 0.007 4.61 (1.44–14.83) 0.010 1 OR and 95% CI were estimated by multivariable logistic regression models (high alcohol intake versus non-intake + moderate); Model 1: adjusted for sex and age; Model 2: additionally adjusted for type 2 diabetes, hypertension, dyslipidemia, obesity, smoking, physical activity and total energy intake. ijms-17-01338-t005_Table 5Table 5 Sensitivity analysis of the association between the pre-miR-27a-rs895819 A>G polymorphism and risk of having a high alcohol intake 1. Variable % Drinker High 2 Risk 3 Sex AA + AG GG p 4 OR 95% CI p 5 Men (n = 368) 13.0% 30.0% 0.011 2.84 (1.12–7.17) 0.028 Women (n = 639) 2.9% 10.7% 0.003 3.79 (1.36–11.64) 0.012 p 6 for interaction: 0.774 Variable % Drinker High 2 Risk 3 Obesity AA + AG GG p 4 OR 95% CI p 5 Non-obese (n = 495) 8.2% 21.0% 0.005 3.31 (1.34–8.18) 0.010 Obese (n = 512) 5.1% 13.6% 0.022 3.87 (1.21–12.35) 0.022 p 6 for interaction: 0.934 Adherence to MedDiet AA + AG GG p 4 OR 95% CI p 5 Low < 9 (n = 511) 5.1% 17.1% 0.002 4.56 (1.71–14.34) 0.003 High ≥ 9 (n = 496) 8.2% 17.8% 0.033 2.49 (0.09–6.60) 0.069 p 6 for interaction: 0.546 Variable % Drinker High 2 Risk 3 Diabetes AA + AG GG p 4 OR 95% CI p 5 No (n = 539) 9.0% 24.5% 0.001 3.56 (1.54–8.23) 0.003 Yes (n = 468) 3.9% 8.1% 0.221 2.06 (0.52–8.18) 0.304 p 6 for interaction: 0.547 Variable % Drinker High 2 Risk 3 Hypertension AA + AG GG p 4 OR 95% CI p 5 No (n = 163) 8.0% 23.1% 0.103 4.59 (0.77–27.59) 0.096 Yes (n = 844) 6.4% 16.4% 0.004 3.22 (1.50–6.90) 0.003 p 6 for interaction: 0.818 1 OR and 95% CI were estimated by multivariable logistic regression models adjusted for the covariates indicated below; 2 % of subjects having a high alcohol intake (>26.4 g/day in men and >13.2 g/day in women) depending on the pre-miR-27a-rs895819 polymorphism; 3 OR of being a high alcohol drinker in comparison with non-drinker + moderate, depending on the variable considered for GG individuals versus AA + AG (recessive model); 4 unadjusted p-value for comparison of percentages; 5 model adjusted for sex, age, type 2 diabetes, hypertension, dyslipidemia, obesity, smoking, physical activity and total energy intake; 6 p-value for the interaction term between the corresponding variable (sex, obesity, adherence to MedDiet, diabetes or hypertension) and the pre-miR-27a-rs895819 polymorphism (recessive) in the multivariable adjusted model. ==== Refs References 1. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081339ijms-17-01339ReviewThe Role of α1-Adrenoceptor Antagonists in the Treatment of Prostate and Other Cancers Batty Mallory 1†Pugh Rachel 1†Rathinam Ilampirai 1†Simmonds Joshua 1†Walker Edwin 1†Forbes Amanda 2†Anoopkumar-Dukie Shailendra 13McDermott Catherine M. 2Spencer Briohny 1Christie David 12Chess-Williams Russ 2*Cho William Chi-shing Academic Editor1 School of Pharmacy, Griffith University, Gold Coast, QLD 4222, Australia; mallory.batty@griffithuni.edu.au (M.B.); rachel.pugh@griffithuni.edu.au (R.P.); ilampirai.rathinam@griffithuni.edu.au (I.R.); joshua.simmonds@griffithuni.edu.au (J.S.); edwin.walker@griffithuni.edu.au (E.W.); s.dukie@griffith.edu.au (S.A.-D.); b.spencer@griffith.edu.au (B.S.); David.Christie@genesiscare.com.au (D.C.)2 Centre for Urology Research, Faculty of Health Sciences and Medicine, Bond University, Robina, QLD 4226, Australia; amanda.forbes@student.bond.edu.au (A.F.); camcderm@bond.edu.au (C.M.M.)3 Menzies Health Institute Queensland, Griffith University, Gold Coast, QLD 4222, Australia* Correspondence: rchesswi@bond.edu.au; Tel.: +61-7-5595-4420† These authors contributed equally to this work. 16 8 2016 8 2016 17 8 133906 7 2016 08 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).This review evaluates the role of α-adrenoceptor antagonists as a potential treatment of prostate cancer (PCa). Cochrane, Google Scholar and Pubmed were accessed to retrieve sixty-two articles for analysis. In vitro studies demonstrate that doxazosin, prazosin and terazosin (quinazoline α-antagonists) induce apoptosis, decrease cell growth, and proliferation in PC-3, LNCaP and DU-145 cell lines. Similarly, the piperazine based naftopidil induced cell cycle arrest and death in LNCaP-E9 cell lines. In contrast, sulphonamide based tamsulosin did not exhibit these effects. In vivo data was consistent with in vitro findings as the quinazoline based α-antagonists prevented angiogenesis and decreased tumour mass in mice models of PCa. Mechanistically the cytotoxic and antitumor effects of the α-antagonists appear largely independent of α 1-blockade. The proposed targets include: VEGF, EGFR, HER2/Neu, caspase 8/3, topoisomerase 1 and other mitochondrial apoptotic inducing factors. These cytotoxic effects could not be evaluated in human studies as prospective trial data is lacking. However, retrospective studies show a decreased incidence of PCa in males exposed to α-antagonists. As human data evaluating the use of α-antagonists as treatments are lacking; well designed, prospective clinical trials are needed to conclusively demonstrate the anticancer properties of quinazoline based α-antagonists in PCa and other cancers. α1-adrenoceptor antagonistprostate cancercytotoxicity ==== Body 1. Introduction Prostate cancer is the most commonly diagnosed male cancer in the world [1]. In Australia, prostate cancer account for approximately 30% of all newly diagnosed cancers and is the second most common cause of cancer-specific death in men [2]. Early stage prostate cancer is highly manageable using definitive radical prostatectomy and/or radiotherapy techniques. However, an estimated one-fifth of men will experience disease recurrence following curative treatment modalities [3,4,5] and resort to first-generation androgen deprivation therapies for long-term management of their disease. Unfortunately, progression after androgen deprivation therapy indicates the transition to castrate-resistant prostate cancer (CRPC), which is considered to be both inevitable and incurable. Although there has been significant progress in the CRPC treatment landscape (e.g., enzalutamide, abiraterone, cabazitaxel), there are no currently available therapies which provide a survival benefit greater than twelve months [6,7,8,9,10]. Therefore, there is an urgent need for novel agents to improve the oncological and survival outcomes for these last-resort patients. One such modality may be through the use of α1-adrenoceptor (ADR) antagonists. Adrenoceptors (also known as adrenergic receptors) are members of the G protein-coupled receptor (GPCR) superfamily, which can be further broken down into α and β subtypes with several homologous isoforms including α-1 (A, B, and D), -2 (A, B, and C), and β-1, 2, and 3 [11]. While all adrenergic receptors play an important role in regulating human tissue homeostasis, the focus of this review will primarily cover α1-ADRs in the human prostate. α1-ADRs are largely found in the stromal region of the human prostate, with few α1-ADR receptors localised in the prostate epithelium. Although, the α1A-ADR isoform (previously identified as α1C) is known to make up approximately 70% of the prostatic α1-ADRs [12], recent evidence suggests that the distribution of α1-ADR isoforms (A, B and D) change with advancing age and are correlated with the subsequent onset of prostatic hyperplasia [13]. Likewise, receptor localisation and expression appears to be altered in prostate cancer tissues. Unlike normal prostate epithelium which expresses few α1-ADRs, prostate cancer epithelia have been reported to express functional α1A-ADR [14,15], as well as increased mRNA levels of α1B and α1D isoforms [16]. It remains unclear whether α1-ADRs have a role in promoting prostate carcinogenesis remains unclear. However, α1-ADRs have been identified to play a role in cellular proliferation in vitro [14,17,18,19] and therefore may be exploited for treatment of neoplasms. α1-ADR antagonists (referred to here as “α-antagonists”) are commonly used in clinical practice to treat hypertension, and more recently, the urodynamic symptoms associated with benign prostate hyperplasia (BPH). In BPH, α-antagonists block receptor activation to relax the prostatic smooth muscle thereby improving rate of urine flow and other associated lower-urinary tract symptoms (LUTS) [20,21]. There are regional differences in the commonly prescribed α-antagonists for BPH. In the United States, the non-selective doxazosin and terazosin are the most commonly prescribed α1-blockers due to their relatively long half-life [22,23] and clinically significant improvement in BPH-related LUTS. Furthermore, these drugs have been associated with fewer adverse drug-related cardiovascular side effects, compared to prazosin [24]. However, in Australia, the short acting and non-selective prazosin is clinically favored over other α-antagonists primarily due to the rapid mitigation of LUTS. The highly selective tamulosin, also offer significant reduction in BPH-related LUTS symptoms, however, at a cost of ejaculatory dysfunction making this α1-ADR antagonists undesirable for some men [24]. In the late 1990s, monotherapy with α-antagonists was shown to provide long-term clinical benefits that could not be explained solely by acute prostatic relaxation [25,26,27]. In support of these findings, a more recent study uncovered a large proportion of men (70%) experienced continued improvement of BPH-associated LUTS following discontinuation of α-antagonists [28]. Subsequent studies over the next sixteen years have identified that some of these drugs possess novel cytotoxic actions in diseased prostates, including prostate and other cancers. Despite the plethora of original papers investigating the anticancer effects of these drugs, only few systematic reviews since the early 2000s have been carried out to colligate the more recent published findings [29,30,31,32,33]. Therefore, the aim of this systematic literature review is to analyse the current evidence for the use of α-antagonists as potential treatment options for prostate cancer (PCa). Specifically, this review will colligate the anticancer mechanisms of α-antagonists, evaluate the evidence supporting clinical anticancer efficacy of these drugs in PCa, and evaluate the evidence for use of these drugs in other cancers. 2. Results Pubmed, Google Scholar and Cochrane databases were accessed to retrieve articles. The search terms used to find the relevant articles were separated into three categories: terms that describe the α-antagonists, the target tissue and the action of the drugs (Table A1). Four hundred and ninety-six articles were identified using the inclusion criteria by searching three databases: Cochrane, Pubmed and Google Scholar. After exclusion criteria were applied, sixty-two relevant articles were obtained, consisting of fifty-four original manuscripts and eight review articles. (Figure A1). Of the fifty-four research articles identified only four studies examined the role of α-antagonists in PCa development in humans (Table 1). The majority focused on the cytotoxic and anti-tumour activity of α-antagonists in vitro and in animal models (Table 2). These retrospective cohort and observational human studies examined the effects of both quinazoline and non-quinazoline α-antagonists, but show only an overall decreased incidence of PCa. Two additional researchers replicated the search which returned identical results, validating the method used, for robustness. 3. Discussion The overall aim of this literature review was to analyse the current evidence for the clinical use of α-antagonists as a potential treatment modality for PCa. A summary of results from the sixty-two studies identified in this systematic review can be found in Table 2. 3.1. In Vitro Evidence 3.1.1. Quinazoline/Piperazine-Dependence In vitro studies provide substantial evidence that the quinazoline α-antagonists doxazosin, terazosin and prazosin exhibit cytotoxic activity in the prostate cancer cell lines LNCaP (androgen-dependent), DU145 and PC-3 (castrate-resistant) cell lines [38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,84,85,86,87]. The structurally similar piperazine, naftopidil, also produced cytotoxic effects in the androgen-dependent LNCaP and E9 cell lines [88]. However these effects were not seen with the sulphonamide based tamsulosin [88] suggesting that the quinazoline/piperazine ring structure maybe responsible for their cytotoxicity (Figure 1). Furthermore, a number of studies have also investigated the use of doxazosin and naftopidil analogues, which demonstrated similar cytotoxic potential to the parent drug [54,55,89,90]. 3.1.2. α1-Adrenoceptor-Independence The mechanisms for cytotoxicity appear to be independent of α1-blockade [39,40,41] as demonstrated by several studies, through the use of phenoxybenzamine (a non-selective, irreversible α-antagonist). Doxazosin and terazosin were observed to reduce cell viability and induce apoptosis in the presence of phenoxybenzamine [32,38,42,43]. This independent action is supported by two studies that proposed involvement of the 5HT receptor [44,89] which resulted in reduced cell viability and apoptosis in PCa. 3.1.3. Cell Death Mechanisms There are several potential mechanisms accounting for the cytotoxic actions of the quinazoline/piperazosine α-antagonists, including apoptosis, decreased cell proliferation and decreased angiogenesis which are crucial mediators of quinazoline-induced cytotoxicity in PCa cell lines [38]. An illustrative summary of the mechanisms contributing to α-antagonist-induced cytotoxicity is shown in Figure 2. Early work by Kyprianou, N. et al. (2000) [32] showed that doxazosin (15 mM) and terazosin (15 mM) induce apoptosis in a dose dependent manner in PC-3 cell lines using the TUNEL assay [38,42]. As well as inducing apoptosis, doxazosin and terazosin were shown to inhibit cell adhesion to the extracellular matrix by inducing anoikis. Both agents induced apoptosis in prostate epithelial and smooth muscle cells at dose ranges used for the treatment of BPH [38]. Similarly Garrison, J. et al. (2006) [45] proposed that apoptosis was an important mediator of doxazosin-induced cytotoxicity (at 25 mM/L) in both malignant and benign prostate epithelial cells (PC-3 and BPH-1 cell lines). These authors suggested that this occurs through increased caspase 8 activation via formation of the death-inducing signalling complex (DISC) [32,45]. Caspase 8 mediates cell cycle arrest at the G2-M phase [46], and activates both cleaved caspase 3 and tBid at the BAX/Bak receptor [32,56]. This results in the release of mitochondrial stress related pro-apoptotic inducing factors including: cytochrome C, Smac/diablo, AMID and AIF [29,35,47,91]. More recently, Forbes, A. et al. (2016) [47] found that in PCa cells, the activation of caspase 3 was similar for prazosin and doxazosin, and suggested superior activity to terazosin, silodosin and alfuzosin. Prazosin (30 mM) treatment resulted in a six-fold increase in caspase 3 activation in LNCaP versus a two-fold increase in PC-3 cells suggesting androgen-dependent prostate cencer (ADPC) cells have greater sensitivity to these effects [47]. Cleaved caspase 3 is used as a marker for apoptosis [32] and is activated via DISC through FADD recruitment [32,45]. Some studies support Forbes’ finding of a dose-dependent increase in caspase 3 activation and consequent apoptosis when treated with quinazolines [32,47,48]. A decrease in HIF-1 (a mediator of resistance) was also shown in LNCaP cells post quinazoline exposure [47]. Additionally, α-antagonists exhibit cytotoxicity via cell-cycle arrest. Naftopidil induced G1 cell-cycle arrest in PCa cells in vitro, as did silodosin, but to a lesser extent [88]. Similarly, prazosin and doxazosin caused an increase in DNA strand breakage leading to subsequent G2 cell-cycle arrest and apoptosis, possibly through the inactivation of CDK1 [46,57]. Ho, C. et al. (2015) [58] showed that the reversible non-selective α-antagonist, phentolamine (an imidazoline), caused cell-cycle arrest in CRPC cells by inducing microtubule assembly, leading to mitotic arrest of the cell-cycle and mitochondrial damage. This inhibition of mitosis is a similar chemotherapeutic mechanism to taxanes [59]. It was suggested that disruption of the cell-cycle by quinazolines can be explained by competitive inhibition of the ATP attachment of tyrosine kinase and inhibiting phosphorylation of PI3K from the following receptors: HER2/Neu, EGF, and VEGF [32,60]. These receptors are well-identified targets of current chemotherapeutic agents, such as bevacizumab which targets VEGF. Another proposed mechanism underlying the cytotoxic actions of α-antagonists is disruption of DNA integrity. Desiniotis, A. et al. (2011) [32] suggested that quinazolines derivatives cause DNA intercalation, similar to anthracycline chemotherapeutics. DNA fragmentation was also observed in studies that tested doxazosin (25 mM) [32,49]. Doxazosin is proposed to inhibit topoisomerase 1, inducing DNA damage and resulting in synergistic cytotoxic activity with etoposide and adriamycin [86]. Furthermore, apoptosis and cell-cycle arrest lead to decreased cell growth and proliferation of PCa cells. This leads to decreased cell survival, migration and adhesion resulting in anoikis [50,61,90]. In vitro evidence also suggests that quinazolines have the potential to disrupt key mediators of angiogenesis. Quinazolines downregulate VEGF, resulting in reduced repression of TGF-β receptor [32,51,60]. TGF-β is responsible for the transcription of various apoptosis factors as well as increasing IκBα; the inhibitor of NF-κB [32,60]. Forbes, A. et al. (2016) [51] noted an increase in stress related factors such as p38α and MAPKs in PCa cells treated with α-antagonists [47], which is suggestive of TGF-β activation. The α-antagonist-mediated disruption and down-regulation of VEGF results in decreased angiogenesis by increasing apoptosis and anoikis [32,52,61]. The inhibition of this signalling pathway blocks Bcl-2, an anoikis inhibiting factor that is identified in CRPC and is a mediator for cell immunity via bypass pathway mutations [32,52]. Targeting this factor improves selectivity and may improve treatment outcomes in CRPC. This was observed in prostate cells, where treatment with doxazosin resulted in inhibition of VEGF-induced angiogenesis, reduced cell migration and increased cell death due to anoikis [53,61], possibly via EphA2 agonist activity [62]. 3.2. In Vivo Evidence Consistent with in vitro studies, the ability of quinazoline α-antagonists to reduce tumour growth and potentially decrease angiogenesis is also observed in mice models of PCa. PCa xenografts in mice showed that tumour mass was significantly reduced when treated with quinazoline compounds prazosin and doxazosin compared to untreated controls, possibly through the induction of apoptosis [38,42,46,57,62]. Terazosin treatment in nude mice significantly reduced VEGF induced angiogenesis. This effect was also seen in prostate tumour mice models [39] suggesting that terazosin has very potent anti-angiogenic effects, reducing tumour volume over time. Anti-proliferative effects of doxazosin were also observed in Wistar rats treated with doxazosin and finasteride [63]. Interestingly, doxazosin has recently been identified as a novel EphA2 agonist [47,62], which triggers PCa cytotoxicity via cell rounding and detachment in vitro and this mechanism may translate to animal models [62]. In line with previous in vivo studies, doxaozisn was previously found to reduce tumour metastasis and improve survival of PC-3 xenograft nude mice. These anti-tumour effects were proposed to occur, to some extent, by EphA2-mediated cell detachment, inhibition of tumour cell migration [62], and indirect activation of apoptosis. 3.3. Clinical Evidence To determine if the cytotoxic and anti-tumour effects observed in vitro and in mice models translate into a potential therapeutic application in human patients, we examined their effects in patients taking them long term (ranging from 3 to 11 months or longer) [34,36]. However, to date there are only four retrospective human studies that have investigated the benefit of quinazoline based α-antagonists in patients after original treatment ended (Table 1). Interestingly, both quinazoline and non-quinazoline α-antagonists appear to decrease the incidence of PCa at doses indicated for the symptomatic relief of LUTs [35,36,37,64]. It is therefore difficult to evaluate their potential for treating PCa. 3.4. Anticancer Effects of α-Antagonists in Other Cancers Lastly, we examined the potential of cytotoxic and antitumor actions of α-antagonists in other cancers (See Table 2 and Supplementary Material Figure S1 for detailed review). Consistent with in vitro and in vivo animal findings in PCa, classical α-antagonists and their analogues appear to have broad activity, exhibiting cytotoxicity in other cancer cell lines including urogenital [44,65,66,67,68,69,70,71,72,73,74], gastrointestinal [73,74,75,76,77], lung [74,78], blood [79], brain [80,81] and thyroid [82]. Importantly, the cytotoxic and anti-proliferative effects are supported by several in vivo mice studies [48,66], suggesting ubiquitous anticancer actions of these drugs. In support of in vitro findings a retrospective study in 24 patients with bladder cancer, 15 of which had been treated with terazosin over a 3–6 month period, had a reduction in incidence, tissue MVD and increase in apoptotic index [83]. The only trials with doses that are of clinical relevance are with bladder, pituitary and ovarian cancer, all of which are within the standard dosage ranges of the respective medications [48,50,66]. The proposed cytotoxic mechanisms of α-antagonists in other cancers differ from those identified in PCa, suggesting the magnitude of their anticancer effects may vary between cancer types. It is difficult to draw sound conclusions of the efficacy of α-antagonists in other cancers from this data. 4. Conclusions PCa is the most commonly diagnosed cancer in Australia with substantial mortality associated with the castrate resistant form. Current treatments are significantly limited by the development of resistance, as well as severe toxicity. Therefore, there is an urgent need to identify alternate or adjunct treatment options. Given their key role in managing the LUTs associated with PCa and its treatments, as well as in vitro cytotoxicity against PCa cell lines, α-antagonists may offer such a treatment option. However, apart from a series of excellent in vitro studies and limited animal studies, the potential of α-antagonists as a treatment option in human patients remains unclear. Therefore, the purpose of this literary review was to analyse the current evidence for the use of α-antagonists in the treatment of prostate and other cancers, and elucidate mechanisms responsible for their cytotoxic effects. Several elegant in vitro studies demonstrate that quinazoline based α-antagonists (doxazosin, terazosin and prazosin) are cytotoxic to PCa cell lines by inducing apoptosis, inhibiting cell proliferation and angiogenesis. Similarly, these effects were also observed with the piperazine based agent naftopidil. In contrast tamsulosin, a sulphonamide based compound did not exhibit cytotoxic activity, suggesting that structural specificity is important in eliciting cytotoxic action. In vitro studies also suggest that the quinazoline α-antagonists may also target angiogenesis by disrupting VEGF. Furthermore, several studies also suggest that the cytotoxic actions are not limited to PCa cell lines as α-antagonists were also shown to induce cell death in some of the following cell lines: Bladder HT1376, Ovarian SKOV-3, Renal Carcinoma ACHN and Caki-2. However, more robust trials with standardised methodologies are required to strengthen the evidence of α-antagonists as chemotherapeutic agents in cancers other than prostate. The in vitro findings are reflected in mice models of PCa, with many studies showing that tumour growth and angiogenesis is significantly decreased when animals are treated with quinazoline or piperazine agents. However, evidence for the potential of the quinazoline and piperazine α-antagonists to treat PCa in human patients is lacking. We identified only four studies looking at the risk of developing PCa in human patients using α-antagonists. These retrospective and observational cohort studies did not examine the potential of these agents to treat PCa. Instead, they showed a decreased incidence of PCa in long term users of α-antagonists. Therefore, while the in vitro and animal studies clearly demonstrate the potential role of quinazoline and piperazine based α-antagonists in the treatment of prostate and other cancers, well designed, prospective clinical trials in humans are required to ultimately evaluate their efficacy as either a primary treatment option or as an adjunct. It is difficult to draw sound conclusions of the efficacy of α-antagonists in other cancers from the data analysed in this review. More robust trials with standardised methodologies are required to strengthen the evidence of α-antagonists as chemotherapeutic agents in cancers other than prostate. We hope the findings from this literature review will stimulate further research to potentially place α-antagonists as possible treatment options for PCa in the future. Acknowledgments All sources of funding of the study should be disclosed. Please clearly indicate grants that you have received in support of your research work. Clearly state if you received funds for covering the costs to publish in open access. Supplementary Materials Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1339/s1. Click here for additional data file. Author Contributions Shailendra Anoopkumar-Dukie, Catherine M. McDermott and Russ Chess-Williams conceived and designed the study; Mallory Batty, Rachel Pugh, Ilampirai Rathinam, Joshua Simmonds, Edwin Walker and Amanda Forbes carried out the review, analysed the data and prepared the manuscript. Amanda Forbes, Catherine M. McDermott, Shailendra Anoopkumar-Dukie, David Christie and Russ Chess-Williams drafted and edited the manuscript. David Christie and Briohny Spencer provided feedback on the final manuscript. Conflicts of Interest The authors declare no conflict of interest. Appendix A ijms-17-01339-t003_Table A1Table A1 Inclusion/exclusion search terms/filters included in methodology. Search Term 1: Agent Search Term 2: Target Tissue Search Term 3: Action Alfuzosin α Adrenergic antagonist α Adrenoreceptor blockers α Blocker Doxazosin Naftopidil Phentolamine Prazosin Silodosin Terazosin Adenocarcinoma Cancer Carcinoma Neoplasm Prostate cancer Anoikis Anti-angiogenic Anti-proliferative Anticancer Antineoplastic Apoptosis Cytotoxic Filters Applied in PubMed Text—Full text Publication Date—2000–2016 Language—English Subjects—Cancer Search Fields—Title/Abstract Exclusion terms: In Title/Abstract AG1478, α-methyl-DL-tryptophan, α-linolenic, α-methyltryptophan, Biscoumarins, BYL719, Calcium channels, Cardiotoxic, Chitosan, Gambogic acid, Glyceollin, Hepatocarcinogens, HhAntag691, Insulin-like growth factor 1 receptor, LSC, Mast cells, PSC833, R482G isoform, Raloxifene, Stapling, Stilbenes, Toremifene, Triphosphate-binding Combined Search Terms (α adrenergic antagonist OR α adrenoreceptor blocker OR α blocker OR Prazosin OR Doxazosin OR Naftopidil OR Phentolamine OR Alfuzosin OR Terazosin OR Silodosin) AND (Neoplasm OR Cancer OR Prostate cancer OR Carcinoma OR Adenocarcinoma) AND (Anoikis OR Antineoplastic OR Apoptosis OR Anticancer OR Anti-angiogenic OR Anti-proliferative OR Cytotoxic) NOT (Calcium channels OR Cardiotoxic OR LSC OR Biscoumarins OR Glyceollin OR Chitosan OR toremifene OR triphosphate-binding OR α-linolenic OR α-methyl-DL-tryptophan OR HhAntag691 OR α-methyltryptophan OR raloxifene OR AG1478 OR R482G isoform OR PSC833 OR Mast cells OR Gambogic acid OR hepatocarcinogens OR insulin-like growth factor 1 receptor OR stapling OR BYL719 OR stilbenes) Figure A1 Stepwise process used to determine the final list of studies. Figure 1 Structural comparison of the α-antagonists doxazosin, naftopidil and tamsulosin. Figure 2 Proposed cytotoxic mechanisms underlying quinazoline-based α-antagonists. ijms-17-01339-t001_Table 1Table 1 Clinical-based studies investigating the effect of α1-antagonists on prostate cancer (PCa). Author Title Drug Results Study Type Keledjian, K. et al. [34] Reduction of human prostate tumor vascularity by the α1-adrenoceptor antagonist terazosin Terazosin Increased apoptotic index in prostate carcinoma after terazosin treatment. Reduction in prostate tumour vascularity in terazosin-treated BPH patients. Patients were treated for 6–11 months Retrospective Cohort study Harris, A. et al. [35] Effect of α1-adrenoceptor antagonist exposure on prostate cancer incidence: an observational cohort study Doxazosin & Terazosin 4070 men were treated with α-antagonists for Benign prostatic hyperplasia or hypertension or HTN. The incidence of PCa among treated men vs. untreated men was 1.65% and 2.41% respectively. Data showed 7.6 fewer cases developed per 1000 exposed men Observational Cohort study Yamada, D. et al. [36] Reduction of prostate cancer incidence by naftopidil, an α1-adrenoceptor antagonist and transforming growth factor-β signalling inhibitor Naftopidil & Tamsulosin PCa incidence was significantly lower in men treated with naftopidil for ≥3 months compared to men treated with tamsulosin. (p = 0.035) Retrospective Cohort study Bilbro, J. et al. [37] Therapeutic value of quinazoline-based compounds in prostate cancer Doxazosin, terazosin and other quinazolines Patients treated with α1-antagonists: doxazosin and terazosin, at the Markey Cancer centre had reduced risk of developing PCa Retrospective Cohort study ijms-17-01339-t002_Table 2Table 2 Summary of identified studies investigating the anticancer effect of α-antagonists. Ref. Author Title Study Type Cancer Type Drugs Findings (Original Studies) [29] Nishizaki, T. et al. 1-[2-(2-Methoxyphenylamino) ethylamino]-3-(naphthalene-1-yloxy) propan-2-ol may be a promising anticancer drug Review NA NA [30] Kyprianou, N. et al. Apoptosis induction by doxazosin and other quinazoline α1-adrenoceptor antagonists: a new mechanism for cancer treatment? Review NA NA [31] Patane, S. et al. Insights into cardio-oncology: Polypharmacology of quinazoline-based α1-adrenoceptor antagonists Review NA NA [32] Desiniotis, A. et al. Advances in the design and synthesis of prazosin derivatives over the last ten years Review NA NA [33] Tahmatzopoulos, A. et al. The role of α-blockers in the management of prostate cancer Review NA NA [34] Kyprianou, N. et al. Suppression of human prostate cancer cell growth by α1-adrenoceptor antagonists Doxazosin and terazosin via induction of apoptosis In vitro, in vivo (mice) Prostate Cancer (PC-3 and DU145) Doxazosin, terazosin, tamsulosin, phenoxy-benzamine Doxazosin and terazosin induced apoptosis in prostate epithelial and smooth muscle cells in patients with BPH, without affecting rate of cell proliferation in PCa cells. This effect could not be prevented by irreversible inhibition of α1-adrenoceptors (phenoxybenzamine), indicating an in vitro toxicitity occurs via an α-receptor independent mechanism. Doxazosin administration (at tolerated pharmacologically relevant doses) in SCID mice resulted in a significant inhibition of PC-3 tumour growth, presumably via induction of apoptosis. [35] Pan, S. et al. Identification of apoptotic and antiangiogenic activities of terazosin in human prostate cancer and endothelial cells In vitro, in vivo (mice) PCa PC-3 & endothelial HUVEC cells Terazosin It was found that terazosin induced apoptosis in PC-3 and human benign prostatic cells (IC50 > 100 µM), and possessed potent anti-angiogenic effect in endothelial cells compared to PCa cells. Terazosin (IC50 of 7.9 µM) significantly inhibited VEGF-induced angiogenesis and endothelial tube formation in nude mice, demonstrating that terazosin had a more potent anti-angiogenic than cytotoxic effects. Terazosin also effectively inhibited vascular endothelial growth factor induced proliferation and tube formation in cultured human umbilical vein endothelial cells (IC50 9.9 and 6.8 µM, respectively). Doxazosin, but not tamsulosin, mimicked these effects and the anti-tumour effects of these drugs were determined to occur independent of α1-adrenoceptor antagonizing activity. [36] Walden, P. et al. Induction of anoikis by Doxazosin in prostate cancer cells is associated with activation of caspase-3 and a reduction of focal adhesion kinase In vitro PCa (PC-3 and LNCaP) Doxazosin Doxazosin induced changes in morphology consistent with anoikis in both benign and cancerous prostatic cells (rounding up of cells, DNA-degradation in the nucleus, cell shrinkage, the appearance of vacuoles, and cell detachment from the tissue culture plate) and increased caspase-3 activity. The increase of caspase-3 activity by doxazosin promotes anoikis and, subsequently, apoptosis of cancer cells. Treatment of PC-3 cells with doxazosin significantly reduced the protein levels of anti-anoikis kinase, FAK, but did not significantly affect the levels of ILK. Norepinephrine had no effect on doxazosin-induced cell morphology or caspase-3 activity, indicating that the apoptotic/anoikis effects of doxazosin result from mechanism that is a1-adrenoceptor independent. [37] Benning, C. et al. Quinazoline-derived α1-adrenoceptor antagonists induce prostate cancer cell apoptosis via an α1-adrenoceptor-independent action In vitro Prostate cancer cells Doxazosin, terazosin Transfection-mediated overexpression of α1-adrenoreceptors in human prostate cancer cells, DU-145 (AR-independent, and reportedly lack of adrenoceptors), did not alter the ability of prostate cancer cells to undergo apoptosis in response to quinazolines. These findings indicate that apoptotic activity of quinazoline-based α1 adrenoceptor antagonists (doxazosin and terazosin) in prostate cancer cells is independent of α1-adrenoceptor antagonism. [38] Kyprianou, N. Doxazosin and terazosin suppress prostate growth by inducing apoptosis: clinical significance Review, in vitro, in vivo (mice) PC-3, DU-145 and SMC-1 Doxazosin, terazosin Doxazosin and terazosin significantly reduced the viability of PC-3 and LNCaP cells by inducing caspase-3 mediated apoptosis in a dose dependent manner, however only doxazosin induced significant death of PCa cells. Doxazosin (and not terazosin) significantly affect the rate of proliferation of PCa cells. Irreversible inhibition with phenoxybenzamine did not abolish the apoptotic effect of doxazosin or terazosin against PCa or SMC cells, indicating the cytotoxic effects occurred via an α1-independent mechanism. Oral treatment with doxazosin resulted in significant decrease in tumour volume of PCa xenografts compared to controls, presumably via induction of apoptosis. [39] Arencibia, J. et al. Doxazosin induces apoptosis in LNCaP prostate cancer cell line through DNA binding and DNA-dependent protein kinase down-regulation In vitro LNCaP Doxazosin Doxazosin induced dose-dependent LNCaP cytotoxicity and apoptosis, which could not be prevented by phenoxybenzamine, indicating an α1-adrenoceptor independent cytotoxicity. Microarray analysis following doxazosin treatment (8–24 h, 20 µM) identified 70–92 deregulated genes, including several involved in cell-cycle control and drug response, and a few related to other cellular processes such as apoptosis or angiogenesis. An inverse correlation was observed with doxazosin concentration and topoisomers, suggesting that topoisomerase I is inhibited by the binding of doxazosin to DNA. Thus, doxazosin could cause DNA damage, resulting in apoptotic cell death. [40] Siddiqui, E. et al. Growth inhibitory effect of Doxazosin on prostate and bladder cancer cells. Is the serotonin receptor pathway involved? In vitro PCa PC-3, bladder cancer HT1376 Doxazosin Doxazosin was found to significantly reduce PCa PC-3 and bladder cancer HT1376 cell growth, which was partially prevented through pre-treatment with 5HT or 5-HT1B. These findings may be related to the structural similarity between subtype 1 serotonin and adrenergic receptors, and the authors suggests that doxazosin displaces 5-HT from 5-HT receptors. [41] Garrison, J. et al. Doxazosin induces apoptosis of benign and malignant prostate cells via a death receptor-mediated pathway In vitro PC-3 and BPH1 Doxazosin Doxazosin (24 h) causes a dose dependent loss of cell viability and induces apoptosis in PC-3 and BPH1 cells 24 h after treatment. After a short doxazosin treatment (6 h), several genes that play a critical role in apoptosis were upregulated in PC-3 cells. In particular, doxazosin was found to upregulate Bax mRNA transcription and induced caspase-8 mediated apoptosis. [42] Lin, S. et al. Prazosin displays anticancer activity against human prostate cancers: targeting DNA and cell cycle In vitro, in vivo (mice) Prostate Cancer Prazosin Prazosin exhibited anti-proliferative activity superior to that of other α-blockers. It induced G2 checkpoint arrest and subsequent apoptosis. In PC-3 cells, prazosin increase in DNA strand breakage leading to Cdk1 inactivation and subsequent cell cycle arrest. In mice, prazosin significantly reduced tumour mass in PC-3 derived cancer xenografts. [43] Forbes, A. et al. Relative cytotoxic potencies and cell death mechanisms of α-adrenoceptor antagonists in prostate cancer cell lines In vitro PCa PC-3, LNCaP Prazosin Doxazosin, terazosin, silodosin, alfuzosin, tamsulosin The relative potency order was prazosin = doxazosin > terazosin = silodosin = alfuzosin> tamsulosin on both cell types, but LNCaP cells were significantly more sensitive to these effects that PC-3 cells. Prazosin and doxazosin increased levels of apoptosis and autophagy in both cell lines. However, autophagy was found to play a paradoxical role by contributing to survival of LNCaP and cytotoxicity of PC-3 cells. Treatment with prazosin (30 µM) altered the expression of several cell stress-related proteins: elevating phospho-p38α and reducing S6 kinase in both cell lines. The expression of some proteins were differentially affected in PC-3 and LNCaP cell; Akt and p27 increasing and HIF-1α decreasing in LNCaP cells but not PC-3, while ADAMTS1 was increased in PC-3 cells only. Phosphorylation of EphA2 was also reported to play a role in doxazosin, but not prazosin, induced PC-3 cytotoxicity. [44] Fernando, M. et al. α1-Adrenergic receptor antagonists: novel therapy for pituitary adenomas In vitro, in vivo (mice) Pituitary tumour Doxazosin Treatment with Doxazosin results in reduced phosphorylation and down-regulation of NF-κB. Decreased phosphorylated retinoblastoma and PCNA expression, which resulted in cell cycle arrest at G0-G1. Doxazosin treatment also increased cleaved caspase 3. In mice, the tumour mass was lower in the doxazosin treated group. In contrast to current literature, this study suggested that the cytotoxic activity of quinazoline-antagonists was greater in cells that express α1a and 1b. In addition to apoptosis, doxazosin treatment appeared to reduce the circulating ACTH level and therefore may be useful for symptomatic relief. [45] Youm, Y. et al. Doxazosin-induced clusterin expression and apoptosis in prostate cancer cells In vitro PCa PC-3 Doxazosin Doxazosin-induced DNA fragmentation after 24 h treatment, and was statistically significant after 48 h treatment of PC-3 cells. Clusterin expression in PC-3 cells was 3-fold higher in doxazosin treated cells (9 h) compared to untreated controls, and was maintained over 48 h. These findings were found to be consistent with doxazosin-induced apoptosis. Immunocytochemistry analysis (after 9 and 12 h treatment) demonstrated the presence of clusterin in 7% and 18% of total cells respectively. At 24 h treatment, clusterin protein was mainly observed in the cytoplasm and rarely in the nuclei of healthy cells. [46] Tahmatzopoulos, A. et al. Maspin sensitizes prostate cancer cells to Doxazosin-induced apoptosis In vitro PCa DU-145 Doxazosin Maspin (tumour suppressor protein) was shown to increase sensitivity of PCa DU-145 cells to doxazosin, by affect the migration and attachment of malignant prostate cells to the ECM. Also caused mammary MDA-MB-435 cells to undergo apoptosis via increased Bax and caspase-3 activation. [47] Partin, J. et al. Quinazoline-based α1-adrenoceptor antagonists induce prostate cancer cell apoptosis via TGF-β signalling and I κB α induction In vitro PCa PC-3 Doxazosin, tamsulosin Doxazosin, but not tamsulosin, was found to induce PC-3 apoptosis by enhancing TGF-β1 signalling, and subsequently, downstream 1κBα. [48] Keledjian, K. et al. Anoikis induction by quinazoline based α1-adrenoceptor antagonists in prostate cancer cells: antagonistic effect of bcl-2 In vitro PCa PC-3 Doxazosin, terazosin, tamsulosin (at therapeutic doses) Treatment of PC-3 cells with doxazosin or terazosin, but not tamsulosin, resulted in significant down regulation of VEGF. Doxazosin also promoted anoikis. However, these effect was reduced in PC-3s that over-expressed Bcl-2 (an anoikis inhibiting factor). In these experimental conditions, these drugs did not have any effect on HIF1-α expression. [49] Liao, C. et al. Anti-angiogenic effects and mechanism of prazosin In vitro PCa and HUVEC Prazosin Prazosin induced apoptosis in PCa and normal HUVEC cells via different mechanisms, suggesting that prazosin-mediated anti-angiogenic activity and differential modulation of apoptotic pathways are cell-type specific. [50] Kim, S. et al. Dual silencing of Hsp27 and c-FLIP enhances doxazosin-induced apoptosis in PC-3 prostate cancer cells In vitro PCa PC-3 Doxazosin Apoptotic indices increased in a dose-dependent manner when doxazosin was added. In basal conditions (+Hsp27/+c-FLIP), doxazosin (25 µM) induced apoptosis in 52% of cells. In −Hsp27/+c-FLIP cells, apoptotic activity increased to 68% of PC-3 cells. In the opposite case (+Hsp27/−c-FLIP) the apoptotic index was 78%. Even greater number of apoptotic cells were observed (92%) when both Hsp27 and c-Flip were silence. These findings indicate that Hsp27 and c-FLIP play a protective role against doxazosin induced cytotoxicity of PC-3 cells. [51] Lee, S. et al. Expression of heat shock protein 27 in prostate cancer cell lines according to the extent of malignancy and doxazosin treatment In vitro PCa LNCaP, PC-3 Doxazosin RT-PCR studies identified Hsp27 expression to be related to PCa malignancy potential in vitro (e.g., Hsp27 > in PC-3 than LNCaP cells), and was dose-dependently enhanced in some cell lines following doxazosin treatment. Apoptotic cell death triggered by HSP27 siRNA is greater in the androgen receptor-negative cell line PC-3 than in the androgen receptor-positive cell line LNCaP. [52] Cal, C. et al. Doxazosin: a new cytotoxic agent for prostate cancer? In vitro PCa DU145, PC-3 Doxazosin adriamycin, etoposide, paclitaxel. DU-145 and PC3 were sensitive to doxazosin-mediated cytotoxicity, which occurred in a dose- and time-dependent fashion. The combination of doxazosin and adriamycin or etoposide resulted in significant dose-dependent cytotoxic synergism. In contrast, the combination of doxazosin and paclitaxel resulted in antagonistic activity, which was enhanced with increasing concentrations of the drugs. [53] Chang, K. et al. Combined effects of terazosin and genistein on a metastatic, hormone-independent human prostate cancer cell line In vitro PCa DU-145 Terazosin, genistein Terazosin or genistein alone inhibited cell growth in a dose-dependent manner genistein (5 µ/mL) being more effective than terazosin (1 µ/mL—nontoxic dose). Combination treatment significantly increased apoptosis in cells compared to genistein alone. The synergistic effects of these drugs had a greater inhibitory effect the pro-survival Bcl-XL protein, compared to either drug along. Genistein and the combination also were reported to have an effect on angiogenesis-related proteins, causing a significant decrease in VEGF165 mRNA and VEGF121 mRNA levels. [54] Harris, A. et al. Effect of α1-adrenoceptor antagonist exposure on prostate cancer incidence: an observational cohort study Observational cohort PCa Doxazosin and Terazosin Incidence of PCa in men exposed to quinazoline-based α-blockers (for BPH or hypertension) was 1.65% whereas in unexposed men, incidence was 2.41%. This indicates men who were exposed to quinazolines were 1.46 times lower relative risk of developing PCa, compared to unexposed men. However, there was no association between quinazoline exposure and overall patient survival. [55] Liu, C. et al. Piperazine-designed α1A/α1D-adrenoceptor blocker KMUP-1 and Doxazosin provide down-regulation of androgen receptor and PSA in prostatic LNCaP cells growth and specifically in xenografts In vitro, in vivo (mice) PCa: LNCaP, PC-3 and DU-145 Doxazosin and KMUP-1 KMUP-1 and Doxazosin both inhibit LNCaP cell growth and downregulate expression of AR and PSA. KMUP-1 is a Xanthine derivative PDE inhibitor with α-blocking features. It also has a piperazine moiety very similar to that seen in Doxazosin, naftopidil which is reported to lead to its activity. KMUP-1 significantly inhibited LNCaP cell growth and induced apoptosis in time and dose-dependent manner. KMUP-1 and doxazosin further inhibited the expression of AR and PSA. Treatment of LNCaP cells with KMUP-1 resulted in cell cycle arrest and apoptotic activities, increasing p21 and p27 and decreasing expressions of cyclin D1, cyclin E, cyclin dependent kinase (CDK) 4, CDK2 and CDK6. Moreover, KMUP-1 activated p53, cleaved poly (ADP-ribose) polymerase and caspase-3, but reduced the expression of Bcl-2. Regular administration of KMUP-1 suppressed the LNCaP xenograft tumour growth in nude mice. These evidences indicate that KMUP-1 and doxazosin inhibit LNCaP cell growth and downregulate expression of AR and PSA. KMUP-1 might be used as a chemoprevention agent for preventing the development of prostate cancer without cardiovascular adverse effect of doxazosin. [56] Ho, C. et al. Repurposing of phentolamine as a potential anticancer agent against human castration-resistant prostate cancer: A central role on microtubule stabilization and mitochondrial apoptosis pathway In vitro PCa DU145, PC-3 Phentolamine, paclitaxel Phentolamine induced anti-proliferative effects in PC-3 and Du-145, two CRPC cell lines and p-glycoprotein overexpressing cells. This effect was not significantly reduced in paclitaxel resistant cells. Phentolamine induced mitotic arrest of the cell cycle and formation of hyperdiploid cells, followed by an increase of apoptosis. Mitotic arrest was confirmed by cyclin B1 up regulation, CDK1 activation and a dramatic increase of mitotic protein phosphorylation. In vitro cellular identification demonstrated that phentolamine, similar to paclitaxel, induced tubulin polymerization and formation of multiple nuclei. The Data suggests that phentolamine is a potential anti-cancer agent. It induces microtubule assembly, leading to mitotic arrest of the cell cycle, which ‘in turn’ induces subsequent mitochondrial damage, and activation of related apoptotic signalling pathways in CRPC cells. Furthermore, the combination between phentolamine and paclitaxel induces a synergistic apoptotic cell death. Phentolamine has a simple chemical structure and is not P-gp substrate. Optimization of phentolamine structure may also be a potential approach for further development. [57] Anglin, I. et al. Induction of prostate apoptosis by α1-adrenoceptor antagonists: mechanistic significance of the quinazoline component Review [58] Keledjian, K. et al. Doxazosin inhibits human vascular endothelial cell adhesion, migration, and invasion In vitro HUVEC, endothelial cells Doxazosin Doxazosin results in a dose-dependent loss of cell viability after 24 h of treatment. At concentrations as low as 1 mM, 10% loss of cell viability is observed and at 15 mM there is more than 30% cell death. There is also significant increase in the number of apoptotic cells within 24 h of exposure to doxazosin and a further increase after 48 h. Increased protein expression of pro-caspase-3 was observed after 6 and 12 h of doxazosin treatment. Doxazosin markedly suppresses VEGF—mediated endothelial cell adhesion to fibronectin. HUVEC cells were wounded and 24 h post-wounding, doxazosin treatment (15 mM) resulted in a dramatic decrease in HUVEC cell migration in the absence or presence of exogenous VEGF compared to control. Thus doxazosin can cause suppression of VEGF-mediated cell migration. FGF-2, a potent angiogenic factor, results in significant stimulation of HUVEC angiogenic response that was suppressed by doxazosin treatment. TGF-b had no significant impact on HUVEC-tube formation. Doxazosin treatment for 24 h resulted in a significant downregulation of VEGF mRNA. [59] Petty et al. A small moleculre agonist of EphA2 receptor tyrosine kinase inhibits tumor cell migration in vitro and prostate cancer in vivo In vitro, in vivo (mice) PC-3 Doxazosin Doxazosin induced cell rounding and detachment via agonistic actions on EphA2 in vito. Animal studies found that doxazosin reduced number of tumour metastasis and increased survival in PC-3 xenograft nude mice [60] Justulin, L. et al. Combined effect of the finasteride and doxazosin on rat ventral prostate morphology and physiology In vivo PCa Doxazosin and finasteride Wistar rats were treated with finasteride and doxazosin and the ventral prostate was excised at day 3 and day 30. The combination induced a transient increase in testosterone plasma concentration and a permanent reduction in DHT. The ventral prostate and epithelial cell proliferation were reduced and the collagen fibre volume fraction and apoptosis of the epithelial cell were increased. Transcription of MMP-2, TIMPs-1 and -2 mRNA was decreased after 30 days of treatment. [61] Keledjian, K. et al. Reduction of human prostate tumor vascularity by the α1-adrenoceptor antagonist terazosin In vitro, retro-spective PCa Terazosin A significant induction of apoptosis was observed among the cancerous prostatic epithelial cells in the terazosin-treated, as compared to the untreated prostate cancer specimens, while there was no significant change in the proliferative index of the same tumour cell populations after treatment. Furthermore, terazosin resulted in a significant decrease in prostate tissue MVD compared with the untreated group, which correlated with the increased apoptotic index of the cancerous areas. Tissue PSA expression in the prostatic tumour was also markedly reduced after terazosin treatment, while no significant changes in VEGF expression were detected. These findings provide the first evidence that terazosin; a quinazoline-based α-blocker decreases prostate tumour vascularity. Our study has significant clinical implications in identifying selected α-adrenoceptor antagonists as potential anti-tumour agents with apoptotic and anti-angiogenic effects in the human prostate that can be exploited for the treatment of advanced prostate cancer. [62] Yamada, D. et al. Reduction of prostate cancer incidence by naftopidil, an α1-adrenoceptor antagonist and transforming growth factor-β signaling inhibitor In vitro, retro-spective PCa Naftopidil and tamsulosin Prostate cancer incidence was significantly lower in men who received naftopidil for 3 months or longer compared with tamsulosin (p = 0.035). Immunohistochemically positivity for Bcl2, a marker for resistance to apoptosis, was less frequently detected in prostate cancer cells of men who received naftopidil compared with tamsulosin. Naftopidil induced apoptosis and blocked Smad2 phosphorylation activated by transforming growth factor-B in cell lines. [63] Tahmat-zopoulos, A. Apoptotic impact of α1-blockers on prostate cancer growth: a myth or an inviting reality? Review, in vitro, retrospective PCa Terazosin, doxazosin and tamsulosin Description of α-antagonist induced anoikis and angiogenesis. Discusses retrospective study of patients using terazosin, and marked increase in tumour vascularity on autopsy. [64] Bilbro, J. et al. Therapeutic value of quinazoline-based compounds in prostate cancer Review PCa NA NA [65] Hui, F. et al. The α1-adrenergic receptor antagonist Doxazosin inhibits EGFR and NF-κB signalling to induce breast cancer cell apoptosis In vitro Breast cancer Doxazosin Doxazosin reduces phosphorylation of EGFR and decreases pERK1/2 levels, NF-κB, AP-1, SRE, E2F and CRE-mediated transcriptional activity. Doxazosin also decreased phosphorylated retinoblastoma (pRb) protein expression, providing a potential mechanism for the doxazosin-mediated G0/G1 cell cycle arrest. Quinazoline ring structure is similar to the EGFR tyrosine kinase inhibitors. Doxazosin appears to be safe in normal cells due to the main target being EGFR and NF-κB signalling which has greater activation in cancer cells. [66] Park, M. et al. The antihypertension drug Doxazosin suppresses JAK/STATs phosphorylation and enhances the effects of IFN-α/γ-induced apoptosis In vitro, In vivo (mice) Ovarian cancer Doxazosin Doxazosin significantly suppressed tumour growth in an ovarian cancer cell xenograft mouse model (50%–65% reduction in tumour size), inducing apoptotic cell death by up-regulating the expression of p53. There was no additional liver toxicity or loss of body weight. In vitro identified JAK/STAT signaling as potential mediators underlying the anti-tumour effect of doxazosin. [67] Kawahara, T. et al. Silodosin inhibits prostate cancer cell growth via ELK1 inactivation and enhances the cytotoxic activity of gemcitabine In vitro Prostate Cancer Silodosin and gemcitabine Silodosin treatment reduced the expression/activity of ELK1 in these cells as well as viability of AR-positive cells, but not the viability of AR-negative or ELK1 negative cells. Interestingly silodosin significantly increased the sensitivity to gemcitabine, but not cisplatin or docetaxel. ELK1 is likely activated in prostate cancer cells and promote tumour progression. Furthermore, silodosin that inactivates ELK1 in prostate cancer cells not only inhibits their growth but also enhances the cytotoxic activity of gemcitabine. Thus, ELK1 inhibition has the potential of being a therapeutic approach or prostate cancer. [68] Kawahara, T. et al. Silodosin inhibits the growth of bladder cancer cells and enhances the cytotoxic activity of cisplatin via ELK1 inactivation In vitro Bladder Cancer (ELK-1 positive urothelial carcinoma) Silodosin + cisplatin Involvement of ELK1 in bladder cancer progression via modulation cell proliferation/apoptosis, migration and invasion. In bladder and prostate cancers, ELK1 was shown to induce the proliferation of cells only with an activated androgen receptor). Silodosin was found to not only inhibit cell viability and migration, but also enhance the cytotoxic activity of cisplatin in bladder cancer lines via inactivating ELK1. The results suggest that combined treatment with silodosin is useful for overcoming chemoresistance in patients with ELK-1 positive urothelial carcinoma receiving cisplatin. [69] Iwamoto, Y. et al. Oral naftopidil suppresses human renal-cell carcinoma by inducing G(1) cell-cycle arrest in tumor and vascular endothelial cells In vitro, in vivo (mice) Renal Cell Carcinoma (ACHN, Caki-2) ACHN Naftopidil Naftopidil, but not tamsulosin, was found to inhibit proliferation of renal cancer cells via induction G1 cell cycle arrest in in vitro studies. [70] Sakamoto, S. et al. Anoikis disruption of focal adhesion-Akt signaling impairs renal cell carcinoma In vitro Renal cancer 786-0, Caki Doxazosin and derivatives Quinazoline-based drugs trigger anoikis in renal cancer cells by targeting the focal adhesion survival signalling. [71] Takara, K. et al. Effects of α-adrenoceptor antagonist Doxazosin on MDR1-mediated multidrug resistance and transcellular transport In vitro Human cervical carcinoma (HeLa, Hvr100-6) Doxazosin, prazosin, terazosin Co-treatment of chemotheraputics (vinblastine and paclitaxel) with doxazosin (1 µM) enhanced chemosensitivity of overexpressing multi-drug resistant HeLa cells, Hvr100-6. On the other hand, prazosin (1 µM) was found to partially reverse cells sensitivity to vinblastine when used in combination, by dose-dependently increasing intracellular accumulation of chemotheraputics. Whereas terazosin had no effect. All other combinations of chemotheraputic and α1-antagonists were found to have little or no effect on chemosensitivity. Over all this study suggests that doxazosin thus may partly reverse drug resistance by inhibiting MDR-1-mediated drug efflux, and in turn, contribute to maintenance of intracellular cytotoxic concentrations. [72] Powe, D. et al. α- And β-adrenergic receptor (AR) protein expression is associated with poor clinical outcome in breast cancer: an immunohistochemical study In vitro Breast cancer α-Antagonists α Antagonists were found to inhibit proliferation and induce apoptosis in vitro. [73] El Sharkawi, F. et al. Possible anticancer activity of rosuvastatine, Doxazosin, repaglinide and oxcarbazepin In vitro MCF7, HeLa, HepG2,EACC Doxazosin Doxazosin was most effective in the EACC line exhibiting 100% inhibition of cell proliferation. Specific mechanisms of action are not reported or discussed. [74] Kanno, T. et al. 1-[2-(2-Methoxyphenylamino) ethylamino]-3-(naphthalene-1-yloxy)propan-2-ol as a potential anticancer drug In vitro Bladder, prostate, MPM, lung, hepatoma, gastric, renal and colorectal cancer cell lines Caco-2 and CW2 Naftopidil This study is discussed in review above. Discuses caspase activation and cell death. [75] Kaku, Y. et al. The newly synthesized anticancer drug HUHS1015 is useful for treatment of human gastric cancer In vitro, in vivo (mice) Gastric cancer (MKN45 and MKN28) HUHS1015 (naftopidil analogue) HUHS1015 treatment caused upregulation of TNFα receptor and apoptosis was observed in both MKN28 and MKN45. However, no caspase activation was observed in MKN28, indicating that HUHS1015 resulted in caspase-dependent and independent apoptosis activity. Mice bearing MKN45 tumours had higher survival rates when treated with HUHS1015 compared to those treated with cisplatin, paclitaxel and irinotecan. [76] Kaku, Y. et al. HUHS1015 Suppresses Colonic Cancer Growth by Inducing Necrosis and Apoptosis in Association with Mitochondrial Damage In vitro, in vivo (mice) Colon cancer (Caco-2, CW2 cells) HUHS1015 (naftopidil analogue) HUHS1015 triggered apoptosis in colon cancer Caco-2 and CW2 cells by disrupting the mitochondrial membrane potential, lowering ATP levels, cytochrome c release, and initiation of the caspase cascade. In addition, HUHS1015 increased the number of cells in sub-G1 phase of cell cycling, which corresponded to apoptosis in both cell lines. In vivo mice studies demonstrated that treatment with HUHS1015, but not naftopidil, delayed colonic tumour growth compared to untreated controls. Furthermore, the authors report 100% survival rate for mice with colonic xenograft tumours treated with HUHS1015 or naftopidil, which was higher than control (89% survival). [77] Shen, S. et al. Effects of α-adrenoreceptor antagonists on apoptosis and proliferation of pancreatic cancer cells in vitro In vitro Pancreatic cancer (PC-2 and PC-3) Yohimbine and urapidil (α1- and α2-adrenoreceptor antagonists) Yohimbine induced apoptotic cytotoxicity of both pancreatic PC-3 and PC-3 pancreatic cancer. In contrast, urapidil was only cytotoxic to PC-2 cells. However, the positive control 5-FU, was more cytotoxic than yohimbine in the conditions tested. [78] Masachika, E. et al. Naftopidil induces apoptosis in malignant mesothelioma cell lines independently of α1-adrenoceptor blocking In vitro Meso-thelial cancer Naftopidil, prazosin Naftopidil and prazosin both have the potential to induce apoptosis via activating caspase-3 and caspase-8, but not caspase-9, independent of α1 blocking activity in mesothelioma cells. [79] Fuchs, R. et al. The cytotoxicity of the α1-adrenoceptor antagonist prazosin is linked to an endocytotic mechanism equivalent to transport-P In vitro K562 cells erythroleukemia, LNCaP (PCa) Prazosin/QAPB (fluorescent analogue of prazosin) Prazosin has been shown to be a substrate for an amine uptake mechanism called transport-P. The fluorescent analogue of prazosin, QAPB was associated with endocytic mechanism of prazosin/QAPB similar transport-P. Prazosin/QAPB was able to induce caspase 8 activation (apoptosis) and tabulation of lysosomes in LNCaP cells. The cytotoxic actions of prazosin was inhibited by chloroquine (a lysomototropic drug) and bafilomycin (transport-P inhibitor). This indicates that transport-p-mediated uptake, and subsequent endosome/lysosome accumulation and caspase activation underlies prazosin-induced LNCaP and/or K562 toxicity. [80] Albinana, V. et al. Propranolol reduces viability and induces apoptosis in hemanglioblastoma cells from von Hippel-Lindau patients In vitro Hemanglio-blastoma, cervical cancer HeLa9XHRE Propranolol (β-blocker) Propranolol treatment resulted in cytotoxicity and caspase-mediated apoptosis (50–100 µM, 48 h treatment) of hypoxia response element-transfected HeLa 9XHRE cells. Similar findings were also observed in hemanglioblastoma cells. Overall, the authors suggests these effects may due in part to the inhibitory effect of HIF1 transcription and protein expression in HeLA9XHRE and hemanglioblastoma cells. [81] Staudacher, I. et al. HERG K+ channel-dependent apoptosis and cell cycle arrest in human glioblastoma cells In vitro Glioblastoma (LNT-229, U87MG) Doxazosin, terazosin Doxazosin was found to induce apoptosis and G0/G1 cell cycle arrest of glioblastoma LNT-229 and U87MG cells in a time and concentration dependent manner. Also, blocking of doxazosin binding to hERG by the non-apoptotic hERG ligand, terazosin, rescued glioblastoma cells from doxazosin-induced apoptosis. The apoptotic effect of doxazosin was marked by the activation of pro-apoptotic factors/signalling (phospho-erythropoietin-producing human hepatocellular carcinoma receptor tyrosine kinase A2, phospho-p38 mitogen-activated protein kinase, growth arrest and DNA damage inducible gene 153, cleaved caspases 9, 7, and 3), and by inactivation of anti- apoptotic poly-ADP-ribose-polymerase, respectively. Overall, this study suggests doxazosin is a hERG antagonist, which results in the activation of apoptotic signaling cascade. [82] Fuchs, R. et al. The anti-hypertensive drug prazosin induces apoptosis in the medullary thyroid carcinoma cell line TT In vitro Medullary thyroid carcinoma Prazosin Prazosin (24 h, ≥15 µM) was found to induce caspase-3/7 activation and apoptosis of medullary thyroid carcinoma cells (α1A and α1B adrenoceptors-positive). This cytotoxicity was associated with morphological changes such as long polar needle-shaped polar protrusion fibers, an increased in number of intracellular vacuoles and detachment. The fibres present in treated cells seem to impair mobility of the cell and were associated with prazosin-mediated caspase activation. Prazosin was also found to have a similar morphological effect on normal human fibroblasts, suggesting a lack of specificity and risk of cytotoxicity to non-cancerous cells. [83] Tahmatzopoulos, A. et al. Effect of terazosin on tissue vascularity and apoptosis in transitional cell carcinoma of bladder Observational Cohort Transitional cell carcinoma (TCC) of the bladder Terazosin Pathological specimens of 24 men who underwent radical cystectomy for transitional cell carcinoma of the bladder were evaluated for terazosin-induced anti-cancer effects. For this study, patients with a history of 5a-reductase inhibitor use were excluded. For men who were never exposed to terazosin (15 men), markers of apoptosis were limited in the tumour specimens of these men. In contrast, terazosin exposure prior to cystectomy (9 men, 2–10 mg/day; 3–60 months) was associated with a statistically significant increase in tumour apoptosis. Terazosin treatment also significantly decreased microvascular density (MVD) in approximately 27% of specimens compared to specimens of unexposed men. N/A Bajek, A. et al. (2011) Prostate epithelial stem cells are resistant to apoptosis after α1-antagonist treatment. The impact for BPH patients In vitro Prostate cancer Doxazosin Doxazosin induced apoptosis in co-cultures of progenitor (type of stem cell) and differentiated epithelial cells. However, progenitor cells were not susceptible to apoptosis, which can be a reason of treatment failure in BPH patients. N/A Minarini, A. et al. (2006) Recent advances in the design and synthesis of prazosin derivatives - - Found to be irrelevant to our research but still of interest. ==== Refs References 1. Bray F. Ren J.S. Masuyer E. Ferlay J. Global estimates of cancer prevalence for 27 sites in the adult population in 2008 Int. J. Cancer 2013 132 1133 1145 10.1002/ijc.27711 22752881 2. Australian Institute of Health and Welfare Prostate Cancer in Australia Cancer Series no. 79. Cat. no. CAN 76 AIHW Canberra, Australia 2013 3. Freedland S.J. Humphreys E.B. Mangold L.A. Eisenberger M. Dorey F.J. Walsh P.C. Partin A.W. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081340ijms-17-01340ArticleDextran: Influence of Molecular Weight in Antioxidant Properties and Immunomodulatory Potential Soeiro Vinicius C. 1Melo Karoline R. T. 1Alves Monique G. C. F. 1Medeiros Mayara J. C. 2Grilo Maria L. P. M. 1Almeida-Lima Jailma 1Pontes Daniel L. 2Costa Leandro S. 13Rocha Hugo A. O. 1*Boix Ester Academic Editor1 Departamento de Bioquímica, Universidade Federal do Rio Grande do Norte (UFRN), Av. Salgado Filho 3000, Natal-RN 59078-970, Brazil; vihcampelo@gmail.com (V.C.S.); melo.krt@gmail.com (K.R.T.M.); monique.gabi@gmail.com (M.G.C.F.A.); mlporpino@hotmail.com (M.L.P.M.G.); biolottus23@yahoo.com.br (J.A.-L.); leandro-silva-costa@hotmail.com (L.S.C.)2 Instituto de Química (IQ), Universidade Federal do Rio Grande do Norte (UFRN), Av. Salgado Filho 3000, Natal-RN 59078-970, Brazil; mayarajane20049@hotmail.com (M.J.C.M.); pontesdl@yahoo.com (D.L.P.)3 Instituto Federal de Educação, Ciência a Tecnologia do Rio Grande do Norte (IFRN), Av. Planalto, Km 406—Planalto, Ceará-Mirim-RN 59580-000, Brazil* Correspondence: hugo@cb.ufrn.br; Tel.: +55-84-3215-341619 8 2016 8 2016 17 8 134015 6 2016 09 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Dextrans (α-d-glucans) extracted from Leuconostoc mesenteroides, with molecular weights (MW) of 10 (D10), 40 (D40) and 147 (D147) kDa, were evaluated as antioxidant, anticoagulant and immunomodulatory drugs for the first time. None presented anticoagulant activity. As for the antioxidant and immunomodulatory tests, a specific test showed an increase in the dextran activity that was proportional to the increase in molecular weight. In a different assay, however, activity decreased or showed no correlation to the MW. As an example, the reducing power assay showed that D147 was twice as potent as other dextrans. On the other hand, all three samples showed similar activity (50%) when it came to scavenging the OH radical, whereas only the D10 sample showed sharp activity (50%) when it came to scavenging the superoxide ion. D40 was the single dextran that presented with immunomodulatory features since it stimulated the proliferation (~50%) of murine macrophages (RAW 264.7) and decreased the release of nitric oxide (~40%) by the cells, both in the absence and presence of lipopolysaccharides (LPS). In addition, D40 showed a greater scavenging activity (50%) for the hydrogen peroxide, which caused it to also be the more potent dextran when it came to inhibiting lipid peroxidation (70%). These points toward dextrans with a 40 kDa weight as being ideal for antioxidant and immunomodulatory use. However, future studies with the D40 and other similarly 40 kDa dextrans are underway to confirm this hypothesis. α-d-glucansLeuconostoc mesenteroidesimmunomodulatory activity40 kDa dextranantioxidant activity ==== Body 1. Introduction Glucans are polysaccharides of d-glucose monomers linked by glycosidic bonds. Despite the concept, the definition of a glucan is not yet fully established since some authors still refer to heteropolysaccharides that are high in glucose content as glucans [1]. In spite of having a monotone monosaccharide composition in comparison to other polysaccharides, glucans show variation when it comes to their molecular weights, types of glycosidic links, anomeric configurations of the glucose residue and types and degrees of ramifications throughout their chain [2]. These biopolymers are synthesized mainly by fungi, bacteria, plants and algae [3]. In these organisms, glucans perform a widely structural role and are, in an array of cases, the most widely available component in the cellular walls [4]. It is worth highlighting that, in some cases, bacteria and fungi may use glucans as an energy source [5]. There are α-, β- and αβ-glucans. The most widely available are, however, α- and β-glucans [6]. β-glucans are described as presenting with structural variations in numeric correlation to the links between their monomers. Those can be type β-(1→3), β-(1→4) or β-(1→6), with possible ramifications throughout the chain [7]. These polymers have been undergoing investigations as to their biological, physiological and pharmacological potentials both in vitro and in vivo. Data already point toward their possible use in treating different types of cancer due to their antitumor [8], anti-proliferative [9] and immune response modulating [10,11] potential. In addition, the pharmacological potential of β-glucans may also be availed in other areas since they can behave as anti-nociceptive [12] and antioxidant [13] agents. The α-glucans whose monosaccharides present exclusively in the α configuration are widely used as an energy source for living organisms. As an example, one can cite the α-glucans known as glycogen (a source of energy for animals) [14] and starch (α-glucan used as a source of energy by plants) [15]. However, α-glucans can also present with other interesting properties such as anti-inflammatory, antimicrobial [16], immunomodulatory [17] and antioxidant [18]. Not all α-glucans, however, have been extensively evaluated for their pharmacological potential. One example of that are the dextrans. Several groups showed the relationship between molecular weight (MW) and pharmacological activities of polysaccharides including fucans [19], alginates [20], hyaluronic acid [21] and glucans. For instance, Zho et al. [22], working with two β-glucans, showed that high molecular weight β-glucan (552 kDa) inhibits differentiation of 3T3-L1 pre-adipocytes stronger than that of low molecular weight β-glucan (32 kDa). Dextran sulfates (DS) are also affected by their molecular weight, and DS with three different MW (4, 15 and 40 kDa) were used as anti-aggregation agent on cell growth and monoclonal antibody (mAb) production and the data showed that the 40 kDa DS was the most effective in attenuating cell aggregation and showed the highest maximum mAb concentration [23]. However, there are no data regarding dextrans. Dextrans are described as α-glucans with type α-(1→6) links in their main chains and type α-(1→3) (the most common), α-(1→4) or α-(1→2) ramifications [24]. They also present with a variety of molecular weights going from 1 to 2000 kDa [25]. These polymers are synthesized mainly by fungi and bacteria of the Leuconostoc genus [26]. However, very little is known of the biological activities of dextrans, and those that are known are described solely when the dextrans are associated with other molecules [27]. Dextrans with different molecular weights synthesized by Leuconostoc mesenteroides (L. mesenteroides) are produced in large quantities and obtained in a commercial fashion. However, to the best of our knowledge, there has not yet been a study to evaluate the pharmacological activities of these polymers. In this context, three dextrans with different MW (10, 40, and 147 kDa) synthesized by the L. mesenteroides were obtained commercially and evaluated as to their antioxidant, immunomodulatory and anticoagulant activities. 2. Results and Discussion 2.1. Glucan Characterization The glucans called D10, D40 and D147 were acquired commercially and the information made available by the company selling said glucans lead one to believe they are the same compound, being differentiated by the molecular weight each one presents. In this particular stance, D10 presents with 10 kDa, while D40 and D147 present with 40 and 147 kDa, respectively. We chose these dextrans because they represent dextran molecules with low, medium and high molecular mass, respectively, in agreement with other papers that evaluated the influence of the molecular weight in the polysaccharides activity [19,20,21,22,23]. In addition, we did not used dextrans of higher molecular weights like 300 or 500 kDa or even higher because they showed very low water solubility, as well as because they could break during the tests and their fragments could alter the overall result. With the intent of confirming the identities of said glucans, as well as of verifying if any contamination has occurred by impurities that might mask results, we conducted a series of chemical analyses of infrared spectroscopy (FTIR). 2.2. Glucan Fourier Transform Infrared Spectroscopy (FTIR) Analyses Fourier Transform Infrared Spectroscopy (FTIR) Analyses is a technique that can be executed rapidly and allows for trustworthy confirmation of the molecule identity. Therefore, to confirm that samples in fact are dextrans and pure the samples were submitted to FTIR analysis and the spectra obtained can be seen on Figure 1. As can be observed, regardless of molecular weight, glucans D10, D40 and D147 present with very similar spectra, which indicates they are the same compound. Another important fact is that the spectra obtained from the three samples are the same obtained from other glucans [28]. With regards to the main signals noted it can be observed that one strong band in the 3415 cm−1 region corresponds to the asymmetric stretching O–H that overlaps itself over the hydrogen intramolecular link signals [29]. One signal between 2925 and 2932 cm−1 can be attributed to C–H symmetric and asymmetric stretching, respectively [17]. There is a signal in the region around 1648 cm−1 that corresponds to the water solvation layer around the polysaccharide [30]. These signals are characteristic to a number of polysaccharides such as chitosans [31], galactans [32], and glucans [33]. Other characteristic signals of glucans were identified, such as those on the 1457 and 1277 cm−1 regions that correspond to the signals of the glycosidic units, signal around 1156 cm−1 that corresponds to the C–O–C asymmetric stretching; signal around cm−1 that corresponds to C–C [34]; and signals around 915 and 845 cm−1 that indicate the presence of α-glycosidic links [35]. These signals were also identified in other dextran spectra [36] and, therefore, confirm the D10, D40 and D147 samples are dextrans. It is notable that no signs of protein content were found. 2.3. Chemical Analyses All three samples, D10, D40 and D147, were analyzed as to the presence of contaminants: proteins and phenolic compounds. The data are available in Table 1. It can be observed that the presence of these was not identified in the samples. The information is important since both proteins and phenolic compounds are molecules that can influence in biological systems [37,38], which could create doubts as to possible activities that might come to be observed for the D10, D40, and D147 glucans. As it relates to the total amounts of sugars, it was verified that D10, D40 and D147 possess an elevated amount of this compound (~90%). It should be pointed out that the added percentage of total amounts of sugars, proteins and phenolic compounds does not account to 100 percent. This fact was also noted in the chemical composition analyses of other sugars such as ramnanas and fucans [39], and can be explained as decurrent from the hygroscopic property of polysaccharides since, even after lyophilization, they are capable of absorbing moisture from the environment extremely rapidly [40]. Dextrans used in the study are composed exclusively by glucose and, therefore, are homoglucans. Leuconostoc mesenteroides can synthesize, beyond dextrans, a small amount of heteroglucans that contain residue of mannose and galactose [34]. However, as we have not identified other monosaccharides besides glucose, we conclude that D10, D40 and D147 are dextrans with a high degree of purity. 2.4. Antioxidant Activities Antioxidants are described mainly as low molecular weight molecules that have a protective effect both against non-reactive species, such as the hypochlorite, and against reactive oxygen species (ROS) and reactive nitrogen species (RNS) [41]. The formation process of these two reactive species is done through a chain reaction involving three steps (initiation, propagation and termination) in which the antioxidants take effect through a series of mechanisms. Thus, different methods were used to evaluate the effect of dextrans D10, D40 and D147 at the different stages: initiation (total antioxidant capacity and reducing power), propagation (chelation of copper and iron) and termination (scavenging of the hydroxyl superoxide radical and of the hydrogen peroxide). Moreover, the inhibiting lipid peroxidation of the dextrans was also determined. 2.4.1. Chelating of Copper and Iron Ions Assay The D10, D40 and D147 dextrans presented with no chelating activity of Fe2+ and Cu2+ ions (Figure 2A,B). It was not possible to compare the results to those presented by other authors since no article was identified evaluating this specific activity in dextrans. With regards to other glucans, despite few records, it was verified that the activity occurs and that it is detached from the molecular weight, since α-glucans of 9 and 113 kDa extracted from Aconitum kusnezoffii Reichb tubers presented with a similar chelating activity for iron ions. However, it is noteworthy that the activity was only 10% [18]. Low chelating activity of iron was also identified in 5 and 15 kDa dextrans extracted from the Ganoderma lucidum mushroom fruiting body. In this case, authors pointed out a chelating activity of 50%. This activity was only achieved, however, when glucans were at a concentration of 10.0 mg/mL [42], meaning a much superior concentration in comparison to what was used in this paper. For comparison purposes, corncob xylans presented with a chelating iron activity around 80% in a 1.0 mg/mL concentration [43]. To summarize, data seen here lead to the proposition that metal chelating activity seems not to be the main antioxidant mechanism in glucans. 2.4.2. Reducing Power and Total Antioxidant Capacity (TAC) Assays Tested dextrans presented a total antioxidant capacity (TAC) around 9.8, 8.7 and 9.9 equivalent in ascorbic acid milligrams to D10, D40 and D147, respectively, which is the first report of glucans with a TAC activity (Figure 2C). In reducing power test, the data were expressed as percentage activity of ascorbic acid control at 0.1 mg/mL. For the reducing power, dextrans D10 and D40 presented with 12.1% and 12.8% activity, respectively, while D147 reached a superior activity around 21% (Figure 2D). These reducing power values were similar to those obtained in other glucans, particularly β-glucans such as laminarin (extracted from the Laminaria digitate algae), botryosphaeran (extracted from the Botryosphaeria rhodina MAMB-05 fungi), curdlan (extracted from the Alcaligenes faecalis bacteria) and lasiodiplodan (extracted from the Lasiodiplodia theobromae MMPI fungi) [44]. In these two tests, one evaluates the samples’ capacities to donate electrons in a solution. For the polysaccharides, the capacity was possibly connected to the presence of hydroxyls linked along the entire chain [39]. However, it can be observed that this capacity in dextrans, as in other molecules, is dependent on the experimental conditions since the TAC dextran tests present with a much smaller activity than the one observed in the reducing power test. The fact that the dextrans presented with reducing activity in two tests that have different chemical environments indicates these molecules can also present the activity in the different chemical microenvironments found inside the cells. Specifically, in regards to the reducing power test, it was verified that the D147 dextran presented with double the activity of the other dextrans (D10 and D40), which indicates the reducing capacity of the dextrans is dependent on molecular weight. However, it is notable that no other studies that evaluated the correlation between molecular weight in glucans and reducing power were found. Therefore, it would be necessary for other studies with glucans of varied molecular weights be conducted to corroborate data found. 2.4.3. Hydroxyl Radical Scavenging Assay Dextrans D10, D40 and D147 showed scavenging activity for hydroxyl ions of ~50% (Figure 2E). The activity was higher than the observed in a β-glucan obtained from the Dictyophora indusiata fungi, which was ~39% of scavenging [45]. However, as it relates to molecular weight, it was possible to observe the activity in dextrans is not influenced by this property. This, in turn, corroborates data described by Hong and collaborators, which show β-glucans with very distinctive weights, such as 70 and 900 kDa obtained from the Paenibacillus polymyxa JB115 bacteria, present with the same scavenging capacity for hydroxyl radicals [8]. 2.4.4. Inhibiting Lipid Peroxidation Assay When testing for the dextrans capacity to inhibit the lipid peroxidation it was observed that D10 and D147 presented with inhibiting capacity of ~30%, while D40 presented with an elevated capacity of ~70%. The amount obtained with D40 becomes significant when compared to those found in the literature (Figure 2F). For example, homoglucans extracted from the Ganoderma lucidum fungi inhibited the peroxidation by around 77%, while heteroglucans extracted from the Agaricus bisporus fungi inhibited lipid peroxidation by merely 46%. In both cases, however, values were only obtained at 20 mg/mL [46], which is much greater than the concentration used in our experiments. These authors also state that the difference between the two glucans occurred due to the difference in the monosaccharide composition, meaning the presence of other monosaccharides other than glucose would lead heteroglucans to being less effective. The data lead to the belief that homoglucans have a good potential to prevent lipid peroxidation. Data also lead to the observation that dextrans are more active in certain molecular weights than in others, as if there were an ideal weight for a dextran that would lead it to show their maximum lipid peroxidation inhibition capacity, since D40 was much more effective than the other dextrans tested. In addition, one other dextran used in the clinic called Dextran 40 has already presented itself as a lipid peroxidation inhibition agent, and the molecular weight of this dextran is also approximately 40 kDa [47]. New studies that compare other glucans with a molecular weight around 40 kDa to glucans with larger or smaller weights will be able to confirm this hypothesis. This lipid peroxidation inhibition capacity observed in the dextrans stands out since there is a clear correlation between aging, caused by the continuous and elevated exposure to reactive species, and lipid peroxidation. This correlation was made clear once the accumulation of lipofuscin, a pigment capable of detecting the presence of free radicals and lipid peroxidation, was observed in the development of atherosclerosis [48]. In addition, the increased formation of lipid peroxides is also observed in Alzheimer’s disease, cancer, rheumatoid arthritis, and other immune diseases [49]. Therefore, inhibiting the lipid peroxidation could promote a certain protection to the body, once it is clearly related to the chain reaction that causes selective changes to the cell’s signals, protein damage and DNA. 2.4.5. Hydrogen Peroxide Radical Scavenging Assay With the intent of better understanding the dextran’s capacity to inhibit the lipid peroxidation, an assay was conducted to measure the scavenging of hydrogen peroxide radicals, since this particular radical is closely related to reactions that form the peroxide radicals [44]. Dextrans D10 and D147 presented with a scavenging capacity of ~40%, while D40 showed a scavenging capacity of 50% (Figure 2G). This result was bigger than that obtained with 140 kDa glucans extracted from the roots of Aconitum kusnezoffii Reichb, which presented with a 33% capacity [18], as well as two glucans of 5 and 15 kDa obtained from the Ganoderma lucidum fungi that showed a scavenging capacity of around 30% [42]. However, we highlight the fact that, just as for the lipid peroxidation assay, the D40 dextran was more effective than the D10 and D147, which shows the main inhibition mechanism for lipid peroxidation in dextrans is in their capacity to scavenge the hydrogen peroxide. 2.4.6. Superoxide Radical Scavenging Assay With regards to the dextran’s capacity to scavenge the superoxide ions, it was observed that the activity decreases as the molecular weight of the dextran increases, since D147 presented with a 0.7% activity, D40 with 9% activity, and D10 presented with a 52.3% activity (Figure 2H). The data corroborate Liu and collaborators observations that showed a 5.2 kDa glucan from the G. lucidum fungi showed a scavenging activity of around 80%, while only ~50% was obtained from a 15 kDa from the same fungi [42]. In short, the data lead to the proposition that dextrans D10 and D40 are more promising molecules from an antioxidant point of view, when compared to the D147. In addition, still favoring D10 and D40, the search is always for lower molecular weight compounds since those are more easily absorbed and distributed inside the human system, metabolized and discarded in urine [50]. 2.5. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) Mitochondrial Reduction Assay Dextrans D10, D40 and D147 allowed for an increase in the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) reducing activity in RAW 264.7 cells (murine macrophages) (Figure 3A). However, the effect was only significant in the presence of D40, which points to a mitosis action in the dextran. The data suggest that the dextrans in this study do not show cytotoxic activity to the cells. Other glucans are described as having a mitosis action in macrophages [5] and lymphocytes [18,51]. 2.6. The Effect of Dextrans in the Nitric Oxide Production in RAW Cells β-Glucans are cited as being immunomodulatory agents in a variety of studies [44,51,52]. However, there are few data regarding α-glucans and we could not find any data referring to the evaluation of the immunomodulatory ability of dextrans. Since D10, D40 and D147 dextrans were not cytotoxic to RAW cells, it was evaluated if they would affect the production of nitric oxide by those cells, both in the presence and absence of LPS (a widely known macrophage activator). Data showed, however, that D10 and D147 dextrans did not affect the nitric oxide (NO) production by the RAW cells in every tested condition. On the other hand, D40 decreased ~40% the amount of NO released by RAW cells, both in the presence and absence of LPS (Figure 3B). In short, data obtained here lead to the observation that D10 and D147 dextrans are not immunomodulatory agents. However, D40 stimulates the proliferation of macrophages and inhibits the production of NO, which awards this dextran an immunomodulatory action. This also leads to the observation that, as well as for the antioxidant action, the dextrans with molecular weight around 40 kDa will tend toward more noticeable action. 2.7. Anticoagulant Activities (Activated Partial Thromboplastin Time (aPTT) and Prothrombin Time (PT)) Dextrans D10, D40 and D147 did not show anticoagulant activity, neither in activated partial thromboplastin time (aPTT) nor prothrombin time (PT) assays (Figure 4A,B). These results corroborate with those observed in the PT and aPTT assays performed with 420 kDa β-glucan from lichen Parmotrema mantiqueirense Hale, which was also not able to induce any apparent coagulation. On the other hand, this glucan showed anticoagulant activity when it was sulfated [53]. The data found with dextran D10, D40 and D147 are positive because anticoagulant activity could be an obstacle to the use of these dextrans as antioxidant or immunomodulatory agents. 3. Materials and Methods 3.1. Materials The 10, 40 and 147 kDa dextrans (Catalog number: 1179876, 1179865 and D-487); bovine serum albumin (BSA); sodium chloride; ferrozine; butylated hydroxyanisole (BHA); pyrocatechol violet; and ascorbic acid were purchased from Sigma Chemical Co. (St. Louis, MO, USA). Potassium ferricyanide, ferrous sulfate (II), ethylenediaminetetraacetic acid (EDTA), Gallic acid, ammonium molybdate, hydrogen peroxide 30%, acetic acid, Folin–Ciocalteu phenol reagent, ethanol and sulfuric acid were obtained from Merck (Darmstadt, Germany). Culture media components Minimum essential Dulbecco’s modified Eagle medium (DMEM), l-Glutamine, sodium pyruvate, sodium bicarbonate, non-essential amino acids, fetal bovine serum and phosphate buffered saline (PBS) were acquired from Invitrogen Corporation (Burlington, ON, Canada). All other reagents and solvents were of analytical degree. 3.2. Fourier Transformed Infrared (FT-IR) Spectroscopy Analysis The infrared spectra of D10, D40 and D147 were obtained using infrared spectroscopy via Fourier transform (IRAffinity-1 spectrometer, Shimadzu Corp., Kyoto, Japan) equipped with the IRsolution 1.20 software. Samples were mixed completely with the dried potassium bromide powder (KBr) and compressed. The sweep frequency range was 4000–400 cm−1. Representative spectra of three independent experiments are shown [54]. 3.3. Chemical Analysis and Monosaccharide Composition Total amounts of sugars, proteins and phenolic compounds were determined as described previously [55]. Total amounts of sugars were determined by the phenol-H2SO4 method using d-glucose as a standard. Amounts of proteins were measured by employing the Coomassie Brilliant Blue reagent, using bovine serum albumin (BSA) as a standard. Phenolic compounds were measured by the Folin–Ciocalteau phenol reagent method, using Gallic acid as a standard. The monosaccharide composition was verified by the acid hydrolysis of the polysaccharides using HCl in a variety of concentrations (0.5, 1.0, 2.0, and 4.0 M) for different periods (0.5, 1, 2, and 4 h) at 100 °C, as described by Camara and collaborators [30]. It was verified that HCl (2.0 M) for a period of 2 h was the best condition available to break down the polysaccharides without degrading the free monosaccharides in the sample. After the acid hydrolysis, the monosaccharide composition was determined by the VWR-Hitachi Lachrom Elite® HPLC system (Hitachi Co., Tokyo, Japan) with the refraction index detector. The LichroCART® 250-4 (250 mm × 40 mm) column connected to a Lichrospher® 100 NH2 (5 μm) was then attached to the system. The concentration of the sample used was 50 mM and the analysis time was 25 min. The following sugars were used as reference: galactose, fucose, fructose, a rabinose, xylose, glucose and mannose. 3.4. Antioxidant Activities The antioxidant activities were investigated through a series of eight in vitro assays: iron ion chelation, copper ion chelation, total antioxidant capacity (TAC), reducing power, hydroxyl radical scavenging activity, inhibiting lipid peroxidation, hydrogen peroxide scavenging activity and superoxide radical scavenging activity. All eight tests were conducted with the 50 mM concentration for all samples. All assays were conducted three times, always in triplicate. 3.4.1. Chelating of Iron The assay was conducted to investigate the sample’s capacity to chelate iron ions as described earlier [30]. Briefly, the reaction containing ferrous chloride (2 mM) and ferrozine (5 mM) were mixed with the samples and incubated for 10 min at 25 °C. The change in color was measured in a microplate reader (BioTek, Winooski, VT, USA) at 562 nm against a blank. EDTA was used as positive control. The ability of the samples in chelating the iron ion was calculated using the following equation: [(Absorbance of blank − Absorbance of the sample)/Absorbance of the blank)] × 100. 3.4.2. Chelating of Copper The assay investigated the sample’s capacity to chelate copper ions. Pyrocatechol violet, the reagent used in this assay, has the ability to associate with certain cations. In the presence of chelating agents, this combination is not formed, resulting in decreased staining. The test was performed in 96-well microplates with a reaction mixture containing the samples (50 mM), pyrocatechol violet (4 mM), and copper II sulfate pentahydrate (50 mg/mL). All wells were homogenized with the aid of a micropipette and the solution absorbance was measured at 632 nm against a blank [43]. EDTA was used as positive control. The ability of the samples in chelating the copper ion was calculated using the following equation: [(Absorbance of blank − Absorbance of the sample)/Absorbance of the blank)] × 100. 3.4.3. Total Antioxidant Capacity (TAC) The total antioxidant capacity assay consists of the reduction of the Mo+6 to Mo+5 ions by the samples and the subsequent formation of the phosphate–molybdate complex in low pH values. The dextrans and the reagent solution (sulfuric acid 0.6 M, sodium phosphate 28 mM and ammonium molybdate 4 mM) were incubated at 95 °C for 90 min. immediately afterwards, the absorbing capacities of each solution were measured at 695 nm against the blank. The TAC was recorded as ascorbic acid milligrams/dextran grams, described as equivalent of ascorbic acid [55]. 3.4.4. Reducing Power The reducing power assay consists of the reduction of the potassium ferricyanide by the samples. Briefly, the dextrans were mixed with a phosphate buffer 0.2 M (pH 6.6) and incubated with potassium ferricyanide (1% m/v) at 50 °C for 20 min. One solution of trichloroacetic acid (10% m/v) was used to stop the reaction. Distilled water and ferrous chloride (0.1% m/v) were added to the solution and the absorbing capacities were measured at 700 nm. Results were accounted as an activity percentage, considering the largest concentration of ascorbic acid (the standard) as 100% activity [43]. 3.4.5. Hydroxyl Radical Scavenging Activity Assay The hydroxyl radical scavenging activity of the dextrans had its investigation based on the Fenton reaction. Hydroxyl radicals were generated by the reaction containing a sodium phosphate buffer 150 mM (pH 7.4) mixed with ferrous sulfate heptahydrate 10 mM, ethylenediaminetetraacetic acid (EDTA) 10 mM, sodium salicylate 2 mM and hydrogen peroxide 30%. Hydrogen peroxide was replaced with phosphate buffer for the blank sample. The solutions were incubated at 37 °C for 1 h and the scavenging capacity was detected via the absorbing capacity analysis at 510 nm. The results were recorded as scavenging percentage [24]. Gallic acid was used as positive control. 3.4.6. Inhibition of Lipid Peroxidation The assay investigated the oxidation of the β-carotene. Briefly, the dextrans were mixed with 0.5 mg of β-carotene and chloroform, 25 μL of linoleic acid and 200 mg of Tween 40. Initially, the chloroform was evaporated and 50 mL of distilled water saturated with O2 was added. The emulsions were incubated with the dextrans and the inhibition was detected by monitoring the absorbing capacities at 490 nm. The reactive mixture was incubated at 50 °C for 2 h and once again the absorbing capacities were verified. Butylated hydroxyanisole (BHA) was used as positive control. The results were recorded as inhibition percentage [56]. 3.4.7. Hydrogen Peroxide Scavenging Activity Assay (H2O2) The assay was conducted to investigate the dextran’s capacity to scavenge the hydrogen peroxide. Briefly, the reaction was to mix the dextrans with 100 mL of H2O2 0.002%. Then, 0.8 mL of sodium phosphate buffer 0.1 M and sodium chloride 100 mM were added. The solutions were incubated at 37 °C for 10 min. After that, 1 mL of the phenol red indicator (0.2 mg/mL) with 0.1 mg/mL of peroxidase in sodium phosphate buffer 0.1 M was added. After 15 min, 50 mL of sodium hydroxide 1 M were added. The absorbance was immediately measured at 610 nm, having the Gallic acid as positive control. Results were recorded as scavenging percentage [57]. All assays were performed three times in triplicate (n = 3). 3.4.8. Superoxide Radical Scavenging Activity Assay The superoxide radical scavenging activity of the dextrans was investigated through the inhibition of the photochemical reduction of the nitroblue tetrazolium (NBT) in the riboflavin-light-NBT system. The dextrans (50 mM) were added to a solution of phosphate buffer 50 mM (pH 7.8), riboflavin 2 mM, EDTA 100 mM, l-methionine 13 mM and NBT 75 mM. The formation of blue formazan was monitored by the increase in absorbance at 560 nm after the exposure to light for 10 min in a closed box. An identical reaction was maintained in the dark and served as a blank. Gallic acid was used as positive control. Results were recorded as scavenging percentage [43]. All assays were performed three times in triplicate (n = 3). 3.5. Mitochondrial Reduction of MTT The cytotoxicity of the dextrans was evaluated following the mitochondrial reduction of the MTT. Briefly, the assay was conducted to investigate the cellular enzyme’s capacity of reducing the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) to formazan in RAW 264.7 macrophages with an active metabolism. The cells were incubated with the dextrans (50 mM) at 37 °C in the presence of DMEM for 24 h. After incubation, traces of dextrans were removed by washing the cells twice with 200 μL PBS and applying MTT (1 mg/mL) dissolved in 100 μL of fresh medium to determine the effects of the samples on cell viability. Cells were then incubated for 4 h at 37 °C, in 5% CO2. The MTT-formazan product dissolved in 100 μL of ethanol was estimated by measuring the absorbance at 570 nm in a Multiskan Ascent Microplate Reader. Inhibition of MTT reduction is presented as a percentage of cell proliferation under no treatment conditions. Absorbance was measured at 570 nm [43]. 3.6. Production of Nitric Oxide (NO) To determine the effect of glucans in the amount of nitric oxide (NO) released by the RAW cells, the method described by Alves and collaborators [54] was used. Briefly, macrophages (RAW 264.7 cell line) were incubated (3 × 105 cells/well) in culture plates of 24 wells at 37 °C in CO2 5% both with and without lipopolysaccharides (LPS) in the presence of dextrans (50 mM). After 24 h a reaction with Griess reagent was conducted on the supernatant. The absorbance of the reaction was monitored at 540 nm. 3.7. Anticoagulant Assays The anticoagulant activity of the glucans was evaluated using two in vitro tests: the activated partial thromboplastin time (aPTT) and the prothrombin time (PT). Both assays were conducted in the 50 mM concentration for all samples. The aPTT and PT assays were conducted following specification of the manufacturers (Labtest, Sao Paulo, SP, Brazil) and each assay was performed three times in triplicate (n = 3). 3.7.1. Activated Partial Thromboplastin Time (aPTT) The coagulation assay via activated partial thromboplastin time (aPTT) was conducted using normal human plasma treated with citrate in which the dextrans were incubated. Briefly, dextrans were mixed with the plasma treated with citrate and then incubated at 37 °C. After 3 min, cephalin was added and the reaction was once again incubated. Following another 3 min, CaCl2 (100 μL, 20 mM) was added and the coagulation time was measured by the coagulometer. Clexane® (Sanofi Aventis Farmacêutica Ltda, São Paulo, SP, Brazil) and normal human plasma treated with citrate were used both as standard and control, respectively [54]. All assays were performed three times in triplicate (n = 3). 3.7.2. Prothrombin Time (PT) The coagulation assay via prothrombin time (PT) was also conducted using normal human plasma treated with citrate, incubated along with the samples. Briefly, the dextrans were mixed with normal human plasma treated with citrate and incubated at 37 °C for 3 min. Then, soluplastin was added and the coagulation time was measured by the coagulometer [30]. 3.8. Statistical Analyses All data have been expressed as the average ± standard deviation. Statistical analyses were conducted via a simple variation analyses (one-way ANOVA) followed by the Tukey–Kramer (p < 0.05) test. All tests were conducted on the GraphPad Prism 5.01 (GraphPad Softwares, La Jolla, CA, USA). 4. Conclusions Dextrans D10, D40 and D147 are the same type of polysaccharide and their only difference is their molecular weight. They are formed by glucose in the alpha configuration and are free of protein and phenolic contaminants. The same dextran molecules—but with different molecular weights—showed different antioxidant and immunomodulatory activities. In one antioxidant test, the dextran activity increases according to the increase of the molecular weight. In another test, however, the activity might decrease or show no correlation to the molecular weight. It can be further stated that antioxidant dextrans act mainly in the termination of the formation process for reactive species. The great highlight of this study was the D40 dextrans, which presented with antioxidant activity in most of the tests conducted and showed a greater inhibiting activity of the lipid peroxidation: it presented with a greater hydrogen peroxide scavenging capacity when compared to the two other dextrans and it was also the only one to present with immunomodulatory activity. This points to the observation that, in order to attain dextrans that present with excellent immunomodulatory and antioxidant activity, one must find dextrans with molecular weight around 40 kDa. However, more studies, tests and published data are required to confirm this hypothesis. Therefore, future studies hold the intent to best characterize the immunomodulatory and antioxidant activities of D40 dextrans—or other dextrans with similar molecular weights. Acknowledgments The authors wish to thank Conselho Nacional de Desenvolvimento Científico e Tecnológico CNPq (National Council for Scientific and Technological Advancement, in loose translation), Coordenacão de Aperfeiçoamento Pessoal de Nível Superior CAPES (Higher Level Personal Development Coordination, in loose translation) and the Ministerio de Ciência, Tecnologia e Informação (MCTI—the Science and Technology Ministry in Brazil), for the financial support. Maria L. P. M. Grilo had a scientific initiation scholarship from CAPES. Hugo A. O. Rocha is a CNPq fellowship honored researcher. Vinicius C. Soeiro and Karoline R. T. Melo have a Ph.D. scholarship from CAPES. Jailma Almeida-Lima has a Post-doctorate scholarship from CAPES. Author Contributions Vinicius C. Soeiro prepared the samples and performed the chemical analysis. Vinicius C. Soeiro and Maria L. P. M. Grilo performed the antioxidant tests. Vinicius C. Soeiro, Karoline R. T. Melo and Jailma Almeida-Lima performed the immunomodulatory assays. Vinicius C. Soeiro and Monique G. C. F. Alves performed the anticoagulant tests. Mayara J. C. Medeiros and Daniel L. Pontes performed the FTIR analysis. Vinicius C. Soeiro analyzed all the data. Vinicius C. Soeiro, Leandro S. Costa and Hugo A. O. Rocha wrote the paper. Hugo A. O. Rocha funds and revised the paper. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Fourier transform infrared spectroscopy (FTIR) spectra of the dextrans. The characteristics signals are in evidence for the regions between 4000 and 400 cm−1. Figure 2 Antioxidant activities of D10, D40 and D147. (A) Ferrous chelating; (B) copper chelating; (C) total antioxidant capacity; (D) reducing Power; (E) hydroxyl radical scavenging; (F) inhibiting lipid peroxidation; (G) hydrogen peroxide radical scavenging; and (H) superoxide radical scavenging. Letters a,b,c represent the significant difference between the samples by the simple variance analyses (one-way ANOVA) followed by the Tukey–Kramer (p < 0.05) test. Figure 3 Effect of D10, D40 and D147 in RAW cells: (A) 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium (MTT) mitochondrial reduction by cells in the presence of glucans; and (B) nitric oxide production by RAW cells in the presence of glucans. Letters a,b represent the significant difference between the various samples according to the simple variation analyses (one-way ANOVA) followed by the Tukey–Kramer (p < 0.05) test. LPS, lipopolysaccharides. Figure 4 Anticoagulant activities of D1, D40 and D147: (A) activated partial thromboplastin time (aPTT); and (B) prothrombin time (PT). Letter a represent non-significant difference between the various samples according to the variation analyses (one-way ANOVA) followed by the Tukey–Kramer (p < 0.05) test. ijms-17-01340-t001_Table 1Table 1 Chemical composition of the D10, D40 and D147 dextrans. Dextrans Total Sugar (%) Proteins (%) Phenolic Compounds (%) Molar Ratio * (%) Glc Man Gal D10 91.3 n.d. n.d. 1:0 0:0 0:0 D40 90.5 n.d. n.d. 1:0 0:0 0:0 D147 93.1 n.d. n.d. 1:0 0:0 0:0 Glc: glucose; Man: mannose; Gal: galactose. * Molar ratio obtained by high performance liquid chromatography (HPLC) analyses after acid hydrolysis (HCl 2 M; 2 h; 100 °C). n.d.: Not detectable in the evaluated conditions. ==== Refs References 1. Ferreira I.C.F.R. Heleno S.A. Reis F.S. Stojkovic D. Queiroz M.J.R.P. Vasconcelos M.H. Sokovic M. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081341ijms-17-01341ArticlePopulation Genetics of the Endemic Hawaiian Species Chrysodracon hawaiiensis and Chrysodracon auwahiensis (Asparagaceae): Insights from RAPD and ISSR Variation Lu Pei-Luen 12*Yorkson Mitsuko 2Morden Clifford W. 2Zhu Jianhua Academic EditorIriti Marcello Academic Editor1 Department of BioResources, Da-Yeh University, Changhua 51591, Taiwan2 Department of Botany, University of Hawaii at Mānoa, Honolulu, HI 96822, USA; mitsuko@hawaii.rr.com (M.Y.); cmorden@hawaii.edu (C.W.M.)* Correspondence: peiluen@mail.dyu.edu.tw; Tel.: +886-4-8511-888 (ext. 6215)16 8 2016 8 2016 17 8 134101 5 2016 11 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).The genus Chrysodracon has six endemic species in the Hawaii Islands. Chrysodracon hawaiiensis is endemic to Hawaii Island and was described as a distinct species in 1980. It was listed as an endangered species on the International Union for the Conservation of Nature and Natural Resources (IUCN) Red List in 1997. This woody plant species was, at one time, common in exposed dry forests, but it became very rare due to grazing pressure and human development. The tree species Chrysodracon auwahiensis (C. auwahiensis), endemic to Maui and Molokai, still has large adult populations in dry lands of the islands, but unfortunately no regeneration from seed has been reported in those areas for many years. The two endemic species were examined using the molecular technique of random amplified polymorphic DNA (RAPD) and inter simple sequence repeats (ISSR) to determine the genetic structure of the populations and the amount of variation. Both species possess similar genetic structure. Larger and smaller populations of both species contain similar levels of genetic diversity as determined by the number of polymorphic loci, estimated heterozygosity, and Shannon’s index of genetic diversity. Although population diversity of Chrysodracon hawaiiensis (C. hawaiiensis) is thought to have remained near pre-disturbance levels, population size continues to decline as recruitment is either absent or does not keep pace with senescence of mature plants. Conservation recommendations for both species are suggested. ChrysodraconconservationHawaiian speciesISSRPleomelepopulation geneticsRAPD ==== Body 1. Introduction The Hawaiian Islands include a high percentage of endemic species and are one of 25 biodiversity hotspots in the world [1,2,3,4]. Although many genera are species-rich in Hawaii and have special evolutionary histories, few of them have been studied in detail [4,5,6]. Many Pacific Island species, including those from the Hawaiian Islands, have a fragile existence. This is often due to their populations being scattered broadly within or across different islands and a limited genetic diversity due to their recent colonization, isolation from the source population, and/or the population size being restricted within island environments [4,5,7,8]. A consequence of this fragility has resulted in many endemic Hawaiian plant species having become endangered and the level of genetic diversity present becoming severely reduced compounding the problems for species recovery [9,10,11]. For example, the Hawaiian dry forests have been seriously reduced due to habitat loss from commercial or agricultural development and the spread of invasive plant and animal species [5,12]. Notably, more than 90% of Hawaiian dry forests are already lost [13] and 50% of the extant Hawaiian endemic flora is listed as endangered or rare in the International Union for the Conservation of Nature and Natural Resources (IUCN) [14] or by the US Fish and Wildlife Service (USFWS) [15]. Therefore, the study and conservation of genetic resources in populations, species, and ecosystems are essential to maintaining biodiversity and population dynamics. The Hawaiian endemic genus Chrysodracon (Jankaski) Lu and Morden (Asparagaceae), species previously included among the widely distributed tropical genus Pleomele Salisbury, has six endemic species in the Hawaiian Islands [16,17]. Chrysodracon hawaiiensis (Degener and Degener) Lu and Morden was distinguished as a species in 1980 [18]. Unfortunately, populations of this species have declined rapidly in the past few decades and the USFWS listed C. hawaiiensis (as Pleomele hawaiiensis) as an endangered species in 1996 [15]. The IUCN also placed it on their red list of endangered and threatened species in 1997 [14]. Chrysodracon hawaiiensis exists in only 6–8 populations totaling approximately 300–400 individuals in sunny dry forests on the leeward side of Hawaii Island [15] (Figure 1). The largest extant wild population with approximately 200 individuals is located at Puu Waawaa [15]. This species has a unique ability to grow in young lava substrates often on steep slopes. To date, nothing is known about the genetic structure of this species. Due to its rarity and small population sizes, it may possibly go extinct or become more severely restricted in distribution within the next few decades if appropriate conservation management are not adopted. Presently, C. hawaiiensis is the only species of Chrysodracon recognized as occurring on Hawaii Island [17]. However, St. John [19] had previously recognized three distinct species (within Pleomele) based on morphological differences: P. hawaiiensis (sensu stricto), P. kaupulehuensis St. John, and P. konaensis St. John. These three species were distinguished by leaf width and the perianth tube length. The perianth of P. hawaiiensis is 37–40 mm long with a perianth tube longer than 26 mm whereas the perianth of P. konaensis is less than 37 mm, with a perianth tube less than 23 mm, and the perianth of P. kaupulehuensis is greater than 43 mm long. The leaf width of P. hawaiiensis and P. konaensis is less than 22 mm compared to the leaf width of P. kaupulehuensis being greater than 23 mm [19]. The most recent treatment of these species combined them within the single species P. hawaiiensis (sensu lato) [17]. As such, it is also important to examine their population differentiation and genetic variation to gain a better understanding of their interrelations. There were two objectives of this study. First, to investigate the genetic structure within and among populations of the endangered species C. hawaiiensis. In doing so, comparisons will also be made of the level of diversity within populations of different size. Understanding the population genetic structure of the endemic Hawaiian Chrysodracon species will be desirable to provide the insight needed for proper conservation strategies to preserve the biodiversity of island ecosystems, reveal the evolutionary stages of those species, and address genetic resource problems that those populations are facing. It will also provide appropriate recovery suggestions for collecting the seeds and artificial pollination from those populations to incorporate the maximum genetic variation in these efforts. To best examine the population structure of an endangered species, it is also necessary to analyze the population structure of a non-endangered congener species for comparison. Therefore, our second objective was to conduct a genetic survey of Chrysodracon auwahiensis (Lu & Morden), a non-endangered species endemic to Maui (Figure 1) and Molokai Islands, to estimate the level and distribution of genetic diversity among populations. There are several extant populations of C. auwahiensis with thousands of individuals present. After completing the population genetics study of both C. hawaiiensis and C. auwahiensis, a comparison between them will provide an understanding of the type of variation that possibly was present in populations of C. hawaiiensis prior to habitat degradation and alteration. Knowledge of the population structure and level of variation will assist in formulating management practices for this species. 2. Results In general, the genetic diversity measures in both random amplified polymorphic DNA (RAPD) and inter simple sequence repeat (ISSR) analyses were very similar and results obtained were highly compatible. Overall, the genetic diversity values were lower in RAPD than ISSR analyses and the values for population differentiation were higher in ISSR than RAPD analyses. 2.1. Random Amplified Polymorphic DNA (RAPD) and Inter Simple Sequence Repeat (ISSR) Profiles Of the 80 RAPD primers and 48 ISSR primers screened, 11 and three (respectively) produced repeatable amplification in C. hawaiiensis that were scored for band presence/absence. These yielded 180 RAPD and 49 ISSR markers (229 total) scored (Table 1). Combined, there were 218 markers (95%) that were polymorphic. Population specific diagnostic RAPD and ISSR markers were present (although polymorphic) in each of the populations. Three such markers were found among Kohala, Manuka and Puu Waawaa, plants, and four among Kaupulehu plants. The same 11 RAPD and three ISSR primers were also used to produce repeatable amplification products in C. auwahiensis (Table 1). A total of 198 RAPD and 40 ISSR markers (238 total) were scored and 235 markers (98%) were polymorphic. Each of the five populations also had population specific diagnostic markers (also polymorphic). Auwahi, Iao Valley, Kanaio, and Kauaula each had two diagnostic markers, whereas Makawao had eight. Levels of RAPD and ISSR variation in C. hawaiiensis, measured by the percentage of polymorphic markers, exhibited slight differences among populations and displayed a similar relationship to the number of individuals sampled in each population (Table 2). The Puu Waawaa population was the largest population (200 individuals and 20 sampled) and was the most variable (68% and 73% polymorphism for RAPD and ISSR, respectively). The other three populations were considerably smaller (20–50 individuals) and had approximately the same sample size (10–13 individuals), and had similar values of polymorphism. The amount of RAPD and ISSR variation found in populations of C. auwahiensis was relatively consistent among the five populations sampled, ranging from 70%–86% and 60%–73% for RAPD and ISSR, respectively (Table 2). All populations demonstrated greater levels of polymorphism than any C. hawaiiensis population. The two largest populations, Auwahi and Kanaio, also displayed the greatest level of polymorphism among individuals although the differences do not appear great and may be consistent with the number of plants sampled rather than population size. The two West Maui populations, Iao Valley and Kauaula, with the smallest estimated population size, show a reduced level of polymorphism among markers. Makawao, with approximately the same estimated population size as the West Maui populations, demonstrated only slightly higher polymorphism than the West Maui populations. Values of estimated heterozygosity (H) similarly reflected sample sizes in C. hawaiiensis for both RAPD and ISSR data (Table 2). Interestingly, this pattern is not the same when examining H with only polymorphic markers. Kohala plants displayed higher values than was found among Puu Waawaa plants suggesting the Kohala population may have fewer polymorphic markers, but higher expected heterozygosity among those that are polymorphic. Heterozygosity estimates using all markers showed a pattern different from those of the polymorphic markers with much lower variation (Table 2). Total mean estimated heterozygosity over all markers in C. hawaiiensis was 0.254 (RAPD) and 0.316 (ISSR). Estimated H in C. auwahiensis did not show great differences among populations (Table 2) but, rather, values were relatively consistent even among those with the smallest sample size (Iao Valley and Kauaula). The highest H value was found among the Makawao plants for the RAPD markers. However, values for ISSR markers did more consistently reflect the population size with both Auwahi and Kanaio, both higher than other populations, especially for H values based on polymorphic markers alone. The total mean estimated heterozygosity over all markers in C. auwahiensis was 0.401 (RAPD) and 0.352 (ISSR). Shannon’s Diversity Index (SDI) estimates based on RAPD analysis demonstrated very little difference among populations within each species, but was consistently lower among populations of C. hawaiiensis compared to C. auwahiensis (Table 2). Values among populations of C. hawaiiensis ranged from 1.552–1.588 (1.576 among all individuals), whereas values of C. auwahiensis ranged from 1.675–1.693 (1.696 among all individuals). SDI from ISSR data similarly showed little variation among populations, but in contrast to the RAPD data, there was nearly a complete overlap of values between the two species (1.333 to 1.440 and 1.377 to 1.413 for C. hawaiiensis and C. auwahiensis, respectively). 2.2. Population Genetic Structure Analysis of molecular variance population analyses of C. hawaiiensis indicate that variation within, and among, populations are similar based on combined RAPD and ISSR markers, with slightly more variation accounting for among populations (54%) than within populations (46%) (Table 3). The ΦST value of 0.536 (p = 0.001) suggests there is significant differentiation among the populations. In contrast, analysis of AMOVA among C. auwahiensis populations indicates there is considerably higher variation within populations (65%) than among them (35%). The ΦST value is also lower, 0.355 (p = 0.001), suggesting less differentiation among populations within this species. 2.3. Genetic Similarity Indices Patterns of genetic similarity, as measured by RAPD and ISSR markers, were very consistent among populations for both species. Although some patterns were more clearly resolved with RAPD data as compared to the ISSR data and could be interpreted as the RAPD data being a more sensitive measure, this is likely an artifact of more genetic markers being measured for RAPD (180) than ISSR (49) analyses. As such, combined data analyses will be presented here; data for independent analysis is available from the authors. Genetic similarity within and among populations was calculated using the Gower similarity coefficient analysis [20,21], and clearly reflect their relationships within each species (Table 4). As expected, individuals were most similar to members in their own population for both species. Intrapopulation similarity was relatively consistent among all populations for both C. hawaiiensis and C. auwahiensis with values ranging from 75%–87%; the lone exception to this was the Manuka population with only 66% similarity among individuals. Interpopulation comparisons within C. hawaiiensis were relatively consistent with values ranging from 47% (Manuka and Kaupulehu) to 60% (Puu Waawaa and Kohala). However, there was no clear indication of any clustering among population. In contrast, comparisons of the five C. auwahiensis populations examined suggest three population clusters. Plants from Iao Valley and Kauaula shared the highest similarity (67%) and plants from Auwahi and Kanaio shared an equally high value (66%); similarity among these two groups to populations of the other or to the Makawao population all ranged from 51%–55%, much lower than the values within each population cluster. Principal coordinate analysis (PCO) of the combined datasets for each species was consistent with the relationships based on genetic similarity and AMOVA. For C. hawaiiensis, the four populations examined were clearly distinct from one another (Figure 2). The first axis distinguishes the southernmost population of Manuka from northern populations of Puu Waawaa, Kohala, and Kaupulehu. The second axis distinguishes the Kaupulehu population from the other three along axis 2. In contrast, PCO analysis of the C. auwahiensis populations demonstrated three clusters, as suggested by similarity values (Figure 3). The Auwahi and Kanaio populations clustered together (with some degree of structure evident), and the West Maui populations of Iao Valley and Kauaula clustered together. Only the Makawao population was completely distinguishable from other populations. Results from the STRUCTURE analysis of the combined RAPD and ISSR data are consistent with the results of the similarity and PCO analyses (Figure 4). For C. hawaiiensis (Figure 4A), the four populations sampled formed four distinctive groups (K = 4). Within populations, there was a slight indication of mixing of genotypes among some individuals. For C. auwahiensis (Figure 4B), the five populations sampled formed only three groups (K = 3) indicating some of the populations are not distinct. The Auwahi and Kanaio populations and the Iao Valley and Kauaula populations were genetically indistinguishable. As with C. hawaiiensis, there was a slight indication of mixing of genotypes in some individuals. 3. Discussion 3.1. Relative Genetic Variation The present study is the first DNA-level examination within and among populations of Chrysodracon species, and establishes a baseline by which comparisons with other species (Chrysodracon or other dracaenoids) may be made. Genetic diversity within both of the species was moderate compared to other Hawaiian taxa examined, while among population differentiation was very significant. Percent polymorphism at the species level was 92% for all individuals of C. hawaiiensis, and this is higher than what has been found in many other taxa (Dubautia ciliolata: 70% and Dubautia scabra: 59% [22]; Touchardia latifolia: 51% [9]; Alphitonia ponderosa: 47%; and Colubrina oppositifolia: 41%, [10]). However, the population level variation was lower and in a moderate range compared to most other Hawaiian species [10,22]. Notably, the endangered species C. hawaiiensis shows similar genetic dynamics, as did the common species C. auwahiensis. Since these are long-lived plants, both species still maintain considerable genetic variation reflective of what may have existed prior to the start of the species decline. Levels of genetic variation based on percent polymorphism indicate that C. hawaiiensis (70%) exhibited moderate levels of relative genetic diversity in comparison to C. auwahiensis (90%). Populations within C. hawaiiensis showed lower diversity (ranging from 50%–68%) than for the species as a whole. Similarly, levels of variation within populations of C. hawaiiensis show a similar trend. Populations within C. auwahiensis also showed lower diversity (ranging from 70%–86%) than for the species as a whole. Similarly, levels of variation within populations of C. auwahiensis also show a similar trend. Both species are long-living woody perennial tree plants. Unfortunately, natural regeneration of young seedling in the field for either species was not evident. There were no reported wildfires destroying the forest, at least after 1947 (Hank Oppenheimer, Maui PEP Coordinator, personal communication) [23]. As such, the extant genetic diversity is likely representative of the diversity present for at least the past 100 years for C. auwahiensis, and the same is likely true for C. hawaiiensis. Most extant plants are old mature trees in populations that have probably experienced minimal impact from genetic drift despite declining population size. The endangered species C. hawaiiensis has similar, although slightly lower, estimated total polymorphism, heterozygosity, and Shannon’s diversity index over all polymorphic markers as compared to the more common C. auwahiensis. 3.2. Population Size and Genetic Diversity Genetic diversity within populations reflected the estimated population size in species by both RAPD and ISSR analyses. Estimated heterozygosity over all loci and estimated genetic diversity was higher in the common species C. auwahiensis than in the endangered species C. hawaiiensis. These data suggest that C. hawaiiensis populations were, at one time, much larger, and the reduction in population sizes have been recent with some loss in genetic variation. In C. hawaiiensis, the estimated population size of Puu Waawaa was the largest and had higher genetic diversity. The other three populations have similar, but markedly lower, genetic diversity. The disparity in the levels of diversity is undoubtedly related to the estimated population sizes. Individuals in those populations are all long-lived old mature plants, and no evidence of seedlings or juveniles in the wild have been recorded (personal observation; Nick Agorastos, Hawaii NARS staff, personal communication). Both species have frequently produced flowers and seeds, but the lack of seedlings found during several years of observation is likely because of invasive weeds and insects (personal observation; H. Oppenheimer, Maui PEP, and Nick Agorastos, Hawaii NARS Coodinator, personal communication). Attrition of individuals from populations without subsequent regeneration may have contributed to the levels of variation now seen there. Trends in population variation for C. auwahiensis were as predicted. The southern East Maui populations of Auwahi and Kanaio are larger and distributed over a wider geographical area compared to the Makawao or West Maui populations of the Iao Valley and Kauaula, and also showed the greatest genetic diversity. The Makawao population is distinct from the populations of East Maui or West Maui in genetic similarity analysis, and this population’s habitat is a mesic to wet forest rather than the dry forests of the other populations. Overall, data for C. auwahiensis indicates that populations encompassing a larger geographical area retain higher genetic diversity, but not greatly so, compared to those encompassing smaller or more isolated areas. The diversity within populations of C. auwahiensis is more similar than the greater ranges in diversity within populations of C. hawaiiensis, and is likely a consequence of all Maui populations still being relatively larger in size. 3.3. Distribution of Variation The majority of variation in Chrysodracon hawaiiensis was found within, rather than among, populations although this difference was not great. In contrast, variation in C. auwahiensis populations was much greater within populations than among populations. It has been shown that long-lived plants, especially trees such as these, typically harbor a greater percentage of their variation within populations [24,25]. The study here supports these conclusions for C. hawaiiensis, but not C. auwahiensis. This may be a reflection of C. auwahiensis having two sets of populations that were genetically indistinguishable, although the three population clusters were very distinct. The numbers of diagnostic alleles unique to each population are possibly signs of differentiation among the populations following selection or genetic drift from the ancestral genetic environment. On the other hand, these alleles may be the representative of new mutations (such as deletions or insertions) that have appeared within populations following their initial dispersal after speciation events. 3.4. Population Differentiation Isolation, created by geographical distance and subsequent fragmentation, has provided the initial means for divergence in both species. Since there is limited (if any) gene flow in both species due to habitat alteration between the populations, this divergence is likely to continue. Considerable population differentiation occurs in the four populations within C. hawaiiensis. As should be expected, the most geographically isolated of the populations, Manuka, on southern Hawaii Island, is genetically distinct from the other three populations on northern Hawaii Island. Unexpected was the low similarity of the Kaupulehu population from the others although, geographically, in close proximity. Interpopulation similarity of Kaupulehu is the lowest of any population comparisons, including the geographically-close Puu Waawaa population, suggesting this population is uniquely distinct from the others. The habitat at Kaupulehu is also distinct in that plants occur in wet, deep valleys on Hualalai Volcano, rather than the more exposed and/or drier habitats associated with the other populations. For C. auwahiensis, analyses indicate there are three genetic population clusters among the five geographic locations examined. On West Maui (Iao Valley and Kauaula populations) and on southern East Maui, close relationship were anticipated because of their close geographic proximity and similarity of habitats. The habitat for populations on West Maui are typically wet soils in deep valleys. On southern East Maui, the habitat is very dry and exposed. The most genetically distinctive population on Maui is the northern East Maui Makawao population where within population similarity is the highest and interpopulation similarity the lowest. This distinctive population has a typically very wet habitat. Reproductive biology in these species has not been examined up to date, but anecdotal evidence in the course of conservation work suggests that pollen and seed movement among populations is related to bird activities [26]. Neither C. hawaiiensis nor C. auwahiensis has had seedlings observed since the last century mainly due to introduced animals eating the leaves and young shoots, and the numbers of introduced animals drastically increasing in the forests in recent years [15]. Chrysodracon species have large bell-shaped yellowish flowers producing dark berry fruit, and have been hypothesized to share an association with birds for pollination and seed dispersal [26]. Although several potential factors may be important in limiting gene flow at this site (i.e., pollinating and seed-dispersing birds are now extinct), the separation of these populations is likely due to human habitat destruction and invasive species, such as pigs, goats, cattle, deer, rats, slugs, and alien plants. There are several lines of evidence to suspect continued differentiation among populations: (1) individuals among populations of both species share low genetic similarity; (2) gene flow among populations is restricted; and (3) localized inbreeding (or, in the extreme, self-fertilization) may be occurring due to lack of pollinators and Allee effects [27] within populations, which will result in a reduction of variation within populations. Dry forests are typically associated with leeward coast regions of all islands. Chrysodracon species typically survive on steep hillsides or lava substrates with well-drained soils. Thus, seed dispersal and gene flow among island populations may have been considerably greater prior to Polynesian inhabitation and the large-scale destruction of low elevation forests [28], extinction of bird species that followed, invasive weeds competition, and animal and slug grazing pressure [15]. 4. Materials and Methods 4.1. Plant Collection and DNA Extraction Leaf tissues were randomly collected from plants in four extant populations of C. hawaiiensis on Hawaii Island and five extant populations of C. auwahiensis on Maui Island (Hawaii State endangered species permit No. P-159 for C. hawaiiensis; special use permits of natural area reserves system (NARS) for both species were also obtained from the Hawaii Division of Land and Natural Resources (DLNR) (available upon request)). The plants frequently grow on rocky and inaccessible cliffs and, in some cases, sampling was limited due to safety concerns. Since these are rare species, a voucher specimen representative of each population was identified in the collections at Bernice Pauahi Bishop BishopMuseum (BISH) and is listed in Table 5 along with estimates of population size, locality, voucher information, and number of individuals collected. Total genomic DNA was extracted from fresh leaf tissue using the CTAB (cetyltrimethylammonium bromide) extraction protocol [29] with modification [30], or from silica dried samples using the Qiagen DNeasy Plant Mini kit according to the manufacturer’s instructions (Qiagen Corporation, Valencia, CA, USA). Samples were accessioned into the Hawaiian Plant DNA Library [30,31]. 4.2. Genetic Analysis Approximately 25 ng of DNA was amplified via the polymerase chain reaction (PCR) performed in a MJ Research Thermal PCR machine (GMI, Inc., Ramsey, MI, USA) in 15 μL volume reactions. Conditions for RAPD reactions were 0.2 μM random 10-mer oligonucleotide primers, 0.2 mM each of dNTP, 1× Taq polymerase PCR buffer, 1.5 mM MgCl2, 0.01 g/mL concentration 1% Bovine Serum Albumins (BSA) in the total reaction volume, and 1 unit of Taq polymerase (Promega, Madison, WI, USA). RAPD PCR conditions were for one cycle at 94 °C for 3 min, 35 °C for 30 s, and 72 °C for 2 min, followed by 43 cycles at 94 °C for 45 s, 35 °C for 30 s, and 72 °C for 2 min, and a final cycle at 94 °C for 45 s, 35 °C for 30 s, and 72 °C for 6 min. Conditions for ISSR reactions were 0.4 μM primer, 0.2 mM each dNTP, 1× Taq polymerase PCR buffer, 2.5 mM MgCl2, 5% 0.01 g/mL concentration BSA in the total reaction volume, and 1 unit of Taq Polymerase (Promega, Madison, WI, USA). ISSR PCR conditions were 94 °C for 90 s, followed by 34 cycles of 94 °C for 40 s, 45 °C for 45 s, and 72 °C for 90 s, followed by 94 °C for 45 s, and 45 °C for 45 s, ending with 5 min at 72 °C after cycling was completed. Amplification products were mixed with loading dye (20 mm EDTA, 10% glycerol, 1% sarcosyl with bromophenol blue and xylene cyanol) and separated in 1.5% agarose gels in 0.5× TBE (tris-borate-EDTA) buffer with 125 ng ethidium bromide per liter. Sizes of the amplification products were estimated by comparison to a Promega 100 bp ladder (Promega, Madison, WI, USA). RAPD primers (Operon Technology, Alameda, CA, USA; kits OPA-OPI) and ISSR primers (University of British Columbia Primer Kit #9, Vancouver, BC, Canada) were screened for amplification of Chrysodracon DNA, and selected primers were then used for amplification of all individuals. Selected ISSR primers were 5007 (ACACACACACACACAC-C), 5009 (ACACACACACACACAC-T), and 5028 (GAGAGAGAGAGAGAGA-YT). Molecular markers were identified by the primer used to generate them and the approximate size of the band as estimated from a 100 bp ladder. The reproducibility of amplification was tested for each primer prior to data collection. GelAnalyzer 1D image analysis software (Dr. Istvan Lazar, www.gelanalyzer.com) was initially used to estimate the number of base pairs represented by each amplified fragment and manually adjusted based on eye observation. Loci were scored as diallelic (1 = band present, 0 = band absent). Gels were scored independently by the first and second authors to produce unbiased and unambiguous analysis of RAPD and ISSR amplifications. 4.3. Data Analysis Assumptions related to RAPD marker analysis were described by Lynch and Milligan [32] and also apply to ISSR analysis. RAPD and ISSR markers were determined to be polymorphic if estimated allele frequency was less than 95%. In practice, a population marker was considered polymorphic when amplification was present in one or more individuals of the population or if a null (no amplification) occurred in one or more individuals. Absence of a marker within a population, although present in others, was assumed to indicate the individual to be a null/null homozygote rather than there having been a loss of the locus. Expected heterozygosity was calculated for each population (HS) and species (HT) for each locus as follows: H = 1 − (p2 + q2) where p is the frequency of the amplified allele and q is the frequency of the null allele; allele frequencies were estimated from the number of null/null homozygotes present in the population [33]. Lynch and Milligan [32] point out that only markers present with an observed frequency of less than 1 − (3/N) (where N represents the sample size) are used to reduce a potential bias when analyzing dominant markers. Summary statistics of average similarity measures (means, standard errors, and t-tests) were calculated using Excel (Microsoft Office 2007, Microsoft District for Pacific Northwest, Bellevue, WA, USA). Distribution of genetic variation within and among populations was estimated using Shannon’s diversity index (H) [34]. Shannon’s diversity index (H) was calculated as: HO = −Σ pi·log2 pi where pi is the frequency of a given RAPD or ISSR phenotype within a population or species group. Genetic structure among populations of each species was measured by four different methods. Analysis of molecular variance (AMOVA) [35] estimates population differentiation directly from molecular data and was implemented in GenAlex 6.1 [36]. The AMOVA approach computes ΦST, a statistic analogous to FST, that estimates the level of genetic differentiation between populations and ranges from 0 (complete genetic homogeneity) to 1 (complete genetic separation). Population-grouped similarity coefficients based on Gower general similarity coefficient [20,21] were used to calculate an average similarity value within and among populations. Similarity values range between 0 and 1, the former indicative of complete genetic dissociation and the latter genetic identity. Principal coordinate analysis (PCO) was used to graphically represent genetic relationships among each individual using MVSP 3.0 (Multi-Variate Statistical Package; Kovach Computing Services 1986–2011, Kovach Computing Services, Anglesey, Wales) based on Gower general similarity coefficient [20,21]. A Bayesian algorithm, as implemented in STRUCTURE version 2.3.4 [37,38], was used to define genetic groups within each species. This algorithm infers genetic discontinuities from individual multilocus genotypes without a priori knowledge of geographic location or taxonomy. The default settings of the program were used, including an admixture model. To determine the most likely number of groups (K) in the data, a series of analyses were performed from K = 1 to 7 or 8 (upper limit determined by the number of populations plus three [39]), using a burn-in period and MCMC (Markov Chain Monte Carlo) both set at 100,000 repetitions, with twenty iterations per K [40]. These results were examined using the ∆K method [37] to identify the most likely number of groups in the data using STRUCTURE HARVESTOR [41]. 5. Conclusions and Conservation Implications Results of this study demonstrate several important factors regarding the genetic diversity and structure within these species. Patterns of genetic diversity and genetic differentiation within and among populations are similar for both species examined. However, the level of variation found in C. hawaiiensis, an endangered species with smaller and more isolated populations, is consistently lower than that found in C. auwahiensis, a non-endangered species with much more extensive populations. Populations of C. hawaiiensis have been in decline for at least 50 years (Nick Agorastos, Hawaii NARS staff, personal communication), yet a level of genetic diversity nearly equal to that of a non-endangered congener occurring in similar habitats suggests that the effects of inbreeding within populations have not yet had a significantly deleterious impact on their vigor. Genetic diversity at the species level remains very high, as levels of polymorphism are above 90% and nearly equal to those species known to have the highest level of genetic diversity yet measured among Hawaiian species [11]. This is likely a reflection of the species habit (long-lived trees) and habitat (mostly dry forests) that promote slow growth in individuals. Since little to no recruitment of plants within populations has been observed, it is probable that the genetic diversity observed is from individuals that have survived in these environments since before the populations went into decline and that loss of variation is because of population attrition rather than loss of alleles through inbreeding. There are approximately 20 very endangered individuals of C. hawaiiensis in scattered locations at Hawaii Volcano National Park (HAVO) that were not examined here. Any future study of these species should include the HAVO population. Long-term survival of C. hawaiiensis will not be possible by simply maintaining current population numbers without active conservation management. The impacts from animal grazing pressure have played a pivotal role in the erosion of plant diversity of Hawaiian dry forests. For C. auwahiensis, the additional pressure on the populations by invasive weeds competing with seedlings and invasive slugs that eat seedlings are further threats (Hank Oppenheimer Maui PEP, personal communication). The consequence has been zero seedling recruitment in these populations. For future conservation work, it has been suggested that seed collections be made from different populations of each species to increase the genetic variation and benefit the long term survival of endangered species. Based on the polymorphism data, C. auwahiensis on Maui still maintains enough genetic variation (70%–86%) for each population, thus seed collection from individual populations should be made broadly. Future research should focus on the reproductive biology of these species. Virtually nothing is known regarding the pollination, seed survival, and growth of these plants. Pollination observations and open flower vs. closed flower seed set experiments would provide the necessary information regarding inbreeding among the species. Seed germination experiments would be beneficial for understanding breaking of dormancy, germination rates, and seedling survival. Seedlings would then also be available for potential population reintroduction. Several conservation measures are recommended to protect both species. First, and most importantly, any threats to the plants at the early stages of their development must be removed. This can only be accomplished by building predator-proof fences that can exclude introduced herbivorous animals, particularly goats, from those areas. Some snail baits have recently been approved for use in conservation areas, and strategies for their use should be developed to implement this control where snails and slugs are a factor. Second, mature plants readily flower and fruit, and efforts should focus on establishing an ex situ seed bank for both species. Care should be made while collecting to target widely-spaced plants to capture the maximum genetic diversity possible [42]. Third, growing plants ex situ for future reintroduction into the source populations when they have attained a size sufficient to withstand existing threats (i.e., slugs and goats) would help maintain the population’s integrity until other measures have been implemented that will allow natural recruitment. Fourth, because individual population variation of C. hawaiiensis is in decline, yet total species variation is high, limited mixing of population progeny is recommended to maintain higher levels of genetic diversity that has been shown to be beneficial for the long-term survival in a wide variety of species [43]. The loss of genetic variation has been shown to have harmful effects on fitness of individuals of populations [33,44]. Possible problems associated with outbreeding depression that could occur from mixing different population progeny are minimal, if present at all, and are far less than potential future problems associated with inbreeding depression. Performing hand-pollination crosses among plants from different populations and growing the individuals from such crosses with the purpose to outplant them might also attain this. Acknowledgments Special thanks to Hank Oppenheimer (Maui) and Nick Agorastos and Christian Torres-Santana (Hawaii) for assistance in population collection. We thank Tom Ranker and Stacy Jørgensen’s for research advice and two anonymous reviewers for recommendations to improve the manuscript. We thank the Division of Forestry and Wildlife (DOFAW) of the Hawaii Department of Land and Natural Resources for research permits and Edith Adkins (DOFAW), Elliott Parsons (Puu Waawaa Forest Reserve) for assistance with access to populations. We thank Yi-Shao Liang for technical assistance. We thank the Department of Botany, University of Hawaii at Manoa, and Da-Yeh University for research support and the East-West Center, University of Hawaii for student fellowship and travel support. Author Contributions Pei-Luen Lu and Clifford W. Morden conceived and designed experiments; Pei-Luen Lu performed the experiments; Pei-Luen Lu and Mitsuko Yorkson analyzed the data; Pei-Luen Lu and Clifford W. Morden contributed reagents/materials/analysis tools; Pei-Luen Lu, Mitsuko Yorkson and Clifford W. Morden wrote the paper. Conflicts of Interest The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results. Figure 1 Locations of populations used in Chrysodracon genetic studies. (A) The eight Hawaiian Islands; (B) topographic map of Hawaii Island; (C) topographic map of Maui Island. Populations are demarcated with the color used in the principal coordinate analysis (PCO) analysis. Names of volcanoes on each island are indicated for Hawaii (5) and Maui (2); principle cities associated with each islands are indicated with a star (★). Figure 2 Principal coordinate analysis of combined random amplified polymorphic DNA and inter simple sequence repeats dataset using all scored markers for Chrysodracon hawaiiensis. PCO axis 1 and 2 accounted for 18.8% and 18.2% of the variation, respectively. Figure 3 Principal coordinate analysis of combined RAPD and ISSR dataset using all scored markers for Chrysodracon auwahiensis. PCO axis 1 and 2 accounted for 17.1% and 11.2% of the variation, respectively. Figure 4 Genetic STRUCTURE bar graph of Chrysodracon species from combined RAPD and ISSR data. Individuals genotyped in this study are represented by a single vertical bar partitioned into colored segments that represent the individual’s probability of belonging to a particular group (K). (A) C. hawaiiensis, 53 individuals, K = 4; and (B) C. auwahiensis, 77 individuals, K = 3. Graphs represent one of 20 iterations from the indicated K value for each species. ijms-17-01341-t001_Table 1Table 1 Random amplified polymorphic DNA (OPA-OPD) and inter simple sequence repeat primers examined and the number of genetic markers scored. Primer name, number of total scored amplification products (N), number of polymorphic bands (P), percent polymorphism (%P), and the range of size (in base pairs) for amplified products (size) for Chrysodracon auwahiensis and Chrysodracon hawaiiensis. Subtotal for RAPD and ISSR primers separately; and total for RAPD and ISSR primers combined. Primer N P %P Size N P %P Size Chrysodracon hawaiiensis Chrysodracon auwahiensis OPA-02 14 13 93 300–1600 21 20 95 175–1600 OPA-09 13 13 100 300–1600 18 18 100 350–2300 OPB-04 19 18 95 260–1500 12 12 100 300–1800 OPB-07 23 23 100 260–1500 19 19 100 170–2400 OPB-08 20 20 100 150–1300 20 20 100 200–1500 OPB-14 19 19 100 150–1600 20 20 100 250–1500 OPC-07 10 9 90 200–2000 14 14 100 180–2000 OPD-02 16 14 88 200–1000 16 16 100 280–1500 OPD-05 16 15 94 200–1200 21 21 100 170–500 OPD-12 11 11 100 350–1000 14 14 100 180–1800 OPD-15 19 18 95 300–2000 23 21 91 300–2000 Subtotal 180 173 96 - 198 195 98 - ISSR-5007 22 21 95 250–1800 15 13 87 300–1800 ISSR-5009 13 12 92 480–1500 12 9 75 350–1300 ISSR-5028 14 12 86 300–1600 13 13 100 300–1800 Subtotal 49 45 92 - 40 35 88 - TOTAL 229 218 95 - 238 230 97 - ijms-17-01341-t002_Table 2Table 2 Genetic variability among populations of Chrysodracon hawaiiensis (C. hawaiiensis) and Chrysodracon auwahiensis (C. auwahiensis) based on RAPD and ISSR analyses. Percentage of polymorphic markers (%P), estimated mean heterozygosity over all markers (H), estimated mean heterozygosity over polymorphic markers (H (P)), and Shannon’s Diversity Index (SDI). The slash (/) separates values for RAPD and ISSR analyses, respectively. Population %P H H (P) SDI Chrysodracon hawaiiensis Kaupulehu 54/57 0.168/0.210 0.314/0.352 1.552/1.395 Kohala 53/45 0.184/0.187 0.350/0.416 1.588/1.333 Manuka 50/65 0.152/0.223 0.301/0.340 1.576/1.410 Puu Waawaa 68/73 0.218/0.276 0.322/0.375 1.582/1.440 All individuals 96/92 0.254/0.316 0.363/0.418 1.576/1.450 Chrysodracon auwahiensis Auwahi 85/70 0.241/0.216 0.302/0.309 1.675/1.407 Iao Valley 77/60 0.238/0.184 0.309/0.226 1.668/1.386 Kanaio 86/73 0.240/0.257 0.302/0.355 1.680/1.413 Kauaula 70/3 0.226/0.181 0.317/0.271 1.685/1.377 Makawao 80/65 0.263/0.217 0.363/0.230 1.693/1.394 All individuals 98/88 0.401/0.352 0.432/0.403 1.696/1.397 ijms-17-01341-t003_Table 3Table 3 Analysis of molecular variance (AMOVA) for 53 individuals in four populations of C. hawaiiensis and 77 individuals in five populations of C. auwahiensis based on combined RAPD, ISSR, and combined dataset analyses. Abbreviations: d.f., degrees of freedom; SSD, sum of squared deviation; MSD, mean squared deviation; Var. Comp., variance component; %T, percentage of total variance contributed by each component; p, probability of obtaining a more extreme component by chance alone; and ΦST, the degree of differentiation between population divisions. Source of Variation Analysis d.f. SSD MSD Var. Comp. %T p ΦST C. hawaiiensis Among Pops RAPD 3 827.1 275.7 19.6 44 0.001 0.442 ISSR 3 199.0 66.4 4.8 52 0.001 0.519 Combined 3 1218.0 406.0 29.5 54 0.001 0.536 Within Pops RAPD 49 1208.4 26.7 24.7 56 0.001 - ISSR 49 218.7 4.5 4.5 48 0.001 - Combined 49 1255.6 25.6 25.5 46 0.001 - C. auwahiensis Among Pops RAPD 4 975.0 243.7 14.5 35 0.001 0.347 ISSR 4 192.7 48.2 2.9 40 0.001 0.401 Combined 4 1167.7 291.9 17.5 35 0.001 0.355 Within Pops RAPD 72 1973.2 243.7 14.5 65 0.001 - ISSR 72 316.7 4.4 4.4 60 0.001 - Combined 77 2298.9 31.8 31.8 65 0.001 - ijms-17-01341-t004_Table 4Table 4 Matrix of the average of coefficient genetic percent similarity (based on Gower similarity coefficients [20,21]) within and among populations of C. hawaiiensis and C. auwahiensis from combined RAPD and ISSR dataset analysis. Population No. C. auwahiensis 1 2 3 4 5 Population No. C. hawaiiensis 6 7 8 9 1 Auwahi 83 6 Kaupulehu 85 2 Iao Valley 55 75 7 Kohala 54 85 3 Kauaula 55 67 76 8 Manuka 47 50 66 4 Kanaio 66 53 53 84 9 Puu Waawaa 54 60 56 87 5 Makawao 51 52 51 51 86 ijms-17-01341-t005_Table 5Table 5 Samples analyzed for population genetic variation of C. hawaiiensis (Hawaii Island) and C. auwahiensis (Maui Island). Species classification, locality, estimated population size (N), number of plant per population sampled (Ns), DNA accession in the Hawaiian Plant DNA Library (HPDL), and representative population voucher. The topographic map of for the populations’ locations of two species are shown on Figure 1. Due to species rarity, voucher specimens were not collected with this study, but representative specimens from each locality were deposited at the B. P. Bishop Museum (BISH) are indicated. Species Locality N 1 NS HPDL Voucher C. hawaiiensis Kaupulehu 50 10 8170–8179 J. D. Jacobi 251 Kohala 20 10 8193–8202 C. Christensen 1 Manuka 50 13 8180–8182 H. St. John 11343 Puu Waawaa 200 20 8170–8179 Y. Kondo 44 C. auwahiensis Auwahi 600 20 6632–6644, 6661–6667 H. St. John 26869 Iao Valley 300 8 6591–6598 J. C. Price 19 Kanaio 600 20 6611–6630 R.W. Hobdy 2552 Kauaula 300 9 6599–6606 D. R. Wood 11943 Makawao 300 20 6607–6610, 6645–6660 H. L. Oppenheimer H50221 1 Estimated population size of C. hawaiiensis from N. Agorastos (Hawaii Natural Area Reserves System) and C. auwahiensis from H. L. Oppenheimer (Maui Nui Plant Extinction Prevention Program) (personal communication). ==== Refs References 1. Carlquist S. Hawaii: A Natural History The Natural History Press Garden City, NY, USA 1997 2. Carlquist S. Hawaii: A Natural History Pacific Tropical Botanical Garden Lawai, Kauai, HI, USA 1980 3. Myers N. Mittermeier R.A. Mittermeier C.G. da Fonseca G.A.B. Kent J. Biodiversity hotspots for conservation priorities Nature 2000 403 853 858 10.1038/35002501 10706275 4. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081342ijms-17-01342ArticleTLR4 Endogenous Ligand S100A8/A9 Levels in Adult-Onset Still’s Disease and Their Association with Disease Activity and Clinical Manifestations Kim Hyoun-Ah 1Han Jae Ho 2Kim Woo-Jung 3Noh Hyun Jin 3An Jeong-Mi 1Yim Hyunee 2Jung Ju-Yang 1Kim You-Sun 3*†Suh Chang-Hee 1*†Malemud Charles J. Academic Editor1 Department of Rheumatology, Ajou University School of Medicine, 164 Worldcup-ro, Yeongtong-gu, Suwon 443-380, Korea; nakhada@naver.com (H.-A.K.); godofpeace@nate.com (J.-M.A.); serinne20@hanmail.net (J.-Y.J.)2 Department of Pathology, Ajou University School of Medicine, 164 Worldcup-ro, Yeongtong-gu, Suwon 443-380, Korea; hanpathol@naver.com (J.H.H.); Huuneeyim@naver.com (H.Y.)3 Department of Biochemistry and Department of Biomedical Sciences, Ajou University School of Medicine, 164 Worldcup-ro, Yeongtong-gu, Suwon 443-380, Korea; kwj881010@hanmail.net (W.-J.K.); simuner@naver.com (H.J.N.)* Correspondence: yousunkim@ajou.ac.kr (Y.-S.K.); chsuh@ajou.ac.kr (C.-H.S.); Tel.: +82-31-219-4509 (Y.-S.K.); +82-31-219-5118 (C.-H.S.); Fax: +82-31-219-4530 (Y.-S.K.); +82-31-219-5157 (C.-H.S.)† These authors contributed equally to this work. 16 8 2016 8 2016 17 8 134229 6 2016 11 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).S100A8/A9 has been suggested as a marker of disease activity in patients with adult-onset Still’s disease (AOSD). We evaluated the clinical significance of S100A8/A9 as a biomarker and its pathogenic role in AOSD. Blood samples were collected prospectively from 20 AOSD patients and 20 healthy controls (HCs). Furthermore, skin and lymph node biopsy specimens of AOSD patients were investigated for S100A8/A9 expression levels via immunohistochemistry. Peripheral blood mononuclear cells (PBMCs) of active AOSD patients and HCs were investigated for S100A8/A9 cell signals. S100A8/A9, interleukin-1β (IL-1β), and tumor necrosis factor-α (TNF-α) levels in active AOSD patients were higher than those of HCs. S100A8/A9 levels correlated positively with IL-1β, TNF-α and C-reactive protein. The inflammatory cells expressing S100A8/A9 were graded from one to three in skin and lymph node biopsies of AOSD patients. The grading for S100A8/A9 was more intense in the skin lesions with karyorrhexis, mucin deposition, and neutrophil infiltration. Like lipopolysaccharide (LPS), S100A8/A9 induced phosphorylation of p38 and c-Jun amino-terminal kinase (JNK) in PBMCs, suggesting that S100A8/A9 activates Toll-like receptor 4 signaling pathways. These findings suggest that S100A8/A9 may be involved in the inflammatory response with induction of proinflammatory cytokines and may serve as a clinicopathological marker for disease activity in AOSD. adult-onset Still’s diseaseS100A8/A9interleukin-1βbiomarkerdisease activity ==== Body 1. Introduction Adult-onset Still’s disease (AOSD) is an uncommon systemic inflammatory disease of unknown cause. Its clinical manifestations are characterized by a high repetitive spiking fever, an evanescent skin rash, myalgia, lymphadenopathy, and hepatosplenomegaly [1]. Although the initial manifestations of all patients are similar, with systemic symptoms, the clinical course of the patients proceeds according to three distinct patterns: monocyclic, polycyclic, and chronic articular patterns [1,2]. Although the pathogenesis of AOSD remains unclear, multiple factors including genetic background, viral infections, and aberrant immune response have been suggested to be involved in the development of the disease [3,4]. Genetically predisposed hosts seem to develop auto-inflammatory conditions triggered by macrophage activation and Th1 cytokines, such as tumor necrosis factor-α (TNF-α), interleukin-1 (IL-1), IL-6, IL-8, and IL-18 [5]. Proinflammatory cytokines, such as IL-1β and TNF-α are amplified by endogenous factors, like S100 proteins. These proteins induce inflammatory responses through the recruitment of inflammatory cells and are suggested to be one of the “damage-associated molecular patterns” [6,7]. S100A8 and S100A9, which belong to the S100 family, are calcium-binding proteins and form heterodimers that are the biologically relevant form [8]. S100A8/A9 is produced by infiltrating neutrophils and monocytes but not by quiescent resident macrophages or lymphocytes under inflammatory status [9,10]. S100A8/A9 has been shown to be an endogenous ligand of Toll-like receptor-4 (TLR-4), to be associated with human sepsis and endotoxemia, and to play an important role in innate immunity [10,11,12]. S100A8/A9 has been detected at high levels in the serum and body fluids of patients with several inflammatory disorders [11,13,14,15,16]. Some studies showed that systemic juvenile idiopathic arthritis (systemic JIA) is associated with high concentrations of S100A8/A9 [17,18,19]. Furthermore, we showed previously elevated serum S100A8/A9 levels in AOSD patients, compared with healthy controls (HCs), and their correlation with several disease activity markers in AOSD [20]. In this study, we have investigated the correlation between S100A8/A9 and proinflammatory cytokines in active AOSD patients recruited prospectively. Furthermore, to evaluate the in vivo involvement of S100A8/A9 in AOSD, we immunohistochemically stained biopsy specimens obtained from the skin lesions of 26 patients and lymph nodes of eight patients with active untreated AOSD for S100A8/A9. We also investigated the association between IL-1β and S100A8/A9 with peripheral blood mononuclear cells (PBMCs) of active AOSD patients and HCs and a monocyte cell line. 2. Results 2.1. Clinical Characteristics of the 20 Adult-Onset Still’s Disease (AOSD) Patients, 20 Rheumatoid Arthritis (RA) Patietns, and 20 Healthy Controls (HCs) The mean age of the AOSD patients was 38 ± 13.7 years, and 85% were female. There were no differences in age or gender among groups (Table 1). The main symptoms in AOSD patients were fever (100%), skin rash (75%), sore throat (55%), arthritis (52.8%), and lymphadenopathy (40%). 2.2. Serum S100A8/A9 Levels in AOSD, RA Patients, and HCs Figure 1A shows S100A8/A9 levels in AOSD patients, RA patients, and HCs. The levels of S100A8/A9 in AOSD patients (15.43 ± 7.3 µg/mL) were higher than those of RA patients and HCs (4.04 ± 4.18 µg/mL, p < 0.001; 2.01 ± 1.06 µg/mL, p < 0.001, respectively). We evaluated the correlation between systemic score and serum S100A8/A9 in AOSD patients. Serum S100A8/A9 levels correlated with systemic score (r = 0.463, p = 0.04; Figure 1B). 2.3. Serum Interleukin-1β (IL-1β), and Tumor Necrosis Factor-α (TNF-α) Levels in AOSD Patients and HCs Figure 2 shows IL-1β and TNF-α levels in AOSD patients and HCs. Serum IL-1β levels of AOSD patients (3.11 ± 2.34 pg/mL) were higher than those of HCs (1.57 ± 0.58, p = 0.004), and the TNF-α levels of AOSD patients (6.63 ± 5.13 pg/mL) were also higher than those of HCs (2.84 ± 1.58 pg/dL, p = 0.002). We sought to determine whether the levels of IL-1β and TNF-α were associated with the level of S100A8/A9. S100A8/A9 levels correlated positively with IL-1β and TNF-α (r = 0.603, p < 0.001; r = 0.405, p = 0.009, respectively). In addition, they correlated positively with ferritin and CRP (r = 0.698, p < 0.001; r = 0.811, p < 0.001, respectively). 2.4. IL-1β Secretion after Treatment of S100A9 in PBMCs from Active AOSD Patients and HCs We thought that monocytes could be major cells for the inflammatory condition in AOSD. Thus, we stimulated PBMCs from six AOSD patients and six HCs in vitro with S100A9, and evaluated IL-1β levels. Stimulation of PBMCs from AOSD patients and HCs in vitro revealed that S100A9 was an inducer of IL-1β secretion, with levels comparable to those observed with lipopolysaccharide (LPS) (Figure 3A). Priming of PBMCs with interferon gamma (IFN-γ) augmented the effects of both S100A9 and LPS in PBMCs from six AOSD patients and six HCs. However, the secreted levels of IL-1β from the PBMCs of the AOSD patients were not elevated significantly compared with PBMCs from the HCs in medium, S100A9, or LPS. 2.5. Expression of c-JUN Amino-Terminal Kinase (JNK) and p38 after Treatment with S100A9 in PBMCs from Active AOSD Patients and HCs and in THP-1 Cells We sought to determine the mechanism by which the endogenous TLR4 ligand S100A8/A9 induced IL-1β in monocytes. First, we evaluated several transcription factors, such as p100, phosphorylated IκBα, and JNK, in monocytes/macrophages treated with LPS, an exogenous TLR4 ligand. Immunoblot analysis was performed with antibodies specific to p100, p52, phosphorylated IκBα, and JNK in PBMCs from AOSD patients and HCs treated with LPS for 2 h (Figure 3B). IκBα and JNK were significantly more phosphorylated at 0.5 h in PBMCs from AOSD patients and HCs with LPS. In addition, phosphorylated IκBα and JNK were similar to PBMCs of AOSD patients and HCs treated with LPS. However, p100 and p52 were not different for 2 h, and these data were similar between AOSD patients and HCs. We next evaluated phosphorylated JNK and p38 from PBMCs of AOSD patients and HCs with S100A9 compared with LPS. Immunoblot analysis was performed with antibodies specific to phosphorylated JNK and p38 in PBMCs from HCs and AOSD patients treated with S100A9 or LPS for 4 h (Figure 3C). JNK and p38 were significantly more phosphorylated at 0.5 h in PBMCs from HCs and AOSD patients treated with S100A9, similar to those treated with LPS. We evaluated activation of the transcription factors with monocytes/macrophages. We assayed with THP-1 cells treated with S100A8/A9 and LPS, and confirmed that JNK and p38 were significantly phosphorylated with S100A8/A9 at 0.5 h, which was similar to those treated with LPS (Figure 3D). 2.6. Histopathological and Immunohistochemical Characteristics of Skin and Lymph Nodes in AOSD Immunohistochemical analyses of skin and lymph nodes were performed for S100A8/A9 to evaluate the organ involvement of this endogenous TLR4 ligand in AOSD. Most skin lesions showed mild histiocytic or lymphocytic infiltration in the upper dermis. More than half the cases had mucin deposition in interstitium. Nuclear debris was observed in the dermis in 14 (53.8%) cases. Eight lymph node biopsy revealed only paracortical hyperplasia (n = 5) and a mixed pattern (n = 3) with paracortical and diffuse (n = 2) and paracortical, follicular and diffuse (n = 1). Vascular proliferation was shown from moderate to severe in all lymph nodes, and immunoblast proliferation was observed moderate-to-severe in five (62.5%) lymph nodes. Lymphoid cells of the paracortical zone in a tonsil were stained for control for S100A8 and S100A9 immunohistochemical explorations. The antibodies showed a diffuse or granular cytoplasmic pattern. The patterns of inflammatory cells staining for skin biopsies were similar to those of the lymphoid cells in a tonsil (Figure 4A,B). The S100A8/A9 staining positive cells were graded from one to three. The grading of S100A8/A9 staining positive cells was higher when there was karyrrhexis, mucin deposition, and neutrophil infiltration (Table 2). Furthermore, the correlation between inflammatory cell grading of Cluster of Differentiation (CD)68 and that of S100A8/A9 was significant (p < 0.001), but was not significant between CD4 or CD8 and that of S100A8/A9. The S100A8/A9 staining positive cells were graded from one to three in lymph node biopsies (Figure 4C,D). The grading and strength of S100A8/A9 staining positive cells was marginally more intense for severe vascular proliferation (p = 0.05). 3. Discussion Serum S100A8/A9 levels of patients with active untreated AOSD were higher than those of RA patients and HCs, and showed correlations with IL-1β and TNF-α. In addition, S100A8/A9 was immunohistochemically stained in lesional skin and lymph nodes from AOSD patients. Moreover, it was found that S100A9 induced IL-1β expression in monocytes from AOSD patients and HCs. Furthermore, S100A9 and S100A8/A9 were shown to induce signal transduction pathways, including p38 and JNK in PBMCs of AOSD patients, HCs, and THP-1 cells. S100A8/A9 is known as an intracellular differentiation marker for phagocytes but also as an extracellular protein complex involved in inflammatory response, i.e., one of the damage-associated molecular patterns molecules (DAMPs) [21]. S100A8/A9 has shown to be a biomarker of several inflammatory diseases including RA, systemic JIA, inflammatory bowel disease, and AOSD [19,20,22,23,24]. One study showed that serum S100A8/A9 levels were higher in patients with active systemic JIA, compared with those with systemic infections and HCs [18]. In addition, S100A8/A9 discriminated systemic JIA from systemic infections with high specificity, in contrast to CRP levels. A recent study confirmed that serum S100A8/A9 levels correlated well with response to treatment, and suggested that it might be an excellent biomarker for monitoring the treatment in systemic JIA [17]. In this study, we recruited active untreated AOSD patients, RA patients, and HCs prospectively, and evaluated S100A8/A9 with proinflammatory cytokines, such as IL-1β and TNF-α. Serum S100A8/A9 levels of patients with AOSD were significantly higher than those of patients with RA and HCs. Additionally, serum S100A8/A9 levels showed strong positive correlations with several inflammatory markers, such as CRP, ferritin, IL-1β, and TNF-α. We confirmed again that S100A8/A9 could provide reliable clinical information for monitoring disease activity of AOSD. In addition to the clinical implications of S100A8/A9 in AOSD, this data showed the important role of innate immune processes involving S100A8/A9 in the pathogenesis of AOSD. We confirmed that S100A9 was a strong inducer of IL-1β expression in monocytes of AOSD patients and HCs. In addition, we showed that S100A9 induced the phosphorylation of p38 and JNK in PBMCs of AOSD patients and HCs. These results were consistent with previous reports [25,26]. S100A8/A9 is known as an endogenous TLR4 ligand, similar to LPS [10]. S100A8/A9 is also an inducer for a thrombogenic response in human microvascular endothelial cells by decreasing the expression of cell junction proteins and molecules and by increasing the transcription of proinflammatory cytokines or adhesion molecules [27]. The elevation of S100A8/9, an internal TLR4 ligand, in AOSD may suggest a similarity in inflammatory responses and disease manifestations between AOSD and septic conditions. However, IL-1β expression in phagocytes of AOSD patients was not significantly different from that in phagocytes from HCs, and the findings were consistent in the expression of transcription factors between AOSD patients and HCs. We analyzed the expression of S100A8/A9 during the initial phase of AOSD using skin and lymph node biopsy specimens obtained before treatment. S100A8/A9 was expressed in skin and lymph nodes of patients with AOSD. Such staining of S100A8/A9 was correlated significantly with the CD68-stained inflammatory cells in the skin of AOSD. Furthermore, enhanced S100A8/A9 staining was evident in neutrophil infiltration and inflammatory skin lesions with mucin deposition and karyorrhexis. Although we did not compare inflammatory skin or lymph node lesions from AOSD with those from HCs, S100A8 and S100A9 are known to be expressed with low levels in normal epidermis [21]. S100A8/A9 is highly expressed in the skin lesions of several inflammatory skin diseases including psoriasis, systemic lupus erythematosus, and lichen planus [28,29]. S100A8/A9 could be a part of a positive feedback mechanism in the initiation and amplification of inflammatory skin diseases through inducing proinflammatory cytokine production as well as proliferation of keratinocytes [30]. Furthermore, in psoriasis, epidermal S100A8/A9 overexpression seems to be related to increase serum S100A8/A9 levels, which correlate with disease activity [31]. Epidermal overexpression of S100A8/A9 was observed in typical skin rashes of systemic JIA, and correlated with elevated circulating S100A8/A9 levels [19,32]. In this study, we showed that S100A8/A9 was expressed in skin and lymph nodes affected by AOSD, and correlated with neutrophil infiltration and CD68-stained inflammatory cells. These results could suggest that the infiltrating neutrophils and CD68-positive inflammatory cells are major sources of S100A8/A9-related inflammation. Furthermore, staining of S100A8/A9 was related to skin lesions with mucin deposition and karyorrhexis. These results suggest that S100A8/A9 plays a key role for the skin inflammation in AOSD. However, we could not demonstrate a correlation between S100A8/A9 expression in skin and serum S100A8/A9 levels, because we did not have serum samples from the AOSD patients who underwent biopsies. 4. Experimental Sections 4.1. Subjects In total, 20 AOSD patients, 20 rheumatoid arthritis (RA) patients as a disease control, and 20 HCs were prospectively included in this study. AOSD patients satisfied the Yamaguchi’s criteria after the exclusion of viral or bacterial infections, hematologic diseases, and autoimmune disorders [33]. RA patients were diagnosed according to American College of Rheumatology 1987 revised criteria for the classification of RA [34]. Blood samples were collected from patients and controls. All serum samples were stored at −70 °C immediately after collection. This study was approved by the Institutional Review Board of our hospital (AJIRB-MED-SMP-11-094). Informed consent was acquired from all enrolled subjects. Information of the medical history, clinical manifestations, and physical examinations was obtained when blood sampling was done. Each patient also underwent laboratory tests including complete blood count, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), ferritin (normal = 13–150 ng/mL for women and 30–400 ng/mL for men), liver function tests, urinalysis, rheumatoid factor, and anti-nuclear antibody. AOSD disease activity was evaluated with a systemic score using the method suggested by Pouchot et al. [35], which adds one point for the following manifestations: fever, rash, sore throat, myalgia, abdominal pain, pneumonia, pleuritis, pericarditis, leukocytosis (≥15,000/mm3), hepatomegaly or abnormal liver function tests, splenomegaly, and lymphadenopathy. 4.2. S100A8/A9, IL-1β, and TNF-α Assays Serum S100A8/A9 concentrations were measured with commercial enzyme-linked immunosorbent assay (ELISA) kits (Buhlmann Laboratories, Schonenbuch, Switzerland) according to the manufacturer’s protocol. Serum concentrations of IL-1β and TNF-α were measured using commercial ELISA kits (R&D Systems, Minneapolis, MN, USA) according to the manufacturer’s protocol. 4.3. PBMC Preparation and Stimulation of Six Active AOSD Patients and Six HCs PBMCs were isolated from human buffy coats from six patients with active AOSD and six HCs and cultured. PBMCs were incubated for 24 h with S100A9 (5 µg/mL), S100A8/A9 (5 µg/mL), or LPS (10 ng/mL; Sigma, Deisenhofen, Germany) and IL-1β concentrations in supernatants were measured by ELISA (R&D Systems). The interassay variability of IL-1β baseline production between different PBMC culture is the reason that the results are reported as a percentage of the control. In parallel, PBMCs were primed for 16 h incubated with interferon-γ (500 IU/mL; Bender MedSystems, Vienna, Austria) prior to stimulation with LPS, S100A8/A9, or S100A9. 4.4. Cell Culture THP-1 cells were cultured in RPMI 1640 medium supplemented with 10% FBS, 2 mM glutamine, 10 mM HEPES, 1 mM sodium pyruvate, 0.05 mM 2-mercaptoethanol, and 100 U/mL penicillin and streptomycin. No further authentication of the cell line was performed. 4.5. Immunobot With treatment, cell lines or PBMCs were lysed in 20 mM Tris (pH 7.0), 250 mM NaCl, 3 mM EDTA, 3 mM EGTA, 2 mM DTT, 0.5% NP-40, 0.5 mM PMSF, 20 mM β-glycerol phosphate, 1 mM sodium vanadate and 1 µg/mL leupeptin. Cell lysates were loaded on 10% or 12% SDS-PAGE gels. After transfer and blotting, the target proteins were visualized by enhanced chemiluminescence (Pierce, Rockford, IL, USA) and analyzed. 4.6. Histopathology of Skin Biopsy and Lymph Node Skin biopsies of 26 patients with active AOSD and eight lymph node biopsies were obtained. We investigated the hematoxylin and eosin-stained sections, as previously described [36]. The slides were examined by two pathologists (JHH and HY) independently with the following skin histological parameters of epidermal change, and presence of karyorrhexis, vasculitis, and interstitial mucin, and degree of inflammatory cell infiltration. Lymph node histological parameters were evaluated with patterns of reaction (follicular, paracortical, or diffuse hyperplasia), kinds of infiltrating inflammatory cells, immunoblast proliferation, and vascular proliferation. 4.7. Immunohistochemistry and Evaluation Immunohistochemistry was performed with several primary antibodies on 4-µm representative tissue sections of formalin-fixed, paraffin wax-embedded tissue with the Benchmark XT automated immunohistochemistry stainer (Ventana Medical Systems Inc., Tucson, AZ, USA). The primary antibodies were used with CD4, 1:10 and CD8, 1:50 (Thermo Fisher Scientific, Fremont, CA, USA), CD68, 1:200 (Novocastra Laboratories Ltd., Newcastle, UK), S100A8, 1:300 and S100A9, 1:400 dilution (Abcam Systems, Cambridge, UK). Detection was done using the Ventana Optiview DAB Kit (Ventana Medical Systems). Each marker for immunohistochemical staining was recorded as the number of positive inflammatory cells divided by the number of total inflammatory cells, and expressed as grade 1 to grade 3: 1, 1%–33%, 2, 34%–66%, or 3, 67%–100% (CD4, CD8, CD68, and S100A8/A9). 4.8. Statistical Analysis Statistical analyses were performed using the SPSS for Windows software (ver.12.0; SPSS, Chicago, IL, USA). A p-value < 0.05 was regarded as indicating statistical significance. The data are shown as means ± standard deviation (SD) or median and interquartile range, as appropriate. Differences in S100A8/A9 and cytokine levels were determined using the Mann-Whitney U-test. The correlations between their levels and disease activity markers were assessed with Spearman’s correlation test. 5. Conclusions We found higher levels of S100A8/A9 in the serum and upon immunohistochemical staining of pathological skin tissue and lymph node from patients with active untreated AOSD. Furthermore, S100A8/A9 correlated with several inflammatory markers and disease activity markers, and this DAMP induced IL-1β production. These findings support the important role of S100A8/A9 in the pathogenesis of AOSD, and may also suggest novel diagnostic or therapeutic strategies. Acknowledgments This work was supported by the new faculty research fund for Ajou University School of Medicine and Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by grant (No. 2011-0030043 and 2014R1A2A1A11052951) from the Ministry of Education, Science, and Technology. Author Contributions Hyoun-Ah Kim, You-Sun Kim and Chang-Hee Suh conceived, designed the experiments, analyzed the data and wrote the paper; Hyoun-Ah Kim, Woo-Jung Kim, Hyun Jin Noh, and Jeong-Mi An performed the experiment; Jae Ho Han, Hyunee Yim and Ju-Yang Jung contributed materials and analyzed the data. Conflicts of Interest The authors declare no conflict of interest. Figure 1 (A) Levels of S100A8/A9 in 20 active adult-onset Still’s disease (AOSD) patients, 20 rheumatoid arthritis (RA) patients, and 20 healthy controls (HCs). Data are expressed as the means ± standard deviation (SD). A Mann-Whitney U-test was used to perform the statistical analysis; (B) the levels of S100A8/A9 correlated with the systemic scores in AOSD patients (r = 0.463, p = 0.04). Figure 2 Levels of inteleukin-1β (IL-1β) (A) and tumor necrosis factor-α (TNF- α) (B) in 20 patients with active adult-onset Still’s disease (AOSD) and 20 healthy controls (HCs). Data are shown as the means ± SD. A Mann-Whitney U-test was performed for the statistical analysis. Serum S100A8/A9 levels correlated positively with IL-1β (r = 0.603, p < 0.001) (C), TNF-α (r = 0.405, p = 0.009) (D), ferritin (r = 0.698, p < 0.001) (E), and C-reactive protein (CRP) (r = 0.811, p < 0.001) (F). These data were assessed using Spearman’s correlation. Figure 3 (A) Interleukin-1β (IL-1β) secretion after treatment with S100A9 in peripheral blood monocyte cells (PBMCs) of healthy controls (HCs) and patients with active adult-onset Still’s disease (AOSD) patients. PBMCs (1 × 106/mL) were incubated for 24 h with 5 µg/mL S100A9 or 10 ng/mL lipopolysaccharide (LPS) or left untreated as controls (medium). Concentrations of IL-1β in supernatants were evaluated by enzyme-linked immunosorbent assay. Data are shown from six independent experiments. Values are the means and SD. * p ≤ 0.05, vs. controls; (B) Activation of c-Jun amino-terminal kinase (JNK) and p38 after treatment with S100A9 in PBMCs from HCs and patients with active AOSD. PBMCs were treated with LPS or S100A9 for the indicated time. For immunoblot analysis, total cellular proteins were extracted. p100, p52, phosphorylated IκBα, and JNK in PBMC from HC and AOSD treated with LPS; (C) Phosphorylated JNK and p38 in PBMCs from HCs and AOSD patients treated with LPS and S100A9; (D) Activation of JNK and p38 in a human monocyte cell line after treatment with S100A8/A9. THP-1 cells were treated with S100A8/A9 or LPS for the indicated time. For immunoblot analysis, total cellular proteins were extracted. Phosphorylated JNK and p38 in THP-1 cells treated with S100A8/9. Figure 4 S100A8/A9 expression levels of inflammatory cells in skin and lymph node biopsy of patients with active adult-onset Still’s disease (original magnification, 200× (A,B), 100× (C,D)). S100A8/A9 was recorded as the number of positive inflammatory cells divided by the number of total inflammatory cells, then expressed as a graded scale from 1 to 3: 1, 1%–33%, 2, 34%–66%, or 3, 67%–100%. The S100A8/A9 staining positive cells were graded from 1 to 3. Representative examples of frequent expression (grade 3, A) and rare expression (grade 1, B) are shown in the skin. Representative examples of frequent expression (grade 3, C) and rare expression (grade 1, D) are shown in the lymph node. ijms-17-01342-t001_Table 1Table 1 Clinical characteristics of patients. Clinical Manifestations and Laboratory Findings AOSD (n = 20) RA (n = 20) HC (n = 20) Age (year) 38 ± 13.7 41.8 ± 15.2 39.3 ± 8.3 Gender (F/M) 17/3 18/2 19/1 Fever 20 (100) Sore throat 11 (55) Skin rash 15 (75) Lymphadenopathy 8 (40) Splenomegaly 6 (30) Hepatomegaly 4 (20) Pericarditis 3 (15) Pleuritis 2 (10) Arthritis 19 (52.8) 40 (100) Hemoglobin, g/dL 11.1 ± 1.8 Leukocyte, /µL 15,554 ± 4553 Platelet, ×103/µL 324.7 ± 124.4 Ferritin, ng/mL 6100.4 ± 5158.3 72.7 ± 80.1 54.2 ± 58.4 ESR, mm/h 65.5 ± 22.9 30 ± 33.4 CRP, mg/dL 10.6 ± 6.8 0.95 ± 1.7 0.09 ± 0.21 AST, U/L 96 ± 125.1 ALT, U/L 88.7 ± 105 ANA positivity 3 (15) 4 (20) RF positivity 5 (25) 12 (60) Systemic score 5.4 ± 1.43 DAS-28 3.8 ± 1.22 4.05 ± 1.6 AOSD, adult-onset Still’s disease; RA, rheumatoid arthritis; HC, healthy control; F, female; M, male; ESR, erythrocyte sedimentation rate; CRP, C-reactive protein; AST, aspartate transaminase; ALT, alanine transaminase; ANA, antinuclear antibody; RF, rheumatoid factor; DAS-28, disease activity score including 28 joints. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081344ijms-17-01344ArticleCharacterisation and Antioxidant Activity of Crude Extract and Polyphenolic Rich Fractions from C. incanus Leaves Gori Antonella 12Ferrini Francesco 1Marzano Maria Cristina 1Tattini Massimiliano 3Centritto Mauro 2Baratto Maria Camilla 4Pogni Rebecca 4Brunetti Cecilia 12*Arráez-Román David Academic Editor1 Department of Agrifood Production and Environmental Sciences (DiSPAA), University of Florence, 50019 Sesto Fiorentino (Florence), Italy; antonella.gori@unifi.it (A.G.); francesco.ferrini@unifi.it (F.F.); mariacristina.marzano@unifi.it (M.C.M.)2 Trees and Timber Institute (IVALSA), The National Research Council of Italy (CNR), 50019 Sesto Fiorentino (Florence), Italy; mauro.centritto@cnr.it3 Institute for Plant Protection (IPSP), The National Research Council of Italy (CNR), 50019 Sesto Fiorentino (Florence), Italy; massimiliano.tattini@ipsp.cnr.it4 Department of Biotechnology, Chemistry and Pharmacy, University of Siena, 53100 Siena, Italy; mariacamilla.baratto@unisi.it (M.C.B.); rebecca.pogni@unisi.it (R.P.)* Correspondence: cbrunetti@ivalsa.cnr.it; Tel.: +39-055-457-402417 8 2016 8 2016 17 8 134429 6 2016 10 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Cistus incanus (Cistaceae) is a Mediterranean evergreen shrub. Cistus incanus herbal teas have been used as a general remedy in traditional medicine since ancient times. Recent studies on the antioxidant properties of its aqueous extracts have indicated polyphenols to be the most active compounds. However, a whole chemical characterisation of polyphenolic compounds in leaves of Cistus incanus (C. incanus) is still lacking. Moreover, limited data is available on the contribution of different polyphenolic compounds towards the total antioxidant capacity of its extracts. The purpose of this study was to characterise the major polyphenolic compounds present in a crude ethanolic leaf extract (CEE) of C. incanus and develop a method for their fractionation. Superoxide anion, hydroxyl and DPPH (1,1-diphenyl-2-picrylhydrazyl) radical scavenging assays were also performed to evaluate the antioxidant properties of the obtained fractions. Three different polyphenolic enriched extracts, namely EAC (Ethyl Acetate Fraction), AF1 and AF2 (Aqueos Fractions), were obtained from CEE. Our results indicated that the EAC, enriched in flavonols, exhibited a higher antiradical activity compared to the tannin enriched fractions (AF1 and AF2). These findings provide new perspectives for the use of the EAC as a source of antioxidant compounds with potential uses in pharmaceutical preparations. polyphenolic enriched fractionsflavonolsLC–MS/MS (liquid chromatography–tandem Mass Spectrometry)DPPH radical-scavenging activity ==== Body 1. Introduction Plants inhabiting Mediterranean-type ecosystems are usually challenged by multiple stressors, particularly during the summer, when water deficiency co-occurs with high solar irradiance and high temperatures. These environmental constraints induce severe photo-oxidative stress in Mediterranean plants [1,2], resulting in the formation of many reactive oxygen species (ROS). Reactive oxygen species include both radicals, such as superoxide anion and hydroxyl radicals, and non-free radicals, such as hydrogen peroxide and singlet oxygen. Within the plant cell, a first line of defense against reactive oxygen species is constituted by antioxidant enzymes [3]. In particular, superoxide dismutase (SOD) detoxifies superoxide anion (O2· −) by converting two O2· − into H2O2 and O2 [4]. Furthermore, in the presence of O2· − and transition metal ions, H2O2 can generate hydroxyl radical (•OH) via the superoxide-driven Fenton reaction [3]. The •OH is highly reactive, causing damage to DNA and lipid peroxidation [5]. Alterations in cellular ROS/REDOX homeostasis induce the activation of additional antioxidant defense systems constituted by secondary metabolites [2]. In particular, polyphenols have been widely reported to protect plants against oxidative stress [6], neutralising ROS, chelating transition metals and reducing lipid peroxidation [7,8,9]. New evidence suggests that polyphenols also have “indirect” antioxidant effects both in plants and humans [10]. The mechanisms by which polyphenols express these beneficial effects in vivo is not yet clear but it appears to involve their interaction with cellular signaling pathways [11,12]. In particular, polyphenols are thought to have the ability to interact with a wide range of protein kinases that supersede key steps of cell growth and differentiation [13]. Interestingly, the same structural features conferring antioxidant activity to polyphenols are also responsible for their ability to regulate these developmental processes [10]. Though such functions have not been conclusively proven in plant cells, they form the basis of the beneficial effects exerted by polyphenols in a wide range of diseases in animals, including their anti-cancer properties. Mediterranean shrub species, such as Cistus incanus, are naturally rich in polyphenols and might represent a source of bioactive compounds for the development of novel drugs [14]. In traditional medicine, C. incanus herbal infusions have been used as anti-inflammatory agents in the treatment of various skin diseases [15,16]. Furthermore, C. incanus polyphenolic-rich extracts have been reported to possess antimycotic, antibacterial and antiviral properties [17,18,19,20,21]. Recently, aqueous extracts of the aerial parts of this plant have been demonstrated to exert intense antioxidant capacities that could be attributed to their high polyphenol content [22,23]. To the best of our knowledge, a complete chemical characterisation of the polyphenolic composition of the leaves of C. incanus has not yet been reported. Moreover, detailed antioxidant activities of different enriched fractions have not been investigated. Consequently, limited data is available on the contribution of the different polyphenolic compounds to the total free radical scavenging activity of leaves of this species. This study aims to characterise the major polyphenolic compounds contained in a crude ethanolic leaf extract (CEE) of C. incanus, and to develop an extraction protocol to obtain tannin and flavonol enriched fractions. Finally, scavenging activity against superoxide anion, hydroxyl and DPPH (1,1-diphenyl-2-picrylhydrazyl) radicals have been used to compare the antioxidative properties of CEE and its derived fractions. 2. Results and Discussion 2.1. Qualitative Characterisation of Phenolic Compounds Present in Crude Extract of Cistus incanus (C. incanus) Leaves In our study, HPLC–DAD-MS/MS was performed to assess the polyphenolic composition of a crude ethanolic extract (CEE) of C. incanus leaves. Individual polyphenols were identified on the basis of their fragmentation patterns as well as by comparison of their retention time and UV–VIS spectra with those of authentic standards. Our analytical conditions allowed the separation of a large percentage of compounds, as shown in Figure 1. The MS data obtained by liquid chromatography–tandem mass spectrometry (LC–MS/MS) of the most representative phenolics present in the CEE of C. incanus are listed in Table 1, identified with the numbers 1–19 according to their elution order. The compounds identified were classified in to three main classes: gallic acid derivatives (peaks 1, 2), condensed tannins (peaks 3–8), also known as proanthocyanidins, and flavonol glycosides (peaks 9–19). Peak 1 was identified as monogalloyl glucose (m/z at 331), with the main fragments at m/z 169 (gallic acid) and 125 (loss of CO2 from gallic acid). Peak 2 was identified as gallic acid (m/z 169) as previously reported by [22,24]. Condensed tannins, both monomeric, dimeric and polymeric proanthocyanidins have been already reported in C. incanus extracts [16]. Our chromatographic method was suitable for the determination of two dimeric (3, 6) and two monomeric proanthocyanidins (4, 5). In particular, as expected by the general scheme proposed by [25,26], the loss of a phloroglucinol unit (C6H6O3), as well as losses due to Retro-Diels-Alder (RDA) fission and interflavanoid cleavage, were the predominant fragmentation pathways of dimeric proanthocyanidins. On this basis, the fragmentation pattern of the epigallocatechin dimer (peak 3, [M-H]− at 609) was consistent with an RDA fission of the heterocyclic ring resulting in the fragment ion at m/z 441 [23]. Furthermore, the fragments detected at m/z 303 (methylenic quinone) and m/z 305 (flavan-3-ol monomer) derived from an inter-flavanic bond cleavage, through the quinine methane (QM) cleavage mechanism, whereas the fragment ion at m/z 483 resulted from the loss of a phloroglucinol unit (Figure 2), [27]. According to [15], gallocatechin-(4α-8)-gallocatechin or the regio-isomer gallocatechin-(4α-6)-gallocatechin were strongly suggested as molecular structure for this dimeric proanthocyanidin. At 20.6 min (peak 6) another dimeric proanthocyanidin was recorded. Its pseudomolecular ion peak [M-H]− at m/z 593 suggested that this compound consisted of one (epi)gallocatechin and one (epi)catechin subunit [25]. MS/MS fragmentation of m/z 593 gave a fragment ion at m/z 425 from RDA rearrangement [28]. The sequential water elimination produced the ion at m/z 407 and the QM cleavage of the interflavonol bond produced a fragment ion at m/z 289. Finally, the ion at m/z 467 resulted from the loss of a C6H6O3− fragment from the pseudomolecular ion. For this dimeric structure gallocatechin-(4α-8)-catechin or catechin-(4α-8)-gallocatechin is suggested according to [15]. Two monomeric gallocatechins were identified at 19.5 and 19.6 min (peaks 4 and 5). In particular, (−)-gallocatechin and its isomer (−)-epigallocatechin with [M-H]− at 305 m/z were detected. Their molecular weight was confirmed by the presence of the ion at m/z 611 corresponding to [M + M-H]−. Further ions were detected at m/z 137, ([M-H-C8H8O4]−) resulting from retro RDA fission, and at m/z 125, corresponding to the loss of CO2 from gallic acid. In addition, both (+)catechin and (−)epicatechin (289 m/z) were found in the CEE (peaks 7 and 8) by comparison with fragmentation patterns of commercial standards. Polymeric proanthocyanidins could not be resolved by reversed-phase HPLC as revealed by the unresolved hump between 22 and 28 m (Figure 1), as also previously reported by other authors [27,29]. Ten compounds were identified as flavonols. As occurred in other members of Cistus subgenus [28], myricetin-3-O-hexoside (10) and myricitrin (12) were present in the CEE. Fragmentations of the precursor ions at m/z 479 (10) and at m/z 463 (12, Figure 3) had a common fragment at m/z 316 [M-H2O-Hexose-2H]−, which could be attributed to myricetin [30,31,32,33]. The neutral loss of sugar units (losses of 162 for the hexose and 146 for the rhamnose moieties from compounds 10 and 12, respectively) and the product ion at m/z 271, typical of 3-O-monoglycosides [34], confirmed the presence of these compounds. Peaks 13, 14 and 15 with precursor ions at m/z 609, 433 and 447 respectively, were identified as quercetin derivatives on the basis of the presence of their aglycone fragment (m/z 301). Particularly, peak 13 was positively identified as rutin, peak 14 as quercetin-3-O-pentoside, and peak 15 as quercitrin [22,24]. Peak 18 was identified as a kaempferol 3-O-rutinoside, on the basis of the pseudomolecular ion [M-H]− at m/z 593 and the fragment at m/z 285 ([M-146-162-H]−), due to the loss of a glucosyl and a rhamnosyl moiety in an unique fragment (Figure 4). This fragmentation pattern is characteristic of flavonol rutinosides, in which the linkage 1–6 between rhamnose and glucose, that forms rutinose, allows for free rotation and a more accessible fragmentation than other disaccharides [35,36]. In accordance with [19], peak 19 was assigned as kaempferol-3-(3″,6″-dicoumaroyl)-glucose with a molecular ion at m/z 739 and a fragment at m/z 285. Other flavonols have been tentatively identified as myricetin derivatives (peaks 9 and 11) and as quercetin derivatives (peaks 16 and 17) based on their retention times and their UV–VIS spectra, in the absence of conclusive mass-spectrometric data. 2.2. Antiradical Activity Evaluation of Different Extracts of Cistus incanus (C. incanus) Leaves The CEE was partitioned following the protocol in Figure 5. The application of our partitioning process resulted in three different fractions enriched in distinct classes of polyphenols, one ethyl acetate flavonol enriched fraction (EAF) and two aqueous tannin enriched fractions (AF1 and AF2). Compounds contained in the different extracts were identified and quantified by HPLC–DAD. The EAF was mainly composed of flavonol glycosides and oligomeric proanthocyanidins (monomers and dimers) whereas the two aqueous fractions contained only low and high polymeric proanthocyanidins (AF1 and AF2, respectively). These results are shown in Table 2. The potential antioxidant activities of the different fractions were compared using three in vitro assays based on the scavenging of reactive oxygen species or stable free radicals: superoxide anion radical-scavenging, hydroxyl radical-scavenging and DPPH-scavenging assay (Figure S1 in supplementary material). Table 3 illustrates the IC50 values. IC50 denotes the concentration of the sample required to scavenge 50% of free radicals. These values were obtained from the regression equations, plotting extract concentrations against inhibition percentages of free radical formation in the different assays. 2.2.1. Superoxide Anion Radical (O2 · −) and Hydroxyl Radical Scavenging Activities As shown in Table 3, the superoxide scavenging activity of different extracts of C. incanus leaves was found to occur in the following order: EAF >> CEE > AF1 and AF2. Our results indicate that lowest IC50 value is related to the highest concentration of flavonol compounds, as confirmed by the IC50 value of myricitrin standard. As already reported by Salaris et al. [37] polyphenols may act in two ways, by the direct scavenging of O2 · − and by the inhibition of xanthine oxidase enzyme, thus preventing the generation of this radical. In particular, Cos et al. [38] showed that catechin derivatives are superoxide scavengers without inhibitory activity on xanthine oxidase, whereas myricitin and quercetin derivatives display both activities. Furthermore, these flavonols have lower IC50 values for the reduction of superoxide level than for the inhibition of xanthine oxidase [39]. The highest antiradical scavenging activity of EAF was confirmed also by the hydroxyl radical scavenging assay (Table 3). Among the various extracts tested, this fraction displayed the lowest IC50, which is around half the values of the aqueous fractions (AF1 and AF2). The ability of the EAF to quench hydroxyl radicals could be related to the capacity of some flavonols to form stable radicals. This mechanism has not been completely clarified; however, they could act as hydrogen donors, breaking radical chains through the formation of aroxyl radicals. The final products of these reactions are stable quinonic structures [40]. 2.2.2. 1,1-Diphenyl-2-picrylhydrazyl (DPPH) Radical Scavenging Activity Results show that the highest DPPH radical scavenging activity was performed by EAF (IC50 = 0.92 ± 0.097), whereas the aqueous fractions had the highest IC50 values (11.78 ± 0.85 for AF1 and 10.92 ± 0.38 for AF2, respectively). The crude ethanolic extract exhibited an IC50 value of 2.99 ± 1.18, closer to EAF than to AFs (AF1 and AF2). Our results clearly indicate that the DPPH radical-scavenging activity was greatly influenced by the phenolic composition of the samples. In particular, flavonols (myricetin and quercetin derivatives) were dominant contributors to the DPPH radical scavenging activity of C. incanus extracts. Nevertheless, although high levels of proanthocyanidins were found in the aqueous extracts, these compounds did not seem to contribute significantly to the antiradical activity of the CEE measured by the DPPH method. Furthermore, no statistical difference was found between AF1 and AF2, suggesting that differences in the degree of polymerization of proanthocyanidins had relatively little effect on their overall quenching capacity. 2.2.3. Structural Aspects of in Vitro Antiradical Activity of C. incanus Leaf Extracts Our data shows a stronger antiradical capacity of EAF than AFs in all the tested assays. Furthermore, the antiradical capacity of C. incanus extracts is largely influenced by their polyphenolic composition. These results are in agreement with previous studies on other members of Cistus subgenus. For example, n-butanolic and ethyl acetate fractions of C. laurifolius displayed the highest flavonol content and also exerted the highest antioxidant activity in DPPH and FRAP (Ferric Reducing Antioxidant Potential) assays [41]. Tomas et al. [42] observed that the antioxidant capacities of C. salvifolius extracts in FRAP and TBARS (Thiobarbituric Acid Reactive Substances) assays increased considerably when these were concentrated in some specific flavonols. Numerous authors have investigated the antioxidant activity of polyphenols and several studies have been undertaken to establish the relationship between their structure and their radical-scavenging activity. The radical-scavenging activity of polyhenols depends upon the substitution pattern of their hydroxyl groups, i.e., on the availability of phenolic hydrogens and on the possibility of stabilization of the resulting phenoxyl radicals via hydrogen bonding or by electron delocalization [43]. In particular, the structural requirements considered to be essential for effective radical scavenging are: (i) the presence of a ortho-OH structure (catechol group in the B ring); (ii) a 2,3- double bond conjugated with the 4-oxo group. Moreover, compounds that contain multiple hydroxyl substitutions showed stronger antiperoxyl radical activities [44,45,46]. Among the compounds identified in C. incanus leaf extracts, myricitrin satisfies meets all of these criteria. In contrast, a flavan-3-ol such as catechin, which lacks of the 2,3- double bond and the 4-oxo function, is unable to support electron delocalization between the A- and the B-rings limiting its radical scavenging potential. This is supported by the comparison of IC50 values of myricitrin and epicatechin standards, since IC50 of myricitrin was approximately half the value of epicatechin in all the three assays (Table 3). Conversely, some galloylated catechins benefit from the contribution of esterification with gallic acid (3,4,5-trihydroxybenzoic acid), which compensates for the lack of electron delocalization with major electrondonating properties. This is the case of the (epi)gallocatechin dimer present in the EAF that could participate in the enhancement of its antioxidant activity. However, the presence of many hydroxyl groups in polymeric proanthocyanidins did not increase their scavenging capacity. As previously described by other authors [47,48,49], the chemical structure of polymeric proanthocyanidins may cause stereochemical hindrances, resulting in relatively high IC50 values of AF1 and AF2. 3. Materials and Methods 3.1. Plant Material and Extraction Procedure Fully-expanded leaves from adult plants of Cistus incanus growing on seashore dunes in Southern Tuscany (42°46′ N, 10°53′ E) were harvested in July 2015. Plant material was rapidly frozen in liquid nitrogen and stored at −80 °C before proceeding with the analysis. 5 g of fresh plant tissue was ground in a mortar with liquid nitrogen. The obtained powder was extracted with 70% of aqueous ethanol acidified to pH 2.5 by HCOOH (50 mL × 5) and sonicated for 30 m. The solution was then partitioned with n-hexane (50 mL × 5) to completely remove lipophilic compounds, following the protocol previously reported by Romani et al. [50]. The ethanolic phase constituted the crude ethanolic extract (CEE). 125 mL of the CEE were then evaporated under vacuum (Rotavapor 144R, Buchi, Switzerland), re-dissolved in 250 mL of water and extracted five times with 50 mL ethyl acetate (v/v) (Figure 5). 1 g of NaCl was added to break down the emulsion and to accelerate the phase-separation process (“salting out” effect). The organic phase (ethyl acetate fraction, EAF) consisted mostly of flavonols, while the aqueous fraction (AF) contained tannins. Two more distinctive fractions (AF1 and AF2) were obtained by a successive precipitation through the addition of NaCl (1 g) to AF. This process was carried out to obtain the separation between low and high molecular weight polymeric proanthocyanidins following a modified protocol from Saucier et al. [51]. The precipitate formed was collected by filtration on glass filters (AF2), while the filtrate was added with ethanol to precipitate the salt and recover AF1. Finally, the CEE and AF1 were totally evaporated. All fractions were re-dissolved in 2.5 mL of water:ethanol, 80:20. An aliquot of each extract (300 µL) was diluted in 1.20 mL of methanol and acid water (pH 2 by HCOOH) 80:20 (v/v) and used for polyphenol analysis by HPLC–DAD and HPLC–MS. 3.2. Chemicals and Reagents The phenolic standards gallic acid, epicatechin, myricetin 3-O-rhamnoside, quercetin 3-O-rhamnoside, rutine and kaempferol 7-O-glucoside were obtained from Extrasynthese (Genay Cedex, France). FeSO4, hydrogen peroxide, sodium salicylate, xanthine, xanthine oxidase, nitro blue tetrazolium (NBT), ethylenediaminetetraacetic acid (EDTA), formic acid, ethanol, n-hexane, methanol and acetonitrile of HPLC purity were purchased from Sigma Aldrich (Milan, Italy). DPPH (2,2-diphenyl-1-picrylhydrazyl) was obtained from Merck (Darmstadt, Germany). Distilled water was purified in a milli-Q water purification system (Millipore Corporation, Bedford, MA, USA). 3.3. HPLC–DAD and LC–ESI-MS/MS Anlaysis of Phenolic Compounds Identification of individual phenolics was carried out using their retention times and both UV–VIS, MS and MS/MS spectra. The LC–DAD-MS/MS system consisted of a Shimadzu LCMS-8030 quadrupole mass spectrometer (Kyoto, Japan) operated in the electrospray ionization (ESI) mode and a Shimadzu Nexera HPLC system (Kyoto, Japan) equipped with a diode array detector (DAD), a degasser, two eluent pumps, a column oven and an autosampler. The separation was performed on a reversed-phase Waters Nova-Pak C18 column (4.9 × 250 mm, 4 µm), (Water Milford, MA, USA). The mobile phase consisted of 1% aqueous formic acid (solvent A) and 1% formic acid in acetonitrile/methanol (25/75) (solvent B). Separation was obtained using the following elution gradient: 2% B isocratic for 10 min, from 2% to 98% B linear for 30 min, 98% B isocratic for 7 min. The flow rate was 0.6 mL/min, and the injection volume was 10 µL. The column oven was set at 30 °C. The mass spectral data were acquired with the following ESI inlet conditions: nebulising gas and drying gas were nitrogen at a flow rate of 3.0 and 15.0 L/min, respectively; the interface voltage was set to −3.5 kV; desolvation line (DL) temperature was 250 °C and the heat block temperature was 400 °C. The mass spectrometer operated in Negative Ion Scan and in Product Ion Scan mode using analyte-specific precursor ions, with Argon as CID (Collision Induced Dissociation) gas at a pressure of 230 kPa. Quantification of the single phenolic compounds was directly performed by HPLC–DAD in triplicates. In particular, six individual compounds, i.e., gallic acid, epicatechin, myricetin 3-O-rhamnoside, quercetin 3-O-rhamnoside, rutine, were quantified with their own standard curves. Calibration of epicatechin, myricetin and kaempferol derivatives was performed at 280 and 350 nm using epicatechin, myricetin 3-O-rhamnoside and kaempferol 7-O-glucoside as reference compounds, respectively. 3.4. Superoxide Scavenging Activity The scavenging activity of sample extracts on superoxide was measured according to a modified version of the method reported by Nishikimi, Rao and Yagi [52]. Superoxide anion was generated enzymatically by xanthine/xanthine oxidase system. Sample extracts were added in the concentration range of 1.95–40 µM to the reaction mixture consisting of xanthine 0.3 mM and 0.3 mM NBT dissolved in potassium phosphate buffer (pH 7.4) with 0.05 mM EDTA (PBE). Finally, 1 mL of xanthine oxidase (0.09 units/mL PBE) was added to the mixture and incubated at 37 °C for 20 min. The absorbance of NBT was measured at 560 nm. The superoxide scavenging activity was expressed as percent (%) superoxide quenching, which was calculated as (1 − B/A) × 100, where B and A are the activities of xanthine oxidase with and without the addition of sample extracts, respectively. 3.5. Hydroxyl Radical-Scavenging Activity The scavenging activity of sample extracts on hydroxyl radicals was measured according to the method of Smirnoff and Cumbes [53]. The reaction mixture consisted of FeSO4 (1.5 mM), hydrogen peroxide (6 mM), sodium salicylate (20 mM) and various concentrations of extracts (0.065–13 µM). The reaction mixture was incubated at 37 °C for 1 h in a water bath. After incubation the absence of the hydroxylated salicylate complex was measured spectrophotometrically at 562 nm. The percentage of hydroxyl radical scavenging activity was calculated by the following formula: % scavenging activity = [1 − (A1 − A2)/A0] × 100, where A0 was absorbance of the control without extracts, A1 was the absorbance in the presence of the extract, and A2 was the absorbance without sodium salicylate. 3.6. DPPH Radical-Scavenging Activity The extracts were tested for in vitro DPPH Radical-Scavenging activity following the protocol described by Baratto et al. [54] with some modifications. The EPR (Electron Paramagnetic Resonance) signal of the DPPH radical was monitored before and after the addition of extracts and standards. Measurements were performed on a X-band (ν = 9 GHz) Bruker Elexsys E500 Series spectrometer (BRUKER DALTONIK GmbH, Germany) with an ER4122SHQE cavity. Spectra were recorded using the following experimental conditions: temperature 298 K, microwave frequency 9.865 GHz, central field 351.7 mT, scan width 10 mT, microwave power 4 mW, modulation frequency 100 kHz, modulation amplitude 0.1 mT. 0.1 mL of 0.2 mM ethanol solution of DPPH were mixed with 0.1 mL of ethanol (blank) or with an equal volume of each extract, in the concentration range of 0.065–13 µM. The obtained mixture was shaken and left at room temperature for 20 min. To determine the scavenging capacity, the area of the EPR radical signal was calculated through a double integral of the experimental spectrum. DPPH scavenging capacity was obtained by the following equation: % scavenger = (1 − A/A0) × 100 where A is the area of the DPPH signal in the presence of extract or standard and A0 is the area of the DPPH signal alone. IC50 values were calculated and compared with standards of myricitrin and epicatechin. 3.7. Statistical Analysis All the experiments were conducted in triplicates, and the data were presented as mean ± SD (standard deviation). SPSS (version 23; SPSS Inc., Chicago, IL, USA) was used to process the results. For the DPPH assay a one-way ANOVA test followed by Tukey’s test (p < 0.05) was used to analyze the differences among IC50 of the CEE and its various fractions. 4. Conclusions The purpose of this study was to investigate the polyphenolic composition of a crude ethanolic leaf extract of C. incanus. We focused on obtaining three different polyphenolic enriched fractions in an attempt to make systematic comparisons among their antioxidant activities and to identify the major antioxidative components of C. incanus leaves. Among all the fractions analysed, the ethyl acetate fraction was found to be the most effective in terms of radical scavenging activity. These results offer clear evidence that the flavonol enriched fraction obtained from C. incanus leaves could be a suitable target for further in vivo antioxidant studies. Acknowledgments The authors extend their gratitude to “PromoFirenze, divisione laboratorio chimico-merceologico, azienda speciale della Camera di Commercio di Firenze” and to Luca Calamai for their technical support during LC–MS/MS analyses. The authors acknowledge Matthew Haworth for the critical revision of the manuscript. Supplementary Materials Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1344/s1. Click here for additional data file. Author Contributions Antonella Gori, Cecilia Brunetti and Massimiliano Tattini conceived and designed the experiment; Antonella Gori, Cecilia Brunetti, Maria Cristina Marzano and Maria Camilla Baratto performed the experiments; Antonella Gori and Maria Cristina Marzano analysed the data; Francesco Ferrini, Mauro Centritto and Rebecca Pogni contributed reagents, materials and analysis tools; Antonella Gori and Cecilia Brunetti wrote the manuscript; Francesco Ferrini, Mauro Centritto and Massimiliano Tattini revised the manuscript. The final version of the manuscript has been read and accepted by all the authors. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Chromatographic profile of crude ethanolic leaf extract (CEE) of leaves of Cistus incanus acquired by HPLC–DAD detected at the relative maxima of absorbance of proanthocyanins (280 nm) and flavonols (350 nm), respectively. Chromatographic conditions are given in the Materials and Methods section. For compound identification see Table 1. Figure 2 Hypothetical ESI(−)-MS/MS fragmentation pattern for Epigallocatechin dimer (peak 3, [M-H]− at m/z 609). RDA = Retro-Diels Alder fission, QM = Quinone Methide cleavage mechanism. Figure 3 Structure, fragmentation and MS/MS spectrum of peak 12 (myricitrin). Solid arrow indicates the most abundant ion in myricitrin fragmentation; dashed arrow indicates the loss of rhamnose moiety. Figure 4 Structure, fragmentation and MS/MS spectrum of peak 18. Figure 5 Scheme for fractionation of the CEE. EAF = Ethyl acetate Fraction, AF = Aqueous Fraction, AF1 = Aqueous Fraction 1, AF2 = Aqueous Fraction 2. ijms-17-01344-t001_Table 1Table 1 HPLC–DAD-MS/MS characterisation of main polyphenols present in crude ethanolic leaf extract (CEE) of C. incanus. Compounds numbers correspond to those indicated in Figure 1. (n.d *, not detected; sh, shoulder). Peak n tR (min) Λ (nm) [M-H]− (m/z) MS2 (m/z) Tentative Assignement 1 4.6 234,270 331 125, 169 Monogalloyl glucose 2 8.3 234,272 169 125 Gallic acid 3 16.9 236,272 609 441, 423, 483, 305, 303 (Epi)Gallocatechin dimer 4, 5 19.5 234,272 305 611, 125, 137 (−)-Gallocatechin and (−)-epigallocatechin 6 20.6 236,276 593 407, 467, 425, 289, 285 (Epi)gallocatechin-(epi)catechin or (Epi)catechin-(epi)gallocatechin 7, 8 21.5 236,278 289 245, 205 (+) Catechin and (−) Epicatechin 9 24.2 260,360 n.d * - Myricetin derivative 1 10 24.5 254,362 479 316, 271 Myricetin-3-O-hexoside 11 25.4 260,360 n.d * - Myricetin derivative 2 12 25.6 260,358 463 316, 271, 179 Myricitrin 13 25.7 256,356 609 301 Rutin 14 26.6 265,355 433 301, 271 Quercetin-3-O-pentoside 15 26.9 256,350 447 301, 179 Quercitrin 16 27.8 264,352 n.d * - Quercetin derivative 1 17 28.2 264,352 n.d * - Quercetin derivative 2 18 29.5 264,314,346sh 593 285, 145 Kaempferol 3-O-rutinoside 19 33.3 268,314,348sh 739 285, 306, 145, 452 Kaempferol-3-(3″,6″-dicoumaroyl)-glucose ijms-17-01344-t002_Table 2Table 2 Mean concentration of phenylpropanoids (μmol/mL) in CEE and enriched fractions of Cistus incanus leaves (n = 3). Sample Monogalloyl Glucose and Gallic Acid Catechins Derivatives a Myricetin Derivatives b Quercetin Derivatives c Kaempferol Derivatives d Proanthocyanidin Polymers CEE 0.315 ± 0.024 2.256 ± 0.076 2.719 ± 0.148 3.578 ± 0.217 0.055 ± 0.009 55.376 ± 3.067 EAF 0.236 ± 0.019 1.647 ± 0.069 2.202 ± 0.127 3.140 ± 0.162 0.036 ± 0.004 nd AF1 nd nd nd nd nd 25.193 ± 0.597 AF2 nd nd nd nd nd 31.037 ± 0.901 nd = not detectable. a (Epi)gallocatechin dimer, (−)-Gallocatechin, (−)-Epigallocatechin, (Epi)gallocatechin-(epi)catechin, (+)-Catechin, (−)-Epicatechin; b Myricetin derivative 1, Myricetin-3-O-hexoside, Myricetin derivative 2, Myricitrin; c Quercetin-3-O-pentoside, Quercitrin, Quercetin derivative 1, Quercetin derivative 2; d Kaempefol-3-O-rutinoside, Kaempferol-3-(3″,6″-dicoumaroyl)-glucose. EAF = Ethyl acetate Fraction, AF1 = Aqueous Fraction 1, AF2 = Aqueous Fraction 2. ijms-17-01344-t003_Table 3Table 3 IC50 (half maximal inhibitory concentration, μM) of different extracts and standards in superoxide anion, hydroxyl and DPPH (1,1-diphenyl-2-picrylhydrazyl) radical scavenging assays. Each value in the table is represented as Mean ± SD (n = 3). Means not sharing the same letter are significantly different at p < 0.05 probability level in each column. CEE: Crude Ethanolic Extract; EAF = Ethyl acetate Fraction, AF1 = Aqueous Fraction 1, AF2 = Aqueous Fraction 2, MYR = Myricitrin Standard, EPI = Epicatechin Standard. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081345ijms-17-01345ArticleTheophylline-Based KMUP-1 Improves Steatohepatitis via MMP-9/IL-10 and Lipolysis via HSL/p-HSL in Obese Mice Wu Bin-Nan 1Kuo Kung-Kai 2Chen Yu-Hsun 1Chang Chain-Ting 1Huang Hung-Tu 3Chai Chee-Yin 4Dai Zen-Kong 5Chen Ing-Jun 16*Van Craenenbroeck Kathleen Academic EditorMaki Masatoshi Academic Editor1 Department of Pharmacology, Graduate Institute of Medicine, College of Medicine, Lipid Science and Aging Research Center, Kaohsiung Medical University, Kaohsiung 807, Taiwan; binnan@kmu.edu.tw (B.-N.W.); sicaja9999@hotmail.com (Y.-H.C.); a99443@hotmail.com (C.-T.C.)2 Division of Hepatobiliopancreatic Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan; kkkuo@kmu.edu.tw3 Department of Anatomy, School of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan; hungtu@kmu.edu.tw4 Department of Pathology, School of Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan; d750147@kmu.edu.tw5 Department of Pediatrics, Division of Pediatric Pulmonology and Cardiology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan; zenkong@kmu.edu.tw6 Department of Medical Education and Research, Pingtung Christian Hospital, Pingtung 900, Taiwan* Correspondence: ingjun@kmu.edu.tw; Tel.: +886-7-312-1101; Fax: +886-7-323-468617 8 2016 8 2016 17 8 134526 6 2016 10 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).KMUP-1 (7-[2-[4-(2-chlorobenzene)piperazinyl]ethyl]-1,3-dimethylxanthine) has been reported to cause hepatic fat loss. However, the action mechanisms of KMUP-1 in obesity-induced steatohepatitis remains unclear. This study elucidated the steatohepatitis via matrix metallopeptidase 9 (MMP-9) and tumor necrosis factor α (TNFα), and related lipolysis via hormone sensitive lipase (HSL) and adipose triglyceride lipase (ATGL) by KMUP-1. KMUP-1 on steatohepatitis-associated HSL/p-HSL/ATGL/MMP-9/TNFα/interleukin-10 (IL-10) and infiltration of M1/M2 macrophages in obese mice were examined. KMUP-1 was administered by oral gavage from weeks 1–14 in high-fat diet (HFD)-supplemented C57BL/6J male mice (protection group) and from weeks 8–14, for 6 weeks, in HFD-induced obese mice (treatment group). Immunohistochemistry (IHC) and hematoxylin and eosin (H&E) staining of tissues, oil globules number and size, infiltration and switching of M1/M2 macrophages were measured to determine the effects on livers. IL-10 and MMP-9 proteins were explored to determine the effects of KMUP-1 on M1/M2 macrophage polarization in HFD-induced steatohepatitis. Long-term administration of KMUP-1 reversed HFD-fed mice increased in body weight, sGOT/sGPT, triglyceride (TG) and glucose. Additionally, KMUP-1 decreased MMP-9 and reactive oxygen species (ROS), and increased HSL/p-HSL and IL-10 in HFD mice livers. In conclusion, KMUP-1, a phosphodiesterase inhibitor (PDEI), was shown to reduce lipid accumulation in liver tissues, suggesting that it could be able to prevent or treat steatohepatitis induced by HFD. adipose triglyceride lipasefatty liverhormone sensitive lipaseM1/M2 macrophagematrix metallopeptidase 9tumor necrosis factor α ==== Body 1. Introduction Studies of theophylline-based KMUP-1 (7-[2-[4-(2-chlorobenzene)piperazinyl]ethyl]-1,3-dimethylxanthine) have shifted to lipid metabolism and obesity [1] in addition to cardiovascular and neuronal systems [2,3,4]. KMUP-1 has been demonstrated as a phosphodiesterase inhibitor (PDEI) [5,6] and it also proved to reduce inflammation and hyperalgesia in a bilateral chronic constriction injury model by suppressing p38 mitogen-activated protein kinase (p38 MAPK) and NFκB activation [3]. Moreover, KMUP-1 caused hepatic fat loss by increasing protein kinase A (PKA) and protein kinase G (PKG), the cyclic nucleotide dependent protein kinases, resulted from cAMP and cGMP activation [1]. Enhanced PKA or PKG can phosphorylate perilipin on oil globules, resulting in lipolysis by activation of hormone sensitive lipase (HSL)/phosphorylated HSL (p-HSL) and adipose triglyceride lipase (ATGL) [7]. Recently, PDE5 inhibitors-mediated cGMP/PKG accumulations have been approved for the therapeutic uses of erectile dysfunction and pulmonary hypertension [1,5,6], but their effects on obesity are not extensively described. A famous PDE5 inhibitor sildenafil was shown to increase cGMP/PKG in lipid metabolism, which leads to adipogenesis or lipolysis [8,9,10]. Previous reports also showed that inhibition of phosphodiesterases (PDEs) lead to activation of HSL/p-HSL and ATGL, promoting lipolysis of adipocytes [8,9,11]. Those reports inspire us to reconsider the important role of our PDE inhibitor KMUP-1 in obesity. In this report, we observed that KMUP-1 affected high-fat diet (HFD)-induced hyperadiposity in livers through matrix metallopeptidase 9 (MMP-9) and reactive oxygen species (ROS) inhibition, and HSL/p-HSL and IL-10 stimulation, suggesting that it might be a potential candidate for strengthening the therapy of nonalcoholic fatty livers. Hepatic steatosis induced by a HFD-mediated hyperadiposity in liver tissues, involving lipid accumulation combined with inflammation and setting the stage for further liver damage. Steatohepatitis is usually accompanied by oxidative stress via ROS after a long-term supplementation with HFD [12,13,14]. HFD-induced oxidative stress also activated stress pathways involving phosphorylation of p38 MAPK and ERK expression in hepatocytes [15]. In this study, we aimed to explore the pharmacotherapeutic agents that may be able to suppress ROS, inhibit pro-inflammatory cytokine TNFα and MMP-9, increase anti-inflammatory cytokine IL-10 and affect the infiltration of macrophages in liver tissues [16,17,18,19]. Immunostaining results display massive macrophage infiltration in HFD-induced liver inflammation in obese mice, characterized by macrophage types due to M1/M2 polarization [20]. We have measured mice body weight and biochemical parameters, and performed H&E and immunohistochemistry (IHC) staining from the number/diameter changes of oil globules and M1/M2 macrophage polarization in mice livers to investigate whether KMUP-1 reduced hepatic fat accumulation that is attributable to its anti-inflammatory and lipolytic activities. 2. Results 2.1. Body Weight Gain, sGOT, sGPT, Triglyceride (TG) and Glucose in Serum Figure 1A depicts the protocol of protection and treatment groups in HFD-fed mice. Figure 1B shows the difference of body weight in the HFD group and the HFD + KMUP-1 group after oral administration of KMUP-1 (2.5 mg/kg/day) for 14 weeks. At the first week, the mean weekly body weight gain was not significantly different. From the second week, supplementation with oral KMUP-1 for 14 weeks in the protection group significantly prevented the increases of body weight. Treatment with KMUP-1 (1, 2.5, 5 mg/kg/day) for 6 weeks, from week 8 to week 14, reduced body weight in obese mice (Figure 1C). Figure 1D shows the lower serum glutamic-oxaloacetic transaminase (sGOT), serum glutamic-pyruvic transaminase (sGPT), TG and glucose levels in the serum of HFD experimental animals in the protection group. 2.2. Hematoxylin and Eosin (H&E) Staining of Livers Figure 2A exhibits the normal gross morphology of mice livers fed with normal chow diet (ND) for 14 weeks. By contrast, Figure 2B shows the gross morphology of an inflammatory fatty liver fed with HFD for 14 weeks, which was fat tissues rich compared to the reddish-brown livers found in the treatment (Figure 2C) and protection group (Figure 2D) supplemented with KMUP-1 + HFD. Figure 2E shows the liver section of oil globules, also confirmed by Oil Red O staining, in the HFD group. Liver sections of mice treated with KMUP-1 (2.5 mg/kg/day, p.o.) for the last 6 weeks (treatment group) and 14 weeks (protection group) were shown in Figure 2F,G. Mice supplemented with HFD induced obviously oil globules and Mallory’s hyaline bodies (black arrow, Figure 2H). Oral KMUP-1 for the last 6 weeks reduced oil globules and Mallory’s hyaline bodies (Figure 2I). Notably, oil globules are dramatically decreased in the protection group (Figure 2J). Figure 2K displays H&E staining of the mice liver from normal chow (ND) diet as a negative control. The estimated diameter of oil globules from Figure 2F was 6, 21, 24 and 25 µm by using a free software ImageJ (Figure 2L). 2.3. Immunohistochemistry (IHC) Staining of TNFα/MMP-9/HSL/p-HSL/ATGL in Steatohepatitis Figure 3 shows that oral administration of KMUP-1 (2.5 mg/kg/day) for 6 weeks (treatment group) and 14 weeks (protection group) only slightly affected TNFα expression (Figure 3A), but the number and diameter of oil globules significantly reduced. In Figure 3B, MMP-9 was nearly abolished in the protection group and oil globules markedly decreased as well, indicating the anti-inflammatory effect of KMUP-1 (Figure 3B,H). In Figure 3C,D, the HSL/p-HSL is an intracellular enzyme of adipose tissue catalyzes the breakdown of stored TGs into glycerol and fatty acids (this process is called lipolysis), with the latter entering the circulation. The HSL is affected by KMUP-1 in the protection group and the number of oil globules was decreased, but not diameter (Figure 3I). In Figure 3D, KMUP-1 also enhanced the phosphorylated HSL (p-HSL, activated form of HSL) in the protection group, indicating the stimulation of lipolysis in oil globules, and the accompanied changes in number and diameter were reduced exactly (Figure 3J). These results support that KMUP-1 inhibition of steatohepatitis is more prominent in the protection group than the treatment group. Likewise, adipose triglyceride lipase (ATGL) is another key enzyme involved in intracellular degradation of TGs in adipose tissues. The ATGL expression appears little affected by KMUP-1 in both treatment and protection groups (Figure 3E), but the number and diameter of oil globules were markedly diminished (Figure 3K). Even in the negative control, the number of oil globules in the HFD group also significantly decreased after KMUP-1 treatment and/or protection (Figure 3F,L). 2.4. IHC Staining of Type 1 or Type 2 Macrophages (M1 or M2) in Steatohepatitis Figure 4 indicates the decreases in M1 (induce proinflammatory cytokines) and increases in M2 (decrease inflammation and promote tissue repair) macrophages by KMUP-1, staining with F4/80 and CD11c antibodies for the M1 type (Figure 4A,B), and with CD206 and CD209a antibodies for M2 type (Figure 4C,D). The number and diameter changes of oil globules in the treatment and protection groups were significantly different from the HFD group as shown in Figure 4E–H. The bidirectional arrow indicates the decreased M1 (CD11c) and/or increased M2 (CD209a) macrophage responses. 2.5. Expression of IL-10 and MMP-9 in HFD Livers Figure 5A shows that KMUP-1 increased IL-10 and would be related to the tendency of M2 type manifestations of macrophage. Additionally, Figure 5B shows that KMUP-1 decreased MMP-9, upregulated by proinflammatory mediators, and would be related to the tendency of M1 type manifestations of macrophage. Taken together with Figure 4 data, we suggested that KMUP-1 could influence the shift from M1 to M2 macrophages in HFD-induced mice livers. 2.6. Effects of Hyperadiposity on Hepatic Reactive Oxygen Species (ROS) Hyperlipidemia increased the ROS of hepatic tissues detected by H2DCF-DA assay using fluorescence analysis (Figure 6). KMUP-1 reduced HFD-induced the increases of dichlorofluoroscence intensity in livers, suggesting that it could attenuate the levels of hepatic ROS. 3. Discussion This study first provided a simple and reproducible method to measure and analyze the number/diameter of oil globules in mice liver using the digital image processing with the aid of ImageJ software, in comparison with previous investigations [21,22]. The oil globules in liver slices has been further confirmed by Oil Red O staining (data not shown). Oil Red O is widely used to validate the presence of fat or lipids in fresh and frozen tissues. On the other hand, sGOT and sGPT are used as two of biomarkers to measure routinely as a diagnosis of liver function. Suppression of these two biomarkers in mice by theophylline-based KMUP-1 is suggested that HFD-induced liver inflammation could be reduced by KMUP-1. Reduction of serum TG and glucose levels might positively correlate with lipids-associated metabolism syndrome and obesity-related insulin resistance in mice chronic inflammation [23]. Many hormones and drugs have been recognized to play a role in the modulation of lipid metabolism, and various hormones and drugs lead to lipolysis through diverse lipolytic pathways. PKA is involved in catecholamine-induced lipolysis, and PKG is responsible for lipolysis stimulated by atrial natriuretic peptide [8,10,24]. The most studied lipolytic pathway is the PKA pathway in adipocytes, in which catecholamines bind to β-adrenoreceptors and stimulate membrane-bound adenylate cyclases and accordingly raise the cAMP levels [10,25]. Elevated cAMP levels enhance PKA activity, leading to the phosphorylation and activation of HSL and lipid droplet-associated perilipin. Activated HSL and perilipin provoke the hydrolysis of TG stored in oil globules and the release of free fatty acids and glycerol from adipocytes [1,10,26]. In a previous report [1] we also confirmed that elevated cGMP/PKG in liver tissues potentially influences the lipid catabolism of hepatocytes by lipolysis of oil globules through HSL. Interestingly, in our HFD-fed mice model, a relatively large amount of oil globules in the liver slice was mitigated in the treatment and protection groups in spite of some inflammatory and lipolytic proteins expression being little affected by KMUP-1. Oral administration of KMUP-1 resulted in a greater increase in the response of p-HSL than HSL in the protection group, indicating that partial HSL is transferred to the active form of p-HSL, which would be able to decrease the development of hepatic steatosis through stimulating lipolysis. TNFα, a proinflammatory cytokine, enhances hepatic fat deposition by affecting the liver lipogenetic metabolism involving sterol regulatory element binding protein-1c (SREBP-1c) [16,17,18]. It also plays a physiological role to stimulate basal lipolysis through a decrease in the lipid-binding protein, perilipin [18,26]. TNFα also downregulates ATGL in adipocytes [17]. Deficiency in liver ATGL causes progressive hepatic steatosis. In HFD-fed mice livers, the downregulation of ATGL via TNFα caused progressive steatohepatitis [13,14]. MMP-9 is recognized as a more intense mediator than TNFα in liver inflammation [21,22]. In this study, KMUP-1 increased IL-10 and decreased MMP-9 significantly, little affected TNFα, indicating its anti-inflammatory properties in hepatic steatosis. This result can be further confirmed that KMUP-1 reduced Mallory’s hyaline bodies, which is a key pathological feature in alcoholic and non-alcoholic steatohepatitis, in HFD-fed mice. Taken together, KMUP-1 improves steatohepatitis that is attributed to decrease MMP-9, increase IL-10, and stimulate lipolysis via HSL/p-HSL. Infiltration of hepatic macrophages from blood-born monocytes has been found in inflammatory livers. Macrophages that encourage inflammation are called M1 macrophages, whereas those that decrease inflammation and encourage tissue repair are called M2 macrophages; the former can release TNFα and the later can release IL-10 [20,23]. The expression of M1 and M2 type macrophages was analyzed by IHC in HFD-fed mice livers [20,23]. Most of the F4/80-positive/cD11c-positive M1 macrophages and CD206-positive/CD209a-positive M2 macrophages in the liver tissues were clearly separated by IHC staining [23]. Figure 4 shows that hepatic cD11c staining (M1) and CD209a staining (M2) were decreased and increased, respectively, in the protection and treatment groups compared to the HFD group. The number/diameter of oil globules was reduced by KMUP-1 under the same conditions. Thus, we suggested that KMUP-1 can reduce the proinflammatory M1 macrophage phenotype, but enhance the anti-inflammatory M2 macrophage phenotype in mice livers. Additionally, KMUP-1 also can protect against M1 macrophage-derived ROS, and therefore it is suggested to be able to reduce ROS-related oxidative stress, inflammation and steatohepatitis. 4. Materials and Methods 4.1. Animals and Blood Sampling C57BL/6J male mice (20–22 g) were fasted for 24 h and then changed to a HFD (Basal purified Diet W/60% energy from fat, Blue:58G9 Test Diet; St. Louis, MO, USA) to produce an obesity model. At 6 weeks of age, the mice were randomly divided into 5 groups, two control and three treatment groups. The control mice received HFD without KMUP-1 and the pretreatment group received oral KMUP-1 (2.5 mg/kg/day) by gavage for 14 weeks (protection group). The obese mouse treatment group was fed a HFD with oral KMUP-1 (1, 2.5, 5 mg/kg/day) from week 8 to week 14. All animals were separated in plastic cages for feeding and drinking [1]. All procedures and protocols were approved (IACUC 100172, 13 May 2013) by the Animal Care and Use Committee at Kaohsiung Medical University and complied with the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health. TG and glucose in mouse serum were measured by the same methods used in the clinic. In brief, mouse blood was obtained by cardiac puncture followed by centrifugation at 1000 rpm to separate serum, and freezing at −80 °C for biochemical analysis using a Hitachi Clinical Analyzer 7070 (Hitachi High-Technologies Co. Tokyo, Japan). Agents used in the assays were obtained from Sigma-Aldrich Chemical Co. (St. Louis, MO, USA). To measure hepatic protein expression, KMUP-1 was administered for 14 weeks or 6 weeks before the mice were sacrificed [1]. The livers were obtained after cardiac puncture on the last day of the experiments. 4.2. Measurement of Hepatic Oil Globules Diameter The diameter of the scale bar in each image was standardized at 100 µm for measuring the specific diameter of oil globules in a whole liver slice observed by microscope, and analyzed with the aid of ImageJ 2.1.4.9 software. An increase in oil globules diameter and cell number indicated increasing liver steatosis, i.e., steatohepatitis. Oil globules in liver slices were observed with a Nikon Eclipse TE2000-S microscope (Tokyo, Japan). The oil globules were counted from a bigger size to a smaller one until the observation reached its limit. Too small or lysis oil globules in liver slice was excluded in the counting process. Considering the possible damage by alcohol and other organic solvents on cell membrane in staining procedures, the abnormal oil globules was also excluded. 4.3. Hematoxylin-Eosin (H&E) Staining of Liver Tissues Mice livers were cut and soaked in formalin, dehydrated through graded alcohols and embedded in paraffin. Specimens of liver tissues fixed with formalin (4%) were embedded in paraffin for 1 h at 4 °C cut into 4-μm-thick sections from paraffin-embedded and de-paraffinized tissue blocks, immersed in xylene and rehydrated with graded alcohols and subjected to H&E staining before examination by light microscopy. 4.4. Immunohistochemistry (IHC) Staining of Liver Tissues and Macrophages The staining of liver tissues was performed as previously described [1]. Briefly, mice livers were fixed in 10% formalin for 24 h and then embedded in paraffin. For IHC of hepatic TNFα, MMP-9, HSL/p-HSL and ATGL, antigen retrieval of deparaffinized sections was performed in Dako target retrieval solution, pH 9.0 in a vegetable steamer followed by quenching of endogenous peroxidase activity with 3% H2O2 in methanol. Sections were then incubated with specific primary antibodies overnight at 4 °C in a humidified chamber. The antibodies of HSL/p-HSL (Cell Signaling, Boston, MA, USA), MMP-9 (Abcam, Cambridge, UK), TNFα (Abcam) and ATGL (Cell Signaling) were used. The sections were then examined using a DAKO EnVision Detection System kit (DAKO, Carpinteria, CA, USA) and counterstained with hematoxylin. Images were obtained through a Nikon Eclipse TE2000-S microscope. For the staining of macrophages infiltrated into liver tissues, F4/80 and CD11c (Abcam) were used to stain M1 macrophages, and CD206 and CD209a (Santa Cruz Biotechnology, Santa Cruz, CA, USA) were used to stain M2 type macrophages. 4.5. Western Blotting Analysis in Liver Tissues To detect the expression of IL-10 and MMP-9 proteins, liver tissues were cut into small pieces, placed into buffer for protein extraction and centrifuged at 20,000× g for 30 min. The obtained protein extract was boiled to a ratio of 4:1 with sample buffer (Tris 100 mM, pH 6.8, glycerol 20%, SDS 4% and bromophenol blue 0.2%). Electrophoresis was performed using 10% SDS-polyacrylamide gel (1 h, 100 V, 40 mA, 20 µg protein) and then transferred to polyvinylidene difluoride (PVDF) membranes (Millipore, Temecula, CA, USA). The membrane was blocked with 5% milk in Tris-buffered saline with Tween 20 (TBS-T) for 1 h and thereafter incubated with specific protein antibody. After the secondary antibody was conjugated with horse radish peroxidase (HRP) (1:5000 dilutions in 5% milk) for 1 h, the signals on the membrane were identified using enhanced chemiluminescence (ECL)-plus luminal solution and exposed to X-ray film for autoradiography. 4.6. Measurement of Hepatic ROS Hepatic ROS was measured using 2′-7′-dichlorofluorescein (H2DCF-DA, Molecular Probe, Waltham, MA, USA). Briefly, 10 μL of liver tissue extracts was diluted 100-fold with cold PBS and labelled with 5 μmol/L 2′-7′-dichlorofluorescein, and the mixture was incubated at 37 °C for 30 min. Fluorescence was measured at 485 nm excitation and 530 nm emission to determine the concentration of H2O2 [27]. 4.7. Statistical Evaluation The experimental results were expressed as means ± S.E. Statistical differences were determined by one-way analysis of variance (ANOVA) or repeated-measures ANOVA. When appropriate, a Tukey-Kramer pairwise comparison was used for post hoc analysis. A p value less than 0.05 was considered significant in all experiments. 5. Conclusions Obesity, that is, the extravagant accumulation of adipose tissue, is associated with poor health outcomes due to several metabolic and cardiovascular diseases. Adipose tissue inflammation mediates the correlation between excessive body fat accumulation and several inflammatory complications [16]. In this study, we observed that KMUP-1 is able to decrease MMP-9, increase IL-10, and stimulate lipolysis via HSL/p-HSL. In conclusion, theophylline-based KMUP-1 protects and/or inhibits liver inflammation and fat accumulation, suggesting that it could be invaluable for the treatment or prophylaxis of obesity-driven steatohepatitis. Acknowledgments We thank Li-Mei An for her excellent technical assistance with the manuscript. This study was supported by grants NSC 101-2320-B-037-044 and NSC 101-2320-B-037-032-MY3 from the Ministry of Science and Technology, Taiwan; KMU-TP104D04 from Kaohsiung Medical University, Taiwan; KMUH 102-2R26 from Kaohsiung Medical University Hospital, Taiwan; and the Cardiac Children’s Foundation of the Republic of China (CCFT2015-04). Author Contributions Bin-Nan Wu, Kung-Kai Kuo and Yu-Hsun Chen conceived, designed and performed the experiments; Bin-Nan Wu, Kung-Kai Kuo, Yu-Hsun Chen, Chain-Ting Chang, Hung-Tu Huang, Chee-Yin Chai, Zen-Kong Dai and Ing-Jun Chen interpreted and analyzed the data; Bin-Nan Wu and Ing-Jun Chen wrote and revised the manuscript. All authors contributed to manuscript preparations and approved the final manuscript. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Effects of KMUP-1 on high-fat diet (HFD)-induced body weight, serum glutamic-oxaloacetic transaminase (sGOT), serum glutamic-pyruvic transaminase (sGPT), triglyceride (TG), and glucose in serum of mice treated with KMUP-1. (A) Protection/Treatment protocol in the mice model; (B) Oral administration of KMUP-1 (2.5 mg/kg/day for 14 weeks) prevented HFD-induced body weight increase; (C) Treatment with KMUP-1 (1, 2.5, 5 mg/kg/day for last 6 weeks) attenuated HFD-induced body weight gain from week 8 to week 14; (D) HFD-induced sGOT, sGPT, TG and glucose levels were reduced by KMUP-1. Data are means ± Standard error (S.E.), n = 6–8. * p < 0.05; ** p < 0.01; *** p < 0.001 versus HFD group. Figure 2 Morphology of livers protected or treated with KMUP-1 in HFD mice shown by H&E staining and the diameter of oil globules measured. (A) Normal morphology of livers in mice fed with normal chow diet (ND) for 14 weeks; (B) Mice fed with HFD for 14 weeks induced relevant fatty liver; (C,D) represent the Treatment and Protection of liver changes by oral gavage of KMUP-1 (2.5 mg/kg/day) for 14 weeks in HFD mice; (E) Excessive oil globules in HFD liver at 14 weeks; (F) Treatment and (G) protection of fatty livers by KMUP-1 for 6 weeks and 14 weeks, respectively; (H) Large amount of Mallory’s hyaline bodies (purple dots indicated by black arrow) were observed in the HFD group; (I) Obese animal treated with KMUP-1 (2.5 mg/kg/day) for 6 weeks decreased the worst pathologic changes at 14 weeks, i.e., the oil globules and Mallory’s hyaline bodies were attenuated at 14 weeks; and this response was more prominent in the (J) protection group; (K) shows H&E staining of the liver from normal chow diet (ND) mice as a control; (L) A representative example for measuring of hepatic oil globules from Figure 2F. The standardized diameters of 10, 25, 50 and 100 µm are depicted. Figure 3 Immunohistochemistry (IHC) staining of tumor necrosis factor α (TNFα)/matrix metallopeptidase 9 (MMP-9), hormone sensitive lipase (HSL)/phosphorylated HSL (p-HSL) and adipose triglyceride lipase (ATGL) in HFD-induced liver steatosis and oil globules protected/treated with KMUP-1 for 14 weeks/6 weeks. HFD-induced fatty liver at 14 weeks implied that oil globules were rich in liver tissues. The expression of HFD-induced TNFα (A) and MMP-9 (B) in the treatment and protection groups; Treatment and/or protection with KMUP-1 sharply reduced the number and diameter of oil globules (G,H); HFD-induced the expression of HSL/p-HSL showed that KMUP-1 could affect the HSL protein (brown, C); and significantly enhanced the active form of HSL (p-HSL) in the protection group (deep brown, D) and matched data regarding the number and diameter of oil globules depicted in (I,J); ATGL expression is not affected by KMUP-1 in both treatment and protection groups (E); but markedly attenuated the number and diameter of oil globules (K); (F,L) negative control of protein expression, and the number and diameter of oil globules. Data are means ± S.E. of three independent experiments. *** p < 0.001 versus HFD group. Figure 4 IHC staining of infiltrated M1/M2 macrophages and accumulated oil globules treated/protected with KMUP-1. KMUP-1 (2.5 mg/kg/day) administration for 6 weeks/14 weeks modulates the balance of infiltrated macrophages 1 (M1; A,B) and macrophages 2 (M2; C,D). KMUP-1 significantly affected the M1 (CD11c, dark brown)/M2 (CD209a, dark brown) macrophages polarization, but little affected the balance of M1 (F4/80, brown) and M2 (CD206, brown), in treatment and protection groups. All the accompanied oil globules were reduced by KMUP-1. The number and diameter changes of oil globules are in the average from M1/M2-positive cells (E–H). Data are means ± S.E. of three independent experiments. * p < 0.05; ** p < 0.01; *** p < 0.001 versus HFD group. Scale bar: 100 µm. Figure 5 Expression of IL-10 and MMP-9 in mice livers treated with KMUP-1. Treatment/protection with KMUP-1 (2.5 mg/kg/day) for 6 weeks/14 weeks increased the expression of IL-10 (A) and decreased the expression of MMP-9 (B) in mice livers. The protein expression of IL-10 and MMP-9 was described in Materials and Methods. Data are means ± S.E. of six independent experiments. ** p < 0.01 versus HFD group. Figure 6 The levels of hepatic ROS was reduced by treating KMUP-1 (1, 2.5 mg/kg/day). HFD induced accumulation of ROS in livers. Protection/treatment with KMUP-1 for 6 weeks/14 weeks decreased the hepatic ROS. ROS was determined as described in Materials and Methods. Data are means ± S.E. of six independent experiments. * p < 0.05; ** p < 0.01 versus HFD group. ND: normal chow diet. ==== Refs References 1. Kuo K.K. Wu B.N. Liu C.P. Yang T.Y. Kao L.P. Wu J.R. Lai W.T. Chen I.J. Xanthine-based KMUP-1 improves HDL via PPARγ/SR-B1, LDL via LDLRs, and HSL via PKA/PKG for hepatic fat loss J. Lipid Res. 2015 56 2070 2084 10.1194/jlr.M057547 26351364 2. Liu C.P. Dai Z.K. Huang C.H. Yeh J.L. Wu B.N. Wu J.R. Chen I.J. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081346ijms-17-01346ArticleNovel Redox-Dependent Esterase Activity (EC 3.1.1.2) for DJ-1: Implications for Parkinson’s Disease Vázquez-Mayorga Emmanuel 12Díaz-Sánchez Ángel G. 1*Dagda Ruben K. 2Domínguez-Solís Carlos A. 1Dagda Raul Y. 2Coronado-Ramírez Cynthia K. 1Martínez-Martínez Alejandro 13*Tegeder Irmgard Academic Editor1 Instituto de Ciencias Biomédicas, Universidad Autónoma de Ciudad Juárez, Anillo envolvente Pronaf y Estocolmo s/n, Ciudad Juarez, Chihuahua 32310, Mexico; emmanuel.vazquez@uacj.mx (E.V.-M.); al135792@alumnos.uacj.mx (C.A.D.-S.); al113722@alumnos.uacj.mx (C.K.C.-R.)2 Department of Pharmacology, University of Nevada, Reno School of Medicine, Mailstop 318, Manville Building 19A(Office)/18(Lab), Reno, NV 89557, USA; rdagda@medicine.nevada.edu (R.K.D.); rauld@medicine.nevada.edu (R.Y.D.)3 El Colegio de Chihuahua, Calle Partido Díaz 4723 esquina con Anillo Envolvente del Pronaf, colonia Progresista, Ciudad Juárez, Chihuahua 32310, Mexico* Correspondence: angel.diaz@uacj.mx (Á.G.D.-S.); alejandro.martinez@uacj.mx (A.M.-M.); Tel.: +52-656-688-1621 (A.M.-M.); Fax: +52-656-688-1894 (A.M.-M.)22 8 2016 8 2016 17 8 134629 6 2016 09 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Mutations the in human DJ-1 (hDJ-1) gene are associated with early-onset autosomal recessive forms of Parkinson’s disease (PD). hDJ-1/parkinsonism associated deglycase (PARK7) is a cytoprotective multi-functional protein that contains a conserved cysteine-protease domain. Given that cysteine-proteases can act on both amide and ester substrates, we surmised that hDJ-1 possessed cysteine-mediated esterase activity. To test this hypothesis, hDJ-1 was overexpressed, purified and tested for activity towards 4-nitrophenyl acetate (pNPA) as µmol of pNPA hydrolyzed/min/mg·protein (U/mg protein). hDJ-1 showed maximum reaction velocity esterase activity (Vmax = 235.10 ± 12.00 U/mg protein), with a sigmoidal fit (S0.5 = 0.55 ± 0.040 mM) and apparent positive cooperativity (Hill coefficient of 2.05 ± 0.28). A PD-associated mutant of DJ-1 (M26I) lacked activity. Unlike its protease activity which is inactivated by reactive oxygen species (ROS), esterase activity of hDJ-1 is enhanced upon exposure to low concentrations of hydrogen peroxide (<10 µM) and plateaus at elevated concentrations (>100 µM) suggesting that its activity is resistant to oxidative stress. Esterase activity of DJ-1 requires oxidation of catalytic cysteines, as chemically protecting cysteines blocked its activity whereas an oxido-mimetic mutant of DJ-1 (C106D) exhibited robust esterase activity. Molecular docking studies suggest that C106 and L126 within its catalytic site interact with esterase substrates. Overall, our data show that hDJ-1 contains intrinsic redox-sensitive esterase activity that is abolished in a PD-associated mutant form of the hDJ-1 protein. 4-nitrophenyl acetatehuman carboxyl esteraseredox sensoroxidative stressDJ1/PARK7ROSParkinson’s disease ==== Body 1. Introduction Mutations in parkinsonism associated deglycase (PARK7) gene, which encodes for the human DJ-1 (hDJ-1)/PARK7 protein, are associated with autosomal recessive early-onset forms of Parkinson’s disease (PD) [1]. hDJ-1 has been described as a cytoprotective multifunctional enzyme with antioxidant, protease, glyoxalase and deglycase activities [2]. In addition, hDJ-1 has been shown to act as a transcriptional regulator that enables the activation of antioxidant responses to confer cytoprotection against oxidative stress (reviewed elsewhere [3,4]). In further support of the functional role of hDJ-1 as a protease, hDJ-1 has high structural homology to the DJ-1/ThiJ/PfpI superfamily, an evolutionarily conserved superfamily of cysteine proteases [5,6]. Interestingly, the oxidative-sensing and protease activities of DJ-1 have been suggested to require the redox-sensing catalytic cysteine residue (C106) and other hydrophilic amino acid residues (H126, E18) [5,7]. Although the requirement for a catalytic triad for several catalytic functions of DJ-1 remains highly controversial, it is clear that C106 plays critical roles in mediating multiple functions of DJ-1 [8]. The high resolution crystal structure of hDJ-1 has been solved by Wilson and colleagues [9]. hDJ-1 consists of an α/β-fold comprised of 10 α-helices and 12 β-strands with high homology to bacterial proteases PfpI. Several studies have shown that hDJ-1 possesses in vitro redox-sensitive proteolytic activity towards different peptides [8]. In addition, the proteolytic function requires the presence of C106 and dimerization of DJ-1. Moreover, hDJ-1 is a zymogen that undergoes catalytic activation via the cleavage of a small C-terminal molecular region which further increases its proteolytic activity [8]. Given that the autocatalytic cleavage of hDJ-1 is unlikely to occur [8], the endogenous protease that cleaves the zymogen of hDJ-1 remains to be identified. In addition, although the acute exposure of cells transiently expressing heterologous hDJ-1 to oxidative stress enhances the proteolytic cleavage of the zymogen of DJ-1, its proteolytic activity decreases in vitro while the cytoprotective activities of DJ-1 are increased [8], suggesting that prosurvival effects and proteolytic activity of DJ-1 are inversely related. In addition, the proteolytic substrates of DJ-1 in vivo have not been identified to date. The C106 of the catalytic triad C106/H126/E18 has been proposed to serve as a redox sensor that modulates the cytoprotective activities of DJ-1 [9]. The thiol (SH) group in C106 of hDJ-1 is a solvent-exposed chemical group that can be sequentially oxidized to sulfenic (SOH), sulfinic (SO2H), sulfinate (SO2¯) and sulfonate (SO3¯), respectively [9,10,11]. The oxidations of SH groups to SO3¯ are irreversible (e.g., DJ-1) and elicit the ubiquitin-proteasome dependent degradation of DJ-1 due to the inactivation of its cytoprotective activities caused by oxidative damage [7,12]. Given that the activated sulfur in C106 can adapt multiple conformational arrangements within the catalytic pocket, it is conceivable that the catalytic cysteine can recognize multiple substrates upon exposure to reactive oxygen species (ROS) [7,9]. It has been observed that the crystal structure of hDJ-1 contains a papain-like domain that is typical of cysteine proteases [7]. However, the fact that the spatial arrangement of the amino acids of the catalytic triad does not favor protease activity [7], it is conceivable that hDJ-1 favors catalysis of multiple substrates including esters in a similar manner to papain. In addition, as postulated elsewhere [7], it is not known whether the catalytic site of DJ-1 is capable of performing other enzymatic activities besides its known protease, deglycase and glyoxalase activities. A variety of cysteine proteases, like papain, are known to possess other enzymatic activities including esterase activity [13,14,15]. It is worth noting that the superfamily of DJ-1/ThiJ/PfpI cysteine proteases contain a papain-like domain with the potential for hydrolyzing amide (peptide) or ester bonds. Therefore, based on this observation, we surmised that members of the superfamily of DJ-1/ThiJ/PfpI, including hDJ-1, contain intrinsic esterase activity. Furthermore, the temporal dynamics of the oxidation of the catalytic cysteine in relation to the catalytic activity of hDJ-1 has yet to be explored [16]. In this study, we show for the first time that hDJ-1 possesses intrinsic esterase activity with positive cooperativity. The esterase activity of hDJ-1 is robustly enhanced upon exposure to low micromolar concentrations (i.e., <10 µM) of hydrogen peroxide (H2O2) and it is extremely resistant to inactivation by high levels of the oxidant. The preincubation of hDJ-1 with the SH-protecting chemical iodoacetamide (IAA) completely abolishes the redox-sensitive esterase activity suggesting that the oxidation of solvent-exposed cysteines is required for hDJ-1 esterase activity. Mechanistically, the esterase activity of hDJ-1 requires oxidation as an oxido-mimetic mutant of hDJ-1 (C016D) is constitutively active towards 4-nitrophenyl acetate (pNPA) whereas a PD-associated mutant, known to reduce oxidation of cysteines in hDJ-1, suppresses its esterase activity. Finally, molecular docking studies suggest that C106 and L126 within the catalytic site interact with esterase substrates. In aggregate, our data suggest that the esterase activity of DJ-1 plays a role in the cytoprotective role of DJ-1 and, conceivable in the etiology of PD. 2. Results 2.1. Cloning and Overexpression of Recombinant hDJ-1 and Its Mutants C106D and M26I To test for esterase activity of hDJ-1 in vitro, human DJ-1 His-tagged, as well as mutants C106D and M26I were expressed in Escherichia coli BL21 (DE3) and purified in accordance to well-established protocols [8]. The temporal dynamics of the expression of wild-type (wt) hDJ-1, mutant C106D and M26I in E. coli BL21 (DE3) were monitored by Coomassie staining analyses of crude homogenates collected at 0, 1, and 2 h following induction with 100 µM of isopropyl β-d-1-thiogalactopyranoside (IPTG). Coomassie staining analyses demonstrated that recombinant hDJ-1 is expressed in E. coli with a molecular weight of 25 kDa (N-terminally His tagged + full-length hDJ-1) in the soluble fraction (Figure 1A). The supernatants of crude homogenates from all aforementioned clones were subjected to downstream purification by using a Ni2+-affinity column which enriched hDJ-1 and its mutant forms (C106D and M26I) to high purity as determined by Western blotting by employing a rabbit polyclonal anti-DJ-1 antibody raised against a synthetic peptide corresponding to residues near the N-terminus of DJ-1 (anti-hDJ-1/PARK7 antibody [EP2815Y]). Western blot analyses of purified eluted fractions (Figure 1B and Figure S1) revealed three immunoreactive bands (25, 23, and 20 kDa) consistent for full-length and proteolytically processed forms of hDJ-1(wt) and hDJ-1(C106D), whereas a single immunoreactive band (25 kDa) was observed for hDJ1(M26I). Unexpectedly, treating hDJ-1-expressing E. coli with H2O2 did not further increase the modest proteolytic cleavage of hDJ-1 in IPTG-induced E. coli (Figure 1B). In summary, our Western blot analysis confirmed successful inducible overexpression, solubility, and enrichment of hDJ-1(wt), hDJ-1(C106D) and hDJ-1(M26I) from transformed E. coli. 2.2. Kinetics Parameters of Esterase Activity of hDJ-1 and Its Enhancement by Oxidation By using pNPA as an in vitro substrate for esterase activity (EC 3.1.1.2), colorimetric assays showed that hDJ-1 possesses strong esterase activity as monitored by the release of 4-nitro-phenoxide (pNP) induced by the hydrolysis of the ester bond (Figure 2A). The esterase activity of hDJ-1 follows a pseudo-first order kinetics (Figure 2B). The esterase activity of hDJ-1 is consistent with a sigmoidal saturation (Figure 2C, dashed line). However, upon re-examination of the saturation enzyme kinetic assays, the enzyme kinetic curve, based on the initial velocities (V0), followed a sigmoidal pattern (Figure 2C, solid line). Moreover, the enzyme kinetic data demonstrated that the esterase activity of hDJ-1 contains a Hill coefficient (h) of 2.05 ± 0.28. Interestingly, the esterase activity of hDJ-1 under different pH conditions is further enhanced beyond the physiological pH (7.4) and shows maximal esterase activity at a pH of 8.0 (Figure S2). These data suggest that an increase in −OH enhances the esterase activity of hDJ-1. Post-translational processing of the zymogen of hDJ-1 to its mature form (~23 kDa) is required to enhance its proteolytic activity [8]. Contrary of the proteolytic activity, the pNPA esterase activity does not need the proteolytic cleavage of the C-terminal region of hDJ-1. This model is supported by the experiment shown in Figure 3C, where a truncated mutant of hDJ-1 (hDJ-1∆C) shows comparable esterase activity to full-length hDJ-1 (Figure 3C). Over more than 20 mutants of hDJ-1 have been linked to the onset of juvenile PD [4]. To determine the role of PD-associated mutations of hDJ-1 on its esterase activity, we measured the esterase activity in a PD-associated mutant of hDJ-1 (M26I), known to have reduced oxidation of solvent exposed cysteines but has an intact ability to dimerize [17]. In brief, we found that hDJ-1 (M26I) possessed no detectable esterase activity towards pNPA (Figure 2D). These results suggest that hDJ-1 esterase activity is compromised in PD models or PD pathogenesis. 2.3. The Esterase Activity of hDJ-1 Is Enhanced by Exposure to Reactive Oxygen Species Exposure of hDJ-1 to ROS has been shown to robustly reduce the proteolytic activity of hDJ-1 [8]. To this end, we surmised that the esterase activity of hDJ-1 is sensitive to ROS. To address this hypothesis, we assessed the esterase activity of recombinant hDJ-1 pre-incubated with increasing concentrations of H2O2 (0–500 µM). Unexpectedly, we observed that initial velocities (V0) of hDJ-1 were enhanced with increasing concentrations of H2O2 (Figure 2D,E). Moreover, the esterase activity of hDJ-1 subsequently plateaus upon exposure to very high micromolar concentrations (>100 µM) of H2O2 (Figure 2E). 2.4. Thiols Are Needed for the Esterase Activity of hDJ-1 The SH group of the catalytic cysteine in hDJ-1(C106) is highly sensitive to oxidation [11,18]. We surmised that the oxidation of solvent-exposed cysteines (C53 or C106) is required for mediating the esterase activity of hDJ-1. To address this hypothesis, we incubated hDJ-1 with IAA, a chemical which alkylates SH groups in solvent-exposed cysteines and protects thiols from further oxidation. Indeed, incubating hDJ-1 with IAA completely abolished the esterase activity of hDJ-1 incubated with a low concentration of H2O2 (Figure 3A) suggesting that the oxidation of solvent-exposed cysteine residues is required for enhancing the esterase activity of hDJ-1. We then measured the oxidation of solvent-exposed cysteines in hDJ-1 due to exposure to H2O2 by measuring the absorbance of thiolates at an optical density (OD) of 240 nm [11]. Indeed, exposing hDJ-1 to H2O2 led to a time-dependent increase in the accumulation of thiolates (Figure 3B). To further determine whether the oxidation of the catalytic cysteine (C106D) is required for the esterase activity of DJ-1, we tested for esterase activity in a mutant of hDJ-1 that mimics the oxidation of a cysteine to a thiolate (sulfinate). Indeed, we observed that the oxido-mimic mutant of hDJ-1(C106D), which retains its ability to be proteolytically processed (Figure 1B), showed a robust increase in esterase activity compared to wild-type DJ-1(wt) (Figure 3C,D). All together, these results suggest that (1) the catalytic cysteine (C106) is critical for the esterase activity of hDJ-1; (2) the oxidation of its catalytic cysteine is required for an induction of its esterase activity; and (3) the PD-associated mutant (M26I) lacks esterase activity. Next, we wanted to determine whether the redox-activated esterase activity shown by hDJ-1 is shared by other proteases that lack catalytic cysteines and which are not expected to undergo redox-sensitive activation. To address this hypothesis, we assayed the esterase activity in pancreatic lipase, a lipid esterase (EC 3.1.1.3) that lacks solvent exposed catalytic cysteines [19] and possesses the classical S-H-D catalytic triad. Indeed, treating porcine pancreatic lipase with H2O2 did not cause any induction in esterase activity (Figure 4A,B). In addition, co-treatment of pancreatic lipase with IAA did not significantly block its esterase activity (Figure 4C). 2.5. Molecular Docking Studies Suggest a Mechanism by Which hDJ-1 Binds to pNPA Thus far, our data suggest that hDJ-1 possesses intrinsic esterase activity. To identify the interacting amino acid residues in hDJ-1 that bind to pNPA, we performed molecular docking simulations by using UCSF Chimera [20,21]. First, we employed a blind docking approach to identify any other plausible alternate binding sites in hDJ-1 that are distinct from the well-characterized catalytic cavity that harbors the C106 [8,10,11]. To this end, the “searchable” molecular space was set to encompass the complete dimer of hDJ-1. The nine best candidate docked structures, based on low annealing energies, contained pNPA docked to C106 within the catalytic site of hDJ-1. We further refined the docking of pNPA to the catalytic site of the top candidate docked structures by re-focusing the “searchable” grid to the catalytic site. In silico mapping of interacting amino acids suggests that pNPA interacts with the thiol group of C106 and with L128 within the catalytic site of the crystal structure of full-length or C-terminal truncated hDJ-1 [8] (Figure 5). Furthermore, our molecular docking studies predict that the carbonyl group in pNPA is oriented towards the catalytic site in a manner that highly favors C106-mediated catalysis. Finally, we observed that the carbonyl oxygen of the substrate is located near the oxyanion hole comprised by peptidyl nitrogens from G75 and A107 in hDJ-1. 3. Discussion 3.1. Recombinant hDJ-1 Is Proteolytically Processed in E. coli DJ-1 is a multifunctional redox-sensing protein with a myriad of cytoprotective functions in eukaryotic cells. Although intrinsic protease and glyoxylase activities have been reported for hDJ-1 [8,10], its endogenous proteolytic and glyoxylase substrates remain to be elucidated. In addition, it is not known whether the catalytic site of hDJ-1 is capable of performing other enzymatic activities. It is worth noting that the superfamily of DJ-1/ThiJ/PfpI cysteine proteases contain a papain-like domain with the potential for hydrolyzing amide (peptide) or ester bonds. To this end, we surmised that hDJ-1 also possesses esterase activity. To address this hypothesis, we purified recombinant hDJ-1 as well as an oxido-mimic mutant (C106D) and a PD-associated mutant (M26I), all of them derived from IPTG-induced E. coli. The full-length of hDJ-1 (189 aa, 20 kDa) was cloned with an N-terminal 6× histidine-tagged peptide (~1 kDa) (Figure S3). The expected molecular weight of the monomer of hDJ-1 is approximately 20 kDa, as reported elsewhere [9]. Interestingly the molecular weight of histidine-tagged hDJ-1 synthesized by E. coli electrophoretically migrated with an approximate molecular weight of 25 kDa (Figure 1), consistent with previous studies [22], and based on 2D gel electrophoresis data of hDJ-1 (23.2 kDa) analyzed in testis [23]. Interestingly, by employing Ni2+-affinity-column chromatography, western blot analyses revealed three hDJ-1 immunoreactive bands in the eluted fractions including full-length hDJ-1 (25 kDa) as well the C-terminal processed forms of the zymogen (23 kDa, 20 kDa) (Figure 1B) [8]. The fact that full and processed forms elute from the Ni2+-affinity-column strongly suggest that the processed region occurs in the C-terminal region, leaving intact the N-histidine tag. 3.2. hDJ-1 Possesses Intrinsic Esterase Activity The sigmoidal fit of the enzyme kinetic curve associated with esterase activity of hDJ-1 (Figure 2C) is significantly different from the kinetic curves reported for its proteolytic activity [8] or glyoxalase activity [10]. Indeed, the proteolytic activity of hDJ-1 is consistent with the Michaelis–Menten model, as reported for the protease activity of hDJ-1 (Km = 173.4 µM) towards casein [8] and for its deglycase activity (Km = 0.44 mM) [24]. Interestingly, the esterase activity described in this paper does not fit the Michaelis–Menten model. Instead, the esterase activity fits a sigmoidal curve with positive cooperative, including the artificially-engineered mutant C106D (Figure 2E). In addition, the highly similar S0.5 (0.55 and 0.60 mM) observed for both esterase activities hDJ-1 and hDJ-1(C106D) suggest that it is unlikely due to residual enzymatic activity. This suggestion is further strengthened by the fact that a PD-associated mutant hDJ-1(M26I) [25] lacks detectable esterase activity. Finally, the enzyme kinetic data suggest that the esterase activity hDJ-1 contains a Hill coefficient of 2.05 ± 0.28 suggesting positive cooperativity and consistent for the dimeric form [9,26,27] of hDJ-1, or with the presence of two processed forms of hDJ-1 (i.e., full-length and proteolytically processed hDJ-1) [8]. Although C-terminal cleavage of DJ-1 is required for enhancing its protease activity [8], we observed that the mutant lacking the C-terminal region (hDJ-1∆C) contains comparable levels of esterase activity as hDJ-1(wt) (Figure 3C). These results suggest that the proteolytic cleavage of hDJ-1 is not essential for modulating the esterase activity of hDJ-1. Interestingly, the PD-associated mutant DJ-1(M26I), characterized to have reduced oxidation of solvent-exposed cysteines but with intact ability to dimerize, shows impaired esterase activity (Figure 3C). These results suggest that the decreased esterase activity of hDJ-1(M26I) is associated with reduced oxidation of solvent-exposed cysteines (Figure 3C) [28]. However, we recognize that additional PD-associated hDJ-1 mutants need to be screened to determine the extent by which PD-associated mutations in hDJ-1 affects its esterase activity. 3.3. Reactive Oxygene Species Enhance the Esterase Activity of hDJ-1 ROS-mediated activation of esterase activity is consistent with the concept that hDJ-1 is a redox-sensing cytoprotective enzyme [8]. However, unlike the esterase activity reported in this study, the protease activity of hDJ-1 is sensitive to ROS-mediated oxidation, presumably due to the oxidation of the SH group in C106, [11] which can undergo multiple oxidation states [7]. The esterase activity of hDJ-1 is elevated with increased oxidation of SH groups in solvent-exposed cysteines whereas its esterase activity is barely noticeable in the absence of oxidation (Figure 2B and Figure 3A,C). However, unlike its protease activity which is extremely labile to ROS exposure, DJ-1 esterase activity plateaus upon exposure to micromolar concentrations of H2O2 (Figure 2E). Similarly, a basic environment increases the esterase activity of hDJ-1, showing maximal esterase activity at a pH above 8.0 (Figure S2). This unexpected but intriguing data suggest that hDJ-1 possesses intrinsic esterase activity that is highly resistant to ROS and high pH (Figure 2 and Figure S2). Given the inverse relationship in its prosurvival and protease activities when hDJ-1 is exposed to micromolar concentrations of H2O2 [8], it is likely that its protease activity does not participate in cytoprotective functions of hDJ-1 in acutely, or chronically stressed cells [29,30]. Although hDJ-1 is proteolytically cleaved in response to cellular stress, it is likely that C-terminal cleavage of hDJ-1 represents a response to compensate for the loss of protease activity caused by exposure to ROS. One limitation of this study is the inability to infer a physiological role of esterase activity of hDJ-1 based on in vitro studies. Nevertheless, this report warrants future studies to determine whether the esterase activity of hDJ-1 plays a cytoprotective role in neurons or whether its newfound esterase activity can survive the harsh oxidative environment in PD brain tissue and other neurodegenerative diseases [7,31]. 3.4. Oxidation of Solvent-Exposed Cysteines Are Required for the Esterase Activity of hDJ-1 C106 in hDJ-1 has been extensively described as the catalytic residue and sensor of oxidative stress that can undergo sequential oxidation states [16,30,32]. The cysteine-SO2H of C106 is postulated to be reversible and required for certain catalytic functions of DJ-1 (e.g., transcriptional and chaperone activities) [30]. Our data suggest that the esterase activity of hDJ-1 requires the oxidation of cysteine-derived thiols as cotreating hDJ-1 with IAA completely blocked its esterase activity when exposed to low micromolar concentrations of H2O2 required to enhance its esterase activity (Figure 3A). In addition, the observation that increasing concentrations of H2O2 elevate the esterase activity of hDJ-1, suggests that multiple oxidative states of SH groups of solvent-exposed cysteines, modulate the speed of esterase catalysis. Previous studies suggest that C106-SO2¯ and cysteine-SO3¯ facilitate enzymatic catalysis of hDJ-1 by forming coordination distances of 1.5 and 3 Å with substrates [7]. Consistent with the concept that the oxidation of a catalytic cysteine is required for activating hDJ-1 esterase activity, cotreating a serine-directed lipase (from porcine pancreas, E.C. 3.1.1.3) that lacks a catalytic cysteine with IAA, or with increasing concentrations of H2O2, does not affect its esterase activity (Figure 4). In addition, the oxido-mimetic mutant hDJ-1(C106D) shows enhanced esterase activity to greater levels than DJ-1(wt) which is akin to the effects of H2O2-induced esterase activity of hDJ-1 (Figure 3C,D). Finally, our molecular docking studies predict that hDJ-1 can sterically accommodate esterase substrates (e.g., pNPA) within its catalytic site by forming hydrogen bonds with a novel catalytic dyad conformed by the catalytic cysteine (C106) and L128 with similar coordination distances. In aggregate, these results demonstrate that the oxidation of the solvent-exposed catalytic cysteine to a thiolate, presumably a sulfenic acid, is required for the esterase activity of hDJ-1. 3.5. Molecular Docking Studies Predict that C106 in hDJ-1 Mediates Catalysis of Esters Our molecular docking studies predict that C106 acts as a catalytic residue for mediating the esterase activity of hDJ-1 (Figure 5). Our in silico studies are consistent with recent crystallographic and mutagenesis studies that suggest that the catalytic cysteine (C106) is required for the glyoxalase activities of Arabidopsis thaliana-derived DJ-1 and hDJ-1 [10]. Furthermore, our molecular docking studies suggest that the methyl group in L128 and SH moiety in C106 form a catalytic dyad to facilitate the catalysis of pNPA. Although our in silico studies did not reveal other interacting amino acid residues, we do not rule out the possibility that other amino acid residues such as E18, R28, R48, N76 and H126 can bind to pNPA under different conditions (i.e., changes in pH, oxidation state of thiols, salt concentration and temperature fluctuations in the system). Overall, our in silico analyses provide a compelling premise for identifying endogenous esterase substrates of hDJ-1 and other interacting residues that are required for its esterase activity to reduce lipid peroxidation removal under stress [33,34]. 4. Materials and Methods 4.1. Cloning, Mutagenesis and Purification of Recombinant hDJ-1 hDJ-1 cDNA (UniProtKB-Q99497) was obtained from Genscript™ (Piscataway, NJ, USA). N-terminal 6× histidine tagged hDJ-1 (pET-28b+-HsDJ-1) was generated by subcloning hDJ-1 into the pET28b (Novagen®, Madison, WI, USA) vector using the NdeI-XhoI enzyme restriction sites. Mutant hDJ-1(C106D), hDJ-1(M26I), and hDJ-1(ΔC) were generated by site directed mutagenesis. hDJ-1(M26I) is an early-onset PD-associated mutant; hDJ-1(C106D) is an oxido-mimetic mutant; hDJ-1(ΔC) is a C-terminal truncated form of hDJ-1, which mimicking the mature form of the proteolytically cleaved zymogen. All mutants were generated by employing standard mutagenesis techniques by using the Quickchange II XL (Agilent Technology Inc., La Jolla, CA, USA) according to the manufacturer’s instructions with the exception that the KOD Hot Start master mix (EMD Millipore, Billerica, MA, USA) was used for reach mutagenesis reaction. The coding sequence, sense and anti-sense oligonucleotides used for performing site directed mutagenesis in full-plasmid of the pET-28b+-HsDJ-1 vector (EMD Millipore) are reported in Figure S3 and Table S1. E. coli BL21 pLys-(DE3) expression strain (Agilent®, Santa Clara, CA, USA) were transformed either with pET-28b+-HsDJ-1 or with mutant hDJ-1(C106D) and hDJ-1(M26I) constructs by employing the CaCl2 technique followed by heat shock. The cloning of hDJ-1 is explained and the same procedure was done for each clone. In brief, 40 ng (5 µL) of pET-28b+-hDJ-1 were added into 50 µL of bacteria in Luria Broth (LB), gently mixed, and incubated on ice for 30 min. The bacteria were heat-shocked for 30 s at 42 °C, gently mixed, and returned on ice for 5 min. Thereafter, 950 mL of LB (0.5% Tryptone, 0.5% yeast extract, 1% w/v NaCl) were added and bacteria were allowed to grow at 250 rpm at 37 °C. After 2 h, bacteria were centrifuged at 1000 rpm (Eppendorf Centrifuge 5417R, rotor F-45-30-11) for 10 min, and the resulting cell pellet was suspended in 50 mL of LB and plated on selective solid LB-agar (2% agar, 0.5% tryptone, 0.5% yeast extract, 1% w/v NaCl) supplemented with 30 µg/mL kanamycin and incubated overnight at 37 °C. Thereafter, a colony was selected and used to inoculate LB medium (~10 mL) that was cultured for 12 h at 37 °C. An aliquot of 1.5 mL of bacteria was then centrifuged, resuspended in LB containing 50% glycerol and stored at −80 °C for long-term storage. To generate recombinant hDJ-1, 50 µL of bacteria were inoculated in 25 mL of LB medium overnight at 37 °C under a continuous agitation rate of 250 rpm. Fifteen mL of the resultant bacterial culture was then passaged onto 500 mL of fresh media and incubated in the same media conditions. When the culture reached an OD of 0.4–0.6 at 600 nm, the expression of hDJ-1 was induced by adding IPTG) to a final concentration of 100 µM and maintained 2 h at 37 °C. Cells were then harvested by centrifugation at 4500 rpm (Eppendorf Centrifuge 5417R, rotor F-45-30-11) for 10 min, and the cell pellet was resuspended in 30 µL of lysis buffer (50 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES), 50 mM KCl, 1% glycerol, 1 mM 2-mercaptoethanol, pH 6.8) and homogenized with an ultrasonic homogenizer bath for 30 min. To purify hDJ-1, the homogenate was centrifuged at 14,500 rpm (Eppendorf Centrifuge 5417R, rotor F-45-30-11) for 30 min at 4 °C. The resulting cell debris was discarded while the soluble fraction was loaded onto a Ni2+-Sepharose HiTrap™ HP column (GE Healthcare, Wauwatosa, WI, USA) that was previously equilibrated with lysis buffer. The column was then washed with 100 mL of lysis buffer containing 30 mM of ÿmidazole, and histidine-tagged hDJ-1 was eluted with 50 mL of elution buffer (50 mM HEPES, 50 mM KCl, 1% glycerol, 1 mM 2-mercaptoethanol, pH 6.8, 100 mM imidazole). The purity of each fraction was analyzed by SDS-PAGE by electrophoresing the samples on a 15% acrylamide/bisacrylamide, at 70 V for 30 min and then at 100 V for 1.5 h. The purity of eluted recombinant hDJ-1 was corroborated by Western blot. Proteins in the SDS-PAGE gel was electrophoretically transferred onto a polyvinylidene difluoride (PVDF, Thermo Fisher®, Waltham, MA, USA) membrane at 150 mA for 35 min by using the Trans-Blot® SD Semi-Dry Transfer Cell (BioRad, Hercules, CA, USA). The PVDF membrane was then blocked in 5% skim milk in PBS containing 1% Triton X-100 (PBST) for 1 h, washed extensively in PBST (5× for 5 min/wash) and incubated with a rabbit polyclonal anti-hDJ-1 antibody (1:10,000, anti-hDJ1/PARK7/ antibody [EP2815Y] ab76008, abcam, Cambridge, MA, USA) for 2 h at room temperature (RT). Following incubation with primary antibody, the PVDF membrane was washed 3× in PBST for 10 min/wash, incubated for 1 h with rabbit IgG anti-donkey secondary antibody conjugated to horseradish peroxidase (1:5000 in PBST), and washed 3× in PBST (10 min/wash). The immunoreactive bands were detected by incubating the PDVF membrane with SuperSignal® West Pico chemiluminescent reagent (Thermo Scientific, Waltham, MA, USA) and detected using a BioRad Versadoc® Imaging system. 4.2. Evaluation of the Hydrolysis of pNPA by hDJ-1 and Lipase The in vitro esterase activity of either purified full-length hDJ-1, mutants hDJ-1(C106D), hDJ-1(M26I), and hDC-1(ΔC) or pure triacylglycerol lipase (Sigma L3126) were evaluated by using the widely popular esterase substrate pNPA (Sigma N8130). The breakdown of pNPA was followed by spectrophotometry measuring the appearance of pNP at 403 nm. Briefly, 10 µL (15 µg) of hDJ-1 purified protein was added to 290 µL reaction buffer (50 mM phosphate buffer, pH 7.4), after which, 50 µL of increasing concentrations of pNPA was added to the reaction (0–1.6 mM final concentration), or maintained at a constant saturating concentration based on the experimental protocol and hypothesis tested. The absorbance at 403 nm was dynamically measured for every second for 90 s at 37 °C by using an onboard injector of FLUOstar (BMG Labtech, Allmendgruen, Ortenberg, Germany) or a Multi-mode SpectraMax M4 microplate reader (Molecular Devices, Sunnyvale, CA, USA). The spontaneous hydrolysis of pNPA in the absence of DJ-1 was used as a negative control and this background activity was subtracted from specific esterase activity associated with DJ-1. Additional negative controls conducted included enzymatic reactions containing DJ-1 but lacking pNPA. The esterase activity of purified porcine pancreatic lipase was measured as the same as for DJ-1 pretreated with vehicle buffer alone (control) or with 100 µM H2O2. Data was analyzed by fitting to Equations (1)–(3). V0 = Vmax·[S]h/S0.5h + [S]h,(1) V0 = Vmax·[S]/Km + [S],(2) ∆Act = Amax·[S]h/A0.5h + [S]h,(3) where V0 is the initial velocity; Vmax, is the maximum velocity; [S], is the substrate concentration; h, is the Hill coefficient; Km, is the Michaelis–Menten constant, S0.5 is the substrate concentration to accomplish half of Vmax; A0.5, is the hydrogen peroxide concentration that produces half of the maximum activation and ∆Act is the change in velocity. 4.3. Analysis of the Oxidation of hDJ-1 To analyze the role of oxidative stress on mutants and hDJ-1 esterase activity, purified hDJ-1 was exposed to increasing concentrations of H2O2 (0 to 500 µM). In brief, hDJ-1 was incubated for an hour at each concentration of H2O2 to fully ensure the oxidation of SH groups of solvent-exposed cysteines. After incubation with H2O2, the shift in UV-spectral signature associated with cysteine sulfurs was recorded [12,35]. The protonation state of thiols in hDJ-1 was monitored by measuring the absorbance at 240 nm as previously reported [35] with the following minor modifications. In brief, hDJ-1 was incubated in assay buffer (50 mM Tris, 50 mM HEPES, 50 mM MES, 50 mM KCl, 1 mM β-mercaptoethanol, pH 6.8) and with H2O2. The formation of the thiolate anion in hDJ-1 induced by exposure to H2O2 was monitored via a FLUOstar Omega™ spectrophotometer (BMG Labtech, Allmendgruen, Ortenberg, Germany) by measuring the absorbance at 240 nm and normalized with respect to 280 nm which accounts for the total thiolate concentration loaded in the incubation reaction. 4.4. Molecular Docking Studies To identify molecular mechanisms by which hDJ-1 hydrolyzes pNPA, we performed molecular docking simulations by using the open source USCF Chimera algorithm [20,21] to find amino acid residues within the catalytic site of hDJ-1 that interact with pNPA. The crystal structure of hDJ-1, solved at a resolution of 1.5 Å, (PDB code: 4ZGG) was used as template and the three-dimensional structure of pNPA was downloaded from PubChem database (CID 13243). Both “receptor” (full-length or C-terminal truncated hDJ-1) and “ligand” (pNPA) structures were optimized prior to docking by employing the dockPrep function for the USCF Chimera menu [21]. In addition, all of the water molecules and the ligand were removed from hDJ-1 structure. Next, the C-terminal tail of hDJ-1 was conceptually removed as previously published [8], and the resulting molecular model was energy-minimized by using the Amber force field (Amber ff12SB) and the structure editing function of USCF-Chimera located in the tools menu. Next, a “blind” docking approach was used to identify candidate structures of hDJ-1 docked to pNPA by preparing a molecular grid that was large enough to encompass the complete structure of the dimer of hDJ-1 (size: 52.73 Å × 42.26 Å × 57.24 Å). The default docking strategy (simulated annealing) was employed for each molecular docking simulation at standard conditions. The top-ranked docked structures of hDJ-1 that contained the highest number of hits (abundance), and low binding affinities (kcal/mol) were further analyzed by using USCF Chimera. 5. Conclusions The DJ-1/ThiJ/PfpI comprise an ancient superfamily of cysteine-proteases that contain a catalytic cysteine (C106 in hDJ-1) and a papain-like domain [8] with the potential to mediate proteolytic and esterase activities. Previous studies have raised the possibility that the catalytic C106 regulates additional intrinsic enzymatic activities or functional roles of hDJ-1 [7,10]. By using pNPA as an in vitro substrate for esterases, we unveiled novel intrinsic esterase activity of DJ-1 that is presumably modulated by positive cooperativity (Figure 2C). Our data is consistent with the fact that hDJ-1 is a homodimeric protein with two catalytic sites [9,11]. The value of h > 1 (2.053) indicates an apparent positive cooperativity suggesting that the activity of DJ-1 is enhanced upon binding the first substrate, thereby inducing conformational changes which increases the affinity for a second substrate. The reaction that we are describing for hDJ-1 fits to the enzyme classification (EC) number EC 3.1.1.2, which corresponds to an hydrolase (3), acting on ether bonds (.1) being carboxylic esther (.1) in phenolic esthers (EC 3.1.1.2). The systematic name, according to the IUPAC, is aryl-esther hydrolase, arylesterase, A-esterase, paraoxonase, aromatic esterase. Although the possibility for this esterase activity was indirectly touted by one study (formation of thioesters intermediates required for its glyoxylate activity) [10], our study shows direct evidence for C106-mediated hydrolysis of esters by hDJ-1. In brief, the basal esterase activity of DJ-1 requires a homodimer conformation (Figure 2), ROS-mediated oxidation of the catalytic cysteine (C106) which robustly elevates hDJ-1’s esterase activity (Figure 2 and Figure 3). Furthermore, esterase activity may be mediated by the well-characterized catalytic pocket of hDJ-1 as suggested by our molecular docking studies (Figure 5). On the other hand, while the protease and glyoxylase activities require that a reactive SH in C106 interacts with H126, E18 and water [7,10,11], our molecular docking and biochemical analyses of the hDJ-1 mutant hDJ-1(C106D) suggests that hydrolysis of pNPA involves C106 and L128 residues and presumably requires oxidation of C106. It is plausible both the esterase and proteolytic activities of hDJ-1 play distinct physiological roles. Like hDJ-1, other proteases are known to contain esterase and proteolytic activities that are mediated by a common catalytic site that can be allosterically modulated. Similarly, trypsin, a highly characterized pH-sensitive serine protease, possesses both intrinsic esterase and proteinase activities that can be differentially modulated by post-translational modifications [36,37]. However, our data suggest that redox-activation of esterase activity is unique to hDJ-1 (Figure 2D,E), as treating pancreatic porcine lipase, a serine protease, with H2O2 does not affect its esterase activity (Figure 4). Our findings add a major level of complexity to the physiological role of this multifunctional protein. Like many cysteine proteases, our studies suggest that hDJ-1 is a cysteine protease with more than one substrate (e.g., amide, ester) [38]. However, unlike very well-characterized cysteine proteases, it remains to be elucidated whether hydrolysis of pNPA, or mild proteolytic activity, may be a non-physiological extension or a proxy readout for distinct physiological roles of full-length hDJ-1. It is clear that hDJ-1 is a multifunctional redox-sensing protein with broad substrate specificity to enable distinct or convergent physiological roles. On the other hand, while the proteolytic activity of full-length hDJ-1 is weak and sensitive to oxidative stress [8], our studies suggest that the esterase activity for hDJ-1 is potently activated by ROS (Figure 2D-E). However, PD mutations in DJ-1 affect its esterase activity given that a PD-associated mutant of DJ-1 was observed to lack esterase activity (Figure 3C). Hence, our study warrants future experiments to further unravel the molecular mechanisms of the esterase activity of hDJ-1 as well as the biological implications for in vivo and in the context of PD. This report lays the groundwork for future studies that examine the extent by which the esterase activity is affected in Parkinsonian models and in other PD-associated mutants of hDJ-1, in relation to its protease activity, and whether it plays a detrimental or neuroprotective role. Acknowledgments Emmanuel Vázquez-Mayorga performed all the experiments in the UACJ and UNR. This study was supported by a CONACYT grant INFR-2012-01-187983 & CB-2015 (254483) to AA, and PRODEP 178650 to Ángel G. Díaz-Sánchez. To CONACYT for a doctoral fellowship (Emmanuel Vázquez-Mayorga), and by start-up funds from UACJ (Alejandro Martínez-Martínez, Ángel G. Díaz-Sánchez.). Lastly, this study was partially supported by an NIH/NIGMS grant in “Cell Biology of Cell Signaling across Membranes” (GM103554) to Ruben K. Dagda. We will like to acknowledge José Alberto Nuñez-Gastelum, Laura A.A. De la Rosa-Carrillo, and Luis F. Plenge-Tellechea for their excellent assistance and in the design of certain in vitro biochemical experiments of this study. Supplementary Materials Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1346/s1. Click here for additional data file. Author Contributions Emmanuel Vázquez-Mayorga performed all the experiments and was involved in all part of the research and manuscript; Ruben K. Dagda was mentor of the first author and he was also involved in the design of the kinetics assays, all discussion and edition of the manuscript; Carlos A. Domínguez-Solís helped in the production, purification and Western blotting of hDJ-1; Raul Y. Dagda and Cynthia K. Coronado-Ramírez conducted cloning of mutant hDJ-1 forms and measurements of lipase activity, respectively; Alejandro Martínez-Martínez and Ángel G. Díaz-Sánchez were involved in all part of the research, the design of cloning, kinetics experiments, molecular docking, discussion and all manuscript edition. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Purification and proteolytic processing of exogenous hDJ-1 from Escherichia coli. (A) Coomassie staining of protein in crude homogenates and soluble fractions derived from E. coli transformed with human DJ-1 (BL21DE3/hDJ-1) and incubated with 100 µM isopropyl β-d-1-thiogalactopyranoside (IPTG) for 0, 1, and 2 h. Cell lysates were loaded (100 μg protein) and separated in 15% acrylamide/bis-acrylamide by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). H: crude homogenate, S: soluble fraction derived from the crude homogenate; (B) Western blot of lysates from untransformed (BL21DE3) or transformed bacteria with either a wild-type (wt, BL21DE3/hDJ-1), a PD associated mutant of DJ-1 (BL21DE3/hDJ-1 M26I) or a mutant of DJ-1 that mimic oxidized cysteine-SO2H (BL21DE3/hDJ-1 C106D) at the indicated concentration of hydrogen peroxide (H2O2). Supernatants from lysed bacteria were purified by using a Ni2+-Sepharose HiTrap™ high performance (HP) column, subjected to SDS-PAGE, and immunoblotted with rabbit polyclonal antiDJ-1 antibody 1:10,000 (anti-PARK7/DJ-1 antibody [EP2815Y]). Molecular weights are indicated on the right of the image in kDa. Figure 2 hDJ-1 contains intrinsic esterase activity that is enhanced by exposure to reactive oxygen species (ROS). (A) Schematic showing the hydrolysis of 4-nitrophenyl acetate (pNPA) by esterase to 4-nitrophenol (pNP) and acetate (EC 3.1.1.2); (B) Representative enzymatic kinetic time course for hDJ-1 esterase activity as spectrophotometrically monitored by the appearance of pNP at optical density (OD) of 403 nm at the indicated substrate concentrations (µM). To obtain specific esterase activity associated with hDJ-1, the absorbance values from control reactions that lacked hDJ-1 but contained H2O2, were subtracted from the values derived from the reactions containing hDJ-1 and H2O2. The enzymatic kinetic assay shown is representative of three experiments with similar results; (C) Representative enzymatic kinetic activity curve of hDJ-1(wt) (U/mg protein) based on the initial velocities (V0) obtained from the data shown in B, demonstrates a better sigmoidal fit for hDJ-1(wt) based on the Hill kinetic model (solid line, Equation (1) in methods) than a Michaelis–Menten fit (dashed line, Equation (2) in methods). Vmax = 235.10 ± 12.00 µmol of pNPA hydrolyzed/min/mg protein; S0.5 = 0.55 ± 0.040 mM; Hill coefficient (h) = 2.05 ± 0.28; (D) Representative enzyme kinetic trends of the esterase activity of recombinant hDJ-1, pretreated with the indicated increasing concentrations of H2O2, were obtained by spectrophotometrically monitoring the appearance of pNP at 403 nm at the indicated H2O2 concentrations; (E) Representative enzymatic kinetic of the change (∆) activity of hDJ-1(wt) (U/mg protein) based on the initial velocities (ΔActivity = [V0 with peroxide − V0 without peroxide]) obtained from the data shown in (D), shows that pretreating hDJ-1 with increasing concentrations of H2O2 (>10 µm) enhances its esterase activity and plateaus at a concentration of 100 µM. Figure 3 Esterase of hDJ-1 requires the oxidation of C106. (A) Representative enzyme kinetic trends of the esterase activity of hDJ-1, in the presence or absence of the thiol (SH)-protecting chemical iodoacetamide (IAA), co-treated with increasing concentrations of H2O2; (B) Oxidation of the SH group by H2O2 as induced by exposing hDJ-1 to H2O2 led to the appearance of sulfonate that was monitored at OD 240 nm; (C) Esterase activity expressed as relative activity units (RAU). Data are means ± standard error in untransformed bacteria (BL21DE3), purified hDJ-1(wt), oxidant-mimetic mutant of hDJ-1 (hDJ-1∆C), hDJ-1(C106D), or the PD-associated mutant of DJ-1hDJ-1(M26I) at a saturating concentration of pNPA (2 mM). Note that hDJ-1(C106D) shows enhanced esterase activity towards pNPA compared to hDJ-1(wt), whereas hDJ-1(M26I) lacks esterase activity. Multiple comparison was done by performing a one-way analysis of variance (ANOVA, p < 0.05) followed by post-hoc analysis, Tukey´s test. Lowercases indicates groups with statistically significant differences at p < 0.05; (D) Representative enzymatic kinetic curve of the oxido-mimetic mutant of hDJ-1(C106D) based on the initial velocities (V0) obtained from the data shown in (C), the kinetic parameters were: S0.5 = 676.40 ± 0.15 μM; h = 1.59 ± 0.15; Vmax = 1434 ± 183 U/mg protein. Figure 4 Oxidative stress does not modulate the esterase activity of pancreatic porcine lipase. (A) Representative enzymatic kinetic curves of esterase activity from purified porcine pancreatic lipase as spectrophotometrically monitored by the appearance of pNP (Absorbance at 403 nm) at the indicated substrate concentrations (pNPA) pretreated with vehicle buffer alone (control) or with 100 µM H2O2. Note that pre-treating pancreatic porcine lipase with H2O2 does not alter its esterase activity towards pNPA. The experiment shown in this figure is representative of two independent experiments with similar results; (B) Representative enzymatic kinetic curve of porcine lipase pretreated with H2O2, based on the initial velocities (V0) obtained from the data shown in A suggests that H2O2 does not modulate the esterase activity of pancreatic porcine lipase; (C) Representative enzymatic kinetic curves of esterase activity from purified porcine pancreatic lipase as spectrophotometrically monitored by the appearance of pNP (OD of 403 nm) at the indicated substrate concentrations (pNPA) pretreated with vehicle buffer alone (control), or with 2 mM IAA. Note that IAA treatment does not significantly decrease the esterase activity of pancreatic porcine lipase. Figure 5 Binding of pNPA to the catalytic site of hDJ-1. (A) Molecular docking studies predict that pNPA binds the dimer of hDJ-1 at the oxyanion cavity of the catalytic site by forming hydrogen bonds with C106 and L128. The molecular surface of the top-ranked candidate docked structure of hDJ-1 is shown. Subunit A and B of the hDJ-1 dimer are colored green and blue, respectively; (B) A close up of the oxyanion site within the catalytic site of the top-ranked candidate docked structure reveals that hDJ-1 forms hydrogen bonds between pNPA via a novel catalytic dyad comprised of C106 and L128. The hatched lines denote the hydrogen bonds between atoms. For clarity, the α-helices in one of the subunits of hDJ-1 are shown in green while the α-helices belonging to the second subunit are colored blue. ==== Refs References 1. Van Duijn C.M. Dekker M.C. Bonifati V. Galjaard R.J. Houwing-Duistermaat J.J. Snijders P.J. Testers L. Breedveld G.J. Horstink M. Sandkuijl L.A. Park7, a novel locus for autosomal recessive early-onset parkinsonism, on chromosome 1p36 Am. J. Hum. Genet. 2001 69 629 634 10.1086/322996 11462174 2. Clements C.M. McNally R.S. Conti B.J. Mak T.W. Ting J.P.-Y. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081347ijms-17-01347ArticleSex-Based Selectivity of PPARγ Regulation in Th1, Th2, and Th17 Differentiation Park Hong-Jai 12Park Hyeon-Soo 12Lee Jae-Ung 12Bothwell Alfred L. M. 3Choi Je-Min 124*Desvergne Béatrice Academic Editor1 Department of Life Science, College of Natural Sciences, Hanyang University, Seoul 04763, Korea; hongjai@hanyang.ac.kr (H.-J.P.); hspark91@hanyang.ac.kr (H.-S.P.); jaeunglee@hanyang.ac.kr (J.-U.L.)2 Research Institute for Natural Sciences, Hanyang University, Seoul 04763, Korea3 Department of Immunobiology, Yale University School of Medicine, New Haven, CT 06520, USA; alfred.bothwell@yale.edu4 Center for Neuroscience Imaging Research (CNIR), Institute for Basic Science (IBS), Suwon 16419, Korea* Correspondence: jeminchoi@hanyang.ac.kr; Tel.: +82-2-2220-4765; Fax: +82-2-2298-031918 8 2016 8 2016 17 8 134713 6 2016 11 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Peroxisome proliferator-activated receptor gamma (PPARγ) has recently been recognized to regulate adaptive immunity through Th17 differentiation, Treg functions, and TFH responses. However, its role in adaptive immunity and autoimmune disease is still not clear, possibly due to sexual differences. Here, we investigated in vitro treatment study with the PPARγ agonist pioglitazone to compare Th1, Th2, and Th17 differentiation in male and female mouse splenic T cells. Pioglitazone treatment significantly inhibited various effector T cell differentiations including Th1, Th2, and Th17 cells from female naïve T cells, but it selectively reduced IL-17 production in male Th17 differentiation. Interestingly, pioglitazone and estradiol (E2) co-treatment of T cells in males inhibited differentiation of Th1, Th2, and Th17 cells, suggesting a mechanism for the greater sensitivity of PPARγ to ligand treatment in the regulation of effector T cell differentiation in females. Collectively, these results demonstrate that PPARγ selectively inhibits Th17 differentiation only in male T cells and modulates Th1, Th2, and Th17 differentiation in female T cells based on different level of estrogen exposure. Accordingly, PPARγ could be an important immune regulator of sexual differences in adaptive immunity. PPARγpioglitazoneeffector T cellsestrogensex ==== Body 1. Introduction Peroxisome proliferator-activated receptor gamma (PPARγ), a nuclear receptor and master regulator of lipid metabolism, has emerged as an important regulator of adaptive immunity [1,2,3,4,5,6,7,8,9]. Its ligands have negative regulatory functions in T cell activation [10], proliferation [11,12], and differentiation [13] to prevent or inhibit disease pathogenesis of autoimmune [13,14,15,16,17,18,19,20] and allergic disease models [21,22,23,24,25]. Treatment of T cells with the PPARγ ligands rosiglitazone, ciglitazone, pioglitazone, and 15d-PGJ2 inhibits T cell proliferation and IL-2 production [11,26,27,28]. Ciglitazone treatment increases survival in graft-versus-host disease (GVHD) by Treg cells expressing PPARγ [29]. Differentiation of Th17 cells is inhibited in mice by pioglitazone, thereby delaying disease onset or ameliorating the clinical features of experimental autoimmune encephalomyelitis (EAE) [13]. We previously reported that pioglitazone treatment inhibits human allogenic T cell responses in arterial grafts [12]. PPARγ ligands ciglitazone, rosiglitazone, and pioglitazone also effectively inhibited allergic inflammation in a mouse model of asthma through up-regulation of PTEN [21,22]. PPARγ-deficient T cell animal studies have demonstrated that PPARγ-deficient Treg cells show an impaired ability to regulate effector T cell functions, leading to the development of colitis [14]. More recently, PPARγ-deficient Treg cells displayed impaired migration ability into visceral adipose tissue [30], supporting the influence of PPARγ on Treg functions. In addition, PPARγ selectively inhibits Th17 differentiation to ameliorate EAE [13]. We recently demonstrated that PPARγ acts as a negative regulator in the differentiation of follicular helper T (TFH) cells and germinal center (GC) formation by controlling IL-21 and Bcl-6 expression to prevent autoimmunity [31]. Overall, PPARγ plays diverse roles in the regulation of effector T cell functions and autoimmune or allergic diseases. However, it was suggested that PPARγ is required for the development of colitis in a lymphopenic environment due to the increased apoptosis of PPARγ-deficient T cells [32]. Interestingly, we also reported that PPARγ-deficient T cells in males are more apoptotic, with reduced TFH responses or no significant phenotype in T cell differentiation in vitro, while PPARγ-deficient T cells in females are more easily activated and differentiate into Th1, Th2, Th17, and TFH cells [31]. Given the discrepancies observed in previous studies of PPARγ roles in effector T cells, we hypothesized that PPARγ activation during T cell activation and differentiation varies by sex. Here, we investigated the impact of PPARγ ligand pioglitazone treatment on Th1, Th2, and Th17 differentiation in male and female T cells. We found that pioglitazone treatment inhibited lineage-specific cytokine production in Th1, Th2, and Th17 cells in females and selectively inhibited IL-17 production in Th17 cells in males. These results suggest variable roles by sex for PPARγ in effector T cell differentiation. 2. Results 2.1. PPARγ Inhibits Th1, Th2, and Th17 Differentiation in Female Mouse Splenic T Cells To examine the role of PPARγ in Th1, Th2, and Th17 differentiation in female T cells, we investigated the effect of treatment with the PPARγ ligand pioglitazone on Th1, Th2, and Th17 differentiating cells. MACS-purified CD62LhighCD44low naive T cells from six- to eight-week-old female C57BL/6 mice were differentiated into Th1, Th2, and Th17 cells using specific cytokine media for T cell–skewing conditions with or without treatment with 20 μM pioglitazone. Lineage-specific cytokines were examined by intracellular cytokine staining, and the frequencies of cytokine-expressing cells were analyzed by flow cytometry. Pioglitazone treatment reduced the proportion of IFN-γ–secreting cells in Th1 differentiation (Figure 1A,B), IL-4– and IL-13–expressing cells in Th2 differentiation (Figure 1C,D), and IL-17A–producing cells in Th17 differentiation (Figure 1E,F) compared to DMSO-treated cells. Pioglitazone was not effective in PPARγ-deficient T cells, suggesting that the inhibitory effect was PPARγ-dependent (Supplementary Materials Figure S1). Accumulated cytokine expression in culture supernatants measured by specific ELISA assays demonstrated that IFN-γ in Th1 cells (Figure 2A), IL-4 and IL-13 in Th2 cells (Figure 2B), and IL-17A in Th17 cells (Figure 2C) were significantly reduced by pioglitazone treatment compared to the control group treated with DMSO. These results indicate that the PPARγ agonist pioglitazone can inhibit differentiation of female naïve T cells into effector T cells, including Th1, Th2, and Th17, without specificity. 2.2. PPARγ Selectively Inhibits Th17 Differentiation in Male Mouse Splenic T Cells While our results demonstrate a potent effect of pioglitazone in effector T cell differentiation, such treatment has previously shown selective inhibition of Th17 cells [13]. In addition, we recently reported sex-based differences in the effects of pioglitazone treatment on TFH cell responses [33]. Thus, we hypothesized that there could be also sex-based differences in the effect of pioglitazone on regulation of Th1, Th2, and Th17 differentiation. To address this question, T cell differentiation experiments were carried out with naive T cells from six- to eight-week-old male C57BL/6 mice. As shown in Figure 3, pioglitazone treatment of T cells from males selectively reduced the frequency of IL-17A–expressing cells in Th17 differentiation (Figure 3E,F) without any effect on IFN-γ–positive cells in Th1 differentiation (Figure 3A,B) or IL-4– and IL-13–producing cells in Th2 differentiation (Figure 3C,D) compared to DMSO-treated cells. Accumulated cytokine production in culture supernatants was analyzed with cytokine-specific ELISA assays, revealing that only IL-17A production in Th17 cells from males (Figure 4C) was inhibited by pioglitazone treatment; production of other cytokines, including IFN-γ in Th1 cells (Figure 4A) and IL-4 and IL-13 in Th2 cells (Figure 4B), was not altered by pioglitazone treatment compared to the vehicle control group. Collectively, these data demonstrate that pioglitazone selectively inhibits the differentiation of Th17 cells in male T cells, while more strongly regulating effector T cells, including Th1, Th2, and Th17 cells in female T cells. 2.3. Pioglitazone and Estradiol Co-Treatment Inhibits Th1, Th2, and Th17 Differentiation in Male Mouse Splenic T Cells To address the question of whether estradiol treatment helps pioglitazone inhibit Th1 and Th2 cells in addition to Th17 cells of male, as previously demonstrated for their synergy in regulating TFH responses [33], we utilized MACS-purified naïve T cells from six- to eight-week-old male C57BL/6 mice for Th1, Th2, and Th17 differentiation and investigated the effects of co-treatment with 20 μM pioglitazone and 5 nM estradiol. As shown in Figure 5, co-treatment with pioglitazone and estradiol significantly reduced the proportion of IFN-γ–producing cells in Th1 differentiation (Figure 5A,B) and IL-4– and IL-13–expressing cells in Th2 differentiation (Figure 5C,D) compared to the control groups treated with DMSO and estradiol, or pioglitazone, respectively. Co-treatment with pioglitazone and estradiol also effectively inhibited IL-17A–secreting cells in Th17 differentiation compared to DMSO-treated Th17 cells (Figure 5E,F), suggesting that estradiol enhances the negative regulation of pioglitazone on Th1, Th2, and Th17 differentiation. Accumulated cytokine production of IFN-γ in Th1 cells (Figure 6A), IL-4 and IL-13 in Th2 cells (Figure 6B), and IL-17A in Th17 cells (Figure 6C) was also significantly reduced by co-treatment compared to the DMSO-treated control group. These data collectively suggest that estradiol treatment enhances the sensitivity of male effector T cell differentiation to pioglitazone, which might explain the observed sexual differences in pioglitazone effects on Th1, Th2, and Th17 differentiation. 3. Discussion In this study, we observed sex-based differences in the regulation of PPARγ in Th1, Th2, and Th17 differentiation by in vitro pioglitazone treatment of murine naïve CD4 T cells. Differentiation of naïve CD4 T cells into effector T cell subsets was more profoundly affected by PPARγ activation in T cells from females compared to males. Moreover, pioglitizone treatment selectively inhibited the differentiation of Th17 cells only in male T cells, while the addition of estradiol enabled PPARγ activation to regulate Th1 and Th2 differentiation as well. We thus conclude there are sex-based differences in regulatory role of PPARγ in effector T cell differentiation to Th1, Th2, and Th17 cells that are at least partly dependent on the estrogen level. Ligands for PPARγ are negative regulators of effector T cell responses that ameliorate autoimmune or allergic diseases, including GVHD, EAE, and asthma [13,21,22,25,29]. Recently, the role of PPARγ has been highlighted in T cell responses by utilizing a T cell–specific PPARγ-deficient mouse model, but results have not been conclusive. PPARγ-deficient Treg cells did not suppress effector T cell responses or colitis development in one study [14], while another found that PPARγ was required for the development of colitis in lymphopenic conditions due to the increase of cell death without PPARγ [32]. Correspondingly, PPARγ has been highlighted due to its selective inhibition of Th17 differentiation in regulating EAE disease [13]. However, we previously reported that PPARγ negatively regulates effector T cell differentiation, including Th1, Th2, Th17, Th9, and TFH cells, without specificity [31,33,34]. Here, we explored whether sex-based differences in the role of PPARγ activation might be due to different PPARγ expression levels. We recently reported a very important finding that pioglitazone treatment inhibited TFH induction and GC formation only in females, but not in males, and that estradiol treatment enhanced the effect of pioglitazone in suppressing effector T cell responses in males [33]. In the current study, we observed selective inhibition of pioglitazone on Th17 cells by PPARγ activation in male T cells, and estradiol co-treatment enabled pioglitazone to have the same effect on Th1 and Th2 cells from males that pioglitazone does on T cells from females. These findings raise the possibility of different sensitivities to PPARγ agonist treatment in clinical applications in men and women. There have been previous reports that the PPARγ ligands rosiglitazone and pioglitazone show better sensitivity in women than in men in improving symptoms by decreasing the fasting plasma glucose (FPG) level and increasing the incidence of hypoglycemia in type II diabetes mellitus [35,36]. This finding of sex-based differences in the action of PPARγ in effector T cell responses could help devise sex-specific treatment schemes of human diseases. Regardless of the sex-dependent roles of PPARγ, pioglitazone treatment suppressed the differentiation of Th17 cells in both male and female T cells, suggesting that PPARγ is an important target to modulate Th17-mediated autoimmune diseases. PPARγ expression also seems to have relevance to multiple sclerosis (MS), with lower expression levels of PPARγ reported in PBMC from MS patients compared to healthy controls [19]. In addition, pioglitazone treatment modestly ameliorates rheumatoid arthritis (RA) activity by preventing bone loss [37] and suppresses plasma levels of cytokines, including TNF-α, IL-1β, and IL-6 [38]. Furthermore, rosiglitazone also reduces glomerular inflammation and autoantibody production in mouse models of systemic lupus erythematosus (SLE) [39], and pioglitazone inhibits the activation and proliferation of effector CD4 T cells from PBMCs of SLE patients [28], suggesting an important role of PPARγ in suppressing autoimmune diseases. Accordingly, PPARγ activation by agonists offers an important strategy for the treatment of autoimmune diseases. In addition to regulating autoimmune responses by PPARγ agonists, pioglitazone also affects allergic responses. We demonstrated that pioglitazone treatment inhibited female Th2 differentiation by reducing both IL-4 and IL-13 production, which are important for allergic disease pathogenesis. Previously, pioglitazone treatment was shown to effectively suppress allergen-induced bronchial inflammation and airway hyper-responsiveness (AHR) by decreasing IL-4, IL-5, and IL-13 cytokine production and the number of infiltrated eosinophils [22]. Ciglitazone treatment also reduces the production of ovalbumin (OVA)-specific IgE [24], and rosiglitazone treatment increases the production of IL-10 and suppresses the migration of dendritic cells (DCs) to lymph nodes, ameliorating the severity of asthma [23]. Therefore, PPARγ activation by ligands could be also considered for the treatment of allergic diseases such as asthma. Given that the effects of pioglitazone treatment were more potent on T cells from females, PPARγ ligand treatment might be more effective in regulating disease pathogenesis in females than males. Further study is needed to prove any sex-specific sensitivity of PPARγ-mediated disease regulation. For males, pioglitazone treatment could be a specific inhibitor of Th17 responses for the treatment of autoimmune diseases, and estradiol co-treatment also could be considered for more potent suppression of the T cell response. The synergistic effects of pioglitazone and estradiol in inhibiting male Th1, Th2, and Th17 cells should be further studied in vivo to elucidate the underlying mechanisms. 4. Experimental Section 4.1. Mice C57BL/6 wild-type mice were purchased from Orient Bio (Seongnam, Korea). CD4-specific PPARγ-knockout mice (CD4-PPARγKO) were generated by crossing CD4-Cre transgenic mice with PPARγfl/fl mice. Mice were maintained at Hanyang University mouse facilities under pathogen-free conditions. All procedures regarding isolating splenocytes from the mice and related experiments were approved by the Animal Experimentation Ethics Committee of Hanyang University (2015-0014A (2 February 2015~7 August 2017)) and the experiments were performed according to the guidelines of the Institutional Animal Care and Use Committees (IACUC) of Hanyang University. 4.2. Naive T Cell Isolation Spleens and lymph nodes were isolated from six- to eight-week-old male and female mice. Spleens were incubated with RBC lysis buffer at room temperature for 1 min. The single-cell suspension was incubated with naive CD4 T cell biotin-antibody cocktail at 4 °C for 5 min, and then anti-biotin and CD44 microbeads were added and incubated at 4 °C for 10 min. Naive CD4+ T cells were isolated from the mixture by negative selection using a naive CD4+ T cell isolation kit and magnetic cell separator (Miltenyi Biotech, Bergisch Gladbach, Germany) according to the manufacturer’s instructions. Purity of MACS-purified naïve T cells was determined by staining with CD62L-FITC, CD44-PE (eBioscience, San Diego, CA, USA), and CD4-PerCP-Cy5.5 (Biolegend, San Diego, CA, USA) fluorescently labeled antibodies. The proportion of CD62LhighCD44low gated on CD4-positive cells was usually higher than 96%–98%. 4.3. T Cell Differentiation MACS-purified naïve T cells were differentiated for three days under Th1- and Th17-skewing conditions and for five days under Th2-skewing conditions in the presence of pioglitazone and/or estradiol. The purified cells were seeded at 2.5 × 105/well onto a 96-well plate (BD Falcon, San Jose, CA, USA) coated with 2 μg/mL of anti-CD3 and anti-CD28 antibodies (BD Bioscience, San Jose, CA, USA). The following conditions were used to skew differentiation: for Th1 cells, α-IL-4 5 μg/mL, IL-2 50 U/mL, IL-12 1 ng/mL; for Th2 cells, α-IFN-γ 5 μg/mL, IL-2 50 U/mL, IL-4 30 ng/mL; and for Th17 cells, α-IFN-γ and α-IL-4 5 μg/mL each, IL-6 30 ng/mL, TGF-β 1 ng/mL, IL-23 20 ng/mL, and IL-1β 20 ng/mL. For PPARγ ligand and estradiol treatment, pioglitazone (20 μM, Enzo Life Science, Farmingdale, NY, USA) was added, and the culture incubated for three days (Th1 and Th17 cells) or five days (Th2 cells) in the absence or presence of estradiol (5 nM); DMSO served as a vehicle control. 4.4. Flow Cytometry For intracellular staining, differentiated cells were stained with a Near-IR Live/Dead staining kit (Life Technologies, Carlsbad, CA, USA) at room temperature for 15 min to exclude dead cells. After washing, the cells were stained with anti-mouse CD4-PerCP-Cy5.5 antibody (Biolegend, San Diego, CA, USA) at 4 °C for 10 min for surface staining. The cells were then fixed and permeabilized using a Foxp3 staining kit (eBioscience, San Diego, CA, USA) according to the manufacturer’s protocol. Permeabilized cells were stained with the following anti-mouse antibodies at room temperature for 40 min: IFN-γ-FITC and IL-4-PE for Th1 cells, IL-4-PE and IL-13-Alexa Fluor488 for Th2 cells, and IL-17A-PE and Foxp3-APC for Th17 cells. Intracellular cytokine production was examined using the FACSCanto II system (BD Bioscience, San Jose, CA, USA), and data were analyzed using Flow Jo software ver 9.7.6 (Treestar, Ashland, OR, USA). 4.5. ELISA Cytokine production in cultured supernatant from differentiated T cells was measured using mouse IL-4, IL-13, and IL-17A ELISA Ready-SET-Go kits (eBioscience, San Diego, CA, USA) and IFN-γ ELISA MAX sets (BioLegend, San Diego, CA, USA) according to the manufacturers’ instructions. 4.6. Statistical Analysis Data were analyzed statistically with an unpaired two-tailed Student’s t-test using Prism5 (GraphPad, San Diego, CA, USA). p-values (p) less than 0.05 were considered statistically significant. Acknowledgments This work was supported by grants from the Basic Science Research Program through the National Research Foundation of Korea (NRF-2013R1A1A2A10060048) and by the research fund of Hanyang University (HY-2013-00000001472) to Je-Min Choi. Supplementary Materials Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1347/s1. Click here for additional data file. Author Contributions Je-Min Choi conceived and supervised the experiments and revised the manuscript; Hong-Jai Park designed and performed most of the experiments and wrote the draft of the manuscript; Hyeon-Soo Park and Jae-Ung Lee provided technical support, performed flow cytometry and ELISA assays, and harvested splenocytes from the mice; Alfred L. M. Bothwell gave technical advice and discussed the data. All authors analyzed the data and edited the manuscript. Conflicts of Interest The authors declare no conflicts of interest. Figure 1 PPARγ activation by pioglitazone treatment inhibits Th1, Th2, and Th17 differentiation in female mouse T cells. MACS-purified CD62LhighCD44low naïve T cells from the spleens of six- to eight-week-old female C57BL/6 mice were differentiated into Th1 and Th17 cells for three days and Th2 cells for five days in specific cytokine media for T cell–skewing conditions in the presence of DMSO or pioglitazone (20 μM). The proportions of IFN-γ–positive cells in Th1 differentiation (A,B); IL-4– and IL-13–producing cells in Th2 differentiation (C,D); and IL-17A–expressing cells in Th17 differentiation (E,F) were determined by flow cytometry and demonstrated as the dot plots. Values represent mean ± SEM (n = 5~6). * p < 0.05. Figure 2 Cytokine production in female mouse Th1, Th2, and Th17 cells is reduced by pioglitazone treatment. The accumulated production of IFN-γ in Th1 cells (A); IL-4 and IL-13 in Th2 cells (B); and IL-17A in Th17 cells (C) in culture supernatants from female T cells treated with DMSO or pioglitazone was measured by ELISA assay. Values represent mean ± SEM (n = 5~6). * p < 0.05. Figure 3 PPARγ activation by pioglitazone treatment selectively inhibits Th17 differentiation in male mouse T cells. MACS-purified CD62LhighCD44low naïve T cells from the spleens of six- to eight-week-old male wild-type C57BL/6 mice were skewed into Th1 and Th17 cells for three days and Th2 cells for five days. The differentiated effector T cells were treated with DMSO or pioglitazone (20 μM), and the frequencies of IFN-γ–secreting cells in Th1 differentiation (A,B); IL-4– and IL-13–producing cells in Th2 differentiation (C,D); and IL-17A–positive cells in Th17 differentiation (E,F) were determined by flow cytometry and represented as the dot plots. Values represent mean ± SEM (n = 4~5). * p < 0.05; n.s: non-significant. Figure 4 Selective inhibition of IL-17 production in Th17 cells from male mouse T cells by pioglitazone treatment. Accumulated cytokine expression of IFN-γ in Th1 cells (A); IL-4 and IL-13 in Th2 cells (B); and IL-17A in Th17 cells (C) in cultured supernatants from male T cells treated with DMSO or pioglitazone (20 μM) was measured by ELISA. Values represent mean ± SEM (n = 4~5). * p < 0.05; n.s: non-significant. Figure 5 Pioglitazone and estradiol co-treatment inhibits Th1, Th2, and Th17 differentiation in male T cells. CD62LhighCD44low naïve T cells were purified by magnetic-activated cell sorting (MACS) from the spleens of six- to eight-week-old male wild-type C57BL/6 mice and were differentiated into Th1 and Th17 cells for three days and Th2 cells for five days under lineage-specific skewing conditions with DMSO, pioglitazone (20 μM), E2 (5 nM), or pioglitazone (20 μM) + E2 (5 nM). The proportions of IFN-γ–producing cells in Th1 differentiation (A,B); IL-4– and IL-13–expressing cells in Th2 differentiation (C,D) and IL-17A–secreting cells in Th17 differentiation (E,F) were determined by flow cytometry and depicted as the dot plots. Values represent mean ± SEM (n = 4~5). * p < 0.05, ** p < 0.01, *** p < 0.001; n.s: non-significant. Figure 6 Co-treatment with pioglitazone and estradiol suppresses lineage-specific cytokine production in male Th1, Th2, and Th17 cells. Cultured supernatants from male cells treated with DMSO, pioglitazone (20 μM), estradiol (5 nM), or pioglitazone (20 μM) + estradiol (5 nM) were analyzed by ELISA assay to determine the production of IFN-γ in Th1 cells (A); IL-4 and IL-13 in Th2 cells (B) and IL-17A in Th17 cells (C). Values represent mean ± SEM (n = 4~5). * p < 0.05, ** p < 0.01; n.s: non-significant. ==== Refs References 1. Choi J.M. Bothwell A.L. The nuclear receptor PPARs as important regulators of T-cell functions and autoimmune diseases Mol. Cells 2012 33 217 222 10.1007/s10059-012-2297-y 22382683 2. Glass C.K. Saijo K. Nuclear receptor transrepression pathways that regulate inflammation in macrophages and t cells Nat. Rev. Immunol. 2010 10 365 376 10.1038/nri2748 20414208 3. Hamm J.K. el Jack A.K. Pilch P.F. Farmer S.R. Role of PPARγ in regulating adipocyte differentiation and insulin-responsive glucose uptake Ann. N. Y. Acad. Sci. 1999 892 134 145 10.1111/j.1749-6632.1999.tb07792.x 10842659 4. Farmer S.R. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081348ijms-17-01348ArticleSynthesis and Cytotoxicity against K562 Cells of 3-O-Angeloyl-20-O-acetyl Ingenol, a Derivative of Ingenol Mebutate Liu Ming 1*Chen Fangling 1Yu Rilei 1Zhang Weiyi 1Han Mei 2Liu Fei 3Wu Jing 1Zhao Xingzeng 3Miao Jinlai 4*Arráez-Román David Academic Editor1 Key Laboratory of Marine Drugs, Ministry of Education, School of Medicine and Pharmacy, Ocean University of China, Qingdao 266003, China; Chenfangling0410@hotmail.com (F.C.); rileiyu2010@hotmail.com (R.Y.); 13070868219@163.com (W.Z.); 6151504014@vip.jiangnan.edu.cn (J.W.)2 Department of Pharmacology, Medical College Qingdao University, Qingdao 266071, China; maviswga@163.com3 Institute of Botany, Jiangsu Province and Chinese Academy of Sciences (Nanjing Botanical Garden, Mem Sun Yat-sen), Nanjing 210014, China; liufeiseu@163.com (F.L.); zhaoxingzeng@hotmail.com (X.Z.)4 Key Laboratory of Marine Bioactive Substance, The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China* Correspondence: lmouc@hotmail.com (M.L.); miaojinlai@163.com (J.M.); Tel./Fax: +86-532-8203-1980 (M.L.); +86-532-8896-7430 (J.M.)19 8 2016 8 2016 17 8 134801 6 2016 12 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Ingenol mebutate possesses significant cytotoxicity and is clinically used to treat actinic keratosis. However, ingenol mebutate undergoes acyl migration which affects its bioactivity. Compound 3-O-angeloyl-20-O-acetyl ingenol (AAI, also known as 20-O-acetyl-ingenol-3-angelate or PEP008) is a synthetic derivative of ingenol mebutate. In this work, we report the AAI synthesis details and demonstrate AAI has higher cytotoxicity than ingenol mebutate in a chronic myeloid leukemia K562 cell line. Our data indicate that the increased activity of AAI originates from the improved intracellular stability of AAI rather than the increased binding affinity between AAI and the target protein protein kinase Cδ (PKCδ). AAI inhibits cell proliferation, induces G2/M phase arrest, disrupts the mitochondrial membrane potential, and stimulates apoptosis, as well as necrosis in K562 cells. Similar to ingenol mebutate, AAI activates PKCδ and extracellular signal regulated kinase (ERK), and inactivates protein kinase B (AKT). Furthermore, AAI also inhibits JAK/STAT3 pathway. Altogether, our studies show that ingenol derivative AAI is cytotoxic to K562 cells and modulates PKCδ/ERK, JAK/STAT3, and AKT signaling pathways. Our work suggests that AAI may be a new candidate of chemotherapeutic agent. 3-O-angeloyl-20-O-acetyl ingenolPEP00820-O-acetyl-ingenol-3-angelateingenol mebutatapoptosischronic myeloid leukemia ==== Body 1. Introduction Ingenol mebutate (also called PEP005 or ingenol 3-anglate, Figure 1A) is a hydrophobic diterpene ester which is isolated from the sap of the plant Euphorbia peplus. Ingenol mebutate has potent anti-cancer effects both in vitro and in vivo, and is used as the active ingredient in Picato®, a new drug for the treatment of actinic keratosis [1,2]. Preclinical studies demonstrate that ingenol mebutate has potent activities against a number of leukemic cell lines, stimulating both apoptosis and necrosis. It is believed that ingenol mebutate is a potent activator of PKCδ [3]. PKCδ belongs to the protein kinase C (PKC) family of signaling isoenzymes which regulates cell proliferation, differentiation, and apoptosis. Depending on the cell type, PKCδ can function as a tumor suppressor or a pro-apoptotic factor and regulate cell proliferation and survival functions. Induction of PKCδ has been previously shown in promyelocytic leukemia cells, and PKCδ regulates eIF2α through induction of PKR in acute myeloid leukemia cells [4]; other studies have shown that activation of PKCδ can lead to ERK1/2 phosphorylation [3]. In recent years, PKCδ-involved signaling pathways have been considered as targets of several novel anti-cancer agents and the ability of PKC activators to induce apoptosis in a wide range of leukemia-derived cell lines has made them attractive targets for the development of anti-leukemic drugs [5,6]. Previous investigations have revealed that the 5-hydroxyl group of ingenol mebutate is necessary for its bioactivity. However, the 3-angelate undergoes acyl migration in aqueous solution to form 5- and 20-mono-angelates (1:18), and this migration thus affects its bioactivity [7]. In the search of ingenol mebutate analogs with improved chemical stability and efficiency, we synthesized 3-O-angeloyl-20-O-acetyl ingenol (AAI, Figure 1B) by acetylation at the 20 position of ingenol mebutate, which may improve compound stability and enhance its hydrophobicity. AAI has been previously named as 20-O-acetyl-ingenol-3-angelate or PEP008 (PubChem CID: 14136933) and is sensitive against cancer cells derived from melanoma, breast cancer, and colon cancer [8]. Considering AAI is a structural derivative of ingenol mebutate, which shows a wide range of anti-cancer spectra, we hypothesized that AAI could be cytotoxic to other cell lines, such as the leukemia, and the increased stability and hydrophobicity could enhance its bioactivity. Here, we reported the synthesis details of AAI, and the underlying molecular mechanisms of its cytotoxicity. We demonstrated that AAI inhibited cell proliferation, disturbed the cell cycle distribution, and stimulated apoptosis in K562 cells. We also illustrated that AAI modulated multiple signaling pathways, including activating PKCδ/ERK pathway, inhibiting protein kinase B AKT, and on JAK/STAT3 pathway, indicating the potential applications of AAI in anti-cancer research and development. 2. Results 2.1. The Synthesis of 3-O-Angeloyl-20-O-acetyl ingenol (AAI) Considering that the 3-angelate group in ingenol mebutate undergoes acyl migration to form inactive 5- and 20-mono-angelates, we made structural changes of ingenol mebutate by 20-acetylation to obtained AAI (Scheme 1). The established synthesis route was started from ingenol, followed by four steps, as shown in Scheme 1, each step with high yield. The structure of the target compound 5 was confirmed by the nuclear magnetic resonance (NMR, Supplementary Materials Figure S1) data: 1H-NMR (CDCl3, 400 MHz): δ 0.69–0.70 (m, 1H); 0.93–0.98 (m, 4H); 1.05–1.08 (d, 3H); 1.25–1.40 (m, 4H); 1.74–1.78 (m, 3H); 1.80 (s, 3H); 1.92 (s, 3H); 2.00–2.02 (t, 3H); 2.06 (s, 3H); 2.21–2.24 (t, 1H); 2.49 (s, 1H); 3.89 (s, 1H); 4.07–4.11 (q, 1H); 4.46–4.78 (dd, 1H); 5.55 (s, 1H); 6.04 (s, 1H); 6.12–6.14 (m, 1H); 6.16–6.19 (m, 1H). 2.2. AAI Inhibits Cell Proliferation To evaluate the cytotoxicity of AAI in vitro, we tested its effects on cell proliferation using several human tumor cell lines (human myeloid leukemia K562, HL-60, and KT-1; adriamycin-resistant human breast carcinoma MCF-7/ADR, a human breast cancer cell line resistant to adriamycin; human colorectal carcinoma HCT-116; human lung adenocarcinoma H1975, A549; and human cervical carcinoma HeLa), as well as two normal cell lines (human normal liver cells L-02 and fibroblast cells NIH-3T3), and the growth inhibition rates are shown in Figure 2A. AAI (0.78–25 μM) showed different cytotoxicity on the selected cell lines. K562, MCF-7/ADR, KT-1, and HL-60 cells were relatively more sensitive to AAI. At 1 μM concentration, AAI inhibited K562 cells growth at a similar level as ingenol mebutate (Figure 2B). However, the observed cytotoxicity of AAI was not concentration-dependent (Figure 2A), but obviously time-dependent (Figure 2C). It is interesting that AAI showed growth inhibition against K562 cells even at very low concentration (5 × 10−15 to 5 × 10−10 μM), and the inhibition effects were much more potent than ingenol mebutate (Figure 2D). Under such low concentrations (5 × 10−15 to 5 × 10−10 μM), AAI did not show any cytotoxicity to other tested cell lines (data not shown), thus K562 cells were selected for the following studies. 2.3. AAI Induces G2/M Phase Arrest, Apoptosis, and Necrosis in K562 Cells Cell cycle arrest usually contributes to the growth inhibition; we, thus, analyzed the cell cycle distribution in K562 cells. As shown in Figure 3A, the percentage of K562 cells in G2/M phase of the untreated group was 10.56%; after treatment with AAI (250 nm) for 2, 4, and 12 h, it increased to 12.18%, 23.4%, and 48.85%, respectively, in a time-dependent manner (Figure 3C). Similarly, when treated with different concentration (0–25 nM) of AAI for 24 h, K562 cells also showed a marked G2/M phase arrest (Figure 3B,D). These results indicated that AAI could arrest K562 cells at G2/M phase, which may contribute to AAI-induced growth inhibition. To evaluate whether apoptosis and necrosis were account for AAI-induced growth inhibition in K562 cells, we performed Annexin V-FITC/PI double-staining assay. After incubated with AAI (0–500 nM) for 18 h, both the percentage of Annexin V-FITC positive and PI positive cells increased (Figure 3E), indicating AAI induced both apoptosis and necrosis in K562 cells. The percentage of apoptotic cells increased from 1.64% in the control group to 10.6% and 9.7% when treatment with 250 and 500 nM AAI, respectively. In addition, the percentage of the necrosis cells in the control group was 0.16%; after AAI (500 nM) treatment, the percentage increased to 6.59% (Figure 3F). Although the percentage of both apoptosis and necrosis is not very high under the present experimental conditions, considering its high chemical structural similarity with ingenol mebutate and the observed similar apoptosis/necrosis-inducing activity, our present results indicated that AAI could also stimulate both apoptosis and necrosis in K562 cells. 2.4. AAI Disturbs Mitochondrial Membrane Potential (MMP) in K562 Cells The disruption of MMP is an important event in chemical agents-induced apoptosis. To further confirm the AAI-induced apoptosis, we tested the effect of AAI on MMP in K562 cells using a JC-1 probe. The loss of MMP was generally reflected by the increased green fluorescence from JC-1 monomers, as well as the decreased red fluorescence from JC-1 aggregates. As shown in Figure 4A, when treated with different concentrations (0–500 nM) of AAI, there were significant increases in green JC-1 monomers, and decreases in red JC-1 aggregates, indicating the loss of cell MMP. Similarly, AAI (250 nm) time-dependently increased the green fluorescence and decreased the red fluorescence (Figure 4B), further confirming the disruption of MMP in K562 cells. These results demonstrated that AAI disrupted the mitochondrial function which confirmed the AAI-induced apoptosis in K562 cells. 2.5. AAI Modulates Multiple Key Signaling Pathways in K562 Cells To illustrate the intracellular apoptosis- and survival-related signaling events triggered by AAI, we tested its effects on several signaling molecules. We found that AAI-treated K562 cells had much higher expression levels of p-PKCδ and p-ERK compared with the control cells, without any obvious changes in the total levels of PKCδ and ERK. This result indicated the activating of PKCδ/ERK pathway by AAI (Figure 5A). Activation of AKT has been considered as one of the main signaling events in the survival of K562 cells. In the present studies, we found that activation of AKT was inhibited by AAI (250 nM) in a time-dependent manner, with no effect on the total AKT level. Moreover, our results also showed that AAI could time-dependently inhibit the activation of JAK/STAT3 pathway; AAI could inactivate both p-JAK and p-STAT3, which play vital roles in leukemogenesis and are attractive targets for therapeutic agents. As the downstream product of JAK/STAT3, survivin protein expression level decreased, correspondingly. However, p-STAT2/5 remained unchanged (Supplementary Materials, Figure S2). Similarly, with longer treatment period of 24 h, AAI also activated the PKCδ/ERK pathway, inactivated AKT, inhibited the activation of JAK/STAT3, and downregulated the expression level of survivin (Figure 5B). These results showed that AAI could modulate multiple signaling pathways related to leukemogenesis. 2.6. AAI and Ingenol Mebutate Bind PKCδ in a Similar Manner The binding modes of AAI and ingenol mebutate on PKCδ Cys2 domain (PDB code: 1PTR) [9] were very similar, with the cluster of hydroxyl groups oriented to the binding site and the cluster of methyl groups exposed to the solvent (Figure 6). Such orientation similarity between AAI and ingenol mebutate was not surprising in that the former only differentiates an acetyl group from the latter (Figure 6A,C). Despite such an orientation similarity, computational docking and molecular dynamics (MD) studies indicated that ingenol mebutate bound with the receptor more tightly and less solvent-exposed than AAI, and the acetyl group of AAI might blockade its further approaching to the binding pocket (Figure 6B,D). In addition, the hydroxyl group at ingenol mebutate formed two H-bonds with Leu23 and Thr12, whereas the acetyl group merely formed one H-bond with Thr12. The additional H-bond formed by ingenol mebutate might further strengthen its binding. To validate our observations from molecular docking, binding free energy calculation and decomposition were performed using the molecular mechanics poisson-boltzmann surface area (MM-PBSA) method [10]. As shown in Table 1, the G-free energy of ingenol mebutate was about 10 kcal/mol lower than that of AAI, supporting the above observations. However, our experimental studies demonstrated that the biological activity of AAI was comparable or even slightly better at low concentration than that of ingenol mebutate, which contradicted with the results from computational modeling. It was possible that the calculated free energy overestimated the experimental values, but it still could not explain the more favorable binding of ingenol mebutate than that of AAI observed in the modeling studies. Thus, our theoretical calculations supported that the slightly improved biological activity of AAI might have originated from its favorable membrane penetration ability rather than its improved binding to PKCδ. Indeed, acetylation of the hydroxyl ingenol mebutate increases its LogP value from 2.33 to 2.9, making it more facile to penetrate cell membranes. It is likely that the acetyl group of AAI could be deprotected by the acetylase to form ingenol mebutate in the cell. Another possible reason for the enhanced bioactivity of AAI is the increased stability of AAI raised from the inhibition of the acyl migration from position 3 to position 20 due to the acetylation at position 20. To further investigate the structure activity relationship between these two compounds, the PKCδ energy decomposition was performed using MM-PBSA. Energy decomposition indicated that the van der Waals interactions were the main driving force for the binding of AAI and ingenol mebutate, whereas the entropic component was not favorable for their binding (Table 1). In addition, the van der Waals contacts differentiated the binding affinity difference between AAI and ingenol mebutate, which suggested that improvement of the molecular surface fitting of ingenol mebutate to the binding pocket might strengthen its binding affinity to the receptor. Calculation of the energetic contribution for each of the residues of the binding site suggested that Met 9, Ser10, Pro11, Thr12, Leu20, Leu21, Trp22, Gly23, Leu24, and Qln27 were essential to the binding affinity of both AAI and ingenol mebutate (Figure 6E), and interactions with these residues should be considered for future compound modifications. 3. Discussion A number of studies have shown that ingenol derivatives, such as ingenol mebutate, exhibited potent anti-HIV [11] and anti-cancer activities [12]. Here, we reported a synthetic ingenol derivative, AAI, whose only difference to ingenol mebutate was the acetylation in position 20. Due to the chemical structural similarity to ingenol mebutate, AAI bound to the same site in PKCδ in very similar binding modes. The biological activity of AAI was similar or even slightly better at low concentration than that of ingenol mebutate. Our results from computational modeling suggested that the better biological activity of AAI might have originated from its increased hydrophobicity rather than its improved binding affinity to the target protein PKCδ. Another possible reason for the better bioactivity of AAI was the increased stability of AAI raised from the inhibition of the acyl migration due to the acetylation at position 20. A previous report has shown the cytotoxicity of AAI against melanoma, breast cancer, and colon cancer [8]. In the expanded cytotoxicity evaluations, we found, in addition to the solid cancer cells, including MCF-7/ADR, HCT-116 and H1975, AAI was also sensitive to the leukemia cells, such as K562, HL-60, and KT-1 cells. Although AAI inhibited the cellular proliferation, however, unlike most of other chemical agents, the inhibition on the cell proliferation was not in a concentration-dependent manner. This concentration independence of AAI was similar to its parent compound ingenol mebutate [13,14], and the possible reason for this could be partly due to the toxic effect, as this kind of compounds could induce both necrosis and plasma membrane disruption, in addition to the apoptosis [13]. In the cellular model, we found that AAI arrested K562 cells at G2/M phase and induced mitochondrial apoptosis in K562 cell line, which is Bcr-Abl promoted chronic myeloid leukemia. Activation of AKT is one of the major signaling events in the Bcr-Abl leukemogenesis [15] and contributes to the development of drug resistance [16]. Moreover, other signaling molecules also play crucial roles in initiating and development of leukemogenesis. For example, activation of the JAK/STAT3 pathway also contributes to the initiating of the disease [17], and therefore JAK/STAT3 has been considered as an attractive therapeutic target for developing therapeutic agents. To further understand the mechanical basis of AAI-induced apoptosis, we investigated the signaling pathways known to regulate cell proliferation and survival. As Bcr-Abl activation is believed to be the initiating molecular event in chronic myeloid leukemia, we performed the kinase inhibition assay against Bcr-Abl. The results showed that AAI (0–20 μM) revealed no effect against the activity of Bcr-Abl, suggesting AAI inhibiting the growth of K562 cells was not via the inhibition of Bcr-Abl. Although AAI was not as effective as the clinical Bcr-Abl inhibitors, AAI was possibly effective against a novel critical target(s) other than Bcr-Abl, and provided an alternate selection of the treatment of chronic myeloid leukemia. In addition to Bcr-Abl, PKCs also regulate several signaling pathways that are crucial to leukemic cellular malignant transformation [18], and PKCδ has a close relationship with proliferation and apoptosis [14]. The parent compound ingenol mebutate has been shown to induce apoptosis in leukaemic cell lines, an effect that requires the expression of protein PKCδ. Chronic activation of PKCδ/ERK in leukemic cells delivers a pro-apoptotic, rather than a proliferative or survival, signal [14]. In the present study, we found that, as ingenol mebutate did, AAI also revealed excellent repeatability of its activation of PKCδ/ERK. Additionally, AKT activation is necessary and sufficient to inhibit apoptosis and induce transformation. In addition to the interaction with PKCδ [19,20], ingenol mebutate has been reported to regulate the AKT activity directly, which is considered as a non-PKC target for ingenol mebutate [14]. We also observed significant inhibitory effects of AAI against the activation of AKT. Therefore, like ingenol mebutate, AAI could also regulate these two potential targets and induce apoptosis in K562 cells. Furthermore, in addition to the reported effect on PKCδ and AKT, we found that AAI could inactivate the JAK/STAT3 pathway, which might be a new activity of ingenol derivatives. However, p-STAT2/5 remained unchanged under our present experimental conditions (Supplementary Materials, Figure S2). Bcr-Abl activates JAK/STAT [21,22], and AAI selectively inhibited the STAT3 pathway, but not STAT2/5, indicating that the inhibition of the STAT3 pathway was not due to the inhibition on Bcr-Abl, which further confirmed that AAI had no effect on Bcr-Abl. However, whether the effect on JAK/STAT3 pathways is related to the known targets, including PKCδ and AKT, needs further investigation. Since AAI could regulate multiple signaling molecules as presented in this work, it will be interesting to perform the global arrays to illustrate the changes in signaling pathways after AAI treatment in future studies 4. Materials and Methods 4.1. Drugs and Reagents Ingenol mebutate was the product of Nanjing Spring and Autumn Biological Engineering Co., Ltd., Nanjing, China. Antibodies against survivin, AKT, p-AKT, ERK, p-ERK, p-PKCδ, PKCδ, p-JAK, JAK, p-STAT3, and STAT3, were purchased from Cell Signaling Technology, Beverly, MA, USA. Annexin V-FITC/PI apoptosis detection kit was provided by Nanjing KeyGEN BioTECH. Co., Ltd., Nanjing, China. Other reagents and kits were the products of Beyond, Nantong, China. 4.2. Gerneral Procedure for the Synthesis of 3-O-Angeloyl-20-O-acetyl ingenol Compound 2: Ingenol 1 (500 mg, 1.43 mmol) was dissolved in a solution of p-toluenesulfonic acid monohydrate (PTSA·H2O, 80 mg, 0.42 mmol) in acetone (1.5 mL). The solution was stirred at room temperature for 2 h (thin layer chromatography control). To this solution was added saturated aqueous solution of sodium hydrogen carbonate. Then, the obtained mixture was filtered, and the filtrate was concentrated in vacuo. The residue was taken up in ethyl acetate (10 mL). Finally, the mixture was concentrated and purified by chromatography (petroleum ether/ethyl acetate 5:1 to 3:1), giving 360 mg of the title compound 2. The total yield was 64%. Compound 3: To a solution of compound 2 and angelic anhydride in THF was added a solution of LHMDS (lithium hexamethyldisilazide) in THF (0.1 mol/L) at 15 °C. The solution was stirred at room temperature for 40 min. The reaction was quenched by saturated aqueous solution of NH4Cl and the mixture was extracted by ethyl acetate, and then the organic layer was washed with brine and dried by Na2SO4. The organic layer was finally concentrated and purified by chromatography (petroleum ether/ethyl acetate 10:1 to 5:1) to give product 3, as a white solid (115 mg, 95%). Compound 4: A solution of compound 3 (50 mg, 0.11 mmol) in 1% HCl (MeOH) was stirred at 25 °C for 1 h. The mixture was concentrated in vacuo at room temperature. The residue was suspended in water and extracted with ethyl acetate. The organic layer was washed with brine, dried by Na2SO4, and concentrated to give the crude product as a colorless solid (45 mg, 98%). Compound 5: To a solution of compound 4 (100 mg, 0.23 mmol) in pyridine was added Ac2O (25 mg, 0.24 mmol) at 10 °C and the reaction mixture was stirred at room temperature overnight. The mixture was extracted with ethyl acetate and the organic layer was washed by HCl (1 M), water and saturated aqueous solution of NaHCO3 and brine, dried by Na2SO4. The mixture was concentrated and purified by chromatography (petroleum ether/ethyl acetate 10:1 to 5:1) to give the product 5 as a white solid (90 mg, 82%). 4.3. Cell Lines and Cell Culture A549, HCT-116, HeLa, and MCF-7/ADR cell lines were obtained from the American Type Culture Collection (Manassas, VA, USA). K562, HL-60, KT-1, HIN-3T3, L-02, and H1975 cell lines were provided by the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). A549 cells were cultured in F-12K medium with 10% fetal bovine serum (FBS). HeLa, HIN-3T3, and HCT-116 cells were cultured in Dulbecco’s Modified Eagle’s Medium with 10% FBS. The others were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium with 10% FBS. 4.4. Cell Proliferation Inhibition Assay The effect on cell proliferation was evaluated by the MTT method. In general, cells were incubated with different concentrations of AAI or ingenol mebutate at indicated time. MTT solution (20 μL, 0.5 mg/mL) was added in the cell culture medium and incubated for another 4 h. Finally, the dye crystals were dissolved with dimethyl sulfoxide, and the absorbance was measured at 570 nm. The effect on the cell proliferation was expressed as inhibition rate. 4.5. Cell Cycle Distribution Assay The K562 cells were treated with certain concentration of AAI for indicated time. After AAI-treatment, the cells were collected, washed, and fixed with ice-cold 70% (v/v) ethanol at −20 °C for 24 h. The samples were resuspended and then stained with PI (50 μg/mL) containing RNase (10 μg/mL) for 20 min. Finally, the cell cycle distribution was analyzed with flow cytometry (Bection Dickinson, Waltham, MA, USA). 4.6. Apoptosis and Necrosis Assay by Annexin V-FITC/PI Double Staining The stimulation of apoptosis and necrosis by AAI was assayed by Annexin V-FITC/PI double staining kit. K562 ells (5 × 105) were treated with AAI (0–500 nM) for 18 h, and then harvested, washed, and resuspended in binding buffer. Double staining was started by adding Annexin V-FITC and PI, 5 μL, respectively, and incubating in the dark for 10 min. After staining, the samples were analyzed with flow cytometry (Bection Dickinson, Waltham, MA, USA). 4.7. MMP Assay MMP of K562 cells was detected according to the manufacture’s instructions in the kit (Beyond, Nantong, China). Briefly, K562 cells were incubated with different concentrations of AAI (0–500 nM) or different time periods. After the treatment, cells were stained by JC-1 for 20 min at 37 °C. MMP were analyzed by flow cytometry. JC-1 was excited at 488 nm. Emissions at 535 nm and 595 nm were used to quantify the mitochondria with JC-1 green monomers and JC-1 red aggregate fluorescence, respectively. Frequency plots were prepared for FL1 and FL2 to determine the percentage of mitochondria stained green and red. 4.8. Western Blotting Assay K562 cells were treated with AAI for the indicated time and concentrations, and then cells were lysed with RIPA buffer and total proteins were obtained. Protein samples were resolved on sodium dodecyl sulfate polyacrylamide gel electropheresis (SDS-PAGE, 10%–15%), and transferred to nitrocellulose membranes. The proteins were probed with primary antibodies, and followed by incubation with secondary antibodies. Finally, the bands were detected by enhanced chemiluminescence. 4.9. Computational Docking Molecular docking was performed using molecular operating environment software (MOE, chemical computing group, Quebec City, QC, Canada) with AMBER12:EHT force field [23]. AAI and ingenol mebutate were drawn using the molecular scratch module in MOE, and then they were minimized using 10,000 steps of steepest minimization. For the docking studies, the crystal structure of the cys2 activator-binding domain of PKCδ in complex with phorbol ester (PDB code: 1PTR) was obtained from the protein databank (http://www.rcsb.org). The induced fit docking approach was applied for consideration of the flexibility of the side chains of the residues at the binding site. The produced conformation with the best score was selected for the analysis. 4.10. MD Simulations MD simulations were carried out on PKCδ in complex with AAI and ingenol mebutate, respectively, using the Amber 12 package (University of California, Oakland, SF, USA). General AMBER force field and FF03 force field were employed for the ligand and the receptor, respectively. Prior to the MD simulations, the complex was solvated into an octagon box of TIP3P water molecules and neutralized using Na+. Then, it was minimized to remove unfavourable van der Waals interactions. The minimization consisted of two steps. First, only the water molecules and ions were minimized with 1000 steps of steepest descent minimization and 1000 steps of conjugate gradient minimization. Second, the restraint on the solute was removed and the whole system was relaxed with 3000 steps of steepest descent minimization and 3000 steps of conjugate gradient minimization. The cutoff of the non-bonded interactions was set to 12 Å for the energy minimization process. After minimization, MD was performed. First, the solute was restrained and the whole system was gradually heated from 10 to 300 K in 100 ps in the NVT (keep the number of molecules, volume and temperature constant) ensemble. Then the system was equilibrated in the NPT ensemble where the temperature and pressure were kept at 300 K and 1 atm, respectively. Finally, in the production process, the whole system was relaxed except for two cluster of residues bound with Zn+2 and a 30-ns molecular dynamics process was carried out. The two Zn2+ were far from the binding site and covalently bound with two clusters of residues. To simulate the covalent binding effect, we applied for a soft restraint on the two clusters of residues. For all MD steps, we set the time step to 0.002 ps, and the particle mesh Ewald (PME) method [24] was applied to deal with long-range electrostatic interactions; the lengths of the bonds involving hydrogen atoms were fixed with the SHAKE algorithm, as described previously [25]. 4.11. Binding Energy Calculations Binding free energy calculation and decomposition were performed using the MM-PBSA script in AMBER 12, as described previously [26]. Parameter setup was the same as our previous studies [26]. 4.12. Data Analysis One-way ANOVA with Tukey’s post hoc test was used for statistical analysis of the data, and values were expressed as mean ± SD. Differences of p < 0.05 were considered statistically significant. 5. Conclusions In summary, the present study reported that ingenol mebutate acetylated derivative AAI displayed more potent in vitro cytotoxicity than ingenol mebutate at very low concentrations. AAI induced G2/M phase arrest, exhibiting potent apoptotic and necrotic activity in K562 cells. The results of molecular mechanism studies showed that AAI stimulated the activation of PKCδ and ERK, inhibited the activation of AKT, and inhibited JAK/STAT3 signaling pathways. These studies revealed that AAI had cytotoxic effects in different cell lines, and could be developed as an alternate therapeutic agent for solid cancer and leukemia. Acknowledgments This work was supported by NSFC-Shandong Joint Fund (No. U1406402), Natural Science Foundation of China (No. 41576187), Natural Science Foundation of the Shandong Province (No. ZR2015HM010), Public Science and Technology Research Funds Projects of Ocean (No. 201405015), Science and Technology Planning Project of Shandong Province (No. 2014GHY115003), Major Projects of Independent Innovation Achievements Transformation in Shandong Province (No. 2014ZZCX06202), and Qingdao Entrepreneurship & Innovation Pioneers Program (No. 15-10-3-15-(44)-zch). The authors would also thank Wei Zhang in Southern Research Institute, USA, for his critical reading and language revision of the manuscript. Supplementary Materials Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1348/s1. Click here for additional data file. Author Contributions Ming Liu designed the study concept and prepared the manuscript; Xingzeng Zhao and Fei Liu synthesized the compounds; Ming Liu, Weiyi Zhang, Mei Han, and Jing Wu performed the biological experimental studies; Fangling Chen and Rilei Yu performed the MD analysis and analyzed the data; Jinlai Miao helped acquire data and statistical analysis, and revised the article critically for intellectual content. Conflicts of Interest The authors declare no conflict of interest. Figures, Scheme and Table Figure 1 Chemical structures of ingenol mebutate (A) and 3-O-angeloyl-20-O-acetyl ingenol (B). ijms-17-01348-sch001_Scheme 1Scheme 1 Synthesis of 3-O-angeloyl-20-O-acetyl ingenol. (a) PTSA·H2O, acetone; (b) angelic anhydride, lithium hexamethyldisilazide (LHMDS), tetrahydrofuran (THF); (c) 1% HCl, MeOH; and (d) Ac2O, pyridine. Figure 2 (A) Cytotoxicity of AAI assayed by 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) methods. Tested cells in 96-well plates were treated with (0–25 μM) AAI for 72 h, and then the viability was evaluated by MTT assay, as described in the Materials and Methods section; (B) AAI decreases the population of K562 cells in a comparable potency of ingenol mebutate. Cells in 96-well plates were treated with 1 μM AAI or ingenol mebutate for 72 h, and then the cell density was photographed (×200); (C) growth inhibition of AAI against K562 cells. Cells were treated with indicated concentrations of AAI for 36, 48, and 72 h, and then the viability was evaluated by MTT assay; and (D) AAI shows stronger growth inhibition than ingenol mebutate at low concentrations. Cells were treated with indicated concentrations of AAI or ingenol mebutate for 72 h, and then cell viability was evaluated by MTT assay. Figure 3 AAI induces G2/M phase arrest, apoptosis, and necrosis in K562 cells. (A) AAI time-dependently arrests K562 cells at G2/M phase. K562 cells were treated with 250 nM AAI for 0, 2, 4 and 12 h. Then, cells were collected, fixed, digestion with RNase A, and stained by PI. The DNA contents of the cells were determined with the Aria FACS flow cytometry system; red: G0/G1 or G2/M phase, blue: S phase; (B) K562 cells were treated with AAI (0–25 nM) for 24 h, and then the cell cycle distribution was analyzed; red: G0/G1 or G2/M phase, blue: S phase; (C) histograms show the percentage of cells distributed in G2/M phase after treated with 250 nM AAI for 0, 2, 4 and 12 h; * p < 0.05, ** p < 0.01 versus control; (D) histograms show the percentage of cells distributed in G2/M phase after treated with AAI (0–25 nM) for 24 h; * p < 0.05, ** p < 0.01 versus control; (E) AAI induces both apoptosis and necrosis in K562 cells. K562 cells were untreated or treated with 31.25, 62.5, 125, 250 and 500 nM AAI for 18 h, and then the cells were double-stained with Annexin V-FITC/PI and analyzed by flow cytometry. The percentage of Annexin V-FITC positive cells and/or PI positive cells is indicated; and (F) histogram shows the percentage of necrosis and apoptosis in K562 cells induced by AAI. Figure 4 AAI induces the loss of MMP in K562 cells. (A) Treatment with AAI (0–500 nM) for 18 h disrupts the MMP in K562 cells. The scatter plot of the flow cytometry analysis shows the distribution of JC-1 aggregates and JC-1 monomers; and (B) AAI at 250 nM time-dependently induces the loss of MMP in K562 cells. Figure 5 AAI modulates multiple signaling pathways in K562 cells. (A) AAI induces time-dependent activation of PKC and ERK, inactivation of AKT, and inhibition on phosphorylation of JAK and STAT3, and decreases the expression level of surviving; and (B) different concentrations (0–12.5 μM) of AAI treated for 24 h activate PKCδ and ERK, inactivate AKT, inhibit JAK/STAT3 pathway, and decrease the expression level of survivin. Figure 6 Binding modes of AAI (orange, A) and ingenol mebutate (green, C) to PKCδ Cys2 domain. The binding site of the receptor is shown using transparent surface area, and the H-bond is represented using dashed line; the blue spot is used to show the solvent exposure of the atoms for AAI (B) and ingenol mebutate (D), light green circles: hydrophobic amino acids; magenta circles: hydrophilic amino acids; blue circles: ligand exposure; and the darker color at AAI suggests that these atoms are more solvent exposed; and (E) Energetic contribution of residues at the binding site of PKCδ Cys2 domain to the enthalpy change of AAI (blue) and ingenol mebutate (red). ijms-17-01348-t001_Table 1Table 1 Decomposition of free energy of AAI and ingenol mebutate to PKCδ Cys2 domain. Ligand Free Energy (kcal/mol) VDW EEL EGB ESURF TΔS ΔG AAI −30.69 (0.39) −15.80 (0.68) 25.14 (0.41) −3.95 (0.04) −20.39 (2.86) −4.91 (2.54) ingenol mebutate −40.51 (0.34) −19.20 (0.48) 27.91 (0.27) −4.46 (0.02) −20.31 (2.71) −15.95 (2.31) VDW, van der Waals contribution from MM; EEL, electrostatic energy as calculated by the MM force field; EGB, the electrostatic contribution to the solvation free energy calculated by GB; ESURF, nonpolar contribution to the solvation free energy calculated by an empirical model. ==== Refs References 1. Aditya S. Gupta S. Ingenol mebutate: A novel topical drug for actinic keratosis Indian Dermatol. Online J. 2013 4 246 249 10.4103/2229-5178.115538 23984250 2. Tzogani K. Nagercoil N. Hemmings R.J. Samir B. Gardette J. Demolis P. Salmonson T. Pignatti F. The European Medicines Agency approval of ingenol mebutate (Picato) for the cutaneous treatment of non-hyperkeratotic, non-hypertrophic actinic keratosis in adults: Summary of the scientific assessment of the Committee for Medicinal Products for Human Use (CHMP) Eur. J. Dermatol. 2014 24 457 463 25115145 3. Hampson P. Wang K. Milverton L. Ersvaer E. Bruserud O. Lord J.M. Kinetics of ERK1/2 activation determine sensitivity of acute myeloid leukaemia cells to the induction of apoptosis by the novel small molecule ingenol 3-angelate (PEP005) Apoptosis 2010 15 946 955 10.1007/s10495-010-0507-7 20467815 4. Ozpolat B. Akar U. Tekedereli I. Alpay S.N. Barria M. Gezgen B. zhang N. Coombes K. Kornblau S. Lopez-Berestein G. PKCδ regulates translation initiation through PKR and eIF2α in response to retinoic acid in acute myeloid leukemia cells Leuk. Res. Treat. 2012 2012 482905 5. Clamp A. Jayson G.C. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081349ijms-17-01349ArticleEffects of Concentrations on the Transdermal Permeation Enhancing Mechanisms of Borneol: A Coarse-Grained Molecular Dynamics Simulation on Mixed-Bilayer Membranes Dai Xingxing 123†Yin Qianqian 123†Wan Guang 4Wang Ran 4Shi Xinyuan 123*Qiao Yanjiang 123*Zhang Ge Academic Editor1 School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, No. 6 of Zhonghuan South Road, Wangjing, Chaoyang District, Beijing 100102, China; jolly_dai@126.com (X.D.); zmnh08830@126.com (Q.Y.)2 Key Laboratory of TCM-information Engineer of State Administration of TCM, No. 6 of Zhonghuan South Road, Wangjing, Chaoyang District, Beijing 100102, China3 Beijing Key Laboratory of Manufacturing Process Control and Quality Evaluation of Chinese Medicine, No. 6 of Zhonghuan South Road, Wangjing, Chaoyang District, Beijing 100102, China4 School of Traditional Chinese Medicine, Capital Medical University, No. 10 of Xitoutiao Outside Youanmen, Fengtai District, Beijing 100069, China; wungung91@163.com (G.W.); wangran2017@sina.com (R.W.)* Correspondence: shixinyuan01@163.com (X.S.); yjqiao@263.net (Y.Q.); Tel.: +86-10-8473-8621 (X.S. & Y.Q.)† These authors contributed equally to this work. 18 8 2016 8 2016 17 8 134921 6 2016 27 7 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Borneol is a natural permeation enhancer that is effective in drugs used in traditional clinical practices as well as in modern scientific research. However, its molecular mechanism is not fully understood. In this study, a mixed coarse-grained model of stratum corneum (SC) lipid bilayer comprised of Ceramide-N-sphingosine (CER NS) 24:0, cholesterol (CHOL) and free fatty acids (FFA) 24:0 (2:2:1) was used to examine the permeation enhancing mechanism of borneol on the model drug osthole. We found two different mechanisms that were dependent on concentrations levels of borneol. At low concentrations, the lipid system maintained a bilayer structure. The addition of borneol made the lipid bilayer loosen and improved drug permeation. The “pull” effect of borneol also improved drug permeation. However, for a strongly hydrophobic drug like osthole, the permeation enhancement of borneol was limited. When most borneol molecules permeated into bilayers and were located at the hydrophobic tail region, the spatial competition effect inhibited drug molecules from permeating deeper into the bilayer. At high concentrations, borneol led to the formation of water pores and long-lived reversed micelles. This improved the permeation of osthole and possibly other hydrophobic or hydrophilic drugs through the SC. Our simulation results were supported by Franz diffusion tests and transmission electron microscope (TEM) experiments. molecular dynamics simulationcoarse-grained force fieldtransdermal drug delivery systempermeation enhancer ==== Body 1. Introduction Transdermal drug delivery systems (TDDS) are known for their advantages of bypassing first-pass liver metabolism, gastrointestinal irritation, improved higher bioavailability, better patient compliance and reduced side effects [1,2]. A common problem with TDDS design is the transport barrier of skin stratum corneum (SC). To solve this problem, numerous techniques have been used, such as the use of permeation enhancers (PEs) [3,4,5,6]. Essential oils (or volatile oils) are a type of natural permeation enhancers that can effectively promote the permeation of both hydrophilic and hydrophobic drugs [7,8,9]. Essential oils are biocompatible with commonly-used chemical synthetics PEs because they are safe, non-toxic, pharmacologically inert, non-irritating, hypo-allergenic and have a wide range of pharmacological functions, such as anti-inflammatory and anticancer applications [10,11,12,13]. Natural borneol (BO) is a monoterpenoid component extracted from the essential oil of Cinnamomum camphora (L.) Presl. Prior pharmacological studies showed that borneol has anti-inflammatory, antinociceptive and antibacterial uses as well as other biological activities [14,15,16]. According to Traditional Chinese Medicinal (TCM) theory, borneol is not only used as a drug, but also as an excipient for other drugs (referred to as “YAO FU HE YI” in TCM theory). Permeation enhancement is an important mechanism for the excipient effect of borneol. Many drugs, such as berberine, geniposide, ribavirin, tobramycin, ligustrazine, and loratadine [17,18,19,20,21], have significant improvements in permeation when used with borneol. However, most prior studies focused on the permeation enhancing behavior of borneol in in vitro tests, but did not examine the involved molecular mechanisms. Interactions with lipid bilayers are the most important mechanisms for the permeation enhancing effects of borneol [22]. To fully understand these mechanisms, our team used the coarse-grained molecular dynamic (CG-MD) simulation method to study the interactions of borneol on dipalmitoyl phosphatidylcholine (DPPC) phospholipid membranes, since borneol can enhance drug permeation not only through SC but also through mucosa and other bio-membranes. The main idea of CG-MD is to coarse-grain the familiar atomistic representation of the molecule to gain orders of magnitude in both length and time scale relative to traditional atomistic scale simulation [23]. The simulation is based on Newton’s equation of motion and is commonly used to study biological systems [24,25,26,27,28]. It also provides insight into the thermodynamics and dynamics properties of mesoscale substances (1–1000 nm). Similar to atomistic simulations, there are interactions between coarse-grained particles, and these interactions are often called coarse-grained force fields. The Martini force field, developed by Marrink and coworkers in 2007, is a special coarse-grained force field [29]. Because of its portability and expansibility, Martini force field has been widely used in many studies of biomolecules, such as lipids, polymers, proteins, carbohydrates, and so on [29,30,31,32,33,34]. In our previous study, the CG-MD simulation was useful to show the influence of borneol on DPPC membranes and explained the permeation enhancing mechanisms of borneol. However, the lipids in SC are different from those in DPPC membranes. SC lipids are comprised of ceramides (CER), cholesterol (CHOL) and free fatty acids (FFA), and organized in lamellar layers around the corneocytes [35]. This structure is important for SC lipid function. Ceramides are the major components of SC. Over 300 ceramides have been identified in SC with fatty acid lengths varying from 16 to 34 carbons. Among them, Ceramide-N-sphingosine (CER NS) is the most abundant species in human SC, and the most commonly studied ceramide in lipid model of SC [35,36,37]. Cholesterol is also an essential component of the SC. Its presence reduces the ordering of ceramide tails and simultaneously increases lipid fluidity. As a result, the range of phase transition temperatures (Tm) is enlarged. Free fatty acids, however, can increase the density and compact the ordering of the hydrophobic lipid tails [38,39]. In order to get more accurate results, we used a mixed CG model of SC lipid bilayer comprised of CER NS 24:0, CHOL and FFA 24:0 (2:2:1) [40] (the properties of this molar proportion lipid layer have been validated by Das and coworkers in 2009 [41,42]) to measure the enhanced transdermal permeability effects. We conducted several CG-MD simulations based on Martini force field to investigate the interaction of borneol with the SC lipid model we built before [40]. Osthole (OST), an antibacterial drug, is often used with borneol in traditional Chinese transdermal preparations to treat surgical diseases such as gynecologic inflammation, tinea pedis, and psoriasis [43]. It is known that the permeability of osthole can be enhanced when used with borneol [44]. In this work, osthole was used as the model drug to study the enhanced transdermal permeation effects and mechanism of borneol by both theoretical and experimental methods. The simulation results were verified by Franz diffusion tests and transmission electron microscope (TEM) experiments. 2. Results and Discussion 2.1. Interaction of Borneol with SC Lipid Bilayer Figure 1(a1–a4) shows the CG simulation results of bilayer with different concentrations of borneol. We found that the lipid bilayer showed different morphological features at different concentrations of borneol, which indicated the different permeation enhancing mechanisms. Below the concentration of 10%, the lipid systems maintained a whole bilayer structure with most of the borneol molecules located at the hydrophobic lipid tail area (Figure 1(a1–a3)). The calculation of average area per lipid (APL), bilayer thickness and order parameters of CER NS (Figure 1b–d) showed that with the increasing of borneol concentration, there was a correlation between increased APL and decreased bilayer thickness and order. The borneol molecules permeated into lipid bilayer and occupied the intermolecular or intramolecular space of lipid molecules, which resulted in the increased APL. As such, borneol disturbed the orderly arrangement of lipid tails and made them easier to bend, which caused the bilayer thickness to decrease (Figures S1–S3 in Supplementary Materials). This much loosened structure increased the permeation of agents through the bilayer. By using fluorescence recovery after photo-bleaching technology (FRAP), Fu [45] demonstrated that borneol improved the fluidity of lipid membranes and that the fluidity increased as the borneol concentration increased. Fu’s finding is consistent with our simulation results. At high concentrations (above 15%), the bilayer structure was dramatically altered (Figure 1(a4)). The order of lipids dropped significantly (Figure 1d). The borneol molecules extracted the lipids from the bilayer and formed water pores and reversed micelles. Their detailed structures are shown in Figure 2. By using attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) to monitor the borneol-induced alteration in molecular organization of SC lipids, Yi Qifeng found borneol could perturb the structure of SC lipid alkyl chains, and extract part of SC lipids, resulting in the alteration in the skin permeability barrier [22]. These results are consistent with our findings. It has been reported that water pores and reversed micelles can contribute to the permeation enhancing effect of drugs, especially hydrophilic agents and ions [46]. As for hydrophobic agents like osthole, the effect of these structures will be discussed later. The molecular trajectory show that along with the permeation of borneol, some lipid molecules in bilayer spontaneously experienced transmembrane flip from one leaflet to another (flip-flop). A typical flip-flop process of a free fatty acid molecule is shown in Figure 3. Lipid flip-flop is a common and important phenomenon involved in many cellular properties and functions, such as membrane mechanical stability [47,48]. Prior studies found that one of the important mechanisms of lipid flip-flop in protein-free membranes was associated with the water pore. Environmental disturbances on the membrane, such as transmembrane ion concentration gradients, transmembrane tension changes and nanoparticle diffusion can initiate the formation of a water pore. In addition to lipids, cholesterol also experiences transmembrane flip in the membrane. MD studies by Bennett [47] revealed that stronger affinities between cholesterol and surrounding lipids lead to higher energy for cholesterol to transmembrane flip. There may be two mechanisms for the lipid flip-flop caused by the addition of borneol. First, the permeation of borneol molecules destroyed the hydrogen bond network of the bilayer system, which weakens the interaction between free fatty acids and other constituent molecules and allows the lipid molecules to easily flip; Second, the water pore caused by borneol mediated the flip. The flip-flop may reduce the mechanical strength of the bilayer membrane that further improves the permeability of drugs. 2.2. Permeation Enhancing Effect of BO on OST Osthole is the active ingredient derived from Cnidium monnieri (L.) Cuss and has a broad-spectrum antimicrobial effect. It is often used in traditional Chinese topical preparations to treat bacterial diseases. Prior studies reported that borneol improved the permeation of osthole through skin. The enhanced effect is better than the commonly used chemical synthesis PE azone [44,49]. In our study, we simulated the permeation behavior of osthole with and without borneol in lipid bilayer to study the permeation enhancing effect of borneol on osthole. The concentration of osthole was fixed at 10%. 2.2.1. CG-MD Simulation Studies Figure 4 shows the morphology evolution of lipid bilayer systems with time. The osthole concentration was fixed at 10% to study the borneol concentration effect on the permeation behavior of osthole. When the concentration was under 10%, the lipids system still maintained a whole bilayer structure with a slight fluctuation. The comparison of systems with and without 5% borneol concentrations revealed that the borneol molecules had higher permeability than osthole because they have strong interaction with lipids, especially on the hydrophilic layer. After 10 ns of MD simulation, almost all of the borneol molecules permeated into the bilayer while most of the osthole molecules were still located at lipid–water interface (Figure 5). To evaluate the permeability of osthole with borneol, the density distribution of osthole along the vertical direction of bilayer (the Z direction) in systems is shown in Figure 6. The results indicated that, with and without the borneol, osthole could gradually diffuse into the lipid layer over time. During the first 100 ns, the osthole density in the borneol containing system was higher, which indicated faster diffusion. However, it was also higher in the borneol excluding systems after 100 ns. Therefore, the borneol could inhibit the permeation of osthole after 100 ns. However, no data exist regarding the inhibition effects of borneol. To explain this phenomenon, we studied the whole dynamic trajectory (Figure 6 and Figure 8). Osthole had strong hydrophobicity (logP = 3.8). Therefore, the hydrophilic head layers were the main barrier for the permeation of osthole. During the first 100 ns, borneol interacted with lipid layers preferentially because of its stronger interaction with lipid molecules. They acted on the polar head groups of lipids by their polar oxygen groups and destroyed the hydrogen bond network of head groups and weakened the bilayer. Then, the borneol molecules pulled the osthole molecules and permeated into the tail region through hydrophobic interactions, as illustrated in Figure 8. That may be the reason for the faster diffusion of osthole during the first 100 ns. This “pull” effect was previously reported as a possible permeation enhancing mechanism for 1,8-cineole [50], which is another natural terpenoid PE similar to borneol. When the system reached equilibrium (after 100 ns), the ordering of lipids somewhat recovered. This indicated that the lipid disturbance caused by borneol at low concentrations was reversible. Nearly all of the borneol molecules permeated into the bilayer and were located at the hydrophobic tail region. The “pull” effect disappeared. However, the hydrophobic tail region was also the main region for osthole to locate. The occupation of borneol made osthole difficult to permeate into bilayer further, and therefore it caused the inhibition effect. Since the inhibition effect was not previously studied, some in vitro permeation tests were conducted to verify the simulation results. When the concentration of borneol was above 10%, the lipid bilayer structures were dramatically perturbed. Water pores and reversed micelles were found formed at this concentration, similar to the structures mentioned in Section 3.1. As shown in Figure 7, borneol extracted part of SC lipids and induced some lipids to drill into the bilayer, forming reversed micelles. This facilitated the contact of osthole molecules with the hydrophobic alkyl chains of lipids. The disordered condition of the hydrophobic region was further conducive to the diffusion of osthole. Besides, osthole molecules were found that they could also permeate through the water pores. These mechanisms jointly resulted in the permeation enhancement of osthole. It is worth noting that, the concentration (10%) of water pore and the reversed micelle in the system with osthole was lower than that (15%) in the system without osthole. This indicated that the addition of osthole could reduce the effective concentration of borneol. Therefore, when evaluating the dosage of PEs, the effects of drugs should be considered. 2.2.2. In Vitro Permeation Studies The in vitro permeation data of osthole under different borneol concentrations obtained from Franz diffusion experiments is graphed as a function of cumulative amount Qn vs. time in Figure 10. The assessment parameters calculated from the Qn-t equations are shown in Table 1. We were pleased to find that borneol had two different effects on the permeation of osthole. At low concentrations (0.09%~0.54%), the Qn of osthole with borneol was lower than concentrations without borneol at all time points. The PR and ER also indicated that borneol inhibited the permeation of osthole through SC and that the inhibition rate increased as the concentration increased. The same decrease in the permeability coefficient Kp in in vitro experiments and in the diffusion coefficient D in simulations was in accord with our previous calculations (Figure 8). When the concentration was above 0.54%, the Qn of osthole sharply increased. The PR and ER value indicated that borneol significantly enhanced the permeation of osthole through SC. We searched relevant literature and found that most reported effective enhancing concentrations of borneol were above 0.5% [44,51,52,53]. This may just be the results of the bilayer morphology changing, as illustrated in the simulations. Severe lipid disturbance coupled with the formation of water pores and reverse micelles destroyed the reservoir function of SC and caused it to weaken so that the osthole could easily permeate. A significant increase the in diffusion rate of osthole at high concentrations was observed in both the in vitro experiments and in the simulations (Figure 9). The inflection point indicted the bilayer morphology changing concentration. The concentrations in the in vitro experiments did not correspond with the concentrations in the simulations because we ignored non-essential details in the CG-MG simulations so that relevant details could be determined while maintaining computer efficiency. Thus, the concentrations in simulations were slightly higher than the ones in vitro, but the variation tendency was the same. 2.2.3. TEM Studies TEM studies were performed to further assess the effects of borneol on the permeation of osthole. As shown in Figure 10a, in the absence of borneol, the SC exhibited the tightly arranged corneocytes with a surrounding packed lamellar lipid layers. After treatment with 0.54% borneol for 24 h, the SC became irregular. The turbulence and ambiguous lipid layers images are shown in Figure 10b. Focal dilutions occurred within the intercellular space, since the permeation of borneol into hydrophobic tail region disordered the arrangement of lipid molecules. However, when the concentrations increased to 1.02%, the lipid layers of stratum corneum were completely disordered and separated (Figure 10c). That may be the results of the water pores formed by borneol stimulated the permeation of solution containing osthole. In conclusion, the TEM studies verified our theory that intercellular lamellar bilayer turbulence and water pore formation induced by borneol enhanced the permeability of osthole. 3. Materials and Methods 3.1. Simulation Method Figure 11 shows a typical CG-MD simulation process in this study. All simulation systems were built using the Packmol package [54] and the figures depicting molecules were generated by Visual Molecular Dynamics (VMD) [55]. The simulations were conducted using the GROMACS software package (version 4.6.3) [56]. 3.1.1. CG Models The CG models and their force field parameters used in this simulation were based on the Martini force field [57]. The Martini model is based on a four-to-one mapping (on average four heavy atoms are represented by a single interaction center), and a two/three-to-one mapping method is used for ring structures. Our simulation systems involved six molecules: CER NS (24:0), CHOL, FFA (24:0), BO, OST and solvent water (W). Their chemical structures and CG mappings are shown in Figure 12a and their interaction parameters are shown in Table S1 in the Supplementary Materials. Here, the CG models of borneol and osthole are new models developed by our team. The validation of these CG models is also shown in Section S2 in the Supplementary Materials. 3.1.2. Initial Bilayer Structures An initial mixed-bilayer membrane composed of CER NS (252 lipids), CHOL (252 lipids) and FFA (126 lipids) in a molar ratio of 2:2:1 was built in a box of 15 × 15 × 10 nm3. Coarse-grained water molecules (W) were filled into the box. The system energy was minimized using the method of steepest descent to remove the bad contacts between molecules. The minimized structure was equilibrated for 100 ns at 310 K. The structural properties of this mixed-bilayer system are shown in Section S3 of the Supplementary Materials. This equilibrated structure was further used as a starting structure to study the permeation behavior of OST at different concentrations of BO. The bilayer systems with different concentrations of BO and OST in water were also built using the Packmol package (Figure 12b). 3.1.3. Simulation Details All simulations were conducted in the NVT ensemble. The simulation temperature was set at 310 K by using the Berendsen temperature coupling with a time constant of 1.0 ps. The pressure was controlled by the Berendsen barostat and semi-isotropic pressure coupling with a constant time of 3.0 ps and compressibility of 4.5 × 10−4/bar. The method for both electrostatics and Van der Waals had the cut-off length of 1.2 nm. The time step was 20 fs and the total simulation time was 400 ns, which was sufficient for the simulation systems to reach equilibrium (see Section S4 in Supplementary Materials). 3.2. Verification Experiments 3.2.1. Materials Borneol and osthole (purity > 98%) were purchased from National Institutes for Food and Drug Control (Beijing, China) and 2.5% of glutaraldehyde was provided by Biotopped Life Science (Beijing, China). Methanol and acetonitrile of HPLC grade were supplied by Thermo Fisher Scientific (Beijing China). All other reagents were readily available from various commercial sources at analytical grade. 3.2.2. In Vitro Permeation Studies Preparation of Osthole Solutions with Different Concentrations of Borneol Because osthole has poor aqueous solubility, so 80% propanediol, which does not influence SC [55,56,57,58], was used to dissolve the osthole in the in vitro experiments. The solution was placed in an ultrasonic cleaner for 15 min followed by another equilibrium process for 24 h at 35 °C. The solution was filtered through a 0.45 μm millipore filter, and the final concentration of osthole was 0.1%. The osthole solution was then used as solvent to prepare different concentrations of borneol solutions. The concentrations of borneol were 0.09%, 0.31%, 0.54%, 0.73% and 1.02%. Preparation of Skin SPF male Sprague-Dawley rats weighing 190–210 g were purchased from Sibeifu Laboratory Animals Co., Ltd. (Beijing, China). The rats were euthanized after excessive ethyl ether anesthesia. The abdominal skin was excised after the hair was carefully trimmed. The subcutaneous fat and connective tissue were removed. The skin samples were washed with ultrapure water and a 0.9% sodium chloride solution and then equilibrated at 35 °C for 1 h in a receptor medium (80% propanediol) in Franz diffusion cells. All experiments on the animals were conducted in conformity with institutional guidelines for the care and use of laboratory animals in Beijing University of Chinese Medicine, Beijing, China. Skin Permeation Franz diffusion cells with an effective diffusion area of 0.785 cm2 and a receptor volume of 10 mL were used to perform the in vitro skin permeation studies. One-milliliter osthole solutions with different concentrations of borneol were added to corresponding donor chambers. The receptor chambers were filled with 80% propanediol as receptor medium which was maintained at 35 ± 0.5 °C with a magnetic stirrer at 300 rpm. A 1 mL receptor medium was sampled at predetermined time intervals (0, 2, 4, 6, 8, 10, 12, and 24 h) and then the same volume of pure medium was immediately added to the receptor chamber. All solution samples were filtered through a 0.45 μm millipore filter and stored at 4 °C for HPLC later analysis. HPLC Analysis The quantitative determination of osthole was measured with an HPLC system (Agilent 1100, Agilent, Inc., Santa Clara, CA, USA) using acetonitrile–water (65:35 v/v) as mobile phase at a flow rate of 1.0 mL/min. The injection volume was 10 μL. A Waters Xbridge C18 column (250 mm × 4.6 mm, 5 μm, Waters, Inc., Hong Kong, China) was used. The UV detector wavelength was set at 322 nm and the column temperature was maintained at 35 °C. Important Assessment Parameters The main parameters used in this paper to assess the permeation enhancing effect of borneol were: the cumulative amount Qn (μg/cm2), the permeability constant J (μg/cm2·h), the permeability coefficient Kp (cm/h), the enhancement ratio ER and the permeation ratio PR. The quantity of drugs that permeated through SC is presented as cumulative amount Qn (μg/cm2) and is calculated using the following formula: (1) Qn=(Cn × Vr+ ∑i=1n−1Ci × Vi)A where Cn is the drug concentration of the receptor medium at each sampling time, Ci is the drug concentration at ith sampling point, Vr and Vi were the volumes of receptor solutions and samplings, respectively, and A was the effective diffusion area of skin. The zero-order permeating kinetics equation (Q-t) is obtained by regressing the cumulative amount on time: (2) Q=Jt+B where the slope J (μg/cm2·h) is the permeability constant. The permeability coefficient of drugs is related to permeability coefficient Kp (cm/h) using the following formula: (3) Kp=J/C0 where C0 (μg/ mL) is the initial concentration of drug. The overall potency of PE is expressed as enhancement ratio (ER), a ratio of the Kp value before and after enhancer treatment. (4) ER=Kpe/Kp where Kpe is the Kp value after treatment. The enhancing effect of PE on drugs partitioning into the SC is described as permeation ratio (PR) and is calculated by: (5) PR=Qn × AC0 × Vd×100% where Vd is the donor volume. Transmission Electron Microscope (TEM) Studies Twenty-four hours after treatment, the skin samples were fixed in 2.5% glutaraldehyde. Before the TEM study, the samples were washed with phosphate buffer (pH 7.2) and fixed in 1% OsO4. The samples then were dehydrated in a graded series of acetone, embedded in a low-viscosity epon-epoxy mixture (provided by Beijing institute of traditional Chinese medicine) and sectioned. Thin sections were double stained with uranyl acetate and lead citrate and examined on a transmission electron microscope (JEOL JEM-1230, Tokyo, Japan) operated at an acceleration voltage of 80 kV. 4. Conclusions Borneol is a natural permeation enhancer that is effective in drugs used in traditional clinical practices as well as in modern scientific research. However, its molecular mechanism is not fully understood. In this study, we discovered two different concentration dependent mechanisms of permeation that were enhanced by borneol using CG-MD simulations. At low concentrations, the lipid system maintained a bilayer structure. The addition of borneol made the lipid bilayer loosen enough for drug permeation. The “pull” effect of borneol also improved the permeation of drugs. However, for strongly hydrophobic drugs like osthole, permeation enhancement of borneol was limited. When borneol molecules permeated into the bilayer and were located at the hydrophobic tail region, the spatial competition effect inhibited drug molecules further into the bilayer. The results of Franz diffusion tests verified the permeation inhibition effect of borneol at low concentration (0%–0.54%). At high concentrations, borneol led to the formation of water pores and long-lived reversed micelles. This improved the permeation of osthole and possibly other hydrophobic or hydrophilic drugs through the SC. The results of Franz diffusion tests supported the permeation enhancing effect of borneol on osthole at high concentration (>0.054%). The TEM experiments further verified the lipid disturbance and pore-mediated pathway may be the permeation enhancing mechanism, as illustrated in our simulation experiments. To date, borneol has been reported as a permeation enhancer for both hydrophobic and hydrophilic drugs, especially the former. Lipid disturbance and pore-mediated pathway were considered as the most important mechanisms as verified in our study. However, our findings were the first to recognize the “pull” effect mechanism as demonstrated in our simulations. Our study not only provided a detailed explanation of the molecular mechanisms on how the borneol enhanced the permeation of drugs like osthole, but also recommended that the effective concentration of borneol should be above 0.54%. In addition, the simulation method developed in this paper can be used in future studies of other percutaneous permeation systems. Acknowledgments This work was financially supported by the National Natural Science Foundation of China (81473364), the National Natural Science Foundation of China (81073058), the New Century Excellent Talents Program of the Ministry of Education (NCET-12-0803) and Excellent Talents Training Subsidy Scheme of Beijing (2013D009999000003). Supplementary Materials Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1349/s1. Click here for additional data file. Author Contributions Xinyuan Shi and Yanjiang Qiao designed the experiments, revised the manuscript and approved the final version; Xingxing Dai and Qianqian Yin performed the experiments, acquired and analyzed the data and drafted the manuscript; Guang Wan and Ran Wang provided some technical guidance and offered constructive advice to the data analysis. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Morphologies and corresponding analyses of lipid bilayer systems at different borneol concentrations by CG-MD simulations: (a) morphologies of the lipid bilayer at (1) 3%, (2) 5%, (3) 10% and (4) 15% of borneol; (b) APL analysis; (c) bilayer thickness analysis; and (d) order parameter analysis for CER NS. Figure 2 Detailed structures of water pores and reversed micelles formed by borneol in lipid bilayers by CG-MD simulations: (a) water pore; and (b) reversed micelle. Figure 3 A typical lipid flip-flop process by CG-MD simulations. To show the dynamic process clearly, water and lipid tail beads were hidden in the figures. Figure 4 Morphology evolution of lipid bilayer systems with time at different concentrations of borneol by CG-MD simulations. The concentration of osthole was fixed at 10%. Figure 5 (1) Lateral views and (2) corresponding density profiles of osthole in z-axis direction in bilayer systems at different points in time by CG-MD simulations: (a) system without borneol; and (b) system with 5% borneol. Figure 6 Permeation process of osthole molecules with “pull” effect of borneol during the first 100 ns of 5% borneol and 10% osthole system by CG-MD simulations. Figure 7 Detail illustration of osthole molecules interacting with water pores and reversed micelles formed by 10% borneol, 10% osthole and SC lipids by CG-MD simulations: (a) lateral view of the bilayer with all the constituent molecules; (b) lateral and (c) vertical views of the bilayer with lipid and solvent molecules; and (d) lateral view of the bilayer with hydrophilic head groups shown in density map and hydrophobic alkyl chains hidden. Figure 8 Cumulative amount of osthole in the presence of varying concentration of borneol and the corresponding zero-order kinetics equations. Figure 9 (a) Permeability coefficient Kp of osthole obtained from in vitro experiments; and (b) diffusion coefficient D of osthole obtained from CG-MD simulations. Either Kp or D reflects the diffusion rate of osthole in the corresponding lipid bilayer systems. Figure 10 Transmission electron microscope (TEM) views of hairless rat skins with 10% osthole and different concentrations of borneol treated at 35 °C for 24 h: (a) 0%; (b) 0.54%; and (c) 1.02%. Bar = 2 μm. Figure 11 The flow chat of the coarse-grained molecular dynamic (CG-MD) simulation process. Figure 12 CG models for simulation systems: (a) the chemical structure and CG mapping for Ceramide-N-sphingosine (CER NS), cholesterol (CHOL), free fatty acids (FFA), borneol (BO), osthole (OST) and solvent water W based on Martini force field; and (b) a side view of a typical simulation system (membrane with 5% BO and 10% OST in water). ijms-17-01349-t001_Table 1Table 1 Assessment parameters calculated from Qn-t equations. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081350ijms-17-01350ArticlePotential of Pseudoshikonin I Isolated from Lithospermi Radix as Inhibitors of MMPs in IL-1β-Induced SW1353 Cells Lee Dae Young 1Choi Soo-Im 2*Han Se Hee 2Lee Ye-Joo 13Choi Jong-Gil 2Lee Young-Seob 1Choi Je Hun 1Lee Seung-Eun 1Kim Geum-Soog 1*Battino Maurizio Academic EditorMaki Masatoshi Academic Editor1 Department of Herbal Crop Research, National Institute of Horticultural and Herbal Science, RDA, Eumseong 27709, Korea; dylee0809@gmail.com (D.Y.L.); yejoo@rda.go.kr (Y.-J.L.); youngseoblee@korea.kr (Y.-S.L.); jehun@rda.go.kr (J.H.C.); seungeun@rda.go.kr (S.-E.L.)2 YD Life Science Research Institutes, YD Life Science Co., Ltd., Seongnam 13235, Korea; sh.han@ydgls.com (S.-H.H.); Jonggil@rda.go.kr (J.-G.C.)3 Department of Food Science & Biotechnology, Chungbuk National University, Cheongju 28644, Korea* Correspondence: langdeve@naver.com (S.-I.C.); kimgs0725@korea.kr (G.-S.K.); Tel.: +82-10-2602-3867 (S.-I.C.); +82-43-871-5582 (G.-S.K.)18 8 2016 8 2016 17 8 135021 6 2016 12 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Pseudoshikonin I, the new bioactive constituent of Lithospermi radix, was isolated from this methanol extract by employing reverse-phase medium-pressure liquid chromatography (MPLC) using acetonitrile/water solvent system as eluents. The chemical structure was determined based on spectroscopic techniques, including 1D NMR (1H, 13C, DEPT), 2D NMR (gCOSY, gHMBC, gHMQC), and QTOF/MS data. In this study, we demonstrated the effect of pseudoshikonin I on matrix-metalloproteinase (MMPs) activation and expression in interleukin (IL)-1β-induced SW1353 chondrosarcoma cells. MMPs are considered important for the maintenance of the extracellular matrix. Following treatment with PS, active MMP-1, -2, -3, -9, -13 and TIMP-2 were quantified in the SW1353 cell culture supernatants using a commercially available ELISA kit. The mRNA expression of MMPs in SW1353 cells was measured by RT-PCR. Pseudoshikonin I treatment effectively protected the activation on all tested MMPs in a dose-dependent manner. TIMP-2 mRNA expression was significantly upregulated by pseudoshikonin I treatment. Overall, we elucidated the inhibitory effect of pseudoshikonin on MMPs, and we suggest its use as a potential novel anti-osteoarthritis agent. Lithospermi radixnuclear magnetic resonance (NMR)pseudoshikonin Imatrix-metalloproteinase (MMPs) ==== Body 1. Introduction Lithospermum erythrorhizon Sieb. et Zucc., called “Jichi” in Korea and “Zicao” in China, is a perennial herbaceous plant and has been used as traditional herbal medicine and as natural dye for staining fabrics and food colorants, mainly in Korea, Japan, and China [1]. It has been traditionally used for the improvement of vascular circulation, removal of fever, detoxification, wound healing, and treatment of hematemesis, hematuria, constipation, eczema, and urinary tract infection [2]. In particular, it has been shown to possess many diverse activities, including anticancer [3,4], antioxidant [5,6,7,8], anti-inflammatory [9,10], antibacterial [11], antifungal [12], hepatoprotective [13], neuroprotective [14], and cosmetic [15] effects. The bioactive phytochemicals of this herb are naphthoquinone compounds, such as shikonin, and its derivatives, such as acetylshikonin, alkannan, isobutyl-shikonin, β,β-dimehtyl-acrylshikonin, β-hydroxyl isovaloryl shikonin, and tetracrylshikonin [6,7,16]. Our previous study showed that the supercritical fluid extract and napthoquinone compounds, like shikonin and acetylshikonin, from Lithospermi radix have strong protective activities on chondrocytes and MIA-induced osteoarthritis in rats [17]. This study sought to evaluate the molecular structure and cartilage protection activities of a new compound from the Lithospermi radix through the inhibitory effect on matrix-metalloproteinase (MMPs) activation and expression in interleukin-1β-induced SW1353 chondrosarcoma cells. 2. Results and Discussion A crude methanol extract of Lithspermi radix was purified by preparative MPLC to yield a new compound, pseudoshikonin I. Compound 1 was isolated as reddish powder (methanol), and its TLC had a deep purple color after spraying with 10% H2SO4 and heating. The molecular formula was established to be C21H24O4 from the molecular ion peak m/z 339.12884 [M − H]− in the negative QTOF/MS. The full assignment of the 1H and 13C NMR signals of compound 1, secured by gCOSY, gHSQC, and gHMBC spectra, and the comparison of these data with those of shikonin [7] and β,β-dimethylacrylshikonin [18] indicated the similarities of this compound. The significant difference of compound was the presence of OH signals in compound 1, instead of two carbonyl signals in shikonin. The 1H-NMR spectrum showed two olefinic methine proton signals (δH 7.42 (1H, s, H-2), 6.95 (1H, s, H-4)) and three olefin methine proton signals (δH 7.14 (1H, d, J = 3.2 Hz, H-8), 6.69 (1H, d, J = 8.8 Hz, H-5), 6.54 (1H, dd, J = 8.8, 3.2 Hz, H-6)); thus, suggesting that compound 1 has a meta-coupled aromatic, including a 1,2,4-3-substitute aromatic ring in the naphthalene moiety. The 1H and 13C NMR spectra of compound 1 showed characteristic signals for 4-methylpent-3-enyl side chain (δH 5.75 (H-11), 5.11 (H-13), 2.55 (H-12), 1.74 (H-16), 1.56 (H-15); δC 119.5 (C-14), 109.7 (C-13), 69.3 (C-11), 34.8 (C-12), 20.3 (C-15), 18.0 (C-16)) and 3-methylbut-2-enoate side chain (δH 5.69 (H-2′), 2.14 (H-5′), 1.89 (H-4′); δC 167.5 (C-1′), 139.3 (C-2′), 135.6 (C-3′), 27.4 (C-5′), 25.9 (C-4′)), which were also present in shikonin and β,β-dimethylacrylshikonin. In the gHMBC spectrum, long-range correlation was shown between the oxgenated methine proton signal (δH 5.75 (H-11)) and carboxyl carbon signal (δC 167.5 (C-1′)), with the olefine methine carbon signal (δC 139.3 (C-2′)) of the butyl group indicating that the butyl group was linked to the hydroxyl of C-11. In addition, correlations were observed between the oxgenated methine proton signal (H-11) and the quaternary carbon signal (δC 128.4 (C-3)) and between methylene carbon signal (δc 34.8 (C-12)) and olefinic methine carbon signal (δc 109.7 (C-13)) (Figure 1). The absolute configurations of compound 1 were resolved using optical rotation data. The comparison of the specific rotation of compound 1 ([α]D25 +75.0; c 0.10, CHCl3) with alkannin ([α]D20 −150.5; c 0.05, benzene) and shikonin ([α]D20 +135.6; c 0.05, benzene) [19] was consistent with compound 1 possessing an 11S absolute configuration. Based on the data above, the chemical structure of compound 1 was determined to be 1-(1,7-dihydroxynaphthalen-3-yl)-4-methylpent-3-enyl 3-methylbut-2-enoate named pseudoshikonin I. 2.1. Cell Viability of Pseudoshikonin I on SW1353 Cells Following incubation with pseudoshikonin I for 24 h, no significant difference was shown between the viability of cells in the control and that in the 10–100 μM of PSI treatment. As shown in Figure 2; however, more than 100 μM of pseudoshikonin I treatment significantly inhibited the proliferation of SW1353 cells. The IC50 value was 201.5 μM of pseudoshikonin I on SW1353 cells. 2.2. Effect of Pseudoshikonin I on MMPs Activity in IL-1β-Induced SW1353 Cells MMP-1, -2, -3, -9, -13, and TIMP-2 productions in IL-1β-stimulated SW1353 cells in the absence or presence of pseudoshikonin I were profiled. IL-1β contributes to the elevated expression of MMPs by chondrocytes in vitro and in vivo [20,21]. Previous studies demonstrated the expression and activation of MMP-1, MMP-2, MMP-3, MMP-9, and MMP-13 in IL-1β-stimulated human chondrosarcoma cells [22]. Cells were co-treated with pseudoshikonin I and IL-1β (20 ng/mL) for 24 h. MMP concentrations in the cell supernatants were expressed as ng total MMP-13/106 to standardize the amounts between cultures. As a result (Table 1), in the presence of IL-1β (control group), MMP-13 production increased (p < 0.01), with concentrations reaching 4.97 ± 0.50 ng/106 cells. These MMPs significantly increased in the control group compared with the normal group. In particular, MMP-1 decreased in a concentration-dependent manner compared with the control group, with IC50 value of 58.7 μM. In addition, the IC50 value of pseudoshikonin I on MMP-2 and MMP-13 was 60.8 and 63.3 μM, respectively. At a concentration of 100 μM, the inhibition effect of PSI on MMP-13 was 34.0% and 18.7%, respectively. 2.3. Effect of Pseudoshikonin I on the mRNA Expression of MMPs in IL-1β-Induced SW1353 Cells To assay the effect of pseudoshikonin I treatment on MMP-1, -2, -3, -9, -13, and TIMP-2 expression in IL-1β-stimulated SW1353 cells, cells were pretreated with pseudoshikonin I for 30 min alone or further treated with IL-1β (20 ng/mL) for 24 h. The gene expression of MMPs in SW1353 cells was investigated by RT-PCR (Table 2). AS shown in Figure 3, the relative MMP-1, -2, -3 mRNA expression showed no significant difference in the pseudoshikonin I (50 or 100 μM) treatment group; however, mRNA expression of MMP-9 (74.2% ± 1.4%), MMP-13 (70.2% ± 3.9%), iNOS (74.7%± 2.6%), and COX-2 (85.9% ± 3.2%) were suppressed by pseudoshikonin I treatment in a dose-dependent manner. In addition, the TIMP-2 mRNA expression was up-regulated by pseudoshikonin I (141.9 ± 0.6) at 100 μΜ treatment, compared to the IL-1β treated group (control, 100), and showed statistical significance (p < 0.001). MMPs are enzymes that are important in the maintenance of the extracellular matrix. They assist in the creation of interstitial spaces by degrading extracellular matrix proteins, thereby facilitating multiple inflammatory processes [23]. MMP-1 (collagenase 1), the most abundant member of the MMP family, efficiently cleaves type II collagen in the cartilage and gets primarily synthesized by chondrocytes or fibroblasts in the connective tissues. MMP 13 (collagenase-3) is capable of cleaving aggrecans and type II collagen in the cartilage. Selective MMP-13 inhibitors are reported to be capable of blocking collagen degradation in cartilage explants efficiently without any musculoskeletal side effects [24,25]. MMP-3 (Stromelysin-1) cleaves proteoglycans, collagens, gelatins, and links proteins of aggrecan. Moreover, MMP-2 (gelatinase A) and MMP-9 (gelatinase B) are thought to play a key role in the degradation of type IV collagen and gelatin, the two main components of ECM [26]. It was proved that the expression of both MMP-2 and MMP-9 is enhanced in osteoarthritic cartilage. Therefore, the search of MMP inhibitor that modifies the expression and/or activity of MMPs might be considered a promising target for the treatment of OA. 3. Experiment 3.1. General Multiple preparative liquid chromatography (MPLC, YMC LC-Forte/R, Kyoto, Japan) methods were performed on a column packed with reverse phase silica (RP-18). Kieselgel 60 F254 (Merck, Palo Alto, CA, USA) and RP-18 F254S (Merck) were used as solid phases for the TLC experiment. The detection of spots on the TLC plate was performed by observing under a UV lamp (Spectronics Corp., New York, NY, USA) or spraying 10% aqueous H2SO4 on the developed plate followed by heating. Ultraviolet spectra were measured with a Shimadzu model UV-1601 spectrophotometer (Kyoto, Japan). QTOF-MS analysis was performed using a Waters Xevo G2-S QTOF MS (Waters Corp., Milford, MA, USA) operating in the negative ion mode. The NMR spectra were recorded on a Varian Inova AS 400 spectrometer (400 MHz, Varian, Palo Alto, CA, USA). 3.2. Plant Materials Dried one year-old Lithospermi radix (LR) was purchased from Jacheon, Chungbuk Province, South Korea. A voucher specimen (MPS000071) was deposited at the Herbarium of the Department of Herbal Crop Research, National Institute of Horticultural and Herbal Science, Rural Development Administration, Eumseong, South Korea. 3.3. Extraction and Isolation The powder of LR (1.5 kg) was extracted with 70% EtOH (15 L) at 80 °C for 2.5 h using a natural substance extractor (EG-BE1, Siheung, Korea) to obtain 70% EtOH extract. The EtOH extract was concentrated under vacuum using a rotary evaporator (N-1200B, EYELA, Tokyo, Japan) and dried in a freeze dryer (LP20, IlshinBioBase, Dongduchun, Korea) to yield the final test samples (LES, 424 g). The glass column (tayperling type, 50 mm × 150 mm, Dychrome, Sunnyvale, CA, USA) was pre-fitted with a glass guard column (15 mm × 25 mm) and an automatic fraction collector. The column was fitted with MPLC. Both of the columns were packed with RP-18 material (40–63 um). The pump was operated at pressure of 10–15 psi, and the column was preconditioned with acetonitrile/water (75:25) for 1 h. at flow rate of 10 mL/min to ensure that no other contaminants were present on the column. Crude extracts (4 g) were dissolved in acetonitrile/water (75:25, v/v, 10 mL) and loaded onto the guard column. Among the various solvent systems tried as eluents, acetonitrile/water (75:25) at flow rate of 10 mL·min−1 provided nigh teen fractions (F1-F19). Fraction 14 (150 mg) was further separately applied to the MPLC system and eluted with acetonitrile/water (80:20) at flow rate of 5 mL·min−1, yielding compound 1 (LE14-6, 28 mg). Pseudoshikonin I (1). Reddish powder, [α]D25 +75.0° (c = 0.10, CHCl3); negative QTOF/MS m/z 339.12884 [M − H]−; 1H-NMR (400 MHz, CD3OD, δH) 7.42 (1H, s, H-2), 7.14 (1H, d, J = 3.2 Hz, H-8), 6.95 (1H, s, H-4), 6.69 (1H, d, J = 8.8 Hz, H-5), 6.54 (1H, dd, J = 8.8, 3.2 Hz, H-6), 5.75 (1H, dd, J = 6.8, 6.8 Hz, H-11), 5.69 (1H, m, H-2′), 5.11 (1H, m, H-13), 2.55 (2H, m, H-12), 2.14 (3H, s, H-5′), 1.89 (3H, s, H-4′), 1.74 (3H, s, H-16), 1.56 (3H, s, H-15); 13C-NMR (100 MHz, CD3OD, δc) 167.5 (C-1'), 158.5 (C-1), 152.6 (C-7), 151.0 (C-9), 147.9 (C-10), 139.3 (C-2′), 135.6 (C-3′), 128.4 (C-3), 120.2 (C-2), 119.5 (C-14), 117.6 (C-8), 117.0 (C-6), 115.9 (C-4), 112.5 (C-5), 109.7 (C-13), 69.3 (C-11), 34.8 (C-12), 27.4 (C-5′), 25.9 (C-4′), 20.3 (C-15), 18.0 (C-16). 3.4. Cell Culture Human SW1353 chondrosarcoma cells (American Type Culture Collection, Rockville, MD, USA) were cultured in Dulbecco’s modified Eagle’s medium (DMEM) containing 100 units/mL penicillin, 100 μg/mL streptomycin, and 10% fetal bovine serum (FBS; Gibco®, Grand Island, NY, USA) in six-well plates at 37 °C with 5% CO2. For the experiments, SW1353 cells were seeded in a six-well plate at 1 × 106 cells/well. At confluence, cells were rinsed with PBS (pH 7.4, Gibco®) twice and treated with or without IL-1β at 20 ng/mL in the absence or presence of pseudoshikonin I for 24 h. 3.5. Determination of Cell Viability Cell viability following treatment with PSI was determined by the 3′-(4,5,-dimethylthiazole-2yl)-2,5-diphenyl-tetrazolium bromide (MTT) assay. To examine the cytotoxicity of Pseudoshikonin I on SW1353 cells, the cells (1 × 105 cells/well) were seeded in triplicate in 96-well plates and cultured in DMEM medium for 24 h. After pseudoshikonin I treatment, medium was removed from the plate and rinsed with PBS twice, 120 μL MTS solution (Promega, Madison, WI, USA) was added, and the plate was incubated at 37 °C with 5% CO2 for 2 h. Optical density (O.D.) was measured at 540 nm using a spectrophotometer (Multiskan™ Go Microplate Spectrophotometer, Thermo scientific, Foster City, CA, USA). Cell viability was calculated relative to untreated control cells as follows: (viability (% control) = 100 × (absorbance of treated sample)/(absorbance of control). 3.6. Measurement of MMPs Production in IL-1β-Induced SW1353 Cells Following treatment with pseudoshikonin I, active MMP-1, -2, -3, -9, -13 and TIMP-2 were quantified in the cell culture supernatants using a commercially available ELISA kit (SensoLyte® 520 MMPs assay kit, AnaSpec, Fremont, CA, USA) according to the manufacturer’s instructions. The fluorescence of 5-FAM can be read and monitored at excitation/emission wavelengths of 490/520 nm. 3.7. mRNA Isolation and RT-PCR in IL-1β-Induced SW1353 Cells Following treatment with PS, chondrocytes were lysed and total cellular RNA was extracted with TRIZOL™ reagent (Invitrogen Life Technologies, Rockville, MD, USA) according to the manufacturer’s instructions. RNA integrity was assessed by electrophoresis on a denaturing 1.5% agarose gel. Total RNA (1 μg) was converted to cDNA using Maxime RT PreMix Oligo dT for RT-PCR (IntronBio, Seongnam, Korea). PCR was performed with incubation at 94 °C for 30 s, 57 °C for 45 s, and 72 °C for 45 s, with the final incubation at 72 °C for 7 min, with a Maxime PCR PreMix i-StarTaq (IntronBio, Seongnam, Korea). Gene-specific primers (Bioneer, Deajeon, Korea) used for the amplification of gene fragments are described in Table 1. The finished cDNA products were stored in aliquots at −80 °C until required. The RT-PCR products were electrophoresed in a 2% agarose gel and visualized by ethidium bromide (EtBr) staining. The relative expression of mRNA was normalized as the ratio to the expression of GAPDH mRNA, as a reference gene. All quantities were expressed as n-fold relative to a calibrator. 3.8. Statistical Analysis All results were analyzed by GraphPad Prism® Version 4.0 (GraphPad Software, San Diego, CA, USA) program for statistical analysis. Data are presented as means ± standard deviation (S.D.). Statistical significance was set to p < 0.05 and analysis of one-way ANOVA with Tukey’s post hoc comparisons. 4. Conclusions In conclusion, this study shown that pseudoshikonin I (1) was isolated from Lithospermi radix. We have been generally successful in our attempt to the protective effects of pseudoshikonin I against IL-1β-induced MMPs production and mRNA expression in vitro using human chondrosarcoma cells. Pseudoshikonin I revealed any effect on the chondrocyte morphology and viability, or any significant cell death when compared with the control. This suggests that pseudoshikonin I was able to enhance the protective response of the matrix substrates in human chondrocytes and, possibly, had a favorable chondro-protective effect. However, the precise mechanism of pseudoshikonin I in relation of matrix degradation still needs to be clarified. Therefore, the action mechanism of pseudoshikonin I should be investigated in further studies. Acknowledgments This work was supported by the Next Generation Bio-Green 21 (PJ01122301) Project from Rural Development Administration, Korea. Author Contributions Dae Young Lee and Soo-Im Choi conceived and designed the experiments; Se Hee Han and Ye-Joo Lee performed the experiments; Dae Young Lee and Soo-Im Choi analyzed the data; Jong-Gil Choi, Young-Seob Lee, Je Hun Choi and Seung-Eun Lee contributed reagents/materials/analysis tools; Dae Young Lee, Soo-Im Choi and Geum-Soog Kim wrote the paper. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Chemical structure of compound 1 isolated from the Lithospermi radix and key gHMBC (arrow) correlations of compound 1. Figure 2 Cell viability of pseudoshikonin I in SW1353 cells. SW1353 cells were treated with indicated concentrations of pseudoshikonin I for 24 h. Normal represents samples only supplied with DMEM and cell viability assay being conducted by the MTT method. Results are expressed as the percentage change of control condition in which cells were grown in medium without pseudoshikonin I. Results shown are mean S.D. of experiments in triplicate. * p < 0.05; ** p < 0.01; *** p < 0.001 compared with control. Figure 3 Effect of pseudoshikonin I on the gene expression of MMP-1, 2, 3, 9, 13, and TIMP-2 in IL-1β-induced SW1353 cells by RT-PCR. SW1353 cells incubated in the presence of 50 or 100 μM pseudoshikonin I stimulated with 20 ng/mL of IL-1β for 24 h. GAPDH was used as internal control. Values were expressed as means ± S.D. of triple experiments. * p < 0.05, ** p < 0.01, *** p < 0.001 compared with IL-1β treated group (control). ijms-17-01350-t001_Table 1Table 1 IC50 value of pseudoshikonin I on the activity of MMP-1, 2, 3, 9, and 13 in IL-1β-induced SW1353 cells a. Matrix Metalloproteinase (MMP) IC50 Value (μM) MMP-1 58.7 MMP-2 60.8 MMP-3 >100 MMP-9 >100 MMP-13 63.3 TIMP-2 – a SW1353 cells incubated in the presence of 10, 25, 50, or 100 μM pseudoshikonin I stimulated with 20 ng/mL of IL-1β for 24 h. Results are expressed as the means ± S.D. of triple experiments. ijms-17-01350-t002_Table 2Table 2 Primer sequences for reverse transcription PCR. Gene Code Accession ID Sequence (5′→3′) Temperature (°C) Product Length (bp) MMP-1 NM_001145938.1 Forward AGTGACTGGGAAACCAGATGA 57 159 Reverse CGTCTTGGCAAATCTGGCCTGTAA MMP-2 NM_001127891.2 Forward GCAGTGGGGGCTTAAGAAGA 57 969 Reverse AGCCGTACTTGCCATCCTTC MMP-3 NM_002422.3 Forward ATTCCATGGAGCCAGGCTTTC 57 142 Reverse CATTTGGGTCAAACTCCACTGTG MMP-9 NM_004994.2 Forward CATCCGGCACCTCTATGGTC 57 637 Reverse CATCGTCCACCGGACTCAAA MMP-13 NM_002427.3 Forward AAATTATGGAGGAGATGCCCATT 57 125 Reverse TCCTTGGAGTGGTCAAGACCTAA TIMP-2 NM_003255.4 Forward GTAGTGATCAGGGCCAAAGC 57 160 Reverse GGGGGCCGTGATAAACT GAPDH NM_001256799.2 Forward AGAAGGCTGGGGCTCATTTG 52 271 Reverse AGGGGCCATCAGTCTTC ==== Refs References 1. Cho M.H. Paik Y.S. Hahn T.R. Physical stability of shikonin derivatives from the roots of Lithospermm erythrorhizon cultivated in Korea J. Agric. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081351ijms-17-01351ArticleMicroRNA-331-3p Suppresses Cervical Cancer Cell Proliferation and E6/E7 Expression by Targeting NRP2 Fujii Tomomi 1*Shimada Keiji 2Asano Aya 1Tatsumi Yoshihiro 1Yamaguchi Naoko 3Yamazaki Masaharu 3Konishi Noboru 1Taguchi Y-h. Academic EditorUI-TEI Kumiko Academic Editor1 Department of Pathology, Nara Medical University School of Medicine, Nara 634-8521, Japan; ayaasano1018@yahoo.co.jp (A.A); tatsumi3@naramed-u.ac.jp (Y.T.); nkonishi@naramed-u.ac.jp (N.K.)2 Department of Diagnostic Pathology, Nara City Hospital, Nara 630-8305, Japan; k-shimada@nara-jadecom.jp3 Department of Central Clinical Laboratory, Nara Medical University Hospital, Nara 634-8521, Japan; nyama@naramed-u.ac.jp (N.Y.); masayama@naramed-u.ac.jp (M.Y.)* Correspondence: fujiit@naramed-u.ac.jp; Tel.: +81-744-22-3051 (ext. 2238); Fax: +81-744-23-568718 8 2016 8 2016 17 8 135123 7 2016 12 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Aberrant expression of microRNAs (miRNAs) is involved in the development and progression of various types of cancers. In this study, we investigated the role of miR-331-3p in cell proliferation and the expression of keratinocyte differentiation markers of uterine cervical cancer cells. Moreover, we evaluated whether neuropilin 2 (NRP2) are putative target molecules that regulate the human papillomavirus (HPV) related oncoproteins E6 and E7. Cell proliferation in the human cervical cancer cell lines SKG-II, HCS-2, and HeLa was assessed using the 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium, inner salt (MTS) assay. Cellular apoptosis was measured using the TdT-mediated dUTP nick end labeling (TUNEL) and Annexin V assays. Quantitative RT-PCR was used to measure the messenger RNA (mRNA) expression of the NRP2, E6, E7, p63, and involucrin (IVL) genes. A functional assay for cell growth was performed using cell cycle analyses. Overexpression of miR-331-3p inhibited cell proliferation, and induced G2/M phase arrest and apoptosis in SKG-II, HCS-2 and HeLa cells. The luciferase reporter assay of the NRP2 3′-untranslated region revealed the direct regulation of NRP2 by miR-331-3p. Gene expression analyses using quantitative RT-PCR in SKG-II, HCS-2, and HeLa cells overexpressing miR-331-3p or suppressing NRP2 revealed down-regulation of E6, E7, and p63 mRNA and up-regulation of IVL mRNA. Moreover, miR-331-3p overexpression was suppressed NRP2 expression in protein level. We showed that miR-331-3p and NRP2 were key effectors of cell proliferation by regulating the cell cycle, apoptosis. NRP-2 also regulates the expression of E6/E7 and keratinocyte differentiation markers. Our findings suggest that miR-331-3p has an important role in regulating cervical cancer cell proliferation, and that miR-331-3p may contribute to keratinocyte differentiation through NRP2 suppression. miR-331-3p and NRP2 may contribute to anti-cancer effects. cervical cancerhuman papillomavirusmiR-331-3pneuropilin 2E6/E7 mRNAkeratinocyte differentiation ==== Body 1. Introduction Cervical cancer is one of the most common neoplasias in females worldwide. The pathogenesis of cervical cancer occurs following persistent infection with high-risk types of human papillomavirus (HPV) such as HPV types 16, 18, 31, 33, 39, 45, 52, 58 and 69; it progresses slowly by interrupting the normal differentiation of the cervical squamous epithelium [1,2]. The E5, E6 and E7 proteins present in the high-risk HPV types, act as viral oncoproteins and are considered to be associated with human cervical carcinogenesis [3,4,5]. E6 and E7 bind to the p53 and retinoblastoma (Rb) family proteins, respectively, which are tumor suppressor proteins involved in cell cycle regulation [5,6]. The inactivation of p53 and Rb proteins by E6 and E7 is likely an important step in cervical carcinogenesis. The recent clinical study suggests that the combination of radiation and molecular targeting therapy with E6/E7 silencing significantly decreased tumor growth in vivo [7]. MicroRNAs (miRNAs) are short regulatory RNAs that control gene expression through translational inhibition by binding to the complementary sites of their target gene transcripts [8,9,10]. miRNAs affect cell proliferation, apoptosis, cell differentiation and the epithelial-mesenchymal transition (EMT) in various types of cancers such as bladder, prostate, breast, pancreatic and gastric cancers [11,12,13,14,15,16]. There are some candidate miRNAs for oncogenic or anti-oncogenic factors in cervical cancer [17]. To evaluate abnormally high-levels of miRNA can potentially serve as useful biomarkers for cervical cancer diagnosis [18]. For example, miR-129-5p induces interferon-β which down-regulates E6 and E7 expression [19], miR-26a and miR-342-3p inhibit cell proliferation and invasion through each protein tyrosine phosphatase type IVA 1 and the mitogen-activated protein kinase (MAPK) pathway or forkhead box M1 [20,21], and miR-101 regulates the cell cycle by inhibiting the G1-to-S transition [22]. miR-196a and miR-181b promote cell proliferation by regulating Forkhead box protein O1 (FOXO1) and p27Kip1 or adenylyl cyclase 9, respectively [23,24]. We have previously reported that syndecan-1 (CD138) up-regulates miR-331-3p expression by directly targeting neuropilin 2 (NRP2) and nucleus accumbens-associated protein 1 (NACC1) to mediate the EMT in prostate cancer cells [13]. miR-331-3p inhibits tumor growth, improves prognosis, and regulates the expression of NRP2, deoxyhypusine hydroxylase and the long non-coding RNA Hox transcript antisense intergenic RNA (HOTAIR) and human epithelial growth factor receptor 2 (HER2) in glioblastoma, prostate, and gastric cancers [25,26,27]. In this study, we evaluated whether miR-331-3p regulates cervical cancer proliferation and the expression of the HPV-related proteins E6 and E7 by the candidate targets NRP2. Additionally, to evaluate whether miR-331-3p contributes to keratinocyte differentiation by altering keratinocyte marker expression, we assessed p63 and involucrin (IVL) expression by quantitative reverse transcription-PCR (qRT-PCR). 2. Results 2.1. miR-331-3p Overexpression Suppresses Cell Proliferation in Cervical Cancer Cells To investigate the roles of miR-331-3p in cervical cancer cells, we performed the MTS assay to evaluate cell proliferation. Overexpression of miR-331-3p significantly decreased cell proliferation in SKG-II, HCS-2 and HeLa cells (Figure 1A). We also assessed the effect of miR-331-3p inhibitor and found that miR-331-3p inhibition did not affect cell proliferation (data not shown). To determine the mechanisms underlying this suppression, we assessed apoptosis by the TdT-mediated dUTP nick end labeling (TUNEL) and Annexin V assays and found that the number of apoptotic cells was significantly increased in SKG-II cells (Figure 1B and Figure 2). The transfection efficiency of miR-331-3p precursor was shown in Figure 1C. The expression level of miR-331-3p was up-regulated 1292-, 820- and 715-fold in SKG-II, HCS-2 and HeLa cells, respectively. We assessed the expression of actin mRNA as the internal control. There was no significant difference in treatment experiments (Ct value (control/miR-331-3p pre transfected/NRP2 siRNA transfected); SKG-II cells: 13.43 ± 0.08/13.50 ± 0.11/13.55 ± 0.16), HCS-2 cells: 15.17 ± 0.18/15.56 ± 0.10/14.69 ± 0.03, HeLa cells: 14.18 ± 0.17/14.37 ± 0.01/14.28 ± 0.07). Taken together, these data demonstrate that miR-331-3p regulates cell proliferation in cervical cancer cells by inducing apoptosis. 2.2. miR-331-3p Significantly Decreases the Expression of HPV-Related Proteins E6 and E7 To assess whether miR-331-3p has functional roles in the regulation of E6 and E7 expression in the HPV-infected squamous cell carcinoma cell lines, we performed qRT-PCR analysis. Overexpression of miR-331-3p significantly suppressed E6 and E7 mRNA expression in SKG-II, HCS-2 and HeLa cells (Figure 3A). miR-331-3p overexpression induced down-regulation of p63 and up-regulation of IVL (Figure 3B); however, suppression of miR-331-3p induced no such changes (data not shown). The data show that miR-331-3p controls expression of E6/E7 and keratinocyte differentiation markers. 2.3. Inhibition of Cell Proliferation by miR-331-3p Is Directly Mediated by NRP2 Expression in SKG-II Cells We have previously screened several putative targets of miR-331-3p using the TargetScan analysis (release 6.2, June 2012) and RNAhybrid 2.2 (Bielefeld BioInformatics Service, Bielefeld, Germany) in silico, and identified NRP2 and NACC1 as the predicted targets for miR-331-3p [13]. In this study, we assessed whether these proteins acted as target molecules of miR-331-3p in cervical cancer cells. NRP2 expression was the highest in SKG-II and significantly decreased by miR-331-3p overexpression in SKG-II, HeLa and HCS-2 cells (Figure 4A–C) but NACC1 was not changed by miR-331-3p overexpression in SKG-II cells (data not shown); therefore, NRP2 might act as a target of miR-331-3p in cervical cancer cells. To confirm this, we constructed a Gluc and SEAP reporter cloning vector (pEZX-GA01) and cloned the full-length NRP2 3′-untranslated region (UTR). The effect of miR-331-3p precursor transfection was determined using the luciferase reporter assay (Figure 4D). Suppression of NRP2 expression significantly decreased cell proliferation, whereas the number of apoptotic cells was significantly increased (Figure 5A,B and Figure 6) in SKG-II, HCS-2 and HeLa cells. Moreover, suppression of NRP2 inhibited E6, E7, and p63 expression and induced IVL expression (Figure 7). These results suggest that NRP2 acts directly as a target molecule and is an important for the effect of miR-331-3p on cell proliferation through the expression of E6/E7 and keratinocyte differentiation markers. 2.4. Suppression of NRP2 by miR-331-3p Induces G2/M-Phase Cell Cycle Arrest To address the mechanism of the regulation of cell proliferation by miR-331-3p and NRP2, we evaluated the DNA content index. miR-331-3p overexpression or NRP2 suppression increased the number of cells in the G2/M-phase (Figure 8A). To confirm the G2/M-phase arrest in the cell cycle, we performed qRT-PCR using a primer array for cell cycle-related factors. Some molecules related to the G2/M-phase transition were found to be decreased (Figure S1). Western blotting and immunocytochemistry showed that miR-331-3p overexpression and NRP2 inhibition suppressed cytoplasmic p16INK4a protein levels (Figure 8B). These results indicate that miR-331-3p overexpression and NRP2 suppression induce G2/M-phase arrest and down-regulate p16INK4a in cervical cancer cells. 3. Discussion Viral oncogenes derived from HPV play crucial roles in cervical cancer progression. The oncogenic transformation of HPV-infected cancers is triggered by the integration of the viral genome into the host chromosome, which leads to increased E6 and E7 protein expression [28]. In the present study, we showed that miR-331-3p overexpression regulated cell proliferation by inducing cell cycle arrest at the G2/M phase and apoptosis in human cervical cancer cell lines. In addition, NRP2 was found to be a direct target of miR-331-3p, and silencing NRP2 exhibited the same effects on cell proliferation as those observed by miR-331-3p overexpression. Our study clearly suggests that the miR-331-3p and NRP2 axis may play an essential role in the growth of cervical cancer cells. p63, a member of the p53 family of transcription factors is expressed in basal and parabasal cells of the non-neoplastic squamous epithelium as well as in the neoplastic counterparts including cervical cancer. Immunohistochemical analysis of p63 expression may be an important tool for evaluating most squamous cell carcinomas, such as cervical cancer [29,30]. The life cycle of human HPV is closely related to keratinocyte differentiation. Once the basal cells of squamous epithelium are infected and undergo integration by HPV, the viral genome products E6 and E7 mediate epithelial differentiation [6]. The E6 protein directly affects the IVL promoter to specifically down-regulate IVL expression [31]. In the current study, miR-331-3p overexpression significantly down-regulates NRP2, E6 and E7 proteins, and sequentially up-regulates IVL expression, which may induce keratinocytic differentiation as identified by decreased p63 protein expression in human cervical cancer cells. Taken together, we conclude that miR-331-3p is closely associated with cervical cancer progression by regulating HPV activity. The cellular protein p16INK4a, which is overexpressed in HPV-infected cervical epithelium, is transformed in response to E7 protein expression [32,33]. p16INK4a is a cyclin-dependent kinase inhibitor that decelerates the cell cycle and functions as a tumor-suppressor gene [34] in many human cancers, in contrast, overexpression of p16INK4a in the nucleus and the cytoplasm strongly correlates with cancer progression in cervical squamous cell carcinomas [35,36]. Therefore, the combined detection of the E6/E7 mRNA and p16INK4a protein is a useful biomarker for histological diagnosis and evaluating of clinical prognosis [34,37,38,39,40,41]. MiR-331-3p has been thought one of cancer-associated miRNAs. In gastric, colorectal and prostate cancers and glioblastoma, miR-331-3p expression was markedly down-regulated [25,27,42,43,44]. In colorectal cancer cells, overexpression of miR-331-3p inhibits cell growth, promotes apoptosis and activates caspase-3 by suppressing HER2 expression [42]. In gastric cancer, miR-331-3p functions as key regulator in cell proliferation by leading to cell cycle arrest through directly suppression of E2F1 gene expression [43]. Therefore, the miR-331-3p could be considered as tumor suppressor genes. On the other hand, miR-331-3p functions as oncogenic miRNA in hepatocellular carcinoma by promoting proliferation and metastasis by directly targeting PH domain and leucine-rich repeat protein phosphatase [45]. Thus, it depends on tumor types whether miR-331-3p acts as tumor-suppressive or oncogenic. Up-to-date, in cervical cancer, there is no report of miR-331-3p and the role of miR-331-3p and NRP2 is still unknown. In our current study, we have shown that miR-331-3p have functioned as the tumor-suppressive miRNA in cervical cancer. NRP2, a putative target of miR-331-3p, which is a co-receptor of semapholin 3 and vascular endothelial growth factor family members, acts as an oncogene and shows high expression in some human carcinomas including lung, colorectal, and pancreatic cancers and glioblastoma [25,46,47,48,49]. In the current study, p16INK4a was significantly decreased with simultaneously suppressed cell proliferation and apoptosis induction through keratinocyte differentiation; decreased p63 and increased IVL expression, by miR-331-3p overexpression or NRP2 suppression were used as indicators of keratinocyte differentiation. These findings suggest that miR-331-3p contributes to the inhibition of progression or to the transformation from high-grade to low-grade through keratinocyte differentiation. We provide here the key molecules involving the mechanisms by which miR-331-3p overexpression could induce cell cycle arrest at the G2/M phase in human cervical cancer cells using the Primer Array system with emphasis on the E6-p53 and E7-Rb pathways. As a result, Rb mRNA expression was unchanged, and the expression of the p53 and p21 mRNAs was decreased. These results may be mediated by a significant decrease in E6 and E7 following suppression of NRP2 by miR-331-3p, and p53 simultaneously interacts with NRP2 [50], to decrease p21. It is possible that G2/M-phase arrest is caused by the suppression of several G2/M-phase related molecules by miR-331-3p overexpression. In our current study, we have not shown the clinical significance data in cervical cancer tissues. To offer more evidence of availability as clinical diagnostic tools, it is important to evaluate the miR-331-3p and its target molecules, NRP2 expression using qRT-PCR and immunohistochemistry in cervical cancer and/or dysplasia. In the future, we are going to evaluate expression of miR-331-3p and NRP2 in cervical cancer and dysplasia. 4. Materials and Methods 4.1. Cell Lines The human cervical cancer cell lines SKG-II, HeLa and HCS-2 were purchased from the JECB cell bank (National Institutes of Biomedical Innovation, Osaka, Japan). SKG-II cells were cultured in Ham′s F12 media supplemented with 10% fetal bovine serum (NICHIREI BIOSCIENCES INC. Tokyo, Japan) and 50 U/mL penicillin-streptomycin (Nacalai tesque, Kyoto, Japan). The HeLa and HCS-2 cells was cultured in Dulbecco's Modified Eagle Medium (Nacalai tesque) media supplemented with each 10% or 15% fetal bovine serum and 50 U/mL penicillin-streptomycin. 4.2. miRNA Precursor and siRNA Transfection in Cervical Cell Lines For transfection, SKG-II, HeLa and HCS-2 were seeded at each 1.5 × 105 cells/well in a 6-well dish and were transfected with Pre-miR™ miRNA Precursor hsa-miR-331-3p (Life Technologies, Carlsbad, CA, USA) or 100 ng/L siRNA against NRP2 and NACC1 for 72 h. Pre-miR miRNA Precursor Molecules Negative control #2 (Life Technologies) were used as control molecules. Transfection with Pre-miR™ miRNA Precursor or NRP2 siRNA was performed using Lipofectamine RNAiMAX (Life Technologies) in accordance with the manufacturer′s instructions. NRP2 siRNA sequences were designed after selecting appropriate DNA targets as follows: NRP2; 5′-CAGGCTCTGAAGATTGCTCAA-3′. 4.3. qRT-PCR Analysis of miRNA and mRNA For purification of total RNA, including miRNA from cells, we used the miRNeasy Mini kit (QIAGEN, Hilden, Germany). For qRT-PCR, first-strand cDNA was synthesized from 1 µg of total RNA using PrimeScript RT Master Mix (Perfect Real Time) and SYBR Premix Ex Taq II (TliRNaseH Plus) (Takara, Otsu, Japan). The qPCR conditions were 95 °C for 30 s followed by 55–63 °C for 30 s for a total of 35–45 cycles. The primers used were as follows: NRP2 sense 5′-CTGGAAGTCAGCACTAATGGAGAG-3′; NRP2 antisense 5′-GCATCGTTGTTGGCTTGAAATACC-3′; E6 sense 5′-CTCTGTGTATGGAGACACATTGGAA-3′; E6 antisense 5′-GGCACCGCAGGCACCTTA-3′; E7 sense 5′-TAGAAAGCTCAGCAGACGACCTT-3′; E7 antisense 5′-GCACACCACGGACACACAAA-3′; p63 sense 5′-GAAAGCAGCAAGTTTCGGAC-3′; p63 antisense 5′-TTTCATAAGTCTCACGGCCC-3′; IVL sense 5′-AGCCTTACTGTGAGTCTGGTTGA-3′; IVL antisense 5′-GGGTATTGACTGGAGGAGGAACA-3′; Actin sense 5′-CTCTTCCAGCCTTCCTTCCT-3′; Actin antisense 5′-AGCACTGTGTTGGCGTACAG-3′. Gene expression analysis of cell cycle-related genes was performed by qPCR using PrimerArray Cell Cycle (Takara, Otsu, Japan). 4.4. Cell Proliferation Assay The CellTiter 96 AQueous One Solution Cell Proliferation Assay (Promega, Madison, WI, USA) was used for the MTS assay as previously described [51] to measure cell proliferation. Data were collected from triplicate experiments. 4.5. Luciferase Reporter Assay For the luciferase reporter assay, SKG-II and HeLa cells were seeded at 1 × 105 cells per well in a 24-well plate and were transfected after 24 h with 100 ng of Gaussia luciferase (Gluc) and secreted alkaline phosphatase (SEAP) reporter plasmid DNA (NRP2 or negative control; Genecopoeia, Rockville, MD, USA) and 5 nM of miR-331-3p precursor. After 24 h, the lysates were assayed for Gluc and SEAP using the Secrete-Pair Dual Luminescence Assay Kit (Genecopoeia, Rockville, MD, USA). 4.6. Cell Viability and Cell Cycle Analysis The cell cycle and Annexin V assays were performed using Muse™ Cell Analyzer from Millipore (Hayward, CA, USA) following the manufacturer’s instructions. Briefly, after transfection of miRNA precursor or siRNA into SKG-II, HCS-2 or HeLa cells, the treated cells were washed with PBS. The cell cycle and Annexin V were then analyzed using the Muse™ Cell Cycle Kit and the MuseTM Annexin V & Dead Cell Assay (Millipore), respectively, according to the manufacturer’s protocol. 4.7. Western Blot and Immunocytochemistory Assay For western blot analysis, the treated cell lysates were separated by Sodium dodecyl sulfate (SDS)-polyacrylamide electrophoresis and transferred onto polyvinylidene difluoride membranes (Millipore), which were then blocked in 5% skim milk at room temperature for 1 h. The membranes were incubated with the indicated primary antibodies, such as anti-NRP2 (Abcam, London, UK) and anti-CDKN2A/p16INK4a (Abcam) for 1 h and then with horseradish peroxidase-conjugated anti-rabbit IgG (Amersham Pharmacia Biotech, Amersham, UK). Peroxidase activity was detected on X-ray films using an enhanced chemiluminescence detection system. For immunocytochemistry, the treated cells were fixed with CytoRich Red Preservative Fluid (BD Biosciences, Franklin Lakes, NJ, USA) and prepared following the SurePath™ method (BD Biosciences). The prepared slides were incubated with the primary antibody (anti-CDKN2A/p16INK4a, Abcam, London, UK) for 1 h at room temperature and the reactions were visualized using a Histofine kit Nichirei, Tokyo, Japan), using diaminobenzidine as the chromogen and hematoxylin as the counterstain. 4.8. TdT-Mediated dUTP Nick End Labeling (TUNEL) Assay SKG-II, HCS-2 and HeLa cells transfected with the miR-331-3p precursor or NRP2 siRNA for 72 h were collected and fixed with CytoRich Red Preservative Fluid (BD Biosciences) and prepared following the SurePath™ method (BD Biosciences). The prepared slides were stained with the ApopTag Plus Peroxidase In Situ Apoptosis Detection Kit (Millipore). 4.9. Statistical Analysis Statistical analysis was performed with GraphPad Prism 6.0 (GraphPad Software, Inc., La Jolla, CA, USA) using a two-tailed Student’s t-test to compare between two groups. Graphical data are presented as the mean ± SEM. Results with p < 0.05 were considered significant. All experiments were performed in triplicate. 5. Conclusions In conclusion, these findings show that miR-331-3p overexpression suppresses cervical cancer cell proliferation by regulating the cell cycle and apoptosis (Figure 9). Our future studies will examine whether miR-331-3p and its target, NRP2, are useful clinical diagnostic and/or prognostic markers for histological and cytological examination using tissue specimens and liquid-based cytology in the screening and diagnosis of cervical cancer. Acknowledgments This research was supported in part by a Grant-in-Aid from the Ministry of Education, Culture, Sports, Science and Technology, Japan (26462424). Supplementary Materials Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1351/s1. Click here for additional data file. Author Contributions Tomomi Fujii designed the study with Noboru Konishi, Masaharu Yamazaki and Keiji Shimada. Tomomi Fujii and Aya Asano also cultured cells, collected data from quantitative RT-PCR, MTS assay and immunocytochemistory, performed statistical analysis, and drafted the manuscript. Keiji Shimada, Yoshihiro Tatsumi and Naoko Yamaguchi participated in cellular functional assays. Tomomi Fujii, Keiji Shimada, Aya Asano, Yoshihiro Tatsumi, Naoko Yamaguchi, Masaharu Yamazaki, and Noboru Konishi interpreted results and prepared the manuscript. Noboru Konishi coordinated and designed the study and critically revised the manuscript. All authors approved the final manuscript. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Cell proliferation and apoptosis assay in the cervical cancer cells. (A) MTS assay in SKG-II, HCS-2 and HeLa cells. Cell proliferation was suppressed by transient transfection of the miR-331-3p precursor. (pre: precursor, * p < 0.05 (24 h), + p < 0.05 (48 h), # p < 0.05 (72 h)); (B) TdT-mediated dUTP nick end labeling (TUNEL) assay for SKG-II, HCS-2 and HeLa cells. The Y-axis shows the number of positive cell counts per 10 or 20 high-power fields (10 or 20 HPFs). Positive cells were induced by transfection of the miR-331-3p precursor (* p < 0.05); (C) Expression of miR-331-3p was up-regulated in SKG-II, HCS-2 and HeLa cells by miR-331-3p precursor transfection compared to control cells (* p < 0.05). Figure 2 Annexin V assay for cervical cancer cells. Early and total apoptotic cells were significantly increased by miR-331-3p overexpression in SKG-II, HCS-2 and HeLa cells (* p < 0.05). Figure 3 mRNA expression of HPV-related oncogenes, E6/E7 and keratinocyte related genes, p63 and IVL. E6/E7 (A) p63 and IVL (B) mRNA expression in SKG-II, HCS-2 and HeLa cells. The Y-axis (fold) shows relative expression compared with control (normalized with actin mRNA expression). E6/E7 and p63 mRNA expression was significantly decreased in SKG-II, HCS-2 and HeLa cells transiently transfected with miR-331-3p precursor, whereas IVL mRNA was significantly increased in SKG-II, HCS-2 and HeLa cells transiently transfected with the miR-331-3p precursor (* p < 0.05). Figure 4 mRNA expression of neuropilin 2 (NRP2), which are the putative target molecules of miR-331-3p. (A) mRNA expression of NRP2 in cervical cancer cell lines. NRP2 expression was higher in SKG-II than other two cervical cancer cell lines; (B,C) NRP2 expression under overexpression of miR-331-3p in cervical cancer cell lines. Images were shown from quantitative RT-PCR (B) and western blot (C). miR-331-3p down-regulates NRP2 mRNA (B) and protein (C) expression in SKG-II, HCS-2 and HeLa cells; (D) Luciferase reporter activity for NRP-2 3′-UTR. NRP2 3′-UTR reporter activity was reduced by miR-331-3p overexpression (* p < 0.05). Figure 5 The effect of NRP2 on cell proliferation in SKG-II cells. (A) MTS assay in SKG-II, HCS-2 and HeLa cells. Cell proliferation was suppressed by transient transfection with NRP2 siRNA. (* p < 0.05 (24 h), + p < 0.05 (48 h), # p < 0.05 (72 h)); (B) TUNEL assay for SKG-II, HCS-2 and HeLa cells. The Y-axis shows the number of positive cell counts per 10 or 20 high-power fields (10 or 20 HPFs). Positive cells were induced by transfection of NRP2 siRNA (* p < 0.05). Figure 6 Annexin V assay for cervical cancer cells. Early and total apoptotic cells were significantly increased by NRP2 suppression in SKG-II, HCS-2 and HeLa cells (* p < 0.05). Figure 7 mRNA expression of HPV-related oncogenes, E6/E7 and keratinocyte related genes, p63 and IVL. E6/E7 and p63 mRNAs were significantly decreased by transient transfection with NRP2 siRNA. whereas IVL mRNA was significantly increased in SKG-II, HCS-2 cells transiently transfected with NRP2 siRNA (* p < 0.05). Figure 8 Cell cycle analysis in the SKG-II cells. Cell cycle analysis using the Muse™ Cell Analyzer (Millipore; Hayward, CA, USA). (A) The upper panels show the DNA content profile by transient transfection of with the miR-331-3p precursor (miR-331-3p pre) or NRP2 siRNA. The DNA histogram results show the distribution of the cell cycle phases; G0/G1 (blue), S (red) and G2/M (green). The lower panel shows the percentage of G1, S, or G2/M phase; (B) Western blotting (upper panel) and immunocytochemistory (lower panel) for p16INK4a (p16) protein expression in SKG-II cells. Figure 9 (Upper panel) miR-331-3p down-regulates NRP2, a putative direct target of miR-331-3p, and inhibits cell proliferation by regulating the expression of E6/E7, keratinocyte differentiation, cell cycle, and apoptosis in cervical cancer cells (thin black arrows); (Lower panel) HPV infection promotes cell proliferation through up-regulation of E6/E7, p16 and p63, and progression to cervical cancer (thin blue arrows). miR-331-3p acts as an anticancer factor through suppression of NRP2 (the thick blue arrow). ==== Refs References 1. Muñoz N. Castellsagué X. de González A.B. Gissmann L. Chapter 1: HPV in the etiology of human cancer Vaccine 2006 24 1 10 10.1016/j.vaccine.2006.05.115 16122853 2. Hausen H. Papillomaviruses causing cancer : Evasion from host-cell control in early events in carcinogenesis J. Natl. Cancer Inst. 2000 92 690 698 10.1093/jnci/92.9.690 10793105 3. Ganguly N. Human papillomavirus-16 E5 protein: Oncogenic role and therapeutic value Cell. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081352ijms-17-01352ReviewPlant Polyphenols as Chemopreventive Agents for Lung Cancer Amararathna Madumani 1Johnston Michael R. 2Rupasinghe H. P. Vasantha 13*Lamuela-Raventos Rosa M. Academic Editor1 Department of Environmental Sciences, Faculty of Agriculture, Dalhousie University, P.O. Box 550, Truro, NS B2N 5E3, Canada; madu.ama@dal.ca2 Department of Surgery, Dalhousie University, Halifax, NS B3H 4R2, Canada; mrj2@mac.com3 Department of Pathology, Faculty of Medicine, Dalhousie University, P.O. Box 15000, Halifax, NS B3H 4R2, Canada* Correspondence: vrupasinghe@dal.ca; Tel.: +1-902-893-662319 8 2016 8 2016 17 8 135203 7 2016 10 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Lung cancer may be prevented by a diet rich in fruits and vegetables as they are enriched with dietary antioxidant polyphenols, such as flavonoids, proanthocyanidins, lignans, stilbenes, and phenolic acids. Dietary polyphenols exert a wide range of beneficial biological functions beyond their antioxidative properties and are involved in regulation of cell survival pathways leading to anticarcinogenic and antimutagenic functions. There are sufficient evidence from in vitro, in vivo, and epidemiological studies to suggest that the dietary intervention of polyphenols in cancer prevention, including the chemopreventive ability of dietary polyphenols, act against lung carcinogens. Cohort and epidemiological studies in selected risk populations have evaluated clinical effects of polyphenols. Polyphenols have demonstrated three major actions: antioxidative activity, regulation of phase I and II enzymes, and regulation of cell survival pathways against lung carcinogenesis. They have also shown an inverse association of lung cancer occurrences among high risk populations who consumed considerable amounts of fruits and vegetables in their daily diet. In in vitro cell culture experimental models, polyphenols bind with electrophilic metabolites from carcinogens, inactivate cellular oxygen radicals, prevent membrane lipid peroxidation and DNA oxidative damage, and adduct formation. Further, polyphenols enhance the detoxifying enzymes such as the phase II enzymes, glutathione transferases and glucuronosyl transferases. polyphenolslung cancercarcinogenesischemopreventiondietfruits and vegetables ==== Body 1. Introduction Worldwide cancer statistics document the devastating effects of lung cancer. Among all types of cancer, lung cancer has caused almost one fifth of cancer deaths (19.4%). Incidence and mortality rates of lung cancer were estimated to be higher in less developed regions than developed regions. Lung cancer is the most common cancer in men worldwide [1]. In addition to the effect from life loss, the global economic impact (disability, treatment cost, palliative care) of lung cancer alone was $188 billion in 2008 [2]. Thus, lung cancer has become a serious concern throughout the world. Lung cancer is a multi-step process of accumulating genetic and epigenetic alterations caused by chronic exposure to carcinogens, such as smoking, domestic radon, fossil fuel burning, automobile exhaust fumes and pesticide inhalation. As per the World Health Organization (WHO), at least one-third of all cancer cases are preventable through dietary modifications and lifestyle changes. Eliminating risk factors, such as cigarette smoke and polluted environments, and adopting safety measures at work are a few actions for lung cancer prevention suggested by the WHO [3,4,5]. Many scientists have observed an inverse association between the consumption of fresh fruits and vegetables and the risk of lung cancer [6,7]. Therefore, in this review, a polyphenols-rich dietary preventive strategy is discussed in relation to lung cancer. 2. Nature of Polyphenols and Their Classification Plant-based biologically active compounds have gained interest due to their disease prevention capability. Polyphenols present in plant-based foods, such as fruits and vegetables, reduce the risk of a variety of cancers. Polyphenols are the largest group of over 40,000 secondary plant metabolites that provide plants with chemical defense mechanisms against pathogens and environmental stress, as well as establishing many plant-ecological interactions [8]. Plant-based diets consumed around the world are rich in polyphenols such as phenolic acids, flavonoids, stilbenes and lignans. There are a wide variety of polyphenols identified so far and they contain at least one aromatic ring with one or more hydroxyl groups [9,10]. Among them, flavonoids have a basic structure, consisting of two aromatic rings (A and B rings) linked by three carbon atoms that are confined in an oxygenated heterocycle ring (C ring). Based on their differences in the C ring, flavonoids are further classified as flavonols, flavones, catechins/condensed tannins, anthocyanidins, and isoflavones. Other polyphenols differ from each other by the presence of hydroxyl, methoxyl, and/or glycosyl groups in their structures. (Figure 1). 3. Characteristics of Polyphenols in Cancer Prevention Hydroxyl groups present in polyphenols (phenol ring) have the capability to donate hydrogen molecules for free radicals and convert free radicals into chemically stable less reactive molecules. Some polyphenols, such as epigallocatechin-3-gallate (EGCG), epicatechin gallate (ECG), and epicatechin (EC), can form metal chelates, thus preventing the formation of metal-catalyzed free radicals through Fenton reactions. Oxidative damage of lipids, proteins, and nucleic acids is prevented as a result of lower accumulation of free radicals and oxidant species in the biological system [18]. Moreover, hydroxyl groups are able to make hydrogen bonds with biological membranes and alter regulation of membrane-bound enzymes and receptors. Quercetin has been identified as a potent antagonist for the aryl hydrocarbon receptor (AhR) which regulates expression of cytochrome P450 (CYP) enzymes (capable of activating procarcinogens) and a transcription factor that can be activated by polycyclic aromatic hydrocarbons (PAHs) implicated in lung carcinogenicity [19,20,21]. Phenolic hydroxyl groups donate hydrogen groups and react with reactive oxygen and reactive nitrogen species [22], which break the cycle of new radical generation and stabilize the radicals. The presence of a hydrophobic benzoic ring and the capability for hydrogen bonding help polyphenols to interact with proteins present in cells. Hence, they suppress the activity of radical generating enzymes such as cytochrome 450 isoforms, lipoxygenases and cyclooxygenase and reduce intracellular oxidative stress [23,24]. Phase II enzymes generally detoxify endogenous and xenobiotic electrophilics (from chemical carcinogens) through glucuronidation, sulfation, methylation, acetylation, glutathione, and amino acid conjugation. The resulting hydrophilic compounds are easily excreted via bile or urine [25]. For example, isoflavone genistein, which is rich in soybean, has exhibited the ability of inducing phase II detoxifying and antioxidant enzymes in cell culture models through activating the extracellular signal-regulated protein kinase 1/2 (ERK1/2) and protein kinase C (PKC) signaling pathway, and increasing nuclear factor E2-related protein 2 (Nrf2), which interact with antioxidant response element (ARE). Most genes encoding phase II enzymes contain an ARE sequence in their promoter regions [26]. Nrf2 protects the cells against the formation of DNA adducts and/or gene mutations from benzo[a]pyrene (BaP). Diesel exhaust fumes and up-regulation of ARE-driven genes, by activated Nrf2, enables the cells to protect against increased concentration of electrophiles, free radicals, and reactive oxygen, nitrogen, and sulphur species [27,28,29,30]. Consumption of fruits and vegetables has given significant protection in 24 of 25 studies in lung cancer [31]. Quercetin from onions and apples was found to be inversely associated with lung cancer risk, especially against squamous cell carcinoma [32,33]. A population-based case control study (1061 cases and 1425 controls) conducted by Christensen and colleagues (2012) has shown that lower dietary flavonoid intake can increase the lung cancer risk. In addition, they found an inverse association between intake of flavone and flavanone-rich diets and squamous cell carcinoma incidences, but no association was found with adenocarcinoma incidences [34]. Many in vitro studies have justified the role of polyphenols in lung cancer control (Table 1). 4. Environmental and Occupation Lung Carcinogens Lung cancer is not a sudden event; it arises from long-term accumulation of genetic and epigenetic modifications occurring in lung cells. It is a heterogeneous disease condition [42]. Lung cancer is classified pathohistologically into two broad categories: small-cell lung cancer (SCLC) (about 15%) and non-small cell lung cancer (NSCLC) (about 85%) [43]. In all SCLC tumors, deletion of 3p(14–23) in the region containing the tumour suppressor gene FHIT (a member of the histidine triad gene family) is seen. Tyrosine kinase signaling genes, including KRAS and EGFR, are rarely mutated [44]. The loss of tumour-suppressor gene retinoblastoma (RB1) and the mutation of tumour suppressor gene TP53 are more common in SCLC patients than among NSCLC patients. Loss of the activity of tumour suppressor genes at the early stage of SCLC development can decrease apoptosis, induce cell proliferation and increase the survival of cancer cells [45]. NSCLC is the leading cause of cancer deaths worldwide with a 14% five-year survival across all stages of the disease [46]. NSCLC is classified into three major sub-groups: squamous cell carcinomas (SCC), adenocarcinomas (ADC), and large cell carcinomas (LCC) and into several minor sub-groups: adenosquamous and sarcomatoid carcinomas [47]. SCC are located centrally while ADC and LCC are usually found in the peripheral lung tissues. In lung cancer histology, SCC consists of keratinized cells tightly attached by intracellular cell junctions, but ADC shows glandular formation and/or mucin production whereas LCC have undifferentiated characteristics [48]. Early stage lung cancer can be treated with curative intent by surgery or, in some cases, with radiotherapy. However, most lung cancers are diagnosed at the later stage of disease with extensive local-regional involvement and systemic metastases. These patients have a poor prognosis and are treated mostly with systemic chemotherapy and palliative radiotherapy [49]. The International Agency for Research on Cancer (IARC) has classified lung carcinogenic agents into five broad groups: Group 1: Carcinogenic to human. Group 2A: Probably carcinogenic to human. Group 2B: Possibly carcinogenic to human. Group 3: Not classifiable as it’s carcinogenic to human. Group 4: Probably not carcinogenic to humans. Carcinogens which have demonstrated sufficient evidence of lung carcinogenesis have been classified as group I lung carcinogens (Table 2). Only 1% of lung cancers originate from the inheritance of a germ line mutation. Most are associated with somatic mutations due to environmental or occupational exposures and lifestyle factors. These mutations may occur in oncogenes, tumor suppressor genes, cell cycle control genes, DNA repair genes, apoptosis regulator genes, and telomerase associate genes [75]. Lung carcinogenesis is a complex cascade of molecular and cellular alterations in the lung epithelial cells. Cancer initiation is a rapid process compared with the promotion and progression phases (Figure 2). Lung cell microenvironment is changed as a result of frequent exposure to carcinogens. Carcinogens form inflammatory, reactive electrophilic metabolites and oxidative stress (reactive oxygen and nitrogen species (ROS, RNS)), which have the ability to interact with DNA and cause DNA damage [8]. Ionizing radiation can produce reactive oxygen intermediates, causing oxidative DNA damage and double strand break [76]. Polycyclic aromatic hydrocarbons, present in tobacco smoke, diesel exhaust, and soot, form DNA adducts and oxidative DNA damage leading to somatic mutation. Persistent DNA damage can cause miscoding during replication and loss of normal cell functions resulting in uncontrolled cell growth and proliferation. Genomic instability, a hallmark of cancer, is the main reason for sustained cell proliferation signals, cell death resistance and suppression invasion [42]. Prevention of genotoxicity and maintenance of genome stability at the early phase of cancer development is the most effective method of lung cancer prevention. Genome stability can be achieved by balancing oxidative stress through scavenging free radicals and/or by inducing the activity of phase II detoxifying enzymes that can detoxify and excrete carcinogenic metabolites from the body. A brief description of lung carcinogenesis is demonstrated in Figure 2. 5. Evidences for Lung Cancer Prevention by Dietary Polyphenols Three chemopreventive strategies have been suggested for polyphenols by Soria et al. [77]. They are: (1) primary prevention, the prevention of cancer in healthy high risk individuals; (2) secondary prevention, preventing cancer development in individuals who are having precancerous lesions; and (3) tertiary prevention, the prevention of recurrence or metastasis in individuals who have experienced cancer before. The prevention of primary lung cancer with polyphenols has been summarized in Figure 3. 5.1. In Vitro Studies Cancer preventive properties of phytochemicals have been studied extensively; however, very little in relation to lung carcinogens. Epigallocatechin-3-gallate (EGCG), a known polyphenol present in green tea, dose dependently suppresses the hexavalent chromium (Cr (VI))-induced apoptosis, reduces activation of caspase-3 and nuclear poly (ADP-ribose) polymerase (PARP), and intracellular ROS and DNA-protein cross links in BEAS-2B cells [81]. It has been demonstrated that bisdemethoxycurcumin (curcuminoid separated from Curcuma longa (turmeric)), can prevent the premature senescence of WI-38 normal lung fibroblast cells treated with tert-butyl hydroperoxide (t-BHP), through surtuins 1/AMP-activated protein kinase (Sirt1/AMPK) signaling pathway [82]. Caffeic acid (3,4-dihydroxy cinnamic acid) can scavenge intracellular ROS and 1,1-diphenyl-2-picrylhydrazyl radical and hence prevent lipid peroxidation in WI-38 cells exposed to hydrogen peroxide (H2O2). It could increase the activity of catalase and protein expression and activate the extracellular signal regulated kinase protein and protect cells from H2O2 damage [83]. Methoxylated flavonoids and resveratrol (found abundantly in grapes) block the DNA binding of BaP, a polycyclic aromatic hydrocarbon present in tobacco smoke, and CYP1A1 protein activation in BEAS-2B normal bronchial epithelial cells [84]. Quercetin has induced Nrf2-driven heme oxygenase 1 (HO-1) expression system and its antioxidative properties respond to the changes in the cellular redox environment in BEAS-2B cells [85]. Luteolin, a flavone, is rich in kiwi fruit and melons. Tan and his colleagues have explored the chemopreventive ability of luteolin in cigarette smoke extract (CSE)-induced normal human bronchial epithelial cells (NHBE). They found that luteolin could attenuate CSE-induced apoptosis, noticeably reduce CSE-induced expression of Nrf2, nicotinamide adenine dinucleotide phosphate (NAD(P)H):Quinone oxidoreductase 1 (NQO1) and HO-1. Further, cellular glutathione (GSH) level was increased with luteolin, which triggered suppression of the ROS generation [86]. Quercetin, a common flavonol present in apples and onions, has suppressed the expression of CYP1A1 and CYP1B1 phase I enzymes in BaP treated BEAS-2B cells. Kaempferol, another member of flavonol, suppresses phase I enzymes activated by cigarette smoke condensate (CSC) in BEAS-2B cells. Further, Kaempferol could prevent the CSC-induced cell transformation and colony formation in BEAS-2B cells [84,87]. 5.2. In Vivo Studies Polyphenol studies of lung cancer prevention in experimental mice models are presented in Table 3. The chemopreventive properties of mangiferin (C-glycosylxanthone structure) have been shown against BaP-induced lung carcinogenesis in male Swiss albino mice, possibly through enhancing phase II enzymes [88]. 6. Epidemiological Evidence of Lung Cancer Prevention Quitting smoking and eating healthy diets are the most important healthy habits to reduce lung cancer incidence. Many studies have confirmed the reduction of lung cancer risk among people consuming fruit- and vegetable-rich diets [99]. Zhong and colleagues (2001) have found a 35% reduction of lung cancer risk in non-smoking women who regularly drink green tea compared with women who did not drink tea regularly in Shanghi, China [100]. In a health professionals’ follow-up study, over 77,200 women and 47,700 men were examined for an association between lung cancer risk and fruits and vegetables consumption. A 21% reduction in lung cancer risk was found in women with the highest fruits and vegetable consumption [101]. A similar risk reduction was not found in the men. A case-control study was conducted in Poland with 118 women diagnosed with lung cancer and 141 healthy women. It showed that cigarette smoking and drinking vodka increased lung cancer risk while frequent consumption of carrots reduced the risk [102]. However, a pooled analysis of seven cohort studies done in North America and Europe failed to show any association between β-carotene intake and lung cancer risk,. But an inverse association was found with β-cryptoxanthin from citrus fruits and lung cancer risk. A study in Singapore has further confirmed the chemopreventive effect of β-cryptoxanthin in reducing lung cancer risk [103,104]. Moreover, a population-based, case-control study in Hawaii has found a significant inverse association between lung cancer risk and regular consumption of onions, apple (main dietary source of flavonoid quercetin) and white grape fruit (naringin). The effect of onions was particularly strong against squamous cell carcinoma and it suggests that decreased activation of PAHs and other carcinogens by inhibiting the activity of cytochrome 450 enzymes. Inhibition of the activation of procarcinogens through cytochrome enzymes may be a major mechanism of polyphenols in lung cancer prevention. A cohort study (consisting of 521,468 subjects; both men and women between 25–70 years old) conducted in 10 European countries showed an inverse association of lung cancer risk in relation to the varied consumption of fruits and vegetables among smokers. The risk of developing squamous cell carcinoma was reduced among smokers consuming a variety of fruits and/or vegetables, but no association was observed in adenocarcinomas and small cell carcinoma. Lung cancer risk is lower with increasing consumption of a variety of fruits and vegetables, independent from quantity of consumption [105]. Once the variety of fruits and vegetables in the diet is higher, diversity of bioactive compounds also increases. A greater variety of fruits and vegetable consumption therefore represents a more diverse bioactive phytochemical intake. Contrast to this finding, Linseisen et al., [7] found only fruit (apples and pears) consumption has an inverse relationship with lung cancer risk, not vegetables. However, lung cancer risk was significantly decreased in smokers who consume more vegetables, specifically root vegetables. Lycopene, β-cryptoxanthin, total carotenoids and lutein significantly reduced lung cancer risk among male smokers. In addition, lycopene reduced the risk of small cell and squamous cell carcinoma, but not adenocarcinoma [106]. 7. Conclusions Genome instability is the primary cause of lung cancer initiation. Almost all lung carcinogens are able to alter the cell microenvironment, which favors DNA damage. Oxidative DNA damage is the central process causing lung carcinogenesis. Plant polyphenols have exhibited multiple modes of cancer prevention functions: (i) polyphenols act as antioxidants and regulate oxidative stress caused by the lung carcinogens; hence, they mediate oxidative DNA damage, lipid autoxidation, and cell membrane damage; (ii) polyphenols inhibit the activation of cytochrome 450 enzymes (phase I enzymes), which form reactive electrophilic metabolites from pro-carcinogens. These reactive intermediary metabolites can covalently bind with DNA specific sites and form DNA adducts which activate oncogenes and suppress tumor suppressor genes, leading to lung cancer; and (iii) polyphenols enhance the activity of phase II detoxification enzymes, which are able to detoxify electrophilic metabolites from phase I enzymes and excrete them through urine and bile. Furthermore, many polyphenols enhance the activity of enzymatic (SOD, CAT, GPx) and non-enzymatic antioxidants (Vitamin E, C, and GST) that are able to balance the ROS and RNS in cells. Polyphenols can be found ubiquitously in fruits, vegetables, grains, and other plant-based food. Further investigations are required to identify specific polyphenols and their dietary sources that are involved in oxidative protection, regulation of phase I and II enzymes, and regulation of cell survival pathways in relation to lung carcinogenesis. Even though there is some controversy in reported literature, this review recognizes many in vitro, in vivo, and epidemiological studies supporting the notion that habitual dietary intervention of polyphenols can reduce the risk of lung cancer. Acknowledgments We would like to thank for the traineeship award (Madumani Amararathna) received from the Beatrice Hunter Cancer Research Institute with funds provided by the Saunders-Matthey award for cancer prevention research as part of the Terry Fox strategic health research training program in cancer research at Canadian Institute of Health Research (CIHR). Author Contributions H. P. Vasantha Rupasinghe designed the outline of the review. Madumani Amararathna reviewed the literature and wrote the first draft of the manuscript. All authors (Madumani Amararathna, H. P. Vasantha Rupasinghe and Michael R. Johnston) contributed to writing and editing and approved the manuscript. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Classification of polyphenols, examples and dietary sources [11,12,13,14,15,16,17]. EGC—epigallocatechin. Figure 2 Stages of developing lung cancer, in summary (Modified from Rupasinghe et al., 2014 [8]). Figure 3 Intervention of polyphenols at lung cancer initiation stage [78,79,80]. 1, Quercetin; 2, Naringin; 3, Luteolin; 4, Keampferol; 5, Proanthocyanidine; 6, Baicalein; 7, Catechins; 8, Isothiocyanate; 9, Epigallocatechin gallate; and 10, Rutin. ijms-17-01352-t001_Table 1Table 1 In vitro studies of anti-proliferative effect of polyphenols in lung cancer. Cell Type Polyphenol Proposed Mechanism of Action Reference A549, H460 & H1299 Grape seed proanthocyanidins Inhibit cell migration and endogenous nitric oxide Inhibit activation of ERK1/2 Induce apoptosis Activate caspases-9 and -3 Activate poly (ADP-ribose) polymerase [35,36,37] NCI-H209 Quercetin glucuronides Decrease cell viability (dose and time dependent) Arrest cell cycle at G2/M phase via caspase-3 cascade [38] A549 & H460 Curcumin Inhibit cell proliferation Induce fork head box protein O1 (FOXO1) expression [39] PC-9 Curcumin Inhibit cell growth Induce G1/S arrest via activating CDK inhibitor genes p21 and p27 [40] A549 Polyphenol rich brown alga (Ecklonia cava) extract Suppress migration and invasion Down-regulate MMP-2 activity Anti-metastatic effect [41] Non-small cell lung cancer (NSCLC) cell—A549, H460, H1299; human adenocarcinoma cell—PC-9; small cell lung cancer (SCLC) cell—NCI-H209; extracellular signal-regulated kinase—ERK; adenosine diphosphate—ADP; cyclin dependent kinase—CDK; and matrix metalloproteinase—MMP. ijms-17-01352-t002_Table 2Table 2 Group I lung carcinogens classified by International Agency for Research on Cancer (IARC) (2012). Group 1 Carcinogens Type of Exposure Personal Habits and Indoor Combustion Tobacco Smoking and Second Hand Smoke E Household Combustion of Coal Tar E Diesel Exhaust E, O Chemical Agents and Related Occupation benzo[a]pyrene (BaP) O Coal Gasification O Coal-tar Pitch O Coke Production O Soot (Contains BaP) E, O Aluminium Production O Bis(chloromethyl)ether and Chloromethyl Methyl Ether O Sulfur Mustard O Iron and steel founding O Painting O Rubber Manufacturing O Radiation X-radiation and γ-radiation O Internalized α-particle Emitting Radionuclides Radon (Rn)—222Rn Produced from Uranium (238U) and 220Rn Produced from Thorium Plutonium-239 E, O Metal, Fiber and Dust Arsenic and Inorganic Arsenic Compounds E, O Beryllium and its Compounds E, O Cadmium and Cadmium Compounds E, O Chromium(VI) Compounds E, O Nickel compounds E, O Asbestos E, O Crystalline silica in the form of quartz or cristobalite E, O Pharmaceuticals Mechlorethamine, Oncovin, Procarbazine, and Prednisone (MOPP) combination therapy O Second-hand smoke—side stream smoke emitted into the environment from the smoldering of cigarettes and other tobacco products between puffs and from the mainstream smoke exhaled by the smoker; environment—E; occupation—O [50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74]. ijms-17-01352-t003_Table 3Table 3 Polyphenols in cancer prevention: in vivo studies. Animal Model Carcinogen Compound or Extract Observation Reference Swiss ICR Mice Cigarette smoke (CS) Black chokeberry and strawberry aqueous extracts Reduce cytogenetic damage, liver degeneration, pulmonary emphysema and lung adenomas Inhibit CS-related body weight loss [89] Mice BaP Hesperidin Attenuate mast cell density Down regulate expressions of COX-2, MMP-2 and MMP-9 Exert anti-carcinogenic activity against lung cancer [90] Swiss Albino Mice BaP Baicalein Increase enzyme antioxidants and non-enzyme antioxidants Decrease the activity of phase I enzymes Increase the activity of phase II detoxification enzymes Preserve pulmonary microvasculature and normal growth pattern [91] Swiss Albino mice BaP Mangiferin Prevent decrement of electron transport chain complexes and TCA cycle key enzymes in lung cancer bearing mice [92] ICR Mice Tobacco smoke Apple polyphenol Reduced inflammation Reverse oxidative stress in lung tissues Regulate the MMP-9 in cells [93] Swiss Albino Mice BaP Naringenin Activate the enzymatic antioxidants (SOD, CAT, GPx, GST) Suppress unregulated expression of CYP1A1, PCNA and NF-κB Reduce pro-inflammatory cytokines (TNF-α, IL-6 and IL-1β) Reduce proliferative lesions in lung [94] Swiss Albino Mice BaP Fisetin Restore lipid peroxidase, enzymatic and non-enzymatic antioxidants levels Reduce the lung lesions Reduce PCNA [95] A/J Mice NNK EGCG Attenuate the induction of DNMT1 Reduce phospho-histone H2AX (γ-H2AX) and phospho-AKT (p-AKT) [96] Sprague-Dawley Rats NNK Cape gooseberry extract Reduce pulmonary hyperplasia Improve the DNA content Reduce expression of cell proliferation marker Ki-67 Enhance expression of tumor suppressor gene p53 [97] Mongolian Gerbils BaP Quercetin Suppress the expression of TNF-α, IL-1β, phospho-c-Jun and phospho-JNK [98] Cyclooxygenase-2—COX-2; matrix metalloprotein—MMP; tricarboxylic acid—TCA; superoxide dismutase—SOD; catalase—CAT; glutathione peroxidase—GPx; Glutathione—GST; proliferating cell nuclear antigen—PCNA; nuclear factor-κ light-chain-enhancer of activated B cells—NF-κB; Tumor necrosis factor-α—TNF-α; Interleukin—IL; 4-(methylnitro-samino)-1-(3-pyridyl)-1-butanone—NNK; and DNA methyltransferase 1—DNMT 1. ==== Refs References 1. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081353ijms-17-01353ArticleMajor Ampullate Spider Silk with Indistinguishable Spidroin Dope Conformations Leads to Different Fiber Molecular Structures Dionne Justine Lefèvre Thierry *Auger Michèle Hardy John G. Academic EditorHolland Chris Academic EditorRegroupement québécois de Recherche sur la Fonction, l'Ingénierie et les Applications des Protéines (PROTEO), Centre de Recherche sur les Matériaux Avancés (CERMA), Centre Québécois sur les Matériaux Fonctionnels (CQMF), Département de Chimie, Université Laval, Pavillon Alexandre-Vachon, Ville de Québec, QC G1V 0A6, Canada; justine.dionne.1@ulaval.ca (J.D.); michele.auger@chm.ulaval.ca (M.A.)* Correspondence: thierry.lefevre@chm.ulaval.ca; Tel.: +1-418-656-2131 (ext. 6460); Fax: +1-418-656-791618 8 2016 8 2016 17 8 135321 7 2016 15 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).To plentifully benefit from its properties (mechanical, optical, biological) and its potential to manufacture green materials, the structure of spider silk has to be known accurately. To this aim, the major ampullate (MA) silk of Araneus diadematus (AD) and Nephila clavipes (NC) has been compared quantitatively in the liquid and fiber states using Raman spectromicroscopy. The data show that the spidroin conformations of the two dopes are indistinguishable despite their specific amino acid composition. This result suggests that GlyGlyX and GlyProGlyXX amino acid motifs (X = Leu, Glu, Tyr, Ser, etc.) are conformationally equivalent due to the chain flexibility in the aqueous environment. Species-related sequence specificity is expressed more extensively in the fiber: the β-sheet content is lower and width of the orientation distribution of the carbonyl groups is broader for AD (29% and 58°, respectively) as compared to NC (37% and 51°, respectively). β-Sheet content values are close to the proportion of polyalanine segments, suggesting that β-sheet formation is mainly dictated by the spidroin sequence. The extent of molecular alignment seems to be related to the presence of proline (Pro) that may decrease conformational flexibility and inhibit chain extension and alignment upon drawing. It appears that besides the presence of Pro, secondary structure and molecular orientation contribute to the different mechanical properties of MA threads. Raman spectromicroscopyspider silk fibersspinning dopesilk protein structuremolecular orientationorientation distribution function ==== Body 1. Introduction Spider silk proteins (spidroins) represent the basic constituent of a range of natural filaments that are used to fulfill specific biological functions. Relying on its biocompatibility and remarkable mechanical properties, this raw material may become a promising resource for materials science. It may lead in the future to diverse and useful applications in various fields including biomedicine, optoelectronics, high-tech threads or textiles, particularly in a context where society’s sustainability requires, among other societal progresses, the development of green and durable technologies [1,2,3,4,5]. It is then desirable to reproduce natural silk in the laboratory and at industrial scale, or to produce silk-inspired materials with tailored characteristics. To achieve this goal, a deep understanding of the structure and formation of silk is necessary. Silk threads are produced by spiders in specialized glands located in their abdomen. The archetype of silk is the dragline which is produced by the major ampullate (MA) gland and is thus termed MA silk. Its basic structural organization can be described by stiff crystalline nanodomains constituted by highly oriented polypeptide chain segments folded into β-sheets and distributed in an amorphous soft matrix composed of protein chains that adopt less oriented and more disordered secondary structures [6,7,8]. This semicrystalline architecture governs silk mechanical properties as it is influenced by the level of molecular orientation, the fraction of the crystalline phase, the limited size of the β-sheet nanocrystals, and by the hydrogen bonded network of the amorphous chains [9,10,11,12,13,14], among other factors. The final material results from the molecular transformations of spidroins and their assembly during the course of the spinning process which is in turn generated by the mechanical constraints imposed by the spider. The sequence of the spidroin amino acid building blocks is segmented into successive motifs that undergo conformational and orientational changes during silk formation. MA spidroins include polyalanine (poly-Ala) segments which form the β-sheets [15,16] and glycine-rich segments GlyGlyX and GlyProGlyXX (GGX and GPGXX) (with X = Leu, Glu, Tyr, Ser, etc.) which constitute the disordered phase (31-helices, turns) [17,18,19,20]. The MA fiber of orb-weaving spiders is actually made of two analogous spidroins which vary in proportion and slightly differ in sequence depending on the species, particularly in terms of proline (Pro) content. For example, in the case of Nephila clavipes (NC), the main protein (MaSp1, 80% mole) is Pro-free while MaSp2 is marked by the presence of Pro under the form of GPGXX motifs [21,22]. In the case of Araneus diadematus (AD), the two proteins (termed ADF3 and ADF4) [23] are both rich in Pro, leading to a higher global Pro content (16%, while NC contains only 3.5%) [24,25]. Major advances have been made to understand how spidroin primary structure (repetitive segment [26,27,28,29], non-repetitive N- and C-terminal regions [30,31]), spinning process (rheology [32,33], anatomy and ultrastructure of the gland [34,35] as well as physicochemical conditions of the lumen [36,37,38]) contribute to the formation of the final structure and to its properties. Among other factors, the role of Pro on the mechanical behavior of MA silk has been emphasized, especially since the discovery that the Pro content is positively correlated with the extent of supercontraction [13,39], a phenomenon by which silk fibers shrink longitudinally and swell radially upon exposure to water. The influence of Pro residues has been attributed to an increase in water plasticization due to a disordering effect on the polypeptide chains in the amorphous phase [13,39,40,41,42]. The improvement in the mechanics of MA silk upon evolution (extensibility, toughness) has been correlated with the appearance of the Pro-rich MaSp2 spidroins in the Orbiculariae clade [43]. Silk thread structure is determined by the protein sequence characteristics and spinning process (Figure 1). The importance of both factors has been emphasized [26,39,44,45,46,47,48,49]. Whereas it may be inferred that silk structure is mainly “encoded” in the sequence (such that the spinning process may appear relatively unimportant), the MA gland environment and the way molecular transformations are induced are essential to lead to the appropriate fiber structure. Both factors are actually interconnected: the spinning process converts the spidroins conformation occurring in the sac of the MA gland into the highly organized structure of the fiber. However, the initial and final states as well as the response of the polypeptide chain to mechanical and physicochemical stimuli depend intimately on the sequence. The initial conformation may also by itself influence the molecular transformations that lead to the final structure. Overall, the relative contributions of the composition and architecture of the spidroins sequence on one hand and the spinning process on the other hand remains unclear. To better understand the initial-to-final structure transition and the role of the sequence and spinning process, structural differences that may exist between MA silks from different spiders have to be evaluated in both the liquid (dope) and solid (fiber) states. The comparison of silks reeled in the same conditions and at the same speed may minimize the effect of the spinning process, thus potentially evidencing small structural differences due to the specificities of the spidroin’s primary structure. This is particularly relevant since the sequence, in particular the Pro content, can influence the conformation of the spidroins both in the dope and in the fiber. Although tensile properties of MA silk of numerous spider species have been extensively investigated [13,39,50,51,52,53,54,55,56,57], researches devoted to comparisons of their structures are scarce. Even for the two most widely investigated spiders (AD and NC), data are lacking, in particular regarding the level of molecular orientation. A lower birefringence of AD with respect to NC has however been measured [13] and a higher level of random coil conformation has been proposed for AD from Raman spectromicroscopy [58]. A major advance has been made recently by a broad phylogenetic X-ray diffraction investigation of MA silk fibers [51]. Systematic data regarding the comparison of spidroin conformations in the MA dope of different species are inexistent. The aim of this study is thus to compare quantitatively the molecular structure of NC and AD MA silk dopes and fibers. Raman spectromicroscopy has been used to investigate single monofilament and gland content and to determine the orientation level and secondary structure content. The present data suggest that besides the role of Pro residues, other molecular structural characteristics such as β-sheet content and molecular orientation have to be considered to exhaustively describe species-related differences in the mechanical properties of the MA spider silks. The sequence specificities also appear essential for the formation of the fiber structure. 2. Results 2.1. Spidroins Conformation in the Major Ampullate (MA) Dope Raman spectra of the native silk dope of the NC and AD MA glands are shown in Figure 2. The normalized Raman intensity displays unique information about the chemical constituents of the spinning dopes. Since the silk dope is isotropic, Raman intensity is only influenced by the chemical composition and conformation of the samples. In other words, molecular orientation does not affect the Raman spectra. Some bands are conformation-sensitive whereas others are representative of the amino acid residues. Bands representative of the secondary structure are mainly due to the peptide bonds, especially regarding the amide modes. The shape and position of the amide I bands (1658 cm−1) are superimposable for the two spider species as revealed by the inset scheme in Figure 2. This result indicates that the conformation of the spidroins is identical for NC and AD. As discussed elsewhere [16,59], these amide I bands reveal that the silk dope is essentially disordered with a small contribution from α-helix [60]. The amide III band is also very similar for both species, with two components at 1244 and 1260 cm−1 which are characteristic of disordered structure. The skeletal Cα–Cβ stretching band appears at 1103 cm−1 and supports the previous random coil assignment. The spectra also exhibit a peak at 525 cm−1 due to poly-Ala with an α-helical conformation. The lower intensity for AD indicates lower percentage of poly-Ala in the sequence. Bands due to the amino acid side-chains allow discrimination between different silks. In particular, Pro bands at 877, 921, and 1043 cm−1 only appear in the spectrum of the AD dope. This was expected since, as previously mentioned, the MA spidroins of AD contain more Pro compared to NC [24,25]. Peaks due to tyrosine (Tyr) at 643, 829, 851, and 1615 cm−1 and to poly-Ala at 904 cm−1 are found in both spectra of liquid MA silk although that those related to Tyr are more intense for AD. Bands due to phenylalanine (Phe) residues arise at 1003 and 1601 cm−1 and the peak at 1207 cm−1 is characteristic of Phe and Tyr. A non-protein constituent previously identified as an isoquinoline compound [61] arises at 702, 1387, and 1547 cm−1 in the spectrum of NC [62]. 2.2. Molecular Structure of the MA Fibers 2.2.1. Qualitative Analysis Figure 3 shows the polarized IXX and IZZ spectra of the NC and AD MA threads. The complete series of polarized spectra are given in Figure S1. Unlike the dopes, silk fibers display anisotropy. Raman intensity thus depends not only on the conformation but also on molecular orientation. Regarding amino acid side-chains, the peaks due to Tyr at 641, 827, 851, and 1615 cm−1, to Phe at 1002 and 1602 cm−1, to Phe/Tyr at 1209 cm−1 and to poly-Ala at 903 cm−1 are still prominent for AD and NC. Consistently with the spectra of the liquid silk, the Pro bands at 877, 919, and 1041 cm−1 are only observed for AD. Peaks associated with isoquinoline in NC come out at 700 and 1550 cm−1. The band previously found at 1387 cm−1 overlaps with another vibrational mode due to the chain backbone at around 1396 cm−1 [63]. As confirmed by the positions of the IXX amide I band at 1669 cm−1 and the amide III bands at 1225 and 1246 cm−1, both fibers exhibit a predominance of β-sheets. Other conformation-sensitive bands at 961, 1315, 1367, and 1396 cm−1 and skeletal Cα–Cβ stretching at 1069 and 1092 cm−1 confirm that the β-sheet conformation is preponderant for the two spiders. The bands at 1367 and 1396 cm−1 are more intense in the IZZ spectrum of NC compared to AD, which suggests that NC MA silk contains more β-sheets than AD. However, a component of the isoquinoline contaminant described earlier contributes to the band at 1396 cm−1. Furthermore, the IXX amide III band of NC is less intense and is located at 1235 cm−1 rather than at 1246 cm−1 for AD. This difference is explained below. The variations of band intensities with polarization indicate that the proteins in MA filaments exhibit a preferential orientation. The β-sheets of MA fiber are known to be oriented along the fiber axis [64,65]. Consequently, the carbonyl groups of the peptide bonds, which are principally responsible for the amide I signal, are mainly perpendicular to the fiber axis (Figure S2). A higher intensity of the amide I band in the IXX spectrum is then expected over the IZZ. For comparison purposes, the IXX amide I bands of all series of spectra have been normalized to unity. Consequently, the weaker the IZZ amide I band, the higher the orientation of the fiber. Conversely, a stronger IZZ amide III band, or a weaker IXX amide III band, means a higher orientation of the chains since the amide III band is mostly due to the C–N stretch of the peptide bond which is oriented along the fiber axis in β-sheets [66]. Figure 3 shows that the IZZ amide I band of NC is weaker and its IZZ amide III band is more intense as compared to AD (its IXX amide III band is also weaker). Those observations suggest that the NC MA spidroins are more oriented than those of AD. Furthermore, the inset illustration in Figure 3 reveals that the amide I band of AD is broader compared to NC. This observation indicates that the β-sheets of the AD fiber are less oriented and/or less abundant than for NC. 2.2.2. Secondary Structure Content Figure 4 presents the orientation-insensitive spectra of NC and AD MA fibers in the amide I region calculated from the average polarized spectra. The Raman intensity of these spectra is only representative of the abundance of spidroin conformational elements, so that they can be used to evaluate the amount of secondary structures. Since the peak maximum of the amide I band at 1669 cm−1 is higher for NC than for AD, it appears that the NC MA silk contains more β-sheets. Alternatively, the spectrum of AD is broader compared to NC, especially at around 1645 cm−1, an observation that suggests that disordered structures are more abundant for the AD silk. The percentage of each secondary structure that contributes to the amide I band has been further estimated by curve-fitting (Figure 5). Consistently with the above qualitative analysis, the β-sheets are the predominant conformation in both MA fibers, the β-sheets being more prevalent in NC (37%) than in AD (29%) silk. The AD filament contains more disordered structures which is in agreement with previous data [58]. A slight difference in the amount of β-turns at 1682 cm−1 is also found between the AD (23%) and NC (19%) threads. The approximate error on the band areas being 3%, this distinction between the two species appears minor. Finally, NC and AD MA fibers have the same relative abundance of 31-helices (19%) and β-turns at 1697 cm−1 (12%–15%). 2.2.3. Quantitative Orientation Analysis The molecular orientation of the NC and AD fibers was characterized using the polarized amide I vibration bands (mainly due to the C=O stretching vibration). As this band is dominated by the β-sheet component, it is assumed that the orientation mainly reflects peptide bonds adopting a β-sheet conformation in the threads. The peak maximum of the amide I band was used to calculate the qualitative orientation parameter R′ and the order parameters P2 and P4 from the intensity ratios R1 and R2. MA silk samples have a weaker IZZ intensity in the amide I region, i.e., R′ < 1 and P2 < 0. A more negative value of R′ = 1 − (IXX/IZZ) indicates a higher degree of orientation of the β-sheets. Similarly, the more negative the P2 value, the higher the molecular orientation. The parameter R′ as well as P2 and P4 for AD and NC threads are presented in Table 1. R′ values of −1.63 and −1.24 were found for NC and AD fibers, respectively, which confirm that the NC silk is more oriented than AD. This qualitative result is confirmed by the quantitative P2 and P4 values calculated with the DC (depolarization constant) method (see the Experimental section). The P2 values are negative due to the fact that the carbonyl groups are mainly perpendicular to the fiber axis. With the DC method, the P2 value of the MA silk of NC is lower (more negative) than for AD indicating that NC spidroins are more oriented than AD ones. The most probable distribution functions Nmp(θ) estimated from the order parameters and the information theory is shown in Figure 6. The full width at half maximum (FWHM) is 57° for NC and 75° for AD. The distribution of orientation is thus narrower for NC than for AD, showing quantitatively that the level of orientation of MA silk is higher for NC. The maximum of the function appears at 90° which is consistent with a perpendicular orientation of the structural units (carbonyl groups) for both species. As can be seen, the top of Nmp(θ) of AD is flattened, which actually seems not physically realistic. This flaw may be due to uncertainties on the intensity ratios and/or accuracy of the depolarization ratio. The values of order parameter P4 is particularly sensitive to such variations and, as noticed previously [67], small differences in P4 values can lead to significant differences in the orientation distributions. A second method, called the MPD (most probable distribution) method (see the Experimental section), assumes that the orientation distribution is Gaussian and has thus been tested to determine P2 and P4. The two methods give closely the same P2 but different P4 values (Table 1). The shape of Nmp(θ) is not strongly affected by the calculation method for NC, but more important changes are observed for AD. As the orientation distribution functions of the two species are Gaussian with the MPD method, they are more appropriately compared. The FWHM calculated with the MPD method is 51° for NC and 58° for AD confirming that NC spidroins are more oriented than those of AD silk. 2.3. Sequences Analysis The amino acid sequences of the spidroins forming the MA silk are presented in Figure 7. ADF3, ADF4, and MaSp2 all belong to spidroin-2 type [23] and exhibit the same motifs: blocks of An and GA with a predominance of GPGXX motif. The spidroin-1 type MaSp1 is quite different with a prevalence of GGX motif, more An-GA regions and an absence of Pro. AD and NC also exhibit similar length of poly-Ala. Since AD MA silk seems to be composed of ADF3 and ADF4 proteins in a 3:2 ratio [23], the Pro content estimated from the sequence is about 15% whereas NC MA silk contains 3% of Pro since MaSp1 accounts for 80% of the total protein content [13]. These Pro percentages are consistent with those determined by chemical methods [24,25]. The proportion of poly-Ala estimated from the sequence, including the GA motifs [18,20], is 34% for NC and 23% for AD. This difference is consistent with the lower intensity of the poly-Ala band at 525 cm−1 for AD silk dope. Assuming that the poly-Ala regions form the β-sheets [15,17,68], there is a good agreement between the β-sheet content evaluated from the Raman and sequence analyses. There is a small discrepancy between both methods (Raman analysis gives higher values than the sequence analysis), but the amount of β-sheets seems to be roughly predictable from the poly-Ala content. 3. Discussion 3.1. The Conformation of the Spidroins in the Dope Is Not Critical for the Fiber Structure The amino acid composition of the NC and AD MA silks are similar but distinct, the former fiber being mainly composed of the Pro-deficient MaSp1, the latter being formed of two Pro-rich MaSp2-like spidroins. These different chemical compositions do not lead to distinctive conformation in the sac of the MA gland. This observation is consistent with the fact that recombinant MaSp1 and MaSp2 spidroins do not reveal conformational differences in solution by vibrational spectroscopy [69]. It may then be hypothesized that the sequence difference is not determinant for the initial spidroin conformation. Overall, vibrational bands of the backbone show that for both species the secondary structure is typical of disordered proteins, with a minor contribution of α-helix. Contrarily to the dope, the spidroins of the two spiders exhibit clear molecular differences in the fibers, although they share the same structural pattern. It may thus be suggested that the initial conformation in the dope is relatively unimportant with regards to the final structure of MA threads. The molecular characteristics of the dope (disordered chains involved in 31-helical structures without intramolecular bonds) may however promote or be important for the efficient folding of the polypeptide chains into oriented β-sheets in the fiber, but some details of the final structure (accurate β-sheet content and level of orientation) may be modulated by the sequence and the spinning process. The fact that the spidroin conformations of NC and AD exhibit disparities in the final state but not in the initial state suggests that sequence specificities are more extensively expressed in the solid than in the solution state. The intrinsic secondary structure propensity of NC and AD MA spidroins thus appear identical in solution. In particular, the GGX and GPGXX motifs, that are predominant in NC and AD MA silk, respectively, seem to be conformationally equivalent in the dope. This may mostly be due to the chain flexibility in the aqueous environment (high conformational freedom), in particular to the prevalence of interactions between polypeptide chains and water (i.e., the absence of chain-chain interactions). After the spinning process, when the polypeptide chains are in close proximity and the intermolecular interactions maximized, the sequences seem to express their divergent secondary structures in the silk fiber. 3.2. Molecular Orientation and β-Sheet Content Influence Silk Mechanical Properties The MA silks of the Nephila and Araneus genera differ in their tensile properties, the latter fibers being generally tougher and more extensible. Although mechanical differences have initially been observed in the dry state [52,55,56,70], they appeared more recently not to be so clear when comparing NC and AD [13]. By contrast, distinctions in the mechanical behavior seem to be particularly marked in the wet (maximum supercontracted) state [13]. In recent years, deviation in the mechanical properties of the MA silk with species has been accounted for by the Pro content, for example by the fact that the AD fiber is composed of two MaSp2-like proteins whereas that of NC is mainly made of MaSp1 [13,41,71]. The presence of Pro is expected to prevent the formation of regular secondary structure (mainly β-sheets), tight packing of the chains, and intermolecular H-bonding [13,39,41,42]. Contrarily, the Gly motifs, which are predominant in NC, would be more appropriate to form regular structures and an ordered organization [13]. The present data show that other structural parameters have to be taken into consideration when comparing MA silks. Molecular orientation and β-sheet content exhibit significant difference depending on the species. These two parameters obviously can strongly influence tensile properties. Their influence on MA silk properties is in line with findings regarding flagelliform silk. The large extensibility of this thread has long been related to the predominance of the GPGXX motif [72,73]. However, this silk also contains a small proportion of oriented β-sheets, which de facto includes this fiber into the archetypal MA silk family [74]. Thus, the increase in the breaking stress and breaking strain of this silk observed with different species seems to be related to higher β-sheet content and molecular alignment [74]. The amount of β-sheet of the NC and AD MA silk fibers can confidently be rationalized from the proportion of poly-Ala and GA motifs of the sequence, suggesting that the secondary structure content is in large part determined by the primary structure and is thus slightly influenced by the spinning process (although it is necessary to induce the conformational conversion towards β-sheets). The estimation of the β-sheet content by Raman spectromicroscopy and primary structure show that β-sheets are more abundant in NC than in AD MA silk. The lower β-sheet content of the AD MA silk, i.e., the higher proportion of amorphous phase, is likely to contribute to the higher extensibility of the AD MA silk with respect to NC. It has been proposed that the presence of Pro (under the form of GPGXX motifs) may globally promote disordered structures, which in turn may make the MA fiber more flexible [13,39,43,73]. However, the present results indicate that the β-sheet content, and reversibly the global amount of disordered chains, is not affected by Pro residues. Pro may rather make the AD amorphous phase more disordered than that of NC, which may in turn affect the tensile properties of these MA silks. Since the present results and conclusions have been obtained from only two spider species, they need to be generalized to a broader range of MA silks. The Raman data also show that the level of molecular orientation of MA silk is higher for NC than for AD, which is in agreement with birefringence measurements [13]. This structural characteristic may also contribute to the fact that the fiber is more brittle for NC than for AD. Since the MA silks investigated here have been reeled at the same speed, it may be questioned whether the different levels of orientation are determined by the spinning process. More precisely, the difference may arise from the different physicochemical composition, differences in the gland anatomy or, more probably, rheological properties of the dope. However, no data such as spidroin concentration in the dope actually supports such an assumption. A more likely hypothesis lies in the fact that the sequence may be at the origin of these structural differences. As a matter of fact, due to its pyrrolidine ring structure, Pro residues may limit the conformational flexibility of the chain, thus inhibiting chain extension and molecular alignment upon drawing. 4. Materials and Methods Adult NC and AD females were collected in Florida (USA) and Québec (Canada), respectively. They were raised in the laboratory at 24 ± 2 °C and 58% ± 5% relative humidity (RH) and fed with small crickets and 10% w/v glucose solution. For the analysis of liquid silk, the MA glands were extracted, deposited on glass slides or polystyrene petri dishes and immersed in a phosphate saline buffer. The epithelium was gently removed or pierced to expose the native liquid silk to the laser beam. Great care was taken to perturb minimally the silk material during the dissection procedure. In order to obtain fibers by forced reeling, spiders were anesthetized with CO2 and fixed on a support. The MA silk was then reeled on 1.3-cm diameter test tubes from awaken spiders at 1 cm/s. MA silks were stored hidden from sunlight to avoid degradation. About 5-cm long monofilaments were gently fixed on glass slides with one-sided tape at its extremities and at different locations along the fiber. The spectra were recorded at 22.0 ± 0.5 °C and under 20% ± 5% RH using a LabRam 800HR Raman spectrometer (Jobin Yvon Horiba, Villeneuve d’Ascq, France) coupled to an Olympus BX 30 motorized stage microscope. The 514.5-nm line of an argon-ion laser (Coherent, INNOVA 70C Series Ion Laser, Santa Clara, CA, USA) was used as excitation light. The laser beam was focused by a 100× objective (Numerical Aperture (NA) = 0.9, Olympus, Richmond Hill, ON, Canada) generating an intensity of 3–5 mW at the sample. The confocal hole and the entrance slit of the monochromator were fixed at 400 μm and 200 μm, respectively. A 600 lines/mm holographic grating was used to disperse the different wavenumbers of the samples on the one-inch open electrode Peltier-cooled CCD detector (1024 × 256 pixels) (Andor Technologies, Belfast, Northern Ireland). Spectra of the silk dope were recorded in the dry state as it has been shown that they are virtually identical to those obtained in the hydrated state [16]. Five spectra were recorded at different points on two distinctive dope samples. Orientation and secondary structure of the fibers were determined from the amide I band using linearly polarized light. A half-wave plate (Melles Griot, Carlsbad, CA, USA) was used to change the polarization of the incident light either perpendicular (x) or parallel (z) to the fiber axis (Figure S2). A polarizer was placed before the entrance slit of the monochromator to orient the polarization of the scattered light along the x or z direction. A broad-band quarter-wave plate was also used after the polarizer to eliminate the polarization dependence of the grating. Since the system works in a backscattering configuration, it allows the acquisitions of four independent polarized spectra (thereafter called a “series”) with an acquisition time of 2 × 30 s and identified as IXX, IXZ, IZZ, IZX (the first index corresponds to the incident light, the second to the scattered light). To ensure that the focus was constant through the acquisitions, i.e., that non-desired intensity variation occur during measurements, a second IXX spectrum was collected at the end of the series and compared with the first IXX measurement. No sign of sample deterioration was observed under these experimental conditions. Spectra treatments were all performed using GRAMS/AI 7.0 (ThermoGalactic, Salem, NH, USA). No smoothing was applied on the spectra. A cubic baseline was subtracted to correct the fluorescence background over the spectral range of 400–1800 cm−1. To take into account the polarization dependence of the instrument, correction factors were calculated from the totally depolarized band of liquid chloroform at 262 cm−1, which were then applied on the spectra of silk samples. For each series, the peak maximum of the amide I band IXX spectra was normalized to unity and the other polarized spectra normalized accordingly. The spectra were aligned along the wavenumbers axis using the tyrosine band at 1615 cm−1. For the present study we investigated three fibers, probed 1–3 points on each fiber and measured 1–3 series of polarized spectra on each point. The overall standard deviation on the measurement of the amide I intensity ratios is lower than 0.03. Averaged polarized spectra were obtained from 12–14 series for each spider species over three different fibers. For the determination of the secondary structure, orientation-insensitive spectra were calculated from a linear combination of the average polarized spectra [75]. The spectral decomposition of the amide I band was then carried out accordingly to the method previously described [59]. Briefly, a linear baseline was first subtracted in the amide I region (1490–1750 cm−1). The amide I band decomposition was carried out with five components located at 1640 (unordered structure), 1655 (31-helix), 1669 (β-sheet), 1682 (turn), and 1697 (turn) cm−1, respectively [59]. Mixed Lorentzian and Gaussian functions were used. Curve-fitting calculations were implemented using the initial band parameters (positions, widths and shapes) optimized previously for other MA silks with constraints on the variations of the parameters [59]. Band area of each component was divided by the total area of the amide I band to determine the structure content. Molecular orientation was estimated qualitatively from the parameter R′ defined as R′ = 1 − (IXX/IZZ). Positive and negative values of R′ of the amide I band indicate that the polypeptide chains are mainly parallel and perpendicular to the fiber axis, respectively. R′ = 0 correspond to an isotropic sample. Molecular orientation was also assessed quantitatively in terms of order parameters P2 and P4 [76]. P2 follows the same interpretation rules than R′ but values are limited between −0.5 and 1. The method to determine the order parameters P2 and P4 from polarized Raman measurements was first described by Bower (1972) [77]. It was extended to Raman spectromicroscopy (i.e., in backscattering configuration with four spectra) by Turrell and coworkers [78,79], and adapted to the amide I tensor (assuming a cylindrical symmetry) by Rousseau et al. [80]. This latter method, referred to as depolarization constant (DC) method by Richard-Lacroix et al. [67], requires the determination of the shape of the Raman tensor from an isotropic sample with the same chemical composition as the oriented sample, assuming that the depolarization ratio is identical for both samples. For silk, this ratio has previously been determined to be 0.21 ± 0.01 [80]. The details of the determination of P2 and P4 are determined from two polarized intensity ratios, noted R1 = IZX/IZZ and R2 = IXZ/IXX, and are given elsewhere [80]. The order parameter values and the associated standard deviations were calculated from the mean intensity ratio values of each (three) fiber. A variant of the DC method, called the most probable distribution (MPD) method, was proposed by Richard-Lacroix et al. [67]. This procedure eliminates the determination of the depolarization ratio and instead assumes that the orientation distribution is Gaussian. This assumption in turn provides a relationship between P4 and P2, which then allows their determination using the two ratios R1 and R2. Finally, the distribution of orientation was estimated by both methods by calculating the most probable distribution of orientation (Nmp(θ)) from the values of P2 and P4 using the information entropy theory [81,82,83,84]. 5. Conclusions This work provides a structural comparison between the MA dope and fiber silk of the two widely studied spiders, NC and AD. Quantitative values of the secondary structure contents and orientation of the MA spidroins have been determined. The AD MA fiber exhibits lower structural and orientational orders compared to the NC thread. Such structural differences certainly contribute to the mechanical properties of these MA silks. Secondary structure (i.e., β-sheet content) of the fiber seems to be mainly driven by the amino acid sequence (poly-Ala content). It is proposed that Pro residues (partly) inhibit molecular alignment of AD MA spidroins during the spinning process. The differences in the sequence of NC and AD spidroins do not result in distinctive conformation in the dope solution. This identical spidroin conformation suggests that it is not a decisive factor for the formation of specific fiber structure. Since differences in the mechanical properties of MA silk fibers are more obvious after supercontraction [13,85], it appears necessary to examine the orientation level and secondary structure of these two silks in this state. Moreover, details regarding the effect of reeling speed on silk molecular structure may also be informative to better understand the effect of the spinning process on the orientation level. Acknowledgments This work is supported by grants from the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Fonds de Recherche du Québec en Nature et Technologies (FRQNT). Justine Dionne acknowledges FRQNT and PROTEO for the award of graduate scholarships. The authors thank François Paquet-Mercier for his technical support with the Raman spectrometer, Simon Boudreault for his help with the MA gland dissection procedure and Philippe Bilodeau for his implication in the calculations of the order parameters and Nmp(θ). Supplementary Materials Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1353/s1. Click here for additional data file. Author Contributions The experiments were designed by Justine Dionne, Thierry Lefèvre and Michèle Auger. The experiments and the spectral treatments were conducted by Justine Dionne and Thierry Lefèvre. The interpretation of the data was performed by Justine Dionne and Thierry Lefèvre. The manuscript was written by Justine Dionne, Thierry Lefèvre and Michèle Auger. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Schematics showing the relationships between the structure of major ampullate (MA) spidroins in the silk dope, the fiber structure, the spinning process and the primary structure. Silk formation corresponds to the transformation converting the spidroins in the dope (initial conformational state) into the silk fiber (final structural state). The spinning process (mechanical constraints applied, physicochemical environment) and primary structure (chemical composition) are the two factors that govern structural changes. On one hand, the spinning process triggers and induces conformational and orientational changes to the polypeptide chains. On the other hand, the response of spidroins and the final secondary structure they adopt are dictated by the sequence. Figure 2 Raman spectra of the spinning dope contained in the sac of the Nephila clavipes (NC) and Araneus diadematus (AD) MA glands. Figure 3 Polarized IXX and IZZ Raman spectra of Nephila clavipes and Araneus diadematus MA silk fibers. Spectra are normalized so that the IXX peak maximum is equal in the amide I region. They represent the mean spectra calculated over 12–14 series of measurements. Figure 4 Orientation-insensitive spectra in the amide I region calculated from the average polarized Raman spectra of Nephila clavipes and Araneus diadematus fibers shown in Figure 3 and Figure S1. The spectra are normalized with respect to the total area of the amide I band. Figure 5 Relative band areas of the different amide I components of Nephila clavipes and Araneus diadematus fibers. The approximate error on the secondary structure content due to curve-fitting is ±3% and is represented by the error bars on the figure. Figure 6 Most probable orientation distribution (MPD) functions, Nmp(θ), of the carbonyl groups as determined from the order parameter values (Table 1) of Nephila clavipes and Araneus diadematus MA silk fibers according to the MPD and depolarization constant (DC) methods. Figure 7 Amino acid sequences for the spidroins composing the MA gland silk of Araneus diadematus (ADF3 and ADF4) [23] and Nephila clavipes (MaSp1 and MaSp2) [21,22]. Single letter code for amino acids is used to simplify the analysis. The An, GA, GGX, and GPGXX motifs are inspired from those of Gatesy et al. [44] and are indicated in red, orange, blue, and green respectively, where X represents a small subset of amino acids (X = L, Q, T, S, etc.). Amino acids that have undergone substitution in the motifs are colored in black and those that do not contribute to any motifs are colored in grey. ijms-17-01353-t001_Table 1Table 1 Qualitative parameter R′ = 1 − (IXX/IZZ) and order parameters P2 and P4 of Nephila clavipes and Araneus diadematus major ampullate (MA) silk fibers as estimated from the polarized amide I bands and evaluated according to the depolarization constant (DC) and most probable distribution (MPD) methods. Species R′ * P2 * P4 * DC Method MPD Method DC Method MPD Method N. clavipes −1.63 ± 0.008 −0.306 ± 0.005 −0.307 ± 0.005 0.075 ± 0.005 0.089 ± 0.004 A. diadematus −1.24 ± 0.008 −0.256 ± 0.007 −0.258 ± 0.006 0.02 ± 0.02 0.059 ± 0.003 * The order parameter values and the associated standard deviations were calculated from the mean intensity ratio values of each (three) fiber. ==== Refs References 1. Hardy J.G. Römer L.M. Scheibel T.R. Polymeric materials based on silk proteins Polymer 2008 49 4309 4327 10.1016/j.polymer.2008.08.006 2. Kundu B. Kurland N.E. Bano S. Patra C. Engel F.B. Yadavalli V.K. Kundu S.C. Silk proteins for biomedical applications: Bioengineering perspectives Prog. Polym. Sci. 2014 39 251 267 10.1016/j.progpolymsci.2013.09.002 3. Lefèvre T. Auger M. Spider silk as a blueprint for greener materials: A review Int. Mater. Rev. 2016 61 127 153 10.1080/09506608.2016.1148894 4. Lefèvre T. Auger M. Spider silk inspired materials and sustainability: Perspective Mater. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081354ijms-17-01354ArticleTrichostatin A Enhances the Apoptotic Potential of Palladium Nanoparticles in Human Cervical Cancer Cells Zhang Xi-Feng 1Yan Qi 1Shen Wei 2Gurunathan Sangiliyandi 3*Sivakov Vladimir Academic Editor1 College of Biological and Pharmaceutical Engineering, Wuhan Polytechnic University, Wuhan 430023, China; zhangxf9465@163.com (X.-F.Z.); yanyy75@163.com (Q.Y.)2 Key Laboratory of Animal Reproduction and Germplasm Enhancement in Universities of Shandong, College of Animal Science and Technology, Qingdao Agricultural University, Qingdao 266109, China; shenwei427@126.com3 Department of Stem Cell and Regenerative Biology, Konkuk University, Seoul 143-701, Korea* Correspondence: gsangiliyandi@yahoo.com; Tel.: +82-2-450-3687; Fax: +82-2-544-464519 8 2016 8 2016 17 8 135407 7 2016 09 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Cervical cancer ranks seventh overall among all types of cancer in women. Although several treatments, including radiation, surgery and chemotherapy, are available to eradicate or reduce the size of cancer, many cancers eventually relapse. Thus, it is essential to identify possible alternative therapeutic approaches for cancer. We sought to identify alternative and effective therapeutic approaches, by first synthesizing palladium nanoparticles (PdNPs), using a novel biomolecule called saponin. The synthesized PdNPs were characterized by several analytical techniques. They were significantly spherical in shape, with an average size of 5 nm. Recently, PdNPs gained much interest in various therapies of cancer cells. Similarly, histone deacetylase inhibitors are known to play a vital role in anti-proliferative activity, gene expression, cell cycle arrest, differentiation and apoptosis in various cancer cells. Therefore, we selected trichostatin A (TSA) and PdNPs and studied their combined effect on apoptosis in cervical cancer cells. Cells treated with either TSA or PdNPs showed a dose-dependent effect on cell viability. The combinatorial effect, tested with 50 nM TSA and 50 nMPdNPs, had a more dramatic inhibitory effect on cell viability, than either TSA or PdNPs alone. The combination of TSA and PdNPs had a more pronounced effect on cytotoxicity, oxidative stress, mitochondrial membrane potential (MMP), caspase-3/9 activity and expression of pro- and anti-apoptotic genes. Our data show a strong synergistic interaction between TSA and PdNPs in cervical cancer cells. The combinatorial treatment increased the therapeutic potential and demonstrated relevant targeted therapy for cervical cancer. Furthermore, we provide the first evidence for the combinatory effect and cytotoxicity mechanism of TSA and PdNPs in cervical cancer cells. cervical cancerpalladium nanoparticlestrichostatin Acell viabilityoxidative stressmitochondrial membrane potentialcaspasesapoptosis ==== Body 1. Introduction Cervical cancer is a common cancer and ranks seventh overall, among all types of cancer in women. Data, recorded in 2012, showed that 528,000 new cases of cervical cancer were identified, and 266,000 resulted in death worldwide; this accounted for 7.5% of all female cancer-related deaths [1]. The American Cancer Society reported that 12,990 new cases of invasive cervical cancer will be diagnosed in 2016, and 4120 women will die from the disease [2]. The current position of cancer treatment highlights the importance of developing new effective anticancer therapeutic molecules and protocols for preventing cervical cancer [2]. The occurrence and progression, of cervical cancer are believed to be related to abnormal genetic and epigenetic regulation, including phosphorylation and acetylation of histone H3, global DNA hypo-methylation and hyper-methylation of tumor suppressor genes [3,4]. The acetylation and de-acetylation of the N-terminal histone tails, by specific histone acetylases and deacetylases, are involved in gene regulation [5]. Histone acetyl transferase (HAT) and histone deacetylase (HDAC) are two classes of enzymes involved in maintaining chromatin structure and function through acetylation and deacetylation, respectively [6]. An imbalance, between these two enzymes, causes several dysfunctions, including an anti-proliferative effect, mitotic defects, cell growth, gene silencing, malignant transformation and aberrant cell signaling [6,7,8,9]. HDAC inhibitors are a new class of potent HDAC-specific inhibitors that lead to tumor cell arrestin a variety of cancers [10,11]. Mishra et al. (2001) reported that HDAC inhibitors are able to reduce cell survival in human breast cancer cells through remodeling of the human epidermal growth factor receptor 2 (HER2) promoter and HER2 expression [12]. Trichostatin A (TSA) is one of several inhibitors, which shows a potential therapeutic effect in various types of cancer cells, when combined with radiotherapy or chemotherapy [13,14]. TSA causes apoptosis through the inhibition of cell viability, as well as proliferation and activation of apoptosis-related proteins in a variety of cancer cells, including human gastric, ovarian and small cell lung cancer cells [15,16,17]. TSA shows potential as an inducer of apoptosis. However, few reports describe the effect of a combination of TSA and nanoparticles on apoptosis. Several studies reported that nanoparticles play a crucial role in cancer therapeutics [18,19]. The synthesis of nano-sized noble metals possessing unique physical, chemical and biological properties is gaining considerable interest [20,21]. Palladium nanoparticles (PdNPs), among several other metal nanoparticles, play a major role in the industry. This is due to their heterogeneous and homogeneous catalytic properties, high surface-to-volume ratio and high surface energy and nano architectonics [22,23,24]. A unique property of Pd complexes of polyamides containing sulfones is their high antimicrobial potency [25]. PdNPs exhibited a significant synergistic, high efficacy effect, compared to the sum of the individual efficacies of chemotherapy and photothermal therapy [26]. PdNPs showed high cytotoxicity against five different human cancer cell lines [27]. Additionally, PdNPs induce concentration-dependent cytotoxicity, apoptosis and alterations in the release and expression of numerous cytokines [28,29] and induction of apoptosis and autophagy in human ovarian cancer cells [30]. PdNPs have been used as antibacterial, antifungal and anticancer agents, but their effect in cancer studies remains elusive. Therefore, analyzing the effective role of PdNPs in biological and biomedical applications is essential. Significant numbers of studies have reported the synthesis and characterization of PdNPs by using chemical methods; however, the significance of biological molecule-mediated synthesis of PdNPs and cytotoxicity-mediated mechanisms by PdNPs is not well known. Therefore, the synthesis of biocompatible, non-toxic, environmentally-friendly PdNPs is essential. The resulting biocompatible PdNPs will be useful and effective for cancer studies with chemotherapeutic agents. Chemotherapy has advanced in cancer therapy using a variety of targeted drugs. The antitumor efficacies of current therapies are limited due to the high degree of cancer clonal heterogeneity and drug resistance [31]. A single therapeutic agent is not able to eradicate the cancer; the use of combinatorial therapy, which could inhibit multiple targets or redundant pathways simultaneously, is essential and inevitable [31]. For example, a combination of vascular endothelial growth factor (VEGFR) antibody DC101 and vinblastine results in full and sustained regression of large, established tumors in neuroblastoma xenograft models [32]. Combinations of TSA and quercetin (10–40 µM) induce cell death in human leukemia HL-60 cells [33]. The combination of Akt inhibitor and TCA potentiates apoptosis in ovarian carcinoma cell lines by increasing the activation of the caspase-8-dependent pathway and the mitochondria-mediated cell death pathway [34]. Targeting signaling pathways, such as the RAS/RAF/MEK/ERKandPI3K/Akt/Src/mTOR pathways, is important for combination therapy. These are the main pathways for extracellular-mediated cell survival, cell proliferation, differentiation and development [31,34,35,36,37,38,39,40]. Currently, several approaches have been developed to improve the activity and overcome multi-drug resistance; new combinations of novel anticancer drugs, which specifically target cancer cells, reduce the side effects of chemotherapy [2]. Combinations of chemotherapeutic drugs are most effective because each drug is effective against a different mechanism, which decreases drug-resistant cancer cells. A considerable number of studies was performed with a combination of various chemotherapeutic agents in different kinds of cancer cells. None of the studies has pursued the combination of TSA and palladium nanoparticles. Particularly, the combination of chemotherapeutic agents with nanoparticles for the treatment of cervical cancer has not been reported. This study was based on three main objectives. The first aim was to synthesize and characterize PdNPs by using a biomolecule called saponin. The second aim was to investigate the growth-inhibitory effects of HDAC inhibitor, TSA and palladium nanoparticles in cervical cancer cells. The final objective was to evaluate the mechanistic effect of a TCA and palladium nanoparticle combination, on apoptosis. 2. Results and Discussion 2.1. Synthesis and Characterization of Palladium Nanoparticles (PdNPs) The synthesis of the PdNPs was performed at 60 °C, using an aqueous solution of PdCl2 and saponin, which served as a reducing and stabilizing agent. Figure 1A shows the ultraviolet-visible spectroscopy (UV-VIS)spectra of the aqueous PdCl2 solution and the Pd colloidal suspensions, after reduction. The UV-VIS spectrum of the PdCl2 reference sample showed a peak at 415 nm due to the absorption of Pd(II) ions. The peak at 415 nm was absent in the reduced samples. A broad continuous absorption was observed, which indicated complete reduction of Pd(II) ions to PdNPs [30]. Further characterization was carried out by X-ray diffraction (XRD). The results showed four peaks at 40.0°, 50.0°, 65.0° and 88.0°, which corresponded to reflections from the (1 1 1), (2 0 0), (2 2 0), (3 1 1) and (2 2 2) planes of the face-centered cubic (fcc) lattice, respectively [30,41,42]. The most intensive and predominant peak of the PdNPs crystals was observed at 50.0°, which corresponded to (1 1 1) planes. The broad peak at 40.0° is the characteristic peak of the (1 1 1) indices of Pd(0), which is a face-centered cubic structure (Figure 1B). Fourier transform infrared spectroscopy (FTIR) analysis confirmed the involvement of the biological molecule in the reduction processes. As shown in Figure 1C, the major absorbance bands were observed at 3420 and 1620 cm−1, which corresponded to the hydrogen-bonded hydroxyl (OH) and amide I. The bands found at 1620 cm−1 could be due to the characteristic asymmetrical stretch of the carboxylate and carbonyl groups and the characteristic peaks of aromatics C–C stretching. The peak at 1040 cm−1 was due to the C–O stretching vibration of the alcoholic groups. The bands at 1050 cm−1 indicated the presence of C–O stretching of alcohols, carboxylic acids, ester and ether groups. The size of the particle derived from the biological method shows an average size of 5 nm (Figure 1D). Transmission electron microscopy (TEM) images of the PdNPs showed that all of the particles were spherical in shape, dispersed within a range of 5–20 nm, and had an average particle size of 5 nm (Figure 1E,F); the images matched the dynamic light scattering (DLS) data exactly. All of the characterization data were significantly consistent with earlier reports using tea extract and leaf extract of Anacardium occidentale, Pulicaria glutinosa and Evolvulus alsinoides [30,42,43,44]. 2.2. Trichostatin A (TSA) and PdNPs Inhibit Breast Cancer and HeLa Cell Viability The potential cytotoxic effect of TSA and PdNPs in breast and cervical cancer cells was evaluated. First, we examined their inhibitory potential on the growth of the MCF-7 breast cancer cell line. Cells were treated with different concentrations of TSA (25–300 nM) and PdNPs (25–300 nM) for 24 h, and cell viability was measured using the WST-8 (5-(2,4-Disulfophenyl)-3-(2-methoxy-4-nitrophenyl)-2-(4-nitrophenyl)-2H-tetrazolium) assay. A significant inhibitory effect of TSA was measured from 25–300 nM and PdNPs at 50–300 nM. Figure 2A shows that the MCF-7 cell viability decreased significantly in a dose-dependent manner. MCF-7 cells treated with either 200 nM of TSA or 300 nM of PdNPs had a 50% reduction in cell viability (Figure 2A). The IC50 values for TSA and PdNPs in MCF-7 cells were 200 and 300 nM, respectively. Vigushin et al. [45] demonstrated the anti-proliferative activity and anti-tumor activity of TSA in eight breast carcinoma cell lines with an IC50 value between 26.4 and 308.1 nM and in carcinogen-induced rat mammary cancer model, respectively. Next, we examined the dose-dependent effect of TSA or PdNPs on cervical cancer cells. TSA and PdNPs inhibited the survival of cervical cancer cells in a concentration-dependent manner. The cytotoxic effects of TSA were more pronounced, compared to those of PdNPs. TSA, at a 100 nM concentration, inhibited cervical cancer cell viability by approximately 50%, whereas 125 nM PdNPs inhibited the viability by approximately the same percentage (Figure 2B). TSA exhibited a stronger toxic effect than PdNPs. Wu et al. [46] reported that HeLa cells treated with lower concentrations of TSA (0.1–1.0 µM) slightly activated cell growth within 12 h. Then, it marginally suppressed cell growth, but did not induce cell death after 24 h. An increased TSA concentration (1.0 and 2.0 µM) completely inhibited cell growth after 24 h of treatment. Our results are consistent with this report. We demonstrated that TSA inhibited cervical cancer cell growth in a dose- and time-dependent manner [47]. Yan et al. [6] demonstrated that a combination of curcumin and TSA enhanced anticancer effects in breast cancer cells by decreasing cell viability. Recently, we reported that PdNPs effectively induced cell death in ovarian cancer cells by decreasing cell viability in a dose-dependent manner [30]. The combined data suggest that either TSA or PdNPs effectively and significantly decreased cervical cancer cell viability to a greater degree than breast cancer cells. Therefore, further experiments were focused on HeLa cells. 2.3. A Combination of TSA and PdNPs Dose-Dependently Inhibits HeLa Cell Viability The effective combined cytotoxic dose was examined by simultaneously adding TSA (50–200 nM) and a fixed concentration of PdNPs (50 nM) to HeLa cells. The results showed that increasing concentrations of TSA with PdNPs significantly reduced cell viability, compared to singular treatment (Figure 3A). Similarly, we examined a combination of increasing concentrations of PdNPs (from 50–200 nM) and a fixed concentration of TSA (50 nM). The results suggested that the increasing concentration of PdNPs significantly influenced the combinatorial effect, which was comparable to the effect of increasing the TSA concentration. Notably, an increased concentration of TSA from 50–200 nM, in combination with 50 nM PdNPs, further inhibited the HeLa cell growth (Figure 3B). The higher concentration of TSA and PdNPs caused a higher cytotoxic effect; therefore, we selected a combination of TSA (50 nM) and PdNPs (50 nM). This was obviously a better strategy to improve the anticancer activity of PdNPs, in HeLa cells. Therefore, the remaining experiments were carried out in cells treated with a combination of TSA (50 nM) and PdNPs (50 nM), unless specified otherwise. Previous studies reported that a combination of TSA and curcumin produced significant anti-proliferative and apoptotic effects than either agent alone [6]. A combination of quercetin (5 µM) and 82.5 nM of TSA significantly increased the cytotoxic effect in A549 cells [48]. 2.4. The Combination of TSA and PdNPs Inhibits Cell Viability and Histone Deacetylase (HDAC) Activity The next series of experiments addressed the question of whether there is a synergistic effect of TSA and PdNPs on HeLa cell cytotoxicity. Therefore, HeLa cells were incubated with TSA (50 nM) or PdNPs (50 nM) for 24 h, either alone or in combination. HeLa cells treated with TSA had a 25%–30% decrease in cell viability, compared to the untreated control; whereas, PdNPs treatment resulted in a 20%–25% decrease in cell viability, compared to the untreated control. The viability of cells co-incubated with TSA and PdNPs decreased by 75%, compared to a 20%–30% decrease in cells treated with either TSA or PdNPs alone (Figure 4A). These results indicated a synergistic effect between TSA and PdNPs, on HeLa cell cytotoxicity. Moreover, the lower concentrations of these compounds would reduce the side effects; therefore, it is a better potential therapeutic approach. It could be a novel and effective tool to kill cancer cells effectively in a time- and dose-dependent manner. Sack et al. [49] demonstrated a synergistic cytotoxicity effect of cerium oxide nanoparticles (CNP) and doxorubicin in A375 melanoma tumor cells. The treatment consisted of 300 mmol/L CNP for 48 h, 0.5 mmol/L doxorubicin for 24 h or a combination of both [49]. Our results showed that the synergistic effect of TSA and PdNPs at 24 h of treatment was much higher than that of other nanoparticles, such as CNP [49]. We examined the effects of TSA, PdNPs or a combination of both for 24 h on the HDAC activity in HeLa cells. As shown in Figure 4B, TSA, at low concentrations, significantly inhibited higher HDAC activity, compared to PdNPs at the same concentration. The potential explanation for the specific TSA enhancement of HDAC activity could be specific targeting of HDAC, while PDNPs may inhibit HDAC activity through secondary effects. These results indicated that PdNPs had an effect on HDAC activity, but it was less than TSA. However, the TSA and PdNP combination exhibited a significant effect. The TSA inhibitory effect on HDAC activity was reported in several breast cancer cell lines, with an IC50 of 2.4 nM. The mechanism was through pronounced histone H4 hyperacetylation [45]. Treatment of HeLa cells with different concentrations of TSA (10–50 nM) for 72 h resulted in dose-dependent inhibition of HDAC activity by increasing the forms of acetylated histone 3 and 4 [47]. Our results were in line with previous reports in cervical cancer cells. 2.5. Combination of TSA and PdNPs Enhances Cytotoxicity Lactate dehydrogenase (LDH) is released into extracellular space when the plasma membrane is damaged. There are several cytotoxicity assays, but measurement of LDH leakage is considered to be the essential toxicity assay [30,50]. Therefore, we examined TSA and PdNPs cytotoxic effects. LDH leakage was measured in cells treated with TSA, PdNPs or a combination of TSA and PdNPs in the presence and absence of N-acetylcysteine (NAC) (Figure 5A), which is an antioxidant compound [51]. The data clearly indicated that all treatment groups increased LDH leakage, compared to control cells. The combination treatment increased LDH leakage to a higher level than TSA or PdNPs treatments alone. Our results are in line with previous studies, which demonstrated that submicromolar concentration of TSA, such as 1, 10 and 100 nM, increased LDH leakage in primary hepatic stellate cells [52]. Next, we examined reactive oxygen species (ROS) generation in cells treated with each of the three groups, in the presence and absence of NAC. ROS generation is a collective term for molecules generating oxidative stress, such as hydrogen peroxide (H2O2) and hydroxyl radicals (HO) [53]. The results from our data suggested that either TSA or PdNPs enhanced the generation of ROS; however, the effect was more pronounced in the combinatorial treatment. The amount of ROS was almost equal to H2O2-treated cells. Notably, NAC strongly suppressed ROS generation in TSA- or PdNP-treated HeLa cells, as well as cells treated with both TSA and PdNPs (Figure 5B). Several previous studies suggested that HDAC inhibitors induce leukemia, prostate and cervical cancer cell death through the generation of ROS [47,54,55]. PdNPs induce LDH leakage and ROS generation in ovarian cancer cells [31]. Ungerstedt et al. [56] demonstrated that oxidative stress is a critical factor in HDAC inhibitor-induced cell death [56]. Recently, several studies showed that histone deacetylase inhibitors enhance ROS production, the mitochondrial respiratory chain and are the major source of ROS production [57]. Our results showed that the combination of TSA and PdNPs has a greater effect on LDH and ROS generation, compared to singular treatments. The combinatorial treatment also induces significantly more cytotoxicity than the singular treatments. 2.6. Effect of TSA and PdNPs on Oxidative Stress Markers The maintenance of ROS level could be an effective therapy for killing cancer cells, rather than normal cells, and ROS has a dual role in cell survival and death by the elevation of ROS production or a decline of ROS-scavenging capacity [57,58,59,60,61,62]. Oberley et al. demonstrated that the levels of ROS-scavenging enzymes are significantly altered in malignant cells and in primary cancer tissues [63,64]. Thus, we investigated whether TSA, PdNPs or a combination of both could influence the level of pro- and anti-oxidative markers, such as malondialdehyde (MDA), glutathione (GSH), superoxide dismutase (SOD) and catalase (CAT), in HeLa cells (Figure 6A–D). The levels of MDA in control, TSA-treated, PdNPs-treated and TSA plus PdNPs-treated cells were 1, 1.5 and 3 nanomole/mg of protein, respectively. It was significantly higher in all three treatment groups, compared to the control. Interestingly, the combined TSA and PdNPs treatment significantly increased the MDA level (Figure 6). Previous findings suggested that ovarian cancer cells treated with PdNPs could result in an abundance of lipid peroxides, increased LDH release and increased MDA levels [30]. The level of MDA was higher in cells treated with PdNPs rather than TSA, the reason for the higher level of MDA being that PdNPs could target multiple pathways responsible for stress compared to TSA. Next, we investigated the levels of GSH, SOD and CAT in cells exposed to TSA and PdNPs (Figure 6). Their contents have become important indicators of the antioxidant capacity of cells [65]. The intracellular GSH content influences the effect of apoptosis induced by anticancer drugs [66,67]. The levels of GSH, SOD and CAT were significantly lower in TSA, PdNPs and the combinatorial treatment, compared to those in the control (Figure 6). Our results are consistent with previous studies, which demonstrated that TSA increased O2•− and decreased GSH in HeLa cells. Huang et al. [68] described how SOD is considered to be the target for selective killing of cancer cells. Inhibition of SOD causes the accumulation of cellular O2−, which leads mitochondria-mediated apoptosis. Previous studies demonstrated that decreased catalase activity in mouse liver cancer cells is due to increasing ROS levels [69]. 2.7. Combination of TSA and PdNPs Disrupts Membrane Potential (MMP) and Enhances Caspase-3 Activity Apoptosis is mediated by an intrinsic pathway that is an essential event involved in TSA anti-tumor activity [70]. Bcl-2 family proteins play a crucial role in the disruption of mitochondrial membrane potential (MMP) and the release of cytochrome c [71]. For example, Vorinostat and TSA disrupted MMP, through increased expression of BH3-only Bcl-2 family genes [72]. Cells were treated with TSA and PdNPs, followed by JC-1 dye measurement of MMP, to determine if the combined treatment had an effect on MMP. A dramatic decrease in the ratio of red-green fluorescence intensity was observed in cells treated for 24 h with the combination of TSA and PdNPs. The data indicated that the treatment resulted in rapid depolarization of the mitochondrial membranes with a 3–4-fold decrease in the ΔΨm (Figure 7A). These results suggested that the collapse of the ΔΨm was an early event in PdNPs-induced apoptosis [30]. The loss of MMP (ΔΨm) in HeLa cells by TSA implied that TSA-induced apoptotic cell death was tightly correlated with the collapse of MMP (ΔΨm) [45]. A high ratio of Bax to Bcl-2 caused the disruption of MMP (ΔΨm) and apoptosis in cells [73]. Similarly, HDAC inhibitors downregulated Bcl-2 expression and induced apoptosis in many cancer cells [47,74]. Human renal carcinoma cells co-treated with TSA and TRAIL effectively induced apoptosis through loss of MMP [75]. These results supported the view that the relative loss of MMP could trigger HeLa cell apoptosis. Caspases are cysteine proteases involved in the execution of apoptosis. The caspase-9-caspase-3 cascade is activated by pro-apoptotic molecules, such as cytochrome c released from mitochondria [76]. Therefore, we examined the involvement of caspase-3 in cells that were treated for 24 h with TSA, PdNPs or a combination of TSA and PdNPs, in the presence or absence of a caspase-3 inhibitor (Z-Asp(O-Me)-Glu(O-Me)-Val-Asp(O-Me) fluoromethyl ketone, Z-DEVD). The combination of TSA and PdNPs had a significantly higher level of caspase-3 activity, compared to cells treated with either one singularly. This indicated that the combinatorial treatment could promote cell death (Figure 7B). The elevated caspase-3 activity declined in the presence of caspase-3 inhibitor. Additionally, we used etoposide as a benchmark to show the clear involvement of caspase-3-mediated apoptosis. Similarly, caspase-3/7 activity in human vertebral-cancer of the prostate (VCaP) prostate cancer cells increased dramatically after 24-h incubation with 5 mM valproate (VPA) or 100 nM TSA [77]. The results from these experiments clearly indicated that TSA- or PdNP-induced HeLa cell apoptosis was mediated by the activation of caspase-3. Previous studies demonstrated that TSA induced apoptosis through activation of various caspase cascades, including the caspase-8 cell death receptor pathway and the caspase-9 mitochondrial pathway [47]. PdNPs induced human ovarian cancer cell apoptosis by caspase-3. Human lung cancer cells treated with TSA had increased caspase-3 activity, whereas quercetin enhanced TSA induction of caspase-3 activity by 113% [48]. Collectively, the data demonstrated that the combination of TSA with PdNPs affects HeLa cells through the activation of caspase-3. 2.8. Combination of TSA and PdNPs Enhances Apoptosis Caspase-3 activation induces DNA fragmentation, which is a biochemical hallmark of apoptosis. HeLa cells were treated with TSA, PdNPs or a combination of TSA and PdNPs for 24 h to verify their induction of apoptosis. The TUNEL assay was used to analyze their effect. The number of cells positively stained by the TUNEL reagents increased significantly with either singular or combined TSA and PdNP treatment (Figure 8). However, the combined treatment stimulated a greater level of apoptosis than the single treatments. Our results were consistent with previous studies, which demonstrated that TSA and sodium butyrate induced apoptosis by DNA fragmentation in a concentration-dependent manner by increased chromatin relaxation and enhanced accessibility of DNA in thymocytes [78]. Yee et al. [79] showed that the loss of p815 mastocytoma cell viability was due to apoptotic events, such as DNA fragmentation. TSA was not only involved in DNA fragmentation, but it also had a significant impact on arresting HeLa cells in the S phase; it eventually induced apoptosis [80]. Several studies claimed that several HDAC inhibitors and TSA induced apoptosis in HeLa cells [81,82] and eosinophils and neutrophils in the presence and absence of growth factors [83]. Piacentini et al. [84] reported that TSA, in combination with five different chemotherapeuticdrugs, induced apoptosis in 10 pancreatic adenocarcinoma cell lines; the data indicated that TSA is a suitable combinatorial agent. 2.9. Combination of TSA and PdNPs Upregulates Apoptotic Genes It is well known that various pro- and anti-apoptotic proteins regulate cell death pathways. Therefore, we examined the link between apoptosis and the three treatment groups, by measuring p53, Bax, Bak, caspase-3/9 and Bcl-2 mRNA levels. Cells treated with TSA, PdNPs or a combination of both had increased expression of p53, Bax, Bak and caspase-3/9, while the expression of Bcl-2 decreased (Figure 9). However, the enhancement of TSA and PdNPs, on apoptosis-induced gene expression, was greater than that on TSA or PdNPs alone. This indicated that the combinatorial effect worked better than the single treatment [85]. Dysregulation of HDAC activity by TSA leads to silencing tumor suppressor genes, such as p53, which plays an important role in apoptosis [48,86,87]. Mouse tumors treated with TSA in combination with quercetin had higher p53 and apoptosis levels, compared to the control and TSA-treated mice [48]. Wu et al. [88] recently reported that genistein, in combination with TSA, markedly increased p53 and caspase expression in A549 cells. The data indicated that TSA plays an important role in enhancing TSA-induced apoptosis in HeLa cells via p53. Several HDAC inhibitors, including TSA, decreased expression Bcl-2, Bcl-xL and XIAP expression. They also enhanced the expression of pro-apoptotic proteins, such as Bax and Bak [89,90]. MCF-7 and MDA-MB-231 breast cancer cells treated with a combination of suberoylbis-hydroxamic acid (SBHA) and a proteasome inhibitor had significantly higher p53, Bax, Bcl-xS and Bak protein levels and decreased the Bcl-2 level [91]. A combination of TSA and TRAIL effectively increased caspase-3, -8, and -9 activation and degradation, in human RCC Caki cells [92]. Our results clearly suggested that TSA, PdNPs or the combination of TSA and PdNPs increased the expression of caspase-3 and -9. However, the combinatorial effect was greater than the single treatment. Altogether, the data from this study clearly suggested that the molecular mechanism of apoptosis induced by TSA, PdNPs or their combination may modulate the Bcl-2 family of proteins. 3. Materials and Methods 3.1. Materials Penicillin-streptomycin, trypsin-EDTA, RPMI 1640 medium and 1% antibiotic-antimycotic were obtained from Life Technologies/Gibco (Grand Island, NY, USA). PdCl2, for the preparation of the PdNPs, was purchased from Sigma-Aldrich (St. Louis, MO, USA). Trichostatin A, NAC, H2O2, fetal bovine serum (FBS) and the in vitro toxicology assay kit were purchased from Sigma-Aldrich (St. Louis, MO, USA). All other chemicals were purchased from Sigma-Aldrich, unless otherwise stated. 3.2. Synthesis of PdNPs The PdNPs were prepared with saponin, according to a previously-described method with some modifications [30]. Saponin (1 mg) was suspended in 90 mL of sterile distilled water, mixed well for 5 min and then used in PdNPs’ synthesis. The saponin solution was combined with 10–100 mL of a 1 mM aqueous PdCl2 solution, then incubated for 6 h at 60 °C, with constant stirring. The reduction reaction occurred rapidly and was indicated by a solution color change, from light to bright brown. 3.3. Characterization of PdNPs The synthesized PdNPswere characterized according to the method described previously [30]. 3.4. Cell Culture Human MCF-7 breast cancer cells and cervical adenocarcinoma HeLa cells were from Dr. Zhang’s laboratory stock. Both human cancer cells were maintained in a humidified incubator at 5% CO2 and 37 °C. HeLa cells were cultured in RPMI-1640 (Sigma-Aldrich, St. Louis, MO, USA), supplemented with 10% fetal bovine serum (FBS; Sigma-Aldrich) and 1% penicillin-streptomycin (Gibco BRL, Grand Island, NY, USA). Cells were routinely grown in 100-mm plastic tissue culture dishes (Nunc, Roskilde, Denmark) and harvested with a solution of trypsin-EDTA, while in a logarithmic phase of growth. 3.5. Cell Viability Assay The WST-8 assay was performed, as previously described [30]. Typically, 2 × 105 cells were seeded into a 96-well plate and cultured, at 37 °C under 5% CO2 for 24 h, in RPMI-1640 standard medium, supplemented with 10% FBS. Next, the cells were washed twice with 100 µL of serum-free RPMI-1640 and incubated, for 24 h, with 100 µL of media, containing TSA (25–300 nM) or PdNPs (25–300 nM). Cells that were not exposed to TSA or PdNPs served as controls. The cells were washed twice with serum-free RPMI-1640, after 24 h of exposure. WST-8 solution (15 µL) was added to each well, which contained 100 µL of serum-free RPMI-1640. The mixture (80 µL) was transferred to another 96-well plate, after 1-h incubation at 37 °C under 5% CO2. Absorbance was measured at 450 nm, using a microplate reader. 3.6. Histone Deacetylase Activity Histone deacetylase activity was assayed, as described [93]. HeLa cells were treated with TSA, PdNPs or in combination for 24 h. The cells were washed in PBS and suspended in 5 volumes of lysis buffer (R&D Systems, Inc., Minneapolis, MN, USA). Next, cells were harvested, and whole cell protein was extracted, using RIPA lysis buffer. Protein concentrations were measured, using BCA kit reagents. Supernatant samples, containing 20 µg of total protein, were used to assay HDAC activity. The samples and HDAC substrate, provided by the assay kit, were added to each well of a 96-well microtiter plate and incubated at 37 °C for 1 h. HDAC activity was measured using the HDAC Activity Assay kit (Sigma-Alrich, St. Louis, MO, USA). Experimental procedures were performed, according to the manufacturer’s instructions. 3.7. Cytotoxicity Assay The integrity of the human ovarian cancer cell membrane was evaluated by measuring the cellular release of lactate dehydrogenase (LDH), according to the manufacturer’s instructions (in vitro toxicology assay kit, TOX7, Sigma-Alrich, St. Louis, MO, USA) and as described previously [18]. Briefly, the cells were exposed to each of the 3 treatment groups, for 24 h, and then LDH was measured. Additionally, cells from each treatment group were incubated, with or without 2 mM NAC. Reactive oxygen species (ROS) were estimated, according to a method described previously [18]. The cells were seeded into 24-well plates, at a density of 5 × 104 cells per well and cultured for 24 h. They were washed twice with phosphate-buffered saline (PBS) before adding fresh media, containing each of the 3 treatment groups, then incubated for 24 h. Cells were also incubated with the same treatment groups with or without 2 mM NAC. The cells were then supplemented with 20 µM DCFH-DA; the incubation continued for 30 min at 37 °C. The cells were rinsed with PBS before adding 2 mL of PBS to each well. Fluorescence intensity was determined, using a spectrofluorometer (Gemini EM, CA, USA), with excitation at 485 nm and emission at 530 nm. 3.8. Measurement of Oxidative Stress Markers Oxidative stress markers, such as malondialdehyde (MDA), glutathione (GSH), superoxide dismutase (SOD) and catalase (CAT), were assayed with reagents from various kits, according to each manufacturer’s instructions (Sigma). Briefly, the cells were cultured in 75 cm2 culture flasks and exposed to TSA, PdNPs or a combination of both, for 24 h. The cells were harvested in chilled PBS, by scraping and washing twice with 1× PBS at 4 °C for 6 min at 1500 rpm. The cell pellet was sonicated at 15 W for 10 s (3 cycles) to obtain the cell lysate. The resulting supernatant was stored at 70 °C, until analyzed. 3.9. Measurement of Mitochondrial Membrane Potential Briefly, the cells were cultured in 75-cm2 culture flasks and exposed to TSA, PdNPs or a combination of both, for 24 h. MMP was measured, as described previously, using a cationic fluorescent indicator JC-1 (Molecular Probes, Eugene, OR, USA) [30]. JC-1 is a lipophilic cation, which, in a reaction driven by ΔΨm in normal polarized mitochondria, assembles into a red fluorescence-emitting dimer, forming JC-1-aggregates. Cells were incubated with 10 µM JC-1 at 37 °C for 15 min, washed with PBS and resuspended in PBS; then, fluorescence intensity was measured. MMP was expressed as the ratio of the fluorescence intensity of the JC-1 aggregates to monomers. 3.10. Measurement of Caspase-3 Activity and TUNEL Assay The caspase-3 and TUNEL assays were performed, according to the methods described earlier [18]. The cells were treated with each of the 3 experimental groups, in the presence of a caspase-3 inhibitor, for 24 h. Caspase-3 activity was measured in the cancer cells, according to the manufacturer’s instructions in a kit from Sigma-Aldrich (St. Louis, MO, USA). Apoptosis was examined in cells treated with all 3 groups, for 24 h, using a DNA Fragmentation Imaging Kit (Roche, Mannheim, Germany), following the manufacturer’s instruction. The culture medium was aspirated after the incubation period, and the cell layers were trypsinized. The detached cells were reattached on 0.01% polylysine-coated slides, fixed with 4% methanol-free formaldehyde solution, and stained, according to the manufacturer’s instructions for the TUNEL protocol. 3.11. Extraction and Amplification of mRNA Total RNA was extracted from cells, treated with TSA, PdNPs or a combination of both for 24 h, using an Arcturus picopure RNA isolation kit (eBioscience, San Diego, CA, USA); samples were prepared according to the manufacturer’s instructions. Real-time RT-PCR was conducted using a Vill7 (Applied Biosystems, Foster City, CA, USA) and SYBR Green, as the double-stranded DNA-specific fluorescent dye (Applied Biosystems) Target gene expression levels were normalized to GAPDH expression, which was unaffected by treatment. The RT-PCR primer sets are shown in Table 1. RT-PCR was performed independently in triplicate, for each of the different samples; the data are presented as the mean values of gene expression measured in treated samples versus the control. 3.12. Statistical Analyses All assays were conducted in triplicate, and each experiment was repeated at least 3 times. The results represent the mean of at least 3 independent experiments (mean ± SD). Student’s t-test or one-way analysis of variance (ANOVA), followed by Tukey’s test for multiple comparisons, were calculated, using the Graph-Pad Prism software (GraphPad Software, San Diego, CA, USA). The differences were considered significant at p < 0.05. 4. Conclusions Cervical cancer is the leading cause of cancer death in women worldwide, which accounts for 7.5% of all female cancer-related deaths; however, it is predominant in certain ethnic and socio-economic groups. Therefore, we hypothesized that TSA, together with PdNPs, may be an effective nanoparticle-mediated chemotherapy, which could significantly inhibit cancer cell viability. We assessed the effects, of all three treatments, in human cervical cancer cells via a series of biochemical assays. This is the first study to show the combinatorial effect of TSA and PdNPs in cervical cancer cells. The fact that the combination treatment resulted in a significant reduction of cell viability, increased oxidative stress, loss of MMP and enhanced caspase-3/9 activity suggests that cervical cancer cells become more sensitive to lower doses of PdNPs, when treated with TSA. It is important to focus on the synergistic cytotoxicity effects and the oxidative stress measured in the combination treatment. Furthermore, our results show that the apoptotic responses, induced by the three treatments, are a result of upregulated pro-apoptotic and downregulated anti-apoptotic proteins. The potential mechanisms, stimulated by the combinatorial treatment, are the modulation of p53 and Bcl-2 family proteins. This finding suggests that cells treated with TSA plus PdNPs experienced significantly higher toxicity, compared to cells treated with TSA or PdNPs alone. Our data supports a strong synergistic interaction between TSA and PdNPs in the human cervical cancer cell line that we used. This approach could be an alternative approach for women experiencing chemoresistance; it may, also, free them from any side effects. Our data suggest that the combination of TSA and PdNPs could provide a novel, effective, supplemental treatment for cervical cancer patients. Acknowledgments This paper was supported by the KU-Research Professor Program of Konkuk University. This work is also supported by the Science and Technology Research Program, from the Department of Education of Hubei Province in China (D20151701). Author Contributions Sangiliyandi Gurunathan came up with the idea and he performed synthesis and characterization of nanoparticles and participated in writing of the manuscript. Xi-Feng Zhang performed all biochemical assays. Qi Yan and Wei Shen analyzed the data. All authors read and approved the final manuscript. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Synthesis and characterization of palladium nanoparticles (PdNPs). (A) Ultraviolet-visible spectroscopy (UV-VIS) spectra of PdNPs. Pdcl2: Palladium(II) chloride; (B) X-ray diffraction (XRD) pattern of PdNPs; (C) Fourier transform infrared spectroscopy (FTIR) spectra of PdNPs; (D) size distribution analysis of PdNPs by dynamic light scattering (DLS); (E) Transmission electron microscopy (TEM) images of PdNPs; (F) size distributions based on TEM images of PdNPs, ranging from 5–20 nm. Figure 2 The dose-dependent effect of trichostatin A (TSA) and PdNPs on cell viability in human breast and cervical cancer cells. (A) The human breast cancer cells (MDA-MB-231) were incubated with various concentrations of TSA (0–300 nM) or PdNPs (0–300 nM), for 24 h. Cell viability was measured by WST-8; (B) Human cervical cancer cells were incubated with various concentrations of TSA (0–300 nM) or PdNPs (0–300 nM) for 24 h. Cell viability was measured using WST-8. The results are expressed as the mean ± standard deviation of three separate experiments. The treated groups showed statistically-significant differences from the control group, as determined by Student’s t-test (* p < 0.05). Figure 3 Increasing concentrations of TSA or PdNPs enhance the loss of cell viability in human cervical cancer cells. (A) Human cervical cancer cells were co-incubated, for 24 h, with increasing concentrations of TSA (50–200 nM) and 50 nMPdNPs or increasing concentrations of PdNPs (50–200 nM) and 50 nM TSA (B). The results are expressed as the mean ± standard deviation of three separate experiments. The treated groups showed statistically-significant differences from the control group, as determined by the Student’s t-test (* p < 0.05). Con: Control. Figure 4 The effect of TSA or PdNPs alone or the combinatorial effect of TSA and PdNPs on cell viability and HDAC activity, in human cervical cancer cells. Human cervical cancer cells were incubated with TSA (50 nM) or PdNPs (50 nM) or both TSA (50 nM) and PdNPs (50 nM) for 24 h. (A) Cell viability was measured using WST-8; (B) HDAC activity was measured. Figure 5 The effect, of TSA, PdNPs or a combination of TSA and PdNPs, on human cervical cancer cell cytotoxicity. The cells were treated, for 24 h, with TSA (50 nM), PdNPs (50 nM) or a combination of TSA (50 nM) and PdNPs (50 nM). (A) Lactate dehydrogenase (LDH) activity was measured at 490 nm, using an LDH cytotoxicity kit (Aldrich, St. Louis, MO, USA); (B) Reactive oxygen species were measured, as the relative fluorescence of 2′,7′-dichlorofluorescein, with a spectrofluorometer. The results are expressed as the mean ± standard deviation of three independent experiments. The treated groups showed statistically-significant differences from the control group, as determined by Student’s t-test (* p < 0.05). NAC: N-acetylcysteine. Figure 6 The effect of TSA, PdNPs or both TSA and PdNPs on oxidative stress markers, in human cervical cancer cells. Cells were treated for 24 h with TSA (50 nM), PdNPs (50 nM) or the combination of TSA (50 nM) and PdNPs (50 nM). (A) The concentration of malondialdehyde, expressed as nanomoles per milligram of protein; (B) the concentration of glutathione, expressed as milligram per gram of protein; (C) the specific activity of superoxide dismutase, expressed as units per milligram of protein; (D) the specific activity of catalase, expressed as units per milligram of protein. The results are expressed as the mean ± standard deviation of three independent experiments. There was a significant difference in the treated cells compared to the untreated cells, as determined by Student’s t-test (* p < 0.05). Figure 7 The effect of TSA or PdNPs alone or a combination of TSA and PdNPs on mitochondrial membrane potential (MMP) and caspase-3 activities. Cells were treated for 24 h with TSA (50 nM), PdNPs (50 nM) or a combination of TSA (50 nM) and PdNPs (50 nM). (A) MMP (ratio of JC-1 aggregate to monomer) in cervical cancer cells was determined after treatment; (B) the cells were treated for 24 h with TSA (50 nM), PdNPs (50 nM) or a combination of TSA (50 nM) and PdNPs (50 nM), with and without caspase inhibitor. The concentration of p-nitroanilide released from the substrate was calculated from the absorbance at 405 nm. The results are expressed as the mean ± standard deviation of three separate experiments. The treated groups showed statistically-significant differences from the control group, as determined by Student’s t-test (* p < 0.05). ET: Etoposide. Figure 8 Effect of TSA or PdNPs alone or in combination on apoptosis in human cervical cancer cells. The human cervical cancer cells were treated for 24 h with TSA (50 nM), PdNPs (50 nM) or a combination of TSA (50 nM) and PdNPs (50 nM). Apoptosis was assessed in a TUNEL assay; the nuclei were counterstained with DAPI. Representative images show apoptotic (fragmented) DNA (red staining) and the corresponding cell nuclei (blue staining). Figure 9 The impact of TSA, PdNPs or a combination of TSA and PdNPs on the expression of apoptotic and anti-apoptotic genes. The relative mRNA expression of apoptotic and anti-apoptotic genes was analyzed by qRT-PCR, in human cervical cancer cells treated for 24 h with TSA (50 nM), PdNPs (50 nM) or a combination of TSA (50 nM) and PdNPs (50 nM). The results are expressed as the mean ± standard deviation of three separate experiments. The treatment groups showed statistically-significant differences from the control group, as determined by Student’s t-test (* p < 0.05). ijms-17-01354-t001_Table 1Table 1 Primers used for quantitative real-time polymerase chain reaction for analysis of apoptotic, and anti-apoptotic, gene expression. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081355ijms-17-01355ArticleComparative Genomics of the Extreme Acidophile Acidithiobacillus thiooxidans Reveals Intraspecific Divergence and Niche Adaptation Zhang Xian 12Feng Xue 1Tao Jiemeng 1Ma Liyuan 1Xiao Yunhua 1Liang Yili 12Liu Xueduan 12Yin Huaqun 12*Woo Patrick C. Y. Academic Editor1 School of Minerals Processing and Bioengineering, Central South University, Changsha 410083, China; zixuange2010@126.com (X.Z.); fengxue@csu.edu.cn (X.F.); taojiemeng@csu.edu.cn (J.T.); maliyuan2008@csu.edu.cn (L.M.); huazipiaoling.123@163.com (Y.X.); liangyili@hotmail.com (Y.L.); xueduanliu@yahoo.com (X.L.)2 Key Laboratory of Biometallurgy of Ministry of Education, Central South University, Changsha 410083, China* Correspondence: yinhuaqun@gmail.com; Tel.: +86-731-8883-054619 8 2016 8 2016 17 8 135503 7 2016 11 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Acidithiobacillus thiooxidans known for its ubiquity in diverse acidic and sulfur-bearing environments worldwide was used as the research subject in this study. To explore the genomic fluidity and intraspecific diversity of Acidithiobacillus thiooxidans (A. thiooxidans) species, comparative genomics based on nine draft genomes was performed. Phylogenomic scrutiny provided first insights into the multiple groupings of these strains, suggesting that genetic diversity might be potentially correlated with their geographic distribution as well as geochemical conditions. While these strains shared a large number of common genes, they displayed differences in gene content. Functional assignment indicated that the core genome was essential for microbial basic activities such as energy acquisition and uptake of nutrients, whereas the accessory genome was thought to be involved in niche adaptation. Comprehensive analysis of their predicted central metabolism revealed that few differences were observed among these strains. Further analyses showed evidences of relevance between environmental conditions and genomic diversification. Furthermore, a diverse pool of mobile genetic elements including insertion sequences and genomic islands in all A. thiooxidans strains probably demonstrated the frequent genetic flow (such as lateral gene transfer) in the extremely acidic environments. From another perspective, these elements might endow A. thiooxidans species with capacities to withstand the chemical constraints of their natural habitats. Taken together, our findings bring some valuable data to better understand the genomic diversity and econiche adaptation within A. thiooxidans strains. Acidithiobacillus thiooxidanscomparative genomicsintraspecific diversityniche adaptation ==== Body 1. Introduction Extraordinarily extreme environments [1,2,3] are the habitats for extremophiles, although they were previously thought of as almost insurmountable physical and chemical barriers to life [4]. These extreme environmental conditions are inhospitable to the growth of most life [5,6]. However, acidophilic microorganisms, especially prokaryotic acidophiles (eubacteria and archaea), are considerably diverse in natural and man-made acidic environments (pH < 3) [7]. Thus, it is of great interest to identify the potential mechanisms that ensure microorganisms survive and proliferate in these extreme environments. With the advance of high-throughput sequencing, numerous genomes derived from a wide range of organisms were sequenced continuously, thereby fueling the development of comparative genomics [8]. In contrast to standard genetic researches, which have inherent limits to elucidate the hereditary traits of species, acquisition of genomic sequences generated by high-throughput sequencing provides plentiful gene contents and further enables future studies to explore the primary issues that investigators are interested in. Acidophiles such as Acidithiobacillus, Sulfobacillus, and Leptospirillum were isolated from the extremely acidic environments, and their sequenced genomes were used for comparative survey [6,9,10]. Nevertheless, limited information is currently known about intraspecific variability of acidophiles at the genome level. Comparative genomics determining genomic differences among multiple strains of individual species can unravel the extent of intraspecific diversity [11]. The gene repertoire represented across all strains reveals the genomic diversity of a species, i.e., pan-genome, which consists of “core genome” (common genes to all strains of a species) and “dispensable or accessory genome” (genes shared by some but not all strains of the species as well as strain-specific genes) [12]. The core genome includes all common genes that are essential for its basic lifestyle and major phenotypic traits, while dispensable genome confers selective advantages such as niche adaptation, antibiotic resistance, and colonization of new hosts [12,13]. However, it remains unclear whether these intriguing findings concerning the genomic analyses of other organisms could be applied to acidophiles isolated from the harsh environments that are physico-chemically and ecologically distinct from the “normal” environments. Acidithiobacillus, the acidophilic and obligately chemolithoautotrophic bacterium, is widely found in various acidic environments worldwide [7,14]. Acidithiobacillus thiooxidans (A. thiooxidans) is an aerobic mesophilic microorganism belonging to the genus Acidithiobacillus and is considered to play an important role in industrial bioleaching [10]. Until recently, three genomes of A. thiooxidans strains including ATCC 19377, A01, and Licanantay from various habitats (Table 1) have been sequenced and submitted to National Center for Biotechnology Information (NCBI) [15,16,17]. When the draft genome sequences were released, numerous genes were annotated and public, thus providing a wealth of valuable information. As a result, much research could be invested to identify novel insights into the genotypic characteristics. In this study, six new genomic DNA of A. thiooxidans strains isolated from different acidic environments in China (Table 1) were extracted and sequenced. Together with three aforementioned genomes publicly available in the GenBank database, a global genomic comparison was executed. Our work showed a data-driven approach to elucidate the similarities and differences among A. thiooxidans genomes, aiming to explore the genetic diversity and niche adaptation within A. thiooxidans strains. 2. Results and Discussion 2.1. General Features of Acidithiobacillus thiooxidans (A. thiooxidans) Genomes Six new genomic DNA were subjected to Illumina MiSeq sequencing platform, and an average of 240 Mb raw reads (short DNA sequences) in each genome was yielded. After quality control using NGS QC Toolkit, high quality (HQ) reads (85.3% to 87.80%) were retained for subsequent analyses. All HQ reads aforementioned were used for sequence assembly, and an in-house Perl script was then employed to filter the assembled sequences under 200 bp, resulting in the draft genome assemblies. Genome characteristics were summarized in Table S1. Of note, the draft genome of A. thiooxidans ATCC 19377 sequenced previously [15] was much smaller than these of the others, preliminarily inferring that the low-coverage genome sequencing (9.6-fold) might contribute to the missing of large fragments genomic DNA. Nevertheless, high quality and completeness of genome assemblies was acquired in this strain (Table S1). Thus, pan-genome analysis could be reliable as the high quality of genome completeness was estimated in all strains. As listed in Table S1, total genome sizes varied among all strains (3.02 to 3.95 Mb). As stated by Nuñez et al. [18], genome size in prokaryotes is related to metabolic diversity, effective population size, regulatory complexity, and horizontal transfer rates. And larger genomes might have a high adaptive plasticity compared to smaller genomes [17]. Previous studies showed that the genome of A. ferrooxidans DSM 16786 isolated from mining processes [19] was much larger than that of A. ferrooxidans ATCC 23270, which was from the bituminous effluent of coal mine [17]. Likewise, the larger genome size of A. thiooxidans strains GD1-3, DXS-W, and Licanantay obtained from copper mining implied that putative gene acquisition might enable them to adapt to the high concentration of metals in these metal mines. Also, the number of putative coding sequence (CDS) (3087 to 4200) and mean mole percentage GC content (52.84% to 53.17%) varied among all sequenced genomes (Figure 1 and Table S1). There is slight difference about mean percentage GC content among strains, while the number of CDS varied greatly. The CDS counts of strain Licanantay (4200) and GD1-3 (4171) were slightly more than those of the others. 2.2. Pan-Genome Analysis of A. thiooxidans Strains To understand the pan-genome of A. thiooxidans more deeply, 7186 protein CDSs obtained from the six newly sequenced genomes plus three available genomes from the public database were clustered using the program PanOCT with a 50% sequence identity cut-off. Herein, 2043 (28.31%) orthologs were identified as the A. thiooxidans core genome, and the remaining variable genes were defined as the accessory genome of A. thiooxidans (Figure 2A). In particular, our results showed that Licanantay had the largest number of unique genes (1001), followed by ATCC 19377 (421). These were similar to what Travisany et al. [17] reported previously. In their study, comparative analysis showed that strain-specific genes in the Licanantay might be involved in adaptation to its specific biomining environment and several genetic motility genes likely acquired by horizontal gene transfer in mining environments. Considering the closely relation between Licanantay and ATCC 19377 [17], we further identified their shared genes. 2304 orthologous genes were present, and the unique genes in Licanantay and ATCC 19377 were 1795 and 715, respectively (Figure 2B), further indicating that Licanantay with much more strain-specific genes had the advantage to adapt the environmental conditions. Additionally, core orthologous genes and unique genes within seven other strains were examined. 2717 orthologs were identified, and the number of strain-specific genes in each strain varied from 34 to 106 (Figure 2C). In particular, GD1-3 and DXS-W shared a large number of genes (1070) only between the two of them, suggesting a closely correlation with each other. 2.3. Phylogenomic Tree Based on Core Genome Since the 16S rRNA gene sequences among each pair of A. thiooxidans strains are highly similar, we could not assess the phylogenetic distance between strains using these sequences alone. In this study, a phylogenomic tree based on their core genome (Figure 3) showed that nine strains were apparently divided into three main groupings. As depicted in Figure 3, A. thiooxidans Licanantay was genetically distinct from other strains included in this study, and strains GD1-3, DXS-W, A02, A01, BY-02, DMC, and TYC-17 were closely related to each other. In fact, the strains ATCC 19377 and Licanantay were originally isolated from Kimmeridge clay and Chilean copper mine respectively [15,17], and the others from various acidic environments in China (Table 1). Thus, a hypothesis was proposed that hereditary difference might be related to the geographic distribution. Further inspection showed that these six strains from China were classified into three clusters. As reported by Douillard et al. [20], phylogenomic analysis showed that multiple groupings of Lactobacillus rhamnosus partly be related to their ecological niches. In our study, A. thiooxidans strains GD1-3 and DXS-W were isolated from the similar environments, and strains A01 and A02 from the same sampling points. While strains BY-02 and TYC-17 were isolated from copper mine, and DMC from coal heap drainage, these three strains gathered in a cluster. Unfortunately, the detailed geochemical conditions of these six bacteria, at that time, were not measured. Thus, the limited samples and experimental data restricted our further analysis to determine whether genetic difference was correlated with the geochemical characteristics in these acidic environments. 2.4. Functional Features of the Pan-Genome To identify possible intraspecific diversification in functions, the functional annotation of core genome and accessory genome were performed against the specialized database Clusters of Orthologous Groups (COG). As shown in Figure 4, the abundances of metabolism-related genes assigned to COG categories (C) (energy production and conversion), (E) (amino acid transport and metabolism), (G) (carbohydrate transport and metabolism), (F) (nucleotide transport and metabolism), (H) (coenzyme transport and metabolism) and (I) (lipid transport and metabolism) in the core genome were greater in these A. thiooxidans strains compared to those in the accessory genome. These findings were reasonable given that these shared genes were involved in microbial basic activities, which might support the view that core genome was essential for basic lifestyle of species [12,13]. Additionally, the core genome was highly enriched in COG category (J) (translation, ribosomal structure and biogenesis) relative to accessory genome. These features were similar to what other researchers have been found in their respective pan-genome analyses [21,22]. Especially, the core genome in all strains was commonly enriched in the COG category (M) (cell wall/membrane/envelope biogenesis). We interpreted this as an indication that A. thiooxidans strains inhabiting the acidic environments shared distinctive structural and functional properties to maintain a stable pH gradient, as specialized cellular structures were regarded to be important for acidophile pH homeostasis [23]. In contrast, the accessory genome among A. thiooxidans strains consisted of putative 5143 CDSs, and COG class assignment revealed the abundant CDSs were involved in replication, recombination and repair (COG category (L); Figure 4). Considering that the high concentration of toxic substances such as heavy metals in these acidic environments [7,24], and the high level of heavy metals concentration might cause a high rate of DNA injury [25], it appears to be reasonable that there were the abundant genes in the accessory genome assigned to COG category (L), which might be related to niche adaptation. This finding was in line with previous comparative genomics showing that the accessory genome of 48 strains of sinorhizobia Sinorhizobium comprising five genospecies might be related to the different strategies to interact with diverse host plant and soil environments [21]. Also, comparative analysis of Klebsiella pneumoniae Kp13 showed that genomic plasticity occurring at multiple hierarchical levels might play a role of the lifestyle [26]. Besides, both core genome and accessory genome had high proportion of genes in COG categories (R) (general prediction only) and (S) (function unknown). The amino acid sequences associated with these CDSs which lacked a functional assignment were then chosen for functional annotation against the NCBI-NR database (E-value ≤ 10−5). Results indicated that various proportions of CDSs (20.55%~74.03%) were hypothetical proteins, and the others were annotated as proteins with known function (Table S2). For these functional proteins, we found most CDS showing hits with A. thiooxidans. We emphasized the reasonability of these findings that CDS in COG categories (R) and (S) could be re-annotated as proteins with known function in our study, due to the continuously updated database. We also performed a Blast search against the NCBI-nr database using the protein sequences related to strain-specific genes. Similar to several other comparative genomic analyses [27,28,29], a large number of CDS were annotated as hypothetical proteins. Furthermore, most of them were not assigned to the COG category (Figure S1A). Especially, we further inspected the non-shared CDS from A. thiooxidans Licanantay and ATCC 19377 considering that the numbers were larger compared to those in other strains. The most abundant strain-specific CDS were assigned into COG category (L) and (M) (Figure S1B), which was consistent with previous study [17]. These strain-specific genes might confer them some advantages to adapt to the environmental conditions. 2.5. Identification of Metabolic Traits and Management Strategies to Environmental Stress 2.5.1. Feature of Central Metabolism The assignment of CDSs to the COG classification revealed inspection concerning the metabolic traits of A. thiooxidans strains, highlighting the high abundance of metabolic profiles in the core genome (Figure 4). In this study, the number of assigned CDSs involved in central metabolism, including carbon assimilation, nitrogen uptake, and sulfur metabolism, was discussed (Table S3). Subsequently, the metabolic potentials of A. thiooxidans were reconstructed and compared to each other in order to determine the shared or strain-specific metabolic feature. As depicted in Figure 5, all strains have the ability to fix carbon atmospheric CO2 via Calvin Benson Bassham cycle. In particular, A. thiooxidans harbors a gene cluster potentially encoding carbon dioxide-concentrating protein, carboxysome shell protein, carboxysomal shell carbonic anhydrase, and ribulose-1,5-bisphosphate carboxylase/oxygenase, allowing a higher efficiency for CO2 fixation within the carboxysome [30]. The product 3-phosphoglycerate (G3P) generated in the process of CO2 fixation was predicted to be converted to be the precursors for the macromolecular biosynthesis such as amino acids, fatty acids. Particularly, the conversion of G3P was expected to result in the formation of UDP-glucose, a major precursor for biosynthesis of extracellular polysaccharide [31]. The latter could mediate bacterial adhesion and biofilm formation [32], and provide a reaction space between bacterial cell and mineral surface, thus increasing the dissolution of metal sulfides [32,33]. Unlike closely related A. ferrooxidans, which has the set of genes required for N2 fixation via nitrogenase [34], A. thiooxidans strains shared the potential to assimilate the nitrate, nitrite, as well as ammonium as nitrogen sources necessary for their growth (Figure 5). The accumulated ammonium derived from the reduction of nitrate and nitrite, or from the uptake of extracellular ammonium via Amt family transporter, would enter into the biosynthesis of glutamate and other vital compounds. Results showed that all strains were found to harbor a full suite of genes involved in nitrogen metabolism except for strain ATCC 19377. For example, several genes associated with assimilatory and assimilatory nitrate reduction were not identified in its genome (Table S3). Also, genes encoding transporters for nitrate or nitrite were absent. One possible explanation was that strain ATCC 19377 could take up environmental ammonium as its sole pathway for the acquisition of nitrogen source. Referred to the well-studied models for sulfur oxidation in A. thiooxidans species [10,35], a hypothetical model for sulfur oxidation system and electron transportation was reconstructed in our study (Figure 5). In this model, a series of complicated enzymatic reactions were performed by various enzymes with distinguished features in specific cellular position. The sor gene encoding sulfur oxygenase reductase, an enzyme directing the disproportionation reaction of sulfur to generate sulfide, thiosulfate, as well as sulfate [36], was absent in strain ATCC 19377. It seems that horizontal gene transfer might occur in this strain. However, it remains to be further validated. Our results showed that the vast majority of homologous genes were identified by aligning against the public database as well as reported sequences in other literatures, although there were few exceptions such as genes involved in nitrate reduction in strain ATCC 19377. Taken together, we proposed that there were few intraspecific differences with respect to metabolic pathways, at least central metabolism. 2.5.2. Predicted Stress Tolerance Mechanisms The comparison of metabolic profiles of A. thiooxidans strains was extended to potential mechanisms to respond to environmental stress, including acidic pH, high concentration of toxic substrates such as heavy metal ion and organic compounds (not shown in Figure 5). Also, cell mobility was taken into account. In this study, all strains shared a core set of genes potentially related to stress management. Like most other acidophiles, A. thiooxidans species highly relied on intracellular pH homeostasis, allowing it to grow at distinct ranges of pH values [37]. Numerous genes assigned to COG category (N) (cell mobility) were predicted to be involved in flagella formation (Table S3). The presence of these putative genes indicated that all A. thiooxidans strains had the capacity to actively move in aquatic habitats. Additionally, the identification of several gene clusters related to the resistance of heavy metals including arsenic, mercury, cadmium, and copper suggested that all strains might employ various systems to cope with high metal ion concentrations. As for organic solvents such as Lix984n, an organic extractant used for metal extraction in industrial operation [38], a putative gene cluster acrAB-tolC encoding a member of resistance-nodulation-cell division (RND) family protein was predicted to potentially transfer this substrate [39]. Additionally, A. thiooxidans strains were found to harbor a complete six-gene cluster for ATP binding cassette (ABC) transport system involved in toluene resistance (Table S3). In conclusion, all strains observed in this study appear to exhibit similar strategies to cope with the chemical constraints of their natural habitats. 2.6. Gene Turnover Analysis Similar to earlier study in the closely related Acidithiobacillus caldus [40], prediction and classification of transposases using ISFinder showed that 3.41%~4.54% of the predicted CDSs of A. thiooxidans strains encoded transposases belonging to 25 different insertion sequence (IS) families (Table 2). The most abundant IS families in all strains were ISL3. High similarity regarding IS type were observed in all A. thiooxidans strains, however, closer inspection demonstrated several differences. For instance, A. thiooxidans strains GD1-3, DXS-W, and Licanantay with larger genome size had more transposases than the others. This finding might be reasonable considering that horizontal transfer was regarded as an evolutionary force to increase microbiological DNA content [41]. On the other hand, a frequent gene flow and genetic drift might endow species with adaptive capacity to the extreme econiche. Besides, finding presented in this study revealed that many genomic island (GI) occurred in the A. thiooxidans population (Table 2). Further analyses suggested that several integrases or mobile genetic elements were presented in the predicted GI (not shown in Table 2), thereby indicating that these putative GI were likely acquired by horizontal gene transfer. As stated by Wu et al. [42], GI were highly related to the niche-specific adaptation. Thus, it was inferred that these GI might play a key role in adapting to specific lifestyles and environmental niches. In short, great frequency of genetic exchange might provide A. thiooxidans species with adaptive advantage in extremely acidic environments. 3. Materials and Methods 3.1. Bacterial Strains Used in This Study We included a total of six strains of A. thiooxidans in this study, which were preserved in the acidophile culture collection maintained at Central South University, Changsha, China (Table 1). Each strain was cultivated in liquid 9K basic medium, including the following ingredients (grams per liter): (NH4)2SO4, 3.0; MgSO4·7H2O, 0.5; KCl, 0.1; Ca(NO3)2, 0.01; and K2HPO4, 0.5. Of this solution, elemental sulfur (autoclave-sterilized, 10g/L) was added as energy source. The culture temperature and shaking speed of rotary platform were 30 °C and 170 rpm, respectively. High-concentration cells (1~2 × 108 cells/mL) were collected at the exponential growth phase (generally 3th day), and their genomic DNA were extracted using TIANamp Bacteria DNA Kit (TIANGEN, Beijing, China) following the manufacturer’s introductions. 3.2. Genome Sequencing and Bioinformatics Analyses The genomes of six strains of A. thiooxidans were sequenced using the Illumina MiSeq platform for 2 × 150 bp paired-end sequencing (Illumina, Inc., San Diego, CA, USA). The raw reads were filtered using NGS QC Toolkit [43] with Phred 20 as a cutoff, and then paired-end read sequences with high-quality were assembled using velvet [44]. For each strain, several kmers were run and the best assembled result was chosen for subsequent analyses. Following the cleanup step, the contigs were further clustered and assembled de novo to obtain unigene sequences using iAssembler tool [45]. The genome completeness of each strain was estimated using the program CheckM [46]. Coding sequences (CDS) prediction was performed by MetaGeneAnnotator [47], and predicted genes were then functionally annotated via homology searching against the generalist databank (NCBI-NR) and specialised databases (COG and KEGG). The hidden Markov models for the protein domains were predicted via searching against the Pfam database [48]. The online platform tRNAscan-SE was used for the identifications of tRNA [49]. Additionally, insertion sequences and transposases were identified by Blastp against the ISFinder database [50] with manual inspection of search hits (E-value ≤ 10−5). The putative genomic islands were also predicted using the online platform IslandViewer 3 [51]. 3.3. Pan-Genome Analysis Gene annotations of assembled contigs of the six strains were performed through the RAST annotation server [52]. The GenBank files containing the genome information of A. thiooxidans strains were then downloaded. Additionally, three other available genome sequences from homologous strains including A. thiooxidans strains A01, ATCC 19377, and Licanantay were obtained from NCBI. For these nine strains, the corresponding protein sequences with Fasta format were extracted using in-house Perl scripts, and were then aligned using an all-versus-all BLASTP. Output files with the m8 BLAST format were used for the identification of single-copy orthologs using PanOCT program (50% identity cut-off; E-value ≤ 10−5) [53]. Finally, the shared and dispensable genes in all strains were functionally annotated as described above. 3.4. Phylogenomic and Phylogenetic Analyses A core of ortholog genes from nine draft genomes of A. thiooxidans strains were extracted using in-house Perl script. These shared genes were then used to construct a genome-based and alignment-free phylogeny using the online platform CVTree3 with K-tuple length 6 [54]. Additionally, the genome of Acidithiobacillus caldus SM-1 was included as an out-group. Visualization for phylogenetic tree was performed using the sofeware MEGA v5.05 [55]. 3.5. Data Deposition These Whole Genome Shotgun projects of six A. thiooxidans strains have been deposited at the DDBJ/ENA/GenBank under the accession LWSC00000000 (GD1-3), LWRY00000000 (DXS-W), LWSA00000000 (A02), LWRZ00000000 (BY-02), LWSB00000000 (DMC), and LWSD00000000 (TYC-17). Additionally, the versions described in this paper are version LWSC01000000, LWRY01000000, LWSA01000000, LWRZ01000000, LWSB01000000, and LWSD01000000, respectively. 4. Conclusions Comparative genomics provided useful information on the genetic and functional features of A. thiooxidans strains. Phylogeny based on their core genome revealed that genetic diversity was potentially related to the geographic distribution and geochemical conditions of their habitats. Functional assignment of common genes uncovered that abundant genes were involved in metabolism, such as COG categories (C), (E), and (G), compared to the accessory genome, indicating that these genes were necessary for the microbial basic activities. Comprehensive analysis further showed little intraspecific diversification with respect to their predicted metabolic profiles. Additionally, most genes belonging to the dispensable genome were assigned to the COG category (L) and (M), suggesting a correlation between accessory genome and niche adaptation. Also, a considerable diverse repertoire of mobile genetic elements including insertion sequences and genomic islands were widespread in the draft genomes of A. thiooxidans strains obtained from various geographic origins, indicating that gain and/or loss of these elements by transferring horizontally might greatly contribute to intraspecific divergence and adaptation to acidic econiches. Acknowledgments This work was supported by the National Natural Science Foundation of China (No. 31570113 and No. 41573072) and the Fundamental Research Funds for the Central Universities of Central South University (No. 2016zzts102). We thank Wei Lin in Chinese Academy of Sciences and Guanyun Wei in Nanjing Normal University for analyzing the data. Also, we thank NCBI for providing the genome sequences of A. thiooxidans strains ATCC 19377, A01, and Licanantay. Supplementary Materials Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1355/s1. Click here for additional data file. Author Contributions Conceived and designed the experiments: Xian Zhang, Xueduan Liu and Huaqun Yin; Performed the experiments: Xue Feng and Jiemeng Tao; Analyzed the data: Xian Zhang; Contributed to sample collection: Liyuan Ma and Yunhua Xiao; Wrote the paper: Xian Zhang; Revised the paper: Yili Liang. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Three-dimensional plots of genome size, coding sequence (CDS) number, and GC content of the nine Acidithiobacillus thiooxidans (A. thiooxidans) strains sequenced in this study. The available genomes from strains Licanantay, ATCC 19377, and A01 were acquired from the public database, and the others were sequenced in this study. Figure 2 The pan-genome of Acidithiobacillus thiooxidans strains. The flower plots and Venn diagram demonstrate the number of shared, accessory and strain-specific genes among A. thiooxidans strains. Each strain was represented by an oval or circle that was colored. (A) Flower plot showing the numbers (in the petals) correspond to the unique genes of each strains, and the number of core genome common to all A. thiooxidans strains (in the center); (B) Venn diagram showing the numbers of unique genes and core orthologous genes between A. thiooxidans ATCC 19377 and Licanantay; (C) flower plot showing the numbers of CDSs among all A. thiooxidans strains in this study except for ATCC 19377 and Licanantay. Figure 3 Phylogenomic tree of sequenced Acidithiobacillus thiooxidans strains based on their core genome. These strains from various geographic origins were clustered into three distinct classes. Classe I represents strain Licanantay isolated from Kimmeridge clay, class II represents strain ATCC 19377 from Chilean copper mine, and class III represents certain strains isolated from different acidic envornments in China. Figure 4 Distribution of core and flexible genes based on Clusters of Orthologous Groups (COG) category in A. thiooxidans strains. Only orthologous genes assigned to COG category were used for analysis. Figure 5 Schematic diagram depicting the predicted central metabolism and potential management strategies to environmental stress of A. thiooxidans strains. Herein, several genes in A. thiooxidans ATCC 19377 were absent. Included were genes involved in nitrate reduction, genes encoding sulfur oxygenase reductase and nitrate/nitrite transporter. More details for genes/enzymes involved in central metabolism and environmental adaptation were presented in Supplementary Table S3. ijms-17-01355-t001_Table 1Table 1 Strains of Acidithiobacillus thiooxidans used for comparison survey in this study. Strain Geographic Origin Reference Licanantay Copper mine, Atacama, Chile [17] ATCC 19377 Kimmeridge clay, UK [15] GD1-3 Copper Mine, Shaoguan, Guangdong, China This study DXS-W Copper Mine, Dongxiang Mountain, Hami, Xinjiang, China This study A02 Coal heap drainage, Pingxiang, Jiangxi, China This study A01 Coal dump, Pingxiang, Jiangxi, China [16] BY-02 Copper Mine, Baiyin, Gansu, China This study DMC Coal heap drainage, Chenzhou, Hunan, China This study TYC-17 Copper Mine, Baiyin, Gansu, China This study ijms-17-01355-t002_Table 2Table 2 The prediction of mobile elements including insertion sequences (IS) and genomic island (GI) in all A. thiooxidans strains observed in this study. A. The Putative Insertion Sequences IS Family DXS-W Licanantay A01 ATCC 19377 GD1-3 DMC A02 BY-02 TYC-17 IS110 9 11 9 4 8 9 9 9 10 IS1182 2 0 0 0 2 0 0 0 0 IS1380 3 2 0 0 3 0 0 0 0 IS1595 5 8 8 4 5 9 7 9 8 IS1634 2 1 3 9 1 3 3 3 3 IS200/IS605 6 15 4 3 5 4 2 4 4 IS21 6 8 11 2 6 8 9 9 9 IS256 1 8 2 15 1 3 3 2 2 IS3 11 12 4 1 10 6 5 5 5 IS30 1 2 0 3 1 2 2 2 2 IS4 7 6 3 12 7 9 7 7 7 IS481 8 1 2 1 8 2 2 1 2 IS5 4 4 5 5 4 6 5 5 6 IS51 0 2 0 2 0 0 0 0 0 IS605 1 0 1 0 1 1 1 1 1 IS607 4 4 1 0 4 4 1 4 1 IS630 11 18 6 9 12 14 12 11 9 IS66 11 0 1 0 9 2 1 1 1 IS91 20 16 16 8 19 15 16 16 15 ISAs1 1 2 1 0 1 1 0 1 0 ISAzo13 1 1 0 0 1 0 0 0 0 ISKra4 7 2 4 2 7 1 4 1 4 ISL3 43 44 39 30 47 41 41 40 42 ISNCY 2 1 1 0 2 1 2 2 2 Tn3 21 16 17 16 20 19 19 20 21 Total 187 184 138 126 184 160 151 153 154 B. The Predicted Genomic Islands Strain DXS-W Licanantay A01 ATCC 19377 GD1-3 DMC A02 BY-02 TYC-17 GI Number 65 54 39 36 56 53 51 50 44 The four most abundant IS families were highlighted in bold. ==== Refs References 1. Kennedy S.P. Ng W.V. Salzberg S.L. Hood L. DasSarma S. Understanding the adaptation of Halobacterium species NRC-1 to its extreme environment through computational analysis of its genome sequence Genome Res. 2001 11 1641 1650 10.1101/gr.190201 11591641 2. Takami H. Takaki Y. Uchiyama I. Genome sequence of Oceanobacillus iheyensis isolated from the Iheya Ridge and its unexpected adaptive capabilities to extreme environments Nucleic Acids Res. 2002 30 3927 3935 10.1093/nar/gkf526 12235376 3. Falb M. Pfeiffer F. Palm P. Rodewald K. Hickmann V. Tittor J. Oesterhelt D. Living with two extremes: Conclusions from the genome sequence of Natronomonas pharaonis Genome Res. 2005 15 1336 1343 10.1101/gr.3952905 16169924 4. Rothschild L.J. Mancinelli R.L. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081357ijms-17-01357ReviewTamoxifen Resistance: Emerging Molecular Targets Rondón-Lagos Milena 1*†Villegas Victoria E. 23*†Rangel Nelson 123Sánchez Magda Carolina 2Zaphiropoulos Peter G. 4Cho William Chi-shing Academic Editor1 Department of Medical Sciences, University of Turin, Turin 10126, Italy; nrangel@unito.it2 Faculty of Natural Sciences and Mathematics, Universidad del Rosario, Bogotá 11001000, Colombia; magda.sanchez@urosario.edu.co3 Doctoral Program in Biomedical Sciences, Universidad del Rosario, Bogotá 11001000, Colombia4 Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge 14183, Sweden; peter.zaphiropoulos@ki.se* Correspondence: srondonl@unito.it (M.R.-L.); victoria.villegas@urosario.edu.co (V.E.V.); Tel.: +39-01-1633-4127 (ext. 4388) (M.R.-L.); +57-1-297-0200 (ext. 4029) (V.E.V.); Fax: +39-01-1663-5267 (M.R.-L.); +57-1-297-0200 (V.E.V.)† These authors contributed equally to this work. 19 8 2016 8 2016 17 8 135705 7 2016 16 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).17β-Estradiol (E2) plays a pivotal role in the development and progression of breast cancer. As a result, blockade of the E2 signal through either tamoxifen (TAM) or aromatase inhibitors is an important therapeutic strategy to treat or prevent estrogen receptor (ER) positive breast cancer. However, resistance to TAM is the major obstacle in endocrine therapy. This resistance occurs either de novo or is acquired after an initial beneficial response. The underlying mechanisms for TAM resistance are probably multifactorial and remain largely unknown. Considering that breast cancer is a very heterogeneous disease and patients respond differently to treatment, the molecular analysis of TAM’s biological activity could provide the necessary framework to understand the complex effects of this drug in target cells. Moreover, this could explain, at least in part, the development of resistance and indicate an optimal therapeutic option. This review highlights the implications of TAM in breast cancer as well as the role of receptors/signal pathways recently suggested to be involved in the development of TAM resistance. G protein—coupled estrogen receptor, Androgen Receptor and Hedgehog signaling pathways are emerging as novel therapeutic targets and prognostic indicators for breast cancer, based on their ability to mediate estrogenic signaling in ERα-positive or -negative breast cancer. tamoxifenbreast cancerG protein-coupled estrogen receptor (GPER)estrogen receptors (ERs)androgen receptor (AR)Hedgehog (HH) signaling pathwayendocrine resistance ==== Body 1. Introduction Breast cancer is the most frequent type of cancer in women from developed and developing countries. It represents 23% of all female cancers, and often leads to death, even though the mortality rates are quite lower than the incidence rates [1]. However, breast cancer shows high morbidity and is commonly related to a wide variety of risk factors, including genetic predisposition and exposure to estrogens. Prolonged exposure to estrogen represents a significant risk factor in the development of breast cancer; however, the mechanisms whereby estrogens enhance the incidence of breast cancer are not completely understood and the subject of a certain controversy. Estrogens could promote de novo breast cancer development though either receptor-dependent or -independent mechanisms [2]. It is known that estrogens bind to a specific nuclear receptor, the estrogen receptor (ER), which generates a potent stimulus for breast gland cell proliferation and increases the risk of DNA mutation during replication [3,4,5,6]. However, some studies in ER knockout mice resulted in a sufficiently high incidence of tumor development, indicating that estrogens can promote breast cancer through ER-independent mechanisms [7]. The G protein-coupled estrogen receptor (GPER) is one candidate for this non-ER signaling that is mediated by estrogens [8]. The therapeutic management of ERα-positive patients consists in the application of endocrine strategies that seek to block ER with anti-estrogens, such as tamoxifen (TAM) or in the depletion of ligand (estrogen) availability, either by suppressing the gonads in premenopausal women (ovariectomy) or by using aromatase inhibitors (AIs) in postmenopausal women. These strategies are implemented both for early and metastatic breast cancer. However, not all patients respond to TAM endocrine therapy, and moreover, patients that initially respond to treatment can acquire resistance to this drug [9,10,11,12]. Currently, the therapeutic management of ERα-positive breast tumors with acquired resistance to TAM consists in the application of second line therapies such as AIs [13], or the synthetic ER antagonist fulvestrant. AIs, including exemestane, letrozole and anastrozole, seek to disrupt estrogen signaling by either irreversible and inactivating binding (exemestane) or reversible and competitive binding (letrozole and anastrazole) to the aromatase enzyme; thus significantly reducing local estrogen biosynthesis and intratumoral levels of estrogen [14,15]. Fulvestrant prevents ER dimerization, leading to degradation and loss of cellular ER, and has proven to be as effective as anastrozole in treating postmenopausal women with acquired TAM resistance [16,17]. Additional approaches involve the use of agents designed to resensitize resistant tumors to endocrine therapy by targeting pathways recognized as drivers of resistance. One such approach has been the combination of TAM endocrine therapies with growth factor receptor kinase inhibitors (RKIs), such as gefitinib, trastuzumab and lapatinib [18,19]. The use of these combined therapies was shown to be a good therapeutic option to either prevent or overcome resistance to TAM in cancers overexpressing epidermal growth factor receptor (EGFR) or EGFR2 (HER2) [18,19,20,21]. To date, several studies have led to the identification of key factors/signaling pathways involved in TAM resistance. These include activation of ER signaling, overexpression/up-regulation of receptor tyrosine kinases (RTKs) signaling pathways (EGFR, HER2, insulin-like growth factor 1 receptor (IGF1R) and fibroblast growth factor receptor (FGFR)), deregulation of the PI3K-PTEN/AKT/mTOR pathway and hyperactivation of NF-κB signaling [22]. However, considering that breast cancer is a heterogeneous disease, multiple mechanisms may contribute to TAM resistance, highlighting the importance of identifying prognostic biomarkers that would allow the application of more effective therapeutic options. Recently, additional receptors/pathways have been postulated to be involved in the development of TAM resistance. In this review, we highlight the role of these new receptors/pathways and their exploitation as both novel prognostication markers and therapeutic targets in breast cancer. 2. Estrogen Receptors (ERα and ERβ) 2.1. Structure ERs are highly involved in the development and progression of breast cancer. Most of the effects of 17β-Estradiol (E2) are mediated through its two nuclear receptors: ERα (ERα) and β (ERβ), which are encoded by different genes, ESR1 encodes ERα on chromosome 6 and ESR2 encodes ERβ on chromosome 14 [23,24]. ERβ is more abundant than ERα in normal human and mouse mammary gland [3,9,25] and both receptors contain in their structure different domains: Two ligand-independent transcriptional activation, N-terminal domains, NTD (A/B domains), also called activation factor 1 (AF1) domains, where MAPKs-mediated phosphorylation is carried out, a DNA-binding domain, DBD (C domain), a nuclear localization and heat shock proteins binding domain (domain D), a ligand-dependent transcriptional activation, ligand binding domain, LBD (domain E), also called activation factor 2 (AF2) domain and a C-terminal domain (domain F), which modulates the transcriptional activation mediated by domains A/B and E [3,26,27,28] (Figure 1). In the absence of ligands, ERs are found predominantly in the nucleus as monomers associated with multiprotein complexes, including heat shock proteins (HSPs) [27,28]. However, recent studies have reported the presence of ERα, ERβ or both on the inner phase of the plasma membrane, bound either to membrane proteins, e.g., caveolin-1, or associated to other membrane receptors, e.g., Insulin-like growth factor receptor (IGFR), EGFR or HER2, or to signal adapter molecules, e.g., SHC (Src Homology 2 Domain Containing) [27,28,29]. 2.2. Function ERs belong to a family of nuclear proteins bound to DNA, which regulate the transcription of a wide variety of genes involved in the development and function of the reproductive organs, in bone density, in regulation of the cell cycle, in DNA replication, differentiation, apoptosis, angiogenesis, survival and tumor progression. Examples of genes regulated by ERs include IGFR, cyclin D1 (CCND1), B-cell CLL/lymphoma 2 (BCL-2), vascular endothelial growth factor (VEGF) and certain growth factors, e.g., heregulins (HER), transforming growth factor β (TGFβ) and amphiregulins, which bind and activate EGFR [28,30,31]. The classical mechanism of action of ERs (genomic signaling) initiates with E2 binding to ER receptors (α and β) in the nucleus and subsequent binding of the ERs to DNA in regulatory regions termed estrogen response elements—EREs. However, ERs can also regulate the expression of many genes without direct binding to DNA, through protein–protein interactions with transcription factors, f. ex. specificity protein 1 (Sp-1), activator protein 1 (AP-1) and GATA binding protein 1 (GATA1) [32,33,34,35]. Genes activated by this route include IGF-1, c-MYC, CCND1, c-FOS and the low-density lipoprotein receptor [36]. Besides this classical mechanism, a non-genomic effect mediated by membrane-associated ERα and ERβ has also been observed, leading to the activation of the cytoplasmic tyrosine kinase Src and other signaling molecules including: (i) IGF1R and EGFR; (ii) mitogen-activated protein kinases (MAPK), phosphatidyl inositol 3 kinase (PI3K) and AKT; (iii) protein kinase C (PKC) and cyclic AMP (cAMP); (iv) p21 and (v) pathways that promote the release of intracellular calcium [37,38,39,40,41]. These signaling cascades can phosphorylate nuclear ERs and their co-activators (AIB1/SRC-3) resulting in their activation as transcriptional regulators of target genes [42]. In addition, the G protein-coupled estrogen receptor (GPER) is another candidate molecule involved in the non-genomic signaling mediated by E2 [8] and also implicated in TAM resistance [8,42,43,44]. In normal breast tissue, ERβ plays a role as the dominant receptor, but during carcinogenesis the amount of ERβ decreases whilst the amount of ERα increases. Thus, ERβ was postulated to act as a tumor suppressor gene in breast cancer [45]. Most of the ERs present in breast tumors are ERα; moreover, high levels of this receptor in benign breast epithelium increase the risk to develop breast cancer, and ERα has particularly been associated with tumor initiation and progression to later stages. Although the detailed ERβ function in breast and ovarian cancer is still unclear, the interaction between ERβ and ERα is essential for normal development and for the functionality of the tissues in which they are expressed [28,31,34]. ER detection is widely used in patients with breast cancer as a prognostic marker to predict the risk of progression and as a response predictor to anti-estrogen therapy. Tumors positive for ER and progesterone receptor (PR) are very well differentiated in histological terms; they show low rates of cell proliferation and diploid content of DNA. Breast tumors can also be associated with poor prognostic markers, e.g., amplification of the HER2, c-MYC and INT-2 genes and TP53 gene mutation [46,47]. 3. Tamoxifen (TAM) 3.1. Function TAM is a non-steroidal anti-estrogen with mixed ER agonist/antagonist activities; its introduction represented a pioneering therapy for the treatment of ERα-positive breast cancer and since then has extensively been used. TAM’s activity is dependent on circulating E2 levels, which are higher in pre-menopausal women and lower in postmenopausal women. Initially considered an antagonist, TAM is currently classified as a Selective Estrogen Receptor Modulator (SERM), a compound that exhibits tissue-specific ER agonist or antagonist activity. It binds competitively to the ERs, thereby inhibiting E2 dependent gene transcription, cell proliferation and tumor growth [48,49,50]. TAM binds to ER with lower affinity compared with E2, and dissociates the heat shock protein 90 (HSP90). The TAM-ER complex homo or hetero dimerizes and translocates to the cell nucleus, causing the activation of the activation factor 1 (AF1) domain and inhibiting the activation factor 2 (AF2) domain. Following this, the TAM-ER dimer binds to DNA at palindromic ERE sequences in the promoter region of E2 responsive genes. Transcription of the E2 responsive gene(s) is attenuated because the AF2, ligand-dependent domain is inactive, and ER co-activator binding is reduced by the TAM-ER complex; partial agonist activity results from the AF1 domain, which remains active in the TAM-ER complex [51] (Figure 2). TAM was demonstrated to reduce the risk of developing ERα-positive breast cancer by at least 50%, in both pre- and post-menopausal women [52]. The use of this anti-estrogen agent (dose of 20 mg/day) reduces the occurrence of breast cancer by 38% in healthy women at high risk of acquiring the disease, decreases the likelihood of recurrence in early breast cancer cases, prevents the development of cancer in the opposite breast, reduces cell proliferation, induces apoptosis and reduces the risk of developing invasive breast cancer in women with Ductal Carcinoma In Situ (DCIS) [53,54] (Figure 3). Five years of TAM treatment prevents ERα-positive breast cancer not only during this time period but also after treatment cessation. Recent studies have found that extending the duration of TAM treatment to 10 years further reduces the risk of breast cancer recurrence in ERα-positive cases [52,55]. Some studies have suggested that TAM causes cell cycle arrest and apoptosis both in vitro and in vivo by modulating growth factors, e.g., down-regulation of TGFα, induction of stromal TGFβ1 and decrease in the production of the potent mitogen IGF-1. Other studies have showed that TAM inhibits cell proliferation by inducing cell cycle arrest in the G0/G1 phase. In addition, it has been reported that TAM can stimulate cellular proliferation by acting on several signaling pathways, including c-MYC and MAPKs. This mitogenic effect might arise as a result of estrogen-altered metabolism [56,57,58,59]. 3.2. TAM Metabolism TAM is extensively metabolized in the liver and to a lesser extent locally in the breast, with the main excretion occurring via the bile and the feces. Cytochrome P450 enzymes (CYP) mediate the biotransformation of TAM to several primary and secondary products, mainly through demethylation and hydroxylation. The major metabolic pathway involves initial conversion of TAM to N-desmethyl-TAM via CYP3A4/5, followed by conversion of N-desmethyl-TAM to endoxifen, via CYP2D6. In addition, some TAM is initially metabolized by CYP2D6 to the active metabolite 4-hydroxy-tamoxifen (4-OH-TAM), which in turn is either degraded or converted by CYP3A4/5 to endoxifen [60,61,62] (Figure 4). Endoxifen and 4-OH-TAM have higher potencies than the parental compound and it has been suggested that these metabolites may be responsible for the anti-tumor effects of TAM in vivo [63,64]. TAM and its metabolites bind to the ERs, albeit with somewhat different affinities. They block the binding of E2 to these receptors, prevent the conformational changes required for binding of co-activators and lead to the preferential recruitment of co-repressors, including nuclear receptor corepressor 1 (NCOR1). The reduced transcriptional activity of the ERs results in attenuation of tumor growth, as the genes regulated by E2 are involved in proliferation, angiogenesis and anti-apoptosis [61,64]. Endoxifen, the major metabolite responsible for the action of TAM in vivo, appears to have differential effects on the two ER receptors. It stabilizes ERβ, promoting receptor hetero-dimerization and has increased inhibitory effects on the expression of target genes. On the other hand, endoxifen targets ERα for proteasomal degradation in breast cancer cells. Polymorphisms in several CYP enzymes involved in TAM metabolism impact on the relative abundance and availability of the metabolites and, consequently, on their effects in E2-dependent breast cancer cell proliferation [63,65,66]. 4. TAM Resistance Despite the obvious benefits of TAM in patients at all stages of ERα-positive breast cancer, several studies have reported that the tumors in almost all patients with metastatic disease and in 40% of patients receiving TAM as adjuvant therapy eventually relapse, with a deadly outcome. Likewise, post-menopausal women with early stage breast cancer that initially responded well to TAM can develop recurrent tumors not only in the breast, but also in the endometrium [31], and over time become resistant to the drug [67]. Several mechanisms are proposed to explain TAM resistance, and intensive research has resulted in the identification of molecular pathways that may be involved. These include ER signaling, RTKs signal transduction pathways (HER2, EGFR, FGFR, and IGF1R), the phosphatidylinositol 3-kinase-phosphatase and tensin homolog (PI3K-PTEN)/V-Akt murine thymoma viral oncogene homolog (AKT)/mechanistic target of rapamycin (mTOR) pathway and NF-κB signaling [11,12,22,68]. Additional mechanisms for TAM resistance implicate imbalances between E2 anabolism and catabolism [69], altered bioavailability of TAM [70], increased angiogenesis, heterogeneity of tumor cell population or overexpression of growth factors. In fact, experimental evidence suggests that patients over-expressing the HER2 protein can develop resistance to TAM [50,71,72], however, the mechanism by which this occurs is unknown. 4.1. ERs and TAM Resistance ERα is expressed in 70%–80% of breast tumors and ERα-positivity is a well-established predictor of a good response to TAM treatment. Patients with higher levels of ERα (ERα-positive tumors) show increased benefits to TAM therapy compared to patients with lower receptor expression [73,74]. Patients with ERα-negative tumors are considered as no responders, although 5%–10% of these do benefit from adjuvant TAM therapy [74,75,76,77]. The response to TAM is frequently limited in duration because the patients can develop resistance [31,32,67,78], with this being one of the major problems of endocrine therapy. The possible causes of resistance to TAM that implicate the ERs are: 4.1.1. Increased Growth Factor Signaling and Membrane-Associated ERs The non-genomic activities of membrane-associated ERs facilitate cross-communication between these receptors and RTKs signaling pathways, including HER2, EGFR, and the PI3K pathway. The membrane ERs can activate RTKs signaling and, in turn, these can phosphorylate ERα at Ser 118 or Ser 167 within the AF1 domain by MAPK and AKT, respectively, which are downstream components of the EGFR/HER2 signaling pathway. This interaction leads to ligand-independent activation of ER and to increased cellular proliferation [12]. This effect is not relevant for breast cancer cells with low levels of membrane ERs and in which RTKs, such as HER2, are poorly expressed [79]. Furthermore, in ERα-positive/HER2-positive tumors, TAM apparently acts as an E2 agonist contributing to increased cell proliferation and survival. In addition, it has also been reported that TAM can activate the membrane-associated ERs in a manner analogous to E2 ligands, thus accounting for its agonistic effects and the observed cellular resistance to this compound [79,80]. Considering the above, cross-communication between ERs and RTKs signaling pathways can contribute to TAM resistance and promote the survival of breast cancer cells [32,35,78]. In fact, the potential involvement of this cross-communication has also been observed in a meta-analysis, in which the tumors of patients with metastatic ERα-positive/HER2-positive breast cancer relapsed after a short period of TAM treatment compared to patients with HER2-negative cancers [81]. Overexpression and activation of EGFR and HER2 lead to proliferation and cell survival through activation of MAPK and PI3K/AKT signaling pathways, thus contributing to the development of resistance to endocrine therapy [28,35]. 4.1.2. Loss of ERα Expression It has been reported that another cause of resistance to TAM is the loss of expression of ERα Since the effects of TAM are primarily mediated through the ERα, and the degree of ERα expression is a strong predictor of a positive response to TAM, loss of ERα expression may be the main mechanism of de novo resistance to endocrine therapy. Loss of ERα expression has mainly been associated with aberrant methylation of CpG islands and with increased deacetylation of histones, which result in a more compact nucleosome structure that limits transcription [82,83]. Additionally, loss of ERα expression has also been linked with invasiveness and poor prognosis [84]. Moreover, it has been hypothesized that loss of ERα expression might be responsible for acquired resistance to TAM; however, some studies have reported that only 17%–28% of tumors with acquired resistance to TAM do not express ERα [85,86], and approximately 20% of TAM-resistant tumors will eventually respond to a second-line of treatment with AIs or fulvestrant [87,88]. 4.1.3. ERα Mutations In hyperplastic breast lesions, a single amino-acid substitution in ERα, which leads to a change at position 303 from lysine to arginine has been detected (K303R). This mutation produces a receptor with enhanced properties in ER-mediated cell growth, as it has increased sensitivity to estrogen and altered crosstalk with various cellular pathways that normally down-regulate ER signaling. These changes of ER activity could contribute to the development of endocrine resistance, but no clinical evidence supporting this claim has yet been reported [84]. In addition, the significance of this mutation is unclear, since it occurs at a low frequency (5%–10%) [89,90] and, moreover, has not been detected in large datasets, including the TCGA dataset [91,92]. Recently, several studies have reported the presence of mutations on the Ligand Binding Domain (LBD) of ERα in ERα-positive breast tumors, including mutations p.Tyr537Ser/Asn, p.Asp538Gly [93,94,95,96] and p.Leu536Gln [97]. These mutations promote the agonist conformation of ERα in the absence of ligand, thus leading to hormone-independent tumor cell growth and clinical resistance to hormonal therapy [93,94,95,97]. The reported incidence on these mutations was low (less than 1%) in primary tumors but high (11%–55%) in metastatic ERα-positive breast cancer [93,96,97]. Therefore, these mutations occur almost exclusively in metastatic breast tumors [96]. Interestingly, ERα mutations appear to be frequently acquired in patients who previously have received hormonal treatment [96]. 4.2. (G-Protein Coupled Estrogen Receptor) GPER and TAM Resistance GPER, formerly known as GPR30, is a candidate molecule that can mediate non-genomic E2 signaling [8] and also may have a role in TAM resistance. GPER, a seven transmembrane domain protein, has recently been identified as a novel estrogen receptor structurally distinct from the classic ERs (ERα and ERβ) [64,98]. This protein is expressed in approximately 50%–60% of all breast carcinomas [64,99], in endometrial and ovarian cancer cells, in thyroid carcinoma cell lines [29], in ERα-positive (MCF7), ERα-negative (SKBR3) and triple negative breast cancer (TNBC) cells [42]. 4.2.1. GPER Subcellular Localization Although the subcellular localization of GPER is still a subject of debate, some studies have indicated that GPER is located mainly in the nucleus, however, this receptor has also been observed in the cytoplasm [100]. This differential subcellular localization could be explained by a retrograde transport of GPER from the plasma membrane towards the nucleus. Interestingly, the subcellular localization of GPER has been associated with different clinicopathological characteristics. While cytoplasmic GPER localization is correlated with low tumor stage and ER and PR positive breast carcinomas, nuclear GPER is linked to poorly differentiated carcinomas and TNBC subtypes [100,101]. These observations reflect the differential biological significance of the two subcellular distributions, one associated with a better clinical outcome (cytoplasmic GPER) and the other with less favorable tumor prognosis (nuclear GPER). 4.2.2. GPER Signaling Signaling through GPER occurs via transactivation of EGFR and involves tyrosine kinases of the Src family. In this mechanism, E2 initially binds to GPER eliciting the activation of heterotrimeric G protein–tyrosine kinase Src-matrix metalloproteinase signaling [64], leading to the production of heparin-binding epidermal growth factor (HB-EGF). HB-EGF binding to EGFR activates the mitogen-activated protein kinase/extracellular regulated protein kinase (MAPK/ERK) signaling cascade [64,102] and increases adenylate cyclase activity. The increased cyclic AMP (cAMP) levels promote the phosphorylation of the cAMP response element-binding (CREB) transcription factor, which subsequently binds to cAMP-response elements (CRE) on promoters of mitogenic genes [103]. In addition to E2, TAM as well as its metabolite, 4-OH-TAM, have also high-affinity binding to GPER and can activate the receptor [104,105,106,107], thus inducing rapid cellular signaling, including ERK activation, PI3K activation, calcium mobilization and cAMP production in breast cancer cells [98,105] (Figure 5). The identification of this distinct class of steroid receptors, i.e., GPER, suggests a role for GPER in non-classical steroid hormone actions [4,7,8,32,108,109]. Consequently, E2 and TAM are regarded as GPER agonists [64]. This activation of GPER signaling often causes tumor progression, which makes GPER a potential therapeutic target in breast cancer. 4.2.3. GPER in Breast Cancer Recent studies have provided evidence that high levels of GPER protein expression in breast cancer patients correlate with clinical and pathological biomarkers of poor outcome, including increased tumor size and metastasis [64,101,110]. Moreover, it has been reported that in GPER-positive patients, TAM activates the crosstalk between the GPER and the EGFR signaling pathways. GPER activation increased ligand-dependent EGFR activity, leading to an ERK1/2-mediated transcriptional response. This crosstalk elicits an increased cellular growth that is associated not only with TAM resistance (Figure 5), but also with metastasis [43,98]. In this regard, several studies have demonstrated that in patients with GPER-positive tumors, treatment with TAM increases GPER expression, with the overall survival decreasing in comparison to patients who did not receive TAM. These results suggest that in breast cancer patients with high GPER expression, potential treatment with TAM should be carefully evaluated [64,101]. In such cases, the capacity of GPER to mediate E2 action is significantly enhanced during the development of TAM resistance [43]. In addition to the above mechanisms, a recent study has shown that ligand-activated GPER also triggers NOTCH activation and expression of NOTCH target genes. Moreover, NOTCH signaling contributes to GPER-mediated migration in ER-negative breast cancer cells and cancer-associated fibroblasts [111]. 4.2.4. GPER in Triple Negative Breast Cancer (TNBC) TNBC cancers, defined as tumors that lack ER, PR and HER2 expression, account for 12%–17% of all invasive breast carcinomas and comprise a heterogeneous group of tumors, with varying histological features and clinical behavior [112]. Recently, GPER has been proposed as a candidate biomarker for TNBC that has a role in growth regulation. Studies in TNBC patients revealed that these tumors strongly express GPER and that this expression correlates with higher TNBC recurrence [113]. While it is well known that ERα-positivity is a predictor of response to TAM, it is also clear that 5%–10% of ERα-negative patients do benefit from adjuvant TAM treatment [75,76,77,114]. In these cells, TAM enhances mRNA and protein expression of CCNA1 and CCND1 after 3 h and 12 h of treatment, indicating not only that TAM regulates cell cycle progression via GPER/EGFR/ERK signaling but also suggesting a link between estrogen, TAM and GPER in TNBC (Figure 5) [42]. Taken together, these studies not only provide evidence for the important role of GPER in the development of TAM resistance in TNBC cells, but also pinpoint a potential target therapy aimed at overcoming the resistance to this endocrine treatment. 4.3. Androgen Receptor (AR) and TAM Resistance The androgen receptor (AR) is a member of the steroid hormone receptor superfamily, a class of receptors that function through their ability to regulate the transcription of specific genes. Furthermore, in women, adrenal and ovarian androgens are sources of pre- and post-menopausal estrogens, as these are converted into E2 [115]. Since it is not yet clear whether AR has a predominantly proliferative or anti-proliferative function, its biological role and significance as an independent predictor of clinical outcome in breast cancer remains controversial. However, an increasing set of data support a possible role of AR as a marker of prognosis in patients with ERα-positive breast cancer [116]. Approximately 90% of ERα-positive patients are also AR-positive (AR+) [117], and this is associated with favorable prognosis i.e., longer relapse-free survival, response to therapy, older age at diagnosis, lower tumor grade, lower Ki67 positivity and smaller tumor size [118,119,120]. 4.3.1. AR Signaling Pathway The transcriptional activity of AR can be controlled by several pathways in order to transduce the AR signal and modulate AR-dependent transcription of target genes. The ability of AR to regulate gene transcription is through its interactions with specific DNA sequences located near or within the target gene promoter [121]. In this regard, AR can act as a ligand-dependent transcription factor via a classical genomic mechanism, which involves homodimerization and translocation to the nucleus upon binding the androgen hormones testosterone and 5-α dihydrotestosterone [122], interaction with AR response elements, and recruitment of co-regulators to elicit transcriptional changes [123]. Moreover, preclinical studies have also revealed a ligand-independent mechanism for triggering AR activation, through signaling pathways that include Janus kinase (JAK)/signal transducer and activator of transcription 3 (STAT3), MAPK, NOTCH and PI3K/mTOR/AKT [124,125]. Interestingly, some studies have indicated that AR can also cause rapid initiation of cytoplasmic signaling cascades, including activation of protein kinase A, protein kinase C and ERK, via a non-genomic mechanism involving binding cytoplasmic and membrane-bound proteins, such as c-Src [123] (Figure 6). 4.3.2. AR and TAM Therapy Although the role of AR in breast cancer is not fully clear, some studies have reported its implications in endocrine therapy response: while in ERα-positive tumors that respond to neoadjuvant endocrine therapy, the AR mRNA and protein expression decreases, in unresponsive tumors, the AR mRNA does not decrease. Moreover, increased AR expression has been observed in TAM-resistant breast cancer models in vitro and in vivo [126,127]. In fact, it has recently been reported that TAM-resistant tumors express higher levels of AR than TAM-sensitive tumors. These observations suggest that high AR expression may be detrimental for the outcome of TAM-treated ERα-positive breast cancer, as increased AR expression could potentially enhance the agonistic properties of TAM [126,127]. Interestingly, a recent study indicated that in ERα-positive breast cancers treated with TAM the ratio of nuclear AR to ER (AR:ER) rather than the level of AR expression may play a role in disease progression and response to treatment. In fact, women with tumors expressing a high AR:ER ratio (>2.0) had over four times higher risk for failure in TAM therapy compared to women with a low ratio (<2.0). Consequently, it was postulated that de novo or acquired resistance to anti-estrogen therapies could result from tumor cell adaptation, from estrogen dependence to androgen dependence [128,129]. These findings suggest that the nuclear AR:ER ratio may critically influence tumor biology and response to endocrine therapy, and that this ratio may be a new, independent predictor of response to traditional E2/ER-directed endocrine treatment [128]. 4.4. Hedgehog(HH) Signaling Pathway and TAM Resistance Components of the Hedgehog (HH) signaling pathway have recently emerged as a new molecular target in TAM-resistant breast tumors. Ramaswamy et al. [130] were first to demonstrate that Glioma-associated oncogene homolog 1 (GLI1) mRNA, a marker of HH signaling activation, and its targets genes SNAIL, BMI1 and MYC, were higher in TAM-resistant cells compared to TAM-sensitive MCF7 cells. Additionally, the MYC and BMI1 polycomb ring finger oncogene (BMI1) protein levels were directly correlated with increased TAM resistance. In the same study, serial passages of the resistant cells in mice, resulted in aggressive metastasic tumors with concomitant increases in the expression of markers of HH signaling and epithelial to mesenchymal transition. In a cohort of 315 patients with breast cancer, high GLI1 expression inversely correlated with disease-free and overall survival [130]. Matevossian and Resh [131] demonstrated that the HH acyltransferase (Hhat) is required for the proliferation of ERα-positive, HER2 positive and TAM-resistant breast cancer cells. Hhat is an enzyme catalyzing the N-terminal palmitoylation of Sonic HH (SHH), the major ligand of the pathway, a modification that is critical for SHH signaling activity [132,133]. Inhibition of Hhat by the small molecule RU-SKI 43, decreased anchorage-dependent and anchorage-independent proliferation of ERα-positive, but not triple negative breast cancer cells, and also reduced proliferation of HER2 amplified as well as TAM-resistant cells [131]. More recently, evidence was provided for a possible role of HH signaling pathway in breast cancer cells treated with TAM [134]. Using a panel of different breast cancer cell lines, we demonstrated that TAM modulates the expression of HH signaling components, including the terminal effector of the pathway, the transcription factor GLI1. Increased GLI1 gene expression and cellular proliferation following TAM treatment was observed in ERα-positive/HER2-negative and ERα-positive/HER2-positive cell lines. Activation of this pathway facilitates tumor growth and progression supporting an association of HH signaling with increased risk of metastasis and breast cancer-specific death [135]. These findings reveal that GLI1 activation can be implicated in the growth and progression of breast cancer, however, the precise mechanism by which GLI1 contributes to TAM resistance remains unclear. 4.4.1. HH Signaling The HH signal transduction cascade is a major pathway involved in embryonic development, cell proliferation, stem cell generation and tissue repair [136,137]. Deregulation of the HH signaling pathway is strongly correlated with various types of cancer, including basal cell carcinoma, medulloblastoma, rhabdomyosarcoma and tumors of the lung, breast, pancreas and prostate [138,139,140,141]. The signaling cascade initiates at the transmembrane receptor Patched (PTCH), which interacts with HH ligands, e.g., sonic hedgehog (SHH), the most broadly expressed ligand [142], relieving its inhibitory effects on the signaling molecule Smoothened (SMO), another membrane-associated protein. The lack of PTCH repression allows SMO to initiate a series of intracellular events that culminate in the activation of the GLI (Glioma) family of zinc finger transcription factors, i.e., GLI1, GLI2 and GLI3 [143,144]. The GLI proteins can function both as activators and repressors, with GLI2 and GLI3 indeed possessing repressor and activator domains [145], whereas GLI1 acts only as an activator, since it lacks a repressor domain [146]. GLI1 is an oncogene and its increased expression is associated with many cancers, acting as a marker of HH signaling activation [147] (Figure 7). The outcome of HH signaling varies depending on the cell type that responds to HH ligands, and may include expression of a variety of cell specific transcription factors mediating different developmental fate responses. Genes generally induced by HH signaling activity include PTCH1 and PTCH2, Hedgehog-interacting protein (HIP) and GLI1, which can trigger positive or negative feedback on this pathway, modifying the strength or duration of the HH signal. Additional GLI targets include genes contributing to the regulation of proliferation and differentiation, e.g., CCND1, CCND2, N-MYC, WNT, PDGFRA, IGF2, FOXM1 and HES1 [146]. 4.4.2. HH Signaling in Breast Cancer An increasing number of recent publications have highlighted a role for canonical and non-canonical HH signaling in breast cancer. Studies carried on the mammary gland have demonstrated the strict regulation of the HH pathway for normal development of this organ, since signaling de-regulation triggers embryonic and postnatal abnormalities [148,149]. It is therefore not surprising to find ample evidence for the involvement of this signaling pathway in breast cancer, as has recently been reviewed [149,150,151] (Figure 7). SHH overexpression was documented to contribute to breast cancer development and progression in both ERα-positive and negative tumors. Kubo and colleagues found high expression of SHH, Patched 1 (PTCH1) and GLI1 in invasive carcinomas, in contrast to normal breast epithelia that do not express detectable levels of these proteins [152]. These data have been corroborated with additional findings in clinical samples and xenografts models [153]. O'Toole et al. reported that different subsets of breast cancer express HH ligands in the epithelium and/or stroma and also demonstrated that epithelial HH ligand expression is an early event in mammary carcinogenesis, strongly associated with a basal-like phenotype and poor outcome, in terms of metastasis and breast cancer-related death [135]. Jeng et al. found that high expression of PTCH1, GLI1 and SMO mRNA in breast cancer tissues correlates with invasiveness, and suggested that overexpression of these genes could be used as potential biomarkers for prediction of postoperative recurrence [154]. Recently, Noman et al. provided evidence for a relationship between high-level SHH expression and poor overall patient survival in TNBC [155]. Additionally, they suggested that the HH pathway in early stages of breast cancer enhances tumor growth and proliferation, while in later stages progression and recurrence. A possible role of hypomethylation of the SHH promoter, as a means to regulate SHH expression, has been put forward by Cui et al. [156]. Moreover, the role of the transcriptional effectors of HH signaling GLI1 and GLI2 has also been addressed. Nuclear GLI1 overexpression in breast cancer has been associated with increased invasiveness, early relapse after radical operation, and metastasis [152,157,158]. Furthermore, up-regulated GLI2 expression has been detected in progesterone receptor negative cases and correlated with increased Ki-67 proliferation index in invasive ductal carcinoma [159]. In an additional study, patients with increased GLI2 expression had a significantly lower overall survival [160]. Finally, it was demonstrated that GLI2 can mediate non-canonical activation of HH signaling in breast cancer [161]. 4.4.3. HH Signaling Crosstalk with Additional Pathways Interactions between HH signaling and other pathways during normal development of the mammary gland and in breast cancer have frequently been reported [162,163,164,165]. A link between ERα and the HH pathway in human breast cancer was first highlighted by Koga et al. [163]. More recent data indicate that HH ligands in mammary epithelial cells can operate through both c-Src and ERα resulting in the activation of ERK1/2 [162]. An additional pathway that can interact with HH signaling in breast cancer is TGFβ, which induces transcriptional up-regulation of GLI1 and GLI2 [166,167]. Moreover, it has been shown that RTK signals, via the PI3K-AKT signaling cascade, can induce stabilization of the GLI1 protein [168]. Similar observations were reported by Ramaswamy et al., who hypothesized not only that the PI3K/AKT pathway is involved in HH signaling activation, but also that it may have a role in promoting TAM resistance [130]. Additionally, WNT signaling regulates GLI2 during development and is involved in modulating the expression and functionality of the GLI proteins in several malignancies, including breast cancer [153,169,170]. It has also been suggested that NF-κB, a key transcription factor that orchestrates numerous biological processes, including proliferation, apoptosis and inflammatory responses, is, at least in part, responsible for up-regulating SHH [156]. Finally, NOTCH receptor activation can also increase SHH expression through rapid modulation of cytoplasmic signals, including AKT and the mammalian target of rapamycin mTOR [171,172] (Figure 7). 4.5. Non-Coding RNAs and TAM Resistance Non-coding RNAs (ncRNAs) are RNA transcripts that do not encode proteins. These include not only the functionally well-established tRNAs, rRNAs, and spliceosomal or snRNAs but also other types of ncRNAs, many of which are cell-type specific and involved in central biological processes [173,174]. Accumulating evidence indicates that most of the mammalian genome is transcribed and the resulting ncRNAs can actively regulate gene expression, exemplified by their role in the process of maturation, stabilization and/or degradation of protein coding mRNAs, and their enhancer/silencer impact on neighboring and distant genes [174,175]. These novel ncRNAs form two classes depending on their size, the short ncRNA (sncRNAs) and the long ncRNAs (lncRNAs) [176,177,178,179]. SncRNAs are RNA transcripts less than 200 nucleotides and include microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), and small interfering siRNAs. In contrast, lncRNAs can range from 200 nucleotides to ~100 kilobases. Both classes of ncRNAs are recognized as key players in the pathogenesis of human cancer, with potential roles as biomarkers or therapeutic targets [180,181]. Expression of certain ncRNAs, and, more specifically, miRNAs and lncRNAs, has clearly been identified as biomarkers in the diagnosis, progression, prognosis, and response to treatment for certain cancers, acting either as tumor suppressors or oncogenes and, in some cases, as both [182]. 4.5.1. miRNAs miRNAs, first described in 1993, are small (22–24 nucleotides), single stranded non-coding, evolutionarily conserved RNA molecules [183,184], which are related to but distinct from siRNAs, and regulate mRNA translation and/or stability [185]. miRNAs are transcribed by RNA polymerase II and processed either from introns of protein-coding transcripts [186] or from lncRNAs (Figure 8), forming precursors called pri-miRNAs [187,188,189]. In cancer, miRNAs are deregulated and can act as tumor suppressors negatively affecting tumor growth or as oncogenes (termed oncomiRs), which are overexpressed in cancer and promote tumor formation [182,190]. miRNAs have been suggested to be important prognosticator markers in breast cancer and some currents studies aim to identify miRNAs with the potential to predict TAM response (Figure 8); however, their role in acquired endocrine-resistant breast cancer is not fully understood [191]. For example, overexpression of miR-221/222 confers resistance to TAM in MCF7 cells and correlates with HER2 positivity in primary human breast tumors [192,193]. Additionally, increased expression of miR-181b has been observed in TAM-resistant breast cancer [192,194], miR-101 promotes estrogen independent growth and causes TAM resistance in ERα-positive breast cancer cells [195], up-regulated miR-301 increases proliferation, migration, invasion, and tumor formation, and also has been associated with TAM resistance in MCF7 cells [196] and miRNA-519a confers TAM resistance in MCF7 cells through regulation of the cell cycle and apoptosis [197]. On the other hand, some miRNAs, which suppress TAM resistance in breast cancer, have also been identified. Hoppe et al. [198] demonstrated that miR-126 and miR-10a are markers for tumor recurrence in postmenopausal patients with early stage ERα-positive breast cancer treated with TAM and suggested that breast cancer metastasis may be favored by the loss of miR-126 expression [198]. The tumor suppressor role of miR-126 is further supported by the findings of decreased expression of this miRNA in highly metastatic MDA-MB231 breast cancer cell derivatives in mice [199]. miR-10a is downregulated in metastatic mouse mammary tumor cells; however, little is known whether a similar scenario is occurring in human breast cancer. It is speculated that the protective effect of miR-10a within the context of TAM treatment may be explained by maintaining the apoptotic capacity of tumor cells, since miR-10a directly targets HOXA1, which plays an oncogenic role in MCF10A cells via modulation of the anti-apoptotic factor BCL2 [198,200]. In addition, recent studies in ERα-positive cell lines have shown that restoring certain miRNAs sensitized these cells to TAM. Such miRNAs include miR-342 [201], miR-375 [202], miR-451 [203], miR-261, miR-575 [204], miR-200b and miR-200c [205,206] (Figure 8). 4.5.2. LncRNAs The majority of lncRNAs are the result of RNA polymerase II activity, and include 5’ capping and other transcriptional modifications, e.g., splicing and polyadenylation. Functional lncRNAs form stable secondary and tertiary structures, which confer unique biological properties, and may be found both in the nucleus or the cytoplasm [207]. Experimental evidence has indicated that lncRNAs contribute to a vast array of biological processes, including physiological as well as pathological conditions. Some studies have revealed that lncRNAs can display enhancer-like functions [208,209], with their mode of action ranging from modulation of protein expression to epigenetic transcriptional regulation and mRNA processing [189,210]. In breast cancer, lncRNAs have been demonstrated to play a leading role in initiation, progression and anti-estrogen resistance [211,212,213,214,215]. Moreover, a new molecular classification of breast cancer based on lncRNA expression has been put forward, and almost two thirds of the lncRNAs expressed in breast cancer were found localized at enhancer regions [216]. Additionally, lncRNA HOTAIR was highly upregulated in tumors of TAM resistant breast cancer patients compared to the primary tumors prior to treatment. These results provide evidence that HOTAIR significantly contributes to the proliferation of TAM resistant cells, suggesting that this lncRNA can promote ER activation in the absence of estrogen to drive TAM resistance [217]. LncRNA breast cancer anti-estrogen resistance 4 (BCAR4), normally found in human placenta and in oocytes, is present at high levels in breast tumors and associated with endocrine resistance and increased invasiveness. BCAR4 overexpression in TAM-sensitive cells blocked the anti-proliferative effects of TAM, likely via interactions with ERBB receptors inducing their phosphorylation [214,215,218] (Figure 8). 5. Clinical Use of TAM in Combination with Other Agents Although TAM is widely used in the treatment of ERα-positive breast cancer patients, it has been reported that the use of AIs, e.g., anastrozole, letrozole and exemestane, as well as fulvestrant, offer better clinical outcomes by improving disease-free survival and reducing the risk of recurrence [10,35,219]. In addition to the development to endocrine resistance, another limitation of TAM is the toxicity associated with its use. Specifically, increases in endometrial cancer and thromboembolic events have limited the use of the drug by high-risk women, who would, otherwise, benefit from it. In order to overcome these adverse side effects, novel strategies have been proposed. Some of these approaches include the use of lower doses, which are anticipated to be associated with lower toxicity, the topical application of either TAM or its active metabolites and the use of combination therapies. 5.1. Lower Doses of TAM A lot of research effort has focused on the use of lower doses of TAM, with these studies indicating that this approach minimizes toxicity without affecting the drug’s chemopreventive activity in the breast [220,221]. Data from animal studies indicate that a reduction in the TAM standard dosage of 20 mg/day to 1 mg/day does not diminish the drug’s inhibitory activity on mammary tumor formation [222] and does not affect a large number of biomarkers, most of which are surrogate markers of cardiovascular disease [223]. 5.2. Topical Application of Either TAM or Its Active Metabolites Another strategy to overcome the adverse effects of TAM is the topical application of either TAM or its active metabolite (4-OH-TAM) directly onto the breast. The purpose of this approach is to reduce the distribution of drug to tissues susceptible to TAM-induced toxicity. Rouanet and colleagues observed that the daily application of 1 or 2 mg of 4-OH-TAM hydroalcoholic gel on breast skin resulted in sufficient concentrations of the drug in the tissue to achieve inhibition of tumor cell proliferation to the same degree seen with the standard dose of oral TAM, but with much lower plasma levels [224]. Similar results were obtained in a phase II trial of Afimoxifene (4-OH-TAM gel) for cyclical mastalgia in premenopausal women [225]. In this study, the use of 4-OH-TAM gel (4 mg/day) delivered potent and sustained antiestrogenic effects to the target tissue, while avoiding the side effects associated with first-pass metabolism of TAM. The results of these studies indicate that the use of percutaneous 4-OH-TAM gel has a local impact on tumor proliferation, suggesting its possible use in future prospective trials of chemoprevention. 5.3. Use of Combination Therapies: GPER Inhibitors and TAM Considering the recent evidence indicating that TAM can activate non-genomic GPER/ERK signaling and enhance breast cancer cell growth, inhibition of ER/GPER/ERK signaling could provide a new therapeutic option for TAM-resistant breast cancer cells. In this regard, several compounds that specifically inhibit GPER, including estriol and G15, have been described. Estriol binds to GPER and inhibits downstream signaling, while G15, a substituted dihydroquinoline, binds to GPER with high affinity and blocks calcium mobilization by E2 in breast cancer cells [113]. Moreover, it was recently shown that G15 improves the response of TAM-resistant xenografts to endocrine treatment [43]. These combination therapies could therefore restrain tumor progression, by increasing apoptosis, and restore the cytotoxic effects of TAM in drug-resistant cells [42,43]. 5.4. Use of Combination Therapies: ARs Inhibitors and TAM Cochrane et al. presented the first preclinical evidence indicating that inhibition of AR by enzalutamide may be an effective therapeutic strategy not only for ERα-negative/AR-positive but also for ERα-positive/AR-positive breast cancers. Additionally, high levels of AR relative to ER may also pinpoint a subset of breast cancers patients that would respond more favorably to enzalutamide alone or in combination with TAM or AIs [128]. Enzalutamide (formerly MDV3100) is an AR signaling inhibitor that binds AR with high affinity, impairing AR nuclear translocation [226,227] and decreasing ERα-mediated proliferation. The observed effect of enzatulamide in ERα-positive and ERα-negative breast cancers highlights the possible role for ARs in breast tumor growth. 5.5. Use of Combination Therapies: HH Inhibitors and TAM Inactivation of the HH pathway can occur at various steps of the signaling cascade and several inhibitors have been developed and used in preclinical and clinical cancer trials. The selective Hhat inhibitor RU-SKI 43, which blocks SHH formation, has proved to be an effective compound that reduces the proliferation of ERα-positive breast cancer cells [131]. SMO inhibition, via cyclopamine and its derivatives, prevents the downstream activation of the pathway and its transcriptional effectors, the GLI proteins [228]. Studies have revealed that blocking SMO with vismodegib can effectively reduce cellular proliferation [130,229] and inhibit the tumor growth of TAM-resistant xenografts in mice [130]. Vismodegib is a well-tolerated drug that offers an excellent outcome in canonical HH signaling-dependent cancers, e.g., basal cell carcinoma [228,230]. The limitation of SMO antagonists relates to the activation of the GLI factors independently of SMO in certain cancers [231]. In-line with this is the increasing focus on inhibitors that act at downstream steps of the pathway [139]. Interestingly, Fan et al. and Della Corte et al. reported that the antidiabetic drug metformin exerts anticancer effects through the inhibition of the HH signaling pathway in breast cancer cells, decreasing the expression levels of SHH, SMO, PTCH and GLI1 [232,233]. Taken together, the above set of data suggest that combinations of TAM with HH signaling inhibitors may be quite effective in overcoming endocrine resistance in breast cancer. 5.6. ncRNAs: Therapeutic Targets in TAM Resistance Recent studies have focused on drugs targeting nucleic acids in order to develop breast cancer therapies, with the objectives centering on either inactivating ncRNAs, which confer resistance to TAM, or enhancing the effect of ncRNAs that restore susceptibility to anti-estrogen treatment [206,234,235]. These emerging therapies include ribozymes, siRNAs, antisense oligonucleotides (ASOs) or their chemically tailored analogs (known as locked nucleic acids, LNAs), which can alter RNA splicing and induce degradation of the targeted RNA via RNaseH. Such approaches can inhibit the production of an endogenous miRNA, while synthetic or miRNA mimics can be used to treat a deficiency in miRNA expression. Similar methodologies can also be employed to modulate the production of lncRNAs in order to enhance sensitivity to TAM [206,236,237]. 6. Conclusions Resistance to endocrine therapy is a significant clinical problem in breast cancer treatment. Consequently, efforts to dissect the molecular mechanisms that underlie the development of resistance are well justified. This analysis will also provide the means for the development of new therapeutic targets for breast cancer. The current evidence suggests that combination therapies and lower TAM doses should be applied in order to overcome endocrine therapy resistance and minimize the adverse effects of TAM, respectively. Acknowledgments Work performed in the authors’ laboratory was funded by the Swedish Childhood Cancer Foundation and AFA Insurance. Milena Rondón-Lagos was supported by Colciencias Grant (call 528, Colombia). Victoria E. Villegas was a recipient of an ERACOL scholarship and Nelson Rangel is supported by Colciencias Grant (call 617, Colombia). Graphic designer Elizabeth Cruz Tapias is acknowledged for the drawings. Author Contributions Milena Rondón-Lagos and Victoria E. Villegas, conceived, designed, and wrote the manuscript. Nelson Rangel and Magda Carolina Sánchez contributed with writing sections of the manuscript and made critical revisions. Peter G. Zaphiropoulos supervised the work and contributed to scientific editing of the manuscript. All authors contributed to, and have given approval to, the final version of the manuscript. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Functional domains of estrogen receptors (ERα and ERβ). Receptor domains are indicated in different colors: Purple, activation factor 1 (AF1) domains A/B; green, DNA-binding domain (DBD) C; blue, heat shock proteins binding domain D; red, activation factor 2 (AF2) domain E; dark purple, C-terminal domain F. Modified from Ng et al. [26]. Figure 2 Tamoxifen action. The tamoxifen-estrogen receptor (TAM-ER) complex activates the activation factor 1 (AF1) domain and inhibits the activation factor 2 (AF2) domain. The TAM-ER dimer binds to DNA at estrogen response element (ERE) sequences in the promotor region of E2 responsive genes. Transcription of these genes is attenuated because the AF2 domain is inactive. Figure 3 Tamoxifen’s positive and negative effects. Positive effects are indicated by a plus sign (+) and negative effects by a minus sign (−). Figure 4 TAM metabolism. TAM is extensively metabolized through biochemical reactions mainly catalyzed by the cytochrome P450 family of enzymes (CYP3A4/5, CYP2D6). Based on Stearns et al. [62]. Figure 5 Proposed model of cell proliferation mediated by TAM in breast cancer cells. TAM can activate both GPER and membrane-associated ERs and crosstalk with growth factor signaling pathways, including HER2 and EGFR receptors, in a manner analogous to E2. TAM resistance can therefore be attributed, at least partly, to the GPER receptor, suggesting a role for GPER in non-classical steroid hormone actions. In fact, TAM as well as its metabolite, 4-OH-TAM, have high-affinity binding to GPER and mimic the rapid, non-genomic E2 effects in breast cancer cells. EGFR, epidermal growth factor receptor; ER, estrogen receptor; GPER, G-protein coupled estrogen receptor; HB-EGF, heparin-binding epidermal growth factor; HRG, heregulins; Src, cytoplasmic tyrosine kinase; MAPK, mitogen-activated protein kinases; PI3K/Akt, phosphatidylinositol 3-kinase/protein kinase B; PKC, protein kinase C; cAMP, cyclic AMP. Modified from SABiosciences (http://www.sabiosciences.com/pathway.php?sn=Estrogen_Pathway). Figure 6 Androgen receptor (AR) signaling pathway. Ligand dependent and independent mechanisms. T: testosterone; DHT: 5-α dihydrotestosterone; HSP: heat shock protein; GF: growth factors; RTK: receptor tyrosine kinase. Figure 7 Simplified representation of canonical and non-canonical Hedgehog(HH) signaling. Canonical activation of the HH pathway. (A) In the absence of HH ligand, PTCH1 present in the primary cilium prevents Smoothened (SMO) trafficking and localization to the cilia. GLI mediators are in complex with proteins, including protein kinase A (PKA) and suppressor of fused homolog (SUFU), and generate repressor GLI forms, which translocate to the nucleus and inhibit transcription of HH signaling target genes; (B) In the presence of HH ligand, ligand binding to PTCH1 leads to PTCH1 internalization and degradation, SMO can traffic to the cilium and initiate a signaling cascade that processes the GLI proteins into transcriptional activator forms, which translocate to the nucleus and activate the expression of HH signaling target genes; (C) Activation of GLI1 and GLI2 in breast cancer by non-canonical pathways. Regulatory signaling cascades or regulatory proteins other than canonical Hedgehog signaling can modulate GLI1 and GLI2 expression, transcriptional activity and stability. Transforming growth factor β/transforming growth factor receptor (TGFβ/TGFR) signals can induce transcriptional up-regulation of GLI1 and GLI2 via SMAD. Receptor tyrosine kinases (RTK) signals via the PI3K-AKT signaling cascade can induce stabilization of the GLI1 protein. In ERα-positive breast cancer cells E2 induces expression of SHH and GLI1 independent of SMO activity. WNT signaling activation stimulates GLI2. GLI2 ACT, the activator form of GLI2. Figure 8 MicroRNAs (MiRNAs) and long non-coding RNAs (lncRNAs) with the potential to predict tamoxifen response. The majority of miRNA and lncRNAs are transcribed by RNA polymerase II. MiRNAs are frequently originating from introns of protein-coding transcripts but can also be processed from lncRNAs, via precursors called pri-miRNAs. In breast cancer, miRNAs have been suggested to be important for predicting tamoxifen response, since they can modulate the effects of anti-estrogen therapy. LncRNAs can function either in the nucleus or the cytoplasm and recent studies have demonstrated their role in endocrine resistance. 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Hielscher T. Wiemann S. Sahin O. Re-expression of microRNA-375 reverses both tamoxifen resistance and accompanying EMT-like properties in breast cancer Oncogene 2013 32 1173 1182 10.1038/onc.2012.128 22508479 203. Bergamaschi A. Katzenellenbogen B.S. Tamoxifen downregulation of miR-451 increases 14–3-3zeta and promotes breast cancer cell survival and endocrine resistance Oncogene 2012 31 39 47 10.1038/onc.2011.223 21666713 204. Hrstka R. Nenutil R. Fourtouna A. Maslon M.M. Naughton C. Langdon S. Murray E. Larionov A. Petrakova K. Muller P. The pro-metastatic protein anterior gradient-2 predicts poor prognosis in tamoxifen-treated breast cancers Oncogene 2010 29 4838 4847 10.1038/onc.2010.228 20531310 205. Manavalan T.T. Teng Y. Litchfield L.M. Muluhngwi P. Al-Rayyan N. Klinge C.M. Reduced expression of miR-200 family members contributes to antiestrogen resistance in LY2 human breast cancer cells PLoS ONE 2013 8 1357 10.1371/journal.pone.0062334 23626803 206. Zhang W. Xu J. Shi Y. 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Elsevier Science BV Amsterdam, The Netherlands 1996 10 223. Decensi A. Robertson C. Viale G. Pigatto F. Johansson H. Kisanga E.R. Veronesi P. Torrisi R. Cazzaniga M. Mora S. A randomized trial of low-dose tamoxifen on breast cancer proliferation and blood estrogenic biomarkers J. Natl. Cancer Inst. 2003 95 779 790 10.1093/jnci/95.11.779 12783932 224. Rouanet P. Linares-Cruz G. Dravet F. Poujol S. Gourgou S. Simony-Lafontaine J. Grenier J. Kramar A. Girault J. Le Nestour E. Neoadjuvant percutaneous 4-hydroxytamoxifen decreases breast tumoral cell proliferation: A prospective controlled randomized study comparing three doses of 4-hydroxytamoxifen gel to oral tamoxifen J. Clin. Oncol. 2005 23 2980 2987 10.1200/JCO.2005.06.064 15860853 225. Mansel R. Goyal A. Nestour E.L. Masini-Eteve V. O'Connell K. Afimoxifene Breast Pain Research Group A phase II trial of afimoxifene (4-hydroxytamoxifen gel) for cyclical mastalgia in premenopausal women Breast Cancer Res. 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Choi M. Combination trimodality therapy using Vismodegib for basal cell carcinoma of the face Case Rep. Oncol. Med. 2015 2015 827608 10.1155/2015/827608 26504605 231. Di Magno L. Coni S. di Marcotullio L. Canettieri G. Digging a hole under Hedgehog: Downstream inhibition as an emerging anticancer strategy Biochim. Biophys. Acta 2015 1856 62 72 10.1016/j.bbcan.2015.06.003 26080084 232. Fan C. Wang Y. Liu Z. Sun Y. Wang X. Wei G. Wei J. Metformin exerts anticancer effects through the inhibition of the Sonic Hedgehog signaling pathway in breast cancer Int. J. Mol. Med. 2015 36 204 214 10.3892/ijmm.2015.2217 25999130 233. Della Corte C.M. Ciaramella V. di Mauro C. Castellone M.D. Papaccio F. Fasano M. Sasso F.C. Martinelli E. Troiani T. de Vita F. Metformin increases antitumor activity of MEK inhibitors through GLI1 downregulation in LKB1 positive human NSCLC cancer cells Oncotarget 2016 7 4265 4278 26673006 234. Li Z. Rana T.M. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081358ijms-17-01358Book ReviewQuantum Nanochemistry: 5-Volume Set. By Mihai V. Putz Čársky Petr Sauer Stephan P. A. Academic EditorJ. Heyrovský Institute of Physical Chemistry, Academy of Sciences of the Czech Republic, Dolejškova 3, 18223 Prague 8, Czech Republic; petr.carsky@jh-inst.cas.cz19 8 2016 8 2016 17 8 1358Putz Mihai V.   Quantum Nanochemistry: 5-Volume . 19 7 2016 09 8 2016 © 2016 by the author; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). ==== Body This book, with its 2889 pages in five volumes, represents an impressive piece of work written by a single author. The wide variety of topics covered by the book reveals the author’s deep and encyclopedic knowledge of quantum theory. The author identifies himself as a theoretical physicist, and characterizes his book as a compilation of his lecture notes which he has used within his classes. The author’s favorite topics can be easily recognized in all five volumes. The sections on the history of quantum theory examine concepts and fundamental quantum theories in retrospect, and may prove useful and amusing reading for lecturers teaching quantum theory. Moreover, the unconventional look at some problems related to quantum theory may prove inspiring for experts actively working in the field. Students may find some useful material in the book, although it seems more difficult to read than many other standard textbooks on quantum theory so far published. The title of the book may invoke an expectation that the content deals with theories and quantum chemical computational methods developed for the understanding of elementary steps in complex chemical processes occurring in modern nanotechnologies. However, this is not the case. The book does not deal with nanochemistry at all. Nor is it a textbook, for the most part. As stated in the preface, the book narrates the “story of quantum chemistry” as an extended review paper. This is unlike content currently available in textbooks, which are written as monographs displaying chapters as themes of interest. A somewhat disturbing feature of the book is the author’s overselling of his own research in some places. For instances, the author barely cites the work of established coryphaei in areas of quantum theory. The book may therefore be recommended to libraries of university departments of physics and physically oriented research laboratories, although less so to chemists interested in the theory of nanochemistry. Volume I: (Quantum Theory and Observability) deals with the fundamental concepts of quantum theory. It contains five chapters. Chapter 1 (Phenomenological Quantification of Matter, 63 pages) covers topics that are typical within introductory sections of textbooks on quantum theory. Special attention is paid to the explanation of unification of corpuscular and undulatory natures of matter. Chapter 2 (Formalization of Quantum Mechanics, 96 pages) provides a survey of the main formal concepts of quantum theory that are used in the mathematical description of quantum phenomena treated in Chapter 1. The topics covered are concepts and tools for the mathematical description of the wave function, the correspondence principle, and the bra-ket formalism. Chapter 3 (Postulates of Quantum Mechanics: Basic Applications, 192 pages). In this chapter, attention is paid mainly to quantum tunneling, the Schrödinger equation, Laguerre polynomials, vibrational motion, free electronic states in solids, quantum transition and scattering theory. Derivation of working formulas and equations are presented in a detailed and step-wise fashion, enabling easy understanding. However, to undertake a more in-depth study of vibrational states it would be more practical to use a textbook dedicated to vibrational spectroscopy. Additionally, the section on scattering theory does not bring anything new to the table in terms of nanochemistry. It contains only textbook application of the Yukawa potential and derivation of the Rutherford scattering equation. Chapter 4 (Quantum Mechanics for Quantum Chemistry, 190 pages). This chapter can be viewed as an extension of a series of other previously published textbooks on quantum chemistry. A reader who is interested in quantum theory relevant for nanochemistry may find its contents unbalanced. Some sections are too specialized, such as those dealing with path integrals; whilst others are too simple, such as those dealing with electronegativities and chemical hardness that are not so important for modern nanochemistry. Hartree–Fock and density functional theory are described in detail, but advanced computational methods are noted only by a few sentences. Volume II: Quantum Atoms and Periodicity. It contains five chapters. Chapter 1 (Historical Highlights on the Periodicity of the Chemical Elements, 62 pages). This chapter describes, in a narrative way, the evolution of scientific knowledge from the ancient Greeks to Mendeleev. It is enjoyable to read, however it does not fit in the scope of the book. The following chapters present abrupt change—they present unconventional view on the electronic structure of atoms, with plenty of equations and concepts, making the text difficult to read. Rigorous derivations are presented with the aim of discussing fuzzy chemical concepts, such as electronegativity and chemical hardness. Chapter 2 (Quantum Assessment for Atomic Stability, 42 pages). The two main topics of this chapter are periodic path integrals and the Feynman–Kleinert variational formalism. This chapter is purely theoretical and serves as a preparation for the next section. Chapter 3 (Periodicity by Quantum Propagators in Physical Atom, 55 pages). The path integral approach is used in this chapter to provide a new definition of electronegativity and chemical hardness. It is shown that this new approach can eliminate irregularities in periodicity often found in traditional models. Chapter 4 (Periodicity by Peripheral Electrons and Density in Chemical Atoms, 199 pages). As in other places in this book, electronegativity and hardness are taken as the major electronic indicators of structure and reactivity. They are expressed in different ways and discussed in connection with valence atomic structure, density functionals, electrophilicity, diamagnetic susceptibility, polarizability and ionization potentials. This was undertaken with the aim of showing the periodicity of these atomic properties. The conclusion of this chapter is certainly disappointing for quantum chemists. It claims boldly that this enterprise was undertaken for the future understanding of chemical bonding, reactivity, aromaticity, all the way up to the modelling of biological activity. Chapter 5 (Quantum Algebraic and Stochastic Dynamics for Atomic Systems, 130 pages). This chapter deals with isolated atoms—the term “atomic system” refers to a single atom, not a system of atoms. It deals with two unconventional methods for describing electron interactions in atoms. This is achieved by abstract formalization within the quantum algebra of open systems and by an analytical formulation within stochastic/dissipative systems. Although it may present a novel approach, it is not clear how this methodology could lead to the expected outcome of a better understanding of the binding in molecules and chemical reactivity. Volume III: Quantum Molecules and Reactivity. It contains four chapters. Chapter 1 (Modern Quantum Nature of the Chemical Bond: Valence, Orbitals and Bondons, 91 pages). This chapter represents the author’s original perspective on the nature of the chemical bond. Instead of electronic interactions, a chemical bond is assumed to be formed by a virtual chemical field and its corresponding quasi-particles, called bondons. The chemical field is characterized by a bond length and a bond energy. As a consequence, bondons have a strange features, having differing mass and charge for each chemical bond. The treatment of bondons in this chapter is an interesting exercise in theoretical physics, but it is however difficult to conceive how the concept may become a practical modern theory of chemical bonding. Particularly, when the main focus of this chapter is an application of bondons theory to Bose–Einstein condensation. Bondons are noted also at other places in the book, but again with not much convincing evidence with respect to their utility. Chapter 2 (Molecular Structure by Quantum Chemistry, 131 pages). This chapter begins with an incomprehensible abstract containing a single sentence which straddles over on 15 lines. The formula for the energy of harmonic oscillator is then derived and an analysis of bonding in the van der Waals molecule He2 is selected for its useful application. Following this derivation, sections dealing with the localization of orbitals and the fundamentals of group theory are encountered. Their use for the formation of symmetry adapted orbitals and symmetry in the crystal field theory are well described. However, much of the content may be readily found in standard textbooks on quantum chemistry. Chapter 3 (Quantum Chemical Reactivity of Atoms-In-Molecules, 215 pages). Once a promising computational method, Atoms-In-Molecules (AIM) is nowadays somewhat of an obsolete topic. This chapter advocates its revival by means of combination of electronegativity and hardness. As claimed in the abstract, such an approach can generate a plethora of density functionals for use in quantifying the “many-electronic” structures and their transformation at the conceptual rather than at the computational quantum level of comprehension. Throughout the book, the conceptual aspect is strongly favored. However, the utility of AIM in its new form is not documented enough. Rich tabular material on electronegativity and hardness, obtained in different ways, is limited to atoms and small molecules that can be treated by rigorous quantum chemical methods. Additionally, their electronic structure and reactivity can be better explained in terms familiar to chemists. Anthocyanidins is the only class of larger molecules treated in this chapter, unfortunately, without any clear validation of the utility of an AIM approach. Chapter 4 (Modeling Molecular Aromaticity, 81 pages). This chapter deals with the problem of aromaticity by means of hardness and AIM, i.e., by tools described in previous chapters. Volume IV (Quantum Solids and Orderability) contains 5 chapters. Chapter 1 (Bondons on Graphenic Nanoribbons with Topological Defects, 73 pages). This chapter addresses the topical problem of the formation of topological defects, isomerism and phase transitions in 2D monolayers such as graphene. It presents a detailed theoretical study on the evolution of a nanoribbon defect structures, based on the path integral formalism and bonding by bondons. The description of the model in Sections 1.3.1 to 1.3.3 is difficult to read, and general comments in the “Conclusion” does permit the reader to make a proper assessment of the utility of this new approach. In any case, one is in agreement with the author that, a “further works are needed for validation of the model”. Chapter 2 (Geometrical Crystallography, 173 pages). This chapter is more conventional and it presents an elementary introduction to crystallography by description of fundamental concepts such as geometry of crystallographic cells, crystallographic system, and point and space groups. All this can be found in commonly available literature. This chapter does not belong in a book on quantum nanochemistry. This chapter could be usefully skipped and the volume could be easily continued by the next chapter that follows. Chapter 3 (Quantum Roots of Crystals and Solids, 93 pages) is an elementary introduction to the theory of solid states. The chapter is a useful survey of quantum mechanics relevant to solids, although a more readable survey could be found in Kittel’s “Introduction to Solid State Physics” (as quoted in “Specific References”). Chapter 4 (Chemical Crystallography, 137 pages). This chapter is again a compilation of commonly available literature. Chapter 5 (X-ray Crystallography, 132 pages). This chapter is a comprehensive description of the underlying theory of this technique. Volume V (Quantum Structure—Activity Relationships (Qu-SAR)). In this volume, the author explores how to give a quantum description to more or less empirical QSAR approaches which, by means of orthogonal correlations, should lead to a unified SAR theory of chemical-biological interactions. This volume could not be classified as a textbook because it uses many terms and concepts that are not satisfactorily explained, making the reading of the volume difficult to understand for non-experts. The volume may be more usefully characterized as the author’s personal take on problems commonly encountered in the field of structure–activity relationships. The volume is divided into three chapters. Chapter 1 (Logistic Enzyme Kinetics, 77 pages) starts with the Michaelis–Menton equation and continues with its later variants, thus providing a theoretical background to enzyme kinetics. Chapter 2 (Statistical Space for Multivariate Correlations, 114 pages) presents the derivation of equations used for multiparametric correlations. The aim of this chapter is to make the reader familiar with mathematical tools previously published in special monographs and to prepare the reader for more advanced types of QSAR which is treated in the next chapter. Chapter 3 (Chemical Orthogonal Spaces for Structure-Activity Relationship (COS-SAR), 375 pages). This chapter presents the author’s original and fresh ideas about general and quantitative structure-activity relationships. The general theory is called Quantum-SAR. Its key feature is the transformation of structured data to an orthogonal basis with the aim of eliminating mutual dependence of the structural descriptors. However, the derived equations become exceedingly complex and difficult to understand. It is likely that, because of its complexity, the description loses its chemical and pharmacological context. Instead of general comments in “Conclusions” it would be more beneficial to present a practical assessment of the rich tabular material. Conflicts of Interest The author declares no conflict of interest.

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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081359ijms-17-01359ArticleGrowth Hormone Releasing Peptide-2 Attenuation of Protein Kinase C-Induced Inflammation in Human Ovarian Granulosa Cells Chao Yi-Ning 1†Sun David 2†Peng Yen-Chun 3Wu Yuh-Lin 1*Wong Kwong-Kwok Academic Editor1 Department of Physiology, School of Medicine, National Yang-Ming University, Taipei 11221, Taiwan; stanley11306@hotmail.com2 Department of Obstetrics and Gynecology, Cheng Hsin General Hospital, Taipei 11221, Taiwan; shineway@hotmail.com3 Department of Internal Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan; pychun@vghtc.gov.tw* Correspondence: ylwu@ym.edu.tw; Tel.: +886-2-2826-7081; Fax: +886-2-2826-4049† These authors contributed equally to this work. 19 8 2016 8 2016 17 8 135927 6 2016 16 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Cyclooxygenase-2 (COX-2) and interleukin-8 (IL-8) are two important inflammatory mediators in ovulation. Ghrelin may modulate inflammatory signaling via growth hormone secretagogue receptors. We investigated the role of ghrelin in KGN human ovarian granulosa cells using protein kinase C (PKC) activator phorbol 12, 13-didecanoate (PDD) and synthetic ghrelin analog growth hormone releasing peptide-2 (GHRP-2). GHRP-2 attenuated PDD-induced expression of protein and mRNA, the promoter activity of COX-2 and IL-8 genes, and the secretion of prostaglandin E2 (PGE2) and IL-8. GHRP-2 promoted the degradation of PDD-induced COX-2 and IL-8 proteins with the involvement of proteasomal and lysosomal pathways. PDD-mediated COX-2 production acts via the p38, c-Jun N-terminal kinase (JNK), extracellular signal-regulated kinase (ERK) and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) pathways; PDD-mediated IL-8 production acts via the p38, JNK and ERK pathways. GHRP-2 reduced the PDD-induced phosphorylation of p38 and JNK and activator protein 1 (AP-1) reporter activation and PDD-induced NF-κB nuclear translocation and reporter activation. The inhibitors of mitogen-activated protein kinase phosphatase-1 (MKP-1) and protein phosphatase 2 (PP2A) reduced the inhibitory effect of GHRP-2 on PDD-induced COX-2 and IL-8 expression. Our findings demonstrate an anti-inflammatory role for ghrelin (GHRP-2) in PKC-mediated inflammation of granulosa cells, at least in part, due to its inhibitory effect on PKC-induced activation of p38, JNK and NF-κB, possibly by targeting to MKP-1 and PP2A. granulosa cellinflammationcyclooxygenase-2interleukin-8ghrelingrowth hormone releasing peptide-2 ==== Body 1. Introduction Inflammation plays an important role in the host defense system that occurs in response to internal or external stimuli; it functions to counteract the insults exerted by these stimuli [1]. When acting as a finely tuned acute inflammation consequence that lasts for a short period of time, it is responsible for innate immunity and/or humoral immunity, and thus has therapeutic significance to the host. However, when inflammation becomes chronic and lasts for a long period of time, the outcome may have pathogenic consequences to the body [2]. Ovulation is a key event in the ovarian cycle that involves a number of regulatory networks. It is well-recognized that several aspects of ovulation resemble an inflammation-like reaction. Accumulated studies have demonstrated that various inflammatory factors are involved in ovulation. For example, cyclooxygenase-2 (COX-2) and interleukin-8 (IL-8) have been proposed to be of particular significance during ovulation [3,4]. The critical role of COX-2 in ovulation is well-established and this protein has been shown to regulate the production of prostaglandins (PGs), which are central to the ovulatory process [3]. Interleukin-8 is a chemotactic cytokine that acts to recruit and activate neutrophils and it is also regarded as an important player in ovulation [4,5]. The protein kinase C (PKC) family contains phospholipid-dependent serine/threonine kinases that are involved in a variety of physiological events. Activation of PKCs may lead to the activation of mitogen-activated protein kinases (MAPKs) or the targeting to the IκB kinase (IKK) complex, which results in nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) activation, all of which are responsible for various inflammatory responses [6,7]. PKC signaling has been noted to regulate the expression of a number of inflammatory mediators, including COX-2 and IL-8 [8]. In fact, PKC signaling has been reported to participate in various ovarian activities, including follicular development, ovulation and luteolysis [9,10]. Ghrelin, a 28 amino acid peptide, was first identified in the stomach as an endogenous ligand for the growth hormone secretagogue receptor (GHSR) [11]. Growth hormone secretagogue is a small synthetic molecule that is able to stimulate the secretion of growth hormone from the pituitary [12]. Ghrelin receptor, GHSR, is a typical G protein-coupled receptor (GPCR) with seven transmembrane domains [13]. Two distinct ghrelin receptors can be expressed via splicing from a single GHSR gene, and the two proteins produced are GHSR type 1a (GHSR-1a) and GHSR-1b [14]. GHSR-1a is expressed in numerous different tissues and most prominently in several nuclei of the brain [15] and its expression has also been noted in ovary and testis [16,17]; GHSR-1b is mainly expressed in peripheral organs such as skin, liver, lung, spleen and kidney [18]. Recent studies have reported that not only both ghrelin and GHSR are expressed in the ovary [19], but also that GHSR-1a can be detected in both granulosa cells and theca cells during the human ovarian cycle [16]. In fact, blood ghrelin can be regularly detected in the women receiving in vitro fertilisation (IVF) procedure [20]; Ghrelin was previously shown to stimulate prolactin secretion in normal women [21]. The submaximal dose of ghrelin was not able to induce growth hormone secretion [22], but was able to suppress gonadotropin secretion in women [23]. Human ghrelin was also demonstrated to reduce the pituitary response to gonadotropin-releasing hormone (GnRH) in superovulated ewes [24]. Ghrelin was shown to improve the bovine blastocyst formation [25] and to enhance the maturation of bovine oocytes [26], but it was also reported to inhibit the GnRH-induced preovulatory gonadotropin surge in dairy heifers [27]. Accumulated findings from these studies indicate that ghrelin may play an important role in the ovarian system. In addition to growth hormone production and metabolism being regulated by ghrelin, previous studies have revealed that the plasma level of ghrelin is lower in patients suffering chronic inflammation [28]. This suggests that ghrelin may act as an anti-inflammatory factor. Ghrelin has a short half-life and therefore ghrelin analog growth hormone releasing peptide-2 (GHRP-2) has been extensively used in many studies. For example, administration of GHRP-2 has been shown to prevent the liver inflammatory response during endotoxemia [29]; GHRP-2 has also been demonstrated to attenuate endotoxin-induced IL-6 production and to decrease nitrite/nitrate release from peritoneal macrophages [30]. These findings suggest a novel anti-inflammatory function for ghrelin and GHRP-2 in physiology. Whether ghrelin is able to affect ovarian physiology by modulating the production of the various inflammatory players has not yet been extensively addressed. The aim of the current study was to characterize how the ghrelin analog GHRP-2 affects the various PKC-mediated inflammatory cascades in human granulosa cells by monitoring the COX-2/prostaglandin E2 (PGE2) and the IL-8 pathways, which are two critical components to be associated with inflammation and also known to be involved in ovarian physiology. This study also aimed to clarify the signaling mechanisms targeted by GHRP-2 that are involved in the PKC-regulated production of COX-2, PGE2 and IL-8. 2. Results 2.1. GHRP-2 Inhibition of the PKC-Induced Expression of COX-2 and IL-8 and Secretion of PGE2 and IL-8 To evaluate the role of ghrelin in ovarian inflammation, we used a PKC activator phorbol 12, 13-didecanoate (PDD) as a stimulus to treat KGN human ovarian granulosa cells in order to study whether the ghrelin analog GHRP-2 has an impact on the regulation of COX-2 and IL-8 expression. To ensure the specificity of the PKC-activation effect of PDD, a PKC inhibitor bisindolylmaleimide I (BIM I) was used. BIM I treatment at 1 and 5 µM significantly attenuated the induction of COX-2 and IL-8 expression by PDD (Figure S1). Before directly testing the role of GHRP-2 in PDD-induced inflammation, we first confirmed the presence of the cognate receptors for GHRP-2 in KGN cells by RT-PCR. It was found that GHSR-1a is constitutively expressed and its expression is not affected by either PDD or GHRP-2 (Figure S2); in contrast, the GHSR-1b was not detected. Having demonstrated specific PKC-mediated COX-2 and IL-8 expression using PDD and the presence of the GHRP-2 receptor GHSR-1a on the KGN cells, KGN cells were then pretreated with different doses of GHRP-2 (0.01, 0.1, and 1 µM) for 2 h before the inclusion of PDD (100 nM) for an additional 12 h of incubation. Expression of COX-2 and IL-8 protein, when induced by PDD, was attenuated by all doses of GHRP-2 (0.01, 0.1, and 1 µM) (Figure 1); GHRP-2 at 0.1 and 1 µM also was able to reduce the PDD-induced PGE2 and IL-8 secretion (Figure 1); GHRP-2 alone (1 µM) did not have any effect on the expression of COX-2 and IL-8 or the secretion of PGE2 and IL-8 (Figure 1). To rule out the possibility that GHRP-2 has a cytotoxic effect on the KGN human ovarian granulosa cells used in this study, the viability indices of the KGN cells after the treatments outlined in Figure 1 were determined by alamarBlue and MTT assays. There was no apparent effect on the viability of the cells across all the treatments using either assay (Figure S3). To further confirm the specific effect of GHRP-2, KGN cells were pretreated with a GHSR-1a antagonist (JMV3002), and under this treatment the inhibitory effect of GHRP-2 on induction of COX-2 and IL-8 protein expression by PDD was reversed and the expression manners returned to levels that were comparable with the PDD alone treatment group (Figure 2), which suggests that GHRP-2 acts specifically via the GHSR-1a. 2.2. GHRP-2 Promotion of the Degradation of PKC-Induced COX-2 and IL-8 Proteins via Both Proteasomal and Lysosomal Pathways The GHRP-2 regulation of the PKC-mediated protein expression of COX-2 and IL-8 may occur at either the mRNA or the protein level. We first evaluated whether GHRP-2 was able to affect the stability of the PDD-induced COX-2 and IL-8 proteins. Cycloheximide (CHX, 5 µg/mL) was used to block de novo protein synthesis. It appeared that GHRP-2 was able to promote the degradation of PKC-induced COX-2 protein at 12 h and IL-8 protein at 9 h and 12 h (Figure 3). In this context, two protein degradation mechanisms, namely the proteasomal and the lysosomal proteolytic pathways, are well-recognized to regulate the turnover of a wide range of proteins [31]. Thus, KGN cells were pretreated with either a proteasome inhibitor MG132 (1 µM) or a lysosome inhibitor chloroquine (50 µM) in combination with GHRP-2 (1 µM), followed by PDD treatment (100 nM) for an additional 12 h. Both MG132 and chloroquine appeared to reverse the inhibitory effect of GHRP-2 on PDD-induced COX-2 and IL-8 protein expression (Figure 4). This supports the hypothesis that both the proteasomal pathway and the lysosomal pathway are involved in the promotion by GHRP-2 of the degradation of PDD-induced COX-2 and IL-8 proteins. Within the proteasomal degradation pathway there are a number of critical enzymes: ubiquitin-activating enzyme (E1), ubiquitin-conjugating enzyme (E2), and ubiquitin ligase (E3) [32]. In the ovary, a tumor suppressor gene BRCA1 has been shown to have ubiquitin E3 ligase activity and has been reported to be expressed in granulosa cells [33]. Within the lysosomal degradation pathway, an established lysosomal marker is cathepsin D, which has been detected in ovarian granulosa cells [34,35]. Based on the above findings, we next evaluated whether GHRP-2 is able to regulate BRCA1 and/or cathepsin D expression and thus mediate the degradation of the PDD-induced COX-2 and IL-8 proteins. It was not possible to detect BRCA1 in KGN cells; although cathepsin D was induced by PDD; nevertheless, GHRP-2 had no effect on cathepsin D expression (Figure S4). 2.3. GHRP-2 Attenuation of the Induction of COX-2 and IL-8 Transcription by PKC Next we explored whether GHRP-2 is able to act at transcriptional level. Specifically, we examined the effect of GHRP-2 on induction of COX-2 and IL-8 mRNA expression by PDD using RT-PCR. The results showed that PDD is able to induce both COX-2 and IL-8 mRNA expression (Figure 5A,B). Furthermore, GHRP-2 at 0.1 and 1 µM apparently attenuated PDD-induced COX-2 and IL-8 mRNA expression (Figure 5A,B). GHRP-2 alone had no effect on the expression of either mRNA (Figure 5). To further clarify whether the regulation of COX-2 and IL-8 mRNA expression by PDD and GHRP-2 occurs at transcriptional level, we examined the transcription activity of the COX-2 and IL-8 promoter constructs in KGN cells. PDD alone appeared to induce both COX-2 and IL-8 promoter activation (Figure 5C,D), while GHRP-2 (1 µM) significantly inhibited PDD-mediated COX-2 and IL-8 promoter activation (Figure 5C,D); GHRP-2 alone did not affect either COX-2 or IL-8 promoter activity (Figure 5C,D). 2.4. Inhibition by GHRP-2 of PKC-Mediated Activation of Various MAPKs and NF-κB Previous studies have reported that the various MAPKs and NF-κB may mediate the expression of various inflammatory molecules [36]. Thus, inhibitors of various MAPKs (p38, JNK, and ERK) and NF-κB were used to clarify the signaling pathways post PKC activation. PDD-mediated COX-2 expression was found to be suppressed by inhibitors of p38 (SB2030580), JNK (SP600125), ERK (PD98059) and NF-κB (APDC) (Figure 6); PDD-mediated IL-8 expression was suppressed by inhibitors of p38, JNK and ERK (Figure 6). It has been noted in many cell types that PKC is able to activate the MAPKs as well as the NF-κB signaling pathways, namely the PKC-induced activation (phosphorylation) of MAPKs and the translocation of NF-κB from the cytosol to the nucleus, and then leads to the induction of various downstream target genes. Thus, the effect of GHRP-2 on MAPKs phosphorylation and NF-κB translocation was monitored in KGN cells. It was noted that PDD was able to induce p38 and JNK phosphorylation at 15 min (Figure 7A,B) and ERK phosphorylation at 15 and 30 min (Figure 7C); GHRP-2 appeared to reduce PDD-induced p38 and JNK phosphorylation at 15 min (Figure 7A,B), but it was not able to affect PDD-induced ERK phosphorylation (Figure 7C). The MAPKs p38 and JNK pathways have been shown to result in activator protein 1 (AP-1)-dependent gene expression [37]. Therefore we further investigated the regulation of AP-1 reporter activity by PDD and GHRP-2 in KGN cells. GHRP-2 alone did not affect AP-1 reporter activity, but PDD treatment did induce AP-1 reporter activity and such induction was abrogated by GHRP-2 (Figure 7D). In addition, we also determined the impact of GHRP-2 on PDD-mediated NF-κB activation in KGN cells. PDD treatment for 60 min significantly increased p65 translocation from the cytosol to the nuclear compartment (Figure 8A), while PDD in combination with GHRP-2 down-regulated p65 nuclear translocation (Figure 8A). We also further examined the regulation of NF-κB reporter activity in the presence/absence of PDD and GHRP-2. It was apparent that GHRP-2 alone did not affect NF-κB reporter activity, but PDD was able to induce NF-κB reporter activation and such induction was suppressed by GHRP-2 (Figure 8B). 2.5. Involvement of MKP-1 and PP2A in the Inhibitory Effect of GHRP-2 on PKC-Mediated COX-2 and IL-8 Expression The MAPKs pathways have been documented to be modulated by phosphorylation via the action of kinases or via dephosphorylation by phosphatases [38]. It has also been shown that the MAPK phosphatases (MKPs) are able to act as negative regulators of the MAPK pathways [39]. More than ten MKPs have been identified in mammalian cells and in particular, MKP-1 has been shown to dephosphorylate p38 and JNK [40], and thus may play a role in attenuating inflammatory response [41]. In addition, protein phosphatases have also been reported to be important during the regulation of NF-κB activation [42]. For example protein phosphatase 2A (PP2A) has been shown to directly dephosphorylate IκB and consequently attenuate the activation of NF-κB [43]. Thus, we next investigated whether the inhibitory effect of GHRP-2 on MAPK-mediated and NF-κB-mediated COX-2 and IL-8 expression also involves an interaction with either MKP-1 or PP2A. Firstly, we found that both MKP-1 and PP2A mRNA expression seemed to be inhibited by PDD as compared with the control treatment, while GHRP-2 was able to reverse the PDD suppression of MKP-1 and PP2A mRNA expression (Figure 9A). With the same treatments, we also monitored the protein expression of MKP-1 and PP2A and surprisingly it appeared that PDD did not affect the protein expression of either, but a lower dose of GHRP-2 was able to induce protein expression of both MKP-1 and PP2A (Figure 9B). To further examine whether MKP-1 and PP2A may act downstream of GHRP-2 during the anti-inflammatory effect, KGN cells were pretreated with GHRP-2 (1 µM) or with GHRP-2 in combination with an inhibitor, either a MKP-1 inhibitor (sanguinarine; 0.01, 0.1, and 1 µM) or a PP2A inhibitor (okadaic acid; 10, and 30 µM) for 2 h before the addition of PDD for an additional 12 h. Sanguinarine at 0.1 or 1 µM was able to attenuate the suppression effect of GHRP-2 on PDD-induced COX-2 expression (Figure 10A) and at 1 µM, it was also able to attenuate the inhibitory effect of GHRP-2 on PDD-induced IL-8 expression (Figure 10A). Similarly, okadaic acid at 10 or 30 µM significantly reversed the inhibitory effect of GHRP-2 on PDD-induced COX-2 and IL-8 expression (Figure 10B). 2.6. Involvement of the Akt Pathway in the Regulation by GHRP-2 of the PKC-Mediated Production of COX-2 and IL-8 Previously, GHRP-2 has been demonstrated to activate the PI3K-Akt signaling pathway, bringing about a cellular effect [44]. Thus, we next evaluated whether the inhibitory effect of GHRP-2 on PKC-mediated COX-2 and IL-8 expression might be mediated via the PI3K-Akt signaling pathway. Firstly, the effect of GHRP-2 on Akt activation (phosphorylation) was monitored at 5, 10, 15, 30, or 60 min. GHRP-2 appeared to induce Akt phosphorylation at 10, 15, and 30 min (Figure 11A), and this Akt phosphorylation was found to be inhibited by the PI3K inhibitor wortmannin (Figure 11A). In addition, treatment of KGN cells with wortmannin (3, 10, and 30 µM) apparently neutralized the suppression effect of GHRP-2 on PDD-induced COX-2 and IL-8 expression (Figure 11B,C). In addition, wortmannin at all doses was able to reverse the GHRP-2’s suppression effect on PDD-mediated PGE2 secretion (Figure 11B) and at 10 or 30 µM, it also reversed the GHRP-2’s suppression effect on PDD-induced IL-8 secretion (Figure 11C). 2.7. Inhibition of PKC-Induced COX-2 Expression by GHRP-2 in Primary Rat Ovarian Granulosa Cells To confirm the role of ghrelin (GHRP-2) in ovarian inflammation using primary ovarian granulosa cells, we also evaluated the effect of GHRP-2 on rat primary ovarian granulosa cells. It was found that GHRP-2 at 0.1 or 1 µM appeared to attenuate the PDD-induced expression of COX-2. In this context GHRP-2 (1 µM) alone had no effect on COX-2 expression (Figure S5). 3. Discussion This in vitro study mimics the inflammation microenvironment within the ovary and allows the investigation of the potential inhibitory impact of a ghrelin analog GHRP-2 on human granulosa cells using a PKC-induced local production of two inflammation mediators COX-2 and IL-8. Our results demonstrate that promoter activity, mRNA expression and protein expression of the COX-2 and IL-8 genes are all induced by the PKC activator PDD. GHRP-2 would seem to target the PKC-mediated activation of MAPKs p38, JNK, and NF-κB, as well as Akt, and this subsequently reduces the PKC-mediated induction of COX-2 and IL-8 production. MKP-1 and PP2A seemed to act downstream of GHRP-2 within an anti-inflammatory activity under conditions where PKC is activated. Two GHSRs, namely GHSR-1a and GHSR-1b, have been identified [45] and we found that that only GHSR1a is detected in KGN human granulosa cells (Figure S2). Our findings are in accordance with previous studies that intense GHSR-1a immunoreactivity was noted in the granulosa cells of developing follicles [16,46], suggesting an important role for ghrelin and GHSR-1a in the ovary [47]. Some previous studies have indicated that ghrelin has an anti-inflammation effect. For examples, ghrelin has been shown to inhibit tumor necrosis factor (TNF-α)-induced IL-8 production in human endothelial cells [48] and the angiotensin II-induced expression of TNF-α, IL-8 and monocyte chemoattractant protein-1 (MCP-1) in human umbilical vein endothelial cells [49]. Furthermore, the ghrelin analog GHRP-2 has been shown to exert an antioxidant effect both in vivo and in vitro, but it does not seem to have any anti-atherogenic impact [50]. Similarly, GHRP-2 administration has been demonstrated to inhibit LPS-induced liver inflammation and endotoxemia in rats [29], as well as Freund's adjuvant-induced inflammation in arthritic rats [30]. In contrast to the above findings, it has also been reported that ghrelin is able to induce COX-2 expression and prostaglandin E2 production in human colonic epithelial cells [51]. Nevertheless, in the present study, we observed a clear anti-inflammatory effect of GHRP-2 on PKC-induced inflammation using KGN human ovarian granulosa cells (Figure 1). Involvement of the cognate receptor of ghrelin or GHRP-2, namely GHSR-1a was confirmed by treatment with a selective GHSR-1a antagonist JMV3002 (Figure 2). These findings provide good support for the hypothesis that ghrelin (GHRP-2) acts as an anti-inflammatory player via GHSR-1a in the ovarian system. When considering the cellular target sites required for a molecule to modulate agonist-induced cytokine expression and subsequent secretion, there is a panel of potential sites. In this study, we have addressed the importance of ghrelin (GHRP-2) to PKC-mediated production of COX-2 and IL-8. In fact, several signaling pathways, including the various MAPKs and NF-κB, are known to be involved in regulating COX-2 [52,53] and IL-8 expression [5,54]. In this study we demonstrated that the PKC-mediated COX-2 expression involves, at least in part, all of the MAPKs (p38, ERK, JNK) as well as NF-κB, while IL-8 expression involves, at least in part, all of the MAPKs pathways but not the NF-κB (Figure 6). In addition, we have also shown that GHRP-2 may potentially target to p38, JNK, and NF-κB in order to down-regulate PDD-induced phosphorylation of p38 and JNK (Figure 7A,B), AP-1 reporter activation (Figure 7D) and PDD-activated NF-κB nuclear translocation and reporter activation (Figure 8). In fact, the involvement of the AP-1 and NF-κB responsive elements in the regulation of the COX-2 [53,55] and IL-8 [56,57] promoters has been reported previously, which supports our findings that AP-1 and NF-κB are two important signaling targets involved in the anti-inflammatory function of GHRP-2 during the PKC-induced transcription of COX-2 and IL-8. Interestingly, ERK phosphorylation was induced by GHRP-2 (Figure 7C). This is in accordance with a previous study showing that ghrelin is able to induce ERK phosphorylation during cell proliferation [58]. Furthermore, previous studies have demonstrated that MKP-1 is able to inactivate the MAPKs p38 and JNK pathways [59] and that PP2A is able to inactivate the NF-κB pathway [60]. Consistently with these two reports, we found that, although PDD did not affect the protein level of either MKP-1 or PP2A, GHRP-2 is able to in fact up-regulate both of them (Figure 9B). Why the impacts of PDD and GHRP-2 on mRNA and protein levels of MKP-1 and PP2A appear different and the lower dose, but not the higher dose of GHRP-2 is more effective are currently a mystery to us and will need further investigation. Previous studies have reported that ghrelin has an inhibitory effect on sepsis-induced inflammation via the up-regulation of MKP-1 expression [41], and the administration of a GHSR-1a agonist to aging mice has been shown to restore a young liver phenotype; the latter occurs via an increase in PP2A activity [61]. In the present study we have further noted that inhibitors of MKP-1 and PP2A are able to neutralize the suppression effect of GHRP-2 on PKC-induced COX-2 and IL-8 expression (Figure 10A,B). All of these findings, when taken together, strongly support the hypothesis that, in human granulosa cells, GHRP-2 (ghrelin) is able to inactivate the p38 and JNK pathways and NF-κB signaling by acting via the MKP-1 and PP2A, resulting in an attenuation of PKC-mediated COX-2 and IL-8 production. It has been previously reported that ghrelin seems to activate the PI3K-Akt signaling pathway by interacting with its cognate receptor GHSR-1a [62]. Indeed in our study, GHSR-1a was detected in KGN human granulosa cells (Figure S2) and GHRP-2 was able to induce Akt phosphorylation (Figure 11A), and the GHRP-2’s inhibitory effect on PKC-induced inflammation in terms of COX-2 and IL-8 production was dramatically suppressed by the PI3K-Akt inhibitor wortmannin (Figure 11B,C). Both lines of evidence support the idea that Akt signaling may act downstream of GHRP-2 to perform the anti-inflammatory features. In fact, similar to our findings, it has been reported previously that, using the human embryonic kidney 293 cell line, ghrelin is able to induce Akt phosphorylation [44]. This also supports the hypothesis that the use of GHRP-2 in our study is an appropriate way of mimicking the action of ghrelin in the ovarian system. Down-regulation of COX-2 and IL-8 proteins has also been reported to be regulated at the post-translational stage [8]. Intriguingly as shown in the current study, the degradation pattern of IL-8 appears to be different from that of COX-2, as the expression of COX-2 involved as lower down-regulation over time, but, nevertheless, GHRP-2 was able to significantly accelerate COX-2 degradation (Figure 3); although GHRP-2 did accelerate IL-8 protein degradation, IL-8 could be seen suddenly to disappear as early as 1 h and 3 h, and then bounced back with time (Figure 3). At this moment, we have no solid interpretation to explain this phenomenon. However, intriguingly, there are some previous articles reporting the novel effect of CHX on transcription and stability of IL-8 mRNA. In human neutrophils (PMNs), the degradation of IL-8 mRNA was shown to be finely regulated and CHX treatment was able to superinduce the IL-8 mRNA accumulation in a dose- and time-dependent manner [63]. In fact, in human hepatoma Huh7 cells, it was demonstrated that IL-8 and several other inflammatory genes all contain the AU rich elements (AREs) in the 3’-untranslated region (3’-UTR), which potentially plays an important role to regulate the mRNA stability; CHX together with TNF-a were noted to perform a superinduction effect of mRNAs of IL-8 and these inflammatory genes [64]. Besides the mRNA stability aspect, in lung epithelial H292 cells, CHX was also shown to enhance IL-8 mRNA transcription with the involvement of two important responsive elements AP-1 and NF-κB, resulting in IL-8 mRNA superinduction [65]. In HL-60 promyelocytic leukemia cell line, IL-8 mRNA was rapidly induced at high levels by PKC activator phorbol 12-myristate 13-acetate and unidentified negatively-acting transcriptional regulator(s) was suggested to involve in the modulatory effect of CHX on IL-8 mRNA induction [66], and consistently in human PMN, the IL-8 mRNA superinduction effect by CHX was also proposed to be due to its ability to prevent the de novo protein synthesis of NRF, a protein shown to repress IL-8 mRNA synthesis [67]. Interestingly in human keratinocytes, it was reported that CHX, but not another protein synthesis inhibitor puromycin, was able to induce IL-8 mRNA expression [68]. Therefore, in our study for monitoring the IL-8 protein stability (Figure 3), as PDD can rapidly induce IL-8 transcription, and although the PDD was removed before the inclusion of CHX in our study, there might be still some minor amount of PDD remaining inside the cells and by acting together with CHX, IL-8 mRNA was then superinduced by promoting either transcription or stabilization or both. The CHX treatment may not completely block the translation machinery 3 h later, and thus the accumulated superinduced IL-8 mRNA can then be translated into IL-8 protein. According to our findings, PDD in combination with the proteasome inhibitor MG132 is able to enhance IL-8 expression compared to PDD alone treatment (Figure 4B), which suggests a possibility that CHX treatment is able to target the proteasome pathway leading to degradation of IL-8 protein. However, this speculation needs more in-depth investigation to solve this puzzle. Regardless of the above anomalies, our findings clearly demonstrated that GHRP-2 promoted degradation of the PKC-induced COX-2 and IL-8 proteins; these phenomena can be reversed by the proteasome inhibitor MG132 or the lysosome inhibitor chloroquine (Figure 4). These findings suggest that GHRP-2 may interact with both the proteasome and lysosome pathways to mediate the degradation of the COX-2 and IL-8 proteins in human granulosa cells. In addition to our studies using human granulosa cells, we also evaluated the anti-inflammatory effect of GHRP-2 on PDD-mediated COX-2 expression in rat ovarian granulosa cells. Consistent with our findings in human granulosa cells, GHRP-2 appeared to significantly attenuate PDD-induced COX-2 expression (Figure S5), which supports the anti-inflammatory role of ghrelin (GHRP-2) in the granulosa cells of both humans and rats. The use of cell lines to mimic the in vivo physiology is a good tool; however, the cell lines may not act identically as the primary cells within the body. For example, the KGN cell line used in this study was originally from a 63-years old woman [69] and thus it may not represent the same granulosa cells from the other aged ovaries. Regardless, in addition to our current findings of ghrelin in ovarian inflammation, previous studies have already revealed various impacts of ghrelin in the reproductive system [19,20,21,22,23,24,25,26,27]. Thus, there is a possibility that ghrelin administration might be a potential strategy to treat inflammation-related diseases in reproductive tissues. In conclusion, our study reveals a novel role for GHRP-2 as a potent anti-inflammation molecule that is able to impact ovary inflammation in vitro. Our findings highlight a new role for the anti-inflammatory molecule ghrelin in the ovarian granulosa cells that involves control of PKC-mediated COX-2 and IL-8 expression and the secretion of PGE2 and IL-8. These effects of GHRP-2 occur, at least in part, by targeting to a number of signaling cascades involving various MAPKs, NF-κB, and Akt (Figure 12). 4. Experimental Section 4.1. Chemicals and Reagents Phorbol 12, 13-didecanoate (PDD) was purchased from Enzo Life Sciences (Farmingdale, NY, USA). Bisindolylmaleimide I (BIM I, a PKC inhibitor) was purchased from Cayman Chemical (Ann Arbor, MI, USA). Fetal bovine serum was obtained from HyClone (Logan, UT, USA). Reverse transcriptase and Taq polymerase were purchased from Promega (Madison, WI, USA). The mouse monoclonal antibody against human IL-8 was purchased from R&D systems. The mouse monoclonal antibody against COX-2 was purchased from Cayman Chemicals. The rabbit polyclonal antibodies against p65 were purchased from Neomarkers (Fremont, CA, USA). The mouse monoclonal antibody against MKP-1 was from Abnova (Taipei, Taiwan). The goat polyclonal antibody against PP2A and the mouse monoclonal antibody against histone H1 were purchased from Santa Cruz Biotechnology (Santa Cruz, CA, USA). The mouse monoclonal antibody against α-tubulin and the horseradish peroxidase-conjugated donkey anti-rabbit IgG secondary antibodies were purchased from Amersham Life Science Inc. (Arlington Heights, IL, USA). Unless otherwise specified, all other chemicals and reagents used in this project were from Sigma. 4.2. Cell Culture The immortalized human granulosa cell line KGN [69] was purchased from the RIKEN BioResource Center (Iberaki, Japan) and was maintained using Dulbecco’s modified Eagle medium: nutrient mixture F-12 (Ham) (1:1) (DMEM/F-12) with 10% fetal bovine serum (FBS), 2 g/L sodium bicarbonate, 100 U/mL penicillin and 100 µg/mL streptomycinin an atmosphere of 5% CO2 at 37 °C. 4.3. Western Blotting Analysis Protein extracts of total cellular proteins and of nuclear proteins were harvested and their protein concentrations were determined using the Bio-Rad protein assay reagent (Bio-Rad, Hercules, CA, USA), and the total protein concentration was adjusted with SDS-PAGE loading buffer and heated to 100 °C for 10 min and then subject to regular Western blotting assay to determine the expression profile of various proteins. Samples of equal amounts of proteins (50 µg) were separated on 10% SDS-PAGE, transferred onto a nitrocellulose membrane, blocked with 5% milk for 1 h, and incubated overnight with various specific antibodies, followed by incubation for 2 h with the corresponding horseradish peroxidase-coupled secondary antibodies. The membrane was exposed to film and the bands of interest on the film were quantified with ImageQuant 5.2 software (Molecular Dynamics, Sunnyvale, CA, USA). 4.4. Enzyme-Linked Immunosorbent Assay (ELISA) The concentrations of IL-8 and PGE2 in the culture medium were determined using enzyme-linked immunosorbent assay kits for IL-8 (R&D systems, Minneapolis, MN, USA) and PGE2 (Assay Designs, Ann Arbor, MI, USA) according to the manufacturers’ instructions. 4.5. Semi-Quantitative Reverse Transcription Polymerase Chain Reaction (RT-PCR) The total cellular RNAs were extracted using Tri-Reagent (Sigma) according to the manufacturer's instructions. The isolated RNA samples were resuspended in RNase-free diethylpyrocarbonate (DEPC)-treated water and followed by a regular two-step semi-quantitative RT-PCR method to examine the levels of various mRNAs, namely those encoding COX-2, IL-8, GHSR-1a, BRCA1, Cathepsin D, MKP-1, PP2A, GAPDH, and β-actin. In brief, 1 µg of total RNAs from each sample was used to perform the reverse transcription, and to detect the cDNA contents, 2 µL cDNA from RT reaction was added into the PCR reaction tube and mixed with 10× PCR buffer, 0.5 mM dNTP, 0.5 µM sense and antisense primers (MDBio Inc., Taipei, Taiwan), and 0.2 U TaqDNA polymerase, using a Program Temp Control System PC 818 (Astec Technology, Fukuoka, Japan). The primer sequences used are listed in Table 1. The PCR products were analyzed by electrophoresis in 2% agarose gel with 1 µg/mL ethidium bromide. The final cDNA yields were then determined from the amplified DNA signals by comparing them against the internal control GAPDH or β-actin. The DNA signals were captured and analyzed by ImageQuant 5.2 software. 4.6. Transfection and Analysis of COX-2 and IL-8 Promoter Activity Levels as well as NF-κB and Activator Protein-1 (AP-1) Reporter Activity Levels Transfection was performed using Lipofectin reagent (Invitrogen, Paisley, UK) according to the manufacturer’s instructions. To examine the levels of COX-2 and IL-8 promoter activity, plated KGN cells were transfected with either a COX-2 [70] or an IL-8 promoter construct (a gift from Dr. N Mukaida, Kanazawa University, Kanazawa, Japan). To analyze activation of the NF-κB and AP-1 promoters, either a minimal promoter sequence bearing multiple NF-κB binding sites and driving a luciferase reporter gene (a gift from Dr. Bing-Chang Chen, Taipei Medical University, Taipei, Taiwan) or a reporter plasmid with AP-1 responsive elements fused to a luciferase reporter gene was separately transfected into KGN cells. During all of these experiments, a pCMV-β-Gal plasmid was co-transfected as a control. 4.7. Statistical Analysis Experimental data are expressed as means plus/minus the standard errors of the means (mean ± SEM). The results were analyzed by one-way analysis of variance (ANOVA), which was followed by the least-significant difference (LSD) test; this approach was used to compare the differences between the various treatment groups and the control groups. Differences with a p value of less than 0.05 were considered to be statistically significant. Acknowledgments This work was supported by Taiwan Ministry of Science and Technology (MOST 100-2320-B-010-018-MY3; and MOST 102-2320-B-010-010-MY3), the Cheng Hsin General Hospital (103F003C03, 104F003C04, and 105F003C04), and Taiwan Ministry of Education, Aim for the Top University Plan. The authors thank Dr. Ralph Kirby, Department of Life Sciences, National Yang-Ming University, for his help with language editing; Dr. Bing-Chang Chen, Taipei Medical University, for providing the NF-κB and AP-1 reporter plasmids; Hiroyasu Inoue, Nara Women’s University, Japan, for providing the COX-2 promoter construct; and Naofumi Mukaida, Kanazawa University, Japan for providing the IL-8 promoter construct. Supplementary Materials Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1359/s1. Click here for additional data file. Author Contributions The study was conceived and designed by Yi-Ning Chao, David Sun and Yuh-Lin Wu. Experiments were performed by Yi-Ning Chao. Data analysis and interpretation were performed by Yi-Ning Chao, David Sun, Yen-Chun Peng and Yuh-Lin Wu. All authors were involved in writing the paper and approved the submitted and published versions. Conflicts of Interest The authors declare no conflict of interests. Figure 1 Growth hormone releasing peptide-2 (GHRP-2) inhibition of protein kinase C (PKC)-induced cyclooxygenase-2 (COX-2) and interleukin-8 (IL-8) expression and secretion of prostaglandin E2 (PGE2) and IL-8 in KGN cells. Overnight plated KGN cells were pretreated with GHRP-2 (0.01, 0.1, and 1 µM) for 2 h, and then phorbol 12,13-didecanoate (PDD) (100 nM) was included for an additional 12 h. The intracellular COX-2 (A) and IL-8 (B) protein expression levels and the resulting PGE2 (A) and IL-8 (B) protein concentrations in the cultured media were determined by Western blotting assay and ELISA, respectively. The results represent the means ± SEM (standard error of mean) (n = 4). * p < 0.05 compared with the control; # p < 0.05 compared with the PDD treatment. Figure 2 Specific effect of GHRP-2 on PKC-induced COX-2 and IL-8 protein expression. Plated KGN cells were pretreated with GHRP-2 (1 µM) in the absence or presence of the GHSR type 1a antagonist JMV3002 (0.5, 1.5, and 4.5 µM) for 2 h, and then PDD (100 nM) was included for an additional 12 h. The intracellular COX-2 (A) and IL-8 (B) protein expression levels were determined by Western blotting assay. The results represent the means ± SEM (n = 3). * p < 0.05 compared with the control; # p < 0.05 compared with the PDD treatment; $ p < 0.05 compared with the combined GHRP-2 and PDD treatment. Figure 3 Promotion of the degradation of PKC-induced COX-2 and IL-8 proteins by GHRP-2. Plated KGN cells were either untreated or treated with PDD (100 nM) for 6 h to induce COX-2 and IL-8 protein expression (defined as 0 h), and then the cells were treated with cycloheximide (CHX, 5 µg/mL) or CHX in combination with GHRP-2 (1 µM) for 1, 3, 6, 9, and 12 h. The intracellular COX-2 and IL-8 protein expression levels were determined by Western blotting assay. The results represent the means ± SEM (n = 4) * p < 0.05 compared with the control; # p < 0.05 compared between with GHRP-2 and without GHRP-2 groups. Figure 4 Involvement of the proteasome and lysosome pathways in the GHRP-2-enhanced degradation of COX-2 and IL-8 proteins. Plated KGN cells were pretreated with either the proteasome inhibitor MG132 (1 µM) or the lysosome inhibitor chloroquine (50 µM) in combination with GHRP-2 (1 µM) for 2 h, and then PDD (100 nM) was added for an additional 12 h. The intracellular COX-2 (A) and IL-8 (B) protein expression levels were determined by Western blotting assay. The results represent the means ± SEM (n = 4) * p < 0.05 compared with the control; # p < 0.05 compared with the PDD treatment; $ p < 0.05 compared with the combined GHRP-2 and PDD treatment. Figure 5 Attenuation of PKC-induced COX-2 and IL-8 transcription and mRNA expression by GHRP-2. Plated KGN cells were pretreated with GHRP-2 (0.01, 0.1, and 1 µM) for 2 h, and then PDD (100 nM) was included for an additional 12 h. The COX-2 (A) and IL-8 (B) mRNA expression levels were determined by RT-PCR. (C,D) To determine the promoter activity of COX-2 and IL-8 genes, plated KGN cells were transfected with a human COX-2 or a human IL-8 promoter construct, each with a luciferase reporter gene; these were co-transfected with pCMV-β-Gal as a control plasmid. The transfected cells were untreated, treated with either PDD (100 nM) or GHRP-2 (1 µM) alone or a combination of PDD and GHRP-2 for 12 h. Cells were then harvested with Glo lysis buffer and the COX-2 (C) or IL-8 (D) promoter activity was determined by luciferase assay; the luciferase activity was normalized against the β-galactosidase activity from pCMV-β-Gal within the same samples. The results represent the means ± SEM ((A,B): n = 4; (C,D): n = 3). * p < 0.05 compared with the control; # p < 0.05 compared with the PDD treatment. Figure 6 Identification of the signaling pathway(s) mediating PKC-induced COX-2 and IL-8 expression. Plated KGN cells were pretreated with mitogen-activated protein kinases (MAPKs) inhibitors individually (SB: p38 inhibitor, 20 µM; SP: c-Jun N-terminal kinase (JNK) inhibitor, 40 µM; PD: extracellular signal-regulated kinase (ERK) inhibitor, 20 µM), or nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) inhibitor ammonium pyrrolidinedithiocarbamate (APDC, 50 µM) for 1 h and then PDD (100 nM) was included for an additional 12 h. The intracellular COX-2 (A) and IL-8 (B) protein expression levels were assessed by Western blotting assay. The results represent the means ± SEM (n = 4) * p < 0.05 compared with the control; # p < 0.05 compared with the PDD treatment. Figure 7 Attenuation of PKC-induced p38 and JNK phosphorylation and PKC-mediated activator protein 1 (AP-1) reporter activity by GHRP-2. Plated KGN cells were pretreated with GHRP-2 for 10 min and then the cells were exposed to PDD (100 nM) for 15 or 30 min. The effects of GHRP-2 on p38 (A); JNK (B); and ERK (C) phosphorylation were monitored by Western blotting assay; (D) To examine the impact of GHRP-2 on PKC-induced AP-1 reporter activation, plated KGN cells were transfected with an AP-1 reporter plasmid that controlled a luciferase reporter gene and co-transfected with pCMV-β-Gal as a control plasmid. Transfected cells were untreated, treated with either PDD (100 nM) or GHRP-2 (1 µM) alone, or treated with a combination of PDD and GHRP-2 for 12 h. The AP-1 reporter activity was determined by luciferase assay and normalized against the β-galactosidase activity from pCMV-β-Gal of the same samples. The results represent the means ± SEM ((A): n = 5; (B–D): n = 4) separate experiments. * p < 0.05 compared with the control; # p < 0.05 compared with the PDD treatment. Figure 8 Inhibition of PKC-induced NF-κB nuclear translocation and activation by GHRP-2. (A) Plated KGN cells were pretreated with GHRP-2 for 10 min before the inclusion of PDD (100 nM) for additional 30 or 60 min. The amount of nuclear and cytosolic NF-κB p65 subunit present in these fractions was monitored by Western blotting assay. (B) To monitor NF-κB reporter activity, plated KGN cells were transfected with a plasmid bearing NF-κB responsive elements fused with a luciferase reporter gene and co-transfected with the pCMV-β-Gal as a control plasmid. Transfected cells were untreated, treated with either PDD (1 µM) or GHRP-2(1 µM) alone or treated with a combination of PDD and GHRP-2 for 12 h. The NF-κB reporter activity was determined by luciferase assay and normalized against the β-galactosidase activity from pCMV-β-Gal of the same samples. The results represent the means ± SEM ((A): n = 4; (B): n = 3) separate experiments. * p < 0.05 compared with the control; # p < 0.05 compared with the PDD treatment. Figure 9 Regulation of mitogen-activated protein kinase phosphatase-1 (MKP-1) and protein phosphatase 2A (PP2A) expression by PDD and GHRP-2. Plated KGN cells were pretreated with GHRP-2 (0.01, 0.1, 1 µM) for 2 h, and PDD (100 nM) was included for an additional 12 h. The MKP-1 and PP2A mRNA or protein expression levels were determined by RT-PCR (A) and Western blotting assay (B), respectively. The results represent the means ± SEM ((A): n = 3; (B): n = 4) separate experiments. * p < 0.05 compared with the control; # p < 0.05 compared with the PDD treatment. Figure 10 Involvement of MKP-1 and PP2A in the inhibitory effect of GHRP-2 on PKC-mediated COX-2 and IL-8 expression. To test the involvement of MKP-1 or PP2A in GHRP-2 inhibition of PKC-induced COX-2 and IL-8 expression, plated KGN cells were pretreated with GHRP-2 (1 µM) or a combination of GHRP-2 with either the MKP-1 inhibitor sanguinarine (0.01, 0.1, and 1 µM) (A) or the PP2A inhibitor okadaic acid (10, and 30 µM) (B) for 2 h, and then PDD (100 nM) was included for an additional 12 h. The intracellular COX-2 and IL-8 protein expression levels were monitored by Western blotting assay. The results represent the means ± SEM (n = 4) separate experiments. * p < 0.05 compared with the control; # p < 0.05 compared with the PDD treatment; $ p < 0.05 compared with the combined GHRP-2 and PDD treatment. Figure 11 Involvement of the PI3K-Akt pathway in regulation by GHRP-2 of the PKC-mediated production of COX-2 and IL-8. (A) To examine whether Akt is under GHRP-2 regulation, plated KGN cells were treated with to GHRP-2 (1 µM) alone for 5, 10, 15, 30 or 60 min or with GHRP-2 in combination with wortmannin (10 µM) for 10 or 30 min. The Akt phosphorylation was monitored by Western blotting assay. (B,C) To evaluate the role of Akt in the GHRP-2 regulation of PKC-induced inflammation, plated KGN cells were pretreated with GHRP-2 (1 µM) alone or in combination with the PI3K-Akt inhibitor wortmannin (3, 10, and 30 µM) for 2 h, and then PDD (100 nM) was included for an additional 12 h. The intracellular COX-2 (B) and IL-8 (C) protein expression levels and the resulting PGE2 (B) and IL-8 (C) concentrations in the cultured media were determined by Western blotting assay and ELISA, respectively. The results are expressed as means ± SEM. ((A): n = 3; (B,C): n = 4). * p < 0.05 compared with the control treatment; # p < 0.05 compared with the GHRP-2 treatment group at the same time point; @ p < 0.05 compared with the PDD treatment; $ p < 0.05 compared with the combined GHRP-2 and PDD treatment. Figure 12 Outline of the PKC regulation of COX-2 and IL-8 production and the potential acting points of ghrelin (GHRP-2) in ovarian granulosa cells. PKC activates the intracellular signaling pathways, including the MAPKs (p38, JNK, ERK) as well as the NF-κB pathways, both of which would lead to transcription of COX-2 and IL-8, and consequently mediate the output of PGE2 and IL-8. Ghrelin (GHRP-2) may regulate MKP-1 and PPA2 to affect the PKC-mediated activation of MAPKs and NF-κB, as well as activate the Akt pathway, and subsequently to attenuate the PKC-induced COX-2 and IL-8 transcription, resulting in reduction of COX-2 and IL-8 protein levels as well as PGE2 and IL-8 secretion. ijms-17-01359-t001_Table 1Table 1 Oligonucleotide primers for RT-PCR. Gene Sequences Direction Size (bp) COX-2 5′-GCATCAGTTTTTCAAGACAG-3′ 5′-TCGCATACTCTGTTGTGTTC-3′ Sense Antisense 324 IL-8 5′-ACTTCCAAGCTGGCCGTGGCT-3′ 5′-TCACTGGCATCTTCACTGATT-3′ Sense Antisense 318 PP2A 5′-AAGGTTCGTTACCGTGAACG-3′ 5′-ACCTCTTGCACGTTGGATTC-3′ Sense Antisense 641 MKP-1 5′-CCGGAGCTGTGCAGCAAA-3′ 5′-CTCCACAGGGATGCTCTT-3′ Sense Antisense 282 GHSR-1a 5′-AGCGCTACTTCGCCATC-3′ 5′-CCGATGAGACTGTAGAG-3′ Sense Antisense 289 GHSR-1b 5′-TCTTCCTTCCTGTCTTCTGT-3′ 5′-GATAGGACCCGCGAGAGAAA-3′ Sense Antisense 179 β-actin 5′-GGCACCACACCTTCTACAAT-3′ 5′-CGTCATACTCCTGCTTGCTG-3′ Sense Antisense 834 GAPDH 5′-ATCACCATCTTCCAGGAGCG-3′ 5′-CCTGCTTCACCACCTTCTTG-3′ Sense Antisense 574 BRCA1 5′-ACAGCTGTGTGGTGCTTCTGTG-3′ 5′-CATTGTCCTCTGTCCAGGCATC-3′ Sense Antisense 107 Cathepsin D 5′-CATTGTGGACACAGGCACTTC-3′ 5′-GACACCTTGAGCGTGTAGTCC-3′ Sense Antisense 201 ==== Refs References 1. Hanada T. Yoshimura A. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081360ijms-17-01360ArticleIsolation and Characterization of a Glycosyl Hydrolase Family 16 β-Agarase from a Mangrove Soil Metagenomic Library Mai Zhimao Su Hongfei Zhang Si *Woo Patrick C. Y. Academic EditorKey Laboratory of Tropical Marine Bio-Resources and Ecology, Guangdong Key Laboratory of Marine Materia Medica, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China; maizhimao@scsio.ac.cn (Z.M.); suhongfei17@126.com (H.S.)* Correspondence: zhsimd@scsio.ac.cn; Tel.: +86-20-8902-3105; Fax: +86-20-8445-167219 8 2016 8 2016 17 8 136017 6 2016 15 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).A mangrove soil metagenomic library was constructed and a β-agarase gene designated as AgaML was isolated by functional screening. The gene encoded for a 659-amino-acids polypeptide with an estimated molecular mass of 71.6 kDa. The deduced polypeptide sequences of AgaML showed the highest identity of 73% with the glycoside hydrolase family 16 β-agarase from Microbulbifer agarilyticus in the GenBank database. AgaML was cloned and highly expressed in Escherichia coli BL21(DE3). The purified recombinant protein, AgaML, showed optimal activity at 50 °C and pH 7.0. The kinetic parameters of Km and Vmax values toward agarose were 4.6 mg·mL−1 and 967.5 μM·min−1·mg−1, respectively. AgaML hydrolyzed the β-1,4-glycosidic linkages of agar to generate neoagarotetraose (NA4) and neoagarohexaose (NA6) as the main products. These characteristics suggest that AgaML has potential application in cosmetic, pharmaceuticals and food industries. agarasemetagenomic libraryagarneoagaro-oligosaccharides ==== Body 1. Introduction Agar is an important polysaccharide that consists of agarose and agaropectin. Agarose has a linear chain structure of alternating residues of 3-O-linked β-d-galactopyranose and 4-O-linked 3,6-anhydro-α-l-galactopyranose [1]. For the stabilizing properties and gelling ability of agarose, it is widely applied in cosmetics, pharmaceuticals and food industries [2]. However, due to the high viscosity, low water solubility and undigested characteristic, its application value has not been fully developed. Instead, agaro-oligosaccharides exhibit not only excellent dissolubility and easily absorbed, but also physiological activity, such as anticancer, anti-oxidation, anti-inflammation, antivirus and immune enhancement [3,4,5,6]. Recently, there is increasingly interest in conversion of agar into agaro-oligosaccharides. Compared with traditional technologies of acid hydrolysis, enzymatic conversion of agar into agaro-oligosaccharides has the advantages of mild reaction conditions, high efficiency and specificity, simple processing and being environmentally friendly. Thus, it is considered to be a feasible approach with broad prospects for the development of novel drugs and functional foods. Agarases that catalyze the hydrolysis of agarose into oligosaccharides have been divided into two classes based on their hydrolysis sites. The α-agarases (E.C. 3.2.1.158) hydrolyze α-1,3-linkages in agarose to generate agaro-oligosaccharides, while the β-agarases (E.C. 3.2.1.81) hydrolyze β-1,4 linkage in agarose to form neoagaro-oligosaccharides [7,8]. Most agarases currently studied and applied are β-agarases. On the basis of amino acid sequences homology in the carbohydrate-active enzymes (CAZY) database, β-agarases are classified into four families: glycoside hydrolases family 16 (GH16), GH50, GH 86 and GH118 [9]. To date, many β-agarases have been isolated from various bacteria, such as from the genera Pseudomonas, Agarivorans, Vibrio, Alteromonas and Bacillus [10,11,12,13,14]. However, these reported agarases were still not satisfactory due to their low catalytic activity, thermal stability and productivity. Thus, screening and isolation of novel agarases from microorganisms is urgently in demand. Current estimates indicate that less than 1% of microorganisms are readily culturable with known cultivation techniques [15]. The traditional method for microbial enzyme mining is based on the culturable microorganisms, which may result in the loss of major portions of the microbial communities. Metagenomics circumvent the traditional cultivation, and directly isolate and clone the target gene from the environmental microorganism DNA. It provides an effective approach for mining novel biocatalysts from uncultured microorganism. Many novel genes encoding different enzymes and secondary metabolites have been isolated from microbial communities without cultivation, such as cellulase [16], lipase [17], protease [18], amylase [19], etc. However, to our knowledge, no agarase has been isolated and characterized from metagenomic library, except for several putative agarase genes identified from a soil metagenome [20]. In this study, we exploited the metagenome for isolation of genes encoding β-agarases. The detailed DNA sequence of the selected β-agarase was analyzed. The recombinant β-agarase was purified and characterized for the further study. 2. Results and Discussion 2.1. Construction of Metagenomic Library and Isolation of Agarase Gene The size and quantity of DNA extracted from the mangrove soil was met the requirement of fosmid library construction. The crude extracted metagenomics DNA was then purified using pulsed-field gel electrophoresis (PFGE). Finally, a fosmid library with 100,000 clones was constructed. Restriction analysis of randomly selected clones showed that the DNA fragments insert sizes ranged from 20 to 55 kb and the average size was about 30 kb. The total insertion DNA of the fosmid library was estimated as more than 3 Gb [21]. The positive fosmid clones were identified when they generated pits after incubation. Three independent clones with agarase activity from the fosmid library were obtained. After enzymatic activity analysis, one clone with highest agarase activity was selected and the recombinant plasmid in this clone was designated as Fos84. Restriction analysis of Fos84 showed that the inserted DNA fragment was approximately 31 kb (date not shown). One agarase-producing clone was identified by screening the subclone library. The plasmid from positive clones was sequenced. A single open-reading frame encoded agarase gene, designated AgaML, was obtained. The currently reported agarases are mostly isolated from marine bacteria. Mangrove is a unique ecosystem that possesses both terrestrial ecosystem and marine ecosystem characteristics. However, there has not yet been agarase isolated from the mangrove environment. The metagenomic approach has been widely used for exploiting novel enzymes [22]. But for agarase, only one study reported that four clones encoding 12 putative agarase genes were identified by activity-based screening from a soil metagenome [20]. In this study, three independent clones with agarase activity were isolated, which exhibited lower hit rate of positive clone. 2.2. Sequence Analysis Sequence analysis revealed that the gene AgaML consists of 1980 bp with the overall G + C content of 55%. It encoded a protein, designated AgaML, with 659 amino acids. The estimated molecular mass of AgaML was 71.6 kDa and the isoelectric point (pI) was 5.05. Analysis of SignalP 4.1 server (http://www.cbs.dtu.dk/services/SignalP/) revealed that there was no signal peptide in AgaML. According to the NCBI search program of conserved-domain database, a β-agarase catalytic domain of the glycoside hydrolases family 16 (GH16) and two carbohydrate-binding modules (CBM6) were found (Figure 1). The CBM6 modules were found in many glycoside hydrolases, including xylanases, endoglucanase and mannanases, which were described as binding to xylan, cellulose, mixed β-(1,3)(1,4)-glucan and β-1,3-glucan [23,24,25,26]. However, the deletion of one or two CBM6 modules in AgaML had no effect on the catalytic activity and stability (data not shown). It was reported that CBM6 modules in GH16 β-agarases functioned as special targets to the non-reducing end of agarose chains [27,28]. The deduced protein of AgaML showed high identity with β-agarases in the NCBI database: 73% to the β-agarase from Microbulbifer agarilyticus (GenBank accession no. BAE06228.1), 71% to the β-agarase from Saccharophagus degradans 2-40 (GenBank accession no. AAT67062.1), 66% to the β-agarase from Saccharophagus sp. AG21 (GenBank accession no. AFR90184.1) and 66% to the β-agarase from Simiduia sp. TM-2 (GenBank accession no. BAQ95400.1). Multiple sequence alignments of the GH16 β-agarase catalytic domain in AgaML with these β-agarases were observed and the catalytic residues were also predicted (Figure 2). Based on the sequence similarities analysis of AgaML with the known GH16 family β-agarases, we assumed that the active site of AgaML were Glu-220 (as the nucleophile) and Glu-225 (as the acid/basic) [29]. Another conserved acidic amino acid residue Asp-222 might be important for maintaining the charge in the environment of the catalytic amino acids. To analyze the relationship of AgaML with the known β-agarase members from various species, a phylogenetic tree was constructed (Figure 3). The selected agarases were comprised into five clades, represented by GH16, GH50, GH86, GH96 and GH118, respectively. AgaML was grouped into GH16. 2.3. Expression and Purification of Recombination Agarase The β-agarase gene AgaML was cloned into pET22b(+) vector, and then transformed into Escherichia coli BL21(DE3) cells. The agarase activity was detected in the recombinant Escherichia coli BL21(DE3) cells after isopropyl-β-d-galactopyranoside (IPTG) induction. The crude β-agarase AgaML was purified by Ni2+-NTA affinity chromatography. The purified AgaML was analyzed using sodium dodecyl sulfate polyacrylamide gel electropheresis (SDS-PAGE). A single band with an apparent molecular mass of 71.6 kDa corresponding to the calculated size of AgaML was observed (Figure 4). The AgaML exhibited maximum catalytic activity of 967.5 μM·min−1·mg−1 and a Km of 4.6 mg·mL−1 for agarose on the optimal condition. The kinetic parameters of Km and Vmax for different agarases are various. Most reported agarases exhibited a Km between 1 and 50 mg·mL−1, and a Vmax between 10 and 1000 μM·min−1·mg−1 [27,30,31,32,33,34,35]. AgaML displayed a relatively higher specific activity than most of the reported agarases. 2.4. Effects of pH and Temperature on the Activity of Recombination Agarase The effects of pH and temperature on the activity of AgaML were measured (Figure 5). The maximum activity of AgaML was observed at pH 7.0 and 50 °C. It displayed high thermostability at the temperature below 45 °C, which retained more than 60% activity after incubation for 1 h (Figure 5b). Similar to most of the reported β-agarases, AgaML exhibited optimal temperature at >40 °C, which was higher than the gelling temperature of agar (~38 °C) [36]. The high catalytic activity and thermostability at temperatures above the gelling temperature could offer advantage for enzymatic conversion of agar or marine algae into oligosaccharide. 2.5. Effects of Various Metal Ions and Reagents on the Activity of Recombination Agarase The effects of different metal ions and reagents on AgaML activity were measured at pH 7.0 and 50 °C in the presence of the tested metal ions and reagents. As shown in Table 1, no significant effects on the activity of AgaML was observed in Na+, K+, Fe3+ and Co2+; however, 1 mM metal ions (Mn2+, Ca2+ and Ba2+) had a slight positive effect on the AgaML activity. However, the AgaML activity was inhibited by Mg2+, Zn2+, Cu2+ and Cd2+, and its activity was also inhibited by 10 mM chelator (EDTA) and detergent (SDS). As reported, metal ions commonly existing in seawater, including Na+, K+, Fe3+, Ca2+, Mn2+ and Ba2+, had no significant inhibition in many agarase while Cu2+ was a potential inhibitor to most agarase [27,30,37]. The AgaML activity was inhibited significantly by EDTA suggested that it might be a metal ion-dependent enzyme. 2.6. Analysis of the Hydrolysis Pattern and Products of Recombination Agarase To determine the hydrolysis type and products of AgaML on agar, the hydrolysates at different reaction times were identified by thin layer chromatography (TLC). The result (Figure 6) showed that the AgaML hydrolyzed agar to generate neoagaro-oligosaccharides with various degrees of polymerization (DPs) during the initial stage of the reaction. This hydrolysis pattern suggested that AgaML was an endo-type β-agarase. The final products of the enzyme reaction after prolonged incubation were neoagarotetraose (NA4) and neoagarohexaose (NA6). When neoagarotetraose and neoagarohexaose were used as substrates hydrolyzed by AgaML, no hydrolysis products were observed. The hydrolysis products of different agarases toward agar are varied, but the same GH family agarases exhibit similar digestion pattern. The hydrolysis products of reported agarases toward agar are listed in Table 2 (these are not comprehensive but represent a selection of previous studies). Generally, the main products toward agar are NA4 and NA6 by agarases of the GH16 family, NA2 or NA4 by those of GH50 family, and NA6, neoagarooctaose (NA8) or higher degrees of polymerization of neoagaro-oligosaccharides by agarases of GH86 family. The main products were NA4 and NA6 by AgaML (Figure 6), indicating it belonged to GH16 family. 3. Materials and Methods 3.1. Strains, Plasmids, and Culture Conditions The Escherichia coli EPI300-T1R strain served as host and Copycontrol pCC2FOS (Epicentre, Madison, WI, USA) served as vector for metagenomic library construction. The Escherichia coli DH5α and Escherichia coli BL21(DE3) (Novagen, Madison, WI, USA) strains served as the hosts for gene cloning and expression, respectively. The Escherichia coli strains were cultured at 37 °C in Luria-Bertani (LB) broth. The plasmids pMD18-T (Takara, Kyoto, Japan) and pET22b(+) (Novagen) were used for cloning and expression vectors, respectively. All restriction enzymes, ligases, Ex TaqTM DNA polymerase and related reagents were purchased from Takara. The standards neoagarobiose (NA2), neoagarotetraose (NA4), neoagarohexaose (NA6), and neoagarooctaose (NA8) were from Qingdao BZ Oligo Biotech Co., Ltd., Qingdao, China. All other chemicals used were of analytical grade. 3.2. Construction of Metagenomic Library The topsoil (0–10 cm) was sampled from Mangrove Reserve of Sanya City (18°15′16.32″ N, 109°30′28.10″ E), Hainan Province, China. The DNA extraction from soil samples was conducted based on direct lysis methods with minor modifications [48]. The soil DNA was purified by pulsed-field gel electrophoresis (PFGE) and sheared to approximately 40 kb fragments. The blunt-ended DNA was ligated to the cloning-ready Copycontrol pCC2FOS vector, and then packaged and plated on EPI300-T1R cells. The constructed libraries were collected in 96-well plates and stored at −80 °C. 3.3. Library Screening and AgaML Gene Isolation The metagenomic library clones in 96-well plates were cultured onto Luria-Bertani (LB) agar supplemented with 25 μg/mL of chloramphenicol at 37 °C for 16 h. The positive clones showing pits were selected and then stained by Lugol’s iodine solution (containing 5% I2 and 10% KI). The clones with agarolytic activity were visualized as clear zones on a brown background. The plasmids of positive clones from fosmid library were extracted d and digested with restriction enzymes Sau3AI. The DNA fragments with the size ranging from 3 to 9 kb were recovered, purified and ligated to BamH I digested pUC19 vector. The ligated mixture was transformed into Escherichia coli DH5α for subcloned library construction. Clones with agarolytic activity were selected and the plasmids from positive clones were sequenced. 3.4. Sequence Analysis and Classification of AgaML The sequence similarities and the conserved domain search were performed by BLAST program (http://www.ncbi.nlm.nih.gov/BLAST). The signal peptide sequence prediction was performed using SignalP 4.1 Server (http://www.cbs.dtu.dk/services/SignalP/). Multiple sequence alignment was conducted using ClustalW (http://www.ch.embnet.org/software/ClustalW.html) and DNAMAN (Version 6.0, Lynnon, San Ramon, CA, USA). A phylogenetic tree was created using MEGA 5.0 software (http://www.megasoftware.net/) with the neighbor-joining (NJ) method. 3.5. Cloning and Expression of AgaML The AgaML gene was amplified from the plasmid of positive clone by using the primer pair of aga-F: 5′-GCATGCCATGGATGTGTATCCACCTTCAATCCCCTACCG-3′ and aga-F: 5′-GCACGCGGATCCGGTTGGCTGTGAGGACTAATCTGTCCAG-3′. The nucleotides underlined in the primers aga-F and aga-F indicated Nco I and BamH I digestion site, respectively. The PCR product was purified and digested with Nco I and BamH I, and then ligated into Nco I-BamH I site of expression vector pET22b(+).The recombinant plasmid, designated as pET-AgaML, was transformed into Escherichia coli BL21(DE3). The transformed Escherichia coli BL21(DE3) cells were cultured in LB medium supplemented with ampicillin (100 μg/mL) at 37 °C. When the optical density value at 600 nm reached 0.6, the cells were induced with 1 μM (final concentration) isopropyl-β-d-galactopyranoside (IPTG) and further cultured at 22 °C for 16 h. 3.6. Purification of Recombinant Agarase The induced Escherichia coli BL21(DE3) cells were harvested by centrifugation (6000 rpm, 15 min, 4 °C), washed twice with Tris-HCl buffer (pH 7.5), and disrupted on ice by sonication. The AgaML was purified with Ni2+-NTA chromatography. The purified recombinant protein was analyzed by SDS-PAGE. The concentration of purified protein was measured by the Bradford method [49]. 3.7. Agarase Activity Assay The agarase activity was determined using 3,5-dinitrosalicylic acid (DNS) method [50]. The reaction mixure contained 1 μL diluted enzyme solution (0.8 μg of purified agarase), 199 μL of McIlvaine buffer (0.05 M, pH 7.0) and 1% (w/v) agarose. After incubation at 50 °C for 15 min, the reaction was terminated with 200 μL DNS and then boiled for 10 min. The heat-inactivated recombinant β-agarase served as a negative control. The absorbance was measured at 540 nm and values for reducing sugar were expressed as d-galactose equivalents. Agarase activity (U) was defined as the amount of enzyme that produced 1 μM of reducing sugar per min under the assay conditions. 3.8. Effects of pH and Temperature on Recombinant Agarase Activity The effect of pH on AgaML activity was assayed in 0.05 M McIlvaine buffer (pH 3.0–8.0) and 0.05 M glycine-NaOH buffer (pH 8.0–11.0) at 50 °C. The effect of temperature on AgaML activity was detected at different temperatures (25 °C–65 °C). The thermostability of AgaML was evaluated by determining the residual activity of AgaML after preincubation at different temperatures ranging from 25 to 65 °C for 1 h. 3.9. Effects of Various Metal Ions and Reagents on Recombinant Agarase Activity The sensitivity of AgaML to various metal ions, denaturants and chelators were analyzed by measuring the enzyme activity supplemented with different concentrations of Na+, NH4+, K+, Mg2+, Zn2+, Ca2+, Ba2+, Cu2+, Co2+, Cd2+, Fe3+, EDTA and SDS. All enzyme activities were determined in three independent experiments. The relative activity was expressed as the percentage of activity respect to that determined under the standard condition without metal ions, denaturants and chelators. 3.10. Hydrolysis Products Analysis of Recombinant Agarase The hydrolysis products of AgaML towards to agar were determined by thin-layer chromatography (TLC) [43]. The hydrolysis reaction containing purified AgaML and 0.5% agar in McIlvaine buffer (0.05 M, pH 7.0). After incubating for different times at 50 °C, the reaction was stop by incubation in a boiling water bath for 10 min and the inactivation enzyme was removed by centrifuging at 4 °C for 20 min. The reaction mixture was spotted on silica gel 60 TLC plates (Merck, San Diego, CA, USA). The plates were developed with n-butanol-acetic acid–water (1:2:1, v/v/v) solution and then immersed rapidly in 10% H2SO4 (v/v). The oligosaccharides were visualized by heating the plates at 90 °C. Neoagarobiose (NA2), neoagarotetraose (NA4), neoagarohexaose (NA6), and neoagarooctaose (NA8) were used as standards. 3.11. Nucleotide Sequence Accession Number The AgaML gene nucleotide sequence reported was deposited in the GenBank database under accession numbers KX388156. 4. Conclusions A β-agarase gene AgaML was isolated from a mangrove soil metagenomic library for the first time. The recombination β-agarase AgaML exhibited high catalytic activity toward agarose. It hydrolyzed agar to generate neoagarotetraose and neoagarohexaose as the main products. Moreover, AgaML displayed optimal temperature higher than the gelling temperature of agar, and it was stable at temperatures below 45 °C. Most common metal ions also had no significant inhibition on AgaML activity. These characteristics indicate that AgaML is a good candidate for industrial applications. This study also highlights the utility of metagenomic approach in discovering novel β-agarase for conversion agar into neoagaro-oligosaccharides. Acknowledgments This work was supported by grants from the Administration of Ocean and Fisheries of Guangdong Province (GD2012-D01-002); the “Strategic Priority Research Program” of the Chinese Academy of Sciences (No. XDA10030400); and the Natural Science Foundation of Guangdong Province, China (Grant No. 2015A030310270, and Grant No. 2016A030313157). Author Contributions Zhimao Mai performed the experiments and wrote the manuscript. Hongfei Su contributed reagents/materials/analysis tools. Si Zhang conceived and designed the experiments. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Schematic overview of the domain structure in AgaML. The amino acid numbers that refer to each module are indicated. Figure 2 Multiple sequence alignments of the catalytic domain in AgaML with other known β-agarases belonging to glycoside hydrolases family 16 (GH16). BAE06228.1: β-agarase from Microbulbifer agarilyticus JAMB A3; WP_011467657.1: β-agarase from Saccharophagus degradans; AAT67062.1: β-agarase from Saccharophagus degradans 2-40; WP_015048661.1: β-agarase from Simiduia agarivorans; WP_041522726.1: β-agarase from agarase Gilvimarinus agarilyticus. The predicted active site residues of AgaML (Glu-220, Glu-225 and Asp-222) are represented as solid inverted triangle symbol. Figure 3 Phylogenetic tree analysis of AgaML with other known agarases based on the amino acid sequences. The AgaML is shown with bold solid triangle. All of the agarase sequences are grouped into five families by the carbohydrate-active enzymes (CAZY) database. Their accession numbers and original genus are indicated on the tree. The phylogenetic tree was constructed using MEGA (Version 5.0) with the neighbor-joining method and 1000 bootstrap replications are indicated at branching points. Figure 4 10% sodium dodecyl sulfate polyacrylamide gel electropheresis (SDS-PAGE) analysis of AgaML. (1) Purified AgaML; (2) Recombinant Escherichia coli BL21(DE3) cells harboring pET-AgaML after induced; (3) Uninduced recombinant Escherichia coli BL21(DE3) cells harboring pET-AgaML; and (4) Protein marker. Figure 5 Effects of pH and temperature on the activity of AgaML. (a) Effect of pH on enzyme activity was measured in 0.05 M McIlvaine buffer with pH range from 3.0 to 11.0; and (b) Effect of temperature on enzyme activity were measured at temperatures ranging from 25 to 65 °C. Thermo stability analysis was observed after preincubation at temperatures ranging from 25 to 65 °C for 1 h. The error bars represent the means ± standard deviation (SD) (n = 3). Figure 6 Thin layer chromatography (TLC) analysis of agar degradation by AgaML at different time points. Hydrolysis reactions were measured at pH 7.0 and 50 °C. Hydrolysates were taken at different incubation times and analyzed by TLC. Neoagarobiose (NA2), neoagarotetraose (NA4), neoagarohexaose (NA6), and neoagarooctaose (NA8) were used as standards. ijms-17-01360-t001_Table 1Table 1 Effects of various metal ions and chemical reagents on the activity of AgaML. Reagents Concentration (mM) Relative Activity (%) a None - 100.0 ± 4.2 b Na+ 100 103 ± 3.4 K+ 100 104 ± 3.5 NH4+ 100 69 ± 5.6 Mn2+ 1 115 ± 4.3 Mg2+ 1 87 ± 4.6 Fe3+ 1 98 ± 3.2 Zn2+ 1 74 ± 6.2 Ca2+ 1 108 ± 6.4 Cu2+ 1 75 ± 5.1 Ba2+ 1 113 ± 4.2 Co2+ 1 103 ± 3.6 Cd2+ 1 85 ± 4.3 EDTA 10 17 ± 5.7 SDS 10 73 ± 5.3 a Assay was measured at the optimum conditions; b Values represent the means ± standard deviation (SD) (n = 3); - Standard condition without metal ions, chelators or denaturants. ijms-17-01360-t002_Table 2Table 2 Hydrolysis products of characterized agarases. Family Protein Strain Products References GH16 AgaML Metagenomic library NA4, NA6 This study AgaA Pseudoalteromonas sp. CY24 NA2, NA4, NA6 [38] AgaG1 Alteromonas sp. GNUM1 NA2, NA4 [39] AgaA Agarivorans sp. LQ48 NA4, NA6 [27] AgaYT Flammeovirga yaeyamensis NA2, NA4 [40] GH50 RagaA11 Agarivorans sp. JAMB-A11 NA2 [41] Unnamed Agarivorans sp. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081361ijms-17-01361ArticleInvolvement of Three Esterase Genes from Panonychus citri (McGregor) in Fenpropathrin Resistance Shen Xiao-Min Liao Chong-Yu Lu Xue-Ping Wang Zhe Wang Jin-Jun Dou Wei *Davies T. G. Emyr Academic EditorKey Laboratory of Entomology and Pest Control Engineering, College of Plant Protection, Southwest University, Chongqing 400716, China; sxmshenxiaomin@163.com (X.-M.S.); leochongyu@163.com (C.-Y.L.); luxueping91@163.com (X.-P.L.); zhewangswu@163.com (Z.W.); wangjinjun@swu.edu.cn (J.-J.W.)* Correspondence: douwei80@swu.edu.cn; Tel.: +86-23-6825-0653; Fax: +86-23-6825-126919 8 2016 8 2016 17 8 136113 7 2016 16 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).The citrus red mite, Panonychus citri (McGregor), is a major citrus pest with a worldwide distribution and an extensive record of pesticide resistance. However, the underlying molecular mechanism associated with fenpropathrin resistance in this species have not yet been reported. In this study, synergist triphenyl phosphate (TPP) dramatically increased the toxicity of fenpropathrin, suggesting involvement of carboxylesterases (CarEs) in the metabolic detoxification of this insecticide. The subsequent spatiotemporal expression pattern analysis of PcE1, PcE7 and PcE9 showed that three CarEs genes were all over-expressed after insecticide exposure and higher transcripts levels were observed in different field resistant strains of P. citri. Heterologous expression combined with 3-(4,5-dimethyl-thiazol-2-yl)-2,5-diphenyltetra-zolium bromide (MTT) cytotoxicity assay in Spodoptera frugiperda (Sf9) cells revealed that PcE1-, PcE7- or PcE9-expressing cells showed significantly higher cytoprotective capability than parental Sf9 cells against fenpropathrin, demonstrating that PcEs probably detoxify fenpropathrin. Moreover, gene silencing through the method of leaf-mediated dsRNA feeding followed by insecticide bioassay increased the mortalities of fenpropathrin-treated mites by 31% (PcE1), 27% (PcE7) and 22% (PcE9), respectively, after individual PcE gene dsRNA treatment. In conclusion, this study provides evidence that PcE1, PcE7 and PcE9 are functional genes mediated in fenpropathrin resistance in P. citri and enrich molecular understanding of CarEs during the resistance development of the mite. Panonychus citricarboxylesterasefenpropathrin resistanceheterologous expressionRNA interference ==== Body 1. Introduction The citrus red mite, Panonychus citri (McGregor) (Acari: Tetranychidae), is one of the most important citrus pests responsible for significant economic losses [1,2]. It feeds on more than 112 different plant species [3]. Heavy infestations lead to leaf drop, twig dieback, and fruit drop, and all negatively affect citrus yield and quality. A short life-cycle and high reproductive rate has allowed P. citri to rapidly develop resistances to many insecticides and acaricides [4,5,6,7]. To date, the citrus red mite ranks the third among species that evolved severe resistance from the family Tetranychidae [3]. Pyrethroids insecticides, analogues naturally occurring pyrethrins extracted from dried flowers Chrysanthemum cinerariaefolium, have been widely used in the control of mites and pests, contributing to more than 25% of world insecticide sales for their high efficiency, broad-spectrum and relatively low toxicity [8,9,10,11]. However, extensive and widespread use of pyrethroids has led to pest resistance as a consequence, which impedes pest control efforts [12]. Knock down resistance (kdr) mutation on target genes and elevated activity of detoxification enzymes are two crucial mechanisms to confer high resistance to pyrethroids [13,14,15,16]. The point mutation F1538I in segment 6 of domain III from sodium channel gene, which is known to confer strong resistance to pyrethroids, has been confirmed from comparison between resistant and susceptible strains of Tetranychus urticae [17]. A recent study about the citrus red mite revealed that a Phe1538 to Ile mutation from the sodium channel played a crucial role in fenpropathrin resistance after comparison of field fenpropathrin-resistant (WZ) and susceptible strains [18]. Carboxylesterases (CarEs) belong to a superfamily of multifunctional enzymes ubiquitous in most living organisms, including animals, plants and microbes. Insect CarEs are mainly involved in insecticide resistance or hormone and semiochemical metabolism [19,20]. As one of the most crucial metabolic detoxification systems in insects, CarEs have been shown to be associated with development of resistance to many insecticides including pyrethroids through gene amplification, improvement of mRNA stability and point mutation [19,21]. Overexpressed esterases are invovled in fenvalerate resistance of Helicoverpa armigera [22] and lambda-cyhalothrin resistance of Aphis glycines [23]. Similar studies have also been found in mites and ticks. For instance, there was a correlation between esterase activity and bifenthrin resistance in T. urticae [24] and significant elevation of esterase activity existed in λ-cyhalothrin-resistant populations of Rhipicephalus bursa [25]. Understanding the expression profiles of a detoxifying gene and characteristics of its recombinant protein is crucial to clarify the function of the gene related to insecticides-detoxifying process, but limited data is available in P. citri. Our previous study found the elevated expression of PcGSTm5 in abamectin resistant strain and its sensitive response to abamectin exposure, indicating that PcGSTm5 might be involved in abamectin resistance [26]. Meanwhile, the synergist TPP dramatically increased the toxicity of fenpropathrin, indicating that CarEs-mediated detoxification was probably an important mechanism of P. citri to pyrethroids resistance. In this study, to better understand the underlying molecular mechanism of CarEs-mediated pyrethroids resistance in P. citri, a series of experiments employing biochemical and molecular approaches were conducted. We first sequenced, phylogenetically analyzed and characterized the spatiotemporal expression pattern of two novel CarE genes, PcE7 and PcE9, along with another previously isolated gene PcE1 (GenBank number: GQ144324). The function study combined with cytotoxicity assays using Sf9 cells overexpressing PcE1, PcE7 and PcE9 were subsequently investigated. In addition, the reverse genetic study through the method of leaf-mediated dsRNA feeding was applied to explore the link between the CarE genes and pyrethroids resistance in P. citri. 2. Results 2.1. Synergism Studies To investigate the effect of esterases on resistance of P. citri against the insecticide fenpropathrin, the synergist triphenyl phosphate (TPP) was used here and the mite of Beibei (BB) population was chosen for bioassay (Table 1). After the application of TPP, the LC50 decreased from 4.339 mg/L to 1.405 mg/L and the synergism fold amounted to 3.09, suggesting that CarEs played a crucial role in detoxification process of P. citri to fenpropathrin. 2.2. Characterization and Phylogenetic Analysis Besides PcE1, isolated from our previously reported transcriptome [27], two novel CarE genes (PcE7 and PcE9) were cloned, respectively. No point mutation occurred among different field strains of P. citri. The cDNAs encoded proteins of 569 (PcE7, GenBank accession number: JQ951938), and 566 (PcE9, GenBank accession number: JQ951939) amino acid residues. Both PcE7 and PcE9 contained all the conserved motifs for maintenance of CarEs including the catalytic triad and oxyanion hole. The phylogenetic trees revealed the evolutionary relationships between the three esterases of P. citri as well as their relationships with 55 CarEs from T. urticae (Figure 1). For PcE1, there was a close clustering within clade J′ of the neurodevelopmental class from T. urticae and PcE1 shared an overall amino acid identity of 54% with tetur20g03250. Both PcE7 and PcE9 were assigned to clade J″ of the neurodevelopmental class, with identities of 52% between PcE7 and tetur01g10820 and 95% between PcE9 and tetur37g00330, respectively. 2.3. Expression Profile to Fenpropathrin Exposure The mRNA expression levels of PcE1, PcE7 and PcE9 were all significantly increased after mite exposure to a sub-lethal concentration of fenpropathrin (LC30, 0.818 mg/L) in a time-dependent manner (Figure 2). A consistent increase in PcE9, expression level occurred after fenpropathrin treatment. In contrast, PcE7 responded to fenpropathrin stress more quickly while the up-regulation alleviated at 36 h treatment. Among the three genes, the expression pattern of PcE1 fluctuated little, ranging from 1.92-fold at 12 h exposure to 2.26-fold at 36 h exposure. 2.4. Different Strains Expression Profiles The expression patterns of PcE1, PcE7 and PcE9 from P. citri of different field strains were shown in Figure 3. Compared to the counterparts of SS, generally higher expression levels of three genes were recorded from different field strains. For PcE1, WZ and FJ strains indicated 9.43- and 3.72-fold increase, respectively. PcE7 expressed a dramatic fluctuation among the different field strains, ranging from 23.03-fold in WZ to 1.12-fold in FJ. The expression ratio of PcE9 fluctuated least among three strains, while significantly higher compared to that of SS. 2.5. Enzyme Activity of Recombinant Enzymes with Sf9 Cells An enzymatic assay of the recombinant PcE protein was measured in vitro to determine if there was CarE-specific activity (Figure 4A). The results showed that compared with the GFP-expression protein, the CarE-specific enzyme activities of PcE1, PcE7 and PcE9 toward the substrate of α-naphthyl acetate (α-NA) were 27.31-, 9.21- and 12.17-fold higher, respectively (p < 0.01). 2.6. Cytotoxicity Assay The cytotoxicity assay with 3-(4,5-dimethyl-thiazol-2-yl)-2,5-diphenyltetra-zolium bromide (MTT) were performed to examine the toxicity of fenpropathrin in Sf9 insect cells expressing PcE1, PcE7 or PcE9 (Figure 4B). The value of the median lethal concentration (LC50) was calculated from a plot of percentage of cell viability against different concentrations of fenpropathrin by Probit assay. The results revealed that there were higher cell viability against cytotoxic effects of fenpropathrin in PcE1-, PcE7- or PcE9-expressing cells than that in the enhanced green fluorescent protein (GFP)-expressing cells. LC50 values were recorded in PcE1- (258.30 μg/mL), PcE7- (265.10 μg/mL) and PcE9- (258.30 μg/mL) expressing cells against fenpropathrin, all about 10-fold of that in GFP-expressing cells (26.04 μg/mL). 2.7. Susceptibility of Mites to Fenpropathrin after RNAi of CarE After RNAi by the plant leaf method, RT-qPCR was applied to investigate the knock-down efficiency of the CarE genes expression in P. citri of the field strain BB. The results showed that the transcript levels of PcE1, PcE7 and PcE9 were significantly decreased 81%, 77% and 54%, respectively, compared with control dsGFP (Figure 5A). The results demonstrated that the transcripts of the CarE genes were successfully silenced with RNAi in P. citri. Subsequently, the sensitivity of mites after RNAi to fenpropathrin were detected. When treated with LC50 of fenpropathrin, the mortality of mites after dsPcE1, dsPcE7 and dsPcE9 treatments increased significantly by 31%, 27% and 22%, respectively (Figure 5B). 3. Discussion CarEs play important physiological roles in detoxification of xenobiotics and resistance to insecticides in insects. They have been reported in metabolic resistance to pyrethroids in several insect species and mites [22,28,29,30]. The synergist (TPP) is normally considered as the inhibitor of esterases. Through synergist experiments, we can obtain preliminary evidence of the relationship between insecticide resistance and detoxification pathways. The current bioassay found the synergist TPP dramatically increased the toxicity of fenpropathrin, indicating that CarEs-mediated detoxification was probably an important mechanism of pyrethroid resistance in P. citri. In the current study, besides PcE1 that was identified to participate in the detoxification of acaricides [27], two novel CarE genes (PcE7 and PcE9) were chosen from transcriptome data as the candidate genes based on the qPCR results of their over-expression in fenpropathrin-resistant filed strains of P. citri. The subsequent phylogenetic analysis with CarE genes from T. urticae indicated that PcE1 was clustered into clade J′ while PcE7 and PcE9 were assigned to clade J″ of the neurodevelopmental class. Point mutations within genes that determine substrate specificities, as well as elevation of CarEs activity arising from transcription or gene amplification, are predominantly two molecular basis of CarEs-mediated resistance in target insects [31,32]. The oxyanion hole mutation (G137D) resulted in modest levels of resistance of Lucilia cuprina to a range of diethyl organophosphorus insecticides (OPs) [33]. Two site mutations (K14Q and N354D) of CarEs with high frequency were found to be involved in cotton aphid resistance to malathion [32]. In this study, sequence alignment of three genes (PcE1, PcE7 and PcE9) found no point mutation occurred among different field strains of P. citri. It is no surprise considering the modest resistance (ranging from 7.7- to 21.7-fold) of the mite strains in the paper. Over-expression of CarEs results from up-regulated transcription of a single copy or accumulation of multiple copies of the esterase genes [34]. The up-regulation of two α-esterase genes mediated metabolic resistance to malathion in the oriental fruit fly, Bactrocera dorsalis [35]. The increased transcription levels and gene copy numbers of CarE were responsible for malathion resistance of the cotton aphid [32]. In mites, there are similar records, for example, esterases were proved to be important metabolic factors involved in resistance of T. urticae against abamectin [36]. Metabolic resistance against pyrethroids mediated by CarEs was also well documented [37] and increased esterase activities were observed in bifenthrin-resistant strains of T. urticae [38]. In T. cinnabarinus, an enhanced activity of esterases correlated to mite resistance against abamectin and fenpropathrin and TCE2 was over-produced in fenpropathrin-resistant strain of the mite [39]. In addition, TCE2 was inducible when exposure to the acaricide, indicating the potential involvement of TCE2 in T. cinnabarinus resistance to fenpropathrin. Transcript profiling analysis revealed that three CarEs genes were all significantly elevated in a time-dependent manner after mite exposure to fenpropathrin. Compared to PcE9, PcE1 and PcE7 responded more quickly to fenpropathrin stress in a short-term treatment. A consistent increase in PcE9 transcript level was observed, suggesting the gene probably played a crucial role in a long-term acaricide exposure. The up-regulation of esterase genes provide options for the development of resistance, representing a general xenobiotic detoxification response [40]. For enzyme characteristics studies, heterologous expression is an efficient approach to obtain target gene products and provides chances to explore gene functions in vitro. Therefore, the recombinant CarEs including PcE1, PcE7 and PcE9 were expressed in Sf9 cells and their enzymatic properties were characterized. All recombinant proteins showed significant catalytic activities when α-NA was used as the substrate. A distinct activity toward the conjugates of glutathione and 1-chloro-2,4 dinitrobenzene were recorded for the recombinant protein of PcGSTm5 expressed in Escherichia coli, and the kinetic characters of expression product were systematically investigated [26]. In T. cinnabarinus, TCE2 gene was successfully expressed by E. coli expression system, and subsequent biochemical analysis found the recombinant protein presented 2-fold of the activity of the crude enzyme extracts [41]. As insecticides have previously been reported to express cytotoxic effects, such as oxidative stress in Sf9 cells [35], treatment with fenpropathrin in Sf9 cells can cause cell mortality unless cells are protected by detoxification or sequestration of fenpropathrin. Thus, cell-based inhibition assays employing MTT cytotoxicity assays were conducted to further clarify the detoxification capabilities of recombinant CarEs to fenpropathrin. The treatment of fenpropathrin in Sf9 cells caused cell mortality to different extent. The higher LC50 values were observed in PcE1-, PcE7- and PcE9-expressing cells than that in the control GFP-expressing cells to fenpropathrin exposure, indicating that the recombinant CarEs can protect cells from cytotoxicity of fenpropathrin. Similar results have also been reported in many other insect species. For instance, flavonoids greatly elevated sensitivity of CYP6AA3- and CYP6P7-expressing Sf9 cells to cypermethrin toxicity, due to inhibition effects on mosquito enzymes [42]. In Anopheles minimus, CYP6P7- or CYP6AA3-expressing cells showed higher detoxification capabilities than parental Sf9 cells against cytotoxicity of pyrethroids [43]. Heterologous expression combined with MTT assay revealed the detoxification role of BdCarE4 and BdCarE6 against malathion [35]. RNAi technique was further applied to evaluate possible roles of PcEs in fenpropathrin-resistance of P. citri. The LC50 for BB field strain were used to detect the effect of RNAi on the change of sensitivity of P. citri to fenpropathrin. The results indicated that transcript levels of PcE1, PcE7 and PcE9 were all successfully knocked down by feeding dsRNA of individual PcE gene to the mites from BB strain. The similar method via leaf-mediated dsRNA delivery has been used in T. urticae, T. cinnabarinus and whiteflies [41,44]. In P. citri, though the time-dependent profile of RNAi efficiency was not investigated, a high RNAi efficiency (at least >54%) was recorded at 24 h after dsRNA feeding in this study. These results indicated that the RNAi system applied in this study was useful for gene function research of the citrus red mite. The subsequent bioassay data showed that those mites after feeding dsRNA-PcE exhibited significantly higher susceptibility when exposed to fenpropathrin, suggesting that gene silencing decreased the detoxification capabilities of CarEs encoded by PcEs on fenpropathrin. As an effective method to determine gene function, RNAi possesses the potential for application in pest management in the field because of its high specificity and has been employed in many insects and mites [45,46,47]. The total CarE activity in Aphis gossypii decreased significantly after dsRNA-CarE treatment and the susceptibility to omethoate was suppressed in individuals of the resistant aphid strains [48]. The transcript levels of TCE2 in resistant strains of T. cinnabarinus were effectively silenced after RNAi and the following bioassay results suggested that the resistant levels of the mite to several acaricides were significantly decreased after the down-regulation of TCE2 [41]. The current bioassay data showed that higher mortalities were recorded after leaf-mediated dsRNA feeding of individual gene, further supporting the link between the expression of PcEs and fenpropathrin resistance. 4. Experimental Section 4.1. Mites A laboratory colony of P. citri, which was originally collected from the citrus nursery without pesticide application for more than 10 years at the Citrus Research Institute of the Chinese Academy of Agricultural Sciences, served as the relatively susceptible strain (SS). This strain was found to be susceptible to fenpropathrin based on results of laboratory bioassays and was reared without the exposure to any acaricides. It had been maintained at 25 ± 1 °C and 60% relative humidity under a 14:10 h light:dark condition. Three fenpropathrin-resistant strains were collected in 2015 from the citrus orchards in Beibei (BB), Wanzhou (WZ) and Fengjie (FJ) districts, Chongqing, China, respectively. Previous bioassay results showed that three field strains of P. citri have developed about 7.7- (BB), 57.7- (WZ) and 21.7-fold (FJ) resistance to fenpropathrin compared to that of SS. 4.2. Bioassays and Fenpropathrin Exposure Bioassays was conducted using the leaf-dip method as described previously [41]. Leaf disks with a diameter of 25 mm were made from fully expanded lemon leaves and washed with nuclease-free water (Promega, Fitchburg, MA, USA) before use, and placed on a water-saturated sponge in Petri dishes (9 cm in diameter). The wet sponge was covered with a piece of thin absorbent paper to prevent mites from escaping. Thirty female adults were transferred onto a leaf disk with a soft brush. Then the leaf disks with mites were dipped 5 s into serial dilutions of fenpropathrin with acetone served as control. Triton-100 (0.1% v/v) (Beijing Dingguo Chang Sheng Biotech Co., Ltd., Beijing, China) was used as surfactant in all the solution. Subsequently, the leaf disks with mites were incubated under climate-controlled conditions at 25 ± 0.5 °C, 60% relative humidity, and a photoperiod of 14:10 h light:dark. Mortality was calculated after 24 h. All tests were performed with three biological replicates and a total of 21 leaf disks were used for the bioassay. The effect of the synergist TPP on fenpropathrin was evaluated according the method described above. The only difference was that TPP and fenpropathrin were first mixed according to the proportion of active ingredient of 3:1 (m/m). The field strain of BB was chosen to conduct the synergist-bioassay. The sub-lethal effects of insecticides have been shown to influence population dynamics and facilitate resistance evolution by altering survival and development, fecundity, and sex ratio, etc. Thus a sub-lethal concentration of 0.818 mg/L (LC30, determined by bioassay) was chosen here to evaluate gene response to fenpropathrin exposure. For sub-lethal concentration exposure assay, a total of 1500 adult female mites were dipped into the solution of fenpropathrin (Sigma-Aldrich, St. Louis, MO, USA) at the concentration of LC30 or acetone (control). The surviving mites were collected 12 h, 24 h and 36 h, respectively, after fenpropathrin exposure for RNA extraction. 4.3. Total RNA Extraction and Reverse Transcription Total RNA was extracted using RNeasy plus Micro Kit (Qiagen GmbH, Hilden, Germany) from 200 female adults (3–5 days old) of P. citri from susceptible and resistant strains and subsequently was treated with a gDNA elimination column supplied by the kit to remove genomic DNA. To check the quantity, the absorbance at 260 nm and the ratio of OD260/280 were measured with a Nanovue UV-Vis spectrophotometer (GE Healthcare, Fairfield, CT, USA). The RNA integrity was further confirmed by 1% agarose gel electrophoresis. The reverse transcription was carried out using PrimeScript 1st Strand cDNA Synthesis Kit (Takara Biotechnology Dalian Co., Ltd., Dalian, China) and the synthesized cDNA was stored at −20 °C. 4.4. Molecular Cloning, Identification and Phylogenetic Analysis Besides PcE1, two novel CarE genes PcE7 and PcE9, were selected based on the analysis of our transcriptome data. The open reading frames of the genes were amplified, respectively, using the corresponding pair of specific primers (Table S1) with the following procedure: 98 °C of initial incubation for 2 min followed by 35 cycles of 98 °C for 15 s, 60 °C for 15 s and 68 °C for 90 s; and 68 °C final extension for 10 min. The PCR products were purified from 1% agarose gel by MiniBEST Agarose Gel DNA Extraction Kit (Takara) and cloned into a pGEM-T Easy vector (Promega). Inserts were further sequenced for confirmation (BGI, Beijing, China). The sequences of CarEs from P. citri and T. urticae downloaded from the T. urticae genome portal website (http://bioinformatics.psb.ugent.be/orcae/overview/Tetur) were assembled by multiple sequence alignment using ClustalX [49]. Phylogenetic trees were constructed using a maximum likelihood method in MEGA 5.1, bootstrapping with 500 replicates [50]. 4.5. RT-qPCR RT-qPCR was subsequently carried out to determine the mRNA expression levels of the three genes from susceptible and resistant strains mites exposed to fenpropathrin. All the specific primer pairs of three CarE genes were designed using Primer 3.0 according to the open reading frames obtained in this study (Table S1) and GAPDH was used as an internal reference gene. The RT-qPCR was performed on a Stratagene Mx3000P thermal cycler (Stratagene, La Jolla, CA, USA) and a standard curve of amplification efficiency was constructed using a dilution series 1/2, 1/4, 1/8, 1/16 and 1/32. The reaction procedure was performed as follows: 95 °C for 2 min, followed by 40 cycles of 95 °C for 15 s and 60 °C for 30 s. The results were normalized to the GAPDH expression level using the 2−ΔΔCt method [51]. All data were expressed as mean ± standard error (SE). 4.6. Functional Expression Expression of PcE1, PcE7, PcE9 and GFP in Spodoptera frugiperda Sf9 cells were performed using the Bac-to-Bac baculovirus expression system (Invitrogen Life Technologies, Carlsbad, CA, USA) following the manufacturer’s protocol. First, the target gene sequences were cloned into the pFastBac HTA expression vector (Invitrogen). Then, the recombinant baculovirus DNA was constructed and transfected into Sf9 cells which were cultured in suspension under serum-free conditions (SF-900 II SFM, Invitrogen) at 27 °C and 100 g. The recombinant CarEs or GFP baculovirus stock were collected and used to infect 25 mL of Sf9 cells at a density of 2 × 106 cells/mL. Baculovirus-infected cells were harvested 72 h after infection by centrifugation at 2000× g for 10 min and resuspended in 5 mL 0.05 M PBS (pH 8.0) containing 0.1% Triton X-100, 0.5 M NaCl and 0.05% Tween 20. The homogenate was centrifuged at 10,000× g for 10 min and the supernatant was used as source of enzyme to evaluate the CarE-specific activity. The aforementioned recombinant baculovirus was used to generate baculovirus-infected cells for further cytotoxicity assays. 4.7. Enzymatic Assay The specific activity of recombinant CarEs was measured using the spectrophotometric method as previously reported [52] with slight modifications. First, 125 μL of substrate solution, mixed with 1 μL of 0.03 M α-NA (Sinopharm Chemical Reagent, Shanghai, China), 1 μL of 10−4 M serine, 98 μL of 0.04 M phosphate buffer (pH 7.0), and 25 μL of enzyme source solution was mixed and incubated for 10 min at 30 °C, and then 25 μL of fast blue conjugate dye (Sinopharm Chemical Reagent) was added. The assays were conducted in 96-well microtitre plates and absorbance was determined using xMark™ Microplate Spectrophotometer (Bio-Rad, Hercules, CA, USA) at 600 nm, 30 °C for 10 min. Protein contents were measured using Bio-Rad protein assay reagent (Bio-Rad) with bovine serum albumin as a standard. 4.8. Cytotoxicity Assay The cytotoxicity effect of fenpropathrin was determined using a MTT Cell Proliferation and Cytotoxicity Assay Kit (Solarbio, Shanghai, China). Cells expressing PcE1, PcE7 or PcE9 were produced by infection of Sf9 insect cells with PcE1-, PcE7-, or PcE9- expressed baculovirus. Parental Sf9 cells infected with GFP-expressed baculovirus served as control. The cytotoxic effects of fenpropathrin treatment with different concentration and time course were initially tested with Sf9 parental cells. For the assay, 500 μL of infected cells (1 × 105 cells/well) was transferred into a 24-well plate and pre-incubated for 24 h. Subsequently, 10 μL of fenpropathrin diluted with acetone ranging from 1 to 100 μM (1, 5, 10, 25, 50 and 100 μM) was added to the culture and incubated for 24 h. Then, the culture medium was removed and 220 μL MTT solution (200 μL fresh culture media and 20 μL MTT) was added to each well of the plate and incubated for 4 h. Finally, MTT solution was removed and 300 μL dimethyl sulphoxide was added to each well. After mild shock for 10 min, the absorbance of formazan product was measured at 490 nm using a Mustiskan EX microtiter plate reader (BioTek, Winooski, VT, USA). Cell viability was calculated as the percentage of viable cells relative to cells treated with acetone alone. Three replications were used for each treatment. 4.9. RNAi Bioassay RNAi was applied to further explore the biological functions of CarE genes in P. citri. PcE1, PcE7 and PcE9 were amplified by PCR using primers (Table S1) containing the T7 RNA polymerase promoter. The dsRNA were synthesized in vitro using a TranscriptAid T7 High Yield Transcription Kit (Thermo Scientific, Waltham, MA, USA) according to the manufacturer’s instructions with the purified PCR products. The dsRNA were diluted with nuclease-free water to a final concentration of 500 ng/μL. To assure the quality of synthesized dsRNA, the dsRNA products was analyzed with 1% agarose gel electrophoresis and quantified using a Nanovue UV-Vis spectrophotometer (GE Healthcare, Bucks, UK) and stored at −80 °C. Gene silence was carried out according to the plant leaf method in our previous study [41]. The field strain of BB was chosen to conduct the RNAi bioassay. First, an 8 cm citrus tender leaflet was detached from the citrus seeding (Aurantii fructus) and washed with water. Then, the leaflet was incubated in oven at 60 °C for 10 min and subsequently inserted into a 250 μL Axygen nuclease-free PCR tube containing 200 μL dsRNA or nuclease-free water for 1 h recovery period. After that, 30 female adult mites from BB strain were transferred onto the leaf with a soft brush. The PCR tube with the tender leaflet was moved into a 50 mL plastic tube and covered with a piece of thin gauze tightly held with a rubber band. Finally, the devices were placed in an incubator under the condition of 25 ± 1 °C, 50% ± 5% relative humidity (RH) and a photoperiod of 14:10 h light:dark. The solution in the PCR tube was renewed daily. After incubation for two days, about 20 surviving female adults on the leaf were collected for RNA extraction. In parallel, the surviving mites after RNAi or nuclease-free water treatment were collected for insecticide bioassay. The bioassay was performed according to the procedure above. The surviving mites were transferred on the leaf-disk and then dipped into the solution of fenpropathrin (LC50, 4.519 mg/L) for 5 s. Twenty four hours later, the mortality was calculated to evaluate the sensitivity of mites feeding on dsRNA or nuclease-free water to fenpropathrin. The mites were determined as dead by the criteria that mites express no response to the stimulation with a soft brush. Four biological replicates were performed for each sample. 4.10. Statistical Analysis All of the experiments involved at least three biological replications. The differences in expression levels among different strains and the relative quantity after fenpropathrin exposure were analyzed and the significance was determined by independent sample t-test with a p < 0.05. For the RNA interference (RNAi), the significant differences of gene expression and mortality after fenpropathrin exposure were also determined by independent sample t-test with a p < 0.05. Probit analysis was used to calculate the median lethal concentration (LC50) in insecticide bioassays and MTT bioassays with 95% confidence intervals. All data were analyzed using SPSS version 16.0 software (SPSS Inc., Chicago, IL, USA). In the current study, all data were given as mean ± SE. 5. Conclusions In conclusion, the current study provides insights to explore the mechanism of fenpropathrin resistance through a series of biochemical and molecular approaches in P. citri of several field strains. The spatiotemporal expression pattern analysis found up-regulation of three PcEs genes after insecticide exposure and in several field resistant strains, indicating PcEs may play roles in tolerance to fenpropathrin. Heterologous expression combined with MTT cytotoxicity assays in Sf9 cells demonstrated that PcEs probably detoxify fenpropathrin. The reverse genetic study through leaf-mediated dsRNA feeding further support the hypothesis that PcEs may be involved in the detoxification of fenpropathrin in P. citri. The current data provide evidence that CarE-mediated metabolic resistance through up-regulation is more likely to be developed in modest resistant strains of P. citri. To further illustrate the underlying molecular mechanism of CarEs-mediated pyrethroids resistance in P. citri, in vitro metabolism of fenpropathrin with purified PcE1, PcE7 and PcE9 protein will be expected to conducted to clarify whether sequestration or detoxification as the major mechanism leading to fenpropathrin resistance. Acknowledgments This work was financially supported by the National Natural Science Foundation (31171851, 31672030), Chongqing Research Program of Basic Research and Frontier Technology (CSTC, 2015jcyjBX0061), the Special Fund for Agro-scientific Research in the Public Interest (201203038), the earmarked fund for the Modern Agro-industry (Citrus) Technology Research System and the Fundamental Research Funds for the Central Universities (XDJK2013C148) of China. We would like to thank Gang Li for his help in collecting the mite. Supplementary Materials Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1361/s1. Click here for additional data file. Author Contributions Xiao-Min Shen and Chong-Yu Liao carried out the laboratory experiments and wrote the manuscript. Xue-Ping Lu and Zhe Wang contributed to the data analysis and interpretation. Wei Dou and Jin-Jun Wang designed the experiments and revised the manuscript. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Rooted phylogenetic tree of three CarEs from Panonychus citri with CCE proteins from Tetranychus urticae. All the protein sequences of the T. urticae CCEs were download from http://bioinformatics.psb.ugent.be/orcae/overview/Tetur and PcE1, PcE7 and PcE9 were retrieved from the National Center for Biotechnology Information. All the amino acid sequences were aligned using ClustalW, and a distance neighbor-joining tree was generated using MEGA 5.0. The three CarEs genes examined in this study are marked with triangles. Figure 2 Expression patterns of PcE1, PcE7 and PcE9 in response to fenpropathrin exposure. Relative expression levels were calculated based on the control, which was defined as a basal value of 1. The vertical bars indicated standard errors of the mean (n = 3). The asterisk on the vertical bars indicate significant differences in the mRNA level of between control and treatment (Student’s t-test, * p < 0.05 and ** p < 0.01). Figure 3 Expression patterns of PcE1, PcE7 and PcE9 in different populations. Relative expression levels were calculated based on the control (SS), which was defined as a basal value of 1. The vertical bars indicated standard errors of the mean (n = 3). The asterisk on the vertical bars indicate significant differences in the mRNA level of between control and treatment (Student’s t-test, ** p < 0.01). Figure 4 Specific activity of carboxylesterase (CarE) in recombinant enzymes expressing PcE1, PcE7 or PcE9 toward the substrate of α-NA (A); and cytotoxicity of PcE1-, PcE7-, PcE9- and EGFP-expressing cells against fenpropathrin (B). The percentage of viable cells was detected using 3-(4,5-dimethyl-2-yl)-2,5-diphenyltetrazolium bromide cytotoxicity assays. Data are means ± SE of three independent experiments. Asterisks (*) above the error bars indicate statistical differences determined by the independent samples t-test (** p < 0.01). Figure 5 Susceptibility of Panonychus citri to fenpropathrin after silencing of PcE1, PcE7 and PcE9 by RNA interference. (A) Silencing efficiency of PcE1, PcE7 and PcE9 after P. citri were investigated 48 h after the gene silencing treatment; (B) The mortalities of the citrus red mites were investigated after the fenpropathrin treatment at the concentration of LC50 (4.519 mg/L). Results were mean ± SE of four biological replication (n = 4). 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081362ijms-17-01362ArticleInflammatory Cutaneous Diseases in Renal Transplant Recipients Savoia Paola 1*Cavaliere Giovanni 2Zavattaro Elisa 1Veronese Federica 1Fava Paolo 2Jackson Chris Academic Editor1 Department of Health Sciences, “A. Avogadro” University of Eastern Piedmont, 28100 Novara, Italy; elisa.zavattaro@med.uniupo.it (E.Z.); federica.veronese@med.uniupo.it (F.V.)2 Department of Medical Science, University of Turin, 10126 Turin, Italy; g.cavali_dr@yahoo.it (G.C.); fava_paolo@yahoo.it (P.F.)* Correspondence: paola.savoia@med.uniupo.it; Tel./Fax: +39-0321-373-358619 8 2016 8 2016 17 8 136227 7 2016 09 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Kidney transplant recipients frequently suffer from skin infections and malignancies, possibly due to the effects of long-term immunosuppressive therapy. While the relationships between immunosuppression and these pathological conditions have been widely investigated, little is known about the relative incidence and characteristics of inflammatory skin diseases in this type of patient. In this study, we analyze the incidence of a number of inflammatory cutaneous diseases in a cohort of patients who underwent kidney transplantation. Although our study shows a relatively low incidence of these pathologies in transplanted patients—in agreement with the general action of immunosuppressant therapies in reducing inflammation—we scored a different efficacy of the various immunosuppressive regimens on inflammatory and autoimmune skin diseases. This information can be key for designing immunosuppressive regimens and devising accurate follow-up protocols. cutaneous diseasesinflammatorykidney transplantation ==== Body 1. Introduction Patients with chronic renal insufficiency often suffer from pathological conditions such as skin dryness, alopecia, skin discoloration, hair and nail abnormalities, as well as cutaneous diseases specifically related to kidney failure [1,2,3,4]. Usually, in the months immediately following transplantation, there is a progressive regression of these conditions, with a clinical and histopathological normalization of the skin [5]. However, other cutaneous diseases may progressively develop, which afflict more than half of renal transplant recipients, including infections to different body districts and non-melanoma skin cancers; these side effects are iatrogenic and develop as a consequence of the long-term immunosuppressive treatment. Notably, inflammatory diseases of the skin are rarely reported [6], possible as a consequence of the direct therapeutic effect of immunosuppressive therapy on these diseases. In the most recent Oxford series of kidney transplant recipients [6], the prevalence of psoriasis and atopic dermatitis was very low, around 1.5%, as also reported in other case series [7,8]. This is not surprising, since cyclosporine is commonly used in the treatment of psoriasis and other inflammatory dermatoses [9], while tacrolimus and mycofenolate are currently proposed for the treatment of atopic dermatitis [10]. Here, we describe the experience of our Clinical Units regarding the incidence and characteristics of various inflammatory skin diseases in a cohort of kidney transplant recipients, followed-up from 2009 to 2016. 2. Results We analyzed the records obtained from a cohort of 610 renal transplant recipients (230 females, 380 males) followed-up at our center from January 2009 to April 2016. The median age was 60 years, while the mean age was 59. Clinical characteristics of these patients and the type of immunosuppressive regimen are summarized in Table 1, together with data about the prevalence of cutaneous diseases in this cohort. According to the treatment schedule in use at our center, the majority of patients (491, 81.5%) received an immunosuppressive regimen without mTOR inhibitors (mTOR-Is), whereas the remaining 19.5% (119 patients) received sirolimus or everolimus. In our series, the most frequent dermatological diseases were skin infections, observed in 187 patients (30.7%), of which there were 72 virus-related infections (Human Papilloma Virus-HPV, Varicella Zoster Virus-HZV, Herpes Simplex Virus-HSV type 1 and 2), 57 cases of cutaneous mycosis (Tinea unguium, Pytiriasis versicolor and Tinea corporis), and 28 bacterial infections (folliculitis and impetigo), while 30 patients had concomitant viral and fungal infections. A diagnosis of skin cancer during follow-up was made in 191 cases (31.3%); according to the histological type, we scored basal cell carcinomas (BCCs) in 84 patients, squamous cell carcinomas (SCCs) in 51, melanomas in 16, Kaposi’s Sarcoma in 17, cutaneous T-cell lymphomas (CTCLs) in four and other skin tumors in nine. 2.1. Inflammatory Diseases Among the 610 kidney transplant recipients of our series, 88 showed an inflammatory skin disease (14.4%). Table 2 summarizes their clinical characteristics, together with data about the time and type of immunosuppressive regimen. Twenty-six patients were female (29.5%) and 62 were male (70.5%). The median age was 47 years, whereas no differences were found in the median duration of immunosuppression (9.1 years) and the follow-up duration with respect to the rest of our series. The immunosuppressive schedule included tacrolimus in 54 patients (61.4%) and cyclosporine in 21 (23.9%). Inflammatory skin conditions were diagnosed in 16.8% (21 of 119) of the patients treated with mTOR-Is and in 13.8% (67 of 489) of the patients that were subject to treatment regimens without mTOR-Is. These differences did not achieve a statistical significance. Infective skin diseases were observed in 29 out of 88 patients (33%), of which 12 were viruses (HPV, VZV, HSV 1 and 2), nine were cutaneous mycoses (Tinea unguium, pytiriasis versicolor and tinea corporis), and five were bacterial infections (folliculitis and impetigo); three patients had concomitant viral and fungal infections. Clinical details of the three groups of patients can be described as follow. 2.1.1. Psoriasis We detected psoriasis-related skin conditions in a total of 14 out of 610 patients (2.3%). In all cases, diagnosis was made before the onset of the renal failure. Psoriasis in renal transplant recipients was characterized by a minimal skin involvement (Psoriasis Area Severity Index—PASI score < 3) (Figure 1) and did not require active treatment in addition to the on-going immunosuppressive treatment. The only added treatment was the use of topical emollients in about 30% of cases. Notably, the skin areas usually affected were the elbow, knee and scalp regions, whereas the involvement of other cutaneous areas or the presence of plaque lesions was extremely rare. No cases of pustular, palmo-plantar or generalized psoriasis were scored. In two cases of our series, patients reported a clinical improvement of psoriatic lesions after the beginning of the immunosuppressive treatment, in comparison to the pre-transplant period. 2.1.2. Atopic Dermatitis and Related Skin Conditions Atopic lesions were observed in 43 out of 610 cases (7.1%) (Figure 2). Atopic dermatitis was detected in an extremely low percentage of renal transplant recipients, i.e., three cases out of 610 (0.5%). Other atopic-related skin conditions (such as seborrheic dermatitis, nummular eczema, and allergic contact dermatitis) were found in 40 of 610 cases (6.6%). Seborrheic dermatitis (Figure 3) was the most frequent atopic-related skin disorder with a total of 37 cases (86%). In the majority of patients with atopic and atopic-related skin conditions, emollients were the only topical treatment proposed. No patients required an adjustment of the immunosuppressive treatment to control atopic-related skin conditions. 2.1.3. Other Inflammatory Skin Conditions Beside atopic dermatitis or psoriasis, 31 patients out of 610 cases (5%) developed other dermatitis. Among these, the most common condition reported was prurigo nodularis (45.2%) (Figure 4), followed by minor aphtosis (20.7%). The other inflammatory conditions detected in our series were reported in Table 3. Statistical analyses did not identify clinical and/or epidemiological features significantly associated with the development of inflammatory skin disease. Of note, in our group of patients, 24 (27.3%) reported a skin cancer (11 BCCs, nine SCCs, two melanomas, two Kaposi Sarcomas) after transplantation. No differences in the prevalence of skin cancers were found in the three sub-groups of patients. 3. Discussion Numerous studies have pointed out that skin infections and Non Melanoma Skin Cancers (NMSCs) are commonly occurring complications for transplant recipients [6,8,11,12,13,14] due to the long-term immunosuppression used to prevent transplant rejection. Conversely, scant clinical data are available regarding the incidence of inflammatory skin diseases in patients with solid organ transplantation (Table 4). In a previous study performed to evaluate the incidence of cutaneous diseases in a group of 282 kidney transplant recipients, we reported that inflammatory conditions occur in 14.9% of patients [14], in agreement with previous studies [6,7]; an even lower occurrence of inflammatory diseases in transplanted patients was scored by [8], with only a few cases of acneiform eruptions, rosacea, asteatotic eczema, contact eczema and stasis dermatitis. Notably, the incidence of skin diseases with an immunologic pathogenesis is considered to be an even more uncommon event, with only a few sporadic cases reported [14,15]. Many immunosuppressive drugs—such as tacrolimus, cyclosporine, and mycophenolate—are approved both for preventing chronic transplant rejection and for the treatment of inflammatory skin diseases [9,15,16,17]; this dual therapeutic role can easily explain the observed reduced incidence of inflammatory skin diseases in transplant recipients. In the present study, we observed inflammatory skin diseases in less than 15% of our kidney transplant recipients, versus a more than 60% incidence in the general population [14]. We were not able to identify specific clinical or epidemiological characteristics in patients with inflammatory dermatological diseases, with the exception of a prevalence of males, which, however, is in agreement with the general characteristics of our cohort. The most common inflammatory disease scored in our transplanted patients was seborrheic dermatitis, which affected a total of 37 patients, accounting for 86% of those with atopic-related skin diseases. This finding is consistent with a previous study [6], but in apparent contrast with the fact that most patients of our series are receiving tacrolimus, commonly used to manage seborrhoeic dermatosis [28]. A possible explanation for this paradoxical behavior could be that only a percentage of seborrhoeic patients are full responders to tacrolimus [28], similarly to those affected by psoriasis [27]. In addition, despite tacrolimus activity in the treatment of atopic dermatitis, a number of atopic dermatitis cases in children with solid organ transplantation have been recently observed: (i) Bumbacea and Ghiordanescu [24] described a six-year old patient who developed a “de novo” atopic dermatitis during long-term immunosuppression with tacrolimus following liver transplantation; (ii) Machura et al. [27] reported a similar case in a three-year-old boy treated with tacrolimus and mycofenolate after heart transplantation. The pathogenesis of this post-transplantation condition is not completely understood and probably involves several factors, including a tacrolimus-induced increase in intestinal permeability, facilitating the absorption of potential allergens and promoting the development of allergy [27]. In our case study, atopic dermatitis was detected in an extremely low percentage of renal transplant recipients, i.e., in three cases out of 610 (0.5%). This can be explained on the basis of the characteristics of our cohort, which was only composed of adult subjects: it is well known that the majority of transplanted patients with atopic dermatitis are children and that a significant risk factor for atopic disease during the post-transplant period is represented by the age of the donor and/or recipient [29]; moreover, allergies are less common described after renal transplantation [30,31]. We also observed a very low percentage of patients affected by psoriasis. The low PASI score confirms that, in the majority of cases, no specific treatment was required in addition to the immunosuppressive regimen to manage psoriatic symptoms. This is possibly due to the high efficacy of calcineurin inhibitors in suppressing psoriasis: we actually observed tacrolimus and cyclosporine efficacy in 61.4% and 23.9% of kidney transplant recipients, respectively. Actually, the therapeutic value of tacrolimus in the treatment of psoriasis was firstly described in transplanted patients [32] and then confirmed in randomized trials [33,34]. Similarly, the effectiveness of cyclosporine in psoriasis has been observed in immunocompetent [34,35] as well as in transplanted patients [22]. Calcineurin inhibitors block the transcription of genes controlling the expression of cytokines, primarily IL-2, and also exert a negative action on regulatory T cell activation (CD4+CD25+FOXp3) [36]. However, a small subset of transplanted patients—especially those who have had a liver transplant—has severe psoriasis that does not fully respond to immunosuppression [22,37,38]. This could be explained by the fact that common immunosuppressive regimens do not completely inhibit all the inflammatory pathways of this pathologic condition, especially the TNF-α and the IL17/23 pathways [24] Moreover, some authors reported a possible role of mTOR-Is in the pathogenesis of inflammatory skin lesions in kidney transplant recipients [21,29,30,31,36]. In our series, 119 of 610 (19.5%) patients were being treatment by mTOR-Is. Among these patients, the incidence of inflammatory skin conditions was 16.8%, whereas in patients treated with immunosuppressive regiments without mTOR-Is this percentage was 13.8%; even if a trend could be hypothesized, this difference did not achieve a statistical significance. 4. Materials and Methods 4.1. Patients Data about 610 renal transplant recipients with a dermatological follow-up at our centers were recorded from January 2009 to April 2016. Sixty-two percent of these patients were males, while 38% were females; median age at transplantation was 51 years and the median duration of immunosuppression was 9.1 years. The median follow-up duration was nine years. For each patient, we evaluated the presence of inflammatory dermatological diseases, which were classified in the following three categories: (i) psoriasis; (ii) atopic dermatitis and related skin conditions (including seborrheic dermatitis, allergic contact dermatitis, and nummular eczema); (iii) other inflammatory dermatitis, unrelated to psoriatic as well as to atopic conditions. 4.2. Statistical Analysis Statistical analysis was performed by IBM SPSS Statistics software (IBM Corp., Armonk, NY, USA) and Kaplan-Meier curves (MedCalc Software, Ostend, Belgium). 5. Conclusions In conclusion, our work emphasizes the low incidence of skin diseases with autoimmune or inflammatory pathogenesis in solid organ-transplanted patients, correlating the beneficial therapeutic effect of these immunosuppressive regimens to the various types of skin disorders. Acknowledgments This work was supported by Grants from “Ricerca Sanitaria Finalizzata-Ministero della Salute”. Author Contributions Paola Savoia: conception and design, acquisition of data, analysis and interpretation of data; Giovanni Cavaliere: acquisition of data, analysis and interpretation of data; Elisa Zavattaro: acquisition of data, analysis and interpretation of data; Federica Veronese: acquisition of data, analysis and interpretation of data; Paolo Fava: conception and design, acquisition of data, analysis and interpretation of data. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Minimal psoriasis in renal transplant recipient. Figure 2 Atopic signs in renal transplant recipient. Figure 3 Seborrheic dermatitis in renal transplant recipient. Figure 4 Prurigo nodularis in renal transplant recipient. ijms-17-01362-t001_Table 1Table 1 Incidence of cutaneous diseases in a cohort of kidney transplant recipients. Variable Number % Gender Male 380 62% Female 230 37.7% Immunosuppressive treatment Including mTOR-Is 119 19.5% Without mTOR-Is 491 81.5% Skin infections 187 30.7% Viruses 72 Mycoses 57 Bacterial 28 Mixed 30 Skin cancer 191 31% Basal Cell Carcinoma (BCC) 94 Squamous Cell Carcinoma (SCC) 51 Melanoma 16 Kaposi’s Sarcoma 17 Cutaneous T cell lymphoma (CTCL) 4 others 9 Inflammatory diseases 88 14.4% ijms-17-01362-t002_Table 2Table 2 Clinical characteristics of kidney transplant recipients affected by inflammatory skin diseases. Variable Number % Gender Male 62 70.5% Female 26 29.5% Median age 47 years Median immunosuppression 9.1 years Concomitant diagnosis Skin infections 29 33% Viruses 12 Mycoses 9 Bacterial 5 Mixed 3 Skin cancer 24 27.3% BCC 11 SCC 9 Melanoma 2 Kaposi’s Sarcoma 2 ijms-17-01362-t003_Table 3Table 3 Other inflammatory conditions detected in our series. Inflammatory Skin Disease Number of Patients (%) Treatment Prurigo nodularis 14 (45.2%) Topical Minor aftosis 6 (19.4%) Topical Erythema nodosum 4 (12.8%) Topical/immunosuppressive treatment adjustment Zoon balanitis 3 (9.6%) Topical Bullous pemfigoid 2 (6.5%) Topical/immunosuppressive treatment adjustment Vitiligo 2 (6.5%) Topical ijms-17-01362-t004_Table 4Table 4 Inflammatory cutaneous diseases in solid organ transplant recipients (literature data). Reference Clinical Characteristics Median Follow-up Cutaneous Disease Number of Cases Coehn et al., 1986 [11] 580/kidney 12.2 years Inflammatory dermatoses 32 (5.5%) Hoover et al., 2007 [18] 1/liver; Male Not Available Psoriasis 1 Kaaroud et al., 2007 [19] 1/kidney; Female 31-year-old 31 months Pustular psoriasis 1 Collazo et al., 2008 [20] 1/liver; Male 49-year-old Not Available Psoriasis 1 Brokalaki et al., 2009 [21] 1/ kidney + pancreas 7 years Psoriasis 1 Male 42-year-old Wisgerhof et al., 2009 [12] 2136/kidney + pancreas 10.2 years Psoriasis 4 (0.2%) Atopic-related dermatitis 58 (2.7%) Others 56 (2.6%) Lally et al., 2010 [6] 308/kidney; median age 51 years 10.7 years Psoriasis 5 (1.6%) Atopic dermatitis 4 (1.3%) Seborrhoeic dermatitis 29 (9.4%) Saalman et al., 2010 [22] liver Not Available Orofacial granulomatosis 8 Savoia et al., 2011 [14] 282/kidney; median age 59 years 7.2 years Psoriasis 17 (6%) Atopic-related dermatitis 25 (8.8%) Shroff et al., 2012 [23] 176/liver; median age 16 mo 19 months Atopic dermatitis 24 (13.6%) Bumbacea et al., 2013 [24] 1/liver; Male six-year-old 2 years Atopic dermatitis 1 Moretti de Lima et al., 2013 [8] 53/kidney; median age 44 year >5 years (52.8%) Atopic-related dermatitis 4 (7.5%) Others 7 (13.2%) Foroncewicz et al., 2014 [25] 591/liver; median age 50 year 8.5 years Psoriasis 10 (1.6%) Machura et al., 2015 [26] 1/heart; Male three-year-old 2.5 years Atopic dermatitis 1 Madankumar et al., 2015 [27] 1/liver; Female 52-year-old 5 years Psoriasis 1 ==== Refs References 1. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081363ijms-17-01363ArticleAntimicrobial Protein Candidates from the Thermophilic Geobacillus sp. Strain ZGt-1: Production, Proteomics, and Bioinformatics Analysis Alkhalili Rawana N. 1Bernfur Katja 2Dishisha Tarek 13Mamo Gashaw 1Schelin Jenny 4Canbäck Björn 5Emanuelsson Cecilia 2Hatti-Kaul Rajni 1*Másson Már Academic Editor1 Biotechnology, Department of Chemistry, Lund University, Lund SE-221 00, Sweden; rawana.alkhalili@biotek.lu.se (R.N.A.); tarek.dishisha@pharm.bsu.edu.eg (T.D.); gashaw.mamo@biotek.lu.se (G.M.)2 Center for Molecular Protein Science, Department of Chemistry, Lund University, Lund SE-221 00, Sweden; katja.bernfur@biochemistry.lu.se (K.B.); cecilia.emanuelsson@biochemistry.lu.se (C.E.)3 Department of Microbiology and Immunology, Faculty of Pharmacy, Beni-Suef University, Beni-Suef 62511, Egypt4 Applied Microbiology, Department of Chemistry, Lund University, Lund SE-221 00, Sweden; jenny.schelin@tmb.lth.se5 Department of Biology, Microbial Ecology Group, Lund University, Lund SE-221 00, Sweden; bjorn.canback@biol.lu.se* Correspondence: rajni.hatti-kaul@biotek.lu.se; Tel.: +46-46-222-4840; Fax: +46-46-222-471319 8 2016 8 2016 17 8 136317 6 2016 12 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).A thermophilic bacterial strain, Geobacillus sp. ZGt-1, isolated from Zara hot spring in Jordan, was capable of inhibiting the growth of the thermophilic G. stearothermophilus and the mesophilic Bacillus subtilis and Salmonella typhimurium on a solid cultivation medium. Antibacterial activity was not observed when ZGt-1 was cultivated in a liquid medium; however, immobilization of the cells in agar beads that were subjected to sequential batch cultivation in the liquid medium at 60 °C showed increasing antibacterial activity up to 14 cycles. The antibacterial activity was lost on protease treatment of the culture supernatant. Concentration of the protein fraction by ammonium sulphate precipitation followed by denaturing polyacrylamide gel electrophoresis separation and analysis of the gel for antibacterial activity against G. stearothermophilus showed a distinct inhibition zone in 15–20 kDa range, suggesting that the active molecule(s) are resistant to denaturation by SDS. Mass spectrometric analysis of the protein bands around the active region resulted in identification of 22 proteins with molecular weight in the range of interest, three of which were new and are here proposed as potential antimicrobial protein candidates by in silico analysis of their amino acid sequences. Mass spectrometric analysis also indicated the presence of partial sequences of antimicrobial enzymes, amidase and dd-carboxypeptidase. thermophileGeobacillusantimicrobial proteinsSDS-resistant proteinsimmobilizationcell-recyclingfood spoilage bacteria ==== Body 1. Introduction Competition for nutrients and space in a given habitat leads organisms to develop their own strategies for survival and growth, one of which is the secretion of antimicrobial substances resulting in either killing or impairing the growth of competing organisms [1]. These antimicrobial substances possess promising clinical and industrial value [2]. Nowadays, the growing problem of multidrug resistance and increasing skepticism about the use of chemical additives in food products have led to an urgent need for finding new and more effective antimicrobial agents [2]. Exploring new ecological niches offers opportunities for isolating novel microorganisms with potent novel antimicrobial compounds [2]. Extremophiles, microorganisms living in extreme conditions of pH, temperature, salt concentration, etc., represent one such source [3]. A number of studies have reported the production of antibacterial peptides or bacteriocins from extremophiles such as alkaliphilic and thermophilic Bacillus species [3,4,5,6,7] and also thermophilic Geobacillus species such as G. thermodentrificans [8] and G. stearothermophilus [9]. Thermophiles represent a source of stable proteins, which help the microorganisms to save energy and nutrient resources that would otherwise be dissipated on protein degradation and synthesis [10]. Investigating the potential of thermophiles to antagonize the growth of other thermophiles, particularly the known food-spoiling thermophilic bacteria, paves the way for identifying new stable food biopreservatives. One of the known thermophilic food-spoiling bacteria is G. stearothermophilus, which creates problems in the dairy industry due to its ability to form biofilms on the process equipment, resulting in spoilage of the final product [11]. G. stearothermophilus spores cause spoilage of low-acid canned, and ready-made vegetable- and meat-based meals [11]. In a clinical context, the potential of the antimicrobial molecules from thermophiles to inhibit the growth of mesophilic pathogens would also be an interesting finding. The present study concerns a thermophilic isolate from Zara hot spring in Jordan, identified as a Geobacillus species that displayed antibacterial activity against G. stearothermophilus and some mesophilic bacterial strains including pathogens. A system for cultivation of the organism for the production of antibacterially active proteins was developed, followed by proteomics and bioinformatics analysis of the expressed proteins to identify the potential antimicrobial candidates. 2. Results and Discussion 2.1. Isolation, Identification, and Characterization of the Isolate Isolation of thermophilic bacterial strains from Zara hot spring sample was performed at 60 °C and the isolate Geobacillus sp. (designated as Geobacillus sp. ZGt-1, GenBank accession no. KT02696) was identified based on 16S rRNA gene sequencing (Table S1), which showed 99.9%–100.0% identity to G. thermoleovorans and G. kaustophilus, respectively. Since the assignment of G. thermoleovorans and G. kaustophilus into distinct species has been questioned previously [12], we did not affiliate strain ZGt-1 to any of the species, and designated it as Geobacillus sp. ZGt-1. Its cells appeared as single rods or in pairs after an overnight cultivation on R2A/Mueller Hinton agar. The sporulating cells of ZGt-1 showed one terminal endospore per cell. The colonies on Mueller Hinton (MH) agar were yellowish, rounded, raised, entire, and shiny, while they were creamy white, rounded, raised, entire, and opaque on R2A agar. Strain 10 was also isolated from the same hot spring and identified as G. stearothermophilus (GenBank accession no. KU933578) (Table S1). 2.2. Antibacterial Activity of Geobacillus sp. ZGt-1 The ability of Geobacillus sp. ZGt-1 to inhibit the growth of the closely related G. stearothermophilus strain 10 was confirmed using the agar-deferred spot method (Figure 1). The antibacterial activity disappeared after treatment with proteinase K, indicating the proteinaceous nature of the secreted antibacterial agent. Testing of the antibacterial activity of ZGt-1 against mesophilic bacteria revealed inhibition zones in the case of Bacillus subtilis and the pathogenic Salmonella typhimurium CCUG 31969 (Figure 1), but not with Escherichia coli 1005, Staphylococcus aureus NCTC 83254, Staphylococcus epidermidis, and Proteus vulgaris. The inhibition zone of ZGt-1 developed against the Gram-positive B. subtilis was more prominent than that against Gram-negative S. typhimurium (Figure 1), which may be ascribed to the different cell wall structures. In contrast to the Gram-negative bacteria, in which the cell wall peptidoglycan (PG) layer is protected by an outer membrane mainly composed of lipopolysaccharides (LPS), the PG in the Gram-positive bacteria is directly exposed to the external factors, including the cell wall lytic enzymes and antimicrobial peptides (AMPs) [13,14,15]. Although AMPs, but not cell wall lytic enzymes, can interact with the negatively charged outer membrane, some Gram-negative bacteria can develop species-specific mechanisms to eliminate the effect of the AMPs under certain environmental conditions [16,17,18]. Moreover, the variations in the structure of LPS, especially in Lipid A, among the different bacteria influence the AMP affinity and insertion into the outer membrane [16,17]. For example, there are differences in the polysaccharide chains and in Lipid A between E. coli and S. typhimurium; the latter has an additional fatty acid and different substituents of phosphate groups in Lipid A [19]. The differences in the susceptibility of the various Gram-positive bacteria (B. subtilis and S. aureus) to the antimicrobial action of ZGt-1 could be ascribed to the differences in the degree of crosslinking between the stem peptides (short peptide chains of 4–5 amino acids) linked to N-acetyl muramic acid residues in the PG [15]. In case of B. subtilis, 56%–63% of the stem peptides contribute to the cross-links, in contrast to S. aureus, in which up to 90% of the stem peptides are involved in the cross-linkages in the cell wall [15]. Moreover, the cross-links in B. subtilis as well as in G. stearothermophilus PG are direct (known as diaminopimelic acid (DAP)-direct), between d-alanine in the stem peptide of one glycan strand and the meso-diaminopimelic acid (meso-DAP) in another [20], while the cross-links in S. aureus form a pentaglycine bridge between l-lysine in the stem peptide of one glycan strand and d-alanine in another [21]. Even the charged polymers, teichoic- and teichuronic acids, which are covalently linked to the PG of Gram-positive bacteria, can influence the sensitivity of the cell wall to the lytic enzymes and AMPs, as the structure and amount of those polymers are strain- and species-specific [14,22,23]. Under certain conditions, B. subtilis produces atypical teichoic acid, which contains only the negatively charged phosphate groups that help in attracting the cationic AMPs to the cell surface [14]. However, some bacteria, like S. aureus, can alter the net surface negative charges of the bacterial surface or produce proteolytic enzymes capable of degrading AMPs as a resistance mechanism [14]. 2.3. Production of the Antibacterial Substance(s) Using Immobilized Cells in Sequential Batch Mode with Cell Recycling Although Geobacillus sp. ZGt-1 grown on solid MH agar medium exhibited good antibacterial activity against G. stearothermophilus, no antibacterial activity was observed on analysis of the culture supernatant when the isolate was grown overnight in a liquid medium (reaching an OD620 of 1.5). Similar observations have been reported previously with other strains [4,24]. Hence, it seems that the cell growth and production of antimicrobial agents is facilitated on the solid-state medium. To confirm this and to enable cultivation in larger scale for the production of antimicrobial molecule(s), the ZGt-1 cells were immobilized by entrapment in agar beads that were suspended in a liquid culture medium. This approach resulted in the appearance of antibacterial activity in the medium. The immobilized cells could be recovered and recycled for sequential batch cultivations in fresh medium with increasing antibacterial activity over consecutive cycles up to the 14th batch (Figure 2a–d). The bacterial growth during the cultivations was noted by the appearance of free cells in the medium. Entrapment inside the gel beads provides a protective environment for the cells that are more active at producing certain metabolites than the free cells and show increased tolerance to inhibitory compounds, which might otherwise limit the cell growth [25,26]. After the 14th cycle, however, the antimicrobial activity started to decrease and then disappeared almost completely during the 25th batch (Figure 2d,e). This decrease may be due to the nutritional limitations for the larger number of immobilized cells present and also the competition by the free cells that are not efficient in production of the antimicrobial metabolites [26], or due to difficulties with exporting the antimicrobial compound to the extracellular environment. 2.4. Antimicrobial Proteins Produced by Strain ZGt-1 Protease treatment gave us an indication of the antimicrobial activity to be associated to protein(s). Therefore, we proceeded with isolation of the proteins from the cell-free supernatant collected from the sequential batches with immobilized ZGt-1 cells by precipitation with ammonium sulphate. The protein precipitate obtained with 60% salt saturation was dialyzed against distilled water, and its antibacterial activity was confirmed using the spot-on-lawn method (Figure S1). The activity was found to be stable after heating at 70 °C for 45 min but was lost on heating to 80 °C for 10 min (Figure S2). The proteins in the sample were resolved on SDS-PAGE and the gel was subjected to the antibacterial activity test (Scheme 1). An area corresponding to 15–20 kDa molecular mass displayed the inhibition zone with G. stearothermophilus (Figure 3 and Figure S3). Interestingly, the protein fraction was seen to be active even after being exposed to SDS under reducing conditions (with dithiothreitol; DTT), which was confirmed during at least eight separate SDS-PAGE-antibacterial activity assays performed using two different batches of the protein molecular weight standards. Higher abundance of SDS-resistant proteins has been reported in thermophilic microorganisms than that in the mesophilic ones [10]. Production of such proteins seems to be a defense strategy developed by prokaryotes, especially thermophiles, to protect some of their proteins against aggregation and premature degradation in order to save energy for thriving in extreme environments [10]. Moreover, the insensitivity of the antimicrobial activity to the reducing agent is possibly due to the lack of cysteine residues and hence the disulfide bridges, as is the case for several antimicrobial peptides [16]. 2.5. Targeted Proteomics Analysis of the Antibacterial Protein-Containing Gel Samples The gel area corresponding to 15–20 kDa, which displayed the inhibition zone against G. stearothermophilus strain 10, was analyzed for its protein content by liquid chromatography tandem mass spectrometry (LC-MS/MS). At the same time, the genome of Geobacillus sp. ZGt-1 was sequenced and annotated (GenBank accession no. LDPD00000000.1) [27], and used to construct a ZGt-1 strain-specific protein database. The collected MS/MS data were searched against the locally created ZGt-1 database to identify the proteins present in the excised gel samples. To be considered true protein identification, the following criteria had to be fulfilled: significant peptide sequences greater than or equal to 5, a total protein score greater than or equal to 200, and an individual peptide score greater than or equal to 20. Many proteins larger than 20 kDa were identified (Table S2), which could possibly be attributed to proteolysis. The high resolving power and sensitivity of the mass spectrometric analysis carried out in this study enables the detection of peptides of truncated or degraded proteins even if present in a small amount. Some proteolysis is indeed to be expected due to the action of bacterial proteases during the sequential batch cultivations and subsequent processing and storage of the extract at 4 °C until the time of analysis. Besides proteolysis, SDS-resistance could be another factor underlying the identification of proteins larger than 20 kDa. SDS-resistant proteins preserve their compact structure and display an apparent molecular weight in the gel that is smaller than their theoretical molecular weights [28,29]. Based on the antibacterial activity being limited to a molecular weight of 15–20 kDa, we decided to screen for proteins within the range of 10–30 kDa to take into account the “gel shifting” phenomenon, as noted above for the SDS-resistant proteins [28,29,30]. Table 1 lists the identified proteins, most of which are not known to display antibacterial activity. Only three uncharacterized/hypothetical proteins within 10–30 kDa were identified in two of the three excised gel samples, with 5–8 significant peptide sequences and a score of 292–517 (Table S3). We combined two approaches in a non-mutually exclusive way to predict the antibacterial activity of the uncharacterized proteins: calculation of the physiochemical properties of the proteins based on their amino acid sequences and comparing the inferred properties to those of known antimicrobial peptides/proteins, and in silico prediction of their antibacterial activity by employing the web-based antimicrobial peptides/proteins prediction algorithms, in an approach similar to that reported earlier [31,32] with some modifications. 2.6. Prediction of Antibacterial Potency Based on Physicochemical Properties Table 2 summarizes the physicochemical properties depicted from analysis of the amino acid sequences of the three uncharacterized proteins. The proteins are in the molecular weight range of approximately 14–19 kDa, and a size range of 129–173 amino acid residues, which is in the range (10 to 100 s) for AMPs [33]. The antibacterial activity of many AMPs is brought about by lysis of the cells or membrane permeabilization due to electrostatic and/or hydrophobic interaction with the membrane of the target cell [34]. This interaction requires the AMP to have a net positive charge at neutral pH to help in initiating the interaction with the anionic surface of the bacterial cell, and to be rich in hydrophobic amino acid residues for insertion into the hydrophobic core of the bacterial cell membrane to destabilize the lipid bilayer and eventually kill the bacteria [32,34,35,36]. A minimum net charge of +2 is one of the unique features of cationic α-helical AMPs [37]. The three proteins have isoelectric points (pI) in the range of 7.72–8.80. Two of the proteins (ID 6_35, and 23_543) have a net charge of +2 at neutral pH, while all have a hydrophobic ratio of 36%–40% (Table 2). The AMPs typically adopt an amphipathic conformation, i.e., the hydrophobic and polar residues are located on opposite sides. Linear amphipathic α-helical AMPs represent an important class of AMPs [34,38]. Analyzing the amino acid sequences of the three uncharacterized proteins using “The Antimicrobial Peptide Database (APD3)” showed that the three proteins have the potential to form amphipathic α-helices, where a certain number of hydrophobic amino acid residues group on the same side of the α-helix (Table 2), they are free of disulfide bridges, and have a linear amphipathic α-helical conformation. This is in agreement with the insensitivity of the antimicrobial effect to the reducing environment, as observed above (Section 2.4). Grand average of hydropathy (GRAVY) index, a measure of the peptide/protein solubility, is within the range of −0.257 to −0.044 for the uncharacterized proteins (Table 2), in accordance with the predominant range of −1 to 0 for AMPs listed in the BIOPEP database as well as the predicted AMPs from milk proteins [32]. The Boman index values, reflecting the potential of a peptide/protein to interact with other proteins [38], for 6_35, 23_543, and 4_4 are 1.12, 1.57, and 1.19, respectively (Table 2), which also fit within the range of 1–2 for AMPs in the BIOPEP database [32]. In vitro stability of the AMPs reflects their bioavailability over a period of time [32]. The proteins 6_35, 23_543, and 4_4 have instability index values of 14.04, 17.61, and 36.7, respectively (Table 2), and are classified as stable in vitro, according to a criterion of instability index below 40 to be good for stability [39]. The tolerance to heat and SDS shown above could be a reflection of the stability of the proteins. The Alpha index is a measure of the relative volume of the aliphatic amino acids side chains (alanine, leucine, isoleucine, and valine) [32,40], and has a positive correlation with thermostability [40]. The proteins 6_35, 23_543, and 4_4 scored 80.08, 110.33, and 96.76 on the aliphatic index, respectively (Table 2), again within the predominant range of 40–120 for AMPs in the BIOPEP database [32]. The potential of the AMPs to form aggregates at their site of interaction with the bacterial cell membranes is a necessary step for their mechanism of action [35]. AGGRESCAN software [41] predicted that there are three, six, and six putative aggregation “hot spots” within the sequences of the proteins, 6_35, 23_543, and 4_4, respectively (Table 2). The Na4vSS values were 5.3, −6.6, and −2.4 for 6_35, 23_543, and 4_4, respectively (Table 2). Previously reported AMPs have Na4vSS values within the range of −40 to 60 [36,42]. 2.7. In Silico Prediction of the Antibacterial Potency The bioinformatics tools used for predicting the antibacterial potency of the uncharacterized proteins based on the amino acid sequences were the sequence-based prediction tools available on “The Collection of Anti-Microbial Peptides (CAMPR3)” [43], the antimicrobial peptide calculator and predictor tool available on the “APD3” [44], and the “AMPA” application [45]. The CAMPR3 prediction tool uses machine learning algorithms to predict the antimicrobial activity of peptides/proteins including the novel ones. CAMPR3 uses four different prediction models (Support Vector Machines (SVM), Random Forests (RF), Artificial Neural Network (ANN), and Discriminant Analysis (DA)). SVM, RF, and DA predict the antimicrobial activity and state the AMP probability, while ANN makes a qualitative description of the peptide/protein as either an AMP (for “antimicrobial peptide/protein”) or NAMP (for “non-antimicrobial peptide/protein”). The accuracy of the prediction results for the models is within the range of 87%–93% [32,46]. APD3 predicts the potential of an amino acid sequence (up to 200 residues) to have an antimicrobial activity by analyzing each amino acid residue and comparing the physicochemical properties of the sequence with those of the natural AMPs already deposited in the APD3 database [32,44,47]. AMPA uses a sliding window algorithm that calculates an “antimicrobial index” for individual amino acid residues and estimates the tendency of the amino acid to be found within an AMP sequence. By doing so, AMPA identifies the antimicrobial domain within the protein/peptide and hence predicts the overall antimicrobial activity [45]. The algorithms indicated that the three uncharacterized proteins have antimicrobial potential to different extents (Table 2). Combining the prediction results inferred from the sequence-derived physicochemical properties with the results of the six prediction algorithms used (the four algorithms of CAMPR3, the APD3 algorithm, and the AMPA algorithm), we can conclude that protein 23_543 is the most likely antibacterial protein candidate (Table 2). In the case of the protein 6_35, not all six prediction algorithms confirmed its potential as an antimicrobial protein. Protein 4_4 does not fulfill all the physicochemical properties of AMPs because its pI is 7.72 and its positive net charge is +1 [37]. Moreover, not all six prediction algorithms confirmed the potential of protein 4_4 as an antimicrobial protein. However, the probability of the antimicrobial potential of these two proteins cannot be ignored since most of the sequence-derived properties match with the features known about reported AMPs, and four out of the six algorithms predicted them to display antimicrobial activity (Table 2). 2.8. Identified Protein Sequences Matching Parts of Antimicrobial Enzymes As described above (Section 2.5), many proteins larger than 20 kDa were identified by LC-MS/MS analysis. Among these are two enzymes (26_23 and 2_3 in Table S2) already reported as antibacterial; N-acetylmuramoyl-l-alanine amidase (referred to as amidase; EC.3.5.1.28), and serine-type d-alanyl-d-alanine carboxypeptidase (referred to as dd-carboxypeptidase; EC: 3.4.16.4). The predicted amino acid sequences of amidase and dd-carboxypeptidase in strain ZGt-1 give a theoretical molecular weight of approximately 87 and 47 kDa, respectively, after cleavage of the enzyme’s signal peptide. These enzymes are known to catalyze the lysis of bacterial cells by hydrolyzing the covalent bonds in the peptidoglycan layer of the bacterial cell wall [13,48]. Amidase cleaves the amide bond between the glycan and the peptide chain [13], while dd-carboxypeptidase cleaves the terminal d-alanyl-d-alanine bond in the stem peptides of the peptidoglycan layer, resulting in the removal of the terminal d-alanine [49]. The lytic enzymes of bacterial origin are known to have a role in bacterial cell growth and division [13,48], and also act as antimicrobials by attacking the cell wall of competing bacteria [13]. Matching of the MS/MS-detected significant peptide sequences of the ZGt-1 amidase (Table S3) to the enzyme domains that we previously identified using InterProScan analysis [50] of the predicted amino acid sequence, showed alignment with segments in the catalytic domain at N-terminus (data not shown). Similar analysis of ZGt-1 dd-carboxypeptidase domains showed a match between the MS/MS-detected significant peptide sequences of the enzyme (Table S3) with N-terminus segments (the catalytic domain), C-terminus segments (supposed to act as the enzyme’s binding domain as indicated by the InterProScan tool), and parts of the region in between the two domains (data not shown). However, whether the detected partial sequences of the two enzymes would be responsible for the antibacterial activity and the clearance zone in the 15–20 kDa region is not clear. 3. Materials and Methods 3.1. Materials R2A broth, containing per liter: 0.5 g meat peptone, 0.5 g casamino acids, 0.5 g yeast extract, 0.5 g dextrose, 0.5 g soluble starch, 0.3 g dipotassium hydrogen phosphate, 0.05 g magnesium sulphate, and 0.3 g sodium pyruvate (pH 7.2 ± 0.2), was purchased from Lab M (Heywood, UK), while Bacto agar was from Difco, BD (Detroit, MI, USA). MH agar, having a composition per liter of 2 g meat infusion, 17.5 g casein hydrolysate, 1.5 g starch and 17.0 g agar (pH 7.3 ± 0.2), was procured from Merck (Darmstadt, Germany). MH broth, having per liter 17.5 g acid hydrolysate of casein, 3.0 g beef extract, 1.5 g starch (pH 7.3 ± 0.1), was purchased from BBL, BD (Sparks, MD, USA). Proteinase K (≥30 Units/mg) was obtained from Sigma-Aldrich (St. Louis, MO, USA). 3.2. Isolation of Bacteria from Zara Hot Spring in Jordan, Strain Maintenance and Cultivation Conditions Water samples were collected from the shores of Zara hot spring (with water temperature of 46 °C and pH 7) in Jordan (32 N 36 E) from 15 to 25 cm depth under the water surface level using sterile pipettes and dispensed into sterile falcon tubes. Within an hour after sampling, 500 μL of the samples were spread on the surface of solid R2A agar (1.7% w/v) plates, which were incubated aerobically at 60 °C for two days. The resulting bacterial colonies were isolated and further purified by streaking onto new R2A agar plates, followed by overnight incubation at 60 °C. The pure colonies were then suspended in 16% v/v glycerol to prepare a stock culture suspension and stored at −80 °C. Cultivation of the bacterial isolates was done by streaking one loopful of the culture stock onto the surface of R2A agar plates for isolation of DNA, or MH agar plates for production of antibacterial molecules. The plates were incubated overnight at 60 °C. Subsequently, 1–2 colonies were chosen, re-streaked on the respective media, and incubated under similar conditions. The resulting colonies were used either for DNA extraction, testing the antibacterial activity (agar-deferred spot method), or as an inoculum for production experiments. 3.3. Identification of the Bacterial Isolates by 16S rRNA Sequencing DNA was extracted from the pure cultures obtained above using ZR Fungal/Bacterial DNA MiniPrep (Zymo Research, Orange, CA, USA). 16S rRNA genes were amplified by PCR using universal primers, 10–30 F (5′-GAGTTTGATCCTGGCTCA-3′) and 1500 R (5′-AGAAAGGAGGTGATCCAGCC-3′) (Eurofins, Ebersberg, Germany). The reaction mixture (50 μL) contained 1× Phusion® GC buffer with 1.5 mM MgCl2, 200 μM of each dNTP, 0.4 μM of each primer, 3% dimethyl sulfoxide, 1 U Phusion® Hot Start II DNA polymerase (Finnzymes, Thermo Fisher Scientific, Helsinki, Finland), 5 ng of the purified DNA, and the final volume was adjusted with nuclease free water. The PCR reaction was carried out in Whatman Biometra T-gradient thermocycler (Biometra GmbH, Göttingen, Germany) under the following conditions: initial denaturation at 98 °C for 30 s, followed by 25 cycles of denaturation at 98 °C for 10 s, primer annealing at 61 °C for 30 s, extension at 72 °C for 23 s, and final extension at 72 °C for 600 s. The PCR products were visualized on 1.2% (w/v) agarose gel stained with GelRed™ 3× (Biotium Inc., Fremont, CA, USA), purified and sequenced in the forward and reverse directions by ABI sequencing reaction (GATC Biotech, Konstanz, Germany). The 16S rRNA sequences obtained were compared against sequences available in GenBank by applying the BLASTn 2.3.1+ [51] using the Megablast option on the RefSeq_RNA database (NCBI Transcript Reference Sequences). 3.4. Detection of Antibacterial Activity of Geobacillus sp. ZGt-1 3.4.1. Agar-Deferred Spot Method For determination of antibacterial activity by this method, several bacteria were used as test strains including a closely related thermophilic strain Geobacillus stearothermophilus (strain 10), isolated from the same environmental niche, and mesophilic Bacillus subtilis TMB94 and pathogenic bacterial strains, E. coli 1005, S. aureus NCTC 83254, S. epidermidis TMB96, S. typhimurium CCUG 31969, and P. vulgaris TMB02. The bacterial cultures were streaked onto MH agar plates that were incubated overnight at 60 and 37 °C, respectively. Colonies of the respective test strains were then suspended in sterile saline solution (0.85% w/v NaCl) and used to seed soft MH agar (0.5×) pre-warmed at 55 °C in case of G. stearothermophilus, or at 45 °C for the mesophiles, such that the final OD550 was ~0.125 (equivalent to 0.5 McFarland turbidity standard). One or two colonies of Geobacillus ZGt-1 were spot-inoculated onto the center of MH agar plates that were incubated aerobically at 60 °C for 24 h, and subsequently overlaid with soft agar seeded with the test strain, and incubated for 15–18 h at 60 °C (G. stearothermophilus strain 10) or for 22–24 h at 37 °C (mesophiles) to allow the test strains to grow. The presence/absence of the inhibition zone around the spot of strain ZGt-1 was detected. 3.4.2. Spot-on-Lawn Method Ten milliliters of soft agar seeded with the test strain (G. stearothermophilus strain 10) were poured into Petri dishes and allowed to solidify. Fifty microliters of the cell-free culture supernatant or desalted protein fraction (after ammonium sulphate precipitation) from Geobacillus sp. ZGt-1 were spotted on the agar surface and incubated at 60 °C for 15–18 h. The presence/absence of the inhibition zone around the spot was detected. 3.5. Sensitivity of the Antibacterial Substance(s) to Proteolysis by Proteinase K Sensitivity of the antibacterial activity towards proteinase K was tested as described earlier [52] with slight modifications. Geobacillus sp. ZGt-1 cells were spot-inoculated at the centre of MH agar plates and incubated overnight at 60 °C. Thereafter 3 μL of proteinase K solution (2 mg/mL) was spotted on one side of the ZGt-1 grown spot, while the other side was not treated, and the plate was incubated for 2 h at 37 °C. Finally, the plate was overlaid with 5 mL of soft MH agar seeded with G. stearothermophilus strain 10 using the agar-deferred spot method described in Section 3.4.1, and then incubated overnight at 60 °C. 3.6. Batch Production of the Antibacterial Substance(s) in Shake Flasks by Free Cells of Geobacillus sp. ZGt-1 Ten milliliters of MH broth in a 50-mL sterile Falcon tube were inoculated with 1–2 colonies of Geobacillus sp. ZGt-1 and incubated in an orbital shaker incubator (IKA KS 4000 ic, Staufen, Germany) at 60 °C and 240 rpm to reach a final OD620 of 0.468. Two milliliters of the resulting culture were transferred to 200 mL of the broth in a 500-mL baffled Erlenmeyer flask that was incubated under similar conditions to reach the final OD620 of 1.5. Finally, the culture was centrifuged at 10,000× g for 20–30 min at room temperature and the supernatant was tested for antibacterial activity. 3.7. Sequential Batch Production of the Antibacterial Substance(s) in Shake Flasks Using Immobilized Cells of Geobacillus sp. ZGt-1 Cells were cultivated and harvested as described above (Section 3.6). The cell pellet was then re-suspended in 10 mL of fresh sterile MH broth, and mixed with 20 mL of 3% (w/v) sterile molten agar solution pre-warmed at 60 °C. The mixture was dropped into a sterile cold solution of oil: saline solution (1:2) using a syringe needle to form beads with an average diameter of 5 mm. The beads were washed thoroughly with sterile saline solution and distilled water to remove the oil and then transferred to the 500-mL cultivation flask containing 200 mL of fresh sterile MH broth. The bead suspension was incubated at 60 °C, 240 rpm for 22–25 h, after which the culture broth was separated from the beads by carefully pouring it into sterile centrifuge bottles. The broth was then centrifuged at 10,000× g for 20–30 min at room temperature and the supernatant was filtered using 0.2 µm syringe filters, and saved. The beads were transferred back to the cultivation flask with 100 mL of fresh sterile MH broth, and another cultivation cycle was carried out. The beads were recycled 25 times in the same manner (except during cycles 12 and 17, where 10 µM FeCl3 was supplemented to the medium to be used for another study), and the cell-free supernatants were tested for antibacterial activity by the spot-on-lawn approach. 3.8. Towards Identification of the Antibacterial Protein Candidates The cell-free supernatant collected from sequential cultivation batches in Section 3.7, except for batches 12 and 17, were pooled together, and 1400 mL of the solution were treated with 60% (w/v) ammonium sulphate and allowed to stand with mild stirring at 4 °C for about 15 h. The precipitate was separated by centrifugation at 10,000× g, 4 °C for 40 min and re-suspended in 10 mL distilled water, yielding 140 times enrichment, and dialyzed using a dialysis membrane (3.5 kDa cutoff) (Spectra Pro) against 5 liters of distilled water that was freshly replaced several times a day for three days to remove the ammonium sulphate. To ensure removal of ammonium sulphate, which in itself may have an inhibitory effect, an aliquot of fresh MH culture medium was also subjected to a similar treatment and used as a negative control. The dialyzed samples were tested for their antibacterial activity by the spot-on-lawn approach. 3.9. Sensitivity of the Antibacterial Protein(s) to Heat Thermostability of the protein fraction isolated above was tested by heating 500 μL aliquots of the desalted sample at 70 °C for 10, 15, 30, and 45 min, or at 80 °C for 10 and 15 min, respectively, in a heating block and then placed on ice for 45–60 min. The samples were then centrifuged for 10 min at 13,000× g at room temperature to separate the denatured proteins, and the supernatant was tested for antibacterial activity by applying the spot-on-lawn technique. 3.10. Sensitivity of the Antibacterial Protein(s) to SDS and Fractionation by SDS-PAGE Twenty microliters of the protein sample was mixed with 5 μL of loading buffer (50 mM Tris-HCl pH 6.8, 100 mM DTT, 2% (w/v) SDS, 0.1% bromophenol blue, 10% (v/v) glycerol). The mixture was incubated at room temperature for 30–60 min, and then loaded on polyacrylamide gel (with 15% w/v acrylamide) in duplicates. After electrophoresis, the gel was cut into two halves, each with a lane of the sample and molecular weight standard (Precision Plus Protein All Blue standards, Biorad, Hercules, CA, USA); one half was stained with Coomassie Brilliant Blue R 250 while the other was tested for antibacterial activity by the method described earlier [53]. For the latter, the gel was fixed immediately in a solution of 20% isopropanol and 10% acetic acid for 2 h, washed in distilled water for 6 h, placed in a sterile Petri dish and overlaid with 15 mL of soft MH agar (0.5×) seeded with the test strain (G. stearothermophilus strain 10), and incubated at 60 °C for 12 h for development of the inhibition zone (Scheme 1). 3.11. Targeted Proteomics Analysis of the Antibacterially Active Protein Fraction Using Mass Spectrometry The gel area that showed the inhibition zone against G. stearothermophilus strain 10 was excised and divided into three gel samples. Each gel sample was cut into 1 × 1 mm pieces and placed in high-recovery Eppendorf tubes, washed twice in 75 µL of 50 mM ammonium bicarbonate/50% ethanol mixture to remove the Coomassie Brilliant Blue stain, dehydrated in 75 µL 100% ethanol, and then subjected to reduction (10 µL of 10 mM DTT, incubated at 37 °C for 30 min) and alkylation (10 µL of 55 mM iodoacetamide, incubated in dark for 30 min), with a second dehydration step in between. The gel pieces were washed and dehydrated once again as described above, and then 10 µL of digestion buffer (50 mM ammonium bicarbonate with 12 ng/µL sequencing-grade modified trypsin (Promega, Madison, WI, USA)) were added. After incubation on ice for 1 h, 15 µL of 50 mM ammonium bicarbonate was added and the samples were digested overnight at 37 °C. Subsequently, the digestion solution was withdrawn and the peptides were further extracted by adding 15 µL of 1% trifluoroacetic acid (TFA). After incubation for 2 h, this extract was collected and pooled with the overnight digestion solution. The tryptic peptide mixtures were further fractionated by reversed phase nano-LC prior to mass spectrometric analyses, using an LTQ-Orbitrap Velos Pro mass spectrometer (Thermo Fisher Scientific) equipped with a nanoEasy spray ion source (Proxeon Biosystems, Odense, Denmark). The chromatographic separation was performed at 40 °C on a 15 cm (75 μm i.d.) EASY-Spray column packed with 3 μm resin (Proxeon Biosystems). The nanoHPLC intelligent flow control gradient was 5%–20% solvent B (0.1% v/v FA, 100% v/v acetonitrile in water) in solvent A (0.1% v/v FA in water) for 60 min, and then 20%–40% solvent B for 30 min, followed by an increase to 90% for 5 min. A flow rate of 300 nL/min was used throughout the whole gradient. The MS scan (usually 350–2000 m/z) was recorded in the Orbitrap mass analyzer set at a resolution of 60,000 at 400 m/z, 1 × 106 automatic gain control target, and 500 ms maximum ion injection time. The MS was followed by data-dependent collision-induced dissociation MS/MS scans on the eight or 10 most intense multiply charged ions in the LTQ at 15,000 signal threshold, 30,000 automatic gain control target, 300 ms maximum ion injection time, 2.5 m/z isolation width, 10 ms activation time at 35 normalized collision energy, and dynamic exclusion enabled for 30 or 60 s with a repeat count of 1. The general mass spectrometric conditions were as follows: spray voltage, 2.0 kV; no sheath or auxiliary gas flow; S-lens 60%; ion transfer tube temperature, 275 °C. The Mascot Server software v. 2.4 (http://www.matrixscience.com) [54] was used for protein identification and Mascot Distiller was used to generate high-quality, de-isotoped peak lists from the raw data files. In parallel, gene prediction was carried out on the ZGt-1 genome [27] using prodigal (v2_60.linux) [55] and identified coding sequences of genes were then translated into proteins. A database of ZGt-1 proteins was constructed, and the MS/MS-identified peptides were searched against the ZGt-1 protein database (parameter settings used: trypsin-specific digestion, digestion with one missed cleavage site, peptide mass tolerance 10 ppm, fragment ion mass tolerance ±0.15 Da, carbamidomethylation set as fixed modification) and the proteins that accounted for those peptides were interpreted. In another step, protein identification was also obtained by matching the mass spectrometric data by Mascot searches directly in UniProt, with protein identification based on sequence homology to proteins in other bacterial species. The proteins that had significant peptide sequences more than or equal to 5, with ion score ≥20, and a total protein score ≥200, were considered as true matches. 3.12. UniProt Database Search for Identification of the MS/MS-Identified Proteins Using BLASTp The proteins from strain ZGt-1 interpreted from the MS/MS-detected peptides (Section 3.11) were identified with the aid of BLASTp version 2.2.30+ [56] using UniProt [57] as database. The e-value threshold was set to 1 × 10−10 and the top hit was used for annotation of protein description. 3.13. In Silico Analysis of the Proteins Identified as Possible Antimicrobials 3.13.1. Calculating the Physicochemical Properties of the Uncharacterized Proteins The physicochemical properties of the uncharacterized proteins (6_35, 23_543, and 4_4) were calculated using computational tools freely available on the internet. The number of amino acid residues, molecular mass, pI, net charge, instability index, aliphatic index, and GRAVY index were calculated using the ProtParam tool on the ExPasy webserver [39]. The amphipathic helix formation, total hydrophobic ratio, number of hydrophobic residues located on the same side of the helix, Boman index, and confirmation of the net charge were calculated using the antimicrobial peptide calculator and predictor available on the Antimicrobial Peptide Database (APD3) [44]. Aggregation of the proteins in vivo was predicted using AGGRESCAN [41], where putative aggregation hot spots and normalized average of aggregation propensity (Na4vSS) of the input protein were calculated. “Hot spot” is a protein aggregation region with a minimum of 5 continuous amino acid residues with an average aggregation propensity higher than a certain threshold calculated by the AGGRESCAN algorithm and none of the residues is a proline, which is an aggregation breaker [41]. The Na4vSS value is the sum of aggregation propensities for the amino acids of the input protein divided by the number of amino acid residues in the protein sequence and multiplied by 100 [41]. 3.13.2. Prediction of Antimicrobial Activity of the Uncharacterized Proteins The antimicrobial activity of the uncharacterized proteins (6_35, 23_543, and 4_4) was predicted using the sequence-based prediction tools freely available on the web, the Collection of Anti-Microbial Peptides (CAMPR3), where we employed the “AMP prediction” tool and used the four available prediction models (Support Vector Machines (SVM), Random Forests (RF), Artificial Neural Network (ANN), and Discriminant Analysis (DA)) [43]. The prediction results were retrieved as either a score evaluating the probability of the protein to be antimicrobially active (in case of using SVM, RF, and DA), or as a description of the protein either as AMP (for antimicrobial peptide/protein), or NAMP (for non-antimicrobial peptide/protein) (in case of using ANN). We also used another prediction tool, the antimicrobial peptide calculator and predictor available on the APD3 [44]. The third tool used was the AMPA server [45], where the default settings were used. Afterwards, we compared the output results retrieved from the different prediction tools to assess the antimicrobial potential of the respective protein. 3.13.3. Domain Architecture Analysis of Antimicrobial Enzyme Sequences The functional domain prediction of the amidase and dd-carboxypeptidase enzyme sequences was performed using the InterProScan sequence search tool [50]. 4. Conclusions The thermophilic bacterial isolate, Geobacillus sp. ZGt-1, seems to be a promising source of several antimicrobial molecules including proteins, which was confirmed by the availability of its genome sequence [27]. This study showed the potential of the isolate in inhibiting the growth of the dairy- and food-spoiling thermophilic bacteria and some other mesophiles including pathogens. Importantly, the potential of combining the immobilized cell technology with cell-recycling for the production of antimicrobial proteins was demonstrated that could be applicable even for other environmental isolates. Combining the proteomics data with the genome sequence information revealed the presence of as yet uncharacterized proteins with antimicrobial potential, and also the presence of antimicrobial enzymes. Further work will deal with empirical confirmation of the antimicrobial activity of the individual protein candidates suggested in this study (typed in bold in Table S2). The putative candidates will be cloned and expressed in order to investigate the scope of their antibacterial activity and the possibility of their synergistic action. Acknowledgments This work was supported by Erasmus Mundus Partnership (JOSYLEEN), and Bertil Andersson’s fund. The authors would like to thank Martin O. Andersson for his valuable comments on the manuscript and Klas Flärdh for sharing his knowledge on antimicrobial compounds. Supplementary Materials Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1363/s1. Click here for additional data file. Author Contributions Rawana N. Alkhalili, Tarek Dishisha, and Gashaw Mamo conceived and designed the experiments on the production, enrichment, and detection of the antibacterial proteins. Rawana N. Alkhalili also performed the experiments and analyzed the data. Katja Bernfur performed the mass spectrometry analysis. Jenny Schelin provided the mesophilic bacterial strains and supervised the work with pathogens. Björn Canbäck performed sequence analysis, while Cecilia Emanuelsson supervised the mass spectrometry analysis. Rajni Hatti-Kaul supervised the research project. Rawana N. Alkhalili wrote the manuscript and all the authors reviewed and edited it. Conflicts of Interest The authors declare no conflict of interest. Abbreviations Amidase N-acetylmuramoyl-l-alanine amidase AMP Antimicrobial peptide/protein ANN Artificial neural network APD3 Antimicrobial peptide database CAMPR3 The collection of anti-microbial peptides DA Discriminant analysis dd-carboxypeptidase Serine-type d-alanyl-d-alanine carboxypeptidase DTT Dithiothreitol GRAVY Grand average of hydropathicity LPS Lipopolysaccharide LC-MS/MS Liquid chromatography tandem mass spectrometry meso-DAP Meso-diaminopimelic acid MH Mueller Hinton culture medium Na4vSS Normalized average of aggregation propensity NAMP Non-antimicrobial peptide/protein PG Peptidoglycan pI Isoelectric point RF Random forests SVM Support vector machines Figures, Scheme and Tables Figure 1 Antibacterial activity of Geobacillus sp. strain ZGt-1 against (a) G. stearothermophilus strain 10; (b) B. subtilis; and (c) S. typhimurium CCUG 31969. The arrows are pointing to the inhibition zone. Figure 2 The antibacterial activity of the cell-free supernatant obtained by sequential batch cultivation of the immobilized cells of Geobacillus sp. ZGt-1 against G. stearothermophilus strain 10 at 60 °C, at the end of (a) cycle # 1; (b) cycle # 5; (c) cycles # 11, 12, and 13; (d) cycles # 14, 15, 16, and 17; (e) cycles # 24 and 25. The yellow dot denotes the site where the supernatant was spotted; and (f) Summary of the antibacterial activity over the 25 cycles, the number of + symbols representing increasing degree of antibacterial activity. ijms-17-01363-sch001_Scheme 1Scheme 1 Workflow for identification of potential antimicrobial protein candidates in Geobacillus sp. ZGt-1: Desalted protein extract from Geobacillus sp. ZGt-1 was fractionated by SDS-PAGE, followed by detection of antibacterial activity on the gel against test microorganism (M.O.) and analysis of the active zone by mass spectrometry. S: sample, M: protein marker in the SDS-PAGE cartoons in the scheme. The experimental details are provided in the text. Figure 3 Analysis of the antibacterial activity of the desalted protein fraction isolated from the culture supernatant produced by Geobacillus sp. ZGt-1 at 60 °C. The test organism was G. stearothermophilus strain 10. After SDS-PAGE separation of the protein in duplicates, the gel was divided into two, one part stained with Coomassie Brilliant Blue R 250 (a) and the other used for antibacterial assay (b). (a) Image of the SDS-PAGE separated protein fraction. Lane 1: Precision Plus Protein All Blue standards; Lane 2: Desalted protein fraction; (b) Antibacterial activity of the desalted protein extract after separating it on SDS-PAGE separated protein; the gel strip was placed in a Petri dish and covered with soft agar layer seeded with strain 10 and incubated at 60 °C. The white arrow is pointing to the inhibition zone ascribed to the antibacterial activity of the protein fraction. The inhibition zone corresponded to 15–20 kDa. ijms-17-01363-t001_Table 1Table 1 Proteins (10–30 kDa) identified by mass spectrometry. Query ID MS Score Mw (kDa) 1 Homologous Protein Name 2 UniProt ID 2_80 253 17.118 2-C-methyl-d-erythritol 2,4-cyclodiphosphate synthase G8N0X9 23_188 469 16.072 6,7-Dimethyl-8-ribityllumazine synthase L8A0J9 28_41 488 20.475 ATP synthase subunit b G8MZV8 186_1_184_1 219 22.862 Capsid protein A0A0K9I0I6 190_1_188_1 243 15.718 Capsid protein A0A0K9I0I6 23_393 258 23.554 Deoxyribose-phosphate aldolase A0A063YQK6 4_30 219 17.987 DinB family protein U2WSJ5 26_1 403 19.041 Flagellin L8A2E4 23_543 517 16.846 Hypothetical conserved protein Q5KWM5 23_84 241 19.549 Menaquinol-cytochrome c reductase iron-sulfur subunit S7U299 6_3 293 17.044 N5-carboxyaminoimidazole ribonucleotide Q5L3D8 23_103 610 16.741 Nucleoside diphosphate kinase G8MZM9 13_48 231 20.528 Peptide deformylase Q5L138 23_492 803 21.126 Peroxiredoxin Q5KWS6 23_704 866 18.266 Probable thiol peroxidase Q5KW64 28_65 267 23.049 Probable transaldolase L8A4Q9 23_775 357 16.545 Starvation-induced protein controlled by σ-B Q5KVZ0 25_145 314 27.457 Triose phosphate isomerase A0A063YNF6 4_4 432 19.025 Uncharacterized protein Q5L3L9 6_35 406 13.884 Uncharacterized protein Q5L3A8 18_68 208 27.963 Uroporphyrin-III C-methyltransferase Q5KZ09 21_9 315 19.432 YceI family protein/uncharacterized protein G8MXK2 1 Theoretical molecular weight based on the amino acid sequence; 2 Top hits of the UniProt BLASTp searches, all e-values were significant and much less than or equal to 0. ijms-17-01363-t002_Table 2Table 2 Prediction of the antimicrobial potential of the uncharacterized proteins based on their physicochemical properties and algorithm models. Physicochemical Properties Property Protein Query ID 6_35 23_543 4_4 Length 129 153 173 Molecular weight (kDa) 13.8927 16.8564 18.979.1 Net charge +2 +2 +1 pI 8.80 8.61 7.72 Instability index 14.04 17.61 36.7 Aliphatic index 80.08 110.33 96.76 GRAVY index −0.044 −0.257 −0.253 Boman index (kcal/mol) 1.12 1.57 1.19 Na4vSS 5.3 −6.6 −2.4 Number of aggregation hot spot regions 3 6 6 Total hydrophobic ratio 40% 39% 36% Potential of forming amphipathic helix Yes Yes Yes Number of hydrophobic residues on the same side ≥38 ≥42 ≥31 Algorithm Models CAMPR3 Models SVM 1.000 1 1.000 1 1.000 1 RF 0.987 1 0.9575 1 0.991 1 DA 1.000 1 1.000 1 1.000 1 ANN NAMP 2 AMP 3 NAMP 2 APD3 AMP 3 AMP 3 AMP 3 AMPA NAMP 2 AMP 3 NAMP 2 (0.86) 1 Summary of the Fulfilled Antimicrobial Potential Parameters Inferred from the Physicochemical Properties and Prediction Algorithms Physicochemical properties All All Majority Prediction algorithms Majority All Majority 1 Probability of being an antimicrobial peptide/protein; 2 Non-antimicrobial peptide/protein; 3 Antimicrobial peptide/protein. ==== Refs References 1. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081364ijms-17-01364ArticleProtection of Historical Wood against Microbial Degradation—Selection and Application of Microbiocides Koziróg Anna 1*Rajkowska Katarzyna 1Otlewska Anna 1Piotrowska Małgorzata 1Kunicka-Styczyńska Alina 1Brycki Bogumił 2Nowicka-Krawczyk Paulina 3Kościelniak Marta 4Gutarowska Beata 1Schaschke Carl Joseph Academic Editor1 Institute of Fermentation Technology and Microbiology, Lodz University of Technology, 90-924 Łódź, Poland; katarzyna.rajkowska@p.lodz.pl (K.R.); anna.otlewska@p.lodz.pl (A.O.); malgorzata.piotrowska@p.lodz.pl (M.P.); alina.kunicka@p.lodz.pl (A.K.-S.); beata.gutarowska@p.lodz.pl (B.G.)2 Laboratory of Microbiocide Chemistry, Faculty of Chemistry, Adam Mickiewicz University, 60-780 Poznań, Poland; brycki@amu.edu.pl3 Department of Algology and Mycology, University of Lodz, 90-237 Lódź, Poland; paulina_nowicka84@interia.pl4 Auschwitz-Birkenau State Museum, 32-603 Oświęcim, Poland; marta.koscielniak@auschwitz.org* Correspondence: anna.kozirog@p.lodz.pl; Tel.: +48-426-313-47022 8 2016 8 2016 17 8 136425 7 2016 11 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).The aim of this study was to select effective and safe microbiocides for the disinfection and protection of historical wooden surfaces at the former Auschwitz II-Birkenau concentration and extermination camp. We tested seven active compounds against bacteria and moulds, of which didecyldimethylammonium chloride and N-(3-aminopropyl)-N-dodecylpropane-1,3-diamine were effective even at 0.02%–2%. Subsequently, eight microbiocides containing the selected active ingredients were chosen and applied three times on the surface of wood samples colonized by bacteria and moulds. ABM-1 and ABM-2—6% solution; Rocima 101—8%; Preventol R 80—12%; Acticide 706 LV—15% and Boramon—30% were the most effective disinfectants. Under laboratory conditions, ABM-1, Boramon and Rocima 101 ensured antimicrobial protection of new wood samples for six months. In situ, 30% Boramon and 8% Rocima 101 applied by spraying effectively protected the historical wood from bacterial and mould growth for 12 and 3 months, respectively. Colour and luminance of the new wood were not altered after exposure to the biocides. Boramon and Rocima 101, applied by the spraying method, caused no significant change in the colour of the historical wood. Results from this study were used to develop a procedure for the protection of wood in historical buildings against biodeterioration. historical wood protectionmicrobiocidesquaternary ammonium compoundsdisinfection ==== Body 1. Introduction For centuries, wood has been used in the construction of houses, ships, weapons and various tools, among other things. Due to its chemical composition, this natural organic material is easily colonized by microorganisms and insects, leading to its deterioration under conditions favourable for their growth [1,2,3,4]. Wood damage by bacteria and fungi involves degradation of cellulose, hemicellulose and lignin. This leads to aesthetic deterioration of the surface (peeling, delamination, discoloration) and, above all, structural and mechanical changes (reduced strength, hardness) [1,5,6,7]. However, properly maintained and treated wood can last for several years. Different chemical compounds have been regularly used, for about 200 years, in order to protect wood from damaging factors [8]. Initially, fluorine, arsenic, chromium and copper compounds were utilized, which were often toxic to humans. Chemicals used to protect wood were first mentioned in ancient times, e.g., precious wood soaked in cedar oil by Egyptians [2,9]. Currently, the most commonly used preservatives are copper compounds, chromium, boric acid, azoles or quaternary ammonium compounds [10,11]. Research is also being conducted on ionic liquids and fatty acids, which can be used in the protection of timber [12,13,14]. Chemical wood preservatives can be divided into impregnates and preservatives, which are used to protect wood against biological agents and fire, and biocides, which are applied to remove and neutralize biological agents already present in the wood [2,15]. Biocides include water-borne, oil-borne and organic solvent-based substances. Water-borne preservatives have been widely used in recent years [11]. However, these chemical preservatives have an impact on the environment and humans. The use of biocides for wood preservation must be authorized in accordance with the Biocidal Regulation 528/2012 [16]. Currently, preservation of wood (especially historical wood) is often a complex and multistage process. The following factors must be taken into account: structural (type of wood, its durability and storage conditions); biological; and chemical (previous impregnation, preservatives, etc.). Furthermore, both environmental and economic aspects must be considered when using biocides. In the case of historical wood, the impact of chemicals on the material is extremely important, as it can lead to its discoloration [2,17]. Improper use of preservatives may result in irreversible damage of historical objects that are often an invaluable heritage of past centuries. The aim of this study was to select safe and effective microbiocides, for disinfection and protection of historical wooden surfaces at the former Auschwitz II-Birkenau concentration and extermination camp; this camp was part of the Auschwitz-Birkenau in Oświęcim, Poland. The scope of the work included: Selection of active biocidal substances against the microflora isolated from historical wood at the former Auschwitz II-Birkenau concentration and extermination camp; Selection of biocides containing the selected active compounds; Checking the activity and assessing the effectiveness of the biocides against microbes colonizing the wood surface; Protecting the wood samples from further contamination with microorganisms, under laboratory and in situ conditions; Evaluating the impact of biocides on new and historical wood colour and luminance; Developing a procedure for the protection of historical buildings from biodeterioration. 2. Results 2.1. Active Compounds The quantitative and qualitative analysis of microorganisms colonizing wooden surfaces, such as doors, floors, bunk beds, door frames, beams and structural walls in the barracks at the former Auschwitz II-Birkenau concentration and extermination camp, was carried out in a previous study [18]. The results enabled us to determine the extent of contamination, and to select the dominant species of microorganisms. This was followed by inhibiting the microbial growth through the use of microbiocides. We evaluated the sensitivity of microorganisms to seven compounds commonly used as active ingredients in disinfectants: didecyldimethylammonium chloride (DDAC), N-(3-aminopropyl)-N-dodecylpropane-1,3-diamine (APDA), hydrogen peroxide (HP), glutaraldehyde (GA), sodium hypochlorite (SH), boric acid (BA), and lactic acid (LA). The susceptibility of bacteria to five of the seven compounds tested was similar (Table 1). The only exception was boric acid, for which the minimal inhibitory concentration (MIC) ranged from 2% to 8%, and was different for each of the tested strains. DDAC and APDA showed maximum bactericidal activity, with an MIC of 0.02%. Only Pseudomonas fluorescens showed low sensitivity to DDAC (MIC 7%). Engyodontium album was considered the most sensitive of all moulds tested (Table 1). DDAC and APDA had the lowest MICs and maximum antimicrobial activity. The compounds evaluated are ranked by their decreasing antimicrobial activity: N-(3-aminopropyl)-N-dodecylpropane-1,3-diamine (APDA) > didecyldimethylammonium chloride (DDAC) > sodium hypochlorite > hydrogen peroxide > glutaraldehyde > boric acid > lactic acid. Based on these results, two active ingredients were selected for further study: didecyldimethylammonium chloride, belonging to quaternary ammonium salts, and N-(3-aminopropyl)-N-dodecylpropane-1,3-diamine, a polyamine. These compounds are active ingredients in a variety of formulations available on the market. 2.2. Biocides Based on data from the Office for Registration of Medicinal Products, Medical Devices and Biocidal Products (List of Biocides, Part I, including information about products, which have been granted a marketing authorization [19]), six biocides were selected for testing, and two preparations (ABM-1 and ABM-2) were prepared specifically for the project: “Research on the biological corrosion of objects in the Auschwitz-Birkenau State Museum in terms of identification and control of biological agents, Phase II. A study on the selection of chemicals for the control of microorganisms and algae, and for wood and mineral surface protection against their development”. In order to determine the spectrum of activity of the compounds, sensitivity of mixed cultures of microorganisms was evaluated to the selected biocides. Since the generation times and resultant growth in the test environment differed for bacteria and moulds, the assays were performed separately for each group (Table 2). Mixed strains of bacteria showed higher sensitivity than moulds. All biocides are based on quaternary ammonium compounds (see Section 4.3. Materials and Methods). Acticide LV 706 (10%) was the only biocide that inhibited growth of both bacteria and moulds. The results allowed the determination of optimal formulation concentrations (Table 2), which are effective in growth inhibition of microorganisms. 2.3. The Activity of Biocides against Microorganisms Colonizing the Wood Surface In addition to concentration, the number of biocide applications is a very important factor in the disinfection process. In the present study, each disinfectant was sprayed onto the surface three times at two different concentrations. In order to determine the activity of the biocides, a five-level scale was used (see Section 4.8. Materials and Methods). ABM-1, ABM-2, Atoxyn and Rocima 101 exhibited the highest antibacterial activity when applied to the wood (Figure 1). These substances showed antibacterial activity at a concentration of 6% (i.e., lower of the two studied concentrations) after a single spray. According to the scale adopted in this study (see Section 4.8. Materials and Methods), the number of bacteria reduced by 8 log units, indicating the efficiency of the formulation at 5 units of biocidal activity. To achieve the same effect, Mycetox B’ had to be applied twice at a concentration of 20%, while Boramon and Acticide LV 706 were effective after three applications at concentrations of 20% and 10%, respectively. Preventol R80 showed antimicrobial activity only at the higher concentration (12%) after a single application. Moulds colonizing the wood samples were less sensitive to the applied disinfectants (Figure 1). ABM-1 and ABM-2 were the most effective antifungal biocides; moulds were eliminated from the surface of the material after three applications at the lower concentration (6%), and two applications of 8%. Antifungal activity was also exhibited by Acticide LV 706, Boramon, Preventol R80 and Rocima 101, after three applications at the higher of the tested concentrations (15%, 30%, 12% and 8%, respectively). The other disinfectants did not show expected antifungal activity (biocidal activity < 4). Although the growth of moulds was reduced, it was not eliminated. ABM-1 and ABM-2 at a concentration of 6%; Rocima 101 at 8%; Preventol R80 at 12%; Acticide LV 706 at 15% and Boramon at 30% were the most effective against mixed populations of bacteria and moulds on the wood. Each of these disinfectants must be applied three times for effective disinfection. In addition to biocidal activity, the economic calculation was also considered i.e., the average cost of disinfectant consumption per 100 m2 of surface in a single spraying procedure. Preventol R80 was the most expensive (€22.8 for a solution at a concentration of 6%). ABM-1 and ABM-2 were the cheapest, not exceeding €2.5, which is nine times less than the cost of other disinfectants. 2.4. Protection of Wood Samples from the Development of Microorganisms: Laboratory Tests Next, the biocides were evaluated for their effectiveness in wood preservation, against mixed cultures of bacteria and moulds. The results obtained under model conditions show that Rocima 101 and ABM-1 at concentrations of 8% applied by the fogging method, as well as Boramon at 30% and Rocima 101 at 8% applied by the spraying method, are highly effective in protecting non-historical wood against bacteria and moulds. No growth of microorganisms was recorded on the samples protected with these biocides, after six months of incubation. Differences between methods of biocides application are described in Section 4.6. Materials and Methods. 2.5. Protection of Historical Wood Samples from the Development of Microorganisms—In Situ Tests In order to verify the laboratory tests, the same disinfectants were applied under in situ conditions. They were checked for effectiveness in preserving historical wood; the treated wood was then exposed in one of the barracks at the former Auschwitz II-Birkenau concentration and extermination camp, for 12 months (Figure 2). Under in situ conditions, spraying with 30% Boramon and 8% Rocima 101, effectively protected the historical wood samples against bacteria for up to 12 months (Figure 3). Mould growth on the wood surface was effectively inhibited for a period of three months. After 6 and 9 months, the effectiveness of the disinfectants was estimated at 3 units of biocidal activity, according to the proposed scale (see Section 4.8. Materials and Methods), indicating that mould growth was noted on 50% of the sample surface. Two disinfectants, Rocima 101 and ABM-1, at a concentration of 8% were applied by fogging. Both of them effectively protected the historical wood samples from bacterial growth for 12 months (Figure 4). The mould growth on the wood surface was suppressed for a period of 3 months after applying ABM-1 at a concentration of 8%. However, more than half of the sample surfaces were covered by moulds as early as three months after application of 8% Rocima 101. As with the new material, Boramon was not fogged on the historical material, since its effective concentration of 30% would adversely affect the environment. The methods of application onto wood surfaces were also compared under in situ conditions utilizing Rocima 101. We found that spraying was more effective for protecting against the development of microorganisms on the historical samples. 2.6. Evaluation of Changes in the Colour and Luminance of Wood after Application of the Tested Disinfectants The appropriate disinfectant must not only be selected based on its antimicrobial activity, but the colour and luminance of the treated wood should also be checked after application. Samples of new and historical wood were disinfected by spraying and fogging. Post disinfection treatments, the colour difference (ΔE) and change in luminance (ΔL) were evaluated by visual and spectrophotometric methods. Values ΔE < 2 and ΔL < 1 determined by the instrumental method, were considered the levels at which the observer using the visual method does not notice any changes in the colour and luminance of the sample [20]. In the case of the new wood samples disinfected by spraying Boramon and Rocima 101, and by fogging ABM-1 and Rocima 101, there were no statistically significant (p < 0.05) colour differences expressed as ΔE. The values of ΔE < 1 means that the observer did not notice any colour difference. The observed changes in the luminance of the material ΔL did not exceed 0.5, which also means that these changes were unnoticeable to the observer (Table 3). The highest colour difference (ΔE = 2.82) was observed in the case of the historical material fogged with Rocima 101, where the wood slightly darkened. At the same time, its application by spraying caused no significant change in the colour of the historical wood. The statistically significant (p < 0.05) differences of luminance and colour between the historical material and the new material after fogging may be connected with the inhomogeneous surface of the historical material. 3. Discussion Various types of microbiocides are used for wood decontamination and preservation. Modern disinfectants are no longer monocomponent solutions, but mixtures of compounds with multidirectional mechanisms of action. It is unlikely that one synthetic or natural compound will eliminate biological factors adversely affecting wood [13,21]. In the first stage of this study, single compounds (N-(3-aminopropyl)-N-dodecylpropane-1,3-diamine, didecyldimethylammonium chloride, sodium hypochlorite, hydrogen peroxide, glutaraldehyde, boric acid and lactic acid) were evaluated to determine their activity on microorganisms isolated from the tested wood surfaces. This allowed us to select the compounds with the highest activity. Quaternary ammonium compounds (QACs) inhibited the growth of bacteria and moulds at the lowest concentrations, compared to other substances. The high effectiveness of QACs against moulds, decay fungi and insects that attack wood has been previously described [9,10,15]. These compounds are often used in commercially available wood preservatives. They affect cell membranes, causing the leakage of cell constituents [2,9,22]. In the next phase of the study, Boramon, which additionally contains boric acid, was used in addition to QAC-based preservatives. According to the European Chemicals Agency (ECHA) and the Biocidal Regulation 528/2012, due to its harmful effects on reproduction, this substance is currently used as a biocidal product only for wood preservation (biocides—Category II, Group 8). Boron compounds, however, are often used as wood preservatives [9,10,15,23], and we were able to demonstrate their effectiveness in this study (Figure 1). Boric acid and borates inhibit the function of enzymes and influence cell-to-cell transport mechanisms [2,15]. After evaluating the disinfectant properties, experiments were performed to protect the wood surface against re-infection by bacteria and moulds. In laboratory conditions, preservatives containing QACs: Rocima 101 and ABM-1 at concentrations of 8%, and Boramon at 30%, effectively protected the wood samples against microbial growth despite the high relative humidity of 80% and temperature of 28 °C. However, these results were not reproducible under variable in situ conditions. Similar observations were made by Young et al. [24] who also studied the effect of various biocides on biofilm development on stone substrates, under laboratory and in situ conditions. In studies on wood preservatives, it is important to check the impact of these compounds on the material. A change in the colour of wood is a measurable parameter and, at the same time, an essential visual element. In the case of new materials, colour differences are readily noticed by consumers and often result in lowering its value. A change in the colour of historical wood caused by the action of chemical compounds may deteriorate its aesthetic value and, above all, contribute to the total destruction of the historical object [2,25,26]. In this study, microbiocides Rocima 101 and Boramon, applied onto the wood samples by spraying, did not change their colour ΔE and luminance ΔL. The results are lower than those obtained by Tomak et al. [26], who observed the discoloration of pine wood from ΔE = 2.29 to ΔE = 3.48 under the influence of boric acid at concentrations of 1% and 5%. The authors concluded that the values obtained indicate a slight colour difference. In contrast, Ozgen and Yildiz [27], who used didecyldimethylammonium chloride (DDAC) for pine wood impregnation, reported significant changes in colour and luminance, amounting to 12.2 and 17.8, respectively. In both cases, the wood was subjected to vacuum impregnation. After removal of the organisms responsible for biodeterioration, it is necessary to provide appropriate environmental conditions. Moisture is one of the main factors contributing to the development of not only mould and bacteria, but also algae and insects. Excess moisture can result from faulty construction of a building, poor site drainage, a leaking roof or a leaking plumbing system, insufficient insulation, or inadequate ventilation. All this may cause rainwater to leak into the interior of the building. Improving the structural condition of the building will significantly reduce the growth of microorganisms on historical materials [21,28,29]. In this study, we developed a procedure for the protection of historical wooden buildings from biodeterioration (Scheme 1). Both, biological factors that cause wood biodeterioration and the historical material undergoing deterioration, should first be identified. This will allow the selection of appropriate disinfection methods. When using a variety of chemical compounds, a model study is necessary to determine their concentrations, as well as the number and methods of applications. It is also necessary to check the impact of the biocide on historical material, and its effectiveness after disinfection. Apart from removing the cause of wood degradation, it is crucial to protect the wooden surface against microbial re-infection. Such a comprehensive procedure can contribute to the preservation of many monuments that are invaluable witnesses to history and the past. 4. Materials and Methods 4.1. Microorganisms The effectiveness of active compounds and biocides was tested for three selected strains of bacteria and five strains of moulds, isolated from the wooden surfaces of the historical barracks at the Auschwitz II-Birkenau State Museum in Oświęcim. These include the bacteria Pseudomonas fluorescens, Staphylococcus equorum, and Bacillus cereus; and moulds Alternaria alternata, Chaetomium globosum, Cladosporium cladosporioides, Engyodontium album, and Penicillium citreonigrum. The nucleotide sequences of the 16S rRNA gene of the bacteria used in the study were deposited in GenBank, the National Centre for Biotechnology Information (Pseudomonas fluorescens KM036083.1; Staphylococcus equorum KM036089.1; Bacillus cereus KM036070.1). Mould strains were deposited in the Culture Collection ŁOCK 105 under the collection numbers Alternaria alternata ŁOCK 0594, Chaetomium globosum ŁOCK 0591, Cladosporium cladosporioides ŁOCK 0592, Engyodontium album ŁOCK 0590, Penicillium citreonigrum ŁOCK 0597. The bacteria were maintained on tryptic soy agar slants (TSA, Merck, Germany) and the moulds were stored on malt extract agar slants (MEA, Merck, Germany) at 4 °C. In order to activate the strains, the biomass was collected from slants and sub-cultured: the bacteria into tryptic soy broth (TSB, Merck, Germany) and the moulds onto MEA slants. The cultures were incubated at 30 °C for 24–48 h (bacteria), and 28 °C for 5 days (moulds). 4.2. Determining the Minimum Inhibitory Concentrations of Active Compounds in Biocides Antimicrobial activity was determined for seven commercially available active compounds, selected on the basis of biocides listed on the website of The Office for Registration of Medicinal Products, Medical Devices and Biocidal Products (http://bip.urpl.gov.pl/pl/biuletyny-i-wykazy/produkty-biob%C3%B3jcze). These included didecyldimethylammonium chloride (CAS 7173-51-5, LONZA AG, Basel, Switzerland), N-(3-aminopropyl)-N-dodecylpropane-1,3-diamine (CAS 2372-82-9, LONZA AG, Basel ,Switzerland), hydrogen peroxide (CAS 7722-84-1, Evonik, Essen, Germany), glutaraldehyde (CAS 111-30-8, BASF, Ludwigshafen, Germany), sodium hypochlorite (CAS 7681-52-9, Solvay SA, Brussels, Belgium), boric acid (CAS 10043-35-3, Alfa Aesar, Karlsruhe, Germany) and l-lactic acid (CAS 79-33-4, Sigma Aldrich, St. Louis, MO, USA). The concentrations of these active compounds were determined based on the concentrations of these ingredients in various commercially available biocides in Poland (Table 4). The sensitivity of microorganisms to the active compounds was determined by the disc-diffusion assay. The surfaces of TSA and MEA were inoculated with bacterial and mould monocultures, respectively (106 CFU/mL and 106 conidia/mL), which were then uniformly spread on the surfaces of the media. Sterile paper disks (ø 6 mm, Oxoid) were soaked with 15 mL solutions of the compounds, at the test concentrations (Table 1); subsequently, the discs were placed on the surface of the media. The plates were incubated at 30 °C for 24–48 h (bacteria) and at 28 °C for 48 h (moulds). Macroscopic observations of microbial growth were carried out, and the diameters of inhibition zones were measured. The MIC value was the lowest tested concentration of an active ingredient, for which the inhibition zone was observed with a diameter ≥ 10 mm. 4.3. Determining the Antimicrobial Activity of Biocides by Disc Diffusion Method Eight commercial biocides, containing didecyldimethylammonium chloride as the active compound and various excipients, were used in the study (Table 5). Each biocide was tested at concentrations recommended by its manufacturer. Antimicrobial activity of biocides was tested by the disc diffusion method, as described in Section 4.2. Mixed populations of bacteria and mixed populations of moulds were tested. The bacterial cultures activated on TSB were centrifuged (6000× g, 10 min), and the biomass was suspended in saline solutions (0.85% NaCl). Bacterial strains were combined in equal volumes to obtain mixed cultures. The density of the suspension was adjusted to 108 CFU/mL. The MEA slants of five-day fungal cultures were washed with sterile distilled water supplemented with 0.01% of Tween80. The density of the mould inoculum in the mixed cultures was determined using a haemocytometer, and adjusted to 106 conidia/mL. 4.4. New and Historical Wood Samples of new material, in the form of white poplar wood fragments measuring 50 × 20 × 10 mm, were used in the study. The samples were sterilized twice at 121 °C for 15 min., and then stabilized in a constant climate chamber (Binder, Germany) for seven days, at 28 °C and relative humidity (RH) 80%. However, historical wood fragments, collected from the Auschwitz II-Birkenau State Museum in Oświęcim, were used under in situ conditions. 4.5. Activity of Biocides against Microorganisms Colonizing Wood Surface The mixed cultures (1 mm each) of bacteria (108 CFU/mL) and moulds (106 conidia/mL), activated in the media ((NH4)2SO4 0.075%, K2HPO4 0.025%, MgSO4·7H2O 0.125%, yeast extract 0.125%, glucose 0.5% and agar 0.1% pH 6.0), were applied onto the surface of new wood samples [22]. The samples were incubated in a constant climate chamber with a relative humidity of 80% and a temperature of 28 °C for 7 days (bacteria) and 21 days (moulds). After incubation, each of the test biocides at two concentrations, was sprayed (using a professional sprayer Mercury Super Pro 360, Quasar, Poland) onto the wood surface one, two, and three times, at intervals of 24 h. The number of microorganisms was determined after successive applications of each biocide, by the contact plate method in TSA (bacteria) and MEA (moulds). The cultures were incubated at 30 °C for 48 h (bacteria) and at 28 °C for 5 days (moulds). Control samples were materials not subjected to the biocide treatment. 4.6. Protecting New Wood Samples against Microorganism Growth: Laboratory Tests Selected biocides (ABM-1 8%, Boramon 30%, Rocima 101 8%) were applied by the spraying method to the surface of each sterile, conditioned sample of new wood (two times at an interval of 30 min) or fogging method (in accordance with the procedure: 1 g of biocide per 1 m3 of air with the addition of 5% of MIST-60 containing polyols). Twenty-four hours after the application of biocides, the sample surfaces were inoculated with mixed cultures of bacteria (108 CFU/mL) and moulds (106 conidia/mL). The samples were incubated in a constant climate chamber at 28 °C and 80% RH for seven days, one month, three months and six months. The effectiveness of antimicrobial activity was determined by the contact plate method, as described above. Control samples were materials not subjected to the biocide treatment. 4.7. Protecting Historical Wood Samples against Microorganism Growth—In Situ Tests Selected biocides (ABM-1 8%, Boramon 30%, Rocima 101 8%) were applied onto the surface of each sterile, conditioned sample of historical wood by the spraying method and by the fogging method. The samples were placed on metal shelves in the washroom located in the western part of the brick barracks B-65 at the Auschwitz II-Birkenau State Museum (Figure 1). The effectiveness of these biocides against the growth of microorganisms present in the museum environment was determined by the contact plate method after 3, 6, 9 and 12 months of storage under in situ conditions. Control samples were historical wood not subjected to the biocide treatment. 4.8. The Evaluation Scale for the Antimicrobial Activity of the Biocides Applied to the New and Historical Wood Surfaces In order to determine the activity of the biocides against bacteria and moulds, a calibration scale was used according to Table 6. 4.9. The Impact of Biocides on the Wood Colour The change in colour and luminance of wood samples was quantified by the spectrophotometric method [32,33]. The theoretical colour model was developed by the International Commission on Illumination (CIE). The model takes into account all the colours recognizable by the human eye, including all RBG and CMYK colours. The CIE model is a three-dimensional colour space, which is described by three parameters: L—luminance ranging from 0 (black) to 100 (white). a—the scope from red to green. b—the scope from yellow to blue. The ΔE00 model was used to describe colour differences. In addition to the trichromatic components, it takes into account four characteristics: saturation, hue, brightness and blue colour. The following correlation was used to develop the results. It is the Euclidean distance between two points in a three-dimensional space of colour: ΔE=(ΔL)2+(Δa)2+ (Δb)2 Trichromatic components were measured at three points of each tested area, and the results were averaged. Samples were analysed by a spectrophotometer (Konica Minolta CM-2500d). Measurements were performed on the new and historical wood samples before biocide application, after two applications of Boramon and Rocima 101 by the spraying method, and after one application of ABM 1 and Rocima 101 by the fogging method. Control samples consisted of historical wood not subjected to the biocide treatment. The results of ΔE and ΔL represent means from three independent samples ± SD. Differences between means were tested by variance analysis (one-way ANOVA) with the post-hoc Tukey test. Probability (p) values of <0.05 were considered significant. Statistica v.10.0 (Stat Soft. Inc., Tulsa, OK, USA) was used for calculations. 5. Conclusions In this work, effective and safe microbiocides for the disinfection and protection of historical wooden surfaces at the former Auschwitz II-Birkenau concentration and extermination camp were indicated. Their concentration, times and method of application has been taken into account and the impact of microbiocides on historical material was examined. The most effective were didecyldimethylammonium chloride and N-(3-aminopropyl)-N-dodecylpropane-1,3-diamine. The majority of the microbiocides (six of eight tested) contain these compounds and in laboratory conditions they effectively inhibit the growth of microorganisms after triple application by spraying. In addition, in situ Boramon 30% and Rocima 101 8% applied by spraying, effectively protected the historical wood from growth of bacteria for 12 months and moulds for 3 months. In the article, a monitoring scheme for the protection of historical wooden buildings from biodeterioration was suggested. It covers all stages of comprehensive recognition of the same object from disinfection to conservation. The presented procedure may help to perform similar analyses by other researchers. Acknowledgments The authors would like to thank the staff of the Auschwitz-Birkenau State Museum for making the facilities available for the research. The study was conducted as a part of the Auschwitz-Birkenau Preservation Plan and funded by the Auschwitz-Birkenau Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author Contributions Conceived and designed the experiments: Beata Gutarowska, Anna Koziróg, Katarzyna Rajkowska, Małgorzata Piotrowska, Anna Otlewska, Bogumił Brycki. Performed the experiments: Anna Koziróg, Katarzyna Rajkowska, Anna Otlewska, Małgorzata Piotrowska, Paulina Nowicka-Krawczyk. Analyzed the data: Anna Koziróg, Małgorzata Piotrowska. Contributed to the writing of the manuscript: Anna Koziróg, Katarzyna Rajkowska, Anna Otlewska, Małgorzata Piotrowska, Alina Kunicka-Styczyńska. Critically revised: Alina Kunicka-Styczyńska, Bogumił Brycki, Beata Gutarowska. Assistances in sampling: Marta Kościelniak. Conflicts of Interest The authors declare no conflict of interest. Figures, Scheme and Tables Figure 1 Effectiveness of disinfectants: ABM-1 (A); ABM-2 (B); Acticide LV 706 (C); Atoxyn (D); Boramon (E); Mycetox B’ (F); Preventol R80 (G); and Rocima 101 (H) against mixed populations of bacteria and moulds on wood: one (white bar graph), two (grey bar graph) and three (dark grey bar graph) applications. Figure 2 Historical wood samples after application of disinfectants, exposed in a barrack at the former Auschwitz II-Birkenau concentration and extermination camp. Figure 3 Effectiveness of biocides (Boramon at 30%, Rocima 101 at 8%) applied by spraying. Figure 4 Effectiveness of biocides (ABM-1 at 8%, Rocima 101 at 8%) applied by fogging. ijms-17-01364-sch001_Scheme 1Scheme 1 Procedure for the protection of historical wooden buildings from biodeterioration [30,31,32]. ijms-17-01364-t001_Table 1Table 1 Minimal inhibitory concentration of active compounds in % (v/v). Microorganism Compound DDAC APDA HP GA SH BA LA Bacillus cereus KM036070.1 0.02 0.02 2 2 0.2 5 4 Pseudomonas fluorescens KM036083.1 7 0.02 2 2 0.2 2 4 Staphylococcus equorum KM036089.1 0.02 0.02 2 2 0.2 8 4 Alternaria alternate ŁOCK 0594 0.2 0.2 2 2 2 2 >10 Cladosporium cladosporioides ŁOCK 0592 0.2 0.2 2 4 0.2 12 >10 Engyodontium album ŁOCK 0590 0.2 0.2 0.2 0.2 0.02 8 10 Penicillium citreonigrum ŁOCK 0597 2 2 2 0.2 2 2 >10 Cheatomium globosum ŁOCK 0591 0.2 0.2 0.2 2 7 2 >10 DDAC—didecyldimethylammonium chloride, APDA—N-(3-aminopropyl)-N-dodecylpropane-1,3-diamine, HP—hydrogen peroxide, GA—glutaraldehyde, SH—sodium hypochlorite, BA—boric acid, LA—lactic acid. ijms-17-01364-t002_Table 2Table 2 Concentrations of biocides (%, v/v) where growth inhibition zone ≥ 10 mm in diameter was observed. Microorganisms Biocide ABM-1 ABM-2 A LV 706 AT B M P R80 R101 Bacteria 1 1 10 1 15 10 2.5 1 Moulds 6 6 10 6 20 20 6 6 ALV706—Acticide LV 706; AT—Atoxyn; B—Boramon; M—Mycetox B’; P R80—Preventol R80; R101—Rocima 101. ijms-17-01364-t003_Table 3Table 3 The colour and luminance of wood after application of disinfectants. Application Biocide New Material Historical Material ΔE ΔL ΔE ΔL Spraying Boramon 0.44 ± 0.31 a,A −0.43 ± 0.19 a,B 0.23 ± 0.06 a,A −0.07 ± 0.02 a,C Rocima 101 0.45 ± 0.21 a,A −0.42 ± 0.28 a,b,B 0.78 ± 0.33 b,A −0.63 ± 0.13 b,C Fogging ABM-1 0.49 ± 0.29 a,A −0.15 ± 0.02 b,C 1.38 ± 0.34 b,B −1.38 ± 0.25 c,D Rocima 101 0.48 ± 0.23 a,A −0.33 ± 0.17 a,b,C 2.82 ± 0.99 c,B −1.81 ± 0.53 c,D Trichromatic components: ΔE—colour difference; ΔL—luminance difference; values in the table represent means from three samples ± SD; lowercase letters (a–c) in the columns indicate statistically significant differences in the ΔE or ΔL means within disinfectants and application methods (one-way Anova, p < 0.05); capital letters (A–D) in the rows indicate statistically significant differences in the ΔE or ΔL means within various types of material (one-way Anova, p < 0.05). ijms-17-01364-t004_Table 4Table 4 Active chemical compound of biocides. Compound Acronym Concentration Used in the Tests (% v/v) Didecyldimethylammonium Chloride DDAC 12, 10, 7, 3, 2, 0.2, 0.02 N-(3-aminopropyl)-N-dodecylpropane-1,3-diamine APDA 5, 2, 0.5, 0.2, 0.02 Hydrogen Peroxide HP 15, 10, 7, 5, 2, 0.2, 0.02 Glutaraldehyde GA 10, 5, 4, 2, 0.2, 0.02 Sodium Hypochlorite SH 7, 5, 2, 0.2, 0.02 Boric Acid BA 12, 8, 5, 2, 0.2, 0.02 l-lactic Acid LA 10, 8, 4, 2, 0.2 ijms-17-01364-t005_Table 5Table 5 Biocides used in the tests. Biocide Active Components Concentrations (% v/v) ABM-1 (MDA Sp. z o.o., Poland) N-3-aminopropyl-N-alkyl(C10-C14)-1,3-propanediamine N,N-dialkyl(C10-C16)-N-methyl-N-poly(oxyethylene)ammonium propionate N,N-dialkyl(C10-C14)-N,N-dimethyl ammonium chloride 2-[3-(dodecanoylamino)]propyl dimethyl ammonium acetate 1, 2, 4, 6, 8 ABM-2 (MDA Sp. z o.o., Poland) N-3-aminopropyl-N-alkyl(C10-C14)-1,3-propanediamine N,N-dialkyl(C10-C16)-N-methyl-N-poly(oxyethylene)ammonium propionate N,N-dialkyl(C10-C14)-N,N-dimethyl ammonium chloride 1, 2, 4, 6, 8 Acticide LV 706 (THOR GmbH, Germany) 2.5%–10% (v/v) (benzyl alkyl(C8–18) dimethylammonium chlorides 2.5%–10% (v/v) diethylene glycol <2.5% (v/v) 2-octyl-2H-isothiazole-3-one 10, 15, 20 Atoxyn (Polfa S.A., Poland) 10% (v/v) benzyl alkyl (C8–18)dimethylammonium bromides 1, 2.5, 6, 8, 12 Boramon (Altax Sp. z o.o., Poland) 24% (v/v) benzyl alkyl(C12–16)dimethylammonium chlorides 5% (v/v) boric acid 10, 15, 20, 30 Mycetox B’ (ADW Sp. z o.o., Poland) <9.5% (v/v) N,N-didecyl-N,N-dimethylammonium chloride 2% (v/v) citric acid; 0.5% (v/v) (2-metoksymetyloetoksy)propanol 10, 20, 30 Preventol R80 (Bayer AG, Germany) 80% (v/v) benzyl alkyl dimethylammonium chloride 8%–12% (v/v) (2-methoxymethylethoxy)propanol 2.5, 6, 12 Rocima 101 (H.S.H. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081365ijms-17-01365ArticleMelatonin Alleviates Liver Apoptosis in Bile Duct Ligation Young Rats Sheen Jiunn-Ming 12Chen Yu-Chieh 1*Hsu Mei-Hsin 1Tain You-Lin 1Huang Ying-Hsien 1Tiao Mao-Meng 1Li Shih-Wen 1Huang Li-Tung 1*Lemarié Anthony Academic Editor1 Department of Pediatrics, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, 123 Ta-Pei Road, Niao Sung, Kaohsiung 833, Taiwan; ray.sheen@gmail.com (J.-M.S.); a03peggy@adm.cgmh.org.tw (M.-H.H.); tainyl@hotmail.com (Y.-L.T.); yhhuang@cgmh.org.tw (Y.-H.H.); tmm@adm.cgmh.org.tw (M.-M.T.); violet7053@gmail.com (S.-W.L.)2 Graduate Institute of Clinical Medical Sciences, Chang Gung University College of Medicine, Linkou, Taoyuan 333, Taiwan* Correspondence: gesicht27@gmail.com (Y.-C.C.); litung.huang@gmail.com or huang_li@pie.com.tw (L.-T.H.); Tel.: +886-9-7505-6169 (L.-T.H.); Fax: +886-7-733-8009 (L.-T.H.)20 8 2016 8 2016 17 8 136506 6 2016 15 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Bile duct ligation (BDL)-treated rats display cholestasis and liver damages. The potential protective activity of melatonin in young BDL rats in terms of apoptosis, mitochondrial function, and endoplasmic reticulum (ER) homeostasis has not yet been evaluated. Three groups of young male Sprague-Dawley rats were used: one group received laparotomy (Sham), a second group received BDL for two weeks (BDL), and a third group received BDL and intraperitoneal melatonin (100 mg/day) for two weeks (BDL + M). BDL group rats showed liver apoptosis, increased pro-inflamamtory mediators, caspases alterations, anti-apoptotic factors changes, and dysfunction of ER homeostasis. Melatonin effectively reversed apoptosis, mainly through intrinsic pathway and reversed ER stress. In addition, in vitro study showed melatonin exerted its effect mainly through the melatonin 2 receptor (MT2) in HepG2 cells. In conclusion, BDL in young rats caused liver apoptosis. Melatonin rescued the apoptotic changes via the intrinsic pathway, and possibly through the MT2 receptor. Melatonin also reversed ER stress induced by BDL. bile duct ligationmelatoninapoptosisendoplasmic reticulummitochondria ==== Body 1. Introduction Bile duct ligation (BDL) is extensively used as a model of acute and chronic liver injury accompanied by cholestasis [1,2,3,4]. Obstructive jaundice occurs immediately and evolves to cirrhosis in four to six weeks [5,6]. BDL in rats is characterized by both peripheral and central inflammation [7,8]. It is well documented that oxidative stress is implicated in the pathogenesis of liver damage and systemic organ dysfunction in young and adult rats after BDL [1,4,7,9,10]. Apoptosis, which is also named programmed cell death, is strongly associated with mitochondrial function and is considered an important component of various physiological processes including normal cell turnover, adequate development and function of the immune system, embryonic development, hormone-dependent atrophy, and chemical substance-induced cell death [11]. Melatonin has been shown to be able to attenuate hepatocyte apoptosis in BDL adult rats [12]; however, the underlying mechanisms are not clear. Mitochondrial structures are highly susceptible to oxidative injury and mitochondrial damages take an important role in cell death [13]. Melatonin is effective in protecting liver mitochondrial damages in diabetic obese rats [14]. Endoplasmic reticulum (ER) plays a pivotal role in biosynthesis and maturation of proteins, synthesis of lipids, adjustment of calcium, and preservation of cell homeostasis [15]. A variety of disorders such as hypoxia, glucose starvation, and oxidative stress may lead to endoplasmic reticulum (ER) disorder, which can provoke ER stress. Recently, increasing studies show ER stress is a salient feature of numerous liver diseases [16,17]. Recently, melatonin is shown to be effective in reducing ER stress-induced hepatic steatosis [18] and tetrachloride-induced liver fibrosis [19]. Melatonin is a strong antioxidant [20,21] and confers ability to protect mitochondrial from damage [22,23]. Melatonin is widely used in various pre-clinical studies [23] with doses ranging from 0.1 to 100 mg/kg/day for various days via different routes. Though the beneficial effects of melatonin in BDL liver damages are well delineated [1,23,24,25], the underlying molecular mechanisms are largely unexplored. In the present study, we aimed to evaluate the therapeutic effect of melatonin in alleviating liver apoptosis in BDL young rats and focused on apoptosis pathway, mitochondrial function, and ER stress. 2. Results 2.1. Bile Duct Ligation (BDL) Resulted in Increased Hepatic Apoptosis BDL rats had higher plasma total and direct bilirubin, aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels than the Sham group (Table 1). Melatonin treatment caused reduction of direct bilirubin levels (Table 1). As shown in Figure 1, BDL resulted in increased liver apoptosis and melatonin treatment reduced the process, which was evidenced by Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) stain. 2.2. BDL Increased the Mrna Expression of Proinflammatory Mediators and Melatonin Treatment Altered the Changes As shown in Figure 2, BDL resulted in increased mRNA expression of tumor necrosis factor-α (TNF-α) mRNA (F (2, 25) = 16.982, p < 0.001), nuclear factor-kappa B (NFκB) mRNA (F (2, 22) = 63.263, p < 0.001), and p53 mRNA (F (2, 22) = 35.768, p < 0.001). Melatonin treatment caused significant reduction of the mRNA expression of all the three pro-inflammtory mediators. 2.3. BDL Induced Liver Apoptosis via the Caspase-Dependent Pathway Caspases can be activated through intrinsic pathway or extrinsic pathway and lead to apoptosis. We analyzed the caspase mRNA expressions because previous reports have shown that the caspase mRNA levels may be increased in the apoptotic process [26,27,28]. As shown in Figure 3, BDL increased caspase 3, 8, and 9 mRNA expressions (caspase 3 mRNA (F (2, 22) = 47.488, p < 0.001); caspase 8 mRNA (F (2, 24) = 48.954, p < 0.001); caspase 9 mRNA (F (2, 24) = 5.683, p < 0.01)). Increased caspase 3, 8, and 9 mRNA expressions indicated that BDL in young rats led to liver apoptosis. Bonferroni post hoc showed melatonin treatment effectively down-regulated the mRNA expression of caspase 3, (BDL vs. BDL + M, p < 0.01). In addition, Western blot revealed increased protein expression of cleaved caspase 3, 8 and 9 in BDL group (cleaved caspase 3 (F (2, 14) = 6.559, p = 0.010); cleaved caspase 8 (F (2, 14) = 15.469, p < 0.001); cleaved-caspase 9 (F (2, 13) = 6.431, p = 0.011)), and melatonin treatment significantly reduced the protein expression of cleaved caspase 3 and cleaved caspase 9 (BDL vs. BDL + M, both p < 0.05), but had no significant effects on the protein expression of cleaved caspase 8 (Figure 4). The discrepancy of mRNA and protein levels of caspase 3 in BDL + M group could be due to downregulation of miRNA targeting mRNA or a feedback loop inhibits further transcription process. Moreover, fluorescent activity study for caspase activity found that melatonin treatment effectively reduced caspase 3 and caspase 9 activities (caspase 3 activity (F (2, 15) = 9.440, p = 0.002); caspase 9 activity (F (2, 34) = 7.413, p = 0.002)), but had no significant effects on the caspase 8 activity (Figure 5). The above findings indicated that BDL altered the apoptotic pathway through a caspase-dependent manner and melatonin might exert its therapeutic function through a caspase-dependent, chiefly the intrinsic, pathway. 2.4. Melatonin Blocked the Apoptotic Signal Mediation Mainly through the Intrinsic Pathway Induced by BDL Western blot revealed increased TNF-α after BDL and melatonin treatment significantly reversed the change (F (2, 23) = 28.673, p < 0.001; BDL vs. Sham, p < 0.01; BDL vs. BDL + M, p < 0.05). Further examination of the extent of the involvement of the extrinsic pathway in the apoptotic process after BDL, Fas-Associated protein with Death Domain (FADD) was examined. However, FADD was not affected by BDL or melatonin treatment (F (2, 23) = 0.230, p = 0.796). To explore the intrinsic pathway, we examined cytochrome c and Second mitochondria-derived activator of caspase/direct inhibitor of apoptosis-binding protein with low pl (Smac/Diablo) and found cytosolic cytochrome c and Smac/Diablo were increased in response to BDL and melatonin treatment successfully rescued the process (cytochrome c, (F (2, 15) = 6.257, p = 0.011; BDL vs. BDL + M, p < 0.05); Smac/Diablo (F (2, 15) = 16.841), p < 0.001; BDL vs. BDL + M, p < 0.05) (Figure 6). Besides, we studied apoptosis-related genes, TNF-Related Apoptosis-Inducing Ligand Receptor 2 (TRAIL-R2)/death receptor 5 (DR5) and cytochrome c, which represented extrinsic and intrinsic pathway, respectively. As shown in Figure 7, BDL induced up-regulation of DR5 and cytochrome c mRNA expression (DR5 (F (2, 24) = 34.866, p < 0.001); cytochrome c (F (2, 24) = 18.621, p < 0.001)). However, melatonin treatment had not effect in lowering these alterations. Taken together, melatonin rescued the liver apoptotic process mainly through the intrinsic pathway. 2.5. Anti-Apoptotosis Factors Change in Response to BDL and Melatonin Treatment Cellular inhibitor of apoptosis (cIAP1) and survivin mRNA were increased in response to BDL, but melatonin had no significant effects (cIAP1 (F (2, 24) = 5.574, p < 0.010); surviving (F (2, 24) = 68.549, p < 0.001); BND vs. BDL + M, all p > 0.05) (Figure 8). As for apoptosis related inhibitors, such as cIAP1 and X-linked inhibitor of apoptosis protein (XIAP), BDL only decreased the protein expression of cIAP1 (F (2, 23) = 4.004, p = 0.032) and melatonin treatment revealed no significant change in cIAP1. On the other hand, survivin protein expression was increased in BDL and BDL + M groups (F (2, 23) = 20.937, p < 0.001) (Figure 9). Together, these results suggested BDL and melatonin treatment might also alter the anti-apoptotic process. 2.6. Endoplasmic Reticulum (ER) Stress in BDL Rat mRNA expression of the three key regulators of ER homeostasis, protein kinase RNA-like ER kinase (PERK), inositol-requiring enzyme 1 (IRE 1) and activating transcription factor 6 (ATF6) were all increased in BDL group (PERK mRNA (F (2, 23) = 13.431, p < 0.001); IRE1 mRNA (F (2, 21) = 8.742, p = 0.002); ATF6 mRNA (F (2, 23) = 4.246, p = 0.027)). Bonferroni post hoc analysis revealed that PERK and IRE 1 mRNA expression were decreased after melatonin treatment (BDL vs. BDL + M, all p < 0.01). Moreover, further tests for the downstream regulators of ER homeostasis including CCAAT-enhancer-binding protein homologous protein (CHOP), eukaryotic translation initiation factor 2α (eIF2α) and activating transcription factor 4 (ATF4) revealed significant increases of eIF2α and AT4 mRNA expression in BDL rats (eIF2α mRNA (F (2, 23) = 23.894, p < 0.001); ATF4 mRNA (F (2, 23) = 9.641, p = 0.001)). Bonferroni post hoc analysis revealed that eIF2α and AT4 mRNA expression were decreased after melatonin treatment (BDL vs. BDL + M, all p < 0.01) (Figure 10). Taken together, BDL induced disturbance of ER homeostasis and melatonin treatment might effectively reverse the effect. A shown in Figure 11, mRNA expression of Binding immunoglobulin protein (Bip) was increased in response to BDL and melatonin treatment effectively decreased its expression ((F (2, 23) = 15.660, p < 0.001); BDL vs. BDL + M, p < 0.01). To elucidate the crosstalk between apoptosis and ER homeostasis, we examined caspase 12 mRNA expression and found increased mRNA expression in BDL group, which responded well to melatonin treatment (caspase 12 mRNA (F (2, 23) = 24.087, p < 0.001); BDL vs. BDL + M, p < 0.05). Besides, As growth arrest and DNA damage-inducible protein (GADD) 34 plays a role in autoregulation (feedback mechanism) of ER stress response, we tested GADD34 mRNA expression, but found no significant difference of GADD34 mRNA among the three groups (F (2, 23) = 2.079, p = 0.148). 2.7. Melatonin Reduced Cleaved Caspase 3 Expression through the Melatonin 2 (MT2) Receptor There are three subtypes of melatonin receptors identified: MT1, MT2, and MT3 [29,30]. In mammals, the effects of melatonin are mediated mainly through MT1 and MT2 receptors. MT1 receptor is inhibited primarily by luzindole and MT2 receptor is inhibited primarily by 4-phenyl-2-propionamidotetralin (4P-PDOT). Therefore, we focused on MT1 and MT2 receptors for delineating the pathway that mediates the work of melatonin on liver apoptosis. As shown in Figure 12, there was a taurolithocholic acid (TLCA) dose-dependent expression of cleaved caspase 3 at six hours of incubation (F (4, 31) = 11.031, p < 0.001) (Figure 12a). Besides, melatonin effectively decreased the cleaved caspase 3 expression in a dose-dependent fashion (F (5, 34) = 4.206, p = 0.003) (Figure 12b). Furthermore, incubation the cells with 4P-PDOT showed melatonin reduced cleaved caspase 3 expression through MT2 receptor (F (4, 30) = 14.167, p < 0.001) (Figure 12c). The above findings indicated that melatonin acted in a caspase-dependent apoptosis and worked through the MT2 receptor. 3. Discussion The main findings of the present study were summarized as the following: (1) BDL in young rat caused liver apoptosis; (2) melatonin rescued the apoptotic changes mainly through the intrinsic pathway and acted through the MT-2 receptor; and (3) BDL itself served as a source of ER stress and melatonin might reverse the ER stress. In rats, two weeks of BDL represents the liver fibrogenesis stage [5]. Recently, increasing evidence support the protective role of melatonin against oxidative injury in cholestasis [31,32] and melatonin works via eliminating the oxidants [24]. In line with our previous report, melatonin partially restored liver function in BDL rats [4,25]. The present study found increased liver mRNA expression of TNF-α, NFκB and p53 in BDL young rats and melatonin treatment caused significant reduction of the mRNA expression of all the three pro-inflammatory mediators. For liver apoptosis regulation, there is growing evidence that melatonin may directly work in the pathways of apoptosis [33,34]. The caspase-dependent apoptosis process may also be initiated via Fas ligand or oxidative stress. Molecules involving in the extrinsic (death factor-related) pathway include FADD/Tumor necrosis factor receptor type 1-associated DEATH domain protein (TRADD) and caspase 8 and intrinsic (mitochondria-related) pathway include Apaf-1 and IAPs family (caspase inhibitor family) are key regulators of apoptosis. In the present study, we tested caspase 3, 8, and 9 mRNA and protein expression in young BDL rats and found BDL altered the apoptotic pathway and melatonin had a therapeutic role. Melatonin has been shown to be effective in protecting liver mitochondrial damages in rodent studies [14,21]. To clarify whether BDL-induced apoptosis alterations acted through intrinsic or extrinsic pathway, FADD, cytochrome c, Smac/Diablo and TRAIL-R2/DR5 were examined. We found BDL + M group rats had lower cytochrome c and Smac/Diablo protein levels as compared with BDL rats. Collectively, melatonin treatment rescues the hepatic apoptotic process in BDL young rats mainly through the intrinsic pathway. Furthermore, to understand the interaction between apoptosis and anti-apoptosis pathways induced by BDL, we tested the anti-apoptotic proteins, including cIAP1 and XIAP and found decreased level of cIAP1 in BDL rats. Both BDL and melatonin treatment did not alter the XIAP expression. Liver apoptosis is a pathogenic event in some liver diseases, and may be related to unresolved ER stress [35]. ER homeostasis is essential for maintenance of the normal liver function. Various liver diseases are known to be lined to unresolved ER stress, which may lead to hepatocyte apoptosis. Here, we examined the essential regulators of ER homeostasis and try to evaluate the ER homeostasis and apoptosis change in BDL young rats and found increased key regulators, including Bip, PERK, IRE 1 and ATF6 mRNA expression in BDL young rats and melatonin treatment effectively reversed the effect, which indicated that BDL disrupt ER homeostasis and melatonin treatment might effectively reverse the effect. In addition, further tests for the downstream regulators of ER homeostasis found that BDL altered ER homeostasis through the CHOP-dependent pathway. Furthermore, increased caspase 12 mRNA expression in BDL rats indicated that BDL disrupted the homeostasis between mitochondria and ER. It has been known that melatonin exerts its effect through melatonin (MT) receptor [36]. Three subtypes of melatonin receptors were identified in animals, MT1, MT2, and MT3 receptors [29], while only MT1 and MT2 but not MT3 receptors were found [29,30] in mammals. In the present study, we used melatonin receptor antagonists, luzindole (MT1 antagonist) and 4P-PDOT (MT2 antagonist), to evaluate whether melatonin acted through MT1 or MT2 receptor and found that the increased cleaved caspase 3 expression was in a taurolithocholic acid (TLCA) dose-dependent manner. Besides, melatonin effectively decreased the cleaved caspase 3 expression in a dose-dependent fashion. Furthermore, melatonin reduced cleaved caspase 3 expression in HepG2 cells incubated with 4P-PDOT, suggesting that melatonin acted through MT2 receptor. Many trials about melatonin on various human diseases are ongoing [37,38]. Few studies used melatonin to treat human liver disease [39,40]. Celinski et al. reported that melatonin can attenuate the levels of pro-inflammatory cytokines and decrease the plasma levels of gammaglutamyl transferase, triglycerides, and low-density lipoprotein in patients who had non-alcoholic fatty liver disease [40]. Our study provides the theoretical foundation to test the therapeutic role of melatonin in children with cholestatic liver disease. 4. Materials and Methods 4.1. Subjects This study was done under the Guidelines for Animal Experiments of Chang Gung Memorial Hospital and Chang Gung University, Taoyuan, Taiwan. We designated the day of delivery as Day 0. Male Sprague-Dawley rats postnatal day (PND) 17 ± 1 weighing about 50 g were used. We made attempts to minimize the numbers of animals used. All animals were housed in the animal care center maintained at 24 °C with a 12-h light/dark cycle. All animals had free access to water and standard chow. 4.2. Experimental Procedures All surgical procedures were done with clean surgical techniques under anesthesia with ketamine (50 mg/kg) and xylazine (23 mg/kg), as previously described [1]. In brief, the common bile duct was ligated through a midline incision then divided with double ligatures on proximal duct to induce obstructive jaundice. We designated rats that underwent sham BDL for two weeks at PND 17 ± 1 as the sham-control group (Sham) (n = 10). Rats that received BDL for two weeks at PND 17 ± 1 were defined as the BDL group (n = 10). BDL rats had daily melatonin 100 mg/kg/day intraperitoneally injected as a dose proposed by Tahan et al. [24] were designated as BDL + M (n = 10). All rats were sacrificed 14 days after surgery. To reduce the number of animals used, we did not perform sham group receiving melatonin because we previously found that there were no significant effects of melatonin in plasma direct and total bilirubin, AST, and ALT levels in young sham operation rats [41]. 4.3. Plasma Biochemistry Parameters Measurement We collected blood samples by cardiocentesis. Plasma was analyzed for aspartate aminotransferase (AST), alanine aminotransferase (ALT), and total and direct bilirubin as we previously reported [1]. 4.4. Liver Histopathology TUNEL stain was done as we previously reported [42]. TUNEL stain among the three groups was compared under 100× magnification to calculate the integrated optical density (IOD) using Image Pro Plus, Version 6.0 (Media Cybernetics, Inc., Rockville, MD, USA) [43]. 4.5. Western Blot We performed Western blot analysis on liver tissue as previously described [25]. The parameters examined include FADD, TRADD, Fas, and IAP family including cIAP1, XIAP, and cytochrome c. We verified the purity of the mitochondrial fraction by the selective expression of cytochrome c oxidase subunit IV, which is a mitochondrial inner membrane specific protein. 4.6. Quantitative Real-Time Polymerase Chain Reaction (PCR) analysis PCR analysis was performed as we reported previously [25]. Table 2 shows the primer sequences investigated in this study. We ran the whole sample in duplicate (2.5 µL of cDNA per well in a 96-well format). We used the comparative threshold cycle (Ct) method to quantify the relative gene expression. The averaged Ct was subtracted from the corresponding averaged 18S value for each sample to result in ΔCt. We obtained ΔΔCt by subtracting the average control ∆Ct value from the average experimental ΔCt. We established the fold increase by calculating 2−ΔΔCt for experimental vs. control samples. 4.7. HepG2 Cell Culture HepG2 cells (human hepatoma, ATCC HB-8065) were grown at 37 °C under 5% of CO2 incubator. The medium used was minimal essential medium (pH 7.4) containing 10% fetal bovine serum, 1% nonessential amino acids, 2 mmol/L of l-glutamine, 1 mmol/L of sodium pyruvate, 100 units/mL of penicillin and 100 μg/mL of streptomycin. To explore whether melatonin exerted its effect on the caspase-dependent apoptosis pathway through MT1 or MT2, HepG2 cells were incubated with different concentrations of chenodeoxycholic acid and melatonin [44]. Furthermore, melatonin receptor antagonists, luzindole (MT1 antagonist) and 4P-PDOT (MT2 antagonist) were used to determine whether melatonin exerted its effects through melatonin receptors. 4.8. Statistical Analysis We analyzed the blood biochemistry, PCR, Western blotting, and ELISA results among the groups by one-way analysis of variance (ANOVA) with Bonferroni post hoc tests. We used mean ± standard error of mean (SEM) to express the values. Significance was defined as p < 0.05 for all tests. 5. Conclusions The present study delineates a comprehensive view of liver apoptosis in BDL young rats and highlights the therapeutic role of melatonin in this process. BDL induces overwhelming dysregulation of liver apoptosis pathways, involving both mitochondria dysfunction and ER stress. Besides, the effect of BDL on apoptosis after BDL is mediated mainly through intrinsic pathway, which can be rescued by melatonin treatment. Acknowledgments This work was supported by Grant CMRPG8F0181 from Chang Gung Memorial Hospital, Kaohsiung, Taiwan to Li-Tung Huang; Grant CMRPG8E0311 from Chang Gung Memorial Hospital to Jiunn-Ming Sheen; and Grants CMRPG8D0351 and CMRPG8D0352 from Chang Gung Memorial Hospital to Yu-Chieh Chen. Author Contributions Jiunn-Ming Sheen performed the majority of the experiments and wrote this manuscript. Yu-Chieh Chen conceived, designed the experiments and edited this manuscript. Mei-Hsin Hsu and You-Lin Tain aided in experimental design. Ying-Hsien Huang and Shih-Wen Li assisted with experimental design and contributed analysis tools. Miao-Meng Tiao analyzed the data. Li-Tung Huang designed the study, edited and provided final approval of this manuscript. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Representative Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining of liver in bile duct ligation (BDL) rats without and with melatonin treatment. TUNEL staining was increased in BDL rat and this was significantly reduced by melatonin treatment: (a) Sham; (b) BDL; (c) BDL + M; (d) negative control; and (e) integrated optical density (IOD) value of TUNEL stain. Data are shown as mean ± standard error of mean (SEM). ** p < 0.01 vs. Sham; ## p < 0.01 vs. BDL. Figure 2 Melatonin treatment reduced the mRNA expression of pro-inflammatory mediators induced by BDL: (a) BDL induced increased mRNA expression of TNF-α and melatonin treatment decreased the effect; (b) BDL induced increased mRNA expression of nuclear factor-kappa B (NFκB) and melatonin treatment decreased the effect; and (c) BDL induces increased mRNA expression of p53 and melatonin treatment decreased the effect. Significant difference among three groups was analyzed by one-way ANOVA followed by Bonferroni post hoc. All data are shown as mean ± SEM. * p < 0.05 vs. Sham; ** p < 0.01 vs. Sham; # p < 0.05 vs. BDL; ## p < 0.01 vs. BDL. Figure 3 mRNA expression of caspase 3, 8 and 9: (a) expression of caspase 3 mRNA was increased in BDL group and down-regulated in BDL + M group; (b) BDL group had higher caspase 8 mRNA than Sham group; and (c) caspase 9 mRNA was increased in BDL and BDL + M groups. Significant difference among three groups was analyzed by one-way ANOVA followed by Bonferroni post hoc. All data are shown as mean ± SEM. * p < 0.05 vs. Sham; ** p < 0.01 vs. Sham; ## p < 0.01 vs. BDL. Figure 4 Caspase 3 (a) and caspase 8 (b) protein expressions were not affected by BDL. BDL for two weeks increased: caspase 9 (c); cleaved caspase 3 (d); cleaved caspase 8 (e); and cleaved caspase 9 protein expression (f). Melatonin treatment decreased cleaved caspase 3 and cleaved caspase 9. Significant difference among three groups was analyzed by one-way ANOVA followed by Bonferroni post hoc. All data are shown as mean ± SEM. * p < 0.05 vs. Sham; ** p < 0.01 vs. Sham; # p < 0.05 vs. BDL; ## p < 0.01 vs. BDL. Figure 5 BDL for two weeks did not affect caspase 3 (a), caspase 8 (b), or caspase 9 (c) activities. Melatonin treatment decreased caspase 3 and caspase 9 activities. Significant difference among three groups was analyzed by one-way ANOVA followed by Bonferroni post hoc. All data are shown as mean ± SEM. * p < 0.05 vs. Sham; ** p < 0.01 vs. Sham; ## p < 0.01 vs. BDL. RFU: Relative fluorescence units. Figure 6 (a) BDL for two weeks increased TNF-α protein expression while melatonin treatment reduced this effect; (b) cytosolic cytochrome c was highly expressed in response to BDL and melatonin treatment rescued this process; (c) mitochondrial cytochrome c was decreased in response to BDL; (d) Fas-Associated protein with Death Domain (FADD) was not affected by BDL or melatonin treatment; (e) cytosolic second mitochondria-derived activator of caspase/direct inhibitor of apoptosis-binding protein with low pl (Smac/Diablo) was increased after BDL and melatonin effectively reversed the effect; and (f) mitochondrial Smac/Diabo change was not reversed by melatonin treatment. Significant difference among three groups was analyzed by one-way ANOVA followed by Bonferroni post hoc. All data are shown as mean ± SEM. * p < 0.05 vs. Sham; ** p < 0.01 vs. BDL; # p < 0.05 vs. BDL. Figure 7 (a) BDL and melatonin treatment induced the up-regulation of TNF-Related Apoptosis-Inducing Ligand Receptor 2/death receptor 5 (TRAIL-R2/DR 5) and melatonin treatment showed no effect on it; (b) BDL increased cytochrome c mRNA expression and melatonin treatment showed no effect on it. Significant difference among three groups was analyzed by one-way ANOVA followed by Bonferroni post hoc. All data are shown as mean ± SEM. ** p < 0.01 vs. BDL. Figure 8 mRNA expression of anti-apoptosis factors. Cellular inhibitor of apoptosis (cIAP1) (a) and survivin mRNA (b) were increased in BDL rats. Significant difference among three groups was analyzed by one-way ANOVA followed by Bonferroni post hoc. All data are shown as mean ± SEM. * p < 0.05 vs. Sham; ** p < 0.01 vs. Sham. Figure 9 (a) Western blot revealed decreased level of cIAP1 in BDL rats; (b) both BDL and melatonin treatment did not alter the X-linked inhibitor of apoptosis protein (XIAP) expression; and (c) survivin was increased in BDL and BDL + M groups. Significant difference among three groups was analyzed by one-way ANOVA followed by Bonferroni post hoc. All data are shown as mean ± SEM. * p < 0.05 vs. Sham; ** p < 0.01 vs. Sham. Figure 10 (a) Increased mRNA expression of protein kinase RNA-like endoplasmic reticulum kinase (PERK) in BDL rats and responded well to melatonin treatment; (b) increased activating transcription factor 6 (ATF6) mRNA expression in BDL rats; (c) increased inositol-requiring enzyme 1 (IRE1) mRNA expression and responded to melatonin treatment; (d) increased eukaryotic translation initiation factor 2α (eIF2α) mRNA expression and responded to melatonin treatment; (e) increased activating transcription factor 4 (ATF4) mRNA expression and responded to melatonin treatment; and (f) BDL + M group had lower CCAAT-enhancer-binding protein homologous protein (CHOP) mRNA expression than BDL group rats. Significant difference among three groups was analyzed by one-way ANOVA followed by Bonferroni post hoc. All data are shown as mean ± SEM. * p < 0.05 vs. Sham; ** p < 0.01 vs. Sham, # p < 0.05 vs. BDL; ## p < 0.01 vs. BDL. Figure 11 (a) mRNA expression of binding immunoglobulin protein (Bip) was increased in response to BDL, and melatonin treatment effectively decreased its expression; (b) There was increased caspase 12 mRNA expression in young BDL rats; (c) There was no significant difference of growth arrest and DNA-damage-inducible protein (GADD34) mRNA expression among the three groups. Significant difference among three groups was analyzed by one-way ANOVA followed by Bonferroni post hoc. All data are shown as mean ± SEM. ** p < 0.01 vs. Sham; ## p < 0.01 vs. BDL. Figure 12 HepG2 cell culture: (a) There was a taurolithocholic acid (TLCA) dose-dependent protein expression of cleaved caspase 3 at six hours of incubation. * p < 0.05 vs. baseline; ** p < 0.01 vs. baseline; # p < 0.05 vs. 100 μM TLCA treated cells; ## p < 0.01 vs. 100 μM TLCA treated cells; (b) melatonin (Mel) effectively decreased the cleaved caspase 3 expression in a dose-dependent fashion. ** p < 0.01 vs. baseline ; # p < 0.05 vs. 175 μM TLCA with 0 μM melatonin treated cell; (c,d) incubating the cells with luzindole and 4-phenyl-2-propionamidotetralin (4P-PDOT) showed that melatonin reduced cleaved caspase 3 in a dose-dependent fashion, indicating melatonin acted in a caspase-dependent manner and worked through the MT2 receptor. ** p < 0.01 vs. control; # p < 0.05 vs. 175 μM TLCA only; $$ p < 0.01 vs. 500 μM melatonin with 175 μM TLCA treated cells. ijms-17-01365-t001_Table 1Table 1 Plasma liver function profiles in different experimental groups. Groups Sham (n = 10) BDL (n = 10) BDL + M (n = 10) AST (IU/L) 97.8 ± 3.6 411.5 ± 21.8 ** 493.6 ± 40.2 ** ALT (IU/L) 34.7 ± 1.8 108.2 ± 6.7 ** 99.8 ± 7.8 ** Direct Bilirubin (mg/dL) 0.17 ± 0.02 4.71 ± 0.32 ** 3.63 ± 0.39 **,# Total bilirubin (mg/dL) 0.41 ± 0.28 5.63 ± 0.38 ** 5.07 ± 0.50 ** Values are shown as means ± standard error of mean (SEM); AST, aspartate aminotransferase; ALT, alanine aminotransferase; BDL, bile duct ligation. ** p < 0.01 vs. sham ; # p < 0.05 vs. BDL. ijms-17-01365-t002_Table 2Table 2 Primer sequences used in the Real-Time PCR. Gene Forward 5′–3′ Reverse 5′–3′ Caspase-3 GGCCGACTTCCTGTATGC GCGCAAAGTGACTGGATG Caspase-8 ACGATATTGCTGAACGTCTGG CCGACTGATATGGAAAAGCAG Caspase-9 GGAAGATCGAGAGACATGCAG CCGTGACCATTTTCTTAGCAG 18S GCGATGCGGCGGCGTTAT AGACTTTGGTTTCCCGGAAGC Cytochrome c AACCTCCATGGTCTGTTTGG GTCTGCCCTTTCTCCCTTCT cIAP1 AGCTTGCAAGTGCTGGATTT CTCCTGACCCTTCATCCGTA Survivin TGCAAAGGAGACCAACAACA AAGCTGGGACAAGTGGCTTA NFκB GCTTACGGTGGGATTGCATT GCACAATCTCTAGGCTCGTTTTTAA TNFα GGCTGCCCCGACTACGT AGGGCAAGGGCTCTTGATG DR5 AAATGCTGCTGAAGTGGCT ACTAATAAAGATCCTCTCGGCTC BiP GACCACCTATTCCTGCGTCGGT CGCCAATCAGACGCTCCCCT Caspase12 GGAAGGTAGGCAAGAGT GTAGAAGTAGCGTGTCATA GADD34 TGAATGTTGAGAGAAGAACC TTGTTTAGAAGTCGCTCTG PERK GCTTGCTCCCACATCGGATA TGCGGCAATTCGTCCATCTA IRE1 TTGACTATGCAGCCTCACTTC AGTTACCACCAGTCCATCGC eIF2 α ATAGGCGTTTGACCCCACAA ATCACATACCTGGGTGGAGC ATF4 CCTTCGACCAGTCGGGTTTG CTGTCCCGGAAAAGGCATCC ATF6 AAGTGAAGAACCATTACTTTATATC TTTCTGCTGGCTATTTGT p53 TATGACTTTAGGGCTTGTTA AGCAACTACCAACCCATTC ==== Refs References 1. Huang L.T. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081366ijms-17-01366ArticleSynthesis and Evaluation of Novel Oxyalkylated Derivatives of 2′,4′-Dihydroxychalcone as Anti-Oomycete Agents against Bronopol Resistant Strains of Saprolegnia sp. Flores Susana 1Montenegro Iván 2Villena Joan 3Cuellar Mauricio 4Werner Enrique 5Godoy Patricio 6Madrid Alejandro 1*Hayashi Yujiro Academic Editor1 Departamento de Química, Facultad de Ciencias Naturales y Exactas, Universidad de Playa Ancha, Avda. Leopoldo Carvallo 270, Playa Ancha, Valparaíso 2340000, Chile; s.flore.gonzalez@gmail.com2 Escuela de Obstetricia y Puericultura, Facultad de medicina, Campus de la Salud, Universidad de Valparaíso, Angamos 655, Reñaca, Viña del Mar 2520000, Chile; ivan.montenegro@uv.cl3 Centro de Investigaciones Biomédicas (CIB), Escuela de Medicina, Universidad de Valparaíso, Av. Hontaneda N° 2664, Valparaíso 2340000, Chile; juan.villena@uv.cl4 Facultad de Farmacia, Universidad de Valparaíso, Av. Gran Bretaña N° 1093, Valparaíso 2340000, Chile; mauricio.cuellar@uv.cl5 Departamento De Ciencias Básicas, Campus Fernando May Universidad del Biobío, Avda. Andrés Bello s/n casilla 447, Chillán 3780000, Chile; ewerner@ubiobio.cl6 Instituto de Microbiología Clínica, Facultad de Medicina, Universidad Austral de Chile, Los Laureles s/n, Isla Teja, Valdivia 5090000, Chile; patricio.godoy@uach.cl* Correspondence: alejandro.madrid@upla.cl; Tel.: +56-032-250-052622 8 2016 8 2016 17 8 136621 7 2016 16 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).A series of novel oxyalkylchalcones substituted with alkyl groups were designed and synthesized, and the antioomycete activity of the series was evaluated in vitro against Saprolegnia strains. All tested O-alkylchalcones were synthesized by means of nucleophilic substitution from the natural compound 2′,4′-dihydroxychalcone (1) and the respective alkyl bromide. The natural chalcone (1) and 10 synthetic oxyalkylchalcones (2–11) were tested against Saprolegnia parasitica and Saprolegnia australis. Among synthetic analogs, 2-hydroxy,4-farnesyloxychalcone (11) showed the most potent activity against Saprolegnia sp., with MIC and MOC values of 125 µg/mL (similar to bronopol at 150 µg/mL) and 175 µg/mL, respectively; however, 2′,4′-dihydroxychalcone (1) was the strongest and most active molecule, with MIC and MOC values of 6.25 µg/mL and 12.5 µg/mL. oxyalkylchalconesfish pathogenSaprolegnia parasiticaSaprolegnia australis ==== Body 1. Introduction Oxyprenylated chalcones are an important subclass of naturally occurring chalcones that have been considered merely biosynthetic intermediates of C-prenylated chalcones for decades [1]. Only in the last 10 years have they been characterized as phytochemicals exerting interesting and valuable biological activities [2,3]. In this context, some structure-activity relationship studies have suggested that the antifungal/antimicrobial effects of chalcones are mainly attributable to the presence of phenolic hydroxyl groups, which are known to have high affinity for proteins [4,5,6]; and that the substitution of the chalcone ring system with alkyl groups, especially O-alkyl, is thought to increase their lipophilicity, which enhances their antimicrobial activity through interaction with cellular membranes [7]. Based on these premises, the unexplored potential of oxyprenylated chalcones is of great interest; however, low yields of these compounds have traditionally been obtained from natural sources [1,8]. Because of these reasons, research efforts have intensified in chemically synthesizing these compounds, their structural analogs, and their derivatives in recent decades [9,10]. As such, oxyalkylated chalcones present interesting biological properties, being both synthetically and biosynthetically related to flavonoids [11]. These facts motivated us to accomplish the synthesis of novel oxyalkylated derivatives from 2′,4′-dihydroxychalcone (1) with different alkyl moieties. In terms of application, our final aim was the evaluation of their biological activity as potential anti-oomycete agents against S. parasitica and S. australis. 2. Results and Discussion 2.1. Synthesis The goal of our synthesis was to introduce an alkyl group connected to ring A in the 4′ position of the chalcone with an ether link. Interestingly, our chalcone derivatives all possessed a hydroxyl group attached to ring A in the 2′ position. All of the O-alkylated chalcones 2–11 were synthesized according to previous literature, i.e., via reaction of 2′,4′-dihydroxychalcone (isolated naturally from Adesmia balsamica) with each desired alkyl bromide in the presence of potassium carbonate and acetone (Scheme 1) [12]. Alkyl bromides are a readily available starting material, and, under basic conditions, react with 2′,4′-dihydroxychalcone (1), forming high yields of derivatives. The selectivity of the O-alkylation at position 4′ of 1 could be explained by the intramolecular hydrogen bond between the carbonyl group and the hydrogen of the hydroxyl group in position 2′ of the aromatic ring A [13]. The hydrogen bond prevents the alkylation of phenol group from occurring in position 2′. 2.2. Structure Determination All compounds have been characterized on the basis of spectral studies (FT-IR, NMR and HRMS data). Assignments of the 1H NMR and 13C NMR resonances of these compounds were deduced on the basis of signal multiplicities, and by the concerted application of the two-dimensional NMR technique (HMBC). The 1H and 13C NMR data for the derivatives of 2′,4′-dihydroxychalcone 2–11 were nearly identical in the aromatic region of the spectra. The spectra of these compounds showed two multiplets at δ: 7.66–7.64 ppm and δ: 7.43 ppm integrating for two and three protons, which were assigned to the H-2,6 and H-3,4,5, respectively, pattern characteristic of chalcones with an unsubstituted ring B, and resonances due to the trans-α,β-unsaturated ketone protons at δ 7.65 ppm (1H, J = 15.5 Hz, H-7) and 7.57 (1H, J = 15.5 Hz, H-8). The ketone carbonyl carbon occurred at δ 191.8–189.9 ppm in the 13C NMR spectrum. The 1H NMR spectrum of 2–11 showed a singlet of a hydrogen-bonded hydroxy proton at δ 13.49–13.40 ppm, confirming the selectivity of the O-alkylation at position 4′ of compound 1. However, alkylation resulted in significant changes in the 1H NMR spectra. The spectrum of compound 2 shows the presence of a singlet at 3.88 ppm. This is characteristic of methoxy groups on aromatic rings, resulting from the methyl groups added by the alkylation reaction. For saturated compound 4, the proton signal at 3.78 ppm (d, 2H) ascribed to O–CH2 protons of alkoxy chain linking (E)-chalcone ring is also observed. The correlation between the signal at 19.3 and 166.0 ppm with the 1H NMR signal cited above allows the assignment of this signal to the O–CH2 carbon (Figure 1). For allylic compounds 3, 5, 6, 9, 10 and 11, the proton signal at 4.47–4.60 ppm (d, 2H) ascribed to O–CH2 protons of alkoxy chain linking (E)-chalcone ring is also observed. The structural determination of these compounds was established by comparison of the spectral data of compound 4 and using the same criteria. For example, the 1H NMR spectrum of compound 5 showed the signal at 4.47 ppm (d, J = 6.5 Hz, 2H, H-1″) correlated (by 2D HSQC) with a carbon atom at δ 75.7 ppm (C-1″), indicating the coupling point between the allyl fragment and the aromatic hydroxyl group. These data also were corroborated by 2D HMBC correlations, where H-1″ showed heteronuclear 3J correlations with the carbons signal at δ 19.3 (C-4″), 139.3 (C-3″) and 165.4 (C-4′) ppm. Heteronuclear 2J correlations were also observed at δ 139.9 (C-2″) (Figure 2). For unsaturated compounds 7 and 8, the proton signal at 4.08 ppm (t, 2H) ascribed to O–CH2 protons of alkoxy chain linking (E)-chalcone ring is also observed. For compounds 7–8, the correlation between the signal at 166.7 ppm with the 1H NMR signal cited above allows the assignment of this signal to the O–CH2 carbon. However, the difference between 7 and 8 was HMBC correlations between H-1″ and C-3″, where H-1″ showed heteronuclear 3J correlations with C-3″ at δ 133.9 and 28.1 ppm, respectively (Figure 3). 2.3. Antioomycete Activity against S. parasitica and S. australis in Vitro In recent years, Bronopol (2-bromo-2-nitropropane-1,3-diol), a broad-spectrum biocide, has seen usage as an effective and economically acceptable alternative in the treatment and control of Saprolegnia sp. [14]; however, there are growing concerns regarding its use within the industry, due to its acute toxicity to several fish species and its potentially harmful effects to human health [15]. Furthermore, it has been reported that prolonged use of this product has caused Saprolegnia to develop multiresistance mechanisms to the action of this compound [16]. Tests of the antioomycete activities of natural compound 1 and oxyalkylchalcones 2–11 were performed, and the results compared to commercial antifungal “Bronopol″; the minimum inhibitory concentrations (MIC) (Table 1) and the minimum oomycidal concentrations (MOC) (Table 2) for 1–11 were determined. Several studies have demonstrated that oomycete fungicide resistance may be linked to specific mutations in target sites [17,18], or other forms of tolerance in some species of oomycete [19]. Further analyses of bronopol resistance in resistant strains of Saprolegnia indicates that the former withstood standard concentrations (i.e., regularly used for treating saprolegniasis in farms [17,18]) of the latter. Even though bronopol concentrations used in these studies had a fungistatic effect, and were able to inhibit sporulation, full growth and sporulation ability were recoverable after being cultivated with bronopol at the lesser concentration of 175 (µg/mL) after initial treatment applications. Table 1 presents the MIC values obtained for isolates of S. parasitica and S. australis, which, in initial screenings, presented some susceptibility to chalcone 1 and synthetic compounds 2–11. In addition, Table 1 shows comparison values for bronopol, safrole, eugenol, fluconazole and ketoconazole. The MIC values for bronopol varied between 200 and 175 µg/mL for S. parasitica and S. australis, respectively. In previous studies, azoles (fluconazole and Ketoconazole) had exhibited promising activity, with MIC values of 256 and 64 µg/mL against Saprolegnia diclina, Saprolegnia ferax and S. parasitica [20]. Building on this, our study determined MIC values for fluconazole (>200 µg/mL) and ketoconazole (75 and 50 µg/mL). Another study [21] had shown that safrole and eugenol are effective anti-oomycete agents against S. parasitica, S. diclina, and S. ferax. For compound 1, the MIC values ranged between 12.5 and 6.25 µg/mL for both species. For compound 5, the MIC values ranged between 175 and 150 µg/mL. Compound 11 was the most effective synthetic derivative, with MIC values of 150 and 125 µg/mL, respectively. Results for compound 1 suggested that cell wall integrity may be affected. In short, this compound caused 100% damage (see Table 1) to cell integrity, which may be linked to the β-1,3-glucan synthesis axis; this explanation is feasible, since the oomycete family Saprolegniaceae (i.e., Saprolegnia ferex, Achlya bisexualis, and Achlya ambisexualis) are unable to adjust hyphal turgor pressure in response to increasing extracellular osmotic potentials, and thus secrete endoglucanases to reduce the strength of their cell walls [22]. Compound 11 presents membrane damage of 30%–32% in S. parasitica and S. australis, similar values to those of bronopol. Next, terms of the minimum oomycetidal concentration (MOC), compound 1 presents a value of 12.5 μg/mL for both species of Saprolegnia. Compound 7 had an MOC effect at 175 µg/mL, except in S. parasitica, where it was more effective. Compound 11 had oomycetidal activity at concentrations of 125 µg/mL for S. parasitica and S. australis. The corresponding MOC values in traditional anti-oomecyte agents were similar: Bronopol varies between 200 and 175 µg/mL for S. parasitica and S. australis. In previous studies, bronopol exhibited promising activity, with MOC value of 20 µg/mL against S. parasitica [20]. Indeed, another study showed that bronopol has even better anti-oomycete effects against Saprolegnia with MOC range 100 to 200 µg/mL [23]. For safrole, MOC values were at 200 µg/mL for S. australis. Mycelial growth inhibition (MIG) on S. parasitica and S. australis was estimated by measuring the radial growth of the isolate on PDA plates with a concentration of 200 µg/mL for all compounds and bronopol (see Table 3). The effect of natural compound 1 on sporulation was assessed by exposing mycelial colonies to compound 1 at a concentration of 200 µg/mL for 30 min. The number of zoospores released was calculated after 48 h. Results for 1 present 100% inhibition of growth respectively for both species. The other compounds 2–11 and bronopol had less inhibitory effects. Beyond the practical uses of the compounds tested here, and of interest for future studies, a novel characteristic of the study found that chalcones inhibit β(1,3)-glucan and chitin synthases [24]. It has been reported that Saprolegnia cells exhibit similar distributions of cellulose and β-(1→3)-glucan in their walls as those found in Achlya [25,26]. In concluding, the results obtained in this work reveal that the highest susceptibility of S. parasitica and S. australis is to natural compound (1) when compared to its synthetic derivates 2–11, bronopol, safrole, or eugenol. In general, 1 showed higher inhibition of growth values as compared to positive controls tested. The oxyalkylchalcone 11 tested was more efficient than bronopol, safrole, and eugenol. Among the group of synthetic O-alkylated chalcones, the farnesyloxy derivate was a close second in terms of antioomycete activity against S. parasitica and S. australis. These results demonstrate a correlation between biological activity and alkyl group length, providing further evidence of the potent activity of farnesyl phenyl ether, as had been touched on in previous studies [27,28]. In the event that natural chalcone compound 1 were to replace synthetic compounds, hatchery operating costs would very likely be reduced, due to the low cost with which this compound can be obtained, and workplace safety would be improved. Moreover, the usage of natural 2′,4′-dihydroxychalcone instead of bronopol or formalin would relieve some of the environmental pressures on fish farms, which have been encouraged to pollute less in operations and reduce impacts on the environment in recent years. 3. Materials and Methods 3.1. General Unless otherwise stated, all chemical reagents and positive controls purchased (Aldrich, Darmstadt, Germany) were of the highest commercially available purity and were used without previous purification. Structures of synthesized products were confirmed by spectroscopic methods have been given elsewhere [29]. The course of synthesis was controlled by means of thin layer chromatography and products were separated by column chromatography following a standard method [29]. 3.2. Plant Material Aerial parts of Adesmia balsamica were collected in Viña del Mar, Valparaíso Region, Chile, in March 2015. A voucher specimen (VALPL 1899) was deposited at the VALP Herbarium, Department of Biology, Universidad de Playa Ancha, Valparaíso, Chile. 3.3. Isolation of 2,4-Dihydroxychalcone (1) The natural chalcone 1 was isolated from resinous exudate of A. balsamica (Fabaceae). The extraction methodology and isolation of pure compound was performed according to reported procedures [30]. Compound 1 was identified by melting point, spectroscopic data, including 1H and 13C NMR and comparisons with data reported in the literature [31]. The % purity of compound 1 (95%) was confirmed by analytical HPLC. 3.4. Synthesis 3.4.1. Oxyalkylation Reaction In a round bottom flask compound 1 (5 mmol) was added, and different alkyl bromides (6 mmol) and anhydrous potassium carbonate (0.70 g; 5 mmol) in dry acetone (10 mL) were refluxed for 8 h at 70 °C. Then, the mixture was cooled and diluted with water (30 mL) and extracted with ethyl acetate (2 × 20 mL). The organic layer was washed with brine, dried over anhydrous Na2SO4, filtered and concentrated under vacuum. The products were purified by crystallization from methanol or the crude was redissolved in CH2Cl2 (5 mL) and chromatographed on silica gel with hexane/ethylacetate mixtures of increasing polarity (20.0:0.0→16.8:3.2). 3.4.2. Synthesis of Oxyalkylated Chalcones (2E)-1-(2-Hydroxy-4-methoxyphenyl)-3-phenylprop-2-en-1-one (2). Compound 2 was obtained as a yellow solid (89%) by coupling of compound 1 (5 mmol) and iodomethane (6 mmol) in acetone. NMR data of 2 was consistent with that reported in the literature [32]. (2E)-1-[4-(Allyloxy)-2-hydroxyphenyl]-3-phenylprop-2-en-1-one (3). The compound 3 was obtained as a yellow solid (79%) by coupling of compound 1 (5 mmol) and allyl bromide (6 mmol) in acetone. NMR data of 3 was consistent with that reported in the literature [33]. (2E)-1-(2-Hydroxy-4-isobutoxyphenyl)-3-phenylprop-2-en-1-one (4). Compound 4 was obtained as a pale yellow viscous oil (78% yield) by standard nucleophilic substitution reaction of compound 1 (5 mmol) with 1-bromo-2-methylpropane (6 mmol) in acetone. Compound 4: IR (cm−1) 2961, 1632 (C=O), 1521 (C=C), 1245, 1131; 1120, 1023. 1H NMR (CDCl3, 400.1 MHz) δ 13.36 (s, 1H, 2′-OH), 7.89 (s, 1H, H-7); 7.84 (m, 1H, H-6′); 7.66 (m, 2H, H-2 and H-6); 7.57 (s, 1H, H-8); 7.43 (m, 3H, H-3, H-4 and H-5); 6.47 (m, 2H, H-3′ and H-5′); 3.78 (d, J = 6.52 Hz, 2H, H-1″); 2.27 (m, 1H, H-2″); 1.12 (s, 6H, H-3″ and H-4″); 13C NMR (CDCl3, 100.6 MHz) δ 191.5 (C-9); 166.0 (C-4′); 164.04 (C-2′); 144.1 (C-7); 134.6 (C-1); 131.1 (C-6′); 130.5 (C-4); 129.1 (C-3 and C-5); 128.6 (C-2 and C-6); 121.8 (C-1′); 120.2 (C-8); 107.2 (C-5′); 101.4 (C-3′); 75.7 (C-1″); 28.6 (C-2″); 19.3 (C-3″and C-4″). MS: M + H ion m/z 297.3671 (calculated for C19H20O3: 296.3604). (2E)-1-{2-Hydroxy-4-[(2-methylprop-2-en-1-yl)oxy]phenyl}-3-phenylprop-2-en-1-one (5). The compound 5 was obtained as a yellow oil (72% yield) by standard nucleophilic substitution reaction of compound 1 (5 mmol) with 3-bromo-2-methyl-1-propene (6 mmol) in acetone. Compound 4: IR (cm−1) 2958, 1635 (C=O), 1519 (C=C), 1243, 1131; 1H NMR (CDCl3, 400.1 MHz) δ 13.40 (s, 1H, 2′-OH), 7.91 (s, 1H, H-7); 7.84 (m, 1H, H-6′); 7.64 (m, 2H, H-2 and H-6); 7.57 (s, 1H, H-8); 7.43 (m, 3H, H-3, H-4 and H-5); 6.51 (m, 2H, H-3′ and H-5′); 5.10 (s, 1H, H-3″b); 5.03 (s, 1H, H-3″α); 4.47 (d, J = 11.12 Hz, 2H, H-1″); 1.84 (s, 3H, H-4″); 13C NMR (CDCl3, 100.6 MHz) δ 191.8 (C-9); 166.6 (C-2′); 165.4 (C-4′); 144.8 (C-7); 139.9 (C-2″); 134.8 (C-1); 131.2 (C-6′); 130.6 (C-4); 129.0 (C-3 and C-5); 128.7 (C-2 and C-6); 122.4 (C-1′); 120.4 (C-8); 113.4 (C-3″); 108.2 (C-5′); 102.0 (C-3′); 71.9 (C-1″); 19.3 (C-4″). MS: M + H ion m/z 295.3565 (calculated for C19H18O3: 294.3345). (2E)-1-[4-(Crotyloxy)-2-hydroxyphenyl]-3-phenylprop-2-en-1-one (6). Compound 6 was obtained as an orange solid (72% yield) by standard nucleophilic substitution reaction of compound 1 (5 mmol) with crotyl bromide (6 mmol) in acetone. Compound 6: mp 108–110 °C. IR (cm−1) 2958, 1635 (C=O), 1519 (C=C), 1243, 1131; 1H NMR (CDCl3, 400.1 MHz) δ 13.43 (s, 1H, 2′-OH), 7.91 (s, 1H, H-7); 7.84 (m, 1H, H-6′); 7.64 (m, 2H, H-2 and H-6); 7.57 (s, 1H, H-8); 7.43 (m, 3H, H-3, H-4 and H-5); 6.49 (m, 2H, H-3′ and H-5′); 5.93 (m, 1H, H-2″); 5.73 (m, 1H, H-3″); 4.51 (d, J = 6.1 Hz, 2H, H-1″); 1.77 (s, 3H, H-4″); 13C NMR (CDCl3, 100.6 MHz) δ 191.8 (C-9); 166.6 (C-2′); 165.4 (C-4′); 144.8 (C-7); 134.8 (C-1); 131.6 (C-2″); 131.1 (C-6′); 130.6 (C-4); 129.0 (C-3 and C-5); 128.5 (C-2 and C-6); 125.0 (C-1′); 120.4 (C-8); 114.1 (C-3″); 108.3 (C-5′); 101.8 (C-3′); 69.0 (C-1″); 17.9 (C-4″). MS: M + H ion m/z 295.3512 (calculated for C19H18O3: 294.3444). (2E)-1-[4-(But-3-en-1-yloxy)-2-hydroxyphenyl]-3-phenylprop-2-en-1-one (7). The compound 7 was obtained as a yellow oil (79% yield) by standard nucleophilic substitution reaction of compound 1 (5 mmol) with 4-bromo-butene (6 mmol) in acetone. Compound 7: IR (cm−1) 2950, 1636 (C=O), 1522 (C=C), 1245, 1133; 1H NMR (CDCl3, 400.1 MHz) δ 13.43 (s, 1H, 2′-OH), 7.91 (s, 1H, H-7); 7.84 (m, 1H, H-6′); 7.66 (m, 2H, H-2 and H-6); 7.57 (s, 1H, H-8); 7.43 (m, 3H, H-3, H-4 and H-5); 6.49 (m, 2H, H-3′ and H-5′); 5.90 (s, 1H, H-3″); 5.17 (m, 2H, H-4″); 4.08 (t, J = 6.4 Hz, 2H, H-1″); 2.57 (m, 2H, H-2″); 13C NMR (CDCl3, 100.6 MHz) δ 191.8 (C-9); 166.7 (C-4′); 165.4 (C-2′); 144.4 (C-7); 134.8 (C-1); 133.9 (C-3″); 131.2 (C-6′); 130.6 (C-4); 129.0 (C-3 and C-5); 128.5 (C-2 and C-6); 120.4 (C-8); 117.4 (C-4″); 114.1 (C-1′); 108.1 (C-5′); 101.6 (C-3′); 67.6 (C-1″); 33.3 (C-2″). MS: M + H ion m/z 295.3514 (calculated for C19H18O3: 294.3445). (2E)-1-[2-Hydroxy-4-(pent-4-en-1-yloxy)phenyl]-3-phenylprop-2-en-1-one (8). Compound 8 was obtained as a pale brown oil (78% yield) by standard nucleophilic substitution reaction of compound 1 (5 mmol) with 5-bromo-pentene (6 mmol) in acetone. Compound 8: IR (cm−1) 2958, 1633 (C=O), 1517 (C=C), 1240, 1130; 1H NMR (CDCl3, 400.1 MHz) δ 13.44 (s, 1H, 2′-OH), 7.91 (s, 1H, H-7); 7.84 (m, 1H, H-6′); 7.65 (m, 2H, H-2 and H-6); 7.57 (s, 1H, H-8); 7.43 (m, 3H, H-3, H-4 and H-5); 6.48 (m, 2H, H-3′ and H-5′); 5.85 (m, 1H, H-4″); 5.06 (m, 2H, H-5″); 4.08 (t, J = 6.4 Hz, 2H, H-1″); 2.25 (m, 2H, H-3″); 1.92 (m, 2H, H-2″); 13C NMR (CDCl3, 100.6 MHz) δ 191.8 (C-9); 166.7 (C-4′); 165.8 (C-2′); 144.3 (C-7); 137.4 (C-4″); 134.8 (C-1); 131.2 (C-6′); 130.6 (C-4); 129.0 (C-3 and C-5); 128.5 (C-2 and C-6); 120.4 (C-8); 115.5 (C-5″); 114.0 (C-1′); 108.1 (C-5′); 101.6 (C-3′); 67.6 (C-1″); 30.0 (C-2″); 28.1 (C-3″). MS: M + H ion m/z 309.3782 (calculated for C20H20O3: 308.3710). (2E)-1-[2-Hydroxy-4-(prenyloxy)phenyl]-3-phenylprop-2-en-1-one (9).The compound 9 was obtained as a yellow solid (70% yield) by standard nucleophilic substitution reaction of compound 1 (5 mmol) with prenyl bromide (6 mmol) in acetone. NMR data of 9 was consistent with that reported in the literature [33]. (2E)-1-[4-(Geranyloxy)-2-hydroxyphenyl]-3-phenylprop-2-en-1-one (10). The compound 10 was obtained as an orange oil (47% yield) by standard nucleophilic substitution reaction of compound 1 (5 mmol) with geranyl bromide (6 mmol) in acetone. Compound 10: IR (cm−1) 2940, 2865, 1631 (C=O), 1530 (C=C), 1512, 1338, 1241, 1129, 817. 1H NMR (CDCl3, 400.1 MHz) δ 13.43 (s, 1H, 2′-OH), 7.91 (s, 1H, H-7); 7.84 (d, J = 9.6 Hz, 1H, H-6′); 7.65 (m, 2H, H-2 and H-6); 7.57 (s, 1H, H-8); 7.43 (m, 3H, H-3, H-4 and H-5); 6.50 (m, 2H, H-3′ and H-5′); 5.48 (m, 1H, H-2″); 5.09 (m, 1H, H-7″); 4.60 (d, J = 6.6 Hz, 2H, H-1″); 2.12 (m, 4H, H-5″ and H-6″), 1.75 (s, 3H, H-4″); 1.68 (s, 3H, H-9″), 1.61 (s, 3H, H-10″). 13C NMR (CDCl3, 100.6 MHz) δ 191.8 (C-9); 166.7 (C-2′); 166.0 (C-4′); 144.3 (C-7); 141.2 (C-3″); 134.9 (C-1); 131.2 (C-6′ and C-8″); 130.6 (C-4); 129.0 (C-3 and C-5); 128.5 (C-2 and C-6); 123.7 (C-7″); 120.4 (C-8); 118.5 (C-2″); 114.0 (C-1′); 108.3 (C-5′); 101.8 (C-3′); 65.3 (C-1″); 39.5 (C-5″); 26.3 (C-6″); 25.6 (C-10″); 17.7 (C-9″); 16.7 (C-4″). MS: M + H ion m/z 377.5221 (calculated for C25H28O3: 376.5130). (2E)-1-[4-(Farnesyloxy)-2-hydroxyphenyl]-3-phenylprop-2-en-1-one (11). The compound 11 was obtained as a pale yellow oil (31% yield) by standard nucleophilic substitution reaction of compound 1 (5 mmol) with farnesyl bromide (6 mmol) in acetone. Compound 11: IR (cm−1) 2955, 1632 (C=O), 1541 (C=C), 1374, 1203, 1140, 978. 1H NMR (CDCl3, 400.1 MHz) δ 7.85 (d, J = 9.6 Hz, 1H, H-6′); 7.70 (s, 1H, H-7); 7.69 (s, 1H, H-8); 7.58 (m, 2H, H-2 and H-6); 7.35 (m, 3H, H-3, H-4 and H-5); 6.56 (m, 1H, H-5′); 6.52 (m, 1H, H-3′); 5.50 (m, 1H, H-2″); 5.10 (m, 2H, H-7″ and H-12″); 4.60 (d, J = 6.6 Hz, 2H, H-1″); 2.07 (m, 4H, H-5″ and H-6″), 1.99 (m, 4H, H-10″ and H-11″), 1.76 (s, 3H, H-4″); 1.73 (s, 3H, H-10″); 1.67 (s, 6H, H-9″ and H-14″). 13C NMR (CDCl3, 100.6 MHz) δ 189.9 (C-9); 163.7 (C-2′); 160.1 (C-4′); 142.0 (C-7); 142.0 (C-3″); 135.8 (C-8″); 135.6 (C-1); 133.1 (C-6′); 131.3 (C-13″); 129.7 (C-4); 128.7 (C-3 and C-5); 128.2 (C-2 and C-6); 124.3 (C-7″); 123.6 (C-12″); 122.1 (C-8); 118.9 (C-2″); 118.7 (C-1′); 106.1 (C-5′); 100.3 (C-3′); 65.6 (C-1″); 39.7 (C-5″); 39.6 (C-10″); 26.2 (C-11″ and C-6″); 25.7 (C-15″); 17.7 (C-14″); 16.7 (C-4″); 16.0 (C-9″). MS: M + H ion m/z 445.6162 (calculated for C30H36O3: 444.605). 3.5. Oomycete Isolate and Culture Condition In this study, the strains of S. parasitica and S. australis were used in all experiments. These strains were isolated from a naturally infected salmon (Salmo salar) [34] and was maintained on yeast dextrose agar medium at 4 °C [35]. The inoculum of the pathogen was grown on Saboraud dextrose agar medium at 18 °C for three days [36]. Molecular characterization and identification of strains of S. parasitica and S. australis was carried out according to the method detailed elsewhere [37]. 3.6. Microwell Enumeration Method Biological Assays This method is based on inoculating water samples, and experimental conditions have been detailed elsewhere [38]. 3.7. Determination of Minimum Inhibitory Concentration (MIC) The antioomycete activities of all tested compounds were evaluated using the dilution test at final concentrations of 3.125, 6.25, 12.5, 25.0, 75.0, 100.0, 125.0, 150.0 and 200.0 µg/L in Griffin's sporulation medium [39]. Bronopol was used as the positive control, whereas 1% solution of EtOH/tween 20 was considered as the negative control. Experimental conditions have been detailed elsewhere [37]. 3.8. Spores Germination Inhibition Test Spores from the culture on PDA plates were taken and suspensions of spores were made separately with different compounds [40]. The minimum oomyeticidal concentration (MOC) was defined previously [39]. Experimental conditions have been detailed elsewhere [37]. 3.9. Mycelial Growth Inhibition Test The anti-saprolegnia activities of all tested compounds were evaluated using the radial growth test at final concentrations of 200 µg/L in PDA medium. The growth inhibition rate will be calculated from mean values as: %IR = 100 (x − y)/(x − z)(1) where IR is the growth inhibition rate; x, the mycelial growth in control; y, the mycelia growth in sample; and z, the average diameter of the rapeseeds. Experimental conditions have been detailed elsewhere [37]. 3.10. Measurement of Cellular Leakage Cell leakage were measured in order to determine the effectiveness of compounds 1–11 on membrane integrity. This method was assessed according to Lunde [41]. 3.11. Statistical Analysis The data was reported following a standard method [37]. 4. Conclusions The results of this research evidence how the presence of a lipophilic alkyloxy chain modifies biological activity; the data so far has suggested that oxyalkylated chalcones may represent a new frontier and a challenge for the development of novel anti-oomycete compounds against Saprolegnia sp. in the near future. There appear to be no published studies investigating the effectiveness of 2′,4′-dihydroxychalcone (1) or its synthetic analogs 2–11 in terms of combating Saprolegnia infection. The natural compounds proved to be more effective at inhibiting Saprolegnia growth in vitro than ketoconazol or bronopol. Acknowledgments The authors thank to FONDECYT (grant No. 11140193) and the Dirección General de Investigación of Universidad de Playa Ancha. Author Contributions Alejandro Madrid supervised the whole work. Susana Flores performed the synthesis of all compounds. Mauricio Cuellar collaborated in structure determination of oxyalkylchalcones by spectroscopic methods. Patricio Godoy contributed with identification and sequencing of Saprolegnia parasitica strains. Iván Montenegro conceived and designed the biologic experiments; Iván Montenegro, Joan Villena and Enrique Werner performed the biologic experiments. Alejandro Madrid, Iván Montenegro, Joan Villena and Enrique Werner collaborated in the discussion and interpretation of the results. Alejandro Madrid wrote the manuscript. Conflicts of Interest The authors declare no conflict of interest. Figures, Scheme and Tables ijms-17-01366-sch001_Scheme 1Scheme 1 General procedure for the synthesis of O-alkylated chalcones. Figure 1 Most important correlations 2D 1H–13C HMBC, compound 4. Figure 2 Most important correlations 2D 1H–13C HMBC, compound 5. Figure 3 Most important correlations 2D 1H–13C HMBC, compounds 7 and 8. ijms-17-01366-t001_Table 1Table 1 Minimum inhibitory concentrations (MIC) and damage values of oxyalkylchalcones 1–11 against Saprolegnia species. Compounds MIC (µg/mL) Damage (%) a S. parasitica S. australis S. parasitica S. australis 1 12.5 6.25 100 100 2 >200 200 0 0 3 200 175 16 20 4 >200 >200 10 15 5 175 150 28 30 6 >200 >200 0 0 7 200 175 28 30 8 >200 200 0 0 9 200 175 10 15 10 200 175 12 17 11 150 125 30 32 Bronopol 175 150 30 35 Safrole 175 150 30 35 Eugenol 150 150 35 38 Fluconazole >200 >200 Nd Nd Ketoconazole 75 50 Nd Nd Sodium Dodecyl Sulfate - - 100 100 Nd: Not determined; a Damage produced by compounds 1–11 compared to the damaged produced by the Sodium Dodecyl Sulfate (SDS). SDS was utilized at a final concentration of 2% that produces a 100% of cell lysis. The assay was performed in duplicates. ijms-17-01366-t002_Table 2Table 2 Minimum oomycidal concentrations (MOC) values a of oxyalkylchalcones 1–11 against mycelium at 48 h. Compounds MOC (µg/mL) S. parasitica S. australis 1 12.5 12.5 2 200 200 3 200 175 4 >200 >200 5 175 150 6 >200 >200 7 175 175 8 >200 >200 9 >200 200 10 200 175 11 125 125 Bronopol >200 175 Safrole >200 200 Eugenol >200 175 Fluconazole >200 >200 Ketoconazole 100 75 a Each value represents the mean ± SD of three experiments, performed in quadruplicate. ijms-17-01366-t003_Table 3Table 3 Mycelial Growth Inhibition values of oxyalkylchalcones 1–11 against Saprolegnia spp. at 48 h. Compounds (200 µg/mL) MIG (%) S. parasitica S. australis 1 100 100 2 32 35 3 33 36 4 0 0 5 35 38 6 0 0 7 30 33 8 0 0 9 0 0 10 10 13 11 50 55 Bronopol 0 33 ==== Refs References 1. Epifano F. Genovese S. Menghini L. Curini M. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081367ijms-17-01367ArticlePlasma LncRNA-ATB, a Potential Biomarker for Diagnosis of Patients with Coal Workers’ Pneumoconiosis: A Case-Control Study Ma Jixuan 12Cui Xiuqing 12Rong Yi 3Zhou Yun 12Guo Yanjun 12Zhou Min 12Xiao Lili 12Chen Weihong 12*Pichler Martin Academic Editor1 Department of Occupational & Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; xjmajixuan@163.com (J.M.); cuixiuqing2008@163.com (X.C.); sarayunzhou@gmail.com (Y.Z.); hustghkan@126.com (Y.G.); mzhou90@126.com (M.Z.); Yeyushunong@163.com (L.X.)2 Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China3 Long Hua Center for Disease Control and Prevention, Shenzhen 518109, China; rongyi1007@126.com* Correspondence: wchen@mails.tjmu.edu.cn; Tel.: +86-27-836-916-7722 8 2016 8 2016 17 8 136723 6 2016 11 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).LncRNA-ATB (lncRNA was activated by transforming growth factor-β) has been reported to be involved in specific physiological and pathological processes in human diseases, and could serve as biomarkers for cancers. However, the role of lncRNA-ATB in coal workers’ pneumoconiosis (CWP) is still unknown. This study aimed to investigate the association between lncRNA-ATB and CWP. Quantitative real-time polymerase chain reaction was performed to detect plasma lncRNA-ATB expression in 137 CWP patients, 72 healthy coal miners and 168 healthy controls. LncRNA-ATB was significantly upregulated in CWP (p < 0.05). Compared with the healthy controls and healthy coal miners, the odds ratios (ORs) (95% confidence interval (CI)) for CWP were 2.57 (1.52–4.33) and 2.17 (1.04–4.53), respectively. LncRNA-ATB was positively associated with transforming growth factor-β1 (TGF-β1) (r = 0.30, p = 0.003) and negative correlated with vital capacity (VC) (r = −0.18, p = 0.033) and forced vital capacity (FVC) (r = −0.18, p = 0.046) in CWP patients. Compared with healthy controls, the area under the curve (AUC) was 0.84, resulting in a 71.17% sensitivity and 88.14% specificity. When compared with healthy coal miners, the AUC was 0.83, the sensitivity and specificity were 70.07% and 86.36%, respectively. LncRNA-ATB expression is commonly increased in CWP and significantly correlates with the TGF-β1 in CWP patients. Furthermore, elevated lncRNA-ATB was associated with CWP risk and may serve as a potential biomarker for CWP. coal workers’ pneumoconiosis (CWP)long noncoding RNAlncRNA-ATBepithelial-mesenchymal transition (EMT)biomarker ==== Body 1. Introduction Coal workers’ pneumoconiosis (CWP), identified by pulmonary parenchyma fibrosis, is a chronic occupational lung disease caused by long-term inhalation of dust in the workplace [1]. Pathologic features of CWP involve focal collections of dust and reticulin around the small airways, fibrotic lesions exhibiting irregularly arranged collagen, and lesions of massive fibrosis [2,3]. Although prevention efforts have been required to implement for decades, CWP is still one of high incidence occupational diseases worldwide, especially in China [4]. Currently, periodic medical screenings for pneumoconiosis normally include chest radiography and spirometry. However, abnormal signs of both tests are often displayed in the late course of the underlying disease. Many workers could not get timely diagnoses and lost opportunities for prevention or treatment. Therefore, novel potential biomarkers for detecting CWP deserved more attention. Long non-coding RNAs are types of transcripts that are greater than 200 nucleotides in length, can exert their biological functions by binding to RNA, DNA and protein, and often do not have the capability to coding proteins [5,6]. LncRNAs have recently been found to be involved in specific physiological and pathological processes in a wide range of human diseases, and can be stable in the plasma and other body fluids [7,8,9,10]. Therefore, lncRNAs could serve as biomarkers for the diagnosis and poor prognosis of human diseases, such as cancers and lung fibrosis [11,12,13]. For example, a high expression of lncRNA-ATB participated in the development of colorectal cancer (CRC) and was a novel indicator of poor prognosis in patients with CRC [14]. Low expression of lncRNA-ATB plays a critical role in pancreatic cancer progression and prognosis, and may serve as a potential prognostic biomarker in pancreatic cancer patients [15]. However, the study investigated the role of lncRNAs in CWP is limited. LncRNA-ATB, which was named lncRNA, was activated by transforming growth factor-β (TGF-β). LncRNA-ATB can induce epithelial-mesenchymal transition (EMT) and promote invasion via competitively binding and sequestering the miR-200 family in hepatocellular carcinoma (HCC) [16]. It is widely recognized that EMT regulated by TGF-β is considered a critical signaling pathway in organ fibrosis process [17,18,19,20]. In our previous study, we found that silica-induced fibrosis was regulated by EMT, which was activated by the upregulated TGF-β1 [21]. Moreover, a genome-wide analysis also showed that the miR-200 family was an aberrant expression in CWP [22]. However, the relationship between lncRNA-ATB and CWP remains unclear. In this study, we hypothesize that lncRNA-ATB may play a potential role in CWP. A case-control study was designed to determine the expression of lncRNA-ATB in human plasma for groups with or without CWP. The objectives of this study were to investigate the association between lncRNA-ATB expression and CWP, and to assess the specificity and sensitivity of using plasma lncRNA-ATB as a potential diagnostic tool for detection of CWP. 2. Results 2.1. LncRNA-ATB Expression in the Plasma of All Participants The basic characteristics for all participants were shown in Table 1. This case-control study included 137 patients with CWP patients, 72 were healthy coal miners, and 168 were healthy controls. Subjects with CWP had significantly higher plasma TGF-β1, matrix metalloproteinase-9 (MMP-9), matrix metalloproteinase-2 (MMP-2), Collagen I (Col-1) and Collagen III (Col-3) (p < 0.05) when compared with both healthy groups. As expected, in comparison with healthy controls and healthy coal miners, CWP patients had remarkably lower levels of spirometry parameters (percent of predicted forced vital capacity (% PRED FVC), percent of predicted forced expiratory volume in 1 s (% PRED FEV1), and % FEV1/FVC) (p < 0.05). The distribution of net years in dust was difference between healthy coal miners and CWP patients. Compared with healthy controls and healthy coal miners, lncRNA-ATB was a higher expression in the plasma of CWP patients (p < 0.05) (Figure 1). 2.2. Association between LncRNA-ATB Expression and Coal Workers’ Pneumoconiosis (CWP) The association of lncRNA-ATB and CWP was shown in Table 2. Compared with healthy controls, single factor logistic regression analysis showed a positive relationship between lncRNA-ATB and the CWP. The association was still strongly after adjusting multiple potential confounders. Compared with the subjects in the lowest group of lncRNA-ATB, the multi-variate adjusted odds ratios (ORs) (95% confidence interval (CI)) for CWP were 1.41 (0.73–2.72) and 2.39 (1.29–4.42) from the second group to the third group of lncRNA-ATB. In the secondary analysis, compared with healthy coal miners, lncRNA-ATB was strongly associated with CWP risk with adjusting potential confounders. The OR (95% CI) for CWP was 2.17 (1.04–4.53) for a one-unit increase in log lncRNA-ATB. 2.3. Relationship between LncRNA-ATB Expression and Clinical/Biological Features in CWP Patients Our results showed that plasma TGF-β1, MMP-9 and Col-1 were closely related to spirometry parameters in patients (p < 0.05). LncRNA-ATB was positively associated with TGF-β1 (Spearman correlation coefficient r = 0.30, p = 0.003) and negatively correlated with VC (% PRED VC) (r = −0.18, p = 0.033) and FVC (% PRED FVC) (r = −0.18, p = 0.046) (Table 3). 2.4. Plasma LncRNA-ATB Expression Can Be a Potential CWP Biomarker Receiver Operating Characteristic (ROC) curve analysis was used to evaluate the discriminatory power of lncRNA-ATB in plasma. We adjusted for age, body mass index (BMI), systolic and diastolic blood pressure (BP). Compared with healthy controls, we found that the area under the curve (AUC) was 0.84 with a cutoff value greater than 0.92, resulting in 71.17% sensitivity and 88.14% specificity. When compared with healthy coal miners, the AUC was 0.83 after adjusting for age and BMI, with a cutoff of greater than 0.70. The sensitivity and specificity were 70.07% and 86.36%, respectively (Figure 2). 3. Discussion In this study, our data clearly demonstrated that an increase in lncRNA-ATB expression level in CWP compared with healthy controls and healthy coal miners. Higher lncRNA-ATB levels were associated with elevated odds of CWP after adjusted for a wide range of risk factors. In CWP patients, lncRNA-ATB had a significantly positive correlation with TGF-β1 and negatively associated with VC (% PRED VC) and FVC (% PRED FVC). Moreover, ROC curve analysis showed that lncRNA-ATB distinguishes patients with CWP from healthy controls and healthy coal miners, and the AUCs were 0.84 and 0.83, respectively. Long non-coding RNAs are types of transcripts that are greater than 200 nucleotides in length [5,23]. Several previous studies have shown that lncRNAs could serve as key regulators of important biological processes, such as proliferation apoptosis, or cell migration [24,25]. LncRNA-ATB is a novel lncRNA that was first profiled by Yuan et al. in HCC cells [16]. LncRNA-ATB was activated by TGF-β, increased ZEB1 and ZEB2 mRNA and protein levels through competitively binding and sequestering miR-200s family and then induce EMT [16]. Altered expression of lncRNA-ATB has been documented in some human diseases, such as HCC, CRC and pancreatic cancer. In this study, we found that increased lncRNA-ATB were associated with elevated odds of having CWP. Compared with healthy controls and healthy coal miners, higher lncRNA-ATB expression had higher ORs of CWP after adjusted for the potential confounders. Similarly, Saito et al. found that gastric cancer (GC) patients in the high lncRNA-ATB group had a significantly worse prognosis than patients in the low lncRNA-ATB group [26]. The elevated expression of lncRNA-ATB was associated with tumor stages, histological grade and distant metastasis in renal cell carcinoma [27]. In colon cancer patients, increased lncRNA-ATB played an important role in disease recurrence and decreased survival [28]. To our knowledge, this study is the first report about the expression of lncRNA-ATB in CWP patients. Currently published papers indicated that lncRNA-ATB was activated by TGF-β, involved in the EMT signaling pathway, which may be participated in the progression and prognosis of CWP. It is widely recognized that EMT regulated by TGF-β is considered an important signaling pathway in lung fibrosis process [29,30]. The previous study in our group found that silica-induced fibrosis was regulated by EMT, and the upregulated TGF-β1 was involved in the process of EMT [21]. In this study, TGF-β1 was significantly higher in CWP patients and positively correlated with lncRNA-ATB. These results suggested that upregulated lncRNA-ATB probably influences the process of CWP through a TGF-β-mediated EMT signaling pathway. Similarly, Zhu et al. found lncRNA-ATB, a transcriptional activator of TGF-β, was overexpressed and associated with miR-200c in keloid fibroblasts [31]. In our study, we matched the average net years in dust between healthy coal miners and CWP patients. However, there was still a difference in distributions between two groups. The major reason was that average net years in dust hid the distribution of net years for single workers. Moreover, we also noted that there was no difference in lncRNA-ATB expression between healthy controls and healthy coal miners. It indicates that fibrosis, rather than dust exposure, might be related to lncRNA-ATB expression in plasma. Recent evidence demonstrates that lncRNA-ATB also could serve as novel biomarkers for diagnosis and poor prognosis. Qu et al. reported that lncRNA-ATB was directly correlated with clinical endpoints (overall survival) and could serve as an independent prognosis maker for pancreatic cancer patients [15]. Saito et al. showed that increased lncRNA-ATB expression was a significant prognostic for increased recurrence and decreased survival of GC patients [26]. Iguchi et al. found that higher lncRNA-ATB was involved in the progression of CRC and was a novel indicator of poor prognosis in patients with CRC [14]. In this study, good AUCs of 0.84 when compared with healthy coal miners suggested that lncRNA-ATB may be used as a biomarker for detection of CWP. In addition, lncRNA-ATB was negatively associated with VC and FVC in CWP patients. These results indicated that upregulated lncRNA-ATB may participate in the progression of decreased lung function in CWP. In China, there are over 2.65 million coal mine workers, and over 12,500 new CWP patients were reported annually from 2010 to 2014 [32,33]. More preventive measures, such as being removed from dust exposed jobs, could be taken if pulmonary fibrosis could be detected in an earlier stage. Further investigation is needed to confirm the diagnosis role of lncRNA-ATB in big groups. Our results had several major strengths. First, we evaluated the associations between lncRNA-ATB and clinical/biological features in CWP, and the results were comprehensively to explain the possible role of lncRNA-ATB. Moreover, all of the subjects were in the same place in China, minimizing the confounding effects of other characteristics, such as environment and socioeconomic factors. However, three limitations should also be addressed. First, in our study, we only observed the relationship between lncRNA-ATB and CWP, but the underlying mechanisms are unknown. Further studies are needed to explore potential mechanisms. Second, although the ROC curves were adjusted for potential confounders for plasma lncRNA-ATB, which permits a better differentiation between patients and controls, the unadjusted ROC curve may be more suitable in clinical practice. This needs further evaluation. Moreover, this study matched the average age among three groups, but the distribution of age was still a difference. Although we had adjusted age as a continuous variable in related analyses and found the trend of the results were similar after deleting subjects (age > 65). However, the role of age in the association between lncRNA-ATB and CWP is still needed to explore. Overall, we discovered that lncRNA-ATB is significantly upregulated in CWP and positively associated with TGF-β1 in CWP patients. Moreover, elevated lncRNA-ATB was related with CWP risk and may be considered as a new biomarker for CWP in coal miners. 4. Materials and Methods 4.1. Study Population We recruited 137 CWP patients and 72 healthy coal miners (net years in dust >15 years) from the Huangshi coal mine, which located in the central China, and 168 age-matched healthy controls recruited from the same city between November 2012 and June 2014. All subjects were male. The patients with CWP were diagnosed based on the China National Diagnostic Criteria for pneumoconiosis (GBZ 70-2009), which is consistent with the 1980 International Labor Organization on the classification of pneumoconiosis. In this study, we excluded the subjects with chronic diseases such as asthma, chronic obstructive pulmonary disease, pulmonary tuberculosis and cardiovascular disease. Trained investigators used a structured standardized questionnaire to collect information through face-to-face interviews, which included personal information, medical history, working history including net years in dust, and smoking status. Approximately 5 mL of venous serum was collected from each participant and then put into a tube containing EDTA (Ethylenediaminetetraacetic acid). Plasma was obtained by centrifugation at 1500 rpm for 20 min and stored at −80 °C until use. Lung function tests were performed by a specialist using electronic spirometer (Chestgraph HI-101, CHEST Ltd., Tokyo, Japan). The lung function tests method were used as described in previous study [34]. Values used in this analysis included the percent of predicted FVC, percent of predicted FEV1, percent of predicted VC and % FEV1/FVC. This study was approved by the Ethics and Human Subject Committees of the Tongji Medical College Huazhong University of Science and Technology (Identification code: (2013) IEC (S017); date: 5 March 2013; Wuhan, China). 4.2. Total RNA Extraction and Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) Total RNA from plasma was extracted by TRIzol LS Reagent (Life Technologies, Foster, CA, USA) according to the manufacturer’s protocol. After purification, RNA was extracted from 1.5 mL of plasma and dissolved in 25 μL of diethylpyrocarbonate (DEPC) water. The quantity and quality of the total RNA was determined with NanoDrop (Thermo, ND-1000, Waltham, MA, USA), and approximately 200 ng/µL RNA was obtained from the 1.5 mL plasma. Complementary DNA (cDNAs) was synthesized using Reverse Transcription Kit (TOYOBO, Osaka, Japan). qRT-PCR was performed to detect lncRNA-ATB expression by using the primer sequences as follows: F: 5′-CTTCACCAGCACCCAGAGA-3′ and R: 5′-AAGACAGAAAAACAGTTCCGAGTC-3′. GADPH was used as a gene reference. The primer sequences was as follows: F: 5′-CAGGAGGCATTGCTGATGAT-3′ and R: 5′-GAAGGCTGGGGCTCATTT-3′. cDNAs was amplified using the SYBR Green PCR Master Mix Kit (TOYOBO), and qRT-PCR was performed using the Applied Biosystems 7900HT Fast Real-Time PCR System (Life Technologies) according to the supplied manufacturer’s instructions. Relative quantification of RNA expression was calculated by using the 2−∆∆Ct method. Each sample was examined in triplicate. 4.3. Enzyme-Linked Immunosorbent Assay for Plasma Measurements (ELISA) Plasma TGF-β1, MMP-2, MMP-9, Col-1 and Col-3 levels were measured by ELISA using commercially available kits. TGF-β1, MMP-9, MMP-2 assay ELISA kits purchased from R&D Systems Inc. (Minneapolis, MN, USA). Col-1 and Col-3 assay ELISA kits purchased from Uscn Life Science Inc. (Wuhan, China). All plasma samples were assayed in duplicate and the mean was calculated. The ELISA method processed manufacturer’s protocol. 4.4. Statistical Analysis Comparisons were made by ANOVA test or Student’s t-test for variables with normal distribution. Least significant difference (LSD) was used to test for pairwise comparisons for normally distributed data. Kruskal–Wallis test was used for non-normally distributed data. Categorical variables were compared using a chi-Squared test. Spearmans’ correlation coefficients were calculated to determine the associations between lncRNA-ATB expression and clinical/biological features in patients. We further investigated the association between lncRNA-ATB expression and CWP risk. Compared with healthy controls, subjects were classified into three groups according to tertlile of lncRNA-ATB expression in the healthy controls. Then, compared with healthy coal miners, subjects were classified into three groups according to tertlile of lncRNA-ATB expression in the healthy coal miners. The classification of this study has been described previously [35]. Multivariable logistic regression models were performed to calculate ORs and 95% confidence interval (CI) for CWP according to the lncRNA-ATB expression. Mean ± standard deviation (SD) are reported for normally distributed data, unless stated otherwise. ROC curves were analyzed to assess the specificity and sensitivity of lncRNA-ATB for CWP. An optimal cut-off value was based on the Youden index. A two-sided p-value < 0.05 was considered statistically significant. Data were analyzed using the SAS, version 9.3, software (SAS Institute Inc., Cary, NC, USA). 5. Conclusions In this study, we found lncRNA-ATB was significantly upregulated in CWP patients. Moreover, lncRNA-ATB may be considered as a new biomarker for CWP in coal miners. Acknowledgments This work was supported by the National Natural Science Foundation of China under Grant (81372967), and Key Program of the National Natural Science Foundation of China under Grant (91543207). Author Contributions Jixuan Ma drafted the original manuscript, carried out the analysis and interpretation the data; Xiuqing Cui, Yi Rong, Yun Zhou and Yanjun Guo assisted in the organization of the survey and collection of the data; Min Zhou and Lili Xiao performed the experiments; Weihong Chen designed the study, interpretation of data and revised the manuscript. All authors approved the final version of the article, including the authorship list. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Scatter plots of lncRNA-ATB expressions in different groups. Solid circles, healthy controls; Solid squares, healthy coal miners; Solid triangles, CWP patients. (* p < 0.05). Figure 2 Receiver operating characteristic (ROC) curve analysis for lncRNA-ATB in plasma adjusted for risk factors; (a) Compared with healthy controls; and (b) compared with healthy coal miners. ijms-17-01367-t001_Table 1Table 1 Basic characteristics of the study population. Variables Normal Controls (n = 168) Healthy Coal Miners (n = 72) CWP Patients (n = 137) p-Value Age (years, mean ± SD) 58.83 ± 10.63 57.69 ± 10.86 59.27 ± 7.88 0.65 a Age (n, %) <55 45 (26.79) 54 (75.00) 40 (29.19) <0.05 b ≥55 123 (73.21) 18 (25.00) 97 (70.81) BMI (kg/m2, mean ± SD) 24.61 ± 3.33 25.50 ± 2.88 22.36 ± 3.46 #,* <0.05 a Net year in dust (n, %) ≤30 / 62 (86.11) 90 (65.69) <0.05 b >30 / 10 (13.89) 47 (34.31) Smoking status (n, %) Non-smoking 74 (44.05) 27 (37.50) 40 (29.20) <0.05 b Smoking 94 (55.95) 45 (62.50) 97 (70.80) Blood pressure systolic (mm Hg, mean ± SD) 127.86 ± 21.51 n.d. 140.20 ± 23.76 <0.05 d Blood pressure diastolic (mm Hg, mean ± SD) 82.85 ± 16.67 n.d. 76.20 ± 13.06 <0.05 d FVC (% PRED FVC) 90.65 (64.90–120.30) 93.55 (71.40–109.40) 66.20 (28.20–104.50) #,* <0.05 c FEV1 (% PRED FEV1) 97.25 (67.10–95.44) 98.65 (89.00–111.20) 62.00 (17.70–108.40) #,* <0.05 c FEV1/FVC (% PRED) 80.75 (68.10–86.83) 83.94 (63.11–97.15) 74.81 (43.83–98.64) #,* <0.05 c TGF-β1 (pg/mL) 303.41 ± 28.38 425.64 ± 33.98 # 569.99 ± 64.13 #,* <0.05 a Col-3 (ng/mL) 67.28 ± 7.79 70.26 ± 8.17 112.15 ± 9.16 #,* <0.05 a MMP2 (ng/mL) 142.27 (114.16–172.00) 166.73 (157.68–183.63) # 206.32 (186.32–219.16) #,* <0.05 c MMP9 (ng/mL) 55.84 (45.03–68.81) 98.88 (75.03–112.32) # 123.68 (105.16–143.27) #,* <0.05 c Col-1 (ng/mL) 28.65 (26.53–29.53) 29.90 (27.28–32.25) 34.15 (31.90.90–36.53) #,* <0.05 c Abbreviations: FVC, forced vital capacity; FEV1, forced expiratory volume in 1 s; % PRED FVC, percent of predicted FVC; % PRED FEV1, percent of predicted FEV1. BMI: body mass index. SD, standard deviation. a Calculated by ANOVA test, pair-wise comparisons calculated by Least significant difference; b Calculated by χ-Squared test; c Calculated by Kruskal–Wallis test; d Calculated by t-test; # Compared with Healthy controls, p < 0.05; * Compared with Healthy coal miners, p < 0.05; n.d., not done. ijms-17-01367-t002_Table 2Table 2 Odds ratio of coal workers’ pneumoconiosis (CWP) according to the lncRNA-ATB expression. Variables LncRNA-ATB Expression Levels (Fold Change) Per 1 Log-Unit Increment p-Value * First Group <0.7559 Second Group 0.7559–1.1433 Third Group >1.1433 Healthy controls vs. CWP No. of cases/control subjects 26/55 39/55 72/55 Model 1 (OR: 95% CI) 1.00 (referent) 1.47 (0.79–2.74) 2.67 (1.49–4.78) 2.75 (1.69–4.48) <0.05 Model 2 (OR: 95% CI) 1.00 (referent) 1.48 (0.80–2.75) 2.68 (1.50–4.80) 2.76 (1.70–4.49) <0.05 Model 3 (OR: 95% CI) 1.00 (referent) 1.43 (0.75–2.74) 2.34 (1.28–4.30) 2.56 (1.54–4.27) <0.05 Model 4 (OR: 95% CI) 1.00 (referent) 1.41 (0.73–2.72) 2.39 (1.29–4.42) 2.57 (1.52–4.33) <0.05 Healthy coal miners vs. CWP <0.7542 0.7542–1.2161 >1.2161 No. of cases/control subjects 49/23 75/24 85/25 Model 1 (OR: 95% CI) 1.00 (referent) 1.77 (0.85–3.70) 2.25 (1.08–4.68) 1.82 (1.05–3.17) <0.05 Model 2 (OR: 95% CI) 1.00 (referent) 1.86 (0.84–4.15) 2.25 (1.02–4.97) 1.83 (1.01–3.35) <0.05 Model 3 (OR: 95% CI) 1.00 (referent) 1.54 (0.64–3.70) 1.78 (0.74–4.31) 1.90 (0.97–3.72) 0.06 Model 4 (OR: 95% CI) 1.00 (referent) 1.47 (0.59–3.65) 2.05 (0.81–5.19) 2.25 (1.07–4.71) <0.05 Model 5 (OR: 95% CI) 1.00 (referent) 1.65 (0.65–4.18) 1.90 (0.74–4.89) 2.17 (1.04–4.53) <0.05 Model 1: single factor logistic regression. Model 2: adjusted for age (continuous). Model 3: adjusted for age (continuous), body mass index (BMI) (continuous). Model 4: adjusted for age (continuous), body mass index (BMI) (continuous), smoking status (no, yes). Model 5: adjusted for age (continuous), body mass index (BMI) (continuous), smoking status (no, yes), net years in dust (continuous). * p-values for the estimated changes by original log-transformed lncRNA-ATB expressions as a continuous variable. ijms-17-01367-t003_Table 3Table 3 Correlations between lncRNA-ATB and clinical-/biological features in CWP patients †. Variables LncRNA-ATB TGF-β1 MMP-2 MMP-9 Col-1 Col-3 % PRED VC % PRED FVC % PRED FEV1 FEV1/FVC LncRNA-ATB 1.00 0.30 * 0.07 −0.03 0.09 0.02 −0.18 * −0.18 * −0.10 0.05 TGF-β1 1.00 −0.04 0.09 0.18 −0.06 0.47 * −0.55 * −0.50 * −0.30 * MMP-2 1.00 0.04 0.09 −0.03 −0.02 −0.04 −0.08 −0.06 MMP-9 1.00 0.29 0.07 0.23 * −0.20 −0.29 * −0.31 * Col-1 1.00 0.14 0.28 * −0.25 * −0.31 * −0.20 Col-3 1.00 −0.15 −0.08 −0.13 −0.07 % PRED VC 1.00 0.93 * 0.88 * 0.47 * % PRED FVC 1.00 0.90* 0.42 * % PRED FEV1 1.00 0.75 * FEV1/FVC 1.00 Abbreviations: VC, vital capacity; FVC, forced vital capacity; FEV1, forced expiratory volume in 1 s; % PRED VC, percent of predicted VC; % PRED FVC, percent of predicted FVC; % PRED FEV1, percent of predicted FEV1; † Adjusted for age (continuous), body mass index (BMI) (continuous), smoking status (no, yes), net years in dust (continuous); * p < 0.05. ==== Refs References 1. Pascolo L. Borelli V. Canzonieri V. Gianoncelli A. Birarda G. Bedolla D.E. Salome M. Vaccari L. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081368ijms-17-01368ArticleSyngonanthus nitens Bong. (Rhul.)-Loaded Nanostructured System for Vulvovaginal Candidiasis Treatment dos Santos Ramos Matheus Aparecido 1de Toledo Luciani Gaspar 1Calixto Giovana Maria Fioramonti 2Bonifácio Bruna Vidal 1de Freitas Araújo Marcelo Gonzaga 3dos Santos Lourdes Campaner 4de Almeida Margarete Teresa Gottardo 5Chorilli Marlus 2Bauab Taís Maria 1*Másson Már Academic Editor1 Department of Biological Sciences, School of Pharmaceutical Sciences, UNESP-Univ Estadual Paulista, Araraquara, São Paulo 14800-903, Brazil; matheusramos_91@hotmail.com (M.A.d.S.R.); lucianitoledo@hotmail.com (L.G.d.T.); brunavidalb@gmail.com (B.V.B.)2 Department of Drugs and Medicines, School of Pharmaceutical Sciences, UNESP-Univ Estadual Paulista, Araraquara, São Paulo 14800-903, Brazil; giovana.calixto@gmail.com (G.M.F.C.); chorilli@fcfar.unesp.br (M.C.)3 Federal University of São João Del-Rey, Divinópolis, Minas Gerais 36307-352, Brazil; mgfaraujo@ufsj.edu.br4 Department of Organic Chemistry, Chemisty Institute, UNESP-Univ Estadual Paulista, Araraquara, São Paulo 14800-060, Brazil; loursant@gmail.com5 Department of Infectious Diseases, Faculty of Medicine of São José do Rio Preto-FAMERP, São José do Rio Preto, São Paulo 15090-900, Brazil; margarete@famerp.br* Correspondence: bauabtm@fcfar.unesp.br; Tel.: +55-16-3301-6955; Fax: +55-16-3322-007322 8 2016 8 2016 17 8 136824 5 2016 30 6 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Herbal-loaded drug delivery nanotechnological systems have been extensively studied recently. The antimicrobial activity of medicinal plants has shown better pharmacological action when such plants are loaded into a drug delivery system than when they are not loaded. Syngonanthus nitens Bong. (Rhul.) belongs to the Eriocaulaceae family and presents antiulcerogenic, antioxidant, antibacterial, and antifungal activity. The aim of this study was to evaluate the antifungal activity of Syngonanthus nitens (S. nitens) extract that was not loaded (E) or loaded (SE) into a liquid crystal precursor system (S) for the treatment of vulvovaginal candidiasis (VVC) with Candida albicans. The minimal inhibitory concentration (MIC) was determined by the microdilution technique. Additionally, we performed hyphae inhibition and biofilm tests. Finally, experimental candidiasis was evaluated in in vivo models with Wistar female rats. The results showed effective antifungal activity after incorporation into S for all strains tested, with MICs ranging from 31.2 to 62.5 μg/mL. Microscopic observation of SE revealed an absence of filamentous cells 24 h of exposure to a concentration of 31.2 μg/mL. E demonstrated no effective action against biofilms, though SE showed inhibition against biofilms of all strains. In the in vivo experiment, SE was effective in the treatment of infection after only two days of treatment and was more effective than E and amphotericin B. The S. nitens is active against Candida albicans (C. albicans) and the antifungal potential is being enhanced after incorporation into liquid crystal precursor systems (LCPS). These findings represent a promising application of SE in the treatment of VVC. liquid crystal precursor systemCandida albicansSyngonanthus nitensvulvovaginal candidiasistherapeutic treatment ==== Body 1. Introduction Microorganisms of the genus Candida are associated with shallow, deep and systemic infections, particularly in patients with underlying diseases such as diabetes and in immunosuppressed patients, patients with neutropenia and transplant patients, due to the opportunistic profile presented by the members of this genus [1]. The Candida albicans (C. albicans) species is presented as the main agent of opportunistic fungal diseases that affect women of all ages, such as vulvovaginal candidiasis (VVC). The pathogenicity of C. albicans species involves mechanisms of aggression attributed to virulence factors. These mechanisms include the ability to defend itself against the host immune system, hyphae proliferation, biofilm formation in tissue or on medical devices, and production of harmful hydrolytic enzymes including proteases, phospholipases and hemolysin [2,3,4]. Drug therapy targeting fungal infections has limitations such as the high cost of drugs available in the clinic, high toxicity, drug interactions, insufficient bioavailability of the active ingredient and the emergence of resistant strains [5]. Several antifungals have been indicated for the treatment of these infections, including those belonging to the polyenic, azole, and echinocandin classes; however, due to the indiscriminate use of these antimicrobials targeting genetic and physiological characteristics of the fungus, there has been a significant increase in profile resistance to drug members of these classes [6,7]. The health sciences have been increasingly concerned with the exaggerated growth of multidrug-resistant strains to antibiotics currently in use in clinical practice. Therefore, medicinal plants have emerged in the search for new antifungal drugs due to their demonstration of important bioactive properties and the presence of secondary metabolites such as tannins, flavonoids and phenols [8]. Several plants have been studied to investigate their antimicrobial potential. Some families of plant species have been featured in science, such as Eriocaulaceae. Eriocaulaceae are popularly known as “evergreens” and are used in the manufacture of decorative ornaments and accessories [9]. Among these is the genus Syngonanthus, which is found in the states of Goiás and Tocantins, Brazil, where it is known as “golden grass” [10,11]. The species Syngonanthus nitens (Bong.) Ruhland has received substantial attention recently. Pacifico et al. [12] analyzed the scapes characteristics and found approximately 17 compounds derived from flavones and apigenin plus six new novel molecules. Araújo et al. [13] detected a significant potential antifungal present in the scapes of Syngonanthus nitens (S. nitens) that exhibited activity against four species of the genus Candida, which is a characteristic therapeutic profile of plant species when subjected to an experimental model of VVC. Although the use of natural products in screening for new drugs is very important, certain limitations have been observed in the development of clinical and pharmacological investigations. These include the difficulty of solubilizing the sample to be analyzed, which may reduce its bioavailability. In this context, nanotechnological tools are commonly used to establish secure platforms for the placement of drugs to develop drug delivery systems. Microemulsions, polymer nanoparticles, solid lipid nanoparticles, liposomes, and, more recently, liquid crystals have been used to enhance therapeutic action and drug selectivity in the vaginal environment [14]. The liquid crystal precursor systems (LCPS) are classified as promising alternatives for the treatment of vulvovaginal candidiasis. Their features are interesting for vaginal administration because they may include liquid forms that facilitate the administration of the formulation. However, upon contact with the vaginal mucosa, LCPS has the ability to incorporate water from the vaginal mucus, thereby becoming a liquid-crystalline mesophase that can promote more viscous controlled release and result in greater applicability of the components of the vegetal extract [15,16]. Recently, the biological activity of S. nitens was evaluated by our research group in an investigation of prophylactic potential against vaginal infection caused by Candida krusei [17]. After incorporation in a similar system, it was found that the system is able to enhance the action of the plant extract in all in vitro biological assays as well as promoting prevention of infection by this fungal species according to the model of in vivo infections. In this sense, we propose in this study to investigate the antifungal potential of the methanolic extract of scapes of S. nitens loaded (or not) into a new liquid crystal precursor system, to highlight their biological applicability for the treatment of vulvovaginal candidiasis caused by C. albicans azole-resistant derivatives. 2. Results 2.1. Development of Liquid Crystal Precursor Systems (LCPS) 2.1.1. Preparation of the Ternary Phase Diagram Figure 1 shows the 36 formulations obtained from the mixture of the system components. The purpose of choice of the components that comprise the system was due to the possibility of obtaining an adhesive formulation and a possible interference in the membrane of fungal cell. A transparent liquid system (S) composed of 40% w/w oleic acid (OA), 40% Procetyl (PRO) and 20% w/w polymer dispersion (PD) was selected as the liquid crystal precursor system because its liquid phase facilitated its vaginal administration by syringe. 2.1.2. Assay with Artificial Vaginal Mucus (AVM) Table 1 shows the results of analysis of the Polarized Light Microscopy (PLM) mesophases found for each proportion of Artificial Vaginal Mucus (AVM) added to formulations. The viscosity of formulation S (formulation without extract) increased in a manner that was directly proportional to the concentration of AVM, demonstrating that the greater the amount of AVM, the higher the system viscosity. The same procedure was repeated with SE (formulation with extract) and the same results were found; thus, the incorporation of the extract into the system did not interfere structurally with the precursor behavior of the liquid crystals. The results obtained by PLM (Figure 2) showed that the transition of S to S100 modified the structure. S no longer possessed the characteristic of microemulsion and began to display hexagonal structures. This transition proved that S had the profile of a liquid crystal precursor. The same data were obtained with SE. In this sense, these results showed that there exists no difference after the incorporation of the extract. 2.1.3. Rheological Analysis The continuous rheological behavior data were plotted on a shear rate (Pa) versus shear stress (1/s) graph, as shown in Figure 3. The results demonstrate ascending and descending curves that indicate the flow behavior of the formulations. Both formulations without AVM (S and SE) behave as a Newtonian liquid (n = 1) without thixotropy in which shear stress is directly proportional to the shear rate. Additionally, the descending curve overlaps with the ascending curve because their initial structure has not been changed with shear, which is characteristic of microemulsion [18]. These formulations also showed a low viscosity, as indicated by the low consistency index (K) in Table 2. Therefore, the incorporation of E did not change the flow behavior of the liquid crystal precursor and both S and SE had a flow property that rendered them easily applicable to the required site with a syringe. However, when these formulations (S and SE) came into contact with the AVM, the resulting diluted formulations (S100 and SE100) underwent a change in flow behavior (also shown in the graph of Figure 2). These formulations had a pseudoplastic behavior (n < 1) with thixotropy (i.e., they were able to return to their original structures when the shear stress was removed) and showed an increase in their consistency indices (K). This type of behavior is characteristic of liquid crystalline systems due to the formation of the semisolid crystalline structure [19]. 2.1.4. In Vitro Evaluation of Mucoadhesive Force Figure 4 illustrates the mucoadhesive strength and the mucoadhesive strength of all formulations. The data were collected at 37 ± 0.5 °C. The results showed that the incorporation of the extract did not cause significant changes in the mucoadhesion of the formulations (p > 0.05). However, when the liquid crystal precursor compositions (S and SE) were diluted by the artificial vaginal mucus, there was a significant increase in the mucoadhesive parameters. 2.2. In Vitro Antifungal Assays 2.2.1. Determination of the Minimum Inhibitory Concentration (MIC) Table 3 showed the minimal inhibitory concentration (MIC) results obtained in the evaluation of the antifungal activity of E and SE against all strains used in the study and the results obtained with the antifungal agents used as positive controls. 2.2.2. Analysis of the Inhibitory Profile of Hyphae in Candida albicans (C. albicans) Figure 5 and Figure 6 show the test images from photomicrographs obtained by inverted light microscopy under 40× magnification with the results of the evaluation of the inhibitor effect of E targeting hyphae growth against C. albicans (ATCC 10231). From the presented results, we could conclude that E was able to exercise hyphae inhibition in both periods. At 12 h, inhibition was observed at concentrations of 1000–250 μg/mL; however, this profile changed after 24 h of incubation, when the concentrations capable of inhibiting hyphae growth were 1000–500 μg/mL. The SE inhibition of hyphae occurred at a concentration 31.2 after 12 and 24 h of testing. These results demonstrated that the action of SE was superior to E. Figure 7 and Figure 8 show test images from photomicrographs obtained by inverted light microscopy under 40× magnification. 2.2.3. In Vitro Assay of Inhibition of the Biofilm Table 4 shows the results of the in vitro test of biofilm formation inhibition against all of the C. albicans strains used in this study. The results showed that E was not able to inhibit biofilm formation by all of the yeast strains, but SE exercised significant potential inhibition. 2.2.4. Time Kill Assay Figure 9 and Figure 10 show the interference of the extract (both unloaded and loaded) on the growth of the strains tested over a 48-h time course. Note that both strains were similar with regards to fungal growth time, which was maintained until the balanced time of 8 h; after this period, the CFU proliferation remained more constant in greater quantities. 2.2.5. In Vivo Assay of Vulvovaginal Candidiasis Treatment Table 5 and Table 6 show the number of infected animals and the fungal burden of all experimental groups in this study. 3. Discussion The results obtained from the characterization of the system showed the applicability of the formulation S to be a precursor system of liquid crystal. The formulations containing 5% and 10% AVM showed both a liquid and dark field, which was characteristic of microemulsion. The 30% and 50% AVM preparations showed Malta crosses (30%) and striae (50%), which were characteristic of the lamellar and hexagonal phases, respectively. The formulation with 100% AVM was more viscous and showed a hexagonal structure (striae) [19]. Then, a phase transition was observed for the region of the translucent viscous system when AVM was added to S (100%), indicating that the conformation was changed. Therefore, based on these results, we proposed that the formulation could behave as a precursor system of liquid crystals because formation of the liquid crystalline system occurred following the addition of increasing amounts of mucus. The rheological behavior of the formulations is desirable because, at the moment of the administration, the SE formulation will flow easily from the syringe, thereby facilitating its administration. Contact with vaginal mucus will increase the formulation viscosity, which allows it to stay in contact with the vaginal mucosa so it can release the extract for a longer time, thereby improving the clinical performance of the treatment. Liquid crystalline hexagonal phases have been widely reported to be mucoadhesive systems due to their high viscosity; however, this property makes their applicability for vaginal administration difficult. Therefore, the system administration of a precursor Newtonian liquid crystal that will form a liquid crystalline mesophase that is mucoadhesive in situ represents one way to overcome this difficulty. The S100 and SE100 formulations were able to interact more strongly with the vaginal mucosa and consequently remained for a longer time in the vaginal environment. Thus, the system designed here represents a novel system for vaginal release because it combines the advantage of forming a strong matrix liquid crystal in situ with high vaginal mucoadhesive strength [20]. The results of the in vitro determination of antifungal potential showed the antifungal effect exerted by the plant extract, which was shown to be active against all of the strains used in this study. According to the data presented in Table 3, we concluded that the incorporation of the extract in S showed promise because there were decreases in the MICs of the extract for all tested strains. These results may be related to the interaction characteristics with the fungal cell membranes exerted by S (i.e., the use of oleic acid as the oil phase of the formulation). Because oleic acid is a constituent of the cell plasma membrane, it can facilitate the passage of the active compound through the fungal membranes [21]. The polymer dispersion used as the aqueous phase of the formulation may also be related to the increased biological activity of E by promoting the controlled release of the active principle (i.e., the extract is deposited on the polymer chain and is thus released in a more gradual and controlled manner) [22]. The literature does not provide a consensus in terms of the MIC values obtained for natural products. Aligiannis and co-workers [23] considered MICs with values lower than 500 μg/mL to be potent inhibitors, MICs between 600 and 1500 μg/mL to be moderate inhibitors and MICs above 1600 μg/mL to be weak inhibitors. Webster and co-workers [24] established a satisfactory MIC as an MIC with a value equal to or less than 1000 μg/mL. In this sense, the results reported in this investigation are extremely important. Secondary metabolites are the major molecules able to develop action against several pathogenic microorganisms by different action mechanisms. The cytoplasmic membrane is classified as the most common site of action of secondary metabolites, with action on this structure triggering with extravasation of cellular contents and, consequently, death of fungal. The interaction with genetic material and protein synthesis is also a predisposing factor in the promotion of therapeutic action. In this case, the contact of the genetic material with secondary metabolites promotes changes in DNA, results in ineffective transcripts promoting disorganization of vital functions of the cell [9]. The chemical characterization developed by Pacífico [12] and co-workers showed the presence of secondary metabolites in S. nitens extract such as flavones and xanthones. In this sense, the promising antifungal activity presented by extract in this study can be attributed by presence of these compounds. The MIC results were confirmed by Time kill assay, and showed that E and SE are able to control the microbial growth of the strains. Comparing the results between E and S in the assay revealed that the growth inhibitory action exerted by SE was higher than E, where the growth was controlled. After this period, the growth increased at a higher intensity but remained lower compared to the growth control at the end of 48 h, indicating a possible fungistatic mechanism for E and SE. Besides of these important results, the potential hyphae inhibition exerted by E against C. albicans was superior when it was loaded into S, which could be explained using the same parameters reported in the MIC results. These data are important because the mechanism of cell infestation exercised by C. albicans in the host mucosa occurs mainly through the hyphae of the fungi in epithelial cells [25]. In the biofilm inhibition assay, it was observed that the adhesiveness of S increased its contact with the C. albicans biofilm, causing the direct and more intense release of SE with the microbial surface; this finding may explain the inhibition observed in relation to E. It is possible that the observed action is linked to the increased permeability of the fungal membrane that promoted an increase in the substantivity of E. Likewise, it is also possible that the mucoadhesive property interfered directly with the inhibition of the biofilm because it maintained direct contact with the system containing E in a uniform manner with more intense delivery and was fixed in place. The mucoadhesive components (PD and PRO) may have been the main action behind this result because we were able to fix the formulation containing the extract directly onto the biofilms formed in the well of the microplate, which in turn were presented as fixed and uniform biofilms. The search for new compounds that promote the inhibition of fungal biofilms is a focus of research in several countries to stimulate the improvement of drug administration [2]. The use of mucoadhesive drug delivery systems on biofilms is more suitable when employing polymers that promote direct adhesion with the biofilm as the most promising and effective route for this treatment [26]. The development of a drug delivery system that is reliable, effective, and safe for treatments against diseases is a goal for various researchers. Furthermore, drug delivery systems should enable the distribution of the drug through the intended route of administration and that prioritize the best drug–receptor interaction and the reduction of harmful effects [14]. So, an ideal drug delivery system should provide a targeted therapy that will allow effective concentrations of a drug to reach the disease site without exposing other tissues to toxicity. In this way, therapeutic treatment employing liquid crystals as drug release from vaginal diseases caused by pathogenic or opportunistic microorganisms is classified as potentially promising, especially because they can incorporate water from the vaginal mucus to promote structural changes in the system, resulting in a liquid crystalline phase and thereby optimizing the efficiency of the drug [27]. The use of S. nitens in the treatment of VVC caused by C. albicans was studied by Araújo and co-workers [13]. The authors reported the therapeutic action of the plant extract when it was incorporated in a conventional formulation. The present study showed that the incorporation of the extract in a nanotechnology-based drug delivery system showed superior results compared to the previous study, thereby indicating that S substantially increased the action of the bioactive agent. In recent years, the use of nanotechnology for drug delivery systems for the incorporation of plant extracts has been shown to be important for vaginal applications [14]. Bonifácio and co-workers [28] showed that the antifungal potential of the ethanolic extract of the leaves of Astronium urundeuva was improved with the incorporation into a nanostructured lipid system (lipid microemulsion) in the treatment of VVC. The results showed by these authors proved that the therapeutic profile in vivo model using the system cured the animals with only six days of treatment. However, the unloaded extract was not effective during the eight days of treatment. In this study, SE exerted a cured profile on the animals of experimental groups (7 and 13) after only 2 days of treatment, which may be due to the interaction with the fungal membranes described in this work. Therefore, the inhibitory profile in relation to the mucoadhesive properties was presented by S. Infected animals produced excess vaginal discharge that came into contact with the system and promoted membership in the vaginal mucosa, there by stimulating a direct interaction of the active principle with the mucous membranes and triggering a direct release at the desired site of action [29,30,31]. The activity observed with E was significant because it promoted healing of the animals infected with the ATCC strain four days after administration. The animals infected with CAV3 were healed after six days of treatment. The late action compared to the incorporated extract can be explained based on the low viscosity of the vehicle (DMSO 20%) used to solubilize E because, at the time of administration, the animals cast out the contents deposited in the vaginal canal after the application, which led to a low concentration of E in the intravaginal environment. The therapeutic action with the standard drug (amphotericin B) was inferior to the action shown by the non-incorporated and incorporated extracts, thereby demonstrating that the use of E in both forms is more promising. Reports of the importance of this result concerning the use of amphotericin B are limited due to its nephrotoxicity-related reactions [32]. We highlighted the results of the therapeutic profile shown by the extract loaded into S because it may be related to the adhesion property. The objective of the present system was based on the fixation characteristics of the vaginal epithelium; thus, materials with adhesive properties were used. The aqueous phase of the system (polymeric dispersion) was also developed to promote adhesion in the vaginal environment. The adhesive behavior is due to physical and chemical processes (i.e., hydrophobic interactions, hydrogen bonds and van der Waals forces) [17]. In addition to this characteristic, the use of polymers in the aqueous phase was intended to promote slower liberation and a controlled manner of the plant extract. The adhesive materials in the pharmaceutical formulations may be hydrophilic molecules of natural or synthetic origin that contain numerous organic components (i.e., carboxyl groups, hydroxyl groups and amines) that form chemical bonds with the biological surface and promote the increase of permeability (i.e., membrane cells) [33]. The choice of surfactant (PEG-5 Ceteth-20 Procetyl®, Wickliffe, OH, USA) and the constituent of aqueous phase was based on adhesive characteristics because they can come into contact with substances that has water to promote the adhesiveness. Therefore, we aimed to trigger adhesion when the system came into contact with vaginal mucus. Systems that have polymeric network compositions, the drug may be homogenously dispersed in the polymer matrix or adsorbed on their surface or within a reservoir. This phenomenon involves the liberation of the same physical and chemical processes, such as water penetration into the matrix, diffusion of the drug through the pores of the matrix, polymer degradation or a combination of the last two mechanisms [14]. In another study, the use of polymeric dispersion (chitosan) as aqueous phase in liquid crystalline system for incorporation of curcumin showed antifungal activity against C. albicans according to in vitro tests. The authors proved that adhesive property exercised by this polymer was important to promote the intense contact of the system with the fungal membrane cells. This contact may be responsible for the increase of membrane permeability that facilitated the entrance of active constituents within the intracellular environment [27]. 4. Material and Methods 4.1. Materials Polyoxypropylene (5) polyoxyethylene (20) cetyl alcohol (PPG-5-CETETH-20) was purchased from Croda (Campinas, Sao Paulo, Brazil). Oleic acid was purchased from Synth (Diadema, Sao Paulo, Brazil). Polycarbophil® and Carbopol® 974P was purchased from Lubrizol® (Wickliffe, OH, USA) Advanceds materials (Cleveland, OH, USA). Sterile fetal bovine serum was purchased from Laborclin® (Pinhais, Paraná, Brazi) laboratory products, Pinhais, Paraná-Brazil. The high-purity water was prepared with a Millipore Milli-Q Plus purification system, and its resistivity was 18.2 MΩ-cm. Sabouraud Dextrose Ágar, Both and supplemented with chloramphenicol were purchased from Difco®-Becton (Franklin Lakes, NJ, USA) Dickinson and Company Sparks (Le Pont de Claix, France). Methanol was purchased from Merck® (Darmstadt, Germany). Triethanolamine, Mucin from porcine stomach type II, amphotericin B, fluconazole, estradiol, cyclophosphamide, XTT sodium salt, triphenyltetrazolium chloride (TTC), gentamicin were purchased from Sigma Aldrich® (Steinheim, North Rhine-Westphalia, Germany). 4.2. Vegetable Plant and Preparation of the Extract The collection of plant material was performed in Serra do Jalapão in the state of Tocantins, Brazil, after owner of the land gave permission to conductivity the study. A number of SPF 189975 voucher specimen was deposited in the IB-USP Sao Paulo, Brazil. The plant extract was prepared by an exhaustive extraction method consisting of simple percolation using methanol as the solvent [34]. The extract was concentrated under reduced pressure by rotary evaporation (48 h) at a temperature below 40 °C and then lyophilized. 4.3. Development of the Liquid Crystal Precursor Mucoadhesive System (LCPS) To prepare the LCPS, we used oleic acid (OA) as the oil phase and polyoxypropylene (5) polyoxyethylene (20) cetyl alcohol (PPG-5-CETETH-20)—Procetyl (PRO) as the surfactant. The aqueous phase was comprised of a 5% polymer dispersion (PD) synthesized from two polymers: (0.5% Polycarbophil® (PP) and 0.5% Carbopol C974P® (CP)) suspended in Milli-Q® water with mechanical stirring and the pH adjusted to 7.0 with triethanolamine (TRI). For preparation of the ternary phase diagram [19], different proportions (0%–100% w/w) of each phase of the system were mixed at room temperature (25 ± 0.5 °C) with stirring, resulting in the construction of the ternary phase diagram with 36 formulations. All formulations that were not at pH 7.0 were adjusted with TRI. All systems were visually classified as Transparent Liquid System (TLS), Transparent Viscous System (TLS), Translucent Liquid System (TrLS), Translucent Viscous System (TrVS), Viscous and Opaque System and Phase Separation (PS). Thus, it was possible to delineate the different regions of the phase diagrams. From this data, the regions of the systems used for physical-chemical characterization were selected. The choice of the optimal formulation for the development of antifungal in vivo and in vitro screening was based on the characteristics observed in each of the 36 formulations. One formulation was obtained to produce a stable liquid with the lowest concentration of surfactant and thus reduce the potential toxicity of the formulation and demonstrate that the incorporation of the water-formulation caused increased viscosity, thereby simulating the vaginal environment. Formulation S (composed of 40% oil phase, 40% surfactant and 20% aqueous phase) was selected as the precursor system of the mucoadhesive liquid crystal. The E was incorporated into S, which resulted in the formulation SE. 4.4. Structural Characterization of the System and Pharmacotechnique Analysis 4.4.1. Polarized Light Microscopy (PLM) A drop of each formulation was placed onto a glass slide covered with a cover slip and then examined through the polarized light microscope (Olympus BX41) coupled with the QColor3 Chamber (Olympus America Inc., New York, NY, USA) at 25 ± 0.5 °C. The isotropic or anisotropic behavior of the samples was noted. Photomicrographs were taken at a magnification of 20,000×. 4.4.2. Assay with Artificial Vaginal Mucus (AVM) To verify the property of the precursor of the liquid crystal, we developed artificial vaginal mucus (AVM) prepared as described by Owen and Katz [35]. For the preparation of 1 L of AVM, each constituent was weight (g), is as follows: NaCl, 3.51; KOH, 1.40; Ca(OH)2, 0.222; bovine serum albumin, 0.018; lactic acid, 2.00; acetic acid, 1.00; glycerol, 0.16; urea, 0.4; and glucose, 5.0. After complete solubilization, 15 g mucin was added. The pH was adjusted to 4.2 using 0.1% HCl. The evaluation of the AVM effect in S and SE (2 mg/mL) were assessed when 5%, 10%, 30%, 50% and 100% of the AVM was added in relation to the initial mass (2 g). Representative samples were analyzed by PLM, and structural changes were elucidated. This test allowed us to evaluate the behavior exercised by the in vitro system developed to simulate the formation of liquid crystals in contact with AVM. 4.4.3. Rheological Analysis After interference analysis, the amount of AVM that promoted better organization of selected formulations was chosen; therefore, 100% AVM was added to the formulations. Rheological testing was also performed with S and SE, resulting in S + 100% AVM (S100) and SE + 100% AVM (SE100). Continuous flow was analyzed on a controlled-stress AR2000 (TA Instruments, New Castle, DE, USA) equipped with parallel plate geometry (40 mm diameter) and a sample gap of 200 μm at 37 ± 0.1 °C in triplicate. Samples of the systems were carefully applied to the lower plate, thereby ensuring that sample shearing was minimized, and allowed to equilibrate for 3 min prior to analysis. Continual testing was performed using a controlled shear rate procedure in the range from 0.01 to 100 s−1 and back, each stage lasting 120 s with an interval of 10 s between the curves. The consistency index and flow index were determined from the Power law described in Equation (1) for the quantitative analysis of flow behavior [19]. (1) τ=k·γη where “τ” is the shear stress, “γ” is the shear rate, “k” is the consistency index and “η” is the flow index. 4.4.4. In Vitro Evaluation of Mucoadhesive Force We used the vaginal mucosa of pigs courtesy of a local producer and the use was approved by the Animal research committee CEUA-UNESP. Freshly excised pig vaginal mucosa was frozen at −30 °C. A section with a 2 mm thickness was excised from the inner part of the surface of the frozen vaginal mucosa and fitted on the mucoadhesion test rig. Then, 50 μL of AVM was applied to the surface of the tissue prior to the experiment [19]. The samples were packed into shallow cylindrical vessels. The test began by lowering the analytical probe that contained the skin at a constant speed (1 mm·s−1) onto the surface of the sample. The mucosa and the sample were kept in contact for 60 s; no force was applied during this interval. After 60 s, the mucosa was drawn upwards (0.5 mm·s−1) until the contact between the surfaces was broken. The mucoadhesive force of the samples was measured as the maximum detachment force of the resistance to the withdrawal of the probe, which reflected the mucoadhesion characteristic. Seven replicates were analyzed at 37 ± 0.5 °C. 4.5. In Vitro Antifungal Assays 4.5.1. Fungal Strains One strain of C. albicans (ATCC 10231) and five clinical strains (CAV1, 2, 3, 4 and 5) were used in the in vitro assays. The clinical strains were donated to the Microbiology Laboratory of the Faculty of Medicine of Sao Jose do Rio Preto for purposes of scientific research through a written consent of the donors and with was approved by the Human Research Ethics Committee CEP-FAMERP (Protocol number 152/2006). Table 7 shows the strains used in this study with their respective general considerations. 4.5.2. Determination of the Minimum Inhibitory Concentration (MIC) In the MIC, determination was performed by the dilution in microplate technique [36] with modifications. The solutions of E and SE (100 μL) were added at concentrations ranging from 1000 to 7.8 μg/mL (serial dilution). Yeast cultures incubated for 48 h were adjusted to 103 CFU/mL which was then distributed into each well of the microplate. Amphotericin B and fluconazole were used as the positive controls. Additional controls also included the culture medium, yeast growth, E, solvent (DMSO) and formulation S. The microplates were incubated at 37 °C for 48 h. The 20 μL of an aqueous 2% solution of 2,3,5-triphenyltetrazolium chloride (TTC) was used as developer [37] and the microplates were incubated at 37 °C for 2 h. The assays were carried out in triplicate. 4.5.3. Analysis of the Inhibitory Profile of Hyphae in C. albicans The C. albicans (ATCC 10231) culture was grown for 24 h to obtain filamentous yeasts. The yeasts at a concentration of 2.5 × 10³ cells/mL in PBS (pH 7.2). A total of 20 μL of this suspension was added to microplate wells containing RPMI 1640 medium with 10% fetal bovine serum and gentamicin (1%). E and SE were evaluated at concentrations ranging from 1000 to 7.81 μg/mL. After 12 and 24 h, a reduction in hyphae growth was observed under the inverted light microscope (400×). Amphotericin B (16 μg/mL) was used as the positive control; additional controls included fungal growth, DMSO, sterile S and culture medium [13]. 4.5.4. In Vitro Assay of Biofilm Inhibition The biofilm adhesion method was performed as described by Pitangui et al. [38] with modifications. The samples were incubated under rotation at 37 °C for two hours at 80 rpm. After the pre-adhesion period, the supernatant was removed and 100 μL of RPMI medium was added to each microplate well; then, incubation proceeded at 37 °C for 48 h, with the RPMI renewed after 24 h. After the incubation period, the supernatant was removed and the wells were washed with 100 mL of 0.9% saline solution. A total of 100 mL of E and SE were added at concentrations of 20, 10, 5, 2.5, 1.2 and 0.6 mg/mL, and the microplates were re-incubated for 48 h at 37 °C. The 2,3-bis (2-methoxy-4-nitro-5-sulfophenyl)-5-[carbonyl(phenylamino)]-2H-tetrazolium hydroxide (XTT®) was used as the developer are shown the reduction of the medium. 4.5.5. Time Kill Assay This test was performed with the standard strain (ATCC 10231) and the clinical strain that was most sensitive in the MIC assay (CAV 3). In a test tube containing culture medium and yeast (2.5 × 103 cells/mL), we added 1 mL of E and SE at a concentration that was 2× the obtained MIC and the mixture was incubated at 37 °C. The aliquot of the contents were diluted in sterile PBS (1:1) at ratio 0, 0.5, 1, 2, 4, 8, 12, 24, 36 and 48 h. This suspension was seeded onto the surface of SDA and the colonies were counted after 48 h of incubation [39]. 4.6. In Vivo Antifungal Assay 4.6.1. Experimental Assay of Vulvovaginal Candidiasis Treatment This assay was approved by the Animal research committee CEUA-UNESP (Protocol number 34/2013). The assays were performed with two strains of C. albicans: a clinical isolate (CAV3- most sensitive in the MIC determination) and the standard strain (ATCC 10231). Wistar female rats were employed (Rattus novergicus) (8 weeks old, 200–300 g). The animals were maintained throughout the experiment in the bioterium of the School of Pharmaceutical Sciences of Araraquara—UNESP, with adequate temperature and ventilation under a 12-h light/dark cycle and free access to water and food during the course of all experiments. The animals were housed in cages with previously sterilized wood shavings and were acclimated to the experimental room for 7 days prior to the start of the procedures. We used the in vivo experimental model of Araújo et al. [13] with modifications. The animals were subjected to a state of immunosuppression by the administration of cyclophosphamide (0.3 mL−50 mg/kg) in a single intraperitoneal dose. To obtain the pseudo-estrus state, the rats were injected with a 0.1 mL solution of estradiol (0.2 mg/mL) subcutaneously on the day of immunosuppression and 10 days later. The hormonal status was verified by microscopic analysis of cell morphology found in the vaginal fluid of animals obtained by washing with 0.1 mL of PBS solution; the presence of cornfield anucleate epithelial cells indicated the pseudo-estrus phase [40]. After adjusting the estrous cycle of all animals, they were infected intravaginally with a suspension of 5.0 × 108 cells/mL of C. albicans (ATCC 10231 and CAV 3) prepared in PBS by injecting 0.1 mL of the fungal suspension with the aid of a micropipettor with a sterile tip. Two days after inoculation, vaginal washings were performed with 0.1 mL of sterile PBS buffer solution. The washes were subjected to microscopic examination and cultured on sabouraud dextrose agar with chloramphenicol (SDA + clo) plates which were incubated at 37 °C for 48 h. The presence of cells or free budding yeast observed under a light microscope and the growth of CFUs in the culture medium were considered positive for infection. 4.6.2. Experimental Groups and Therapeutic Treatment We used 13 experimental groups consisting of 6 animals each (n = 78). Table 8 shows the experimental groups and their respective treatment parameters. The concentration of vegetal extract used in the therapeutic groups was based in the MIC value obtained in the in vitro determination of antifungal potential by microdilution technique. A value of 2× MIC was employed as a therapeutical dose [17,38]. The treatments used in the assays were performed twice daily for 8 days. 4.6.3. Analysis of the Effectiveness of Treatment Microscopic analysis and culture of vaginal fluids were performed to determine the vaginal fungal burden. On days 2, 4, 6 and 8 of the treatment period, the animals were subjected to the collection of vaginal fluids which were obtained by intravaginal washing with 0.1 mL of PBS solution (pH 7.4) with the aid of a sterile micropipettor with sterile tips. Microscopic analyses were performed to verify the presence or absence of yeast in the vaginal environment. The washes were also cultured in SDA + clo plates which were incubated at 37 °C for 48 h and quantified by the number of CFU on each day to analyze the treatment. 4.6.4. Analysis of Recurrence of Infection and Euthanasia Eight days after the treatment period, the animals were subjected to recurrence of the infection to identify and diagnose infectious states that may eventually emerge after the treatment period. To this end, vaginal fluids were collected daily over a 7 day period for microscopic analysis and culture as described above for the analysis of the efficacy of treatment. The animals were euthanized by intoxication in a CO2 chamber. Statistical data were analyzed using ANOVA. We used the Tukey test to compare the results of the treatments and the Dunnett test to compare the results of the treatment and control. 5. Conclusions This study confirms the antifungal potential of S. nitens extract and shows that the incorporation into the drug delivery system is important for the increase of its pharmacological parameters. The use of the LCPS (S) proves to be suitable for the treatment of VVC which make them more efficient than E unloaded. The improvement in antifungal activity can be based on the mucoadhesive properties of the formulation and the components that might have facilitated the interaction with the fungal cells. Besides the prophylactic potential showed in our previous studies, in this study we conclude that the incorporation of S. nitens in LCPS is able to promote the treatment of VVC caused by C. albicans. Acknowledgments We thank grant#2013/25432-0 and grant#2016/13784-8, São Paulo Research Foundation (FAPESP); and “Programa de Apoio ao Desenvolvimento Científico da Faculdade de Ciências Farmacêuticas, UNESP-PADC”. Author Contributions Matheus Aparecido dos Santos Ramos, Luciani Gaspar de Toledo and Giovana Maria Fioramonti Calixto made substantial contributions to concept and design, acquisition of data, analysis and interpretation of data. Bruna Vidal Bonifácio made the statistical analysis. Marcelo Gonzaga de Freitas Araújo supervised the execution of the in vivo assay. Lourdes Campaner dos Santos supervised the acquisition of S. nitens extract. Margarete Teresa Gottardo de Almeida, Marlus Chorilli and Taís Maria Bauab supervised the design and data interpretation and they gave final approval of the version to be published. All authors discussed the results and contributed to the manuscript. Conflicts of Interest The authors declare no conflicts of interest. Figure 1 Ternary phase diagram with procetyl, oleic acid, and polymeric dispersion. The dashed lines represent the different regions of the diagram. The formulation S (chosen for to study) is highlighted in red. TLS = Transparent Liquid System; TrLS = Translucent Liquid System; TrVS = Translucent Viscous System; VOS = Viscous and Opaque System; PS = Phase Separation; VTS = Viscous Transparent System. Figure 2 Analyses by PLM of the formulations before and after addition of AVM (20× magnification). Legend: S = Formulation without extract; SE = Formulation with extract; S100 = Formulation + 100% of AVM; SE100 = Formulation with extract + 100% of AVM. Figure 3 Rheogram of the formulations. Filled symbol upslope and downslope open symbol. Legend: S = Formulation; SE = Formulation with extract; S100 = Formulation + 100% of AVM; SE100 = Formulation with extract + 100% of AVM; Up Arrow = ascending; Down Arrow = descending. Figure 4 Parameters of in vitro bioadhesion test of all formulations. Each value represents the mean (±SD) of at least seven replicates. Legend: S = Formulation; SE = Formulation with extract; S100 = Formulation + 100% of AVM; SE100 = Formulation with extract + 100% of AVM. Figure 5 Photomicrography of 12 h of the hyphae inhibition test of Candida albicans (C. albicans) realized with E. Legend: (a) growth control; (b) amphotericin B 16 μg/mL; (c–j) E from 1000 to 7.8 μg/mL in descending order. E = Extract solution (without incorporation). Figure 6 Photomicrography of 24 h of the hyphae inhibition test of C. albicans realized with E. Legend: (a) growth control; (b) amphotericin B 16 μg/mL; (c–j) E from 1000 to 7.8 μg/mL in descending order. E = Extract solution (without incorporation). Figure 7 Photomicrography of 12 h of the hyphae inhibition test of C. albicans realized with SE. Legend: (a) growth control; (b) amphotericin B 16 μg/mL; (c–j) SE from 1000 to 7.8 μg/mL in descending order. SE = Formulation with extract. Figure 8 Photomicrography of 24 h of the hyphae inhibition test of C. albicans realized with SE. Legend: (a) growth control; (b) amphotericin B 16 μg/mL; (c–j) SE from 1000 to 7.8 μg/mL in descending order. SE = Formulation with extract. Figure 9 Time kill assay of ATCC 10231 strain. Legend: E = Extract solution (without incorporation); SE = Formulation with extract. Figure 10 Time kill assay of CAV3 strain. Legend: E = Extract solution (without incorporation); SE = Formulation with extract. ijms-17-01368-t001_Table 1Table 1 Results of the evaluation of precursor behavior the formulation of liquid crystals by Polarized Light Microscopy (PLM). Test Viscosity Structure Viewed Mesophase Formulation + 5% of AVM + Dark field Microemulsion Formulation + 10% of AVM ++ Dark field Microemulsion Formulation + 30% of AVM +++ Cross of Malta Lamellar Formulation + 50% of AVM ++++ Striae Hexagonal Formulation + 100% of AVM +++++ Striae Hexagonal + = viscosity; AVM, Artificial Vaginal Mucus. ijms-17-01368-t002_Table 2Table 2 Flow behavior (n) and consistency index (K) of the formulations. Formulations n K SE100 0.30 17.64 S100 0.23 55.11 S 1.0 0.059 SE 1.0 0.069 Each value represents the mean (±SD) of three replicates. S = Formulation; SE = Formulation with extract; S100 = Formulation + 100% of AVM; SE100 = Formulation with extract + 100% of AVM. ijms-17-01368-t003_Table 3Table 3 Minimum Inhibitory Concentration (MIC) of E and SE against Candida albicans (C. albicans). Sample Analyzed MIC a E SE Fluconazole Amphotericin B ATCC 10231 250 62.5 R 0.12 CAV 1 125 62.5 R 0.12 CAV 2 250 62.5 R 0.12 CAV 3 125 31.2 R 0.12 CAV4 125 62.5 R 0.50 CAV5 250 62.5 R 0.25 a Values in μg/mL; (R) resistance. E = Extract solution (not loaded); SE = Formulation with extract. ijms-17-01368-t004_Table 4Table 4 Results of inhibition biofilm assay. C. albicans Biofilm Inhibition a SE E AMB DMSO S ATCC 10231 1.25 >20.0 4.0 - - CAV 1 10.0 >20.0 8.0 - - CAV 2 20.0 >20.0 4.0 - - CAV 3 10.0 >20.0 8.0 - - CAV 4 1.25 >20.0 16.0 - - CAV 5 1.25 >20.0 16.0 - - a values in mg/mL; (-) without inhibition. AMB = amphotericin B; E = Extract solution (not loaded); SE = Formulation with extract; DMSO = dimethyl sulfoxide (20%); S = Formulation. ijms-17-01368-t005_Table 5Table 5 Fungal loads (CFUs) obtained from the culture of vaginal fluid collected during the treatment period for therapeutic treatment against C. albicans ATCC 10231. Groups Treatment Day 2 Day 4 Day 6 Day 8 Positive control (infection) 8030.0 ± 254.6 5830.0 ± 56.6 6700.0 ± 141.4 8123.0 ± 113.1 Positive control (tetracycline + amphotericin B) 2507.0 ± 42.4 947.0 ± 84.9 160.0 ± 0.0 0.0 ± 0.0 Solvent control (DMSO) 6007.0 ± 42.4 6447.0 ± 70.7 5303.0 ± 28.3 8043.0 ± 28.3 Treatment 1 (E) 170.0 ± 0.0 a,* 253.0 ± 0.0 a,* 0.0 ± 0.0 a 0.0 ± 0.0 a S formulation 4587.0 ± 28.3 5793.0 ± 28.3 6587.0 ± 84.9 6287.0 ± 53.5 Treatment 2 (SE) 0.0 ± 0.0 a,* 0.0 ± 0.0 a,* 0.0 ± 0.0 a 0.0 ± 0.0 a a The same letter denotes non-significant differences among groups according to parametric post hoc test (p < 0.05—Tukey test); * significant difference (p < 0.05) when compared with positive control group (tetracycline + amphotericin B—Dunnett test). ijms-17-01368-t006_Table 6Table 6 Fungal loads (CFUs) obtained from the culture of vaginal fluid collected during the treatment period for therapeutic treatment against CAV3. Groups Treatment Day 2 Day 4 Day 6 Day 8 Positive control (infection) 7203.3 ± 42.4 16716.0 ± 172.1 16683.3 ± 14.1 16130.0 ± 28.3 Positive control (tetracycline + amphotericin B) 7270.0 ± 14.1 4256.7 ± 141.4 2143.3 ± 200.3 416.7 ± 118.5 Solvent control (DMSO) 5726.7 ± 56.6 8356.7 ± 127.3 8936.7 ± 198.0 8563.3 ± 0.0 Treatment 1 (E) 546.7 ± 30.0 a 110.0 ± 91.9 a 63.3 ± 44.8 a 0.0 ± 0.0 a S formulation 5340.0 ± 28.3 10470.0 ± 28.3 10683.3 ± 186.2 9476.7 ± 80.9 Treatment 2 (SE) 0.0 ± 0.0 a 0.0 ± 0.0 a 0.0 ± 0.0 a 0.0 ± 0.0 a a The same letter denotes non-significant differences among groups according to parametric post hoc test (p < 0.05—Tukey test); * significant difference (p < 0.05) when compared with positive control group (tetracycline + amphotericin B—Dunnett test). ijms-17-01368-t007_Table 7Table 7 Candida albicans strains. C. albicans Strains Origin a Resistance Profile b Symptomatology ATCC 10231 Pulmonary ketoconazole, fluconazole and itraconazole Not described CAV1 Vaginal ketoconazole, fluconazole, itraconazole and nystatin Symptomatic CAV2 Vaginal ketoconazole, fluconazole and itraconazole Asymptomatic CAV3 Vaginal ketoconazole, fluconazole, itraconazole and nystatin Symptomatic CAV4 Vaginal ketoconazole, fluconazole and itraconazole Symptomatic CAV5 Vaginal ketoconazole, fluconazole and itraconazole Asymptomatic a All strains are of human origin; b according to microdilution tests (CLSI protocol). ijms-17-01368-t008_Table 8Table 8 Experimental groups of in vivo treatment assay of vulvovaginal candidiasis (VVC). Experimental Group Specification Treatment * Group 1 Negative control PBS solution sterile Group 2 Positive control-ATCC 10231 PBS solution sterile Group 3 Positive control with antifungal-ATCC 10231 0.1 mL antifungal cream (amphotericin B and tetracycline) Group 4 Control of solvent-ATCC 10231 0.1 mL solution with 20% of DMSO Group 5 Treatment 1-ATCC 10231 0.1 mL of E (500 μg/mL) Group 6 Control of S-ATCC 10231 0.1 mL of S Group 7 Treatment 2-ATCC 10231 0.1 mL of SE (500 μg/mL) Group 8 Control of infection-CAV3 PBS solution sterile Group 9 Positive control with antifungal-CAV3 0.1 mL antifungal cream Group 10 Control of the solvent-CAV3 0.1 mL solution with 20% of DMSO Group 11 Treatment 3-CAV3 0.1 mL of E (500 μg/mL) Group 12 Control of S-CAV3 0.1 mL of S Group 13 Treatment 4-CAV3 0.1 mL of SE (500 μg/mL) * Treatment performed twice a day. S = Formulation; SE = Formulation with extract. ==== Refs References 1. Kim J. Sudbery P. Candida albicans , a major human fungal pathogen J. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081369ijms-17-01369ArticleTNFSF4 Gene Variations Are Related to Early-Onset Autoimmune Thyroid Diseases and Hypothyroidism of Hashimoto’s Thyroiditis Song Rong-Hua 1†Wang Qiong 2†Yao Qiu-Ming 1Shao Xiao-Qing 1Li Ling 1Wang Wen 1An Xiao-Fei 1Li Qian 1Zhang Jin-An 1*Grimm Daniela Gabriele Academic Editor1 Department of Endocrinology, Jinshan Hospital of Fudan University, No. 1508 Longhang Road, Jinshan District, Shanghai 201508, China; someonesrh66@163.com (R.-H.S.); 14211270001@fudan.edu.cn (Q.-M.Y.); shaoxq2015@163.com (X.-Q.S.); alana3344@126.com (L.L.); wwen910108@163.com (W.W.); anxiaofei2000@163.com (X.-F.A.); ellen38668@163.com (Q.L.)2 The hemodialysis center of Nephropathy Department, Shaanxi Provincial People’s Hospital, No. 256 West Youyi Road, Beilin District, Xi’an 710068, China; wq9930@126.com* Correspondence: zhangjinan@hotmail.com; Tel.: +86-21-5703-9815; Fax: +86-21-6722-6910† These authors contributed equally to this work. 20 8 2016 8 2016 17 8 136910 7 2016 16 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).The aim of the current study was to examine whether the polymorphism loci of the tumor necrosis factor superfamily member 4 (TNFSF4) gene increase the risk of susceptibility to autoimmune thyroid diseases (AITDs) in the Han Chinese population, and a case-control study was performed in a set of 1,048 AITDs patients and 909 normal healthy controls in the study. A total of four tagging single nucleotide polymorphisms (SNPs) in the TNFSF4 region, including rs7514229, rs1234313, rs16845607 and rs3850641, were genotyped using the method of ligase detection reaction. An association between GG genotype of rs3850641 in TNFSF4 gene and AITDs was found (p = 0.046). Additionally, the clinical sub-phenotype analysis revealed a significant association between GG genotype in rs7514229 and AITDs patients who were ≤18 years of age. Furthermore, rs3850641 variant allele G was in strong association with hypothyroidism in Hashimoto’s thyroiditis (HT) (p = 0.018). The polymorphisms of the TNFSF4 gene may contribute to the susceptibility to AITDs pathogenesis. tumor necrosis factor superfamily member 4 (TNFSF4)single nucleotide polymorphism (SNP)autoimmune thyroid diseases (AITDs)Graves’ disease (GD)Hashimoto’s thyroiditis (HT) ==== Body 1. Introduction Autoimmune thyroid diseases (AITDs) are a group of organ-specific and polygenic inherited autoimmune diseases, with an estimated prevalence of up to 1%–5% of the general population [1]. AITDs mainly consist of two clinical subtypes of Graves’ disease (GD) and Hashimoto’s thyroiditis (HT). GD is predominantly characterized by a variable combination of hyperthyroidism, diffused goiter and high level of thyroid stimulating hormone receptor antibody (TRAb). Meanwhile, some GD patients may present extrathyroidal manifestations, including ophthalmopathy, pretibial myxedema and clubbed fingers. Clinical features of HT include the presence of antibody against thyroid peroxidase (TPOAb) or thyroglobulin (TgAb). Additionally, some patients with HT harbor extensive apoptosis of thyrocytes leading to hypothyroidism. Although there are some common characteristics in GD and HT, such as destruction of thyroid tissue and the existence of circulating thyroid autoantibodies including TRAb, TPOAb and TgAb, the clinical presentations and mechanisms of the two subtypes are different from each other to some extent; for example, our previous studies found that GD, HT or even Graves’ ophthalmopathy (GO) have specific genetic backgrounds [2,3]. The pathogenesis of AITDs remains unclear, although there is much evidence demonstrating that the interaction between genetic factors and environmental components may be involved in their etiology [4,5]. More recently, an increasing body of research has confirmed that several specific genes are associated with multiple autoimmune diseases [6,7], implicating that many autoimmune diseases may share some genetic risk factors. For instance, TNFAIP3 has been identified to be related to the genetic etiology of systemic lupus erythematosus (SLE) [8], rheumatoid arthritis (RA) [9], systemic sclerosis (SSc) [10]. Additionally, we also found the relationship between this gene and GD [11]. All these data documented that variants in several genes probably contribute to dysregulation of common immune pathways, and then are involved in the pathological procedure of diverse autoimmune diseases. The tumor necrosis factor superfamily member 4 (TNFSF4) gene encodes a cytokine (OX40L), which is expressed on antigen-presenting cells (APCs) to provide co-stimulatory signals to T cells. In recent years, the TNFSF4 gene polymorphisms have been reported to be an important predisposition factor to SLE [12], RA [6], SSc [13] and primary Sjogren’s syndrome (pSS) [14]. However, to date, whether TNFSF4 gene variations are associated with AITDs has not been investigated. In the present study, we evaluated whether mutations in TNFSF4 gene are genetically predisposed in Han Chinese populations to AITDs via a case-control study. Single nucleotide polymorphisms (SNPs) tagging four independent susceptibility loci were genotyped in a large cohort of AITDs patients and normal healthy controls. We also analyzed the association between each polymorphism locus and the predisposition to different subtypes of AITDs, including GD, HT and ophthalmopathy. 2. Results 2.1. Clinical Phenotype Analysis The clinical characteristics of the AITDs cohort are displayed in Table 1. Among the investigated 1,048 AITDs patients, 693 were GD patients including 30.736% male and 69.264% female, with mean disease-onset age of 34.010 ± 14.395 years old; 355 were HT patients, including 12.676% male and 87.324% female, with mean disease-onset age of 32.720 ± 13.511 years old. There were 162 (15.458%) teenaged AITDs patients with disease-onset age ≤18 years old, 130 (12.405%) AITDs patients with ophthalmopathy, 198 (55.775%) HT patients with hypothyroidism, and 216 (20.611%) AITDs patients, comprised of 143 GD patients and 73 HT patients, with family history. 2.2. Allelic and Genotypic Association There is a Hardy-Weinberg equilibrium (HWE) in the genotype distributions of TNFSF4 SNPs in the control group (p > 0.05). Additionally, we evaluated the HWE for the loci in our AITDs cases; the HWE of the four loci for AITDs cases all had p-value higher than 0.01. In addition, variant genotype GG of rs3850641 in TNFSF4 gene is associated with AITDs (p = 0.046), as shown in Table 2. Further analysis found that the frequencies of TT genotype in rs7514229 and GG genotype in rs3850641 were lower in the AITDs group than the control group (Table 3), which suggested that people with these genotypes are less susceptible to AITDs (p = 0.016, OR = 0.236, 95% CI = 0.066–0.850 and p = 0.027, OR = 0.492, 95% CI = 0.259–0.935, respectively). Those subjects whose genotypes of the four loci failed to be determined were ruled out from the statistical analysis. Moreover, the frequency of genotype GG in rs3850641 was lower in GD patients than the control group in analysis of sub-clinical types of AITDs (GD and HT) although without statistical significance (p = 0.086), as shown in Table 4. 2.3. Haplotypic Association Haplotypic analysis using the Haploview software (Whitehead Institute for Biomedical Research, MIT Media Lab, and Broad Institute of Harvard and MIT, Cambridge, MA, USA) revealed that in the HapMap Han Chinese Beijing database, rs7514229 and rs1234313 were in the same block (Figure 1), which contained three haplotypes, namely GA, GG and TG. However, these haplotypes were not associated with AITDs (p > 0.05, data not shown). 2.4. Genotyping-Clinical Sub-Phenotype Association To further investigate the relation between polymorphisms of TNFSF4 and clinical phenotypes, clinical sub-phenotype analyses were conducted. The results showed that the frequency of genotype TT in rs7514229 marginally declined in AITDs patients with disease-onset age ≥19 years old (p = 0.049, as shown in Table 5). However, our present study displayed that TNFSF4 gene variants were not associated with AITDs patients with ophthalmopathy or family history. Interestingly, the frequency of allele G in rs3850641 was significantly more decreased in HT patients with hypothyroidism than in HT patients without hypothyroidism, suggesting that HT patients with allele G in rs3850641 had increased susceptibility risk to hypothyroidism (p = 0.018, Table 6). 3. Discussion The TNFSF4 gene, also known as the OX40 ligand (OX40L), encodes the OX40L protein which is a co-stimulatory cytokine and belongs to the TNF ligand family. The protein mainly participates in the interaction of T-cell and antigen-presenting cell (APC), T-cell activation and B-cell differentiation, providing CD28-independent co-stimulatory signals for activated CD4+ T cells [15]. TNFSF4, located in chromosome 1 (1q25), contains three exons and two introns (in NCBI database). Previous studies have shown that polymorphisms of TNFSF4 can confer risk to diverse autoimmune diseases, such as SLE, RA, SSc and pSS, but it remains unknown whether genetic mutations of TNFSF4 region may induce occurrence of AITDs, which attracts our interest. AITDs are also regarded as autoimmune diseases targeting the thyroid with a complex genetic and environmental etiology, manifesting mainly as GD and HT. It is notable that genetic factors play a prominent role in the occurrence and persistence of AITDs. Given that autoimmune diseases may share a common genetic predisposition, and that immune dysregulation plays a vital role in AITDs [16,17], we hypothesized that variants within the TNFSF4 gene, which is a crucial immune regulator, could also elicit abnormal OX40L expression and dysfunction, thus affecting T-cell activation and leading to unbalanced immune regulation and its resultant occurrence of AITDs. In the present work, we observed the association between four loci of TNFSF4 gene and AITDs patients in the Han Chinese population. We found that the frequency of genotype GG in rs3850641 was slightly lower in AITDs patients, probably suggesting it could decrease susceptibility to AITDs. In addition, frequencies of GG genotype in rs3850641 and TT genotype in rs7514229 also decreased in AITDs subjects, confirming that variant genotype GG in rs3860541 was indeed a factor protecting people from AITDs, as was variant genotype TT in rs7514229. Our results suggested polymorphisms in the TNFSF4 gene region, one SNP in 3′UTR (rs7514229) and two intronic SNPs (rs3860541 and rs1234313), may be associated with AITDs susceptibility. To our knowledge, variants in the intron of a gene may influence its expression and regulate its function [18], 3′UTR polymorphisms in the gene region are of important regulation function. We therefore speculated that the molecular action underlying genetic pathology of AITDs is that TNFSF4 SNPs may affect the expression of TNFSF4 gene and down-regulate T-cell activation, which requires further in-depth research to confirm. Further, to investigate the association between genotype and clinical manifestations, we carried out the clinical sub-phenotype analysis. The AITDs occurrence in teenagers (≤18 young patients) may be due to their genetic family history of this disease [19,20], which corresponded with our results showing the frequency of family history was much higher in AITDs patients with disease-onset age ≤18 years old. Meanwhile, marginally significant differences in frequencies of rs7514229 genotype TT and disease-onset age were found between AITDs patients with disease-onset age ≤18 years old and AITDs patients with disease-onset age ≥19 years old. Similar correlations between gene mutations and disease-onset age were reported in RA [21], type 1 diabetes [22] and multiple sclerosis [23]. Furthermore, we revealed that frequency of GG genotype of rs3850641 declined slightly in GD subgroup of AITDs, although without significance. Nevertheless, we observed that TNFSF4 SNPs were not associated with AITDs patients with ophthalmopathy or family history. Several studies provided clues that thyroid-associated ophthalmopathy (TAO) was correlated with the impact of environmental elements, especially current smoking history [24,25]. Recent studies are suggesting that genetic markers also affect the susceptibility of TAO [26], including genetic variants in the STAT3 [27], TSHR [28] and HLA-DR3 [29] regions. However, our results cannot add the TNFSF4 gene to the list of the predisposition of thyroid-associated ophthalmopathy (TAO). Moreover, allele A from rs3850641 was associated with the decreased risk for the HT subgroup of hypothyroidism by 41.5%. In HT, hypothyroidism is more associated with a family history of thyroid dysfunction [20]. Our study showed HT hypothyroidism patients with higher ratio of family history, which was consistent with the previous research [20]. To our best knowledge, we were the first to find that genetic factors are also involved in etiology of hypothyroidism in HT. Why do these SNPs not show their susceptibility to GD or TAO? It is possible that thyroid eye disease or TAO is a different disease than Graves’ disease and Hashimoto’s thyroiditis. In addition, a recent paper found that polymorphisms in calsequestrin (CASQ1) are correlated with HT and GO, but not Graves’ hyperthyroiditis (GH) [30]. Interestingly, our study found SNPs in TNFSF4 are associated with hypothyroidism of Hashimoto’s thyroiditis, but not thyroid orbitopathy or GD. These two studies do not show contradictory results, and illustrate the complexity of the diseases, GD, HT and TAO or GO. For instance, our previous studies indeed found UBE2L3 and CLEC16A gene polymorphisms to be associated with susceptibility to HT rather than GD and TAO or GO [2,3]. Obviously, the genetic mechanisms of these diseases are still unclear, so more research is needed to reveal the pathomechanism of thyroid ophthalmopathy. Overall, we provided the first evidence for genetic association between four susceptibility loci in the TNFSF4 gene in Chinese AITDs patients, with samples exclusively from the Han Chinese population. Nevertheless, considering the validation of a convincing association and discovery of population differences, the importance of replication studies in some different populations should not be overlooked. The statistical power calculated in this research was very strong (larger than 0.8) to detect the association, and it has adequately reached a significant result. Simultaneously, the sample size in this study was large enough with 1,048 cases and 909 controls to effectively reduce the type of errors (type 1 error and type 2 error). 4. Materials and Methods 4.1. Subjects A total of 1,048 Chinese patients with AITDs (693 GD and 355 HT) and 909 healthy Chinese controls were recruited. All AITDs patients were enrolled from the Out-Patient Department of Endocrinology of Jinshan Hospital of Fudan University. Ethnically and geographically matched and unrelated healthy controls were recruited from the Healthy Check-Up Center of the same hospital. All AITDs patients were diagnosed as previously described [2,27]. GD patients were diagnosed based on their clinical manifestations and biochemical assessments of hyperthyroidism and the positive circulating TRAb, with or without positive TPOAb or TgAb and diffusive goiter of the thyroid. HT was defined based on the high level of either TPOAb or TgAb, with or without clinical and biochemical hypothyroidism and the presence of an enlarged thyroid. A minority of HT patients were further confirmed by fine needle aspiration biopsies. All the control subjects showed negative thyroid antibodies against TPO. In the current study, TPOAb, TgAb and TRAb were detected with highly specific and sensitive immunochemiluminescence kits from Roche Company (Shanghai, China). All the subjects, including AITDs patients and controls, were ethnic Han Chinese. Written informed consent was obtained from all participants and the research was approved by the Ethics Committee of Jinshan Hospital of Fudan University (JYLL-2014-06, 2014/2/21), respectively. 4.2. DNA Sample Preparation Genomic DNA were extracted from 2 mL of peripheral venous blood from each subject using RelaxGene Blood DNA System (Tiangen Biotech Company, Beijing, China), according to the manufacturer’s protocol. The concentration and A260/A280 ratio of all DNA samples were measured by NANO DROP 2000 Spectrophotometer (Thermo Scientific Company, Waltham, MA, USA). Finally, the DNA samples with great purity and concentration were used for next genotyping. 4.3. Single Nucleotide Polymorphism (SNP) Selection and Genotyping Marker-tagging SNPs were chosen from the Hapmap CHB data using the Tagger programme of Haploview software (Whitehead Institute for Biomedical Research, MIT Media Lab, and Broad Institute of Harvard and MIT) to satisfy the following criteria: minor allele frequency (MAF) >0.1, Hardy-Weinberg equilibrium (HWE) with p > 0.001 and logarithm of odds (LOD) >3.0. For the TNFSF4 gene of 23 kb with 42 SNPs in Hapmap CHB population, we selected four loci covering the whole region of the TNFSF4 gene to capture all the most common variants. Four tag SNPs were selected including rs7514229 located in the 3′ untranslated region (UTR), as well as rs1234313, rs16845607 and rs3850641 in intron 1 of the TNFSF4 region. Genotyping of the four SNPs was undertaken using the ligase detection reaction (LDR) platform according to the manufacturer’s instructions. Moreover, to ensure detection quality, each reaction was performed in duplicate, and blank samples without DNA were used as negative controls. Furthermore, only SNPs and samples that passed the 95% quality control threshold were subjected to further statistical analysis and SNPs with allele frequencies not meeting Hardy-Weinberg equilibrium (HWE) were removed from the next analysis. The primers specific to the four SNPs at the TNFSF4 loci are “rs7514229” forward-GATAACACAGAATCATCCAG and reverse-TTGTAGCACATGTTTCCCTG; “rs1234313” forward-ATCTAACACTGGCTCTAGTC and reverse-GCCATTCTGACTAGAATAGG; “rs16845607” forward-AGATATAGCTACCAAGCTCC and reverse-GATGAGAAAACAGAGGCTAC; “rs3850641” forward-GCTGTCACTTTGAAGCTTTG and reverse-TGCCTGATCAAACACATTAC. 4.4. Clinical Sub-Phenotype Analysis Clinical sub-phenotype stratification analysis was conducted using a case-only approach, in which basic allelic and genotypic examination was performed by comparing minor allele and genotype frequency of cases with a specific sub-phenotype to the whole case group. The clinical sub-phenotypes include: (1) the age of disease onset (≤18 years old versus ≥19 years old); (2) presence or absence of ophthalmopathy which was defined as a distinctive disorder characterized by inflammation and swelling of the extraocular muscles, eyelid retraction, periorbital edema, episcleral vascular injection, conjunctive swelling and proptosis; (3) presence or absence of hypothyroidism in HT patients; and (4) presence or absence of AITDs family history, which was defined as the subjects’ first-degree relatives including parents, children and siblings or second-degree relatives such as grandparents, uncles and aunts who had AITDs. 4.5. Statistical Analysis Clinical data were described as M ± SD (mean ± standard deviation). Hardy-Weinberg equilibrium (HWE) concordance test in the controls and patient samples, linkage disequilibrium (LD) test and haplotype frequency calculation were performed using HaploView 4.2 (Whitehead Institute for Biomedical Research, MIT Media Lab, and Broad Institute of Harvard and MIT). In order to analyze whether the four predisposing loci are associated with AITDs, allele and genotype frequencies were compared between AITDs cases and healthy controls using the Chi-square test (χ2-test) or Fisher’s exact test. LD among the selected SNPs was measured using the pairwise LD measures D’ and r2. All data were statistically calculated with the SPSS 18.0 software (International Business Machines Corporation, Armonk, NY, USA). A p value of less than 0.05 was considered statistically significant. Odds ratio (OR) and 95% confidence interval (95% CI) were applied to assess the association between each genotype and AITDs. 4.6. Power Calculation Power calculations for AITDs in this research considered allele frequency of SNPs from 0.05 to 0.5, a population prevalence of 1%–5% for AITDs, and OR of 0.2–0.5 at a 0.05 significant level. As a result, this study had sufficient power (larger than 0.8) to detect the association of OR of 0.2 or above with 1,048 cases and 909 controls. 5. Conclusions In conclusion, the preliminary findings of our present study are the first to indicate the association of novel genetic susceptibility loci of the TNFSF4 region with the predisposition to AITDs. Additionally, our results support the importance of T cells in the pathology of AITDs, and reveal the frequency overlap of risk loci in immune pathways between AITDs and other autoimmune diseases. Acknowledgments This project is supported by grants from the National Natural Science Foundation of China (No. 81471004) and the Key Disciplines Development of Shanghai Jinshan District (No. JSZK2015A02). The authors would like to thank all of the people who took part in the study. Author Contributions Rong-Hua Song and Qiong Wang carried out the work and contributed equally to the work. Qiu-Ming Yao, Xiao-Qing Shao, Ling Li, Wen Wang, Xiao-Fei An and Qian Li helped specimens collection. Rong-Hua Song conducted the data analysis and wrote the manuscript. Jin-An Zhang designed the study and coordinated the research team. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Linkage disequilibrium (LD) block of TNFSF4 from controls in the Hapmap CHB data. ijms-17-01369-t001_Table 1Table 1 Clinical data of AITDs patients and controls. Clinical Phenotype AITDs (%) GD (%) HT (%) Control (%) Number 1048 693 355 909 Gender – – – – Male 260 (24.809) 213 (30.736) 45 (12.676) 314 (34.543) Female 788 (75.191) 480 (69.264) 310 (87.324) 595 (65.457) Onset of age 33.570 ± 14.108 34.010 ± 14.395 32.720 ± 13.511 – ≤18 years 162 (15.458) 113 (16.306) 49 (13.803) – ≥19 years 886 (84.542) 580 (83.694) 306 (86.197) – Ophthalmopathy – – – – (+) 130 (12.405) 124 (17.893) 6 (1.690) – (−) 918 (87.595) 569 (82.107) 349 (98.310) – Family history – – – – (+) 216 (20.611) 143 (20.635) 73 (20.563) – (−) 832 (79.389) 550 (79.365) 282 (79.437) – ijms-17-01369-t002_Table 2Table 2 Allele and genotype distribution of TNFSF4 in AITDs patients and controls. SNP ID Control AITDs p OR (95% CI) rs7514229 – – – – GG 732 (81.424) 838 (81.518) 0.052 – TT 11 (1.224) 3 (0.292) – – GT 156 (17.353) 187 (18.191) – – G 1620 (90.100) 1863 (90.613) 0.590 1.061 (0.856–1.314) T 178 (9.900) 193 (9.387) – – rs1234313 – – – – AA 365 (40.466) 445 (42.871) 0.536 – GG 110 (12.195) 117 (11.272) – – AG 427 (47.339) 476 (45.857) – – A 1157 (64.135) 1366 (65.800) 0.278 1.076 (0.943–1.228) G 647 (35.865) 710 (34.200) – – rs16845607 – – – – AA 4 (0.443) 5 (0.480) 0.993 – GG 797 (88.359) 920 (88.292) – – AG 101 (11.197) 117 (11.228) – – A 109 (6.042) 127 (6.094) 0.946 1.009 (0.775–1.314) G 1695 (93.958) 1957 (93.906) – – rs3850641 – – – – GG 26 (2.879) 15 (1.438) 0.046 – AA 660 (73.090) 750 (71.908) – – AG 217 (24.031) 278 (26.654) – – G 269 (14.895) 308 (14.765) 0.910 0.990 (0.829–1.182) A 1537 (85.105) 1778 (85.235) – – ijms-17-01369-t003_Table 3Table 3 Genotype frequency of TNFSF4 loci in AITDs patients and controls. SNP Name Genotype Control (%) AITDs (%) p OR 95% CI rs7514229 GG 732 (81.424) 838 (81.518) 0.958 1.006 0.799–1.267 TT + GT 167 (18.576) 190 (18.482) TT 11 (1.233) 3 (0.292) 0.016 0.236 0.066–0.850 GG + GT 888 (98.776) 1025 (99.708) GT 156 (17.353) 187 (18.191) 0.631 1.059 0.838–1.339 GG + TT 743 (82.647) 841 (81.809) rs1234313 AA 365 (40.466) 445 (42.871) 0.284 1.104 0.921–1.323 GG + AG 537 (59.534) 593 (57.129) GG 110 (12.195) 117 (11.272) 0.528 0.915 0.693–1.206 AA + AG 792 (87.805) 921 (88.728) AG 427 (47.339) 476 (45.857) 0.514 0.943 0.788–1.126 AA + GG 475 (52.661) 562 (54.143) rs16845607 AA 4 (0.443) 5 (0.480) 0.906 1.082 0.290–4.049 GG+AG 898 (99.557) 1037 (99.520) GG 797 (88.359) 920 (88.292) 0.963 0.993 0.752–1.311 AA + AG 105 (11.641) 122 (11.708) AG 101 (11.197) 117 (11.228) 0.983 1.003 0.756–1.330 AA + GG 801 (88.803) 925 (88.772) rs3850641 GG 26 (2.879) 15 (1.438) 0.027 0.492 0.259–0.935 AA + AG 877 (97.121) 1028 (98.562) AA 660 (73.090) 750 (71.908) 0.561 0.943 0.772–1.151 GG + AG 243 (26.910) 293 (28.092) AG 217 (24.031) 278 (26.654) 0.185 1.149 0.935–1.410 AA + GG 686 (75.969) 765 (73.346) ijms-17-01369-t004_Table 4Table 4 Distribution of genotype and allele of TNFSF4 gene in sub-clinical types of AITDs patients and controls. SNP Control GD p OR (95% CI) HT p OR (95% CI) rs7514229 – – – – – – – GG 732 (81.424) 560 (82.474) 0.128 – 278 (79.656) 0.182 – TT 11 (1.224) 2 (0.295) – – 1 (0.287) – – GT 156 (17.353) 117 (17.231) – – 70 (20.057) – – G 1620 (90.100) 1237 (91.090) 0.347 1.123 (0.881–1.432) 626 (89.685) 0.756 0.955 (0.716–1.275) T 178 (9.900) 121 (8.910) – – 72 (10.315) – – rs1234313 – – – – – – – AA 365 (40.466) 298 (43.504) 0.474 – 147 (41.643) 0.832 – GG 110 (12.195) 78 (11.387) – – 39 (11.048) – – AG 427 (47.339) 309 (45.109) – – 167 (47.309) – – A 1157 (64.135) 905 (66.058) 0.261 1.088 (0.939–1.261) 461 (65.297) 0.584 1.052 (0.877–1.263) G 647 (35.865) 465 (33.942) – – 245 (34.703) – – rs16845607 – – – – – – – AA 4 (0.443) 4 (0.581) 0.897 – 1 (0.283) 0.859 – GG 797 (88.359) 605 (87.808) – – 315 (89.235) – – AG 101 (11.197) 80 (11.611) – – 37 (10.482) – – A 109 (6.042) 88 (6.386) 0.69 1.061(0.794–1.418) 39 (5.524) 0.62 0.909 (0.624–1.325) G 1695 (93.958) 1290 (93.614) – – 667 (94.476) – – rs3850641 – – – – – – – GG 26 (2.879) 9 (1.306) 0.086 – 6 (1.695) 0.172 – AA 660 (73.090) 502 (72.859) – – 248 (70.056) – – AG 217 (24.031) 178 (25.835) – – 100 (28.249) – – G 269 (14.895) 196 (14.224) 0.595 0.948(0.776–1.156) 112 (15.819) 0.561 1.074 (0.845–1.364) A 1537 (85.105) 1182 (85.776) – – 596 (84.181) – – ijms-17-01369-t005_Table 5Table 5 Allele and genotype distribution of TNFSF4 in AITDs patients with or without early-onset age. SNP ID Onset Age of AITDs Patients p OR (95% CI) ≤18 ≥19 rs7514229 – – – – GG 128 (80.503) 710 (81.703) 0.049 – TT 2 (1.258) 1 (0.115) – – GT 29 (18.239) 158 (18.182) – – G 285 (89.623) 1578 (90.794) 0.51 1.142 (0.769–1.696) T 33 (10.377) 160 (9.206) – – rs1234313 – – – – AA 71 (44.375) 374 (42.597) 0.41 – GG 22 (13.750) 95 (10.820) – – AG 67 (41.875) 409 (46.583) – – A 209 (65.312) 1157 (65.888) 0.842 1.026 (0.799–1.318) G 111 (34.688) 599 (34.112) – – rs16845607 – – – – AA 1 (0.625) 4 (0.454) 0.246 – GG 135 (84.375) 785 (89.002) – – AG 24 (15.000) 93 (10.544) – – A 26 (8.125) 101 (5.726) 0.099 0.687 (0.439–1.075) G 294 (91.875) 1663 (94.274) – – rs3850641 – – – – GG 2 (1.250) 13 (1.472) 0.639 – AA 120 (75.000) 630 (71.348) – – AG 38 (23.750) 240 (27.180) – – G 42 (13.125) 266 (15.062) 0.369 1.174 (0.827–1.664) A 278 (86.875) 1500 (84.938) – – ijms-17-01369-t006_Table 6Table 6 TNFSF4 genotype and allele distribution in clinical sub-phenotype of HT patients. TNFSF4 SNP HT p OR (95% CI) Non-Hypothyroidism Hypothyroidism rs7514229 – – – – GG 107 (80.451) 153 (78.462) 0.668 – TT 0 (0) 1 (0.513) – – GT 26 (19.549) 41 (21.026) – – G 240 (90.226) 347 (88.974) 0.608 0.874 (0.523–1.462) T 26 (9.774) 43 (11.026) – – rs1234313 – – – – AA 50 (37.037) 90 (45.685) 0.287 GG 16 (11.852) 19 (9.645) – – AG 69 (51.111) 88 (44.670) – – A 169 (62.593) 268 (68.020) 0.148 1.271 (0.918–1.759) G 101 (37.407) 126 (31.980) – – rs16845607 – – – – AA 1 (0.741) 0 (0) 0.136 GG 115 (85.185) 180 (91.371) – – AG 19 (14.074) 17 (8.629) – – A 21 (7.778) 17 (4.315) 0.059 0.535 (0.277–1.034) G 249 (92.22) 377 (95.685) – – rs3850641 – – – – GG 1 (0.741) 5 (2.525) 0.051 AA 104 (77.037) 129 (65.152) – – AG 30 (22.222) 64 (32.323) – – G 32 (11.852) 74 (18.687) 0.018 1.709 (1.096–2.674) A 238 (88.148) 322 (81.313) – – ==== Refs References 1. Tomer Y. Mechanisms of autoimmune thyroid diseases: From genetics to epigenetics Annu. Rev. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081370ijms-17-01370ArticleMiR-132-3p Regulates the Osteogenic Differentiation of Thoracic Ligamentum Flavum Cells by Inhibiting Multiple Osteogenesis-Related Genes Qu Xiaochen Chen Zhongqiang *Fan Dongwei Sun Chuiguo Zeng Yan UI-TEI Kumiko Academic EditorTaguchi Y-h. Academic EditorDepartment of Orthopaedics, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, China; xiaochen13@hotmail.com (X.Q.); fdw_puth@163.com (D.F.); scg_puth@163.com (C.S.); zy_puth@163.com (Y.Z.)* Correspondence: puth_czq@126.com; Tel.: +86-10-8226-525220 8 2016 8 2016 17 8 137018 7 2016 16 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Ossification of the ligamentum flavum (OLF) is a disorder of heterotopic ossification of spinal ligaments and is the main cause of thoracic spinal canal stenosis. Previous studies suggested that miR-132-3p negatively regulates osteoblast differentiation. However, whether miR-132-3p is involved in the process of OLF has not been investigated. In this study, we investigated the effect of miR-132-3p and its target genes forkhead box O1 (FOXO1), growth differentiation factor 5 (GDF5) and SRY-box 6 (SOX6) on the osteogenic differentiation of ligamentum flavum (LF) cells. We demonstrated that miR-132-3p was down-regulated during the osteogenic differentiation of LF cells and negatively regulated the osteoblast differentiation. Further, miR-132-3p targeted FOXO1, GDF5 and SOX6 and down-regulated the protein expression of these genes. Meanwhile, FOXO1, GDF5 and SOX6 were up-regulated after osteogenic differentiation and the down-regulation of endogenous FOXO1, GDF5 or SOX6 suppressed the osteogenic differentiation of LF cells. In addition, we also found FOXO1, GDF5 and SOX6 expression in the ossification front of OLF samples. Overall, these results suggest that miR-132-3p inhibits the osteogenic differentiation of LF cells by targeting FOXO1, GDF5 and SOX6. ossification of the ligamentum flavummiR-132-3pFOXO1GDF5SOX6osteogenic differentiation ==== Body 1. Introduction Ossification of the ligamentum flavum (OLF) is a rare disorder of heterotopic ossification of spinal ligaments that is almost exclusively reported in Eastern Asian countries. OLF primarily occurs in the thoracolumbar spine and is the main cause of thoracic spinal canal stenosis and myelopathy [1,2]. In the past several years, many studies have examined OLF development and progression at both histopathological and cellular levels. While these studies identified potential contributing factors, such as mechanical [3,4,5,6], metabolic [7,8], degenerative [9] and genetic factors [10,11], OLF development and progression continues to be inadequately understood. One potential way that osteogenic differentiation is modulated is through microRNAs (miRNAs). MiRNAs, which comprise a substantial family of small (18–24 nucleotides), single-stranded non-coding RNAs, function in the regulation of mammalian cell gene expression. miRNAs regulate a target mRNA by binding its 3′-untranslated region (UTR) and subsequently mediating its degradation via the RNA-induced silencing complex (RISC) [12]. MiRNAs regulate a variety of physiological and pathological processes, with previous studies showing that particular miRNAs have the potential to positively or negatively regulate cells’ osteogenic differentiation and bone development [13,14]. For instance, miR-29b [15], miR-21 [16], miR-548d-5p [17], and miR-22 [18] were found to promote osteogenic differentiation and miR-92a [19], miR-214 [20], miR-30 [21], and miR-103a [22] were discovered to suppress osteogenesis, while miR-34a may have dimorphic effects [12,23]. Further, as reviewed by Huang C, studies on the miRNA revealed that miRNAs regulate osteogenesis by targeting osteoblast-related genes, particularly runt related transcription factor 2 (RUNX2) and Osterix, and targeting signaling pathways such as Wnt/β-catenin, transforming growth factor beta (TGF-β)/bone morphogenetic protein (BMP) and Notch [24]. The miR-212/132 family is highly conserved in vertebrates [25]. miR-132 and miR-212 are found in an intergenic region, exhibit similar mature sequences and share the same seed region, but the expression levels and the physiological functions of miR-132 and miR-212 are largely different [26]. Previous studies suggested that miR-132-3p can inhibit osteoblast differentiation and participate in the regulation of bone loss [27,28]. In addition, miR-132-3p was also found to regulate osteosarcoma [29,30,31,32], an osteogenic tumor that has the capability of osteoblast differentiation. However, whether miR-132-3p is involved in the process of OLF has not been investigated. To explore the important role of miR-132-3p in thoracic ossification of the ligamentum flavum (TOLF), the present study investigated the effect of miR-132-3p and its target genes on the osteogenic differentiation of ligamentum flavum (LF) cells. Our results implied that miR-132-3p, which is down-regulated during osteogenic differentiation of LF cells, inhibits the differentiation process by targeting forkhead box O1 (FOXO1), growth differentiation factor 5 (GDF5) and SRY-box 6 (SOX6). 2. Results 2.1. MiR-132-3p Inhibits the Osteogenic Differentiation of Ligamentum Flavum (LF) Cells We first determined the temporospatial expression pattern of miR-132-3p in human LF cells cultured in osteogenic medium by qPCR to investigate the potential roles of miR-132-3p in the osteogenic differentiation of LF cells. Expression of miR-132-3p decreased at day 3 compared with that at day 0 and continuously decreased to day 14 (Figure 1A). This result suggests that miR-132-3p might negatively regulate the osteogenic differentiation of LF cells. To further elucidate the role of miR-132-3p in the regulation of osteogenic differentiation of LF cells, synthetic mimics of miR-132-3p and inhibitors were transfected into LF cells, and the osteogenic capacity was examined. Intracellular miR-132-3p levels were markedly up-regulated by miR-132-3p mimics (Figure 1B) and substantially down-regulated by miR-132-3p inhibitors (Figure 1C). Furthermore, osteogenic differentiation was significantly inhibited after over-expression of miR-132-3p (Figure 1D,E) and significantly promoted after reduction of miR-132-3p (Figure 1F,G), as indicated by the expression changes of the osteogenic transcription factors, RUNX2 and Osterix, and osteoblastic markers, alkaline phosphatase (ALP), osteopontin (OPN) and osteocalcin (OCN), as well as ALP and Alizarin Red staining. 2.2. MiR-132-3p Directly Targets FOXO1, GDF5 and SOX6 To reveal the molecular mechanism by which miR-132-3p regulates the osteogenic differentiation of LF cells, TargetScan (http://www.targetscan.org/vert_71/) was utilized to forecast potential miR-132-3p targets. Among the candidates, we found that three osteogenesis-related genes, FOXO1, GDF5 and SOX6, contain miR-132-3p binding sites in their 3′-UTRs. Next, we constructed luciferase reporters for each gene that contained either a wild-type (WT) 3′-UTR or a mutant (mut) 3′-UTR with mutant sequences of the miR-132-3p binding site (Figure 2A). The results showed that miR-132-3p repressed the luciferase activity of the 3′-UTR of each gene when compared to the nonspecific microRNA (miR-NC) control group, respectively. Additionally, no statistically significant alteration in luciferase activity was observed in the presence of the mutated 3′-UTR site (Figure 2B). Next, we detected the gene expression of FOXO1, GDF5 and SOX6 after transfecting LF cells with the mimics of miR-132-3p and the inhibitor. We confirm that overexpression of miR-132-3p resulted in down-regulation of FOXO1, GDF5 and SOX6 in LF cells based on Western blot analysis (Figure 2C) and the reduction of miR-132-3p resulted in the opposite effects (Figure 2D). These results suggested that the effect of miR-132-3p during the osteogenic differentiation of LF cells is mediated by targeting these osteogenesis-related genes. 2.3. FOXO1, GDF5 and SOX6 Knockdown Inhibits Osteogenic Differentiation in LF Cells The mRNA and protein expression levels of FOXO1, GDF5 and SOX6 were determined by quantitative real-time polymerase chain reaction (qRT-PCR) (Figure 3A) and Western blot analyses (Figure 3B). All three were up-regulated in expression after osteogenic differentiation compared with day 0. Specifically, FOXO1 was continually increased to day 14, while GDF5 and SOX6 were decreased (still higher than day 0) after day 10 and 7, respectively. To examine the functional effects of FOXO1, GDF5 and SOX6 on the osteogenic differentiation of LF cells, siRNA-induced mRNA knockdown for each gene was employed and it significantly reduced both mRNA and protein expression of FOXO1, GDF5 and SOX6 (Figure 3C,D). Furthermore, FOXO1, GDF5 or SOX6 knockdown inhibited the osteogenic differentiation of LF cells, as indicated by reduced RUNX2, Osterix, ALP, OPN and OCN expression (Figure 3E) and reduced ALP and Alizarin Red staining (Figure 3F). 2.4. FOXO1, GDF5 and SOX6 Protein Expression in OLF Samples Structurally, pathological specimens of endochondral ossification exhibit an ossification front, including a fibrocartilage area (FCA) and calcified cartilage area (CCA), between the ossified area (OA) and ligamentous fiber area (FA). The HE staining, tissue-specific staining and immunohistochemical (IHC) staining in each area are shown in Figure 4. In FA, the Elastic Fibers staining showed positive and regular expression, and the IHC for FOXO1, GDF5 and SOX6 was negative. In FCA and CCA, the expressions of Elastic Fibers staining were decreased and irregular, and the Alcian Blue staining was positive. The IHC results suggested that FOXO1, GDF5 and SOX6 were positive in the round cells of the FCA and CCA. Further, in OA, the Fast Green staining was positive. IHC results showed that FOXO1 and GDF5 were strongly stained in the nuclei of osteoblasts, but the nuclei of osteoblasts showed no nuclear reactivity for SOX6. 3. Discussion miR-132, which is located on chromosome 17p13.3, has been widely researched in recent years and is mainly implicated in neuropsychiatric disorders [25,26,33,34] and tumor progression in tumors, especially in osteosarcoma [29,30,31,32,35,36]. Recent evidence suggested that miRNA-132-3p inhibits osteoblast differentiation in simulated microgravity [27] and Type 2 Diabetes Mellitus-induced osteoporosis [28]. Furthermore, in a recent integrated microRNA-mRNA study, miR-132-3p was found to be one of the top 10 down-regulated miRNAs in ossified posterior longitudinal ligament (PLL) cells compared with normal PLL cells [37], thus suggesting that miR-132-3p may be involved in the process of ligament ossification. The present study provides the first evidence that miR-132-3p suppresses the osteogenic differentiation of LF cells. Our results showed that miR-132-3p was down-regulated during osteogenic differentiation. Further, inhibition of the expression of miR-132-3p promoted osteogenic differentiation, while miR-132-3p overexpression inhibited osteogenic differentiation. These findings suggest that miR-132-3p acts as a negative regulator of the osteogenic differentiation of LF cells. It is well known that one miroRNA can regulate multiple target genes. Among the potential miR-132-3p targets predicted using Targetscan, three osteogenesis-related genes, FOXO1, GDF5 and SOX6, were selected in this study. The dual luciferase reporter assays identified these three genes as direct targets of miR-132-3p. Furthermore, miR-132-3p overexpression resulted in their down-regulation at the protein level, whereas inhibition of miR-132-3p led to their up-regulation, suggesting that FOXO1, GDF5 and SOX6 are regulated by miR-132-3p during osteogenic differentiation. Though the regulatory connection between miR-132-3p and FOXO1 has already been reported in gastric cancer cells [38], the direct targeting of miR-132-3p with GDF5 and SOX6 was newly discovered. Besides, miR-132-3p regulated osteoblast differentiation in osteoporosis by targeting EP300 [27] and SIRT1 [28]. Whether these two genes were involved in osteogenic differentiation of LF cells requires further study. The pathological process of OLF involves the differentiation of fibroblasts into osteoblasts. Many cytokines, including transcription factors [39], growth factors [40] and inflammatory cytokines [41], have been reported to be involved in the ossification process. In the present study, FOXO1, GDF5 and SOX6 were all found to encourage the osteogenic differentiation of LF cells. We demonstrated that down-regulation of endogenous FOXO1, GDF5 or SOX6 suppressed osteogenesis to varying degrees, respectively. In addition, during the osteogenic differentiation of LF cells, the three osteogenesis-related genes were up-regulated, and the highest value of each gene expression was found at different times. The results indicated that these three genes may take effect in different stages of osteogenic differentiation and also suggested that miR-132-3p may regulate different genes in different stages of osteogenic differentiation. Location expressions of FOXO1, GDF5 and SOX6 were visually observed by immunohistochemistry. From the perspective of histopathology, the progression of OLF is viewed as a process of endochondral ossification. FOXO1 and GDF5 were positively expressed in both the cartilage and ossified area, while SOX6 was only positively expressed in the cartilage area. The results were in agreement with the cell experiment. FOXO1 is the main member of the orkhead box O (FoxO) family expressed in bone and is a positive regulator of osteoblast differentiation [42,43,44]. GDF5, also called bone morphogenetic protein (BMP)-14 and cartilage-derived morphogenetic protein (CDMP)-1, is a member of the transforming growth factor β (TGF-β) superfamily and plays critical roles in organ development processes including bone, cartilage, ligament, and joint formation [45]. SOX6, a member of the D subfamily of sex determining region y-box (SRY-box)-related transcription factors, is a crucial transcription factor that regulates chondrogenesis and endochondral ossification [46]. A previous study showed that rhGDF-5 induces the osteogenic differentiation of LF cells through the activation of ERK1/2 and p38 mitogen-activated protein kinase (MAPK) [40]. However, the molecular mechanisms of FOXO1 and SOX6 in the osteogenic differentiation of LF cells were unclear. More research is required to understand the detailed mechanism of these osteogenesis-related genes in LF cells. 4. Materials and Methods 4.1. Patient Specimens All experimental protocol involving humans (including the acquisition, processing and detection of the specimens) were approved by the Medical Scientific Research Ethics Committee of Peking University Third Hospital (PUTH-REC-SOP-06-3.0-A27, 2014003). All the methods were performed in accordance with relevant guidelines and regulations. TOLF patients who visited the orthopedic clinic and provided written informed consent for the study were utilized. Specialists diagnosed TOLF based on clinical symptoms and radiological examination as previously described [47]. Ligamentum flavum samples were obtained from TOLF patients during spinal surgery via en bloc resection of the lamina and ligamentum flavum as previously described [48]. 4.2. Cell Cultures and Osteogenic Differentiation Ligaments (approximately 0.5–1 cm2) were aseptically harvested from patients during surgery and rinsed with phosphate-buffered saline (PBS), while surrounding tissues were removed under a dissecting microscope to avoid possible osteogenic cell contamination. The collected ligaments were minced into approximately 0.5 mm3 pieces and digested using 0.25% trypsin, followed by 250 U/mL type I collagenase (Sigma-Aldrich, St. Louis, MO, USA). The specimen were washed with serum-containing medium and placed in 100-mm culturing dishes containing Dulbecco’s Modified Eagle′s medium (DMEM; GIBCO, Grand Island, NY, USA) supplemented with 10% fetal bovine serum (GIBCO), 100 U/mL penicillin G sodium and 100 mg/mL streptomycin sulfate in a humidified atmosphere with 5% CO2 at 37 °C. Explant-derived cells derived were harvested using 0.25% trypsin for further passaging, with passages (P) 2 and 3 used for subsequent experimentation. To induce osteogenic differentiation, cells were cultured in osteogenic medium consisting of DMEM supplemented with 50 µM ascorbic acid (Sigma-Aldrich), 10 mM β-glycerophosphate (Sigma-Aldrich) and 10−8 M dexamethasone (Sigma-Aldrich). 4.3. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) Analysis Total RNA was isolated using Trizol (Invitrogen, Carlsbad, CA, USA). Reverse transcription and qPCR for miR-132-3p were performed using a miDETECTA Track™ miRNA qRT-PCR Starter kit (RiboBio, Guangzhou, China) according to the manufacturer’s instructions on a BioRad IQ5 system. Each value was normalized to that of RnU6. Reverse transcription and qPCR for the mRNA levels of FOXO1, GDF5 and SOX6 were carried out as described previously [49]. Expression levels were normalized to glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and relative gene expression levels were calculated using the 2-ΔΔCt method. All experiments were performed in triplicate. The primers were described in Table 1. 4.4. Western Blot Analysis Cell lysates were obtained using RIPA lysis buffer (Beyotime, Shanghai, China) containing 10 mM phenylmethylsulphonylfluoride as a protease inhibitor (PMSF; Beyotime) and 50 µg of total protein was separated in a Bis-Tris polyacrylamide gel and transferred onto a nitrocellulose membrane. The membrane was then incubated in 5% bovine serum albumin (BSA) containing primary rabbit-anti-human polyclonal antibodies at 4 °C overnight. After incubating with horseradish peroxidase (HRP)-conjugated goat-anti-rabbit antibody at room for 1 h, protein was detected using electrochemiluminescence (ECL; Millipore, Darmstadt, Germany). The following primary rabbit-anti-human antibodies were used: anti-FOXO1 (1:1000; ab39670, Abcam, Cambridge, MA, USA); anti-GDF5 (1:1000; ab93855, Abcam); anti-SOX6 (1:1000; ab30455, Abcam); anti-Runx2 (1:1000; ab23981, Abcam); anti-Sp7/Osterix (1:2000; ab22552, Abcam); anti-ALP (1:2000; ab95462, Abcam); anti-OCN (1:500; ab93876, Abcam); anti-OPN (1:1000; ab8448, Abcam); and anti-GAPDH (1:2500; ab9485, Abcam). The results of Western blots were quantified using image-J software (http://imagej.net) analysis. 4.5. Alkaline Phosphatase (ALP) Activity Assay and Alizarin Red Staining Cells were seeded in six-well plates at a density of 1 × 105 cells/well and cultured in osteogenic medium for 14 days. ALP activity was determined using an ALP activity staining kit (DE0004; Leagene Biotech, Beijing, China) and mineralization was assessed using an Alizarin Red S kit (DS0002; Leagene Biotech). 4.6. miRNA/siRNA Transfection Synthetic miRNA and siRNA were purchased from RiboBio. ligamentum flavum cells were transfected with miRNA/siRNA using Lipofectamine® 2000 Transfection Reagent (Life Technologies, New York, NY, USA), according to the manufacturer′s instructions. MiR-132-3p mimics or miR-132-3p inhibitor (anti-miR-132-3p) were transfected into LF cells at a concentration of 20 nM with nonspecific microRNA (miR-NC) or nonspecific microRNA inhibitor (anti-miR-NC) used as negative controls. SiRNA targeting FOXO1, GDF5 or SOX6 were transfected at a concentration of 50 nM with non-targeting siRNA (siNC) used as negative control. 4.7. Luciferase Constructs and Reporter Assay The DNA sequences of FOXO1, GDF5 or SOX6 3′-UTR were amplified by PCR using HEK293T genomic DNA as a template. The amplified DNA sequences were inserted into pmiR-RB-REPORT™ vectors (RiboBio) to generate wild type (WT) FOXO1, GDF5 or SOX6 3′-UTR, with a mutated (mut) FOXO1, GDF5 or SOX6 3′-UTR luciferase vector generated using site-directed mutagenesis. For the reporter assay, HEK293T cells were cultured in a 96-well plate with 1.5 × 104 cells/well in 100 μL of culture medium/well for 24 h. Cells were then co-transfected with 50 nM miR-132-3p mimic or miR-NC and 100 ng of vector per well and cultured in fresh medium for an additional 48 h. The luciferase reporter assay was carried out using the Dual-Glo® Luciferase Assay System (Promega, Madison, WI, USA) according to the manufacturer’s instructions and luminescence was quantified using a Veritas™ 9100-002 luminometer (Promega). 4.8. Hematoxylin-Eosin (HE) Staining, Tissue-Specific Staining and Immunohistochemical (IHC) Analysis Serial 5-mm-thick sections were prepared from paraffin-embedded specimens for staining. Hematoxylin-eosin staining was performed in an autostainer machine (ST5010 XL; Leica Microsystems, Mannheim, Germany) using standard procedures. Elastic Fibers staining kit for ligament, Alcian Blue staining kit for cartilage and Fast Green staining kit for bone were purchased from Leagene Biotech (DC0066; DB0060; DZ0046). Sections for immunohistochemical staining were carried out as described previously [49]. The primary rabbit anti-human antibodies were: anti-FOXO1 (1:200; ab39670, Abcam); anti-GDF5 (1:200; ab93855, Abcam); anti-SOX6 (1:200; ab30455, Abcam). 5. Conclusions Overall, our study provides a comprehensive profiling of miR-132-3p as a novel regulator during the osteogenic differentiation of LF cells. The findings presented herein show that miR-132-3p can suppress osteogenic differentiation of LF cells by targeting FOXO1, GDF5 and SOX6. Further, we demonstrated that FOXO1, GDF5 and SOX6 are expressed in LF cells and ossified ligamentum flavum tissues, and these osteogenesis-related genes may take effect at different stages of the osteogenic differentiation of LF cells. Our findings reveal a new mechanism of the TOLF pathological process and suggest that miR-132-3p and its target genes could possibly be viable therapeutic targets for TOLF and other skeletal disorders. Acknowledgments This work was supported by the National Natural Science Foundation of China (Grant numbers 81272031 and 81071505). We would like to extend a special thanks to all of the patients who provided specimens and thank Guangzhou RiboBio Technology Co. Ltd. (http://www.ribobio.com/) for their assistance in constructing the luciferase vectors. Author Contributions Xiaochen Qu designed the study, performed the majority of the laboratory work, statistics and data analysis, and drafted the manuscript. Zhongqiang Chen designed the study, secured funding, analyzed and interpreted data, and approved the final submitted manuscript. Dongwei Fan designed the study and performed some of the laboratory work, statistics, and data analysis. Chuiguo Sun and Yan Zeng specimen and clinical data acquisition. All authors have read and approved the final submitted manuscript. Conflicts of Interest The authors declare no conflict of interest. Figure 1 MiR-132-3p inhibits the osteogenic differentiation of ligamentum flavum cells. (A) Endogenous miR-132-3p expression levels were measured via quantitative real-time polymerase chain reaction (qRT-PCR) at different time points during osteogenic differentiation of ligamentum flavum cells; * p < 0.05 compared with day 0; (B) miR-132-3p expression was assessed via qRT-PCR in ligamentum flavum cells transfected with miRNA mimics; * p < 0.05 compared with nonspecific microRNA (miR-NC) group; (C) miR-132-3p expression was assessed via qRT-PCR in ligamentum flavum cells transfected with miRNA inhibitors; * p < 0.05 compared with anti-miR-NC group; (D,E) Western blot analysis of osteogenic marker protein expression after miR-132-3p overexpression (D) and reduction (E) at day 14; (F,G) alkaline phosphatase (ALP) staining and Alizarin Red staining at day 14 showed ALP activity and calcification after miR-132-3p overexpression (F) and reduction (G). Scale bar represents 200 µm and is fitted for every figure. Figure 2 MiR-132-3p directly targets FOXO1, GDF5 and SOX6. (A) The wild-type and mutated type miR-132-3p binding sites in the FOXO1, GDF5 and SOX6 3′-UTR. The sequences in red font showed the blinding sites; (B) The wild-type (WT) 3′-UTR or mutant (MUT) 3′-UTR reporter plasmids of the three genes were co-transfected into HEK293T cells with either miR-132-3p or miR-NC and fluorescence was quantified; * p < 0.05 compared with miR-NC group; (C,D) FOXO1, GDF5 and SOX6 protein expression levels were examined via Western blot following miR-132-3p mimics (C) and inhibitors (anti-miR-132-3p) (D) transfection in ligamentum flavum cells. Figure 3 FOXO1, GDF5 and SOX6 knockdown inhibits osteogenic differentiation of ligamentum flavum cells. (A,B) FOXO1, GDF5 and SOX6 mRNA and protein expression levels examined via qRT-PCR and Western blot at different time points during osteogenic differentiation of ligamentum flavum cells; (C,D) FOXO1, GDF5 and SOX6 mRNA and protein expression level examined via qRT-PCR and Western blot following siRNAs transfection in ligamentum flavum cells; * p < 0.05 compared with non-targeting siRNA (si-NC) group; (E) Osteogenic marker protein expression examined via Western blot at day 14 after FOXO1, GDF5 or SOX6 knockdown; (F) ALP staining and Alizarin Red staining at day 14 showed inhibited ALP activity and calcification following FOXO1, GDF5 or SOX6 knockdown when compared with siNC group. Scale bar represents 200 µm and is fitted for every figure. Figure 4 Representative hematoxylin-eosin staining, tissue-specific staining and immunohistochemical staining for FOXO1, GDF5 or SOX6 in OLF samples. Scale bar represents 100 µm and is fitted for every figure. ijms-17-01370-t001_Table 1Table 1 Primer sequences for quantitative real-time polymerase chain reaction (qRT-PCR). Gene Primer (5′–3′) FOXO1 Fw: AAGCTCCCAAGTGACTTGGATG Rv: CTGCTCACTAACCCTCAGCCTGA GDF5 Fw: AAAAGGACAGCTTCCCGGAG Rv: GCCTCCCTTTCTGTCAGCAT SOX6 Fw: TCAACATGTGGCCTCCCATC Rv: GATGACAGAACGCTGTCCCA GAPDH Fw: TCAAGGCTGAGAACGGGAAG Rv: TGGACTCCACGACGTACTCA miR-132-3p RT: GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACCGACCATG Fw: GCGCGCGTAACAGTCTACAGC Rv: GTCGTATCCAGTGCAGGGTCC U6 RT: AAAATATGGAACGCTTCACGAATTTG Fw: CTCGCTTCGGCAGCACATATACT Rv: CGCTTCACGAATTTGCGTGT ==== Refs References 1. Hou X. Sun C. Liu X. Liu Z. Qi Q. Guo Z. Li W. Zeng Y. Chen Z. Clinical features of thoracic spinal stenosis-associated myelopathy: A retrospective analysis of 427 cases Clin. Spine Surg. 2016 29 86 89 10.1097/BSD.0000000000000081 26885607 2. Feng F.B. Sun C.G. Chen Z.Q. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081371ijms-17-01371ArticleInhibitory Effect of 2,3,5,6-Tetrafluoro-4-[4-(aryl)-1H-1,2,3-triazol-1-yl]benzenesulfonamide Derivatives on HIV Reverse Transcriptase Associated RNase H Activities Pala Nicolino 1*†Esposito Francesca 2†Rogolino Dominga 3Carcelli Mauro 3Sanna Vanna 1Palomba Michele 1Naesens Lieve 4Corona Angela 2Grandi Nicole 2Tramontano Enzo 25Sechi Mario 1*Supuran Claudiu T. Academic Editor1 Dipartimento di Chimica e Farmacia, Università di Sassari, Via Vienna 2, I-07100 Sassari, Italy; vsanna@uniss.it (V.S.); mpalomba@uniss.it (M.P.)2 Dipartimento di Scienze della Vita e dell’Ambiente-Sezione Biomedica, Università di Cagliari, Cittadella Universitaria SS554, I-09042 Monserrato, Italy; francescaesposito@unica.it (F.E.); angela.corona@unica.it (A.C.); nicole.grandi2@gmail.com (N.G.); tramon@unica.it (E.T.)3 Dipartimento di Chimica, Università di Parma, Parco Area delle Scienze 17/A, I-43124 Parma, Italy; dominga.rogolino@unipr.it (D.R.); mauro.carcelli@unipr.it (M.C.)4 Rega Institute for Medical Research, KU Leuven, B-3000 Leuven, Belgium; lieve.naesens@kuleuven.be5 Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche (CNR), I-09042 Monserrato, Italy* Correspondence: nikpal@uniss.it (N.P.); mario.sechi@uniss.it (M.S.); Tel.: +39-079-228-756 (N.P.); +39-079-228-753 (M.S.)† These authors contributed equally to this work. 20 8 2016 8 2016 17 8 137128 6 2016 15 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).The HIV-1 ribonuclease H (RNase H) function of the reverse transcriptase (RT) enzyme catalyzes the selective hydrolysis of the RNA strand of the RNA:DNA heteroduplex replication intermediate, and represents a suitable target for drug development. A particularly attractive approach is constituted by the interference with the RNase H metal-dependent catalytic activity, which resides in the active site located at the C-terminus p66 subunit of RT. Herein, we report results of an in-house screening campaign that allowed us to identify 4-[4-(aryl)-1H-1,2,3-triazol-1-yl]benzenesulfonamides, prepared by the “click chemistry” approach, as novel potential HIV-1 RNase H inhibitors. Three compounds (9d, 10c, and 10d) demonstrated a selective inhibitory activity against the HIV-1 RNase H enzyme at micromolar concentrations. Drug-likeness, predicted by the calculation of a panel of physicochemical and ADME properties, putative binding modes for the active compounds, assessed by computational molecular docking, as well as a mechanistic hypothesis for this novel chemotype are reported. HIV-1 RNase Hclick-chemistry4-[4-(aryl)-1H-1,2,3-triazol-1-yl]benzenesulfonamidesdocking ==== Body 1. Introduction Infections with Human Immunodeficiency Virus Type-1 (HIV-1), the causative agent of Acquired Immunodeficiency Syndrome (AIDS), constitute a serious and global health problem [1,2,3]. Due to the lack of an effective vaccine, antiretroviral treatment remains the only option for control, allowing HIV-1 infection to transform from a highly lethal syndrome into a chronic condition. However, the use and long-term effectiveness of these drugs have severe limitations, in particular the intrinsic drug toxicity during long-term administration, the emergence of drug-resistant strains, poor patient compliance necessitating careful monitoring, and considerable costs. The current approach (also termed High Active Anti-Retroviral Therapy, HAART) involves the combined inhibition of two or three viral enzymes, protease (PR), integrase (IN), and especially reverse transcriptase (RT), which are all essential for the virus to replicate. Besides these validated pharmacological targets, the inhibition of the ribonuclease H (RNase H) catalytic activity of the multifunctional RT enzyme has not yet been extensively explored as a suitable antiviral strategy [4,5,6,7,8]. The HIV RT enzyme is associated with RNA- and DNA-dependent polymerase, RNase H strand displacement, and strand transfer activities, which are required to transcribe the single-stranded vRNA into double-stranded proviral vDNA. From the structural point of view, RT is an asymmetrical heterodimer originating from two copies of the p66 polypeptide that is encoded by the HIV pol gene. During viral maturation the C-terminal portion of one p66 subunit is shortened by about 15 kDa, thus yielding the p51 polypeptide, which is assembled with an uncut copy of p66 to form the mature enzyme. All the enzymatic activities reside on the p66 subunit, whereas p51 lacks catalytic functions and only acts as a structural support. Subunit p66 can be divided into five distinct domains; three of these are organized in a thumb-palm-fingers fashion, and are the depository of the polymerase activities. A fourth connection domain separates the hand from the RNase H domain located at the C-terminus of p66 (Figure 1). At present, more than 30 HIV blockers have been approved, with RT being the most successful viral target. All marketed RT inhibitors belong to the classes of nucleoside/nucleotide RT inhibitors (N(t)RTIs) or non-nucleoside RT inhibitors (NNRTIs), and act by interfering with the polymerase activities of RT. However, despite the relatively high number of available compounds for combination therapy, drug effectiveness is hampered by the rapid emergence of drug-resistant viruses. One possible way to overcome this problem would be to inhibit the RNase H activity of RT. In fact, it is highly plausible that an additional RT inhibition mechanism could contribute synergistically to supress viral replication. From the functional point of view, HIV-1 RNase H, which catalyzes hydrolysis of the RNA part of the replicative intermediate DNA:RNA hybrid, is a member of the polynucleotidyl transferase superfamily. The tertiary structure of the HIV-1 RT RNase H active site is similar to that of all known RNases H, and contains five highly conserved residues that are essential for the catalysis: Asp443, Glu478, Asp498, Asp549 (the “DEDD motif”), and His539 [5,8,9]. Moreover, high resolution crystallography of HIV-1 RT RNase H complexed to a RNA/DNA hybrid revealed that the DEDD motif stabilizes two Mg2+ ions, which promotes cleavage of the phosphodiester bond of the RNA chain, according to the “two-metal ions mechanism” [10,11]. As demonstrated for other polynucleotidyl transferases, HIV-1 RNase H can also be inhibited through selective chelation of the metal cofactors [12]. During the last years, a few classes of compounds capable of inhibiting HIV-1 RNase H activity by involving such a mechanism have been identified (Figure 2), and include diketoacids/esters (1) [13,14,15,16], triketoacids (2) [17], β-thujaplicinol (3) [18], 2-hydroxyisoquinoline-1,3(2H,4H)-diones (HID, 4) [19], pymidinol carboxylic acids (5) [20], naphthyridinones (5,6) [21,22], 3-hydroxy-2-oxo-1,2-dihydroquinoline-4-carboxamides (7) [23], and 4-(aryl)piperazin-1-yl)methyl)-7,8-dihydroxy-2H-chromen-2-ones (8) [24]. All these compounds share a common pharmacophoric pattern constituted by at least three metal chelating features, which usually consist of three oxygen atoms (compounds 1–5, and 8). In other examples, as outlined by compounds 6 and 7, the minimal pharmacophore is formed by the combination of two oxygen atoms and one nitrogen atom. However, neither of these compounds is currently moving into advanced preclinical development. In the course of a focused screening campaign using an in-house collection of molecules specifically designed as metalloenzyme inhibitors, we found that compounds bearing the 4-(aryl–triazolyl)-benzenesulfonamide scaffold can act as novel HIV-1 RNase H inhibitors. Herein, we report results on the HIV-1 RNase H, RT-associated DNA polymerase (DP) and HIV-1 integrase (IN) inhibitory activities of two sets of 4-[4-(substituted)-1H-1,2,3-triazol-1-yl]benzenesulfonamide derivatives (9a–d and 10a–d, Figure 3), which we selected to also evaluate the influence of the perfluorination on the benzenesulfonamide chemotype. Moreover, we present a typical panel of physicochemical ADME parameters for the most active compounds, the interaction between a model ligand and the Mg2+ ions, as well as computational docking studies to predict the possible binding mode of our compounds within the enzyme active site. 2. Results and Discussion 2.1. Compound Identification The possibility that some metal-chelating inhibitors can block not only the specific target for which they were designed, but also other metalloenzymes, has proven to be a consolidated strategy for medicinal chemists [24]. In particular, this approach led us to identify novel classes of influenza virus endonuclease inhibitors (i.e., salicyl thiosemicarbazones [25], and acyl hydrazones [26]), as well as original prototypes (i.e., pyrazole-carboxylic acids) of carbonic anhydrase inhibitors (CAIs) [27]. On this basis, we evaluated some representative compounds belonging to a general class of 4-(4-substituted-triazole)-benzenesulfonamides, specifically designed as CAIs, as putative HIV-1 RT-associated RNase H inhibitors. To start, a library of selected metal-chelating compounds was preliminarily screened in order to assess their ability to inhibit the HIV-1 RNase H activity. First, from an in-house library of about 250 compounds, we chose structural backbones carrying metal-coordinating functionalities. Next, after a preliminary screening against the HIV-1 RNase H enzyme, a few hit compounds belonging to a novel class of 4-(4-substituted-triazole)-benzenesulfonamides were identified. Among them, our attention was drawn to derivatives 9a–d and 10a–d (Figure 3), which were submitted for further investigation. Although the interaction between sulfonamides and Zn2+ ions is widely investigated and established, coordination with Mg2+ appeared relatively novel; however, it is well known that deprotonated oxygens are likely to establish an interaction with hard Lewis acid-like ions such as Mg(II). It is worth nothing that the selected compounds 9a–d and 10a–d are representative of a collection previously prepared by us using copper-catalyzed azide-alkyne cycloaddition (CuAAC) [28]. CuAAC is one of the most successful click chemistry methods because it allows us to create large libraries of complex compounds with chemical diversity, starting from simple reactants [29,30]. Moreover, CuAAC reactions are usually high-yielding under facile and mild conditions, with the possibility to generate and manage stereoselectivity. In this scenario, compounds 9a–d and 10a–d were selected to preliminarily explore this chemical space, in order to achieve a certain chemical diversity by modulating both the steric hindrance and hydrophilic/lipophilic balance. Namely, the nature of the R moiety on the triazole was planned considering various chemical functionalities including alkyl-(substituted)aromatic, hydroxyalkyl- or aminoalkyl-substituents (Figure 3). On the other hand, complete substitution of the hydrogens of an aromatic ring with fluorine atoms is a well-known bioisosteric replacement strategy that, among other effects, dramatically increases the acidity of ring substituents without significantly affecting the steric properties of the molecule [31]. In fact, the atomic size of fluorine is comparable to that of hydrogen, but its high electronegativity produces several changes such as decrease of pKa, the modulation of lipophilicity and other physicochemical parameters, which can lead to substantial variation of physicochemical properties [31]. In order to study the influence of perfluorination on the activity of homologous compounds, we re-synthesized two subclasses of derivatives (Scheme S1), where the first bears a simple benzenesulfonamide scaffold (9a–d), while the second one carries a 2,3,5,6-tetrafluorobenzenesulfonamide group (10a–d). Moreover, we sought to predict the pKa of the sulfonamide group of these two sets of compounds, under the presence or absence of perfluorination on the benzenesulfonamide ring. From a computational prediction [32], the pKas were lowered approximately from six to 3.5, for compounds 9a–d and 10a–d, respectively, and this should dramatically affect the ion speciation profile of the tested compounds. In particular, at pH 7.4 the sulfonamide functionality of compounds 9a–d would prevalently exist in non-deprotonated form, while at the same pH deprotonation of the sulfamoyl group on fluorinated 10a–d should be favored. It was also supposed that the presence of a negatively charged sulfonamide could increase the affinity of these compounds for the metal cofactors of HIV-1 RNase H. 2.2. Interaction with Mg2+ Since RNase H active site inhibitors have been reported to chelate the Mg2+ ions [12,13,14,15,16,17,18,19,20,21,22,23,24], and to support the hypothesis that the sulfonamide group could interact with the metal cofactors in the RNase H catalytic pocket, we measured the capability of the model compound 10d to coordinate Mg2+ ions by means of UV-visible (UV-vis) experiments. A series of UV-vis absorbance spectra of a methanolic solution of 10d in the presence of increasing equivalents of Mg(CH3COO)2 were recorded (Figure 4). We observed that increasing concentrations of Mg2+ produced a reduction of the maximum UV-vis absorption, supporting the involvement of coordinative interactions between the sulfonamide and metal ions. 2.3. Biology An enzymatic assay using recombinant HIV-1 RT and IN proteins was performed to assess the ability to inhibit HIV-1 RT-associated RNase H and DP functions, and/or the IN catalytic process. Within the series of 9a–d and 10a–d, three compounds (9d, 10c, and 10d) were endowed with selective inhibitory activity towards RNase H (Table 1). With an IC50 value of 6.6 ± 0.5 µM, 10d was the most active compound of the series. Marginal or moderate activities were registered for 9d (IC50 = 63 ± 7 µM) and 10c (IC50 = 26 ± 3 µM), respectively. However, 10d and 10c also exhibited inhibition of the DP function (IC50 33.4 ± 5.8 and 90 ± 5 µM, for 10d and 10c, respectively, Table 1), meaning that the potency for DP inhibition was much lower compared to that for RNase H inhibition. In fact, their specificity indexes (SpI, expressed as the ratio of the RNase H IC50 value vs. the DP IC50 value), were favorable to the RNase H function (SpI = ~5 and ~3.5, for 10d and 10c, respectively). Moreover, these compounds were inactive when tested for IN inhibition (in the presence of the LEDGF cofactor and with 100 µM as the highest test concentration). Overall, these data would support a relative selectivity toward RNase H enzymatic activity. Although the number of tested compounds was not sufficient to perform a detailed structure-activity relationships (SAR) analysis, these results allowed us to identify two key features that seem important for activity: (a) the biaryl-triazole moiety, free or with an appropriate substituent (i.e., a n-pentyl tail); and (b) the perfluorination on the benzenesulfonamide ring. For example, since two of the three active compounds possess both these features, we can hypothesize that the simultaneous presence of the biaryl-triazole moiety and a small aliphatic chain in the position para may be a crucial role for the activity. This can also be supported by the observation that small polar substituents (such as OH or NH2) in position 4 of the triazole ring, as in compounds 9a,b, and 10a,b, dramatically abolish the activity. These findings are consistent with those reported by Williams [21], Gerondelis [33], and Vernekar [34], who suggested that the presence of an accessory biaryl motif, in addition to the aromatic scaffold bearing the chelating group, is an important factor for HIV-1 RNase H inhibition. As far as the perfluorination is concerned, the presence of tetrafluorine on the benzenesulfonamide group led to an increase in potency, as observed by comparing the IC50 values of 9c and 10c (from >100 µM to 26 µM), and 9d and 10d (from 63 µM to 6.6 µM), respectively. Finally, the presence of the n-pentyl tail on the biaryltriazole shares an additional positive effect, as observed by comparing the activity of compounds 9c and 9d (IC50 > 100 µM and 63 µM, respectively), and of 10c and 10d (IC50 26 µM and 6.6 µM, respectively). 2.4. Absorption, Distribution, Metabolism and Excretion Prediction Determination of physicochemical properties and the related absorption, distribution, metabolism and excretion (ADME) properties is a useful approach to predict the druggability and therapeutic potential of a lead candidate [35]. Therefore, calculation of a panel of selective parameters important to predict the solubility and membrane permeability for the most interesting compounds 9d, 10c, and 10d was performed by considering Lipinski’s rule-of-five method (Table 2). According to this procedure, a compound is likely to be well absorbed when it possesses a molecular weight <500, number of H-bond acceptors <10, number of H-bond donors <5, and log P < 5 [36]. Moreover, other parameters, such as the log S (−6.5 to 0.5) and the topological polar surface area (TPSA < 140 Å2), that correlate with membrane permeability have been considered [37,38,39]. As shown in Table 2, the calculated atom-based values (i.e., molecular weight, H-bond acceptor and donor counts, log P, log S and TPSA) for 9d, 10c, and 10d meet desirable ADME criteria for good absorption and membrane permeability, and are likely to yield favorable pharmacokinetic properties and bioavailability. 2.5. Docking To predict the putative binding mode of the selected compounds 9d, 10c, and 10d, a series of computational docking studies were performed. As displayed in Figure 5, docking results for the three active compounds (9d, 10c, and 10d) revealed a tight affinity within the amino acid pocket located near the catalytic site, which includes the following residues: Asp443-Arg448, Asn474, Glu478, Asp498, Ala538-Lys540, and Asp549 (Figure 5, Figure S1). Although the common sulfonamide function of the selected compounds is directed toward the metal ions, the orientation of their aromatic backbones appears different within two pockets, probably depending on the presence (9d and 10d) or absence (10c) of the aliphatic n-pentyl tail. In particular, the most favorable conformation of 9d and 10d is placed along the surface of the protein, with the 4-pentylphenyltriazole accommodated in a cavity surrounding the active site formed by residues Asn265, Trp266, Ser268, Gln269, Ile274, and Lys275 (Figure 5A,C for 9d and 10d, respectively). Strong arene-cation interactions between the 4-pentylbenzene ring and Lys540, for both 9d and 10d, were revealed. Otherwise, compound 10c (Figure 5B) is engaged into a second pocket, with the benzene ring placed in a narrow cavity lined by residues Ala446-Glu449, Ile556, and Arg557, in close proximity to the catalytic site. Similarly to 9d and 10d, the distal benzene ring of 10c is also involved in an arene-cation interaction, in this case with Arg448, which establishes a second arene-cation interaction with the triazole ring. Significant differences could be rationalized on the coordination of metal cofactors by each compound. In particular, in 9d (dG = 11.9648 kcal·mol−1, IC50 = 63 µM), which bears a neutral sulfonamide group, one oxygen of the sulfonamide group is bridging between the two metal cofactors (Mn2+ ions are present in the 3LP2 X-ray structure, Figure S1A). Comparatively, each oxygen of the sulfonamide group of 10c (dG = 14.2330 kcal·mol−1, IC50 = 26 µM) appeared involved in one bond (two bonds in total) with one metal ion (Figure S1B): in this way, the ligand produced µ2-bridging between the two metal ions. Interestingly, the most active derivative 10d (dG = 19.4250 kcal·mol−1, IC50 = 6.6 µM) is predicted to coordinate one manganese cation with both sulfonamide oxygens; one of the two chelating oxygens shows an additional interaction with the second cation (Figure S1C). These interactions would presumably concur, together with the molecules of the solvent and with amino acids on the catalytic pocket, to complete the coordination geometry around the metal, favoring chelation of metal cofactors and, consequently, the inhibitory activity. These results suggest that the activity of these compounds can be explained by their arrangement in the active site of the enzyme. The superior potency of 10d (IC50 = 6.6 ± 0.5 µM) can be attributed to the optimal pKa of the sulfonamide group, as well as to a favorable orientation of the chelating motif, which should significantly contribute to its metal coordination. 3. Materials and Methods 3.1. Chemistry Compounds 9a–d and 10a–d, were prepared following a synthetic procedure previously reported by us [28]. The experimental details and characterization data are detailed in the Supporting Information. Briefly, the title compounds 9a–d and 10a–d were synthesized by reacting the azides 11 and 12 with alkynes 13b–d,e in the presence of nanosized metallic copper as catalyst (Scheme S1) [28]. 3.2. UV-Visible Titration UV-vis absorption spectra of 10d were registered by a spectrophotometer uniSPEC 2 (LLG Labware, BDL Czech Republic sro, Turnov, Czech Republic) using a 0.025 mM solution in methanol. Each metal/ligand (M:L) system was studied by titrating a 3.0 mL of the ligand solution with a methanolic solution of Mg(CH3COO)2 (5 mM); spectra of samples with M:L molar ratio ranging from 0 to 10 were measured. 3.3. Biology 3.3.1. Protein Expression and Purification The recombinant HIV-1 RT protein, the coding gene of which was subcloned in the p6HRT_prot plasmid, was expressed in E. coli strain M15 [40,41]. The bacteria cells were grown up to an optical density (at 600 nm) of 0.8 and induced with 1.7 mM isopropyl β-d-1-thio galactopyranoside (IPTG) for 5 h. HIV-1 RT purification was performed as described [42]. Briefly, cell pellets were re-suspended in lysis buffer (20 mM HEPES, pH 7.5; 0.5 M NaCl; 5 mM β-mercaptoethanol; 5 mM imidazole; 0.4 mg·mL−1 lysozyme), incubated on ice for 20 min, sonicated, and centrifuged at 30,000× g for 1 h. The supernatant was applied to a His-binding resin column and washed thoroughly with wash buffer (20 mM HEPES, pH 7.5; 0.3 M NaCl; 5 mM β-mercaptoethanol; 60 mM imidazole; 10% glycerol). RT was eluted by imidazole gradient, and the enzyme-containing fractions were pooled and dialyzed and aliquots were stored at −80 °C. 3.3.2. HIV-1 RNase H Polymerase-Independent Cleavage Assay The HIV-1 RT-associated RNase H activity was measured as described [42] in 100 μL reaction volume containing 50 mM Tris HCl, pH 7.8; 6 mM MgCl2, 1 mM dithiothreitol (DTT), 80 mM KCl, 0.25 µM hybrid RNA/DNA (5′-GTT TTC TTT TCC CCC CTG AC-3′-fluorescein, 5′-CAA AAG AAA AGG GGG GAC UG-3′-dabcyl) and 3.8 nM RT. The reaction mixture was incubated for 1 h at 37 °C. The enzymatic reaction was stopped with the addition of ethylenediaminetetraacetic acid (EDTA) and measured with a Victor3 instrument (Perkin) at 490/528 nm. 3.3.3. HIV-1 RT-Associated RNA-Dependent DNA Polymerase Activity Determination The HIV-1 RT-associated RNA-dependent DP activity was measured as previously described [23]. Briefly, 20 ng of HIV-1 wt RT was incubated for 30 min at 37 °C in 25 mL volume containing 60 mM Tris HCl, pH 8.1, 8 mM MgCl2, 60 mM KCl, 13 mM DTT, 2.5 mM poly(A)-oligo(dT), 100 mM dTTP. Enzymatic reaction was stopped by addition of EDTA. Reaction products were detected by picogreen addition and measured with a PerkinElmer Victor 3 multilabel counter plate reader at excitation-emission wavelength of 502/523 nm. Chemical reagents were purchased form Sigma Aldrich srl. RNA-DNA labelled sequences were purchased from Metabion international AG. 3.3.4. HIV-1 IN/LEDGF HTRF LEDGF-Dependent Assay Recombinant IN and LEDGF/p75 were purified as described by Esposito et al. [43]. The INLEDGF/p75-dependent assay allow to measure the inhibition of 3′-processing and strand transfer IN reactions in presence of recombinant LEDGF/p75 protein, as previously described [44]. Briefly, 50 nM IN was pre-incubated with increasing concentration of compounds for 1 h at room temperature in reaction buffer containing 20 mM HEPES pH 7.5, 1 mM DTT, 1% Glycerol, 20 mM MgCl2, 0.05% Brij-35 and 0.1 mg/mL BSA. DNA donor substrate, DNA acceptor substrate and 50 nM LEDGF/p75 protein were added and incubated at 37 °C for 90 min. After the incubation, 4 nM of Europium-Streptavidine were added at the reaction mixture and the HTRF signal was recorded using a Perkin Elmer Victor 3 plate reader using a 314 nm for excitation wavelength and 668 and 620 nm for the wavelength of the acceptor and the donor substrates emission, respectively. 3.4. Molecular Modeling 3.4.1. Hardware Specifications All calculations were performed on a 64 bit Intel 8-Core i7-2600 CPU (Hewlett Packard, Palo Alto, CA, USA) running at 3.40 GHz with 8 GB RAM. 3.4.2. Protein Preparation The coordinates of full-length mutant HIV-1 RT were retrieved from RCSB Protein Data Bank (accession code 3LP2). Wild-type enzyme was obtained by retro-mutation of Asp103 to Lysine, then the missed residue Arg557 belongings to the HIV-1 RNase H active site was modeled using the crystal complex 3K2P, as previously described [14]. The protein was prepared using Molecular Operating Environment software package platform (MOE, version 2009.10, Chemical Computing Group Inc., Montreal, QC, Canada) [45] as follows: solvent molecules were removed, and chains termini were capped; then all hydrogens were added to the system, partial atomic charges were assigned according OPLS_AA force field, and minimization procedure was applied in order to optimize atoms positions. 3.4.3. Ligands Preparation The ligands were built using MOE builder mask. For each ligand the predicted most representative species at pH 7.4 was considered. Thus, compounds 9c was modeled as neutral species, whereas for compounds 10c and 10d, due to the tetrafluorination, the mono-deprotonated sulfonamide form was considered. The geometries of the ligands were optimized by an energy minimization pass until a convergence gradient of 0.01 kJ (mol·Å)−1 was reached using the MMFF94x force field. Solvent effect was calculated using the Generalized Born Solvation Model. 3.4.4. Docking Procedures Triangle Matcher Placement docking method implemented in MOE platform was used to re-dock the co-crystallized ligand of 3LP1 on the HIV-1 RNase H active site. The results were scored using London dG as rescoring, Forcefield as refinement, and Affinity dG as second rescoring functions. The same protocol was applied to the database containing our ligands. 4. Conclusions In summary, we identified original 4-[4-(substituted)-1H-1,2,3-triazol-1-yl]benzenesulfonamides as potential inhibitors of the HIV-1 RNase H function. This study highlighted that key features such as a (substituted) biaryl moiety and perfluorination on the benzenesulfonamide ring are crucial for the inhibitory activity of this class of compounds. These insights resulted in compound 10d as a representative and interesting lead compound for drug optimization. Moreover, spectroscopic analyses and molecular modeling studies indicated that such derivatives can mechanistically act by interfering with the two-metal ions through direct coordination of the metal cofactors in the enzyme catalytic site. This is in agreement with the behavior of diketo/triketoacid derivatives [14], but differs from what was observed for allosteric inhibitors [6,18] and dual inhibitors of RNase H and RT-associated DP activity, which were recently proposed to bind to a site close, but not coincident, to the RNase H active site [46]. Finally, calculated ADME properties and the preliminary finding that 10d displayed cellular cytotoxicity in human MT-4 cells at >7 μM, which was evaluated within the range of its inhibition concentration, predict that this compound can have desirable drug-like properties, thus making it suitable for further development. Acknowledgments We thank the Italian Ministero dell’Istruzione, dell’Università e della Ricerca (PRIN 2010, grant 2010W2KM5L_003) (to Mario Sechi, Dominga Rogolino, Mauro Carcelli, Enzo Tramontano) for the financial support. The authors thank Andrea Brancale for the use of the MOE program. Supplementary Materials Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1371/s1. Click here for additional data file. Author Contributions Nicolino Pala, Francesca Esposito, Lieve Naesens, Enzo Tramontano, and Mario Sechi conceived and designed the experiments; Nicolino Pala, Francesca Esposito, Dominga Rogolino, Vanna Sanna, Michele Palomba, Angela Corona, Nicole Grandi performed the experiments; Nicolino Pala, Francesca Esposito, Mauro Carcelli, Enzo Tramontano, and Mario Sechi analyzed the data; Dominga Rogolino and Mauro Carcelli contributed reagents/materials/analysis tools; Nicolino Pala and Mario Sechi wrote the paper. Conflicts of Interest The authors declare no conflict of interest. Abbreviations RNase H Ribonuclease H RT Reverse Transcriptase RNA Ribonucleic Acid DNA Deoxyribonucleic Acid ADME Absorption Distribution Metabolism Excretion HIV-1 Human Immunodeficiency Virus Type-1 AIDS Acquired Immune Deficiency Syndrome HAART Highly Active Antiretroviral Therapy PR Protease IN Integrase vRNA Viral RNA vDNA Viral DNA NRTI Nucleoside Reverse Transcriptase Inhibitor NtRTI Nucleotide Reverse Transcriptase Inhibitor NNRTI Non-Nucleoside Reverse Transcriptase Inhibitor HID 2-Hydroxyisoquinoline-1,3(2H,4H)-dione CAIs Carbonic Anhydrase Inhibitors SAR Structure-Activity Relationships CuAAC Copper-Catalyzed Azide-Alkyne Cycloaddition IPTG β-d-1-thio Galactopyranoside HEPES 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid DTT Dithiothreitol EDTA Ethylenediaminetetraacetic Acid Figure 1 Overall view of the full HIV-1 RT heterodimer. Subunits p51 (grey) and p66 (which include fingers, palm, thumb, connection, and RNase H sites) are depicted as ribbon. Polymerase and RNase H active sites are circled in black. The RNase H metal cofactors are represented as orange spheres. Figure 2 Structures of some representative HIV-1 RNase H inhibitors. Figure 3 General structure of compounds 9a–d and 10a–d. Figure 4 UV-vis titration of ligand 10d (bold line, compound alone) with increasing amount of Mg(CH3COO)2. The arrow indicates the direction of absorbance change as Mg(CH3COO)2 increases. Figure 5 Predicted binding modes for the most active compounds 9d, 10c, and 10d within the HIV-1 RNase H catalytic site: (A–C) best docking pose for 9d (purple), 10c (cyan), and 10d (white), respectively, with the protein shown as grey surface, and with the contact residues colored in the same color as the docked ligand; (D–F) close views of the binding site, where the protein is depicted as cartoon (blue lagoon), side chains of relevant residues are provided as thick line (pink), and Mn2+ metal cofactors as spheres (orange). ijms-17-01371-t001_Table 1Table 1 HIV-1 RT-associated RNase H, RT-associated DNA polymerase, and HIV-1 IN inhibition activities for 9a–d and 10a–d, and the previously described inhibitor RDS1759 [14], used as reference compound. Compound RNase H IC50 (µM) a DP IC50 (µM) b IN-LEDGF IC50 (µM) c 9a >100 ND ND 9b >100 ND ND 9c >100 ND ND 9d 63 ± 7 >100 >100 10a >100 ND ND 10b >100 ND ND 10c 26 ± 3 90 ± 5 >100 10d 6.6 ± 0.5 33.4 ± 5.8 >100 RDS1759 7.0 ± 1.5 ND ND a Compound concentration required to inhibit the HIV-1 RNase H activity by 50%; b Compound concentration required to inhibit the HIV-1 DP activity by 50%; c Compound concentration required to inhibit the HIV-1 IN catalytic activity by 50% in the presence of LEDGF. ND, not determined. Inhibition activities of the most active compound 10d are highlighted in bold. ijms-17-01371-t002_Table 2Table 2 Predicted physicochemical/ADME properties for compounds 9a–d, 10a–d, and RDS1759. Compound MW H-acc H-don Rbond log P (o/w) log S TPSA 9d 370.5 4 1 7 3.328 −6.216 91 10c 372.3 2 1 3 1.829 −4.885 65 10d 442.4 2 1 7 3.928 −7.396 65 RDS1759 359.8 3 2 8 4.466 −4.103 69 Abbreviations: MW, molecular weight; H-acc, number of hydrogen bond acceptors; H-don, number of hydrogen bond donors; Rbond, number of rotatable bonds; log P (o/w), log of the octanol-water partition coefficient ; log S, log of the aqueous solubility; TPSA, topological polar surface area. ==== Refs References 1. Zhang J. Crumpacker C. Eradication of HIV and cure of AIDS, now and how? Front. Immunol. 2013 4 337 10.3389/fimmu.2013.00337 24151495 2. Siliciano J.D. Siliciano R.F. HIV-1 eradication strategies: Design and assessment Curr. Opin. HIV AIDS 2013 8 318 325 10.1097/COH.0b013e328361eaca 23698561 3. Xing S. Siliciano R.F. Targeting HIV latency: Pharmacologic strategies toward eradication Drug Discov. Today 2013 18 541 551 10.1016/j.drudis.2012.12.008 23270785 4. Su H.P. Yan Y. Prasad G.S. Smith R.F. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081372ijms-17-01372ArticleHow Many Conformations of Enzymes Should Be Sampled for DFT/MM Calculations? A Case Study of Fluoroacetate Dehalogenase Li Yanwei 1Zhang Ruiming 1Du Likai 2Zhang Qingzhu 1*Wang Wenxing 1Chermette Henry Academic Editor1 Environment Research Institute, Shandong University, Jinan 250100, China; lyw@sdu.edu.cn (Y.L.); zrm0118@163.com (R.Z.); wxwang@sdu.edu.cn (W.W.)2 Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.; dulikai@mail.hzau.edu.cn* Correspondence: zqz@sdu.edu.cn; Tel.: +86-531-8836-443520 8 2016 8 2016 17 8 137221 7 2016 16 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).The quantum mechanics/molecular mechanics (QM/MM) method (e.g., density functional theory (DFT)/MM) is important in elucidating enzymatic mechanisms. It is indispensable to study “multiple” conformations of enzymes to get unbiased energetic and structural results. One challenging problem, however, is to determine the minimum number of conformations for DFT/MM calculations. Here, we propose two convergence criteria, namely the Boltzmann-weighted average barrier and the disproportionate effect, to tentatively address this issue. The criteria were tested by defluorination reaction catalyzed by fluoroacetate dehalogenase. The results suggest that at least 20 conformations of enzymatic residues are required for convergence using DFT/MM calculations. We also tested the correlation of energy barriers between small QM regions and big QM regions. A roughly positive correlation was found. This kind of correlation has not been reported in the literature. The correlation inspires us to propose a protocol for more efficient sampling. This saves 50% of the computational cost in our current case. quantum mechanics/molecular mechanicsenzymatic dehalogenationBoltzmann-weighted average barrierdisproportionate effect ==== Body 1. Introduction The quantum mechanics/molecular mechanics (QM/MM) method is a powerful tool to model biochemical processes. It can probe enzymatic mechanisms at the atomic level which is generally hard to be experimentally explored [1,2,3,4,5]. Ever since the pioneer study by Warshel and Levitt on a realistic enzyme system in 1976 [6], the QM/MM technique has been improved and widely applied. Currently, there are generally two ways to calculate the features (e.g., structural parameters, energies) of enzymatic reactions. One is “dynamic sampling” (e.g., the umbrella sampling method) [7,8,9]. Combined with QM/MM, dynamic sampling gives a relatively accurate description of the dynamic behavior of an enzyme. This method, however, is highly computationally inefficient and is in most cases applicable with only low-level QM methods (e.g., PM3) [10]. Alternatively, adiabatic mapping was proposed. This method samples a random enzymatic conformation of enzyme residues using molecular dynamics (MD), and locates the reaction pathway in this conformation (the enzyme-substrate-water complex). Although high-level QM methods (e.g., density functional theory (DFT), or even ab initio) can be used in adiabatic mapping, this method likely generates biased results [11,12]. To address this dilemma, adiabatic calculations using DFT/MM in enzymes with multiple sampled conformations have been widely reported [13,14,15,16,17,18]. Reaction pathways are generally optimized in each of the sampled conformations. The average energy barrier is computed by averaging all the barriers from multiple conformations. Two averaging methods, the direct arithmetic average and the Bolzmann-weighted average (also known as the exponential average), have been proposed [19,20,21]. Recently Kästner and coworkers have tested the two averaging methods. They concluded that the Bolzmann-weighted average better reproduces the experimental value than the direct arithmetic average method [22]. Although the advantage of the Bolzmann-weighted average method has been proposed for 20 years [23], it was only recently applied in studying enzymatic reactions. A challenging problem, however, is how to determine the minimum number of enzymatic conformations required for the convergence of the averaged energy barrier. Different articles have tentatively proposed different numbers of conformations, considering 3–10 [21] and 5–10 [24] conformations have been recommended, and three [13,14,15], four [16], five [17], six [18,19], seven [20], eight [21], 10 [25], 20 [26], or even 40 [27] conformations have been recently used. The justification, however, is supposed to be provided for the number of conformations being used. An effective strategy is to manually pick conformations from MD that already resemble the transition state geometry [22]. However, this strategy has two limitations. First, the “transition-state-like” is conceptually ambiguous. Identification of important structural parameters could be arbitrary. For instance, only after analyzing results from 20 conformations, an angle was found to be more sensitive toward the energy barrier than all the other tested structural parameters for fluoroacetate dehalogenase catalyzed processes [28]. Second, this “manually picking” strategy is not applicable to the cases in which the first step of a multi-step enzymatic process is not rate-determining. Here, we provide a strategy to determine the minimum number of conformations required for the convergence of energies in enzymatic reactions. Defluorination and dechlorination reactions catalyzed by fluoroacetate dehalogenase (FAcD) [29,30] were selected as representative. The mechanism of the reaction has been well demonstrated. As shown in Scheme 1, the reaction is triggered by residue Asp110 attacking the Cδ atom of the substrate fluoroacetate, which leads to the cleavage of the C–F bond. Using the strategy proposed in the present study, about 50% and 30% computational costs were saved for the defluorination and dechlorination processes. The results will be valuable in guiding the work-flow of future DFT/MM studies and enhance its application in modeling enzymatic chemical processes. 2. Results and Discussion We studied the defluorination of FAc, and the dechlorination of FAc catalyzed by FAcD. Based on the substrate and size of the QM region used, four systems are generated. They are FAcD-FAc-S, FAcD-FAc-B, FAcD-ClAc-S, and FAcD-ClAc-B. Here “FAc” and “ClAc” refer to the substrates fluoroacetate and chloroacetate while “S” and “B” refer to the small or big QM regions used during DFT/MM calculations. 2.1. Convergence Criteria Currently it is still challenging to determine the number of conformations that are supposed to be sampled for converging the averaged energy barrier of a given enzyme system. To address this issue, two convergence criteria were proposed. The first criterion is the convergence of the Boltzmann-weighted average barrier. The Bolzmann-weighted average method has been shown to be an excellent approach for analyzing the fluctuations of energy barriers in multiple conformations compared to the direct arithmetic average method [21,22]: (1) ΔE=−RT ln{1n∑i=1nexp(−ΔEiRT)} where ΔE is the average barrier, n is the total number of sampled conformations, ΔEi is the energy barrier of conformation i. The second criterion is the convergence of the disproportionate effect. For a small n, if the set of starting conformations includes one conformation with an anomalously low energy barrier, this will have a dramatic disproportionate effect (DE) on the Boltzmann-weighted average barrier [28]: (2) DE=ΔEa−l−ΔEaΔEa×100% where ΔEa−l is the Boltzmann-weighted average barrier calculated by neglecting the conformation with the lowest energy barrier, ΔEa is the Boltzmann-weighted average barrier with all the conformations considered. DE is another important indicator for determining whether the sampled number of conformations is sufficient. To decrease the number of conformations needed, the Boltzmann-weighted average barrier and disproportionate effect were considered as “converged” when the corresponding numerical changes are less than 5% in at least five consecutive iterations. This can prevent the results from being converged to an incorrect value. Figure 1 shows the Boltzmann-weighted average barrier versus the number of sampled conformations for FAcD-FAc-S and FAcD-ClAc-S. The Boltzmann-weighted average barrier is converged after 13 and nine conformations (Figure 1a) while the disproportionate effect is converged after 18 and 17 conformations (Figure 1b) for the systems FAcD-FAc-S and FAcD-ClAc-S. However, taking both of the convergence criteria into consideration, 18 and 17 are the minimum number of conformations that should be sampled for studying the systems FAcD-FAc-S and FAcD-ClAc-S. 2.2. Small QM Region versus Big QM Region As shown in Figure 1a, the Boltzmann-weighted average barrier for defluorination (20.1 kcal·mol−1) is much higher than dechlorination (7.7 kcal·mol−1), which is inconsistent with the experimental observation that FAcD favors defluorination over dechlorination [31]. This is presumably because some key residues of the strong interaction with the substrates were not included in the QM region in FAcD-FAc-S and FAcD-ClAc-S. To test our assumption, FAcD-FAc-B and FAcD-ClAc-B were calculated with residues His155, Trp156, and Tyr219 in the QM region. The Boltzmann-weighted average barriers for defluorination and dechlorination are then 11.4 and 14.5 kcal·mol−1, respectively, which are consistent with the experiment [28]. This shows the necessity of the test on the QM boundary. This has also been found by many other studies [32,33]. What we are interested in here is whether there is a correlation of energy barriers between small and big QM region systems. The correlation is helpful for the qualitative prediction of the energy barriers for big QM region systems (accurate, computationally inefficient) based on small QM region calculations (inaccurate, computationally efficient). As shown in Figure 2a (FAcD-FAc-S/FAcD-FAc-B) and Figure 2b (FAcD-ClAc-S/FAcD-ClAc-B), a positive relationship between energy barriers in small and big QM systems was found by analyzing results from 20 independent conformations. The positive relationship suggests that if the energy barrier of a typical conformation with a small QM region were low, the corresponding energy barrier with a large QM region is likely low. Take the system FAcD-FAc-S as an example; the lowest energy barrier among 20 studied conformations is 18.7 kcal·mol−1 (conformation sampled at 6.5 ns), while the energy barrier of the same conformation in the system FAcD-FAc-B is 15.0 kcal·mol−1, the fourth lowest among 20 studied conformations (details are shown in Table S1). Based on the correlation, a protocol for efficient adiabatic DFT/MM calculations is proposed. Firstly, pre-screen the enzymatic reactions with the minimum number of QM atoms. The number of conformations can be determined by checking the convergence of the Boltzmann-weighted average barrier and the disproportionate effect; Secondly, align the obtained barriers from lowest to highest. Thirdly, choose the lowest energy barrier and calculate the corresponding energy barrier with the big QM region considered; Finally, successively choose more conformations based on the alignment and calculate the corresponding energy barriers (big QM region calculations) until the convergence of the Boltzmann-weighted average barrier and the disproportionate effect are reached. It should be noted that the correlation between energy barriers in small and big QM systems will not influence the reliability of the protocol proposed. Worse correlation generally means more computational cost: more conformations (big QM region calculations) are needed before the criteria are reached. 2.3. Determine the Minimum Number of Conformations to Be Sampled The convergence of the Boltzmann-weighted average barrier and the disproportionate effect for systems with a big QM region (FAcD-FAc-B and FAcD-ClAc-B) were shown in Figure 3. Two cases were tested: (1) use the protocol (FAcD-FAc-B-Pro and FAcD-ClAc-B-Pro); (2) do not use the protocol, randomly increase the number of conformations and test the convergence (FAcD-FAc-B and FAcD-ClAc-B). According to two proposed convergence criteria, the systems FAcD-FAc-B-Pro and FAcD-ClAc-B-Pro (protocol used) are converged after eight and 11 conformations, which indicates that the energy barrier correlation between the systems FAcD-FAc-S and FAcD-FAc-B is slightly better than the correlation between the systems FAcD-ClAc-S and FAcD-ClAc-B. The systems FAcD-FAc-B and FAcD-ClAc-B (protocol not used) are only converged after 20 and 18 conformations. It is worth mentioning that the converged results from using the protocol reached the same accuracy by directly calculating all the 20 configurations (big QM region calculations). Combining the fact that the demanded computational resource for small QM region calculations only accounts for 10% of that demanded for big QM region calculations in the present study, applying the protocol will approximately save 50% and 30% computational cost for defluorination and dechlorination processes catalyzed by FAcD. 3. Materials and Methods The dehalogenation of substrates fluoroacetate (FAc) and chloroacetate (ClAc) by fluoroacetate dehalogenase (FAcD) were selected as two representatives in this study. The QM/MM protocol used here is similar to our previous study [28] and only important details of the methods will be briefly summarized. The QM/MM calculations were performed by using ChemShell [34,35] platform, which integrates programs Turbomole [36] and DL-POLY [37]. The geometries of the intermediates and transition states were optimized by using hybrid delocalized internal coordinates optimizer and microiterative transition state (TS) optimizer under the B3LYP/6-31G(d,p)//CHARMM22 level [38]. Frequency calculations were performed to verify the one imaginary frequency character of transition state structures, and the suitability of the transition vector was also confirmed. In pre-screen, QM atoms were treated by B3LYP/6-31G(d,p) method while MM atoms were treated by CHARMM22 force field. All QM atoms and MM atoms within 20 Å of element F or Cl were allowed to move while the other MM atoms were fixed during QM/MM calculations. The QM region (14 atoms) used for pre-screening in the present study only contain residues Asp110 and substrate (FAc or ClAc) rather than relatively bigger QM region (90 atoms) described in our previous study [28]. Twenty conformations were extracted from 10 ns molecular dynamics simulation with 0.5 ns interval. They were used for the QM/MM calculations for both defluorination and dechlorination processes. This strategy may avoid biased sampling to some extent and has been extensively applied in many other studies [17,20,27]. 4. Conclusions The convergence of the Boltzmann-weighted average barrier and the disproportionate effect are used here to determine the number of conformations that are sufficient to converge the energetics of defluorination (18 for FAcD-FAc-S, 20 for FAcD-FAc-B) and dechlorination (17 for FAcD-ClAc-S, 18 for FAcD-ClAc-B) catalyzed by fluoroacetate dehalogenase. A protocol on the basis of the roughly positive correlation between energy barriers of small and big QM regions was proposed. The protocol saves about 50% and 30% computational cost for defluorination and dechlorination in the present study. Acknowledgments The work was financially supported by the NSFC (National Natural Science Foundation of China, project No. 21337001, 21577082, and 21507073), the SPNSFC (Shandong Provincial Natural Science Foundation, China, project no. ZR2015PB002), the FRFSU (Fundamental Research Funds of Shandong University, project No. 2015GN007), the CPSF (China Postdoctoral Science Foundation, project No. 2015M570594, 2016T90635), and the SFPIPSP (Special Funds for Postdoctoral Innovative Projects of Shandong Province, project No. 201501009). The authors thank Zhongyue Yang and Kendall N. Houk from the University of California, Los Angeles, CA, USA, for helpful discussions. Supplementary Materials Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1372/s1. Click here for additional data file. Author Contributions Yanwei Li, Qingzhu Zhang, and Wenxing Wang conceived and designed the project; Yanwei Li performed all the experiments; Yanwei Li, Ruiming Zhang, and Likai Du analyzed the data; Yanwei Li and Qingzhu Zhang wrote the manuscript. Conflicts of Interest The authors declare no conflict of interest. Figures and Scheme ijms-17-01372-sch001_Scheme 1Scheme 1 The dehalogenation process catalyzed by fluoroacetate dehalogenase. The atoms in small quantum mechanics (QM) regions are indicated in bold blue while atoms considered in big QM regions are indicated in black. The boundaries between the QM and molecular mechanics (MM) regions are indicated by wavy lines. Figure 1 The convergence of (a) Boltzmann-weighted average barrier and (b) disproportionate effect of systems FAcD-FAc-S and FAcD-ClAc-S in 20 conformations. Figure 2 The correlation of energy barriers between systems (a) FAcD-FAc-S and FAcD-FAc-B and systems (b) FAcD-ClAc-S and FAcD-ClAc-B. Figure 3 The convergence of (a) Boltzmann-weighted average barrier and (b) disproportionate effect of systems with protocols (FAcD-FAc-B-Pro and FAcD-ClAc-B-Pro) considered, and systems without protocols considered (FAcD-FAc-B-noPro and FAcD-ClAc-B-noPro). ==== Refs References 1. Lonsdale R. Ranaghan K.E. Mulholland A.J. Computational enzymology Chem. Commun. 2010 46 2354 2372 10.1039/b925647d 20309456 2. Brunk E. Rothlisberger U. Mixed quantum mechanical/molecular mechanical molecular dynamics simulations of biological systems in ground and electronically excited states Chem. Rev. 2015 115 6217 6263 10.1021/cr500628b 25880693 3. Jiang L. Althoff E.A. Clemente F.R. Doyle L. Rothlisberger D. Zanghellini A. Gallaher J.L. Betker J.L. Tanaka F. Barbas C.F. III De novo computational design of retro-aldol enzymes Science 2008 319 1387 1391 18323453 4. Martí S. Andrés J. Moliner V. Silla E. Tuñón I. Bertrán J. Computational design of biological catalysts Chem. Soc. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081373ijms-17-01373ArticleExpression and Critical Role of Interleukin Enhancer Binding Factor 2 in Hepatocellular Carcinoma Cheng Shaobing 123Jiang Xu 1Ding Chaofeng 1Du Chengli 1Owusu-Ansah Kwabena Gyabaah 1Weng Xiaoyu 1Hu Wendi 1Peng Chuanhui 1Lv Zhen 1Tong Rongliang 1Xiao Heng 1Xie Haiyang 1Zhou Lin 1Wu Jian 12*Zheng Shusen 123*Haybaeck Johannes Academic Editor1 Department of Hepatobiliary Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China; showcheng@126.com (S.C.); drjiangxu@126.com (X.J.); dcf25@126.com (C.Di.); 496722098@163.com (C.Du); Kobby01@live.com (K.G.O.-A.); wengxiaoyu2009@sina.com (X.W.); huwendi1988@gmail.com (W.H.); clarkpch@163.com (C.P.); lvzhen@zju.edu.cn (Z.L.); trl55555@126.com (R.T.); xiaohengdoctor@126.com (H.Xia.); xiehy@zju.edu.cn (H.Xie); linzhou19@163.com (L.Z.)2 Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China3 Key Laboratory of Combined Multi-organ Transplantation, Ministry of Public Health, Key Laboratory of Organ Transplantation, Hangzhou 310003, China* Correspondence: drwujian@hotmail.com (J.W.); shusenzheng@zju.edu.cn (S.Z.); Tel.: +86-571-8723-6567 (J.W.); +86-571-8723-6570 (S.Z.); Fax: +86-571-8723-6739 (J.W.); +86-571-8723-6466 (S.Z.)22 8 2016 8 2016 17 8 137306 4 2016 08 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Interleukin enhancer binding factor 2 (ILF2), a transcription factor, regulates cell growth by inhibiting the stabilization of mRNA. Currently, its role has gained recognition as a factor in the tumorigenic process. However, until now, little has been known about the detailed role ILF2 plays in hepatocellular carcinoma (HCC). In this study, we investigated the expression levels of ILF2 in HCC tissue with Western blot and immunohistochemical assays. To examine the effect of ILF2 on liver cancer cell growth and apoptosis, small interfering RNAs (siRNAs) targeting ILF2 were recombined to create lentiviral overexpression vectors. Our results showed higher expression levels of ILF2 mRNA and ILF2 protein in HCC tissue compared with matched peritumoral tissue. Expression of ILF2 may regulate cell growth and apoptosis in liver cancer cells via regulation of B-cell lymphoma 2 (Bcl-2), Bcl-2 related ovarian killer (Bok), Bcl-2-associated X protein (BAX), and cellular inhibitor of apoptosis 1 (cIAP1). Moreover, we inoculated nude mice with liver cancer cells to investigate the effect of ILF2 on tumorigenesis in vivo. As expected, a rapid growth was observed in cancer cells inoculated with a lentiviral vector coding Flag-ILF2 (Lenti-ILF2) compared with the control cells. Hence, these results promote a better understanding of ILF2’s potential role as a therapeutic target in HCC. interleukin enhancer binding factor 2hepatocellular carcinomacell growthapoptosis ==== Body 1. Introduction Hepatocellular carcinoma (HCC) is one of the most common solid tumors, with an estimated 782,500 new cases and 745,500 deaths reported in 2012 worldwide [1]. Although much progress has been made in treatment options for patients with HCC, surgery remains the main treatment option for such patients, with a five-year survival rate of about 30%–40% [2]. Other treatment options, such as radiation therapy, radiofrequency ablation, transarterial chemoembolization, and liver transplantation, have been performed in some HCC patients when tumor resection was difficult [3]. Patients with HCC show poor prognosis, in part due to the lack of effective therapeutic options. Hence, determination of the genes and molecular mechanisms involved in HCC is considered extremely urgent for improving the diagnosis of multiple stages of HCC and facilitating drug development with specific liver cancer targets. Interleukin enhancer binding factor 2 (ILF2), also known as nuclear factor 45 (NF45), is encoded by a gene located on human chromosome 1 (1q11-qter and 1p11-p12). It is confirmed by polymerase chain reaction (PCR) amplification of ILF2-specific DNA sequences [4], and is consistent with a prior localization of the NF45 gene to chromosome 1q21.3 by fluorescence in situ hybridization (FISH) [5]. ILF2/NF45 associates with ILF3/NF90 in the nucleus and regulates IL-2 gene transcription at the antigen receptor response element (ARRE)/nuclear factor of activated T-cells (NFAT) DNA target sequence [6]. Also, cells deficient in ILF2 exhibit reduced internal ribosome entry site (IRES)-mediated translation of X-linked inhibitor of apoptosis protein (XIAP) and cellular inhibitor of apoptosis protein 1 (cIAP1) [7,8]. Furthermore, knockdown of NF90 destabilized NF45, and vice versa. Similarly, NF110 and NF45 are coregulated [5]. ILF2/NF45 was shown to be regulated by meiosis and is associated with transcriptionally active chromatin [9]. In addition to its potential role in regulating transcription, NF45 may be associated with RNAs in ribonucleoprotein complexes that regulate delayed translation of mRNAs [10,11]. NF45 may directly bind to pri-miRNAs to reduce miRNA production by hindering the accessibility of the microprocessor complex [12]. Cotransfection of ILF2 and ILF3 has been demonstrated to augment transcriptional activation. NF45 has also been identified as a component of the spliceosome [13,14]. The recovery of ILF2 in the ribosomal salt wash and the physical association with protein kinase R (PKR) suggest that ILF2 is involved in regulating translation [15,16,17]. A growing pool of evidence has indicated that overexpression of ILF2 is frequently observed in glioma, non-small cell lung cancer, esophageal squamous cell carcinoma, and childhood endodermal sinus tumors and is related to poor clinical outcomes [18,19,20,21]. Results on the induction of p53 and p21 by knockdown of ILF2 in human cervical carcinoma cells have recently been reported [22]. Upregulation of ILF2 in HCC has been validated by Western blot and immunohistochemistry [23]. Although the regulation and function of ILF2 have been extensively investigated, the biological functions as well as the molecular mechanisms of ILF2 in tumorigenesis and tumor progression have not been fully demonstrated. In this study, we discovered the aberrant expression of ILF2 in HCC, and its regulation of liver cancer cell proliferation, cell growth, and apoptosis. Furthermore, we discovered that ILF2 promotes cell proliferation and enhances the tumorigenic capacity in vivo. Our data revealed that ILF2 might play a crucial role in liver carcinogenesis and serve as a potential target for HCC therapy. 2. Results 2.1. Interleukin Enhancer Binding Factor 2 Is Upregulated in Human Hepatocellular Carcinoma First, ILF2 expression was analyzed in 27 paired HCC and corresponding neighboring normal tissues using quantitative real-time PCR (qRT-PCR). ILF2 was found to be upregulated in HCC tissues (p < 0.05, Figure 1A). As shown in Figure 1B, higher levels of ILF2 were observed in liver cancer cell lines than in a normal liver cell line (LO2). To confirm the qRT-PCR results, we first measured ILF2 expression in 72 paired HCC tissues by immunohistochemical assay. The results showed higher expression levels in HCC compared with adjacent non-cancerous tissues (Figure 1C). Moreover, the results also revealed that expression of ILF2 was correlated with tumor size (p = 0.043) (Table 1). In agreement with the above results in HCC, ILF2 protein expression was upregulated in HCC as assessed by Western blot (Figure 1D). In order to determine the impact of high ILF2 expression levels in the prognosis of HCC patients, the Kaplan–Meier method was used to compare the prognosis among HCC patients with high and low ILF2 expression levels in The Cancer Genome Atlas (TCGA) dataset [24]. We analyzed the differences in the overall survival after surgery between high and low ILF2 expression groups. Interestingly, HCC patients with high ILF2 expression levels experienced shorter survival time compared with the low expression group by TCGA data analysis (p = 0.0135) (Figure 1E). The first quartile was used to define the high ILF2 expression group and low ILF2 expression group, with 4287.62 as the cut-off value. To determine whether ILF2 upregulation occurs via transcription and/or degradation, we used the proteasome inhibitor MG132. The cells were incubated with MG132 several times, after which cell lysates were subjected to immunoblot analysis. No significant changes in the level of ILF2 were observed in HCC cell lines (Huh7 and MHCC-LM3) treated with MG132 (Figure 1F). Therefore, our results indicate that ILF2 expression is upregulated in both HCC tissues and liver cancer cell lines, and upregulation of ILF2 occurs via transcription and not via protein turnover. 2.2. Effects of Interleukin Enhancer Binding Factor 2 on the Proliferation of Liver Cancer Cells To explore its potential role in HCC proliferation, we utilized small interfering RNA (siRNA) transfection and lentivirus overexpression methods to remove the endogenous ILF2 and enhance the expression of exogenous ILF2 in liver cancer cells, respectively. HuH7 and MHCC-LM3 cells were transduced with a lentiviral vector coding Flag-ILF2 (Lenti-ILF2) and a lentiviral vector negative control (Lenti-NC). Based on the expression patterns of ILF2 in the liver cancer cell lines, MHCC-LM3 and Huh7 were selected to perform the experiments. The levels of endogenous ILF2 knockdown and Flag-ILF2 in Huh7 and MHCC-LM3 cells were validated (Figure 2A,B). For objective quantification of bands, we used densitometry with the ImageJ 1.44 software [25]. As shown in Figure S1, Western blot densitometric analysis allowed quantification of ILF2 protein bands in comparison to β-actin. Notably, since lentiviral vectors carrying Flag-ILF2 or a negative control vector and corresponding viruses at 1 × 108 plaque forming units (pfu)/mL were constructed in Lenti-ILF2 or Lenti-NC cells, whole-cell extracts were used for anti-Flag immunoprecipitation, consistent with previous results. Flag-ILF2 was detected by Western blot in the Lenti-ILF2 group but not in the Lenti-NC group (Figure S2). Next, we demonstrated the effects of ILF2 silencing and overexpression on the proliferation of liver cancer cells by Cell Counting Kit-8 (CCK-8) assay and colony formation assay. While negative control transfected cells grow rapidly, ILF2-depleted cells display a slow rate of proliferation, as shown in the CCK-8 assay (Figure 2C). Conversely, the proliferation rate of cells transfected with Lenti-ILF2 was significantly faster than that in cells treated with the Lenti-NC (Figure 2D). Similarly, the results were also validated from the colony formation assay in Huh7 and MHCC-LM3 cells (Figure 2E,F). Data presented here indicate that ILF2 plays a significant role in liver cancer cell growth, cell proliferation, and survival. 2.3. Effects of Interleukin Enhancer Binding Factor 2 on Apoptosis of Liver Cancer Cells Next, to determine whether the increase in cell proliferation was due to inhibition of apoptosis and how ILF2 expression correlates with apoptosis in liver cancer cells, we performed an apoptosis assay by flow cytometry. As expected, ILF2 overexpression significantly decreased apoptosis in cancer cells infected with recombinant ILF2 lentivirus compared with the control group. Consistent with these results, ILF2 downregulation increased the apoptotic cell count in the ILF2 siRNA group (Figure 3). Together, these results suggest that ILF2 might be involved in liver cancer cell proliferation and apoptosis. 2.4. Overexpression of Interleukin Enhancer Binding Factor 2 Promotes Tumor Growth in a Xenograft Model The findings suggesting that ILF2 might play a role in apoptosis and promotion of HCC cell proliferation in vitro prompted us to confirm whether ILF2 exerts a similar effect in vivo. Lenti-NC or Lenti-ILF2 cells were subsequently implanted into the right axilla of nude mice. The mice were sacrificed five weeks after inoculation and tumors were excised, measured, and photographed (Figure 4A). Compared with the Lenti-NC group, the volume and weight of the tumors were significantly larger in mice bearing Lenti-ILF2 (Figure 4B). Consistently, the expression of Ki-67, a cell proliferation marker, was increased in nude mice after subcutaneous injection of Lenti-ILF2 cells (Figure 4C). Five weeks after inoculation, Western blot analysis revealed high expression levels of ILF2 in tumors formed from the Lenti-ILF2 group. This indicated that Lenti-ILF2 effectively overexpresses ILF2 in vivo for more than a month (Figure S3). Taken together, these results demonstrate the ability of ILF2 to modulate the proliferative capacity of liver cancer cells in vivo. 2.5. Interleukin Enhancer Binding Factor 2 May Suppress Apoptosis in Tumors via Regulation of Pro-Apoptotic Proteins and Anti-Apoptotic Proteins We next explored the molecular mechanism underlying ILF2 apoptosis suppression. As B-cell lymphoma 2 (Bcl-2) is a key regulator of apoptosis and ILF2 enhances IRES-dependent translation of endogenous cIAP1 [11], the expression of anti-apoptotic Bcl-2 and cIAP1 were measured in nude mice bearing Lenti-ILF2 or Lenti-NC cells. Interestingly, immunohistochemistry proved that Bcl-2 and cIAP1 levels were significantly increased in tumors from Lenti-ILF2-treated mice compared with the control group (Figure 5A). To further confirm the relationship between ILF2 and the expression of pro-apoptotic and anti-apoptotic proteins, we analyzed the levels of pro-apoptotic proteins such as Bcl-2-associated X protein (Bax), Bcl-2 antagonist/killer 1 (Bak), and Bcl-2 related ovarian killer (Bok); and anti-apoptotic proteins such as Bcl-2 and cIAP1 by Western blot in cancer cells infected with Lenti-ILF2 and Lenti-NC or transfected with ILF2 siRNA (siILF2) and siRNA negative control (siNC). Bcl-2 and cIAP1 protein levels were significantly upregulated, and levels of Bax and Bok were downregulated in Lenti-ILF2 cells; similar results were also validated in siILF2 cells, but there were no changes on Bak levels in cancer cells infected with Lenti-NC and Lenti-ILF2 or transfected with siNC and siILF2 (Figure 5B). The results were further confirmed by terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay, which was employed to detect apoptosis in xenograft tumors. As shown in Figure 5C, the control group revealed more extensive apoptosis compared with cells from Lenti-ILF2 xenografts. These results suggest that ILF2 might suppress apoptosis in liver cancer cells via regulation of pro-apoptotic proteins and anti-apoptotic proteins in vivo and in vitro. 3. Discussion HCC has fairly high morbidity and is difficult to treat, with very poor prognosis if not diagnosed early. Despite decades of study on its etiology, pathogenesis, and epidemiology, targeted agents (Sunitinib) have currently failed to provide meaningful improvements in the prognosis of HCC. Increasing evidence indicates that the majority of tumors show enhanced expression of ILF2 compared with their normal healthy counterparts [18,19,20,21]. Thus, we focused on the correlation between ILF2 expression and outcome in HCC patients, and its role in HCC proliferation. ILF2 is a nucleic acid-binding protein that binds to ILF3 [6]. Besides binding to double-stranded (ds) RNA, the ILF2–ILF3 complex has the ability to bind to single-stranded and dsDNA [26]. In addition, ILF2 may be an important interacting factor for DNA-dependent protein kinase (DNA-PK), which plays a key role in DNA double-strand break repair [27]. It has been reported that ILF2 might be highly expressed in HCC specimens, compared to the normal liver, and serves as a potential molecular target of HCC [23], in agreement with their results, we also showed that higher ILF2 expression was observed in most HCC cell lines as well as HCC tissues from patients (Figure 1). To explore whether ILF2 upregulation occurs via transcription and/or degradation, the Huh7 and MHCC-LM3 cells were incubated with the proteasome inhibitor MG132 for various times, and our results revealed that ILF2 upregulation happens via transcription and not via degradation of ILF2 or protein turnover (Figure 1F). In addition to HCC, increased ILF2 expression has also been detected in glioma, non-small cell lung cancer, and esophageal cancer [18,19,20]. Therefore, overexpression of ILF2 might provide a beneficial effect to tumor cells during tumor progression. Moreover, a recent report has indicated a close correlation between ILF2 expression and tumor size in pancreatic ductal adenocarcinoma (PDAC) and that ILF2 could be a valuable prognostic indicator for survival in PDAC patients [28]. Consistent with these findings, we found a significant association between ILF2 expression and tumor size (Table 1). HCC patients with high ILF2 expression have dismal clinical outcomes from TCGA data analysis, strongly supporting the clinical relevance of ILF2 in HCC prognosis. Loss of proliferation control is a hallmark of cancer, including liver cancer. Cells fail to proliferate in part due to apoptosis. Apoptosis, the first identified programmed cell death process, has been widely investigated in the chemical treatment of tumors [29]. Evasion of apoptosis, a characteristic of tumor cells, occurs by altering the expression levels and functions of apoptosis regulators [30]. In apoptosis, the mitochondrial outer membrane permeabilization (MOMP) is considered as a “point of no return” and is tightly regulated by the well-known Bcl-2 family proteins [31]. Other apoptosis regulators frequently changed in tumors are the inhibitors of apoptosis proteins (IAPs). Our results showed that ILF2 could regulate cell proliferation and apoptosis in vitro (Figure 2 and Figure 3), which is consistent with our hypothesis. In vivo, overexpression of ILF2 could promote tumor growth in a xenograft model (Figure 4). The findings prompted us to search for ILF2 target genes involved in cell proliferation and apoptosis regulation. ILF2 has been shown to regulate IRES activity and translation of the human AU-rich IRES-containing mRNAs of cIAP1, XIAP, the E26 transformation-specific (ETS) family transcription factor ELG, and nuclear respiratory factor (NRF) [7]. Also, ILF2 enhances translation of endogenous cIAP1 mRNA, and ILF2-dependent translation of cIAP1 is mediated by its IRES [8]. The expression and functions of IAPs have been reported in various human cancers, such as esophageal, colon, cervical, and prostate cancer [32,33,34,35]. It has been reported that the biological activities exerted by ILF2 are mediated by apoptosis pathways [22,36]. Furthermore, Bcl-2 and cIAP1 are involved in apoptosis and proliferation processes in HCC [37,38]. Here we show that ILF2 might regulate Bcl-2 and cIAP1 expression in xenografted tumors and liver cancer cells, suggesting that the oncogenic function of ILF2 was at least in part by promoting the expression of Bcl-2 and cIAP1 (Figure 5B). In addition to these two anti-apoptotic proteins, it also regulates the expression of pro-apoptotic proteins, such as Bax and Bok. Notably, the control group showed more extensive apoptosis compared with cells from Lenti-ILF2 xenografts by TUNEL assay (Figure 5C). The findings indicated that Bcl-2, Bok, Bax, and cIAP1 are likely to mediate the effect of ILF2 on apoptosis regulation in vivo and in vitro. However, Bak expression levels are not affected in the overexpression or knockdown groups, probably because Bak is not critically involved in the process of ILF2-induced cell proliferation or apoptosis. Furthermore, we proved that the expression of Ki-67, a cell proliferation marker, was upregulated in nude mice bearing Lenti-ILF2 cells (Figure 4C). Huang et al. [18] also reported that the expression of NF45 positively and significantly correlates with Ki-67 expression in glioma patients. It has been previously reported that repression of either ILF2 or its binding partner ILF3 leads to retardation of HeLa cell growth and accumulation of multinucleate giant cells [39]. In addition, ILF3 is upregulated in HCC and its expression increases HCC growth both in vitro and in vivo [40]. Similar to that observation, our results showed upregulation of ILF2 in HCC and that ILF2 plays a significant role in liver cancer cell growth in vitro and in vivo. Therefore, elucidating the molecular mechanisms underlying ILF2 ability to induce cancer cell proliferation and whether this is mediated by ILF2–ILF3 interactions in HCC pathogenesis, requires further study. In conclusion, our work shows that ILF2 expression is associated with cell proliferation and apoptosis progression in vitro and in vivo. Moreover, our findings shed light into the mechanisms by which ILF2 influences apoptosis progression via regulation of Bcl-2, Bok, Bax, and cIAP1; with cIAP1 acting as a key regulatory protein that communicates between apoptotic and cell proliferation pathways. Birinapant is a specific cIAP1 inhibitor that could potentially be used in HCC treatment. Therefore, the features of this ILF2–cIAP1 crosstalk support its exploration as a potential therapeutic target for HCC. The ILF2–ILF3 complex, is involved in several steps of RNA metabolism [41]. Thus, the downstream signaling pathways and interactions between ILF2 andILF3 in tumorigenesis require further investigation. 4. Materials and Methods 4.1. Cell Lines and Cultures All cell lines were purchased from the Shanghai Institute of Cell Biology, Chinese Academy of Sciences (Shanghai, China). Cells were cultured in Dulbecco’s modified Eagle medium (Gibco, Grand Island, NY, USA) supplemented with 10% fetal bovine serum (FBS), 100 U/mL of penicillin and streptomycin, and maintained at 37 °C in a humidified 5% CO2 incubator. 4.2. Lentiviral Transfection and siRNA Knockdown A lentiviral vector carrying Flag-ILF2 or a negative control vector and corresponding viruses (1 × 108 pfu/mL) were constructed and prepared by GeneChem Co., Ltd. (Shanghai, China). Lentiviral transfection was carried out in the presence of polybrene (GeneChem Co., Ltd.) according to the manufacturer’s guidelines. The siIFL2 for ILF2 knockdown and siNC as a negative control were purchased from Invitrogen (Cat. No. 1299001; Waltham, MA, USA). Transfection was performed using lipofectamine 2000 transfection reagent (Invitrogen) and Opti-MEM (Thermo Fisher, Waltham, MA, USA), as the datasheet suggests. 4.3. Terminal Deoxynucleotidyl Transferase dUTP Nick-End Labeling Assay Apoptosis in xenograft tumors was also measured using the TUNEL assay, which was conducted using the Trevigen Apoptotic Cell Sysem (TACS) XL-Blue label in situ apoptosis detection kit (Trevigen, Gaithersburg, MD, USA) according to the manufacturer’s guidelines. Briefly, the sections were rinsed in water and then equilibrated with phosphate-buffered saline (PBS). The specimens were permeabilized with 0.1% Triton X-100 for 2 min on ice. After rinsing, a reaction buffer was used to incubate the sections in a humidified chamber. Next, visualization of the reaction was carried out using TACS Blue Label solution. 4.4. Immunohistochemistry The formalin-fixed paraffin-embedded tissues were deparaffinized and rehydrated. Antigen repair was conducted in 10 mM citric acid buffer (pH 6.0) for 10 min using a microwave oven. Endogenous peroxidase activity was blocked by 3% hydrogen peroxide in methanol for 20 min at room temperature. The sections were further incubated with Dako Liquid DAB Large-Volume Substrate-Chromogen System (Dako, Glostrup, Denmark). After counterstaining with Mayer’s hematoxylin, the slides were dehydrated, cleared, and mounted. Immunostaining was evaluated by an Olympus BX-50 light microscope (Olympus, Tokyo, Japan). The stain density was analyzed using an Image Pro-Plus 6.0 analysis system (Media Cybernetics Inc., Silver Spring, MD, USA). 4.5. Cell Viability Assay A cell counting kit (CCK-8, Dojindo Molecular Technologies, Inc., Kumamoto, Japan) was used to detect cell viability. Briefly, 3000 viable cells per well were seeded into 96-well culture plates. Each plate was subjected to the CCK-8 assay according to the manufacturer’s protocol. After a 2 h incubation at 37 °C, the relative viable cell numbers were measured by the absorbance optical density at 450 nm using a microplate reader (BioTek, Winooski, VT, USA). The results represented the mean ± standard deviation (SD) of three independent experiments. 4.6. Western Blotting Protein concentration was measured using a BCA Protein Assay Kit (Pierce, Rockford, IL, USA). Proteins were denatured by heating at 90 °C for 10 min in 4× nuPAGE LDs sample buffer (Life Technologies, Carlsbad, CA, USA). Equal amounts of protein from each sample were separated by 6%–18% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS–PAGE) (Life Technologies). Then, the proteins were transferred to a polyvinylidene fluoride membrane (Millipore, Billerica, MA, USA). After blocking with 5% non-fat milk, the membrane was incubated overnight at 4 °C with the primary anti-ILF2 antibody (1:1000; HPA007484; Sigma-Aldrich, St. Louis, MO, USA), anti-Flag (1:1000; SAB4200071; Sigma-Aldrich), anti-Bcl-2 (1:1000; #15071; Cell Signal, Danvers, MA, USA), anti-cIAP1 (1:1000; ab108361; Abcam, Cambridge, MA, USA), anti-Bax (1:1000; #5023; Cell Signal), and anti-β-actin (1:1000; #3700; Cell Signal) overnight. After washing three times with Tris-buffered saline with 0.05% Tween-20 for 10 min each, signal was detected with the SuperSignal West Pico Chemiluminescent Substrate (Pierce). The band signals were quantified using the Image J 1.44 software from Wayne Rasband (National Institutes of Health, Bethesda, MD, USA). β-Actin was used as a protein control. 4.7. Colony Formation Assay Liver cancer cells were seeded in six-well plates at a density of 500 cells per well and continually cultured at 37 °C in a humidified 5% CO2 incubator for two weeks. After incubation, the supernatants were discarded, and cells were rinsed three times with PBS. The cells were fixed with methanol for 15 min and stained with 0.1% crystal violet for 10 min. Colony numbers containing more than 50 cells were manually counted. The experiments were performed in triplicate. 4.8. Tumor Xenografts MHCC-LM3 cells were plated and infected with lentivirus carrying either Lenti-ILF2 or Lenti-NC at multiplicity of infection (MOI) of 100. Cells were subcutaneously injected into the right anterior armpit of six-week-old female BALB/c nude mice. Tumor volume was calculated using a caliper every three days. Mice were sacrificed after tumor inoculation for five weeks, and the volume and weight of each tumor were measured. Xenograft tumors were fixed in 10% buffered formalin and embedded in paraffin for immunohistochemical staining assay. All experimental procedures were performed in accordance with the Guide for the Care and Use of Laboratory Animals and approved by our institutional ethical guidelines (The project code: 2016-294, Date: 2016-08-16) for animal experiments. 4.9. Apoptosis Assay Apoptosis was detected by flow cytometry, measured by annexin V-fluorescein isothiocyanate (FITC) and propidium iodide (PI) (BD Bioscience, San Jose, CA, USA). Experimental procedures were performed as described by the manufacturer’s guidelines. After incubation with 5 μL of annexin V and 5 μL of PI at room temperature in the dark for 15 min, stained cells were analyzed in a flow cytometer model BD FACS Canto II (BD Biosciences, San Diego, CA, USA), and 10,000 events were analyzed by sample. 4.10. Statistical Analysis Measurement data were presented as mean ± SD from at least three independent experiments. The comparisons between the two groups were analyzed by Student’s t-test. The statistical analysis was performed using GraphPad Prism software 5.0 (GraphPad Software Inc., La Jolla, CA, USA) and SPSS 15.0 (IBM, Chicago, IL, USA). All tests were two-tailed, and a p value of less than 0.05 was considered statistically significant. Acknowledgments This research was funded by the Science and Technology Department of Zhejiang Province (No. 2015C03034), the Innovative Research Groups of the National Natural Science Foundation of China (No. 81421062), the National S&T Major Project (No. 2016ZX10002020), and the National Health and Family Planning Commission of China (No. 2016138643). Supplementary Materials Supplementary materials can be found at www.mdpi.com/1422-0067/17/8/1373/s1. Click here for additional data file. Author Contributions Shusen Zheng and Jian Wu conceived and designed the experiments; Shaobing Cheng, Xu Jiang, Chaofeng Ding, Chengli Du, and Xiaoyu Weng performed the experiments; Wendi Hu, Chuanhui Peng, Zhen Lv, Rongliang Tong, Heng Xiao, Haiyang Xie, and Lin Zhou analyzed the data; Shaobing Cheng and Kwabena Gyabaah Owusu-Ansah wrote the manuscript. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Expression patterns of interleukin enhancer binding factor 2 (ILF2) in hepatocellular carcinoma (HCC) tissues and cell lines. (A,B) mRNA levels of ILF2 were analyzed using quantitative real-time PCR (qRT-PCR) in HCC tissues and HCC cell lines; bars, standard deviation (SD) * p < 0.05; (C) Immunohistochemical image of ILF2 expression in liver tumor tissues (T) and normal tissues (N). Representative image of ILF2 at 40×, 100×, and 200× magnification; (D) ILF2 protein expression in HCC (T) and normal (N) tissues; (E) Kaplan–Meier survival curves of overall survival of HCC patients with high and low ILF2 expression; p = 0.0135; Regression coefficient (Coef) = 0.7187; Hazard ratio exp(coef) = 2.0518; 95% lower confidence limits (lower 0.95) = 1.1453; 95% upper confidence limits (upper 0.95) = 3.6757; (F) Huh7 and MHCC-LM3 cells following treatment with 10 μM MG132 at indicated time points. ILF2 and β-actin levels were measured by Western blot. Figure 2 Effects of ILF2 silencing and overexpression on liver cancer cell proliferation. (A) Downregulation of ILF2 expression by small interfering RNA (siRNA) in Huh7 and MHCC-LM3 cells; (B) ILF2 expression in Huh7 and MHCC-LM3 cells infected with recombinant ILF2 lentivirus; (C,D) proliferation ability of cells in vitro after transfection with siRNA. Infection with recombinant lentivirus was evaluated by Cell Counting Kit-8 (CCK-8) assay. Data shown as mean (n = 3) ± SD ** p < 0.01, *** p < 0.001; (E) Representative images of the colony formation assay in Huh7 and MHCC-LM3 cells; (F) Quantification of colony number. Data shown as mean (n = 3) ± SD * p < 0.05. siILF2: ILF2 siRNA; siNC: siRNA negative control. Figure 3 Effects of ILF2 knockdown and overexpression on apoptosis. Cells were transfected with siRNAs (siNC and siILF2) and infected with recombinant lentivirus (Lenti-NC and Lenti-ILF2). Annexin V-fluorescein isothiocyanate (FITC)/propidium iodide (PI) assay for determination of apoptosis was then performed. Cells were analyzed by flow cytometry. Representative images of each group are shown. Data shown as mean (n = 3) ± SD, * p < 0.05. Figure 4 ILF2 overexpression promotes MHCC-LM3 cell tumorigenicity in vivo. (A) Tumors dissected from nude mice bearing Lenti-NC or Lenti-ILF2 cells; (B) Tumor growth curves measured after subcutaneous injection of Lenti-NC or Lenti-ILF2 cells. The tumor volume was calculated with calipers every three days for 38 days. Data shown as mean (n = 5) ± SD, *** p < 0.001; with tumor weights measured just after mice were sacrificed in the two groups; (C) Immunohistochemical images of Ki-67 expression. Representative images at 200× magnification. Figure 5 ILF2 regulates the expression of apoptosis-related proteins in xenografted tumors and liver cancer cells. (A) Immunohistochemistry of ILF2, cellular inhibitor of apoptosis 1 (cIAP1) and B-cell lymphoma 2 (Bcl-2) in xenografted Lenti-NC and Lenti-ILF2 tumors. Representative images at 100× magnification; (B) Effect of ILF2 overexpression and downregulation on apoptosis-related protein expression in cancer cells infected with Lenti-ILF2 or transfected with siILF2, respectively. Relative band intensity from Western blot analysis was normalized by the expression level of β-actin; (C) Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining of xenografted tumors in the Lenti-NC and Lenti-ILF2 groups. Representative images at 200× magnification. Red arrows indicate apoptotic cells. Data shown as mean (n = 3) ± SD, ** p < 0.01. ijms-17-01373-t001_Table 1Table 1 Correlation of ILF2 expression with the observed clinicopathological features of 72 HCC patients. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081374ijms-17-01374ArticleEffect of Pulsed Electric Field on Membrane Lipids and Oxidative Injury of Salmonella typhimurium Yun Ou 1Zeng Xin-An 1*Brennan Charles S. 12*Han Zhong 1Battino Maurizio Academic Editor1 School of Food Science and Engineering, South China University of Technology, Guangzhou 510641, China; cloud_ou@126.com (O.Y.); fezhonghan@scut.edu.cn (Z.H.)2 Centre for Food Research and Innovation, Department of Wine, Food and Molecular Biosciences, Lincoln University, Lincoln 85084, New Zealand* Correspondence: xazeng@scut.edu.cn (X.-A.Z.); charles.brennan@lincoln.ac.nz (C.S.B.); Tel.: +86-20-3938-1191 (X.-A.Z.); +64-03-325-6678 (C.S.B.)22 8 2016 8 2016 17 8 137417 5 2016 12 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Salmonella typhimurium cells were subjected to pulsed electric field (PEF) treatment at 25 kV/cm for 0–4 ms to investigate the effect of PEF on the cytoplasmic membrane lipids and oxidative injury of cells. Results indicated that PEF treatment induced a decrease of membrane fluidity of Salmonella typhimurium (S. typhimuriumi), possibly due to the alterations of fatty acid biosynthesis-associated gene expressions (down-regulation of cfa and fabA gene expressions and the up-regulation of fabD gene expression), which, in turn, modified the composition of membrane lipid (decrease in the content ratio of unsaturated fatty acids to saturated fatty acids). In addition, oxidative injury induced by PEF treatment was associated with an increase in the content of malondialdehyde. The up-regulation of cytochrome bo oxidase gene expressions (cyoA, cyoB, and cyoC) indicated that membrane damage was induced by PEF treatment, which was related to the repairing mechanism of alleviating the oxidative injury caused by PEF treatment. Based on these results, we achieved better understanding of microbial injury induced by PEF, suggesting that micro-organisms tend to decrease membrane fluidity in response to PEF treatment and, thus, a greater membrane fluidity might improve the efficiency of PEF treatment to inactivate micro-organisms. pulsed electric fieldsSalmonella typhimuriummembrane lipidscytoplasmic membrane fluidityoxidative injury ==== Body 1. Introduction Pulsed electric field (PEF) technology has been researched extensively in the area of non-thermal pasteurization as an innovative and environmental friendly technology which causes minimal changes to the sensory quality and nutritional ingredient of liquid foods [1,2,3,4]. In addition, the combination of PEF with other hurdles, such as heat and antimicrobial compounds, has been proposed to improve the pasteurization efficiency of PEF treatment [5,6] and the saftey of semi-solid foods [7,8,9]. The mechanism by which PEF inactivates micro-organisms is mainly ascribed to electroporation [10]. The reversible pores on cytoplasmic membrane induced by PEF result in the injury of micro-organisms, while the irreversible pores on cytoplasmic membrane caused by PEF has been shown to lead to microbial death [11,12,13]. Studies have also illustrated that oxidative damage of cell may be caused by PEF treatment [14]. For instance, a previous study illustrated that an oxidative jump could be induced by PEF treatment at 0.9 kV/cm [15]. In addition, red blood cells have been shown to have suffered from oxidative injury after exposure to electric fields [16]. However, micro-organism damage induced by PEF, especially in the aspect of oxidative damage, still required more work to achieve better understanding of the mechanism by which PEF inactivates bacteria. Cytoplasmic membrane fluidity and membrane fatty acid composition play important roles in the response of micro-organisms to external stress [17]. The key target of PEF inactivating micro-organisms is the membrane, PEF has the capability of modifying organizational changes of membrane, which ultimately affects vital activities of micro-organisms and leads to cell damage or cell death [18]. Although several studies have demonstrated that PEF treatment decreased membrane fluidity [19] and altered membrane fatty acid composition [20] of Saccharomyces cerevisiae, little is known about the relationship between PEF treatment and modification of membrane fatty acid composition at the transcriptional level of micro-organisms. The objective of the present study was to investigate the effect of PEF on lipids and oxidative injury of Salmonella typhimurium based on the cytoplasmic membrane fluidity, fatty acid composition, and lipid peroxidation of S. typhimurium. In addition, the effects of PEF on the transcription of cytochrome bo oxidase genes (cyoA, cyoB, cyoC) [18] and fatty acid biosynthesis-associated genes (cfa, fabA, fabD) [21] were determined to identify stress behavior of S. typhimurium in response to PEF and obtain more information on microbial inactivation by PEF. 2. Results 2.1. Salmonella typhimurium (S. typhimurium) Inactivation by Pulsed Electric Field (PEF) Treatment Figure 1 illustrates the inactivation of S. typhimurium by PEF treatment at 25 kV/cm with treatment time from 0–4 ms. It can be noted that with the increase of PEF treatment time from 0–4 ms the reduction of log10 cycles dramatically increased to 3.21 ± 0.12 (p < 0.05). 2.2. Modification in Cytoplasmic Membrane Fluidity 1,6-diphenyl-1,3,5-hexatriene (DPH), was used as a fluorescence probe to estimate the membrane fluidity of S. typhimurium. Figure 2 illustrates the effects of PEF on fluorescence polarization (P), fluorescence anisotropy (r), and micro-viscosity (η). Compared to the control sample, P values of cells increased by 2.02% and 9.76% when PEF treatment times were 0.8 and 4 ms, respectively (Figure 2A). In addition, as depicted in Figure 2A, the r value significantly increased by 10.91% compared to the control sample after PEF treatment for 4 ms (p < 0.05). Similarly, the alteration of η values in cells increased as the PEF treatment time increased. Figure 2B illustrates a 1.32-fold increase in η value of cells after PEF treatments at 25 kV/cm for 4 ms compared to those without PEF treatment (p < 0.05), which indicates that the membrane became relatively more rigid after relatively longer PEF treatment time. 2.3. Changes in Lipid Composition Table 1 shows the influence of the PEF treatment on fatty acid composition of S. typhimurium. Apart from nine main fatty acids shown in Table 1, three other fatty acids were also found, including C15:0 (pentadecanoic acid), C17:0 (margaric acid) and C18:0 (stearic acid). When subjected to PEF treatment for 1.6 and 4.0 ms, the content of C18:1 (octadecenoic acid) and C18:2 (octadecadienoic acid) significantly decreased and the content of C16:0 (palmitic acid) increased (p < 0.05). However, compared to the control sample, the contents of cyclic fatty acids after PEF treatment for 1.6 ms exhibited no significant difference (p > 0.05), while those after PEF treatment for 4 ms exhibited significant decrease (p < 0.05). 2.4. Lipid Peroxidation and Cell Morphology Figure 3 illustrates the content of malondialdehyde (MDA) generated after PEF with different treatment time. The results show an increased trend of the content of MDA with the increased PEF treatment time. The content of MDA was 79.90 ± 2.52 nmol/mg after PEF exposure for 4 ms. When the treatment time reached 1.6 ms, the content of MDA exhibited a significant difference compared to the control sample (p < 0.05). In addition, and consistent with the results previously described, scanning electron microscope (SEM) images illustrated that with the increment of PEF treatment time S. typhimurium cells suffered from severe cell injury (Figure 4). It can be revealed from SEM images that the modifications of cell morphology and cell debris had taken place after cells subjected to PEF treatment. 2.5. Alteration in Expression of Fatty Acid Biosynthesis-Associated Genes and Cytochrome Bo Oxidase Genes The transcription levels of fatty acid biosynthesis-associated genes (cfa, fabA, and fabD) were determined to investigate membrane fatty acid composition of S. typhimurium at the transcriptional level. The results showed that compared to the control sample, the down-regulation of the expression of cfa and fabA genes were in very significant difference, while the transcription of fabD was very significantly up-regulated (p < 0.01). For instance, a 1.91-fold up-regulation of fabD, a 1.47-fold down-regulation of fabA and a 1.35-fold down-regulation of cfa were observed when cells were subjected to PEF treatment for 1.6 ms, respectively (Figure 5). The expression of cytochrome bo oxidase genes (cyoA, cyoB, and cyoC) were also determined before and after PEF treatment. All cytochrome bo oxidase genes exhibited very significant up-regulation after PEF treatment by comparison with the control sample (p < 0.01). For instance, the expression of cyoC gene after PEF treatment for 1.6 ms was more than triple of that without PEF treatment (Figure 5). 3. Discussion 3.1. The Effect of PEF Treatment Time on S. typhimurium Inactivation With an increase of PEF treatment time, the reduction of log10 cycles was also increased. Compared to previous research, there was less treatment time required to inactivate three log10 cycles of S. typhimurium under similar PEF treatment conditions [4]. This might be due to the low conductivity (0.18 ms/cm) used in this study which may, in turn, influence the specific energy delivered to the sample. In addition, previous study illustrated that the specific energy input was 188 kJ/L, the Escherichia coli ATCC 26 of inactivation by PEF was from 1.5–3.6 log10 cycles when the pH of the samples ranged from 6–7 [22], which was similar to our results. 3.2. The Effect of PEF on Cytoplasmic Membrane Fluidity of S. typhimurium In this paper, the results of cytoplasmic membrane fluidity modified by PEF were similar to previous studies [19,20], which also demonstrated that PEF treatment decreased membrane fluidity. Previous research illustrated that dipalmitoyl phosphatidylcholine (DPPC) and soya phosphatidylcholine (PC) vesicles subjected to heat-assisted PEF treatment exhibited greater electro-permeability than those subjected to PEF treatment [23], which was due to a greater membrane fluidity that the vesicles possessed when the temperature of thermal treatment reached the phase transition temperature of the vesicles. Remarkably, heat-assisted PEF treatment enhanced the efficiency of microbial inactivation in various liquid foods [6,18]. Thus, it is probable that a lower membrane fluidity is required to protect cells from the damage caused by PEF treatment. In addition, the occurrence of disorder in cell metabolism or cell death may result from the organizational changes of membrane induced by PEF treatment [18]. Therefore, it is also possible that PEF treatment can induce the modification in fatty acid composition of membrane. Previous research has illustrated that the alteration in content of unsaturated fatty acids (UFA) and saturated fatty acids (SFA) results in changes of the UFA to SFA ratio, which is regaded as the predominant factor affecting membrane fluidity [24]. 3.3. The Effect of PEF on on Lipid Composition and Oxidative Injury Lipid composition plays an important role in cytoplasmic membrane integrity and function [25]. The alterations in lipid composition of membrane are induced by bacterial adaptation to external stress [16,26], resulting in modifications in membrane integrity [27]. In this paper, all identified membrane fatty acids of S. typhimurium were polar lipids, which could be affected by PEF treatment [28]. The occurrence of a significant decrease in the ratio of UFA to SFA after PEF treatment was predominantly ascribed to an increase of the C16:0 and a decrease of the C18:1. These results were similar to the results of Zhao et al. (2014) [21], which indicated that PEF treatment decreased the content of UFA and increase the content of SFA of membrane in S. cerevisiae. A relatively lower ratio of UFA to SFA represented a relatively lower membrane fluidity [29], which was consistent with the results of modifications of fluorescence anisotropy and micro-viscosity of membrane induced by PEF. It is probable that the regulation of fatty acid desaturases is stimulated after PEF exposure, which could modulate membrane lipids composition [30]. Additionally, it is also possible that the oxidative injury induced by PEF generates free radicals that could react with fatty acids [31], which results in the decrease in the degree of unsaturation and cell damage. MDA was regarded as an indicator of oxidative damage mainly due to the lipid peroxidation reaction between polyunsaturated fatty acid (PUFA) and the free radicals [31]. Therefore, the occurrence of a significant decrease of C18:2 in membrane lipids after PEF exposure may be ascribed to the fact that S. typhimurium cells had insufficient ability to adapt to PEF treatment, contributing to oxidative injury or cell death. Similarly, previous research has illustrated that oxidative injury was induced by electrostatic field in red blood cells [16]. It has been reported that membrane damage could be induced by PEF treatment and contribute to disrupting cell structure and function as well as leakage of intracellular substances [32]. Therefore, it is possible that membrane repair may be triggered when S. typhimurium cells are exposed to PEF treatment, which may result in the up-regulation of expression of genes related to membrane function. 3.4. The Effect of PEF on Relative Expression of Fatty Acid Biosynthesis-Associated Genes and Cytochrome Bo Oxidase Genes FabA regulates the key fatty acid desaturase and the synthesis of UFA derived from SFA [33,34]. FabD encodes long-chain-fatty-acid–CoA ligase and involved in the regulation of the processes of fatty acid biosynthesis [35]. Cfa controls the cyclopropane fatty acyl phospholipid syntheses and participates in the biosynthesis of CFA [36]. Thus, the down-regulation of fabA induced by PEF treatment might imply a decrease in the biosynthesis of UFA [21]. A down-regulation of cfa induced by PEF might indicate a decrease in the formation of CFA. These events may be related to the decrease in the degree of unsaturation. It was interesting to mention that compared to the control sample, the contents of cyclic fatty acids after PEF treatment for 1.6 ms exhibited no significant difference (p > 0.05), while those after PEF treatment for 4 ms exhibited significant decrease (p < 0.05). It was possible that the formation of CFA was not just determined by cfa. For instance, Kim et al. (2005) have reported that the rpoS gene plays an important role in the formation of CFA of S. typhimurium [36]. In addition, these results were consistent with the results of membrane fluidity and lipid composition caused by PEF treatment. Therefore, PEF treatment has a tremendous impact on the expression of fatty acid biosynthesis-associated genes, which leads to the decrease in UFA to SFA ratio and membrane fluidity for adaptation of micro-organisms to PEF treatment. The cytochrome bo oxidase is regarded as one of the predominant respiratory cytochrome oxidases on the membrane and plays an important role in generating the proton motive force [37]. The cytochrome bo oxidase genes (cyoA, cyoB, and cyoC) were significantly up-regulated after PEF treatment, which indicated that a proper response related to membrane function was activated by PEF treatment [9,38]. These results were in agreement of the view that membrane was the predominant target for microbial inactivation by PEF [39]. Previous reports have shown that damaged E. coli cells required the involvement of phospholipids synthesis and energy production to recover from membrane damage induced by PEF [40]. Additionally, the repair of membrane damages in Listeria monocytogenes cells was intimately related to energy production [41]. It is possible, therefore, that cell repair may be triggered after PEF treatment. Calderon et al. [38] illustrated that the cytochrome bo oxidase genes of S. typhimurium cells were up-regulated when subjected to NO stress, reducing their susceptibility to oxidative damage. Therefore, it is possible that S. typhimurium cells tended to alleviate oxidative injury caused by PEF treatment by up-regulation of cyoA, cyoB, and cyoC. 4. Materials and Methods 4.1. Materials S. typhimurium strain (ATCC 14028) was purchased from the American Type Culture Collection (Manassas, VA, USA). Tryptic soy broth (TSB), yeast extract, peptone, and agar were obtained from Guangdong Huankai Microbial Sci. and Tech. Co., Ltd. (Guangzhou, China). 1,6-Diphenyl-1,3,5-hexatriene (DPH), methanol, hexane, and methyl tert-butyl ether were purchased from Aladdin Chemistry Co., Ltd. (Shanghai, China), all other chemicals used were of reagent grade and obtained from Guangzhou local market. 4.2. Growth Condition of S. Typhimurium With one single colony from slant culture, broth subculture was carried out in 100 mL of sterile TSB supplemented with yeast extract (0.6%, w/v) (TSBYE) and incubated at 37 °C for 10 h in an orbital shaker (200 rpm; OS-200, Hangzhou Allsheng Instruments Co., Ltd., Hangzhou, China). After incubation, 5 mL of these subcultures, containing 20% glycerol, was transferred into sterile test tubes and then stored at −80 °C. The culture of one tube was transferred into a sterile 500 mL flask containing 200 mL of TSBYE and cultivated at 37 °C with agitation (200 rpm) until the cells reached a stationary growth phase with a final concentration of 5 × 109 CFU/mL. 4.3. PEF System and Treatment Before PEF treatment, the pellets were obtained by centrifuging at 4000× g for 5 min (JW-3021HR, Anhui Jiaven Equipment Industry Co., Ltd., Hefei, China) and then adjusted to a final concentration of 109 CFU/mL by re-suspension in sterile deionized water. Two mol/L sterile potassium chloride solution was used to adjust the electrical conductivity of all samples to 0.18 ms/cm. For PEF treatment, all samples were continuously circulated in the PEF system described in previous studies [42,43] through a rotary pump (323E/D, Watson-Marlow Inc., Wilmington, MA, USA). The PEF treatment temperature was maintained at 10 ± 1 °C by a heat exchanger (DLSK-3/10, Ketai Instrument Co., Ltd., Zhenzhou, China). The parameters of PEF system and treatment were as follows: bipolar square wave; pulse frequency: 1 kHz; pulse width: 40 μs; gap between electrodes: 0.30 cm; flow volume: 0.02 mL; flow rate: 1 mL/s; electric field strength: 25 kV/cm; times of treatment cycle from 1 to 5. The PEF treatment time t (s) was calculated based on Equations (1) and (2): (1) t=n×Np×Wp (2) Np=V×fF where n, Np, and Wp represent the times of treatment cycle, the number of pulses, and the pulse duration (μs), respectively. V, f, and F represent the flow volume (mL), the pulse frequency (Hz), and the flow rate (mL/s), respectively. According to Equations (1) and (2), the PEF treatment time and the number of pulses were 0.8 ms (20 pulses), 1.6 ms (40 pulses), 2.4 ms (60 pulses), 3.2 ms (80 pulses), and 4.0 ms (100 pulses), respectively. The control sample was prepared without PEF treatment. The specific PEF energy applied to the samples during one cycle was 90 kJ/L and the temperature rise was lower than 25 °C in all process. In addition, the final pH of the treatment media was 6.70 ± 0.30. 4.4. Enumeration of Survivors One milliliter of control sample was diluted by 0.1% (w/v) sterile peptone solution with gradient dilution. Then, 0.1 mL of diluted control sample was immediately placed on tryptic soy agar with 0.6% yeast extract. PEF-treated cells were conducted to the same operation as the control sample. All plates were incubated at 37 °C for 24 h. After incubation, the enumeration of viable cells was determined by computing the log10 reduction of the PEF-treated samples compared to the control sample. 4.5. Measurement of Cytoplasmic Membrane Fluidity The determination of fluorescence polarization, fluorescence anisotropy and micro-viscosity of cells was carried out according to the methods of Zhang et al. [20]. Briefly, the pellets were collected by centrifugation (4000× g, 5 min, 4 °C), followed by washing three times by sterile 10 mL of buffer solution, containing 0.06 mol/L disodium phosphate, 0.02 mol/L monosodium phosphate, and 0.15 mol/L sodium chloride. 4 mL of 2 μmol/L DPH solution was used to re-suspend the pellets in test tubes. Subsequently, the tubes were maintained at 37 °C for 30 min in a water bath with darkness. After centrifugation (4000× g, 5 min, 4 °C), the pellets were washed three times by the buffer solution and then re-suspended in 4 mL of buffer solution. Finally, the suspensions were used to detect membrane fluidity of S. typhimurium cells by a spectrofluorometer (F-4500, Hitachi, Japan). The parameters of the spectrofluorometer were as follow: slit width of excitation light: 5 nm; slit width of emission light: 5 nm; excitation wavelength: 362 nm; and emission wavelength: 432 nm. The control sample was prepared without being DPH-labeled. Equations (3)–(5) were used to calculate fluorescence polarization (P), fluorescence anisotropy (r) and micro-viscosity (η), respectively. (3) P=IVV−GIVHIVV+GIVH (4) r=IVV−GIVHIVV+2GIVH (5) η=2P0.46−P where IVV and IVH denote the fluorescence intensities of emission polarizer oriented vertically and horizontally when the excitation polarizer is determined at the vertical position, respectively. G denotes the grating factor. 4.6. Determination of Membrane Fatty Acid Composition Based on the method of Sasser et al. [44], pellets (40 mg) were obtained by centrifugation (4000× g, 5min) and re-suspended in 1 mL of water: methanol (1:1 v/v) mixture solution containing 3.75 mol/L NaOH. After vortexing (7.5 ± 2.5 s), samples were pipetted into test tubes and heated at 100 ± 1 °C for 5 min. A further 25 min heat processing (100 ± 1 °C) was conducted after a vortexing (7.5 ± 2.5 s). Afterwards, the test tubes were cooled before the addition of 2 mL of HCl (6.0 mol/L):methanol (13:11, v/v) mixture solution to each tube. The tubes were then vortexed (7.5 ± 2.5 s) and placed in a water bath at 80 ± 1 °C for 10 min followed by immediate cooling. Then 1.25 mL of hexane:methyl tert-butyl ether (1:1, v/v) mixture solution was added into each tube, and the tubes were tumbled for 10 min. The lower phase was then removed and 3 mL of 0.3 mol/L NaOH was added to each tube. After tumbling for a further 5 min, 2/3 of the upper phase was transferred into a gas chromatography (GC) vial which was stored at −80 °C until analysis. The membrane fatty acid composition of S. typhimurium was analyzed by a GC Agilent 7820A (Agilent Technologies, Wilmington, DE, USA) loaded with a capillary column HP-5 (30 m × 0.32 mm × 0.25 μm, Agilent Technologies) using pure nitrogen as carrier gas at 1 mL/min in a split mode (20:1). Under the following temperature program: the first ramp, initial temperature was increased from 150 to 170 °C at 10 °C·min−1 and held for 0.5 min; the second ramp, the temperature was increased to 200 at 5 °C·min−1 and maintained for 1 min; the third ramp, the temperature was increased to 260 at 2 °C·min−1 for detection. The comparison was made between the retention times of fatty acid methyl esters and known standard (Supelco 37 Component FAME Mix, Sigma, St. Louis, MO, USA). The fatty acid methyl esters were finally identified by analysis of gas chromatograph-mass spectrometer analyses (GCMS-QP2010 Ultra, SHIMADZU, Tokyo, Japan) with the same temperature program. The percentage composition of each fatty acid was calculated as the ratio of the surface area of the considered peak to that of all peaks. 4.7. Detection of Membrane Lipid Peroxidation PEF-treated cells and control cells were collected by centrifugation (4000× g, 5 min, 4 °C). The MDA concentrations of samples were analyzed following the instructions of Malondialdehyde (MDA) Assay Kit (Nanjing Jiancheng Bioengineering Institute, Nanjing, China). Finally, the samples were detected spectrophotometer (UV-1800, Tokyo, Japan) at 532 nm to determine oxidative damage of S. typhimurium cells induced by PEF treatment. 4.8. RNA Extraction and Analysis of Gene Expression by Quantitative Real-Time RT-PCR (qRT-PCR) The extraction of total RNA in S. typhimurium cells was immediately carried out according to the instructions of Qiagen RNeasy Mini Kit 74106 (Qiagen Inc., Hilden, Germany) after PEF treatment. Reverse transcription was carried out following the instructions of iScript cDNA synthesis kit (Bio-Rad Inc., Philadelphia, PA, USA) and the first strand cDNA was synthesized based on 150 ng of total RNA. Analysis of genes expression was performed by 7900 HT Sequence Detection System (ABI, Foster City, CA, USA) to investigate the effects of PEF on the transcription of membrane function genes (cyoA, cyoB, cyoC) and fatty acid biosynthesis-associated genes (cfa, fabA, fabD). The synthesis of all primers was made by Sangon Biotech Ltd. (Shanghai, China) according to the data from the NCBI database (accession number NC_003197.1), which were listed in Table 2. Gene expression was detected by 10 μL of qPCR reaction mix (ABI Power SYBR Green PCR Master Mix, (ABI, Foster City, CA, USA) according to the protocol of manufacturer. The reaction mix was composed of 5 μL of 2× qPCR Master Mix, 0.5 of 10 μM forward primer, 0.5 of 10 μM reverse primer, 1 ng of template cDNA, and PCR water used to maintain the total volume to 10 μL. Control sample was prepared by reactions without reverse transcriptase. The reaction conditions were as follows: 1 cycle at 50 °C maintained for 2 min, one cycle at 95 °C held for 10 min, 40 cycles at 95 °C for 15 s and 60 °C for 1 min. 16S rRNA of S. typhimurium was regarded as the endogenous control. Fold changes of these gene expression were computed based on the method of 2−ΔΔCt [45]. 4.9. Scanning Electron Microscope (SEM) The morphological changes of S. typhimurium were observed by scanning electron microscope (JSM-6360LV, Tokyo, Japan). The PEF-treated and without PEF treatment suspensions were performed to centrifugation (4000× g, 5 min) and the pellets were re-suspended with paraformaldehyde:glutaraldehyde (2%:2.5%, w/w) mixture for 12 h at 4 °C. Subsequently, 0.1 mol/L phosphate solution (pH 7.2) was used to wash the samples for three times, followed by 1% osmium tetroxide fixing for 2.5 h at 25 °C. After being washed by the same buffer for three times, samples were dehydrated by graded ethanol from 30%–100% and substituted by tert-butyl alcohol. Then, samples were stored at −20 °C for 1 h and dried by vacuum freeze drier (SCIENTZ-18N, Shanghai, China) for 24 h. Finally, all samples were transferred into a sputter coater (JEOLJFC-1600, Tokyo, Japan) for gold sputtering and then observed by SEM. 4.10. Statistics Analysis Mean values were computed based on the three replicate measurements of duplicate experiments. All data were analyzed with least significant difference (LSD) (p < 0.05 or p < 0.01) based on analysis of variance (ANOVA) by SPSS software (Statistical Package for the Social Sciences, version 22.0, IBM, Armonk, NY, USA). All data were expressed as mean ± standard deviation. 5. Conclusions The present study investigated the effect of PEF on cytoplasmic membrane lipids of S. typhimurium by focusing on the membrane fluidity, fatty acid composition, and lipid peroxidation. Results indicate that PEF appears to alter the membrane function and cause damage to cells, contributing to decrease in membrane fluidity and oxidative injury. S. typhimurium, which was subjected to PEF treatment for 1.6 ms, showed a decrease in membrane fluidity. The micro-viscosity of S. typhimurium was increased significantly compared to the untreated cells, largely due to the significant down-regulation of fabA genes and up-regulation of fabD, which was characterized by the in alterations in composition of membrane lipid (decrease in ratio of UFA to SFA). In addition, PEF treatment resulted in oxidative injury of S. typhimurium, which was characterized by an increase in the content of MDA and a decrease in the content of PUFA. Exposure to PEF treatment for 1.6 and 4.0 ms, resulted in a significant up-regulation the expression of cyoA, cyoB, and cyoC genes. This might mitigate the oxidative injury, suggesting that S. typhimurium might tend to a decreased membrane fluidity in response to PEF and a greater membrane fluidity, which could improve the efficiency of PEF treatment to inactivate micro-organisms. More attention should be focused on the effect of PEF on membrane function regulation of micro-organisms to reveal the microbial response behavior to PEF treatment. Acknowledgments The present work received financial support from the National Natural Science Foundation of China (21576099, 21376094 and 31301559) and S&T projects of Guangdong province (2015A030312001, 2013B051000010, 2013B020203001). Author Contributions Ou Yun and Xin-An Zeng conceived and designed the experiments; Ou Yun and Zhong Han performed the experiments; Charles S. Brennan analyzed the data and revised the paper; Xin-An Zeng contributed all materials and experiment apparatus; Ou Yun wrote the paper. Conflicts of Interest The authors declare no conflict of interest. Figure 1 The inactivation of Salmonella typhimurium (S. typhimuriumi) by pulsed electric field (PEF) treatment at 25 kV/cm for 0–4 ms. Figure 2 The alterations of (A) fluorescence polarization (P), fluorescence anisotropy (r); and (B) micro-viscosity (η) of cytoplasmic membrane of S. typhimurium after PEF treatments at 25 kV/cm for 0–4 ms. Figure 3 The alterations of content of malondialdehyde of S. typhimurium after PEF treatments at 25 kV/cm for 0–4 ms. Different letters indicate the significant difference (p < 0.05). Figure 4 The effect of PEF treatment at 25 kV/cm with treatment time for 0, 1.6, and 4.0 ms on the morphology of S. typhimurium. (a) Control; (b) PEF-treated for 1.6 ms; and (c) PEF-treated for 4.0 ms. Arrows indicate the modifications of cell morphology and cell debris. Figure 5 The effect of PEF treatment at 25 kV/cm on the expression of cfa, fabA, fabD, cyoA, cyoB, and cyoC genes of S. typhimurium (** p < 0.01). ijms-17-01374-t001_Table 1Table 1 Fatty acids composition of stationary phase cells of Salmonella typhimurium (S. typhimuriumi) before and after pulsed electric field (PEF) treatment at 25 kV/cm. Fatty Acid Composition Content (%) Control PEF for 1.6 ms PEF for 4.0 ms Saturated fatty acid (SFA) C12:0 2.11 ± 0.32 a 1.96 ± 0.15 a 1.65 ± 0.12 b C14:0 6.71 ± 0.48 a 5.35 ± 0.12 ab 5.81 ± 0.31 a C16:0 46.04 ± 1.23 a 50.90 ± 0.23 b 55.68 ± 0.62 c Unsaturated fatty acid (UFA) C16:1 5.56 ± 0.41 a 5.06 ± 0.98 a 4.84 ± 0.37 a C18:1 7.30 ± 0.12 a 4.54 ± 0.21 b 3.01 ± 0.16 c Polyunsaturated fatty acid (PUFA) C18:2 1.33 ± 0.11 a 0.63 ± 0.09 b 0.22 ± 0.08 c Cyclic fatty acid (CFA) C17:cyclo 19.21 ± 0.37 a 18.91 ± 0.93 a 16.22 ± 0.21 b C19:cyclo 3.23 ± 0.62 a 2.94 ± 0.21 a 1.75 ± 0.14 b C14:0 (3-OH) 7.40 ± 0.12 a 7.76 ± 0.15 a 8.13 ± 0.16 ab Total minor fatty acids 1.11 ± 0.59 a 1.95 ± 0.49 a 2.69 ± 0.68 ab Different letters indicate the significant difference (p < 0.05). ijms-17-01374-t002_Table 2Table 2 Primers of fatty acid biosynthesis-associated genes and cytochrome bo oxidase genes used in this study. Gene Sequence (5′ to 3′) Product Length (bp) 16S rRNA F: TCGTGTTGTGAAATGTTGGGTTA 66 R: ACCGCTGGCAACAAAGGAT fabA F: GGTTCTTCGGATGCCACTTTAT 65 R: CATAGCATCCAGACCCAGACAA fabD F: AGTGGACGAAGAGCGTGGAAT 67 R: CCTGGACCCACTTCATAAAGATG cfa F: CCCCCACCATGTTAAAGATACG 74 R: AGGCGCGTTTTTTACTTTGTAGA cyoA F: TGGTTTCGCCTGGAAGTATC 64 R: GTGTGACCAGTTCGGGCTAT cyoB F: GGCACCCATTTCTTTACCAA 105 R: GACCGGCAGAATCAGAATGT cyoC F: GGATGGCGGTGCTGATG 67 R: ATGATGCGGGTACGGTTAGTG ==== Refs References 1. Aadil R.M. Zeng X.-A. Ali A. Zeng F. Farooq M.A. Han Z. Khalid S. Jabbar S. Influence of different pulsed electric field strengths on the quality of the grapefruit juice Int. J. Food Sci. Technol. 2015 50 2290 2296 10.1111/ijfs.12891 2. Aadil R.M. Zeng X.-A. Wang M.-S. Liu Z.-W. Han Z. Zhang Z.-H. Hong J. Jabbar S. A potential of ultrasound on minerals, micro-organisms, phenolic compounds and colouring pigments of grapefruit juice Int. J. Food Sci. Technol. 2015 50 1144 1150 10.1111/ijfs.12767 3. Aadil R.M. 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==== Front Int J Mol SciInt J Mol SciijmsInternational Journal of Molecular Sciences1422-0067MDPI 10.3390/ijms17081375ijms-17-01375ArticleDevelopment of a Patient-Derived Xenograft (PDX) of Breast Cancer Bone Metastasis in a Zebrafish Model Mercatali Laura 1*†La Manna Federico 12†Groenewoud Arwin 3Casadei Roberto 4Recine Federica 1Miserocchi Giacomo 1Pieri Federica 5Liverani Chiara 1Bongiovanni Alberto 1Spadazzi Chiara 1de Vita Alessandro 1van der Pluijm Gabri 2Giorgini Andrea 6Biagini Roberto 7Amadori Dino 1Ibrahim Toni 1‡Snaar-Jagalska Ewa 3‡Desiderio Maria Alfonsina Academic EditorMarchetti Dario Academic Editor1 Osteoncology and Rare Tumors Center, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Via P. Maroncelli 40, 47014 Meldola, Italy; federico.lamanna@gmail.com (F.L.M.); federica.recine@irst.emr.it (F.R.); giacomo.miserocchi@irst.emr.it (G.M.); chiara.liverani@irst.emr.it (C.L.); alberto.bongiovanni@irst.emr.it (A.B.); chiara.spadazzi@irst.emr.it (C.S.); alessandro.devita@irst.emr.it (A.d.V.); dino.amadori@irst.emr.it (D.A.); toni.ibrahim@irst.emr.it (T.I.)2 Department of Urology, Leiden University Medical Center, J-3-100, Albinusdreef 2, 2333ZA Leiden, The Netherlands; g.van.der.pluijm@umail.leidenuniv.nl3 Department of Molecular Cell Biology, Institute of Biology, Leiden University, Sylviusweg 72, 2333BE Leiden, The Netherlands; a.groenewoud@biology.leidenuniv.nl (A.G.); b.e.snaar-jagalska@biology.leidenuniv.nl (E.S.-J.)4 Department of Orthopedics, Istituto Ortopedico Rizzoli, University of Bologna, Via Pupilli, 1, 40136 Bologna, Italy; roberto.casadei@ior.it5 Pathology Unit, Morgagni-Pierantoni Hospital, 47121 Forlì, Italy; federica.pieri@auslromagna.it6 Department of Medical and Surgical Sciences for Children and Adults, Modena Polyclinic, Viale del Pozzo, 71, 41124 Modena, Italy; andreagiorgini@hotmail.it7 UOC Orthopedic Surgery, Regina Elena National Cancer Institute, 00144 Rome, Italy; biagini@ifo.it* Correspondence: laura.mercatali@irst.emr.it; Tel.: +39-0543-739-239† These authors contributed equally to this work. ‡ These authors contributed equally to this work. 22 8 2016 8 2016 17 8 137527 6 2016 16 8 2016 © 2016 by the authors; licensee MDPI, Basel, Switzerland.2016This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).Bone metastasis is a complex process that needs to be better understood in order to help clinicians prevent and treat it. Xenografts using patient-derived material (PDX) rather than cancer cell lines are a novel approach that guarantees more clinically realistic results. A primary culture of bone metastasis derived from a 67-year-old patient with breast cancer was cultured and then injected into zebrafish (ZF) embryos to study its metastatic potential. In vivo behavior and results of gene expression analyses of the primary culture were compared with those of cancer cell lines with different metastatic potential (MCF7 and MDA-MB-231). The MCF7 cell line, which has the same hormonal receptor status as the bone metastasis primary culture, did not survive in the in vivo model. Conversely, MDA-MB-231 disseminated and colonized different parts of the ZF, including caudal hematopoietic tissues (CHT), revealing a migratory phenotype. Primary culture cells disseminated and in later stages extravasated from the vessels, engrafting into ZF tissues and reaching the CHT. Primary cell behavior reflected the clinical course of the patient’s medical history. Our results underline the potential for using PDX models in bone metastasis research and outline new methods for the clinical application of this in vivo model. bone metastasiszebrafish modelbreast cancerpatient-derived xenograft ==== Body 1. Introduction Metastasis from solid tumors is the main cause of cancer-related death [1]. It is a dynamic, complex and multistep process involving tumor cell intravasation, spread to different organs, extravasation and cancer cell colonization, and final outgrowth of secondary lesions [2]. Bone is the most frequent site of cancer relapse after lung and liver and is one of the most common complications in several primary tumors. In particular, 70% of patients with breast or prostate cancer relapse to bone during the course of their disease [3,4,5,6]. The disease is usually not curable, and about 80% of patients die within five years of the diagnosis of bone metastasis [3]. It was discovered in the late 19th century [7] that primary tumors have distinctive sites of metastasis. Cancer cells that reach bone tissue may stop there and are capable of developing into a secondary lesion through a process known as osteomimicry [1,8], i.e., cells acquire specific features characteristic of bone resident cells which enable them to survive and proliferate in the new microenvironment. For example, cancer cells, such as hematopoietic stem cells (HSCs), are capable of expressing C-X-C motif chemokine receptor 4 (CXCR4) and thus take advantage of the same physiological mechanism of chemoattraction used by HSCs. Cells expressing the same pattern of bone cell integrins may remain in the blood endothelium and escape to bone parenchyma. When breast cancer cells finally arrive in bone tissue, the balance between bone resorption and bone formation is lost, usually in favor of bone resorption, with the consequent development of lytic or mixed lesions. Osteoclast activity causes the release of growth factors from the bone matrix such as transforming growth factor β (TGFβ), which contributes to cancer cell proliferation. Moreover, cancer cells in the bone microenvironment are stimulated to produce cytokines and to secrete factors such as interleukin 6 (IL6) and parathyroid hormone-related protein (PThrP), the latter inducing osteoblasts to increase receptor activator of nuclear factor κB ligand (RANKL) and decrease osteoprogesterin (OPG) production. The change of the RANKL/OPG ratio is one of the major causes of osteoclast differentiation and activity. Briefly, these described relations constitute the backbone of a more complex network of interactions between bone and cancer cells occurring in the bone after their arrival and which are described as the “vicious cycle of bone metastases” [8]. The outstanding scientific findings of recent years have upgraded the role of the bone as one of the most frequent sites of metastasis to that of a key actor and director in the different phases of the entire metastatic process. Cancer cells induce the migration of vascular endothelial growth factor receptor 1 (VEGFR1)-positive bone marrow-derived hematopoietic cells to sites of future metastasis where they form cellular clusters preceding tumor cell arrival and increase fibronectin production and matrix stiffness [9], creating a pre-metastatic niche in tissue [8]. It is worthy of note that cancer cells can remain quiescent in bone marrow for several years until some stimuli determine their reactivation with consequent spreading and formation of a secondary lesion to the bone, to other organs, or to the site of the primary tumor [8,10]. One of the most widely used in vivo models to study cancer cell metastasis is that of xenografts in immunodeficient mice. However, this model is hampered by being time-consuming and labor-intensive. Zebrafish (Danio rerio) (ZF), a small freshwater tropical fish, and their transparent embryos recently emerged as a promising xenograft tumor model [11,12,13]. ZF display distinct features that facilitate the exploration of tumor development, angiogenesis, invasion and metastasis. Moreover as a vertebrate, the ZF model shows high levels of physiologic and genetic similarities to mammals, closely mimicking the clinical setting and permitting the natural history of the tumor to be monitored. It is thus thought to be a potentially powerful model for preclinical studies on bone metastases [13]. The majority of preclinical studies on bone metastases use commercially available immortalized cancer cell lines or cancer cell lines selected in vivo to form metastases in bone tissue [14,15,16,17]. However, cell lines, after being cultured in plastic supports and going through hundreds of freeze/thaw passages, modify their phenotypic behavior in order to survive in the new microenvironment, which can seriously compromise results. Thus, the use of primary cultures obtained from tissue resected during surgery could be the answer to overcoming these problems [13,18,19]. The present work focuses on the development of a patient-derived xenograft of a bone metastasis in ZF embryos to study cell behavior during the metastatic process. 2. Results 2.1. Patient History A 67-year-old female with breast cancer previously treated with surgery and systemic therapies presented at our institute with bone and liver metastases. The patient was diagnosed with breast cancer in 1999 and a left radical mastectomy was performed, revealing invasive ductal carcinoma G2, pT1c (m), pN0, M0. ER 80% PgR 10%, Ki-67 20% and c-erbB2 0%. Adjuvant hormone therapy with tamoxifen was administered. About 15 years later, the patient developed pain in her left arm and an X-ray revealed the presence of an osteolytic lesion of about 60 × 33 mm in the humerus, suggestive of a secondary bone lesion. Magnetic resonance imaging (MRI) of the left shoulder confirmed the presence of the bone lesion, together with an intra-articular fracture. A total body computed tomography (CT) scan also revealed the presence of a liver lesion 67 × 62 mm in the fifth and sixth hepatic segments. The α-fetoprotein value was normal. A total body bone scan showed a single area of pathological uptake in the left humerus. Biopsy of the bone lesion confirmed the diagnosis of metastasis from ductal carcinoma of the breast with mucinous aspects, estrogen receptor (ER) 100% progesteron receptor (PgR) 70%, Ki-67 15% and c-erbB2 0%. The multidisciplinary medical team examined the case and recommended surgery and hormone treatment with anastrozole and bone-targeted therapy with zoledronic acid. The patient underwent resection and prosthetic replacement of the humerus. Histology confirmed metastasis compatible with breast cancer, ER 100% PgR 5%, Ki-67 5% and c-erbB2 0%. Surgical margins were negative (Figure 1). The hepatic lesion was removed, histological analysis confirming metastasis of breast cancer origin (ER 100%, Ki-67 20%, c-erbB2 0%). There was no evidence of macroscopic disease at the most recent radiological evaluation (CT scan). Treatment with anastrozole and zoledronic acid is ongoing and is well tolerated by the patient. 2.2. Breast Cancer Cell Lines The in vivo behavior of cells from a bone metastasis (BM) primary culture was compared with that of two different breast cancer cell lines: MCF7, a non-invasive, hormone receptor–positive line (like the BM primary culture), and MDA-MB 231, a highly invasive, triple-negative breast cancer line widely used for in vivo studies on breast cancer metastasis. MCF7 cells did not develop a phenotype in the in vivo model and none survived the five-day duration of the experiment. Conversely, MDA-MB-231 disseminated and colonized different parts of the zebrafish (ZF), including caudal hematopoietic tissue (CHT), indicating a migratory phenotype (Figure 2). 2.3. Invasive Phenotype of Primary Culture of Bone Metastasis in Zebrafish Model Positivity to pan-cytokeratin staining confirmed the presence of cancer cells in the primary culture. In particular, a mean of 12% of cells per field was observed (20× magnification). Cells derived from the primary culture and selected by colony picking, as reported in the Experimental Section, were injected into the duct of Cuvier of 2 dpf Tg(kdrl:mCherry) ZF. The zebrafish model was used primarily to stabilize the surgical material with an in vivo near-patient model and to assess the invasiveness of the selected cancer cell population. It also enabled us to track migratory movements of injected cells across the tissues of the developing zebrafish embryos at the single-cell level. Injected cells survived and disseminated in the ZF embryos, extravasating and engrafting mainly in the perivascular milieu of the CHT (Figure 3). The cells remained visible for the entire duration of the in vivo experiment. 2.4. Overexpression of Osteomimicry Markers by Primary Culture of Bone Metastasis Several markers involved in breast cancer cell aggressiveness and osteomimicry were evaluated in the BM primary culture and in the MCF7 and MDA-MB-231 cell lines. Secreted protein acidic and rich in cysteine (SPARC) expression in the primary culture was almost 16,000-fold higher than that of MCF7 cell lines and 2000-fold higher than that of MDA-MB-231. Integrin-binding sialoprotein (IBSP), another osteomimicry marker, and matrix metalloprotease 9 (MMP9), a marker of cancer cell aggressiveness expressed by osteoclasts in bone tissue, were also overexpressed in the BM primary culture. Furthermore, higher levels of heparanase (HPSE), JAGGED1 (JAG1) and receptor activator of nuclear factor κB (RANK) were observed in BM cells than in MCF7, a cell line with a hormone receptor arrangement similar to that of the primary culture (Figure 4). 3. Discussion Although the prognosis of patients with bone metastases from breast cancer is poor, in recent years bone-targeted therapies used in combination with anticancer agents have substantially improved quality of life and survival [8,20]. Numerous studies have used immunodeficient mice to develop human-to-mouse xenograft tumor models to study the metastasis process and identify key steps and genes involved so that novel markers and new targets for the development of innovative drugs can be derived [8,14,16]. Current experimental procedures have limitations because of the length of time it takes for tumors to grow and spread, the variation in the tumor growth rate in vivo, and the high costs of breeding and housing large numbers of mice. The ZF model has a number of important advantages over traditional mouse models, i.e., simplicity of genetic manipulation, inexpensive housing, rapid embryonic development, and easy visualization of internal structures. Furthermore, as fish produce 100–200 eggs/mating, in vivo experiments are designed with a higher number of replicates than those of mice models, leading to a higher statistical power and opening up new possibilities for detecting rare phenotypes. Some studies have shown that human tumor cells proliferate and interact with vessel tissues in ZF embryos without the risk of rejection as the latter are devoid of a mature immune system [21,22,23,24]. ZF models have thus become a valid alternative for overcoming mice model limitations, e.g., when a high number of cells need to be injected. The majority of studies on the metastasis process take advantage of commercially available immortalized cancer cell lines [14,15,16,17] which, in adapting to their new environment, tend to lose cell heterogeneity and compensate for the loss of stromal contribution. During the process of adaptation, clones with a higher proliferative rate than that of the primary tumor and thus not representative of the cancer cell population may be selected [13]. Surgical material would perhaps be more suitable to develop patient-derived xenografts in which the stromal counterpart and cancer cell heterogeneity are both preserved. However, as the quantity of material available is often insufficient for use in murine models, the ZF becomes the perfect candidates to substitute mice models. As such, 100–200 replicates can be performed with as few as 400–500,000 cells, providing high reproducibility and robust results. Some researchers have developed PDXs in ZF models to study human cancer cell behavior, including response to therapy [18,19,25,26]. The ZF has also shown to be a suitable model to study metastases as cancer cells with different in vitro invasive potential preserve this behavior after injection into ZF embryos [26]. The current study focused on the development of a PDX from a breast cancer bone metastasis in ZF embryos. We first validated this in vivo model by testing the ability of two well-established breast cancer cell lines to colonize the CHT of two-day post-fertilization (dpf) ZF embryos. We used a red dye, CM-DiI dye, commonly used in this model, to label the cell lines. One disadvantage of this dye is that, after injection into the ZF embryos, labeled cells frequently produce debris and apoptotic bodies at later time points, lowering the signal-to-noise ratio and thus hampering the correct quantification of the phenotype. We therefore set up a labeling strategy based on the green dye carboxyfluorescein succinimidyl ester (CFSE) to obtain a clearer phenotype for the injection of the primary bone metastasis culture. The hormone receptor–positive MCF7 cell line did not colonize the CHT efficiently, whereas the highly aggressive and triple-negative cell line MDA-MB-231 showed efficient and widespread colonization of the ZF embryo including the CHT. A comparison between the grafting potential of the patient-derived bone metastasis and that of the two breast cancer cell lines enabled us to evaluate whether the detected phenotype of the patient-derived cells could be ascribed to the common tissue of origin of the cells. Despite the histological and molecular similarities, i.e., tissue of origin and hormone receptor pattern of MCF7 cells, respectively, the patient-derived cells showed a specific grafting pattern with cells mainly colonizing the CHT of the ZF, suggesting that the in vivo behavior of these cells may be due to an adaptation process to the bone tissue of the patient. As the CHT is the first homing and expansion site for hematopoietic stem cells in the ZF embryo [27], the experimental phenotype can be considered as an expression of bone marrow tropism [28]. A more detailed and mechanistic description of the process of dissemination and colonization of the CHT by cancer cells can be found in the study by He et al. [29]. This technique produced an aspecific signal detectable in the intestinal tract of the zebrafish due to dye leakage at early time points, but provided a crisp fluorescent signal at later time points. Although the hormone receptor–positive MCF7 cell line did not efficiently colonize the CHT, the highly aggressive and triple-negative cell line MDA-MB-231 showed efficient widespread CHT colonization and local growth. We then assayed cells collected from a lytic bone lesion of a patient with metastatic hormone receptor–positive breast cancer and showed, for the first time in this model, engraftment and local growth of the ex vivo cells. Furthermore, cells colonized different parts of the embryos, showing an aggressive phenotype. As the CHT is the primary hematopoietic organ of the zebrafish, the experimental phenotype can be considered as an expression of bone tropism. Although the primary culture had the same hormonal receptor pattern as that of MCF7, its behavior differed substantially as MCF7 cells did not show a phenotype in vivo, as previously reported. The phenotype of the primary culture resembled that of the invasive cell line MDA-MB-231. Our in vivo results appear to reflect the patient’s clinical history; cells were initially quiescent and then re-activated, going on to develop concurrent bone and hepatic lesions. Fifteen years after the primary diagnosis of breast cancer, the patient developed bone and hepatic disease, both as single metastatic localizations. The presence of visceral disease is suggestive of a different and more aggressive phenotype than that of the indolent primary tumor. We also studied the gene expression profile of the breast cancer cell lines and the primary culture for markers of osteomimicry and aggressiveness. As the primary cell population was heterogeneous, its gene expression profile was not ascribable to either of the two cancer cell lines. Of note, SPARC expression was higher in the BM primary culture than in MCF7 and MDA-MB-231. This protein, also known as osteonectin, is normally expressed by resident bone cells and overexpressed by cancer cells in osteomimicry [30]. It is also thought to play a role in the chemoattraction of both breast and prostate cancer cells [31] and has been proposed as a biomarker for bone metastasis [32]. As SPARC is expressed by normal bone cells, it was no surprise to find high levels of it in the bone metastasis primary culture. The primary culture also overexpressed other markers (Jagged1 and RANK) [33,34,35,36]. 4. Experimental Section 4.1. Cell Cultures The experiments were performed on MDA-MB-231, a triple-negative human breast cancer cell line, and on MCF7, a hormone receptor–positive breast cancer cell line, both obtained from the America Type Culture Collection (Rockville, MD, USA). Cells were cultured as a monolayer in 75-cm2 flasks at 37 °C in Dulbecco’s Modified Eagle’s medium (DMEM) medium (PAA, Piscataway, NJ, USA) supplemented with 10% fetal bovine serum and 1% glutamine (PAA) and 10% penicillin/streptomycin in a 5% CO2 atmosphere. Cells were cultured until they reached 90%–100% confluence and then supplemented with fresh DMEM medium, which was collected after 24 h, filtered through a 0.22 μm filter, aliquoted and stored at −20 °C. 4.2. Isolation of Primary Bone Metastasis Cells The bone tumor specimen was obtained from a bone metastasis resected from the left humerus of a 67-year-old woman with a previous history of breast cancer. The protocol IRST B039 was reviewed and approved by the ethics Committee IRST IRCCS AVR and by the Medical Scientific Committee of IRST in February 2015. The study was performed in accordance with the principles of Good Clinical Practice and the Helsinki Declaration. The patient gave written informed consent to take part in the study. The surgical material was analyzed and selected by an experienced pathologist and processed within 3 h of removal. The specimen was washed twice in sterile phosphate buffered saline (PBS) supplemented with 10% penicillin/streptomycin and disaggregated into 1–2 mm3 pieces with sterile surgical blades. The obtained fragments were incubated with 2 mg/mL collagenase type I (Millipore Corporation, Billerica, MA, USA) at 37 °C under stirring conditions. The enzymatic digestion was stopped after 2 h by adding complete DMEM medium. The cell suspension was then filtered with 100 µm sterile Filters (CellTrics, Partec, Münster, Germany). Cells were counted and cultured at a density of 80,000 cells/cm2 in standard monolayer cultures. All cells were maintained in complete DMEM medium at 37 °C in a 5% CO2 atmosphere. After a seven-day culture, a colony of cells that morphologically resembled cancer cells was selected with a plastic ring (Sigma, St. Luois, MO, USA), trypsinized and reseeded in a different well. The obtained cell population was expanded through a further three to four passages so that it could be used for subsequent experiments. 4.3. Immunocytochemistry The primary culture suspension was counted and cytocentrifuged for 8 min at 900 rpm onto glass slides at a concentration of 2 × 105 cells/spot. The cytospin preparations were fixed in acetone and chloroform, air-dried overnight and stored at −20 °C. Immunostaining to detect CK-positive cells was performed with the Epimet® kit (Micromet, Deutschland GmbH, Düsseldorf, Germany) which uses the monoclonal antibody A45-B/B3, a pancytokeratin marker. 4.4. Prepapration of Cells for Implantation into Zebrafish (ZF) Embryos MDA-MB-231 and MCF7 cell lines and the primary culture were gently washed with PBS and harvested with trypsin, counted and resuspended at a density of 106/mL. The breast cancer cell lines were stained with CMDiI dye (Sigma), while the primary culture was stained with CellTrace™ CFSE Cell Proliferation Kit (Life Technologies, Carlsbad, CA, USA), in accordance with the manufacturers’ instructions. Each labeled cell suspension was loaded into borosilicate glass capillary needles (1 mm outer diameter × 0.78 mm inner diameter; Harvard Apparatus, Saint-Laurent, QC, Canada) and injected within 3 h of cell harvest. 4.5. In Vivo Experiments with the ZF Model The ZF transgenic lines Tg(kdrl:mCherry) and Tg(fli1:GFP) were used for the in vivo studies of the breast cancer cell lines and primary culture, respectively. Zebrafish and embryos were raised, staged and maintained according to standard procedures in compliance with the local animal welfare regulations and the EU Animal Protection Directive 2010/63/EU. N-phenylthiourea 0.2 mM (Sigma) was applied to prevent pigment formation from the first dpf. Two-dpf ZF embryos were anesthetized with 0.003% tricaine (Sigma) and positioned on a 10 cm petri dish coated with 1% agarose. Then 50–400 manually counted cells were injected into the duct of Cuvier using a Pneumatic Pico pump and a micromanipulator (WPI, Sarasota, FL, USA). After implantation with cancer cells, the ZF embryos (including non-implanted controls) were maintained at 34 °C as a compromise between the optimal temperature requirements for fish and mammalian cells [24]. Up to 400 implantations were manually achieved per h, with survival rates of >80% up to the 6th dpi. Fluorescent image acquisition was performed using a Leica MZ16FA stereo microscope (Leica Microsystems GmbH, Wetzlar, Germany). Separate images of the various segments of the ZF embryos were blended together to form a composite image using Adobe Photoshop CS6 software (Adobe Systems, Mountainview, CA, USA). 4.6. Quantitative Real-Time PCR (qPCR) Total mRNA of the primary culture and cancer cell lines was isolated using TRIzol Reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s instructions. Five hundred nanograms of RNA were reverse-transcribed using the iScript cDNA Synthesis Kit (BioRad, Hercules, CA, USA). Real-Time PCR was performed on the 7500 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) using the TaqMan gene expression assay mix (Applied Biosystems). Amplification was performed in a final volume of 20 µL containing 2× Universal master Mix (Applied Biosystems), 2 µL of cDNA in a total volume of 20 µL. The following markers were analyzed: OPG, JAG1, CXCR4, RANK, IBSP, TFF1, SPARC, HPSE, CTGF, MMP-9, LOX and CDH1. The stably expressed endogenous β-actin and HPRT and were used as reference genes. The amount of transcripts was normalized to the endogenous reference genes and expressed as n-fold mRNA levels relative to a calibrator using a comparative threshold cycle (Ct) value method (∆∆Ct). RNA extracted from MCF7 cell line was used as calibrator. Gene expression analyses were graphed by heatmap using R software (https://www.R-project.org/). 5. Conclusions Finally, we propose an original approach to study the metastatic process and cancer cell aggressiveness comprising the use of patient-derived primary cultures in the in vivo ZF model. The primary culture in the ZF showed behavior resembling that of the patient’s medical history but differing from that of the cancer cell line sharing the same hormonal receptor status and could thus be used to better understand drug sensitivity and to identify both prognostic markers and markers that are predictive of response to therapy. These results highlight the importance of using near-patient models in bone metastasis research and outline new methods for the clinical implementation of this in vivo model. Acknowledgments The authors thank Cristiano Verna and Gráinne Tierney for editorial assistance. Author Contributions Laura Mercatali, Toni Ibrahim, Gabri van der Pluijm, Dino Amadori and Ewa Snaar-Jagalska conceived the idea for the study; Laura Mercatali and Federica Recine wrote the paper; Federico La Manna and Arwin Groenewoud performed the in vivo experiments; Roberto Casadei provided the surgical material; Federica Recine and Alberto Bongiovanni provided the clinical records; Giacomo Miserocchi, Chiara Liverani, Alessandro de Vita and Chiara Spadazzi performed the experiments; Federica Pieri performed the pathological diagnosis; Andrea Giorgini, Roberto Biagini, Federica Recine and Alberto Bongiovanni contributed to data interpretation; Gabri van der Pluijm, Dino Amadori and Ewa Snaar-Jagalska critically reviewed the manuscript for important intellectual content. All authors read the final version of the paper and approved its submission. Conflicts of Interest The authors declare no conflict of interest. Abbreviations BM bone metastasis CDH1 cadherin-1 CHT caudal hematopoietic tissues CTGF connective tissue growth factor CXCR4 C-X-C chemokine receptor type 4 dpf days post fertilization HSCs hematopoietic stem cells HPSE heparanase HPRT hypoxanthine phosphoribosyltransferase 1 IBSP integrin binding sialoprotein IL6 interleukin 6 LOX lysyl oxidase MMP-9 matrix metallopeptidase 9 JAG1 jagged1 OPG osteoprotegerin PBS phosphate buffered saline PDX patient-derived material PThrP parathyroid hormone-related protein RANK receptor activator of nuclear factor κB RANKL receptor activator of nuclear factor κB ligand TGFβ transforming growth factor β SPARC secreted protein acidic and cysteine rich TFF1 trefoil factor 1 Ct threshold cycle VEGFR1 vascular endothelial growth factor receptor 1 ZF Zebrafish Figure 1 Breast cancer bone metastasis. (A): (a) Histological hematoxylin and eosin (H & E) staining of bone metastasis (BM) primary culture (5× magnification). Mucinous areas show low cellularity; (b) H & E staining of BM primary culture (20× magnification). Monomorphic cells with round nucleus and nucleolus are seen in nests with a minor mucus quantity; (c) ER staining showing positivity; (B): Cytospin of BM primary cells. White arrows show pancytokeratin-positive cells. Figure 2 Representative image of MDA-MB-231 cell line five days after injection into the duct of Couvier of 2 day post fertilization (dpf), Tg(fli1:GFP) ZF embryo. MDA-MB-231, labeled in red, were monitored on a daily basis for the duration of the experiment (five days) and showed progressive and extensive dissemination throughout the developing embryo (25× magnification). Figure 3 Images of carboxyfluorescein succinimidyl ester (CFSE)-labeled, patient-derived breast cancer bone metastasis (primary cells) xenografted in Tg(kdrl:mcherry) ZF embryos. (A) Whole-body image of the zebrafish embryo at 5 dpi (25× magnification). Primary cells disseminated predominantly in the caudal hematopoietic tissues (CHT) of the embryo; (B) Details of (A), showing the interactions of primary cells with the zebrafish vessels in the CHT. Cells extravasated in the CHT of the ZF embryo and engrafted in close proximity of the vessels. Fluorescent images merged with brightfield image, 63× magnification; (C) Combined picture of the CHT of embryo in (A,B). Individual images were taken at 63× magnification. Figure 4 Gene expression analysis of aggressiveness and osteomimicry markers. (A) Heatmap configuration of gene expression analysis in BM primary culture and breast cancer cell lines; green/red bars refer to high/low gene expression, respectively; black bars refer to intermediate levels of expression; (B) Gene expression quantification by comparative threshold cycle (Ct) value method (∆∆Ct). The primary culture was chosen as calibrator. See the Experimental Section for selected genes. ==== Refs References 1. Weidle U.H. 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==== Front Am J Physiol Regul Integr Comp PhysiolAm. J. Physiol. Regul. Integr. Comp. PhysiolajpreguajpreguAJPREGUAmerican Journal of Physiology - Regulatory, Integrative and Comparative Physiology0363-61191522-1490American Physiological Society Bethesda, MD 26676251R-00405-201510.1152/ajpregu.00405.2015Hormones, Reproduction and DevelopmentPhenylalanine transfer across the isolated perfused human placenta: an experimental and modeling investigation Lofthouse E. M. 14Perazzolo S. 2Brooks S. 1Crocker I. P. 3Glazier J. D. 3Johnstone E. D. 3Panitchob N. 2Sibley C. P. 3Widdows K. L. 3Sengers B. G. 24*http://orcid.org/0000-0003-4044-9104Lewis R. M. 14*1Faculty of Medicine, University of Southampton, Southampton, United Kingdom; 2Bioengineering Science Research Group, Faculty of Engineering and the Environment, University of Southampton, Southampton, United Kingdom; 3Maternal and Fetal Health Research Centre, Institute of Human Development, University of Manchester, and St. Mary's Hospital and Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom; and 4Institute for Life Sciences, University of Southampton, Southampton, United Kingdom* B. G. Sengers and R. M. Lewis contributed equally to this work. Address for reprint requests and other correspondence: R. M. Lewis, Univ. of Southampton Faculty of Medicine, MP 887 IDS Bldg., Southampton General Hospital, Southampton SO16 6YD, UK (e-mail: Rohan.Lewis@soton.ac.uk).16 12 2015 1 5 2016 16 12 2015 310 9 R828 R836 22 9 2015 13 12 2015 Copyright © 2016 the American Physiological Society2016American Physiological SocietyLicensed under Creative Commons Attribution CC-BY 3.0: © the American Physiological Society.Membrane transporters are considered essential for placental amino acid transfer, but the contribution of other factors, such as blood flow and metabolism, is poorly defined. In this study we combine experimental and modeling approaches to understand the determinants of [14C]phenylalanine transfer across the isolated perfused human placenta. Transfer of [14C]phenylalanine across the isolated perfused human placenta was determined at different maternal and fetal flow rates. Maternal flow rate was set at 10, 14, and 18 ml/min for 1 h each. At each maternal flow rate, fetal flow rates were set at 3, 6, and 9 ml/min for 20 min each. Appearance of [14C]phenylalanine was measured in the maternal and fetal venous exudates. Computational modeling of phenylalanine transfer was undertaken to allow comparison of the experimental data with predicted phenylalanine uptake and transfer under different initial assumptions. Placental uptake (mol/min) of [14C]phenylalanine increased with maternal, but not fetal, flow. Delivery (mol/min) of [14C]phenylalanine to the fetal circulation was not associated with fetal or maternal flow. The absence of a relationship between placental phenylalanine uptake and net flux of phenylalanine to the fetal circulation suggests that factors other than flow or transporter-mediated uptake are important determinants of phenylalanine transfer. These observations could be explained by tight regulation of free amino acid levels within the placenta or properties of the facilitated transporters mediating phenylalanine transport. We suggest that amino acid metabolism, primarily incorporation into protein, is controlling free amino acid levels and, thus, placental transfer. blood flowamino acid transferexchangerfacilitated transportmetabolism501100000268 Biotechnology and Biological Sciences Research Council (BBSRC)BB/I011315/1 ==== Body understanding the determinants of placental function and fetal growth are important, as poor fetal growth is associated with impaired health throughout life (14). Amino acid transfer, a key placental function required for fetal growth, is reduced in growth-restricted pregnancies (23). To understand why placental amino acid transfer becomes restricted in these pregnancies, we need to define the factors that may be limiting to this process. It is clear that net placental amino acid flux to the fetus is dependent on membrane transport proteins localized to the microvillous membrane (MVM) and basal plasma membrane (BM) of the syncytiotrophoblast (9). However, other variables, such as blood flow and metabolism, could be equally limiting to net placental amino acid transfer (18). In growth-restricted pregnancies, umbilical blood flow may be reduced by 50%, impairing transfer of oxygen and, potentially, other nutrients to the fetus (1). Substances predominantly transferred by diffusion, such as small hydrophobic solutes, are most likely to be sensitive to flow, as their net flux is driven by concentration gradients maintained by maternal and fetal blood flows. Under these circumstances, maintenance of a transplacental concentration gradient is a key determinant of oxygen transfer by simple diffusion and glucose transfer by facilitated diffusion (5, 10, 29). For substances predominantly transferred by active transport (charged and/or hydrophilic solutes), maternal blood flow is necessary to deliver substrates for transfer to the transporting plasma membrane, but flow is less likely to be the rate-limiting step, as transfer is not directly dependent on transplacental concentration gradients. Amino acid transfer across the placenta is an active process that occurs against a concentration gradient (6, 9). As such, placental amino acid transfer has not generally been considered to be flow-limited. Nevertheless, many of the amino acid transporters involved in this process do rely on transmembrane concentration gradients (8, 22). In particular, transfer of amino acids from the placenta to the fetal circulation, across the BM, is mediated by facilitated transporters and exchangers, both of which rely on transmembrane amino acid concentration gradients across the plasma membrane for their activity (7, 8). With fetoplacental blood flow determining amino acid concentrations in the fetal capillaries, the issue of flow dependency of amino acid transfer from the placenta to the fetus is raised in the context of presiding concentration gradients across the BM. As amino acid concentrations are believed to be much higher within placental tissue than in fetal capillaries, any change in transmembrane concentration gradient due to flow is likely to be relatively small (24). Hence, the effect of fetal flow on transfer would be predicted to be small but requires experimental validation. Flow-limited transfer has been studied previously in the isolated perfused human placenta and has been clearly established, as expected, for antipyrine (28). There is also evidence that maternal flow rate affects transfer of glucose across the isolated perfused human placenta (15). Modeling of placental amino acid transfer also suggests that flow may be an important determinant (18). Phenylalanine is a good candidate amino acid with which to study possible flow effects, as it is transported by exchangers [SLC7A5 (LAT1) and/or SLC7A8 (LAT2)] and facilitated transporters [SLC16A10 (TAT1), SLC43A1 (LAT3), and SLC43A2 (LAT4)], the activity of which is dependent on concentration gradients that are sensitive to flow (8, 22). Phenylalanine, taken up by the placenta, may be incorporated into protein; however, as there is little or no phenylalanine hydroxylase in the human placenta, loss via catabolism is likely to be limited (20). Using experimental and modeling approaches, we set out to investigate whether factors such as maternal and fetal blood flow influence placental transfer of the amino acid phenylalanine as a model for essential amino acid transport by exchangers and facilitated transporters across the human placenta. METHODS Human placentas were collected from daytime full-term vaginal deliveries from uncomplicated pregnancies at the Princess Anne Hospital in Southampton, in accordance with ethical approval from the Southampton and Southwest Hampshire Regional Ethics Committee (approval no. 11/sc/0323). Perfusion methodology. Placentas were perfused using the methodology of Schneider et al. (28), as adapted in our laboratory (10, 11). Placentas were collected within 30 min of delivery and placed on ice for transport to the laboratory, where fetal-side perfusion was established within 30 min of collection. The fetal and maternal circulations were perfused at 6 and 14 ml/min, respectively, with Earle's bicarbonate buffer (EBB; in mM: 1.8 CaCl2, 0.4 MgSO4, 116.4 NaCl, 5.4 KCl, 26.2 NaHCO3, 0.9 NaH2PO4, and 5.5 glucose) containing 0.1% (wt/vol) bovine serum albumin and 5,000 IU/l heparin and equilibrated with 95% O2-5% CO2. Perfusion of the fetal circulation was established, and, if fetal venous outflow was ≥95% of fetal arterial inflow, maternal arterialerfusion was established 15 min later. Maternal arterial catheters are placed through the maternal decidual surface of the placenta and into the intervillous space. The maternal venous outflows are not catheterized, but maternal venous exudate appearing on the surface of the cotyledon was channeled to a collection point. Phenylalanine experiment methodology. Phenylalanine was chosen as the candidate amino acid, as it is a substrate for both exchangers and facilitated transporters (8). It is also not catabolized (i.e., phenylalanine hydroxylase is not expressed) within the human placenta (20). Tracer concentrations of phenylalanine were used to study flow in an experimental design where transporters were not saturated to ensure that any effects of flow were apparent. Glutamate and taurine were added to support metabolic and tissue homeostasis within the perfused placental tissue (11, 12). The maternal circulation was perfused with EBB containing 2.7 nmol/l [14C]phenylalanine [50 μCi (1.85 Mbq), NEC284E050UC], 50 μmol/l glutamate, and 50 μmol/l taurine, along with 1.8 mmol/l creatinine as a marker of paracellular diffusion. Initial baseline maternal and fetal flow rates were 14 and 6 ml/min, respectively, for 30 min. As outlined in Fig. 1, maternal flow rate was then changed from 14 ml/min to 10 ml/min back to 14 ml/min and then to 18 ml/min for 1 h each. During each 1-h period, fetal flow rates were ramped to 3, 6, and 9 ml/min for 20 min each. In each 20-min block, maternal and fetal venous exudates were sampled at 5, 10, 15, and 18 min. Finally, the tissue was washed by perfusion of both circulations for 15 min with buffer that did not contain [14C]phenylalanine. After the perfusion protocol, the cotyledon was trimmed of nonperfused areas (perfused areas become blanched), and the cotyledon was frozen for analysis of intracellular amino acids. Fig. 1. Experimental design and modeling schematic. A: experimental design. Stepwise changes in maternal and fetal perfusion flow rates from the beginning of [14C]phenylalanine tracer infusion. After an initial 20-min equilibration period, flow rates were varied every 20 min and maternal and fetal venous outflow samples were collected at 15 and 18 min to determine uptake and transfer, respectively. B: conceptual outline of phenylalanine transport across the human placenta showing the classes of transporters involved on the microvillous (MVM) and basal (BM) plasma membranes of the placental syncytiotrophoblast, as well as incorporation into the protein pool (catabolism is not shown, as phenylalanine hydroxylase is not expressed in the placenta). C: compartmental computational modeling of transporter-mediated phenylalanine transfer. F is flow in maternal or fetal arteries, [Phe] is [14C]phenylalanine concentration in the respective compartments of the maternal intervillous space (m), syncytiotrophoblast (s), and fetal capillaries (f), and v is compartment volume. J represents net flux between compartments for exchangers (ex) or facilitated transporters (fa), and metabolism is given by Jmetab. While it was assumed that metabolism of phenylalanine was predominantly protein synthesis, which is reversible, given the short experimental time frame, flux of [14C]phenylalanine back from protein to the free amino acid pool was not modeled (dashed arrow). Phenylalanine uptake in the placenta via exchange is driven by high intracellular concentrations of endogenous substrates of the facilitated and exchange (fa and ex) or exchange-only (ex) transporters. Placental uptake (mol/min) was calculated from the difference in concentration (mol/l) between maternal arterial and maternal venous outflow, multiplied by maternal flow rate (l/min). Placental transfer (mol/min) was calculated from fetal vein concentrations (mol/l) multiplied by fetal flow rate (l/min). Tissue amino acid measurements. To study intracellular [14C]phenylalanine, the cotyledons were homogenized in three volumes of distilled water and centrifuged at 10,000 g for 10 min to remove cellular debris. A 1-ml sample of homogenate was mixed with an equal volume of 10% trichloroacetic acid to precipitate protein. The pellet and supernatant were counted separately in a liquid scintillation counter (Tri-Carb 2100TR, Perkin Elmer Life Sciences) on standard counting windows for 14C to determine [14C]phenylalanine incorporated into protein and free 14C tracer. To assess potential quenching of radioactive counts by tissue components, the supernatant and protein pellet were prepared as described above, and serial double dilutions were performed, with each sample being spiked with a standard amount of 14C tracer and counted as described above. No quenching was observed in the supernatant, but within the protein pellet, efficiency of counting was 31%, and these counts were adjusted accordingly. Computational modeling. A compartmental model to represent the intervillous space, syncytiotrophoblast, and fetal capillaries was constructed using relative volume fractions from the literature (30). Flow rates were based on the experimental protocol outlined in Fig. 1, and overall cotyledon volume was based on the average value from these experiments (with the assumption of 1 ml/g tissue). The placenta was modeled with generic exchange and facilitated transporters as outlined in Fig. 1, B and C. Model equations were implemented in MATLAB (R2014b) as outlined previously (22, 30). Transport modeling. A carrier-based model was used to represent the transporters, as outlined previously (22, 32). Net flux JAI→II (mol/min) of substrate A from compartment I to compartment II for the exchanger (ex) model is given by JA,exI→II=Vex[A]j[R]exII−[A]II[R]exLKex([Tot]exI+[Tot]exII)/2+[Tot]BxI[Tot]BxII and by JA,faI→II=Vfa([A]IKfa+[Tot]faI−[A]IIKfa+[Tot]faII) for the facilitated transporter (fa). [A]I is the concentration (mol/l) of substrate A in compartment I and [Tot]I is the total sum of all substrates of the exchanger or the facilitative transporter in compartment I, while [R]exI is the sum of all exchanger substrates, excluding substrate A. K is the dissociation constant (mol/l) for the exchanger (Kex) or facilitated transporter (Kfa), and the maximum transport rate (Vmax, mol/min) is Vex for the exchanger or Vfa for the facilitated transporter. Intracellular amino acids were represented by two generic amino acids, to differentiate between substrates of the transporters that transport phenylalanine by both exchange and facilitated transporters and substrates transported by exchange transporters only. For the first generic amino acid, i.e., substrates transported by facilitated transporters [SLC16A10 (TAT1), SLC43A1 (LAT3), and SLC43A2 (LAT4)] and exchangers [SLC7A5 (LAT1) and SCL7A8 (LAT2)], which includes phenylalanine (alanine, isoleucine, leucine, methionine, phenylalanine, tyrosine, tryptophan, and valine), the sum of the intracellular concentrations available in the literature for these amino acids, 3,132 μmol/l, was applied (19, 24). For the second generic amino acid, the sum of those intracellular concentrations available in the literature for amino acids transported by exchangers (but not facilitated transporters), including phenylalanine (asparagine, cysteine, glutamine, glycine, histidine, serine, and threonine), 4,491 μmol/l, was applied. Previous data indicate few instances of significant decline in intracellular amino acid concentrations in perfused human placentas provided with glutamate over the course of an experiment (11). As such, the intracellular concentrations of the two generic amino acids within the model were kept constant throughout the experiment. MVM and BM exchangers were assumed to be symmetrical, with the same dissociation constants on either side of the membrane in MVM and BM (Kex = 200 μmol/l and Kfa = 1,000 μmol/l) (22). At steady state, net transfer in the model will be the same whether or not transporters are symmetrical. While it remains to be established how this affects the system for different operating conditions, we did not want to introduce additional model parameters without experimental justification. Vmax values were fitted by manual adjustment of the parameters, so that the model matched the average of the experimental steady-state placental uptake and transfer values over all flow conditions. In the first instance, the BM exchanger and facilitated transporter were assumed to have the same Vmax to reduce the number of parameters required for the model. For uptake under physiological amino acid concentrations, we represented maternal input amino acid concentrations with the approaches of the two generic amino acids described above using literature values (19). With maternal values for the facilitated substrates, this value was 615 μmol/l, and for the exchanger-only substrates, this value was 915 μmol/l. Flow modeling. Blood flow into and out of the maternal and fetal compartments results in a net molecular flux JA,flowi (mol/min) as follows JA,flowi=Fi([A]ini−[A]i) where [A]ini is the inlet concentration (mol/l) of substrate A in compartment i and [A]i is the concentration of substrate A in compartment i. Fi is the constant flow rate into and out of compartment i (l/min). Metabolic modeling. Metabolism of amino acids was represented by linear kinetics with the assumption of an unsaturated process with rate constant kmetab. The rate constant was determined simultaneously by fitting average steady-state amino acid uptake and transfer JA,metabS=Kmetab[A]s where JA,metabs is the metabolic rate (mol/min), [A]s is the concentration (mol/l) of substrate A in the syncytiotrophoblast, and kmetab is the rate constant (l/min). This equation represents all metabolic removal of phenylalanine, and as there is no phenylalanine hydroxylase activity, this equation is likely to represent primarily protein synthesis incorporation. The release of amino acids from the protein pool was not modeled, as median protein half-lives are considerably longer than the course of this experiment (25). Diffusion modeling. To determine if diffusion could explain the transfer of phenylalanine, the effective diffusive permeability was fitted by manual adjustment of this parameter, so that the model matched the experimental average steady-state placental uptake. The following equation was used to model the flux due to simple diffusion JA,difm→f=Vdif([A]m−[A]f) where Vdif is the effective diffusive permeability constant (l/min). Compartmental modeling. A compartmental modeling approach was adopted on the basis of our previous work (30), in which the placenta was represented as three separate volumes corresponding to the maternal intervillous space, syncytiotrophoblast, and fetal capillaries. All compartments were assumed to be well mixed. The transfer of amino acids between the compartments was modeled as fluxes mediated by the exchange transporters at the MVM and facilitative and exchange transporters at the BM d[A]mdt=1vm(JA,flowm−JA,exm→s−JA,difm→f)  (intervillous space) d[A]sdt=1vs(JA,exm→s−JA,exs→f−JA,fas→f−JA,metabs) (syncytiotrophoblast) d[A]fdt=1vf(JA,flowf+JA,exs→f+JA,fas→f+JA,difm→f)  (fetal capillary) where [A]i is the concentration (mol/l) of substrate A in compartment i and vi is the volume of compartment i (liters). JA,xi→j represents the net molecular flux (mol/min) of substrate A from compartment i to compartment j mediated by transporter x. Here m, s, and f represent the maternal, syncytiotrophoblast, and fetal compartments, respectively, while ac, ex, and fa denote the accumulative, exchange, and facilitative transporters, respectively. JA,flowi is the net molecular flux of substrate A due to blood flow, JA,metabs is the consumption of substrate A by metabolic processes (for phenylalanine, primarily protein synthesis), and JA,difm→f is flux due to paracellular diffusion. Model equations were implemented in MATLAB (R2014b). Simulations were carried out for transporters alone or for diffusive transfer alone by omission of the relevant terms in the compartmental model equations. Parameter variation was undertaken in the final transport model to mimic metabolism by modeling the effect of a fivefold increase and decrease in model parameters on uptake or transfer of phenylalanine. Statistics Data were analyzed by two-way ANOVA, with maternal and fetal flow as discrete variables. Linear regression analysis was performed to compare experimental data with model predictions. Values are means ± SE; n is the number of placentas. RESULTS Perfusion data. The average cotyledon weight was 42.0 ± 9.7 g (n = 5 placentas) and maternal flow rates were 10, 14, and 18 ml/min; these values equate to maternal flow rates of 0.31 ± 0.08, 0.43 ± 0.11, and 0.55 ± 0.14 ml/g placental cotyledon. For fetal flow rates of 3, 6, and 9 ml/min, these values equate to fetal flow rates of 0.09 ± 0.02, 0.18 ± 0.05, and 0.28 ± 0.07 ml/g placental cotyledon. Average fetal perfusate recovery was 5.88 ± 0.06 ml/min at the beginning and 5.76 ± 0.09 ml/min at the end (at 6 ml/min flow rate) of the experiment. Average maternal recovery was 13.92 ± 0.08 ml/min at the beginning and 13.92 ± 0.08 ml/min at the end (at 14 ml/min flow rate) of the experiment. Placental uptake of [14C]phenylalanine. Placental uptake of [14C]phenylalanine increased with increasing maternal flow (n = 5 placentas, P = 0.011) but was not related to fetal flow (Fig. 2A). There were no significant interactions between maternal and fetal flow. The computational model was used to predict placental uptake, with the assumption of simple diffusion (Fig. 2B) and transport (MVM exchange and BM-facilitated transport and exchange; Fig. 2C). The experimental data were most consistent with uptake by transport (R2 = 0.77), rather than simple diffusion (R2 = 0.02). The simulation was also conducted in the presence of physiological maternal concentrations of amino acids. In this case, there was only a marginal effect of flow on phenylalanine uptake (Fig. 2D). Predicted uptake was essentially identical if modeled with or without placental metabolism; therefore, in Fig. 2, C and D, the predictions show uptake for the model that included metabolism, to match Fig. 3D. Fig. 2. Placental phenylalanine uptake from maternal circulation: experimental data and predicted transfer under certain assumptions. 10M, 14M, and 18M, maternal flow of 10, 14, and 18 ml/min; 3F, 6F, and 9F, fetal flow of 3, 6, and 9 ml/min. A: experimental uptake of [14C]phenylalanine across the perfused placental lobule. Uptake of [14C]phenylalanine from the maternal circulation was associated with maternal (P = 0.011), but not fetal (P = 0.41), flow rates. There were no significant interactions between maternal and fetal flow (P = 0.96). Values are means ± SE; n = 5 placentas. B: predicted uptake of phenylalanine if transfer is mediated by simple diffusion. Maternal uptake levels could not be matched, and there was no correlation between predicted uptake and experimental data (R2 = 0.02). C: predicted uptake of phenylalanine if transfer is mediated by facilitated and exchange transporters. There was good correlation between predicted uptake and experimental data (R2 = 0.77). D: predicted uptake of phenylalanine tracer at physiological maternal arterial amino acid levels if transfer is mediated by transporters and with the assumption of intracellular metabolism or compartmentalization. Because amino acid (aa) concentrations within the perfusate are much higher, delivery is no longer rate-limiting and maternal flow does not determine uptake. Fig. 3. Placental phenylalanine transfer: experimental data and predicted transfer under certain assumptions. A: experimental transfer of [14C]phenylalanine across the perfused placental lobule. Transfer of phenylalanine to the fetal circulation was not related to maternal (P = 0.89) or fetal (P = 0.94) flow rates, and there were no interactions between maternal and fetal flow (P = 0.95). Values are means ± SE; n = 5 placentas. B: predicted transfer of phenylalanine if transfer is mediated by simple diffusion. Uptake and transfer are equal, and fetal flow has the predominant effect on transfer. C: predicted transfer of phenylalanine if transfer is mediated by transporters and with the assumption of no intracellular metabolism or compartmentalization. Because uptake is greater than transfer, intracellular phenylalanine concentrations rise over time, driving a progressive increase in transfer over the course of the experiment. This scenario does not reflect the experimental data. D: predicted transfer of phenylalanine if transfer is mediated by transporters and with the assumption of intracellular metabolism or compartmentalization. While transport with metabolism demonstrates the closest agreement with the experimental data (R2 = 0.14), none of the model outputs showed good correlation, indicating that other factors are required to fully account for the mechanisms underlying transfer of phenylalanine. Placental transfer of [14C]phenylalanine. Net flux of [14C]phenylalanine (mol/min) to the fetus was unaffected by varying fetal (P = 0.89) or maternal (P = 0.94) flow rates, nor was there an interaction between maternal and fetal flow (P = 0.95, n = 5; Fig. 3A). As such, the increase in placental uptake with increasing maternal flow did not translate into an increased transfer to the fetal circulation. The computational model was used to predict placental transfer with the assumption of simple diffusion (Fig. 3B) and transport (MVM exchange and BM-facilitated transport and exchange), first, in the absence of metabolism or compartmentalization (Fig. 3C) and, second, with the assumption of metabolism and/or compartmentalization (Fig. 3D). The second model, with the assumption of syncytiotrophoblast metabolism and/or compartmentalization, provided the best overall representation of the experimental data, as observed in Fig. 3D. Nonetheless, there was still a progressive increase in phenylalanine transfer associated with increasing maternal flow, although the fit was less convincing than that for uptake (R2 = 0.12). The model could be effectively fitted to the experimental data only if intracellular phenylalanine concentration was kept constant within the syncytiotrophoblast. Baseline uptake and transfer over time. [14C]phenylalanine uptake and transfer were compared at baseline maternal and fetal flow rates (14 and 6 ml/min, respectively) at the beginning, middle, and end of the experiment. At baseline flow rates, there were no differences in maternal venous concentration (mol/l) or placental uptake (mol/min) over the course of the experiment. Creatinine transfer. Creatinine transfer was significantly related to fetal (P = 0.015), but not maternal, flow rate, and there was no interaction between fetal and maternal flow rate (n = 5 placentas; Fig. 4). Fig. 4. Creatinine transfer across the perfused human placenta. Creatinine transfer was not significantly related to maternal flow rate (P = 0.84), but there was a significant relationship with fetal flow rate (P = 0.015). There was no interaction between maternal and fetal flows (P = 0.94). Values are means ± SE; n = 5 placentas. Sensitivity analysis for the transport model, including metabolism. Parameter variation was undertaken in the transport model with metabolism included, in which the effect of a fivefold increase and decrease in model parameter on the uptake/transfer of phenylalanine was considered. The sensitivity analysis for uptake indicated that Vmax and K for the exchanger on the MVM are the major determinants of transfer (Fig. 5). The sensitivity analysis for transfer indicated that the Vmax of the facilitated transporter on the BM and then the Vmax of the exchanger on the MVM are major determinants of transfer when metabolic rate is applied as a major limiting factor. Sensitivity analysis showed that uptake under experimental conditions was dependent on the ratio of K to Vmax in the linear regimen (Fig. 5A). In the case of the BM, transfer was highly sensitive to the rate of metabolism and Vmax of the facilitated transporter (Fig. 5B). However, when sensitivity analysis, including physiological amino acid concentrations, was performed, distinct differences were observed, particularly in regard to uptake, where metabolism and, to a lesser degree, facilitated transporter Vmax now affected the model (Fig. 5, C and D). Fig. 5. Parameter variation showing the predicted effect of a 5-fold increase and decrease, respectively, in model parameters on uptake and transfer of phenylalanine for the experimental paradigm (A and B) and modeled with physiological amino acid concentration (C and D). Lines for basal plasma membrane (BM)-facilitated Vmax and Kfa are obscured by BM exchanger Vmax. A: sensitivity analysis for placental uptake indicates that placental uptake is dependent on the ratio of K to Vmax in the linear transport regimen. B: sensitivity analysis for placental transfer indicates that placental transfer is highly sensitive to metabolic rate and Vmax of the BM-facilitated transporter. This sensitivity analysis is based on the low uterine arterial phenylalanine concentration used in the experimental model. Parameter variation shows the predicted effect of a 5-fold increase and decrease, respectively, in model parameter on uptake or transfer of phenylalanine under conditions assumed for physiological modeling. C: sensitivity analysis for placental uptake under conditions assumed for physiological modeling indicates that placental uptake is dependent on MVM exchanger Vmax and metabolic rate. There is a marked difference between predicted sensitivities under experimental and physiological conditions. D: sensitivity analysis for placental transfer under conditions assumed for physiological modeling indicates that placental transfer is sensitive to metabolic rate and Vmax of the MVM and BM-facilitated transporters. Tissue and protein counts and mass balance. On the basis of the steady-state measurements over the course of the experiment, [14C]phenylalanine uptake per cotyledon was 4.6 ± 0.7 nmol, 15%, 0.7 ± 0.02 nmol, of which was transferred to the fetal circulation, leaving 3.9 ± 0.01 nmol retained within the perfused cotyledon (n = 5 placentas). After protein precipitation, 14C label was measured in the supernatant and in the protein pellet derived from the perfused cotyledon. The concentration of free [14C]phenylalanine in the tissue was 1.0 ± 0.7 nmol/cotyledon and the amount of [14C]phenylalanine incorporated into protein was 1.2 ± 0.5 nmol/cotyledon. Total recovery of [14C]phenylalanine was 2.2 ± 0.8 nmol/cotyledon, which equates to 56% of the tracer retained in the tissue (n = 5 placentas). Estimation of [14C]phenylalanine gradient across the BM. On the basis of a tissue [14C]phenylalanine content of 1.03 nmol/cotyledon, with a mean wet weight of 42 g, and the assumption that the trophoblast occupies 15% of placental volume (21), the placental [14C]phenylalanine concentration was calculated to be ∼163 nmol/l vs. fetal vein concentrations of 1 nmol/l at low flow rates and 0.3 nmol/l at high flow rates. DISCUSSION This study demonstrates that factors additional to transporter activity and flow determine placental transfer of phenylalanine to the fetal circulation. Understanding the key determinants of amino acid transfer is essential if we are to identify the underlying causes of impaired amino acid transfer associated with fetal growth restriction and mechanistic targets for potential interventions. The observation that increased placental uptake of phenylalanine did not lead to corresponding increases in transfer to the fetal circulation suggests that other factors, such as incorporation into protein, are potentially rate-limiting. This has important implications for our understanding of the regulation of amino acid transfer in the human placenta. The experiments performed here allowed investigation of the dependence of placental uptake of phenylalanine from the maternal circulation and transfer to the fetal circulation across intact placental tissue on flow and the application of computational modeling to interpret the transfer mechanisms that underlie these processes. The observation that phenylalanine uptake was limited by maternal flow was consistent with modeling predictions and illustrates that, at the concentration of [14C]phenylalanine used in these experiments (2.7 pmol/l), supply was inadequate to saturate transport capacity. However, at physiological concentrations of amino acids (∼40 μmol/l for phenylalanine), supply is unlikely to become limiting; while this needs to be demonstrated experimentally, the model implies that phenylalanine uptake would not be flow-limited at physiological concentrations. The demonstration that the experimental uptake data matched the transport model, rather than the diffusion model, illustrates the role of transporters and confirms that phenylalanine transfer in the perfusion system is occurring by predicted mechanisms. The pattern of uptake could not be fitted by the diffusion model, which showed a strong effect of fetal flow that was not observed in the experimental data. We initially proposed that phenylalanine transfer could be flow-limited on the BM, with its transfer across the BM of the placental syncytiotrophoblast mediated by facilitated diffusion (8). However, phenylalanine transfer occurred at a near-constant rate across the range of fetal flow rates. This is consistent with a high intracellular concentration within placental tissue relative to the capillary concentration, as, in this case, changes in fetal flow will have a relatively small effect on the overall gradient and transfer. However, the transmembrane gradient should have increased during the experiment both in response to changes in [14C]phenylalanine uptake with increasing flow and because uptake exceeded efflux. Although phenylalanine uptake was ∼40% greater at the fastest than at the slowest maternal flow rate, this did not translate into increased transfer to the fetal circulation. Moreover, as illustrated by computational modeling, the observation that phenylalanine uptake was greater than efflux implies that intracellular phenylalanine concentration should have been increasing with time, driving increased transfer to the fetal circulation (Fig. 3C). Explanations for this discrepancy between uptake and transfer include the following: 1) the intracellular concentration of free [14C]phenylalanine, available for transfer, is controlled by another factor, such as metabolism, or 2) the activity of the facilitated transporters on the BM is not primarily determined by the transmembrane concentration gradient. BM-facilitated transport might not be determined by the transmembrane concentration gradient for the following reasons. First, BM-facilitated transport was saturated; however, this is unlikely, as tracer concentration is well below the Km, and, in this range, flux should increase proportionally with concentration (even if transfer of the unlabeled substrate were saturated). Second, facilitated amino acid transporters do not operate as we would expect on the basis of observations of other facilitated transporters such as GLUT1 (SLC2A1). Facilitated transport of glucose by GLUT1 (SLC2A1) appears to be dependent on the transplacental concentration gradient, and it seems reasonable to assume that facilitated amino acid transporters may share this characteristic (10, 29). However, the facilitated transporters LAT3 and LAT4 are reported to have complex kinetics with multiple apparent affinities for phenylalanine (3). It is therefore possible that they do not operate in a manner consistent with previous observations for other facilitated transporters. Further characterization of these transporters is therefore warranted to help clarify this issue. It is noteworthy that up to two-thirds of the [14C]phenylalanine retained within the placenta was incorporated into protein. If this phenomenon applies to other amino acids, this would reduce the intracellular concentration of amino acids and, therefore, the amino acid concentration gradient driving transfer to the fetus. Previous work in the guinea pig has suggested an important role for protein metabolism in amino acid transfer (4). Alternatively, phenylalanine catabolism or sequestration within intracellular organelles could regulate the free amino acid pool available for transport. When metabolism was included in the model, the predicted transfer to the fetus was much closer to the observed experimental data. Nevertheless, if we assume linear kinetics for metabolism, the model could not fully reproduce the constant rate of phenylalanine transfer observed experimentally. The reason for this is that, in the model, increased maternal uptake would directly lead to a higher equilibrium of intracellular amino acid concentration, increasing the concentration gradients that drive amino acid transport across the BM. Only if intracellular phenylalanine concentration was fixed could the model provide a good representation of our experimental data. Therefore, for metabolism to fully explain the data, regulation of metabolism would be needed to maintain a constant free intracellular phenylalanine concentration at the BM interface. A relatively high proportion of 14C label was unaccounted for; this has been reported previously, but the cause for this remains elusive (26). Catabolism is a possibility, but as phenylalanine hydrolase is not expressed in the placenta, we consider this unlikely (25). It is possible that phenylalanine uptake may have been overestimated, as our calculations were based on steady-state values, which may not fully reflect equilibration time following changes in flow rate. Another possibility is that the observed quenching in protein extracts was not fully accounted for. In both cases, the proportion of [14C]phenylalanine incorporated into protein would have been underestimated, affecting estimates of tracer recovery. The observation that much of the [14C]phenylalanine taken up was incorporated into protein suggests that metabolism and integration of phenylalanine into protein make a significant contribution. However, whether metabolism can fully explain the discrepancy between the model and experimental data or whether there is a combination of metabolism and some other factor, such as compartmentalization or facilitated transporter function, remains to be determined. For example, compartmentalization of the amino acid arginine has been proposed as an explanation of the arginine paradox in nitric oxide production (13). Protein synthesis inhibitors have been shown to be effective in inhibition of protein synthesis in the perfused placenta, and it would be interesting to determine if protein synthesis inhibitors also stimulated the transfer of amino acids to the fetus (2). While all the factors modeled are necessary for transfer, it is important to identify those that are most likely to become rate-limiting and, thus, have the greatest clinical relevance. The model sensitivity analysis identifies those factors that, if we assume that the model is correct, have the greatest impact on phenylalanine transfer. It is important to note that the sensitivity analysis favored different factors under experimental (low phenylalanine concentrations) and physiological amino acid concentrations. However, it appears that MVM exchanger activity, BM-facilitated transporter activity, and incorporation of amino acids into protein were predicted to be the primary determinants of placental transfer. As the experiments themselves were not performed with physiological concentrations of amino acids, we should be careful about extrapolating our findings to the physiological situation. However, while experimental validation is required, we are confident that the modeling framework is capable of effectively representing the main transport processes relevant for physiological fluids such as serum. We should also note that the placenta is a more complex tissue than the model currently reflects and that processes such as metabolism may occur in cell types other than the syncytiotrophoblast. In the normal placenta, maternal and fetal flow rates are on the order of 2 and 0.2 ml·g placenta−1·min−1, respectively (17). In this study our maternal flow rates were below normal (15–30% of physiological), while our fetal flow rates spanned the normal range (50–140% of physiological). Maternal blood flows in this range or fetal flow of 50% would normally be associated with placental disease, leading to preeclampsia or fetal growth restriction (16). This model is based on human full-term placenta but could be applied to other gestational ages and species. By substitution of the data on the volumes of the different tissue compartments, rates of uterine and umbilical blood flow, and the localization of transporters, the basic model would be applicable to a range of species or gestational ages. Creatinine transfer across the human placenta is generally believed to occur via paracellular diffusion (31), and, consistent with this notion, the experimental data followed the pattern predicted by the diffusion model, providing confidence in our modeling approaches. The perfusion system provides an excellent model for study of placental transfer (27). Nevertheless, there are issues that should be considered when the data are interpreted. First, maternal-side perfusion may not fully represent the uteroplacental perfusion that occurs in vivo via the spiral arteries (26). This may affect the efficiency of mixing within the intervillous space and, thus, the efficiency of transfer. Second, this study measured the transfer of one amino acid in the absence of other amino acids that would normally be present. Amino acid transfer is likely to be determined by interaction between amino acids, and future studies including all amino acids would be informative (18). In considering these observations, we also need to be mindful of the time course of these experiments, as the factors limiting transfer over the course of 3 h may be different from those limiting transfer over days, weeks, and longer. While incorporation of phenylalanine into the protein pool and metabolism may predominate over short time frames, in the longer term, transport may affect amino acid availability for protein synthesis, the size of the protein pool, and, thus, transfer over extended periods. It is likely that protein synthesis and breakdown are in quasi-steady state, with input and output matched over time. This would allow the placenta to maintain supply to the fetus in response to short-term variations in maternal supply. If a significant proportion of placental amino acids enters a protein pool before being transported to the fetus, we may need to rethink the time frames over which amino acid transfer is regulated. In fetal growth-restricted pregnancies, many placental factors have been shown to be altered; some of these will be key determinants of placental function, while others will not. A key aim of the model is to be able to identify the factors that are most likely to be rate-determining for placental transfer. These factors are the most likely to become rate-limiting in fetal growth-restricted pregnancies and need to be targeted for successful interventions. By modeling the phenotypes observed in fetal growth restriction, we hope to identify the factors that are having the greatest effect on placental function and, thus, fetal growth, as these are the most important targets for future research. In conclusion, this study suggests that transporter activity is a major determinant of phenylalanine transfer across the perfused human placenta, but flow is not. However, our combined experimental and computational modeling approach leads us to conclude that other factors, such as metabolism and integration into protein within the placenta, play a previously underappreciated role. GRANTS This work was funded by Biotechnology and Biological Sciences Research Council Project Grants BB/I011250/1 and BB/I011315/1. DISCLOSURES No conflicts of interest, financial or otherwise, are declared by the authors. AUTHOR CONTRIBUTIONS E.M.L. and S.B. performed the experiments; E.M.L., S.P., S.B., N.P., B.G.S., and R.M.L. analyzed the data; E.M.L., S.P., N.P., B.G.S., and R.M.L. interpreted the results of the experiments; E.M.L., S.P., B.G.S., and R.M.L. prepared the figures; E.M.L., B.G.S., and R.M.L. drafted the manuscript; E.M.L., S.P., S.B., I.P.C., J.D.G., E.D.J., N.P., C.P.S., K.L.W., B.G.S., and R.M.L. edited and revised the manuscript; E.M.L., S.P., S.B., I.P.C., J.D.G., E.D.J., N.P., C.P.S., K.L.W., B.G.S., and R.M.L. approved the final version of the manuscript; S.B., I.P.C., J.D.G., E.D.J., N.P., C.P.S., K.L.W., B.G.S., and R.M.L. developed the concept and designed the research. ACKNOWLEDGMENTS We thank the midwives at the Princess Anne Hospital for help in the placental collection process. ==== Refs REFERENCES 1. Battaglia FC Clinical studies linking fetal velocimetry, blood flow and placental transport in pregnancies complicated by intrauterine growth retardation (IUGR) . 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==== Front Clin NutrClin NutrClinical Nutrition (Edinburgh, Scotland)0261-56141532-1983Elsevier S0261-5614(15)00201-010.1016/j.clnu.2015.07.022Original ArticleIs adductor pollicis muscle thickness a good predictor of lean mass in adults? Bielemann Renata Moraes renatabielemann@hotmail.comab∗Horta Bernardo Lessa aOrlandi Silvana Paiva bBarbosa-Silva Thiago Gonzalez aGonzalez Maria Cristina cAssunção Maria Cecília aGigante Denise Petrucci aa Post-Graduate Program in Epidemiology, Federal University of Pelotas, Brazilb Nutrition Department, Federal University of Pelotas, Brazilc Post-Graduate Program in Health and Behavior, Catholic University of Pelotas, Brazil∗ Corresponding author. Programa de Pós-Graduação em Epidemiologia, Rua Marechal Deodoro, 1160, 3° andar, CEP: 96020-220 Pelotas, Rio Grande do Sul, Brazil. renatabielemann@hotmail.com1 10 2016 10 2016 35 5 1073 1077 30 5 2015 29 7 2015 © 2015 The Authors2015This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Summary Background & aims Lean mass (LM) is an important parameter in clinical outcomes, which highlights the necessity of reliable tools for its estimation. The adductor pollicis muscle thickness (APMT) is easily accessible and suffers minimal interference from the adjacent subcutaneous fat tissue. Objective To assess the relationship between the APMT and LM in a sample of Southern Brazilian adults. Methods Participants were adults from the 1982 Pelotas (Brazil) Birth Cohort. LM was measured by dual energy X-ray absorptiometry (DXA). LM and lean mass index (LMI – LM divided by the square of height – kg/m2) were the outcomes. APMT was measured using a skinfold caliper. The mean of three measurements in the non-dominant hand was used in the analyses. APMT was described according to socio-demographic characteristics and nutritional status. The relationship between APMT and both LM and LMI was evaluated by correlation coefficient and linear regression using APMT as a single anthropometric parameter and also in addition to BMI. Results APMT was assessed in 3485 participants. APMT was higher in males, non-whites, less-schooled and obese individuals. APMT was moderately correlated to LM and LMI (ranged from 0.44 to 0.57). Correlation coefficients were higher for LMI as outcome and in females (LM: 0.51 and LMI: 0.57). APMT explained 19% and 26% of the variance in LM in males and females, respectively, whereas it explained 26% and 33% of the variance in LMI. APMT increased the prediction for LM in 3 and 4 percentage points in males and females, in comparison to explained by BMI. BMI explained 48% and 59% of the variance of LMI in males and females whereas APMT increased it to 51% and 62% for both sexes, respectively. Conclusions Results were not good enough to promote the APMT as a single predictor of LM or LMI in epidemiological studies. APMT has a little predictive capacity in estimating LM or LMI when BMI is also considered. Keywords AnthropometryAdductor pollicis muscleLean massAdults ==== Body 1 Introduction Nowadays, there is a growing importance of body composition evaluation in several fields [1]. The measurement of body composition allows documenting the efficiency of nutrition support, tailoring the choice of nutritional behaviors and therapies, whereas only body weight does not allow objectively the same approach [1]. Assessment of fat mass has been the main focus of several studies in the last decades due to the importance of the evaluation of the body fat per se as well as its corporal distribution [2], [3]. However, lean mass (LM) has also recently attracted major attention in the scientific literature, given its role as an important predictor of clinical outcomes [4], [5]. It has been reported that LM is a fundamental determinant of growth and development [6], as well as an important clinical marker of diseases and aging processes [7]. Several methods to evaluate body compartments have been developed and, subsequently, adapted for use in different scenarios. Devices such as dual energy X-ray absorptiometry (DXA) and air-displacement plethysmography have been proven reliable in epidemiological scenario [8]. Unfortunately, given the high costs, technical complexity and low availability of the methods, their use is restricted in clinical and research environments. In population-based studies, the availability of simple and minimally invasive methods with lower costs is important. With that in mind, anthropometric measurements have been largely used in epidemiological studies to assess fat mass – such as waist and hip circumference and skinfold thickness [9], [10]. However, the growing attention to LM as a predictor of clinical outcomes highlights the necessity of reliable tools, which can easily assess LM in different cohorts. Previous studies have reported that low adductor pollicis muscle thickness (APMT) could be used as a proxy of low lean mass in clinical scenario [11], [12], [13]. This muscle has an easily accessible location in the hands and suffers minimal interference of the subcutaneous fat tissue in its thickness' assessment. APMT has been used mainly in the clinical environment, particularly in surgical, renal, long-term hospitalized or critical care patients [11], [14], [15], [16], [17], as a predictor of malnutrition, length of stay and mortality. However, its use in the general healthy population has been scarcely studied. Few studies have described APMT in healthy subjects according to demographic characteristics. Lameu et al. [13] observed a positive correlation between APMT and arm muscle circumference, arm muscle area and calf circumference, but did not find any meaningful correlations with fat parameters. Gonzalez et al. [18] found a positive correlation of APMT with BMI, but weak correlations with weight, height and age. To our knowledge, no previous study has compared APMT and LM measured by reference methods are inexistent. The present study aimed to assess the relationship between the APMT and LM among young adults in South Brazil. 2 Materials and methods Data used for this analysis were collected as part of the last follow-up of the 1982 Pelotas Birth Cohort Study. These subjects (n = 5914 at birth) were followed-up on several occasions, and further details about this cohort are available elsewhere [19], [20]. From June, 2012 to February, 2013, the cohort members were invited to visit the research clinic, where they were interviewed and examined. All procedures were approved by the Ethics Committee in Research of the Faculty of Medicine at Federal University of Pelotas and a written informed consent was obtained from all subjects. Subjects were categorized by BMI according to the World Health Organization recommendation [21]. Standing height was measured to the nearest 1 mm, using a wooden stadiometer with the barefooted subjects. Weight was assessed using a pletismography scale (BodPod® – Cosmed, Italy), with the precision of 0.01 kg. Their economic status was also assessed, based on asset index, having a full-time maid and the head of the family's schooling. This allowed us to stratify subjects in wealth groups from A – richest – to E – poorest, according to the Brazilian Research Association Institute criterion. APMT measurement (mm) was performed using a Lange® skinfold caliper (Beta Technology – Santa Cruz, CA, USA). Measurements were taken as subjects sat upright in a chair with their legs, arms and backs supported. Arms were set at a 90° angle from the elbow using the chairs arm rest. APTM was measured with the skinfold caliper in the vertex of an imaginary triangle formed by the extension of the thumb and the index finger, under the continuous pressure of 10 g/mm. The mean of three measurements was used [18]. The non-dominant APMT was chosen for consideration in this study – therefore, the values obtained from the left hand of right-handed subjects, and from the right hand of the left-handed ones, were used. Examiners were trained and standardized using acceptable technical errors of measurement calculated based on Habicht's publication [22] for all anthropometric measurements. Exclusion criteria for APMT were factors that could influence the execution of daily movements, such as pregnancy; tendinitis; current injuries or deterioration of mobility due to previous injuries or accidents in at least one of the arms or hands; fractures in the upper limbs in the last six months; wheelchair use, mental disorders and degenerative diseases (e.g. fibromyalgia). LM was assessed using DXA (Lunar Prodigy Advance – GE®, Germany). Total body DXA scans were not performed in pregnant women and subjects weighing more than 120 kg or taller than 1.92 m. Subjects with metal surgical implants and irremovable metal items were excluded from examination. Subjects that could not fit in the DXA scan area were submitted to half-body scans of their right side to estimate total body composition. Lean Mass Index (LMI) was also calculated by dividing the LM (kg) by the square of height (m), as proposed by VanItallie [23]. All analyses were stratified by sex. Student's t-test or Analysis of Variance (ANOVA) was used in the bivariate analysis. Scatter plots were used to show the relationship between APMT and LM (kg) or LMI (kg/m2), and Pearson's correlation was also determined. Regression coefficients and adjusted coefficient of determination (adjusted R2) were both estimated using linear regression: first, for APMT only; later, using anthropometric variable in addition to BMI. Significance level was set in 5%. 3 Results In 2012–3, 3701 participants from the original 1982 Pelotas Birth Cohort were interviewed. The follow-up rate was 68.1% (including 325 known deaths). After exclusion, 3338 individuals were DXA scanned. APMT was, on average, 24.2 mm (sd = 4.2) and 19.4 mm (sd = 3.9) for males and females, respectively. Table 1 shows that APMT was higher among non-white subjects. Females from the highest economic status presented lower APMT (p < 0.001), whereas among males the same relationship was observed but it was not statistically significant. The highest schooling group showed lower APTM than the two lowest groups in both males (p < 0.001) and females (p < 0.001). Nutritional status was positively associated with APMT (p < 0.001). Fig. 1 shows that APMT was positively correlated with LM and LMI, regardless of the sex. Pearson's coefficients were higher in females than in males. In females, the correlation between APMT and LM was r = 0.51, whereas, in males, r = 0.44. For LMI, the correlation coefficient was 0.51 and 0.57, for males and females, respectively. Regression coefficients of APMT in the LM prediction were similar for males (β = 0.71, 95% CI = 0.64; 0.78) and females (β = 0.71, 95% CI = 0.65; 0.76), though the coefficient of determination was slightly higher for females (26.3%) than males (19.1%). Coefficient of determination for APMT was higher in the LMI prediction than for the LM prediction. APMT explains 26% and 33% in the variation of LMI in males and females, respectively (Table 2). BMI predicted around 30% and 41% of the LM variation in males and females, respectively (Fig. 2). APMT increased the LM prediction by 3 and 4 percentage points in males and females. BMI explained 48% and 59% of the LMI variation in males and females, whereas APMT increased it to 51% and 62% for both sexes, respectively. 4 Discussion This was the first study that evaluated the relationship between APMT and LM assessed by DXA, an accurate and reliable method in the measurement of body composition compartments. APMT was higher in males, lower in high-educated and richer individuals and was positively related to the nutritional status. Correlation coefficients for the relationship between APMT and both LM and LMI were higher in females. Coefficient of determination of APMT was higher for LMI. APMT alone was able of predicting about 33% of the variation of LMI in females. However, the increase in the prediction of LM or LMI promoted by the APMT when used in conjunction with BMI was low. Concerning the description of APMT in our young population, APMT values from our study were similar to those found in healthy males and females with approximately the same age described by Gonzalez et al. [18]. However, another Brazilian study found APMT values much lower than our results [13]. Methodological differences from these studies should be considered since Gonzalez et al. [18] reported that lower values found by Lameu et al. [13] can be possibly attributable to measurement errors derived from misplacement of the skinfold caliper from the correct anatomic point. In this case, the lower measurements obtained would be from the skinfold thickness near the muscle, not the APMT. The current study trained and standardized the examiners, filling the existing gap concerning reliable APMT measurements. Still, there are several other studies that evaluated the APMT performance in the clinical scenario, using unhealthy populations. However, due to the subjects' demographic characteristics and, specially, health status, the comparison with our results is unviable. This study was aimed in assess the prediction of LM by APMT. However, the adductor pollicis muscle was first used to study muscle function through electric stimulation of the ulnar nerve [24]. The use of its thickness as a possible nutritional assessment parameter is recent. Given the method's appliance practicality, portability and low cost, it would be a promissory tool for epidemiological field situations, if it was able to generate an adequate prediction of LM. However, results from the current study were not good enough to encourage the use of APMT in the estimative of LM in large healthy adult populations, mainly because it adds little to the explanation of the total variance in lean mass already promoted by BMI. APMT could be a good predictor of appendicular skeletal muscle mass (ASM), a lean mass measurement from arms and legs that reflects mainly muscle. However, correlation coefficients between APMT and ASM were 0.42 and 0.51 in males and females, respectively (data not shown). Regarding other LM predictors, there is a large number of anthropometrics measurements used as such. They are generally combined to other anthropometrical assessments, as weight and height, and included in prediction equations with variables such as sex and, sometimes, skin color. Variables such as skinfolds, waist and hip circumferences are usually included as negative predictors of LM in those equations [25], [26], [27], whereas knee height [28], arm [26], [29], calf [26], [29] and thigh circumferences [27], [29] seem to improve the explained variance of those equations, presenting a positive relationship with LM or skeletal muscle mass of adults and elderly. The use of anthropometric-based methods, such as thigh or calf muscle cross-sectional areas and volumes derived from circumference and skinfold thickness measurements, overestimated the same measurement from magnetic resonance imaging [30], [31]. Overestimation of muscle mass by anthropometric measurements was also suggested by several studies included in a recent systematic review from Al-Gindan et al. [25]. It is suggested that APMT is not only influenced by the amount of skeletal muscle mass, but is also influenced by other variables. For example, it is suggested an important influence of the body frame in APMT. Lameu et al. [13] found a progressive increase in the APMT of individuals with a small, medium or large body frame, evaluated by the wrist circumference. In addition, APMT have been previously associated with occupation [18], which requires greater attention, since APMT could be positively biased by the occupation with physical hand effort. On the other hand it could also be a marker of higher levels of occupational physical activity, reflecting higher LM values. In the previously referred study with healthy individuals from Lameu et al. [13], despite of the possibility of methodological peculiarities already described above, interesting findings must be considered. APMT failed to correlate with triceps skinfold thickness and arm fat area (fat parameters), but had a positive low-to-moderate correlation with calf circumference (r = 0.35), arm muscle area (r = 0.40) and arm muscle circumference (r = 0.42). Correlation coefficients of APMT with LM and LMI in the current study were around 0.50, although increase in explained LM and LMI variance was low when BMI is already considered in the prediction model. Limitations of this study mainly concern the assessment of a population with the same age, failing to explore variations in the prediction related to the aging process. In addition, the muscle compartment could not be isolated from the LM. This may have biased the results, because APMT reflects mainly the muscle measurement, with low interference from the body water compartment that is also included in the total lean mass. On the other hand, the current study was able to fill the existing knowledge gap concerning the comparison between APMT and whole-body LM evaluated by a reliable method such as DXA, Another strong point of the study was the concern with adequate training and standardization of examiners. Finally, the peak of physical capacity (muscle and bone strength and mass) is reached up to the end of the third decade of life [32]. In summary, APMT was moderately positively correlated with LM and LMI. The performance of APMT in predicting LM was better if height was taken into account (LMI), and in females. However, increases in the coefficient of determination promoted by APMT were low when BMI is already considered. Based on these results, APMT was not considered a good predictor for LM in a generally healthy adult population. Statement of authorship RMB performed the statistical analyses and conceived the study. RMB, BLH, SPO, TGBS, MCG, MCA and DPG drafted the manuscript. TGBS and MCG performed the literature review. SPO performed the training of the anthropometrists. BLH and DPG were the principal investigators of last follow-up of 1982 Pelotas Birth Cohort. All authors revised and approved the final version of the manuscript. Conflict of interest The authors declare that they have no conflict of interest. Funding sources The 1982 birth cohort study was supported by the Wellcome Trust Initiative entitled Major Awards for Latin America on Health Consequences of Population Change, grant entitled: “Implications of early life and contemporary exposures on body composition, human capital, mental health and precursors of complex chronic diseases in three Brazilian cohorts (1982, 1993 and 2004)”. Previous phases of the study were supported by the International Development Research Center, The World Health Organization, Overseas Development Administration, European Union, National Support Program for Centers of Excellence (PRONEX), the Brazilian National Research Council (CNPq) and Brazilian Ministry of Health. Acknowledgments This article is based on data from the study “Pelotas birth cohort, 1982” conducted by Postgraduate Program in Epidemiology at Universidade Federal de Pelotas. The 1982 birth cohort study was supported by the Wellcome Trust Initiative entitled Major Awards for Latin America on Health Consequences of Population Change (grant N° 086974/Z/08/Z). Previous phases of the study were supported by the International Development Research Center, The World Health Organization, Overseas Development Administration, European Union, National Support Program for Centers of Excellence (PRONEX), the Brazilian National Research Council (CNPq) and Brazilian Ministry of Health. Fig. 1 Relationship of adductor pollicis muscle thickness with lean mass and lean mass index by sex in young adults from Southern Brazil. (a) Adductor pollicis muscle thickness in relation to lean mass; (b) adductor pollicis muscle thickness in relation to lean mass index. Fig. 2 Adjusted coefficients of determination (r2) of adductor pollicis muscle thickness in the prediction of lean body mass (LBM) and lean mass index (LMI) of young males and females from a Southern Brazilian cohort. BMI – Body mass index; APMT – adductor pollicis muscle thickness. Table 1 Adductor pollicis muscle thickness (mm) according to socio-demographic characteristics and nutritional status in young adults from Pelotas, Brazil. APMT (mm) Males Females n Mean (sd) p n Mean (sd) p Skin color 0.006 <0.001  White 1296 24.0 (4.1) 1341 19.1 (3.8)  Non-white 438 24.6 (4.3) 408 20.6 (3.9) Economic status 0.054 <0.001  A/B (richest) 939 24.1 (4.2) 856 18.9 (3.8)  C 395 24.7 (4.2) 425 20.7 (3.8)  D/E (poorest) 41 25.1 (4.1) 61 20.3 (4.5) Schooling (years) <0.001 <0.001  0–8 489 25.0 (4.2) 396 20.5 (3.7)  9–11 548 24.7 (4.2) 490 20.5 (3.8)  ≥12 669 23.2 (4.0) 841 18.4 (3.7) Nutritional status (BMI) <0.001a <0.001a  <18.5 24 18.8 (2.8) 44 15.7 (3.1)  18.5–24.9 615 22.0 (3.6) 789 17.6 (3.1)  25.0–29.9 702 25.5 (3.5) 497 19.9 (2.9)  ≥30 380 27.4 (4.0) 411 22.9 (3.8) APMT – adductor pollicis muscle thickness. Economic status according to Brazilian Research Association Institute criterion. a Linear trend test. Table 2 Linear regression coefficients of prediction of lean body mass and lean mass index by adductor pollicis muscle thickness in young adults from Pelotas, Brazil. Lean body mass (kg) Lean mass index (kg/m2) Coefficient (95% CI) p Adj R2 Coefficient (95% CI) p Adj R2 Males APMT (mm) <0.001 0.191 <0.001 0.259  α 39.74 (37.99; 41.49) 13.31 (12.85; 13.77)  β 0.71 (0.64; 0.78) 0.23 (0.21; 0.25) Females APMT (mm) <0.001 0.263 <0.001 0.325  α 24.90 (23.78; 26.02) 9.61 (9.25; 9.98)  β 0.71 (0.65; 0.76) 0.27 (0.25; 0.29) APMT – adductor pollicis muscle thickness. ==== Refs References 1 Thibault R. Genton L. Pichard C. Body composition: why, when and for who? Clin Nutr 31 4 2012 435 447 22296871 2 Muller M.J. Lagerpusch M. Enderle J. Schautz B. Heller M. Bosy-Westphal A. Beyond the body mass index: tracking body composition in the pathogenesis of obesity and the metabolic syndrome Obes Rev 13 Suppl. 2 2012 6 13 23107255 3 Oliveros E. Somers V.K. Sochor O. Goel K. Lopez-Jimenez F. The concept of normal weight obesity Prog Cardiovasc Dis 56 4 2014 426 433 24438734 4 Amundson D.E. Djurkovic S. Matwiyoff G.N. The obesity paradox Crit Care Clin 26 4 2010 583 596 20970043 5 Gonzalez M.C. Pastore C.A. Orlandi S.P. Heymsfield S.B. Obesity paradox in cancer: new insights provided by body composition Am J Clin Nutr 99 5 2014 999 1005 24572565 6 Cameron N. Bogin B. Human growth and development 2nd ed. 2002 Academic Press Elsevier 7 Cruz-Jentoft A.J. Baeyens J.P. Bauer J.M. Boirie Y. Cederholm T. Landi F. Sarcopenia: European consensus on definition and diagnosis: report of the European working group on Sarcopenia in older people Age Ageing 39 4 2010 412 423 20392703 8 Orlandi S.P. Bielemann R.M. Martínez-Mesa J. Barros A.J.D. Gigante D.P. Assunção M.C.F. The precision of human body composition measurements using air-displacement plethysmography and dual X-ray absorptiometry. Is there any difference between fasting and non-fasting measurements? Int J Body Compos Res 11 2 2013 43 50 9 Brodie D. Moscrip V. Hutcheon R. Body composition measurement: a review of hydrodensitometry, anthropometry, and impedance methods Nutrition 14 3 1998 296 310 9583375 10 Wang J. Thornton J.C. Kolesnik S. Pierson R.N. Jr. Anthropometry in body composition. An overview Ann N Y Acad Sci 904 2000 317 326 10865763 11 Bragagnolo R. Caporossi F.S. Dock-Nascimento D.B. Aguilar-Nascimento J.E. Espessura do músculo adutor do polegar: um método rápido e confiável na avaliação nutricional de pacientes cirúrgicos Rev Col Bras Cir 36 5 2009 371 376 20069147 12 Gonzalez M.C. Pureza Duarte R.R. Orlandi S.P. Bielemann R.M. Barbosa-Silva T.G. Adductor pollicis muscle: a study about its use as a nutritional parameter in surgical patients Clin Nutr 2014 13 Lameu E.B. Gerude M.F. Campos A.C. Luiz R.R. The thickness of the adductor pollicis muscle reflects the muscle compartment and may be used as a new anthropometric parameter for nutritional assessment Curr Opin Clin Nutr Metab Care 7 3 2004 293 301 15075921 14 de Oliveira C.M. Kubrusly M. Mota R.S. Choukroun G. Neto J.B. da Silva C.A. Adductor pollicis muscle thickness: a promising anthropometric parameter for patients with chronic renal failure J Ren Nutr 22 3 2012 307 316 22056150 15 Guerra R.S. Fonseca I. Pichel F. Restivo M.T. Amaral T.F. Handgrip strength and associated factors in hospitalized patients J Parenter Enteral Nutr 2013 16 Melo C.Y. Silva S.A. Adductor pollicis muscle as predictor of malnutrition in surgical patients Arq Bras Cir Dig 27 1 2014 13 17 24676291 17 Pereira R.A. Caetano A.L. Cuppari L. Kamimura M.A. Adductor pollicis muscle thickness as a predictor of handgrip strength in hemodialysis patients J Bras Nefrol 35 3 2013 177 184 24100736 18 Gonzalez M.C. Duarte R.R. Budziareck M.B. Adductor pollicis muscle: reference values of its thickness in a healthy population Clin Nutr 29 2 2010 268 271 19744751 19 Barros F.C. Victora C.G. Horta B.L. Gigante D.P. Metodologia do estudo da coorte de nascimentos de 1982 a 2004-5, Pelotas, RS Rev Saude Publ 42 2008 7 15 20 Victora C.G. Barros F.C. Cohort profile: the 1982 Pelotas (Brazil) birth cohort study Int J Epidemiol 35 2 2006 237 242 16373375 21 World Health Organization Obesity: preventing and managing the global epidemic [Report on a WHO consultation on obesity, Geneva, 3–5 June, 1997] 2000 World Health Nutrition Geneva 22 Habicht J.P. [Standardization of quantitative epidemiological methods in the field] Boletin Oficina Sanit Panam Pan Am Sanit Bureau 76 5 1974 375 384 23 VanItallie T.B. Yang M.U. Heymsfield S.B. Funk R.C. Boileau R.A. Height-normalized indices of the body's fat-free mass and fat mass: potentially useful indicators of nutritional status Am J Clin Nutr 52 6 1990 953 959 2239792 24 Detsky A.S. Smalley P.S. Chang J. The rational clinical examination. Is this patient malnourished? JAMA 271 1 1994 54 58 8258889 25 Al-Gindan Y.Y. Hankey C.R. Leslie W. Govan L. Lean M.E. Predicting muscle mass from anthropometry using magnetic resonance imaging as reference: a systematic review Nutr Rev 72 2 2014 113 126 24447263 26 Kulkarni B. Kuper H. Taylor A. Wells J.C. Radhakrishna K.V. Kinra S. Development and validation of anthropometric prediction equations for estimation of lean body mass and appendicular lean soft tissue in Indian men and women J Appl Physiol 115 8 2013 1156 1162 23950165 27 Ross R. Shaw K.D. Rissanen J. Martel Y. de Guise J. Avruch L. Sex differences in lean and adipose tissue distribution by magnetic resonance imaging: anthropometric relationships Am J Clin Nutr 59 6 1994 1277 1285 8198051 28 Baumgartner R.N. Waters D.L. Gallagher D. Morley J.E. Garry P.J. Predictors of skeletal muscle mass in elderly men and women Mech Ageing Dev 107 2 1999 123 136 10220041 29 Lee R.C. Wang Z. Heo M. Ross R. Janssen I. Heymsfield S.B. Total-body skeletal muscle mass: development and cross-validation of anthropometric prediction models Am J Clin Nutr 72 3 2000 796 803 10966902 30 Fuller N.J. Hardingham C.R. Graves M. Screaton N. Dixon A.K. Ward L.C. Predicting composition of leg sections with anthropometry and bioelectrical impedance analysis, using magnetic resonance imaging as reference Clin Sci (Lond) 96 6 1999 647 657 10334971 31 Mathur S. Takai K.P. Macintyre D.L. Reid D. Estimation of thigh muscle mass with magnetic resonance imaging in older adults and people with chronic obstructive pulmonary disease Phys Ther 88 2 2008 219 230 18056754 32 Kuh D. New dynamics of ageing preparatory N A life course approach to healthy aging, frailty, and capability J Gerontol A Biol Sci Med Sci 62 7 2007 717 721 17634317

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==== Front AIP AdvAIP AdvAAIDBIAIP Advances2158-3226AIP Publishing LLC 036608ADV1.496122810.1063/1.49612282016-1957-TRRegular ArticlesSeed layer technique for high quality epitaxial manganite films Graziosi P. 1,2a)Gambardella A. 1Calbucci M. 1O’Shea K. 3MacLaren D. A. 3http://orcid.org/0000-0003-0976-1810Riminucci A. 1Bergenti I. 1http://orcid.org/0000-0001-7944-0735Fugattini S. 1http://orcid.org/0000-0002-3318-9132Prezioso M. 1,4Homonnay N. 5Schmidt G. 5,6Pullini D. 7Busquets-Mataix D. 2Dediu V. 11 CNR - ISMN, Consiglio Nazionale delle Ricerche - Istituto per lo Studio dei Materiali Nanostrutturati, v. Gobetti 101, 40129 Bologna, Italy2 Instituto de Tecnología de Materiales, Universitat Politécnica de Valencia, Camino de Vera s/n, 46022, Valencia, Spain3 Scottish Universities Physics Alliance, School of Physics and Astronomy, University of Glasgow, Glasgow, United Kingdom4 University of California, Santa Barbara, Electrical & Computer Engineering Harold Frank Hall, Santa Barbara, CA 93106-9560, USA5 Institut für Physik, Universität Halle, 06120 Halle, Germany6 Interdisziplinäres Zentrum für Materialwissenschaften, Martin-Luther University Halle-Wittenberg, Nanotechnikum Weinberg, 06120 Halle, Germany7 Centro Ricerche Fiat, 10043, Orbassano, Torino, Italya) Corresponding author: patrizio.graziosi@gmail.com 12 8 2016 8 2016 12 8 2016 6 8 08510913 7 2016 04 8 2016 © 2016 Author(s).2016Author(s)2158-3226/2016/6(8)/085109/9/$0.00All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).We introduce an innovative approach to the simultaneous control of growth mode and magnetotransport properties of manganite thin films, based on an easy-to-implement film/substrate interface engineering. The deposition of a manganite seed layer and the optimization of the substrate temperature allows a persistent bi-dimensional epitaxy and robust ferromagnetic properties at the same time. Structural measurements confirm that in such interface-engineered films, the optimal properties are related to improved epitaxy. A new growth scenario is envisaged, compatible with a shift from heteroepitaxy towards pseudo-homoepitaxy. Relevant growth parameters such as formation energy, roughening temperature, strain profile and chemical states are derived. European Commission (EC)http://dx.doi.org/10.13039/501100000780NMP-2010-SMALL-4-263104NMP3-LA-2010-246102NMP3-SL-2010-246073Deutsche Forschungsgemeinschaft (DFG)http://dx.doi.org/10.13039/501100001659SFB762Ministero dell'Istruzione, dell'Università e della Ricerca (MIUR)http://dx.doi.org/10.13039/501100003407RBAP117RWN crossmark ==== Body Ferromagnetic manganites are a prototypical example of half metals: materials with 100% spin polarization at zero temperature. Although their application in commercial devices is limited by a relatively low Curie temperature (TC ≤ 370 K), manganites represent an excellent research model for testing spin injection into various materials and to search for pioneering device paradigms.1–5 For instance, they have contributed significantly to the field of organic spintronics, where almost half of the reported devices have La0.7Sr0.3MnO3 (LSMO) as a spin injector.6,7 In these and other devices, it is imperative to optimize the spin injection efficiency, which is intimately linked to the quality of the ferromagnetic layer and its interfaces.6,8 In many cases, however, keeping the manganite film thickness relatively low (around 12 nm) offered the best trade-off between maintaining a smooth morphology and optimizing the magnetic and transport properties.9 In this article, we propose a new way to increase this limit to much higher thicknesses, up to 75 nm at least, in order to push the LSMO based devices temperature operations closer to manganite TC. We report on epitaxial LSMO thin films deposited on SrTiO3 (100) (STO) by pulsed electron beam deposition in the channel spark ablation (CSA) configuration.8,10,11 Using atomic force microscopy (AFM) and scanning tunneling microscopy (STM), we confirm that the roughness of the films grown directly on STO depends on the deposition temperature. In particular, below a threshold deposition temperature of TR ∼ 1050 K, the growth is bi-dimensional (for thicknesses up to at least 100 nm) and the films are smoother than those grown above TR, which show a thickness induced roughening with a three dimensional growth above a certain thickness, ∼ 10 nm for STO (100) substrates. We also find that a strong magnetism is achievable only above TR where the films surfaces are rougher above 10 nm in thickness. The magnetic properties are nevertheless fully recovered, even for deposition below TR, when the film is deposited in a LSMO/seed-layer/STO design, where the seed layer is a film of 0.5 to 1.5 LSMO unit cells deposited at room temperature (RT) and then rapidly heated (∼ 100 °C/min) in oxygen to the film deposition temperature. Such film/substrate interface engineering preserves the film flatness characteristic of the depositions below TR and the magnetism typical of the depositions above TR. The seed layer approach acts on a roster of parameters rather than focusing on only ne and enables a comprehensive improvement of the LSMO film quality, rather than develop just one. This approach allows the deposition of thick (75 nm at least) LSMO films with atomically flat surfaces and robust bulk and surface magnetism, even though the growth is carried out at temperatures below TR. LSMO thin films were deposited at a rate of 0.1 ± 0.02 Å/pulse (∼0.025 unit cells per pulse, at a frequency of 6 Hz) by pulsed electron beam deposition in the CSA configuration using a commercial target; details of the deposition condition have been previously reported.10 The substrates were purchased from Crystal GmbH and Crystec GmbH and treated as already described.8 AFM measurements were conducted by a Nanoscope Multimode III in tapping mode, STM measurements were performed in an ultra high vacuum Omicron STM system with Pt/Ir tips. Magnetotransport and magneto-optic Kerr effect measurements were performed in an homemade system. X-ray diffraction (XRD) measurements were executed in a Siemens / Bruker D5000 XRD System. Scanning transmission electron microscopy (STEM) was performed with a JEOL 200cF ARM scanning instrument, equipped with a cold field emission gun and operated at 200 kV. Standard cross-sectional specimens for STEM investigation were fabricated using a FEI Dual Beam FIB Nova 200. The AFM images in Figure 1 show the surface evolution of LSMO thin films grown on identical STO substrates (same lot) deposited at Tdep ∼ 1100 K without the seed layer (left column) and deposited at Tdep ∼ 1000 K with the seed layer (right column). Details on the substrate cleaning procedure and on film deposition have been previously reported elsewhere.10 Films deposited at Tdep ∼ 1000 K without the seed layer and films deposited at Tdep ∼ 1100 K with the seed layer are not shown because the morphology is not distinguishable from the ones reported here for the same Tdep. The STO surface features terraces and 0.4 nm high steps, consistent with a single chemical termination. The LSMO surface evolution was studied by AFM on 15 nm and 35 nm thick films and by STM on 75 nm thick films. The root mean square (rms) roughness is reported in each image. The vertical scale for the films deposited at Tdep > TR without the seed layer increases up to 9 nm for the 75 nm thick film while for the films deposited at Tdep < TR with the seed layer the peak to valley roughness is confined below 1 nm – in the 75 nm film the brighter mounds are 2 nm height. It is evident that the rms roughness increases with the thickness of the films deposited directly on the STO, while it is constant, at about half unit cell, for films grown on the seed layer. The films deposited on the seed layer were grown below TR to ensure bi-dimensional growth, which is the reason for their low roughness – in the 75 nm film deposited with the seed layer approach the slightly higher rms roughness value (0.20 nm instead of 0.15 nm) is just ascribed to the higher sensitivity of the STM. In addition, the seed layer has a impressive impact on the magnetotransport properties as it will be shown later. FIG. 1. Surface evolution of the LSMO thin films, starting from the substrate (on top), for standard (left column) and engineered (right column) films. The evolution is studied by imaging films of different thicknesses (15, 35, 75 nm) as reported on the top of each row. All images were acquired using AFM, apart from the 75 nm film which was investigated by STM. The number in each image is the rms roughness. The existence of a temperature-dependent roughening transition is well known in inorganic semiconductor epitaxy and is related to the thermodynamics of the stepped surface. Below TR, the surface evolves by the evolution of each single step, while above it, the surface is free to reorganize under the influence of infinitesimally small thermodynamic driving forces.12,13 In the case of crystal growth from melt or from vapor, the roughening transition takes place when: α=ξε/kBTR∼2.5,(1) where ξ is the ratio between the number of first neighboring atoms in the surface and in the bulk (2/3 in a cubic lattice), ε is the energy required to extract a unit cell from the crystal and put it in the phase into which the crystal is growing (melt, vapor …) and can be regarded as the enthalpy of formation, TR is the roughening transition temperature. The surface roughens when α ≤ 2.5.14,15 This picture has also been applied to metal thin films deposited by sputtering.16 A cube-on-cube epitaxy picture is applicable for LSMO/STO because the substrates are cubic and LSMO grows epitaxially on top. Assuming TR ∼ 1050 ± 30 K, we obtain ε ∼ 0.34 eV ± 0.01 eV per unit cell, which corresponds to 33 KJ/mol. Such a value is in fair agreement with the value of about 98 KJ/mol found for bulk LSMO obtained by solid state reaction from simple oxides.17 This difference can be due to three reasons. (i) The solid state reaction happens at thermodynamic equilibrium but additional kinetic effects are expected for the highly energetic ionized species produced in the CSA technique.18 (ii) The key steps in the formation of the epitaxial manganite films occur at the sub-unit-cell level, before the formation of a complete perovskite unit cell layer.19 (iii) The co-deposition of multiple elements leads to a variety of potential side-reactions and bonding configurations not encapsulated by the simple model of Equation (1). Most notably, the value of 33 KJ/mol is very close to the oxidation enthalpy of Mn3+ in Mn4+ (23.8 KJ/mol) in LSMO,17 suggesting that the oxidation processes play a crucial role in the stabilization of the LSMO phase during thin film growth. We found a fascinating dependence of the magnetic properties of LSMO on the use of the seed layer and on the deposition temperature. Due to our setup temperature limitations, the study has been carried out on 15 nm nominal thick LSMO films having a TC < 360 K. All the films presented here are metallic over the whole temperature range apart from those deposited at lower temperature without the seed layer, which show a metal-insulator transition at 335 K. Figure 2 reports the data for these 15 nm thick LSMO. Figure 2(a) show the low field magnetoresistance, LFMR = (R0 – RH)/R0, versus temperature, where R0 is the resistance without an external magnetic field and RH is the resistance with an in-plane magnetic field of 80 mT parallel to the current direction. The R(H) curves are linear in this range of fields at all the temperatures below TC. The films were deposited at Tdep = 1100 K (Tdep > TR) or at 1000 K (Tdep < TR) as specified by the legend. For both temperatures, we report on films deposited with and without the seed layer – for films deposited with the seed layer, it is specified in the legend. It is possible to estimate TC from a linear extrapolation to zero of the LFMR(T) plot to the right of the peak.10 Using this method, the highest TC value is achieved at Tdep > TR without the seed layer (330 K); TC drops of about 25 K when the substrate temperature is reduced below TR (ΔT in the figure) indicating that the bi-dimensional growth impairs the magnetism. Nevertheless, TC = 325 K is found where the seed layer engineering is adopted even for Tdep < TR. Such a variation is inside the (thickness) reproducibility of our technique.10 As for the role of the seed layer in films deposited at Tdep > TR, we notice that the effect is absent. An even detrimental effect has been observed for LSMO films deposited on NdGaO3 (110) (NGO) and on (LaAlO3)0.3(Sr2TaAlO6)0.7 (100) (LSAT) (data not shown here), suggesting that the role of the seed layer depends on the strain. Indeed the mismatch between LSMO and NGO is 0.26 % and 0.39 % (compressive strain) along the two directions of the strained LSMO unit cell, while it is -0.89 % (tensile strain) in the case of STO and less than 0.05 % in the case of LSAT substrates. FIG. 2. LFMR versus T (a) for 15 nm LSMO films deposited on STO above and below TR and with and without the seed layer, according to the graphs legends; MOKE signal at room temperature (b) for the couples of samples with the higher TC. We can conclude that the seed layer has a great influence on the magnetic properties of LSMO when the films are deposited below TR, while no (or detrimental) effect is observed when the LSMO films are grown above TR. This is consistent with the fact that the low roughness in films deposited below TR is assumed to be related to a decreased surface diffusion constant,20 which can be influenced by the seed layer. Differently, above TR the diffusion constant is greater, the adatoms can reach their optimal position (from a crystalline point of view) and the effect of a seed layer is minimized. Therefore in the reminder of the paper we will compare the films deposited at Tdep > TR without the seed layer with the films deposited at Tdep < TR and with the seed layer (referred as “seed layer approach”). Magneto-optic Kerr effect (MOKE) measurements of the same films, with the field along the easy axis of the samples, are summarized in figures 2(b). Interestingly, the films grown on the seed layer show a harder surface magnetism, as evidenced by the larger coercivity of 0.5 mT compared with 0.2 mT. The coercive field (HC) values appear to be not linked to the TC values, defined by extrapolation to zero of the LFMR.10 Rather than HC, it is the shape of the magnetization cycle to be truly informative. Indeed the samples with the seed layer have more square cycles with the closing field corresponding to the HC, as single domain sample. On the contrary, the samples without the seed layer approach have closing fields higher than HC, which is an indication of an inhomogeneous magnetization process or multi domain sample. Thus the seed layer approach enables higher homogeneity and improved crystalline order. The geometrical phase analysis of the transmission electron microscopy data (Figure 4(e),4(f) and related comment below) support this picture. It is interesting to correlate the magnetic properties to the structure of standard films with those grown on the seed layer, as they exhibit similar magnetic properties but different morphologies. XRD and TEM were used to collect structural information on the films. In the case of XRD, a large set of films of different thicknesses, ranging from 5 nm to 34 nm (as measured by x-ray reflectivity) was studied. The main result is shown in Figure 3, where the XRD results for (a) a 15 nm thick film deposited at T > TR without the seed layer and (b) a 25 nm thick film deposited at T < TR with the seed layer are compared. From the (003) reflection it is possible to observe that both the films started to relax, the main difference comes from the full width at half maximum (FWHM) which is higher for the film deposited without the seed layer (0.653° versus 0.212°), although it is thinner. This implies that the seed layer approach improves the degree of order in the out of plane parameter. FIG. 3. XRD characterization (a) of a 15 nm LSMO/STO film deposited above TR without the seed layer and (b) of a 25 nm LSMO/STO deposited with the seed layer approach. The arrow marks the (003) reflection from the film, which is used to calculate the FWHM: 0.653 for (a) and 0.212 for (b). These results are confirmed by scanning transmission electron microscopy (STEM) analysis, reported in figure 4. STEM images are given in figures 4(a)-4(d), where the growth direction is from right to left across the image; the protective Pt layer can be seen to the left of the LSMO. As these are high-angle annular dark field images, the contrast derives primarily from atomic number variations, and the LSMO film appears brighter than the STO substrate. In the film deposited without the seed layer, figures 4(a), 4(c), the interface with the STO substrate is somewhat diffuse, indicating a degree of intermixing at the interface as the result of the high energy deposition. This is in direct contrast to that of the sample deposited with the seed layer approach (figures 4(b), 4(d)) where a sharper interface is observed. As the seed layer was deposited at room temperature in the engineered interface sample, the thermal energy for surface diffusion is lower and the atomic species in the plume can be considered as already thermalized since the oxygen pressure in the chamber is about 4 Pa,21 resulting in less atomic intermixing and a sharp interface. FIG. 4. (a)-(d): STEM characterization and strain analysis of (left column) a LSMO film deposited without the seed layer approach and (right column) a LSMO film deposited with the seed layer approach. (e) and (f): GPA analysis is performed over the regions highlighted by the red rectangles in (a) and (b). The seed layer is not noticeable at the interface in the TEM images, thus it should be part of the epitaxial system. This seems to be in contradiction with the fact that it is deposited at room temperature and hence most likely to be amorphous.22,23 The explanation we propose is that the amorphous seeded layer was transformed into epitaxial manganite film. Although the high growth temperature cannot be sufficient to restructure the seed layer,24,21 it looks realistic that the kinetic energy of the species in the plume can induce the crystallization of the seed layer during the subsequent film deposition. This also means that the material in the seed layer is utilized during the growth by the subsequent film; this available material not only changes the interaction between the early adatoms and the substrate but also acts as a material reservoir for the following film. This can explain why the out-of-plane parameters are more uniform in the films deposited with the seed layer approach (confirmed by the lower FWHM in XRD), suggesting also a different mechanism of the conservation of the unit cell volume. We can infer that the seed layer, which is a sort of material stock, moves the growth far from the intermediate proto-perovskite phase occurring when the seed layer is not used,19 because it provides a sufficient number of atoms to form a complete perovskite unit cell since the early atoms arrive during the subsequent deposition at Tdep. The geometric phase analysis (GPA) reported in Figure 4(e), 4(f), confirms that the interface engineering via the seed layer approach gives rise to excellent epitaxial growth with the substrate, comparable to or better than the optimized films deposited at Tdep > TR.10 When the seed layer approach is employed to engineer the substrate/film interface, the out-of-plane lattice parameter (i.e. along the growth direction) is more uniform across the interface and along the film growth direction, with less scattered data. The more uniform lattice constant at the substrate side in the sample with the seed layer is likely due to the seed layer itself, which imply a different film/substrate interaction at the interface, as we discuss in the next paragraph. A real understanding of the role played by the initial material reservoir is lacking and has not yet been considered in other growth models. We suggest that the improved performance, compared with the standard films, is related to (i) an improved epitaxial growth, due to the departure from the proto-perovskite intermediate scenario and to a modified density of the nucleation centers, and to (ii) a different accommodation of crystalline defects, accompanied by local strain relaxation, in the substrate during the substrate heating after the room temperature deposition of the seed layer.25 This looks likely when we keep in mind that even the best single crystal STO substrates have a dislocations surface density of 1011 cm−2, which corresponds to 100 dislocations per μm2.26 A different mechanism for their relaxation is expected to have a great impact on the film properties. FIG. 5. EELS analysis on the O-K edge of the sample regions highlighted in Figure 4(a), 4(b). The arrow indicates the growth direction. The main difference is in the pre-peak at 530 eV as discussed in the text. Electron energy loss spectroscopy (EELS) on the O-K and Mn-L2,3 edges were performed in order to obtain chemical information about the engineered interface. While in the case of the Mn-L2,3 edge no obvious changes were observed, a difference in the so-called pre-peak at 530 eV27 was observed on the O-K edge (Figure 5). In the sample with the seed layer approach (Fig. 5(b)) the distance between the pre-peak and the main peak is about 1 eV larger. Moreover this larger split feature appears throughout the whole film. In order to understand this point it has to be stressed that the O-K edge pre-peak has a strong contribution from Mn 3d band and so it is extremely sensitive to bonding features. For instance it has been observed in the Ca analogue of LSMO (LaxCa1−xMnO3) that the split between the two peaks increases with the divalent atomic content (Ca or Sr).27 Because the samples with the seed layer approach have a greater spacing between the pre-peak and the main peak, indicating a more Sr content,27 the seed layer approach also enables us to fix (at least partially) the known problem that that films without seed layer are slightly deficient in Sr.11 In summary, we show that LSMO deposited by pulsed electron deposition at T > TR undergoes a thickness induced roughening which is suppressed by decreasing the substrate temperature, resulting in a persistent bi-dimensional growth but with weakened magnetic and transport properties. The latter are recovered, while preserving the bi-dimensional growth, by using a seed layer (0.5 to 1.5 unit cells) of LSMO deposited at room temperature, resulting in the LSMO/LSMO(seed)/STO structure. Such interface engineered films display high quality epitaxial growth and exhibit excellent magnetic properties and low roughness. Although a possible growth mechanism responsible for this improvement is proposed, many important questions remain open, especially considering the quantitative description. We believe that engineering the interface by seed layers represents a powerful tool for the simultaneous control of the film properties and roughness in various complex oxides. ACKNOWLEDGMENTS The authors thank Federico Bona for technical help. The use of the scanning probe microscopy laboratory at the “Centro Interfacoltà Misure” of the University of Parma is acknowledged. Financial support from the FP7 projects NMP3-LA-2010-246102 (IFOX), NMP-2010-SMALL-4-263104 (HINTS), NMP3-SL-2010-246073 (GRENADA), the DFG in the SFB762 project, and the Italian government FIRB project n°RBAP117RWN is acknowledged. ==== Refs REFERENCES 1. M. Bowen , J.-L. Maurice , a Barthélémy , M. Bibes , D. 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==== Front eNeuroeNeuroeneuroeneuroeNeuroeNeuro2373-2822Society for Neuroscience 10.1523/ENEURO.0058-16.2016eN-NWR-0058-165New ResearchIntegrative SystemsPhotoperiodic Regulation of Cerebral Blood Flow in White-Footed Mice (Peromyscus leucopus) Photoperiod and Brain Blood Flowhttp://orcid.org/0000-0002-5419-0271Borniger Jeremy C. 123Teplitsky Seth 4http://orcid.org/0000-0001-8980-4889Gnyawali Surya 4http://orcid.org/0000-0002-8194-4016Nelson Randy J. 123http://orcid.org/0000-0002-3149-3624Rink Cameron 41 The Behavioral Neuroendocrinology Group, Wexner Medical Center, The Ohio State University, Columbus, Ohio 432102 Department of Neuroscience, Wexner Medical Center, The Ohio State University, Columbus, Ohio 432103 Neuroscience Research Institute, Wexner Medical Center, The Ohio State University, Columbus, OH 432104 Department of Surgery, Wexner Medical Center, The Ohio State University, Columbus, Ohio 43210The authors declare no competing financial interests. Author contributions: J.C.B., R.J.N., and C.R. designed research; J.C.B., S.T., S.G., and C.R. performed research; C.L.R. contributed unpublished reagents/analytic tools; J.C.B., S.T., S.G., and C.R. analyzed data; J.C.B., R.J.N., and C.R. wrote the paper. This research was supported by National Science Foundation Division of Integrative Organismal Systems Grant 11-18792 to R.J.N., Presidential and Pelotonia Fellowships to J.C.B., and, in part, by American Heart Association Grant 12SDG11780023 to C.R. Correspondence should be addressed to Jeremy C. Borniger, The Ohio State University Wexner Medical Center, 636 Biomedical Research Tower, 460 West 12th Avenue, Columbus, OH 43210. E-mail: jeremy.borniger@osumc.edu.14 7 2016 27 7 2016 Jul-Aug 2016 3 4 ENEURO.0058-16.201624 2 2016 5 7 2016 5 7 2016 Copyright © 2016 Borniger et al.2016Borniger et al.This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.Abstract Individuals living outside the tropics need to adjust their behavioral and physiological repertoires throughout the year to adapt to the changing seasons. White-footed mice (Peromyscus leucopus) reduce hippocampal volumes, hippocampal-dependent memory function, long-term potentiation, and alter neurogenesis in response to short (winter-like) day lengths (photoperiods). During winter, these mice putatively shunt energy away from the brain to maximize peripheral thermogenesis, immune function, and survival. We hypothesized that these changes in brain function are accompanied by alterations in brain vasculature. We maintained white-footed mice in short (8 h light/16 h dark) or long (16 h light/8 h dark) photoperiods for 8–9 weeks. Mice were then perfused with fluorescein isothiocyanate (FITC)-conjugated tomato (Lycopersicon esculentum) lectin to visualize the perfused cerebrovasculature. Short-day mice reduced hippocampal and cortical capillary density (FITC+ area); vessels isolated from short day-exposed mice expressed higher mRNA levels of the gelatinase matrix metalloproteinase 2 (MMP2). Additionally, short-day mice reduced cerebral blood flow ∼15% compared with their long-day counterparts, as assessed by laser speckle flowmetry. Immunohistochemistry revealed higher levels of MMP2 in the hippocampus of mice maintained in short days compared with long days, potentially contributing to the observed vascular remodeling. These data demonstrate that a discrete environmental signal (i.e., day length) can substantially alter cerebral blood flow in adult mammals. brain blood flowhippocampusperomyscusphotoperiodNational Science FoundationIOS 11-18792American Heart Association12SDG11780023 cover-dateJuly/August 2016 ==== Body Significance Statement Individuals living in nontropical climates show seasonal changes in physiology and behavior primarily controlled by day length (photoperiod). Specifically, white-footed mice (Peromyscus leucopus) display reduced hippocampal function, neurogenesis, and cognitive capacity in response to short days. It is unknown, however, whether these changes are preceded or accompanied by alterations in cerebral blood flow. This study provides evidence that short days elicit a reduction in hippocampal and cortical blood flow, and that this reduction is accompanied by increased mRNA and protein expression of the gelatinase matrix metalloproteinase 2, a primary component in vascular remodeling. These results have broad implications for our understanding of postnatal brain plasticity, environmental modulation of behavior, and seasonal changes in brain function. Introduction Adult mammalian brains have some level of plasticity; however, environmental contributors to brain plasticity remain poorly understood. Nontropical rodents display seasonal variation in many aspects of their physiology and behavior (for review, see Follett, 2015), providing attractive models in which to study brain plasticity, development, and natural variation (Vrana et al., 2014; Bedford and Hoekstra, 2015). White-footed mice (Peromyscus leucopus) are among the most well studied photoperiodic small rodents (Lynch, 1973; Johnston and Zucker, 1980; Demas et al., 1996; Walton et al., 2014; Sharp et al., 2015;). Many seasonal changes in their physiology and behavior are driven by predictable changes in photoperiod (day length) across the year. For instance, in response to autumnal short day (SD) lengths, many white-footed mice regress their reproductive systems and reputedly shift energy toward survival, although there is substantial variation among populations living at different latitudes (Dark et al., 1983; Pyter et al., 2005a). Pineal melatonin plays an important role in this process; most short photoperiod-dependent phenotypic changes can be recapitulated under long-photoperiod conditions via the administration of exogenous melatonin (Bartness et al., 1993; Goldman, 2001; Hiebert et al., 2006; Walton et al., 2013). Additionally, short days impair spatial learning and hippocampal long-term potentiation, reduce hippocampal volumes, alter hippocampal dendritic complexity, and reduce hippocampal neurogenesis (Pyter et al., 2005b; Walton et al., 2011, 2013, 2014). In tandem with changes in the hippocampus, white-footed mice alter neuronal spine densities within the basolateral amygdala in response to short photoperiods, and these changes are associated with enhanced fear memory (Walton et al., 2012). These alterations in brain function are likely preceded or mirrored by changes in brain vascularity, as the brain is an energetically expensive organ (Engl and Attwell, 2015), and any reduction in size or vascular perfusion would confer significant energetic savings. To test this hypothesis, we maintained adult male P. leucopus mice in long- or short-photoperiod conditions for 8 weeks, and then assessed capillary density and cerebral blood flow via fluorescein isothiocyanate (FITC)-lectin perfusion and laser speckle flowmetry (LSF). We further laser captured FITC+ hippocampal endothelial cells from brain tissue and assessed the expression of genes related to vascular remodeling. We predicted that animals maintained under short-day conditions would reduce cerebrovascular density and flow compared with their counterparts maintained in long day (LD) conditions, and changes in these measures would be accompanied by altered gene expression profiles in cerebral capillaries. Materials and Methods Animals Adult (>8 weeks old), male, white-footed mice (P. leucopus) were purchased from the Peromyscus Genetic Stock Center (University of South Carolina, Columbia, SC; RRID: SCR_002769). These mice were born into long-photoperiod (16 h light/8 h dark) conditions and maintained in this lighting condition until shipment to our laboratory. Upon arrival at our facility, mice were allowed to recover from the stress of shipping and then assigned randomly to either SDs (8 h light/16 h dark) or LDs (16 h light/8 h dark). Mice were singly housed and maintained in their experimental lighting conditions for 8–9 weeks. Throughout the course of the experiment, mice were supplied with ad libitum filtered tap water and chow (catalog #7912, Harlan Teklad), a cotton nestlet, and a piece of plastic housing enrichment. Weekly cage changes and body mass measures were the only physical disturbances throughout the experiment. Following each experiment, the brain (cohort 1), seminal vesicles (cohort 2), and paired testes were dissected. Organ masses were taken using an analytical balance (AE 240, Mettler Toledo). All procedures and experiments described below were approved by our affiliated Institutional Animal Care and Use Committee. Cohort 1 was used for lectin, immunohistochemistry, and laser capture experiments (described below). From this cohort, we originally started with 11 mice in the LD condition and 10 mice in the SD condition. During lectin perfusion, two LD mice had cardiac arrest prior to 5 min of lectin circulation and were excluded from analyses (LD = 9). Of the remaining mice in LD and SD conditions, four were randomly selected from each group for lectin visualization alone. The remaining samples were conserved to enable laser capture microdissection (LCM) studies. For LCM, sufficient sample from two SD lectin mice remained to include with untouched sample blocks (eight SD mice for LCM, five LD mice for LCM). After LCM, three blocks from the SD and LD groups remained for matrix metalloproteinase 2 (MMP2) staining of hippocampus (three SD mice for MMP2 staining, three LD mice for MMP2 staining). Cohort 2 was used for laser speckle flowmetry (LSF), where no mice were excluded from analyses (nine SD mice, eight LD mice). All mice were used for somatic and reproductive tissue measurements. Only mice in cohort 1 had their brains weighed, and only mice in cohort 2 had their seminal vesicles weighed, as this is the most robust measure of reproductive regression in response to photoperiod. Blood vessel quantification After 8 weeks in their respective photoperiod conditions, mice were deeply anesthetized via intraperitoneal injection of 120 mg/kg ketamine and 24 mg/kg xylazine in a vehicle containing 0.9% sodium chloride. Upon sedation, mice were transcardially perfused with 250 μl of FITC-tagged tomato (Lycopersicon esculentum) lectin over the course of 1 min (0.5 mg/ml; Vector Laboratories). This lectin binds to complex-type N-glycans glycoproteins found on the luminal surface of capillary endothelial cells, enabling morphological visualization and quantification specifically of capillary bed features (Robertson et al., 2015). This technique specifically allows for the visualization of patent (open) vessels and leaves inactive vessels unstained (Inai et al., 2004). Importantly, cerebral capillary density is recognized as a key variable in the study of brain angioplasticity in response to metabolic acclimatization (Boero et al., 1999; Benderro et al., 2012; Benderro and LaManna, 2014). To that end, our lectin perfusion approach was not designed for staining/quantification of large arteries or veins, but speaks to adaptive capillary remodeling in response to changes in photoperiod. FITC–lectin was allowed to circulate for 5 min, after which the mouse was decapitated; and brains were dissected, weighed, embedded in O.C.T. compound (Sakura), and flash frozen in liquid nitrogen. Twelve micrometer serial sections were cut on a cryostat directly onto Superfrost Plus slides. DAPI was used as a nuclear counterstain. The FITC–lectin signal in the hippocampus, cortex, and amygdala (Fig. 1, representative image) was determined by a condition-blinded observer using the AutoMeasure plug-in within Axiovert software (version 4.8, Zeiss), as described previously (Khanna et al., 2013, 2015). Figure 1. Representative 12 μm coronal section of P. leucopus brain. Regions used for FITC–lectin blood vessel quantification are delineated as hippocampus (1), cortex (2), and amygdala (3). The section is counterstained with DAPI. Immunohistochemistry Immunohistochemical determination of MMP2 expression was performed as previously described (Rink et al., 2011; Khanna et al., 2013). Twelve micrometer sections were blocked in 10% normal goat serum, followed by an overnight (4°C) incubation with primary antibody (0.5 µg/ml; anti-MMP2, Abcam; RRID:AB_10864041). Signal was visualized by reaction with fluorescent secondary antibody (30 min incubation at room temperature; goat anti-rabbit Alexa Fluor 568, Life Technologies). Sections were placed in DAPI for 5 min prior to being coverslipped. Images were captured using an Axiovert 200M microscope (Zeiss), and expression was quantified as the percentage area in hippocampus, amygdala, and S1 cortex regions of interest (ROIs) using the AutoMeasure plug-in within Axiovert software (version 4.8, Zeiss; Fig. 1). ROIs were standardized across sections and localized using a mouse stereotaxic atlas (Paxinos and Franklin, 2004). Observation of no staining following omission of the primary antibody from the primary incubation acted as a negative control. Laser capture and quantitative PCR The 12-µm-thick sections for LCM were mounted onto RNase inhibitor-treated thermoplastic (polyethylene napthalate)-covered glass slides (PALM). FITC–lectin perfused blood vessels from the hippocampus were collected using MicroLaser, MicroBeam, and RoboStage/RoboMover systems (PALM). More than 100,000 µm2 of capture elements were collected from each sample for downstream RNA isolation, cDNA synthesis, and quantitative PCR (qPCR). After laser cutting, the isolated vessels were catapulted directly into 35 µl of RNA extraction buffer (PicoPure RNA Isolation Kit, Life Technologies) situated directly above the section in a microtube cap. An additional 15 µl of extraction buffer was added after collection, and RNA was isolated from captured and catapulted elements. cDNA was synthesized from >250 ng of RNA using oligo-dT primer and Superscript III. cDNA was then quantified using SYBR Green-I in a real-time PCR reaction. Relative gene expression was standardized to 18S ribosomal RNA expression. Primer sequences are available in Table 1. These genes were chosen because they are associated with vascular remodeling, and we have previously examined many of them in the context of reproductive regression in P. leucopus (Pyter et al., 2005a). Verification of endothelial cell enrichment in laser-captured samples was confirmed via high expression of Von Willebrand factor (VWF), and low expression of glial fibrillary acidic protein (GFAP) and neurofilament heavy (NFH). Table 1: Primer sequences used in qPCR Gene Forward primer 5'-3' Reverse primer 5'-3' Tm Vegf CCA GGC TGC ACC CAC GAC AG TGA GGT GTG GGG GCT GCT GT F: 63.5R: 64.1 Hif1α CTG TGA TGA AAG AAT TAC TGA GTT GAT G CAT AAA TTG AGC GGC CCA AA F: 54.3R: 53.9 Tgfβr3 CAG GAC CAG CTC GAT GGA A CAC CAG GAA GAG GTC TGT TGT TAT ACA F: 56.9R: 58.0 Timp1 CAG TCC CTG CCG CCA TCG TC TGT GGG TGG AGT GGG GCA CA F: 63.2R: 64.1 Clic1 TGG ACC GAG CGG AGG GTC TG CCA TGG TTG CGT CGG GGA CC F: 63.8R: 63.6 Mmp2 CAC AAG TGG CCT GGG GAG CG GCG TGG CTT CCG CAT GGT CT F: 63.6R: 63.2 Csf2 CTG CTC CCA CTC GCT CAC CC AGG TTG CCC CGT AGG CCC TT F: 62.8R: 64.0 18s GTA ACC CGT TGA ACC CCA TT CCA TCC AAT CGG TAG TAG CG F: 55.3R: 55.1 Vwf CCG GAA GCG ACC CTC AGA CGG TCA ATT TTG CCA AAG ATC T F: 59.5R: 54.0 Gfap CAC GTG GAG ATG GAT GTG GC CAG TTG GCG GCG ATA GTC ATT A F: 58.4R: 57.2 Nfh CGA GCT GTA CGA GCG CGA GG AGC TCG CCC ACC TCC TCC TG F: 62.8R: 64.1 F, Forward; R, reverse. Laser speckle flowmetry A second subset of mice was housed in photoperiodic (8 h light/16 h dark and 16 h light/8 h dark) and housing conditions similar to those used in the first experiment. These mice were supplied tap water and food (catalog #TD 01432-I, Harlan-Teklad, supplemented with fenbendazole) ad libitum. After 8–9 weeks in their respective photoperiod conditions, mice were deeply anesthetized in isoflurane (4–5% induction, 1.5% maintenance) and positioned for laser speckle flowmetry. The skin of the scalp was cut along the sagittal plane to expose the braincase for imaging. A bolus of saline was applied to the skullcap to normalize the effect of light refraction between animals. LSF recordings of the neocortex were acquired from a 1 × 1 cm field of view using a 785 nm, 80 mW laser with a sampling rate of 60 Hz at a working distance of 10 cm (PeriCam PSI HR System, PeriMed). Relative perfusion units were averaged over a 10 s sampling period. Statistics Group means were compared using two-tailed independent-samples t tests. If groups displayed unequal variances or data was non-normally distributed, nonparametric tests (i.e., Mann–Whitney U test) were used. Statistical significance was set at p ≤ 0.05. Statistics were completed with SPSS version 22 (IBM) and visualized using Prism version 5.0 (GraphPad Software). Table 2 contains more information regarding the statistical results presented. Table 2: Statistical table Figure Panel *Data structure Test type Observed power†/p value 2 A Normal t test 0.108/p = 0.476 2 B Normal t test 0.052/p = 0.901 2 C Normal t test 0.097/p = 0.512 2 D Normal t test 0.238/p = 0.213 3 D (hippocampus) Normal t test 0.742/p = 0.02 3 D (cortex) Non-normal Mann–Whitney U 0.517/p = 0.029 3 D (amygdala) Normal t test 0.052/p = 0.871 4 B Normal t test 0.619/p = 0.027 5 A (MMP2) Non-normal Mann–Whitney U 0.635/p = 0.019 5 B Normal t test 1/p = 0.00019 *Normality tested using Shapiro-Wilk Test. †Observed power calculated with G*Power version 3.1.7; statistical analyses were completed with SPSS Statistics (IBM) version 22. Results Somatic and reproductive masses After 8 weeks in their respective photoperiod conditions, both cohorts of mice did not differ in body mass (cohort 1: t = 1.596, p = 0.126; cohort 2: t = 0.6, p = 0.557), brain mass (cohort 1: t = 0.913, p = 0.373), brain mass corrected for body mass (cohort 1: t = 0.126, p = 0.901), testes mass (cohort 1: t = 0.649, p = 0.524; cohort 2: t = 1.196, p = 0.250), or mass of the seminal vesicles (cohort 2: t = 0.672, p = 0.512; Fig. 2). These data indicate that reproductive responses to photoperiod were not apparent, suggesting a photoperiod-nonresponsive phenotype (Majoy and Heideman, 2000). Alternatively, previous reports have suggested that an exposure of 10–12 weeks elicits a maximal photoperiodic response (Pyter et al., 2005a; Walton et al., 2012). Figure 2. A–D, Eight weeks of short-day exposure did not cause gross body mass loss (A), reduction in brain mass (B), or reproductive regression (C, D), as measured by seminal vesicle and testes masses (note: brain weights were measured only for cohort 1, and seminal vesicle weights were measured only for cohort 2). Error bars represent the SEM. Short photoperiods reduce central capillary density and cerebral blood flow Mice maintained in short-day conditions had reduced FITC–lectin signal in the hippocampus (SD mean, 0.0108 ± 0.003; LD mean, 0.0268 ± 0.009; t = 3.13, p = 0.02) and cortex (SD mean, 0.0232 ± 0.015; LD mean, 0.0775 ± 0.041; U = 16, p = 0.029), but not the amygdala (SD mean, 0.033 ± 0.0175; LD mean, 0.0363 ± 0.034; t = 0.169, p = 0.871; Fig. 3) compared with their LD counterparts. Furthermore, SD mice had an ∼15% reduction in cortical blood flow compared with their LD counterparts (SD mean, 311.95 ± 52.44; LD mean, 367 ± 40.37; t = 2.442, p = 0.027; Fig. 4). These data provide evidence that short photoperiods decrease hippocampal and cortex capillary density, suggesting reduced cortical blood flow. Figure 3. Short days reduce blood vessel density in the hippocampus and cortex. A–C, Representative images of hippocampus (A), cortex (B), and amygdala (C) in LD (top row) and SD (bottom row) mice. Quantification of the mean ± SEM per group are presented for these brain regions in D. *p < 0.05. N = 4/group. Figure 4. Short days reduce cerebral blood flow by ∼15%. A, Representative speckle contrast images of the brain of an LD and an SD mouse. B, Quantification of relative perfusion units between LD and SD animals. N = 9 LD, 8 SD. Error bars represent the SEM. *p < 0.05. Short photoperiods increase hippocampal MMP2 expression Hippocampal FITC+ blood vessels from SD mice expressed higher levels of the gelatinase MMP2 (fold change from LD, 5.77 ± 5.37; U = 4, p = 0.019) compared with LD mice (Fig. 5C). No changes were detected in any of the other gene candidates examined (p > 0.05 in all cases). Additionally, SD mice increased MMP2 expression in the hippocampus (Fig. 5B) compared with LD mice (SD mean, 0.069 ± 0.0063; LD mean, 0.02 ± 0.00085; t = 13.18, p = 0.00019). We focused on the hippocampus for these measures because a large amount of research has demonstrated short-day reductions in hippocampal function and hippocampal-dependent tasks (Pyter et al., 2005b; Walton et al., 2013). These data indicate that short-day exposed mice had greater hippocampal expression of MMP2 at the mRNA and protein level compared with LD mice. Figure 5. Short days increased MMP2 mRNA expression in brain endothelial cells and MMP2 protein in the hippocampus. A, Gene expression in laser-captured hippocampal FITC+ endothelial cells reveal higher MMP2 expression in short days. B, Immunohistochemical quantification of MMP2 staining in hippocampus (N = 3/group). C, Representative immunofluorescent images of an LD and an SD housed mouse stained with anti-MMP2 primary Ab, DAPI, and FITC-lectin. Error bars represent the SEM. *p < 0.05, ***p < 0.001. Discussion Together, these data demonstrate that day length can alter brain blood vessel dynamics in adult mammals. Mice maintained in short-day conditions had decreased cortical and hippocampal perfusion (Figs. 3, 4) and increased MMP2 mRNA expression in brain capillaries and MMP2 in the brain parenchyma (Fig. 5). Previous research has examined other environmental contributors that increase central blood vessel formation. It is well documented that environmental enrichment or motor activity can increase central angiogenesis and synaptogenesis in rats (Black et al., 1987, 1990; Sirevaag et al., 1988; Ding et al., 2006). Environmental contributors that decrease central blood flow, however, remain undefined. P. leucopus display seasonal changes in cognitive capacity; short days decrease performance on hippocampal dependent tasks, hippocampal neurogenesis, and dendritic spine densities (Pyter et al., 2005b; Walton et al., 2014). Our data suggest that this phenomenon is accompanied by capillary remodeling in the hippocampus and cortex; thus, short-day reductions in blood flow may precede declines in brain function. It is important to note that due to limitations in the penetration depth of the laser speckle instrument, we were unable to reliably measure hippocampal-specific blood flow using this method. However, changes in blood flow within the hippocampus are evident as FITC-lectin binds only active capillary beds when administered via transcardial perfusion, leaving inactive blood vessels unstained. Additionally, because our lectin perfusion method selectively stains capillary beds (Robertson et al., 2015), it was important to test whether changes in these microvessels resulted in measurable changes in blood flow. Regulation of testicular angiogenesis in P. leucopus has been studied in the context of photoperiod-induced changes in reproduction. In the testes, short days trigger the expression of Hif1α, Serpine1, and Tgfβr3, inhibiting angiogenesis and halting reproductive function during testicular regression (Pyter et al., 2005a). Reduced blood flow to the brain coincident with gonadal regression may allow for animals to trade off reproductive function and cognitive capacity for somatic maintenance and thermogenesis during the impending winter months. We found no evidence of reproductive regression in the present study, indicating that changes within the brain can occur independently from photoperiodic regulation of the reproductive system. MMPs act to degrade a variety of extracellular matrix proteins and participate in vessel remodeling, apoptosis, cell migration, proliferation, and host defense (Chakraborti et al., 2003). MMP2 (gelatinase A) primarily catalyzes the breakdown of type IV collagen, a component of endothelial basement membranes. In the brain, MMP2 can be produced by neurons, glia, and endothelial cells (Planas et al., 2001). It also plays a large role in endometrial remodeling during menses and the regulation of vascularization in response to immune activation (Freitas et al., 1999; Parks et al., 2004). MMP2 is specifically implicated in seasonal changes in reproductive function in Siberian hamsters (Phodopus sungorus), another seasonally breeding small rodent (Salverson et al., 2008; Shahed et al., 2015a,b). In this species, MMP2 facilitates photostimulated ovarian recrudescence after extended short-day exposure. That is, MMP2 is rapidly induced in the ovaries after transfer from short to long photoperiods. It is interesting to note that MMP2 in the ovaries of P. sungorus is elevated in response to long photoperiods, while in the brains of P. leucopus that were used in our study it was increased in response to short photoperiods. Although widely implicated in angiogenesis, the initial steps of MMP2 action involve the fragmentation of the capillary basal lamina, leading to downstream migration of endothelial cells in response to angiogenic factors (Sang, 1998). In P. leucopus, the initial steps of basement membrane breakdown occur (as evidenced by increased MMP2 expression), but the subsequent formation of functional blood vessels seems to be impaired in SD mice (reduced FITC–lectin signal). In support of this, we found no evidence of increased vascular endothelial growth factor (VEGF) expression in short-day mouse brain endothelium (Fig. 5A). In sum, our data demonstrate a role of photoperiod in the regulation of central blood flow in an adult mammal. Further experiments should address whether the transition back into a long photoperiod reverses short day-induced vessel reductions, the time course of blood vessel remodeling, and the role that melatonin or other seasonally regulated molecules play in the phenotype we observed. Additionally, these data provide an impetus for investigating seasonal rhythms in cardiovascular disease (Ricci et al., 1992; Sheth et al., 1999), because the short days of winter may be an overlooked but important contributor, although this remains to be determined. It is important to note that our data represent a “snapshot” of the dynamic changes occurring in response to short photoperiods; and because we did not observe reproductive regression, our findings may represent an intermediate phenotype. However, independent photoperiodic regulation of the reproductive system and the brain has been described in a closely related species of California mice (Peromyscus californicus), where short photoperiods induce aggressive behavior without reproductive regression, a phenotype that can be recapitulated with the administration of exogenous melatonin (Nelson et al., 1995; Laredo et al., 2014). Additionally, there is high heritability in reproductive nonresponsiveness to short photoperiods in P. leucopus (Heideman et al., 1999), and photoperiodic responsiveness within the same Peromyscus species depends on their latitude of origin (Dark et al., 1983). Furthermore, short photoperiod-induced changes in hippocampal neurogenesis are evident in as few as 2 weeks of short-photoperiod exposure, well before reproductive regression occurs (Walton et al., 2014). These data indicate that changes within the CNS of Peromyscus species can be uncoupled from alterations in the reproductive system. Acknowledgments: We thank The Ohio State University Laboratory Animal Resource personnel for the excellent care they provided the animals used in these studies. Synthesis The decision was a result of the Reviewing Editor Rae Silver and the peer reviewers coming together and discussing their recommendations until a consensus was reached. A fact-based synthesis statement explaining their decision and outlining what is needed to prepare a revision is listed below. The following reviewer(s) agreed to reveal their identity: Michael Lehman The rebuttal letter is very clear and well documented with suitable references. However, some of the points made in the rebuttal letter are not included in the revised manuscript. It is necessary to provide the missing information in order to strengthen the manuscript. The following information appears to be lacking in the revised manuscript. 1)A more detailed quantification (per vessel type: arteries, veins, cortical penetrating vessels, capillaries) seems fundamental to understand the vascular remodeling dynamics. This might require additional time-points along the 8 weeks (and in the reversal process, as in comment above). FITC-tagged tomato (Lycopersicon esculentum) binds to complex-type N-glycans glycoproteins found on the luminal surface of capillary endothelial cells, enabling morphological visualization and quantification of capillary bed features (Robertson et al, 2015). ....etc 2)It is unclear why some animals were excluded from analysis. The rebuttal makes the numbers of animals clear, and this information should be provided somewhere in the text. It is not clearly explained at present. We apologize for the lack of clarity in this regard, and have amended the manuscript to include reasoning for the number of animals used in each analysis. Cohort 1 was used for lectin, MMP2, and laser capture experiments. From this cohort, we originally started with 11 mice in the LD condition, and 10 mice in the SD condition. During lectin perfusion, 2 LD mice had cardiac arrest prior to 5min of lectin circulation and were excluded from analyses (LD = 9). Of the remaining mice in LD and SD conditions, 4 were randomly selected from each group for lectin visualization alone. Remaining samples were conserved to enable laser capture microdissection (LCM) studies. For LCM, sufficient sample from 2 SD lectin mice remained to include with untouched sample blocks (SD = 8, LD = 5 for LCM). After LCM, 3 blocks from SD and LD groups remained for MMP2 staining of hippocampus (SD = 3, LD = 3 for MMP2 staining). Cohort 2 was used for laser speckle flowmetry, where no mice were excluded from analyses (SD = 9, LD = 8). All mice were used for somatic and reproductive tissue measurements. Only mice in cohort 1 had their brains weighed, and only mice in cohort 2 had their seminal vesicles weighed, as this is the most robust measure of reproductive regression in response to photoperiod. 3)Please explain what an "active" blood vessel might be in contrast to an "inactive" blood vessel, and provide a reference. We apologize for the lack of clarity on this subject. Because the FITC-tagged tomato lectin only travels through (and binds) active capillary vasculature when transcardially perfused, it is a reliable index of blood flow (i.e., it does not reach inactive vessels). 4)Some references in the rebuttal letter are omitted from the revised text. Please correct or explain the rationale for the omissions.( For example, Heideman et al 1999). ==== Refs References Bartness TJ , Powers JB , Hastings MH , Bittman EL (1993 ) The timed infusion paradigm for melatonin delivery: what has it taught us about the melatonin signal, its reception, and the photoperiodic control of seasonal responses? J Pineal Res 15 :161 -190 . 8120796 Bedford NL , Hoekstra HE (2015 ) Peromyscus mice as a model for studying natural variation . eLife 4 :e06813 . 10.7554/eLife.06813 26083802 Benderro GF , LaManna JC (2014 ) HIF-1α/COX-2 expression and mouse brain capillary remodeling during prolonged moderate hypoxia and subsequent re-oxygenation . Brain Res 1569 :41 -47 . 10.1016/j.brainres.2014.04.035 24796880 Benderro GF , Sun X , Kuang Y , LaManna JC (2012 ) Decreased VEGF expression and microvascular density, but increased HIF-1 and 2α accumulation and EPO expression in chronic moderate hyperoxia in the mouse brain . Brain Res 1471 :46 -55 . 10.1016/j.brainres.2012.06.055 22820296 Black JE , Sirevaag AM , Greenough WT (1987 ) Complex experience promotes capillary formation in young rat visual cortex . Neurosci Lett 83 :351 -355 . 2450317 Black JE , Isaacs RK , Anderson BJ , Alcantara AA , Greenough WT (1990 ) Learning causes synaptogenesis, whereas motor activity causes angiogenesis, in cerebellar cortex of adult rats . Proc Natl Acad Sci U S A 87 :5568 -5572 . 10.1073/pnas.87.14.5568 1695380 Boero JA , Ascher J , Arregui A , Rovainen C , Woolsey TA (1999 ) Increased brain capillaries in chronic hypoxia . J Appl Physiol 86 :1211 -1219 . 10194205 Chakraborti S , Mandal M , Das S , Mandal A , Chakraborti T (2003 ) Regulation of matrix metalloproteinases: an overview . Mol Cell Biochem 253 :269 -285 . 14619979 Dark JO , Johnston PG , Healy M , Zucker IR (1983 ) Latitude of origin influences photoperiodic control of reproduction of deer mice (Peromyscus maniculatus) . Biol Reprod 28 :213 -220 . 6681991 Demas GE , Klein SL , Nelson RJ (1996 ) Reproductive and immune responses to photoperiod and melatonin are linked in Peromyscus subspecies . J Comp Physiol A 179 :819 -825 . 8956499 Ding YH , Li J , Zhou Y , Rafols JA , Clark JC , Ding Y (2006 ) Cerebral angiogenesis and expression of angiogenic factors in aging rats after exercise . Curr Neurovasc Res 3 :15 -23 . 16472122 Engl E , Attwell D (2015 ) Non‐signalling energy use in the brain . J Physiol 593 :3417 -3429 . 10.1113/jphysiol.2014.282517 25639777 Follett BK (2015 ) "Seasonal changes in the neuroendocrine system": some reflections . Front Neuroendocrinol 37 :3 -12 . 10.1016/j.yfrne.2014.11.003 25462591 Freitas S , Meduri G , Le Nestour E , Bausero P , Perrot-Applanat M (1999 ) Expression of metalloproteinases and their inhibitors in blood vessels in human endometrium . Biol Reprod 61 :1070 -1082 . 10491646 Goldman BD (2001 ) Mammalian photoperiodic system: formal properties and neuroendocrine mechanisms of photoperiodic time measurement . J Biol Rhythms 16 :283 -301 . 11506375 Heideman PD , Bruno TA , Singley JW , Smedley JV (1999 ) Genetic variation in photoperiodism in Peromyscus leucopus: geographic variation in an alternative life-history strategy . J Mammal 80 :1232 -1242 . 10.2307/1383173 Hiebert SM , Green SA , Yellon SM (2006 ) Daily timed melatonin feedings mimic effects of short days on testis regression and cortisol in circulation in Siberian hamsters . Gen Comp Endocrinol 146 :211 -216 . 10.1016/j.ygcen.2005.11.004 16386252 Inai T , Mancuso M , Hashizume H , Baffert F , Haskell A , Baluk P , Hu-Lowe DD , Shalinsky DR , Thurston G , Yancopoulos GD , McDonald DM ( 2004 ) Inhibition of vascular endothelial growth factor (VEGF) signaling in cancer causes loss of endothelial fenestrations, regression of tumor vessels, and appearance of basement membrane ghosts . Am J Pathol 165 :35 -52 . 10.1016/S0002-9440(10)63273-7 15215160 Johnston PG , Zucker I (1980 ) Photoperiodic regulation of reproductive development in white-footed mice (Peromyscus leucopus) . Biol Reprod 22 :983 -989 . 7397311 Khanna S , Rink C , Ghoorkhanian R , Gnyawali S , Heigel M , Wijesinghe DS , Chalfant CE , Chan YC , Banerjee J , Huang Y , Roy S , Sen CK (2013 ) Loss of miR-29b following acute ischemic stroke contributes to neural cell death and infarct size . J Cereb Blood Flow Metab 33 :1197 -1206 . 10.1038/jcbfm.2013.68 23632968 Khanna S , Heigel M , Weist J , Gnyawali S , Teplitsky S , Roy S , Sen CK , Rink C (2015 ) Excessive α-tocopherol exacerbates microglial activation and brain injury caused by acute ischemic stroke . FASEB J 29 :828 -836 . 10.1096/fj.14-263723 25411436 Laredo SA , Orr VN , McMackin MZ , Trainor BC (2014 ) The effects of exogenous melatonin and melatonin receptor blockade on aggression and estrogen-dependent gene expression in male California mice (Peromyscus californicus) . Physiol Behav 128 :86 -91 10.1016/j.physbeh.2014.01.039 24518867 Lynch GR (1973 ) Seasonal changes in thermogenesis, organ weights, and body composition in the white-footed mouse, Peromyscus leucopus . Oecologia Dec 1;13 (4 ):363 -376 . Majoy SB , Heideman PD (2000 ) Tau differences between short-day responsive and short-day nonresponsive white-footed mice (Peromyscus leucopus) do not affect reproductive photoresponsiveness . J Biol Rhythms 15 :501 -513 . 10.1177/074873000129001611 11106067 Nelson RJ , Gubernick DJ , Blom JMC (1995 ) Influence of photoperiod, green food, and water availability on reproduction in male California mice (Peromyscus californicus) . Physiol Behav 57 :1175 -1180 7652040 Parks WC , Wilson CL , López-Boado YS (2004 ) Matrix metalloproteinases as modulators of inflammation and innate immunity . Nat Rev Immunol 4 :617 -629 . 10.1038/nri1418 15286728 Paxinos G , Franklin KB (2004 ) The mouse brain in stereotaxic coordinates . Houston, TX : Gulf Professional Publishing . Planas AM , Solé S , Justicia C (2001 ) Expression and activation of matrix metalloproteinase-2 and -9 in rat brain after transient focal cerebral ischemia . Neurobiol Dis 8 :834 -846 10.1006/nbdi.2001.0435 11592852 Pyter LM , Hotchkiss AK , Nelson RJ (2005a ) Photoperiod-induced differential expression of angiogenesis genes in testes of adult Peromyscus leucopus . Reproduction 129 :201 -209 . 10.1530/rep.1.00448 15695614 Pyter LM , Reader BF , Nelson RJ (2005b ) Short photoperiods impair spatial learning and alter hippocampal dendritic morphology in adult male white-footed mice (Peromyscus leucopus) . J Neurosci 25 :4521 -4526 . 10.1523/JNEUROSCI.0795-05.2005 15872099 Ricci S , Celani MG , Vitali R , La Rosa F , Righetti E , Duca E (1992 ) Diurnal and seasonal variations in the occurrence of stroke: a community-based study . Neuroepidemiology 11 :59 -64 . 1495575 Rink C , Christoforidis G , Khanna S , Peterson L , Patel Y , Khanna S , Abduljalil A , Irfanoglu O , Machiraju R , Bergdall VK , Sen CK (2011 ) Tocotrienol vitamin E protects against preclinical canine ischemic stroke by inducing arteriogenesis . J Cereb Blood Flow Metab 31 :2218 -2230 10.1038/jcbfm.2011.85 21673716 Robertson RT , Levine ST , Haynes SM , Gutierrez P , Baratta JL , Tan Z , Longmuir KJ (2015 ) Use of labeled tomato lectin for imaging vasculature structures . Histochem Cell Biol 143 :225 -34 . 10.1007/s00418-014-1301-3 25534591 Salverson TJ , McMichael GE , Sury JJ , Shahed A , Young KA (2008 ) Differential expression of matrix metalloproteinases during stimulated ovarian recrudescence in Siberian hamsters (Phodopus sungorus) . Gen Comp Endocrinol 155 :749 -761 . 10.1016/j.ygcen.2007.09.003 17980368 Sang A (1998 ) Complex role of matrix metalloproteinases in angiogenesis . Cell Res 8 :171 -177 . 10.1038/cr.1998.17 9791730 Shahed A , Simmons JJ , Featherstone SL , Young KA (2015a ) Matrix metalloproteinase inhibition influences aspects of photoperiod stimulated ovarian recrudescence in Siberian hamsters . Gen Comp Endocrinol 216 :46 -53 . 10.1016/j.ygcen.2015.04.010 25910436 Shahed A , McMichael CF , Young KA (2015b ) Rapid changes in ovarian mRNA induced by brief photostimulation in Siberian hamsters (Phodopus sungorus) . 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PMC005xxxxxx/PMC5000816.txt

==== Front Genome AnnouncGenome AnnouncgagaGAGenome Announcements2169-8287American Society for Microbiology 1752 N St., N.W., Washington, DC genomeA00165-1610.1128/genomeA.00165-16VirusesComplete Genome Sequence of Pseudomonas aeruginosa Phage AAT-1 Genome AnnouncementAndrade-Domínguez and KolterAndrade-Domínguez Andrés Kolter Roberto Department of Microbiology and Immunobiology, Harvard Medical School, Boston, Massachusetts, USAAddress correspondence to Roberto Kolter, roberto_kolter@hms.harvard.edu.25 8 2016 Jul-Aug 2016 4 4 e00165-164 2 2016 1 7 2016 Copyright © 2016 Andrade-Domínguez and Kolter.2016Andrade-Domínguez and KolterThis is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.Aspects of the interaction between phages and animals are of interest and importance for medical applications. Here, we report the genome sequence of the lytic Pseudomonas phage AAT-1, isolated from mammalian serum. AAT-1 is a double-stranded DNA phage, with a genome of 57,599 bp, containing 76 predicted open reading frames. HHS | NIH | NIH Clinical Center (Clinical Center) http://dx.doi.org/10.13039/100000098GM58213Roberto Kolter cover-dateJuly/August 2016 ==== Body GENOME ANNOUNCEMENT It has been shown that vertebrates are widely exposed to phages, which penetrate them quite freely (1, 2). Recently, it was shown that phages can adhere to mucus and provide a non-host-derived antimicrobial defense on the mucosal surfaces of animals (3). Here, we report the genome sequence of the lytic phage AAT-1, which was isolated from fetal bovine serum using Pseudomonas aeruginosa PA14 as the host strain. This phage was able to infect 17 of 23 P. aeruginosa clinical and environmental isolates. The phage genome was sequenced using the Illumina MiSeq system at the MGH sequencing DNA core facility (Cambridge, MA, USA). The 100-bp reads were de novo assembled using Velvet (4). The coverage was on the order of 1,000× and two contigs were obtained. Primers were designed from the ends of contigs with an outward orientation and used in PCR, with the genomic DNA of phages used as templates. The sequences of PCR products were determined by Sanger sequencing. Annotation of open reading frames was performed with RAST (5) and PHAST (6). Sequence similarity searches were performed with the translation of each predicted coding sequence against the NCBI protein database, using BLASTp (7), in order to assign putative protein functions. tRNAscan-SE (8) was used for tRNA annotation, but no putative genes coding for tRNAs were found in phage AAT-1. Phage AAT-1 has a genome of 57,599 bp, with a coding percentage of 92.61% and a G+C content of 65.84%. In a dot plot alignment, the AAT-1 genome showed a completely colinear similarity and 91% overall nucleotide identity to Pseudomonas phage PaMx28 (GenBank accession no. JQ067089.2), isolated from sewage in central Mexico. Because Pseudomonas is a bacterium that lives in different environments and is an opportunistic pathogen of mammals, it is not surprising that phages AAT-1 and PaMx28 are closely related. We predicted 76 unique coding sequences, of which 34 were assigned a predicted function and 42 are hypothetical. We identified genes for DNA replication, including a DNA ligase (AAT1_02003), a putative helicase (AAT1_02051), a DNA polymerase (AAT1_02053), a putative primase/polymerase (AAT1_02071), and the small terminase subunit (AAT1_02072). A predicted HNH-type intron broke the large terminase gene into two fragments (AAT1_02073 and AAT1_02076), and 15 genes for head and tail morphogenesis were identified. Furthermore, we identified genes that encode proteins for DNA and nucleotide metabolism, such as a GTP cyclohydrolase II (AAT1_02009), a dCMP deaminase (AAT1_02044), a thymidylate synthase (AAT1_02046), a nucleotide pyrophosphohydrolase (AAT1_02047), a nucleotide triphosphate hydrolase (AAT1_02063), and a ribonucleotide reductase (AAT1_02001). The genes for host-cell lysis encode for an endolysin (AAT1_02033), an i-spanin (AAT1-02034), and an o-spanin (AAT1-02035). Accession number(s). The complete genome of P. aeruginosa phage AAT-1 was deposited in GenBank under the accession number KU204984. The version described in this paper is version KU204984.2. Citation Andrade-Domínguez A, Kolter R. 2016. Complete genome sequence of Pseudomonas aeruginosa phage AAT-1. Genome Announc 4(4):e00165-16. doi:10.1128/genomeA.00165-16. ACKNOWLEDGMENTS This study was supported by postdoctoral research fellowships to A.A.D. from the Consejo Nacional de Ciencia y Tecnologia (Mexico) and Fundación México en Harvard, A.C. and from NIH grant GM58213 to R.K. ==== Refs REFERENCES 1. Dabrowska K , Switala-Jelen K , Opolski A , Weber-Dabrowska B , Gorski A 2005 Bacteriophage penetration in vertebrates . J Appl Microbiol 98 :7 –13 . doi:10.1111/j.1365-2672.2004.02422.x .15610412 2. Letarov A , Kulikov E 2009 The bacteriophages in human- and animal body-associated microbial communities . J Appl Microbiol 107 :1 –13 . doi:10.1111/j.1365-2672.2009.04143.x .19239553 3. Barr JJ , Auro R , Furlan M , Whiteson KL , Erb ML , Pogliano J , Stotland A , Wolkowicz R , Cutting AS , Doran KS , Salamon P , Youle M , Rohwer F 2013 Bacteriophage adhering to mucus provide a non-host-derived immunity . Proc Natl Acad Sci U S A 110 :10771 –10776 . doi:10.1073/pnas.1305923110 .23690590 4. Zerbino DR , Birney E 2008 Velvet: algorithms for de novo short read assembly using de Bruijn graphs . Genome Res 18 :821 –829 . doi:10.1101/gr.074492.107 .18349386 5. Aziz RK , Bartels D , Best AA , DeJongh M , Disz T , Edwards RA , Formsma K , Gerdes S , Glass EM , Kubal M , Meyer F , Olsen GJ , Olson R , Osterman AL , Overbeek RA , McNeil LK , Paarmann D , Paczian T , Parrello B , Pusch GD , Reich C , Stevens R , Vassieva O , Vonstein V , Wilke A , Zagnitko O 2008 The RAST server: Rapid Annotations using Subsystems Technology . BMC Genomics 9 :75 . doi:10.1186/1471-2164-9-75 .18261238 6. Zhou Y , Liang Y , Lynch KH , Dennis JJ , Wishart DS 2011 PHAST: a fast phage search tool . Nucleic Acids Res 39 :W347 –W352 . doi:10.1093/nar/gkr485 .21672955 7. Altschul SF , Gish W , Miller W , Myers EW , Lipman DJ 1990 Basic local alignment search tool . J Mol Biol 215 :403 –410 . doi:10.1016/S0022-2836(05)80360-2 .2231712 8. Schattner P , Brooks AN , Lowe TM 2005 The tRNAscan-SE, snoscan and snoGPS web servers for the detection of tRNAs and snoRNAs . Nucleic Acids Res 33 :W686 –W689 . doi:10.1093/nar/gki366 .15980563

PMC005xxxxxx/PMC5000817.txt

==== Front Genome AnnouncGenome AnnouncgagaGAGenome Announcements2169-8287American Society for Microbiology 1752 N St., N.W., Washington, DC genomeA00431-1610.1128/genomeA.00431-16VirusesGenome Sequence of Jumbo Phage vB_AbaM_ME3 of Acinetobacter baumanni Genome AnnouncementButtimer et al.Buttimer Colin aO’Sullivan Lisa aElbreki Mohamed aNeve Horst bMcAuliffe Olivia cRoss R. Paul cHill Colin dO’Mahony Jim aCoffey Aidan aa Department of Biological Sciences, Cork Institute of Technology, Co. Cork, Irelandb Department of Microbiology and Biotechnology, Max Rubner-Institut, Kiel, Germanyc Biotechnology Department, Teagasc, Moorepark Food Research Centre, Fermoy, Co. Cork, Irelandd Department of Microbiology, University College Cork, Co. Cork, IrelandAddress correspondence to Aidan Coffey, aidan.coffey@cit.ie.25 8 2016 Jul-Aug 2016 4 4 e00431-1619 4 2016 28 6 2016 Copyright © 2016 Buttimer et al.2016Buttimer et al.This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.Bacteriophage (phage) vB_AbaM_ME3 was previously isolated from wastewater effluent using the propagating host Acinetobacter baumannii DSM 30007. The full genome was sequenced, revealing it to be the largest Acinetobacter bacteriophage sequenced to date with a size of 234,900 bp and containing 326 open reading frames (ORFs). This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. cover-dateJuly/August 2016 ==== Body GENOME ANNOUNCEMENT Acinetobacter baumannii has emerged in recent times as an important nosocomial pathogen. Health care-acquired A. baumannii infections include pneumonia and urinary tract and bloodstream infections (1). There is only a small number of bacteriophages (phages) with genomes greater than 200 kbp (termed “jumbo” phages) that have had their genomes sequenced to date. Most of their encoded proteins do not have any homologues in current sequence databases, and the diversity of these phages has been great enough that it has limited comparative genomics studies (2). A phage with the ability to lyse A. baumannii strain DSM 30007 was isolated from effluent obtained from a wastewater treatment plant in Cork, Ireland. Transmission electron microscopy revealed that the phage belonged to the Myoviridae family, and according to nomenclature proposed by Kropinski et al. was named vB_AbaM_ME3 (ME3) (3). A high titer phage suspension was concentrated by ultracentrifugation, and DNA extraction was performed as previously described (4). DNA was sequenced using the 454 FLX Titanium PLUS Sequencing approach (LGC Genomics, Mannheim, Germany). Open reading frames (ORFs) were identified using GLIMMER and GenemarkS (5, 6), with possible function of these ORFs’ proteins being predicted with BLASTp, pFam, InterProScan, THMHMM v.2.0, LipoP v1.0 (7–11), with tRNAscan.SE 1.21 being used to locate any tRNA present in the genome (12). To date, this is the largest Acinetobacter phage genome sequenced, with a size of 234,900 bp (the genome ends of ME3 are not known). The overall %G+C is 40%, similar to that of its host (13). The genome was predicted to have 326 ORFs with four tRNA genes. On the basis of homology, putative functions were assigned to 77 ORFs, with 19 ORFs annotated as putative membrane proteins, two ORFs annotated as putative lipoproteins, and the remaining 228 ORFs being annotated as hypothetical proteins. Phage ME3 is an orphan phage, however, it has eight ORFs encoding structural proteins that share homology to those of the novel Bacillus phage 0305phi8-36 (GenBank accession number NC_009760.1), although showing high levels of divergence (percentage identity of 26% to 34%). The major head protein (ME3_22), portal protein (ME3_19), and tail sheath subunit (ME3_29) are examples of such proteins. Until now, these proteins of 0305phi8-36 have only been found to share homology with those of phage-like elements found in the genomes of B. thuringiensis serovar israelensis and B. weihenstephanensis (14). With regard to these structural proteins and the large terminase subunit (ME3_13), phages ME3 and 0305phi8-36 may share a distant ancestor. Phage ME3 appears to encode its own DNA replication machinery including DNA polymerase subunits (ME3_60 and 61), thymidylate synthase enzymes (ME3_107 and 108), helicases, and enzymes involved in DNA degradation and repair. ME3 also possesses two cell wall degrading enzymes, ME3_8, a lysozyme with proven lytic activity against A. baumannii and ME3_113, a putative cell wall hydrolase. Phage ME3 also has a curiously large number of genes associated with Ter-stress response (ME3_286, 284, 289, 290, and 291) and a massive protein of 5,419 amino acids (ME3_104) possessing domains relating to host specificity and binding (IPR015406, IPR008979). Accession number(s). The full genome sequence of A. baumannii phage vB_AbaM_ME3 was deposited in GenBank under the accession number KU935715. Citation Buttimer C, O’Sullivan L, Elbreki M, Neve H, McAuliffe O, Ross RP, Hill C, O’Mahony J, Coffey A. 2016. Genome sequence of jumbo phage vB_AbaM_ME3 of Acinetobacter baumanni. Genome Announc 4(4):e00431-16. doi:10.1128/genomeA.00431-16. ==== Refs REFERENCES 1. Fournier PE , Richet H , Weinstein RA 2006 The epidemiology and control of Acinetobacter baumannii in health care facilities . Clin Infect Dis 42 :692 –699 . doi:10.1086/500202 .16447117 2. Hendrix RW 2009 Jumbo bacteriophages . Curr Top Microbiol Immunol 328 :229 –240 . doi:10.1007/978-3-540-68618-7_7 .19216440 3. Kropinski AM , Prangishvili D , Lavigne R 2009 Position paper: the creation of a rational scheme for the nomenclature of viruses of bacteria and archaea . Environ Microbiol 11 :2775 –2777 . doi:10.1111/j.1462-2920.2009.01970.x .19519870 4. Keary R , McAuliffe O , Ross RP , Hill C , O’Mahony J , Coffey A 2014 Genome analysis of the staphylococcal temperate phage DW2 and functional studies on the endolysin and tail hydrolase . Bacteriophage 4 :e28451 . doi:10.4161/bact.28451 .25105056 5. Delcher AL , Bratke KA , Powers EC , Salzberg SL 2007 Identifying bacterial genes and endosymbiont DNA with Glimmer . Bioinformatics 23 :673 –679 . doi:10.1093/bioinformatics/btm009 .17237039 6. Besemer J , Lomsadze A , Borodovsky M 2001 GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions . Nucleic Acids Res 29 :2607 –2618 . doi:10.1093/nar/29.12.2607 .11410670 7. Gish W , States DJ 1993 Identification of protein coding regions by database similarity search . Nat Genet 3 :266 –272 . doi:10.1038/ng0393-266 .8485583 8. Finn RD , Coggill P , Eberhardt RY , Eddy SR , Mistry J , Mitchell AL , Potter SC , Punta M , Qureshi M , Sangrador-Vegas A , Salazar GA , Tate J , Bateman A 2016 The Pfam protein families database: towards a more sustainable future . Nucleic Acids Res 44 :D279 –D285 . doi:10.1093/nar/gkv1344 .26673716 9. Quevillon E , Silventoinen V , Pillai S , Harte N , Mulder N , Apweiler R , Lopez R 2005 InterProScan: protein domains identifier . Nucleic Acids Res 33 :W116 —W120 . doi:10.1093/nar/gki442 .15980438 10. Krogh A , Larsson B , von Heijne G , Sonnhammer EL 2001 Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes . J Mol Biol 305 :567 –580 . doi:10.1006/jmbi.2000.4315 .11152613 11. Juncker AS , Willenbrock H , von Heijne G , Nielsen H , Brunak S , Krogh A 2003 Prediction of lipoprotein signal peptides in gram-negative bacteria . Protein Sci 12 :1652 –1662 . doi:10.1110/ps.0303703 .12876315 12. Lowe TM , Eddy SR 1997 tRNAscan-SE: A program for improved detection of transfer RNA genes in genomic sequence . Nucleic Acids Res 25 :955 –964 . doi:10.1093/nar/25.5.0955 .9023104 13. Davenport KW , Daligault HE , Minogue TD , Bruce DC , Chain PS , Coyne SR , Jaissle JG , Koroleva GI , Ladner JT , Li PE , Palacios GF , Scholz MB , Teshima H , Johnson SL 2014 Draft genome assembly of Acinetobacter baumannii ATCC 19606 . Genome Announc 2 (4):e00832-14 . doi:10.1128/genomeA.00832-14 .25146140 14. Thomas JA , Hardies SC , Rolando M , Hayes SJ , Lieman K , Carroll CA , Weintraub ST , Serwer P 2007 Complete genomic sequence and mass spectrometeric analysis of highly diverse, atypical Bacillus thuringiensis phage 0305phi8-36 . Virology 127 :358 –366 .

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==== Front Genome AnnouncGenome AnnouncgagaGAGenome Announcements2169-8287American Society for Microbiology 1752 N St., N.W., Washington, DC genomeA00712-1610.1128/genomeA.00712-16Letter to the EditorIsolating Viable Ancient Bacteria: What You Put In Is What You Get Out http://orcid.org/0000-0002-3843-0749Eisenhofer Raphael Cooper Alan Weyrich Laura S. Australian Centre for Ancient DNA, University of Adelaide, Adelaide, AustraliaAddress correspondence to Raphael Eisenhofer, raphael.eisenhoferphilipona@adelaide.edu.au.25 8 2016 Jul-Aug 2016 4 4 e00712-16Copyright © 2016 Eisenhofer et al.2016Eisenhofer et al.This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license. cover-dateJuly/August 2016 ==== Body LETTER In the recent publication “Draft Genome Sequence of Enterococcus faecium Strain 58m, Isolated from Intestinal Tract Content of a Woolly Mammoth, Mammuthus primigenius” in Genome Announcements (1), Goncharov et al. claim to have isolated and grown in pure culture a 28,000-year-old Enterococcus faecium strain. However, the authors ignored a breadth of literature about the authentication of ancient DNA, failed to adhere to recommended guidelines (2), and did not provide the appropriate experimental controls and analyses required to substantiate such a claim. Here, we present a subsequent reanalysis of the Goncharov et al. isolate and demonstrate by multilocus sequence typing (MLST) that this strain likely represents a modern contaminant. Previous efforts aimed at isolating viable ancient bacteria have been consistently controversial (3). Viable bacteria have been reported from a 250 million-year-old salt crystal (4) and 25- to 40 million-year-old amber (5). These unlikely findings have not been independently replicated, and failed molecular phylogenetic tests (6–8). In light of such dubious claims, a set of rigorous authentication criteria have been proposed (2). These include evolutionary rates tests, whereby phylogenetic comparisons of the ancient organism with its modern counterparts are expected to show substantial genetic differences, accumulated through time. In the Goncharov et al. study, the authors admit that E. faecium is a common member of the human gut community and can be found from numerous environmental sources, yet strangely they did nothing to prevent or control for modern contamination at various stages of their experiment. Modern contaminants can enter during the sampling procedure (2) or during laboratory analysis (i.e., culturing or DNA sequencing). Contamination during laboratory analysis is especially probable when the isolate is cultured using broad-spectrum media (2), as used by the authors. Clearly, the authors should have considered these factors and demonstrated or minimally investigated to determine that their isolate did not represent a modern human or environmental contaminant, something they failed to do. To test the authenticity of the authors’ claims, we queried the genome assembly of the “ancient” E. faecium isolate against published sequences in the E. faecium MLST database (http://pubmlst.org/efaecium/), which contains >2,800 modern E. faecium isolates. The MLST sequence from the putatively ancient E. faecium isolate matches the previously identified sequence type 32 (ST32) with 100% sequence homology; this is unexpected if the genome is ancient. Modern isolates of ST32 are known from the Russian Federation, where this study took place. If the bacterium was an ancient resident of the mammoth gut, it should not be identical to a modern human isolate, given that many gut microorganisms coevolved with their hosts and that humans and mammoths diverged over 100 million years ago (9). The lack of even a single nucleotide difference within seven genetic loci, coupled with the fact that this bacterium is commonly found in the modern human gut community and other environmental sources, is damning evidence that the authors’ isolate represents a modern contaminant. The authors’ “ancient” E. faecium isolate is highly similar to modern human isolates and is therefore almost certainly not an ancient mammoth strain. For the author reply, see doi:10.1128/genomeA.00734-16. ==== Refs REFERENCES 1. Goncharov A , Grigorjev S , Karaseva A , Kolodzhieva V , Azarov D , Akhremenko Y , Tarasova L , Tikhonov A , Masharskiy A , Zueva L , Suvorov A 2016 Draft genome sequence of Enterococcus faecium strain 58m, isolated from intestinal tract content of a woolly mammoth, Mammuthus primigenius . Genome Announc 4 (1):e01706-15 . doi:10.1128/genomeA.01706-15 .26868396 2. Hebsgaard MB , Phillips MJ , Willerslev E 2005 Geologically ancient DNA: fact or artefact? Trends Microbiol 13 :212 –220 . doi:10.1016/j.tim.2005.03.010 .15866038 3. Fischman J 1995 Have 25-million-year-old bacteria returned to life? Science 268 :977 . doi:10.1126/science.7754393 .7754393 4. Vreeland RH , Rosenzweig WD , Powers DW 2000 Isolation of a 250 million-year-old halotolerant bacterium from a primary salt crystal . Nature 407 :897 –900 . doi:10.1038/35038060 .11057666 5. Cano RJ , Borucki MK 1995 Revival and identification of bacterial spores in 25- to 40-million-year-old Dominican amber . Science 268 :1060 –1064 . doi:10.1126/science.7538699 .7538699 6. Nickle DC , Learn GH , Rain MW , Mullins JI , Mittler JE 2002 Curiously modern DNA for a “250 million-year-old” bacterium . J Mol Evol 54 :134 –137 . doi:10.1007/s00239-001-0025-x .11734907 7. Yousten AA , Rippere KE 1997 DNA similarity analysis of a putative ancient bacterial isolate obtained from amber . FEMS Microbiol Lett 152 :345 –347 . doi:10.1111/j.1574-6968.1997.tb10450.x . 8. Weyrich LS , Llamas B , Cooper A 2014 Reply to Santiago-Rodriguez et al.: was luxS really isolated from 25- to 40-million-year-old bacteria? FEMS Microbiol Lett 353 :85 –86 . doi:10.1111/1574-6968.12415 .24617861 9. Meredith RW , Janečka JE , Gatesy J , Ryder OA , Fisher CA , Teeling EC , Goodbla A , Eizirik E , Simão TL , Stadler T , Rabosky DL , Honeycutt RL , Flynn JJ , Ingram CM , Steiner C , Williams TL , Robinson TJ , Burk-Herrick A , Westerman M , Ayoub NA , Springer MS , Murphy WJ 2011 Impacts of the Cretaceous terrestrial revolution and KPg extinction on mammal diversification . Science 334 :521 –524 . doi:10.1126/science.1211028 .21940861

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==== Front Genome AnnouncGenome AnnouncgagaGAGenome Announcements2169-8287American Society for Microbiology 1752 N St., N.W., Washington, DC genomeA00734-1610.1128/genomeA.00734-16Author ReplyReply to “Isolating Viable Ancient Bacteria: What You Put In Is What You Get Out” Goncharov A. abcGrigorjev S. ehttp://orcid.org/0000-0002-9570-4769Karaseva A. aKolodzhieva V. abAzarov D. bAkhremenko Y. eTarasova L. eTikhonov A. dMasharskiy A. cZueva L. bSuvorov A. aca FSBSI Institute of Experimental Medicine, Saint Petersburg, Russiab North-West State Medical University named after I. I. Mechnikov, Saint Petersburg, Russiac Saint Petersburg State University, Saint Petersburg, Russiad Zoological Institute of the Russian Academy of Sciences, Saint Petersburg, Russiae M. K. Ammosov North-Eastern Federal University, Yakutsk, RussiaAddress correspondence to A. Karaseva, xathaelru@gmail.com.25 8 2016 Jul-Aug 2016 4 4 e00734-16Copyright © 2016 Goncharov et al.2016Goncharov et al.This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license. cover-dateJuly/August 2016 ==== Body REPLY Investigation and description of viable bacteria isolated from ancient permafrost are an essential part of modern paleomicrobiology (1, 2), despite the difficulties with evidence of autochthony of isolates. In our work, we have described the draft genome of an unusual strain of Enterococcus faecium isolated from a paleontological specimen (3). The possibility of contamination with modern microbiota, of course, was considered by us. In this regard, we have used the term “putative ancient” for determination of the isolates. However, we cannot agree with the opinion that multilocus sequence typing (MLST) allelic profile investigation is sufficient for the estimation of absolute age of bacterial strains, as suggested by Eisenhofer et al. (4). It is clear that housekeeping genes coding for proteins with important functions for bacterial survival are considered for use in MLST schemes because they are stable with respect to rapid genetic modifications. Regarding Enterococcus faecium, it has been assumed by Galloway-Peña et al. (5) that divergence of this species into the hospital and commensal phylogenetic clusters occurred no earlier than 300,000 years ago. However, nucleotide differences between sequence types representing the hospital and commensal clusters are relatively small. For example, we have found by MLST profile comparison between ST17 (belonging to the hospital clade) and ST32 (commensal clade) that four of the seven MLST genes are 100% intact and the other three have only 11 single nucleotide polymorphisms (SNPs) in total (95, C → T; 128, C → T; 314, T → C; and 542, T → G in the internal fragment of atpA; 171, T → G; 204, G → A; 327, T → C; 348, C → T; 387, C → A; and 435, G → A in ddl; and 115, C → T in purK). This fact demonstrates that the molecular clock for different housekeeping genes artificially chosen for MLST analysis might go with different speeds depending on the evolutional pressure on the bacteria. As we believe that the rate of molecular evolution is low, we do not expect significant accumulation of mutations in these genes over the last 28,500 years. Besides, the isolation of ST32 among taxonomically distant species of animals and the wide distribution of ST32 in geographically distant regions may indicate that this sequence type is relatively ancient. Irrespective of the age of the E. faecium 58m strain, the presence of genetic regions in its genome that do not have any significant similarity with the genomes of modern prokaryotes (for example, genomic regions from positions 13577 to 23686 in contig 46 and positions 22200 to 23200 in contig 43) is of considerable interest for further study. This is a response to a letter by Eisenhofer et al. (doi:10.1128/genomeA.00712-16). ==== Refs REFERENCES 1. Steven B , Léveillé R , Pollard WH , Whyte LG 2006 Microbial ecology and biodiversity in permafrost . Extremophiles 10 :259 –267 . doi:10.1007/s00792-006-0506-3 .16550305 2. Gilichinsky DA , Wilson GS , Friedmann EI , McKay CP , Sletten RS , Rivkina EM , Vishnivetskaya TA , Erokhina LG , Ivanushkina NE , Kochkina GA , Shcherbakova VA , Soina VS , Spirina EV , Vorobyova EA , Fyodorov-Davydov DG , Hallet B , Ozerskaya SM , Sorokovikov VA , Laurinavichyus KS , Shatilovich AV , Chanton JP , Ostroumov VE , Tiedje JM 2007 Microbial populations in Antarctic permafrost: biodiversity, state, age, and implication for astrobiology . Astrobiology 7 :275 –311 . doi:10.1089/ast.2006.0012 .17480161 3. Goncharov A , Grigorjev S , Karaseva A , Kolodzhieva V , Azarov D , Akhremenko Y , Tarasova L , Tikhonov A , Masharskiy A , Zueva L , Suvorov A 2016 Draft genome sequence of Enterococcus faecium strain 58m, isolated from intestinal tract content of a woolly mammoth, Mammuthus primigenius . Genome Announc 4 (1):e00734-16. doi:10.1128/genomeA.01706-15 . 4. Eisenhofer R , Cooper A , Weyrich LS 2016 Isolating viable ancient bacteria: what you put in is what you get out . Genome Announc 4 (4):e00734-16. doi:10.1128/genomeA.00712-16 . 5. Galloway-Peña J , Roh JH , Latorre M , Qin X , Murray BE 2012 Genomic and SNP analyses demonstrate a distant separation of the hospital and community-associated clades of Enterococcus faecium . PLoS One 7 :e00734-16. doi:10.1371/journal.pone.0030187 .

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==== Front Genome AnnouncGenome AnnouncgagaGAGenome Announcements2169-8287American Society for Microbiology 1752 N St., N.W., Washington, DC genomeA00773-1610.1128/genomeA.00773-16ProkaryotesDraft Genome Sequences of Acinetobacter baumannii Isolates from Wounded Military Personnel Genome AnnouncementArivett et al.Arivett Brock A. abReam Dave C. aFiester Steven E. aKidane Destaalem bActis Luis A. aa Department of Microbiology, Miami University, Oxford, Ohio, USAb Biology Department, Middle Tennessee State University, Murfreesboro, Tennessee, USAAddress correspondence to Luis A. Actis, actisla@miamioh.edu.25 8 2016 Jul-Aug 2016 4 4 e00773-168 6 2016 6 7 2016 Copyright © 2016 Arivett et al.2016Arivett et al.This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.Acinetobacter baumannii is a Gram-negative bacterium capable of causing hospital-acquired infections that has been grouped with Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species as ESKAPE pathogens because of their extensive drug resistance phenotypes and increasing risk to human health. Twenty-four multidrug-resistant A. baumannii strains isolated from wounded military personnel were sequenced and annotated. U.S. Department of Defense (DOD) http://dx.doi.org/10.13039/100000005W81XWH-12-2-0035Luis A. Actis cover-dateJuly/August 2016 ==== Body GENOME ANNOUNCEMENT The Gram-negative coccobacillus Acinetobacter baumannii is an opportunistic human pathogen causing myriad human diseases, including pneumonia, bacteremia, urinary tract infections, meningitis, and wound infections. A. baumannii is the fifth most common Gram-negative pathogen associated with nosocomial infections (1, 2). Of concern is the increasing multidrug resistance of A. baumannii isolates, which has caused this bacterium to be included as an ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) pathogen, underscoring its ability to “escape” antimicrobials (3). In fact, A. baumannii strains resistant to all known antibiotics have been encountered, demonstrating the paramount impact of this pathogen on public health (2). The genomes of 24 A. baumannii strains isolated from wounded warriors at Walter Reed Army Medical Center (WRAMC) and San Antonio Military Medical Center (SAMMC), Fort Sam Houston, San Antonio, TX, were sequenced using next-generation sequencing for future analyses to investigate the resistance and virulence mechanisms of this emerging pathogen. As described previously, strains were routinely stored at -80°C in 10% glycerol (4). DNA was isolated from overnight LB cultures grown with agitation at 37°C using the DNeasy blood and tissue kit (Qiagen, Valencia, CA, USA). Absorption at 260 nm and 280 nm was measured for each sample to determine quantity and quality using the NanoDrop 2000 (Thermo Scientific, Wilmington, DE, USA). DNA concentrations for library preparation were determined by the SYBR green (Life Technologies, Grand Island, NY) standard curve method in a black 96-well plate (Corning, Tewksbury, MA, USA) using a FilterMax F5 spectrophotometer with multimode analysis software version 3.4.0.25 (Molecular Devices, Sunnyvale, CA, USA). Whole DNA was sheared to approximately 500 bp in a microTUBE-50 using an M220 focused ultrasonicator (Covaris, Woburn, MA, USA). Fragmentation of the resultant libraries was examined with a Bioanalyzer 2100 high-sensitivity DNA analysis kit (Agilent Technologies, Santa Clara, CA, USA) using version B.02.08.SI648 software. Individual libraries were normalized, pooled, and then sequenced using the MiSeq version 3 600-cycle kit (Illumina, San Diego, CA, USA) to perform 300-bp paired-end sequencing on a MiSeq instrument (Illumina), per the manufacturer’s instructions. De novo assembly was performed using Genomics Workbench 8.0 with the Bacterial Genome Finishing module (CLC bio, Boston, MA, USA) run on a workstation with an AMD Opteron 2.10 GHz 16-core processor with 128 Gb DDR3 ECC random access memory (RAM). Genomes were annotated with Prokka version 1.10 on a quad-core i7 workstation with 32 Gb DDR3 running Ubuntu 14.04 LTS (5). The de novo assembly statistics for 24 A. baumannii isolates are shown in Table 1. TABLE 1  Assembly metrics and accession numbers of A. baumannii genomes Strain ID No. of contigs N50 contig size (bp) Total size (bp) Coverage (×) G+C content (%) No. of ORFsa No. of RNAs Accession no. AB2828 107 124,070 4,426,896 30 39.21 4,274 53 LRDT00000000 AB3340 76 132,604 4,010,248 28 38.86 3,864 49 LRDU00000000 AB3560 58 247,914 4,012,126 30 38.92 3,894 59 LRDV00000000 AB967 27 401,652 3,795,032 29 38.84 3,633 62 LRDS00000000 AB3785 70 134,647 3,894,584 29 39.01 3,745 58 LRDX00000000 AB3638 78 108,414 4,294,582 31 38.72 4,113 62 LRDW00000000 AB3806 86 96,852 4,295,294 33 38.75 4,117 59 LRDY00000000 AB3927 45 227,995 4,113,781 30 38.82 3,978 58 LRDZ00000000 AB4026 67 160,728 3,905,198 30 38.99 3,749 50 LREB00000000 AB4027 72 152,887 3,903,961 32 39.00 3,749 54 LREC00000000 AB4025 69 152,887 3,902,672 29 39.00 3,741 55 LREA00000000 AB4456 58 182,799 4,001,807 27 38.92 3,857 47 LREF00000000 AB4052 43 262,160 3,921,338 33 39.00 3,739 51 LRED00000000 AB4448 43 369,360 3,992,257 28 38.92 3,854 58 LREE00000000 AB4490 98 84,980 3,947,403 31 38.99 3,786 60 LREG00000000 AB4498 76 128,212 3,905,177 32 39.00 3,753 57 LREH00000000 AB4795 78 113,293 3,882,341 33 39.03 3,727 62 LREI00000000 AB4878 45 223,470 3,862,567 26 38.98 3,685 50 LREJ00000000 AB4957 50 223,470 3,882,040 33 38.97 3,722 60 LREL00000000 AB4932 39 237,199 3,865,974 33 38.99 3,703 60 LREK00000000 AB5001 33 223,470 3,789,469 30 38.99 3,586 52 LREN00000000 AB4991 52 310,788 3,877,107 28 39.09 3,686 58 LREM00000000 AB5674 34 419,504 3,869,253 29 39.03 3,679 52 LREP00000000 AB5197 58 184,472 3,959,484 33 39.04 3,799 58 LREO00000000 a ORFs, open reading frames. Accession number(s). The whole-genome shotgun projects were deposited into GenBank under BioProject ID PRJNA261239 with accession numbers listed in Table 1. Citation Arivett BA, Ream DC, Fiester SE, Kidane D, Actis LA. 2016. Draft genome sequences of Acinetobacter baumannii isolates from wounded military personnel. Genome Announc 4(4):e00773-16. doi:10.1128/genomeA.00773-16. ACKNOWLEDGMENTS This work was supported by funds from Miami University and U.S. Department of Defense W81XWH-12-2-0035 award to L.A.A. We are grateful to Daniel V. Zurawski from Walter Reed Army Institute of Research for providing the A. baumannii strains listed in Table 1. We also thank Andor Kiss and the Miami University Center for Bioinformatics and Functional Genomics for assistance in sequence acquisition. The findings and opinions expressed herein belong to the authors and do not necessarily reflect the official views of the WRAIR, the U.S. Army, or the Department of Defense. ==== Refs REFERENCES 1. Hidron AI , Edwards JR , Patel J , Horan TC , Sievert DM , Pollock DA , Fridkin SK , National Healthcare Safety Network Team , Participating National Healthcare Safety Network Facilities 2008 NHSN annual update: antimicrobial-resistant pathogens associated with health care-associated infections: annual summary of data reported to the National Healthcare Safety Network at the Centers for Disease Control and Prevention, 2006–2007 . Infect Control Hosp Epidemiol 29 :996 –1011 . doi:10.1086/591861 .18947320 2. Peleg AY , Seifert H , Paterson DL 2008 Acinetobacter baumannii: emergence of a successful pathogen . Clin Microbiol Rev 21 :538 –582 . doi:10.1128/CMR.00058-07 .18625687 3. Rice L 2008 Federal funding for the study of antimicrobial resistance in nosocomial pathogens: no ESKAPE . J Infect Dis 197 :1079 –1081 . doi:10.1086/533452 .18419525 4. Arivett BA , Ream DC , Fiester SE , Mende K , Murray CK , Thompson MG , Kanduru S , Summers AM , Roth AL , Zurawski DV , Actis LA 2015 Draft genome sequences of Klebsiella pneumoniae clinical type strain ATCC 13883 and three multidrug-resistant clinical isolates . Genome Announc 3 (1 ):e01385-14 . doi:10.1128/genomeA.01385-14 . 5. Seemann T 2014 Prokka: rapid prokaryotic genome annotation . Bioinformatics 30 :2068 –2069 . doi:10.1093/bioinformatics/btu153 .24642063

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==== Front Genome AnnouncGenome AnnouncgagaGAGenome Announcements2169-8287American Society for Microbiology 1752 N St., N.W., Washington, DC genomeA00801-1610.1128/genomeA.00801-16VirusesComplete Genome Sequence of Foot-and-Mouth Disease Virus Serotype SAT3 Zimbabwe/4/81 Genome AnnouncementYou Su-Hwa Pyo Hyun Mi Lee Seo-Yong Ko Mi-Kyeong Choi Joo-Hyung Shin Sung Ho Lee Kwang-Nyeong Kim Byounghan Park Jong-Hyeon Animal and Plant Quarantine Agency, Gimcheon City, Gyeongsangbuk-do, Republic of KoreaAddress correspondence to Jong-Hyeon Park, parkjhvet@korea.kr.S.-H.Y. and H.M.P. contributed equally to this work. 25 8 2016 Jul-Aug 2016 4 4 e00801-1614 6 2016 6 7 2016 Copyright © 2016 You et al.2016You et al.This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.The complete genome sequence of a foot-and-mouth disease (FMD) serotype SAT3 virus ZIM/4/81, which belongs to a topotype 1 (SEZ), is reported here. This work was supported by the Animal and Plant Quarantine Agency (QIA) of the Ministry of Agriculture, Food and Rural Affairs, Republic of Korea. cover-dateJuly/August 2016 ==== Body GENOME ANNOUNCEMENT Foot-and-mouth disease (FMD) is a highly contagious vesicular disease in cloven-hooved ungulates (1, 2). Countries where the FMD outbreaks occur have implemented a culling policy and vaccination to contain the disease (3), but the FMD epidemic prevails in many countries, according to the World Reference Laboratory for Foot-and-Mouth Disease (WRLFMD [http://www.wrlfmd.org/fmd_genotyping/]). A causative agent of FMD is a single-stranded positive-sense RNA virus that belongs to the genus Aphthovirus of the Picornaviridae family (4). FMD virus (FMDV) is divided into seven antigenically distinct serotypes, A, O, C, Asia 1, and South African Territories (SATs) 1, 2, and 3. We took initiative to develop FMD vaccines to counter the disease after the recent FMD outbreaks in South Korea (5). Although serotype C and SAT 1, 2, and 3 FMDVs are at low probability to be introduced in Asian countries, these serotype FMDVs were included as vaccine antigens in an antigen bank for emergency use. As for serotype SAT3 FMDV, Zimbabwe/4/81 (ZIM/4/81) was received from the WRLFMD. Although ZIM/4/81 has been featured in several FMDV research articles as a representative SAT3 topotype 1 virus, its complete genome sequence was not available. The WRLFMD has a record of an SAT3 FMDV outbreak in Zimbabwe in 1981, but the origin of this virus is not clear (B. E. Clarke, unpublished data). We report here the complete genome sequence of the ZIM/4/81 strain. Viral RNA was extracted from the cell culture supernatant of BHK-21 cell line, and cDNA was synthesized using an oligo(dT) primer with Superscript III reverse transcriptase (Thermo Fisher Scientific). We designed pairs of primers to produce seven overlapping amplicons spanning the entire viral genome based on the sequence of the SAT3 ZIM/6/91 strain (6). Amplified PCR products were subjected to direct sequencing, and the sequencing reads were assembled into a single contig using BioEdit (version 7.2.3). The complete genome of ZIM/4/81 is 8,124 nucleotides (nt) in length, including a 996-nt 5′ untranslated region (5′ UTR) with 12 nt of a poly(C) tract of unknown length and a 120-nt 3′ UTR with a ≥27-nt poly(A) tail. A single open reading frame of 7,008 nt was predicted to encode a polyprotein of 2,336 amino acids (aa) containing four structural and 10 nonstructural proteins. A putative polyprotein sequence shared 95 to 97% homologies to known SAT 3 FMDV isolates. The most closely related isolate was ZIM/6/91, which shared 92% nucleotide homology of the complete genome. Accession number(s). The complete genome sequence of FMDV SAT3/ZIM/4/81 has been deposited in GenBank under the accession no. KX375417. Citation You S-H, Pyo HM, Lee S-Y, Ko M-K, Choi J-H, Shin SH, Lee K-N, Kim B, Park J-H. 2016. Complete genome sequence of foot-and-mouth disease virus serotype SAT3 Zimbabwe/4/81. Genome Announc 4(4):e00801-16. doi:10.1128/genomeA.00801-16. ACKNOWLEDGMENTS SAT3/ZIM4/81 was kindly provided by the OIE/FAO FMD Reference Laboratory in the Pirbright Institute, United Kingdom. This work was supported by the Animal and Plant Quarantine Agency (QIA) of the Ministry of Agriculture, Food and Rural Affairs, Republic of Korea. ==== Refs REFERENCES 1. Jamal SM , Belsham GJ 2013 Foot-and-mouth disease: past, present and future . Vet Res 44 :116 . doi:10.1186/1297-9716-44-116 .24308718 2. Alexandersen S , Mowat N 2005 Foot-and-mouth disease: host range and pathogenesis . Curr Top Microbiol Immunol 288 :9 –42 .15648173 3. Smith MT , Bennett AM , Grubman MJ , Bundy BC 2014 Foot-and-mouth disease: technical and political challenges to eradication . Vaccine 32 :3902 –3908 . doi:10.1016/j.vaccine.2014.04.038 .24785105 4. Brown F 2003 The history of research in foot-and-mouth disease . Virus Res 91 :3 –7 . doi:10.1016/S0168-1702(02)00268-X .12527434 5. Park JH , Tark D , Lee KN , Lee SY , Ko MK , Lee HS , Kim SM , Ko YJ , Seo MG , Chun JE , Lee MH , Kim B 2016 Novel foot-and-mouth disease virus in Korea, July–August 2014 . Clin Exp Vaccine Res 5 :83 –87 . doi:10.7774/cevr.2016.5.1.83 .26866028 6. Logan G , Freimanis GL , King DJ , Valdazo-González B , Bachanek-Bankowska K , Sanderson ND , Knowles NJ , King DP , Cottam EM 2014 A universal protocol to generate consensus level genome sequences for foot-and-mouth disease virus and other positive-sense polyadenylated RNA viruses using the Illumina MiSeq . BMC Genomics 15 :828 . doi:10.1186/1471-2164-15-828 .25269623

PMC005xxxxxx/PMC5000822.txt

==== Front Genome AnnouncGenome AnnouncgagaGAGenome Announcements2169-8287American Society for Microbiology 1752 N St., N.W., Washington, DC genomeA00806-1610.1128/genomeA.00806-16ProkaryotesDraft Genome Sequence of Lactobacillus reuteri Strain CRL 1098, an Interesting Candidate for Functional Food Development Genome AnnouncementTorres Andrea C. Suárez Nadia E. Font Graciela Saavedra Lucila Taranto María Pía Centro de Referencia para Lactobacilos (CERELA-CONICET), Chacabuco, San Miguel de Tucumán, Tucumán, ArgentinaAddress correspondence to María Pía Taranto, ptaranto@cerela.org.ar.25 8 2016 Jul-Aug 2016 4 4 e00806-1630 6 2016 6 7 2016 Copyright © 2016 Torres et al.2016Torres et al.This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.We report here the draft genome sequence of Lactobacillus reuteri strain CRL 1098. This strain represents an interesting candidate for functional food development because of its proven probiotic properties. The draft genome sequence is composed of 1,969,471 bp assembled into 45 contigs and an average G+C content of 38.8%. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) http://dx.doi.org/10.13039/501100002923PIP0406Lucila SaavedraMINCyT | ANPCyT | Fondo para la Investigación Científica y Tecnológica (FonCyT) http://dx.doi.org/10.13039/501100006668PICT 2011-0175Lucila Saavedra cover-dateJuly/August 2016 ==== Body GENOME ANNOUNCEMENT Lactobacillus reuteri CRL 1098, a lactic acid bacterium isolated from sourdough, has gained attention for the development of functional foods as a potential probiotic. This strain is a well-known cobalamin producer strain (1). Furthermore, Malpeli et al. (2) demonstrated the hypocholesterolemic effect associated with the daily consumption of a yogurt containing L. reuteri CRL 1098. To further investigate the probiotic potential of L. reuteri CRL 1098, we have determined its genome sequence. Genomic DNA was isolated by the method of Pospiech and Neumann (3) and used to generate reads sequences by a whole-genome shotgun (WGS) strategy on an Illumina MiSeq sequencer. Quality-filtered reads were de novo assembled into 45 contigs using the NGen DNAStar (versus 12.2.0) assembler (MR DNA, Shallowater, TX). Genome annotation was done by the NCBI Prokaryotic Genome Annotation Pipeline (PGAP) service and the Rapid Annotations using Subsystems Technology (RAST) server (4). The draft genome of L. reuteri CRL 1098 is 1,969,471 bp in length and has an average G+C content of 38.8%. A total of 1,968 coding sequences (CDSs) and 85 structural RNAs were predicted. Among all CDSs, 1,448 (73.6%) were assigned to known protein functions, while 520 (26.4%) remain as hypothetical proteins. Additionally, there are 303 RAST subsystems represented in the chromosome, which represent only 48% of the sequences assigned. In silico studies revealed that L. reuteri CRL 1098 carries 29 genes involved in the cobalamin biosynthesis and two components of the cobalamin transporter in the cbi-cob-hem cluster. Detailed analysis of the L. reuteri CRL 1098 genome will be useful to predict the competitiveness of the strain as a probiotic. Accession number(s). This whole-genome shotgun project has been deposited at DDBJ/ENA/GenBank under the accession no. LYWI00000000. The version described in this paper is version LYWI01000000. Citation Torres AC, Suárez NE, Font G, Saavedra L, Taranto MP. 2016. Draft genome sequence of Lactobacillus reuteri strain CRL 1098, an interesting candidate for functional food development. Genome Announc 4(4):e00806-16. doi:10.1128/genomeA.00806-16. ACKNOWLEDGMENTS This work was supported by the Agencia Nacional de Promoción Científica y Tecnológica (FONCyT PICT 2011-0175) and the Consejo Nacional de Investigaciones Científicas y Técnicas (PIP0406). ==== Refs REFERENCES 1. Taranto MP , Vera JL , Hugenholtz J , de Valdez GF , Sesma F 2003 Lactobacillus reuteri CRL1098 produces cobalamin . J Bacteriol 185 :5643 –5647 . doi:10.1128/JB.185.18.5643-5647.2003 .12949118 2. Malpeli A , Taranto M , Cravero R , Tavella M , Fasano V , Vicentin D , Ferrari G , Magrini G , Hébert E , Valdez G , Varea A , Tavella J , González H 2015 Effect of daily consumption of Lactobacillus reuteri CRL 1098 on cholesterol reduction in hypercholesterolemic subjects . Food Nutr Sci 6 :1583 –1590 . 3. Pospiech A , Neumann B 1995 A versatile quick-prep of genomic DNA from Gram-positive bacteria . Trends Genet 11 :217 –218 . doi:10.1016/S0168-9525(00)89052-6 .7638902 4. Aziz RK , Bartels D , Best AA , DeJongh M , Disz T , Edwards RA , Formsma K , Gerdes S , Glass EM , Kubal M , Olsen GJ , Olson R , Osterman AL , Overbeek RA , McNeil LK , Paarmann D , Paczian T , Parrello B , Pusch GD , Reich C , Stevens R , Vassieva O , Vonstein V , Wilke A , Zagnitko O 2008 The RAST server: Rapid Annotations using Subsystems Technology . BMC Genomics 9 :75 . doi:10.1186/1471-2164-9-75 .18261238

PMC005xxxxxx/PMC5000823.txt

==== Front Genome AnnouncGenome AnnouncgagaGAGenome Announcements2169-8287American Society for Microbiology 1752 N St., N.W., Washington, DC genomeA00815-1610.1128/genomeA.00815-16ProkaryotesComplete Genome Sequence of Neisseria weaveri Strain NCTC13585 Genome AnnouncementAlexander Sarah aFazal Mohammed-Abbas aBurnett Edward aDeheer-Graham Ana aOliver Karen bHolroyd Nancy bhttp://orcid.org/0000-0002-7069-5958Parkhill Julian bRussell Julie E. aa Culture Collections, Public Health England, London, United Kingdomb Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, United KingdomAddress correspondence to Sarah Alexander, sarah.alexander@phe.gov.uk.25 8 2016 Jul-Aug 2016 4 4 e00815-1622 6 2016 23 6 2016 Copyright © 2016 Alexander et al.2016Alexander et al.This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.Neisseria weaveri is a commensal organism of the canine oral cavity and an occasional opportunistic human pathogen which is associated with dog bite wounds. Here we report the first complete genomic sequence of the N. weaveri NCTC13585 (CCUG30381) strain, which was originally isolated from a patient with a canine bite wound. the Wellcome Trust http://dx.doi.org/10.13039/100004440101503/Z/13/Z cover-dateJuly/August 2016 ==== Body GENOME ANNOUNCEMENT Neisseria weaveri is a Gram-negative, nonmotile, aerobic, rod-like bacterium that is a commensal of the canine oral flora (1–3). It is an infrequent opportunistic pathogen of dog bite wounds and there are also isolated reports of N. weaveri causing lower respiratory tract and septic infection (4, 5). There is a paucity of information on N. weaveri within the literature and historically there has been confusion with its taxonomy, with two different type strains being described (1–3). To date there are only two draft genomes available for N. weaveri, LMG 5135 and ATCC 51223 (both the proposed type strains), which have been assembled into 46 and 40 contigs, respectively (6, 7). Here we report the first complete genome for N. weaveri strain NCTC13585, which was isolated from a human dog bite wound in Danderyd, Sweden, in 1982. High-molecular-weight genomic DNA was extracted from a pure culture using the Masterpure DNA extraction kit (Epicentre, WI, USA) and the quality was confirmed (>50 kb) using the Agilent 2200 TapeStation. Whole-genome sequencing (WGS) was performed using the PacBio single-molecule real-time (SMRT) DNA sequencing technology utilizing C4/P6 chemistry followed by genome assembly using an automated assembly pipeline and annotation with Prokka. The genomic size of NCTC13585 was determined to be 2,188,497 bp. The average G+C content of the sequence was 49.0%. No plasmids were identified in this strain. In total there were 2,060 coding sequences, four rRNA operons, and 55 tRNA genes identified. Accession number(s). The complete genome sequence has been deposited in the European Nucleotide Archive under the BioSample accession number SAMEA3174300 and the assembly accession number LT571436. Citation Alexander S, Fazal M-A, Burnett E, Deheer-Graham A, Oliver K, Holroyd N, Parkhill J, Russell JE. 2016. Complete genome sequence of Neisseria weaveri strain NCTC13585. Genome Announc 4(4):e00815-16. doi:10.1128/genomeA.00815-16. ==== Refs REFERENCES 1. Holmes B , Costas M , On SL , Vandamme P , Falsen E , Kersters K 1993 Neisseria weaveri sp. nov. (formerly CDC group M-5), from dog bite wounds of humans . Int J Syst Bacteriol 43 :687 –693 . doi:10.1099/00207713-43-4-687 .8240951 2. Barnham M , Holmes B 1992 Isolation of CDC group M-5 and Staphylococcus intermedius from infected dog bites . J Infect 25 :332 –334 . doi:10.1016/0163-4453(92)91759-5 .1474272 3. Andersen BM , Steigerwalt AG , O’Connor SP , Hollis DG , Weyant RS , Weaver RE , Brenner DJ 1993 Neisseria weaveri sp. nov., formerly CDC group M-5, a Gram-negative bacterium associated with dog bite wounds . J Clin Microbiol 31 :2456 –2466 .8408570 4. Panagea S , Bijoux R , Corkill JE , Al Rashid F , Hart CA 2002 A Case of lower respiratory tract infection caused by Neisseria weaveri and review of the literature . J Infect 44 :96 –98 . doi:10.1053/jinf.2001.0965 .12076070 5. Carlson P , Kontiainen S , Anttila P , Eerola E 1997 Septicemia caused by Neisseria weaveri . Clin Infect Dis 24 :739 . doi:10.1093/clind/24.4.739 .9145755 6. Yi H , Chun J 2015 Neisseria weaveri Andersen et al. 1993 is a later heterotypic synonym of Neisseria weaveri Holmes et al. 1993 . Int J Syst Evol Microbiol 65 :463 –464 . doi:10.1099/ijs.0.070664-0 .25389153 7. Yi H , Cho YJ , Yoon SH , Park SC , Chun J 2012 Comparative genomics of Neisseria weaveri clarifies the taxonomy of this species and identifies genetic determinants that may be associated with virulence . FEMS Microbiol Lett 328 :100 –105 . doi:10.1111/j.1574-6968.2011.02485.x .22188430

PMC005xxxxxx/PMC5000824.txt

==== Front Genome AnnouncGenome AnnouncgagaGAGenome Announcements2169-8287American Society for Microbiology 1752 N St., N.W., Washington, DC genomeA00823-1610.1128/genomeA.00823-16ProkaryotesComplete Genome Sequence of a Cylindrospermopsin-Producing Cyanobacterium, Cylindrospermopsis raciborskii CS505, Containing a Circular Chromosome and a Single Extrachromosomal Element Genome AnnouncementFuentes-Valdés et al.Fuentes-Valdés Juan J. abPlominsky Alvaro M. cdAllen Eric E. efTamames Javier gVásquez Mónica ba Department of Chemical and Bioprocess Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chileb Department of Molecular Genetics and Microbiology, Faculty of Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chilec Department of Oceanography, Universidad de Concepción, Concepción, Chiled Instituto Milenio de Oceanografía, Universidad de Concepción, Concepción, Chilee Marine Biology Research Division, Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, USAf Division of Biological Sciences, University of California San Diego, La Jolla, California, USAg Centro Nacional de Biotecnología (CNB-CSIC), Madrid, SpainAddress correspondence to Mónica Vásquez, mvasquez@bio.puc.cl.25 8 2016 Jul-Aug 2016 4 4 e00823-1628 6 2016 29 6 2016 Copyright © 2016 Fuentes-Valdés et al.2016Fuentes-Valdés et al.This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.Cylindrospermopsis raciborskii is a freshwater cyanobacterium producing bloom events and toxicity in drinking water source reservoirs. We present the first genome sequence for C. raciborskii CS505 (Australia), containing one 4.1-Mbp chromosome and one 110-Kbp plasmid having G+C contents of 40.3% (3933 genes) and 39.3% (111 genes), respectively. Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT) http://dx.doi.org/10.13039/50110000285011310371161232Mónica VásquezFondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT) http://dx.doi.org/10.13039/5011000028503140191Alvaro M. PlominskyJ. J. Fuentés-Valdés has a Chilean Government Fellowship for Graduate Students (CONICYT #21120837). cover-dateJuly/August 2016 ==== Body GENOME ANNOUNCEMENT Cylindrospermopsis raciborskii is defined as a planktonic nitrogen-fixing freshwater cyanobacterium (1). Strain CS505 (CSIRO culture collection) was isolated from Solomon Dam, North Queensland, Australia (2) and characterized based on its production of the hepatotoxin cylindrospermopsin (CYL), a potent protein synthesis inhibitor (3). In 2010, the draft genome of this strain was analyzed by a combination of 454 and Sanger sequencing, yielding 95 scaffolds with a total length of 3,879,017 bp (4). The cluster associated with the synthesis of CYL toxin was identified, as it had been previously described (5). The strain was grown in MLA liquid medium (6) at 25°C to 28°C under 12:12-h light/dark with a photon flux density of 40 µmol·m−2·s−1 with no aeration. In this work, the full genome of the C. raciborskii CS505 strain was sequenced using the Pacific Biosciences (PacBio) RS II single-molecule real-time (SMRT) whole-genome sequencing system and assembled using the hierarchical genome assembly process (HGAP) implemented in the PacBio SMRT Analysis software suite (version 2.2.0). The assembly resulted in six scaffolds with a total length of 4,159,260 bp. The average length of the scaffolds was 693,210 bp, with longest and shortest scaffolds sizes of 4,011,384 bp and 2,519 bp, respectively. CheckM analysis (7) indicated a genome completeness of 99.85% with a contamination level of 0.22% and no strain heterogeneity identified. The complete genome and plasmid were annotated using RAST (8) and curated using GenomeMatcher (9). Accession number(s). This whole-genome shotgun project has been deposited at DDBJ/ENA/GenBank under the accession number LYXA00000000. The version described in this paper is version LYXA01000000. Citation Fuentes-Valdés JJ, Plominsky AM, Allen EE, Tamames J, Vásquez M. 2016. Complete genome sequence of a cylindrospermopsin-producing cyanobacterium, Cylindrospermopsis raciborskii CS505, containing a circular chromosome and a single extrachromosomal element. Genome Announc 4(4):e00823-16. doi:10.1128/genomeA.00823-16. ACKNOWLEDGMENTS This work was supported by Grant Fondecyt Regular 1131037, 1161232 and FONDECYT de postdoctorado 3140191. ==== Refs REFERENCES 1. Woloszynska J 1912 Das phytoplankton einiger javanischer seen mit berücksichtigung des Sawa-planktons . Bull Int Acad Sci Cracovie Ser B 6 :649 –709 . 2. Saker ML 2014 Cyanobacterial blooms in tropical north Queensland water bodies . Ph.D. Thesis James Cook University , Townsville, Australia . 3. Griffiths DJ , Saker ML 2003 The Palm Island mystery disease 20 years on: a review of research on the cyanotoxin cylindrospermopsin . Environ Toxicol 18 :78 –93 . doi:10.1002/tox.10103 .12635096 4. Stucken K , John U , Cembella A , Murillo AA , Soto-Liebe K , Fuentes-Valdés JJ , Friedel M , Plominsky AM , Vásquez M , Glöckner G 2010 The smallest known genomes of multicellular and toxic cyanobacteria: comparison, minimal gene sets for linked traits and the evolutionary implications . PLoS One 5 :e9235 . doi:10.1371/journal.pone.0009235 .20169071 5. Mihali TK , Kellmann R , Muenchhoff J , Barrow KD , Neilan BA 2008 Characterization of the gene cluster responsible for cylindrospermopsin biosynthesis . Appl Environ Microbiol 74 :716 –722 . doi:10.1128/AEM.01988-07 .18065631 6. Castro D , Vera D , Lagos N , García C , Vásquez M 2004 The effect of temperature on growth and production of paralytic shellfish poisoning toxins by the cyanobacterium Cylindrospermopsis raciborskii C10 . Toxicon 44 :483 –489 . doi:10.1016/j.toxicon.2004.06.005 .15450922 7. Parks DH , Imelfort M , Skennerton CT , Hugenholtz P , Tyson GW 2015 Assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes . Genome Res 25 :1043 –1055 . doi:10.1101/gr.186072.114 .25977477 8. Aziz RK , Bartels D , Best AA , DeJongh M , Disz T , Edwards RA , Formsma K , Gerdes S , Glass EM , Kubal M , Meyer F , Olsen GJ , Olson R , Osterman AL , Overbeek RA , McNeil LK , Paarmann D , Paczian T , Parrello B , Pusch GD , Reich C , Stevens R , Vassieva O , Vonstein V , Wilke A , Zagnitko O 2008 The RAST server: rapid annotations using subsystems technology . BMC Genomics 9 :75 . doi:10.1186/1471-2164-9-75 .18261238 9. Ohtsubo Y , Ikeda-Ohtsubo W , Nagata Y , Tsuda M 2008 GenomeMatcher: a graphical user interface for DNA sequence comparison . BMC Bioinformatics 9 :376 . doi:10.1186/1471-2105-9-376 .18793444

PMC005xxxxxx/PMC5000825.txt

==== Front Genome AnnouncGenome AnnouncgagaGAGenome Announcements2169-8287American Society for Microbiology 1752 N St., N.W., Washington, DC genomeA00826-1610.1128/genomeA.00826-16ProkaryotesMultiple Genome Sequences of the Important Beer-Spoiling Species Lactobacillus backii Genome AnnouncementGeissler et al.Geissler Andreas J. aBehr Jürgen bVogel Rudi F. aa Technische Universität München, Lehrstuhl für Technische Mikrobiologie, Freising, Germanyb Technische Universität München, Bavarian Center for Biomolecular Mass Spectrometry, Freising, GermanyAddress correspondence to Jürgen Behr, juergen.behr@wzw.tum.de.25 8 2016 Jul-Aug 2016 4 4 e00826-1617 6 2016 6 7 2016 Copyright © 2016 Geissler et al.2016Geissler et al.This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.Lactobacillus backii is an important beer-spoiling species. Five strains isolated from four different breweries were sequenced using single-molecule real-time sequencing. Five complete genomes were generated, which will help to understand niche adaptation to beer and provide the basis for consecutive analyses. Allianz Industrie Forschung (AiF) http://dx.doi.org/10.13039/501100002723Aif 17576NRudi F. VogelPart of this work was funded by the German Ministry of Economics and Technology (via AiF) and the Wifoe (Wissenschaftsförderung der Deutschen Brauwirtschaft e.V., Berlin) in project AiF 17576N. None of the funding sources had any influence on the study design, the collection, analysis, and interpretation of data, the writing of the report, or the decision to submit the article for publication. cover-dateJuly/August 2016 ==== Body GENOME ANNOUNCEMENT Beer is a selective environment for the growth of bacteria. Restrictive parameters in beer include ethanol, carbon dioxide, antibacterial hops, and anaerobicity. In addition, beer is characterized by a low pH (3.8 to 4.7) and a selective nutrient content (1–3). Nevertheless, lactic acid bacteria (LAB) of the genus Lactobacillus are capable of growing in and spoiling beer. Between 2010 and 2013, Lactobacillus backii caused 4.8 to 10% of all beer spoilage incidents in Germany, while spoiled beers are characterized by visible turbidity and slight acidification (4, 5). In order to gain insights into the genomic adaptation of L. backii to beer, we sequenced the complete genomes of five brewery isolates with the ability to spoil beer. Beer spoilage ability was tested as described previously (6). High-molecular-weight DNA was purified from de Man, Rogosa, and Sharpe (MRS) liquid cultures using the Genomic-tip 100/G kit (Qiagen), as described previously (6). Single-molecule real-time sequencing (7) (PacBio RS II) was carried out at GATC Biotech (Konstanz, Germany). An insert size of 8 to 12 kb was selected for library creation, resulting in at least 200 Mb raw data from 1 to 2 SMRT cells (1 × 120-min movies) applying P4-C2 chemistry. Assembly was done with SMRT Analysis version 2.2.0.p2, using the Hierarchical Genome Assembly Process (HGAP) (8), and completed by manual curation (https://github.com/PacificBiosciences/Bioinformatics-Training/wiki/Finishing-Bacterial-Genomes). Genomes were annotated using the NCBI Prokaryotic Genome Annotation Pipeline (PGAP) and the Rapid Annotations using Subsystems Technology (RAST) server (9–11). Pan- and core genomes were calculated using CMG-Biotools and BADGE (6, 12). Strain characteristics, sequencing statistics, genome information, and accession numbers are listed in Table 1. The chromosome sizes range from 2.55 Mbp to 2.67 Mbp, with G+C contents of 40.8 to 40.9%. We found seven to 10 plasmids (per strain) with G+C contents from 34.7 to 43.9%. Plasmid sizes range from 7,030 bp to 70,980 bp, resulting in overall genome sizes of 2.78 to 2.85 Mbp. The analysis of RAST-annotated genomes resulted in an L. backii core genome with 1,924 gene families and a pangenome with 2,889 gene families. The chromosomes encode five complete rRNA operons and 66 to 68 tRNAs. TABLE 1  Strain characteristics, sequencing statistics, genome information, and accession numbersa Strain Source BioSample no. Accession no. Avg coverage of HGAP assembly (×) Size (Mbp) No. of contigs G+C content (%) No. of PEGs No. of CDSs TMW 1.1988 Light wheat beer SAMN04505726 CP014623 to CP014633 121 2.82 11 40.8 2,671 2,495 TMW 1.1989 Beer SAMN04505727 CP014873 to CP014880 89 2.85 8 40.8 2,646 2,496 TMW 1.1991 Brewery environment SAMN04505728 CP014881 to CP014889 99 2.82 9 40.7 2,590 2,437 TMW 1.1992 Brewery environmentb SAMN04505729 CP014890 to CP014898 109 2.78 9 40.8 2,621 2,450 TMW 1.2002 Brewery environmentb SAMN04505730 CP014899 to CP014906 168 2.84 8 40.7 2,653 2,478 a All strains (BioSamples) have beer spoilage ability and have been isolated from German breweries. All BioSamples are part of the BioProject PRJNA290141. Accession numbers are listed for all contigs of each whole genome (as range). Number of contigs are from chromosome plus plasmids and partial plasmids (only the case for TMW 1.1992). PEG, protein-encoding genes based on RAST annotation; CDS, coding sequences (coding) based on NCBI PGAP. b TMW 1.1992 and TMW 1.2002 are from the same brewery. The analysis of all five L. backii genomes revealed the presence of the same brewery-specific (99% sequence similarity, 99% coverage to each other) and plasmid-encoded fatty acid biosynthesis cluster as found in case of Pediococcus damnosus (6). Similarly, L. backii encodes an incomplete chromosomal fatty acid biosynthesis. Long-chain fatty acids are scarce in beer (13), while it was shown that the ability to produce fatty acids de novo is essential for P. damnosus growth in beer (6). The availability of these five L. backii genome sequences provides the basis for consecutive analyses (e.g., transcriptomics and plasmid curing experiments), with the objective to derive novel lifestyle genes of beer-spoiling L. backii. It will further help understand the role of plasmids for LAB niche adaptation. Accession number(s). The five complete L. backii genomes have been deposited in DDBJ/EMBL/GenBank under the accession numbers stated in Table 1. Citation Geissler AJ, Behr J, Vogel RF. 2016. Multiple genome sequences of the important beer-spoiling species Lactobacillus backii. Genome Announc 4(4):e00826-16. doi:10.1128/genomeA.00826-16. ==== Refs REFERENCES 1. Vriesekoop F , Krahl M , Hucker B , Menz G 2012 125th anniversary review: bacteria in brewing: the good, the bad and the ugly . J Inst Brew 118 :335 –345 . doi:10.1002/jib.49 . 2. Suzuki K 2011 125th anniversary review: microbiological instability of beer caused by spoilage Bacteria . J Inst Brew 117 :131 –155 . doi:10.1002/j.2050-0416.2011.tb00454.x . 3. Geissler AJ , Behr J , von Kamp K , Vogel RF 2016 Metabolic strategies of beer spoilage lactic acid bacteria in beer . Int J Food Microbiol 216 :60 –68 . doi:10.1016/j.ijfoodmicro.2015.08.016 .26398285 4. Bohak I , Thelen K , Beimfohr C 2006 Description of Lactobacillus backi sp. nov., an obligate beer-spoiling bacterium . Monatsschrift für Brauwissenschaft 59 :78 –82 . 5. Suzuki K 2015 Gram-positive spoilage bacteria in brewing , p 141 –174 . In Hill AE (ed) , Brewing microbiology managing microbes, ensuring quality and valorising waste. Woodhead publishing series in food science, technology, and nutrition , Woodhead Publishing , Cambridge, United Kingdom . 6. Behr J , Geissler AJ , Schmid J , Zehe A , Vogel RF 2016 The identification of novel diagnostic marker genes for the detection of beer spoiling Pediococcus damnosus strains using the BLAST diagnostic gene finder . PLoS One 11 :e0152747 . doi:10.1371/journal.pone.0152747 .27028007 7. Eid J , Fehr A , Gray J , Luong K , Lyle J , Otto G , Peluso P , Rank D , Baybayan P , Bettman B , Bibillo A , Bjornson K , Chaudhuri B , Christians F , Cicero R , Clark S , Dalal R , Dewinter A , Dixon J , Foquet M , Gaertner A , Hardenbol P , Heiner C , Hester K , Holden D , Kearns G , Kong X , Kuse R , Lacroix Y , Lin S , Lundquist P , Ma C , Marks P , Maxham M , Murphy D , Park I , Pham T , Phillips M , Roy J , Sebra R , Shen G , Sorenson J , Tomaney A , Travers K , Trulson M , Vieceli J , Wegener J , Wu D , Yang A , Zaccarin D 2009 Real-time DNA sequencing from single polymerase molecules . Science 323 :133 –138 . doi:10.1126/science.1162986 .19023044 8. Chin CS , Alexander DH , Marks P , Klammer AA , Drake J , Heiner C , Clum A , Copeland A , Huddleston J , Eichler EE , Turner SW , Korlach J 2013 Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data . Nat Methods 10 :563 –569 .23644548 9. Angiuoli SV , Gussman A , Klimke W , Cochrane G , Field D , Garrity G , Kodira CD , Kyrpides N , Madupu R , Markowitz V , Tatusova T , Thomson N , White O 2008 Toward an online repository of Standard Operating Procedures (SOPs) for (meta)genomic annotation . Omics 12 :137 –141 . doi:10.1089/omi.2008.0017 .18416670 10. Aziz RK , Bartels D , Best AA , DeJongh M , Disz T , Edwards RA , Formsma K , Gerdes S , Glass EM , Kubal M , Meyer F , Olsen GJ , Olson R , Osterman AL , Overbeek RA , McNeil LK , Paarmann D , Paczian T , Parrello B , Pusch GD , Reich C , Stevens R , Vassieva O , Vonstein V , Wilke A , Zagnitko O 2008 The RAST server: Rapid Annotations using Subsystems Technology . BMC Genomics 9 :75 . doi:10.1186/1471-2164-9-75 .18261238 11. Overbeek R , Olson R , Pusch GD , Olsen GJ , Davis JJ , Disz T , Edwards RA , Gerdes S , Parrello B , Shukla M , Vonstein V , Wattam AR , Xia F , Stevens R 2014 The SEED and the rapid annotation of microbial genomes using subsystems technology (RAST) . Nucleic Acids Res 42 :D206 –D214 . doi:10.1093/nar/gkt1226 .24293654 12. Vesth T , Lagesen K , Acar Ö , Ussery D 2013 CMG-Biotools, a free workbench for basic comparative microbial genomics . PLoS One 8 :e60120 . doi:10.1371/journal.pone.0060120 .23577086 13. Preedy VR 2009 Beer in health and disease prevention . Elsevier/Academic Press , Amsterdam, The Netherlands .

PMC005xxxxxx/PMC5000826.txt

==== Front Genome AnnouncGenome AnnouncgagaGAGenome Announcements2169-8287American Society for Microbiology 1752 N St., N.W., Washington, DC genomeA00858-1610.1128/genomeA.00858-16ProkaryotesDraft Genome Sequence of the Aureocin A53–Producing Strain Staphylococcus aureus A53 Genome AnnouncementSantos et al.Santos Olinda Cabral Silva aDuarte Andreza Freitas Souza aAlbano Rodolpho Mattos bBastos Maria Carmo Freire aa Departamento de Microbiologia Geral, Instituto de Microbiologia Paulo de Góes, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brasilb Departamento de Bioquímica, Instituto de Biologia Roberto Alcântara Gomes, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, BrasilAddress correspondence to Maria Carmo Freire Bastos, mcbastos@micro.ufrj.br.25 8 2016 Jul-Aug 2016 4 4 e00858-1623 6 2016 29 6 2016 Copyright © 2016 Santos et al.2016Santos et al.This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.Here, we present the 2,658,363-bp draft genome sequence of the aureocin A53–producing strain Staphylococcus aureus A53. This genome information may contribute to the optimal and rational exploitation of aureocin A53 as an antimicrobial agent and to its production in large scale. National Council for Scientific and Technological Development (Brazil)470.443/2012-0Olinda Cabral Silva SantosAndreza Freitas Souza DuarteMaria Carmo Freire BastosFoundation for Research Support in Rio de Janeiro (Brazil)E-26/102.336/2013Olinda Cabral Silva SantosAndreza Freitas Souza DuarteMaria Carmo Freire Bastos cover-dateJuly/August 2016 ==== Body GENOME ANNOUNCEMENT Staphylococcus aureus A53 was isolated from pasteurized commercial milk (1) and produces an atypical class II bacteriocin, aureocin A53, a 51-residue peptide produced and secreted without any posttranslational modification (2). Aureocin A53 exhibits a broad spectrum of antimicrobial activity (3), being highly bacteriolytic against multidrug-resistant nosocomial staphylococcal strains (4), streptococci, and staphylococci involved in bovine mastitis (5, 6). It also exhibits an activity against strains of the foodborne pathogen Listeria monocytogenes, even in a food matrix, and several features relevant to application in food biopreservation (7). Therefore, aureocin A53 has a great potential for biotechnological applications. Aureocin A53 is encoded by plasmid pRJ9 (1). The complete sequence of pRJ9 showed that this plasmid carries 14 open reading frames, from which eight are involved in aureocin A53 production and in immunity to this bacteriocin (2, 8). As bacteriocin synthesis is an energy-consuming process, it is generally closely controlled (9). However, as pRJ9 carries no functions involved in regulation of aureocin A53 production, these functions may be encoded on the bacterial chromosome. Description of the genome of strain A53 may help us in future investigations on the regulation of aureocin A53 production and, therefore, on how to increase aureocin A53 yields. The sequencing library was prepared using the Nextera XT DNA sample preparation kit (Illumina) following the manufacturer’s recommendations. Whole-genome shotgun sequencing was performed on the Illumina MiSeq system. De novo assembly of 1,838,024 reads was conducted using the A5-miseq pipeline (10), yielding 142-fold average genome coverage and resulting in a draft genome comprising 27 scaffolds ranging from 16,057 to 277,790 bp that represent the single chromosome and the 10,406-bp plasmid pRJ9. Genome annotation was performed using the Rapid Annotation using Subsystem Technology (RAST) server (11) and by Prokka (12). The total scaffold size was determined to be 2,658,363 bp, featuring a G+C content of 32.7%. Gene prediction by Prokka revealed 2,418 coding sequences, 21 tRNA genes, and 5 rRNA genes. Gene-category analysis showed that most genes are related to membrane transport and carbohydrate, lipid, and amino acid metabolisms. Genes involved in the regulation and expression of teicoplanin and fluoroquinolone resistances, as well as in the production of beta-lactamases and multidrug-resistance efflux pumps were also found. Moreover, genes encoding several virulence factors, including alpha-hemolysin, nonclassical enterotoxins, and proteins involved in biofilm formation, were also identified. Availability of the S. aureus A53 draft genome sequence may contribute to the optimal and rational exploitation of aureocin A53 as an antimicrobial agent and to its production in large scale. The fact that the S. aureus A53 genome encodes secreted virulence factors also encourages the research of aureocin A53 production through heterologous expression of its gene cluster in staphylococcal species used in the food industry and graded as nonpathogenic (13). Accession number(s). This whole-genome shotgun project has been deposited in DDBJ/ENA/GenBank under the accession number LVID00000000. The version described in this paper is the first version, LVID01000000. The plasmid pRJ9 sequence has been deposited at GenBank under the accession number AF4478813 (2). Citation Santos OCS, Duarte AFS, Albano RM, Bastos MCF. 2016. Draft genome sequence of the aureocin A53–producing strain Staphylococcus aureus A53. Genome Announc 4(4):00858-16. doi:10.1128/genomeA.00858-16. ACKNOWLEDGMENTS This work was supported by research programs funded by the National Council for Scientific and Technological Development (CNPq) and by the Foundation for Research Support in Rio de Janeiro (FAPERJ), Brasil. Genome sequencing was undertaken by the University of the State of Rio de Janeiro (UERJ), Brasil. A.F.S.D. was a recipient of a scholarship from CNPq. O.C.S.S. was a recipient of a postdoctoral fellowship from FAPERJ/CAPES. This study was supported by grants from CNPq (470.443/2012-0) and FAPERJ (E-26/102.336/2013) to M.C.F.B. ==== Refs REFERENCES 1. Giambiagi-Marval M , Mafra MA , Penido EG , Bastos MCF 1990 Distinct groups of plasmids correlated with bacteriocin production in Staphylococcus aureus . J Gen Microbiol 136 :1591 –1599 . doi:10.1099/00221287-136-8-1591 .2175767 2. Netz DJA , Pohl R , Beck-Sickinger AG , Selmer T , Pierik AJ , Sahl H-G , Bastos MCF 2002 Biochemical characterization and genetics analysis of aureocin A53, a new, atypical bacteriocin from Staphylococcus aureus . J Mol Biol 137 :445 –456 . 3. Oliveira SS , Nascimento JS , Póvoa DC , Araújo SA , Gamon MR , Bastos MCF 1998 Genetic analysis of the bacteriocin-encoding plasmids pRJ6 and pRJ9 of Staphylococcus aureus by transposon mutagenesis and cloning of genes involved in bacteriocin production . J Appl Microbiol 85 :972 –984 . doi:10.1111/j.1365-2672.1998.tb05261.x .9871317 4. Nascimento JS , Ceotto H , Nascimento SB , Giambiagi-deMarval M , Santos KR , Bastos MCF 2006 Bacteriocins as alternative agents for control of multiresistant staphylococcal strains . Lett Appl Microbiol 42 :215 –221 . doi:10.1111/j.1472-765X.2005.01832.x .16478507 5. Oliveira SS , Abrantes J , Cardoso M , Sordelli D , Bastos MC 1998 Staphylococcal strains involved in bovine mastitis are inhibited by Staphylococcus aureus antimicrobial peptides . Lett Appl Microbiol 27 :287 –291 . doi:10.1046/j.1472-765X.1998.00431.x . 9830147 6. Coelho MLV , Nascimento JS , Fagundes PC , Madureira DJ , Oliveira SS , Brito MAVP , Bastos MCF 2007 Activity of staphylococcal bacteriocins against Staphylococcus aureus and Streptococcus agalactiae involved in bovine mastitis . Res Microbiol 158 :625 –630 . doi:10.1016/j.resmic.2007.07.002 .17719749 7. Fagundes PC , Farias FM , Santos OCS , Oliveira NEM , Paz JAS , CeottoCeotto-Vigoder H , Alviano DS , Romanos MTV , Bastos MCF 2016 The antimicrobial peptide aureocin A53 as an alternative agent for biopreservation of dairy products . J Appl Microbiol 121 :435 –444 . doi:10.1111/jam.13189 .27225974 8. Nascimento JS , Coelho ML , Potter A , Ceotto H , Fleming LR , Salehian Z , Nes IF , Bastos Mdo C 2012 Genes involved in immunity to and secretion of aureocin A53, an atypical class II bacteriocin produced by Staphylococcus aureus A53 . J Bacteriol 194 :875 –883 . doi:10.1128/JB.06203-11 .22155775 9. Heng NCK , Wescombe PA , Burton JP , Jack RW , Tagg JR 2007 The diversity of bacteriocins in Gram-positive bacteria , p. 45 –83 . In Riley MA , Chavan MA (ed) , Bacteriocins: ecology and evolution . Springer , New York, NY. 10. Coil D , Jospin G , Darling AE 2015 A5-miseq: an updated pipeline to assemble microbial genomes from Illumina MiSeq data . Bioinformatics 31 :587 –589 . doi:10.1093/bioinformatics/btu661 .25338718 11. Aziz RK , Bartels D , Best AA , DeJongh M , Disz T , Edwards RA , Formsma K , Gerdes S , Glass EM , Kubal M , Meyer F , Olsen GJ , Olson R , Osterman AL , Overbeek RA , McNeil LK , Paarmann D , Paczian T , Parrello B , Pusch GD , Reich C , Stevens R , Vassieva O , Vonstein V , Wilke A , Zagnitko O 2008 The RAST server: Rapid Annotations using Subsystems Technology . BMC Genomics 9 :75 . doi:10.1186/1471-2164-9-75 .18261238 12. Seemann T 2014 Prokka: rapid prokaryotic genome annotation . Bioinformatics 30 :2068 –2069 . doi:10.1093/bioinformatics/btu153 .24642063 13. Rosenstein R , Götz F 2013 What distinguishes highly pathogenic staphylococci from medium- and non-pathogenic? Curr Top Microbiol Immunol 358 :33 –89 .23224647

PMC005xxxxxx/PMC5000827.txt

==== Front Genome AnnouncGenome AnnouncgagaGAGenome Announcements2169-8287American Society for Microbiology 1752 N St., N.W., Washington, DC genomeA00861-1610.1128/genomeA.00861-16ProkaryotesDraft Genome Sequences of Two Bacillus anthracis Strains from Etosha National Park, Namibia Genome AnnouncementValseth et al.Valseth Karoline abNesbø Camilla L. acEasterday W. Ryan aTurner Wendy C. dOlsen Jaran S. bStenseth Nils C. ahttp://orcid.org/0000-0002-3070-9745Haverkamp Thomas H. A. aa Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, University of Oslo, Blindern, Oslo, Norwayb Norwegian Defence Research Establishment, Kjeller, Norwayc Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canadad State University of New York, Albany, New York, USAAddress correspondence to Thomas H. A. Haverkamp, thhaverk@ibv.uio.no.25 8 2016 Jul-Aug 2016 4 4 e00861-1624 6 2016 29 6 2016 Copyright © 2016 Valseth et al.2016Valseth et al.This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.Bacillus anthracis strains K1 and K2 were isolated from two plains zebra anthrax carcasses in Etosha National Park, Namibia. These are draft genomes obtained by Illumina MiSeq sequencing of isolates collected from culture of blood-soaked soil from each carcass. Research Council of Norway180444/V40Camilla L. NesbøNational Science Foundation (NSF) http://dx.doi.org/10.13039/100000001OISE-1103054Wendy C. TurnerThis work was supported by a Research Council of Norway award (project no. 180444/V40) to C.L.N. and NSF OISE-1103054 to W.C.T. cover-dateJuly/August 2016 ==== Body GENOME ANNOUNCEMENT Bacillus anthracis is a Gram-positive, rod-shaped bacterium which sporulates to survive the transmission between hosts (1). It belongs to the genetically similar B. cereus group, and is the causative agent of anthrax (2). Anthrax is endemic to Etosha National Park, Namibia, which has yearly outbreaks of the mortal infection arising from spore-contaminated soil of carcass sites from previous years (3). Two B. anthracis isolates were obtained by culturing serial dilutions on polymyxin-lysozyme-EDTA-thallous acetate (PLET) agar culture medium following previously described protocols (4, 5) from contaminated soil beneath two plains zebra (Equus quagga) carcasses (Ca1 and Ca2) (located on 3 March 2014—coordinates, Ca1: S19.031/E015.548; Ca2: S19.037/E015.553). One colony per carcass was picked from the PLET plates and regrown as a lawn on a fresh PLET plate, then colonies were scraped of the PLET agar with a spatula and dispensed in 300 µL phosphate buffered saline (PBS) buffer (1 tablet from Sigma-Aldrich, USA, dissolved in 200 mL dH2O, autoclaved at 121°C for 20 min) before isolation. DNA isolation was performed using a FastDNA spin kit for soil (MP Biomedicals, CA, USA) following the manufacturer’s protocol with these alterations: tubes were homogenized with a MiniBead Beater, model 607 (BioSpec Products, OK, USA) for 80 s at 3,450 oscillations/min, before being centrifuged at 14,000 rpm (rpm) for 10 min. Six hundred fifty  µL of the supernatant was removed in step 10 in protocol. In step 13 of the protocol, spin filters were centrifuged for 3 min without emptying the catch tube. DES buffer was heated to 55°C in Dri-Bath, type 17600 (Thermolyn, IA, USA) before addition. Then, tubes were put on a heat block for 5 min at 55°C. Finally, the DNA extracts were sterilized using an Ultrafree Durapore PVDF 0.1 µM spin filter (Millipore, Darmstadt, Germany) by centrifugation at 11,000 rpm for 3 min. The bacterial DNA isolates were whole-genome sequenced at the Norwegian Sequencing Centre (NSC) using the TruSeq Nano reagents (Illumina, Inc., San Diego, CA, USA) and Illumina MiSeq with the following parameters: paired-end, insert size of 500 bp and a read length of 300 bp. Sequences were cleaned with AdapterRemoval 1.5.4 (6) and assembled in CLC workbench v8 using the AdapterRemoval 1.5.4 output files for singleton, pair1, and pair2. The genomes were annotated using the NCBI PGAP pipeline. The sequenced B. anthracis isolates from Ca1 and Ca2 were named K1 and K2, respectively, after the Norwegian word for carcass, “kadaver.” The K1 draft genome consisted of 38 contigs covering of 5,456,561 bp, with a G+C content of 35.1%, 5,837 genes, and 5,549 coding sequences (CDSs). The K2 draft genome is very similar with 38 contigs covering 5,461,141 bp, G+C content of 35.1%, 5,856 genes, and 5,549 CDSs. Accession number(s). Both whole-genome shotgun projects have been deposited in DDBJ/ENA/GenBank under the accession no. LBBZ00000000 (K1) and LBCA00000000 (K2). Citation Valseth K, Nesbø CL, Easterday WR, Turner WC, Olsen JS, Stenseth NC, Haverkamp THA. 2016. Draft genome sequences of two Bacillus anthracis strains from Etosha National Park, Namibia. Genome Announc 4(4):e00861-16. doi:10.1128/genomeA.00861-16. ACKNOWLEDGMENTS We thank the Ministry of Environment and Tourism (MET) in Namibia for permission to conduct this research (MET research permit 1857/2013); Mari Espelund for help and input for optimization of DNA extractions from soil; Claudine C. Cloete and Zoe Barandongo for help with sample collecting and DNA extractions; the Etosha Ecological Institute for providing laboratory facilities; and Institutt for Biovitenskap (IBV) student funding at University of Oslo (UiO) for providing travel money. This work was supported by a Research Council of Norway award (project 180444/V40) to C.L.N. and NSF OISE-1103054 to W.C.T. ==== Refs REFERENCES 1. Sharp RJ , Roberts AG 2006 Anthrax: the challenges for decontamination . J Chem Technol Biotechnol 81 :1612 –1625 . doi:10.1002/jctb.1591 . 2. Helgason E , Økstad OA , Caugant DA , Johansen HA , Fouet A , Mock M , Hegna I , Kolstø A-B 2000 Bacillus anthracis, Bacillus cereus, and Bacillus thuringiensis—one species on the basis of genetic evidence . Appl Environ Microbiol 66 :2627 –2630 . doi:10.1128/AEM.66.6.2627-2630.2000 .10831447 3. Turner WC , Kausrud KL , Beyer W , Easterday WR , Barandongo ZR , Blaschke E , Cloete CC , Lazak J , Van Ert MN , Ganz HH , Turnbull PC , Stenseth NC , Getz WM 2016 Lethal exposure: an integrated approach to pathogen transmission via environmental reservoirs . Sci Rep 6 :27311 . doi:10.1038/srep27311 .27265371 4. OIE 2008 Anthrax in humans and animals , 4th ed. World Organisation for Animal Health , Paris, France : http://www.who.int/csr/resources/publications/anthrax_webs.pdf 5. Turner WC , Kausrud KL , Krishnappa YS , Cromsigt JP , Ganz HH , Mapaure I , Cloete CC , Havarua Z , Küsters M , Getz WM , Stenseth NC 2014 Fatal attraction: vegetation responses to nutrient inputs attract herbivores to infectious anthrax carcass sites . Proc R Soc Lond B Biol Sci 281 :20141785 . doi:10.1098/rspb.2014.1785 . 6. Lindgreen S 2012 AdapterRemoval: easy cleaning of next-generation sequencing reads . BMC Res Notes 5 :337 . doi:10.1186/1756-0500-5-337 .22748135

PMC005xxxxxx/PMC5000828.txt

==== Front Genome AnnouncGenome AnnouncgagaGAGenome Announcements2169-8287American Society for Microbiology 1752 N St., N.W., Washington, DC genomeA00866-1610.1128/genomeA.00866-16ProkaryotesComplete Genome Sequences of the Historical Legionella pneumophila Strains OLDA and Pontiac Genome AnnouncementMercante et al.Mercante Jeffrey W. Morrison Shatavia S. Raphael Brian H. Winchell Jonas M. Division of Bacterial Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USAAddress correspondence to Jonas M. Winchell, jwinchell@cdc.gov.25 8 2016 Jul-Aug 2016 4 4 e00866-1624 6 2016 29 6 2016 Copyright © 2016 Mercante et al.2016Mercante et al.This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.Here, we report the complete genome sequences of Legionella pneumophila serogroup 1 strains OLDA and Pontiac, which predate the 1976 Philadelphia Legionnaires’ disease outbreak. Strain OLDA was isolated in 1947 from an apparent sporadic case, and strain Pontiac caused an explosive outbreak at a Michigan health department in 1968. This study was supported, in part, by funds made available through the Office of Advanced Molecular Detection at the Centers for Disease Control and Prevention. cover-dateJuly/August 2016 ==== Body GENOME ANNOUNCEMENT Legionnaires’ disease (LD) is a severe and sometimes fatal bacterial pneumonia caused by a growing list of Legionella spp., the most clinically relevant is L. pneumophila. One of the first high-profile LD outbreaks occurred in 1976 at an American Veterans’ convention in Philadelphia (1). Eight years prior to the Philadelphia outbreak, a large epidemic of febrile, nonpneumonic disease, termed “Pontiac Fever,” sickened 144 individuals at the Oakland County health department in Pontiac, Michigan (2). The cause of this illness was identified in 1977 when CDC researchers successfully isolated L. pneumophila serogroup 1 (sg1) from previously frozen guinea pig tissues experimentally inoculated with the Pontiac agent in 1968 (3). The “rickettsia-like” organism, OLDA, among the earliest cultured legionellae, was originally isolated in 1947 (4) from an apparent sporadic LD case; in 1977, this organism was identified as the same species and serogroup as the Philadelphia outbreak bacterium (5). Both strains were recently propagated from frozen stocks in the CDC archival collection with storage dates of 25 July 1978 for strain OLDA and 10 April 1978 for strain Pontiac. Pacific Biosciences RSII-compatible (Menlo Park, CA, USA) long-insert DNA libraries were constructed for both strains according to the manufacturer’s 10-kb protocol (PN100-286-100-04) and sequenced on single SMRT cells using 240-min movies. Assembly was performed with the Hierarchical Genome Assembly Process 3 (HGAP3) (6) through the PacBio SMRT Analysis System at ~85× coverage using 80,689 and 70,696 reads for the OLDA and Pontiac genomes, respectively. Overlapping contig ends were identified with Gepard version 1.3 (7) and trimmed to produce closed, circular genomes; 2,541,000 (OLDA) and 2,869,376 (Pontiac) paired-end 250-bp Illumina MiSeq (San Diego, CA, USA) reads were mapped to the PacBio assemblies to verify nucleotide accuracy >99.99%. The main OLDA genome is 3,486,082 bp with one circular plasmid, pLP3 (8) (G+C of 37.5%), of 129,883 bp, while strain Pontiac is 3,544,954 bp without extrachromosomal elements; both genomes have a G+C content of 38.4%. The NCBI Prokaryotic Genome Annotation Pipeline (PGAP) (9) identified 3,135 and 3,084 predicted protein coding genes for OLDA and Pontiac, respectively, and nine rRNAs, 43 tRNAs, and four ncRNAs in both strains; strain OLDA harbored a single CRISPR array. Genome analyses with Geneious version 9 (10) revealed that the previously described ~30-kb unstable genetic element (11) was absent from strain OLDA, but a pP36-like element (12) is maintained. A deletion of the lag-1 O-acetyltransferase also corroborates its monoclonal antibody-2 (mAb2 = mAb3/1) negative phenotype (13, 14). Strain OLDA is sequence type 1 (ST1) (15) and highly similar (≥98.5%) to the ST1 L. pneumophila reference strain Paris (NC_006368) (16). Strain Pontiac (ST62) is 93.4% identical to the L. pneumophila reference strain L10-023 (NC_002942.5) of the same sequence type, but lacks a 70.5-kb genomic island (position 3,042,379), and exhibits a genomic rearrangement near a trb/vir conjugal transfer locus (at position ~2,729,000). The complete genomes of strains OLDA and Pontiac, among the oldest known clinical and environmental L. pneumophila isolates, respectively, will be valuable as reference sequences as we attempt to understand the diversity of this species and its public health importance. Accession number(s). The whole-genome sequences described here have been deposited at NCBI/GenBank under the accession numbers CP016030 (OLDA chromosome), CP016031 (OLDA plasmid), and CP016029 (Pontiac). Citation Mercante JW, Morrison SS, Raphael BH, Winchell JM. 2016. Complete genome sequences of the historical Legionella pneumophila strains OLDA and Pontiac. Genome Announc 4(4):e00866-16. doi:10.1128/genomeA.00866-16. ACKNOWLEDGMENTS The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. ==== Refs REFERENCES 1. McDade JE , Shepard CC , Fraser DW , Tsai TR , Redus MA , Dowdle WR 1977 Legionnaires’ disease—isolation of a bacterium and demonstration of its role in other respiratory disease . N Engl J Med 297 :1197 –1203 . doi:10.1056/NEJM197712012972202 .335245 2. Glick TH , Gregg MB , Berman B , Mallison G , Rhodes WW Jr, Kassanoff I 1978 Pontiac fever. An epidemic of unknown etiology in a health department: I. Clinical and epidemiologic aspects . Am J Epidemiol 107 :149 –160 .623097 3. Kaufmann AF , McDade JE , Patton CM , Bennett JV , Skaliy P , Feeley JC , Anderson DC , Potter ME , Newhouse VF , Gregg MB , Brachman PS 1981 Pontiac fever: isolation of the etiologic agent (Legionella pneumophilia) and demonstration of its mode of transmission . Am J Epidemiol 114 :337 –347 .7304569 4. Jackson EB , Crocker TT , Smadel JE 1952 Studies on two rickettsia-like agents probably isolated from guinea pigs . Bacteriol Proc 52 :119 . 5. McDade JE , Brenner DJ , Bozeman FM 1979 Legionnaires’ disease bacterium isolated in 1947 . Ann Intern Med 90 :659 –661 . doi:10.7326/0003-4819-90-4-659 .373548 6. Chin CS , Alexander DH , Marks P , Klammer AA , Drake J , Heiner C , Clum A , Copeland A , Huddleston J , Eichler EE , Turner SW , Korlach J 2013 Nonhybrid, finished microbial genome assemblies from long-read SMRT sequencing data . Nat Methods 10 :563 –569 . doi:10.1038/nmeth.2474 .23644548 7. Krumsiek J , Arnold R , Rattei T 2007 Gepard: a rapid and sensitive tool for creating dotplots on genome scale . Bioinformatics 23 :1026 –1028 . doi:10.1093/bioinformatics/btm039 .17309896 8. Mikesell P , Ezzell JW , Knudson GB 1981 Isolation of plasmids in Legionella pneumophila and Legionella-like organisms . Infect Immun 31 :1270 –1272 .7228404 9. Tatusova T , DiCuccio M , Badretdin A 2013 Prokaryotic genome annotation pipeline . In The NCBI Handbook , 2nd ed. NCBI , Bethesda, MD. http://www.ncbi.nlm.nih.gov/books/NBK174280. 10. Kearse M , Moir R , Wilson A , Stones-Havas S , Cheung M , Sturrock S , Buxton S , Cooper A , Markowitz S , Duran C , Thierer T , Ashton B , Meintjes P , Drummond A 2012 Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data . BioInformatics 28 :1647 –1649 . doi:10.1093/bioinformatics/bts199 .22543367 11. Lüneberg E , Mayer B , Daryab N , Kooistra O , Zähringer U , Rohde M , Swanson J , Frosch M 2001 Chromosomal insertion and excision of a 30 kb unstable genetic element is responsible for phase variation of lipopolysaccharide and other virulence determinants in Legionella pneumophila . Mol Microbiol 39 :1259 –1271 . doi:10.1111/j.1365-2958.2001.02314.x .11251842 12. Doléans-Jordheim A , Akermi M , Ginevra C , Cazalet C , Kay E , Schneider D , Buchrieser C , Atlan D , Vandenesch F , Etienne J , Jarraud S 2006 Growth-phase-dependent mobility of the lvh-encoding region in Legionella pneumophila strain Paris . Microbiology 152 :3561 –3568 . doi:10.1099/mic.0.29227-0 .17159208 13. Petzold M , Thürmer A , Menzel S , Mouton JW , Heuner K , Lück C 2013 A structural comparison of lipopolysaccharide biosynthesis loci of Legionella pneumophila serogroup 1 strains . BMC Microbiol 13 :198 . doi:10.1186/1471-2180-13-198 .24069939 14. Joly JR , McKinney RM , Tobin JO , Bibb WF , Watkins ID , Ramsay D 1986 Development of a standardized subgrouping scheme for Legionella pneumophila serogroup 1 using monoclonal antibodies . J Clin Microbiol 23 :768 –771 .3517064 15. Gaia V , Fry NK , Afshar B , Lück PC , Meugnier H , Etienne J , Peduzzi R , Harrison TG 2005 Consensus sequence-based scheme for epidemiological typing of clinical and environmental isolates of Legionella pneumophila . J Clin Microbiol 43 :2047 –2052 . doi:10.1128/JCM.43.5.2047-2052.2005 .15872220 16. Brudno M , Do CB , Cooper GM , Kim MF , Davydov E , Program NCS , Green ED , Sidow A , Batzoglou S 2003 LAGAN and Multi-LAGAN: efficient tools for large-scale multiple alignment of genomic DNA . Genome Res 13 :721 –731 . doi:10.1101/gr.926603 .12654723

PMC005xxxxxx/PMC5000829.txt

==== Front Genome AnnouncGenome AnnouncgagaGAGenome Announcements2169-8287American Society for Microbiology 1752 N St., N.W., Washington, DC genomeA00871-1610.1128/genomeA.00871-16ProkaryotesDraft Genome Sequences of Pseudoalteromonas telluritireducens DSM 16098 and P. spiralis DSM 16099 Isolated from the Hydrothermal Vents of the Juan de Fuca Ridge Genome AnnouncementZhang et al.Zhang Huan aLiu Rui aWang Mengqiang aWang Hao aGao Qiang aHou Zhanhui aZhou Zhi aGao Dahai aWang Lingling aba Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, Chinab Key Laboratory of Mariculture and Stock Enhancement in North China's Sea, Ministry of Agriculture, Dalian Ocean University, Dalian, ChinaAddress correspondence to Huan Zhang, zhanghuan@qdio.ac.cn, or Lingling Wang, wanglingling@dlou.edu.cn.25 8 2016 Jul-Aug 2016 4 4 e00871-1627 6 2016 29 6 2016 Copyright © 2016 Zhang et al.2016Zhang et al.This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.This report describes the draft genome sequences of two strains, Pseudoalteromonas telluritireducens DSM 16098 and P. spiralis DSM 16099, which were isolated from hydrothermal vents of the Juan de Fuca Ridge. The reads generated by an Ion Torrent PGM were assembled into contigs with total sizes of 4.4 Mb and 4.1 Mb for DSM 16098 and DSM 16099, respectively. High Technology Project2014AA093501Lingling WangStrategic Priority Research Program of CASXDA11030202Huan Zhang cover-dateJuly/August 2016 ==== Body GENOME ANNOUNCEMENT Pseudoalteromonas (Gammaproteobacteria, Alteromonadales, Alteromonadaceae) spp. are ubiquitously distributed in the ocean and have become organisms of interest to the fields of ecological and pharmaceutical sciences due to their adaptability to the deep-sea environment and their ability to produce large quantities of biologically active metabolites (1, 2). So far, the draft genome sequences of over 40 Pseudoalteromonas strains have been released to public databases, and the complete whole-genome sequences of more than seven strains have been reported (3–5). P. telluritireducens DSM 16098 and P. spiralis DSM 16099 were isolated from samples obtained in June 1998 on the research vessel Atlantis using the deep-ocean-submersible vessel ALVIN at the hydrothermal vents located in the Main Endeavor Segment of the Juan de Fuca Ridge in the Pacific Ocean (47.57′N, 129.05′W, depths of 2,000 to 2,200 m) (6). The colonies of DSM 16098 were transparent and creamy, whereas those of DSM 16099 were transparent and colorless. They were selenite- and tellurite-reducing strains, respectively, which could grow in media containing Na2SeO3 or K2TeO3 and accumulate metallic selenium or tellurium. In addition, they could also produce capsule- or matrix-like extracellular compounds (6). The sequencing reads of both strains were generated by the Ion Torrent PGM using a 314 chip version 2 and a 400-bp sequencing kit, which were assembled by the Torrent SPAdes plugin de novo assembler version 2.3 and then merged using the CISA contig integrator (7). Protein-coding sequences were predicted by Glimmer software version 3.02 (8), and Ribosomal RNA genes were detected using RNAmmer software version 1.2 (9), while tRNA genes were detected using tRNAscan-SE (10). The genome of P. telluritireducens DSM 16098 consisted of 4,406,320 bases in 65 contigs (N50 = 165,877 bp and N90 = 44,541 bp) with a GC content of 40.94%. For P. spiralis DSM 16099, the genome consisted of 4,166,507 bases in 87 contigs (N50 = 108,041 bp and N90 = 31,480 bp), with a GC content of 40.17%. Both the genome size and GC content were similar to the published data from Pseudoalteromonas strains (11). A total of 110 tRNAs and eight rRNAs were predicted for DSM 16098, while 109 tRNAs and 11 rRNAs were predicted for DSM 16099. In DSM 16098, there were 4,278 putative open reading frames (ORFs) with an average size of 892 bp, giving a coding intensity of 84.92%. Meanwhile, 3,990 ORFs were predicted from DSM 16099, with an average size of 910 bp and a coding intensity of 87.22%. A total of 2,869 and 2,750 proteins from DSM 16098 and 16099, respectively, were assigned to cluster of orthologous group families. A comparative genomic study of these two strains is underway to explore the mechanism of selenite and tellurite oxyanion reduction, which could provide new insights into the bioremediation of highly polluted effluents, as well as the biometallurgical applications for their properties as semiconductors. Accession number(s). This whole-genome shotgun project has been deposited in DDBJ/ENA/GenBank under the accession numbers LVCM00000000 for DSM 16098 and LVCN00000000 for DSM 16099. The versions described in this study are the first versions, LVCM010000000 and LVCN010000000. Citation Zhang H, Liu R, Wang M, Wang H, Gao Q, Hou Z, Zhou Z, Gao D, Wang L. 2016. Draft genome sequences of Pseudoalteromonas telluritireducens DSM 16098 and P. spiralis DSM 16099 isolated from the hydrothermal vents of the Juan de Fuca Ridge. Genome Announc 4(4):e00871-16. doi:10.1128/genomeA.00871-16. ACKNOWLEDGMENTS We thank Xiaoxue Wang from the South China Sea Institute of Oceanology, Chinese Academy of Sciences (CAS), for providing the samples. We also thank Ping Zhang and Zengfang Zhao from the High-Performance Computing Center of the Institute of Oceanology, CAS, for the computing resources and the services they kindly provided. This work was supported by the grants from the High Technology Project (863 Project, no. 2014AA093501) of the Chinese Ministry of Science and Technology and from the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA11030202). ==== Refs REFERENCES 1. Holmström C , Kjelleberg S 1999 Marine Pseudoalteromonas species are associated with higher organisms and produce biologically active extracellular agents . FEMS Microbiol Ecol 30 :285 –293 . http://dx.doi.org/10.1111/j.1574-6941.1999.tb00656.x.10568837 2. Qin QL , Li Y , Zhang YJ , Zhou ZM , Zhang WX , Chen XL , Zhang XY , Zhou BC , Wang L , Zhang YZ 2011 Comparative genomics reveals a deep-sea sediment-adapted life style of Pseudoalteromonas sp. SM9913 . ISME J 5 :274 –284 . doi:10.1038/ismej.2010.103 . 20703316 3. Li B , Wang P , Zeng Z , Cai X , Wang G , Wang X 2016 Complete genome sequence of Pseudoalteromonas rubra SCSIO 6842, harboring a putative conjugative plasmid pMBL6842 . J Biotechnol 224 :66 –67 doi:10.1016/j.jbiotec.2016.03.010 .26970053 4. Choe H , Lee SH , Kim SG , Park DS , Nasir A , Kim KM 2016 Complete genome of Pseudoalteromonas phenolica KCTC 12086(T) (= O-BC30T), a marine bacterium producing polybrominated aromatic compounds . J Biotechnol 218 :23 –24 . doi:10.1016/j.jbiotec.2015.11.028 .26654937 5. Shin H , Lee JH , Ahn CS , Ryu S , Cho BC 2014 Complete genome sequence of marine bacterium Pseudoalteromonas phenolica bacteriophage TW1 . Arch Virol 159 :159 –162 . doi:10.1007/s00705-013-1776-6 .23851651 6. Rathgeber C , Yurkova N , Stackebrandt E , Beatty JT , Yurkov V 2002 Isolation of tellurite- and selenite-resistant bacteria from hydrothermal vents of the Juan de Fuca ridge in the Pacific Ocean . Appl Environ Microbiol 68 :4613 –4622 . doi:10.1128/AEM.68.9.4613-4622.2002 .12200320 7. Lin SH , Liao YC 2013 CISA: contig integrator for sequence assembly of bacterial genomes . PLoS One 8 :e60843 . doi:10.1371/journal.pone.0060843 .23556006 8. Delcher AL , Bratke KA , Powers EC , Salzberg SL 2007 Identifying bacterial genes and endosymbiont DNA with Glimmer . BioInformatics 23 :673 –679 . doi:10.1093/bioinformatics/btm009 .17237039 9. Lagesen K , Hallin P , Rødland EA , Staerfeldt H-H , Rognes T , Ussery DW 2007 RNAmmer: consistent and rapid annotation of ribosomal RNA genes . Nucleic Acids Res 35 :3100 –3108 . doi:10.1093/nar/gkm160 .17452365 10. Lowe TM , Eddy SR 1997 tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence . Nucleic Acids Res 25 :955 –964 . doi:10.1093/nar/25.5.0955 .9023104 11. Zeng Z , Dai S , Xie Y , Tian X , Li J , Wang X 2014 Genome sequences of two Pseudoalteromonas strains isolated from the South China sea . Genome Announc 2 (2):e00305-14 . doi:10.1128/genomeA.00305-14 .24744335

PMC005xxxxxx/PMC5000830.txt

==== Front Genome AnnouncGenome AnnouncgagaGAGenome Announcements2169-8287American Society for Microbiology 1752 N St., N.W., Washington, DC genomeA00872-1610.1128/genomeA.00872-16ProkaryotesDraft Genome Sequence of Alcanivorax sp. Strain KX64203 Isolated from Deep-Sea Sediments of Iheya North, Okinawa Trough Genome AnnouncementZhang et al.Zhang Huan aLiu Rui aWang Mengqiang aWang Hao aGao Qiang aHou Zhanhui aGao Dahai aWang Lingling aba Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, Chinab Key Laboratory of Mariculture & Stock Enhancement in North China's Sea, Ministry of Agriculture, Dalian Ocean University, Dalian, ChinaAddress correspondence to Huan Zhang, zhanghuan@qdio.ac.cn, or Lingling Wang, wanglingling@dlou.edu.cn.25 8 2016 Jul-Aug 2016 4 4 e00872-1627 6 2016 29 6 2016 Copyright © 2016 Zhang et al.2016Zhang et al.This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.This report describes the draft genome sequence of Alcanivorax sp. strain KX64203, isolated from deep-sea sediment samples. The reads generated by an Ion Torrent PGM were assembled into contigs, with a total size of 4.76 Mb. The data will improve our understanding of the strain’s function in alkane degradation. Strategic Priority Research Program of CASXDA11030202Huan ZhangHigh Technology Project2014AA093501Lingling Wang cover-dateJuly/August 2016 ==== Body GENOME ANNOUNCEMENT Bacteria of the genus Alcanivorax belong to a group of slow-growing marine hydrocarbonoclastic bacteria that can use oil hydrocarbons as its exclusive source of carbon and energy (1). By now, the genome sequences of six species in this genus isolated from various marine environments have been reported, including A. borkumensis (1), A. pacificus W11-5T (2), A. hongdengensis A-11-3T (3), A. dieselolei B5T (4), and A. jadensis. The Alcanivorax sp. strain KX64203 was isolated from sediment samples which were collected by using an electrohydraulic grab with an underwater television camera during the cruise conducted by the scientific research vessel KEXUE in Okinawa Trough (126°54.32′ E, 27°48.47′ N, ~1190-m depth, around a hydrothermal vent). Analysis of the 16S rRNA gene sequence (GenBank accession number KU954765) showed that it shared 99% identity (100% coverage) with that of A. dieselolei strain PTG4-3, and almost 99% identity with this gene in other Alcanivorax strains. The sequencing reads were generated by an Ion Torrent PGM using 314 Chip version 2 and a 400-bp sequencing kit, assembled by the Torrent SPAdes plugin (version 2.3) de novo assembler, and then merged using a CISA contig integrator (5). Protein-coding sequences were predicted by Glimmer software (version 3.02) (6) Ribosomal RNA genes were detected using RNAmmer software version 1.2 (7) and tRNA genes were detected using tRNAscan-SE (8). The genome of KX64203 consisted of 4,757,396 bases in 79 contigs (N50 159,927 bp and N90 36,849 bp), and had a G+C content of 61.4%. Both the genome size and G+C content were similar to published data for A. dieselolei type strain B5 (total length of 4.86 Mb and G+C% of 61.5%). Forty-three tRNA genes for all 20 amino acids and six rRNA genes were identified in the draft genome sequence. There were a total of 4,464 putative open reading frames (with an average size of 932 bp), giving a coding intensity of 87.43%. A total of 2,988 proteins were assigned to cluster of orthologous group (COG) families. The genes encoding three integral-membrane alkane monooxygenases (AlkB) and only one cytochrome P450 enzyme were found in the genome. Moreover, there were also some other proteins involved in the alkane degradation pathway, including three AlkKs, one AlkL, and one AlkN. Two genes, rubA and rubB encoding a rubredoxin and a rubredoxin reductase, respectively, were likely to be involved in alkane catabolism. The genome sequence and its gene annotation shed new light on the functional genomics of this species. Accession number(s). This whole-genome shotgun project has been deposited in DDBJ/ENA/GenBank under the accession no. LVIC00000000. The version described in this study is the first version LVIC01000000. Citation Zhang H, Liu R, Wang M, Wang H, Gao Q, Hou Z, Gao D, Wang L. 2016. Draft genome sequence of Alcanivorax sp. strain KX64203 isolated from deep-sea sediments of Iheya North, Okinawa Trough. Genome Announc 4(4):e00872-16. doi:10.1128/genomeA.00872-16. ACKNOWLEDGMENTS We thank the research vessel KEXUE of the Chinese Academy of Sciences for collecting samples and the WPOS sample center for providing samples. We also thank Ping Zhang and Zengfang Zhao from the High Performance Computing Center of the Institute of Oceanology, CAS, for the computing resources and service they kindly provided. This work was supported by grants from the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA11030202) and High Technology Project (863 Project, 2014AA093501) from the Chinese Ministry of Science and Technology. ==== Refs REFERENCES 1. Schneiker S , Martins dos Santos VA , Bartels D , Bekel T , Brecht M , Buhrmester J , Chernikova TN , Denaro R , Ferrer M , Gertler C , Goesmann A , Golyshina OV , Kaminski F , Khachane AN , Lang S , Linke B , McHardy AC , Meyer F , Nechitaylo T , Pühler A , Regenhardt D , Rupp O , Sabirova JS , Selbitschka W , Yakimov MM , Timmis KN , Vorholter FJ , Weidner S , Kaiser O , Golyshin PN 2006 Genome sequence of the ubiquitous hydrocarbon-degrading marine bacterium Alcanivorax borkumensis . Nat Biotechnol 24 :997 –1004 . doi:10.1038/nbt1232 .16878126 2. Lai Q , Shao Z 2012 Genome sequence of an alkane-degrading bacterium, Alcanivorax pacificus type strain W11–5, isolated from deep sea sediment . J Bacteriol 194 :6936 . doi:10.1128/JB.01845-12 .23209202 3. Lai Q , Shao Z 2012 Genome sequence of the alkane-degrading bacterium Alcanivorax hongdengensis type strain A-11-3 . J Bacteriol 194 :6972 . doi:10.1128/JB.01849-12 .23209226 4. Lai Q , Li W , Shao Z 2012 Complete genome sequence of Alcanivorax dieselolei type strain B5 . J Bacteriol 194 :6674 . doi:10.1128/JB.01813-12 .23144414 5. Lin SH , Liao YC 2013 CISA: contig integrator for sequence assembly of bacterial genomes . PLoS One 8 :e60843 . doi:10.1371/journal.pone.0060843 .23556006 6. Delcher AL , Bratke KA , Powers EC , Salzberg SL 2007 Identifying bacterial genes and endosymbiont DNA with glimmer . Bioinformatics 23 :673 –679 . doi:10.1093/bioinformatics/btm009 .17237039 7. Lagesen K , Hallin P , Rødland EA , Staerfeldt H-H , Rognes T , Ussery DW 2007 RNAmmer: consistent and rapid annotation of ribosomal RNA genes . Nucleic Acids Res 35 :3100 –3108 . doi:10.1093/nar/gkm160 .17452365 8. Lowe TM , Eddy SR 1997 tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence . Nucleic Acids Res 25 :955 –964 . doi:10.1093/nar/25.5.0955 .9023104

PMC005xxxxxx/PMC5000831.txt

==== Front Genome AnnouncGenome AnnouncgagaGAGenome Announcements2169-8287American Society for Microbiology 1752 N St., N.W., Washington, DC genomeA00875-1610.1128/genomeA.00875-16ProkaryotesComplete Genome Sequence of Streptomyces parvulus 2297, Integrating Site-Specifically with Actinophage R4 Genome AnnouncementNishizawa et al.Nishizawa Tomoyasu aMiura Takamasa bHarada Chizuko aGuo Yong aNarisawa Kazuhiko aOhta Hiroyuki aTakahashi Hideo cShirai Makoto a*a Department of Bioresource Science, Ibaraki University College of Agriculture, Ibaraki, Japanb Biological Resource Center, National Institute of Technology and Evaluation, Tokyo, Japanc Graduate School of Agriculture and Life Sciences, The University of Tokyo, Tokyo, JapanAddress correspondence to Tomoyasu Nishizawa, tomoyasu.nishizawa.agr@vc.ibaraki.ac.jp.* Present address: Makoto Shirai, College of Human and Cultural Sciences, Aikoku Gakuen University, Chiba, Japan. T.N. and T.M. contributed equally to this work. 25 8 2016 Jul-Aug 2016 4 4 e00875-1628 6 2016 29 6 2016 Copyright © 2016 Nishizawa et al.2016Nishizawa et al.This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.Streptomyces parvulus 2297, which is a host for site-specific recombination according to actinophage R4, is derived from the type strain ATCC 12434. Species of S. parvulus are known as producers of polypeptide antibiotic actinomycins and have been considered for industrial applications. We herein report for the first time the complete genome sequence of S. parvulus 2297. cover-dateJuly/August 2016 ==== Body GENOME ANNOUNCEMENT Streptomyces parvulus produces polypeptide antibiotics, which are synthesized by multifunctional enzymes such as polyketide synthases (PKSs) and nonribosomal polyketide synthases (NRPSs) (1–3). S. parvulus 2297 was derived from strain ATCC 12434T by a standard mutagenesis technique (4). Strain 2297 has been utilized as the host of a cosmid vector and in the site-specific recombination of actinophage R4 (5–7), and the integration mechanism between strain 2297 and the R4 phage has been investigated (8–10). These site-specific recombination events have been applied to the gene integration system for hetero-bacterial hosts (11, 12). Although the R4 phage genome sequence has been elucidated (13), the principal host genome sequence currently remains unclear. In order to gain an industrial insight into secondary metabolism and genome engineering by site-specific recombination, the genome sequence of strain 2297 was examined by means of a hybrid assembly based on paired-end sequencing and single-molecule real-time sequencing data. The strain 2297 DNA genome was sequenced using Illumina MiSeq and PacBio RSII (APRO Life Science Institute, Inc., Naruto, Japan). The paired-end reads from MiSeq were trimmed using sickle version 1.200 with default parameters (https://github.com/najoshi/sickle). The hybrid assembly with MiSeq and PacBio RSII data (34,760,398 paired-end and 222,831 single-end reads, and 110,723 long reads, respectively) was performed by SPAdes (v3.5.0 with the option, –careful) (14). Finishing was performed using GenoFinisher software (15) and the BWA-MEM (v0.7.12) algorithm (16). The alignment with telomere sequences was analyzed by BLAST (17) using strain ATCC 12434 (accession numbers AF038454 and AF038455). The genome sequence of strain 2297 was annotated using the NCBI Prokaryotic Genomes Automatic Annotation Pipeline (PGAP), CAZy database with dbCAN HMM v3.0 (18), and antiSMASH (19). The genome of strain 2297 consisted of a 7,149,446-bp linear chromosome (coverage of 252.8-fold) with 72.8% G+C content and containing 6,287 coding sequences (CDSs), 18 rRNA genes and 65 tRNA genes, and a 617,085-bp linear plasmid (coverage of 339.5-fold) with 71.9% G+C content and containing 427 CDSs. The telomere sequences in strain 2297 were conserved between the position of 1 to 180 bases in the linear chromosome and that of 616,906 to 617,085 bases in the linear plasmid, which possess 89% and 85% identities in opposite terminal ends, respectively. According to the antiSMASH analysis, 21 and 3 gene clusters related to secondary metabolites were predicted in the chromosome and plasmid, respectively. The type II PKS module, type III PKS module, 4 NRPS modules, and 2 PKS-NRPS hybrid modules were identified in these gene clusters. On the other hand, it was presumed that the host strain possessed an excisionase for site-specific excision because there was no gene for the excision of a prophage on the R4 phage genome (13). A gene encoding excisionase was also not identified on the strain 2297 genome, suggesting the potential of an excisionase that has not yet been identified in site-specific excision. Accession number(s). The genome sequence of S. parvulus 2297 has been deposited in the DDBJ/EMBL/GenBank database under the accession numbers CP015866 and CP015867. Citation Nishizawa T, Miura T, Harada C, Guo Y, Narisawa K, Ohta H, Takahashi H, Shirai M. 2016. Complete genome sequence of Streptomyces parvulus 2297, integrating site-specifically with actinophage R4. Genome Announc 4(4):e00875-16. doi:10.1128/genomeA.00875-16. ACKNOWLEDGMENTS This work was supported by the Council for Science, Technology and Innovation (CSTI), Cross-ministerial Strategic Innovation Promotion Program (SIP), “Technologies for creating next-generation agriculture, forestry and fisheries” (funding agency: Bio-oriented Technology Research Advancement Institution, NARO) ==== Refs REFERENCES 1. Olano C , Wilkinson B , Sánchez C , Moss SJ , Sheridan R , Math V , Weston AJ , Braña AF , Martin CJ , Oliynyk M , Méndez C , Leadlay PF , Salas JA 2004 Biosynthesis of the angiogenesis inhibitor borrelidin by Streptomyces parvulus Tü4055: cluster analysis and assignment of functions . Chem Biol 11 :87 –97 . doi:10.1016/j.chembiol.2003.12.018 .15112998 2. Shetty PR , Buddana SK , Tatipamula VB , Naga YV , Ahmad J 2014 Production of polypeptide antibiotic from Streptomyces parvulus and its antibacterial activity . Braz J Microbiol 45 :303 –312 . doi:10.1590/S1517-83822014005000022 .24948949 3. Sakurai Y , Inoue H , Nishii W , Takahashi T , Iino Y , Yamamoto M , Takahashi K 2009 Purification and characterization of a major collagenase from Streptomyces parvulus . Biosci Biotechnol Biochem 73 :21 –28 . doi:10.1271/bbb.80357 .19129667 4. Kirby R , Hopwood DA 1977 Genetic determination of methylenomycin synthesis by the SCP1 plasmid of Streptomyces coelicolor A3(2) . J Gen Microbiol 98 :239 –252 . doi:10.1099/00221287-98-1-239 .833570 5. Isogai T , Takahashi H , Saito H 1980 High-frequency protoplast-transfection of Streptomyces parvulus 2297 with actinophage R4 DNA . Agric Biol Chem 44 :2425 –2428 . 6. Morino T , Takagi K , Nakamura T , Takita T , Saito H , Takahashi H 1986 Studies of cosmid transduction in Streptomyces lividans and Streptomyces parvulus . Agric Biol Chem 50 :2493 –2497 . doi:10.1271/bbb1961.50.2493 . 7. Shirai M , Nara H , Sato A , Aida T , Takahashi H 1991 Site-specific integration of the actinophage R4 genome into the chromosome of Streptomyces parvulus upon lysogenization . J Bacteriol 173 :4237 –4239 .2061298 8. Matsuura M , Noguchi T , Aida T , Asayama M , Takahashi H , Shirai M 1995 A gene essential for the site-specific excision of actinophage R4 prophage genome from the chromosome of a lysogen . J Gen Appl Microbiol 41 :53 –61 . doi:10.2323/jgam.41.53 . 9. Matsuura M , Noguchi T , Yamaguchi D , Aida T , Asayama M , Takahashi H , Shirai M 1996 The sre gene (ORF469) encodes a site-specific recombinase responsible for integration of the R4 phage genome . J Bacteriol 178 :3374 –3376 .8655526 10. Miura T , Hosaka Y , Yang Y , Nishizawa T , Asayama M , Takahashi H , Shirai M 2011 In vivo and in vitro characterization of site-specific recombination of actinophage R4 integrase . J Gen Appl Microbiol 57 :45 –57 . doi:10.2323/jgam.57.45 .21478647 11. Miura T , Nishizawa A , Nishizawa T , Asayama M , Takahashi H , Shirai M 2014 Construction of a stepwise gene integration system by the transient expression of actinophage R4 integrase in cyanobacterium Synechocystis sp. PCC 6803 . Mol Genet Genomics 289 :615 –623 . doi:10.1007/s00438-014-0838-0 .24638932 12. Miura T , Nishizawa A , Nishizawa T , Asayama M , Shirai M 2016 Actinophage R4 integrase-based site-specific chromosomal integration of non-replicative closed circular DNA . J Basic Microbiol 56 :635 –644 . doi:10.1002/jobm.201500658 .26870903 13. Smith MC , Hendrix RW , Dedrick R , Mitchell K , Ko C-C , Russell D , Bell E , Gregory M , Bibb MJ , Pethick F , Jacobs-Sera D , Herron P , Buttner MJ , Hatfull GF 2013 Evolutionary relationships among actinophages and a putative adaptation for growth in Streptomyces spp . J Bacteriol 195 :4924 –4935 . doi:10.1128/JB.00618-13 .23995638 14. Nurk S , Bankevich A , Antipov D , Gurevich AA , Korobeynikov A , Lapidus A , Prjibelski AD , Pyshkin A , Sirotkin A , Sirotkin Y , Stepanauskas R , Clingenpeel SR , Woyke T , McLean JS , Lasken R , Tesler G , Alekseyev MA , Pevzner PA 2013 Assembling single-cell genomes and mini-metagenomes from chimeric MDA products . J Comput Biol 20 :714 –737 . doi:10.1089/cmb.2013.0084 .24093227 15. Ohtsubo Y , Maruyama F , Mitsui H , Nagata Y , Tsuda M 2012 Complete genome sequence of Acidovorax sp. strain KKS102, a polychlorinated-biphenyl degrader . J Bacteriol 194 :6970 –6971 . doi:10.1128/JB.01848-12 .23209225 16. Li H , Durbin R 2010 Fast and accurate long-read alignment with Burrows-Wheeler transform . Bioinformatics 26 :589 –595 . doi:10.1093/bioinformatics/btp324 .20080505 17. Altschul SF , Gish W , Miller W , Myers EW , Lipman DJ 1990 Basic local alignment search tool . J Mol Biol 215 :403 –410 . doi:10.1016/S0022-2836(05)80360-2 .2231712 18. Yin Y , Mao X , Yang J , Chen X , Mao F , Xu Y 2012 dbCAN: a web resource for automated carbohydrate-active enzyme annotation . Nucleic Acids Res 40 :W445 –W451 . doi:10.1093/nar/gks479 .22645317 19. Weber T , Blin K , Duddela S , Krug D , Kim HU , Bruccoleri R , Lee SY , Fischbach MA , Müller R , Wohlleben W , Breitling R , Takano E , Medema MH 2015 antiSMASH 3.0-a comprehensive resource for the genome mining of biosynthetic gene clusters . Nucleic Acids Res 43 :W237 –W243 . doi:10.1093/nar/gkv437 .25948579

PMC005xxxxxx/PMC5000832.txt

==== Front Genome AnnouncGenome AnnouncgagaGAGenome Announcements2169-8287American Society for Microbiology 1752 N St., N.W., Washington, DC genomeA00876-1610.1128/genomeA.00876-16VirusesFirst Complete Genome Sequence of a Chikungunya Virus Strain Isolated from a Patient Diagnosed with Dengue Virus Infection in Malaysia Genome AnnouncementOoi et al.http://orcid.org/0000-0002-4983-4984Ooi Man Kwan abGan Han Ming bcRohani Ahmad dhttp://orcid.org/0000-0001-6347-4455Syed Hassan Sharifah aba Virus-Host Interaction Research Group, Infectious Disease Laboratory, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, Selangor, Malaysiab Genomics Facility, Tropical Medicine and Biology Platform, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, Selangor, Malaysiac School of Science, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, Selangor, Malaysiad Medical Entomology Unit, Institute for Medical Research, Jalan Pahang, Kuala Lumpur, MalaysiaAddress correspondence to Sharifah Syed Hassan, sharifah.syedhassan@monash.edu.25 8 2016 Jul-Aug 2016 4 4 e00876-1630 6 2016 1 7 2016 Copyright © 2016 Ooi et al.2016Ooi et al.This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.Here, we report the complete genome sequence of a chikungunya virus coinfection strain isolated from a dengue virus serotype 2-infected patient in Malaysia. This coinfection strain was determined to be of the Asian genotype and contains a novel insertion in the nsP3 gene. Monash University http://dx.doi.org/10.13039/5011000017795140762Sharifah Syed Hassan cover-dateJuly/August 2016 ==== Body GENOME ANNOUNCEMENT Chikungunya virus (CHIKV) is an alphavirus in the family of Togaviridae and was first isolated in Tanzania in 1952 (1). CHIKV outbreaks in Africa and Asia have been reported since the 1960s, and reemergences of CHIKV have been observed (2). Global expansion of CHIKV outbreaks has broadened due to infected travelers, with the first known autochthonous CHIKV infection case in the Western Hemisphere reported in 2013 (3). Due to global expansion and reemergences of CHIKV outbreaks, CHIKV is recognized as an emerging epidemic-prone pathogen by the public health community (4). CHIKV infection has similar clinical manifestations to a few other mosquito-borne global health threat pathogens, particularly flaviviruses, which have a similar Aedes mosquito vector, e.g., dengue virus (DENV) (4). Due to the similar transmitting vector and geographical distribution of outbreaks, coinfection of DENV and CHIKV has emerged as a global threat, as coinfections usually remain undiagnosed (5, 6). Although coinfections of DENV and CHIKV were reported in India, Sri Lanka, Malaysia, Gabon, and Taiwan (7-12), to date, there is no complete genome sequence of CHIKV isolated from a coinfected patient with DENV. Here, we report the complete genome sequence of a CHIKV isolated from serum of a patient coinfected with DENV-2, in Selangor, Malaysia, in 2009. Strain MUM001-2009-Selangor was isolated through in vitro limiting-dilution–plaque purification in Vero cells. The viral RNA was extracted from the virus supernatant using GENEzol reagent (Geneaid), and the cDNA was synthesized using Moloney murine leukemia virus (M-MLV) reverse transcriptase and random primers. Second-strand synthesis was performed using the NEB 2nd-strand synthesis kit (NEB, Ipswich, MA). The purified double-stranded DNA was subsequently prepped using Nextera XT (Illumina, San Diego, CA) and sequenced on the MiSeq (2 × 150 bp setting) located at the Monash University Malaysia Genomics Facility, Selangor, Malaysia. The draft genome was assembled using IDBA_UD (13) and subsequently gap filled using a conventional primer walking strategy. The complete genome of CHIKV consists of 11,900 nucleotides (nt), containing two open reading frames (ORFs) coding for the typical structural and nonstructural proteins. The preliminary phylogenetic analysis based on the neighbor-joining method showed that strain MUM001-2009-Selangor formed a monophyletic clustering (data not shown) with strains from the Asian genotype with close similarity (99%) strain MY/06/37348 (accession no. FN295483.3) and MY/06/37350 (accession no. FN295484.2) isolated from Malaysia. Intriguingly, a unique amino acid insertion in the nsP3 gene was observed in this isolate in positions 376 to 451. This insertion consists of a duplicated sequence of part of the N-terminal domain of nsP3. Deletions of seven amino acids at this similar position starting from amino acid (aa) 376 were observed initially in a few of the Malaysian isolates, later in Indonesian isolates, and isolates currently circulating in the Western Hemisphere (14, 15). nsP3 is involved in the negative-strand RNA synthesis, and the essential role remains enigmatic. Accession number(s). The complete genome of coinfected chikungunya virus, Asian genotype, has been deposited in GenBank under the accession no. KX168429. Citation Ooi MK, Gan HM, Rohani A, Syed Hassan S. 2016. First complete genome sequence of a chikungunya virus strain isolated from a patient diagnosed with dengue virus infection in Malaysia. Genome Announc 4(4):e00876-16. doi:10.1128/genomeA.00876-16. ACKNOWLEDGMENTS We thank the entomology staff of the Institute for Medical Research (IMR) for providing us with the coinfection sample. This work was funded by the Infectious Disease and Health Cluster of Tropical Medicine and Biology Platform, Monash University Malaysia (Grant No. 5140762). ==== Refs REFERENCES 1. Lumsden WHR 1955 An epidemic of virus disease in southern Province, Tanganyika territory, in 1952-1953 II. General description and epidemiology . Trans R Soc Trop Med Hyg 49 :33 –57 . doi:10.1016/0035-9203(55)90081-X .14373835 2. Powers AM , Logue CH 2007 Changing patterns of chikungunya virus: re-emergence of a zoonotic arbovirus . J Gen Virol 88 :2363 –2377 . doi:10.1099/vir.0.82858-0 .17698645 3. Nasci RS 2014 Movement of chikungunya virus into the Western Hemisphere . Emerg Infect Dis 20 :1394 –1395 . doi:10.3201/eid2008.140333 .25061832 4. Staples JE , Breiman RF , Powers AM 2009 Chikungunya fever: an epidemiological review of a re-emerging infectious disease . Clin Infect Dis 49 :942 –948 . doi:10.1086/605496 .19663604 5. Caglioti C , Lalle E , Castilletti C , Carletti F , Capobianchi MR , Bordi L 2013 Chikungunya virus infection: an overview . New Microbiol 36 :211 –227 .23912863 6. Carey DE 1971 Chikungunya and dengue: a case of mistaken identity? J Hist Med Allied Sci 26 :243 –262 . doi:10.1093/jhmas/XXVI.3.243 .4938938 7. Chahar HS , Bharaj P , Dar L , Guleria R , Kabra SK , Broor S 2009 Co-infections with chikungunya virus and dengue virus in Delhi, India . Emerg Infect Dis 15 :1077 –1080 . doi:10.3201/eid1507.080638 .19624923 8. Chang S-F , Su C-L , Shu P-Y , Yang C-F , Liao T-L , Cheng C-H , Hu H-C , Huang J-H 2010 Concurrent isolation of chikungunya virus and dengue virus from a patient with coinfection resulting from a trip to Singapore . J Clin Microbiol 48 :4586 –4589 . doi:10.1128/JCM.01228-10 .20881182 9. Hapaurachchi H , Bandara K , Hapugoda M , Williams S , Abeyewickreme W 2008 Laboratory confirmation of dengue and chikungunya co-infection . Ceylon Med J 53 :104 –105 . doi:10.4038/cmj.v53i3.252 .18982804 10. Leroy EM , Nkoghe D , Ollomo B , Nze-Nkogue C , Becquart P , Grard G , Pourrut X , Charrel R , Moureau G , Ndjoyi-Mbiguino A , De-Lamballerie X 2009 Concurrent chikungunya and dengue virus infections during simultaneous outbreaks, Gabon, 2007 . Emerg Infect Dis 15 :591 –593 . doi:10.3201/eid1504.080664 .19331740 11. Nayar SK , Noridah O , Paranthaman V , Ranjit K , Norizah I , Chem YK , Mustafa B , Chua KM 2007 Co-infection of dengue virus and chikungunya virus in two patients with acute febrile illness . Med J Malaysia 62 :335 –336 .18551940 12. Schilling S , Emmerich P , Günther S , Schmidt-Chanasit J 2009 Dengue and chikungunya virus co-infection in a German traveller . J Clin Virol 45 :163 –164 . doi:10.1016/j.jcv.2009.04.001 .19442576 13. Peng Y , Leung HC , Yiu S-M , Chin FY 2010 IDBA–a practical iterative de Bruijn graph de novo assembler , p 426 –440 . In Berger B (ed) , Research in computational molecular biology . Springer , New York, NY. 14. Leparc-Goffart I , Nougairede A , Cassadou S , Prat C , De Lamballerie X 2014 Chikungunya in the Americas . Lancet 383 :514 . doi:10.1016/S0140-6736(14)60185-9 .24506907 15. Sam I-C , Loong S-K , Michael JC , Chua C-L , Wan Sulaiman WY , Vythilingam I , Chan S-Y , Chiam C-W , Yeong Y-S , AbuBakar S , Chan YF 2012 Genotypic and phenotypic characterization of chikungunya virus of different genotypes from Malaysia . PLoS One 7 :e50476 . doi:10.1371/journal.pone.0050476 .23209750

PMC005xxxxxx/PMC5000833.txt

==== Front Genome AnnouncGenome AnnouncgagaGAGenome Announcements2169-8287American Society for Microbiology 1752 N St., N.W., Washington, DC genomeA00881-1610.1128/genomeA.00881-16ProkaryotesGenome Sequence of a Lactococcus lactis Strain Isolated from Salmonid Intestinal Microbiota Genome AnnouncementOpazo Rafael aGajardo Felipe aRuiz Mauricio bRomero Jaime aa Laboratorio de Biotecnología, Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Chile, Macul, Santiago, Chileb Departamento de Desarrollo, Helios Innovation, Matucana, Casablanca, ChileAddress correspondence to Jaime Romero, jromero@inta.uchile.cl.25 8 2016 Jul-Aug 2016 4 4 e00881-1628 6 2016 1 7 2016 Copyright © 2016 Opazo et al.2016Opazo et al.This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.Lactococcus lactis is a common inhabitant of the intestinal microbiota of salmonids, especially those in aquaculture systems. Here, we present a genome sequence of a Lactococcus lactis strain isolated from the intestinal contents of rainbow trout reared in Chile. Conicyt Inserción de Capital Humano a la AcademiaPAI 791100002Rafael OpazoFondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT) http://dx.doi.org/10.13039/501100002850Fondecyt 1140734Jaime Romero cover-dateJuly/August 2016 ==== Body GENOME ANNOUNCEMENT Currently, it is generally recognized that the gastrointestinal microbiota of animals serves several functions, including nutrition, development, immunity, and xenobiotic metabolism (1). Using molecular approaches to examine the gut microbiota of farmed Atlantic salmon and rainbow trout, several authors reported that lactic acid bacteria (LAB) were among the predominant bacterial groups; hence, Lactococcus and Lactobacillus were observed in most of the molecular profiles derived from samples from those fish (1). Strains of Lactococcus have been proposed to be probiotic; some studies have suggested that protection and stimulation of the immune system are conferred by several strains of this genus. Furthermore, the immunomodulatory properties of Lactococcus lactis strains have been shown recently, for example, in the context of soy-induced enteritis in salmon (2). These authors showed that the addition of Lactococcus lactis strain RBT018 to the soybean meal diet decreased the inflammation parameters in the histological examination compared to soybean meal diet without the probiotic. The bacterial genome was sequenced using the Ion Torrent PGM platform with mate-paired-end 3-kbp span library for each isolate. The data were quality trimmed using Prinseq with a Phred score of 15, and sequencing errors were corrected using the software Pollux and subsequently assembled with Celera Assembler version 8.3. The assembled sequence was annotated by the National Center for Biotechnology Information (NCBI) Prokaryotic Genome Annotation Pipeline (PGAP). The resulting genome assembly of RBT018 has 79 contigs, comprising 2,479,393 bp. The estimated G+C content of the assembled RBT018 genome is 35.4%. The annotation of the RBT018 assembly contains 2,630 genes, including 2,419 coding DNA sequences (CDSs), 123 pseudogenes, 23 rRNA genes (5S, 16S, and 23S), 64 tRNA genes, one ribozyme gene (RNase P), and 110 frameshifted genes. The genome size, G+C content, number of predicted genes, and RNA coding genes are comparable to those of the other published Lactococcus strains (3). RBT018 was compared with the probiotic strain Lactococcus lactis CV56 (accession numbers CP002365 to CP002370) using InParanoid8 (4). Some of the common genes in both strains that could be related to probiotic activity were collagen adhesin (accession no. OAZ16633.1), two bacteriocins (accession numbers OAZ16685.1 and OAZ16145.1), and bacteriocin ABC transporter ATP-binding protein (accession no. OAZ16685.1). After a BLASTn search (with default parameters) against the Virulence Factors Database (VFDB [http://www.mgc.ac.cn/VFs/]), none of the genes in the RBT018 genome were found to be similar to virulence genes in VFDB. Accession number(s). The genome sequence was deposited in DDBJ/EMBL/GenBank under the accession no. JCOB00000000. The version described in this paper is the first version. Citation Opazo R, Gajardo F, Ruiz M, Romero J. 2016. Genome sequence of a Lactococcus lactis strain isolated from salmonid intestinal microbiota. Genome Announc 4(4):e00881-16. doi:10.1128/genomeA.00881-16. ACKNOWLEDGMENTS We gratefully acknowledge the technical support of Omics Solutions. This work was supported by grant FONDECYT 1140734 from CONICYT Chile. ==== Refs REFERENCES 1. Romero J , Ringø E , Merrifield DL 2014 The gut microbiota of fish . In Merrifield D , Ringø E (ed) , Aquaculture nutrition: gut health, probiotics and prebiotics. John Wiley & Sons , Chichester, United Kingdom . 2. Navarrete P , Fuentes P , De la Fuente L , Barros L , Magne F , Opazo R , Ibacache C , Espejo R , Romero J 2013 Short-term effects of dietary soybean meal and lactic acid bacteria on the intestinal morphology and microbiota of Atlantic salmon (Salmo salar) . Aquacult Nutr 19 :827 –836 . doi:10.1111/anu.12047 . 3. Yang CH , Wu CC , Cheng WS , Chung MC , Tsai YC , Chang CH 2015 A17, the first sequenced strain of Lactococcus lactis subsp. cremoris with potential immunomodulatory functions . Genome Announc 3 (1):e01563-14 . doi:10.1128/genomeA.01563-14 .25676767 4. Sonnhammer EL , Östlund G 2015 InParanoid 8: orthology analysis between 273 proteomes, mostly eukaryotic . Nucleic Acids Res 43 :D234 –D239 . doi:10.1093/nar/gku1203 .25429972

PMC005xxxxxx/PMC5000834.txt

==== Front Genome AnnouncGenome AnnouncgagaGAGenome Announcements2169-8287American Society for Microbiology 1752 N St., N.W., Washington, DC genomeA00891-1610.1128/genomeA.00891-16ProkaryotesDraft Genome Sequence of Cellulolytic and Xylanolytic Cellulomonas sp. Strain B6 Isolated from Subtropical Forest Soil Genome AnnouncementPiccinni et al.Piccinni Florencia adMurua Yanina ac*Ghio Silvina bTalia Paola adRivarola Máximo acdCampos Eleonora ada Instituto de Biotecnología, CICVyA, INTA, Buenos Aires, Argentinab Instituto de Suelos, CIRN, INTA, Buenos Aires, Argentinac Facultad de Ingeniería y Ciencias, Exactas, UADE, Buenos Aires, Argentinad Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, ArgentinaAddress correspondence to Eleonora Campos, campos.eleonora@inta.gob.ar.* Present address: Yanina Murua, Fundación Instituto Leloir (FIL), Ciudad Autónoma de Buenos Aires, Buenos Aires, Argentina. 25 8 2016 Jul-Aug 2016 4 4 e00891-1629 6 2016 1 7 2016 Copyright © 2016 Piccinni et al.2016Piccinni et al.This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.Cellulomonas sp. strain B6 was isolated from a subtropical forest soil sample and presented (hemi)cellulose-degrading activity. We report here its draft genome sequence, with an estimated genome size of 4 Mb, a G+C content of 75.1%, and 3,443 predicted protein-coding sequences, 92 of which are glycosyl hydrolases involved in polysaccharide degradation. Instituto Nacional de Tecnología Agropecuaria (INTA)project PNAIyAV 1130034Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) http://dx.doi.org/10.13039/501100002923PIP 11420110100124Eleonora CamposMinisterio de Ciencia, Tecnología e Innovación Productiva (MINCyT) http://dx.doi.org/10.13039/501100003033PICT2011-2735Florencia Elizabeth PiccinniYanina Alejandra MuruaSilvina GhioPaola Monica TaliaMaximo RivarolaEleonora Campos cover-dateJuly/August 2016 ==== Body GENOME ANNOUNCEMENT Cellulases and xylanases are widely used in textile, animal feed, food, and paper industries. They also play a key role in the production of cellulosic ethanol (1). Cellulomonas sp. strain B6 (available from Argentine collection of microorganisms as IMIZA:CEB6) was isolated from the first 10-cm layer of a preserved native subtropical forest soil sample (26°01′34′′S 54°26′59′′W) (2). It is a Gram-positive, rod-shaped, aerobic isolate that can grow on lignocellulosic biomass, such as sugarcane residue, as a sole carbon source. Its secreted protein extract presented cellulose- and xylan-degrading activities (our unpublished data). Based on 16S rRNA analysis, strain B6 formed a cluster with Cellulomonas flavigena (accession no. AF140036.1) and Cellulomonas persica (accession no. NR_024913.1). The genomes of Cellulomonas flavigena DSM 20109 and B6 show an average nucleotide Identity (ANI) value of 81.99%, suggesting that they are different species. Genomic DNA of Cellulomonas sp. strain B6 was extracted from a 24-h culture in LB broth by a commercial extraction kit (Wizard genomic DNA extraction kit; Promega) and sequenced using the Illumina MiSeq platform. The data comprised 1,532,556 paired-end reads of 500 bp, resulting in 83-fold genome coverage. The raw reads were subjected to trimming using Trimmomatic version 0.33 (3) and assembled de novo using Celera Assembler version 8.2 (4), followed by the SPAdes genome assembler version 3.5.0 (5), generating 279 contigs, with a total length of 4,042,435 bp (N50, 24,612 bp) and a G+C content of 75.1%, consistent with the genus. Gene prediction and functional analysis were carried out using the Rapid Annotations using Subsystems Technology (RAST) server version 2.0 (6) and the NCBI Prokaryotic Genome Annotation Pipeline (http://www.ncbi.nlm.nih.gov/genome/annotation_prok/). Using the NCBI pipeline, 3,691 genes, including 3,443 protein-coding sequences, 50 tRNA, and a set of full-length 5S, 23S, and 16S rRNA gene sequences, were predicted. A noncoding RNA (ncRNA) of an RNase P (ATM99_11600) was also predicted. Similar results were obtained by RAST. A comparison of a representative set of FigFam protein-coding genes from Cellulomonas sp. B6 to other bacterial sequences available in RAST identified Cellulomonas flavigena DSM 20109 (score, 413) and Sanguibacter keddieii DSM 10542 as the closest neighbors. Utilizing all functional annotations from CAZy (http://www.cazy.org/) (7) and dbCAN (http://csbl.bmb.uga.edu/dbCAN/) (8), 92 sequences encoding potential glycosyl hydrolases (GH) were identified, including six endo-β-1,4-glucanases (two GH5 and four GH9), two exo-glucanases (GH6 and GH48), 11 β-glucosidases (three GH1 and eight GH3), 10 endo-1,4-β-xylanases (eight GH10, one GH11, and one GH43), two β-xylosidases (two GH39 and one GH43:1), four α-l-arabinofuranosidases (two GH43 and two GH51), two endo-1,5-α-arabinosidases (GH43), and an α-glucuronidase (GH67). These results are consistent with the cellulolytic and xylanolytic activities of this bacterial isolate. The genome information will be useful for studies of microbial enzymes for industrial application in lignocellulosic biomass utilization. Accession number(s). This whole-genome shotgun project has been deposited at NCBI SRA database under the accession no. LNTD00000000. The version described in this paper is version LNTD01000000. Citation Piccinni F, Murua Y, Ghio S, Talia P, Rivarola M, Campos E. 2016. Draft genome sequence of cellulolytic and xylanolytic Cellulomonas sp. strain B6 isolated from subtropical forest soil. Genome Announc 4(4):e00891-16. doi:10.1128/genomeA.00891-16. ACKNOWLEDGMENTS F.P. is a Ph.D. student of the Department of Biological Chemistry (QB) of the School of Natural and Exact Sciences (FCEN) of the University of Buenos Aires (UBA) and has a doctoral fellowship from the Argentine National Council of Research (CONICET). M.R., P.T., and E.C. are members of the Scientific Research Career of CONICET. Sequencing services were performed at INTA, Consorcio Argentino de Tecnología Genómica (CATG) (PPL Genómica, MINCyT), and this work used computational resources from the Bioinformatics Unit, Instituto de Biotecnología, CICVyA, INTA. ==== Refs REFERENCES 1. Lennartsson PR , Erlandsson P , Taherzadeh MJ 2014 Integration of the first and second generation bioethanol processes and the importance of by-products . Bioresour Technol 165 :3 –8 . doi:10.1016/j.biortech.2014.01.127 .24582951 2. Campos E , Negro Alvarez MJ , Sabarís Di Lorenzo G , Gonzalez S , Rorig M , Talia P , Grasso DH , Saéz F , Manzanares Secades P , Ballesteros Perdices M , Cataldi AA 2014 Purification and characterization of a GH43 beta-xylosidase from Enterobacter sp. identified and cloned from forest soil bacteria . Microbiol Res 169 :213 –220 . doi:10.1016/j.micres.2013.06.004 .23838121 3. Bolger AM , Lohse M , Usadel B 2014 Trimmomatic: a flexible trimmer for Illumina sequence data . Bioinformatics 30 :2114 –2120 . doi:10.1093/bioinformatics/btu170 .24695404 4. Myers EW , Sutton GG , Delcher AL , Dew IM , Fasulo DP , Flanigan MJ , Kravitz SA , Mobarry CM , Reinert KH , Remington KA , Anson EL , Bolanos RA , Chou HH , Jordan CM , Halpern AL , Lonardi S , Beasley EM , Brandon RC , Chen L , Dunn PJ 2000 A whole-genome assembly of Drosophila . Science 287 :2196 –2204 . doi:10.1126/science.287.5461.2196 .10731133 5. Bankevich A , Nurk S , Antipov D , Gurevich AA , Dvorkin M , Kulikov AS , Lesin VM , Nikolenko SI , Pham S , Prjibelski AD , Pyshkin AV , Sirotkin AV , Vyahhi N , Tesler G , Alekseyev MA , Pevzner PA 2012 SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing . J Comput Biol 19 :455 –477 . doi:10.1089/cmb.2012.0021 .22506599 6. Aziz RK , Bartels D , Best AA , DeJongh M , Disz T , Edwards RA , Formsma K , Gerdes S , Glass EM , Kubal M , Meyer F , Olsen GJ , Olson R , Osterman AL , Overbeek RA , McNeil LK , Paarmann D , Paczian T , Parrello B , Pusch GD , Reich C , Stevens R , Vassieva O , Vonstein V , Wilke A , Zagnitko O 2008 The RAST server: Rapid Annotations using Subsystems Technology . BMC Genomics 9 :75 . doi:10.1186/1471-2164-9-75 .18261238 7. Lombard V , Golaconda Ramulu H , Drula E , Coutinho PM , Henrissat B 2014 The carbohydrate-active enzymes database (CAZy) in 2013 . Nucleic Acids Res 42 :D490 –D95 . doi:10.1093/nar/gkt1178 .24270786 8. Yin Y , Mao X , Yang J , Chen X , Mao F , Xu Y 2012 dbCAN: a Web resource for automated carbohydrate-active enzyme annotation . Nucleic Acids Res 40 :W445 –W451 . doi:10.1093/nar/gks479 .22645317

PMC005xxxxxx/PMC5000835.txt

==== Front Genome AnnouncGenome AnnouncgagaGAGenome Announcements2169-8287American Society for Microbiology 1752 N St., N.W., Washington, DC genomeA00893-1610.1128/genomeA.00893-16ProkaryotesGenome Sequence of Enterobacter cloacae Strain SENG-6, a Bacterium Producing Histo-Blood Group Antigen-Like Substances That Can Bind with Human Noroviruses Genome AnnouncementIshii et al.http://orcid.org/0000-0003-3600-9165Ishii Satoshi aAmarasiri Mohan bHashiba Satoshi bYang Peiyi bOkabe Satoshi bSano Daisuke ba Department of Soil, Water and Climate and Biotechnology Institute, University of Minnesota, St. Paul, Minnesota, USAb Division of Environmental Engineering, Hokkaido University, Sapporo, Hokkaido, JapanAddress correspondence to Daisuke Sano, dsano@eng.hokudai.ac.jp.25 8 2016 Jul-Aug 2016 4 4 e00893-1629 6 2016 1 7 2016 Copyright © 2016 Ishii et al.2016Ishii et al.This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.Enterobacter sp. strain SENG-6, isolated from healthy human feces, produces histo-blood group antigen (HBGA)-like substances that can bind with human noroviruses. Based on the genome sequence analysis, strain SENG-6 belongs to the species Enterobacter cloacae. The genome sequence of this strain should help identify genes associated with the production of HBGA-like substances. Japan Society for the Promotion of Science (JSPS) http://dx.doi.org/10.13039/50110000169126249075Daisuke SanoJapan Science and Technology Agency (JST) http://dx.doi.org/10.13039/501100002241Satoshi Okabe cover-dateJuly/August 2016 ==== Body GENOME ANNOUNCEMENT Human noroviruses are the leading cause of nonbacterial gastroenteritis (1) and utilize histo-blood group antigens (HBGAs) as binding receptors to infect human cells (2). Because some Gram-negative bacteria were reported to have blood group activity (3), we screened enteric bacteria that carry HBGA-like substances from human feces by using anti-HBGA antibodies to see if these bacteria can capture human noroviruses (4). One such strain, Enterobacter sp. strain SENG-6, showed strong binding activity to norovirus-like particles (4). The HBGA-like substances are located in the extracellular polymeric substances (EPSs) secreted from strain SENG-6; however, the genetic mechanism associated with the production of HBGA-like substances is largely unknown. Genome sequencing of strain SENG-6 was conducted to identify genes associated with the production of HBGA-like substances. Enterobacter sp. strain SENG-6 was grown in Luria-Bertani medium overnight at 37°C, and genomic DNA was extracted using the DNeasy blood and tissue kit (Qiagen). Sequencing libraries were prepared using the TruSeq DNA sample prep kit (Illumina) according to the manufacturer’s instructions. The genome was analyzed using the Illumina HiSeq 2000 with a 101-bp paired-end library. The resulting high-quality sequences (44,316,264 reads) were assembled using Velvet version 1/2/08 (5) to a total length of 5,030,416 bp, providing approximately 889-fold genome coverage. Gene prediction and annotation were performed by the NCBI Prokaryotic Genome Annotation Pipeline. Average nucleotide identity (ANI) was calculated using JSpecies (6). Annotation of the Enterobacter sp. strain SENG-6 genome identified 4,824 genes, 4,650 protein-coding sequences, 14 rRNAs (seven copies), 73 tRNAs, five noncoding RNAs (ncRNAs), and 82 pseudogenes. The average GC content was 53.2%, which is comparable to other Enterobacter spp. (7). The ANI value between the genomes of strains SENG-6 and Enterobacter cloacae strain ATCC 13047T (8) was 98.5%, which is greater than the cutoff value for species discrimination (95 to 96%) (6, 9). This result, together with the highly similar 16S rRNA gene sequences between strains SENG-6 and ATCC 13047T (99.9%), suggests that strain SENG-6 belongs to the species Enterobacter cloacae. The genome of strain SENG-6 contains genes encoding various enzymes for amino-sugar and glycan biosynthesis, which is likely involved in the production of HBGA-like substances. Enterobacter cloacae strain SENG-6 expresses A, B, and H antigens in EPSs (4), while Enterobacter cloacae (ATCC PTA-3882) has been found to carry an H antigen and promote human norovirus infection of B cells in vitro (10, 11). Another strain of Enterobacter cloacae (LMG2783) does not express A, B, and H antigens but has Lea and Leb antigens and contributes to the thermal stability of human norovirus particles (12). These suggest that HBGA expression profiles may vary among strains of this species. Comparative genomics of these strains may allow us to identify the genes associated with the production of the HBGA-like substances that can bind with human noroviruses. Accession number(s). This whole-genome shotgun project has been deposited at DDBJ/ENA/GenBank under the accession number LOMM00000000. The version described in this paper is the first version, LOMM01000000. Citation Ishii S, Amarasiri M, Hashiba S, Yang P, Okabe S, Sano D. 2016. Genome sequence of Enterobacter cloacae strain SENG-6, a bacterium producing histo-blood group antigen-like substances that can bind with human noroviruses. Genome Announc 4(4):e00893-16. doi:10.1128/genomeA.00893-16. ACKNOWLEDGMENTS This study was supported by the Japan Science and Technology Agency (JST) through Core Research for Evolutionary Science and Technology (CREST) and the Japan Society for the Promotion of Science through a Grant-in-Aid for Scientific Research (A) (26249075). ==== Refs REFERENCES 1. Glass RI , Parashar UD , Estes MK 2009 Norovirus gastroenteritis . N Engl J Med 361 :1776 –1785 . doi:10.1056/NEJMra0804575 .19864676 2. Donaldson EF , Lindesmith LC , LoBue AD , Baric RS 2010 Viral shape-shifting: norovirus evasion of the human immune system . Nat Rev Microbiol 8 :231 –241 . doi:10.1038/nrmicro2296 .20125087 3. Springer GF , Williamson P , Brandes WC 1961 Blood group activity of Gram-negative bacteria . J Exp Med 113 :1077 –1093 . doi:10.1084/jem.113.6.1077 .19867191 4. Miura T , Sano D , Suenaga A , Yoshimura T , Fuzawa M , Nakagomi T , Nakagomi O , Okabe S 2013 Histo-blood group antigen-like substances of human enteric bacteria as specific adsorbents for human noroviruses . J Virol 87 :9441 –9451 . doi:10.1128/JVI.01060-13 .23804639 5. Zerbino DR , Birney E 2008 Velvet: algorithms for de novo short read assembly using de Bruijn graphs . Genome Res 18 :821 –829 . doi:10.1101/gr.074492.107 .18349386 6. Richter M , Rosselló-Móra R 2009 Shifting the genomic gold standard for the prokaryotic species definition . Proc Natl Acad Sci USA 106 :19126 –19131 . doi:10.1073/pnas.0906412106 .19855009 7. Liu W-Y , Wong C-F , Chung KM , Jiang J-W , Leung FC 2013 Comparative genome analysis of Enterobacter cloacae . PLoS One 8 :e74487 . doi:10.1371/journal.pone.0074487 .24069314 8. Ren Y , Ren Y , Zhou Z , Guo X , Li Y , Feng L , Wang L 2010 Complete genome sequence of Enterobacter cloacae subsp. cloacae type strain ATCC 13047 . J Bacteriol 192 :2463 –2464 . doi:10.1128/JB.00067-10 .20207761 9. Goris J , Konstantinidis KT , Klappenbach JA , Coenye T , Vandamme P , Tiedje JM 2007 DNA-DNA hybridization values and their relationship to whole-genome sequence similarities . Int J Syst Evol Microbiol 57 :81 –91 . doi:10.1099/ijs.0.64483-0 .17220447 10. Jones MK , Watanabe M , Zhu S , Graves CL , Keyes LR , Grau KR , Gonzalez-Hernandez MB , Iovine NM , Wobus CE , Vinjé J , Tibbetts SA , Wallet SM , Karst SM 2014 Enteric bacteria promote human and mouse norovirus infection of B cells . Science 346 :755 –759 . doi:10.1126/science.1257147 .25378626 11. Jones MK , Grau KR , Costantini V , Kolawole AO , de Graaf M , Freiden P , Graves CL , Koopmans M , Wallet SM , Tibbetts SA , Schultz-Cherry S , Wobus CE , Vinjé J , Karst SM 2015 Human norovirus culture in B cells . Nat Protoc 10 :1939 –1947 . doi:10.1038/nprot.2015.121 .26513671 12. Li D , Breiman A , Le Pendu J , Uyttendaele M 2015 Binding to histo-blood group antigen-expressing bacteria protects human norovirus from acute heat stress . Front Microbiol 6 :659 . doi:10.3389/fmicb.2015.00659 .26191052

PMC005xxxxxx/PMC5000848.txt

==== Front Sci RepSci RepScientific Reports2045-2322Nature Publishing Group srep1696710.1038/srep1696726586412ArticleQuantum secret sharing via local operations and classical communication Yang Ying-Hui 12Gao Fei a1Wu Xia 1Qin Su-Juan 1Zuo Hui-Juan 3Wen Qiao-Yan 11 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommu-nications, Beijing, 100876, China2 School of Mathematics and Information Science, Henan Polytechnic University, Jiaozuo, 454000, China3 Mathematics and Information Science College, Hebei Normal University, Shijiazhuang, 050024, Chinaa gaofei_bupt@hotmail.com20 11 2015 2015 5 1696707 07 2015 22 10 2015 Copyright © 2015, Macmillan Publishers Limited2015Macmillan Publishers LimitedThis work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/We investigate the distinguishability of orthogonal multipartite entangled states in d-qudit system by restricted local operations and classical communication. According to these properties, we propose a standard (2, n)-threshold quantum secret sharing scheme (called LOCC-QSS scheme), which solves the open question in [Rahaman et al., Phys. Rev. A, 91, 022330 (2015)]. On the other hand, we find that all the existing (k, n)-threshold LOCC-QSS schemes are imperfect (or “ramp”), i.e., unauthorized groups can obtain some information about the shared secret. Furthermore, we present a (3, 4)-threshold LOCC-QSS scheme which is close to perfect. ==== Body Quantum secret sharing (QSS) is an important branch of quantum cryptography, which was simultaneously proposed by Hillery et al.1 and Cleve et al.2. It allows a secret to be shared among many participants in such a way that only the authorized groups can reconstruct it. In a (k, n)-threshold QSS scheme, the dealer distributes a shared secret among n participants, and any group of k or more participants can collaboratively recover the shared secret, however, no group of fewer than k participants can. During the past two decades, many interesting QSS schemes1234567891011 were proposed (for an incomplete list). Recently, Rahaman et al. concentrated on the implementation of classical secret sharing by quantum means, and first introduced the theory of local distinguishability of quantum states to the design of QSS scheme12. A novel, simple and efficient model of QSS scheme was presented, where the participants only used local quantum operations and classical communication (LOCC), in other words, any joint quantum operations were not required. This QSS model is called LOCC-QSS model. According to the model, a series of (k, n)-threshold LOCC-QSS schemes were proposed in ref. [12]. The designs of them are based on the local distinguishability of orthogonal multipartite quantum states. That is, some pairs of locally distinguishable orthogonal multipartite entangled states which represent the encoded secret can be collaboratively distinguished by a sufficient number of participants using LOCC, but cannot be distinguished by any fewer than the threshold k participants. The topic of LOCC-QSS is very interesting, meanwhile, it brings us some valuable study points. First, (2, n)-threshold LOCC-QSS scheme in ref. [12] is a nonstandard QSS scheme since it needs a strictly restricted condition, i.e., the two cooperating participants must come from two disjoint groups. A natural question how to design a standard (2, n)-threshold LOCC-QSS scheme is an open question. Second, all the existing (k, n)-threshold LOCC-QSS schemes are ramp (or “imperfect”) QSS schemes, i.e., there exist some information leakages in these schemes. How to quantify the information leakages and design a (k, n)-threshold LOCC-QSS scheme of less information leakages or even a perfect (k, n)-threshold LOCC-QSS scheme is also an interesting topic. In this paper, we revolve around above study points to research and try to solve them. On the one hand, we study the properties of orthogonal multipartite entangled states in d-qudit system. What’s more, a standard (2, n)-threshold LOCC-QSS scheme is presented, i.e., there is no any restricted condition for the two cooperating participants. On the other hand, we find that all the existing (k, n)-threshold LOCC-QSS schemes are ramp schemes, i.e., unauthorized groups can obtain some information about the shared secret. Then a near-perfect (3, 4)-threshold LOCC-QSS scheme is proposed. Results Local distinguishability of quantum states in high dimension system The paradigm of local distinguishability can be described as follows. Suppose some parties shared a multipartite quantum state which is secretly chosen from a known set of orthogonal quantum states. Their aim is to identify the unknown quantum state perfectly using local operations and classical communication. Numerous interesting results have been reported13141516171819202122232425. Now we discuss the distinguishability about a pair of orthogonal multipartite entangled states by restricted local operations and classical communication (rLOCC). Here, rLOCC means only a subset of parties is allowed to communicate with each other12. Let be a standard orthonormal basis of a d-dimensional Hilbert space. Consider the following two orthogonal state , , which can act as generalized Bell states in d-qudit system. where “+” is performed modulo d. For d = 2, the two states are known as Bell states. Now we show that the two states in Eq.(1) have the following properties. Theorem 1. Two orthogonal entangled states , in Eq.(1) can always be exactly distinguished by no less than two cooperating participants using LOCC. But they cannot be distinguished by only one participant. Proof. On the one hand, according to the forms of the two states, it is easy to obtain a distinguishable protocol. All the cooperating participants (no less than two) measure their own particle in the computational basis locally. If they have precisely the same results, then the shared state is . Otherwise, if they have completely different results, the state is . On the other hand, for the two states, it is straightforward to calculate that any single particle reduced density matrices are I/d, where I is the identity operator in d-dimensional system. It means that only one participant cannot obtain any information from his own particle. That is, the two states cannot be distinguished by only one participant. Now we recall the notion of stabilizer state. The generalized Pauli operators in d-dimensional Hilbert space are where ω = e2πi/d. A stabilizer state is a state of an n-qudit system that is the simultaneous eigenvector, with eigenvalues 1, of a subgroup of dn commuting elements of the Pauli group which does not contain multiples of the identity other than the identity itself. We call this subgroup as the stabilizer G of 26. When d is prime, G can always be generated by n suitably chosen group elements gj, where the order of each gj is d. is called a set of generators. When d is not prime, one might need more than n generators in some cases. In d-qudit system, d is a prime. Let us define two sets of quantum operations where According to the definition of stabilizer, we can easily obtain the following lemma. Lemma 1. The elements of in Eq.(3) constitute the generators set of the stabilizer of the state , i = 1, 2. It is easy to see that quantum state is the unique eigenstate of all the elements of with eigenvalues . So we have the following theorem. Theorem 2. If an unknown state satisfy: , . Then (1) eigenvalues λi = 1 if and only if , ; (2) eigenvalues λi = ωi−1 if and only if , . Note that whether d is a prime or not, the two states , are both the eigenstates of all the elements of . However, Theorem 2 holds only when d is a prime. It means that both of the two states can be uniquely determined by the set according to eigenvalues. If d is not a prime, they may not be uniquely determined by the set according to eigenvalues. Theorem 3. Two orthogonal entangled states in Eq. (4) can always be exactly distinguished by no less than three cooperating participants using LOCC. However, they cannot be deterministically distinguished by any two or fewer participants by LOCC. where P is the set of all possible distinct permutations. Proof. All the cooperating participants (no less than three) measure their own particle in the computational basis locally. If they have precisely the same results or completely different results, then the shared state is . Otherwise, if there exist two participants who have the same results, but their results are different from the other participants’ results, then the shared state is . On the other hand, for the two states, it is straightforward to calculate that any one participant have the same reduced density matrix I/d, where I is the identity operator in d-dimensional system. It means that only one participant cannot obtain any information from his own particle. All the bipartite reduced density matrices of are same because of the symmetry of , so are that of . Employing the probability formula of the minimum-error state discrimination 27, where q1, q2 are a priori probabilities and ρ1, ρ2 are two states, we can calculate that the probability with which any two participants can distinguish the states is 0.5536. It means that two participants cannot perfectly distinguish the two states even if they use joint quantum operations. So the two states cannot be exactly distinguished by any two participants by LOCC. That completes the proof. LOCC-QSS Suppose the sender Alice wants to share a key between n separated participants Bob1, Bob2, …, Bobn. Only no less than k participants can collaboratively recover the shared secret. That is, a (k, n)-threshold QSS should be designed. Here, we still adopt the basic model of LOCC-QSS in ref. [12] since the basic model is very simple and efficient. For readability, we still use the same notations. The standard (2, n)-threshold LOCC-QSS scheme Step 1. Alice first prepares a large number (say L > n) of states chosen randomly from a specified pair of orthogonal n-qudit (n = d) entangled states in Eq.(1) according to her requirement. Let us denote the prepared states by to keep details of each prepared state in each run (run t is associated with the prepared state at time t). Here, a represents the state randomly chosen from a pair of orthogonal states, that Alice prepares at time , where represents the positions of all n qudits of a prepared state at time t, i.e., the position of ith qudit of a prepared state a at time t is denoted by . Step 2. Alice prepares at random, a different sequence, for each Bobi, and sends the itth qudit (i = 1, 2, …, n; t = 1, 2, …, L) to Bobi according to the ri sequence order, where is an arbitrary permutation of the sequence (1, 2, …, L). No one has the information about except for Alice. After receiving their associated sequence of qudits, all of the receivers now share L n-qudit entangled states . Here . Step 3. Alice now randomly selects some run, say , and also computes n arbitrarily chosen permutations, pi of {1, 2, …, u}, only known to herself. She then prepares list for Bobi (for i = 1, 2, …, n) and sends it to him. After receiving the list Ci, Bobi measures his qudit in the basis and sends the measurement outcome to Alice. Here Alice choose randomly elements of the set in Eq.(3) to determine Bobi′s measurement basis. Now we interpret it. First, for the set , both and are the eigenstates of the elements of . They have the following relation where eigenvalue and . Therefore, all the product of all the local measurement results for must be equal to the corresponding eigenvalue, i.e., . It should be noted that the generalized Pauli operators X and Z are not Hermite, so X, Z and cannot act as observables. However, they are unitary operators. Since the relation between unitary operator U and Hermite operator H is U = exp(iH), and they have the same eigenstates. While the above measurement can always be completed using Hermite operator H as observables. For simplicity, roughly speaking, one can use the eigenstates of U as measurement basis to complete projective measurement, and measurement results can be denoted by eigenvalues. For example, if Alice chooses , then , , . Bob1 uses the eigenstates of XZd−1 as measurement basis to complete projective measurement, and measurement result can be denoted by eigenvalues. Other Bobi have the similar way to completed measurement. If the unknown state is , then . If the unknown state is , then . In this step, two very important points should be emphasized. First, when Alice prepares list Ci for Bobi and sends it to him, Bobi still does not know which n qudits come from the same entangled state. It is very crucial for design of eavesdropping detection in a concrete LOCC-QSS scheme. Second, Alice starts to send lists Ci only if all of the receivers confirm the receipt of all their L qudits. Step 4. For each selected run ts, Alice check whether or not the the product of local measurement results is equal to the corresponding eigenvalue λ(a, ts), i.e., . If , then and if , then By analyzing the measurement results, Alice can easily detect whether there is an eavesdropper or not. If there is one, she aborts the protocol and starts again from step 1. Step 5. If no eavesdropper is detected, Alice announces, to the respective parties, all qudit positions of an unmeasured state . Alice selects this according to her secret a (=0 or 1). The mapping between classical bit value and orthogonal entangled states is fixed and is communicated securely from Alice and Bobs in advance. If Alice’s secret is more than one bit, then she reveals the qudit positions of a sequence of unmeasured states . According to Theorem 2, the states , can be uniquely determined by the set S1 according to eigenvalues if d is prime. It makes the protocol be more secure. On the other hand, although and cannot be uniquely determined if d is not prime, the protocol is still secure due to the design method of this scheme. It will be shown in the section of security analysis. Employing Theorem 1, the two states can be exactly distinguished by no less than two cooperating participants using LOCC. But they cannot be distinguished by only one participant. Thus, this is a standard (2, n)-threshold LOCC-QSS scheme. Example 1. In a (2,3)-threshold LOCC-QSS scheme, the pair of the states are and . The steps are described in detail in the standard (2, n)-threshold LOCC-QSS scheme. Here, we only consider the Step 4. If , then and if , then The security analysis of standard (2, n)-threshold LOCC-QSS scheme The standard (2, n)-threshold LOCC-QSS scheme can be regarded as secure because the shared secret cannot be eavesdropped without being detected. Usually, there are three eavesdropping strategies for Eve (she may be dishonest Bob). Now we consider the security of our scheme under the three attacks. The first eavesdropping strategy is called “intercept-measure-resend”, that is, Eve intercepts the legal particles when Alice sends them to Bobi, chooses local or global measurement basis to measure them, then resends them to Bobi. (i) If Eve wants to obtain Alice’s secret, she can choose and measure n qudits by global measurement to distinguish the unknown state. However, Eve does not know which n qudits come from the same entangled state because Alice has scrambled the order of qudits using permutation , and no one has the information about except for Alice. Therefore, this attack will be detected in the eavesdropping detection if Eve chooses this method of attack. (ii) If Eve wants to obtain Bobi′s secret or wants to obtain Alice’s secret according to more than t (threshold value) Bobi′s secrets, she can measure one or more qudits by local measurement. However, the original correlations of quantum states will be destroyed. For example, Eve chooses computation basis to locally measure the unknown state . Then collapses to a product state, which does not satisfy the conditions of eavesdropping detection. This attack will be detected in the eavesdropping detection. The second one is “intercept-replace-resend”, i.e., Eve intercepts the legal particles and replaces them by her counterfeit ones. If Eve escapes from the detection of Alice, she will obtain Alice’s secret. Now we show that our scheme is security under the attack. (i) If d is a prime, according to Theorem 2 Eve cannot find a quantum state which satisfies the conditions of eavesdropping detection to replace the legal particles. (ii) if d is not a prime, , cannot be uniquely determined by the set S1 according to eigenvalues, that is, there exists another state which satisfies . However, Alice has scrambled the order of qudits using permutation , according to Step 2 and 3 anyone does not know which n qudits come from the same entangled state except for Alice before the end of the eavesdropping detection. Thus the eavesdropper cannot use the illegal states satisfying to replace the states which are sent by Alice. Otherwise, the eavesdropping will be found by Alice. The third one is “entangle-measure”, i.e., Eve entangles an ancilla with the n-qudit, at some later time she can measure the ancilla to gain information. Without loss of generality, assume that Eve uses a unitary operator such that the ancilla entangles with the quantum state , i.e., , , where the subscripts B and E express the particles belonging to Bobi and Eve, respectively. In fact, This kind of attack is general, it contains the above two attacks. Now we will show that the legal particles (B) and the ancilla (E) must be not entangled if no error is introduced into the QSS procedures. It means that Eve will gain no information about the secret by observing the ancilla. (i) If d is a prime, according to Theorem 2, and are uniquely determined by S1. In other words, the state must be not entangled between B and E, otherwise, this attack will be detected by Alice with certain probability. (ii) Next we consider that d is not a prime. Firstly, since Eve does not know which n qudits come from the same entangled state, the unitary operator can only act on one qudit from and the ancilla. Secondly, note that the operator Z⊗n can be generated by the elements of S1, i.e., , in Eq. (3). Then , where , . Therefore, only if the state satisfies the property that the product of all Bobi′s measurement results measured by computation basis is equal to λ (=1), may Eve escape from the detection of Alice. So and have the form and , where . It should be noted that we do not put constraints on the dimensions of and . Next we will show that this attack will be detected when participants check eavesdropping with the basis . Using the inverse Fourier transform , we consider the form of the n legal qudits of quantum state in the Fourier basis, where is the computation basis, is the Fourier basis and w = e2πi/d. It is easy to calculate the terms j ≠ 0. Obviously, they must be eliminated, i.e., , j ≠ 0. Otherwise, this attack will be found by Alice. It means , where So . Since the matrix contains a Vandermonde submatrix with the order d − 1, we have . Thus . It means that (up to global phase). So is a product state between legal qudits and the ancilla. The similar discussion can be applied for the analysis of quantum state , and we can obtain the same result. Intuitively, maybe it is surprised that the scheme is secure despite cannot be uniquely determined by the set S1 for non-prime d. The reason is that Alice has scrambled the order of qudits such that the states satisfying conditions of eavesdropping detection are excluded. If the unitary operator can only act on one qudit from and the ancilla, Eve cannot obtain any information according to the above proof. The quantification of information leakages It is difficult to design a perfect (without any information leakage) (k, n)-threshold LOCC-QSS scheme. At present all of the existing (k, n)-threshold LOCC-QSS schemes are ramp schemes. We try to quantify the information leakages. Now we consider conspiracy attack for (k, n)-threshold LOCC-QSS scheme. If there exist l(<k) dishonest Bobi, they can recover the secret together. This attack method is called conspiracy attack. For the (k, n)-threshold LOCC-QSS scheme12, according to the following two intentions, they can choose different ways to eavesdrop. (i) No matter whether eavesdroppers obtain the shared secret or not, it is not allowed that they obtain a wrong shared secret and disturb the authorized groups to recover the shared secret. For simplicity, the eavesdropping probability of success is called unambiguous probability. (ii) In order to obtain information about the shared secret as much as possible, it is allowed that eavesdroppers minimize the errors that occur in a state discrimination task and can disturb the authorized groups to recover the shared secret. The eavesdropping probability of success is called guessing probability. For the sake of simplicity, we only analyze the example 3 in ref. [12], i.e., (5, 6)-threshold LOCC-QSS scheme. It is easy to be generalized for (k, n)-threshold LOCC-QSS scheme. First we recall the key steps in the original scheme. Step 1. Alice randomly chooses the states from the pair orthogonal Dicke states Step 4. If , then and if , then Other steps are similar to the standard (2, n)–threshold LOCC-QSS scheme. It should be noted that there is a mistake in original (5, 6)-threshold LOCC-QSS scheme, i.e., if , then , . Now we consider conspiracy attack for (5, 6)-threshold LOCC-QSS scheme. The method of conspiracy attack: these dishonest Bobi will faithfully perform the protocol until Alice believes no eavesdropper. For intention (i): when Alice announces all qubit positions of unmeasured states, these l(<5) dishonest Bobi measure their own qubit in the computational basis locally to recover the secret together. For intention (ii): when Alice announces all qubit positions of unmeasured states, these l(<5) dishonest Bobi use joint quantum measurement to measure their l qubits according to the minimum-error state discrimination. Now we calculate the probability when l(<5) participants recover the secret together. (1) l = 4. For intention (i): if the local measurement results of the four dishonest Bobi are three same states and one state or two states and two states , they can determine the state is . If the local measurement result are four same states , they can determine the state is . So the unambiguous probability is 17/30. On the other hand, for intention (ii), we can calculate that the guessing probability is 0.7 according to the probability formula of the minimum-error state discrimination, i.e., the rate of information leakages is 11.87%. (2) l = 3. For intention (i): if the local measurement results of the three dishonest Bobi are three same states or two states and one state , they can determine the state is . The unambiguous probability is 1/4. For intention (ii): the guessing probability is 0.625, namely, the rate of information leakages is 4.56%. (3) l = 2. For intention (i): Only the local measurement results of the two dishonest Bobi are two same states , they can determine the state is . For other local measurement results they cannot distinguish the states. So the unambiguous probability is 1/10. For intention (ii): the guessing probability is 0.6167, that is, the rate of information leakages 3.97%. (4) l = 1. Obviously, the unambiguous probability is zero. The guessing probability is 7/12. That is, the rate of information leakages 2.01%. All the cases can be shown in Table 1, where l is the number of dishonest Bobi, pu, pg, r are unambiguous probability, guessing probability and the rate of information leakages, respectively. The intention (i) is very interesting. Since dishonest Bobi can always exactly recover the secret with nonzero probability if the unambiguous probability is nonzero, and they cannot disturb the authorized groups to recover the shared secret. Now we introduce two parameters k1, k2 in (k, n)-threshold LOCC-QSS scheme, denoted as (k1, k2, k, n), to describe the information leakages. It means that (i) any fewer than k1 participants cannot obtain any information; (ii) any l (k1 ≤ l < k) participants can obtain the shared secret with guessing probability more than 1/2; (iii) any l (k2 ≤ l < k) participants can obtain the shared secret with nonzero unambiguous probability. Obviously, for ramp LOCC-QSS scheme, it has 1 ≤ k1 ≤ k2 ≤ k. And the more k1, k2 are close to k, the less information leakages are. For perfect LOCC-QSS scheme, it has k1 = k2 = k. For the above (5, 6)-threshold LOCC-QSS scheme, it can be denoted as (1, 2, 5, 6)-threshold LOCC-QSS scheme. Finally, we show that a secure (3, 4)-threshold LOCC-QSS scheme cannot be designed based on the model of (k, n)-threshold LOCC-QSS scheme in ref. [12]. Since threshold k = n − r + 1 = 3, the distance12 r between the pair of states is 2. If the pair of states which Alice chooses contains the Dicke state , the other is , or . It contradicts with the definition of Dicke state. If the pair of states does not contain the state , the pair of states must be and . In the stage of eavesdropping detection, only condition (m = 1 or 3) can be used to detect eavesdropping. Obviously it is insecure. Since the eavesdropper Eve can always measure all the qubit in the computational basis then send the post-measurement states to Bobi, but Alice cannot find Eve’s eavesdropping. The (3, 4)-threshold LOCC-QSS scheme Now we propose a (3, 4)-threshold LOCC-QSS scheme, in which dishonest Bobi cannot obtain the shared secret with nonzero unambiguous probability. All the steps are similar to the standard (2, n)-threshold LOCC-QSS scheme, so we only show the differences. Step 1. Alice prepares the states, the desired pair of the states are in Eq. (4). Step 4. If , then , , and if , then , , where Because both and are the eigenstates of the element in with eigenvalue 1. According to Theorem 3, we know it is a (3, 4)-threshold LOCC-QSS scheme. Employing the forms of the two states in Eq.(4), it is easy to see that the unambiguous probability is zero for any l dishonest Bobi (l < 3). According to the proof of Theorem 3, we know that the guessing probability is zero when l = 1, and the guessing probability is 0.5536 when l = 2. The rate of information leakages is 0.83%. So the scheme can be denoted as (2, 3, 3, 4)-threshold LOCC-QSS scheme. It is close to perfect (3, 4)-threshold LOCC-QSS scheme. Discussion In ref. [11], Gheorghiu et al. also proposed an efficient QSS scheme by LOCC, which is based on quantum error-correcting codes to distribute a quantum secret. In their QSS scheme, they reduced the required quantum communication at the cost of some classical communication. But our schemes are based on local discrimination of quantum states to distribute classical secrets. And any joint quantum operations and quantum communication are not required in secret recovery stage. Although the designs of these schemes have all used LOCC, their essences are completely different. In this paper, based on the distinguishability of orthogonal multipartite entangled states by rLOCC in d-qudit system, we present a standard (2, n)-threshold LOCC-QSS scheme, which work out the open question in ref. [12]. In addition, we take (5, 6)-threshold LOCC-QSS scheme as a example to present that all the existing (k, n)-threshold LOCC-QSS schemes are ramp schemes. Then we propose a (3, 4)-threshold LOCC-QSS scheme, which is close to perfect. We hope that these results will encourage researchers to study generalized (k, n)-threshold LOCC-QSS scheme. Additional Information How to cite this article: Yang, Y.-H. et al. Quantum secret sharing via local operations and classical communication. Sci. Rep. 5, 16967; doi: 10.1038/srep16967 (2015). This work is supported by NSFC (Grant Nos 61272057, 61572081, 61402148), Beijing Higher Education Young Elite Teacher Project (Grant Nos YETP0475, YETP0477), Natural Science Foundation of Hebei Province (F2015205114). Author Contributions Y.Y., F.G., X.W. and S.Q. initiated the idea. Y.Y., F.G., H.Z. and Q.W. wrote the main manuscript text and prepared table. All authors reviewed the manuscript. Table 1 The information leakages of (5, 6) scheme. l 1 2 3 4 pu 0 1/10 1/4 17/30 pg 0.5833 0.6167 0.625 0.7 r 2.01% 3.97% 4.56% 11.87% ==== Refs Hillery M. , Buzek V. & Berthiaume A. Quantum secret sharing . Phys. Rev. A 59 , 1829 (1999 ). Cleve R. , Gottesman D. & Lo H.-K. How to share a quantum secret . Phys. Rev. Lett. 83 , 648 (1999 ). Karlsson A. , Koashi M. & Imoto N. Quantum entanglement for secret sharing and secret splitting . Phys. Rev. A 59 , 162 (1999 ). Gottesman D. Theory of quantum secret sharing . Phys. Rev. A 61 , 042311 (2000 ). Bandyopadhyay S. Teleportation and secret sharing with pure entangled states . Phys. Rev. A 62 , 012308 (2000 ). Nascimento A. C. A. , Mueller-Quade J. & Imai H. Improving quantum secret-sharing schemes . Phys. Rev. A 64 , 042311 (2001 ). Karimipour V. , Bahraminasab A. & Bagherinezhad S. Entanglement swapping of generalized cat states and secret sharing . Phys. Rev. A 65 , 042320 (2002 ). Xiao L. , Long G. L. , Deng F. G. & Pan J. W. 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Three maximally entangled states can require two-way local operations and classical communication for local discrimination Phys. Rev. A 88 , 062316 (2013 ). Zhang Z. C. , Feng K. Q. , Gao F. & Wen Q. Y. Distinguishing maximally entangled states by one-way local operations and classical communication . Phys. Rev. A 91 , 012329 (2015 ). Yang Y. H. et al. Bound on local unambiguous discrimination between multipartite quantum states . Quant. Inf. Proc. 14 , 731 (2015 ). Yang Y. H. et al. Characterizing unextendible product bases in qutrit-ququad system . Sci. Rep. 5 , 11963 (2015 ).26169249 Hostens E. , Dehaene J. & De Moor B. Stabilizer states and Clifford operations for systems of arbitrary dimensions and modular arithmetic . Phys. Rev. A 71 , 042315 (2005 ). Bae J. & Kwek L. C. Quantum state discrimination and its applications . J. Phys. A: Math. Theor. 48 , 083001 (2015 ).

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==== Front Front MicrobiolFront MicrobiolFront. Microbiol.Frontiers in Microbiology1664-302XFrontiers Media S.A. 10.3389/fmicb.2016.01328MicrobiologyOriginal ResearchUsing Matrix-Assisted Laser Desorption Ionization-Time of Flight (MALDI-TOF) Complemented with Selected 16S rRNA and gyrB Genes Sequencing to Practically Identify Clinical Important Viridans Group Streptococci (VGS) Zhou Menglan 12Yang Qiwen 1*Kudinha Timothy 3Zhang Li 1Xiao Meng 1Kong Fanrong 4Zhao Yupei 1Xu Ying-Chun 5*1Department of Clinical Laboratory, Peking Union Medical College Hospital, Chinese Academy of Medical SciencesBeijing, China2Graduate School, Peking Union Medical College, Chinese Academy of Medical SciencesBeijing, China3School of Biomedical Sciences, Charles Sturt UniversityOrange, NSW, Australia4Centre for Infectious Diseases and Microbiology Laboratory Services, Westmead HospitalWestmead, NSW, Australia5Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical SciencesBeijing, ChinaEdited by: Fabrice Merien, Auckland University of Technology, New Zealand Reviewed by: Marta Palusinska-Szysz, Maria Curie-Skłodowska University, Poland; María Pilar Alonso, Servicio Gallego de Salud, Spain *Correspondence: Qiwen Yang yangqiwen81@163.comYing-Chun Xu xycpumch@139.comThis article was submitted to Infectious Diseases, a section of the journal Frontiers in Microbiology 26 8 2016 2016 7 132819 5 2016 11 8 2016 Copyright © 2016 Zhou, Yang, Kudinha, Zhang, Xiao, Kong, Zhao and Xu.2016Zhou, Yang, Kudinha, Zhang, Xiao, Kong, Zhao and XuThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.There are challenges in viridans group streptococci (VGS) identification especially for the mitis group. Few studies have investigated the performance of MALDI-TOF MS system in VGS identification. Using 16S rRNA gene and gyrB gene sequencing as a gold standard, the performance of two MALDI-TOF MS instruments in the identification of 181 VGS clinical isolates was studied. The Bruker Biotyper and Vitek MS IVD systems correctly identified 88.4% and 98.9% of the 181 isolates, respectively. The Vitek MS RUO system was the least reliable, only correctly identifying 38.7% of the isolates to species level with several misidentifications and invalid results. The Bruker Biotyper system was very unreliable in the identification of species within the mitis group. Among 22 non-pneumococci isolates (S. mitis/S. oralis/S. pseudopneumoniae), Biotyper misidentified 21 of them as S. pneumoniae leading to a low sensitivity and low positive predictive value in these species. In contrast, the Vitek MS IVD demonstrated a better resolution for pneumococci and non-pneumococci despite the inability to distinguish between S. mitis/S. oralis. For more accurate species-level identification, further improvements in the VGS spectra databases are needed. Based on MALDI-TOF analysis and selected 16S rRNA gene plus gyrB genes sequencing, we designed a practical VGS identification algorithm. Streptococcusviridans group streptococci (VGS)matrix-assisted laser desorption ionization-time of flight (MALDI-TOF)16S rRNA genegyrB gene ==== Body Introduction The viridans group streptococci (VGS) are a heterogeneous group of gram positive cocci, which form part of the normal human flora of the oral cavity, respiratory, urogenital, and gastrointestinal tracts (Spellberg and Brandt, 2011). Currently, VGS is subdivided into six major groups: S. anginosus, S. bovis, S. mitis, S. mutans, S. salivarius, and S. sanguinis (Facklam, 2002; Doern and Burnham, 2010). Accurate identification of species within the VGS group is important for assessing the clinical significance of the organism and to facilitate appropriate antimicrobial therapy (Sinner and Tunkel, 2009; Doern and Burnham, 2010). However, due to constant taxonomic changes in the VGS group, identification of species is challenging. No phenotypic identification method can be used as a gold standard for VGS as most methods, including API Strep, and Vitek 2, have only 30–80% identification accuracy, depending on the species (Ikryannikova et al., 2011; Teles et al., 2011). Sequence analysis targeting different single genes such as 16S rRNA gene, rpoA, rpoB, rnpB, rodA, soda, and gdh, have been used in the identification of VGS species with various degrees of success (Poyart et al., 1998; Ip et al., 2006; Westling et al., 2008; Konishi et al., 2009; Nielsen et al., 2009; Park et al., 2010). Currently, only multilocus sequence analysis (MLSA) can accurately and reliably identify species within the VGS group. But MLSA is too expensive and laborious for routine laboratory diagnostic use (Bishop et al., 2009). Recently, Galloway-Peña et al. reported that the gyrB amino acid sequence may offer a more practical and accurate method for speciating invasive VGS strains than MLSA (Galloway-Pena et al., 2014). In recent years, matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) has emerged as a rapid and cost-effective alternative assay for bacterial identification (Seng et al., 2009; Bizzini et al., 2010; Neville et al., 2011). Nevertheless, some studies indicate that this assay has problems in distinguishing species within the S. mitis group (Ikryannikova et al., 2011; Davies et al., 2012; Wessels et al., 2012). We investigated the performance of two MALDI-TOF MS systems, namely Bruker Biotyper (Daltonics, German) and Vitek MS (bioMérieux, France) in the identification of species within the VGS group, using sequencing of the 16S rRNA, and gyrB genes as a gold standard. Materials and methods Bacterial strains and cultures Clinically significant VGS isolates (n = 181) from sputum (n = 81), blood cultures (n = 29), tracheal aspirates (n = 11), midstream urine (n = 9), and other various sterile and non-sterile sites (n = 51) from Peking Union Medical College Hospital (PUMCH; 2013–2014), were studied (Supplementary Table S1). Initial identification of these isolates was done by conventional methods (positive Gram stain, coccus morphology in chains, alpha-hemolysis, and a negative catalase test) and Vitek 2 compact system. Optochin and bile solubility tests were performed to differentiate pneumococci from non-pneumococci. The isolates were stored at −70°C in skim milk until further testing. Gene sequencing-based identification Template DNA was prepared as described by. Dubois et al. (2013). The16S rRNA gene was amplified for all the isolates using the universal primers 27F 5′-AGAGTTTGATCMTGGCTCAG-3′ and 1492R 5′-TACGGYTACCTTGTT ACGACTT-3′ (Seng et al., 2009). Purified PCR products and sequencing primers (the same as for amplification) were mixed and sent to Ruibiotech (Beijing, China) for sequencing. Species identification was performed by comparing the obtained sequences against those in the GenBank database using BLASTn (www.ncbi.nlm.nih.gov/blast). A sequence similarity of 99% was applied as species identification “cut-off” value for the 16S rRNA gene region. Amplification and sequencing of the gyrB gene encoding DNA Gyrase subunit B was performed for all the non-pneumococci using primers gyrB7F 5′-GAAGTDGTIAARATYACBAAY CG-3′ and gyrB5R 5′-ACATCDGCATCRGTCAT-3′ (Maeda et al., 2011). Both the gyrB nucleotide and amino acid sequences were analyzed through BLASTn and BLASTp and a nucleotide sequence similarity of 96% or a signature amino acid at a certain position was applied as species identification standard according to Galloway-Pena et al. (2014). Besides, the phylogenetic trees were generated based on 16S rRNA gene and gyrB gene. The region used for phylogenetic analyses and species identification was nucleotides 86–1336 for 16S rRNA gene and nucleotides 1113–1512, corresponding to amino acids 371–503 for gyrB gene respectively. Following alignment with Clustal W, the sequences were analyzed in MEGA version 5.2 to create radial trees using the neighbor-joining statistical method and the maximum likelihood composite model. MALDI-TOF analysis Two MALDI-TOF MS systems, Bruker Biotyper and Vitek MS, including both the In Vitro Diagnosis (IVD) and Research Use Only (RUO) modes were used to identify the 181 VGS isolates, according to the manufacturer's instructions. For both MALDI-TOF MS systems, direct transfer method were used for sample preparation. A small portion of a single colony after 24 or 48 h of incubation was smeared onto a target plate using a wooden cocktail stick, and covered with 1 μl matrix solution (α- cyano-4-hydroxycinnamic acid in 50 acetonitrile/2.5% trifluoroacetic acid, Bruker Daltonics, Bremen, Germany; α-cyano-4-hydroxycinnamic acid, VITEK® MS CHCA) immediately. For Bruker Biotyper, measurements were performed with the Bruker Biotyper MALDI-TOF MS system using FlexControl software with Compass Flex Series version 1.3 software and a 60-Hz nitrogen laser (337 nm wavelength). Spectra ranging from 2000 to 20,000 m/z were analyzed using the MALDI Biotyper system's automation control and the current Bruker Biotyper V.3.3.1.2 software and library [database (DB) 5989 with 5989 entries]. For Vitek MS, measurement was performed using the manufacturer's suggested settings using automated collecting spectra. Captured spectra (mass range of 2–20 kDa in the linear mode) were analyzed using the MALDI-TOF MS IVD MYLA database v2.0 and also the MALDI-TOF MS RUOsystem with the SARAMIS™ database v4.10 of bioMérieux. All identifications displaying a single result with a confidence score ≥1.7 or a confidence value of 99.9% were considered acceptable for Bruker Biotyper MS and Vitek MS, respectively (Karpanoja et al., 2014). For both MS systems, all isolates yielding a single result without acceptable confidence level or multiple results or “no identification” results were re-tested. If a single, species-level identification was obtained upon repeat analysis, this identification was considered to be the final MS result; otherwise no further analysis was performed. Statistical analysis Agreement and validity values were calculated with a 95% confidence interval (CI) based on an exact binomial distribution. Data were analyzed using SPSS, version 15.0 (SPSS Inc, Chicago, IL, USA). Results The 16S rRNA gene and GyrB gene sequencing-based identification Using a combination of 16S rRNA gene and gyrB gene sequencing, all the 181 VGS isolates were assigned to species level on the basis of a ≥99 and ≥96% gene sequence similarity with published sequences in the GenBank. The DNA sequences of the16S rRNA gene and gyrB gene of the 181 VGS isolates studied have been submitted to GenBank database (accession numbers of KX661043–KX661223 for 16S rRNA gene and KX661224–KX661319 for gyrB gene respectively (Supplementary Table S2). Figure 1 shows the phylogenetic trees generated by gyrB gene and 16S rRNA gene. As is shown in Figure 1A, gyrB nucleotide sequence successfully delineated all of the 96 non-pneumococci strains into individual species branches while in Figure 1B, all of the S. oralis isolates were branched with S. mitis isolates by 16S rRNA sequences. Besides, the bootstrap values between the mitis group were rather low compared to gyrB gene, which emphasizs the difficulties of using 16S gene sequences to correctly assign VGS strains to particular species. The identification results of the 181 VGS isolates are summarized in Supplementary Tables S3, S4, and S5, using gene sequencing as the gold standard. Figure 1 Phylogenetic analyses using 16S rRNA and gyrB fragment sequences. (A) Phylogenetic analysis using the gyrB sequence from nucleotides 1113–1512. (B) Phylogenetic analysis using the 16S rRNA sequence from nucleotides 86 to 1336. A high level of similarity in the 16S rRNA gene sequences of S. pneumoniae, S. pseudopneumoniae, S. mitis, and S. oralis, which could make it difficult in distinguishing these species, has been reported (Kawamura et al., 1995; Arbique et al., 2004; Suzuki et al., 2005; Haanpera et al., 2007). Nevertheless, a recent study by Scholz et al. demonstrated that S. pneumoniae has a cytosine at position 203 of the 16S rRNA gene, while all other mitis group streptococci have adenine in that position (Scholz et al., 2012). In this study, we also checked position 203 of the 16S rRNA gene sequences, and confirmed that 85 isolates were pneumococci and the other 96 isolates non-pneumococci. MALDI-TOF analysis Compared to the gold standard, the Bruker Biotyper system correctly identified 88.4% (160/181) of the isolates to species level and misidentified 11.6% (21/181) of the isolates. This system performed poorly in the identification of species in the mitis group, correctly identifying 80.4% (86/107) of the isolates to species level and misidentifying 19.6% (21/107) of the isolates. Optochin tests were performed on all isolates with an identification result of S. pneumoniae in an atmosphere of 5% CO2, among which 85 isolates were sensitive and the other 21 were resistant. Specifically, 100 (11/11), 100 (2/2), and 88.9% (8/9) of S. mitis, S. oralis, and S. pseudopneumoniae isolates were misidentified as S. pneumoniae, respectively. For all other groups, the Bruker Biotyper system performed excellently, accurately identifying all the species within each of the respective groups. Furthermore, the system did not yield an invalid or “no identification result” on any isolates (Supplementary Table S3). Vitek MS IVD correctly identified 98.9 (179/181) of the VGS isolates to species level, 0.55 (1/181) to group level, and 0.55% (1/181) with no identification. In contrast to the Bruker MS system which performed dismally in the identification of S. pseudopneumoniae isolates, the Vitek MS IVD correctly identified the majority (8 of 9, 88.9%) of these isolates to species level and the remaining one isolate to group level (Supplementary Table S4). Furthermore, the Vitek MS IVD gave a “no identification” result on one isolate within the bovis group (S. gallolyticus). In comparison to Bruker Biotyper and Vitek MS IVD, the Vitek MS RUO system performed very poorly in VGS identification, with an overall correct species level, group level, and genus level identification rate of 38.7 (70/181), 42.0 (76/181), and 7.2% (13/181) respectively (Supplementary Table S5). The system especially performed poorly in identifying species within the mitis and bovis groups, with only 15.9 (17/107) and 25% (2/8) of the isolates correctly identified, albeit low number of isolates. In addition, 8.8 (16/181) and 3.3% (6/181) of the isolates studied were misidentified and yielded a “no identification” result, respectively. Three MALDI-TOF systems comparisons Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the three databases are shown in Table 1. Overall, Bruker Biotyper gave a 100% sensitivity for the majority of the species identified, except for the mitis group species, namely S. mitis (0%), S. oralis (0%), and S. pseudopneumoniae (11.1%), most of which were misidentified as S. pneumoniae (21 of 22, 95.6%). Furthermore, PPV was incalculable for S. mitis and S. oralis as none of these isolates were correctly identified by the Bruker Biotyper. In contrast to other species in the mitis group, S. pneumoniae, had a 100% (95 CI: 95.8–100.0%) sensitivity and a 100% (95 CI: 95.5–100.0%) NPV, while the specificity and PPV were relatively low, at 78.1% (95 CI:68.5–85.9%) and 80.2% (95 CI:71.3%–87.3%), respectively. Table 1 Identification performance comparison of three MALDI-TOF MS systems for each group of viridans group streptococci (VGS). No. (%) of isolate Bruker Biotyper MS system Vitek MS IVD system Vitek MS RUO system Se (%) Sp (%) PPV (%) NPV (%) Se (%) Sp (%) PPV (%) NPV (%) Se (%) Sp (%) PPV (%) NPV (%) Mitis group 107 80.4 (71.6–87.4) 71.6 (60.0–81.5) 80.4 (71.6–87.4) 71.6 (60.0–81.5) 98.1 (93.4–99.8) 98.7 (92.7–100) 99.1 (94.9–100) 97.3 (90.7–99.7) 15.0 (8.8–23.1) 33.8 (23.2–45.7) 24.6 (14.8–36.9) 21.6 (14.6–30.2) S. mitis 11 0.0 (0.0–28.5) 100 (97.9–100) IC 93.9 (89.4–96.9) 100 (75.3–100) 99.4 (96.7–100) 92.9 (66.1–99.8) 100 (97.8–100) 0.0 (0.0–28.5) 95.3 (90.9–98.0) 0.0 (0.0–36.9) 93.6 (88.9–96.8) S. oralis 2 0.0 (0.0–84.2) 100 (98.0–100) IC 98.9 (96.1–99.9) 100 (75.3–100) 99.4 (96.7–100) 92.9 (66.1–99.8) 100 (97.8–100) 0.0 (0.0–84.2) 91.6 (86.6–95.2) 0.0 (0.0–21.8) 98.8 (95.7–99.9) S.pseudopneumoniae 9 11.1 (0.3–48.3) 100 (97.9–100) 100 (2.5–100) 95.6 (92.6–98.6) 77.8 (40.0–97.2) 100 (97.9–100) 100 (59.0–100) 98.9 (95.9–99.9) 0.0 (0.0–33.6) 95.9 (91.8–98.4) 0.0 (0.0–41.0) 94.8 (90.4–97.6) S. pneumoniae 85 100 (95.8–100) 78.1 (68.5–85.9) 80.2 (71.3–87.3) 100 (95.2–100) 100 (95.8–100) 100 (96.2–100) 100 (95.8–100) 100 (96.2–100) 18.8 (11.2–28.8) 80.2 (70.8–87.6) 45.7 (28.8–63.4) 52.7 (44.3–61.1) Anginosus group 52 100 (93.2–100) 100 (97.2–100) 100 (93.2–100) 100 (97.2–100) 100 (93.2–100) 100 (97.2–100) 100 (93.2–100) 100 (97.2–100) 93.1 (59.0–84.4) 89.9 (83.4–94.5) 74.5 (60.4–85.7) 89.2 (82.6–94.0) S. anginosus 29 100 (88.1–100) 100 (97.6–100) 100 (88.1–100) 100 (97.6–100) 100 (88.1–100) 100 (97.6–100) 100 (88.1–100) 100 (97.6–100) 100 (88.1–100) 91.5 (85.8–95.4) 69.1 (52.9–82.4) 100 (97.4–100) S. constellatus 19 100 (82.4–100) 100 (97.8–100) 100 (82.4–100) 100 (97.8–100) 100 (82.4–100) 100 (97.8–100) 100 (82.4–100) 100 (97.8–100) 26.3 (9.2–51.2) 100 (97.8–100) 100 (47.8–100) 92.1 (87.0–95.6) S. intermedius 4 100 (39.8–100) 100 (97.9–100) 100 (39.8–100) 100 (97.9–100) 100 (39.8–100) 100 (97.9–100) 100 (39.8–100) 100 (97.9–100) 100 (39.8–100) 100 (97.9–100) 100 (39.8–100) 100 (97.9–100) Sanguinis group 12 100 (73.5–100) 100 (97.8–100) 100 (73.5–100) 100 (97.8–100) 100 (73.5–100) 100 (97.8–100) 100 (73.5–100) 100 (97.8–100) 91.7 (61.5–99.8) 100 (97.8–100) 100 (71.5–100) 99.4 (96.8–100) S. sanguinis 8 100 (63.1–100) 100 (97.9–100) 100 (63.1–100) 100 (97.9–100) 100 (63.1–100) 100 (97.9–100) 100 (63.1–100) 100 (97.9–100) 87.5 (47.4–99.7) 100 (97.9–100) 100 (59.0–100) 99.4 (96.8–100) S. gordonii 4 100 (39.8–100) 100 (97.9–100) 100 (39.8–100) 100 (97.9–100) 100 (39.8–100) 100 (97.9–100) 100 (39.8–100) 100 (97.9–100) 100 (39.8–100) 100 (97.9–100) 100 (39.8–100) 100 (97.9–100) Salivarius group 2 100 (15.8–100) 100 (98.0–100) 100 (15.8–100) 100 (98.0–100) 100 (15.8–100) 100 (98.0–100) 100 (15.8–100) 100 (98.0–100) 100 (15.8–100) 100 (98.0–100) 100 (15.8–100) 100 (98.0–100) S. salivarius 2 100 (15.8–100) 100 (98.0–100) 100 (15.8–100) 100 (98.0–100) 100 (15.8–100) 100 (98.0–100) 100 (15.8–100) 100 (98.0–100) 100 (15.8–100) 100 (98.0–100) 100 (15.8–100) 100 (98.0–100) Bovis group 8 100 (63.1–100) 100 (97.9–100) 100 (63.1–100) 100 (97.9–100) 87.5 (47.4–99.7) 100 (97.9–100) 100 (59.0–100) 99.4 (96.8–100) 25.0 (3.2–65.1) 100 (97.9–100) 100 (15.8–100) 96.7 (92.9–98.8) S. lutetiensis 2 100 (15.8–100) 100 (98.0–100) 100 (15.8–100) 100 (98.0–100) 100 (15.8–100) 100 (98.0–100) 100 (15.8–100) 100 (98.0–100) 0.0 (0.0–84.2) 100 (98.0–100) IC 98.9 (96.1–99.9) S. gallolyticus 6 100 (54.1–100) 100 (97.9–100) 100 (54.1–100) 100 (97.9–100) 83.3 (35.9–99.6) 100 (97.9–100) 100 (47.8–100) 99.4 (96.8–100) 33.3 (4.3–77.7) 100 (97.9–100) 100 (15.8–100) 97.8 (94.4–99.4) Se, sensitivity; Sp, specificity; PPV, positive predictive value; NPV, negative predictive value, IC, incalculable. Group values of Se, Sp, PPV and NPV are in bold formats. Compared with Bruker Biotyper, Vitek MS IVD gave a better resolution for S. mitis/S. oralis identification with a sensitivity of 100% (95 CI: 75.3–100%) though the two species couldn't be distinguished from each other due to database limitation. Similarly, the overall sensitivity for S. pseudopneumoniae identification [77.8% (95 CI: 40.0–97.2%)] was higher than that of Bruker Biotyper [11.1% (95 CI: 0.3–48.3%)]. Expectedly, Vitek MS RUO showed low sensitivity for the identification of most species, except for S. anginosus, S. intermedius, S. sanguinis, S. gordonii, and S. salivarius. Discussion Currently, only three studies have compared the performance of Bruker Biotyper vs. Vitek MS for VGS identification, with two of the studies analyzing a limited number of blood culture isolates (Karpanoja et al., 2014; Angeletti et al., 2015; Isaksson et al., 2015). In two of the studies, 16S rRNA gene and rpoB gene sequencing were used as a gold standard, while the third study didn't have a gold standard. Although the species/group distributions were different in each of these three studies, they all concluded that apart from the misidentification of other species as S. pneumoniae by Bruker Biotyper, the MALDI-TOF technique offers a reliable, rapid and cost saving method for VGS identification. We evaluated the performance of two MALDI-TOF MS systems in VGS identification with a larger number of VGS species and a wider sample type base, using a combination of 16S rRNA gene and gyrB sequencing as a gold standard. Overall, the Vitek MS IVD system performed better than the Bruker Biotyper, accurately identifying 98.9% of the 181 VGS isolates, compared to 88.4% for Bruker Biotyper (P < 0.05). The lower overall performance of the Bruker Biotyper MS was due to misidentification of species within the mitis group, with 21 non-pneumococcal isolates misidentified as S. pneumoniae, which is in agreement with previous studies (Ikryannikova et al., 2011, 2013; Lopez Roa et al., 2013). Notably, we evaluated for the first time, the performance of Vitek MS RUO system though the results were not satisfying. However, this database covers rare VGS species not included in the Vitek MS IVD system (Karpanoja et al., 2014). The superior performance of Vitek MS IVD in distinguishing S. pneumoniae from other mitis group species may be due to use of bin-weighting algorithms in species identification. The system identifies significant peaks of sample isolates and divides them into bins that are weighted according to frequency within a given species. Then the sums of bin weights are calculated using the advanced spectrum classifier algorithm to determine the best match, which may enhance sensitivity (Rychert et al., 2013). In contrast, Bruker Biotyper uses a single reference strain to generate multiple spectra and chooses a consensus spectra based on the reference spectra. After this, matching signals of the sample spectra are compared to the reference spectra and a score value is created (Welker, 2011). Based on our findings, we designed an identification algorithm for the best way to identify species within the VGS group (Figure 2). Laboratories with either of the instruments could refer to this algorithm easily. For Bruker Biotyper MS system, strains with identification scores <1.7 must be re-tested since VGS are very homologous species. Any identification of S. pneumoniae even with a score of >2.0, should be discounted, optochin test and gene-based analysis need to be performed for confirmation. For Vitek MS, initial identification results with a confidence value <99.9% for a single result or with multiple/no identification results, must be repeated. If the repeat analysis fails to provide a high confidence value, these isolates should be identified by molecular methods. Figure 2 An identification testing algorithm for viridans group streptococci (VGS) based on the Bruker Biotyper MS system/Vitek MS system and selective molecular identification (see Supplementary Tables S3 and S4). aFor Bruker Biotyper MS system, strains with identification scores <1.7 must be re-tested. bAny identification of S. pneumoniae even with a score of >2.0, should be preliminatory and then do further sequencing for the high incorrect identification rate. Gene-based analysis confirmed that 85 out of the 106 isolates were S. pneumoniae while the rest are non-S. pneumoniae (Supplementary Table S3). cFor Vitek MS, initial identification results with a confidence value <99.9% for a single isolate or with multiple/no identification results, must be repeated (Supplementary Table S4). Study limitations include possible selection bias as isolates were from a single center, imbalance in group/species distribution of isolates, with some groups and species poorly represented, e.g., the bovis and salivarius groups. And finally, for uniformity, protein extraction step was not performed for all assays. Summary This study shows that both the Bruker Biotyper and the Vitek MS IVD systems can provide a good alternative to phenotypic methods for VGS identification. However, further improvements in the data bases are needed to increase the identification accuracy. In the mean-time, gene-based sequencing remains the best way to correctly identify VGS species. The proposed integrated algorithm is a practical approach in VGS identification at this stage. Author contributions MZ, QY, and YX conceived and designed the experiments, performed the experiments, analyzed the data, and wrote the paper. TK and FK revised the paper critically for important intellectual content. LZ, MX, and YZ read and approved the final version of the manuscript. Funding This work was supported by Research Special Fund for Public Welfare Industry of Health (Grant no. 201402001). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Conflict of interest statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. We thank Weijian Lin from department of Clinical laboratory, WuZhou Red Cross Hospital, Guangxi, China, Xiaodong Gui from department of Clinical Laboratory, Weihaiwei People's Hospital, Shandong, China and Hui Deng from department of Clinical Laboratory, The Second People's Hospital of Panzhihua, Sichuan, China for their technical assistance. Supplementary material The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb.2016.01328 Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Abbreviations MALDI-TOFmatrix-assisted laser desorption ionization-time of flight VGSviridans group streptococci MLSAmultilocus sequence analysis PUMCHPeking Union Medical College Hospital IVDIn Vitro Diagnosis RUOResearch Use Only CIconfidence interval PPVpositive predictive value NPVnegative predictive value ==== Refs References Angeletti S. Dicuonzo G. Avola A. Crea F. Dedej E. Vailati F. . (2015 ). Viridans Group Streptococci clinical isolates: MALDI-TOF mass spectrometry versus gene sequence-based identification . PLoS ONE 10 :e0120502 . 10.1371/journal.pone.0120502 25781023 Arbique J. C. Poyart C. Trieu-Cuot P. Quesne G. Carvalho Mda G. Steigerwalt A. G. . (2004 ). Accuracy of phenotypic and genotypic testing for identification of Streptococcus pneumoniae and description of Streptococcus pseudopneumoniae sp . nov. J. Clin. Microbiol. 42 , 4686 –4696 . 10.1128/JCM.42.10.4686-4696.2004 15472328 Bishop C. J. Aanensen D. M. Jordan G. E. Kilian M. Hanage W. P. Spratt B. G. (2009 ). 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==== Front Front MicrobiolFront MicrobiolFront. Microbiol.Frontiers in Microbiology1664-302XFrontiers Media S.A. 10.3389/fmicb.2016.01307MicrobiologyOriginal ResearchPseudopyronine B: A Potent Antimicrobial and Anticancer Molecule Isolated from a Pseudomonas mosselii Nishanth Kumar S. 1*Aravind S. R. 2†Jacob Jubi 1Gopinath Geethu 1Lankalapalli Ravi S. 1Sreelekha T.T. 2Dileep Kumar B.S. 1*1Agroprocessing and Natural Products Division, National Institute for Interdisciplinary Science and Technology – Council of Scientific and Industrial ResearchThiruvananthapuram, India2Laboratory of Biopharmaceuticals and Nanomedicine, Division of Cancer Research, Regional Cancer CentreThiruvananthapuram, IndiaEdited by: Márcia Vanusa Da Silva, Federal University of Pernambuco, Brazil Reviewed by: Bala Nambisan, Central Tuber Crops Research Institute, India; Prem Dureja, Indian Agricultural Research Institute, India; Carlos Henrique Gomes Martins, University of Franca, Brazil; Ruby Anto Jphn, Rajiv Gandhi Centre for Biotechnology, India *Correspondence: B. S. Dileep Kumar, dileepkumarbs@yahoo.in S. Nishanth Kumar, nishanthctcri@gmail.com†Present address: S. R. Aravind, Sharjah Institute for Medical Research, University of Sharjah, Sharjah, UAE This article was submitted to Antimicrobials, Resistance and Chemotherapy, a section of the journal Frontiers in Microbiology 26 8 2016 2016 7 130729 1 2016 08 8 2016 Copyright © 2016 Nishanth Kumar, Aravind, Jacob, Gopinath, Lankalapalli, Sreelekha and Dileep Kumar.2016Nishanth Kumar, Aravind, Jacob, Gopinath, Lankalapalli, Sreelekha and Dileep KumarThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.In continuation of our search for new bioactive compounds from soil microbes, a fluorescent Pseudomonas strain isolated from paddy field soil of Kuttanad, Kerala, India was screened for the production of bioactive secondary metabolites. This strain was identified as Pseudomonas mosselii through 16S rDNA gene sequencing followed by BLAST analysis and the bioactive metabolites produced were purified by column chromatography (silica gel) and a pure bioactive secondary metabolite was isolated. This bioactive compound was identified as Pseudopyronine B by NMR and HR-ESI-MS. Pseudopyronine B recorded significant antimicrobial activity especially against Gram-positive bacteria and agriculturally important fungi. MTT assay was used for finding cell proliferation inhibition, and Pseudopyronine B recorded significant antitumor activity against non-small cell lung cancer cell (A549), and mouse melanoma cell (B16F10). The preliminary MTT assay results revealed that Pseudopyronine B recorded both dose- and time-dependent inhibition of the growth of test cancer cell lines. Pseudopyronine B induced apoptotic cell death in cancer cells as evidenced by Acridine orange/ethidium bromide and Hoechst staining, and this was further confirmed by flow cytometry analysis using Annexin V. Cell cycle analysis also supports apoptosis by inducing G2/M accumulation in both A549 and B16F10 cells. Pseudopyronine B treated cells recorded significant up-regulation of caspase 3 activity. Moreover, this compound recorded immunomodulatory activity by enhancing the proliferation of lymphocytes. The production of Pseudopyronine B by P. mosselii and its anticancer activity in A549 and B16F10 cell lines is reported here for the first time. The present study has a substantial influence on the information of Pseudopyronine B from P. mosselii as potential sources of novel drug molecule for the pharmaceutical companies, especially as potent antimicrobial and anticancer agent. Pseudomonas mosseliiPseudopyronine Bantimicrobialanticancer activityapoptosis ==== Body Introduction The discovery of antibiotics has decreased the spread and severity of a broad range of infectious diseases caused by human pathogenic microbes. However, due to the wide spread use of antibiotics, the competence of many introduced antibiotics in the market is being threatened by the rise of microbial pathogens which are resistance to the current clinical chemotherapeutic agents (Cowan, 1999). Human infections owing to multi-drug-resistant microbes is a serious challenge to many patients, especially those, are in hospitals (Arias and Murray, 2009; Fischbach and Walsh, 2009). Infectious diseases account for more than 13 billion human deaths worldwide, which accounts for about 25% of all deaths (Zin et al., 2011). The antibiotic treatments of various infectious diseases are also getting limited due to the reemergence of multi-drug-resistance (MDR) pathogenic microbes, which have been frequently reported from many parts of the world. Methicillin-resistant Staphylococcus aureus (MRSA) is one of the important drug resistant pathogen, which is frequently reported by many clinicians worldwide. A part from MRSA and VRSA (vancomycin resistant S. aureus), several other drug resistant and pan-drug-resistant (PDR) microbes especially Gram-negative bacteria, including carbapenem resistant Pseudomonas aeruginosa, Enterococcus faecium, Klebsiella pneumoniae, Acinetobacter baumannii, Enterobacter sp., and Stenotrophomonas maltophilia, are emerging as an important health problem to human being (Huang et al., 2013). Recently, few novel drugs have been industrialized exactly for treating various Gram-negative MDR/PDR bacteria (Payne et al., 2006; Vaara et al., 2010; Velkov et al., 2010). Many bioactive compounds from natural sources have played an important role in the discovery of many antibiotics which are used clinically. Moreover, 70% of the clinically used antimicrobials are either unchanged natural compounds used directly or derived from natural compounds by synthetic tailoring (Singh et al., 2011). Thus, more investigation should be initiated for discovering and developing novel antimicrobial molecules especially from natural sources is urgently needed. At present, one in four deaths in the USA is because of various tumors (Fadeyi et al., 2013). When classified within age groups, the tumor is one of the five important reasons of death among humans (males and females) and the single major cause of human death around the world (Fadeyi et al., 2013). By 2016, cancer morbidity may increase more than 10 million worldwide. This growing tendency also indicates a shortage in the various current tumor treatments including surgical operation, chemotherapy, and radiotherapy. Since the normal endurance rates of cancer have remained essentially unaffected in spite of such above mentioned hostile treatments, so there is an urgent requirement for new anticancer drugs with advanced efficiency, and fewer side effects that can be developed at a reasonable price to common people worldwide. Tawiah et al. (2012) reported that “Throughout the various years, natural compounds are the most constantly fruitful basis of diverge bioactive metabolites having several applications in the field of modern human medicine, pharmaceutics and agriculture.” Therefore, the search for new antimicrobials from natural sources is an important area of many researchers worldwide. Microorganisms from soils play an important source of many novel antibiotic compounds due to their high abundance and amazing diversity. There are many reports of the production of antimicrobial metabolites by Pseudomonas spp. Some of these antimicrobials have been chemically characterized, and their commercial exploitation is attempted (Zhou et al., 2012). As mentioned earlier infectious diseases and cancer is the most leading cause of death worldwide. Therefore, novel and potent therapeutic agents are often required to control these diseases. To achieve this goal, several new sources of antimicrobial/anticancer compounds ranging from natural to synthetic are currently being explored worldwide. Therefore in our continuous search for new bioactive secondary metabolites from soil bacteria, Pseudopyronine B was purified from a Pseudomonas mosselii TR strain. The current manuscript also reported the antimicrobial and anticancer activity of Pseudopyronine B. Materials and Methods Chemicals and Media Used in Current Study All the chemicals and reagents used in the current study were of the finest purity. All the chemicals used for separation and purification of bioactive compounds were from Merck (Mumbai, India). Silica gel (mesh size: 230–400) used for column purification and precoated silica gel 60 plates (GF254) used for thin layer chromatography (TLC) were purchased from Merck (Germany). Various media used for microbiological investigation were purchased from Hi-Media Laboratories Pvt. Ltd, Mumbai, India. The antibiotics ciprofloxacin and amphotericin B, which were used as standard positive control, were procured from Sigma-Aldrich (USA). The Chemsketch Ultra (Toronto, ON, Canada) software was used for drawing the chemical structure. Bioactive Compound Producing Bacterium A fluorescent Pseudomonas, named as TR strain was isolated from paddy field of Kuttanad region of Kerala, India was used in the present study. Kuttanad is a unique agroclimatic zone in India where paddy is cultivated below sea level and is blessed with a rich microbial population which is not fully explored for economic cultivation. The strain was identified as a fluorescent Pseudomonas strain through classical identification methods. Molecular Identification of TR Strain through 16S rDNA Sequencing Extraction of Bacterial DNA and Sequencing of the 16S Gene A pure culture of bacteria was raised in 10 ml of Luria-Bertani broth (LB) for 18 h to obtain OD value of approximately 0.7 at 600 nm. The bacterial culture broth (1.5 ml) was pelleted in a microfuge tube for 2 min at 12,000 rpm. Total DNA was extracted using standard phenol–chloroform extraction procedures (Sambrook et al., 2001). PCR amplification was done with universal primers (f: 5′-GATTAGATACCCTGGTAGTCCAC-3′ and r: 5′-CCCGGGAACGTATTCACCG-3′) specific for the 16S rDNA gene. The PCR reaction was carried out in a total volume of 50 μl [DNA (100 ng), MgCl2 (4 Mm), PCR reaction buffer (1X, Genei, Bangalore, India), each deoxyribonucleotide triphosphate (2.5 mM), Taq polymerase (1.5 U, Genei, Bangalore, India) and each primer (40 pmol)]. PCR cycle conditions were: 2 min initial denaturation at 95°C, followed by 36 cycles of denaturation for 30 s at 95°C, followed by 1 min primer annealing at 60°C and 1 min primer extension at 72°C. Final primer extension step was carried for 2 min at 72°C. PCR amplification was achieved on a Bio-Rad Thermal Cycler (USA). Amplicons were analyzed in 1% (w/v) agarose gels. The gels were stained with an aqueous solution containing ethidium bromide (0.5 mg/mL) and visualized on a UV transilluminator. Amplicons were purified using the QIA quick purification kit (Qiagen, Valencia, CA, USA) and sequenced. The sequencing was performed on an ABI PRISM 310 Genetic analyzer (Perkin-Elmer Applied Biosystems, Foster City, CA, USA) using the universal primers (described for PCR amplification) and the Big Dye Terminator Cycle Sequencing Ready Reaction Kit (Perkin-Elmer, Applied Biosystems). The 16S rDNA gene sequences (partial) obtained were finally aligned using ABI Prism software (Perkin-Elmer, Applied Biosystems) and compared to sequences retrieved from GenBank Database and BLAST. Phylogenetic Analysis The phylogenetic investigation was performed using MEGA 6.0 software program (Molecular Evolutionary Genetics Analysis, Version 6.0; Tamura et al., 2007). The phylogenetic tree topologies were assessed by bootstrap analyses based on 1000 replicates, and final phylogenetic trees were constructed with the neighbor-joining method as reported earlier (Saitou and Nei, 1987). The taxonomic position of the present strain was recognized based on 16S rDNA homology. Fermentation and Extraction of Crude Bioactive Secondary Metabolites The bacterial production and solvent extraction of crude bioactive secondary metabolites from TR strain were done according to George et al. (2015) in King’s B media (KB). The supernatant was collected according to the method of George et al. (2015) and extracted with ethyl acetate trice (1:1 v/v) in a globe shaped separating funnel (2 L) and the ethyl acetate portion was collected, concentrated and dried in a Buchi rota evaporator (40°C) for further detailed investigations. Purification of Bioactive Compound Activated silica gel (230–400 mesh) was filled into a 600 mm × 30 mm long glass column using n-hexane solvent. The crude secondary metabolite (0.85 g) was subjected to silica gel column chromatography purification, with a elution profile of 5% ethyl acetate (EtOAc), 10% EtOAc, 20% EtOAc, 40% EtOAc in hexane (100 ml each), 50% EtOAc in hexane (150 ml), 100% EtOAc (100 ml), 5% MeOH in EtOAc (100 ml), obtained 75 fractions. The purity of the fractions was determined by TLC. An aliquot of each collected fraction was spotted on the activated TLC plates (silica gel 60; 10 cm × 10 cm). Ethyl acetate and hexane (1:1) was used for developing TLC plates. Spots were positioned by UV and further revealing the TLC plate to iodine vapors. One fraction (tubes 35–37) was obtained as pure. This fraction was evaporated to give a faint white residue (17 mg). Initial bioactivity of this fraction was confirmed by testing against Bacillus subtilis, which was used as indicator test microorganism. Identification of the Bioactive Compound The NMR spectra (1H NMR and 13C) of the pure compound were documented using a Bruker DRX500 NMR spectrometer (Bruker, Rheinstetten, Germany) at room temperature and the chemical shifts are reported relative to the corresponding reference solvent (methanol). HR-ESI-MS of the compound was recorded using a Thermo Scientific Exactive Mass Spectrometer (Thermo Fisher 110 Scientific, USA) with an electrospray ionization mode. Antimicrobial Activity of the Compound Pathogenic Microbes Used in the Study Bacterial The following Gram-positive and negative pathogenic bacteria are used for antibacterial study. Gram-positive pathogenic bacteria: B. subtilis (MTCC 2756), Bacillus cereus (MTCC 430), S. aureus (MTCC 902), S. epidermis (MTCC 435), and S. simulans (MTCC 3610); Gram-negative pathogenic bacteria: Escherichia coli (MTCC 2622), Klebsiella pneumoniae (MTCC 109), Proteus mirabilis (MTCC 425), Proteus vulgaris (MTCC 1771), P. aeruginosa (MTCC 2642), Salmonella typhi (MTCC 3216), and Vibrio cholerae (MTCC 3905). Fungal The fungal strains used in the present are Aspergillus flavus (MTCC 183), A. fumigatus (MTCC 3376), A. niger (MTCC 282), A. tubingensis (MTCC 2425), Colletotrichum gloeosporioides (MTCC 2151), Fusarium oxysporum (MTCC 284), Penicillium expansum (MTCC 2006), Rhizoctonia solani (MTCC 4634), and Trichophyton rubrum (MTCC 296). All the test microbes were obtained from MTCC (Microbial Type Culture Collection and GenBank), Council of Scientific and Industrial Research- Institute of Microbial Technology (CSIR-IMTECH), Chandigarh, India. Determining the Antibacterial Activity of Test Compound Minimum Inhibitory Concentration (MIC) and Minimum Bactericidal Concentration (MBC) The MIC of the compound was determined according to broth microdilution method as suggested by Clinical and Laboratory Standard Institute, USA (Clinical and Laboratory Standards Institute [CLSI], 2012a). Briefly, the concentration of the fresh overnight culture of test bacteria was adjusted to 1 × 105 CFU/ml using a spectrophotometer. Dilutions of inocula were cultured on a Nutrient agar (NA) medium to check the validity of the inoculum and the absence of any contaminations. Different solvent and water dilutions of test compound (0.5–1000 μg/ml) and ciprofloxacin (0.25–250 μg/ml) were placed in the wells containing 150 μl of nutrient broth (NB), followed by the addition of 10 μl of inoculum. After that, the ELISA plates were incubated for 18–24 h at 37°C. After incubation MIC was determined by measuring the OD at 600 nm. The lowest concentration of test compound that produced a significant inhibition (around 90%) of the growth of the bacteria in comparison with the positive control was identified as the MIC. About 100 μl from the ELISA wells not displaying any microbial growth in the MIC test were diluted serially using 0.85% saline and plated on NA plates to determine the MBC values. The NA plates were incubated at 37°C for 24 h. MBC is defined as the lowest concentration of antibacterial agent that reduces the viability of the initial bacterial inoculum by 99.99%. Disk Diffusion Experiment of Test Compound The antimicrobial activity of the pure compound was performed by the disk diffusion assay against the test bacterial pathogens as mentioned by CLSI, USA (Clinical and Laboratory Standards Institute [CLSI], 2012b). The test bacterial strain preserved in NA at 4°C were sub-cultured in NB to get the working concentration approximately containing 1 × 106 CFU/ml. The test compound (MIC concentration) was loaded into a sterile disk (Hi-Media) with 6 mm diameter. Then Mueller-Hinton agar (MHA) plates were swabbed with each test bacterial pathogens and the compound loaded disks were placed along with the control antibiotics disk. Here, ciprofloxacin disks (5 μg/disk) were used as the standard positive control and the plates were incubated for 24 h at 37°C until bacteria had developed in a confluent film. The antimicrobial property of the test compound and antibiotic was determined by measuring the zone of inhibition (diameter) expressed in mm. The experiment was performed in triplicate sets. Determining the Antifungal Activity of Test Compound Minimum Inhibitory Concentration The MIC of test compound against the fungi was done by broth microdilution assay as per the recommendations of Clinical and Laboratory Standard Institute, USA (Clinical and Laboratory Standards Institute [CLSI], 2010, 2012c), with RPMI 1640 growth medium supplemented by L-glutamine, without sodium bicarbonate (all from Sigma-Aldrich) and buffered to pH 7.0 according to the previously reported method (Aravind et al., 2014). The lowest concentration of agents that produced a significant inhibition (90%) of the growth of the test fungi in comparison with the standard positive control was defined as the MIC. Disk Diffusion Experiment of Test Compound The compound was screened for their antifungal activity against test fungi by agar disk diffusion experiment as mentioned by CLSI, USA (Clinical and Laboratory Standards Institute [CLSI], 2008, 2009). Briefly, PDA plates were inoculated (0.1 ml) with a spore suspension in 0.85% sterile saline. The concentration of test fungal suspension was adjusted to 1 × 105 CFU/ml using sterile saline. The test compound (MIC concentrations) was loaded into a 6-mm diameter sterile filter paper disks (Hi-Media, India), air dried and then placed on the surface of the PDA plates swabbed previously by test fungi. The plates were incubated at 35°C for 48–72 h. After incubation, the antifungal activity was assessed by measuring the zone of inhibition (diameter) and expressed in millimeter (mm). The assay was performed in triplicate sets. Anticancer Studies of the Test Compound Cancer Cell Lines Used in the Study: Its Source and Maintenance and Treatments The following three cancer cell lines were used in the present investigation (1) Non-small cell lung cancer (A549), (2) mouse melanoma cell (B16F10) and (3) liver cancer cell (HepG2) lines and these cell lines were obtained from NCCS (National Centre for Cell Science), Pune, India and maintained throughout the study in Dulbecco’s Modified Eagle Medium (DMEM) added with 3 mM of L-glutamine, 100 μg/ml of streptomycin, 100 IU/ml of penicillin, 10% heat inactivated (56°C) fetal bovine serum (FBS) and 25 mM of HEPES [4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid]. The final pH was adjusted to 7.2 by bicarbonate solution at 37°C in a CO2 incubator. Stock solutions (3 mg/500 μl) of the test compound, made in dimethyl sulfoxide (DMSO), were further dissolved in the corresponding medium (DMEM) to the necessary working concentrations. A549, B16F10 and HepG2 cell lines were seeded into ELISA plates and incubated for 24 h at 37°C in a CO2 incubator. After the cell adherence, six different concentrations of test compound (0.1, 1, 5, 10, 50, and 100 μg/ml) were added to the wells containing test cancer cell lines, except to the control wells. Control wells contain only DMEM nutrient medium and the test cells. The cultures were incubated for various time periods (24, 48, and 72 h). Anti-proliferative Activity The cytotoxic effect of the test compound was determined by MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] methods as described earlier (Aravind et al., 2015). The concentrations used in this study are 0.1, 1, 5, 10, 50, and 100 μg/ml. Apoptosis Induction Assay Using the Test Compound Acridine Orange and Ethidium Bromide Staining (AO/EB Staining) Apoptotic morphology of cancer cells after treatment with Pseudopyronine B was investigated by staining the cancer cells with a combination of the fluorescent DNA-binding acridine orange (AO) and ethidium bromide (EB) dyes to decide the viable and non-viable cancer cells in a population. Cells were collected and washed three times with PBS after being incubated with IC50 concentration of Pseudopyronine B for 24, 48 and 72 h, and were then stained with 100 μg/ml AO/EB stain (Sigma-Aldrich) for 2 min. Then the color and structure of the cancer cells were immediately recorded under an inverted fluorescence microscope (Aravind et al., 2014). Detection of Morphological Apoptosis with Hoechst 33342 Staining The morphological apoptosis like chromatin condensation in the test cancer cell lines was observed by nuclear staining dye Hoechst 33342 (Aravind et al., 2014). After treatment with Pseudopyronine B (IC50 concentration) for 24, 48 and 72 h, the A549 and B16F10 cells were washed with PBS trice and then further fixed with methanol: acetic acid (3:1) for 10 min at room temperature in the dark. Fixed cells were again washed with PBS and stained with 1 μg/ml of Hoechst 33342 stain (10 mg/ml) for 10 min in the dark at room temperature. Changes in the nuclei of cells after staining with Hoechst 33342 were recorded using an inverted fluorescence microscope (stimulation at 350 nm and emission at 460 nm; Leica, Germany). Annexin V/Propidium Iodide Assay The annexin V/propidium iodide (PI) method was done according to the recommendation given by manufacturer’s (Invitrogen, USA). Briefly, A549 and B16F10 cell lines were seeded into six well plates and incubated for 24, 48, and 72 h with Pseudopyronine B (IC50 concentration). After incubation, the cells were collected and washed with ice-cold PBS and centrifuged. The pellet was resuspended in 100 μl of binding buffer containing 2.5 μl FITC conjugated Annexin V and 1 μl 100 μl/ml PI and incubated for further 15 min at room temperature in the dark. A total of at least 10000 events were composed and examined by BD flow cytometry (BD Biosciences, San Jose, CA, USA; Liu et al., 2013). Cell Cycle Analysis of Cancer Cells after Treatment with Test Compound The cellular DNA content and distribution in the cell cycle were enumerated by BD flow cytometry using PI staining. A549 and B16F10 cells were seeded in the T25 culture flask and treated with Pseudopyronine B (IC50 concentration) for the various time periods (24, 48, and 72 h). Cells were collected, washed with PBS twice, and fixed in 70% ethanol at -20°C. After fixation, the cells were washed in PBS and centrifuged. The pellets thus obtained was treated with RNase (1 mg/ml; Roche, Mannheim, Germany) at 37°C for 30 min and then incubated with PI for at least 30 min. Cell-cycle analysis was recorded with the FACS Calibur Flow Cytometer (BD Biosciences, San Jose, CA, USA) and the data were analyzed with Flowjo software. Determination of Activation of Caspase 3 Caspase-Glo assay kits (Promega) were used to determine the activation of Caspase 3 by following manufacturer’s instructions. A549 and B16F10 cells were plated into ELISA plates and incubated for 24 h. After 24 h seeding, cells were treated with test compound (5, 10, and 50 μg/ml) and incubated for 48 h. Subsequently, 100 ml of caspase-3 assay reagent was added to each well. After adding the reagent, the plate was incubated for 1 h in the dark and the luminescence was quantified with the help of a microplate reader (SpectraMax M5, Molecular Devices). The activity of Caspase was expressed as a percentage (%) of the untreated control treatment (DMSO). The experiment was performed in triplicate (Wang et al., 2014). Proliferation Assay of Normal Lymphocytes The proliferation of normal lymphocytes when treated with test compound was done as described earlier (Aravind et al., 2014). Statistical Analysis Disk diffusion data was presented as mean ± standard deviation. The error bars represent ± SD. taken from three independent experiments. Statistical analysis was performed with SPSS (Version 17.0; SPSS, Inc., Chicago, IL, USA). The significance level was set at P < 0.05. Results Based on 16S rDNA Sequencing and Phylogenetic Analysis, Bacteria Was Identified as P. mosselii Molecular characterization of the TR bacterium (P. mosselii) was performed by 16S rDNA gene sequencing. PCR amplification of 16S rDNA gene yielded -1500 bp amplicon. BLAST analysis recorded 99% similarity to P. mosselii 16S rDNA sequence accessible in the NCBI GenBank database and based on this our TR strain was identified as P. mosselii. The partial 16S rDNA gene sequence data have been deposited in the NCBI GenBank nucleotide database under the accession number KF712283. The phylogenetic tree clearly portrayed the relationships our P. mosselii with other Pseudomonas strains used for the present analysis. The present TR bacterial isolate (P. mosselii strain) was very successfully clustered along with other P. mosselii sequences obtained from the NCBI GenBank database further confirming the authenticity of our isolate (Figure 1). The P. mosselii was currently deposited in CSIR-IMTECH (Institute of Microbial Technology, Chandigarh, India). FIGURE 1 Phylogenic tree displaying the relationships of Pseudomonas mosselii TR strain and other known Pseudomonas species based on 16S rDNA gene sequences (Neighbor-joining Method). The Bioactive Compound Was Identified as Pseudopyronine B Based on Detailed Spectral Analyses Ethyl acetate fraction of P. mosselii yielded one main compound, which having an Rf value 0.68. Initial bioactivity of this compound was confirmed by testing the antimicrobial activity against B. subtilis and the compound recorded significant activity (data not shown). The compound was identified based on the spectral data as Pseudopyronine B (Figure 2). FIGURE 2 (A) Structure of Pseudopyronine B and (B) HR–MS spectra of Pseudopyronine B. Pseudopyronine B:1H NMR (500 MHz, CD3OD) δ 0.91 (2 × t, J = 6.8 Hz, 2 × 3 H), 1.27–1.39 (m, 14 H), 1.42–1.50 (m, 2 H), 1.66 (quin, J = 7.3 Hz, 2 H), 2.38 (t, J = 7.5 Hz, 2 H), 2.48 (t, J = 7.5 Hz, 2 H), 5.99 (s, 1 H); 13C NMR (126 MHz, CD3OD) δ 12.9, 13.0, 22.2, 22.3, 22.4, 26.5, 27.5, 28.5, 28.6, 28.8, 29.2, 31.4, 31.5, 32.9, 99.6, 102.5, 163.7, 166.4, 167.4; ESI-MS [M+H]+ C18H31O3 calc′d for m/z 295.22732, found 295.22729. Antibacterial Activity of Pseudopyronine B The Pseudopyronine B was tested for checking its antibacterial (MIC and MBC) activity against 13 bacterial species using standard CLSI protocol and the values are presented in Table 1. Pseudopyronine B recorded significant antibacterial activity against Gram positive bacteria, while most of the tested Gram negative bacteria recorded no activity. The microorganism that presented highest sensitivity toward Pseudopyronine B was S. epidermis (1 μg/ml), followed by S. aureus (4 μg/ml). The diameter of zone of inhibition obtained from agar disk diffusion experiment was presented in Table 1 and S. epidermis recorded significant diameter of zone of inhibition (33 mm) and is shown in Figure 3. The activity of Pseudopyronine B against S. epidermis was superior to ciprofloxacin, standard antimicrobial agent (Table 1). Table 1 Antibacterial activity of Pseudopyronine B. Test bacteria Pseudopyronine B Ciprofloxacin MIC (μg/ml) MBC (μg/ml) Zone of inhibition (Diagram in mm) MIC (μg/ml) MIC (μg/ml) Zone of inhibition (Diagram in mm) B. subtilis 64 125 20 ± 1 2 2 30 ± 1 B. cereus 16 32 23 ± 0 1 2 31 ± 1.52 S. aureus 4 4 31 ± 0.23 1 2 33 ± 1.14 S. epidermis 1 2 33 ± 0.56 2 4 30 ± 1.77 S. simulans 8 8 28 ± 1.72 4 4 29 ± 0.56 E. coli – – – 1 2 27 ± 0.77 P. mirabilis – – – 0.5 1 29 ± 0.56 P. vulgaris MTCC 1771 – – – 2 4 30 ± 1.15 V. cholerae – – – 2 4 31 ± 1.72 K. pneumonia 4 8 25 1 2 25 ± 1.15 P. aeruginosa – – – 2 4 27 ± 0 S. typhi 32 32 22 4 4 29 ± 1 –, recorded no activity.FIGURE 3 Antimicrobial activity of Pseudopyronine B. Antifungal Activity of Pseudopyronine B Antifungal property of Pseudopyronine B against 11 fungi and the MIC values are shown in Table 2. Pseudopyronine B documented good antifungal property especially against plant pathogenic fungi. Pseudopyronine B displayed best MIC value against P. expansum (4 μg/ml), followed by F. oxysporum and R. solani (16 μg/ml). In agar disk diffusion assay P. expansum recorded significant activity (34 mm; Table 2; Figure 3). Table 2 Antifungal activity of Pseudopyronine B. Test bacteria Pseudopyronine B Amphotericin B MIC (μg/ml) Zone of inhibition (Diagram in mm) MIC (μg/ml) Zone of inhibition (Diagram in mm) A. flavus 16 21 ± 0.52 4 23 ± 1.15 A. fumigatus 64 18 ± 0.77 2 27 ± 1.72 A. niger 32 21 ± 1.12 2 26 ± 0.56 A. tubingensis – – 4 30 ± 0.77 C. gloeosporioides – – 32 25 ± 1.12 F. oxysporum 16 25 ± 1.12 125 26 ± 2.21 P. expansum 4 34 ± 0.52 64 23 ± 2.27 R. solani 16 24 ± 1 64 23 ± 072 T. rubrum – – 2 24 ± 0.56 –, recorded no activity.Anticancer Activity Pseudopyronine B Recorded Significant Anti-proliferative Activity as Evidenced by MTT Assay Anti-proliferative activity of Pseudopyronine B was assessed on non-small cell lung cancer (A549), mouse melanoma cell (B16F10) and liver cancer cell (HepG2) lines. MTT assay was used to evaluate the cell viability after Pseudopyronine B treatment. From the MTT experiment it is clearly evident that the Pseudopyronine B recorded both dose- and time-dependent inhibition in the growth of test cancer cell lines, when treated with 100, 50, 10, 5, 1, and 0.5 μg/ml (Figure 4A). Out of three cell lines tested, A549 and B16F10 cells recorded best activity. A549 and B16F10 cells turned out to be the most sensitive cell lines and were selected for further apoptotic studies. FIGURE 4 (A) Cytotoxicity profile of Pseudopyronine B on A549 and B16F10 cells measured by MTT assay. (B) Phase contract images of A549 and B16F10 cells treated with Pseudopyronine B. The experiments where performed in three replication and results were expressed as mean ± standard deviation. Apoptotic Induction Assays Pseudopyronine B Induces Apoptosis in A549 and B16F10 as Evidenced by Acridine Orange-Ethidium Bromide and Hoechst 33342 Staining The cultured A549 and B16F10 cells were examined for their morphology features after treatment with Pseudopyronine B. It was observed that the Pseudopyronine B is inducing characteristic apoptosis features changes such as nuclear condensation, membrane blebbing and formation of apoptotic bodies, when compared to untreated control as evaluated by phase contrast microscope. Moreover, there was a significant reduction in the number of A549 and B16F10 cells after Pseudopyronine B treatment (Figure 4B). Acridine orange-ethidium bromide staining was done to confirm the nuclear membrane damage, a characteristic feature of apoptosis as observed by yellow/orange coloration in the nuclei of A549 and B16F10 cells treated with Pseudopyronine B (Figure 5). The cells treated with the compound after 24, 48 and 72 h exhibited time depend increase in AO-EB positivity, when compared to untreated control (Figure 5). FIGURE 5 Analysis of A549 and B16F10 cell lines to detect apoptosis by Acridine orange/ethidium bromide staining. In this method live cells were detected as green color, whereas the apoptosis cells documented orange-red due to the co-staining of acridine orange with ethidium bromide stain due to the loss of membrane integrity by the action of Pseudopyronine B. The Hoechst 33342 staining of A549 and B16F10 cells recorded a very significant increase in the number of cancer cells showing nuclear condensation and fragmentation after treatment with Pseudopyronine B when examined through phase contrast microscope (Figure 6). Moreover, cancer cells lost its normal structure after treatment with Pseudopyronine B and presented an expansion of the endoplasmic reticulum. Some very specific signs of early stages of apoptosis (vacuolization in the cytoplasm, nucleus shrinkage and fragmentation and chromatin densification) can also be viewed in Pseudopyronine B treated cells through phase contrast microscope (Figure 6). FIGURE 6 Hoechst staining of A549 and B16F10 cells. Treatment with Pseudopyronine B resulted in apoptotic death. Effect of Pseudopyronine B on Cell Cycle Analysis Distribution of cells on the different phases of the cell cycle was investigated by flow cytometry after treatment of A549 and B16F10 cells with several concentrations of Pseudopyronine B for 24, 48, and 72 h. In both A549 and B16F10 Pseudopyronine B treated cells, G1 and G2/M phase were increased were as S phases found to be decreased. In A549 cells at 48 h of incubation control cells possess 68.96 ± 2.94% in G1 phase were as in Pseudopyronine B treated cells have 76.36 ± 2.67%. Control cells in G2/M phase possess 5.88 ± 0.84% were as in Pseudopyronine B treated cells have 11.88 ± 1.04%. In B16F10 cells at 48 h of incubation control cells possess 70.62 ± 3.56% and 72.79 ± 3.21% for Pseudopyronine B treated cells. In G2/M phase control cells possess 17.59 ± 1.59% and 21.12 ± 1.64% for Pseudopyronine B treated cells suggesting that it can induce significant apoptosis in A549 and B16F10 cells (Figure 7). FIGURE 7 Effect of Pseudopyronine B against A549 and B16F10 on cell cycle. The experiments where performed in three replication and results in bar diagram were expressed as mean ± standard deviation. Pseudopyronine B Induces Apoptotic Events by Altering Membrane Permeability as Evidenced by Flow Cytometry Annexin V-FITC staining, using flow cytometry was used to detect the morphological changes that occur in the early stages of apoptotic cells (Figure 8). After 48 h of treatment, Pseudopyronine B induced apoptosis on A549 [14.1 ± 1.34% in control to 76.5 ± 2.97% in Pseudopyronine B treated cells (P < 0.001)] and B16F10 [21.7 ± 1.68% in control to 64.7 ± 2.67% in Pseudopyronine B treated cells (P < 0.001)] cells. FIGURE 8 Pseudopyronine B induce phosphatidylserine exposure in A549 and B16F10 cells. The cells were stained with annexin V-FITC and propidium iodide. Analyses were performed by flow cytometry. Each data represents mean ± SD from three independent experiments. Pseudopyronine B Induces Caspase 3 Activation in Cells Our next attempt was to examine the basic mechanism behind the cytotoxic effect of Pseudopyronine B. For this, we tested the activation of caspase 3 using a colorimetric assay. Pseudopyronine B treated cells and control lysates were prepared and incubated with Ac-DEVD-pNA caspase 3 specific substrate with the reaction buffer and the caspase 3 pNA release was measured using a spectrophotometer at 405 nm. Significant enhancement in the caspase 3 activity was recorded in Pseudopyronine B treated cells when compared with that of control clearly indicating the involvement of caspase 3 in Pseudopyronine B induced apoptotic cell death (Figure 9). The results are given in Figure 9. FIGURE 9 Effect of Pseudopyronine B on the release of caspase 3 in A549 and B16F10 cells. The experiments where performed in three replication and results were expressed as mean ± standard deviation. Evaluation of Immunomodulatory Properties of Pseudopyronine B Immunomodulatory activity of Pseudopyronine B was checked by the proliferation of lymphocyte. The proliferation lymphocyte was slightly enhanced in Pseudopyronine B treated cells when compared to that of control cells (untreated). The proliferation index of lymphocyte was detected to be 1.08 for 5 μg/ml of Pseudopyronine B in the presence of PHA (Figure 10). FIGURE 10 In vitro lymphocyte proliferative activity of Pseudopyronine B after 72 h of incubation period. Discussion The enormous rise in the value of drug discovery and development is a strong encouragement for many pharmaceutical industries to contempt all but the most economically bioactive lead molecules (Higginbotham et al., 2013). But, the decreasing quantity of medicines reaching the market is putting strong pressure on the pharmaceutical companies to research and find alternative sources for novel lead molecules (Bennani, 2012). Natural molecules with diverse bioactivity have been used since the beginning of various conventional medicines in early years (Graca et al., 2013). These bioactive molecules are present in all forms of life and are usually produced during the secondary metabolism by almost all organisms. A wide variety of structurally interesting and bioactive secondary metabolites produced by various microorganisms including bacteria, fungi, actinomycetes, etc. have been reported worldwide and many of them are taken by the several pharmaceutical companies as lead drug molecules especially antibiotics (Gao et al., 2012). Several microbes especially from terrestrial soils have an inborn capability of producing many novel natural molecules with various biological properties and this represent rich source of biologically active compounds which may play very important role in drug discovery process for pharma industry (Bode et al., 2002). In the present study, a bacterial strain, named as TR, was isolated from soil during a screening for microbes with potent antimicrobial property. The microbial strain used in the present study was identified as a P. mosselii strain based 16 S rDNA sequencing and BLAST analyses. There are many literature reports on the production of compounds with antimicrobial properties by Pseudomonas spp. Some of these antimicrobial compounds have been identified chemically and the structure has been reported (Zhou et al., 2012). Several fluorescent Pseudomonas spp. have currently received world-wide interest due to the production of a board range of bioactive compounds with antibiotics properties such as phenazine-1-carboxylic acid, pyoluteorin, phenazine-1-carboxamide, viscosinamide and tesin (Chin-A-Woeng et al., 2003; Hu et al., 2005; Huang et al., 2009) and several bioactive enzymes. In this study, P. mosselii exhibit remarkable antimicrobial property especially against a broad range of plant pathogenic fungi, Gram-positive and negative human pathogenic bacteria. So far, many antibiotics have been isolated and identified from Pseudomonas species, and most of them were from P. aeruginosa, which is the most studied species of Pseudomonas. P. mosselii is one of the Pseudomonas species that has not been studied extensively. In the present study we have isolated Pseudopyronines B from P. mosselii and the isolation of Pseudopyronine B from P. mosselii is reported here for the first time. The isolation of Pseudopyronines A and B is previously reported from marine sponge associated Pseudomonas species collected from the Fiji islands (Singh et al., 2003, 2011; Kong et al., 2005) and Pseudomonas sp. associated with entomopathogenic nematode (Grundmann et al., 2012). These compounds showed significant MICs against Gram-positive bacteria, including S. aureus, B. subtilis, methicillin-resistant S. aureus and Enterococcus faecium (MIC value: 2–4 μg/ml) and reasonable activity against E. faecalis and S. pneumoniae (MIC value: 16–64 μg/ml) with being the more active of the two compounds (Giddens et al., 2008; Singh et al., 2011). Similar results were observed in our study also (Table 1), where in our study also Staphylococcus spp. recorded best activity. Antimycobacterial activity of pseudopyronines against M. tuberculosis H37Rv is also well-reported in the literature (Giddens et al., 2008). Pseudopyronines are powerful and comparatively selective parasitic protozoa (Leishmania donovani) inhibitors (Blunt et al., 2010). These reports clearly portrait the role of Pseudopyronines in modern drug discovery process. The antifungal activity of Pseudopyronines especially Pseudopyronine B is not reported in literature. In the present manuscript the antifungal activity of Pseudopyronine B especially against plant pathogenic fungi is reported for the first time. Bioactive compounds especially from natural have played a significant role over several years in the growth of various anticancer drugs. Many natural compounds and their derivatives have been successfully categorized according to the standard collection of several cancer drugs, such as paclitaxel, vinblastine, and vincristine (Zhong et al., 2012). Newman et al. (2003) reported that more than 50% of the novel compounds approved between 1982 and 2002 were derived directly or indirectly from natural compounds. This clearly indicated that natural compound play a profound role in the development of various anticancer drugs. Even though cancer is the leading cause of mortality worldwide and most of the chemotherapeutic compounds already in clinic have been reported to exhibit severe toxicity to normal cells, accompanied by other unwanted side effects to our body. Moreover, most of these compound are highly expensive, mutagenic, and carcinogenic (Cao et al., 2010). Therefore, it is very important to find out low toxic anti-cancer agents from natural sources. In this study, Pseudopyronine B was isolated from natural source and was tested for its cytotoxicity effect on A549, B16F10, and HepG2 cancer cell lines. We showed that Pseudopyronine B effectively inhibited cell growth of A549 and B16F10. When compared to that of A549 and B16F10 cells, Pseudopyronine B treated HepG2 cells recorded less activity. Alterations in the apoptosis and its related signaling pathways have a vital role in the development of tumor (Cao et al., 2010). Pseudopyronine B treated A549a and B16F10 cells exhibited typical morphological features of apoptosis including membrane flip-flop, cell shrinkage, higher uptake of stain with apoptotic markers and finally end up in severe DNA damage. The faults in apoptosis pathway are supposed to be one of the important causes of human cancer (Thompson, 1995). Thus, a compound which induces apoptosis in cancer cells are one of the most efficient approaches in treating cancer (Lowe and Lin, 2000). The exact mode of action of Pseudopyronine B remains unclear. Using acridine orange-ethidium bromide and Hoechst staining assay, we observed that Pseudopyronine B induced apoptosis and DNA damage in A549 and B16F10 cell lines. Interesting in all most all cases, cellular DNA is the major goal of many chemotherapeutic drug molecules, which directly or indirectly attach to DNA. These drugs also obstruct DNA metabolism and inhibit the action of DNA polymerases and/or topoisomerases, which intern inhibit cell replication. Usually apoptosis occurs, following cellular DNA damage. Cysteine-containing aspartate-specific proteases (caspases) in the cells play a very important role in the activation of various apoptotic signaling pathways. So far, 10 members have been identified in humans. Caspase-8 and caspase-9 are two initiator caspases which are capable of transducing apoptosis signals by direct activation of downstream executioner caspase-3 (Chen et al., 2003). Caspase-3, a protein on the common path of cell apoptosis, is one of the most important members and the key executor of cell apoptosis. Caspase-3 usually exists in the cytoplasm in the form of an inactive zymogen. When activated by the many external apoptosis signals, caspase-3 can induce the inactivation of many key proteases in the cytoplasm, cell nucleus, and cytoskeleton, and finally cause the apoptosis of cells. In the current study, cleavage of caspase-3 and up regulation of its cleaved form in A549 and B16F10 cells treated with Pseudopyronine B revealed Pseudopyronine B-induced apoptosis occurred through the caspase-3-dependent pathway. Cellular apoptosis (programmed cell death) is a common form of cell death induced by many anticancer compounds/drugs (Vaux and Korsmeyer, 1999). Apoptosis cells are well-categorized by various morphological defects, such as condensation of chromatin, membrane flip-flop, and the formation of apoptotic body. These changes in cancer cells were finally leads to DNA fragmentation which was observed as a ladder on agar gel electrophoresis (Desagher and Martinou, 2000; Nonpunya et al., 2014). Apoptosis is mainly induced by a caspase cascade or translocation of apoptosis inducing factors (Nonpunya et al., 2014). There are two important pathways in the activation of caspase, which are the cell surface death receptor (extrinsic pathway) and mitochondrial initiator (intrinsic pathway). Caspase-3 and caspase-7 is the “execute” caspase for the apoptotic induction, while caspase-8 and caspase-9 are the critical caspases and signify the activation of the extrinsic and intrinsic pathways, respectively (McConkey, 1998; Law et al., 2014). The activation of caspases plays a significant role during apoptotic cell death; especially, the caspase 3 activation and activation of this caspase is an important step in the apoptosis procedure (Elmore, 2007). Interestingly Pseudopyronine B recorded enhanced activation of caspase 3, which may play an important role in apoptosis in A549 and B16F10 cells. Conclusion Here, it is highly evident from the present findings that a P. mosselii which produces a powerful bioactive substance (Pseudopyronine B), active against bacteria especially Gram-positive. The present study contributes to the quest for novel antimicrobial compounds, a vital strategy in emerging alternative therapies to treat many infections caused by pathogenic microbes. Pseudopyronine B also recorded significant antifungal activity against plant pathogenic fungi. Isolation of Pseudopyronine B from P. mosselii and antifungal activity is reported here for the first time. Although, the present study delivers some valuable basic information about Pseudopyronine B from P. mosselii and further detailed investigation are needed to determine their potential for clinical applications. In the present study, we confirmed that Pseudopyronine B inhibiting cell growth in a dose- and time-dependent manner. We also demonstrated that the cell death is due to apoptosis. To our best knowledge, this is the first investigation reporting the anticancer activity of Pseudopyronine B, which has the potential to be evaluated as a novel anticancer drug. Further studies are warranted to decipher the molecular mechanisms by which Pseudopyronine B modulates programmed cell death in various cancer cells. More over the results in the present study clearly point out the potential therapeutic property of Pseudopyronine B as an anticancer agent. Author Contributions SNK performed experiments, analysis, or interpretation of data and manuscript preparation; SA performed anticancer section; JJ performed purification of compound; GG helped in manuscript preparation; TS performed anticancer section; RL performed purification of compound; BDK designed the work and manuscript correction. Conflict of Interest Statement The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. We thank Director, CSIR-NIIST, and Director RCC for providing necessary facilities to carry out the present work. The authors are also grateful to Kerala State Council for Science Technology and Engineering (KSCSTE), Government of Kerala for financial support in term of PDF fellowship. JJ acknowledges the Department of Science and Technology for providing INSPIRE fellowship (IF 130648). SNK thanks DST-SERB for providing the Young Scientist award. ==== Refs References Aravind S. R. Joseph M. M. George S. K. Dileep K. V. Varghese S. Rose-James A. (2015 ). TRAIL-based tumor sensitizing galactoxyloglucan, a novel entity for targeting apoptotic machinery. Int. J. Biochem. Cell Biol. 59 153 –166 . 10.1016/j.biocel.2014.11.019 25541375 Aravind S. R. Sreelekha T. T. Dileep Kumar B. S. 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==== Front Q J Exp Psychol (Hove)Q J Exp Psychol (Hove)PQJEpqje20Quarterly Journal of Experimental Psychology (2006)1747-02181747-0226Routledge 109916310.1080/17470218.2015.1099163ArticleRegular ArticlesHigher body mass index is associated with episodic memory deficits in young adults Cheke Lucy G. a * http://orcid.org/0000-0001-5588-7575Simons Jon S. a Clayton Nicola S. a a Department of Psychology, University of Cambridge, Cambridge, UKCorrespondence should be addressed to Lucy Cheke, Department of Psychology, University of Cambridge, Downing Street, CambridgeCB2 3EB, UK. E-mail: lgc23@cam.ac.ukWe would like to thank Heidi Bonnici for helpful comments, and Robert Hart for experimental assistance. This study was funded by an MRC (Medical Research Council) Centenary Early Career Award. L.G.C. was funded by the Sarah Woodhead Research Fellowship at Girton College Cambridge; J.S.S. was funded by a James S. McDonnell Foundation Scholar Award; and N.S.C. was funded by an Experimental Psychology Society Mid Career Award. The authors declare no conflicts of interest. 1 11 2016 22 2 2016 69 11 2305 2316 25 3 2015 15 9 2015 © 2016 The Experimental Psychology Society2016The Experimental Psychology SocietyThis is an Open Access article distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Obesity has become an international health crisis. There is accumulating evidence that excess bodyweight is associated with changes to the structure and function of the brain and with a number of cognitive deficits. In particular, research suggests that obesity is associated with hippocampal and frontal lobe dysfunction, which would be predicted to impact memory. However, evidence for such memory impairment is currently limited. We hypothesised that higher body mass index (BMI) would be associated with reduced performance on a test of episodic memory that assesses not only content, but also context and feature integration. A total of 50 participants aged 18–35 years, with BMIs ranging from 18 to 51, were tested on a novel what–where–when style episodic memory test: the “Treasure-Hunt Task”. This test requires recollection of object, location, and temporal order information within the same paradigm, as well as testing the ability to integrate these features into a single event recollection. Higher BMI was associated with significantly lower performance on the what–where–when (WWW) memory task and all individual elements: object identification, location memory, and temporal order memory. After controlling for age, sex, and years in education, the effect of BMI on the individual what, where, and when tasks remained, while the WWW dropped below significance. This finding of episodic memory deficits in obesity is of concern given the emerging evidence for a role for episodic cognition in appetite regulation. Keywords ObesityMemoryAppetite regulationEpisodic memoryWhat–where–whenMedical Research Council10.13039/501100000265RG68925 PJAG/229 TASK1 (MRC Centenary Award) ==== Body Obesity has become one the of most significant health concerns facing the western world. In the United Kingdom, around 65% of adults are overweight, and 25% are obese (World Health Organization, 2010). Obesity is a major risk factor for premature mortality (Kopelman, 2000) and carries an enormous financial burden for governments and health care providers worldwide. As such, research into understanding how this problem perpetuates is of high priority. While the physical health impacts of obesity are increasingly well understood, recent research indicates that there may be a significant psychological element to the obese syndrome, with proposals that cognitive deficits may occur both as a result of obesity and potentially as a causal factor in its emergence. Accumulating evidence suggests that obesity-related health issues (such as diabetes and hypertension) along with adiposity (excess body weight) itself have a significant impact on the structure and function of the brain both in rodent models (e.g., Erion et al., 2014; Kanoski, Meisel, Mullins, & Davidson, 2007; Molteni, Barnard, Ying, Roberts, & Gomez-Pinilla, 2002) and in humans (Bruehl, Sweat, Tirsi, Shah, & Convit, 2011; Jagust, Harvey, Mungas, & Haan, 2005; Mueller et al., 2012; Raji et al., 2010; Smith et al., 2015; Ursache, Wedin, Tirsi, & Convit, 2012). These neurological changes are accompanied by evidence of cognitive deficits (e.g., Reinert, Po'e, & Barkin, 2013). Because of the central role of behaviour in the advancement of the obese syndrome through high consumption and low energy expenditure, evidence for obesity-related cognitive change has inspired a number of addiction (see Smith & Robbins, 2013) and “vicious cycle” (Kanoski & Davidson, 2011; Sellbom & Gunstad, 2012) models of obesity that describe a circular pattern of obesity, behavioural change, and consumption. Episodic memory is the ability to store, maintain, and retrieve contextually rich representations of events from one's own life (Tulving & Donaldson, 1972). There is increasing evidence to suggest that this type of memory may play a major role in allowing us to regulate consumption. Manipulations of memory for recent meals have considerable impact on the long-term satiating effect of those meals (e.g., Brunstrom et al., 2012; Higgs & Donohoe, 2011; Higgs, Williamson, & Attwood, 2008; Oldham-Cooper, Hardman, Nicoll, Rogers, & Brunstrom, 2011), while amnesic patients who are unable to remember recent consumption can sometimes eat several consecutive meals without reporting satiety or discomfort (Hebben, Corkin, Eichenbaum, & Shedlack, 1985; Higgs, Williamson, Rotshtein, & Humphreys, 2008; Rozin, Dow, Moscovitch, & Rajaram, 1998). Rodent models suggest that neurotoxic lesions to the hippocampus analogous to those seen in episodic amnesia result in animals that will work harder for food (e.g., Clifton, Vickers, & Somerville, 1998) and are more likely to become overweight (Davidson et al., 2009) than sham-lesioned controls. While studies involving humans and animals with severe brain lesions are difficult to generalize to the general population, this cumulative evidence suggests that episodic memory plays a significant role in the regulation of consumption, and that damage to brain areas associated with memory such as the hippocampus may result in overconsumption and, at least in rodent models, obesity. However, homeostasis is a complex process, and it is clear that other cognitive factors such as executive functions and Pavlovian learning (Davidson, Tracy, Schier, & Swithers, 2014) are highly involved in the control of food intake and may also be disrupted by temporal lobe lesions (Davidson et al., 2010). To date there is no research investigating the impact of minor memory deficits on consumption. However, the findings of Higgs and colleagues suggest that small individual differences in memory accuracy or vividness may be capable of having a considerable influence. It is therefore important to establish whether obesity is associated with episodic memory deficits. There is accumulating evidence that obesity and obesity-related health disorders may be a contributing factor to changes to areas within the “core recollection network” of the brain (Rugg & Vilberg, 2013), and in particular to hippocampal structure and function. Rodent models have produced extensive evidence for changes in hippocampal structure and function in obese animals (Grillo et al., 2011; Li et al., 2002; Molteni et al., 2002), suggesting that both dietary and congenital obesity lead to abnormalities in the hippocampal formation. Recent studies are beginning to show a similar pattern of neural change in humans (Gustafson, Lissner, Bengtsson, Bjorkelund, & Skoog, 2004; Raji et al., 2010; Ursache et al., 2012). There have been a number of mechanisms proposed as potential drivers of the hippocampal changes seen in obesity. Given the association between obesity and type 2 diabetes mellitus (Bonadonna et al., 1990), insulin resistance has been suggested as a key mechanism in hippocampal dysfunction in obesity (Chabot, Massicotte, Milot, Trudeau, & Gagne, 1997; Lamport, Lawton, Mansfield, Moulin, & Dye, 2014; Li et al., 2002; Molteni et al., 2002; Zhao, Chen, Quon, & Alkon, 2004). Indeed, obese individuals with diabetes show hippocampal volumetric reductions (Bruehl et al., 2011; Ursache et al., 2012) and impaired source memory performance (Lamport et al., 2014) compared to their nondiabetic obese peers. However, given the evidence for hippocampal volume reductions in obesity in the absence of diabetes (Jagust et al., 2005; Raji et al., 2010), it is likely that the effect of insulin resistance is additive rather than explanatory. Indeed, adiposity itself is associated with neuroinflammation (Erion et al., 2014), suggesting that being overweight may be sufficient to cause alterations to brain function, independent of how obesity was achieved and what comorbid health problems may exist. Nonetheless, given the high levels of comorbidity of obesity with health issues that, like diabetes, have been associated with cognitive decline (e.g., hypertension, Kilander, Nyman, Boberg, Hansson, & Lithell, 1998; and sleep apnoea, Décary, Rouleau, & Montplaisir, 2000), it may be constructive to address cognitive deficits in the obese “syndrome”, considering not just adiposity but the combination of disorders that often accompany it. Hippocampal abnormalities seen in rodent models of obesity are associated with robust evidence for memory and spatial cognition deficits in these animals (Jurdak, Lichtenstein, & Kanarek, 2008; Molteni et al., 2002; Popovic, Biessels, Isaacson, & Gispen, 2001; Valladolid-Acebes et al., 2011; Winocur et al., 2005). However, the association between obesity and memory in humans is much less clear. Obese adults have been reported to perform poorly on measures of verbal learning such as delayed recall and recognition (Cournot et al., 2006; Elias, Elias, Sullivan, Wolf, & D'Agostino, 2003; Gunstad, Paul, Cohen, Tate, & Gordon, 2006), an effect that is independent from, but interacts with, the effects of normal ageing (Gunstad, Lhotsky, Wendell, Ferrucci, & Zonderman, 2010). However, other studies have failed to find evidence for obesity-related memory impairments (Conforto & Gershman, 1985; Holloway et al., 2011; Nilsson & Nilsson, 2009). This inconsistent picture may be due to methodological issues. So far, memory in obese subjects has only been investigated in the context of verbal recall of word lists, a task that may have little in common with the contextually rich multidimensional episodic recollection that has been linked with consumption. The current study aimed to investigate whether overweight individuals are impaired in memory for complex temporal–spatial events—that is, whether they are less able to recall the what, where, and when (WWW) elements of an episode. Such features are considered to be definitive of episodic memory (Tulving & Donaldson, 1972) and have been extensively used to assess episodic memory behaviourally in nonhuman animals (e.g., Babb & Crystal, 2006; Clayton & Dickinson, 1998). Recent studies in healthy human participants have shown that WWW memories are reliably reported as “remembered” rather than “known” (Easton, Webster, & Eacott, 2012; Holland & Smulders, 2011), indicating a strong dependence on recollection. Furthermore, integrated WWW memories have been shown to be impaired in normal ageing, and related to memory complaints in older adults, to a greater degree than retrieval of the individual what, where, and when elements, or free recall performance (Plancher, Gyselinck, Nicolas, & Piolino, 2010). WWW performance has been shown to be correlated with, but distinct from, free recall performance (Cheke & Clayton, 2013, 2015) motivating the concept that while both are tests of episodic memory, they may be assessing different aspects of this ability. Evidence from rodent models (sometimes using variants in which the “when” element is replaced with “which context”) suggests that WWW memories rely on the integrity of the hippocampus (DeVito & Eichenbaum, 2010; Ergorul & Eichenbaum, 2004). Crucially, it appears that integrated WWW memories are dependent on hippocampal function, whereas the component elements (what, what–where, when/which context) are preserved despite hippocampal lesions (Langston & Wood, 2010). WWW memory and spatial memory (but not object memory alone) are also shown to be sensitive to normal ageing and Alzheimer's-like pathology in mice (Davis, Eacott, Easton, & Gigg, 2013). Thus it appears to be the spatial component, and in particular the requirement for integration of multiple types of information, that depends on intact hippocampal function. While most of this research has concentrated on the hippocampus, it is likely that WWW tests require the healthy function of a range of areas within the “core recollection network”, including the prefrontal and parietal cortices (Rugg & Vilberg, 2013). Here we introduce the “Treasure-Hunt Task”. This task assesses memory for object information (“what”), location information (“where”), and temporal order information (“when”) within the same paradigm, as well as testing the ability to integrate these features into a single “WWW” event recollection. In this way, this task is able to identify not just the extent but the pattern of performance deficits that may characterize different disorders. EXPERIMENTAL STUDY Ethics statement This study was approved by the Cambridge Human Biology Research Ethics Committee. All participants gave written informed consent to take part. Method A total of 60 participants between 18 and 35 years were recruited through posters, online advertisements, and word of mouth. A high proportion of the first participants to be recruited indicated on a demographic questionnaire that they had a history of mental illness, on the basis of which the data of these individuals (n = 10) were excluded prior to analysis. Later recruitment screened participants for a history of mental illness before arrival. This left 50 participants (72% female; mean age 24.62 years, range 18–35 years; mean body mass index [BMI] 25.7, range 18–51.7). Of these, 26 were lean (BMI < 25), and 24 were overweight (n = 16, BMI = 25–30) or obese (n = 8, BMI > 30). Recruitment across the BMI range was balanced for recruitment population (e.g., university/nonuniversity) and age. All participants indicated that they had never received a diagnosis of diabetes. Participants were invited to attend a testing session in the Department of Psychology in the centre of Cambridge. Here they completed a demographic information form and a training task. They then undertook six sessions of the computerized memory task created using PsychoPy (Peirce, 2008). The memory task contained five sections: encoding, WWW, where, what, and when (see Figure 1). These were presented in a fixed order. During the encoding period, participants were instructed to move a number of food items around a complex scene (for example, a desert with palm trees; see Supplemental Material for examples of scenes and items) using the arrow keys, and to “hide” them in the scene by pressing enter. Participants were specifically instructed to use the “scene, not the screen” and not to hide in the corners of the screen. Each item was hidden within a given scene twice, across two hiding periods labelled “day 1” and “day 2”, which occurred consecutively. Within each encoding period, participants hid objects in two different scenes consecutively (such that the order was: Scene 1, Day 1; Scene 1, Day 2; Scene 2, Day 1; Scene 2, Day 2). The WWW retrieval period occurred immediately after encoding; however, because memory for Scene 1 was always assessed first, the encoding of Scene 2 occurred during the retention interval for Scene 1, and retrieval of Scene 1 occurred during the retention interval for Scene 2, meaning that the retention interval was around 5 min. During the WWW retrieval period, participants were instructed to move each of the food items around the screen just as they had during encoding, but this time in order to indicate where they hid that item in that scene on each “day” (“place the item in the same place you hid it on day 1”). This was followed by the “where” retrieval period during which participants observed a series of “X”s in specific locations within the scenes for 5 seconds. After each, they were asked “Did you hide something in that location?”. Half of the “X”s were in a location where the participant had hidden an item; half were in locations in which the participant had not hidden anything. The participants were then shown a series of food items, half of which they had hidden and half of which were new. They were asked “Did you hide this item?” (“what” retrieval period). Finally, subjects were shown two items and were asked “which of these did you hide first?” (“when” retrieval period). This allowed participants to be tested on the order of appearance of items within as well as between scenes, facilitating a greater range of questions. As such, the last item from Scene 1 would be considered to have appeared before the first item of Scene 2, but after the first item of Scene 1. While each item appeared on both “Day 1” and “Day 2” in each scene, the participants were asked to consider when they first hid that item. There were six different sessions of these tasks, divided into two “easy” sessions (with four items, and therefore eight hiding events since each was hidden twice), two “medium” sessions (with eight items, and therefore 16 hiding events), and two “hard” sessions (with 12 items and 24 hiding events). Thus each retrieval task had eight items in the easy sessions, 16 in the medium sessions, and 24 in the hard sessions, with the exception of the easy “when” tasks, which had only six items because that is the maximum number of permutations for four items. Participants undertook these six sessions in a random order, counterbalanced across individuals. Figure 1 Schematic of the memory test. Participants moved items around and “hid” them in two scenes across two “days” (“encoding”). Participants were then asked to indicate in the same manner where they had hidden each food on each day (“WWW retrieval”, where WWW = what–where–when). They were then given the “where” and “what” recognition tests, followed by the “when” order discrimination test. Accuracy on the “WWW” task was calculated as proportion of items in which the participant indicated the correct location. Responses on this task were also coded for the types of errors made. “Imprecision” errors were defined as those responses that were within one key-press of the correct location, but did not match this location completely. This type of error suggests that the correct what–where–when combination has been retrieved, but imprecisely reported. “Binding” errors were defined as those where the correct location had been reported, but for the wrong object, the right object on the wrong day, or the wrong object on the wrong day. Finally “total inaccuracy” errors were defined as those that did not match any features with any correct location. It is difficult to know exactly what failure causes such errors as they could result either from total binding failures or from total failures at spatial memory. Accuracy on the “where” and “what” tasks was computed by calculating d ′ from proportion of correctly identified “old” items/locations against false alarms (new items identified as old). Accuracy for the “when” task was computed by calculating d ′ from proportion of correct answers against proportion of incorrect answers. Formulas for d ′ calculation were taken from Macmillan and Creelman (Macmillan & Creelman, 1990). Analysis was conducted using repeated measures analysis of variance (ANOVA) with difficulty as a between-subjects factor and BMI as a covariate, and stepwise linear regression. Internal consistency for all tests was very high (Cronbach's alpha, WWW: α = .898; “what”: α = .896; “where”: α = .74; “when”: α = .719]. Results There were no differences between men and women on any of the memory measures [“WWW”: t(48) = 1.405, p = .166; “what”: t(48) = 0.237, p = .814; “where”: t(48) = 1.438, p = .157; “when”: t(48) = 0.079, p = .937]. The data from both sexes were pooled for all subsequent analyses. There was a significant negative effect of increased BMI on performance on all tasks [“WWW”: F(1, 48) = 4.567, p = .038; “where”: F(1, 48) = 9.696, p = .003; “what”: F(1, 48) = 5.758, p = .02; “when”: F(1, 48) = 5.181, p = .027]. When controlling for BMI, performance did not significantly reduce with increasing difficulty levels on the WWW or what tasks [“WWW”: F(2, 47) = 3.082, p = .055; “what”: F(2, 47) = 0.093, p = .911] but did on the “where”, F(2, 47) = 4.648, p = .014, and “when” tasks, F(2, 47) = 3.789, p = .03. In no task was there an interaction between BMI and difficulty level [“WWW”: F(2, 47) = 0.242, p = .786; “what”: F(2, 47) = 1.057, p = .355; “where”: F(2, 47) = 0.523, p = .596; “when”: F(2, 47) = 0.462, p = .633]. These results suggest that with increasing BMI, individuals struggled with all aspects of the Treasure-Hunt Task, but did not become more impaired as the difficulty increased (Figure 2). Figure 2 Association between memory score and body mass index (BMI) in what–where–when (r = −.295; top left), where (r = −.346; top right), what (r = −.228; bottom left), and when (r = −.272; bottom right) tasks. In our sample, BMI was significantly related to years in education, r(50) = −.374, p = .008, and gender, t(48) = 2.779, p = .008, but not age, r(50) = 0.212, p = .139. It is therefore important to establish the additional variance in memory score explained by BMI beyond that explained by years in education and gender. When entered into a stepwise regression analysis with age, gender, and years in education, BMI explained an additional 6.1% of variance in WWW score, which was not a significant change from the variance explained by age, gender, and years in education alone (p = .085). However, BMI did account for significant additional variance in performance on the “what”, r 2 change = .094, p = .035, “where”, r 2 change = .239, p < .001, and “when”, r 2 change = .096, p = .03, tasks. On their own, age, years in education and gender predicted very little variance in memory performance, and in no test did the influence of these factors differ significantly from zero [WWW: r 2 = .06, p = .414; “where”: r 2 = .059, p = .422; “when”: r 2 = .044, p = .558; “what”: r 2 = .010, p = .923]. The effect of BMI on memory performance was not reflected in reaction times. There was no relationship between BMI and reaction times on any task [“WWW”: F(1, 48) = 0.071, p = .970; “what”: F(1, 48) = 0.101, p = .752; “where”: F(1) = 0.094, p = .760; “when”: F(1, 48) = 0.338, p = .564]. There was also no relationship between BMI and time taken at encoding, suggesting that overweight participants were not simply less careful when committing events to memory, F(1, 114) = 0.30, p = .586. Given the particular demand on spatial recall in the WWW task, it is possible that poor performance on the WWW task was driven solely by a problem with spatial memory. This was investigated by analysing the types of errors made in the WWW task. People with higher BMI made more errors in total; however, there was no relationship between BMI and number of “imprecision”, F(1, 48) = 0.107, p = .446. or “total inaccuracy”, F(1, 48) = 0.811, p = .372, errors. Instead, there was a significant relationship between BMI and number of “binding” errors, F(1, 48) = 5.278, p = .026. This suggests that the type of error driving the poorer performance in those with higher BMI was incomplete binding or integration of elements, rather than spatial inaccuracy per se. Discussion The present results indicate that with increased BMI, young, otherwise healthy individuals show significant reduced capacity in episodic memory, as assessed by the Treasure-Hunt Task. Those with higher BMI showed impaired performance on spatial, temporal, and item memory, as well as the ability to bind these elements together into a single coherent representation (“what–where–when” memory). BMI explained significant additional variance in memory score for the individual “what”, “where”, and “when” tasks, but not the “WWW” task when variance due to age, gender, and years in education was controlled for. Our findings could suggest that the structural and functional neural changes that have been demonstrated in those with elevated BMI may be accompanied by significantly reduced ability to form and/or retrieve episodic memories. Importantly, this effect is present in young, nondiabetic individuals. This adds to the growing data suggesting that the cognitive impairments that accompany obesity are present early in adult life and are not driven by diabetes. However, it should be noted that the sample assessed in this study were not screened for the many other conditions that are comorbid with obesity (such as hypertension and sleep apnoea) and also associated with cognitive deficits (Décary et al., 2000; Kilander et al., 1998). Furthermore, while the participants had never received a diagnosis of diabetes, this does not rule out the possibility that they had undiagnosed problems with insulin resistance. As such, it is possible that the memory effects reported here may be driven by conditions comorbid with obesity rather than adiposity itself. To date, the association between obesity and memory has yet to be convincingly demonstrated in humans. Given the current findings, it may be that the failure of previous research in humans to reliably replicate the rodent findings of obesity-related memory deficits may have been attributable to the use of word-list paradigms. The study presented here used a nonverbal memory paradigm that required the integration of item, spatial location, and temporal order into a single coherent representation and in this way gets closer to the context-rich nature of episodic memory as it is used and experienced in everyday life. This study had a small sample size, and thus we must be cautious with interpretation, especially given the fact that variance explained by BMI dropped below significance once demographic factors were accounted for, which suggests that the effect may not be robust against variation due to extraneous factors. While there was no significant relationships between memory and these demographic factors, null results should be treated with caution when degrees of freedom are limited. Further research is necessary to establish whether the results of this study can be generalized to overweight individuals in general, and to episodic memory in everyday life. However, the possibility that there may be episodic memory deficits in overweight individuals is of major concern, especially given the growing evidence that episodic memory may have a considerable influence on feeding behaviour and appetite regulation (Robinson et al., 2013). The idea that impaired cognitive performance in obesity may affect the regulation of consumption has been put forward by Gunstad and Sellbom (Sellbom & Gunstad, 2012) and Davidson and colleagues (Davidson, Kanoski, Walls, & Jarrard, 2005; Kanoski & Davidson, 2011). Both groups propose “vicious cycle” models of obesity and cognitive decline, in which cognitive impairments associated with obesity may impact learning, which in turn impacts ability to regulate weight. Davidson and colleagues (Davidson et al., 2014) hypothesize that the reason that memory for recent meals affects later consumption is that it acts as a negative feature stimulus that is informative about the likely postingestive sensory consequences of intake; that is, the presence of the memory is a predictive stimulus as to the physiological consequences of further consumption. Within their framework, memories have no special associative properties per se and therefore operate in the same way as conventional external stimuli when they are embedded in the set of associative relationships. If associative learning is disrupted, therefore, then the ability to form associations between memories and physiological outcomes is also disrupted. Given the present results, it is possible that obesity may also be related to problems with explicit episodic memory itself. These accounts are not mutually exclusive; it is likely that changes to hippocampal function would result in problems with both episodic memory and negative-pattern associative learning, making the resulting problems even more likely to cause issues with consumption regulation. Of course, such an account requires further empirical confirmation, and it is not clear to what degree the present findings can be applied to consumption memories. Caveats and provisos This is the first study to use the Treasure-Hunt Task, and as such there are as yet no established norms for this task, and its validity for assessing episodic memory across diverse populations is yet to be established. For this reason, further research is needed before we can conclude with confidence that the associations between BMI and memory seen in this small, cross-sectional study are generalizable to the general population in everyday life. In the present study the WWW what, where, and when elements differed not only in the information assessed but also in the retrieval support provided. Some tests required only recognition, whereas others provided fewer retrieval cues. It is therefore possible that different performances across these tasks were due in some degree to differences in the requirement for retrieval support rather than the ability to remember particular types of information. Furthermore, it is difficult to identify whether there is a specific deficit in memory integration, given the impairments on the individual elements of the memory. Future studies should control for retrieval support across the different tests and should assess integration independently of spatial memory. Moreover, it is possible that the fixed order of the task presentation may have impacted performance on different tests unevenly. As such, future studies should present these tests in a counterbalanced order. Finally, this study has concentrated on the impact of excess weight on episodic memory. However, as reviewed in the introduction, obesity is associated with impairments in a range of cognitive functions, and it is likely that any impairment would not be specific to memory. Conversely, cognitive impairments have been indicated in a number of conditions comorbid with obesity (such as hypertension and sleep apnoea), ones that were not assessed in this study. As such, future research should investigate memory impairments in the context of other obesity-related conditions and cognitive abilities. In summary, the current findings suggest that individuals with higher BMI may exhibit a deficit in episodic memory relative to lean controls. This study focused on a small cross-section of individuals. However, if generalizable, this finding is concerning, especially given evidence that memory may be an important factor in the regulation of consumption. Further research should investigate whether overweight individuals are less able to encode and/or retrieve meal memories, and whether this impacts later consumption. Supplemental material Supplemental material is available via the “Supplemental” tab on the article's online page (http://dx.doi.org/10.1080/17470218.2015.1099163). ORCID Lucy G. Cheke http://orcid.org/0000-0001-5588-7575 ==== Refs REFERENCES Babb S. J. Crystal J. 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