PCR-TTGE Analysis of 16S rRNA from Rainbow Trout (Oncorhynchus mykiss) Gut Microbiota Reveals HostSpecific Communities of Active Bacteria Paola Navarrete1*, Fabien Magne2, Cristian Araneda3, Pamela Fuentes1, Luis Barros1, Rafael Opazo1, Romilio Espejo1, Jaime Romero1 1 Instituto de Nutrición y Tecnologı́a de los Alimentos, Universidad de Chile, Santiago, Región Metropolitana, Chile, 2 Laboratoire de Biologie, Conservatoire National des Arts et Métiers, Paris, Île-de-France, France, 3 Facultad de Ciencias Agronómicas, Universidad de Chile, Santiago, Región Metropolitana, Chile Abstract This study assessed the relative contributions of host genetics and diet in shaping the gut microbiota of rainbow trout. Full sibling fish from four unrelated families, each consisting of individuals derived from the mating of one male and one female belonging to a breeding program, were fed diets containing either vegetable proteins or vegetable oils for two months in comparison to a control diet consisting of only fish protein and fish oil. Two parallel approaches were applied on the same samples: transcriptionally active bacterial populations were examined based on RNA analysis and were compared with bacterial populations obtained from DNA analysis. Comparison of temporal temperature gradient gel electrophoresis (TTGE) profiles from DNA and RNA showed important differences, indicating that active bacterial populations were better described by RNA analysis. Results showed that some bacterial groups were significantly (P,0.05) associated with specific families, indicating that microbiota composition may be influenced by the host. In addition, the effect of diet on microbiota composition was dependent on the trout family. Citation: Navarrete P, Magne F, Araneda C, Fuentes P, Barros L, et al. (2012) PCR-TTGE Analysis of 16S rRNA from Rainbow Trout (Oncorhynchus mykiss) Gut Microbiota Reveals Host-Specific Communities of Active Bacteria. PLoS ONE 7(2): e31335. doi:10.1371/journal.pone.0031335 Editor: Stefan Bereswill, Charité-University Medicine Berlin, Germany Received July 15, 2011; Accepted January 6, 2012; Published February 29, 2012 Copyright: ß 2012 Navarrete et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This study was supported by Fondo Nacional de Desarrollo Cientı́fico y Tecnológico (FONDECYT Nu3100075, Nu1110253) and INNOVA 07CN13PBT-41. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: firstname.lastname@example.org approach based on 16 S rRNA gene- and rpoB-TTGE analysis to reveal that Lactococcus, Citrobacter, Kluyvera, Obesumbacterium and Shewanella dominated the intestinal microbiota. While these studies shed light on the composition of the gut microbiota, the available information does not fully clarify the factors involved in determining this composition. Exogenous and endogenous factors can affect the initial colonization and nature of the microbial composition, such as the developmental stage of the fish, the gut structure, the surrounding environment (e.g. water temperature), rearing and farming conditions . The host genotype is expected to influence inter-individual variation in the intestinal microbiota. Studies in humans  and animals [13,14] support the hypothesis that host-related factors are involved in the determination of the gut microbiota. The possible involvement of host genotype, particularly as it relates to immuno-phenotype, has been frequently postulated as a major influence on microbiota composition and stability, though this has been difficult to prove. In humans, it is currently unclear how the host’s genetic background influences the gut microbiota because the assessment of the role of genetics in the determination of the microbiota is obscured due to environmental factors, primarily diet. The evaluation of the effect of the host on the gut microbiota can be accomplished in fish by studying different non related families of rainbow trout that are fed controlled diets. Today, limited supplies of fish meal and fish oil for the aquaculture industry can hamper the future growth of this activity; Introduction The importance of intestinal bacteria in the nutrition and well being of the host has been established for several animals and was recently demonstrated in fish. It is known that the gut microbiota of fish contribute to important key functions, such as nutrition, development, immunity and xenobiotic metabolism [1,2]. Fish harbor a microbiota that can reach 107–1011 bacteria/g of intestinal content  that is dominated mainly by the phyla Proteobacteria, Firmicutes and Actinobacteria [2,4–7]. A stable microbiota can be established after the first feeding stages, and its major components can be derived from water and egg epibiota [5,7]. Recent reports have investigated the gut microbiota of rainbow trout using culture-independent methods [3,6,8–11]. These studies, which were conducted in Europe (Scotland, Denmark) and America (Canada, Chile), reported that the composition of the gut microbiota can be dominated by different bacterial groups. Using DGGE, Huber et al.  described Anaerofilum, Carnobacterium and Clostridium as the most important components of the gut microbiota. Kim et al.  found by using DGGE that uncultured Clostridia were the most common bands, and by cloning, .50% of the clones corresponded to Enterobacteriaceae. Recently, Mansfield et al.  used chaperonin (cpn60) instead of ribosomal RNA genes and found that .80% of the clones corresponded to Carnobacterium, followed by Hafnia, which represented approximately 10% of the clones. In another report, Navarrete et al.  used a combined PLoS ONE | www.plosone.org 1 February 2012 | Volume 7 | Issue 2 | e31335 Host Influence on Rainbow Trout Microbiota distributed normally among the enterocytes. The lamina propria appeared as a thin layer beneath the epithelium. The subepithelial mucosa, located between the basal part of the folds and the stratum compactum, showed a normal widening with no abnormal granulocyte infiltration. To assess the effects on growth, the body weight of each fish was measured after two months of diet treatment. All experimental diets were well tolerated by the fish, and no significant differences in total feed intake were observed through the end of the experiment (P.0.05). The inclusion of vegetable protein or oil in the diets did not significantly affect body weight gain (mean+/2SD) over the two months of the experiment (D1: 178+/257 g; D2: 181+/256 g; D3: 192+/247 g; P = 0.889). therefore, great efforts have been made to evaluate the use of other protein and oil sources. Among the alternatives, plant-based formulations are the cheapest; many have a suitable amino acid profile and will be sustainable . The aim of this study was to use molecular approaches to investigate the extent to which the rainbow trout gut microbiota is affected by the host and the inclusion of vegetable components in the diet. The overall contribution of the host to the microbiota composition was determined by analyzing full sibling individuals from four different unrelated rainbow trout families, each derived from a single pair of breeders that had been previously identified and classified in a breeding program. Results Bacterial Counts Fish performance and gut histology The average total bacteria from the intestinal content of fish from the different families and diet treatments are detailed in Table S4. No differences were detected among the families (P.0.05, Kruskal Wallis) or between fish fed different diets (P.0.05, Kruskal Wallis). Full sibling fish from four unrelated families were distributed equally in three tanks and reared under identical conditions except for their diets, which are described in Table S1. Each fish was tagged with a PIT tag that allowed individual identification and monitoring. Each group of fish was fed one of the following diets: diet D1, where 100% of the protein was provided by fish meal and 100% of the oil was provided by fish oil; diet D2, where 50% of the protein was provided by fish meal and 50% was provided by vegetable meal (corn, sunflower and soybean meal); and diet D3, where 50% of the oil was provided by fish oil and 50% was provided by rapeseed oil (Table 1). To evaluate the possible effects of the diets on the intestinal mucosa of the fish, intestines were histologically examined after two months of diet treatment according to a semi-quantitative method (Table S2). No signs of inflammation were detected in fish fed the three diets and no differences were detected between the families or diet treatments groups (Figure S1, Table S3). The villous mucosa appeared to be normal in the intestines of all the fish, with the mucosal fold forming long, finger-like structures. The enterocytes showed basal nuclei and normal round supranuclear vacuoles. Goblet cells were Analysis of TTGE profiles derived from DNA and RNA extraction Temporal temperature gradient gel electrophoresis (TTGE) profiles derived from a DNA analysis showed very low diversity: an intense band was observed in most of the samples (44/47). This band corresponded to the small subunit of the wheat mitochondrial 16 S rRNA gene (Figure 1 lanes D). In a few of the samples, 3 to 5 bands were observed per profile. The wheat meal was included in the three diets at approximately 16% (see Table 1). This result may explain the low diversity observed in the DNA-derived TTGE profiles, which may underestimate the diversity of the trout intestinal microbiota. To overcome this limitation, TTGE profiles were performed using RNA-extracted RT-PCR-amplified bacterial 16 S rRNA. As expected, the TTGE profiles derived from RNA showed more bands in the majority of individuals: up to 7 bands Table 1. Formulation and chemical analysis of experimental diets. Experimental Diets 21 Control Diet (D1) Diet with vegetable protein (D2) Diet with vegetable oil (D3) Fish meal 40.0 20.0 40.0 Fish oil 23.5 24.5 11.3 Rapeseed oil - - 11.3 16.8 Ingredients (g 100 g ) Wheat meal 16.6 14.0 Feather meal 7.0 7.0 7.0 Viscera/Entrail meal 7.3 7.3 7.3 Corn gluten - 13.0 - Sunflower meal - 6.0 - Defatted soybean meal - 6.2 - Vitamin and mineral premix 2.3 2.7 2.5 41.6 43.4 43.6 Chemical composition (g 100 g21) Protein Lipid 31.7 33.1 31.2 Ash 8.1 8.5 7.8 Fiber 1.3 1.3 1.3 Moisture 3.4 3.0 4.4 doi:10.1371/journal.pone.0031335.t001 PLoS ONE | www.plosone.org 2 February 2012 | Volume 7 | Issue 2 | e31335 Host Influence on Rainbow Trout Microbiota Figure 1. PCR-Temporal temperature gradient gel electrophoresis (TTGE) fingerprinting of the intestinal microbiota of rainbow trout (Oncorhynchus mykiss). Comparison of the TTGE profiles based on the amplification of the V3–V4 region of the 16 S rRNA genes from DNA extraction (D) and RNA extraction (R) from different individuals (I) from Family F1, which were fed either the control diet D1 (where 100% of the protein in the diet was provided by fish meal and 100% of the oil was provided by fish oil), diet D2 (where 50% of the protein in the diet was provided by fish meal and 50% was provided by vegetable meal (corn, sunflower and soybean meal), or diet D3 (where 50% of the oil was provided by fish oil and 50% was provided by rapeseed oil). Letters indicate some of the bacterial phylotypes described in Table 2. W: wheat component; S: Sphingomonas, R: Ralstonia, Ce: Cellulomonas, Pd: Pedobacter, B: Blastococcus, L: Lactobacillus aviarus, Lb Lactobacillus sp., St: Streptococcus, Ch: Chelatococcus, Sr: Sinorhizobium, P: Paracoccus, N: Novosphingobium. doi:10.1371/journal.pone.0031335.g001 were identified (Figure 1 lanes R), and the wheat band was not observed. Thus, under our experimental conditions, analysis of the TTGE profiles derived from RNA was more informative than analysis of the TTGE profiles derived from DNA. the hosts rather than the diets (Figure 2A and 2B). Analysis of PCA plots showed that host-related differences were mainly observed along the F1 axis, which accounted for 20.5% of the total variations, whereas the diets had smaller effects along F2 (15.8% of the total variations) (Figure 2B). The microbiota compositions were very different among families, although F2 and F3 were clustered together (Figure 2A and 2B) and shared some bacterial components (Figure 3). The main result was that the response of the microbiota to diet depended on the host: the four trout families responded differently to diet. Families F2 and F3 clustered Effects of host and diet on intestinal microbiota composition Dendrograms and principal components analysis (PCA) based on bacterial identification of the TTGE bands showed that the main variations in microbiota composition could be attributed to PLoS ONE | www.plosone.org 3 February 2012 | Volume 7 | Issue 2 | e31335 Host Influence on Rainbow Trout Microbiota Figure 2. Comparison of intestinal microbiota composition between individuals (I) of the different rainbow trout (Oncorhynchus mykiss) families (F1, F2, F3 and F4) fed different diets (D1, D2 and D3). (A) Clustering analysis of intestinal microbiota based on distances between different groups was performed using the TREECON program with neighbor-joining method (Van de Peer and De Wachter, 1997) as described (Romero and Navarrete, 2006). (B) The principal components analysis (PCA) scores plot based on the bacterial identification data from intestinal bacterial communities. doi:10.1371/journal.pone.0031335.g002 together independent of diet, suggesting that these families were less affected by diet than F1 and F4 (Figure 2A and 2B). In Families F1 and F4, the variations of the microbiota were more pronounced with diet D2. This suggests that the microbiota composition from Families F1 and F4 were more susceptible to the inclusion of vegetable ingredients. The bacterial species identified are presented in Table 2 and Figure 3. Figure 3 shows the prevalence (%) of the different bacteria in each family. A specific association was observed between certain bacterial groups and particular families. Family F1 harbored the richest microbiota: 12 bacterial species were identified (Figure 3). Among these, Ralstonia sp., Sphingomonas sp. and Streptococcus iniae were significantly associated with this Family (P,0.05). In Family F4, 6 bacterial species were identified. Among these, uncultured Rothia, Fusobacterium nucleatum, and uncultured Enterobacteriaceae were significantly associated with this Family (P,0.05). Family F3 also harbored 6 bacterial species; however, none of them were significantly associated with this Family. The microbiota of Family F2 was slightly less rich; 5 bacteria were identified. From these, Delftia acidovorans and uncultured Kocuria showed significant associations with this Family (P,0.05). The microbiota of Families F2 and F3 was more similar and shared some bacterial species (Table 2, Figure 3). When vegetable meals (D2) or vegetable oil (D3) was incorporated into the diet, the richness of the bacterial composition was reduced for most families (Table 2). A higher number of bacterial species were retrieved from fish fed the control D1 (21 bacterial species), in contrast to those fed D2 and D3 (in which 14 and 9 bacterial species were identified, respectively). The PLoS ONE | www.plosone.org incorporation of vegetable oil (D3) significantly reduced the average numbers of bacterial species per fish (P,0.05, Kruskal Wallis). Fish fed D1 had, on average, 3.3+/21.8 bacterial species per fish; in contrast, fish fed D2 and D3 had 2.3+/21.7 and 1.8+/ 20.9 bacterial species, respectively. Effects of host and diet on dominant phyla A total of five phyla were identified in the microbiota of intestinal trout: Proteobacteria, Firmicutes, Actinobacteria, Bacteroidetes and Fusobacteria (Table 2, Figure S2). Some phyla, such as Proteobacteria, Firmicutes and Actinobacteria, were detected in all families. Notably, those were the only phyla detected in Families F2 and F3. In addition to the three phyla mentioned, Families F1 and F4 harbored Bacteroidetes and Fusobacteria phyla, respectively. Regarding the effects of diet (Table 2), we observed that fish fed the control D1 harbored the richest microbiota, composed of all five phyla. The most evident effect of the introduction of vegetable meal (D2) or vegetable oil (D3) was the disappearance of the Bacteroidetes phyla in Family F1. Discussion The aim of this study was to use molecular approaches to investigate the relative contributions of host background and diet to the gut microbiota composition of rainbow trout. Four unrelated trout families were studied, each consisting of individuals of similar weight, derived from the mating of one male and one female that had been previously identified and classified. Each fish was PIT-tagged, allowing different families to be reared in the 4 February 2012 | Volume 7 | Issue 2 | e31335 Host Influence on Rainbow Trout Microbiota Figure 3. Prevalence of the different bacterial phylotypes identified in each rainbow trout family (F1, F2, F3 and 4). Bacteria identified from a specific family are represented by the same color. Prevalence values (%) show the percentage of fish in a given family that harbored a specific bacterium. The association between the bacterial phylotype and a particular family was statistical assessed.* Indicates significant association (0.005,P,0.05), ** Indicates highly significant association (P,0.005). doi:10.1371/journal.pone.0031335.g003 same tank. Three tanks were used, each corresponding to a equilibrium value of MeCpG steps (,+14 deg.) [31,44]. In comparison, methylation has a significantly lower stability cost when happening at major groove positions, such as 211 and 21 base pair from dyad (mutations 9 and 12), where the roll of the nucleosome bound conformation (+10 deg.) is more compatible with the equilibrium geometry of MeCpG steps. The nucleosome destabilizing effect of cytosine methylation increases with the number of methylated cytosines, following the same position dependence as the single methylations. The multiple-methylation case reveals that each major groove meth- PLOS Computational Biology | www.ploscompbiol.org 3 November 2013 | Volume 9 | Issue 11 | e1003354 DNA Methylation and Nucleosome Positioning ylation destabilizes the nucleosome by around 1 kJ/mol (close to the average estimate of 2 kJ/mol obtained for from individual methylation studies), while each minor groove methylation destabilizes it by up to 5 kJ/mol (average free energy as single mutation is around 6 kJ/mol). This energetic position-dependence is the reverse of what was observed in a recent FRET/SAXS study . The differences can be attributed to the use of different ionic conditions and different sequences: a modified Widom-601 sequence of 157 bp, which already contains multiple CpG steps in mixed orientations, and which could assume different positioning due to the introduction of new CpG steps and by effect of the methylation. The analysis of our trajectories reveals a larger root mean square deviation (RMSD) and fluctuation (RMSF; see Figures S2– S3 in Text S1) for the methylated nucleosomes, but failed to detect any systematic change in DNA geometry or in intermolecular DNA-histone energy related to methylation (Fig. S1B, S1C, S4–S6 in Text S1). The hydrophobic effect should favor orientation of the methyl group out from the solvent but this effect alone is not likely to justify the positional dependent stability changes in Figure 2, as the differential solvation of the methyl groups in the bound and unbound states is only in the order of a fraction of a water molecule (Figure S5 in Text S1). We find however, a reasonable correlation between methylation-induced changes in hydrogen bond and stacking interactions of the bases and the change in nucleosome stability (see Figure S6 in Text S1). This finding suggests that methylation-induced nucleosome destabilization is related to the poorer ability of methylated DNA to fit into the required conformation for DNA in a nucleosome. Changes in the elastic deformation energy between methylated and un-methylated DNA correlate with nucleosomal differential binding free energies To further analyze the idea that methylation-induced nucleosome destabilization is connected to a worse fit of methylated DNA into the required nucleosome-bound conformation, we computed the elastic energy of the nucleosomal DNA using a harmonic deformation method [36,37,44]. This method provides a rough estimate of the energy required to deform a DNA fiber to adopt the super helical conformation in the nucleosome (full details in Suppl. Information Text S1). As shown in Figure 2, there is an evident correlation between the increase that methylation produces in the elastic deformation energy (DDE def.) and the free energy variation (DDG bind.) computed from MD/TI calculations. Clearly, methylation increases the stiffness of the CpG step , raising the energy cost required to wrap DNA around the histone octamers. This extra energy cost will be smaller in regions of high positive roll (naked DNA MeCpG steps have a higher roll than CpG steps ) than in regions of high negative roll. Thus, simple elastic considerations explain why methylation is better tolerated when the DNA faces the histones through the major groove (where positive roll is required) that when it faces histones through the minor groove (where negative roll is required). Nucleosome methylation can give rise to nucleosome repositioning We have established that methylation affects the wrapping of DNA in nucleosomes, but how does this translate into chromatin structure? As noted above, accumulation of minor groove methylations strongly destabilizes the nucleosome, and could trigger nucleosome unfolding, or notable changes in positioning or phasing of DNA around the histone core. While accumulation of methylations might be well tolerated if placed in favorable positions, accumulation in unfavorable positions would destabilize the nucleosome, which might trigger changes in chromatin structure. Chromatin could in fact react in two different ways in response to significant levels of methylation in unfavorable positions: i) the DNA could either detach from the histone core, leading to nucleosome eviction or nucleosome repositioning, or ii) the DNA could rotate around the histone core, changing its phase to place MeCpG steps in favorable positions. Both effects are anticipated to alter DNA accessibility and impact gene expression regulation. The sub-microsecond time scale of our MD trajectories of methylated DNAs bound to nucleosomes is not large enough to capture these effects, but clear trends are visible in cases of multiple mutations occurring in unfavorable positions, where unmethylated and methylated DNA sequences are out of phase by around 28 degrees (Figure S7 in Text S1). Due to this repositioning, large or small, DNA could move and the nucleosome structure could assume a more compact and distorted conformation, as detected by Lee and Lee , or a slightly open conformation as found in Jimenez-Useche et al. . Using the harmonic deformation method, we additionally predicted the change in stability induced by cytosine methylation for millions of different nucleosomal DNA sequences. Consistently with our calculations, we used two extreme scenarios to prepare our DNA sequences (see Fig. 3): i) all positions where the minor grooves contact the histone core are occupied by CpG steps, and ii) all positions where the major grooves contact the histone core are occupied by CpG steps. We then computed the elastic energy required to wrap the DNA around the histone proteins in unmethylated and methylated states, and, as expected, observed that methylation disfavors DNA wrapping (Figure 3A). We have rescaled the elastic energy differences with a factor of 0.23 to match the DDG prediction in figure 2B. In agreement with the rest of our results, our analysis confirms that the effect of methylation is position-dependent. In fact, the overall difference between the two extreme methylation scenarios (all-in-minor vs all-in-major) is larger than 60 kJ/mol, the average difference being around 15 kJ/ mol. We have also computed the elastic energy differences for a million sequences with CpG/MeCpG steps positioned at all possible intermediate locations with respect to the position (figure 3B). The large differences between the extreme cases can induce rotations of DNA around the histone core, shifting its phase to allow the placement of the methylated CpG steps facing the histones through the major groove. It is illustrative to compare the magnitude of CpG methylation penalty with sequence dependent differences. Since there are roughly 1.5e88 possible 147 base pairs long sequence combinations (i.e., (4n+4(n/2))/2, n = 147), it is unfeasible to calculate all the possible sequence effects. However, using our elastic model we can provide a range of values based on a reasonably large number of samples. If we consider all possible nucleosomal sequences in the yeast genome (around 12 Mbp), the energy difference between the best and the worst sequence that could form a nucleosome is 0.7 kj/mol per base (a minimum of 1 kJ/mol and maximum of around 1.7 kJ/mol per base, the first best and the last worst sequences are displayed in Table S3 in Text S1). We repeated the same calculation for one million random sequences and we obtained equivalent results. Placing one CpG step every helical turn gives an average energetic difference between minor groove and major groove methylation of 15 kJ/ mol, which translates into ,0.5 kJ/mol per methyl group, 2 kJ/ mol per base for the largest effects. Considering that not all nucleosome base pair steps are likely to be CpG steps, we can conclude that the balance between the destabilization due to CpG methylation and sequence repositioning will depend on the PLOS Computational Biology | www.ploscompbiol.org 4 November 2013 | Volume 9 | Issue 11 | e1003354 DNA Methylation and Nucleosome Positioning Figure 3. Methylated and non-methylated DNA elastic deformation energies. (A) Distribution of deformation energies for 147 bplong random DNA sequences with CpG steps positioned every 10 base steps (one helical turn) in minor (red and dark red) and major (light and dark blue) grooves respectively. The energy values were rescaled by the slope of a best-fit straight line of figure 2, which is 0.23, to source of circulating FGF-21. The lack of association between circulating and muscle-expressed FGF-21 also suggests that muscle FGF-21 primarily works in a local manner regulating glucose metabolism in the muscle and/or signals to the adipose tissue in close contact to the muscle. Our study has some limitations. The number of subjects is small and some correlations could have been significant with greater statistical power. Another aspect is that protein levels of FGF-21 were not determined in the muscles extracts, consequently we cannot be sure the increase in FGF-21 mRNA is followed by increased protein expression. In conclusion, we show that FGF-21 mRNA is increased in skeletal muscle in HIV patients and that FGF-21 mRNA in muscle correlates to whole-body (primarily reflecting muscle) insulin resistance. These findings add to the evidence that FGF-21 is a myokine and that muscle FGF-21 might primarily work in an autocrine manner. Acknowledgments We thank the subjects for their participation in this study. Ruth Rousing, Hanne Willumsen, Carsten Nielsen and Flemming Jessen are thanked for excellent technical help. The Danish HIV-Cohort is thanked for providing us HIV-related data. PLOS ONE | www.plosone.org 6 March 2013 | Volume 8 | Issue 3 | e55632 Muscle FGF-21,Insulin Resistance and Lipodystrophy Author Contributions Conceived and designed the experiments: BL BKP JG. Performed the experiments: BL TH TG CF PH. Analyzed the data: BL CF PH. Contributed reagents/materials/analysis tools: BL. Wrote the paper: BL. References 1. Kharitonenkov A, Shiyanova TL, Koester A, Ford AM, Micanovic R, et al. (2005) FGF-21 as a novel metabolic regulator. J Clin Invest 115: 1627–1635. 2. Coskun T, Bina HA, Schneider MA, Dunbar JD, Hu CC, et al. (2008) Fibroblast growth factor 21 corrects obesity in mice. Endocrinology 149: 6018– 6027. 3. Xu J, Lloyd DJ, Hale C, Stanislaus S, Chen M, et al. 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