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• W2917386722 abstract "•Screening of human gut metagenomes reveals different Prevotella copri pangenomes•Habitual diet and lifestyle can select different P. copri strains•Strains from non-Western subjects show higher potential for complex fiber break-down•Strains from Western subjects have a higher prevalence of drug metabolism genes The role of intestinal Prevotella species in human health is controversial, with both positive and negative associations. Strain-level diversity may contribute to discrepancies in genus and species associations with health and disease. We dissected the gut metagenomes of Italians with varying dietary habits, investigating the presence of distinct Prevotella copri strains. Fiber-rich diets were linked to P. copri types with enhanced potential for carbohydrate catabolism. P. copri strains associated with an omnivore diet had a higher prevalence of the leuB gene—involved in branched-chain amino acid biosynthesis—a risk factor for glucose intolerance and type 2 diabetes. These P. copri pangenomes were compared to existing cohorts, providing evidence of distinct gene repertoires characterizing different P. copri populations, with drug metabolism and complex carbohydrate degradation significantly associated with Western and non-Western individuals, respectively. Strain-level P. copri diversity in gut microbiomes is affected by diet and should be considered when examining host-microbe associations. The role of intestinal Prevotella species in human health is controversial, with both positive and negative associations. Strain-level diversity may contribute to discrepancies in genus and species associations with health and disease. We dissected the gut metagenomes of Italians with varying dietary habits, investigating the presence of distinct Prevotella copri strains. Fiber-rich diets were linked to P. copri types with enhanced potential for carbohydrate catabolism. P. copri strains associated with an omnivore diet had a higher prevalence of the leuB gene—involved in branched-chain amino acid biosynthesis—a risk factor for glucose intolerance and type 2 diabetes. These P. copri pangenomes were compared to existing cohorts, providing evidence of distinct gene repertoires characterizing different P. copri populations, with drug metabolism and complex carbohydrate degradation significantly associated with Western and non-Western individuals, respectively. Strain-level P. copri diversity in gut microbiomes is affected by diet and should be considered when examining host-microbe associations. The gut microbiome plays a key role in human well-being, performing important metabolic functions, such as the biosynthesis of vitamins or the breakdown of indigestible compounds, and interacting with the host through the production of beneficial or detrimental metabolites (De Filippis et al., 2018De Filippis F. Vitaglione P. Cuomo R. Berni Canani R. Ercolini D. Dietary interventions to modulate the gut microbiome: how far away are we from precision medicine.Inflamm. Bowel Dis. 2018; 24: 2142-2154Crossref PubMed Scopus (45) Google Scholar, Derrien and Veiga, 2017Derrien M. Veiga P. Rethinking diet to aid human–microbe symbiosis.Trends Microbiol. 2017; 25: 100-112Abstract Full Text Full Text PDF PubMed Scopus (71) Google Scholar). Indeed, an imbalance among the microbial organisms inhabiting our gut (commonly referred to as dysbiosis) has been linked with the pathogenesis of both intestinal and extra-intestinal diseases, including neurological disorders, obesity, atherosclerosis, inflammatory bowel disease, and cancer (Marchesi et al., 2016Marchesi J.R. Adams D.H. Fava F. Hermes G.D.A. Hirschfield G.M. Hold G. Quraishi M.N. Kinross J. Smidt H. Tuohy K.M. et al.The gut microbiota and host health: a new clinical frontier.Gut. 2016; 65: 330-339Crossref PubMed Scopus (1354) Google Scholar, Sharon et al., 2016Sharon G. Sampson T.R. Geschwind D.H. Mazmanian S.K. The central nervous system and the gut microbiome.Cell. 2016; 167: 915-932Abstract Full Text Full Text PDF PubMed Scopus (732) Google Scholar, Blum, 2017Blum H.E. The human microbiome.Adv. Med. Sci. 2017; 62: 414-420Crossref PubMed Scopus (107) Google Scholar). In healthy adults, the gut microbiome may be influenced by many extrinsic factors, among which diet may be considered one of the most important (Zhernakova et al., 2016Zhernakova A. Kurilshikov A. Bonder M.J. Tigchelaar E.F. Schirmer M. Vatanen T. Mujagic Z. Vila A.V. Falony G. Vieira-Silva S. et al.Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity.Science. 2016; 352: 565-569Crossref PubMed Scopus (1018) Google Scholar, Falony et al., 2016Falony G. Joossens M. Vieira-Silva S. Wang J. Darzi Y. Faust K. Kurilshikov A. Bonder M.J. Valles-Colomer M. Vandeputte D. et al.Population-level analysis of gut microbiome variation.Science. 2016; 352: 560-564Crossref PubMed Scopus (1214) Google Scholar, Sonnenburg and Bäckhed, 2016Sonnenburg J.L. Bäckhed F. Diet-microbiota interactions as moderators of human metabolism.Nature. 2016; 535: 56-64Crossref PubMed Scopus (1213) Google Scholar). Habitual diet shapes the gut microbiome, and several researches have highlighted that dietary “Westernization”— characterized by higher consumption of high-fat and protein products at the expense of foods rich in fiber—may have caused a loss of microbial diversity, with ultimate repercussions on human health (Segata, 2015Segata N. Gut microbiome: westernization and disappearance of intestinal diversity.Curr. Biol. 2015; 25 (R611–613)Abstract Full Text Full Text PDF PubMed Scopus (113) Google Scholar, Sonnenburg and Sonnenburg, 2014Sonnenburg E.D. Sonnenburg J.L. Starving our microbial self: the deleterious consequences of a diet deficient in microbiota-accessible carbohydrates.Cell Metab. 2014; 20: 779-786Abstract Full Text Full Text PDF PubMed Scopus (435) Google Scholar). Among the Bacteroidetes, two genera prevail: Bacteroides and Prevotella, and while Bacteroides species are highly prevalent, they are usually dominated by Prevotella when this genus is present (Falony et al., 2016Falony G. Joossens M. Vieira-Silva S. Wang J. Darzi Y. Faust K. Kurilshikov A. Bonder M.J. Valles-Colomer M. Vandeputte D. et al.Population-level analysis of gut microbiome variation.Science. 2016; 352: 560-564Crossref PubMed Scopus (1214) Google Scholar, Arumugam et al., 2011Arumugam M. Raes J. Pelletier E. Le Paslier D. Yamada T. Mende D.R. Fernandes G.R. Tap J. Bruls T. Batto J.M. et al.Enterotypes of the human gut microbiome.Nature. 2011; 473: 174-180Crossref PubMed Scopus (4458) Google Scholar). Higher abundance of Prevotella was traditionally associated with the consumption of an agrarian-type diet, rich in fruit and vegetables, while the abundance of Bacteroides is usually linked to high-fat and protein-rich diets (David et al., 2014David L.A. Maurice C.F. Carmody R.N. Gootenberg D.B. Button J.E. Wolfe B.E. Ling A.V. Devlin A.S. Varma Y. Fischbach M.A. et al.Diet rapidly and reproducibly alters the human gut microbiome.Nature. 2014; 505: 559-563Crossref PubMed Scopus (5605) Google Scholar, Wu et al., 2011Wu G.D. Chen J. Hoffmann C. Bittinger K. Chen Y.Y. Keilbaugh S.A. Bewtra M. Knights D. Walters W.A. Knight R. et al.Linking long-term dietary patterns with gut microbial enterotypes.Science. 2011; 334: 105-108Crossref PubMed Scopus (4121) Google Scholar). In the past decades, metagenomics deeply increased our knowledge on the role of the gut microbiome and how it is influenced by external factors. Nevertheless, our knowledge often relies on a genus- or species-level taxonomic assignment that, although useful, may not be sufficient for a comprehensive understanding of the complex inter-connections between the gut microbiome and human health. Indeed, each microbial genus in the gut includes several species and strains that may harbor substantial differences in their genomes. Such inter- and intra-species variation endows each species, and even each strain, with potentially distinct functional capacities (Faith et al., 2015Faith J.J. Colombel J.F. Gordon J.I. Identifying strains that contribute to complex diseases through the study of microbial inheritance.Proc. Natl. Acad. Sci. U S A. 2015; 112: 633-640Crossref PubMed Scopus (47) Google Scholar, Greenblum et al., 2015Greenblum S. Carr R. Borenstein E. Extensive strain-level copy-number variation across human gut microbiome species.Cell. 2015; 160: 583-594Abstract Full Text Full Text PDF PubMed Scopus (153) Google Scholar, Lloyd-Price et al., 2017Lloyd-Price J. Mahurkar A. Rahnavard G. Crabtree J. Orvis J. Hall A.B. Brady A. Creasy H.H. McCracken C. Giglio M.G. et al.Strains, functions and dynamics in the expanded Human Microbiome Project.Nature. 2017; 550: 61-66Crossref PubMed Scopus (604) Google Scholar, Schloissnig et al., 2013Schloissnig S. Arumugam M. Sunagawa S. Mitreva M. Tap J. Zhu A. Waller A. Mende D.R. Kultima J.R. Martin J. et al.Genomic variation landscape of the human gut microbiome.Nature. 2013; 493: 45-50Crossref PubMed Scopus (557) Google Scholar, Scholz et al., 2016Scholz M. Ward D.V. Pasolli E. Tolio T. Zolfo M. Asnicar F. Truong D.T. Tett A. Morrow A.L. Segata N. Strain-level microbial epidemiology and population genomics from shotgun metagenomics.Nat. Methods. 2016; 13: 435-438Crossref PubMed Scopus (225) Google Scholar, Wu et al., 2017Wu G. Zhang C. Wu H. Wang R. Shen J. Wang L. Zhao Y. Pang X. Zhang X. Zhao L. et al.Genomic microdiversity of Bifidobacterium pseudocatenulatum underlying differential strain-level responses to dietary carbohydrate intervention.mBio. 2017; 8 (e02348–16)Crossref Scopus (33) Google Scholar, Zhang and Zhao, 2016Zhang C. Zhao L. Strain-level dissection of the contribution of the gut microbiome to human metabolic disease.Genome Med. 2016; 8: 41Crossref PubMed Scopus (58) Google Scholar). The role of Prevotella spp. in the human gut microbiome is controversial and deserves further exploration (Ley, 2016Ley R.E. Gut microbiota in 2015: Prevotella in the gut: choose carefully.Nat. Rev. Gastroenterol. Hepatol. 2016; 13: 69-70Crossref PubMed Scopus (270) Google Scholar, Cani, 2018Cani P.D. Human gut microbiomes: hopes, threats and promises.Gut. 2018; 67: 1716-1725Crossref PubMed Scopus (680) Google Scholar). Its usual connection with agrarian and vegetable-rich diets would suggest that Prevotella, as a fiber-degrader, is an indicator of a microbiome associated with a healthy status. Indeed, it was recognized as positively associated with the production of health-promoting compounds such as short-chain fatty acids (De Filippis et al., 2016aDe Filippis F. Pellegrini N. Vannini L. Jeffery I.B. La Storia A. Laghi L. Serrazanetti D.I. Di Cagno R. Ferrocino I. Lazzi C. et al.High-level adherence to a Mediterranean diet beneficially impacts the gut microbiota and associated metabolome.Gut. 2016; 65: 1812-1821Crossref PubMed Scopus (836) Google Scholar, De Filippo et al., 2010De Filippo C. Cavalieri D. Di Paola M. Ramazzotti M. Poullet J.B. Massart S. Collini S. Pieraccini G. Lionetti P. Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa.Proc. Natl. Acad. Sci. U S A. 2010; 107: 14691-14696Crossref PubMed Scopus (3717) Google Scholar), an improved glucose metabolism (Kovatcheva-Datchary et al., 2015Kovatcheva-Datchary P. Nilsson A. Akrami R. Lee Y.S. De Vadder F. Arora T. Hallen A. Martens E. Björck I. Bäckhed F. Dietary fiber-induced improvement in glucose metabolism is associated with increased abundance of Prevotella.Cell Metab. 2015; 22: 971-982Abstract Full Text Full Text PDF PubMed Scopus (873) Google Scholar, De Vadder et al., 2016De Vadder F. Kovatcheva-Datchary P. Zitoun C. Duchampt A. Bäckhed F. Mithieux G. Microbiota-produced succinate improves glucose homeostasis via intestinal gluconeogenesis.Cell Metab. 2016; 24: 151-157Abstract Full Text Full Text PDF PubMed Scopus (364) Google Scholar), or an overall anti-inflammatory effect (De Angelis et al., 2015De Angelis M. Montemurno E. Vannini L. Cosola C. Cavallo N. Gozzi G. Maranzano V. Di Cagno R. Gobbetti M. Gesualdo L. Effect of whole-grain barley on the human fecal microbiota and metabolome.Appl. Environ. Microbiol. 2015; 81: 7945-7956Crossref PubMed Scopus (107) Google Scholar, Vitaglione et al., 2015Vitaglione P. Mennella I. Ferracane R. Rivellese A.A. Giacco R. Ercolini D. Gibbons S.M. La Storia A. Gilbert J.A. Jonnalagadda S. et al.Whole-grain wheat consumption reduces inflammation in a randomized controlled trial on overweight and obese subjects with unhealthy dietary and lifestyle behaviors: role of polyphenols bound to cereal dietary fiber.Am. J. Clin. Nutr. 2015; 101: 251-261Crossref PubMed Scopus (219) Google Scholar). Nevertheless, some studies also highlighted an association of P. copri with inflammatory conditions (Lozupone et al., 2014Lozupone C.A. Rhodes M.E. Neff C.P. Fontenot A.P. Campbell T.B. Palmer B.E. HIV-induced alteration in gut microbiota: driving factors, consequences, and effects of antiretroviral therapy.Gut Microbes. 2014; 5: 562-570Crossref PubMed Scopus (103) Google Scholar, Maeda et al., 2016Maeda Y. Kurakawa T. Umemoto E. Motooka D. Ito Y. Gotoh K. Hirota K. Matsushita M. Furuta Y. Narazaki M. et al.Dysbiosis contributes to arthritis development via activation of autoreactive T cells in the intestine.Arthritis Rheumatol. 2016; 68: 2646-2661Crossref PubMed Scopus (346) Google Scholar, Scher et al., 2013Scher J.U. Sczesnak A. Longman R.S. Segata N. Ubeda C. Bielski C. Rostron T. Cerundolo V. Pamer E.G. Abramson S.B. et al.Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis.Elife. 2013; 2: e01202Crossref PubMed Google Scholar), as well as insulin resistance and glucose intolerance (Pedersen et al., 2016Pedersen H.K. Gudmundsdottir V. Nielsen H.B. Hyotylainen T. Nielsen T. Jensen B.A. Forslund K. Hildebrand F. Prifti E. Falony G. et al.Human gut microbes impact host serum metabolome and insulin sensitivity.Nature. 2016; 535: 376-381Crossref PubMed Scopus (1097) Google Scholar). Consistently, it has been recently brought to our attention that P. copri represents one of the clearest cases of dissimilar associations with either health or disease (Cani, 2018Cani P.D. Human gut microbiomes: hopes, threats and promises.Gut. 2018; 67: 1716-1725Crossref PubMed Scopus (680) Google Scholar), and such behavior can be most likely explained by a strain-level diversity. Therefore, the current associations of Prevotella with the host may represent an oversimplification that does not consider the wide diversity possibly existing among different P. copri strains. The vastly different genomic repertoires of P. copri strains may help to explain some of the differences observed across individuals in their metabolic responses to diet (Ley, 2016Ley R.E. Gut microbiota in 2015: Prevotella in the gut: choose carefully.Nat. Rev. Gastroenterol. Hepatol. 2016; 13: 69-70Crossref PubMed Scopus (270) Google Scholar, Truong et al., 2017Truong D.T. Tett A. Pasolli E. Huttenhower C. Segata N. Microbial strain-level population structure and genetic diversity from metagenomes.Genome Res. 2017; 27: 626-638Crossref PubMed Scopus (323) Google Scholar, Cani, 2018Cani P.D. Human gut microbiomes: hopes, threats and promises.Gut. 2018; 67: 1716-1725Crossref PubMed Scopus (680) Google Scholar). Any attempt to assess the influence of the gut microbiome on human health or disease must acknowledge that many relevant functions may well be strain specific, and therefore, strain-level dissection of metagenomics data can be crucial to demonstrate a causative role of the gut microbiome in the balance between health and disease. In particular, the response to different dietary regimens or nutritional interventions may be strain dependent and, therefore, unpredictable in the current scenario of genus- or species-scale resolution, complicating the possibility of microbiome-targeted dietary interventions (De Filippis et al., 2018De Filippis F. Vitaglione P. Cuomo R. Berni Canani R. Ercolini D. Dietary interventions to modulate the gut microbiome: how far away are we from precision medicine.Inflamm. Bowel Dis. 2018; 24: 2142-2154Crossref PubMed Scopus (45) Google Scholar, Derrien and Veiga, 2017Derrien M. Veiga P. Rethinking diet to aid human–microbe symbiosis.Trends Microbiol. 2017; 25: 100-112Abstract Full Text Full Text PDF PubMed Scopus (71) Google Scholar, Zmora et al., 2016Zmora N. Zeevi D. Korem T. Segal E. Elinav E. Taking it personally: personalized utilization of the human microbiome in health and disease.Cell Host Microbe. 2016; 19: 12-20Abstract Full Text Full Text PDF PubMed Scopus (157) Google Scholar). In order to study in depth the association between diet and strain-level determinants in the microbiome, we sequenced the gut metagenome of healthy Italian adults with different habitual diets and carried out a strain-level analysis of P. copri to explore the possible diet-driven selection of specific strains and functions. Moreover, we compared the overall P. copri functional potential of our Italian subjects with previously studied non-Westernized cohorts. We analyzed the gut metagenome of 97 Italian omnivores (O, n = 23), vegetarians (VG, n = 38), and vegans (V, n = 36). The relative abundance of P. copri was high enough for a strain-level analysis in 47 samples (9 O, 22 VG, and 16 V). The average age of the 47 subjects was 40.8 ± 8.9, 42.1 ± 7.6, and 40.1 ± 12.3 years, and body mass index (BMI) was 24.5 ± 4.5, 21.9 ± 3.0, and 21.8 ± 3.7 kg/m2 for O, VG, and V, respectively. No significant difference in age and BMI was detected by pair-wise Wilcoxon tests (p > 0.05). Fifty-three subjects were part of a larger cohort previously characterized (De Filippis et al., 2016aDe Filippis F. Pellegrini N. Vannini L. Jeffery I.B. La Storia A. Laghi L. Serrazanetti D.I. Di Cagno R. Ferrocino I. Lazzi C. et al.High-level adherence to a Mediterranean diet beneficially impacts the gut microbiota and associated metabolome.Gut. 2016; 65: 1812-1821Crossref PubMed Scopus (836) Google Scholar), while 44 subjects belonged to a newly recruited cohort. Dietary habits and main demographics are reported in Table S1. The abundance of P. copri in our metagenomes ranged from 0 to 83.2% (Figure S1), and it was not significantly associated with diet type (O, V, or VG), as determined by multivariate analysis of variance (MANOVA) based on Bray Curtis’ dissimilarity matrix. To test the hypothesis that strain-level structures could be associated with diet, we characterized the strain-specific P. copri functional potential by pangenome profiling, using PanPhlAn and grouping orthologous genes into Kyoto Encyclopedia of Genes and Genomes (KEGG) functional categories. No associations of P. copri pangenome with sex was found by MANOVA (p > 0.05). Principal coordinates analysis (PCoA) clearly separated omnivore from non-omnivore (V and VG) subjects (Figure 1A), based on the P. copri gene repertoire. Moreover, by further distinguishing V and VG subjects, we observed a gradient of separation from vegans to omnivores (Figure 1B). Compared to O, thirty-six and eight pangenes occurred differentially in P. copri pangenomes of V and VG, respectively (p < 0.05; Table 1). Interestingly, V-associated P. copri strains showed a higher prevalence of genes involved in complex carbohydrate break-down (Table 1). Vegans showed a higher prevalence of genes identified as acetylxylan esterase, pectate lyase, alpha-L-fucosidase, 1,4 beta-xylanase, phosphoenolpyruvate carboxykinase, and several carbohydrate transporters (susD family). As confirmation, we also used the CAZy database (Lombard et al., 2014Lombard V. Golaconda Ramulu H. Drula E. Coutinho P.M. Henrissat B. The carbohydrate-active enzymes database (CAZy) in 2013.Nucleic Acids Res. 2014; 42: d490-d495Crossref PubMed Scopus (4138) Google Scholar; http://www.cazy.org) for the identification of P. copri pangenes (see STAR Methods). Glycoside hydrolase (GH) and carbohydrate esterase (CE) families were enriched in V compared to O and VG, although only CEs were significantly enriched in V (p < 0.05; Figure S2). In particular, CE7 and CE8, including acetyl xylan esterase and pectin methyl esterase, showed a higher prevalence in vegans. GH5, GH95, and GH127 containing enzymes, involved in complex polysaccharides breakdown, were enriched in V, while GH2, including β-galactosidase, prevailed in O (Table S2). In addition, genes involved in sulfur compound metabolism (cystathionine beta-lyase and O-acetylhomoserine thiol-lyase) were enriched in O compared to V, as well as 3-isopropylmalate dehydrogenase (leuB, EC 1.1.1.85), which is involved in branched-chain amino acid (BCAA) biosynthesis. All O, 67% of VG, and 18% of V harbored the leuB gene in P. copri pangenome. Interestingly, when we divided subjects for presence or absence of leuB in the P. copri pangenome, we found significantly lower urinary BCAA levels in V and VG individuals not harboring the P. copri leuB gene (p < 0.05; Figure S3).Table 1P. copri Genes with a Significantly Different Occurrence in Italian Omnivore, Vegetarian, and Vegan IndividualsComparison of Omnivores, O, versus Vegans, VGene IDGene NameKEGG Metabolism and PathwayE.C.∗p values were calculated by paired chi-squared test. Occurrence was calculated based on the percentage of samples for each diet group showing the gene.p ValuePrevalence in V (%)Prevalence in O (%)g000271TonB-dependent receptorNANA0.05829.40.0g0003383-isopropylmalate dehydrogenaseamino acid metabolism; valine, leucine, and isoleucine biosynthesis1.1.1.850.00923.570.0g0005624-amino-4-deoxychorismatel yasemetabolism of cofactors and vitamins; folate biosynthesis4.1.3.380.00394.141.7g000563para-aminobenzoate synthetase componentImetabolism of cofactors and vitamins; folate biosynthesis2.6.1.850.00194.133.3g000800alpha-L-fucosidaseglycan biosynthesis and metabolism; other glycan degradation3.2.1.510.00682.425.0g0008071,4-beta-xylanaseNANA0.05694.158.3g000920pectate lyaseNANA0.000476.58.3g000922alpha-glucosidaseNANA0.00376.516.7g000924peptidase S24NANA0.01817.766.7g001013arginase 1amino acid metabolism; arginine and proline metabolism3.5.3.10.03888.250.0g001041phosphoenolpyruvate carboxykinasecarbohydrate metabolism; pyruvate metabolism4.1.1.490.04582.441.7g001142rhamnulokinasecarbohydrate metabolism; pentose and glucuronate interconversions2.7.1.50.05358.816.7g001144L-rhamnose-proton symport protein (RhaT)NANA0.05358.816.7g001203Putative glycoside hydrolaseNANA0.04682.441.7g001240nitroreductasexenobiotics biodegradation and metabolism; nitrotoluene degradationNA0.01023.575.0g001419phage associated proteinNANA0.02835.30.0g001539acetylxylanesteraseNA3.1.1.720.00870.616.7g001569N-acetyl transferaseNANA0.02835.30.0g002040RagB/SusD domain proteinNANA0.00358.30.0g002041thiol-disulfide isomerase-like thioredoxinNANA0.00511.866.7g002054SusE outer membrane proteinNANA0.01952.98.3g002058carbohydrate-binding proteinNANA0.01866.70.0g002259nitrate ABC transporter ATPaseNANA0.00117.783.3g002283threonine aldolaseamino acid metabolism; glycine, serine, and threonine metabolism4.1.2.480.00829.483.3g002284arginaseamino acid metabolism; arginine and proline metabolism3.5.3.10.00617.775.0g002334preprotein translocase, SecA subunitNANA0.00111.875.0g002397tripeptidyl aminopeptidaseNANA0.0035.958.3g002408RagB/SusD domain proteinNANA0.01150.00.0g002464putative phage related proteinNANA0.01076.525.0g002465phage uncharacterized proteinNANA0.00182.416.7g002469zinc ABC transporter substrate-binding proteinNANA0.02923.566.7g002508thiolperoxidaseNANA0.01023.575.0g003225thiamine biosynthesis protein ThiHmetabolism of cofactors and vitamins; thiamine metabolismNA0.0565.941.7g003319O-acetylhomoserine (thiol)-lyaseamino acid metabolism; cysteine and methionine metabolism2.5.1.490.00141.2100.0g003320cystathionine beta-lyaseenergy metabolism; sulfur metabolism4.4.1.80.00141.2100.0g003324mannitol 2-dehydrogenasecarbohydrate metabolism; fructose and mannose metabolism1.1.1.670.01023.575.0Comparison of Omnivores, O, versus Vegetarians, VGGene IDGene NameKEGG Metabolism and PathwayE.C.∗p values were calculated by paired chi-squared test. Occurrence was calculated based on the percentage of samples for each diet group showing the gene.p ValuePrevalence in VG (%)Prevalence in O (%)g000046alpha-L-fucosidaseglycan biosynthesis and metabolism; other glycan degradation3.2.1.510.03036.40.0g000271TonB-dependent receptorNANA0.03631.80.0g0005624-amino-4-deoxychorismatel yasemetabolism of cofactors and vitamins; folate biosynthesis4.1.3.380.02682.041.7g000859N-acetylmuramoyl-L-alanine amidaseNANA0.037100.00.0g001013arginase 1amino acid metabolism; arginine and proline metabolism3.5.3.10.01290.950.0g001419phage associated proteinNANA0.03631.80.0g002053SusD family proteinNANA0.03059.116.7g002499Beta-xylosidase, xynBcarbohydrate metabolism; amino sugar and nucleotide sugar metabolism3.2.1.370.04295.566.7g003320cystathionine beta-lyaseenergy metabolism; sulfur metabolism4.4.1.80.03063.6100.0Comparison of Vegans, V, versus Vegetarians, VGGene IDGene NameKEGG Metabolism and PathwayE.C.∗p values were calculated by paired chi-squared test. Occurrence was calculated based on the percentage of samples for each diet group showing the gene.p ValuePrevalence in VG (%)Prevalence in V (%)g000563para-aminobenzoate synthetase component Imetabolism of cofactors and vitamins; folate biosynthesis2.6.1.850.02459.194.0g000800alpha-L-fucosidase 2glycan biosynthesis and metabolism; other glycan degradation3.2.1.510.02040.982.4g003319O-acetylhomoserine (thiol)-lyasecysteine and methionine metabolism2.5.1.490.05972.741.2g003324mannitol 2-dehydrogenasecarbohydrate metabolism; fructose and mannose metabolism1.1.1.670.02363.623.5g000800alpha-L-fucosidaseglycan biosynthesis and metabolism; other glycan degradation3.2.1.510.02040.982.4g000920pectate lyaseNANA0.02336.476.5g001240nitroreductasexenobiotics biodegradation and metabolism; nitrotoluene degradationNA0.05059.123.5g001449putative PTS permease proteinNANA0.02454.617.7g002259nitrate ABC transporter ATPaseNANA0.04950.017.7g002283threonine aldolaseamino acid metabolism; glycine, serine, and threonine metabolism4.1.2.480.01072.729.4g002284arginaseamino acid metabolism; arginine and proline metabolism3.5.3.10.04950.017.7g002341glycosyltransferaseNANA0.03754.688.2g002380TonB-dependent receptorNANA0.00259.1100.0g002416TonB-dependent receptorNANA0.05677.3100.0g002417glucoside-hydrogenasecarbohydrate metabolism; pentose phosphate pathway1.1.1.470.01268.2100.0g002464putative phage related proteinNANA0.01031.876.5g002465phage uncharacterized proteinNANA0.00331.882.4g002496putative bacteriophage integraseNANA0.01068.223.5g002301amino acid carrier proteinNANA0.0560.0100.0NA, not available; VG, vegetarians; V, vegans; O, omnivores; E.C., Enzyme Commission; KEGG, Kyoto Encyclopedia of Genes and Genomes.∗ p values were calculated by paired chi-squared test. Occurrence was calculated based on the percentage of samples for each diet group showing the gene. Open table in a new tab NA, not available; VG, vegetarians; V, vegans; O, omnivores; E.C., Enzyme Commission; KEGG, Kyoto Encyclopedia of Genes and Genomes. In order to confirm the results obtained by reference-based computational profiling, we assembled the metagenomes into contigs and extracted those belonging to P. copri (see STAR Methods). Core genes identified in the assemblies were aligned and used to build a phylogenetic tree. Although only part of the samples had >2.5 Mb total alignment to P. copri genome (due to assembly and coverage limitations), results still showed a sharp separation of P. copri strains present in O and V, while VG subjects were separated in the two groups (Figure 2). We also compared the P. copri gene repertoire of our cohort with Western and non-Western populations from previously published studies (Rampelli et al., 2015Rampelli S. Schnorr S.L. Consolandi C. Turroni S. Severgnini M. Peano C. Brigidi P. Crittenden A.N. Henry A.G. Candela M. Metagenome sequencing of the Hadza hunter-gatherer gut microbiota.Curr. Biol. 2015; 25: 1682-1693Abstract Full Text Full Text PDF PubMed Scopus (222) Google Scholar, Le Chatelier et al., 2013Le Chatelier E. Nielsen T. Qin J. Prifti E. Hildebrand F. Falony G. Almeida M. Arumugam M. Batto J.M. Kennedy S. et al.Richness of human gut microbiome correlates with metabolic markers.Nature. 2013; 500: 541-546Crossref PubMed Scopus (2790) Google Scholar, Obregon-Tito et al., 2015Obregon-Tito A.J. Tito R.Y. Metcalf J. Sankaranarayanan K. Clemente J.C. Ursell L.K. Zech Xu Z. Van Treuren W. Knight R. Gaffney P.M. et al.Subsistenc– strategies in traditional societies distinguish gut microbiomes.Nat. Commun. 2015; 6: 6505Crossref PubMed Scopus (292) Google Scholar, Yatsunenko et al., 2012Yatsunenko T. Rey F.E. Manary M.J. Trehan I. Dominguez-Bello M.G. Contreras M. Magris M. Hidalgo G. Baldassano R.N. Anokhin A.P. et al.Human gut microbiome viewed across age and geography.Nature. 2012; 486: 222-227Crossref PubMed Scopus (4829) Google Scholar). The functional potential of P. copri strains present in Western and non-Western populations was different, and we could identify two main clusters separated by subject-origin (Figure 3A). Interestingly, the few American and Italian controls from the other studies clustered together with Italians from this study and Danes from Le Chatelier et al., 2013Le Chatelier E. Nielsen T. Qin J. Prifti E. Hildebrand F. Falony G. Almeida M. Arumugam M. Batto J.M. Kennedy S. et al.Richness of human gut microbiome correlates with metabolic markers.Nature. 2013; 500: 541-546Crossref PubMed Scopus (2790) Google Scholar (Figure 3B). In particular, 1,368 genes differentiated Western and non-Western subjects (Table S3). Among them, several genes encoding for SusC and SusD tran" @default.
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