Abstract
Both genetic variations and diet-disrupted gut microbiota can predispose animals to metabolic syndromes (MS). This study assessed the relative contributions of host genetics and diet in shaping the gut microbiota and modulating MS-relevant phenotypes in mice. Together with its wild-type (Wt) counterpart, the Apoa-I knockout mouse, which has impaired glucose tolerance (IGT) and increased body fat, was fed a high-fat diet (HFD) or normal chow (NC) diet for 25 weeks. DNA fingerprinting and bar-coded pyrosequencing of 16S rRNA genes were used to profile gut microbiota structures and to identify the key population changes relevant to MS development by Partial Least Square Discriminate Analysis. Diet changes explained 57% of the total structural variation in gut microbiota, whereas genetic mutation accounted for no more than 12%. All three groups with IGT had significantly different gut microbiota relative to healthy Wt/NC-fed animals. In all, 65 species-level phylotypes were identified as key members with differential responses to changes in diet, genotype and MS phenotype. Most notably, gut barrier-protecting Bifidobacterium spp. were nearly absent in all animals on HFD, regardless of genotype. Sulphate-reducing, endotoxin-producing bacteria of the family, Desulfovibrionaceae, were enhanced in all animals with IGT, most significantly in the Wt/HFD group, which had the highest calorie intake and the most serious MS phenotypes. Thus, diet has a dominating role in shaping gut microbiota and changes of some key populations may transform the gut microbiota of Wt animals into a pathogen-like entity relevant to development of MS, despite a complete host genome.
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Acinas SG, Klepac-Ceraj V, Hunt DE, Pharino C, Ceraj I, Distel DL et al. (2004). Fine-scale phylogenetic architecture of a complex bacterial community. Nature 430: 551–554.
Backhed F, Manchester JK, Semenkovich CF, Gordon JI . (2007). Mechanisms underlying the resistance to diet-induced obesity in germ-free mice. Proc Natl Acad Sci USA 104: 979–984.
Beerens H, Romond C . (1977). Sulfate-reducing anaerobic bacteria in human feces. Am J Clin Nutr 30: 1770–1776.
Campbell TC, Campbell TM . (2005). The China Study: The Most Comprehensive Study of Nutrition Ever Conducted and the Startling Implications for Diet, Weight Loss and Long-Term Health. Benbella Books: Texas, USA.
Cani PD, Neyrinck AM, Fava F, Knauf C, Burcelin RG, Tuohy KM et al. (2007a). Selective increases of bifidobacteria in gut microflora improve high-fat-diet-induced diabetes in mice through a mechanism associated with endotoxaemia. Diabetologia 50: 2374–2383.
Cani PD, Amar J, Iglesias MA, Poggi M, Knauf C, Bastelica D et al. (2007b). Metabolic endotoxemia initiates obesity and insulin resistance. Diabetes 56: 1761–1772.
Dandona P, Aljada A, Chaudhuri A, Mohanty P, Garg R . (2005). Metabolic syndrome: a comprehensive perspective based on interactions between obesity, diabetes, and inflammation. Circulation 111: 1448–1454.
DeSantis Jr TZ, Hugenholtz P, Keller K, Brodie EL, Larsen N, Piceno YM et al. (2006). NAST: a multiple sequence alignment server for comparative analysis of 16S rRNA genes. Nucleic Acids Res 34: W394–W399.
Eckel RH, Grundy SM, Zimmet PZ . (2005). The metabolic syndrome. Lancet 365: 1415–1428.
Finegold SM, Attebery HR, Sutter VL . (1974). Effect of diet on human faecal flora: comparison of Japanese and American diets. Am J Clin Nutr 27: 1456–1469.
Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM et al. (2007). A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316: 889–894.
Han R, Lai R, Ding Q, Wang Z, Luo X, Zhang Y et al. (2007). Apolipoprotein A-I stimulates AMP-activated protein kinase and improves glucose metabolism. Diabetologia 50: 1960–1968.
Hayashi H, Sakamoto M, Benno Y . (2002). Phylogenetic analysis of the human gut microbiota using 16S rDNA clone libraries and strictly anaerobic culture-based methods. Microbiol Immunol 46: 535–548.
Holmes E, Loo RL, Stamler J, Bictash M, Yap IK, Chan Q et al. (2008). Human metabolic phenotype diversity and its association with diet and blood pressure. Nature 453: 396–400.
Jia W, Li H, Zhao L, Nicholson JK . (2008). Gut microbiota: a potential new territory for drug targeting. Nat Rev Drug Discov 7: 123–129.
Li H, Wu Y, Loos RJ, Hu FB, Liu Y, Wang J et al. (2008a). Variants in the fat mass- and obesity-associated (FTO) gene are not associated with obesity in a Chinese Han population. Diabetes 57: 264–268.
Li M, Wang B, Zhang M, Rantalainen M, Wang S, Zhou H et al. (2008b). Symbiotic gut microbes modulate human metabolic phenotypes. Proc Natl Acad Sci USA 105: 2117–2122.
Liu WT, Marsh TL, Cheng H, Forney LJ . (1997). Characterization of microbial diversity by determining terminal restriction fragment length polymorphisms of genes encoding 16S rRNA. Appl Environ Microbiol 63: 4516–4522.
Loubinoux J, Mory F, Pereira IA, Le Faou AE . (2000). Bacteremia caused by a strain of Desulfovibrio related to the provisionally named Desulfovibrio fairfieldensis. J Clin Microbiol 38: 931–934.
Lozupone C, Hamady M, Knight R . (2006). UniFrac—an online tool for comparing microbial community diversity in a phylogenetic context. BMC Bioinformatics 7: 371.
Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar et al. (2004). ARB: a software environment for sequence data. Nucleic Acids Res 32: 1363–1371.
Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, Bemben LA et al. (2005). Genome sequencing in microfabricated high-density picolitre reactors. Nature 437: 376–380.
McKenna P, Hoffmann C, Minkah N, Aye PP, Lackner A, Liu Z et al. (2008). The macaque gut microbiome in health, lentiviral infection, and chronic enterocolitis. PLoS Pathog 4: e20.
Muyzer G, de Waal EC, Uitterlinden AG . (1993). Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl Environ Microbiol 59: 695–700.
Nguyen DV, Rocke DM . (2002). Tumor classification by partial least squares using microarray gene expression data. Bioinformatics 18: 39–50.
Osten DW . (1988). Selection of optimal regression models via cross-validation. J Chemom 2: 39–48.
Perez-Enciso M, Tenenhaus M . (2003). Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach. Hum Genet 112: 581–592.
Satokari RM, Vaughan EE, Akkermans AD, Saarela M, de Vos WM . (2001). Bifidobacterial diversity in human feces detected by genus-specific PCR and denaturing gradient gel electrophoresis. Appl Environ Microbiol 67: 504–513.
Schloss PD, Handelsman J . (2005). Introducing DOTUR, a computer program for defining operational taxonomic units and estimating species richness. Appl Environ Microbiol 71: 1501–1506.
Sogin ML, Morrison HG, Huber JA, Mark Welch D, Huse SM, Neal PR et al. (2006). Microbial diversity in the deep sea and the underexplored ‘rare biosphere’. Proc Natl Acad Sci USA 103: 12115–12120.
Sonnenburg JL, Angenent LT, Gordon JI . (2004). Getting a grip on things: how do communities of bacterial symbionts become established in our intestine? Nat Immunol 5: 569–573.
Tajima K, Aminov RI, Nagamine T, Matsui H, Nakamura M, Benno Y . (2001). Diet-dependent shifts in the bacterial population of the rumen revealed with real-time PCR. Appl Environ Microbiol 67: 2766–2774.
Turnbaugh PJ, Backhed F, Fulton L, Gordon JI . (2008). Diet-induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome. Cell Host Microbe 3: 213–223.
Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI . (2006). An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444: 1027–1031.
Wang Y, Holmes E, Nicholson JK, Cloarec O, Chollet J, Tanner M et al. (2004). Metabonomic investigations in mice infected with Schistosoma mansoni: an approach for biomarker identification. Proc Natl Acad Sci USA 101: 12676–12681.
Weglarz L, Dzierzewicz Z, Skop B, Orchel A, Parfiniewicz B, Wisniowska B et al. (2003). Desulfovibrio desulfuricans lipopolysaccharides induce endothelial cell IL-6 and IL-8 secretion and E-selectin and VCAM-1 expression. Cell Mol Biol Lett 8: 991–1003.
Wei G, Pan L, Du H, Chen J, Zhao L . (2004). ERIC-PCR fingerprinting-based community DNA hybridization to pinpoint genome-specific fragments as molecular markers to identify and track populations common to healthy human guts. J Microbiol Methods 59: 91–108.
Wellen KE, Hotamisligil GS . (2005). Inflammation, stress, and diabetes. J Clin Invest 115: 1111–1119.
Wen L, Ley RE, Volchkov PY, Stranges PB, Avanesyan L, Stonebraker AC et al. (2008). Innate immunity and intestinal microbiota in the development of type 1 diabetes. Nature 455: 1109–1113.
Westad F, Martens H . (2000). Variable selection in near infrared spectroscopy based on significance testing in partial least squares regression. Journal of Near Inffrared Spectroscopy 8: 117–124.
Zhang M, Zhang M, Zhang C, Du H, Wei G, Pang X et al. (2009). Pattern extraction of structural responses of gut microbiota to rotavirus infection via multivariate statistical analysis of clone library data. FEMS Microbiol Ecol 70: 21–29.
Zoetendal EG, Akkermans AD, De Vos WM . (1998). Temperature gradient gel electrophoresis analysis of 16S rRNA from human faecal samples reveals stable and host-specific communities of active bacteria. Appl Environ Microbiol 64: 3854–3859.
Zoetendal EG, von Wright A, Vilpponen-Salmela T, Ben-Amor K, Akkermans AD, de Vos WM . (2002). Mucosa-associated bacteria in the human gastrointestinal tract are uniformly distributed along the colon and differ from the community recovered from feces. Appl Environ Microbiol 68: 3401–3407.
Acknowledgements
This work was supported by the National Natural Science Foundation of China Program Grants 30730005, 30821005 and 20875061, 973 Program Grants 2007CB513002 and 2004CB518600, 863 Program Grant 2008AA02Z315, the International Cooperation Program Grants 2007DFC30450 and 075407001, and the Chinese Academy of Sciences (Knowledge Innovation Program KSCX1-YW-02).
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Zhang, C., Zhang, M., Wang, S. et al. Interactions between gut microbiota, host genetics and diet relevant to development of metabolic syndromes in mice. ISME J 4, 232–241 (2010). https://doi.org/10.1038/ismej.2009.112
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DOI: https://doi.org/10.1038/ismej.2009.112
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