Abstract

Assessment and characterization of gut microbiota has become a major research area in human disease, including type 2 diabetes, the most prevalent endocrine disease worldwide. To carry out analysis on gut microbial content in patients with type 2 diabetes, we developed a protocol for a metagenome-wide association study (MGWAS) and undertook a two-stage MGWAS based on deep shotgun sequencing of the gut microbial DNA from 345 Chinese individuals. We identified and validated approximately 60,000 type-2-diabetes-associated markers and established the concept of a metagenomic linkage group, enabling taxonomic species-level analyses. MGWAS analysis showed that patients with type 2 diabetes were characterized by a moderate degree of gut microbial dysbiosis, a decrease in the abundance of some universal butyrate-producing bacteria and an increase in various opportunistic pathogens, as well as an enrichment of other microbial functions conferring sulphate reduction and oxidative stress resistance. An analysis of 23 additional individuals demonstrated that these gut microbial markers might be useful for classifying type 2 diabetes.

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Primary accessions

Sequence Read Archive

Data deposits

The raw Illumina read data of all 368 samples has been deposited in the NCBI Sequence Read Archive under accession numbers SRA045646 and SRA050230. The assembly data, updated metagenome gene catalogue, annotation information, and MGLs are published in the GigaScience database, GigaDB35.

References

  1. 1.

    & Inflammation, stress, and diabetes. J. Clin. Invest. 115, 1111–1119 (2005)

  2. 2.

    , & Dietary fats and prevention of type 2 diabetes. Prog. Lipid Res. 48, 44–51 (2009)

  3. 3.

    The Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661–678 (2007)

  4. 4.

    et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316, 1341–1345 (2007)

  5. 5.

    , & Interactions between gut microbiota and host metabolism predisposing to obesity and diabetes. Annu. Rev. Med. 62, 361–380 (2011)

  6. 6.

    et al. Diversity of the human intestinal microbial flora. Science 308, 1635–1638 (2005)

  7. 7.

    et al. A core gut microbiome in obese and lean twins. Nature 457, 480–484 (2009)

  8. 8.

    et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010)

  9. 9.

    The Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012)

  10. 10.

    The Human Microbiome Project Consortium. A framework for human microbiome research. Nature 486, 215–221 (2012)

  11. 11.

    et al. Metabolic syndrome and altered gut microbiota in mice lacking Toll-like receptor 5. Science 328, 228–231 (2010)

  12. 12.

    et al. The gut microbiota as an environmental factor that regulates fat storage. Proc. Natl Acad. Sci. USA 101, 15718–15723 (2004)

  13. 13.

    et al. Obesity alters gut microbial ecology. Proc. Natl Acad. Sci. USA 102, 11070–11075 (2005)

  14. 14.

    et al. Human gut microbiota in obesity and after gastric bypass. Proc. Natl Acad. Sci. USA 106, 2365–2370 (2009)

  15. 15.

    , , & Mechanisms underlying the resistance to diet-induced obesity in germ-free mice. Proc. Natl Acad. Sci. USA 104, 979–984 (2007)

  16. 16.

    et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006)

  17. 17.

    et al. Reduced diversity of faecal microbiota in Crohn’s disease revealed by a metagenomic approach. Gut 55, 205–211 (2006)

  18. 18.

    et al. Dysbiosis of the faecal microbiota in patients with Crohn’s disease and their unaffected relatives. Gut 60, 631–637 (2011)

  19. 19.

    et al. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS ONE 5, e9085 (2010)

  20. 20.

    et al. Enterotypes of the human gut microbiome. Nature 473, 174–180 (2011)

  21. 21.

    et al. Principal components analysis corrects for stratification in genome-wide association studies. Nature Genet. 38, 904–909 (2006)

  22. 22.

    et al. Bacteremia due to Clostridium hathewayi in a patient with acute appendicitis. J. Clin. Microbiol. 42, 5947–5949 (2004)

  23. 23.

    & Bacteremia caused by Clostridium symbiosum. J. Clin. Microbiol. 42, 4390–4392 (2004)

  24. 24.

    , & Intraabdominal infection: a review. Clin. Inf. Dis. 19, 100–116 (1994)

  25. 25.

    Clostridial infection in children. J. Med. Microbiol. 42, 78–82 (1995)

  26. 26.

    , & Metagenomic systems biology of the human gut microbiome reveals topological shifts associated with obesity and inflammatory bowel disease. Proc. Natl Acad. Sci. USA 109, 594–599 (2012)

  27. 27.

    & Fitting multivariate models to community data: a comment on distance-based redundancy analysis. Ecology 82, 290–297 (2001)

  28. 28.

    et al. Unsupervised binning of environmental genomic fragments based on an error robust selection of l-mers. BMC Bioinformatics 11 (suppl. 2). S5 (2010)

  29. 29.

    et al. Phylogenetic classification of short environmental DNA fragments. Nucleic Acids Res. 36, 2230–2239 (2008)

  30. 30.

    et al. Structural segregation of gut microbiota between colorectal cancer patients and healthy volunteers. ISME J. 6, 320–329 (2012)

  31. 31.

    et al. Through ageing, and beyond: gut microbiota and inflammatory status in seniors and centenarians. PLoS ONE 5, e10667 (2010)

  32. 32.

    & Oxidative stress: key player in gastrointestinal complications of diabetes. Neurogastroenterol. Motil. 23, 111–114 (2011)

  33. 33.

    et al. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N. Engl. J. Med. 359, 2220–2232 (2008)

  34. 34.

    , , , & Molecular microbial diversity of an anaerobic digestor as determined by small-subunit rDNA sequence analysis. Appl. Environ. Microbiol. 63, 2802–2813 (1997)

  35. 35.

    et. al. Type 2 diabetes gut metagenome (microbiome) data from 368 Chinese samples. GigaScience (2012)

  36. 36.

    et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011)

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Acknowledgements

We thank L. Goodman for editing the manuscript and providing comments. This research was supported by the Ministry of Science and Technology of China, 863 program (2012AA02A201), the National Natural Science Foundation of China (30890032, 30725008, 30811130531, 31161130357), the Shenzhen Municipal Government of China (ZYC200903240080A, BGI20100001, CXB201108250096A, CXB201108250098A), the Danish Strategic Research Council grant (2106-07-0021), the Ole Rømer grant from Danish Natural Science Research Council, the Solexa project (272-07-0196), and the European Commission FP7 grant HEALTH-F4-2007-201052. The Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention and Care (LuCamp, http://www.lucamp.org). The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent Research Center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (http://www.metabol.ku.dk). We are also indebted to many additional faculty and staff of BGI-Shenzhen who contributed to this work.

Author information

Author notes

    • Junjie Qin
    • , Yingrui Li
    • , Zhiming Cai
    • , Shenghui Li
    • , Jianfeng Zhu
    •  & Fan Zhang

    These authors contributed equally to this work.

Affiliations

  1. BGI-Shenzhen, Shenzhen 518083, China

    • Junjie Qin
    • , Yingrui Li
    • , Shenghui Li
    • , Jianfeng Zhu
    • , Suisha Liang
    • , Wenwei Zhang
    • , Yuanlin Guan
    • , Dongqian Shen
    • , Yangqing Peng
    • , Dongya Zhang
    • , Zhuye Jie
    • , Wenxian Wu
    • , Youwen Qin
    • , Wenbin Xue
    • , Junhua Li
    • , Xiaoping Li
    • , Weineng Chen
    • , Ran Xu
    • , Mingbang Wang
    • , Qiang Feng
    • , Meihua Gong
    • , Jing Yu
    • , Yanyan Zhang
    • , Ming Zhang
    • , Zhaoxi Zhang
    • , Hua Chen
    • , Ruifu Yang
    • , Weimou Zheng
    • , Songgang Li
    • , Huanming Yang
    • , Jian Wang
    • , Karsten Kristiansen
    •  & Jun Wang
  2. Shenzhen Second People’s Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen 518035, China

    • Zhiming Cai
    • , Xiaojuan Sun
    • , Zesong Li
    •  & Aifa Tang
  3. Peking University Shenzhen Hospital, Shenzhen 518036, China

    • Fan Zhang
    • , Lingchuan Han
    • , Donghui Lu
    • , Peixian Wu
    •  & Yali Dai
  4. Medical Research Center of Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China

    • Shilong Zhong
  5. The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health Sciences, University of Copenhagen, DK-2100 Copenhagen, Denmark

    • Torben Hansen
    • , Oluf Pedersen
    •  & Jun Wang
  6. Department of Integrative Biology and Department of Statistics, University of California Berkeley, Berkeley, CA 94820, USA

    • Gaston Sanchez
    •  & Rasmus Nielsen
  7. Department of Structural Biology, VIB, 1050 Brussels, Belgium

    • Jeroen Raes
    • , Gwen Falony
    •  & Shujiro Okuda
  8. Department of Applied Biological Sciences (DBIT), Vrije Universiteit Brussel, 1050 Brussels, Belgium

    • Jeroen Raes
    • , Gwen Falony
    •  & Shujiro Okuda
  9. Institut National de la Recherche Agronomique, 78350 Jouy en Josas, France

    • Mathieu Almeida
    • , Emmanuelle LeChatelier
    • , Pierre Renault
    • , Nicolas Pons
    • , Jean-Michel Batto
    •  & S. Dusko Ehrlich
  10. State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing 100071, China

    • Ruifu Yang
  11. Institute of Biomedical Sciences, University of Copenhagen & Faculty of Health Science, University of Aarhus, DK-8000 Aarhus, Denmark

    • Oluf Pedersen
  12. Hagedorn Research Institute, DK-2820 Gentofte, Denmark

    • Oluf Pedersen
  13. Department of Biology, University of Copenhagen, DK-2200 Copenhagen, Denmark

    • Karsten Kristiansen
    •  & Jun Wang

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Contributions

The project idea was conceived and the project was designed by Ju.W., K.K., O.P., R.N. and S.D.E.; J.Q., Y.L., Sh.L. and Ju.W. managed the project. F.Z., Z.C., R.X., Su.L., L.H., D.L., P.W., Y.D., X.S., Z.L., A.T., S.Z., M.W., Q.F. and T.H. performed sample collection and clinical study. Wen.Z., M.G., J.Y., Y.Z. and W.X. performed DNA experiments. Ju.W., K.K., O.P., R.N., S.D.E., J.Q., Y.L., Sh.L. and J.Z. designed the analysis. J.Q., Y.L., Sh.L., J.Z., Su.L., Y.G., Y.P., D.S., X.L., W.C., D.Z., Y.Q., M.Z., Z.Z., Z.J., G.S., J.L., J.R., S.O., H.C. and W.W. performed the data analysis. J.Q., Sh.L., J.Z., Y.G., Y.P., M.A., E.L., P.R., N.P. and J.-M.B. worked on metagenomic linkage group method. J.Q., D.S., Su.L., Y.Q., J.R., G.F. and S.O. did the functional annotation analyses. J.Q., Sh.L., D.S., J.Z., Y.P. and Y.L. wrote the paper. Ju.W., O.P., K.K., R.N., S.D.E., Ji.W., H.Y., So.L., Wei.Z. and R.Y. revised the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Jun Wang.

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https://doi.org/10.1038/nature11450

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