1887

Microbial Genomics logo

Volume 3, Issue 4

Human genome-microbiome interaction: metagenomics frontiers for the aetiopathology of autoimmune diseases Open Access

Abstract

A short while ago, the human genome and microbiome were analysed simultaneously for the first time as a multi-omic approach. The analyses of heterogeneous population cohorts showed that microbiome components were associated with human genome variations. In-depth analysis of these results reveals that the majority of those relationships are between immune pathways and autoimmune disease-associated microbiome components. Thus, it can be hypothesized that autoimmunity may be associated with homeostatic disequilibrium of the human-microbiome interactome. Further analysis of human genome–human microbiome relationships in disease contexts with tailored systems biology approaches may yield insights into disease pathogenesis and prognosis.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): autoimmune disease , genome-microbiome interaction and metagenomics
© 2017 The Authors
Loading

Article metrics loading...

/content/journal/mgen/10.1099/mgen.0.000112
2017-04-26
2024-04-19
Loading full text...

Full text loading...

/deliver/fulltext/mgen/3/4/mgen000112.html?itemId=/content/journal/mgen/10.1099/mgen.0.000112&mimeType=html&fmt=ahah

References

  1. Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC et al. Initial sequencing and analysis of the human genome. Nature 2001; 409:860–921 [View Article][PubMed]
     [Google Scholar]
  2. Lander ES. Initial impact of the sequencing of the human genome. Nature 2011; 470:187–197 [View Article][PubMed]
     [Google Scholar]
  3. Venter JC, Smith HO, Adams MD. The sequence of the human genome. Clin Chem 2015; 61:1207–1208 [View Article][PubMed]
     [Google Scholar]
  4. Human Microbiome Project Consortium A framework for human microbiome research. Nature 2012; 486:215–221 [View Article][PubMed]
     [Google Scholar]
  5. Peterson J, Garges S, Giovanni M, McInnes P, Wang L et al. The NIH Human Microbiome project. Genome Res 2009; 19:2317–2323 [View Article][PubMed]
     [Google Scholar]
  6. Gibson G. Hints of hidden heritability in GWAS. Nat Genet 2010; 42:558–560 [View Article][PubMed]
     [Google Scholar]
  7. Lu Y, Loos RJ. Obesity genomics: assessing the transferability of susceptibility loci across diverse populations. Genome Med 2013; 5:55 [View Article][PubMed]
     [Google Scholar]
  8. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA et al. Finding the missing heritability of complex diseases. Nature 2009; 461:747–753 [View Article][PubMed]
     [Google Scholar]
  9. Blekhman R, Goodrich JK, Huang K, Sun Q, Bukowski R et al. Host genetic variation impacts microbiome composition across human body sites. Genome Biol 2015; 16:1–12 [View Article][PubMed]
     [Google Scholar]
  10. Davenport ER. Elucidating the role of the host genome in shaping microbiome composition. Gut Microbes 2016; 7:178–184 [View Article][PubMed]
     [Google Scholar]
  11. Gourh P, Agarwal SK, Divecha D, Assassi S, Paz G et al. Polymorphisms in TBX21 and STAT4 increase the risk of systemic sclerosis: evidence of possible gene-gene interaction and alterations in Th1/Th2 cytokines. Arthritis Rheum 2009; 60:3794–3806 [View Article][PubMed]
     [Google Scholar]
  12. Radstake TR, Gorlova O, Rueda B, Martin JE, Alizadeh BZ et al. Genome-wide association study of systemic sclerosis identifies CD247 as a new susceptibility locus. Nat Genet 2010; 42:426–429 [View Article][PubMed]
     [Google Scholar]
  13. Volkmann E, Chang Y, Barroso N, Furst DE, Clements P et al. Systemic sclerosis is associated with a unique colonic microbial consortium. Arthritis Rheum 2016; 74:151
     [Google Scholar]
  14. Goodacre R. Metabolomics of a superorganism. J Nutr 2007; 137:S259–S266[PubMed]
     [Google Scholar]
  15. van den Abbeele P, Gérard P, Rabot S, Bruneau A, El Aidy S et al. Arabinoxylans and inulin differentially modulate the mucosal and luminal gut microbiota and mucin-degradation in humanized rats. Environ Microbiol 2011; 13:2667–2680 [View Article][PubMed]
     [Google Scholar]
  16. Zhang C, Zhang M, Wang S, Han R, Cao Y et al. Interactions between gut microbiota, host genetics and diet relevant to development of metabolic syndromes in mice. ISME J 2010; 4:232–241 [View Article][PubMed]
     [Google Scholar]
  17. Hacquard S, Garrido-Oter R, González A, Spaepen S, Ackermann G et al. Microbiota and host nutrition across plant and animal kingdoms. Cell Host Microbe 2015; 17:603–616 [View Article][PubMed]
     [Google Scholar]
  18. Lax S, Smith DP, Hampton-Marcell J, Owens SM, Handley KM et al. Longitudinal analysis of microbial interaction between humans and the indoor environment. Science 2014; 345:1048–1052 [View Article][PubMed]
     [Google Scholar]
  19. Tung J, Barreiro LB, Burns MB, Grenier J-C, Lynch J et al. Social networks predict gut microbiome composition in wild baboons. Elife 2015; 4:e05224 [View Article]
     [Google Scholar]
  20. Maurice CF, Haiser HJ, Turnbaugh PJ. Xenobiotics shape the physiology and gene expression of the active human gut microbiome. Cell 2013; 152:39–50 [View Article][PubMed]
     [Google Scholar]
  21. Hoffmann C, Hill DA, Minkah N, Kirn T, Troy A et al. Community-wide response of the gut microbiota to enteropathogenic Citrobacter rodentium infection revealed by deep sequencing. Infect Immun 2009; 77:4668–4678 [View Article][PubMed]
     [Google Scholar]
  22. Li E, Hamm CM, Gulati AS, Sartor RB, Chen H et al. Inflammatory bowel diseases phenotype, C. difficile and NOD2 genotype are associated with shifts in human ileum associated microbial composition. PLoS One 2012; 7:e26284 [View Article][PubMed]
     [Google Scholar]
  23. Belkaid Y, Hand TW. Role of the microbiota in immunity and inflammation. Cell 2014; 157:121–141 [View Article][PubMed]
     [Google Scholar]
  24. Ehrlich SD. The human gut microbiome impacts health and disease. C R Biol 2016; 339:319–323 [View Article][PubMed]
     [Google Scholar]
  25. Qin J, Li Y, Cai Z, Li S, Zhu J et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 2012; 490:55–60 [View Article][PubMed]
     [Google Scholar]
  26. Wu HJ, Ivanov II, Darce J, Hattori K, Shima T et al. Gut-residing segmented filamentous bacteria drive autoimmune arthritis via T helper 17 cells. Immunity 2010; 32:815–827 [View Article][PubMed]
     [Google Scholar]
  27. Lee YK, Menezes JS, Umesaki Y, Mazmanian SK. Proinflammatory T-cell responses to gut microbiota promote experimental autoimmune encephalomyelitis. Proc Natl Acad Sci USA 2011; 108:22
     [Google Scholar]
  28. Li J, Jia H, Cai X, Zhong H, Feng Q et al. An integrated catalog of reference genes in the human gut microbiome. Nat Biotechnol 2014; 32:834–841 [View Article][PubMed]
     [Google Scholar]
  29. Welter D, Macarthur J, Morales J, Burdett T, Hall P et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res 2014; 42:D1001–D1006 [View Article][PubMed]
     [Google Scholar]
  30. Greenblum S, Carr R, Borenstein E. Extensive strain-level copy-number variation across human gut microbiome species. Cell 2015; 160:583–594 [View Article][PubMed]
     [Google Scholar]
  31. Nayfach S, Rodriguez-Mueller B, Garud N, Pollard KS. An integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeography. Genome Res 2016; 26:1612–1625 [View Article][PubMed]
     [Google Scholar]
  32. Truong DT, Franzosa EA, Tickle TL, Scholz M, Weingart G et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat Methods 2015; 12:902–903 [View Article][PubMed]
     [Google Scholar]
  33. Bush WS, Moore JH. Chapter 11: Genome-wide association studies. PLoS Comput Biol 2012; 8:e1002822 [View Article][PubMed]
     [Google Scholar]
  34. Benson AK. Host genetic architecture and the landscape of microbiome composition: humans weigh in. Genome Biol 2015; 16:203 [View Article][PubMed]
     [Google Scholar]
  35. Cai L, Wu H, Li D, Zhou K, Zou F. Type 2 diabetes biomarkers of human gut microbiota selected via iterative sure independent screening method. PLoS One 2015; 10:e0140827 [View Article][PubMed]
     [Google Scholar]
  36. Wang Z, Montana G. The graph-guided group lasso for genome-wide association studies. In Suykens JAK, Signoretto M, Argyriou A. (editors) Regularization, Optimization, Kernels, and Support Vector Machines CRC Press; 2014 pp. 131–157
     [Google Scholar]
  37. Botta V, Louppe G, Geurts P, Wehenkel L. Exploiting SNP correlations within random forest for genome-wide association studies. PLoS One 2014; 9:e93379 [View Article][PubMed]
     [Google Scholar]
  38. Xing EP, Jordan MI, Karp RM. Feature Selection for High-Dimensional Genomic Microarray. Data in ICML vol. 1 2001 pp. 601–608
     [Google Scholar]
  39. Goodrich JK, Davenport ER, Beaumont M, Jackson MA, Knight R et al. Genetic determinants of the gut microbiome in UK twins. Cell Host Microbe 2016; 19:731–743 [View Article][PubMed]
     [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/mgen/10.1099/mgen.0.000112
Loading
Human genome-microbiome interaction: metagenomics frontiers for the aetiopathology of autoimmune diseases
M Gen 3, e000112 (2017); https://doi.org/10.1099/mgen.0.000112
/content/journal/mgen/10.1099/mgen.0.000112
/content/journal/mgen/10.1099/mgen.0.000112
Loading

Data & Media loading...

Most cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error